From 78a8cd7a53072bfcfb92b2813376bf61f6d3bfae Mon Sep 17 00:00:00 2001 From: duyiming Date: Mon, 6 Feb 2023 16:58:27 +0800 Subject: [PATCH 001/126] 1 --- deepmd/descriptor/se_atten.py | 62 +++++++++++++++++++++++++++++++++-- 1 file changed, 59 insertions(+), 3 deletions(-) diff --git a/deepmd/descriptor/se_atten.py b/deepmd/descriptor/se_atten.py index 00190672e4..feee2776e2 100644 --- a/deepmd/descriptor/se_atten.py +++ b/deepmd/descriptor/se_atten.py @@ -652,15 +652,16 @@ def _filter_lower( # with (natom x nei_type_i) x 1 xyz_scatter = tf.reshape(tf.slice(inputs_reshape, [0, 0], [-1, 1]), [-1, 1]) assert atype is not None, 'atype must exist!!' - type_embedding = tf.cast(type_embedding, self.filter_precision) - xyz_scatter = self._lookup_type_embedding( - xyz_scatter, atype, type_embedding) + type_embedding = tf.cast(type_embedding, self.filter_precision) # ntypes * Y + #xyz_scatter = self._lookup_type_embedding( + # xyz_scatter, atype, type_embedding) if self.compress: raise RuntimeError('compression of attention descriptor is not supported at the moment') # natom x 4 x outputs_size if (not is_exclude): with tf.variable_scope(name, reuse=reuse): # with (natom x nei_type_i) x out_size + print(xyz_scatter.shape) xyz_scatter = embedding_net( xyz_scatter, self.filter_neuron, @@ -675,6 +676,61 @@ def _filter_lower( uniform_seed=self.uniform_seed, initial_variables=self.embedding_net_variables, mixed_prec=self.mixed_prec) + out_size = xyz_scatter.get_shape().as_list()[-1] + xyz_scatter = tf.nn.embedding_lookup(xyz_scatter, + self.nei_type_vec) + xyz_scatter = tf.reshape(xyz_scatter, [-1, out_size]) # nframes*natoms[0] * nei * out_size + + if self.type_one_side: + embedding_of_embedding_suffix = suffix + "_ebd_of_ebd" + embedding_of_embedding = embedding_net( + type_embedding, + self.filter_neuron, + self.filter_precision, + activation_fn=activation_fn, + resnet_dt=self.filter_resnet_dt, + name_suffix=embedding_of_embedding_suffix, + stddev=stddev, + bavg=bavg, + seed=self.seed, + trainable=trainable, + uniform_seed=self.uniform_seed, + initial_variables=self.embedding_net_variables, + mixed_prec=self.mixed_prec) # ntypes * out_size + nei_embed = tf.nn.embedding_lookup(embedding_of_embedding, self.nei_type_vec) + #nei_embed = tf.reshape(self.nei_embed, [-1, out_size]) # nframes*natoms[0] * nei * out_size + + xyz_scatter = xyz_scatter + nei_embed + else: + type_embedding_nei = tf.tile(tf.reshape(type_embedding, [1, self.ntypes, -1]), + [self.ntypes, 1, 1]) # (ntypes) * ntypes * Y + type_embedding_center = tf.tile(tf.reshape(type_embedding, [self.ntypes, 1, -1]), + [1, self.ntypes, 1]) # ntypes * (ntypes) * Y + two_side_type_embedding = tf.concat([type_embedding_nei, type_embedding_center], -1) # ntypes * ntypes * (Y+Y) + two_side_type_embedding = tf.reshape(two_side_type_embedding, [-1, two_side_type_embedding.shape[-1]]) + two_side_type_embedding_suffix = suffix + "_two_side_ebd" + embedding_of_two_side_type_embedding = embedding_net( + two_side_type_embedding, + self.filter_neuron, + self.filter_precision, + activation_fn=activation_fn, + resnet_dt=self.filter_resnet_dt, + name_suffix=two_side_type_embedding_suffix, + stddev=stddev, + bavg=bavg, + seed=self.seed, + trainable=trainable, + uniform_seed=self.uniform_seed, + initial_variables=self.embedding_net_variables, + mixed_prec=self.mixed_prec) + #index_of_two_side = self.nei_type_vec * self.ntypes + tf.tile(atype, [1, self.nnei]) + tmpres1 = self.nei_type_vec * self.ntypes + tmpres2 = tf.tile(atype, [self.nnei]) + index_of_two_side = tmpres1 + tmpres2 + two_embd = tf.nn.embedding_lookup(embedding_of_two_side_type_embedding, index_of_two_side) + + xyz_scatter = xyz_scatter + two_embd + if (not self.uniform_seed) and (self.seed is not None): self.seed += self.seed_shift input_r = tf.slice(tf.reshape(inputs_i, (-1, shape_i[1] // 4, 4)), [0, 0, 1], [-1, -1, 3]) input_r = tf.nn.l2_normalize(input_r, -1) From 045b16cacf134e5dd43f132daba2d111445939fa Mon Sep 17 00:00:00 2001 From: duyiming Date: Wed, 8 Feb 2023 13:55:45 +0800 Subject: [PATCH 002/126] bugfix --- deepmd/descriptor/se_atten.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/deepmd/descriptor/se_atten.py b/deepmd/descriptor/se_atten.py index feee2776e2..6047e14269 100644 --- a/deepmd/descriptor/se_atten.py +++ b/deepmd/descriptor/se_atten.py @@ -677,9 +677,9 @@ def _filter_lower( initial_variables=self.embedding_net_variables, mixed_prec=self.mixed_prec) out_size = xyz_scatter.get_shape().as_list()[-1] - xyz_scatter = tf.nn.embedding_lookup(xyz_scatter, - self.nei_type_vec) - xyz_scatter = tf.reshape(xyz_scatter, [-1, out_size]) # nframes*natoms[0] * nei * out_size + #xyz_scatter = tf.nn.embedding_lookup(xyz_scatter, + # self.nei_type_vec) + #xyz_scatter = tf.reshape(xyz_scatter, [-1, out_size]) # nframes*natoms[0] * nei * out_size if self.type_one_side: embedding_of_embedding_suffix = suffix + "_ebd_of_ebd" From 3d803d0274d81fe4ae5fc816048848e1c92a8d73 Mon Sep 17 00:00:00 2001 From: nahso Date: Thu, 9 Feb 2023 19:27:13 +0800 Subject: [PATCH 003/126] log model name --- deepmd/train/trainer.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/deepmd/train/trainer.py b/deepmd/train/trainer.py index 5a5593ff9d..8813b636f2 100644 --- a/deepmd/train/trainer.py +++ b/deepmd/train/trainer.py @@ -722,7 +722,7 @@ def train (self, train_data = None, valid_data=None) : if self.timing_in_training: toc = time.time() test_time = toc - tic - log.info("batch %7d training time %.2f s, testing time %.2f s" + log.info("model2 batch %7d training time %.2f s, testing time %.2f s" % (cur_batch, train_time, test_time)) train_time = 0 if self.save_freq > 0 and cur_batch % self.save_freq == 0 and self.saver is not None: From 8e6c41353356d33e2229bab0ec3db585bbd5663e Mon Sep 17 00:00:00 2001 From: nahso Date: Thu, 9 Feb 2023 21:41:16 +0800 Subject: [PATCH 004/126] =?UTF-8?q?bugfix:=20=E8=A7=A3=E5=86=B3=E5=B0=86ty?= =?UTF-8?q?pe=20embedding=E7=9A=84=E7=AC=AC=E4=B8=80=E7=BB=B4=E4=B8=8D?= =?UTF-8?q?=E7=AD=89=E4=BA=8Entypes=E7=9A=84=E6=83=85=E5=86=B5?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- deepmd/descriptor/se_atten.py | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/deepmd/descriptor/se_atten.py b/deepmd/descriptor/se_atten.py index 6047e14269..6d62c231d6 100644 --- a/deepmd/descriptor/se_atten.py +++ b/deepmd/descriptor/se_atten.py @@ -702,10 +702,11 @@ def _filter_lower( xyz_scatter = xyz_scatter + nei_embed else: - type_embedding_nei = tf.tile(tf.reshape(type_embedding, [1, self.ntypes, -1]), - [self.ntypes, 1, 1]) # (ntypes) * ntypes * Y - type_embedding_center = tf.tile(tf.reshape(type_embedding, [self.ntypes, 1, -1]), - [1, self.ntypes, 1]) # ntypes * (ntypes) * Y + type_embedding_shape = type_embedding.get_shape().as_list() + type_embedding_nei = tf.tile(tf.reshape(type_embedding, [1, type_embedding_shape[0], -1]), + [type_embedding_shape[0], 1, 1]) # (ntypes) * ntypes * Y + type_embedding_center = tf.tile(tf.reshape(type_embedding, [type_embedding_shape[0], 1, -1]), + [1, type_embedding_shape[0], 1]) # ntypes * (ntypes) * Y two_side_type_embedding = tf.concat([type_embedding_nei, type_embedding_center], -1) # ntypes * ntypes * (Y+Y) two_side_type_embedding = tf.reshape(two_side_type_embedding, [-1, two_side_type_embedding.shape[-1]]) two_side_type_embedding_suffix = suffix + "_two_side_ebd" @@ -724,7 +725,7 @@ def _filter_lower( initial_variables=self.embedding_net_variables, mixed_prec=self.mixed_prec) #index_of_two_side = self.nei_type_vec * self.ntypes + tf.tile(atype, [1, self.nnei]) - tmpres1 = self.nei_type_vec * self.ntypes + tmpres1 = self.nei_type_vec * type_embedding_shape[0] tmpres2 = tf.tile(atype, [self.nnei]) index_of_two_side = tmpres1 + tmpres2 two_embd = tf.nn.embedding_lookup(embedding_of_two_side_type_embedding, index_of_two_side) From 497da254cfa9c698ccd9196e38a528a748809f09 Mon Sep 17 00:00:00 2001 From: nahso Date: Thu, 9 Mar 2023 15:07:46 +0800 Subject: [PATCH 005/126] based on model2 --- deepmd/descriptor/se_atten.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/deepmd/descriptor/se_atten.py b/deepmd/descriptor/se_atten.py index 6d62c231d6..08f3123f61 100644 --- a/deepmd/descriptor/se_atten.py +++ b/deepmd/descriptor/se_atten.py @@ -730,7 +730,7 @@ def _filter_lower( index_of_two_side = tmpres1 + tmpres2 two_embd = tf.nn.embedding_lookup(embedding_of_two_side_type_embedding, index_of_two_side) - xyz_scatter = xyz_scatter + two_embd + xyz_scatter = xyz_scatter * two_embd + two_embd if (not self.uniform_seed) and (self.seed is not None): self.seed += self.seed_shift input_r = tf.slice(tf.reshape(inputs_i, (-1, shape_i[1] // 4, 4)), [0, 0, 1], [-1, -1, 3]) From f5c025cb59363569e8eaf7361f41df4baa344603 Mon Sep 17 00:00:00 2001 From: Jinzhe Zeng Date: Mon, 13 Mar 2023 01:39:52 -0400 Subject: [PATCH 006/126] enable openmp for gelu (#2375) Fix #2373. --- source/lib/src/gelu.cc | 3 +++ 1 file changed, 3 insertions(+) diff --git a/source/lib/src/gelu.cc b/source/lib/src/gelu.cc index c9ff7c9f2b..f352fcd1b5 100644 --- a/source/lib/src/gelu.cc +++ b/source/lib/src/gelu.cc @@ -6,6 +6,7 @@ template void deepmd::gelu_cpu(FPTYPE* out, const FPTYPE* xx, const int_64 size) { +#pragma omp parallel for for (int ii = 0; ii < size; ii++) { out[ii] = xx[ii] * (FPTYPE)0.5 * ((FPTYPE)1.0 + @@ -19,6 +20,7 @@ void deepmd::gelu_grad_cpu(FPTYPE* out, const FPTYPE* xx, const FPTYPE* dy, const int_64 size) { +#pragma omp parallel for for (int ii = 0; ii < size; ii++) { const FPTYPE var = tanh((FPTYPE)SQRT_2_PI * @@ -36,6 +38,7 @@ void deepmd::gelu_grad_grad_cpu(FPTYPE* out, const FPTYPE* dy, const FPTYPE* dy_2, const int_64 size) { +#pragma omp parallel for for (int ii = 0; ii < size; ii++) { const FPTYPE var1 = tanh((FPTYPE)SQRT_2_PI * From edf8218f9101516439017c35222de8424b6a29bf Mon Sep 17 00:00:00 2001 From: Duo <50307526+iProzd@users.noreply.github.com> Date: Mon, 13 Mar 2023 13:49:50 +0800 Subject: [PATCH 007/126] Add docs for 'None' activation functions. (#2376) --- deepmd/common.py | 4 +++- deepmd/utils/argcheck.py | 18 +++++++++--------- 2 files changed, 12 insertions(+), 10 deletions(-) diff --git a/deepmd/common.py b/deepmd/common.py index 99328164ed..d0afbf0784 100644 --- a/deepmd/common.py +++ b/deepmd/common.py @@ -126,6 +126,8 @@ def gelu_wrapper(x): "tanh": tf.nn.tanh, "gelu": gelu, "gelu_tf": gelu_tf, + "None": None, + "none": None, } @@ -306,7 +308,7 @@ def get_activation_func( RuntimeError if unknown activation function is specified """ - if activation_fn is None or activation_fn in ["none", "None"]: + if activation_fn is None: return None if activation_fn not in ACTIVATION_FN_DICT: raise RuntimeError(f"{activation_fn} is not a valid activation function") diff --git a/deepmd/utils/argcheck.py b/deepmd/utils/argcheck.py index 09c43e14a3..93135304a9 100644 --- a/deepmd/utils/argcheck.py +++ b/deepmd/utils/argcheck.py @@ -50,7 +50,7 @@ def type_embedding_args(): doc_neuron = "Number of neurons in each hidden layers of the embedding net. When two layers are of the same size or one layer is twice as large as the previous layer, a skip connection is built." doc_resnet_dt = 'Whether to use a "Timestep" in the skip connection' doc_seed = "Random seed for parameter initialization" - doc_activation_function = f'The activation function in the embedding net. Supported activation functions are {list_to_doc(ACTIVATION_FN_DICT.keys())} Note that "gelu" denotes the custom operator version, and "gelu_tf" denotes the TF standard version.' + doc_activation_function = f'The activation function in the embedding net. Supported activation functions are {list_to_doc(ACTIVATION_FN_DICT.keys())} Note that "gelu" denotes the custom operator version, and "gelu_tf" denotes the TF standard version. If you set "None" or "none" here, no activation function will be used.' doc_precision = f"The precision of the embedding net parameters, supported options are {list_to_doc(PRECISION_DICT.keys())} Default follows the interface precision." doc_trainable = "If the parameters in the embedding net are trainable" @@ -162,7 +162,7 @@ def descrpt_se_a_args(): doc_rcut_smth = "Where to start smoothing. For example the 1/r term is smoothed from `rcut` to `rcut_smth`" doc_neuron = "Number of neurons in each hidden layers of the embedding net. When two layers are of the same size or one layer is twice as large as the previous layer, a skip connection is built." doc_axis_neuron = "Size of the submatrix of G (embedding matrix)." - doc_activation_function = f'The activation function in the embedding net. Supported activation functions are {list_to_doc(ACTIVATION_FN_DICT.keys())} Note that "gelu" denotes the custom operator version, and "gelu_tf" denotes the TF standard version.' + doc_activation_function = f'The activation function in the embedding net. Supported activation functions are {list_to_doc(ACTIVATION_FN_DICT.keys())} Note that "gelu" denotes the custom operator version, and "gelu_tf" denotes the TF standard version. If you set "None" or "none" here, no activation function will be used.' doc_resnet_dt = 'Whether to use a "Timestep" in the skip connection' doc_type_one_side = r"If true, the embedding network parameters vary by types of neighbor atoms only, so there will be $N_\text{types}$ sets of embedding network parameters. Otherwise, the embedding network parameters vary by types of centric atoms and types of neighbor atoms, so there will be $N_\text{types}^2$ sets of embedding network parameters." doc_precision = f"The precision of the embedding net parameters, supported options are {list_to_doc(PRECISION_DICT.keys())} Default follows the interface precision." @@ -215,7 +215,7 @@ def descrpt_se_t_args(): doc_rcut = "The cut-off radius." doc_rcut_smth = "Where to start smoothing. For example the 1/r term is smoothed from `rcut` to `rcut_smth`" doc_neuron = "Number of neurons in each hidden layers of the embedding net. When two layers are of the same size or one layer is twice as large as the previous layer, a skip connection is built." - doc_activation_function = f'The activation function in the embedding net. Supported activation functions are {list_to_doc(ACTIVATION_FN_DICT.keys())} Note that "gelu" denotes the custom operator version, and "gelu_tf" denotes the TF standard version.' + doc_activation_function = f'The activation function in the embedding net. Supported activation functions are {list_to_doc(ACTIVATION_FN_DICT.keys())} Note that "gelu" denotes the custom operator version, and "gelu_tf" denotes the TF standard version. If you set "None" or "none" here, no activation function will be used.' doc_resnet_dt = 'Whether to use a "Timestep" in the skip connection' doc_precision = f"The precision of the embedding net parameters, supported options are {list_to_doc(PRECISION_DICT.keys())} Default follows the interface precision." doc_trainable = "If the parameters in the embedding net are trainable" @@ -265,7 +265,7 @@ def descrpt_se_r_args(): doc_rcut = "The cut-off radius." doc_rcut_smth = "Where to start smoothing. For example the 1/r term is smoothed from `rcut` to `rcut_smth`" doc_neuron = "Number of neurons in each hidden layers of the embedding net. When two layers are of the same size or one layer is twice as large as the previous layer, a skip connection is built." - doc_activation_function = f'The activation function in the embedding net. Supported activation functions are {list_to_doc(ACTIVATION_FN_DICT.keys())} Note that "gelu" denotes the custom operator version, and "gelu_tf" denotes the TF standard version.' + doc_activation_function = f'The activation function in the embedding net. Supported activation functions are {list_to_doc(ACTIVATION_FN_DICT.keys())} Note that "gelu" denotes the custom operator version, and "gelu_tf" denotes the TF standard version. If you set "None" or "none" here, no activation function will be used.' doc_resnet_dt = 'Whether to use a "Timestep" in the skip connection' doc_type_one_side = r"If true, the embedding network parameters vary by types of neighbor atoms only, so there will be $N_\text{types}$ sets of embedding network parameters. Otherwise, the embedding network parameters vary by types of centric atoms and types of neighbor atoms, so there will be $N_\text{types}^2$ sets of embedding network parameters." doc_precision = f"The precision of the embedding net parameters, supported options are {list_to_doc(PRECISION_DICT.keys())} Default follows the interface precision." @@ -319,7 +319,7 @@ def descrpt_se_atten_args(): doc_rcut_smth = "Where to start smoothing. For example the 1/r term is smoothed from `rcut` to `rcut_smth`" doc_neuron = "Number of neurons in each hidden layers of the embedding net. When two layers are of the same size or one layer is twice as large as the previous layer, a skip connection is built." doc_axis_neuron = "Size of the submatrix of G (embedding matrix)." - doc_activation_function = f'The activation function in the embedding net. Supported activation functions are {list_to_doc(ACTIVATION_FN_DICT.keys())} Note that "gelu" denotes the custom operator version, and "gelu_tf" denotes the TF standard version.' + doc_activation_function = f'The activation function in the embedding net. Supported activation functions are {list_to_doc(ACTIVATION_FN_DICT.keys())} Note that "gelu" denotes the custom operator version, and "gelu_tf" denotes the TF standard version. If you set "None" or "none" here, no activation function will be used.' doc_resnet_dt = 'Whether to use a "Timestep" in the skip connection' doc_type_one_side = r"If true, the embedding network parameters vary by types of neighbor atoms only, so there will be $N_\text{types}$ sets of embedding network parameters. Otherwise, the embedding network parameters vary by types of centric atoms and types of neighbor atoms, so there will be $N_\text{types}^2$ sets of embedding network parameters." doc_precision = f"The precision of the embedding net parameters, supported options are {list_to_doc(PRECISION_DICT.keys())} Default follows the interface precision." @@ -376,7 +376,7 @@ def descrpt_se_a_mask_args(): doc_neuron = "Number of neurons in each hidden layers of the embedding net. When two layers are of the same size or one layer is twice as large as the previous layer, a skip connection is built." doc_axis_neuron = "Size of the submatrix of G (embedding matrix)." - doc_activation_function = f'The activation function in the embedding net. Supported activation functions are {list_to_doc(ACTIVATION_FN_DICT.keys())} Note that "gelu" denotes the custom operator version, and "gelu_tf" denotes the TF standard version.' + doc_activation_function = f'The activation function in the embedding net. Supported activation functions are {list_to_doc(ACTIVATION_FN_DICT.keys())} Note that "gelu" denotes the custom operator version, and "gelu_tf" denotes the TF standard version. If you set "None" or "none" here, no activation function will be used.' doc_resnet_dt = 'Whether to use a "Timestep" in the skip connection' doc_type_one_side = r"If true, the embedding network parameters vary by types of neighbor atoms only, so there will be $N_\text{types}$ sets of embedding network parameters. Otherwise, the embedding network parameters vary by types of centric atoms and types of neighbor atoms, so there will be $N_\text{types}^2$ sets of embedding network parameters." doc_exclude_types = "The excluded pairs of types which have no interaction with each other. For example, `[[0, 1]]` means no interaction between type 0 and type 1." @@ -441,7 +441,7 @@ def fitting_ener(): doc_numb_fparam = "The dimension of the frame parameter. If set to >0, file `fparam.npy` should be included to provided the input fparams." doc_numb_aparam = "The dimension of the atomic parameter. If set to >0, file `aparam.npy` should be included to provided the input aparams." doc_neuron = "The number of neurons in each hidden layers of the fitting net. When two hidden layers are of the same size, a skip connection is built." - doc_activation_function = f'The activation function in the fitting net. Supported activation functions are {list_to_doc(ACTIVATION_FN_DICT.keys())} Note that "gelu" denotes the custom operator version, and "gelu_tf" denotes the TF standard version.' + doc_activation_function = f'The activation function in the fitting net. Supported activation functions are {list_to_doc(ACTIVATION_FN_DICT.keys())} Note that "gelu" denotes the custom operator version, and "gelu_tf" denotes the TF standard version. If you set "None" or "none" here, no activation function will be used.' doc_precision = f"The precision of the fitting net parameters, supported options are {list_to_doc(PRECISION_DICT.keys())} Default follows the interface precision." doc_resnet_dt = 'Whether to use a "Timestep" in the skip connection' doc_trainable = "Whether the parameters in the fitting net are trainable. This option can be\n\n\ @@ -502,7 +502,7 @@ def fitting_ener(): def fitting_polar(): doc_neuron = "The number of neurons in each hidden layers of the fitting net. When two hidden layers are of the same size, a skip connection is built." - doc_activation_function = f'The activation function in the fitting net. Supported activation functions are {list_to_doc(ACTIVATION_FN_DICT.keys())} Note that "gelu" denotes the custom operator version, and "gelu_tf" denotes the TF standard version.' + doc_activation_function = f'The activation function in the fitting net. Supported activation functions are {list_to_doc(ACTIVATION_FN_DICT.keys())} Note that "gelu" denotes the custom operator version, and "gelu_tf" denotes the TF standard version. If you set "None" or "none" here, no activation function will be used.' doc_resnet_dt = 'Whether to use a "Timestep" in the skip connection' doc_precision = f"The precision of the fitting net parameters, supported options are {list_to_doc(PRECISION_DICT.keys())} Default follows the interface precision." doc_scale = "The output of the fitting net (polarizability matrix) will be scaled by ``scale``" @@ -553,7 +553,7 @@ def fitting_polar(): def fitting_dipole(): doc_neuron = "The number of neurons in each hidden layers of the fitting net. When two hidden layers are of the same size, a skip connection is built." - doc_activation_function = f'The activation function in the fitting net. Supported activation functions are {list_to_doc(ACTIVATION_FN_DICT.keys())} Note that "gelu" denotes the custom operator version, and "gelu_tf" denotes the TF standard version.' + doc_activation_function = f'The activation function in the fitting net. Supported activation functions are {list_to_doc(ACTIVATION_FN_DICT.keys())} Note that "gelu" denotes the custom operator version, and "gelu_tf" denotes the TF standard version. If you set "None" or "none" here, no activation function will be used.' doc_resnet_dt = 'Whether to use a "Timestep" in the skip connection' doc_precision = f"The precision of the fitting net parameters, supported options are {list_to_doc(PRECISION_DICT.keys())} Default follows the interface precision." doc_sel_type = "The atom types for which the atomic dipole will be provided. If not set, all types will be selected." From 71b867484c723d1d8179f9e133ca910b9997a5b8 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Wed, 15 Mar 2023 09:40:16 +0800 Subject: [PATCH 008/126] Bump actions/checkout from 2 to 3 (#2381) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Bumps [actions/checkout](https://github.com/actions/checkout) from 2 to 3.
Release notes

Sourced from actions/checkout's releases.

v3.0.0

  • Updated to the node16 runtime by default
    • This requires a minimum Actions Runner version of v2.285.0 to run, which is by default available in GHES 3.4 or later.

v2.6.0

What's Changed

Full Changelog: https://github.com/actions/checkout/compare/v2.5.0...v2.6.0

v2.5.0

What's Changed

Full Changelog: https://github.com/actions/checkout/compare/v2...v2.5.0

v2.4.2

What's Changed

Full Changelog: https://github.com/actions/checkout/compare/v2...v2.4.2

v2.4.1

  • Fixed an issue where checkout failed to run in container jobs due to the new git setting safe.directory

v2.4.0

  • Convert SSH URLs like org-<ORG_ID>@github.com: to https://github.com/ - pr

v2.3.5

Update dependencies

v2.3.4

v2.3.3

v2.3.2

Add Third Party License Information to Dist Files

v2.3.1

Fix default branch resolution for .wiki and when using SSH

... (truncated)

Changelog

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Changelog

v3.1.0

v3.0.2

v3.0.1

v3.0.0

v2.3.1

v2.3.0

v2.2.0

v2.1.1

  • Changes to support GHES (here and here)

v2.1.0

v2.0.0

... (truncated)

Commits

[![Dependabot compatibility score](https://dependabot-badges.githubapp.com/badges/compatibility_score?dependency-name=actions/checkout&package-manager=github_actions&previous-version=2&new-version=3)](https://docs.github.com/en/github/managing-security-vulnerabilities/about-dependabot-security-updates#about-compatibility-scores) Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting `@dependabot rebase`. [//]: # (dependabot-automerge-start) [//]: # (dependabot-automerge-end) ---
Dependabot commands and options
You can trigger Dependabot actions by commenting on this PR: - `@dependabot rebase` will rebase this PR - `@dependabot recreate` will recreate this PR, overwriting any edits that have been made to it - `@dependabot merge` will merge this PR after your CI passes on it - `@dependabot squash and merge` will squash and merge this PR after your CI passes on it - `@dependabot cancel merge` will cancel a previously requested merge and block automerging - `@dependabot reopen` will reopen this PR if it is closed - `@dependabot close` will close this PR and stop Dependabot recreating it. You can achieve the same result by closing it manually - `@dependabot ignore this major version` will close this PR and stop Dependabot creating any more for this major version (unless you reopen the PR or upgrade to it yourself) - `@dependabot ignore this minor version` will close this PR and stop Dependabot creating any more for this minor version (unless you reopen the PR or upgrade to it yourself) - `@dependabot ignore this dependency` will close this PR and stop Dependabot creating any more for this dependency (unless you reopen the PR or upgrade to it yourself)
Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- .github/workflows/build_wheel.yml | 2 +- .github/workflows/package_c.yml | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/build_wheel.yml b/.github/workflows/build_wheel.yml index bb34fe0bf6..cd4dd728b3 100644 --- a/.github/workflows/build_wheel.yml +++ b/.github/workflows/build_wheel.yml @@ -33,7 +33,7 @@ jobs: platform_id: manylinux_aarch64 dp_variant: cpu steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v3 with: submodules: true # https://github.com/pypa/setuptools_scm/issues/480 diff --git a/.github/workflows/package_c.yml b/.github/workflows/package_c.yml index e57c0a30e1..0a0d16816f 100644 --- a/.github/workflows/package_c.yml +++ b/.github/workflows/package_c.yml @@ -9,7 +9,7 @@ jobs: name: Build C library runs-on: ubuntu-22.04 steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v3 - name: Package C library run: ./source/install/docker_package_c.sh - name: Test C library From ddd94bacb5abe8d57e61ae15cb612c3a9edf9e6a Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Wed, 15 Mar 2023 09:40:50 +0800 Subject: [PATCH 009/126] Bump pypa/cibuildwheel from 2.11.3 to 2.12.1 (#2382) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Bumps [pypa/cibuildwheel](https://github.com/pypa/cibuildwheel) from 2.11.3 to 2.12.1.
Release notes

Sourced from pypa/cibuildwheel's releases.

v2.12.1

  • 🐛 Fix a bug that prevented the use of CIBW_CONFIG_SETTINGS with the 'pip' build backend. (#1430)

v2.12.0

  • ✨ Adds support for PyPy arm64 wheels. This means that you can build PyPy wheels for Apple Silicon machines. Cross-compilation is not supported for these wheels, so you'll have to build on an Apple Silicon machine. (#1372)
  • 🛠 Pinned version updates, including PyPy to v7.3.11 and setuptools to 66.0.0.

v2.11.4

  • 🐛 Fix a bug that caused missing wheels on Windows when a test was skipped using CIBW_TEST_SKIP (#1377)
  • 🛠 Updates CPython 3.11 to 3.11.1 (#1371)
  • 🛠 Updates PyPy 3.7 to 3.7.10, except on macOS which remains on 7.3.9 due to a bug. (#1371)
  • 📚 Added a reference to abi3audit to the docs (#1347)
Changelog

Sourced from pypa/cibuildwheel's changelog.

v2.12.1

11 March 2023

  • 🐛 Fix a bug that prevented the use of CIBW_CONFIG_SETTINGS with the 'pip' build backend. (#1430)

v2.12.0

16 Jan 2023

  • ✨ Adds support for PyPy arm64 wheels. This means that you can build PyPy wheels for Apple Silicon machines. Cross-compilation is not supported for these wheels, so you'll have to build on an Apple Silicon machine. (#1372)
  • 🛠 Pinned version updates, including PyPy to v7.3.11 and setuptools to 66.0.0.

v2.11.4

24 Dec 2022

  • 🐛 Fix a bug that caused missing wheels on Windows when a test was skipped using CIBW_TEST_SKIP (#1377)
  • 🛠 Updates CPython 3.11 to 3.11.1 (#1371)
  • 🛠 Updates PyPy to 7.3.10, except on macOS which remains on 7.3.9 due to a bug on that platform. (#1371)
  • 📚 Added a reference to abi3audit to the docs (#1347)
Commits
  • 02ad79a Bump version: v2.12.1
  • 1d6ee75 Merge pull request #1430 from henryiii/henryiii/fix/pipconfigopts
  • 0b1b539 [Bot] Update dependencies (#1425)
  • 2e48667 refactor: use frontend instead of plural
  • c681860 fix: pip config options were broken
  • 7190b1a Merge pull request #1428 from henryiii/henryiii/ci/appveyor
  • 16380b8 Update CI table
  • 94efa61 ci: fix appveyor (Python 3.8+ needed for macOS)
  • acdbe87 Merge pull request #1427 from pypa/pre-commit-ci-update-config
  • 49b736d [pre-commit.ci] pre-commit autoupdate
  • Additional commits viewable in compare view

[![Dependabot compatibility score](https://dependabot-badges.githubapp.com/badges/compatibility_score?dependency-name=pypa/cibuildwheel&package-manager=github_actions&previous-version=2.11.3&new-version=2.12.1)](https://docs.github.com/en/github/managing-security-vulnerabilities/about-dependabot-security-updates#about-compatibility-scores) Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting `@dependabot rebase`. [//]: # (dependabot-automerge-start) [//]: # (dependabot-automerge-end) ---
Dependabot commands and options
You can trigger Dependabot actions by commenting on this PR: - `@dependabot rebase` will rebase this PR - `@dependabot recreate` will recreate this PR, overwriting any edits that have been made to it - `@dependabot merge` will merge this PR after your CI passes on it - `@dependabot squash and merge` will squash and merge this PR after your CI passes on it - `@dependabot cancel merge` will cancel a previously requested merge and block automerging - `@dependabot reopen` will reopen this PR if it is closed - `@dependabot close` will close this PR and stop Dependabot recreating it. You can achieve the same result by closing it manually - `@dependabot ignore this major version` will close this PR and stop Dependabot creating any more for this major version (unless you reopen the PR or upgrade to it yourself) - `@dependabot ignore this minor version` will close this PR and stop Dependabot creating any more for this minor version (unless you reopen the PR or upgrade to it yourself) - `@dependabot ignore this dependency` will close this PR and stop Dependabot creating any more for this dependency (unless you reopen the PR or upgrade to it yourself)
Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- .github/workflows/build_wheel.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/build_wheel.yml b/.github/workflows/build_wheel.yml index cd4dd728b3..911673f96d 100644 --- a/.github/workflows/build_wheel.yml +++ b/.github/workflows/build_wheel.yml @@ -42,7 +42,7 @@ jobs: name: Setup QEMU if: matrix.platform_id == 'manylinux_aarch64' - name: Build wheels - uses: pypa/cibuildwheel@v2.11.3 + uses: pypa/cibuildwheel@v2.12.1 env: CIBW_BUILD_VERBOSITY: 1 CIBW_ARCHS: all From f0747709ed175e678be4d20acab10e9065b0d1eb Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Wed, 15 Mar 2023 09:41:13 +0800 Subject: [PATCH 010/126] Bump docker/metadata-action from 3.3.0 to 4.3.0 (#2383) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Bumps [docker/metadata-action](https://github.com/docker/metadata-action) from 3.3.0 to 4.3.0.
Release notes

Sourced from docker/metadata-action's releases.

v4.3.0

Full Changelog: https://github.com/docker/metadata-action/compare/v4.2.0...v4.3.0

v4.2.0

  • Add tz attribute to handlebar date function by @​chroju (#251)
  • Bump minimatch from 3.0.4 to 3.1.2 (#242)
  • Bump csv-parse from 5.3.1 to 5.3.3 (#245)
  • Bump json5 from 2.2.0 to 2.2.3 (#252)

Full Changelog: https://github.com/docker/metadata-action/compare/v4.1.1...v4.2.0

v4.1.1

  • Revert changes to set associated head sha on pull request event by @​crazy-max (#239)
    • User can still set associated head sha on PR by setting the env var DOCKER_METADATA_PR_HEAD_SHA=true
  • Bump csv-parse from 5.3.0 to 5.3.1 (#237)

Full Changelog: https://github.com/docker/metadata-action/compare/v4.1.0...v4.1.1

v4.1.0

Full Changelog: https://github.com/docker/metadata-action/compare/v4.0.1...v4.1.0

v4.0.1

Full Changelog: https://github.com/docker/metadata-action/compare/v4.0.0...v4.0.1

v4.0.0

  • Node 16 as default runtime by @​crazy-max (#176)
    • This requires a minimum Actions Runner version of v2.285.0, which is by default available in GHES 3.4 or later.
  • Do not sanitize before pattern matching by @​crazy-max (#201)
    • Breaking change with type=match pattern matching

Full Changelog: https://github.com/docker/metadata-action/compare/v3.8.0...v4.0.0

v3.8.0

... (truncated)

Upgrade guide

Sourced from docker/metadata-action's upgrade guide.

Upgrade notes

v2 to v3

  • Repository has been moved to docker org. Replace crazy-max/ghaction-docker-meta@v2 with docker/metadata-action@v4
  • The default bake target has been changed: ghaction-docker-meta > docker-metadata-action

v1 to v2

inputs

New Unchanged Removed
tags images tag-sha
flavor sep-tags tag-edge
labels sep-labels tag-edge-branch
tag-semver
tag-match
tag-match-group
tag-latest
tag-schedule
tag-custom
tag-custom-only
label-custom

tag-sha

tags: |
  type=sha

tag-edge / tag-edge-branch

tags: |
  # default branch
</tr></table>

... (truncated)

Commits
  • 507c2f2 Merge pull request #257 from crazy-max/env-output
  • 04861f5 update generated content
  • 6729545 Provide outputs as env vars
  • 05d22bf Merge pull request #256 from crazy-max/fix-readme
  • 70b403b Fix README
  • 9e6ae02 Merge pull request #252 from docker/dependabot/npm_and_yarn/json5-2.2.3
  • 3d239e8 Bump json5 from 2.2.0 to 2.2.3
  • 7cb52e2 Merge pull request #251 from chroju/set_timezone
  • 90a1d5c Add tz attribute to handlebar date function
  • c98ac5e Merge pull request #249 from crazy-max/fix-readme
  • Additional commits viewable in compare view

[![Dependabot compatibility score](https://dependabot-badges.githubapp.com/badges/compatibility_score?dependency-name=docker/metadata-action&package-manager=github_actions&previous-version=3.3.0&new-version=4.3.0)](https://docs.github.com/en/github/managing-security-vulnerabilities/about-dependabot-security-updates#about-compatibility-scores) Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting `@dependabot rebase`. [//]: # (dependabot-automerge-start) [//]: # (dependabot-automerge-end) ---
Dependabot commands and options
You can trigger Dependabot actions by commenting on this PR: - `@dependabot rebase` will rebase this PR - `@dependabot recreate` will recreate this PR, overwriting any edits that have been made to it - `@dependabot merge` will merge this PR after your CI passes on it - `@dependabot squash and merge` will squash and merge this PR after your CI passes on it - `@dependabot cancel merge` will cancel a previously requested merge and block automerging - `@dependabot reopen` will reopen this PR if it is closed - `@dependabot close` will close this PR and stop Dependabot recreating it. You can achieve the same result by closing it manually - `@dependabot ignore this major version` will close this PR and stop Dependabot creating any more for this major version (unless you reopen the PR or upgrade to it yourself) - `@dependabot ignore this minor version` will close this PR and stop Dependabot creating any more for this minor version (unless you reopen the PR or upgrade to it yourself) - `@dependabot ignore this dependency` will close this PR and stop Dependabot creating any more for this dependency (unless you reopen the PR or upgrade to it yourself)
Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- .github/workflows/build_wheel.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/build_wheel.yml b/.github/workflows/build_wheel.yml index 911673f96d..b318f2c0e4 100644 --- a/.github/workflows/build_wheel.yml +++ b/.github/workflows/build_wheel.yml @@ -103,7 +103,7 @@ jobs: - name: Extract metadata (tags, labels) for Docker id: meta - uses: docker/metadata-action@98669ae865ea3cffbcbaa878cf57c20bbf1c6c38 + uses: docker/metadata-action@507c2f2dc502c992ad446e3d7a5dfbe311567a96 with: images: ghcr.io/deepmodeling/deepmd-kit From 164a70b1866b8caa79cdfef4bcdd7c52656cc618 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Wed, 15 Mar 2023 09:43:52 +0800 Subject: [PATCH 011/126] Bump actions/upload-artifact from 2 to 3 (#2384) Bumps [actions/upload-artifact](https://github.com/actions/upload-artifact) from 2 to 3.
Release notes

Sourced from actions/upload-artifact's releases.

v3.0.0

What's Changed

  • Update default runtime to node16 (#293)
  • Update package-lock.json file version to 2 (#302)

Breaking Changes

With the update to Node 16, all scripts will now be run with Node 16 rather than Node 12.

v2.3.1

Fix for empty fails on Windows failing on upload #281

v2.3.0 Upload Artifact

  • Optimizations for faster uploads of larger files that are already compressed
  • Significantly improved logging when there are chunked uploads
  • Clarifications in logs around the upload size and prohibited characters that aren't allowed in the artifact name or any uploaded files
  • Various other small bugfixes & optimizations

v2.2.4

  • Retry on HTTP 500 responses from the service

v2.2.3

  • Fixes for proxy related issues

v2.2.2

  • Improved retryability and error handling

v2.2.1

  • Update used actions/core package to the latest version

v2.2.0

  • Support for artifact retention

v2.1.4

  • Add Third Party License Information

v2.1.3

  • Use updated version of the @action/artifact NPM package

v2.1.2

  • Increase upload chunk size from 4MB to 8MB
  • Detect case insensitive file uploads

v2.1.1

  • Fix for certain symlinks not correctly being identified as directories before starting uploads

v2.1.0

  • Support for uploading artifacts with multiple paths
  • Support for using exclude paths
  • Updates to dependencies

... (truncated)

Commits
  • 0b7f8ab ci(github): update action/download-artifact from v1 to v3 (#312)
  • 013d2b8 Create devcontainer for codespaces + update all dev dependencies (#375)
  • 055b8b3 Bump Actions NPM dependencies (#374)
  • 7a5d483 ci(github): update action/checkout from v2 to v3 (#315)
  • e0057a5 README: Bump actions/checkout to v3 (#352)
  • 7fe6c13 Update to latest actions/publish-action (#363)
  • 83fd05a Bump actions-core to v1.10.0 (#356)
  • 3cea537 Merge pull request #327 from actions/robherley/artifact-1.1.0
  • 849aa77 nvm use 12 & npm run release
  • 4d39869 recompile with correct ncc version
  • Additional commits viewable in compare view

[![Dependabot compatibility score](https://dependabot-badges.githubapp.com/badges/compatibility_score?dependency-name=actions/upload-artifact&package-manager=github_actions&previous-version=2&new-version=3)](https://docs.github.com/en/github/managing-security-vulnerabilities/about-dependabot-security-updates#about-compatibility-scores) Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting `@dependabot rebase`. [//]: # (dependabot-automerge-start) [//]: # (dependabot-automerge-end) ---
Dependabot commands and options
You can trigger Dependabot actions by commenting on this PR: - `@dependabot rebase` will rebase this PR - `@dependabot recreate` will recreate this PR, overwriting any edits that have been made to it - `@dependabot merge` will merge this PR after your CI passes on it - `@dependabot squash and merge` will squash and merge this PR after your CI passes on it - `@dependabot cancel merge` will cancel a previously requested merge and block automerging - `@dependabot reopen` will reopen this PR if it is closed - `@dependabot close` will close this PR and stop Dependabot recreating it. You can achieve the same result by closing it manually - `@dependabot ignore this major version` will close this PR and stop Dependabot creating any more for this major version (unless you reopen the PR or upgrade to it yourself) - `@dependabot ignore this minor version` will close this PR and stop Dependabot creating any more for this minor version (unless you reopen the PR or upgrade to it yourself) - `@dependabot ignore this dependency` will close this PR and stop Dependabot creating any more for this dependency (unless you reopen the PR or upgrade to it yourself)
Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- .github/workflows/build_wheel.yml | 2 +- .github/workflows/package_c.yml | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/build_wheel.yml b/.github/workflows/build_wheel.yml index b318f2c0e4..53131f540a 100644 --- a/.github/workflows/build_wheel.yml +++ b/.github/workflows/build_wheel.yml @@ -48,7 +48,7 @@ jobs: CIBW_ARCHS: all CIBW_BUILD: cp${{ matrix.python }}-${{ matrix.platform_id }} DP_VARIANT: ${{ matrix.dp_variant }} - - uses: actions/upload-artifact@v2 + - uses: actions/upload-artifact@v3 with: path: ./wheelhouse/*.whl build_sdist: diff --git a/.github/workflows/package_c.yml b/.github/workflows/package_c.yml index 0a0d16816f..6cc5ed8dc8 100644 --- a/.github/workflows/package_c.yml +++ b/.github/workflows/package_c.yml @@ -16,7 +16,7 @@ jobs: run: ./source/install/docker_test_package_c.sh # for download and debug - name: Upload artifact - uses: actions/upload-artifact@v2 + uses: actions/upload-artifact@v3 with: path: ./libdeepmd_c.tar.gz - name: Release From 27fec1966d3b0a61a6dcc6112af1a328b156b3d3 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Wed, 15 Mar 2023 09:44:04 +0800 Subject: [PATCH 012/126] Bump docker/build-push-action from 2.5.0 to 4.0.0 (#2385) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Bumps [docker/build-push-action](https://github.com/docker/build-push-action) from 2.5.0 to 4.0.0.
Release notes

Sourced from docker/build-push-action's releases.

v4.0.0

Note

Buildx v0.10 enables support for a minimal SLSA Provenance attestation, which requires support for OCI-compliant multi-platform images. This may introduce issues with registry and runtime support (e.g. Google Cloud Run and AWS Lambda). You can optionally disable the default provenance attestation functionality using provenance: false.

Full Changelog: https://github.com/docker/build-push-action/compare/v3.3.1...v4.0.0

v3.3.1

Full Changelog: https://github.com/docker/build-push-action/compare/v3.3.0...v3.3.1

v3.3.0

Note

Buildx v0.10 enables support for a minimal SLSA Provenance attestation, which requires support for OCI-compliant multi-platform images. This may introduce issues with registry and runtime support (e.g. Google Cloud Run and AWS Lambda). You can optionally disable the default provenance attestation functionality using provenance: false.

Full Changelog: https://github.com/docker/build-push-action/compare/v3.2.0...v3.3.0

v3.2.0

Full Changelog: https://github.com/docker/build-push-action/compare/v3.1.1...v3.2.0

v3.1.1

Full Changelog: https://github.com/docker/build-push-action/compare/v3.1.0...v3.1.1

v3.1.0

  • no-cache-filters input by @​crazy-max (#653)
  • Bump @​actions/github from 5.0.1 to 5.0.3 (#619)
  • Bump @​actions/core from 1.6.0 to 1.9.0 (#620 #637)
  • Bump csv-parse from 5.0.4 to 5.3.0 (#623 #650)

Full Changelog: https://github.com/docker/build-push-action/compare/v3.0.0...v3.1.0

... (truncated)

Commits
  • 3b5e802 Merge pull request #784 from crazy-max/enable-provenance
  • 02d3266 update generated content
  • f403daf revert disable provenance by default if not set
  • 1104d47 Merge pull request #781 from crazy-max/disable-provenance
  • 838bf90 update generated content
  • 337a09d disable provenance by default if not set
  • 37abced Merge pull request #760 from crazy-max/test-envs
  • 67109bc test: move envs to jest config
  • d1b0eb0 Merge pull request #759 from crazy-max/fix-provenance-input
  • a0635fe update generated content
  • Additional commits viewable in compare view

[![Dependabot compatibility score](https://dependabot-badges.githubapp.com/badges/compatibility_score?dependency-name=docker/build-push-action&package-manager=github_actions&previous-version=2.5.0&new-version=4.0.0)](https://docs.github.com/en/github/managing-security-vulnerabilities/about-dependabot-security-updates#about-compatibility-scores) Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting `@dependabot rebase`. [//]: # (dependabot-automerge-start) [//]: # (dependabot-automerge-end) ---
Dependabot commands and options
You can trigger Dependabot actions by commenting on this PR: - `@dependabot rebase` will rebase this PR - `@dependabot recreate` will recreate this PR, overwriting any edits that have been made to it - `@dependabot merge` will merge this PR after your CI passes on it - `@dependabot squash and merge` will squash and merge this PR after your CI passes on it - `@dependabot cancel merge` will cancel a previously requested merge and block automerging - `@dependabot reopen` will reopen this PR if it is closed - `@dependabot close` will close this PR and stop Dependabot recreating it. You can achieve the same result by closing it manually - `@dependabot ignore this major version` will close this PR and stop Dependabot creating any more for this major version (unless you reopen the PR or upgrade to it yourself) - `@dependabot ignore this minor version` will close this PR and stop Dependabot creating any more for this minor version (unless you reopen the PR or upgrade to it yourself) - `@dependabot ignore this dependency` will close this PR and stop Dependabot creating any more for this dependency (unless you reopen the PR or upgrade to it yourself)
Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- .github/workflows/build_wheel.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/build_wheel.yml b/.github/workflows/build_wheel.yml index 53131f540a..a20f867e69 100644 --- a/.github/workflows/build_wheel.yml +++ b/.github/workflows/build_wheel.yml @@ -108,7 +108,7 @@ jobs: images: ghcr.io/deepmodeling/deepmd-kit - name: Build and push Docker image - uses: docker/build-push-action@ad44023a93711e3deb337508980b4b5e9bcdc5dc + uses: docker/build-push-action@3b5e8027fcad23fda98b2e3ac259d8d67585f671 with: context: source/install/docker push: ${{ github.repository_owner == 'deepmodeling' && github.event_name == 'push' }} From 2d117143f74c7bd88dfc0c426bc27354a72775da Mon Sep 17 00:00:00 2001 From: Jinzhe Zeng Date: Tue, 14 Mar 2023 21:46:14 -0400 Subject: [PATCH 013/126] fix the version of pypa/gh-action-pypi-publish (#2389) The error was found when v2.2.0 was released. I have manually uploaded v2.2.0 to PyPI. Signed-off-by: Jinzhe Zeng --- .github/workflows/build_wheel.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/build_wheel.yml b/.github/workflows/build_wheel.yml index a20f867e69..c497e86e1d 100644 --- a/.github/workflows/build_wheel.yml +++ b/.github/workflows/build_wheel.yml @@ -79,7 +79,7 @@ jobs: with: name: artifact path: dist - - uses: pypa/gh-action-pypi-publish@v4 + - uses: pypa/gh-action-pypi-publish@release/v1 with: user: __token__ password: ${{ secrets.pypi_password }} From 36cd09c48a091ba2effe87e057c3fbacc7f7c33b Mon Sep 17 00:00:00 2001 From: Jinzhe Zeng Date: Wed, 15 Mar 2023 00:40:00 -0400 Subject: [PATCH 014/126] link op library with protobuf (#2391) --- source/op/CMakeLists.txt | 3 +++ 1 file changed, 3 insertions(+) diff --git a/source/op/CMakeLists.txt b/source/op/CMakeLists.txt index fdc7fa7ea2..059e72b9a8 100644 --- a/source/op/CMakeLists.txt +++ b/source/op/CMakeLists.txt @@ -56,6 +56,9 @@ add_library(${LIB_DEEPMD_OP} MODULE ${OP_SRC} ${OP_REMAPPER_SRC}) # link: libdeepmd libtensorflow_cc libtensorflow_framework target_link_libraries(${LIB_DEEPMD_OP} PRIVATE TensorFlow::tensorflow_framework) target_link_libraries(${LIB_DEEPMD_OP} PRIVATE ${LIB_DEEPMD}) +if(Protobuf_LIBRARY) + target_link_libraries(${LIB_DEEPMD_OP} PRIVATE ${Protobuf_LIBRARY}) +endif() if(APPLE) set_target_properties( ${LIB_DEEPMD_OP} PROPERTIES INSTALL_RPATH From 3c1d6240f2ce24b690c4d6b4fa5ea26c189e3341 Mon Sep 17 00:00:00 2001 From: Jinzhe Zeng Date: Wed, 15 Mar 2023 00:40:12 -0400 Subject: [PATCH 015/126] atype_filter should have the shape of nloc (#2390) Signed-off-by: Jinzhe Zeng Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> --- deepmd/fit/ener.py | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/deepmd/fit/ener.py b/deepmd/fit/ener.py index d65a3a984b..53c3b1d468 100644 --- a/deepmd/fit/ener.py +++ b/deepmd/fit/ener.py @@ -549,13 +549,14 @@ def build( aparam = tf.reshape(aparam, [-1, self.numb_aparam * natoms[0]]) atype_nall = tf.reshape(atype, [-1, natoms[1]]) - atype_filter = tf.cast(atype_nall >= 0, GLOBAL_TF_FLOAT_PRECISION) + self.atype_nloc = tf.slice( + atype_nall, [0, 0], [-1, natoms[0]] + ) ## lammps will make error + atype_filter = tf.cast(self.atype_nloc >= 0, GLOBAL_TF_FLOAT_PRECISION) + self.atype_nloc = tf.reshape(self.atype_nloc, [-1]) # prevent embedding_lookup error, # but the filter will be applied anyway - atype_nall = tf.clip_by_value(atype_nall, 0, self.ntypes - 1) - self.atype_nloc = tf.reshape( - tf.slice(atype_nall, [0, 0], [-1, natoms[0]]), [-1] - ) ## lammps will make error + self.atype_nloc = tf.clip_by_value(self.atype_nloc, 0, self.ntypes - 1) if type_embedding is not None: atype_embed = tf.nn.embedding_lookup(type_embedding, self.atype_nloc) else: From 1e0e8fd96314a4399c68976cf717c2fc3c37f611 Mon Sep 17 00:00:00 2001 From: Duo <50307526+iProzd@users.noreply.github.com> Date: Thu, 16 Mar 2023 09:36:31 +0800 Subject: [PATCH 016/126] Fix incompatibility after fixing the incontinuity of se_atten (#2397) Add `set_davg_zero` para in `se_atten` with default value True. This will fix incompatibility after fixing the incontinuity of se_atten. #2393 #2394 --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> --- deepmd/descriptor/se_atten.py | 11 ++++++++++- deepmd/utils/argcheck.py | 4 ++++ 2 files changed, 14 insertions(+), 1 deletion(-) diff --git a/deepmd/descriptor/se_atten.py b/deepmd/descriptor/se_atten.py index eb3431bf7b..1c9d7b2c09 100644 --- a/deepmd/descriptor/se_atten.py +++ b/deepmd/descriptor/se_atten.py @@ -1,3 +1,4 @@ +import warnings from typing import ( List, Optional, @@ -67,6 +68,8 @@ class DescrptSeAtten(DescrptSeA): exclude_types : List[List[int]] The excluded pairs of types which have no interaction with each other. For example, `[[0, 1]]` means no interaction between type 0 and type 1. + set_davg_zero + Set the shift of embedding net input to zero. activation_function The activation function in the embedding net. Supported options are |ACTIVATION_FN| precision @@ -97,6 +100,7 @@ def __init__( trainable: bool = True, seed: Optional[int] = None, type_one_side: bool = True, + set_davg_zero: bool = True, exclude_types: List[List[int]] = [], activation_function: str = "tanh", precision: str = "default", @@ -107,6 +111,11 @@ def __init__( attn_mask: bool = False, multi_task: bool = False, ) -> None: + if not set_davg_zero: + warnings.warn( + "Set 'set_davg_zero' False in descriptor 'se_atten' " + "may cause unexpected incontinuity during model inference!" + ) DescrptSeA.__init__( self, rcut, @@ -119,7 +128,7 @@ def __init__( seed=seed, type_one_side=type_one_side, exclude_types=exclude_types, - set_davg_zero=True, + set_davg_zero=set_davg_zero, activation_function=activation_function, precision=precision, uniform_seed=uniform_seed, diff --git a/deepmd/utils/argcheck.py b/deepmd/utils/argcheck.py index 93135304a9..ca9afd43ac 100644 --- a/deepmd/utils/argcheck.py +++ b/deepmd/utils/argcheck.py @@ -325,6 +325,7 @@ def descrpt_se_atten_args(): doc_precision = f"The precision of the embedding net parameters, supported options are {list_to_doc(PRECISION_DICT.keys())} Default follows the interface precision." doc_trainable = "If the parameters in the embedding net is trainable" doc_seed = "Random seed for parameter initialization" + doc_set_davg_zero = "Set the normalization average to zero. This option should be set when `se_atten` descriptor or `atom_ener` in the energy fitting is used" doc_exclude_types = "The excluded pairs of types which have no interaction with each other. For example, `[[0, 1]]` means no interaction between type 0 and type 1." doc_attn = "The length of hidden vectors in attention layers" doc_attn_layer = "The number of attention layers" @@ -361,6 +362,9 @@ def descrpt_se_atten_args(): Argument( "exclude_types", list, optional=True, default=[], doc=doc_exclude_types ), + Argument( + "set_davg_zero", bool, optional=True, default=True, doc=doc_set_davg_zero + ), Argument("attn", int, optional=True, default=128, doc=doc_attn), Argument("attn_layer", int, optional=True, default=2, doc=doc_attn_layer), Argument("attn_dotr", bool, optional=True, default=True, doc=doc_attn_dotr), From 1a7f2034299dd14c80811232e67f314484c3c200 Mon Sep 17 00:00:00 2001 From: Jinzhe Zeng Date: Wed, 15 Mar 2023 21:37:22 -0400 Subject: [PATCH 017/126] support mixed natoms training (#2366) Signed-off-by: Jinzhe Zeng --- deepmd/utils/data.py | 10 +++++- deepmd/utils/neighbor_stat.py | 4 +-- doc/model/train-se-atten.md | 6 ++-- source/op/neighbor_stat.cc | 2 ++ source/tests/common.py | 55 ++++++++++++++++++++++++++++++- source/tests/test_virtual_type.py | 32 ++++++++++++++++++ 6 files changed, 103 insertions(+), 6 deletions(-) diff --git a/deepmd/utils/data.py b/deepmd/utils/data.py index 537c5b4868..66870e85b2 100644 --- a/deepmd/utils/data.py +++ b/deepmd/utils/data.py @@ -91,6 +91,10 @@ def __init__( self.type_idx_map = np.array( sorter[np.searchsorted(type_map, self.type_map, sorter=sorter)] ) + # padding for virtual atom + self.type_idx_map = np.append( + self.type_idx_map, np.array([-1], dtype=np.int32) + ) self.type_map = type_map if type_map is None and self.type_map is None and self.mixed_type: raise RuntimeError("mixed_type format must have type_map!") @@ -489,8 +493,12 @@ def _load_set(self, set_name: DPPath): [(real_type == i).sum(axis=-1) for i in range(self.get_ntypes())], dtype=np.int32, ).T + ghost_nums = np.array( + [(real_type == -1).sum(axis=-1)], + dtype=np.int32, + ).T assert ( - atom_type_nums.sum(axis=-1) == natoms + atom_type_nums.sum(axis=-1) + ghost_nums.sum(axis=-1) == natoms ).all(), "some types in 'real_atom_types.npy' of set {} are not contained in {} types!".format( set_name, self.get_ntypes() ) diff --git a/deepmd/utils/neighbor_stat.py b/deepmd/utils/neighbor_stat.py index b07614fd57..eebe33d7a0 100644 --- a/deepmd/utils/neighbor_stat.py +++ b/deepmd/utils/neighbor_stat.py @@ -69,8 +69,8 @@ def builder(): t_natoms = place_holders["natoms_vec"] if self.one_type: # all types = 0, natoms_vec = [natoms, natoms, natoms] - t_type = tf.zeros_like(t_type, dtype=tf.int32) - t_natoms = tf.repeat(t_natoms[0], 3) + t_type = tf.clip_by_value(t_type, -1, 0) + t_natoms = tf.tile(t_natoms[0:1], [3]) _max_nbor_size, _min_nbor_dist = op_module.neighbor_stat( place_holders["coord"], diff --git a/doc/model/train-se-atten.md b/doc/model/train-se-atten.md index e91ae646c4..8b006346a9 100644 --- a/doc/model/train-se-atten.md +++ b/doc/model/train-se-atten.md @@ -103,11 +103,13 @@ ID | Property | File | Requir ---------- | -------------------------------- | ------------------- | -------------------- | ----------------------- | ----------- / | Atom type indexes (place holder) | type.raw | Required | Natoms | All zeros to fake the type input type_map | Atom type names | type_map.raw | Required | Ntypes | Atom names that map to atom type contained in all the frames, which is unnecessart to be contained in the periodic table -type | Atom type indexes of each frame | real_atom_types.npy | Required | Nframes \* Natoms | Integers that describe atom types in each frame, corresponding to indexes in type_map +type | Atom type indexes of each frame | real_atom_types.npy | Required | Nframes \* Natoms | Integers that describe atom types in each frame, corresponding to indexes in type_map. `-1` means virtual atoms. With these edited files, one can put together frames with the same `Natoms`, instead of the same formula (like `H2O`). Note that this `mixed_type` format only supports `se_atten` descriptor. -The API to generate or transfer to `mixed_type` format will be uploaded on [dpdata](https://github.com/deepmodeling/dpdata) soon for a more convenient experience. +To put frames with different `Natoms` into the same system, one can pad systems by adding virtual atoms whose type is `-1`. Virtual atoms do not contribute to any fitting property, so the atomic property of virtual atoms (e.g. forces) should be given zero. + +The API to generate or transfer to `mixed_type` format is available on [dpdata](https://github.com/deepmodeling/dpdata) for a more convenient experience. ## Training example Here we upload the AlMgCu example shown in the paper, you can download it here: diff --git a/source/op/neighbor_stat.cc b/source/op/neighbor_stat.cc index 6c58096824..ba067786f9 100644 --- a/source/op/neighbor_stat.cc +++ b/source/op/neighbor_stat.cc @@ -180,8 +180,10 @@ class NeighborStatOp : public OpKernel { #pragma omp parallel for for (int ii = 0; ii < nloc; ii++) { + if (d_type[ii] < 0) continue; // virtual atom for (int jj = 0; jj < d_nlist_r[ii].size(); jj++) { int type = d_type[d_nlist_r[ii][jj]]; + if (type < 0) continue; // virtual atom max_nbor_size[ii * ntypes + type] += 1; compute_t rij[3] = { d_coord3[d_nlist_r[ii][jj] * 3 + 0] - d_coord3[ii * 3 + 0], diff --git a/source/tests/common.py b/source/tests/common.py index d0dd2d2608..8fc303c1d7 100644 --- a/source/tests/common.py +++ b/source/tests/common.py @@ -85,9 +85,62 @@ def gen_data_mixed_type(nframes=1): ) -def gen_data(nframes=1, mixed_type=False): +def gen_data_virtual_type(nframes=1, nghost=4): + tmpdata = Data(rand_pert=0.1, seed=1, nframes=nframes) + sys = dpdata.LabeledSystem() + real_type_map = ["foo", "bar"] + sys.data["atom_names"] = ["X"] + sys.data["coords"] = tmpdata.coord + sys.data["atom_types"] = np.concatenate( + [ + np.zeros_like(tmpdata.atype), + np.zeros([nghost], dtype=np.int32), + ], + axis=0, + ) + sys.data["cells"] = tmpdata.cell + nframes = tmpdata.nframes + natoms = tmpdata.natoms + sys.data["coords"] = np.concatenate( + [ + sys.data["coords"].reshape([nframes, natoms, 3]), + np.zeros([nframes, nghost, 3]), + ], + axis=1, + ) + sys.data["cells"] = sys.data["cells"].reshape([nframes, 3, 3]) + sys.data["energies"] = np.zeros([nframes, 1]) + sys.data["forces"] = np.zeros([nframes, natoms + nghost, 3]) + sys.to_deepmd_npy("system_mixed_type", prec=np.float64) + np.savetxt("system_mixed_type/type_map.raw", real_type_map, fmt="%s") + np.save( + "system_mixed_type/set.000/real_atom_types.npy", + np.concatenate( + [ + tmpdata.atype.reshape(1, -1).repeat(nframes, 0), + np.full([nframes, nghost], -1, dtype=np.int32), + ], + axis=1, + ), + ) + np.save("system_mixed_type/set.000/fparam.npy", tmpdata.fparam) + np.save( + "system_mixed_type/set.000/aparam.npy", + np.concatenate( + [ + tmpdata.aparam.reshape([nframes, natoms, 2]), + np.zeros([nframes, nghost, 2]), + ], + axis=1, + ), + ) + + +def gen_data(nframes=1, mixed_type=False, virtual_type=False): if not mixed_type: gen_data_type_specific(nframes) + elif virtual_type: + gen_data_virtual_type(nframes) else: gen_data_mixed_type(nframes) diff --git a/source/tests/test_virtual_type.py b/source/tests/test_virtual_type.py index 575f6dc768..98ab84cf93 100644 --- a/source/tests/test_virtual_type.py +++ b/source/tests/test_virtual_type.py @@ -4,15 +4,26 @@ import numpy as np from common import ( + gen_data, + j_loader, tests_path, ) +from deepmd.common import ( + j_must_have, +) from deepmd.infer import ( DeepPot, ) from deepmd.utils.convert import ( convert_pbtxt_to_pb, ) +from deepmd.utils.data_system import ( + DeepmdDataSystem, +) +from deepmd.utils.neighbor_stat import ( + NeighborStat, +) class TestVirtualType(unittest.TestCase): @@ -106,3 +117,24 @@ def test_infer_mixed_type(self): np.testing.assert_almost_equal(v1, v2) np.testing.assert_almost_equal(ae1[:nloc], ae2[nghost:]) np.testing.assert_almost_equal(av1[:nloc], av2[nghost:]) + + +class TestTrainVirtualType(unittest.TestCase): + def setUp(self) -> None: + gen_data(mixed_type=True, virtual_type=True) + + def test_data_mixed_type(self): + jfile = "water_se_atten_mixed_type.json" + jdata = j_loader(jfile) + + systems = j_must_have(jdata, "systems") + batch_size = 1 + test_size = 1 + rcut = j_must_have(jdata["model"]["descriptor"], "rcut") + type_map = j_must_have(jdata["model"], "type_map") + + data = DeepmdDataSystem(systems, batch_size, test_size, rcut, type_map=type_map) + data.get_batch() + # neighbor stat + nei_stat = NeighborStat(len(type_map), rcut, one_type=True) + min_nbor_dist, max_nbor_size = nei_stat.get_stat(data) From 69bdaefaaf877a70b25ae2f807a52cef0fd9b87e Mon Sep 17 00:00:00 2001 From: Jinzhe Zeng Date: Wed, 15 Mar 2023 22:27:15 -0400 Subject: [PATCH 018/126] support mixed systems training (#2372) With the new option, a batch of data could come from all systems with different numbers of atoms and is generated and padded into a mixed system on the fly. This PR is independent of #2366, which provides a manual way to use mixed systems. In theory #2366 should have better performance and this PR is more convenient. --------- Signed-off-by: Jinzhe Zeng Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> --- deepmd/utils/argcheck.py | 3 +- deepmd/utils/data_system.py | 106 ++++++++++++++++++++++++++- examples/nopbc/mixed/input.json | 71 ++++++++++++++++++ source/tests/test_deepmd_data_sys.py | 24 ++++++ 4 files changed, 201 insertions(+), 3 deletions(-) create mode 100644 examples/nopbc/mixed/input.json diff --git a/deepmd/utils/argcheck.py b/deepmd/utils/argcheck.py index ca9afd43ac..9fb977e5eb 100644 --- a/deepmd/utils/argcheck.py +++ b/deepmd/utils/argcheck.py @@ -976,7 +976,8 @@ def training_data_args(): # ! added by Ziyao: new specification style for data - list: the length of which is the same as the {link_sys}. The batch size of each system is given by the elements of the list.\n\n\ - int: all {link_sys} use the same batch size.\n\n\ - string "auto": automatically determines the batch size so that the batch_size times the number of atoms in the system is no less than 32.\n\n\ -- string "auto:N": automatically determines the batch size so that the batch_size times the number of atoms in the system is no less than N.' +- string "auto:N": automatically determines the batch size so that the batch_size times the number of atoms in the system is no less than N.\n\n\ +- string "mixed:N": the batch data will be sampled from all systems and merged into a mixed system with the batch size N. Only support the se_atten descriptor.' doc_auto_prob_style = 'Determine the probability of systems automatically. The method is assigned by this key and can be\n\n\ - "prob_uniform" : the probability all the systems are equal, namely 1.0/self.get_nsystems()\n\n\ - "prob_sys_size" : the probability of a system is proportional to the number of batches in the system\n\n\ diff --git a/deepmd/utils/data_system.py b/deepmd/utils/data_system.py index 28dc799bf8..d87219fcc9 100644 --- a/deepmd/utils/data_system.py +++ b/deepmd/utils/data_system.py @@ -112,6 +112,7 @@ def __init__( # batch size self.batch_size = batch_size is_auto_bs = False + self.mixed_systems = False if isinstance(self.batch_size, int): self.batch_size = self.batch_size * np.ones(self.nsystems, dtype=int) elif isinstance(self.batch_size, str): @@ -121,9 +122,16 @@ def __init__( rule = 32 if len(words) == 2: rule = int(words[1]) + self.batch_size = self._make_auto_bs(rule) + elif "mixed" == words[0]: + self.mixed_systems = True + if len(words) == 2: + rule = int(words[1]) + else: + raise RuntimeError("batch size must be specified for mixed systems") + self.batch_size = rule * np.ones(self.nsystems, dtype=int) else: raise RuntimeError("unknown batch_size rule " + words[0]) - self.batch_size = self._make_auto_bs(rule) elif isinstance(self.batch_size, list): pass else: @@ -361,7 +369,7 @@ def _get_sys_probs(self, sys_probs, auto_prob_style): # depreciated prob = self._process_sys_probs(sys_probs) return prob - def get_batch(self, sys_idx: Optional[int] = None): + def get_batch(self, sys_idx: Optional[int] = None) -> dict: # batch generation style altered by Ziyao Li: # one should specify the "sys_prob" and "auto_prob_style" params # via set_sys_prob() function. The sys_probs this function uses is @@ -375,9 +383,36 @@ def get_batch(self, sys_idx: Optional[int] = None): The index of system from which the batch is get. If sys_idx is not None, `sys_probs` and `auto_prob_style` are ignored If sys_idx is None, automatically determine the system according to `sys_probs` or `auto_prob_style`, see the following. + This option does not work for mixed systems. + + Returns + ------- + dict + The batch data """ if not hasattr(self, "default_mesh"): self._make_default_mesh() + if not self.mixed_systems: + b_data = self.get_batch_standard(sys_idx) + else: + b_data = self.get_batch_mixed() + return b_data + + def get_batch_standard(self, sys_idx: Optional[int] = None) -> dict: + """Get a batch of data from the data systems in the standard way. + + Parameters + ---------- + sys_idx : int + The index of system from which the batch is get. + If sys_idx is not None, `sys_probs` and `auto_prob_style` are ignored + If sys_idx is None, automatically determine the system according to `sys_probs` or `auto_prob_style`, see the following. + + Returns + ------- + dict + The batch data + """ if sys_idx is not None: self.pick_idx = sys_idx else: @@ -390,6 +425,73 @@ def get_batch(self, sys_idx: Optional[int] = None): b_data["default_mesh"] = self.default_mesh[self.pick_idx] return b_data + def get_batch_mixed(self) -> dict: + """Get a batch of data from the data systems in the mixed way. + + Returns + ------- + dict + The batch data + """ + # mixed systems have a global batch size + batch_size = self.batch_size[0] + batch_data = [] + for _ in range(batch_size): + self.pick_idx = dp_random.choice(np.arange(self.nsystems), p=self.sys_probs) + bb_data = self.data_systems[self.pick_idx].get_batch(1) + bb_data["natoms_vec"] = self.natoms_vec[self.pick_idx] + bb_data["default_mesh"] = self.default_mesh[self.pick_idx] + batch_data.append(bb_data) + b_data = self._merge_batch_data(batch_data) + return b_data + + def _merge_batch_data(self, batch_data: List[dict]) -> dict: + """Merge batch data from different systems. + + Parameters + ---------- + batch_data : list of dict + A list of batch data from different systems. + + Returns + ------- + dict + The merged batch data. + """ + b_data = {} + max_natoms = max(bb["natoms_vec"][0] for bb in batch_data) + # natoms_vec + natoms_vec = np.zeros(2 + self.get_ntypes(), dtype=int) + natoms_vec[0:3] = max_natoms + b_data["natoms_vec"] = natoms_vec + # real_natoms_vec + real_natoms_vec = np.vstack([bb["natoms_vec"] for bb in batch_data]) + b_data["real_natoms_vec"] = real_natoms_vec + # type + type_vec = np.full((len(batch_data), max_natoms), -1, dtype=int) + for ii, bb in enumerate(batch_data): + type_vec[ii, : bb["type"].shape[1]] = bb["type"][0] + b_data["type"] = type_vec + # default_mesh + default_mesh = np.mean([bb["default_mesh"] for bb in batch_data], axis=0) + b_data["default_mesh"] = default_mesh + # other data + data_dict = self.get_data_dict(0) + for kk, vv in data_dict.items(): + if kk not in batch_data[0]: + continue + b_data["find_" + kk] = batch_data[0]["find_" + kk] + if not vv["atomic"]: + b_data[kk] = np.concatenate([bb[kk] for bb in batch_data], axis=0) + else: + b_data[kk] = np.zeros( + (len(batch_data), max_natoms * vv["ndof"] * vv["repeat"]), + dtype=batch_data[0][kk].dtype, + ) + for ii, bb in enumerate(batch_data): + b_data[kk][ii, : bb[kk].shape[1]] = bb[kk][0] + return b_data + # ! altered by Marián Rynik def get_test(self, sys_idx: Optional[int] = None, n_test: int = -1): # depreciated """Get test data from the the data systems. diff --git a/examples/nopbc/mixed/input.json b/examples/nopbc/mixed/input.json new file mode 100644 index 0000000000..ab49b4e442 --- /dev/null +++ b/examples/nopbc/mixed/input.json @@ -0,0 +1,71 @@ +{ + "_comment": " model parameters", + "model": { + "type_map": [ + "C", + "H", + "O" + ], + "descriptor": { + "type": "se_atten", + "sel": 120, + "rcut_smth": 1.00, + "rcut": 6.00, + "neuron": [ + 25, + 50, + 100 + ], + "resnet_dt": false, + "axis_neuron": 12, + "seed": 1, + "_comment": " that's all" + }, + "fitting_net": { + "neuron": [ + 240, + 240, + 240 + ], + "resnet_dt": true, + "seed": 1, + "_comment": " that's all" + }, + "_comment": " that's all" + }, + + "learning_rate": { + "type": "exp", + "decay_steps": 4000, + "start_lr": 0.001, + "stop_lr": 3.51e-8, + "_comment": "that's all" + }, + + "loss": { + "type": "ener", + "start_pref_e": 0.02, + "limit_pref_e": 1, + "start_pref_f": 1000, + "limit_pref_f": 1, + "start_pref_v": 0, + "limit_pref_v": 0, + "_comment": " that's all" + }, + + "training": { + "training_data": { + "systems": "../data/", + "batch_size": "mixed:4", + "_comment": "that's all" + }, + "numb_steps": 4000000, + "seed": 10, + "disp_file": "lcurve.out", + "disp_freq": 100, + "save_freq": 1000, + "_comment": "that's all" + }, + + "_comment": "that's all" +} diff --git a/source/tests/test_deepmd_data_sys.py b/source/tests/test_deepmd_data_sys.py index d24291114d..9df48c3f57 100644 --- a/source/tests/test_deepmd_data_sys.py +++ b/source/tests/test_deepmd_data_sys.py @@ -7,6 +7,9 @@ from deepmd.env import ( GLOBAL_NP_FLOAT_PRECISION, ) +from deepmd.utils import ( + random, +) from deepmd.utils.data_system import ( DeepmdDataSystem, ) @@ -398,3 +401,24 @@ def test_sys_prob_floating_point_error(self): ] ds = DeepmdDataSystem(self.sys_name, 3, 2, 2.0, sys_probs=sys_probs) self.assertEqual(ds.sys_probs.size, len(sys_probs)) + + def test_get_mixed_batch(self): + """test get_batch with mixed system.""" + batch_size = "mixed:3" + test_size = 2 + + ds = DeepmdDataSystem(self.sys_name, batch_size, test_size, 2.0) + ds.add("test", self.test_ndof, atomic=True, must=True) + ds.add("null", self.test_ndof, atomic=True, must=False) + random.seed(114514) + # with this seed, the batch is fixed, with natoms 3, 6, 6 + data = ds.get_batch() + np.testing.assert_equal(data["natoms_vec"], np.array([6, 6, 6, 0, 0])) + np.testing.assert_equal(data["real_natoms_vec"][:, 0], np.array([3, 6, 6])) + np.testing.assert_equal(data["type"][0, 3:6], np.array([-1, -1, -1])) + np.testing.assert_equal(data["coord"][0, 9:18], np.zeros(9)) + for kk in ("test", "null"): + np.testing.assert_equal( + data[kk][0, 3 * self.test_ndof : 6 * self.test_ndof], + np.zeros(3 * self.test_ndof), + ) From 9ab62defcc098b173028e43dc79be46e0ccaa768 Mon Sep 17 00:00:00 2001 From: Jinzhe Zeng Date: Wed, 15 Mar 2023 23:31:49 -0400 Subject: [PATCH 019/126] fix pdf docs (#2401) It looks that the documentation is broken Signed-off-by: Jinzhe Zeng --- deepmd/descriptor/se_a.py | 4 ++-- deepmd/descriptor/se_a_mask.py | 4 ++-- deepmd/fit/ener.py | 8 ++++---- deepmd/utils/network.py | 4 ++-- doc/conf.py | 4 ++++ 5 files changed, 14 insertions(+), 10 deletions(-) diff --git a/deepmd/descriptor/se_a.py b/deepmd/descriptor/se_a.py index d49c329353..2bff523608 100644 --- a/deepmd/descriptor/se_a.py +++ b/deepmd/descriptor/se_a.py @@ -96,8 +96,8 @@ class DescrptSeA(DescrptSe): .. math:: (\mathcal{G}^i)_j = \mathcal{N}(s(r_{ji})) - :math:`\mathcal{G}^i_< \in \mathbb{R}^{N \times M_2}` takes first :math:`M_2`$` columns of - :math:`\mathcal{G}^i`$`. The equation of embedding network :math:`\mathcal{N}` can be found at + :math:`\mathcal{G}^i_< \in \mathbb{R}^{N \times M_2}` takes first :math:`M_2` columns of + :math:`\mathcal{G}^i`. The equation of embedding network :math:`\mathcal{N}` can be found at :meth:`deepmd.utils.network.embedding_net`. Parameters diff --git a/deepmd/descriptor/se_a_mask.py b/deepmd/descriptor/se_a_mask.py index 00b23232f1..93256e3910 100644 --- a/deepmd/descriptor/se_a_mask.py +++ b/deepmd/descriptor/se_a_mask.py @@ -70,8 +70,8 @@ class DescrptSeAMask(DescrptSeA): .. math:: (\mathcal{G}^i)_j = \mathcal{N}(s(r_{ji})) - :math:`\mathcal{G}^i_< \in \mathbb{R}^{N \times M_2}` takes first :math:`M_2`$` columns of - :math:`\mathcal{G}^i`$`. The equation of embedding network :math:`\mathcal{N}` can be found at + :math:`\mathcal{G}^i_< \in \mathbb{R}^{N \times M_2}` takes first :math:`M_2` columns of + :math:`\mathcal{G}^i`. The equation of embedding network :math:`\mathcal{N}` can be found at :meth:`deepmd.utils.network.embedding_net`. Specially for descriptor se_a_mask is a concise implementation of se_a. The difference is that se_a_mask only considered a non-pbc system. diff --git a/deepmd/fit/ener.py b/deepmd/fit/ener.py index 53c3b1d468..c9f70117f7 100644 --- a/deepmd/fit/ener.py +++ b/deepmd/fit/ener.py @@ -59,9 +59,9 @@ class EnerFitting(Fitting): \mathbf{y}=\mathcal{L}(\mathbf{x};\mathbf{w},\mathbf{b})= \boldsymbol{\phi}(\mathbf{x}^T\mathbf{w}+\mathbf{b}) - where :math:`\mathbf{x} \in \mathbb{R}^{N_1}`$` is the input vector and :math:`\mathbf{y} \in \mathbb{R}^{N_2}` + where :math:`\mathbf{x} \in \mathbb{R}^{N_1}` is the input vector and :math:`\mathbf{y} \in \mathbb{R}^{N_2}` is the output vector. :math:`\mathbf{w} \in \mathbb{R}^{N_1 \times N_2}` and - :math:`\mathbf{b} \in \mathbb{R}^{N_2}`$` are weights and biases, respectively, + :math:`\mathbf{b} \in \mathbb{R}^{N_2}` are weights and biases, respectively, both of which are trainable if `trainable[i]` is `True`. :math:`\boldsymbol{\phi}` is the activation function. @@ -71,9 +71,9 @@ class EnerFitting(Fitting): \mathbf{y}=\mathcal{L}^{(n)}(\mathbf{x};\mathbf{w},\mathbf{b})= \mathbf{x}^T\mathbf{w}+\mathbf{b} - where :math:`\mathbf{x} \in \mathbb{R}^{N_{n-1}}`$` is the input vector and :math:`\mathbf{y} \in \mathbb{R}` + where :math:`\mathbf{x} \in \mathbb{R}^{N_{n-1}}` is the input vector and :math:`\mathbf{y} \in \mathbb{R}` is the output scalar. :math:`\mathbf{w} \in \mathbb{R}^{N_{n-1}}` and - :math:`\mathbf{b} \in \mathbb{R}`$` are weights and bias, respectively, + :math:`\mathbf{b} \in \mathbb{R}` are weights and bias, respectively, both of which are trainable if `trainable[n]` is `True`. Parameters diff --git a/deepmd/utils/network.py b/deepmd/utils/network.py index e5ce249f45..a718da0b26 100644 --- a/deepmd/utils/network.py +++ b/deepmd/utils/network.py @@ -144,9 +144,9 @@ def embedding_net( \boldsymbol{\phi}(\mathbf{x}^T\mathbf{w}+\mathbf{b}), & \text{otherwise} \\ \end{cases} - where :math:`\mathbf{x} \in \mathbb{R}^{N_1}`$` is the input vector and :math:`\mathbf{y} \in \mathbb{R}^{N_2}` + where :math:`\mathbf{x} \in \mathbb{R}^{N_1}` is the input vector and :math:`\mathbf{y} \in \mathbb{R}^{N_2}` is the output vector. :math:`\mathbf{w} \in \mathbb{R}^{N_1 \times N_2}` and - :math:`\mathbf{b} \in \mathbb{R}^{N_2}`$` are weights and biases, respectively, + :math:`\mathbf{b} \in \mathbb{R}^{N_2}` are weights and biases, respectively, both of which are trainable if `trainable` is `True`. :math:`\boldsymbol{\phi}` is the activation function. diff --git a/doc/conf.py b/doc/conf.py index e338274f0e..2fd0dcdc33 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -305,6 +305,10 @@ def setup(app): "fontpkg": r""" \usepackage{fontspec} \setmainfont{Symbola} +""", + "preamble": r""" +\usepackage{enumitem} +\setlistdepth{99} """, } From 4e5eb6948d0ca30b900a5ca6ef3087e4807feca7 Mon Sep 17 00:00:00 2001 From: Jinzhe Zeng Date: Wed, 15 Mar 2023 23:47:15 -0400 Subject: [PATCH 020/126] clean old handlers before adding new one (#2400) It's not a problem for production, but during tests there might be multiple handlers added. Signed-off-by: Jinzhe Zeng --- deepmd/loggers/loggers.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/deepmd/loggers/loggers.py b/deepmd/loggers/loggers.py index bb2e5f2dad..4249d7fb3d 100644 --- a/deepmd/loggers/loggers.py +++ b/deepmd/loggers/loggers.py @@ -229,6 +229,8 @@ def set_log_handles( ch.setLevel(level) ch.addFilter(_AppFilter()) + # clean old handlers before adding new one + root_log.handlers.clear() root_log.addHandler(ch) # * add file handler *************************************************************** From 4fe3b1be362f825051f5e861df0213ea678c1aa0 Mon Sep 17 00:00:00 2001 From: Koki MURAOKA Date: Thu, 16 Mar 2023 23:31:22 +0900 Subject: [PATCH 021/126] fix typo (#2404) --- deepmd/descriptor/descriptor.py | 4 ++-- deepmd/utils/argcheck.py | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/deepmd/descriptor/descriptor.py b/deepmd/descriptor/descriptor.py index 15f3cfaa4c..38b56a85be 100644 --- a/deepmd/descriptor/descriptor.py +++ b/deepmd/descriptor/descriptor.py @@ -44,7 +44,7 @@ class Descriptor(PluginVariant): @staticmethod def register(key: str) -> "Descriptor": - """Regiester a descriptor plugin. + """Register a descriptor plugin. Parameters ---------- @@ -54,7 +54,7 @@ def register(key: str) -> "Descriptor": Returns ------- Descriptor - the regiestered descriptor + the registered descriptor Examples -------- diff --git a/deepmd/utils/argcheck.py b/deepmd/utils/argcheck.py index 9fb977e5eb..f19300a488 100644 --- a/deepmd/utils/argcheck.py +++ b/deepmd/utils/argcheck.py @@ -80,7 +80,7 @@ def __init__(self) -> None: def register( self, name: str, alias: Optional[List[str]] = None ) -> Callable[[], List[Argument]]: - """Regiester a descriptor argument plugin. + """Register a descriptor argument plugin. Parameters ---------- @@ -92,7 +92,7 @@ def register( Returns ------- Callable[[], List[Argument]] - the regiestered descriptor argument method + the registered descriptor argument method Examples -------- From 9737ad91bfe90b7d660c310668a213b693fbf6cd Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 21 Mar 2023 09:17:08 +0800 Subject: [PATCH 022/126] Bump docker/login-action from 1.10.0 to 2.1.0 (#2411) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Bumps [docker/login-action](https://github.com/docker/login-action) from 1.10.0 to 2.1.0.
Release notes

Sourced from docker/login-action's releases.

v2.1.0

What's Changed

  • Ensure AWS temp credentials are redacted in workflow logs by @​crazy-max (#275)
  • Bump @​actions/core from 1.6.0 to 1.10.0 (#252 #292)
  • Bump @​aws-sdk/client-ecr from 3.53.0 to 3.186.0 (#298)
  • Bump @​aws-sdk/client-ecr-public from 3.53.0 to 3.186.0 (#299)

Full Changelog: https://github.com/docker/login-action/compare/v2.0.0...v2.1.0

v2.0.0

  • Node 16 as default runtime by @​crazy-max (#161)
    • This requires a minimum Actions Runner version of v2.285.0, which is by default available in GHES 3.4 or later.
  • chore: update dev dependencies and workflow by @​crazy-max (#170)
  • Bump @​actions/exec from 1.1.0 to 1.1.1 (#167)
  • Bump @​actions/io from 1.1.1 to 1.1.2 (#168)
  • Bump minimist from 1.2.5 to 1.2.6 (#176)
  • Bump https-proxy-agent from 5.0.0 to 5.0.1 (#182)

Full Changelog: https://github.com/docker/login-action/compare/v1.14.1...v2.0.0

v1.14.1

  • Revert to Node 12 as default runtime to fix issue for GHE users (#160)

v1.14.0

  • Update to node 16 (#158)
  • Bump @​aws-sdk/client-ecr from 3.45.0 to 3.53.0 (#157)
  • Bump @​aws-sdk/client-ecr-public from 3.45.0 to 3.53.0 (#156)

v1.13.0

  • Handle proxy settings for aws-sdk (#152)
  • Workload identity based authentication docs for GCR and GAR (#112)
  • Test login against ACR (#49)
  • Bump @​aws-sdk/client-ecr from 3.44.0 to 3.45.0 (#132)
  • Bump @​aws-sdk/client-ecr-public from 3.43.0 to 3.45.0 (#131)

v1.12.0

  • ECR: only set credentials if username and password are specified (#128)
  • Refactor to use aws-sdk v3 (#128)

v1.11.0

  • ECR: switch implementation to use the AWS SDK (#126)
  • ecr input to specify whether the given registry is ECR (#123)
  • Test against Windows runner (#126)
  • Update instructions for Google registry (#127)
  • Update dev workflow (#111)
  • Small changes for GHCR doc (#86)
  • Update dev dependencies (#85)
  • Bump ansi-regex from 5.0.0 to 5.0.1 (#101)
  • Bump tmpl from 1.0.4 to 1.0.5 (#100)
  • Bump @​actions/core from 1.4.0 to 1.6.0 (#94 #103)

... (truncated)

Commits
  • f4ef78c Merge pull request #299 from docker/dependabot/npm_and_yarn/aws-sdk/client-ec...
  • 9ad4ce3 Update generated content
  • 884eadd Bump @​aws-sdk/client-ecr-public from 3.53.0 to 3.186.0
  • a266232 Merge pull request #298 from docker/dependabot/npm_and_yarn/aws-sdk/client-ec...
  • f97efcf Update generated content
  • 5ae789b Bump @​aws-sdk/client-ecr from 3.53.0 to 3.186.0
  • 71c23b5 Merge pull request #292 from docker/dependabot/npm_and_yarn/actions/core-1.10.0
  • 6401d70 Update generated content
  • 67e8909 Bump @​actions/core from 1.9.1 to 1.10.0
  • 21f251a Merge pull request #275 from crazy-max/redact-aws-creds
  • Additional commits viewable in compare view

[![Dependabot compatibility score](https://dependabot-badges.githubapp.com/badges/compatibility_score?dependency-name=docker/login-action&package-manager=github_actions&previous-version=1.10.0&new-version=2.1.0)](https://docs.github.com/en/github/managing-security-vulnerabilities/about-dependabot-security-updates#about-compatibility-scores) Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting `@dependabot rebase`. [//]: # (dependabot-automerge-start) [//]: # (dependabot-automerge-end) ---
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Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- .github/workflows/build_wheel.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/build_wheel.yml b/.github/workflows/build_wheel.yml index c497e86e1d..e11ae58bbe 100644 --- a/.github/workflows/build_wheel.yml +++ b/.github/workflows/build_wheel.yml @@ -95,7 +95,7 @@ jobs: name: artifact path: source/install/docker/dist - name: Log in to the Container registry - uses: docker/login-action@f054a8b539a109f9f41c372932f1ae047eff08c9 + uses: docker/login-action@f4ef78c080cd8ba55a85445d5b36e214a81df20a with: registry: ghcr.io username: ${{ github.actor }} From b7fd74bba7f29328a734f33142c367ec45f62f63 Mon Sep 17 00:00:00 2001 From: Jinzhe Zeng Date: Mon, 20 Mar 2023 22:45:55 -0400 Subject: [PATCH 023/126] keep the file permission when `make lammps` (#2414) It looks that by default, CMake will remove the execute permission. Signed-off-by: Jinzhe Zeng --- source/cmake/cmake_lammps.cmake.in | 1 + 1 file changed, 1 insertion(+) diff --git a/source/cmake/cmake_lammps.cmake.in b/source/cmake/cmake_lammps.cmake.in index 7db7ec71c0..a18b43daae 100644 --- a/source/cmake/cmake_lammps.cmake.in +++ b/source/cmake/cmake_lammps.cmake.in @@ -5,6 +5,7 @@ string(REGEX REPLACE "\n" "" files "${files}") foreach (cur_file ${files}) file ( INSTALL DESTINATION "${LMP_INSTALL_PREFIX}" + USE_SOURCE_PERMISSIONS TYPE FILE FILES "${cur_file}" ) From 63cc21f3bdf5c13d3540ae7aba70becd986cd8ed Mon Sep 17 00:00:00 2001 From: Jinzhe Zeng Date: Mon, 20 Mar 2023 22:56:45 -0400 Subject: [PATCH 024/126] C API: support fparam and aparam for DeepPot (#2415) Signed-off-by: Jinzhe Zeng --- source/api_c/include/c_api.h | 50 +- source/api_c/include/c_api_internal.h | 4 + source/api_c/include/deepmd.hpp | 273 +++- source/api_c/src/c_api.cc | 53 +- .../tests/test_deeppot_a_fparam_aparam.cc | 381 +++++ .../test_deeppot_a_fparam_aparam_nframes.cc | 1297 +++++++++++++++++ 6 files changed, 1978 insertions(+), 80 deletions(-) create mode 100644 source/api_c/tests/test_deeppot_a_fparam_aparam.cc create mode 100644 source/api_c/tests/test_deeppot_a_fparam_aparam_nframes.cc diff --git a/source/api_c/include/c_api.h b/source/api_c/include/c_api.h index d36bd2206d..341b2f853d 100644 --- a/source/api_c/include/c_api.h +++ b/source/api_c/include/c_api.h @@ -202,8 +202,10 @@ extern void DP_DeepPotComputeNListf(DP_DeepPot* dp, * @param[in] atype The atom types. The array should contain natoms ints. * @param[in] box The cell of the region. The array should be of size 9. Pass *NULL if pbc is not used. - * @param[in] fparam The frame parameters. (not work at this time) - * @param[in] aparam The atom parameters. (not work at this time) + * @param[in] fparam The frame parameters. The array can be of size nframes x + *dim_fparam. + * @param[in] aparam The atom parameters. The array can be of size nframes x + *dim_aparam. * @param[out] energy Output energy. * @param[out] force Output force. The array should be of size natoms x 3. * @param[out] virial Output virial. The array should be of size 9. @@ -239,8 +241,10 @@ extern void DP_DeepPotCompute2(DP_DeepPot* dp, * @param[in] atype The atom types. The array should contain natoms ints. * @param[in] box The cell of the region. The array should be of size 9. Pass *NULL if pbc is not used. - * @param[in] fparam The frame parameters. (not work at this time) - * @param[in] aparam The atom parameters. (not work at this time) + * @param[in] fparam The frame parameters. The array can be of size nframes x + *dim_fparam. + * @param[in] aparam The atom parameters. The array can be of size nframes x + *dim_aparam. * @param[out] energy Output energy. * @param[out] force Output force. The array should be of size natoms x 3. * @param[out] virial Output virial. The array should be of size 9. @@ -280,8 +284,10 @@ extern void DP_DeepPotComputef2(DP_DeepPot* dp, * @param[in] nghost The number of ghost atoms. * @param[in] nlist The neighbor list. * @param[in] ago Update the internal neighbour list if ago is 0. - * @param[in] fparam The frame parameters. (not work at this time) - * @param[in] aparam The atom parameters. (not work at this time) + * @param[in] fparam The frame parameters. The array can be of size nframes x + *dim_fparam. + * @param[in] aparam The atom parameters. The array can be of size nframes x + *dim_aparam. * @param[out] energy Output energy. * @param[out] force Output force. The array should be of size natoms x 3. * @param[out] virial Output virial. The array should be of size 9. @@ -324,8 +330,10 @@ extern void DP_DeepPotComputeNList2(DP_DeepPot* dp, * @param[in] nghost The number of ghost atoms. * @param[in] nlist The neighbor list. * @param[in] ago Update the internal neighbour list if ago is 0. - * @param[in] fparam The frame parameters. (not work at this time) - * @param[in] aparam The atom parameters. (not work at this time) + * @param[in] fparam The frame parameters. The array can be of size nframes x + *dim_fparam. + * @param[in] aparam The atom parameters. The array can be of size nframes x + *dim_aparam. * @param[out] energy Output energy. * @param[out] force Output force. The array should be of size natoms x 3. * @param[out] virial Output virial. The array should be of size 9. @@ -365,8 +373,10 @@ extern void DP_DeepPotComputeNListf2(DP_DeepPot* dp, *ints. * @param[in] box The cell of the region. The array should be of size 9. Pass *NULL if pbc is not used. - * @param[in] fparam The frame parameters. (not work at this time) - * @param[in] aparam The atom parameters. (not work at this time) + * @param[in] fparam The frame parameters. The array can be of size nframes x + *dim_fparam. + * @param[in] aparam The atom parameters. The array can be of size nframes x + *dim_aparam. * @param[out] energy Output energy. * @param[out] force Output force. The array should be of size natoms x 3. * @param[out] virial Output virial. The array should be of size 9. @@ -402,8 +412,10 @@ extern void DP_DeepPotComputeMixedType(DP_DeepPot* dp, *ints. * @param[in] box The cell of the region. The array should be of size 9. Pass *NULL if pbc is not used. - * @param[in] fparam The frame parameters. (not work at this time) - * @param[in] aparam The atom parameters. (not work at this time) + * @param[in] fparam The frame parameters. The array can be of size nframes x + *dim_fparam. + * @param[in] aparam The atom parameters. The array can be of size nframes x + *dim_aparam. * @param[out] energy Output energy. * @param[out] force Output force. The array should be of size natoms x 3. * @param[out] virial Output virial. The array should be of size 9. @@ -551,6 +563,20 @@ double DP_DeepPotGetCutoff(DP_DeepPot* dp); */ int DP_DeepPotGetNumbTypes(DP_DeepPot* dp); +/** + * @brief Get the dimension of frame parameters of a DP. + * @param[in] dp The DP to use. + * @return The dimension of frame parameters of the DP. + */ +int DP_DeepPotGetDimFParam(DP_DeepPot* dp); + +/** + * @brief Get the dimension of atomic parameters of a DP. + * @param[in] dp The DP to use. + * @return The dimension of atomic parameters of the DP. + */ +int DP_DeepPotGetDimAParam(DP_DeepPot* dp); + /** * @brief Get the type map of a DP. * @param[in] dp The DP to use. diff --git a/source/api_c/include/c_api_internal.h b/source/api_c/include/c_api_internal.h index 741f1e88e3..e52b3c9e67 100644 --- a/source/api_c/include/c_api_internal.h +++ b/source/api_c/include/c_api_internal.h @@ -38,6 +38,8 @@ struct DP_DeepPot { deepmd::DeepPot dp; std::string exception; + int dfparam; + int daparam; }; struct DP_DeepPotModelDevi { @@ -46,6 +48,8 @@ struct DP_DeepPotModelDevi { deepmd::DeepPotModelDevi dp; std::string exception; + int dfparam; + int daparam; }; struct DP_DeepTensor { diff --git a/source/api_c/include/deepmd.hpp b/source/api_c/include/deepmd.hpp index cf7285fb41..6f2740077b 100644 --- a/source/api_c/include/deepmd.hpp +++ b/source/api_c/include/deepmd.hpp @@ -586,6 +586,8 @@ class DeepPot { } dp = DP_NewDeepPotWithParam(model.c_str(), gpu_rank, file_content.c_str()); DP_CHECK_OK(DP_DeepPotCheckOK, dp); + dfparam = DP_DeepPotGetDimFParam(dp); + daparam = DP_DeepPotGetDimAParam(dp); }; /** @@ -598,14 +600,25 @@ class DeepPot { * @param[in] atype The atom types. The list should contain natoms ints. * @param[in] box The cell of the region. The array should be of size nframes *x 9 (PBC) or empty (no PBC). + * @param[in] fparam The frame parameter. The array can be of size : + * nframes x dim_fparam. + * dim_fparam. Then all frames are assumed to be provided with the same + *fparam. + * @param[in] aparam The atomic parameter The array can be of size : + * nframes x natoms x dim_aparam. + * natoms x dim_aparam. Then all frames are assumed to be provided with the + *same aparam. **/ template - void compute(ENERGYVTYPE &ener, - std::vector &force, - std::vector &virial, - const std::vector &coord, - const std::vector &atype, - const std::vector &box) { + void compute( + ENERGYVTYPE &ener, + std::vector &force, + std::vector &virial, + const std::vector &coord, + const std::vector &atype, + const std::vector &box, + const std::vector &fparam = std::vector(), + const std::vector &aparam = std::vector()) { unsigned int natoms = atype.size(); unsigned int nframes = coord.size() / natoms / 3; assert(nframes * natoms * 3 == coord.size()); @@ -620,9 +633,15 @@ class DeepPot { virial.resize(nframes * 9); VALUETYPE *force_ = &force[0]; VALUETYPE *virial_ = &virial[0]; + std::vector fparam_, aparam_; + validate_fparam_aparam(nframes, natoms, fparam, aparam); + tile_fparam_aparam(fparam_, nframes, dfparam, fparam); + tile_fparam_aparam(aparam_, nframes, natoms * daparam, aparam); + const VALUETYPE *fparam__ = !fparam_.empty() ? &fparam_[0] : nullptr; + const VALUETYPE *aparam__ = !aparam_.empty() ? &aparam_[0] : nullptr; _DP_DeepPotCompute(dp, nframes, natoms, coord_, atype_, box_, - nullptr, nullptr, ener_, force_, virial_, + fparam__, aparam__, ener_, force_, virial_, nullptr, nullptr); DP_CHECK_OK(DP_DeepPotCheckOK, dp); }; @@ -639,16 +658,27 @@ class DeepPot { * @param[in] atype The atom types. The list should contain natoms ints. * @param[in] box The cell of the region. The array should be of size nframes *x 9 (PBC) or empty (no PBC). + * @param[in] fparam The frame parameter. The array can be of size : + * nframes x dim_fparam. + * dim_fparam. Then all frames are assumed to be provided with the same + *fparam. + * @param[in] aparam The atomic parameter The array can be of size : + * nframes x natoms x dim_aparam. + * natoms x dim_aparam. Then all frames are assumed to be provided with the + *same aparam. **/ template - void compute(ENERGYVTYPE &ener, - std::vector &force, - std::vector &virial, - std::vector &atom_energy, - std::vector &atom_virial, - const std::vector &coord, - const std::vector &atype, - const std::vector &box) { + void compute( + ENERGYVTYPE &ener, + std::vector &force, + std::vector &virial, + std::vector &atom_energy, + std::vector &atom_virial, + const std::vector &coord, + const std::vector &atype, + const std::vector &box, + const std::vector &fparam = std::vector(), + const std::vector &aparam = std::vector()) { unsigned int natoms = atype.size(); unsigned int nframes = coord.size() / natoms / 3; assert(nframes * natoms * 3 == coord.size()); @@ -668,9 +698,15 @@ class DeepPot { VALUETYPE *virial_ = &virial[0]; VALUETYPE *atomic_ener_ = &atom_energy[0]; VALUETYPE *atomic_virial_ = &atom_virial[0]; + std::vector fparam_, aparam_; + validate_fparam_aparam(nframes, natoms, fparam, aparam); + tile_fparam_aparam(fparam_, nframes, dfparam, fparam); + tile_fparam_aparam(aparam_, nframes, natoms * daparam, aparam); + const VALUETYPE *fparam__ = !fparam_.empty() ? &fparam_[0] : nullptr; + const VALUETYPE *aparam__ = !aparam_.empty() ? &aparam_[0] : nullptr; _DP_DeepPotCompute(dp, nframes, natoms, coord_, atype_, box_, - nullptr, nullptr, ener_, force_, virial_, + fparam__, aparam__, ener_, force_, virial_, atomic_ener_, atomic_virial_); DP_CHECK_OK(DP_DeepPotCheckOK, dp); }; @@ -689,17 +725,28 @@ class DeepPot { * @param[in] nghost The number of ghost atoms. * @param[in] nlist The neighbor list. * @param[in] ago Update the internal neighbour list if ago is 0. + * @param[in] fparam The frame parameter. The array can be of size : + * nframes x dim_fparam. + * dim_fparam. Then all frames are assumed to be provided with the same + *fparam. + * @param[in] aparam The atomic parameter The array can be of size : + * nframes x natoms x dim_aparam. + * natoms x dim_aparam. Then all frames are assumed to be provided with the + *same aparam. **/ template - void compute(ENERGYVTYPE &ener, - std::vector &force, - std::vector &virial, - const std::vector &coord, - const std::vector &atype, - const std::vector &box, - const int nghost, - const InputNlist &lmp_list, - const int &ago) { + void compute( + ENERGYVTYPE &ener, + std::vector &force, + std::vector &virial, + const std::vector &coord, + const std::vector &atype, + const std::vector &box, + const int nghost, + const InputNlist &lmp_list, + const int &ago, + const std::vector &fparam = std::vector(), + const std::vector &aparam = std::vector()) { unsigned int natoms = atype.size(); unsigned int nframes = coord.size() / natoms / 3; assert(nframes * natoms * 3 == coord.size()); @@ -714,10 +761,16 @@ class DeepPot { virial.resize(nframes * 9); VALUETYPE *force_ = &force[0]; VALUETYPE *virial_ = &virial[0]; + std::vector fparam_, aparam_; + validate_fparam_aparam(nframes, natoms - nghost, fparam, aparam); + tile_fparam_aparam(fparam_, nframes, dfparam, fparam); + tile_fparam_aparam(aparam_, nframes, (natoms - nghost) * daparam, aparam); + const VALUETYPE *fparam__ = !fparam_.empty() ? &fparam_[0] : nullptr; + const VALUETYPE *aparam__ = !aparam_.empty() ? &aparam_[0] : nullptr; _DP_DeepPotComputeNList( dp, nframes, natoms, coord_, atype_, box_, nghost, lmp_list.nl, ago, - nullptr, nullptr, ener_, force_, virial_, nullptr, nullptr); + fparam__, aparam__, ener_, force_, virial_, nullptr, nullptr); DP_CHECK_OK(DP_DeepPotCheckOK, dp); }; /** @@ -736,19 +789,30 @@ class DeepPot { * @param[in] nghost The number of ghost atoms. * @param[in] nlist The neighbor list. * @param[in] ago Update the internal neighbour list if ago is 0. + * @param[in] fparam The frame parameter. The array can be of size : + * nframes x dim_fparam. + * dim_fparam. Then all frames are assumed to be provided with the same + *fparam. + * @param[in] aparam The atomic parameter The array can be of size : + * nframes x natoms x dim_aparam. + * natoms x dim_aparam. Then all frames are assumed to be provided with the + *same aparam. **/ template - void compute(ENERGYVTYPE &ener, - std::vector &force, - std::vector &virial, - std::vector &atom_energy, - std::vector &atom_virial, - const std::vector &coord, - const std::vector &atype, - const std::vector &box, - const int nghost, - const InputNlist &lmp_list, - const int &ago) { + void compute( + ENERGYVTYPE &ener, + std::vector &force, + std::vector &virial, + std::vector &atom_energy, + std::vector &atom_virial, + const std::vector &coord, + const std::vector &atype, + const std::vector &box, + const int nghost, + const InputNlist &lmp_list, + const int &ago, + const std::vector &fparam = std::vector(), + const std::vector &aparam = std::vector()) { unsigned int natoms = atype.size(); unsigned int nframes = coord.size() / natoms / 3; assert(nframes * natoms * 3 == coord.size()); @@ -768,10 +832,17 @@ class DeepPot { VALUETYPE *virial_ = &virial[0]; VALUETYPE *atomic_ener_ = &atom_energy[0]; VALUETYPE *atomic_virial_ = &atom_virial[0]; - - _DP_DeepPotComputeNList( - dp, nframes, natoms, coord_, atype_, box_, nghost, lmp_list.nl, ago, - nullptr, nullptr, ener_, force_, virial_, atomic_ener_, atomic_virial_); + std::vector fparam_, aparam_; + validate_fparam_aparam(nframes, natoms - nghost, fparam, aparam); + tile_fparam_aparam(fparam_, nframes, dfparam, fparam); + tile_fparam_aparam(aparam_, nframes, (natoms - nghost) * daparam, aparam); + const VALUETYPE *fparam__ = !fparam_.empty() ? &fparam_[0] : nullptr; + const VALUETYPE *aparam__ = !aparam_.empty() ? &aparam_[0] : nullptr; + + _DP_DeepPotComputeNList(dp, nframes, natoms, coord_, atype_, + box_, nghost, lmp_list.nl, ago, fparam__, + aparam__, ener_, force_, virial_, + atomic_ener_, atomic_virial_); DP_CHECK_OK(DP_DeepPotCheckOK, dp); }; /** @@ -786,15 +857,26 @@ class DeepPot { * @param[in] atype The atom types. The list should contain natoms ints. * @param[in] box The cell of the region. The array should be of size nframes *x 9 (PBC) or empty (no PBC). + * @param[in] fparam The frame parameter. The array can be of size : + * nframes x dim_fparam. + * dim_fparam. Then all frames are assumed to be provided with the same + *fparam. + * @param[in] aparam The atomic parameter The array can be of size : + * nframes x natoms x dim_aparam. + * natoms x dim_aparam. Then all frames are assumed to be provided with the + *same aparam. **/ template - void compute_mixed_type(ENERGYVTYPE &ener, - std::vector &force, - std::vector &virial, - const int &nframes, - const std::vector &coord, - const std::vector &atype, - const std::vector &box) { + void compute_mixed_type( + ENERGYVTYPE &ener, + std::vector &force, + std::vector &virial, + const int &nframes, + const std::vector &coord, + const std::vector &atype, + const std::vector &box, + const std::vector &fparam = std::vector(), + const std::vector &aparam = std::vector()) { unsigned int natoms = atype.size() / nframes; assert(nframes * natoms * 3 == coord.size()); if (!box.empty()) { @@ -808,9 +890,15 @@ class DeepPot { virial.resize(nframes * 9); VALUETYPE *force_ = &force[0]; VALUETYPE *virial_ = &virial[0]; + std::vector fparam_, aparam_; + validate_fparam_aparam(nframes, natoms, fparam, aparam); + tile_fparam_aparam(fparam_, nframes, dfparam, fparam); + tile_fparam_aparam(aparam_, nframes, natoms * daparam, aparam); + const VALUETYPE *fparam__ = !fparam_.empty() ? &fparam_[0] : nullptr; + const VALUETYPE *aparam__ = !aparam_.empty() ? &aparam_[0] : nullptr; _DP_DeepPotComputeMixedType(dp, nframes, natoms, coord_, atype_, - box_, nullptr, nullptr, ener_, + box_, fparam__, aparam__, ener_, force_, virial_, nullptr, nullptr); DP_CHECK_OK(DP_DeepPotCheckOK, dp); }; @@ -828,17 +916,28 @@ class DeepPot { * @param[in] atype The atom types. The list should contain natoms ints. * @param[in] box The cell of the region. The array should be of size nframes *x 9 (PBC) or empty (no PBC). + * @param[in] fparam The frame parameter. The array can be of size : + * nframes x dim_fparam. + * dim_fparam. Then all frames are assumed to be provided with the same + *fparam. + * @param[in] aparam The atomic parameter The array can be of size : + * nframes x natoms x dim_aparam. + * natoms x dim_aparam. Then all frames are assumed to be provided with the + *same aparam. **/ template - void compute_mixed_type(ENERGYVTYPE &ener, - std::vector &force, - std::vector &virial, - std::vector &atom_energy, - std::vector &atom_virial, - const int &nframes, - const std::vector &coord, - const std::vector &atype, - const std::vector &box) { + void compute_mixed_type( + ENERGYVTYPE &ener, + std::vector &force, + std::vector &virial, + std::vector &atom_energy, + std::vector &atom_virial, + const int &nframes, + const std::vector &coord, + const std::vector &atype, + const std::vector &box, + const std::vector &fparam = std::vector(), + const std::vector &aparam = std::vector()) { unsigned int natoms = atype.size() / nframes; assert(nframes * natoms * 3 == coord.size()); if (!box.empty()) { @@ -857,9 +956,15 @@ class DeepPot { VALUETYPE *virial_ = &virial[0]; VALUETYPE *atomic_ener_ = &atom_energy[0]; VALUETYPE *atomic_virial_ = &atom_virial[0]; + std::vector fparam_, aparam_; + validate_fparam_aparam(nframes, natoms, fparam, aparam); + tile_fparam_aparam(fparam_, nframes, dfparam, fparam); + tile_fparam_aparam(aparam_, nframes, natoms * daparam, aparam); + const VALUETYPE *fparam__ = !fparam_.empty() ? &fparam_[0] : nullptr; + const VALUETYPE *aparam__ = !aparam_.empty() ? &aparam_[0] : nullptr; _DP_DeepPotComputeMixedType( - dp, nframes, natoms, coord_, atype_, box_, nullptr, nullptr, ener_, + dp, nframes, natoms, coord_, atype_, box_, fparam__, aparam__, ener_, force_, virial_, atomic_ener_, atomic_virial_); DP_CHECK_OK(DP_DeepPotCheckOK, dp); }; @@ -896,9 +1001,59 @@ class DeepPot { void print_summary(const std::string &pre) const { DP_PrintSummary(pre.c_str()); } + /** + * @brief Get the dimension of the frame parameter. + * @return The dimension of the frame parameter. + **/ + int dim_fparam() const { + assert(dp); + return dfparam; + } + /** + * @brief Get the dimension of the atomic parameter. + * @return The dimension of the atomic parameter. + **/ + int dim_aparam() const { + assert(dp); + return daparam; + } private: DP_DeepPot *dp; + int dfparam; + int daparam; + template + void validate_fparam_aparam(const int &nframes, + const int &nloc, + const std::vector &fparam, + const std::vector &aparam) const { + if (fparam.size() != dfparam && fparam.size() != nframes * dfparam) { + throw deepmd::hpp::deepmd_exception( + "the dim of frame parameter provided is not consistent with what the " + "model uses"); + } + + if (aparam.size() != daparam * nloc && + aparam.size() != nframes * daparam * nloc) { + throw deepmd::hpp::deepmd_exception( + "the dim of atom parameter provided is not consistent with what the " + "model uses"); + } + } + template + void tile_fparam_aparam(std::vector &out_param, + const int &nframes, + const int &dparam, + const std::vector ¶m) const { + if (param.size() == dparam) { + out_param.resize(nframes * dparam); + for (int ii = 0; ii < nframes; ++ii) { + std::copy(param.begin(), param.end(), out_param.begin() + ii * dparam); + } + } else if (param.size() == nframes * dparam) { + out_param = param; + } + } }; /** diff --git a/source/api_c/src/c_api.cc b/source/api_c/src/c_api.cc index ffe860545c..253790008c 100644 --- a/source/api_c/src/c_api.cc +++ b/source/api_c/src/c_api.cc @@ -25,13 +25,15 @@ DP_Nlist* DP_NewNlist(int inum_, } DP_DeepPot::DP_DeepPot() {} -DP_DeepPot::DP_DeepPot(deepmd::DeepPot& dp) : dp(dp) {} +DP_DeepPot::DP_DeepPot(deepmd::DeepPot& dp) : dp(dp) { + dfparam = dp.dim_aparam(); + daparam = dp.dim_fparam(); +} DP_DeepPot* DP_NewDeepPot(const char* c_model) { std::string model(c_model); - deepmd::DeepPot dp(model); - DP_DeepPot* new_dp = new DP_DeepPot(dp); - return new_dp; + DP_NEW_OK(DP_DeepPot, deepmd::DeepPot dp(model); + DP_DeepPot* new_dp = new DP_DeepPot(dp); return new_dp;) } DP_DeepPot* DP_NewDeepPotWithParam(const char* c_model, @@ -45,7 +47,10 @@ DP_DeepPot* DP_NewDeepPotWithParam(const char* c_model, DP_DeepPotModelDevi::DP_DeepPotModelDevi() {} DP_DeepPotModelDevi::DP_DeepPotModelDevi(deepmd::DeepPotModelDevi& dp) - : dp(dp) {} + : dp(dp) { + dfparam = dp.dim_aparam(); + daparam = dp.dim_fparam(); +} DP_DeepPotModelDevi* DP_NewDeepPotModelDevi(const char** c_models, int n_models) { @@ -119,10 +124,19 @@ inline void DP_DeepPotCompute_variant(DP_DeepPot* dp, // pbc cell_.assign(cell, cell + nframes * 9); } + std::vector fparam_; + if (fparam) { + fparam_.assign(fparam, fparam + nframes * dp->dfparam); + } + std::vector aparam_; + if (aparam) { + aparam_.assign(aparam, aparam + nframes * natoms * dp->daparam); + } std::vector e; std::vector f, v, ae, av; - DP_REQUIRES_OK(dp, dp->dp.compute(e, f, v, ae, av, coord_, atype_, cell_)); + DP_REQUIRES_OK(dp, dp->dp.compute(e, f, v, ae, av, coord_, atype_, cell_, + fparam_, aparam_)); // copy from C++ vectors to C arrays, if not NULL pointer if (energy) std::copy(e.begin(), e.end(), energy); if (force) std::copy(f.begin(), f.end(), force); @@ -184,11 +198,19 @@ inline void DP_DeepPotComputeNList_variant(DP_DeepPot* dp, // pbc cell_.assign(cell, cell + nframes * 9); } + std::vector fparam_; + if (fparam) { + fparam_.assign(fparam, fparam + nframes * dp->dfparam); + } + std::vector aparam_; + if (aparam) { + aparam_.assign(aparam, aparam + nframes * (natoms - nghost) * dp->daparam); + } std::vector e; std::vector f, v, ae, av; DP_REQUIRES_OK(dp, dp->dp.compute(e, f, v, ae, av, coord_, atype_, cell_, - nghost, nlist->nl, ago)); + nghost, nlist->nl, ago, fparam_, aparam_)); // copy from C++ vectors to C arrays, if not NULL pointer if (energy) std::copy(e.begin(), e.end(), energy); if (force) std::copy(f.begin(), f.end(), force); @@ -253,11 +275,20 @@ inline void DP_DeepPotComputeMixedType_variant(DP_DeepPot* dp, // pbc cell_.assign(cell, cell + nframes * 9); } + std::vector fparam_; + if (fparam) { + fparam_.assign(fparam, fparam + nframes * dp->dfparam); + } + std::vector aparam_; + if (aparam) { + aparam_.assign(aparam, aparam + nframes * natoms * dp->daparam); + } std::vector e; std::vector f, v, ae, av; - DP_REQUIRES_OK(dp, dp->dp.compute_mixed_type(e, f, v, ae, av, nframes, coord_, - atype_, cell_)); + DP_REQUIRES_OK( + dp, dp->dp.compute_mixed_type(e, f, v, ae, av, nframes, coord_, atype_, + cell_, fparam_, aparam_)); // copy from C++ vectors to C arrays, if not NULL pointer if (energy) std::copy(e.begin(), e.end(), energy); if (force) std::copy(f.begin(), f.end(), force); @@ -883,6 +914,10 @@ double DP_DeepPotGetCutoff(DP_DeepPot* dp) { return dp->dp.cutoff(); } int DP_DeepPotGetNumbTypes(DP_DeepPot* dp) { return dp->dp.numb_types(); } +int DP_DeepPotGetDimFParam(DP_DeepPot* dp) { return dp->dfparam; } + +int DP_DeepPotGetDimAParam(DP_DeepPot* dp) { return dp->daparam; } + const char* DP_DeepPotCheckOK(DP_DeepPot* dp) { return string_to_char(dp->exception); } diff --git a/source/api_c/tests/test_deeppot_a_fparam_aparam.cc b/source/api_c/tests/test_deeppot_a_fparam_aparam.cc new file mode 100644 index 0000000000..c2247a868d --- /dev/null +++ b/source/api_c/tests/test_deeppot_a_fparam_aparam.cc @@ -0,0 +1,381 @@ +#include +#include +#include +#include + +#include +#include +#include +#include + +#include "deepmd.hpp" +#include "test_utils.h" + +template +class TestInferDeepPotAFParamAParam : public ::testing::Test { + protected: + std::vector coord = {12.83, 2.56, 2.18, 12.09, 2.87, 2.74, + 00.25, 3.32, 1.68, 3.36, 3.00, 1.81, + 3.51, 2.51, 2.60, 4.27, 3.22, 1.56}; + std::vector atype = {0, 0, 0, 0, 0, 0}; + std::vector box = {13., 0., 0., 0., 13., 0., 0., 0., 13.}; + std::vector fparam = {0.25852028}; + std::vector aparam = {0.25852028, 0.25852028, 0.25852028, + 0.25852028, 0.25852028, 0.25852028}; + std::vector expected_e = { + -1.038271183039953804e-01, -7.285433575272914908e-02, + -9.467600174099155552e-02, -1.467050086239614082e-01, + -7.660561620618722145e-02, -7.277295998502930630e-02}; + std::vector expected_f = { + 6.622266817497907132e-02, 5.278739055693523058e-02, + 2.265727495541422845e-02, -2.606047850915838363e-02, + -4.538811686410718776e-02, 1.058247569147072187e-02, + 1.679392490937766935e-01, -2.257828022687320690e-03, + -4.490145670355452645e-02, -1.148364103573685929e-01, + -1.169790466695089237e-02, 6.140402504113953025e-02, + -8.078778132132799494e-02, -5.838878056243369807e-02, + 6.773639989682191109e-02, -1.247724708090079161e-02, + 6.494523955924384750e-02, -1.174787188812918687e-01}; + std::vector expected_v = { + -1.589185553287162656e-01, 2.586163333170100279e-03, + -1.575127933809472624e-04, -1.855360380105876630e-02, + 1.949822090859933826e-02, -1.006552056166355388e-02, + 3.177029853276916449e-02, 1.714349636720383010e-03, + -1.290389175187874483e-03, -8.553510339477603253e-02, + -5.654637257232508415e-03, -1.286954833787038420e-02, + 2.464156457499515687e-02, -2.398202886026797043e-02, + -1.957110465239037672e-02, 2.233492928605742764e-02, + 6.107843207824020099e-03, 1.707078295947736047e-03, + -1.653994088976195043e-01, 3.894358678172111371e-02, + -2.169595969759342477e-02, 6.819704294738503786e-03, + -5.018242039618424008e-03, 2.640664428663210429e-03, + -1.985298275686078057e-03, -3.638421609610945767e-02, + 2.342932331075030239e-02, -8.501331914753691710e-02, + -2.181253413538992297e-03, 4.311300069651782287e-03, + -1.910329328333908129e-03, -1.808810159508548836e-03, + -1.540075281450827612e-03, -1.173703213175551763e-02, + -2.596306629910121507e-03, 6.705025662372287101e-03, + -9.038455005073858795e-02, 3.011717773578577451e-02, + -5.083054073419784880e-02, -2.951210292616929069e-03, + 2.342445652898489383e-02, -4.091207474993674431e-02, + -1.648470649301832236e-02, -2.872261885460645689e-02, + 4.763924972552112391e-02, -8.300036532764677732e-02, + 1.020429228955421243e-03, -1.026734151199098881e-03, + 5.678534096113684732e-02, 1.273635718045938205e-02, + -1.530143225195957322e-02, -1.061671865629566225e-01, + -2.486859433265622629e-02, 2.875323131744185121e-02}; + int natoms; + double expected_tot_e; + std::vector expected_tot_v; + + deepmd::hpp::DeepPot dp; + + void SetUp() override { + std::string file_name = "../../tests/infer/fparam_aparam.pbtxt"; + deepmd::hpp::convert_pbtxt_to_pb(file_name, "fparam_aparam.pb"); + + dp.init("fparam_aparam.pb"); + + natoms = expected_e.size(); + EXPECT_EQ(natoms * 3, expected_f.size()); + EXPECT_EQ(natoms * 9, expected_v.size()); + expected_tot_e = 0.; + expected_tot_v.resize(9); + std::fill(expected_tot_v.begin(), expected_tot_v.end(), 0.); + for (int ii = 0; ii < natoms; ++ii) { + expected_tot_e += expected_e[ii]; + } + for (int ii = 0; ii < natoms; ++ii) { + for (int dd = 0; dd < 9; ++dd) { + expected_tot_v[dd] += expected_v[ii * 9 + dd]; + } + } + }; + + void TearDown() override { remove("fparam_aparam.pb"); }; +}; + +TYPED_TEST_SUITE(TestInferDeepPotAFParamAParam, ValueTypes); + +TYPED_TEST(TestInferDeepPotAFParamAParam, cpu_build_nlist) { + using VALUETYPE = TypeParam; + std::vector& coord = this->coord; + std::vector& atype = this->atype; + std::vector& box = this->box; + std::vector& fparam = this->fparam; + std::vector& aparam = this->aparam; + std::vector& expected_e = this->expected_e; + std::vector& expected_f = this->expected_f; + std::vector& expected_v = this->expected_v; + int& natoms = this->natoms; + double& expected_tot_e = this->expected_tot_e; + std::vector& expected_tot_v = this->expected_tot_v; + deepmd::hpp::DeepPot& dp = this->dp; + double ener; + std::vector force, virial; + dp.compute(ener, force, virial, coord, atype, box, fparam, aparam); + + EXPECT_EQ(force.size(), natoms * 3); + EXPECT_EQ(virial.size(), 9); + + EXPECT_LT(fabs(ener - expected_tot_e), EPSILON); + for (int ii = 0; ii < natoms * 3; ++ii) { + EXPECT_LT(fabs(force[ii] - expected_f[ii]), EPSILON); + } + for (int ii = 0; ii < 3 * 3; ++ii) { + EXPECT_LT(fabs(virial[ii] - expected_tot_v[ii]), EPSILON); + } +} + +TYPED_TEST(TestInferDeepPotAFParamAParam, cpu_build_nlist_atomic) { + using VALUETYPE = TypeParam; + std::vector& coord = this->coord; + std::vector& atype = this->atype; + std::vector& box = this->box; + std::vector& fparam = this->fparam; + std::vector& aparam = this->aparam; + std::vector& expected_e = this->expected_e; + std::vector& expected_f = this->expected_f; + std::vector& expected_v = this->expected_v; + int& natoms = this->natoms; + double& expected_tot_e = this->expected_tot_e; + std::vector& expected_tot_v = this->expected_tot_v; + deepmd::hpp::DeepPot& dp = this->dp; + double ener; + std::vector force, virial, atom_ener, atom_vir; + dp.compute(ener, force, virial, atom_ener, atom_vir, coord, atype, box, + fparam, aparam); + + EXPECT_EQ(force.size(), natoms * 3); + EXPECT_EQ(virial.size(), 9); + EXPECT_EQ(atom_ener.size(), natoms); + EXPECT_EQ(atom_vir.size(), natoms * 9); + + EXPECT_LT(fabs(ener - expected_tot_e), EPSILON); + for (int ii = 0; ii < natoms * 3; ++ii) { + EXPECT_LT(fabs(force[ii] - expected_f[ii]), EPSILON); + } + for (int ii = 0; ii < 3 * 3; ++ii) { + EXPECT_LT(fabs(virial[ii] - expected_tot_v[ii]), EPSILON); + } + for (int ii = 0; ii < natoms; ++ii) { + EXPECT_LT(fabs(atom_ener[ii] - expected_e[ii]), EPSILON); + } + for (int ii = 0; ii < natoms * 9; ++ii) { + EXPECT_LT(fabs(atom_vir[ii] - expected_v[ii]), EPSILON); + } +} + +TYPED_TEST(TestInferDeepPotAFParamAParam, cpu_lmp_nlist) { + using VALUETYPE = TypeParam; + std::vector& coord = this->coord; + std::vector& atype = this->atype; + std::vector& box = this->box; + std::vector& fparam = this->fparam; + std::vector& aparam = this->aparam; + std::vector& expected_e = this->expected_e; + std::vector& expected_f = this->expected_f; + std::vector& expected_v = this->expected_v; + int& natoms = this->natoms; + double& expected_tot_e = this->expected_tot_e; + std::vector& expected_tot_v = this->expected_tot_v; + deepmd::hpp::DeepPot& dp = this->dp; + float rc = dp.cutoff(); + int nloc = coord.size() / 3; + std::vector coord_cpy; + std::vector atype_cpy, mapping; + std::vector > nlist_data; + _build_nlist(nlist_data, coord_cpy, atype_cpy, mapping, coord, + atype, box, rc); + int nall = coord_cpy.size() / 3; + std::vector ilist(nloc), numneigh(nloc); + std::vector firstneigh(nloc); + deepmd::hpp::InputNlist inlist(nloc, &ilist[0], &numneigh[0], &firstneigh[0]); + convert_nlist(inlist, nlist_data); + + double ener; + std::vector force_, virial; + dp.compute(ener, force_, virial, coord_cpy, atype_cpy, box, nall - nloc, + inlist, 0, fparam, aparam); + std::vector force; + _fold_back(force, force_, mapping, nloc, nall, 3); + + EXPECT_EQ(force.size(), natoms * 3); + EXPECT_EQ(virial.size(), 9); + + EXPECT_LT(fabs(ener - expected_tot_e), EPSILON); + for (int ii = 0; ii < natoms * 3; ++ii) { + EXPECT_LT(fabs(force[ii] - expected_f[ii]), EPSILON); + } + for (int ii = 0; ii < 3 * 3; ++ii) { + EXPECT_LT(fabs(virial[ii] - expected_tot_v[ii]), EPSILON); + } + + ener = 0.; + std::fill(force_.begin(), force_.end(), 0.0); + std::fill(virial.begin(), virial.end(), 0.0); + dp.compute(ener, force_, virial, coord_cpy, atype_cpy, box, nall - nloc, + inlist, 1, fparam, aparam); + _fold_back(force, force_, mapping, nloc, nall, 3); + + EXPECT_EQ(force.size(), natoms * 3); + EXPECT_EQ(virial.size(), 9); + + EXPECT_LT(fabs(ener - expected_tot_e), EPSILON); + for (int ii = 0; ii < natoms * 3; ++ii) { + EXPECT_LT(fabs(force[ii] - expected_f[ii]), EPSILON); + } + for (int ii = 0; ii < 3 * 3; ++ii) { + EXPECT_LT(fabs(virial[ii] - expected_tot_v[ii]), EPSILON); + } +} + +TYPED_TEST(TestInferDeepPotAFParamAParam, cpu_lmp_nlist_atomic) { + using VALUETYPE = TypeParam; + std::vector& coord = this->coord; + std::vector& atype = this->atype; + std::vector& box = this->box; + std::vector& fparam = this->fparam; + std::vector& aparam = this->aparam; + std::vector& expected_e = this->expected_e; + std::vector& expected_f = this->expected_f; + std::vector& expected_v = this->expected_v; + int& natoms = this->natoms; + double& expected_tot_e = this->expected_tot_e; + std::vector& expected_tot_v = this->expected_tot_v; + deepmd::hpp::DeepPot& dp = this->dp; + float rc = dp.cutoff(); + int nloc = coord.size() / 3; + std::vector coord_cpy; + std::vector atype_cpy, mapping; + std::vector > nlist_data; + _build_nlist(nlist_data, coord_cpy, atype_cpy, mapping, coord, + atype, box, rc); + int nall = coord_cpy.size() / 3; + std::vector ilist(nloc), numneigh(nloc); + std::vector firstneigh(nloc); + deepmd::hpp::InputNlist inlist(nloc, &ilist[0], &numneigh[0], &firstneigh[0]); + convert_nlist(inlist, nlist_data); + + double ener; + std::vector force_, atom_ener_, atom_vir_, virial; + std::vector force, atom_ener, atom_vir; + dp.compute(ener, force_, virial, atom_ener_, atom_vir_, coord_cpy, atype_cpy, + box, nall - nloc, inlist, 0, fparam, aparam); + _fold_back(force, force_, mapping, nloc, nall, 3); + _fold_back(atom_ener, atom_ener_, mapping, nloc, nall, 1); + _fold_back(atom_vir, atom_vir_, mapping, nloc, nall, 9); + + EXPECT_EQ(force.size(), natoms * 3); + EXPECT_EQ(virial.size(), 9); + EXPECT_EQ(atom_ener.size(), natoms); + EXPECT_EQ(atom_vir.size(), natoms * 9); + + EXPECT_LT(fabs(ener - expected_tot_e), EPSILON); + for (int ii = 0; ii < natoms * 3; ++ii) { + EXPECT_LT(fabs(force[ii] - expected_f[ii]), EPSILON); + } + for (int ii = 0; ii < 3 * 3; ++ii) { + EXPECT_LT(fabs(virial[ii] - expected_tot_v[ii]), EPSILON); + } + for (int ii = 0; ii < natoms; ++ii) { + EXPECT_LT(fabs(atom_ener[ii] - expected_e[ii]), EPSILON); + } + for (int ii = 0; ii < natoms * 9; ++ii) { + EXPECT_LT(fabs(atom_vir[ii] - expected_v[ii]), EPSILON); + } + + ener = 0.; + std::fill(force_.begin(), force_.end(), 0.0); + std::fill(virial.begin(), virial.end(), 0.0); + std::fill(atom_ener_.begin(), atom_ener_.end(), 0.0); + std::fill(atom_vir_.begin(), atom_vir_.end(), 0.0); + dp.compute(ener, force_, virial, atom_ener_, atom_vir_, coord_cpy, atype_cpy, + box, nall - nloc, inlist, 1, fparam, aparam); + _fold_back(force, force_, mapping, nloc, nall, 3); + _fold_back(atom_ener, atom_ener_, mapping, nloc, nall, 1); + _fold_back(atom_vir, atom_vir_, mapping, nloc, nall, 9); + + EXPECT_EQ(force.size(), natoms * 3); + EXPECT_EQ(virial.size(), 9); + EXPECT_EQ(atom_ener.size(), natoms); + EXPECT_EQ(atom_vir.size(), natoms * 9); + + EXPECT_LT(fabs(ener - expected_tot_e), EPSILON); + for (int ii = 0; ii < natoms * 3; ++ii) { + EXPECT_LT(fabs(force[ii] - expected_f[ii]), EPSILON); + } + for (int ii = 0; ii < 3 * 3; ++ii) { + EXPECT_LT(fabs(virial[ii] - expected_tot_v[ii]), EPSILON); + } + for (int ii = 0; ii < natoms; ++ii) { + EXPECT_LT(fabs(atom_ener[ii] - expected_e[ii]), EPSILON); + } + for (int ii = 0; ii < natoms * 9; ++ii) { + EXPECT_LT(fabs(atom_vir[ii] - expected_v[ii]), EPSILON); + } +} + +TYPED_TEST(TestInferDeepPotAFParamAParam, cpu_lmp_nlist_2rc) { + using VALUETYPE = TypeParam; + std::vector& coord = this->coord; + std::vector& atype = this->atype; + std::vector& box = this->box; + std::vector& fparam = this->fparam; + std::vector& aparam = this->aparam; + std::vector& expected_e = this->expected_e; + std::vector& expected_f = this->expected_f; + std::vector& expected_v = this->expected_v; + int& natoms = this->natoms; + double& expected_tot_e = this->expected_tot_e; + std::vector& expected_tot_v = this->expected_tot_v; + deepmd::hpp::DeepPot& dp = this->dp; + float rc = dp.cutoff(); + int nloc = coord.size() / 3; + std::vector coord_cpy; + std::vector atype_cpy, mapping; + std::vector > nlist_data; + _build_nlist(nlist_data, coord_cpy, atype_cpy, mapping, coord, + atype, box, rc * 2); + int nall = coord_cpy.size() / 3; + std::vector ilist(nloc), numneigh(nloc); + std::vector firstneigh(nloc); + deepmd::hpp::InputNlist inlist(nloc, &ilist[0], &numneigh[0], &firstneigh[0]); + convert_nlist(inlist, nlist_data); + + double ener; + std::vector force_(nall * 3, 0.0), virial(9, 0.0); + dp.compute(ener, force_, virial, coord_cpy, atype_cpy, box, nall - nloc, + inlist, 0, fparam, aparam); + std::vector force; + _fold_back(force, force_, mapping, nloc, nall, 3); + + EXPECT_EQ(force.size(), natoms * 3); + EXPECT_EQ(virial.size(), 9); + + EXPECT_LT(fabs(ener - expected_tot_e), EPSILON); + for (int ii = 0; ii < natoms * 3; ++ii) { + EXPECT_LT(fabs(force[ii] - expected_f[ii]), EPSILON); + } + for (int ii = 0; ii < 3 * 3; ++ii) { + EXPECT_LT(fabs(virial[ii] - expected_tot_v[ii]), EPSILON); + } + + ener = 0.; + std::fill(force_.begin(), force_.end(), 0.0); + std::fill(virial.begin(), virial.end(), 0.0); + dp.compute(ener, force_, virial, coord_cpy, atype_cpy, box, nall - nloc, + inlist, 1, fparam, aparam); + _fold_back(force, force_, mapping, nloc, nall, 3); + + EXPECT_EQ(force.size(), natoms * 3); + EXPECT_EQ(virial.size(), 9); + + EXPECT_LT(fabs(ener - expected_tot_e), EPSILON); + for (int ii = 0; ii < natoms * 3; ++ii) { + EXPECT_LT(fabs(force[ii] - expected_f[ii]), EPSILON); + } + for (int ii = 0; ii < 3 * 3; ++ii) { + EXPECT_LT(fabs(virial[ii] - expected_tot_v[ii]), EPSILON); + } +} diff --git a/source/api_c/tests/test_deeppot_a_fparam_aparam_nframes.cc b/source/api_c/tests/test_deeppot_a_fparam_aparam_nframes.cc new file mode 100644 index 0000000000..6c7558402e --- /dev/null +++ b/source/api_c/tests/test_deeppot_a_fparam_aparam_nframes.cc @@ -0,0 +1,1297 @@ +#include +#include +#include +#include + +#include +#include +#include +#include + +#include "deepmd.hpp" +#include "test_utils.h" + +template +class TestInferDeepPotAFparamAparamNFrames : public ::testing::Test { + protected: + std::vector coord = { + 12.83, 2.56, 2.18, 12.09, 2.87, 2.74, 00.25, 3.32, 1.68, + 3.36, 3.00, 1.81, 3.51, 2.51, 2.60, 4.27, 3.22, 1.56, + 12.83, 2.56, 2.18, 12.09, 2.87, 2.74, 00.25, 3.32, 1.68, + 3.36, 3.00, 1.81, 3.51, 2.51, 2.60, 4.27, 3.22, 1.56}; + std::vector atype = {0, 0, 0, 0, 0, 0}; + std::vector box = {13., 0., 0., 0., 13., 0., 0., 0., 13., + 13., 0., 0., 0., 13., 0., 0., 0., 13.}; + std::vector fparam = {0.25852028, 0.25852028}; + std::vector aparam = { + 0.25852028, 0.25852028, 0.25852028, 0.25852028, 0.25852028, 0.25852028, + 0.25852028, 0.25852028, 0.25852028, 0.25852028, 0.25852028, 0.25852028}; + std::vector expected_e = { + -1.038271183039953804e-01, -7.285433575272914908e-02, + -9.467600174099155552e-02, -1.467050086239614082e-01, + -7.660561620618722145e-02, -7.277295998502930630e-02, + -1.038271183039953804e-01, -7.285433575272914908e-02, + -9.467600174099155552e-02, -1.467050086239614082e-01, + -7.660561620618722145e-02, -7.277295998502930630e-02}; + std::vector expected_f = { + 6.622266817497907132e-02, 5.278739055693523058e-02, + 2.265727495541422845e-02, -2.606047850915838363e-02, + -4.538811686410718776e-02, 1.058247569147072187e-02, + 1.679392490937766935e-01, -2.257828022687320690e-03, + -4.490145670355452645e-02, -1.148364103573685929e-01, + -1.169790466695089237e-02, 6.140402504113953025e-02, + -8.078778132132799494e-02, -5.838878056243369807e-02, + 6.773639989682191109e-02, -1.247724708090079161e-02, + 6.494523955924384750e-02, -1.174787188812918687e-01, + 6.622266817497907132e-02, 5.278739055693523058e-02, + 2.265727495541422845e-02, -2.606047850915838363e-02, + -4.538811686410718776e-02, 1.058247569147072187e-02, + 1.679392490937766935e-01, -2.257828022687320690e-03, + -4.490145670355452645e-02, -1.148364103573685929e-01, + -1.169790466695089237e-02, 6.140402504113953025e-02, + -8.078778132132799494e-02, -5.838878056243369807e-02, + 6.773639989682191109e-02, -1.247724708090079161e-02, + 6.494523955924384750e-02, -1.174787188812918687e-01}; + std::vector expected_v = { + -1.589185553287162656e-01, 2.586163333170100279e-03, + -1.575127933809472624e-04, -1.855360380105876630e-02, + 1.949822090859933826e-02, -1.006552056166355388e-02, + 3.177029853276916449e-02, 1.714349636720383010e-03, + -1.290389175187874483e-03, -8.553510339477603253e-02, + -5.654637257232508415e-03, -1.286954833787038420e-02, + 2.464156457499515687e-02, -2.398202886026797043e-02, + -1.957110465239037672e-02, 2.233492928605742764e-02, + 6.107843207824020099e-03, 1.707078295947736047e-03, + -1.653994088976195043e-01, 3.894358678172111371e-02, + -2.169595969759342477e-02, 6.819704294738503786e-03, + -5.018242039618424008e-03, 2.640664428663210429e-03, + -1.985298275686078057e-03, -3.638421609610945767e-02, + 2.342932331075030239e-02, -8.501331914753691710e-02, + -2.181253413538992297e-03, 4.311300069651782287e-03, + -1.910329328333908129e-03, -1.808810159508548836e-03, + -1.540075281450827612e-03, -1.173703213175551763e-02, + -2.596306629910121507e-03, 6.705025662372287101e-03, + -9.038455005073858795e-02, 3.011717773578577451e-02, + -5.083054073419784880e-02, -2.951210292616929069e-03, + 2.342445652898489383e-02, -4.091207474993674431e-02, + -1.648470649301832236e-02, -2.872261885460645689e-02, + 4.763924972552112391e-02, -8.300036532764677732e-02, + 1.020429228955421243e-03, -1.026734151199098881e-03, + 5.678534096113684732e-02, 1.273635718045938205e-02, + -1.530143225195957322e-02, -1.061671865629566225e-01, + -2.486859433265622629e-02, 2.875323131744185121e-02, + -1.589185553287162656e-01, 2.586163333170100279e-03, + -1.575127933809472624e-04, -1.855360380105876630e-02, + 1.949822090859933826e-02, -1.006552056166355388e-02, + 3.177029853276916449e-02, 1.714349636720383010e-03, + -1.290389175187874483e-03, -8.553510339477603253e-02, + -5.654637257232508415e-03, -1.286954833787038420e-02, + 2.464156457499515687e-02, -2.398202886026797043e-02, + -1.957110465239037672e-02, 2.233492928605742764e-02, + 6.107843207824020099e-03, 1.707078295947736047e-03, + -1.653994088976195043e-01, 3.894358678172111371e-02, + -2.169595969759342477e-02, 6.819704294738503786e-03, + -5.018242039618424008e-03, 2.640664428663210429e-03, + -1.985298275686078057e-03, -3.638421609610945767e-02, + 2.342932331075030239e-02, -8.501331914753691710e-02, + -2.181253413538992297e-03, 4.311300069651782287e-03, + -1.910329328333908129e-03, -1.808810159508548836e-03, + -1.540075281450827612e-03, -1.173703213175551763e-02, + -2.596306629910121507e-03, 6.705025662372287101e-03, + -9.038455005073858795e-02, 3.011717773578577451e-02, + -5.083054073419784880e-02, -2.951210292616929069e-03, + 2.342445652898489383e-02, -4.091207474993674431e-02, + -1.648470649301832236e-02, -2.872261885460645689e-02, + 4.763924972552112391e-02, -8.300036532764677732e-02, + 1.020429228955421243e-03, -1.026734151199098881e-03, + 5.678534096113684732e-02, 1.273635718045938205e-02, + -1.530143225195957322e-02, -1.061671865629566225e-01, + -2.486859433265622629e-02, 2.875323131744185121e-02}; + int natoms; + int nframes = 2; + std::vector expected_tot_e; + std::vector expected_tot_v; + + deepmd::hpp::DeepPot dp; + + void SetUp() override { + std::string file_name = "../../tests/infer/fparam_aparam.pbtxt"; + deepmd::hpp::convert_pbtxt_to_pb("../../tests/infer/fparam_aparam.pbtxt", + "fparam_aparam.pb"); + + dp.init("fparam_aparam.pb"); + + natoms = expected_e.size() / nframes; + EXPECT_EQ(nframes * natoms * 3, expected_f.size()); + EXPECT_EQ(nframes * natoms * 9, expected_v.size()); + expected_tot_e.resize(nframes); + expected_tot_v.resize(nframes * 9); + std::fill(expected_tot_e.begin(), expected_tot_e.end(), 0.); + std::fill(expected_tot_v.begin(), expected_tot_v.end(), 0.); + for (int kk = 0; kk < nframes; ++kk) { + for (int ii = 0; ii < natoms; ++ii) { + expected_tot_e[kk] += expected_e[kk * natoms + ii]; + } + for (int ii = 0; ii < natoms; ++ii) { + for (int dd = 0; dd < 9; ++dd) { + expected_tot_v[kk * 9 + dd] += + expected_v[kk * natoms * 9 + ii * 9 + dd]; + } + } + } + }; + + void TearDown() override { remove("fparam_aparam.pb"); }; +}; + +TYPED_TEST_SUITE(TestInferDeepPotAFparamAparamNFrames, ValueTypes); + +TYPED_TEST(TestInferDeepPotAFparamAparamNFrames, cpu_build_nlist) { + using VALUETYPE = TypeParam; + std::vector& coord = this->coord; + std::vector& atype = this->atype; + std::vector& box = this->box; + std::vector& fparam = this->fparam; + std::vector& aparam = this->aparam; + std::vector& expected_e = this->expected_e; + std::vector& expected_f = this->expected_f; + std::vector& expected_v = this->expected_v; + int& natoms = this->natoms; + int& nframes = this->nframes; + std::vector& expected_tot_e = this->expected_tot_e; + std::vector& expected_tot_v = this->expected_tot_v; + deepmd::hpp::DeepPot& dp = this->dp; + std::vector ener; + std::vector force, virial; + dp.compute(ener, force, virial, coord, atype, box, fparam, aparam); + + EXPECT_EQ(ener.size(), nframes); + EXPECT_EQ(force.size(), nframes * natoms * 3); + EXPECT_EQ(virial.size(), nframes * 9); + + for (int ii = 0; ii < nframes; ++ii) { + EXPECT_LT(fabs(ener[ii] - expected_tot_e[ii]), EPSILON); + } + for (int ii = 0; ii < nframes * natoms * 3; ++ii) { + EXPECT_LT(fabs(force[ii] - expected_f[ii]), EPSILON); + } + for (int ii = 0; ii < nframes * 3 * 3; ++ii) { + EXPECT_LT(fabs(virial[ii] - expected_tot_v[ii]), EPSILON); + } +} + +TYPED_TEST(TestInferDeepPotAFparamAparamNFrames, cpu_build_nlist_atomic) { + using VALUETYPE = TypeParam; + std::vector& coord = this->coord; + std::vector& atype = this->atype; + std::vector& box = this->box; + std::vector& fparam = this->fparam; + std::vector& aparam = this->aparam; + std::vector& expected_e = this->expected_e; + std::vector& expected_f = this->expected_f; + std::vector& expected_v = this->expected_v; + int& natoms = this->natoms; + int& nframes = this->nframes; + std::vector& expected_tot_e = this->expected_tot_e; + std::vector& expected_tot_v = this->expected_tot_v; + deepmd::hpp::DeepPot& dp = this->dp; + std::vector ener; + std::vector force, virial, atom_ener, atom_vir; + dp.compute(ener, force, virial, atom_ener, atom_vir, coord, atype, box, + fparam, aparam); + + EXPECT_EQ(ener.size(), nframes); + EXPECT_EQ(force.size(), nframes * natoms * 3); + EXPECT_EQ(virial.size(), nframes * 9); + EXPECT_EQ(atom_ener.size(), nframes * natoms); + EXPECT_EQ(atom_vir.size(), nframes * natoms * 9); + + for (int ii = 0; ii < nframes; ++ii) { + EXPECT_LT(fabs(ener[ii] - expected_tot_e[ii]), EPSILON); + } + for (int ii = 0; ii < nframes * natoms * 3; ++ii) { + EXPECT_LT(fabs(force[ii] - expected_f[ii]), EPSILON); + } + for (int ii = 0; ii < nframes * 3 * 3; ++ii) { + EXPECT_LT(fabs(virial[ii] - expected_tot_v[ii]), EPSILON); + } + for (int ii = 0; ii < nframes * natoms; ++ii) { + EXPECT_LT(fabs(atom_ener[ii] - expected_e[ii]), EPSILON); + } + for (int ii = 0; ii < nframes * natoms * 9; ++ii) { + EXPECT_LT(fabs(atom_vir[ii] - expected_v[ii]), EPSILON); + } +} + +TYPED_TEST(TestInferDeepPotAFparamAparamNFrames, cpu_lmp_nlist) { + using VALUETYPE = TypeParam; + std::vector& coord = this->coord; + std::vector& atype = this->atype; + std::vector& box = this->box; + std::vector& fparam = this->fparam; + std::vector& aparam = this->aparam; + std::vector& expected_e = this->expected_e; + std::vector& expected_f = this->expected_f; + std::vector& expected_v = this->expected_v; + int& natoms = this->natoms; + int& nframes = this->nframes; + std::vector& expected_tot_e = this->expected_tot_e; + std::vector& expected_tot_v = this->expected_tot_v; + deepmd::hpp::DeepPot& dp = this->dp; + float rc = dp.cutoff(); + std::vector coord_first(coord.begin(), coord.begin() + 3 * natoms); + std::vector box_first(box.begin(), box.begin() + 9); + int nloc = coord_first.size() / 3; + std::vector coord_cpy; + std::vector atype_cpy, mapping; + std::vector > nlist_data; + _build_nlist(nlist_data, coord_cpy, atype_cpy, mapping, + coord_first, atype, box_first, rc); + int nall = coord_cpy.size() / 3; + std::vector ilist(nloc), numneigh(nloc); + std::vector firstneigh(nloc); + deepmd::hpp::InputNlist inlist(nloc, &ilist[0], &numneigh[0], &firstneigh[0]); + convert_nlist(inlist, nlist_data); + std::vector coord_cpy2(nframes * nall * 3); + for (int ii = 0; ii < nframes; ++ii) { + for (int jj = 0; jj < nall * 3; ++jj) { + coord_cpy2[ii * nall * 3 + jj] = coord_cpy[jj]; + } + } + + std::vector ener; + std::vector force_, virial; + dp.compute(ener, force_, virial, coord_cpy2, atype_cpy, box, nall - nloc, + inlist, 0, fparam, aparam); + std::vector force; + _fold_back(force, force_, mapping, nloc, nall, 3, nframes); + + EXPECT_EQ(ener.size(), nframes); + EXPECT_EQ(force.size(), nframes * natoms * 3); + EXPECT_EQ(virial.size(), nframes * 9); + + for (int ii = 0; ii < nframes; ++ii) { + EXPECT_LT(fabs(ener[ii] - expected_tot_e[ii]), EPSILON); + } + for (int ii = 0; ii < nframes * natoms * 3; ++ii) { + EXPECT_LT(fabs(force[ii] - expected_f[ii]), EPSILON); + } + for (int ii = 0; ii < nframes * 3 * 3; ++ii) { + EXPECT_LT(fabs(virial[ii] - expected_tot_v[ii]), EPSILON); + } + + std::fill(ener.begin(), ener.end(), 0.0); + std::fill(force_.begin(), force_.end(), 0.0); + std::fill(virial.begin(), virial.end(), 0.0); + dp.compute(ener, force_, virial, coord_cpy2, atype_cpy, box, nall - nloc, + inlist, 1, fparam, aparam); + _fold_back(force, force_, mapping, nloc, nall, 3, nframes); + + EXPECT_EQ(ener.size(), nframes); + EXPECT_EQ(force.size(), nframes * natoms * 3); + EXPECT_EQ(virial.size(), nframes * 9); + + for (int ii = 0; ii < nframes; ++ii) { + EXPECT_LT(fabs(ener[ii] - expected_tot_e[ii]), EPSILON); + } + for (int ii = 0; ii < nframes * natoms * 3; ++ii) { + EXPECT_LT(fabs(force[ii] - expected_f[ii]), EPSILON); + } + for (int ii = 0; ii < nframes * 3 * 3; ++ii) { + EXPECT_LT(fabs(virial[ii] - expected_tot_v[ii]), EPSILON); + } +} + +TYPED_TEST(TestInferDeepPotAFparamAparamNFrames, cpu_lmp_nlist_atomic) { + using VALUETYPE = TypeParam; + std::vector& coord = this->coord; + std::vector& atype = this->atype; + std::vector& box = this->box; + std::vector& fparam = this->fparam; + std::vector& aparam = this->aparam; + std::vector& expected_e = this->expected_e; + std::vector& expected_f = this->expected_f; + std::vector& expected_v = this->expected_v; + int& natoms = this->natoms; + int& nframes = this->nframes; + std::vector& expected_tot_e = this->expected_tot_e; + std::vector& expected_tot_v = this->expected_tot_v; + deepmd::hpp::DeepPot& dp = this->dp; + float rc = dp.cutoff(); + std::vector coord_first(coord.begin(), coord.begin() + 3 * natoms); + std::vector box_first(box.begin(), box.begin() + 9); + int nloc = coord_first.size() / 3; + std::vector coord_cpy; + std::vector atype_cpy, mapping; + std::vector > nlist_data; + _build_nlist(nlist_data, coord_cpy, atype_cpy, mapping, + coord_first, atype, box_first, rc); + int nall = coord_cpy.size() / 3; + std::vector ilist(nloc), numneigh(nloc); + std::vector firstneigh(nloc); + deepmd::hpp::InputNlist inlist(nloc, &ilist[0], &numneigh[0], &firstneigh[0]); + convert_nlist(inlist, nlist_data); + std::vector coord_cpy2(nframes * nall * 3); + for (int ii = 0; ii < nframes; ++ii) { + for (int jj = 0; jj < nall * 3; ++jj) { + coord_cpy2[ii * nall * 3 + jj] = coord_cpy[jj]; + } + } + + std::vector ener; + std::vector force_, atom_ener_, atom_vir_, virial; + std::vector force, atom_ener, atom_vir; + dp.compute(ener, force_, virial, atom_ener_, atom_vir_, coord_cpy2, atype_cpy, + box, nall - nloc, inlist, 0, fparam, aparam); + _fold_back(force, force_, mapping, nloc, nall, 3, nframes); + _fold_back(atom_ener, atom_ener_, mapping, nloc, nall, 1, nframes); + _fold_back(atom_vir, atom_vir_, mapping, nloc, nall, 9, nframes); + + EXPECT_EQ(ener.size(), nframes); + EXPECT_EQ(force.size(), nframes * natoms * 3); + EXPECT_EQ(virial.size(), nframes * 9); + EXPECT_EQ(atom_ener.size(), nframes * natoms); + EXPECT_EQ(atom_vir.size(), nframes * natoms * 9); + + for (int ii = 0; ii < nframes; ++ii) { + EXPECT_LT(fabs(ener[ii] - expected_tot_e[ii]), EPSILON); + } + for (int ii = 0; ii < nframes * natoms * 3; ++ii) { + EXPECT_LT(fabs(force[ii] - expected_f[ii]), EPSILON); + } + for (int ii = 0; ii < nframes * 3 * 3; ++ii) { + EXPECT_LT(fabs(virial[ii] - expected_tot_v[ii]), EPSILON); + } + for (int ii = 0; ii < nframes * natoms; ++ii) { + EXPECT_LT(fabs(atom_ener[ii] - expected_e[ii]), EPSILON); + } + for (int ii = 0; ii < nframes * natoms * 9; ++ii) { + EXPECT_LT(fabs(atom_vir[ii] - expected_v[ii]), EPSILON); + } + + std::fill(ener.begin(), ener.end(), 0.0); + std::fill(force_.begin(), force_.end(), 0.0); + std::fill(virial.begin(), virial.end(), 0.0); + std::fill(atom_ener_.begin(), atom_ener_.end(), 0.0); + std::fill(atom_vir_.begin(), atom_vir_.end(), 0.0); + dp.compute(ener, force_, virial, atom_ener_, atom_vir_, coord_cpy2, atype_cpy, + box, nall - nloc, inlist, 1, fparam, aparam); + _fold_back(force, force_, mapping, nloc, nall, 3, nframes); + _fold_back(atom_ener, atom_ener_, mapping, nloc, nall, 1, nframes); + _fold_back(atom_vir, atom_vir_, mapping, nloc, nall, 9, nframes); + + EXPECT_EQ(ener.size(), nframes); + EXPECT_EQ(force.size(), nframes * natoms * 3); + EXPECT_EQ(virial.size(), nframes * 9); + EXPECT_EQ(atom_ener.size(), nframes * natoms); + EXPECT_EQ(atom_vir.size(), nframes * natoms * 9); + + for (int ii = 0; ii < nframes; ++ii) { + EXPECT_LT(fabs(ener[ii] - expected_tot_e[ii]), EPSILON); + } + for (int ii = 0; ii < nframes * natoms * 3; ++ii) { + EXPECT_LT(fabs(force[ii] - expected_f[ii]), EPSILON); + } + for (int ii = 0; ii < nframes * 3 * 3; ++ii) { + EXPECT_LT(fabs(virial[ii] - expected_tot_v[ii]), EPSILON); + } + for (int ii = 0; ii < nframes * natoms; ++ii) { + EXPECT_LT(fabs(atom_ener[ii] - expected_e[ii]), EPSILON); + } + for (int ii = 0; ii < nframes * natoms * 9; ++ii) { + EXPECT_LT(fabs(atom_vir[ii] - expected_v[ii]), EPSILON); + } +} + +TYPED_TEST(TestInferDeepPotAFparamAparamNFrames, cpu_lmp_nlist_2rc) { + using VALUETYPE = TypeParam; + std::vector& coord = this->coord; + std::vector& atype = this->atype; + std::vector& box = this->box; + std::vector& fparam = this->fparam; + std::vector& aparam = this->aparam; + std::vector& expected_e = this->expected_e; + std::vector& expected_f = this->expected_f; + std::vector& expected_v = this->expected_v; + int& natoms = this->natoms; + int& nframes = this->nframes; + std::vector& expected_tot_e = this->expected_tot_e; + std::vector& expected_tot_v = this->expected_tot_v; + deepmd::hpp::DeepPot& dp = this->dp; + float rc = dp.cutoff(); + std::vector coord_first(coord.begin(), coord.begin() + 3 * natoms); + std::vector box_first(box.begin(), box.begin() + 9); + int nloc = coord_first.size() / 3; + std::vector coord_cpy; + std::vector atype_cpy, mapping; + std::vector > nlist_data; + _build_nlist(nlist_data, coord_cpy, atype_cpy, mapping, + coord_first, atype, box_first, rc * 2); + int nall = coord_cpy.size() / 3; + std::vector ilist(nloc), numneigh(nloc); + std::vector firstneigh(nloc); + deepmd::hpp::InputNlist inlist(nloc, &ilist[0], &numneigh[0], &firstneigh[0]); + convert_nlist(inlist, nlist_data); + std::vector coord_cpy2(nframes * nall * 3); + for (int ii = 0; ii < nframes; ++ii) { + for (int jj = 0; jj < nall * 3; ++jj) { + coord_cpy2[ii * nall * 3 + jj] = coord_cpy[jj]; + } + } + + std::vector ener; + std::vector force_(nall * 3, 0.0), virial(nframes * 9, 0.0); + dp.compute(ener, force_, virial, coord_cpy2, atype_cpy, box, nall - nloc, + inlist, 0, fparam, aparam); + std::vector force; + _fold_back(force, force_, mapping, nloc, nall, 3, nframes); + + EXPECT_EQ(ener.size(), nframes); + EXPECT_EQ(force.size(), nframes * natoms * 3); + EXPECT_EQ(virial.size(), nframes * 9); + + for (int ii = 0; ii < nframes; ++ii) { + EXPECT_LT(fabs(ener[ii] - expected_tot_e[ii]), EPSILON); + } + for (int ii = 0; ii < nframes * natoms * 3; ++ii) { + EXPECT_LT(fabs(force[ii] - expected_f[ii]), EPSILON); + } + for (int ii = 0; ii < nframes * 3 * 3; ++ii) { + EXPECT_LT(fabs(virial[ii] - expected_tot_v[ii]), EPSILON); + } + + std::fill(ener.begin(), ener.end(), 0.0); + std::fill(force_.begin(), force_.end(), 0.0); + std::fill(virial.begin(), virial.end(), 0.0); + dp.compute(ener, force_, virial, coord_cpy2, atype_cpy, box, nall - nloc, + inlist, 1, fparam, aparam); + _fold_back(force, force_, mapping, nloc, nall, 3, nframes); + + EXPECT_EQ(ener.size(), nframes); + EXPECT_EQ(force.size(), nframes * natoms * 3); + EXPECT_EQ(virial.size(), nframes * 9); + + for (int ii = 0; ii < nframes; ++ii) { + EXPECT_LT(fabs(ener[ii] - expected_tot_e[ii]), EPSILON); + } + for (int ii = 0; ii < nframes * natoms * 3; ++ii) { + EXPECT_LT(fabs(force[ii] - expected_f[ii]), EPSILON); + } + for (int ii = 0; ii < nframes * 3 * 3; ++ii) { + EXPECT_LT(fabs(virial[ii] - expected_tot_v[ii]), EPSILON); + } +} + +TYPED_TEST(TestInferDeepPotAFparamAparamNFrames, cpu_lmp_nlist_type_sel) { + using VALUETYPE = TypeParam; + std::vector& coord = this->coord; + std::vector& atype = this->atype; + std::vector& box = this->box; + std::vector& fparam = this->fparam; + std::vector& aparam = this->aparam; + std::vector& expected_e = this->expected_e; + std::vector& expected_f = this->expected_f; + std::vector& expected_v = this->expected_v; + int& natoms = this->natoms; + int& nframes = this->nframes; + std::vector& expected_tot_e = this->expected_tot_e; + std::vector& expected_tot_v = this->expected_tot_v; + deepmd::hpp::DeepPot& dp = this->dp; + float rc = dp.cutoff(); + + // add vir atoms + int nvir = 2; + std::vector coord_vir(nvir * 3); + std::vector atype_vir(nvir, 2); + std::vector aparam_vir(nvir * 1); + for (int ii = 0; ii < nvir; ++ii) { + coord_vir[ii] = coord[ii]; + } + for (int ii = 0; ii < nvir; ++ii) { + aparam_vir[ii] = aparam[ii]; + } + coord.insert(coord.begin(), coord_vir.begin(), coord_vir.end()); + atype.insert(atype.begin(), atype_vir.begin(), atype_vir.end()); + natoms += nvir; + std::vector expected_f_vir(nvir * 3, 0.0); + // two frames + expected_f.insert(expected_f.begin(), expected_f_vir.begin(), + expected_f_vir.end()); + expected_f.insert(expected_f.begin() + natoms * 3, expected_f_vir.begin(), + expected_f_vir.end()); + std::vector coord_first(coord.begin(), coord.begin() + 3 * natoms); + std::vector box_first(box.begin(), box.begin() + 9); + + // build nlist + int nloc = coord_first.size() / 3; + std::vector coord_cpy; + std::vector atype_cpy, mapping; + std::vector > nlist_data; + _build_nlist(nlist_data, coord_cpy, atype_cpy, mapping, + coord_first, atype, box_first, rc); + int nall = coord_cpy.size() / 3; + std::vector ilist(nloc), numneigh(nloc); + std::vector firstneigh(nloc); + deepmd::hpp::InputNlist inlist(nloc, &ilist[0], &numneigh[0], &firstneigh[0]); + convert_nlist(inlist, nlist_data); + std::vector coord_cpy2(nframes * nall * 3); + for (int ii = 0; ii < nframes; ++ii) { + for (int jj = 0; jj < nall * 3; ++jj) { + coord_cpy2[ii * nall * 3 + jj] = coord_cpy[jj]; + } + } + aparam.insert(aparam.begin(), aparam_vir.begin(), aparam_vir.end()); + aparam.insert(aparam.begin() + nloc, aparam_vir.begin(), aparam_vir.end()); + + // dp compute + std::vector ener; + std::vector force_(nall * 3, 0.0), virial(nframes * 9, 0.0); + dp.compute(ener, force_, virial, coord_cpy2, atype_cpy, box, nall - nloc, + inlist, 0, fparam, aparam); + // fold back + std::vector force; + _fold_back(force, force_, mapping, nloc, nall, 3, nframes); + + EXPECT_EQ(ener.size(), nframes); + EXPECT_EQ(force.size(), nframes * natoms * 3); + EXPECT_EQ(virial.size(), nframes * 9); + + for (int ii = 0; ii < nframes; ++ii) { + EXPECT_LT(fabs(ener[ii] - expected_tot_e[ii]), EPSILON); + } + for (int ii = 0; ii < nframes * natoms * 3; ++ii) { + EXPECT_LT(fabs(force[ii] - expected_f[ii]), EPSILON); + } + for (int ii = 0; ii < nframes * 3 * 3; ++ii) { + EXPECT_LT(fabs(virial[ii] - expected_tot_v[ii]), EPSILON); + } +} + +TYPED_TEST(TestInferDeepPotAFparamAparamNFrames, + cpu_lmp_nlist_type_sel_atomic) { + using VALUETYPE = TypeParam; + std::vector& coord = this->coord; + std::vector& atype = this->atype; + std::vector& box = this->box; + std::vector& fparam = this->fparam; + std::vector& aparam = this->aparam; + std::vector& expected_e = this->expected_e; + std::vector& expected_f = this->expected_f; + std::vector& expected_v = this->expected_v; + int& natoms = this->natoms; + int& nframes = this->nframes; + std::vector& expected_tot_e = this->expected_tot_e; + std::vector& expected_tot_v = this->expected_tot_v; + deepmd::hpp::DeepPot& dp = this->dp; + float rc = dp.cutoff(); + + // add vir atoms + int nvir = 2; + std::vector coord_vir(nvir * 3); + std::vector atype_vir(nvir, 2); + std::vector aparam_vir(nvir * 2); + for (int ii = 0; ii < nvir; ++ii) { + coord_vir[ii] = coord[ii]; + } + for (int ii = 0; ii < nvir * 2; ++ii) { + aparam_vir[ii] = aparam[ii]; + } + coord.insert(coord.begin(), coord_vir.begin(), coord_vir.end()); + atype.insert(atype.begin(), atype_vir.begin(), atype_vir.end()); + aparam.insert(aparam.begin(), aparam_vir.begin(), aparam_vir.end()); + natoms += nvir; + std::vector expected_f_vir(nvir * 3, 0.0); + // two frames + expected_f.insert(expected_f.begin(), expected_f_vir.begin(), + expected_f_vir.end()); + expected_f.insert(expected_f.begin() + natoms * 3, expected_f_vir.begin(), + expected_f_vir.end()); + std::vector coord_first(coord.begin(), coord.begin() + 3 * natoms); + std::vector box_first(box.begin(), box.begin() + 9); + + // build nlist + int nloc = coord_first.size() / 3; + std::vector coord_cpy; + std::vector atype_cpy, mapping; + std::vector > nlist_data; + _build_nlist(nlist_data, coord_cpy, atype_cpy, mapping, + coord_first, atype, box_first, rc); + int nall = coord_cpy.size() / 3; + std::vector ilist(nloc), numneigh(nloc); + std::vector firstneigh(nloc); + deepmd::hpp::InputNlist inlist(nloc, &ilist[0], &numneigh[0], &firstneigh[0]); + convert_nlist(inlist, nlist_data); + std::vector coord_cpy2(nframes * nall * 3); + for (int ii = 0; ii < nframes; ++ii) { + for (int jj = 0; jj < nall * 3; ++jj) { + coord_cpy2[ii * nall * 3 + jj] = coord_cpy[jj]; + } + } + + // dp compute + std::vector ener; + std::vector force_(nall * 3, 0.0), virial(nframes * 9, 0.0), + atomic_energy, atomic_virial; + dp.compute(ener, force_, virial, atomic_energy, atomic_virial, coord_cpy2, + atype_cpy, box, nall - nloc, inlist, 0, fparam, aparam); + // fold back + std::vector force; + _fold_back(force, force_, mapping, nloc, nall, 3, nframes); + + EXPECT_EQ(ener.size(), nframes); + EXPECT_EQ(force.size(), nframes * natoms * 3); + EXPECT_EQ(virial.size(), nframes * 9); + + for (int ii = 0; ii < nframes; ++ii) { + EXPECT_LT(fabs(ener[ii] - expected_tot_e[ii]), EPSILON); + } + for (int ii = 0; ii < nframes * natoms * 3; ++ii) { + EXPECT_LT(fabs(force[ii] - expected_f[ii]), EPSILON); + } + for (int ii = 0; ii < nframes * 3 * 3; ++ii) { + EXPECT_LT(fabs(virial[ii] - expected_tot_v[ii]), EPSILON); + } +} + +template +class TestInferDeepPotAFparamAparamNFramesSingleParam : public ::testing::Test { + protected: + std::vector coord = { + 12.83, 2.56, 2.18, 12.09, 2.87, 2.74, 00.25, 3.32, 1.68, + 3.36, 3.00, 1.81, 3.51, 2.51, 2.60, 4.27, 3.22, 1.56, + 12.83, 2.56, 2.18, 12.09, 2.87, 2.74, 00.25, 3.32, 1.68, + 3.36, 3.00, 1.81, 3.51, 2.51, 2.60, 4.27, 3.22, 1.56}; + std::vector atype = {0, 0, 0, 0, 0, 0}; + std::vector box = {13., 0., 0., 0., 13., 0., 0., 0., 13., + 13., 0., 0., 0., 13., 0., 0., 0., 13.}; + std::vector fparam = {0.25852028}; + std::vector aparam = {0.25852028, 0.25852028, 0.25852028, + 0.25852028, 0.25852028, 0.25852028}; + std::vector expected_e = { + -1.038271183039953804e-01, -7.285433575272914908e-02, + -9.467600174099155552e-02, -1.467050086239614082e-01, + -7.660561620618722145e-02, -7.277295998502930630e-02, + -1.038271183039953804e-01, -7.285433575272914908e-02, + -9.467600174099155552e-02, -1.467050086239614082e-01, + -7.660561620618722145e-02, -7.277295998502930630e-02}; + std::vector expected_f = { + 6.622266817497907132e-02, 5.278739055693523058e-02, + 2.265727495541422845e-02, -2.606047850915838363e-02, + -4.538811686410718776e-02, 1.058247569147072187e-02, + 1.679392490937766935e-01, -2.257828022687320690e-03, + -4.490145670355452645e-02, -1.148364103573685929e-01, + -1.169790466695089237e-02, 6.140402504113953025e-02, + -8.078778132132799494e-02, -5.838878056243369807e-02, + 6.773639989682191109e-02, -1.247724708090079161e-02, + 6.494523955924384750e-02, -1.174787188812918687e-01, + 6.622266817497907132e-02, 5.278739055693523058e-02, + 2.265727495541422845e-02, -2.606047850915838363e-02, + -4.538811686410718776e-02, 1.058247569147072187e-02, + 1.679392490937766935e-01, -2.257828022687320690e-03, + -4.490145670355452645e-02, -1.148364103573685929e-01, + -1.169790466695089237e-02, 6.140402504113953025e-02, + -8.078778132132799494e-02, -5.838878056243369807e-02, + 6.773639989682191109e-02, -1.247724708090079161e-02, + 6.494523955924384750e-02, -1.174787188812918687e-01}; + std::vector expected_v = { + -1.589185553287162656e-01, 2.586163333170100279e-03, + -1.575127933809472624e-04, -1.855360380105876630e-02, + 1.949822090859933826e-02, -1.006552056166355388e-02, + 3.177029853276916449e-02, 1.714349636720383010e-03, + -1.290389175187874483e-03, -8.553510339477603253e-02, + -5.654637257232508415e-03, -1.286954833787038420e-02, + 2.464156457499515687e-02, -2.398202886026797043e-02, + -1.957110465239037672e-02, 2.233492928605742764e-02, + 6.107843207824020099e-03, 1.707078295947736047e-03, + -1.653994088976195043e-01, 3.894358678172111371e-02, + -2.169595969759342477e-02, 6.819704294738503786e-03, + -5.018242039618424008e-03, 2.640664428663210429e-03, + -1.985298275686078057e-03, -3.638421609610945767e-02, + 2.342932331075030239e-02, -8.501331914753691710e-02, + -2.181253413538992297e-03, 4.311300069651782287e-03, + -1.910329328333908129e-03, -1.808810159508548836e-03, + -1.540075281450827612e-03, -1.173703213175551763e-02, + -2.596306629910121507e-03, 6.705025662372287101e-03, + -9.038455005073858795e-02, 3.011717773578577451e-02, + -5.083054073419784880e-02, -2.951210292616929069e-03, + 2.342445652898489383e-02, -4.091207474993674431e-02, + -1.648470649301832236e-02, -2.872261885460645689e-02, + 4.763924972552112391e-02, -8.300036532764677732e-02, + 1.020429228955421243e-03, -1.026734151199098881e-03, + 5.678534096113684732e-02, 1.273635718045938205e-02, + -1.530143225195957322e-02, -1.061671865629566225e-01, + -2.486859433265622629e-02, 2.875323131744185121e-02, + -1.589185553287162656e-01, 2.586163333170100279e-03, + -1.575127933809472624e-04, -1.855360380105876630e-02, + 1.949822090859933826e-02, -1.006552056166355388e-02, + 3.177029853276916449e-02, 1.714349636720383010e-03, + -1.290389175187874483e-03, -8.553510339477603253e-02, + -5.654637257232508415e-03, -1.286954833787038420e-02, + 2.464156457499515687e-02, -2.398202886026797043e-02, + -1.957110465239037672e-02, 2.233492928605742764e-02, + 6.107843207824020099e-03, 1.707078295947736047e-03, + -1.653994088976195043e-01, 3.894358678172111371e-02, + -2.169595969759342477e-02, 6.819704294738503786e-03, + -5.018242039618424008e-03, 2.640664428663210429e-03, + -1.985298275686078057e-03, -3.638421609610945767e-02, + 2.342932331075030239e-02, -8.501331914753691710e-02, + -2.181253413538992297e-03, 4.311300069651782287e-03, + -1.910329328333908129e-03, -1.808810159508548836e-03, + -1.540075281450827612e-03, -1.173703213175551763e-02, + -2.596306629910121507e-03, 6.705025662372287101e-03, + -9.038455005073858795e-02, 3.011717773578577451e-02, + -5.083054073419784880e-02, -2.951210292616929069e-03, + 2.342445652898489383e-02, -4.091207474993674431e-02, + -1.648470649301832236e-02, -2.872261885460645689e-02, + 4.763924972552112391e-02, -8.300036532764677732e-02, + 1.020429228955421243e-03, -1.026734151199098881e-03, + 5.678534096113684732e-02, 1.273635718045938205e-02, + -1.530143225195957322e-02, -1.061671865629566225e-01, + -2.486859433265622629e-02, 2.875323131744185121e-02}; + int natoms; + int nframes = 2; + std::vector expected_tot_e; + std::vector expected_tot_v; + + deepmd::hpp::DeepPot dp; + + void SetUp() override { + std::string file_name = "../../tests/infer/fparam_aparam.pbtxt"; + deepmd::hpp::convert_pbtxt_to_pb("../../tests/infer/fparam_aparam.pbtxt", + "fparam_aparam.pb"); + + dp.init("fparam_aparam.pb"); + + natoms = expected_e.size() / nframes; + EXPECT_EQ(nframes * natoms * 3, expected_f.size()); + EXPECT_EQ(nframes * natoms * 9, expected_v.size()); + expected_tot_e.resize(nframes); + expected_tot_v.resize(nframes * 9); + std::fill(expected_tot_e.begin(), expected_tot_e.end(), 0.); + std::fill(expected_tot_v.begin(), expected_tot_v.end(), 0.); + for (int kk = 0; kk < nframes; ++kk) { + for (int ii = 0; ii < natoms; ++ii) { + expected_tot_e[kk] += expected_e[kk * natoms + ii]; + } + for (int ii = 0; ii < natoms; ++ii) { + for (int dd = 0; dd < 9; ++dd) { + expected_tot_v[kk * 9 + dd] += + expected_v[kk * natoms * 9 + ii * 9 + dd]; + } + } + } + }; + + void TearDown() override { remove("fparam_aparam.pb"); }; +}; + +TYPED_TEST_SUITE(TestInferDeepPotAFparamAparamNFramesSingleParam, ValueTypes); + +TYPED_TEST(TestInferDeepPotAFparamAparamNFramesSingleParam, cpu_build_nlist) { + using VALUETYPE = TypeParam; + std::vector& coord = this->coord; + std::vector& atype = this->atype; + std::vector& box = this->box; + std::vector& fparam = this->fparam; + std::vector& aparam = this->aparam; + std::vector& expected_e = this->expected_e; + std::vector& expected_f = this->expected_f; + std::vector& expected_v = this->expected_v; + int& natoms = this->natoms; + int& nframes = this->nframes; + std::vector& expected_tot_e = this->expected_tot_e; + std::vector& expected_tot_v = this->expected_tot_v; + deepmd::hpp::DeepPot& dp = this->dp; + std::vector ener; + std::vector force, virial; + dp.compute(ener, force, virial, coord, atype, box, fparam, aparam); + + EXPECT_EQ(ener.size(), nframes); + EXPECT_EQ(force.size(), nframes * natoms * 3); + EXPECT_EQ(virial.size(), nframes * 9); + + for (int ii = 0; ii < nframes; ++ii) { + EXPECT_LT(fabs(ener[ii] - expected_tot_e[ii]), EPSILON); + } + for (int ii = 0; ii < nframes * natoms * 3; ++ii) { + EXPECT_LT(fabs(force[ii] - expected_f[ii]), EPSILON); + } + for (int ii = 0; ii < nframes * 3 * 3; ++ii) { + EXPECT_LT(fabs(virial[ii] - expected_tot_v[ii]), EPSILON); + } +} + +TYPED_TEST(TestInferDeepPotAFparamAparamNFramesSingleParam, + cpu_build_nlist_atomic) { + using VALUETYPE = TypeParam; + std::vector& coord = this->coord; + std::vector& atype = this->atype; + std::vector& box = this->box; + std::vector& fparam = this->fparam; + std::vector& aparam = this->aparam; + std::vector& expected_e = this->expected_e; + std::vector& expected_f = this->expected_f; + std::vector& expected_v = this->expected_v; + int& natoms = this->natoms; + int& nframes = this->nframes; + std::vector& expected_tot_e = this->expected_tot_e; + std::vector& expected_tot_v = this->expected_tot_v; + deepmd::hpp::DeepPot& dp = this->dp; + std::vector ener; + std::vector force, virial, atom_ener, atom_vir; + dp.compute(ener, force, virial, atom_ener, atom_vir, coord, atype, box, + fparam, aparam); + + EXPECT_EQ(ener.size(), nframes); + EXPECT_EQ(force.size(), nframes * natoms * 3); + EXPECT_EQ(virial.size(), nframes * 9); + EXPECT_EQ(atom_ener.size(), nframes * natoms); + EXPECT_EQ(atom_vir.size(), nframes * natoms * 9); + + for (int ii = 0; ii < nframes; ++ii) { + EXPECT_LT(fabs(ener[ii] - expected_tot_e[ii]), EPSILON); + } + for (int ii = 0; ii < nframes * natoms * 3; ++ii) { + EXPECT_LT(fabs(force[ii] - expected_f[ii]), EPSILON); + } + for (int ii = 0; ii < nframes * 3 * 3; ++ii) { + EXPECT_LT(fabs(virial[ii] - expected_tot_v[ii]), EPSILON); + } + for (int ii = 0; ii < nframes * natoms; ++ii) { + EXPECT_LT(fabs(atom_ener[ii] - expected_e[ii]), EPSILON); + } + for (int ii = 0; ii < nframes * natoms * 9; ++ii) { + EXPECT_LT(fabs(atom_vir[ii] - expected_v[ii]), EPSILON); + } +} + +TYPED_TEST(TestInferDeepPotAFparamAparamNFramesSingleParam, cpu_lmp_nlist) { + using VALUETYPE = TypeParam; + std::vector& coord = this->coord; + std::vector& atype = this->atype; + std::vector& box = this->box; + std::vector& fparam = this->fparam; + std::vector& aparam = this->aparam; + std::vector& expected_e = this->expected_e; + std::vector& expected_f = this->expected_f; + std::vector& expected_v = this->expected_v; + int& natoms = this->natoms; + int& nframes = this->nframes; + std::vector& expected_tot_e = this->expected_tot_e; + std::vector& expected_tot_v = this->expected_tot_v; + deepmd::hpp::DeepPot& dp = this->dp; + float rc = dp.cutoff(); + std::vector coord_first(coord.begin(), coord.begin() + 3 * natoms); + std::vector box_first(box.begin(), box.begin() + 9); + int nloc = coord_first.size() / 3; + std::vector coord_cpy; + std::vector atype_cpy, mapping; + std::vector > nlist_data; + _build_nlist(nlist_data, coord_cpy, atype_cpy, mapping, + coord_first, atype, box_first, rc); + int nall = coord_cpy.size() / 3; + std::vector ilist(nloc), numneigh(nloc); + std::vector firstneigh(nloc); + deepmd::hpp::InputNlist inlist(nloc, &ilist[0], &numneigh[0], &firstneigh[0]); + convert_nlist(inlist, nlist_data); + std::vector coord_cpy2(nframes * nall * 3); + for (int ii = 0; ii < nframes; ++ii) { + for (int jj = 0; jj < nall * 3; ++jj) { + coord_cpy2[ii * nall * 3 + jj] = coord_cpy[jj]; + } + } + + std::vector ener; + std::vector force_, virial; + dp.compute(ener, force_, virial, coord_cpy2, atype_cpy, box, nall - nloc, + inlist, 0, fparam, aparam); + std::vector force; + _fold_back(force, force_, mapping, nloc, nall, 3, nframes); + + EXPECT_EQ(ener.size(), nframes); + EXPECT_EQ(force.size(), nframes * natoms * 3); + EXPECT_EQ(virial.size(), nframes * 9); + + for (int ii = 0; ii < nframes; ++ii) { + EXPECT_LT(fabs(ener[ii] - expected_tot_e[ii]), EPSILON); + } + for (int ii = 0; ii < nframes * natoms * 3; ++ii) { + EXPECT_LT(fabs(force[ii] - expected_f[ii]), EPSILON); + } + for (int ii = 0; ii < nframes * 3 * 3; ++ii) { + EXPECT_LT(fabs(virial[ii] - expected_tot_v[ii]), EPSILON); + } + + std::fill(ener.begin(), ener.end(), 0.0); + std::fill(force_.begin(), force_.end(), 0.0); + std::fill(virial.begin(), virial.end(), 0.0); + dp.compute(ener, force_, virial, coord_cpy2, atype_cpy, box, nall - nloc, + inlist, 1, fparam, aparam); + _fold_back(force, force_, mapping, nloc, nall, 3, nframes); + + EXPECT_EQ(ener.size(), nframes); + EXPECT_EQ(force.size(), nframes * natoms * 3); + EXPECT_EQ(virial.size(), nframes * 9); + + for (int ii = 0; ii < nframes; ++ii) { + EXPECT_LT(fabs(ener[ii] - expected_tot_e[ii]), EPSILON); + } + for (int ii = 0; ii < nframes * natoms * 3; ++ii) { + EXPECT_LT(fabs(force[ii] - expected_f[ii]), EPSILON); + } + for (int ii = 0; ii < nframes * 3 * 3; ++ii) { + EXPECT_LT(fabs(virial[ii] - expected_tot_v[ii]), EPSILON); + } +} + +TYPED_TEST(TestInferDeepPotAFparamAparamNFramesSingleParam, + cpu_lmp_nlist_atomic) { + using VALUETYPE = TypeParam; + std::vector& coord = this->coord; + std::vector& atype = this->atype; + std::vector& box = this->box; + std::vector& fparam = this->fparam; + std::vector& aparam = this->aparam; + std::vector& expected_e = this->expected_e; + std::vector& expected_f = this->expected_f; + std::vector& expected_v = this->expected_v; + int& natoms = this->natoms; + int& nframes = this->nframes; + std::vector& expected_tot_e = this->expected_tot_e; + std::vector& expected_tot_v = this->expected_tot_v; + deepmd::hpp::DeepPot& dp = this->dp; + float rc = dp.cutoff(); + std::vector coord_first(coord.begin(), coord.begin() + 3 * natoms); + std::vector box_first(box.begin(), box.begin() + 9); + int nloc = coord_first.size() / 3; + std::vector coord_cpy; + std::vector atype_cpy, mapping; + std::vector > nlist_data; + _build_nlist(nlist_data, coord_cpy, atype_cpy, mapping, + coord_first, atype, box_first, rc); + int nall = coord_cpy.size() / 3; + std::vector ilist(nloc), numneigh(nloc); + std::vector firstneigh(nloc); + deepmd::hpp::InputNlist inlist(nloc, &ilist[0], &numneigh[0], &firstneigh[0]); + convert_nlist(inlist, nlist_data); + std::vector coord_cpy2(nframes * nall * 3); + for (int ii = 0; ii < nframes; ++ii) { + for (int jj = 0; jj < nall * 3; ++jj) { + coord_cpy2[ii * nall * 3 + jj] = coord_cpy[jj]; + } + } + + std::vector ener; + std::vector force_, atom_ener_, atom_vir_, virial; + std::vector force, atom_ener, atom_vir; + dp.compute(ener, force_, virial, atom_ener_, atom_vir_, coord_cpy2, atype_cpy, + box, nall - nloc, inlist, 0, fparam, aparam); + _fold_back(force, force_, mapping, nloc, nall, 3, nframes); + _fold_back(atom_ener, atom_ener_, mapping, nloc, nall, 1, nframes); + _fold_back(atom_vir, atom_vir_, mapping, nloc, nall, 9, nframes); + + EXPECT_EQ(ener.size(), nframes); + EXPECT_EQ(force.size(), nframes * natoms * 3); + EXPECT_EQ(virial.size(), nframes * 9); + EXPECT_EQ(atom_ener.size(), nframes * natoms); + EXPECT_EQ(atom_vir.size(), nframes * natoms * 9); + + for (int ii = 0; ii < nframes; ++ii) { + EXPECT_LT(fabs(ener[ii] - expected_tot_e[ii]), EPSILON); + } + for (int ii = 0; ii < nframes * natoms * 3; ++ii) { + EXPECT_LT(fabs(force[ii] - expected_f[ii]), EPSILON); + } + for (int ii = 0; ii < nframes * 3 * 3; ++ii) { + EXPECT_LT(fabs(virial[ii] - expected_tot_v[ii]), EPSILON); + } + for (int ii = 0; ii < nframes * natoms; ++ii) { + EXPECT_LT(fabs(atom_ener[ii] - expected_e[ii]), EPSILON); + } + for (int ii = 0; ii < nframes * natoms * 9; ++ii) { + EXPECT_LT(fabs(atom_vir[ii] - expected_v[ii]), EPSILON); + } + + std::fill(ener.begin(), ener.end(), 0.0); + std::fill(force_.begin(), force_.end(), 0.0); + std::fill(virial.begin(), virial.end(), 0.0); + std::fill(atom_ener_.begin(), atom_ener_.end(), 0.0); + std::fill(atom_vir_.begin(), atom_vir_.end(), 0.0); + dp.compute(ener, force_, virial, atom_ener_, atom_vir_, coord_cpy2, atype_cpy, + box, nall - nloc, inlist, 1, fparam, aparam); + _fold_back(force, force_, mapping, nloc, nall, 3, nframes); + _fold_back(atom_ener, atom_ener_, mapping, nloc, nall, 1, nframes); + _fold_back(atom_vir, atom_vir_, mapping, nloc, nall, 9, nframes); + + EXPECT_EQ(ener.size(), nframes); + EXPECT_EQ(force.size(), nframes * natoms * 3); + EXPECT_EQ(virial.size(), nframes * 9); + EXPECT_EQ(atom_ener.size(), nframes * natoms); + EXPECT_EQ(atom_vir.size(), nframes * natoms * 9); + + for (int ii = 0; ii < nframes; ++ii) { + EXPECT_LT(fabs(ener[ii] - expected_tot_e[ii]), EPSILON); + } + for (int ii = 0; ii < nframes * natoms * 3; ++ii) { + EXPECT_LT(fabs(force[ii] - expected_f[ii]), EPSILON); + } + for (int ii = 0; ii < nframes * 3 * 3; ++ii) { + EXPECT_LT(fabs(virial[ii] - expected_tot_v[ii]), EPSILON); + } + for (int ii = 0; ii < nframes * natoms; ++ii) { + EXPECT_LT(fabs(atom_ener[ii] - expected_e[ii]), EPSILON); + } + for (int ii = 0; ii < nframes * natoms * 9; ++ii) { + EXPECT_LT(fabs(atom_vir[ii] - expected_v[ii]), EPSILON); + } +} + +TYPED_TEST(TestInferDeepPotAFparamAparamNFramesSingleParam, cpu_lmp_nlist_2rc) { + using VALUETYPE = TypeParam; + std::vector& coord = this->coord; + std::vector& atype = this->atype; + std::vector& box = this->box; + std::vector& fparam = this->fparam; + std::vector& aparam = this->aparam; + std::vector& expected_e = this->expected_e; + std::vector& expected_f = this->expected_f; + std::vector& expected_v = this->expected_v; + int& natoms = this->natoms; + int& nframes = this->nframes; + std::vector& expected_tot_e = this->expected_tot_e; + std::vector& expected_tot_v = this->expected_tot_v; + deepmd::hpp::DeepPot& dp = this->dp; + float rc = dp.cutoff(); + std::vector coord_first(coord.begin(), coord.begin() + 3 * natoms); + std::vector box_first(box.begin(), box.begin() + 9); + int nloc = coord_first.size() / 3; + std::vector coord_cpy; + std::vector atype_cpy, mapping; + std::vector > nlist_data; + _build_nlist(nlist_data, coord_cpy, atype_cpy, mapping, + coord_first, atype, box_first, rc * 2); + int nall = coord_cpy.size() / 3; + std::vector ilist(nloc), numneigh(nloc); + std::vector firstneigh(nloc); + deepmd::hpp::InputNlist inlist(nloc, &ilist[0], &numneigh[0], &firstneigh[0]); + convert_nlist(inlist, nlist_data); + std::vector coord_cpy2(nframes * nall * 3); + for (int ii = 0; ii < nframes; ++ii) { + for (int jj = 0; jj < nall * 3; ++jj) { + coord_cpy2[ii * nall * 3 + jj] = coord_cpy[jj]; + } + } + + std::vector ener; + std::vector force_(nall * 3, 0.0), virial(nframes * 9, 0.0); + dp.compute(ener, force_, virial, coord_cpy2, atype_cpy, box, nall - nloc, + inlist, 0, fparam, aparam); + std::vector force; + _fold_back(force, force_, mapping, nloc, nall, 3, nframes); + + EXPECT_EQ(ener.size(), nframes); + EXPECT_EQ(force.size(), nframes * natoms * 3); + EXPECT_EQ(virial.size(), nframes * 9); + + for (int ii = 0; ii < nframes; ++ii) { + EXPECT_LT(fabs(ener[ii] - expected_tot_e[ii]), EPSILON); + } + for (int ii = 0; ii < nframes * natoms * 3; ++ii) { + EXPECT_LT(fabs(force[ii] - expected_f[ii]), EPSILON); + } + for (int ii = 0; ii < nframes * 3 * 3; ++ii) { + EXPECT_LT(fabs(virial[ii] - expected_tot_v[ii]), EPSILON); + } + + std::fill(ener.begin(), ener.end(), 0.0); + std::fill(force_.begin(), force_.end(), 0.0); + std::fill(virial.begin(), virial.end(), 0.0); + dp.compute(ener, force_, virial, coord_cpy2, atype_cpy, box, nall - nloc, + inlist, 1, fparam, aparam); + _fold_back(force, force_, mapping, nloc, nall, 3, nframes); + + EXPECT_EQ(ener.size(), nframes); + EXPECT_EQ(force.size(), nframes * natoms * 3); + EXPECT_EQ(virial.size(), nframes * 9); + + for (int ii = 0; ii < nframes; ++ii) { + EXPECT_LT(fabs(ener[ii] - expected_tot_e[ii]), EPSILON); + } + for (int ii = 0; ii < nframes * natoms * 3; ++ii) { + EXPECT_LT(fabs(force[ii] - expected_f[ii]), EPSILON); + } + for (int ii = 0; ii < nframes * 3 * 3; ++ii) { + EXPECT_LT(fabs(virial[ii] - expected_tot_v[ii]), EPSILON); + } +} + +TYPED_TEST(TestInferDeepPotAFparamAparamNFramesSingleParam, + cpu_lmp_nlist_type_sel) { + using VALUETYPE = TypeParam; + std::vector& coord = this->coord; + std::vector& atype = this->atype; + std::vector& box = this->box; + std::vector& fparam = this->fparam; + std::vector& aparam = this->aparam; + std::vector& expected_e = this->expected_e; + std::vector& expected_f = this->expected_f; + std::vector& expected_v = this->expected_v; + int& natoms = this->natoms; + int& nframes = this->nframes; + std::vector& expected_tot_e = this->expected_tot_e; + std::vector& expected_tot_v = this->expected_tot_v; + deepmd::hpp::DeepPot& dp = this->dp; + float rc = dp.cutoff(); + + // add vir atoms + int nvir = 2; + std::vector coord_vir(nvir * 3); + std::vector atype_vir(nvir, 2); + std::vector aparam_vir(nvir * 1); + for (int ii = 0; ii < nvir; ++ii) { + coord_vir[ii] = coord[ii]; + } + for (int ii = 0; ii < nvir; ++ii) { + aparam_vir[ii] = aparam[ii]; + } + coord.insert(coord.begin(), coord_vir.begin(), coord_vir.end()); + atype.insert(atype.begin(), atype_vir.begin(), atype_vir.end()); + natoms += nvir; + std::vector expected_f_vir(nvir * 3, 0.0); + // two frames + expected_f.insert(expected_f.begin(), expected_f_vir.begin(), + expected_f_vir.end()); + expected_f.insert(expected_f.begin() + natoms * 3, expected_f_vir.begin(), + expected_f_vir.end()); + std::vector coord_first(coord.begin(), coord.begin() + 3 * natoms); + std::vector box_first(box.begin(), box.begin() + 9); + + // build nlist + int nloc = coord_first.size() / 3; + std::vector coord_cpy; + std::vector atype_cpy, mapping; + std::vector > nlist_data; + _build_nlist(nlist_data, coord_cpy, atype_cpy, mapping, + coord_first, atype, box_first, rc); + int nall = coord_cpy.size() / 3; + std::vector ilist(nloc), numneigh(nloc); + std::vector firstneigh(nloc); + deepmd::hpp::InputNlist inlist(nloc, &ilist[0], &numneigh[0], &firstneigh[0]); + convert_nlist(inlist, nlist_data); + std::vector coord_cpy2(nframes * nall * 3); + for (int ii = 0; ii < nframes; ++ii) { + for (int jj = 0; jj < nall * 3; ++jj) { + coord_cpy2[ii * nall * 3 + jj] = coord_cpy[jj]; + } + } + aparam.insert(aparam.begin(), aparam_vir.begin(), aparam_vir.end()); + + // dp compute + std::vector ener; + std::vector force_(nall * 3, 0.0), virial(nframes * 9, 0.0); + dp.compute(ener, force_, virial, coord_cpy2, atype_cpy, box, nall - nloc, + inlist, 0, fparam, aparam); + // fold back + std::vector force; + _fold_back(force, force_, mapping, nloc, nall, 3, nframes); + + EXPECT_EQ(ener.size(), nframes); + EXPECT_EQ(force.size(), nframes * natoms * 3); + EXPECT_EQ(virial.size(), nframes * 9); + + for (int ii = 0; ii < nframes; ++ii) { + EXPECT_LT(fabs(ener[ii] - expected_tot_e[ii]), EPSILON); + } + for (int ii = 0; ii < nframes * natoms * 3; ++ii) { + EXPECT_LT(fabs(force[ii] - expected_f[ii]), EPSILON); + } + for (int ii = 0; ii < nframes * 3 * 3; ++ii) { + EXPECT_LT(fabs(virial[ii] - expected_tot_v[ii]), EPSILON); + } +} + +TYPED_TEST(TestInferDeepPotAFparamAparamNFramesSingleParam, + cpu_lmp_nlist_type_sel_atomic) { + using VALUETYPE = TypeParam; + std::vector& coord = this->coord; + std::vector& atype = this->atype; + std::vector& box = this->box; + std::vector& fparam = this->fparam; + std::vector& aparam = this->aparam; + std::vector& expected_e = this->expected_e; + std::vector& expected_f = this->expected_f; + std::vector& expected_v = this->expected_v; + int& natoms = this->natoms; + int& nframes = this->nframes; + std::vector& expected_tot_e = this->expected_tot_e; + std::vector& expected_tot_v = this->expected_tot_v; + deepmd::hpp::DeepPot& dp = this->dp; + float rc = dp.cutoff(); + + // add vir atoms + int nvir = 2; + std::vector coord_vir(nvir * 3); + std::vector atype_vir(nvir, 2); + std::vector aparam_vir(nvir); + for (int ii = 0; ii < nvir; ++ii) { + coord_vir[ii] = coord[ii]; + } + for (int ii = 0; ii < nvir; ++ii) { + aparam_vir[ii] = aparam[ii]; + } + coord.insert(coord.begin(), coord_vir.begin(), coord_vir.end()); + atype.insert(atype.begin(), atype_vir.begin(), atype_vir.end()); + aparam.insert(aparam.begin(), aparam_vir.begin(), aparam_vir.end()); + natoms += nvir; + std::vector expected_f_vir(nvir * 3, 0.0); + // two frames + expected_f.insert(expected_f.begin(), expected_f_vir.begin(), + expected_f_vir.end()); + expected_f.insert(expected_f.begin() + natoms * 3, expected_f_vir.begin(), + expected_f_vir.end()); + std::vector coord_first(coord.begin(), coord.begin() + 3 * natoms); + std::vector box_first(box.begin(), box.begin() + 9); + + // build nlist + int nloc = coord_first.size() / 3; + std::vector coord_cpy; + std::vector atype_cpy, mapping; + std::vector > nlist_data; + _build_nlist(nlist_data, coord_cpy, atype_cpy, mapping, + coord_first, atype, box_first, rc); + int nall = coord_cpy.size() / 3; + std::vector ilist(nloc), numneigh(nloc); + std::vector firstneigh(nloc); + deepmd::hpp::InputNlist inlist(nloc, &ilist[0], &numneigh[0], &firstneigh[0]); + convert_nlist(inlist, nlist_data); + std::vector coord_cpy2(nframes * nall * 3); + for (int ii = 0; ii < nframes; ++ii) { + for (int jj = 0; jj < nall * 3; ++jj) { + coord_cpy2[ii * nall * 3 + jj] = coord_cpy[jj]; + } + } + + // dp compute + std::vector ener; + std::vector force_(nall * 3, 0.0), virial(nframes * 9, 0.0), + atomic_energy, atomic_virial; + dp.compute(ener, force_, virial, atomic_energy, atomic_virial, coord_cpy2, + atype_cpy, box, nall - nloc, inlist, 0, fparam, aparam); + // fold back + std::vector force; + _fold_back(force, force_, mapping, nloc, nall, 3, nframes); + + EXPECT_EQ(ener.size(), nframes); + EXPECT_EQ(force.size(), nframes * natoms * 3); + EXPECT_EQ(virial.size(), nframes * 9); + + for (int ii = 0; ii < nframes; ++ii) { + EXPECT_LT(fabs(ener[ii] - expected_tot_e[ii]), EPSILON); + } + for (int ii = 0; ii < nframes * natoms * 3; ++ii) { + EXPECT_LT(fabs(force[ii] - expected_f[ii]), EPSILON); + } + for (int ii = 0; ii < nframes * 3 * 3; ++ii) { + EXPECT_LT(fabs(virial[ii] - expected_tot_v[ii]), EPSILON); + } +} From c5fc6dabf07cc675e80f9def42dd286e036c707f Mon Sep 17 00:00:00 2001 From: Jinzhe Zeng Date: Tue, 21 Mar 2023 21:19:41 -0400 Subject: [PATCH 025/126] fix lmp_version.sh (#2417) Signed-off-by: Jinzhe Zeng --- source/lmp/lmp_version.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/source/lmp/lmp_version.sh b/source/lmp/lmp_version.sh index 3f769cf3a3..99c1c5552e 100755 --- a/source/lmp/lmp_version.sh +++ b/source/lmp/lmp_version.sh @@ -6,4 +6,4 @@ version_line=$(grep LAMMPS_VERSION ../version.h) tmp=${version_line#*\"} # remove prefix ending in " version=${tmp%\"*} # remove suffix starting with " # string to int -date --date="$(printf $version)" +"%Y%m%d" +date --date="$(printf "$version")" +"%Y%m%d" From 112988aacb6796e4007fe9e91d01f47a867157d9 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Wed, 22 Mar 2023 10:15:58 +0800 Subject: [PATCH 026/126] [pre-commit.ci] pre-commit autoupdate (#2416) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit updates: - [github.com/psf/black: 22.12.0 → 23.1.0](https://github.com/psf/black/compare/22.12.0...23.1.0) - [github.com/charliermarsh/ruff-pre-commit: v0.0.243 → v0.0.257](https://github.com/charliermarsh/ruff-pre-commit/compare/v0.0.243...v0.0.257) --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Jinzhe Zeng --- .pre-commit-config.yaml | 4 ++-- deepmd/calculator.py | 2 +- deepmd/descriptor/se_a.py | 2 +- deepmd/descriptor/se_atten.py | 2 +- deepmd/entrypoints/compress.py | 2 +- deepmd/infer/deep_dipole.py | 3 +-- deepmd/infer/deep_polar.py | 4 +--- deepmd/infer/deep_pot.py | 1 - deepmd/infer/deep_wfc.py | 3 +-- deepmd/loggers/loggers.py | 1 - deepmd/model/ener.py | 1 - deepmd/model/multi.py | 1 - deepmd/model/tensor.py | 1 - deepmd/nvnmd/entrypoints/wrap.py | 2 +- deepmd/nvnmd/utils/config.py | 2 +- deepmd/train/trainer.py | 2 -- deepmd/utils/compat.py | 1 - deepmd/utils/neighbor_stat.py | 2 +- deepmd/utils/path.py | 8 ++++---- doc/conf.py | 1 - source/install/build_tf.py | 2 -- source/tests/common.py | 6 +++--- source/tests/test_argument_parser.py | 1 - source/tests/test_compat_input.py | 1 - source/tests/test_deepmd_data_sys.py | 2 +- source/tests/test_descrpt_sea_ef_rot.py | 2 +- source/tests/test_get_potential.py | 1 - 27 files changed, 21 insertions(+), 39 deletions(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 017c54c544..e630577dc5 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -16,7 +16,7 @@ repos: - id: check-toml # Python - repo: https://github.com/psf/black - rev: 22.12.0 + rev: 23.1.0 hooks: - id: black-jupyter - repo: https://github.com/PyCQA/isort @@ -26,7 +26,7 @@ repos: files: \.py$ - repo: https://github.com/charliermarsh/ruff-pre-commit # Ruff version. - rev: v0.0.243 + rev: v0.0.257 hooks: - id: ruff args: ["--fix"] diff --git a/deepmd/calculator.py b/deepmd/calculator.py index cd361eed5f..4777bc80bc 100644 --- a/deepmd/calculator.py +++ b/deepmd/calculator.py @@ -75,7 +75,7 @@ def __init__( model: Union[str, "Path"], label: str = "DP", type_dict: Dict[str, int] = None, - **kwargs + **kwargs, ) -> None: Calculator.__init__(self, label=label, **kwargs) self.dp = DeepPotential(str(Path(model).resolve())) diff --git a/deepmd/descriptor/se_a.py b/deepmd/descriptor/se_a.py index 2bff523608..73221c1f91 100644 --- a/deepmd/descriptor/se_a.py +++ b/deepmd/descriptor/se_a.py @@ -866,7 +866,7 @@ def _filter_lower( trainable=True, suffix="", ): - """input env matrix, returns R.G.""" + """Input env matrix, returns R.G.""" outputs_size = [1] + self.filter_neuron # cut-out inputs # with natom x (nei_type_i x 4) diff --git a/deepmd/descriptor/se_atten.py b/deepmd/descriptor/se_atten.py index 1c9d7b2c09..cab26f4390 100644 --- a/deepmd/descriptor/se_atten.py +++ b/deepmd/descriptor/se_atten.py @@ -793,7 +793,7 @@ def _filter_lower( name="filter_", reuse=None, ): - """input env matrix, returns R.G.""" + """Input env matrix, returns R.G.""" outputs_size = [1] + self.filter_neuron # cut-out inputs # with natom x (nei_type_i x 4) diff --git a/deepmd/entrypoints/compress.py b/deepmd/entrypoints/compress.py index 6e5ea4f087..36d38c2f8b 100644 --- a/deepmd/entrypoints/compress.py +++ b/deepmd/entrypoints/compress.py @@ -55,7 +55,7 @@ def compress( mpi_log: str, log_path: Optional[str], log_level: int, - **kwargs + **kwargs, ): """Compress model. diff --git a/deepmd/infer/deep_dipole.py b/deepmd/infer/deep_dipole.py index 8988083a5e..e7eb1061de 100644 --- a/deepmd/infer/deep_dipole.py +++ b/deepmd/infer/deep_dipole.py @@ -37,7 +37,6 @@ def __init__( load_prefix: str = "load", default_tf_graph: bool = False, ) -> None: - # use this in favor of dict update to move attribute from class to # instance namespace self.tensors = dict( @@ -45,7 +44,7 @@ def __init__( # output tensor "t_tensor": "o_dipole:0", }, - **self.tensors + **self.tensors, ) DeepTensor.__init__( diff --git a/deepmd/infer/deep_polar.py b/deepmd/infer/deep_polar.py index d76ae5ccad..f08ea9f64b 100644 --- a/deepmd/infer/deep_polar.py +++ b/deepmd/infer/deep_polar.py @@ -41,7 +41,6 @@ def __init__( load_prefix: str = "load", default_tf_graph: bool = False, ) -> None: - # use this in favor of dict update to move attribute from class to # instance namespace self.tensors = dict( @@ -49,7 +48,7 @@ def __init__( # output tensor "t_tensor": "o_polar:0", }, - **self.tensors + **self.tensors, ) DeepTensor.__init__( @@ -84,7 +83,6 @@ class DeepGlobalPolar(DeepTensor): def __init__( self, model_file: str, load_prefix: str = "load", default_tf_graph: bool = False ) -> None: - self.tensors.update( { "t_sel_type": "model_attr/sel_type:0", diff --git a/deepmd/infer/deep_pot.py b/deepmd/infer/deep_pot.py index 67a371b58e..7e13c77761 100644 --- a/deepmd/infer/deep_pot.py +++ b/deepmd/infer/deep_pot.py @@ -75,7 +75,6 @@ def __init__( default_tf_graph: bool = False, auto_batch_size: Union[bool, int, AutoBatchSize] = True, ) -> None: - # add these tensors on top of what is defined by DeepTensor Class # use this in favor of dict update to move attribute from class to # instance namespace diff --git a/deepmd/infer/deep_wfc.py b/deepmd/infer/deep_wfc.py index ef7866f04d..36a77de35e 100644 --- a/deepmd/infer/deep_wfc.py +++ b/deepmd/infer/deep_wfc.py @@ -37,7 +37,6 @@ def __init__( load_prefix: str = "load", default_tf_graph: bool = False, ) -> None: - # use this in favor of dict update to move attribute from class to # instance namespace self.tensors = dict( @@ -45,7 +44,7 @@ def __init__( # output tensor "t_tensor": "o_wfc:0", }, - **self.tensors + **self.tensors, ) DeepTensor.__init__( self, diff --git a/deepmd/loggers/loggers.py b/deepmd/loggers/loggers.py index 4249d7fb3d..9fe9530482 100644 --- a/deepmd/loggers/loggers.py +++ b/deepmd/loggers/loggers.py @@ -235,7 +235,6 @@ def set_log_handles( # * add file handler *************************************************************** if log_path: - # create directory log_path.parent.mkdir(exist_ok=True, parents=True) diff --git a/deepmd/model/ener.py b/deepmd/model/ener.py index ff8fca6932..a925640cf2 100644 --- a/deepmd/model/ener.py +++ b/deepmd/model/ener.py @@ -151,7 +151,6 @@ def build( suffix="", reuse=None, ): - if input_dict is None: input_dict = {} with tf.variable_scope("model_attr" + suffix, reuse=reuse): diff --git a/deepmd/model/multi.py b/deepmd/model/multi.py index d69e08eea8..dcf7566f36 100644 --- a/deepmd/model/multi.py +++ b/deepmd/model/multi.py @@ -183,7 +183,6 @@ def build( suffix="", reuse=None, ): - if input_dict is None: input_dict = {} with tf.variable_scope("model_attr" + suffix, reuse=reuse): diff --git a/deepmd/model/tensor.py b/deepmd/model/tensor.py index 8446fcad8f..eb32fa88cf 100644 --- a/deepmd/model/tensor.py +++ b/deepmd/model/tensor.py @@ -117,7 +117,6 @@ def build( suffix="", reuse=None, ): - if input_dict is None: input_dict = {} with tf.variable_scope("model_attr" + suffix, reuse=reuse): diff --git a/deepmd/nvnmd/entrypoints/wrap.py b/deepmd/nvnmd/entrypoints/wrap.py index 3fbd8b0b10..4bf12803f2 100644 --- a/deepmd/nvnmd/entrypoints/wrap.py +++ b/deepmd/nvnmd/entrypoints/wrap.py @@ -459,7 +459,7 @@ def wrap( nvnmd_weight: Optional[str] = "nvnmd/weight.npy", nvnmd_map: Optional[str] = "nvnmd/map.npy", nvnmd_model: Optional[str] = "nvnmd/model.pb", - **kwargs + **kwargs, ): wrapObj = Wrap(nvnmd_config, nvnmd_weight, nvnmd_map, nvnmd_model) wrapObj.wrap() diff --git a/deepmd/nvnmd/utils/config.py b/deepmd/nvnmd/utils/config.py index ff379d6dc6..9d756f8ef1 100644 --- a/deepmd/nvnmd/utils/config.py +++ b/deepmd/nvnmd/utils/config.py @@ -163,7 +163,7 @@ def init_fitn(self, jdata: dict, jdata_parent: dict = {}) -> dict: return jdata def init_dpin(self, jdata: dict, jdata_parent: dict = {}) -> dict: - r"""initial members about other deepmd input.""" + r"""Initial members about other deepmd input.""" return jdata def init_size(self, jdata: dict, jdata_parent: dict = {}) -> dict: diff --git a/deepmd/train/trainer.py b/deepmd/train/trainer.py index 14d0ab240a..2b8e24f908 100644 --- a/deepmd/train/trainer.py +++ b/deepmd/train/trainer.py @@ -729,7 +729,6 @@ def _init_session(self): run_sess(self.sess, bcast_op) def train(self, train_data=None, valid_data=None): - # if valid_data is None: # no validation set specified. # valid_data = train_data # using training set as validation set. @@ -793,7 +792,6 @@ def train(self, train_data=None, valid_data=None): total_train_time = 0.0 while cur_batch < stop_batch: - # first round validation: if not self.multi_task_mode: train_batch = train_data.get_batch() diff --git a/deepmd/utils/compat.py b/deepmd/utils/compat.py index 0005e0abdf..4ec47b53a1 100644 --- a/deepmd/utils/compat.py +++ b/deepmd/utils/compat.py @@ -287,7 +287,6 @@ def remove_decay_rate(jdata: Dict[str, Any]): def convert_input_v1_v2( jdata: Dict[str, Any], warning: bool = True, dump: Optional[Union[str, Path]] = None ) -> Dict[str, Any]: - tr_cfg = jdata["training"] tr_data_keys = { "systems", diff --git a/deepmd/utils/neighbor_stat.py b/deepmd/utils/neighbor_stat.py index eebe33d7a0..9b23bc9d76 100644 --- a/deepmd/utils/neighbor_stat.py +++ b/deepmd/utils/neighbor_stat.py @@ -89,7 +89,7 @@ def builder(): self.sub_sess = tf.Session(graph=sub_graph, config=default_tf_session_config) def get_stat(self, data: DeepmdDataSystem) -> Tuple[float, List[int]]: - """get the data statistics of the training data, including nearest nbor distance between atoms, max nbor size of atoms. + """Get the data statistics of the training data, including nearest nbor distance between atoms, max nbor size of atoms. Parameters ---------- diff --git a/deepmd/utils/path.py b/deepmd/utils/path.py index e19a3f8a1d..454eed3d2b 100644 --- a/deepmd/utils/path.py +++ b/deepmd/utils/path.py @@ -106,7 +106,7 @@ def __truediv__(self, key: str) -> "DPPath": @abstractmethod def __lt__(self, other: "DPPath") -> bool: - """whether this DPPath is less than other for sorting.""" + """Whether this DPPath is less than other for sorting.""" @abstractmethod def __str__(self) -> str: @@ -204,7 +204,7 @@ def __truediv__(self, key: str) -> "DPPath": return type(self)(self.path / key) def __lt__(self, other: "DPOSPath") -> bool: - """whether this DPPath is less than other for sorting.""" + """Whether this DPPath is less than other for sorting.""" return self.path < other.path def __str__(self) -> str: @@ -349,11 +349,11 @@ def _connect_path(self, path: str) -> str: return f"{self.name}/{path}" def __lt__(self, other: "DPH5Path") -> bool: - """whether this DPPath is less than other for sorting.""" + """Whether this DPPath is less than other for sorting.""" if self.root_path == other.root_path: return self.name < other.name return self.root_path < other.root_path def __str__(self) -> str: - """returns path of self.""" + """Returns path of self.""" return f"{self.root_path}#{self.name}" diff --git a/doc/conf.py b/doc/conf.py index 2fd0dcdc33..1216914b29 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -150,7 +150,6 @@ def run_apidoc(_): def setup(app): - # Add hook for building doxygen xml when needed app.connect("builder-inited", run_apidoc) diff --git a/source/install/build_tf.py b/source/install/build_tf.py index d2510e75da..883c654b5c 100755 --- a/source/install/build_tf.py +++ b/source/install/build_tf.py @@ -162,7 +162,6 @@ def post_process(self): with tarfile.open(self.path) as tar: def is_within_directory(directory, target): - abs_directory = os.path.abspath(directory) abs_target = os.path.abspath(target) @@ -171,7 +170,6 @@ def is_within_directory(directory, target): return prefix == abs_directory def safe_extract(tar, path=".", members=None, *, numeric_owner=False): - for member in tar.getmembers(): member_path = os.path.join(path, member.name) if not is_within_directory(path, member_path): diff --git a/source/tests/common.py b/source/tests/common.py index 8fc303c1d7..dcbccd221a 100644 --- a/source/tests/common.py +++ b/source/tests/common.py @@ -660,7 +660,7 @@ def stats_energy(self): return np.average(eners) def load_energy(self, set_name, nframes, nvalues, energy_file, atom_energy_file): - """return : coeff_ener, ener, coeff_atom_ener, atom_ener.""" + """Return : coeff_ener, ener, coeff_atom_ener, atom_ener.""" # load atom_energy coeff_atom_ener, atom_ener = self.load_data( set_name, atom_energy_file, [nframes, nvalues], False @@ -765,13 +765,13 @@ def get_set(self, data, idx=None): return new_data def get_test(self): - """returned property prefector [4] in order: + """Returned property prefector [4] in order: energy, force, virial, atom_ener. """ return self.get_set(self.test_set) def get_batch(self, batch_size): - """returned property prefector [4] in order: + """Returned property prefector [4] in order: energy, force, virial, atom_ener. """ set_size = self.batch_set["energy"].shape[0] diff --git a/source/tests/test_argument_parser.py b/source/tests/test_argument_parser.py index 8c881b4829..7b0c9c1205 100644 --- a/source/tests/test_argument_parser.py +++ b/source/tests/test_argument_parser.py @@ -90,7 +90,6 @@ def attr_and_type_check( mapping = {**{"command": dict(type=str, value=command)}, **mapping} for argument, test_data in mapping.items(): - # get expected type expected_type = test_data["type"] diff --git a/source/tests/test_compat_input.py b/source/tests/test_compat_input.py index e8a443931b..df5fcd45f7 100644 --- a/source/tests/test_compat_input.py +++ b/source/tests/test_compat_input.py @@ -39,7 +39,6 @@ def assertDictAlmostEqual(self, d1, d2, msg=None, places=7): self.assertAlmostEqual(d1[kk], d2[kk], places=places, msg=msg) def test_json_yaml_equal(self): - inputs = ("water_v1", "water_se_a_v1") for i in inputs: diff --git a/source/tests/test_deepmd_data_sys.py b/source/tests/test_deepmd_data_sys.py index 9df48c3f57..5ed9d3d3f6 100644 --- a/source/tests/test_deepmd_data_sys.py +++ b/source/tests/test_deepmd_data_sys.py @@ -403,7 +403,7 @@ def test_sys_prob_floating_point_error(self): self.assertEqual(ds.sys_probs.size, len(sys_probs)) def test_get_mixed_batch(self): - """test get_batch with mixed system.""" + """Test get_batch with mixed system.""" batch_size = "mixed:3" test_size = 2 diff --git a/source/tests/test_descrpt_sea_ef_rot.py b/source/tests/test_descrpt_sea_ef_rot.py index ec48f859e1..cdeb51350e 100644 --- a/source/tests/test_descrpt_sea_ef_rot.py +++ b/source/tests/test_descrpt_sea_ef_rot.py @@ -66,7 +66,7 @@ def build_efv(self, dcoord, dbox, dtype, tnatoms, name, op, reuse=None): "rcut_smth": 5.5, "seed": 1, "uniform_seed": True, - } + }, ) else: descrpt = DescrptSeA( diff --git a/source/tests/test_get_potential.py b/source/tests/test_get_potential.py index e07ed3b3e8..869325e1c3 100644 --- a/source/tests/test_get_potential.py +++ b/source/tests/test_get_potential.py @@ -49,7 +49,6 @@ def tearDown(self): f.unlink() def test_factory(self): - msg = "Returned wrong type of potential. Expected: {}, got: {}" dp = DeepPotential(self.work_dir / "deep_dipole.pb") From f91bb9c65ece775d8bec737f6a33118f2ef13d9d Mon Sep 17 00:00:00 2001 From: Jinzhe Zeng Date: Tue, 21 Mar 2023 22:16:31 -0400 Subject: [PATCH 027/126] add read_file_to_string to C API (#2412) Signed-off-by: Jinzhe Zeng --- source/api_c/include/c_api.h | 7 ++++++ source/api_c/include/deepmd.hpp | 11 ++++++++ source/api_c/src/c_api.cc | 7 ++++++ .../api_c/tests/test_read_file_to_string.cc | 25 +++++++++++++++++++ 4 files changed, 50 insertions(+) create mode 100644 source/api_c/tests/test_read_file_to_string.cc diff --git a/source/api_c/include/c_api.h b/source/api_c/include/c_api.h index 341b2f853d..a2d644a7e4 100644 --- a/source/api_c/include/c_api.h +++ b/source/api_c/include/c_api.h @@ -1033,6 +1033,13 @@ extern void DP_ConvertPbtxtToPb(const char* c_pbtxt, const char* c_pb); */ extern void DP_PrintSummary(const char* c_pre); +/** + * @brief Read a file to a char array. + * @param[in] c_model The name of the file. + * @return const char* The char array. + */ +const char* DP_ReadFileToChar(const char* c_model); + #ifdef __cplusplus } /* end extern "C" */ #endif diff --git a/source/api_c/include/deepmd.hpp b/source/api_c/include/deepmd.hpp index 6f2740077b..bb5997883a 100644 --- a/source/api_c/include/deepmd.hpp +++ b/source/api_c/include/deepmd.hpp @@ -1730,5 +1730,16 @@ class DipoleChargeModifier { DP_DipoleChargeModifier *dcm; int nsel_types; }; + +/** + * @brief Read model file to a string. + * @param[in] model Path to the model. + * @param[out] file_content Content of the model file. + **/ +void inline read_file_to_string(std::string model, std::string &file_content) { + const char *c_file_content = DP_ReadFileToChar(model.c_str()); + file_content = std::string(c_file_content); +}; + } // namespace hpp } // namespace deepmd diff --git a/source/api_c/src/c_api.cc b/source/api_c/src/c_api.cc index 253790008c..ed764171f9 100644 --- a/source/api_c/src/c_api.cc +++ b/source/api_c/src/c_api.cc @@ -1169,4 +1169,11 @@ void DP_PrintSummary(const char* c_pre) { deepmd::print_summary(pre); } +const char* DP_ReadFileToChar(const char* c_model) { + std::string model(c_model); + std::string file_content; + deepmd::read_file_to_string(model, file_content); + return string_to_char(file_content); +} + } // extern "C" diff --git a/source/api_c/tests/test_read_file_to_string.cc b/source/api_c/tests/test_read_file_to_string.cc new file mode 100644 index 0000000000..fb27ad8b1d --- /dev/null +++ b/source/api_c/tests/test_read_file_to_string.cc @@ -0,0 +1,25 @@ +#include +#include +#include +#include + +#include +#include +#include +#include +#include +#include + +#include "deepmd.hpp" +TEST(TestReadFileToString, readfiletostring) { + std::string file_content; + deepmd::hpp::read_file_to_string("../../tests/infer/deeppot.txt", + file_content); + + std::string file_name_2 = "../../tests/infer/deeppot.txt"; + std::stringstream buffer; + std::ifstream file_txt(file_name_2); + buffer << file_txt.rdbuf(); + std::string expected_out_string = buffer.str(); + EXPECT_STREQ(expected_out_string.c_str(), file_content.c_str()); +} From e53a56589684e8c22148686fead0385fa1a76c29 Mon Sep 17 00:00:00 2001 From: Jinzhe Zeng Date: Wed, 22 Mar 2023 21:15:51 -0400 Subject: [PATCH 028/126] `source` a relative path (#2420) Fix #2409. By default, `source` searches the file from `PATH` (see the help from `source --help`). This might conflict with other packages. To prevent this behavior, one has to source the relative path instead. Signed-off-by: Jinzhe Zeng --- source/lmp/Install.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/source/lmp/Install.sh b/source/lmp/Install.sh index 87c51a9766..d2a728226e 100644 --- a/source/lmp/Install.sh +++ b/source/lmp/Install.sh @@ -1,6 +1,6 @@ #!/bin/bash -source env.sh +source ./env.sh # Install/unInstall package files in LAMMPS # mode = 0/1/2 for uninstall/install/update From 134215eb0998aec006ae02ea0a460605bc5bc0c4 Mon Sep 17 00:00:00 2001 From: Jinzhe Zeng Date: Sat, 25 Mar 2023 04:58:01 -0400 Subject: [PATCH 029/126] bump to TF 2.12 (#2422) Resolve #2331. Update `setup.py` for TF 2.12. Migrate all workflows to TF 2.12 with `USE_TF_PYTHON_LIBS`. Update documentation to recommend `USE_TF_PYTHON_LIBS`. --------- Signed-off-by: Jinzhe Zeng --- .github/workflows/build_cc.yml | 32 ++++++++++++++---------------- .github/workflows/build_wheel.yml | 8 ++++---- .github/workflows/package_c.yml | 4 ++-- .github/workflows/test_cc.yml | 8 ++++---- .github/workflows/test_python.yml | 10 ++-------- doc/install/install-from-source.md | 15 ++++++++------ doc/install/install-lammps.md | 2 +- setup.py | 5 +++-- source/install/build_cc.sh | 5 +---- source/install/docker_package_c.sh | 8 +++++--- source/install/package_c.sh | 7 ++++++- source/install/test_cc_local.sh | 4 ++-- 12 files changed, 54 insertions(+), 54 deletions(-) diff --git a/.github/workflows/build_cc.yml b/.github/workflows/build_cc.yml index 0ebb80fa98..c2d2613464 100644 --- a/.github/workflows/build_cc.yml +++ b/.github/workflows/build_cc.yml @@ -6,7 +6,6 @@ jobs: buildcc: name: Build C++ runs-on: ubuntu-latest - container: ghcr.io/deepmodeling/deepmd-kit-test-cc:latest strategy: matrix: include: @@ -21,32 +20,31 @@ jobs: - variant: clang dp_variant: clang steps: - - name: work around permission issue - run: git config --global --add safe.directory /__w/deepmd-kit/deepmd-kit - uses: actions/checkout@v3 with: submodules: true - - run: apt-get update && apt-get install -y nvidia-cuda-toolkit + - uses: actions/setup-python@v4 + with: + python-version: '3.11' + - run: python -m pip install tensorflow + - run: sudo apt-get update && sudo apt-get install -y nvidia-cuda-toolkit if: matrix.variant == 'cuda' - run: | - apt-get update \ - && apt-get -y install wget \ - && wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-keyring_1.0-1_all.deb \ - && dpkg -i cuda-keyring_1.0-1_all.deb \ - && apt-get update \ - && apt-get -y install cuda-12-0 + wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-keyring_1.0-1_all.deb \ + && sudo dpkg -i cuda-keyring_1.0-1_all.deb \ + && sudo apt-get update \ + && sudo apt-get -y install cuda-12-0 if: matrix.variant == 'cuda120' env: DEBIAN_FRONTEND: noninteractive - run: | - apt-get update && apt-get install -y gnupg2 \ - && echo 'deb [arch=amd64] https://repo.radeon.com/rocm/apt/5.3/ jammy main' | tee /etc/apt/sources.list.d/rocm.list \ - && printf 'Package: *\nPin: release o=repo.radeon.com\nPin-Priority: 600' | tee /etc/apt/preferences.d/rocm-pin-600 \ - && curl -s https://repo.radeon.com/rocm/rocm.gpg.key | apt-key add - \ - && apt-get update \ - && apt-get install -y rocm-dev hipcub-dev + echo 'deb [arch=amd64] https://repo.radeon.com/rocm/apt/5.3/ jammy main' | sudo tee /etc/apt/sources.list.d/rocm.list \ + && printf 'Package: *\nPin: release o=repo.radeon.com\nPin-Priority: 600' | sudo tee /etc/apt/preferences.d/rocm-pin-600 \ + && curl -s https://repo.radeon.com/rocm/rocm.gpg.key | sudo apt-key add - \ + && sudo apt-get update \ + && sudo apt-get install -y rocm-dev hipcub-dev if: matrix.variant == 'rocm' - - run: apt-get update && apt-get install -y clang + - run: sudo apt-get update && sudo apt-get install -y clang if: matrix.variant == 'clang' - run: source/install/build_cc.sh env: diff --git a/.github/workflows/build_wheel.yml b/.github/workflows/build_wheel.yml index e11ae58bbe..b1b705fd49 100644 --- a/.github/workflows/build_wheel.yml +++ b/.github/workflows/build_wheel.yml @@ -14,17 +14,17 @@ jobs: include: # linux-64 - os: ubuntu-latest - python: 310 + python: 311 platform_id: manylinux_x86_64 dp_variant: cuda # macos-x86-64 - os: macos-latest - python: 310 + python: 311 platform_id: macosx_x86_64 dp_variant: cpu # win-64 - os: windows-2019 - python: 310 + python: 311 platform_id: win_amd64 dp_variant: cpu # linux-aarch64 @@ -61,7 +61,7 @@ jobs: - uses: actions/setup-python@v4 name: Install Python with: - python-version: '3.10' + python-version: '3.11' - run: python -m pip install build - name: Build sdist run: python -m build --sdist diff --git a/.github/workflows/package_c.yml b/.github/workflows/package_c.yml index 6cc5ed8dc8..982dfc44a2 100644 --- a/.github/workflows/package_c.yml +++ b/.github/workflows/package_c.yml @@ -12,13 +12,13 @@ jobs: - uses: actions/checkout@v3 - name: Package C library run: ./source/install/docker_package_c.sh - - name: Test C library - run: ./source/install/docker_test_package_c.sh # for download and debug - name: Upload artifact uses: actions/upload-artifact@v3 with: path: ./libdeepmd_c.tar.gz + - name: Test C library + run: ./source/install/docker_test_package_c.sh - name: Release uses: softprops/action-gh-release@v1 if: startsWith(github.ref, 'refs/tags/') diff --git a/.github/workflows/test_cc.yml b/.github/workflows/test_cc.yml index a6eb4727f1..0c9b2ed487 100644 --- a/.github/workflows/test_cc.yml +++ b/.github/workflows/test_cc.yml @@ -6,11 +6,12 @@ jobs: testcc: name: Test C++ runs-on: ubuntu-latest - container: ghcr.io/deepmodeling/deepmd-kit-test-cc:latest steps: - - name: work around permission issue - run: git config --global --add safe.directory /__w/deepmd-kit/deepmd-kit - uses: actions/checkout@v3 + - uses: actions/setup-python@v4 + with: + python-version: '3.11' + - run: python -m pip install tensorflow - run: source/install/test_cc_local.sh env: OMP_NUM_THREADS: 1 @@ -18,7 +19,6 @@ jobs: TF_INTER_OP_PARALLELISM_THREADS: 1 tensorflow_root: /usr/local # test lammps - - run: apt-get update && apt-get install -y python3-pip python3-venv - run: source/install/build_lammps.sh - run: | python -m pip install -U pip diff --git a/.github/workflows/test_python.yml b/.github/workflows/test_python.yml index c690e16328..3a6c500554 100644 --- a/.github/workflows/test_python.yml +++ b/.github/workflows/test_python.yml @@ -21,16 +21,10 @@ jobs: - python: 3.7 gcc: 8 tf: 1.14 - - python: 3.7 - gcc: 5 - tf: - - python: 3.7 + - python: 3.8 gcc: 8 tf: - - python: "3.10" - gcc: 5 - tf: - - python: "3.10" + - python: "3.11" gcc: 8 tf: diff --git a/doc/install/install-from-source.md b/doc/install/install-from-source.md index 8ce2524806..e326be1598 100644 --- a/doc/install/install-from-source.md +++ b/doc/install/install-from-source.md @@ -39,7 +39,7 @@ deactivate ``` If one has multiple python interpreters named something like python3.x, it can be specified by, for example ```bash -virtualenv -p python3.7 $tensorflow_venv +virtualenv -p python3.8 $tensorflow_venv ``` If one does not need the GPU support of DeePMD-kit and is concerned about package size, the CPU-only version of TensorFlow should be installed by ```bash @@ -142,7 +142,7 @@ Available Tensor Operations: [X] Gloo ``` -From version 2.0.1, Horovod and mpi4py with MPICH support are shipped with the installer. +Since version 2.0.1, Horovod and mpi4py with MPICH support are shipped with the installer. If you don't install Horovod, DeePMD-kit will fall back to serial mode. @@ -150,7 +150,9 @@ If you don't install Horovod, DeePMD-kit will fall back to serial mode. If one does not need to use DeePMD-kit with Lammps or I-Pi, then the python interface installed in the previous section does everything and he/she can safely skip this section. -### Install Tensorflow's C++ interface +### Install Tensorflow's C++ interface (optional) + +Since TensorFlow 2.12, TensorFlow C++ library (`libtensorflow_cc`) is packaged inside the Python library. Thus, you can skip building TensorFlow C++ library manually. If that does not work for you, you can still build it manually. The C++ interface of DeePMD-kit was tested with compiler GCC >= 4.8. It is noticed that the I-Pi support is only compiled with GCC >= 4.8. Note that TensorFlow may have specific requirements for the compiler version. @@ -164,11 +166,12 @@ cd $deepmd_source_dir/source mkdir build cd build ``` -I assume you want to install DeePMD-kit into path `$deepmd_root`, then execute CMake +I assume you have activated the TensorFlow Python environment and want to install DeePMD-kit into path `$deepmd_root`, then execute CMake ```bash -cmake -DTENSORFLOW_ROOT=$tensorflow_root -DCMAKE_INSTALL_PREFIX=$deepmd_root .. +cmake -DUSE_TF_PYTHON_LIBS=TRUE -DCMAKE_INSTALL_PREFIX=$deepmd_root .. ``` -where the variable `tensorflow_root` stores the location where TensorFlow's C++ interface is installed. + +If you specify `-DUSE_TF_PYTHON_LIBS=FALSE`, you need to give the location where TensorFlow's C++ interface is installed to `-DTENSORFLOW_ROOT=${tensorflow_root}`. One may add the following arguments to `cmake`: diff --git a/doc/install/install-lammps.md b/doc/install/install-lammps.md index eb3dc0fea6..22262d9b95 100644 --- a/doc/install/install-lammps.md +++ b/doc/install/install-lammps.md @@ -69,7 +69,7 @@ ${deepmd_root}/bin/lmp -h ``` :::{note} -If `${tensorflow_root}` or `${deepmd_root}` is different from the prefix of LAMMPS, you need to append the library path to [`RUNPATH`](https://man7.org/linux/man-pages/man8/ld.so.8.html) of `liblammps.so`. For example, +If `${tensorflow_root}`, `${deepmd_root}`, or the path to TensorFlow Python package if applicable is different from the prefix of LAMMPS, you need to append the library path to [`RUNPATH`](https://man7.org/linux/man-pages/man8/ld.so.8.html) of `liblammps.so`. For example, ```sh patchelf --set-rpath "${tensorflow_root}/lib" liblammps.so diff --git a/setup.py b/setup.py index f96c119186..e0838e3e90 100644 --- a/setup.py +++ b/setup.py @@ -65,8 +65,7 @@ tf_install_dir, _ = find_tensorflow() tf_version = get_tf_version(tf_install_dir) -# TODO: change to "tf_version == "" or" after tensorflow 2.12 is released -if tf_version != "" and Version(tf_version) >= Version("2.12"): +if tf_version == "" or Version(tf_version) >= Version("2.12"): find_libpython_requires = [] else: find_libpython_requires = ["find_libpython"] @@ -76,6 +75,8 @@ class bdist_wheel_abi3(bdist_wheel): def get_tag(self): python, abi, plat = super().get_tag() if python.startswith("cp"): + if tf_version == "" or Version(tf_version) >= Version("2.12"): + return "py38", "none", plat return "py37", "none", plat return python, abi, plat diff --git a/source/install/build_cc.sh b/source/install/build_cc.sh index 9aaa51bab8..55f4730ac5 100755 --- a/source/install/build_cc.sh +++ b/source/install/build_cc.sh @@ -11,9 +11,6 @@ SCRIPT_PATH=$(dirname $(realpath -s $0)) if [ -z "$INSTALL_PREFIX" ]; then INSTALL_PREFIX=$(realpath -s ${SCRIPT_PATH}/../../dp) fi -if [ -z "$DOWNLOAD_TENSORFLOW" ]; then - DOWNLOAD_TENSORFLOW=TRUE -fi mkdir -p ${INSTALL_PREFIX} echo "Installing DeePMD-kit to ${INSTALL_PREFIX}" NPROC=$(nproc --all) @@ -23,7 +20,7 @@ NPROC=$(nproc --all) BUILD_TMP_DIR=${SCRIPT_PATH}/../build mkdir -p ${BUILD_TMP_DIR} cd ${BUILD_TMP_DIR} -cmake -DCMAKE_INSTALL_PREFIX=${INSTALL_PREFIX} -DINSTALL_TENSORFLOW=${DOWNLOAD_TENSORFLOW} ${CUDA_ARGS} -DLAMMPS_VERSION=stable_23Jun2022_update3 .. +cmake -DCMAKE_INSTALL_PREFIX=${INSTALL_PREFIX} -DUSE_TF_PYTHON_LIBS=TRUE ${CUDA_ARGS} -DLAMMPS_VERSION=stable_23Jun2022_update3 .. make -j${NPROC} make install diff --git a/source/install/docker_package_c.sh b/source/install/docker_package_c.sh index 95291dfbd5..d6fb269acd 100755 --- a/source/install/docker_package_c.sh +++ b/source/install/docker_package_c.sh @@ -3,7 +3,9 @@ set -e SCRIPT_PATH=$(dirname $(realpath -s $0)) docker run --rm -v ${SCRIPT_PATH}/../..:/root/deepmd-kit -w /root/deepmd-kit \ - ghcr.io/deepmodeling/libtensorflow_cc:2.9.2_cuda11.6_centos7_cmake \ - /bin/sh -c "source /opt/rh/devtoolset-10/enable \ + tensorflow/build:2.13-python3.11 \ + /bin/sh -c "pip install tensorflow cmake \ && cd /root/deepmd-kit/source/install \ - && /bin/sh package_c.sh" + && CC=/dt9/usr/bin/gcc \ + CXX=/dt9/usr/bin/g++ \ + /bin/sh package_c.sh" diff --git a/source/install/package_c.sh b/source/install/package_c.sh index ce188425c9..c250956e19 100755 --- a/source/install/package_c.sh +++ b/source/install/package_c.sh @@ -17,12 +17,17 @@ mkdir -p ${BUILD_TMP_DIR} cd ${BUILD_TMP_DIR} cmake -DCMAKE_INSTALL_PREFIX=${INSTALL_PREFIX} \ -DUSE_CUDA_TOOLKIT=TRUE \ - -DOP_CXX_ABI=0 \ -DPACKAGE_C=TRUE \ + -DUSE_TF_PYTHON_LIBS=TRUE \ .. make -j${NPROC} make install #------------------ +# fix runpath +for ii in ${BUILD_TMP_DIR}/libdeepmd_c/lib/*.so*; do + patchelf --set-rpath \$ORIGIN $ii +done + tar vczf ${SCRIPT_PATH}/../../libdeepmd_c.tar.gz -C ${BUILD_TMP_DIR} libdeepmd_c diff --git a/source/install/test_cc_local.sh b/source/install/test_cc_local.sh index a48fdbd4bf..b4594a569c 100755 --- a/source/install/test_cc_local.sh +++ b/source/install/test_cc_local.sh @@ -7,12 +7,12 @@ NPROC=$(nproc --all) #------------------ -echo "try to find tensorflow in ${tensorflow_root}" +echo "try to find tensorflow in the Python environment" INSTALL_PREFIX=${SCRIPT_PATH}/../../dp_test BUILD_TMP_DIR=${SCRIPT_PATH}/../build_tests mkdir -p ${BUILD_TMP_DIR} cd ${BUILD_TMP_DIR} -cmake -DINSTALL_TENSORFLOW=FALSE -DTENSORFLOW_ROOT=${tensorflow_root} -DCMAKE_INSTALL_PREFIX=${INSTALL_PREFIX} -DBUILD_TESTING:BOOL=TRUE -DLAMMPS_VERSION=stable_23Jun2022_update3 .. +cmake -DINSTALL_TENSORFLOW=FALSE -DUSE_TF_PYTHON_LIBS=TRUE -DCMAKE_INSTALL_PREFIX=${INSTALL_PREFIX} -DBUILD_TESTING:BOOL=TRUE -DLAMMPS_VERSION=stable_23Jun2022_update3 .. make -j${NPROC} make install From f87b9c116315fc78399365111bd28997ef8f2518 Mon Sep 17 00:00:00 2001 From: Jinzhe Zeng Date: Sat, 25 Mar 2023 05:04:19 -0400 Subject: [PATCH 030/126] support xla for CUDA pip packages (#2427) Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> --- deepmd/env.py | 11 +++++++++++ setup.py | 2 ++ 2 files changed, 13 insertions(+) diff --git a/deepmd/env.py b/deepmd/env.py index bfb7a268c9..bee03fd240 100644 --- a/deepmd/env.py +++ b/deepmd/env.py @@ -296,6 +296,17 @@ def get_tf_session_config() -> Any: intra, inter = get_tf_default_nthreads() if int(os.environ.get("DP_JIT", 0)): set_env_if_empty("TF_XLA_FLAGS", "--tf_xla_auto_jit=2") + # pip cuda package + if platform.system() == "Linux": + try: + m = import_module("nvidia.cuda_nvcc") + except ModuleNotFoundError: + pass + else: + cuda_data_dir = str(Path(m.__file__).parent.absolute()) + set_env_if_empty( + "XLA_FLAGS", "--xla_gpu_cuda_data_dir=" + cuda_data_dir + ) config = tf.ConfigProto( gpu_options=tf.GPUOptions(allow_growth=True), intra_op_parallelism_threads=intra, diff --git a/setup.py b/setup.py index e0838e3e90..4641326b76 100644 --- a/setup.py +++ b/setup.py @@ -146,6 +146,7 @@ def get_tag(self): "nvidia-cusolver-cu11", "nvidia-cusparse-cu11", "nvidia-cudnn-cu11", + "nvidia-cuda-nvcc-cu11", ], "cu12": [ "nvidia-cuda-runtime-cu12", @@ -155,6 +156,7 @@ def get_tag(self): "nvidia-cusolver-cu12", "nvidia-cusparse-cu12", "nvidia-cudnn-cu12", + "nvidia-cuda-nvcc-cu12", ], }, entry_points={ From 2496de0d97230cadf09e87f718762108c9e41124 Mon Sep 17 00:00:00 2001 From: Jinzhe Zeng Date: Sat, 25 Mar 2023 10:12:51 -0400 Subject: [PATCH 031/126] set mixed_type to True for mixed systems (#2428) When mixed systems training (#2372) is enabled, the mixed_type attribute will affect the data stat. --------- Signed-off-by: Jinzhe Zeng --- deepmd/descriptor/se_atten.py | 2 +- deepmd/utils/data_system.py | 1 + 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/deepmd/descriptor/se_atten.py b/deepmd/descriptor/se_atten.py index cab26f4390..0a64f3807b 100644 --- a/deepmd/descriptor/se_atten.py +++ b/deepmd/descriptor/se_atten.py @@ -508,7 +508,7 @@ def _compute_dstats_sys_smth( sysa2 = [0.0 for i in range(self.ntypes)] for ff in range(nframes): natoms = real_natoms_vec[ff] - dd_ff = np.reshape(dd_all[ff], [-1, self.ndescrpt * natoms[0]]) + dd_ff = np.reshape(dd_all[ff], [-1, self.ndescrpt * natoms_vec[0]]) start_index = 0 for type_i in range(self.ntypes): end_index = ( diff --git a/deepmd/utils/data_system.py b/deepmd/utils/data_system.py index d87219fcc9..e048dccd99 100644 --- a/deepmd/utils/data_system.py +++ b/deepmd/utils/data_system.py @@ -124,6 +124,7 @@ def __init__( rule = int(words[1]) self.batch_size = self._make_auto_bs(rule) elif "mixed" == words[0]: + self.mixed_type = True self.mixed_systems = True if len(words) == 2: rule = int(words[1]) From 00fc8a2b4cc3561d5e50dc5f341b20826c272089 Mon Sep 17 00:00:00 2001 From: HuangJiameng <105633685+HuangJiameng@users.noreply.github.com> Date: Tue, 28 Mar 2023 12:00:36 +0800 Subject: [PATCH 032/126] Support different learning rate settings for each fitting net in multi-task mode (#2348) It is a breaking change to the user interface in multi-task mode. Please review carefully. Replace "learning_rate" with "learning_rate_dict" in multi-task mode to support different learning rate settings for each fitting net, which is familiar to "loss_dict". The format of `lcurve.out` will be like the following: ``` # step sys1_rmse_val sys1_rmse_trn sys1_rmse_e_val sys1_rmse_e_trn sys1_rmse_f_val sys1_rmse_f_trn sys1_lr sys2_rmse_val sys2_rmse_trn sys2_rmse_e_val sys2_rmse_e_trn sys2_rmse_f_val sys2_rmse_f_trn sys2_lr ... ``` The learning rate of each system will be printed separately. --------- Signed-off-by: HuangJiameng <105633685+HuangJiameng@users.noreply.github.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> --- deepmd/entrypoints/freeze.py | 5 + deepmd/train/trainer.py | 302 +++++++++++++++------- deepmd/utils/argcheck.py | 63 ++++- deepmd/utils/multi_init.py | 24 ++ source/tests/test_init_frz_model_multi.py | 5 + source/tests/water_multi.json | 24 +- 6 files changed, 320 insertions(+), 103 deletions(-) diff --git a/deepmd/entrypoints/freeze.py b/deepmd/entrypoints/freeze.py index 761fc419b0..bf7b957a4a 100755 --- a/deepmd/entrypoints/freeze.py +++ b/deepmd/entrypoints/freeze.py @@ -158,6 +158,11 @@ def _modify_model_suffix(output_graph_def, out_suffix, freeze_type): loss_dict = jdata.pop("loss_dict") if out_suffix in loss_dict: jdata["loss"] = loss_dict[out_suffix] + # learning_rate + if "learning_rate_dict" in jdata: + learning_rate_dict = jdata.pop("learning_rate_dict") + if out_suffix in learning_rate_dict: + jdata["learning_rate"] = learning_rate_dict[out_suffix] # fitting weight if "fitting_weight" in jdata["training"]: jdata["training"].pop("fitting_weight") diff --git a/deepmd/train/trainer.py b/deepmd/train/trainer.py index 2b8e24f908..29802d0f75 100644 --- a/deepmd/train/trainer.py +++ b/deepmd/train/trainer.py @@ -262,22 +262,37 @@ def fitting_net_init(fitting_type_, descrpt_type_, params): model_param.get("sw_rmax"), ) + def get_lr_and_coef(lr_param): + scale_by_worker = lr_param.get("scale_by_worker", "linear") + if scale_by_worker == "linear": + scale_lr_coef = float(self.run_opt.world_size) + elif scale_by_worker == "sqrt": + scale_lr_coef = np.sqrt(self.run_opt.world_size).real + else: + scale_lr_coef = 1.0 + lr_type = lr_param.get("type", "exp") + if lr_type == "exp": + lr = LearningRateExp( + lr_param["start_lr"], lr_param["stop_lr"], lr_param["decay_steps"] + ) + else: + raise RuntimeError("unknown learning_rate type " + lr_type) + return lr, scale_lr_coef + # learning rate - lr_param = j_must_have(jdata, "learning_rate") - scale_by_worker = lr_param.get("scale_by_worker", "linear") - if scale_by_worker == "linear": - self.scale_lr_coef = float(self.run_opt.world_size) - elif scale_by_worker == "sqrt": - self.scale_lr_coef = np.sqrt(self.run_opt.world_size).real - else: - self.scale_lr_coef = 1.0 - lr_type = lr_param.get("type", "exp") - if lr_type == "exp": - self.lr = LearningRateExp( - lr_param["start_lr"], lr_param["stop_lr"], lr_param["decay_steps"] - ) + if not self.multi_task_mode: + lr_param = j_must_have(jdata, "learning_rate") + self.lr, self.scale_lr_coef = get_lr_and_coef(lr_param) else: - raise RuntimeError("unknown learning_rate type " + lr_type) + self.lr_dict = {} + self.scale_lr_coef_dict = {} + lr_param_dict = jdata.get("learning_rate_dict", {}) + for fitting_key in self.fitting_type_dict: + lr_param = lr_param_dict.get(fitting_key, {}) + ( + self.lr_dict[fitting_key], + self.scale_lr_coef_dict[fitting_key], + ) = get_lr_and_coef(lr_param) # loss # infer loss type by fitting_type @@ -343,7 +358,7 @@ def loss_init(_loss_param, _fitting_type, _fitting, _lr): loss_param, self.fitting_type_dict[fitting_key], self.fitting_dict[fitting_key], - self.lr, + self.lr_dict[fitting_key], ) # training @@ -553,9 +568,51 @@ def build(self, data=None, stop_batch=0, origin_type_map=None, suffix=""): def _build_lr(self): self._extra_train_ops = [] self.global_step = tf.train.get_or_create_global_step() - self.learning_rate = self.lr.build(self.global_step, self.stop_batch) + if not self.multi_task_mode: + self.learning_rate = self.lr.build(self.global_step, self.stop_batch) + else: + self.learning_rate_dict = {} + for fitting_key in self.fitting_type_dict: + self.learning_rate_dict[fitting_key] = self.lr_dict[fitting_key].build( + self.global_step, self.stop_batch + ) + log.info("built lr") + def _build_loss(self): + if not self.multi_task_mode: + l2_l, l2_more = self.loss.build( + self.learning_rate, + self.place_holders["natoms_vec"], + self.model_pred, + self.place_holders, + suffix="test", + ) + + if self.mixed_prec is not None: + l2_l = tf.cast(l2_l, get_precision(self.mixed_prec["output_prec"])) + else: + l2_l, l2_more = {}, {} + for fitting_key in self.fitting_type_dict: + lr = self.learning_rate_dict[fitting_key] + model = self.model_pred[fitting_key] + loss_dict = self.loss_dict[fitting_key] + + l2_l[fitting_key], l2_more[fitting_key] = loss_dict.build( + lr, + self.place_holders["natoms_vec"], + model, + self.place_holders, + suffix=fitting_key, + ) + + if self.mixed_prec is not None: + l2_l[fitting_key] = tf.cast( + l2_l[fitting_key], get_precision(self.mixed_prec["output_prec"]) + ) + + return l2_l, l2_more + def _build_network(self, data, suffix=""): self.place_holders = {} if self.is_compress: @@ -591,55 +648,46 @@ def _build_network(self, data, suffix=""): reuse=False, ) - if not self.multi_task_mode: - self.l2_l, self.l2_more = self.loss.build( - self.learning_rate, - self.place_holders["natoms_vec"], - self.model_pred, - self.place_holders, - suffix="test", - ) - - if self.mixed_prec is not None: - self.l2_l = tf.cast( - self.l2_l, get_precision(self.mixed_prec["output_prec"]) - ) - else: - self.l2_l, self.l2_more = {}, {} - for fitting_key in self.fitting_type_dict: - self.l2_l[fitting_key], self.l2_more[fitting_key] = self.loss_dict[ - fitting_key - ].build( - self.learning_rate, - self.place_holders["natoms_vec"], - self.model_pred[fitting_key], - self.place_holders, - suffix=fitting_key, - ) - if self.mixed_prec is not None: - self.l2_l[fitting_key] = tf.cast( - self.l2_l[fitting_key], - get_precision(self.mixed_prec["output_prec"]), - ) + self.l2_l, self.l2_more = self._build_loss() log.info("built network") - def _build_training(self): - trainable_variables = tf.trainable_variables() + def _build_optimizer(self, fitting_key=None): if self.run_opt.is_distrib: - if self.scale_lr_coef > 1.0: - log.info("Scale learning rate by coef: %f", self.scale_lr_coef) - optimizer = tf.train.AdamOptimizer( - self.learning_rate * self.scale_lr_coef - ) + if fitting_key is None: + if self.scale_lr_coef > 1.0: + log.info("Scale learning rate by coef: %f", self.scale_lr_coef) + optimizer = tf.train.AdamOptimizer( + self.learning_rate * self.scale_lr_coef + ) + else: + optimizer = tf.train.AdamOptimizer(self.learning_rate) + optimizer = self.run_opt._HVD.DistributedOptimizer(optimizer) else: - optimizer = tf.train.AdamOptimizer(self.learning_rate) - optimizer = self.run_opt._HVD.DistributedOptimizer(optimizer) + if self.scale_lr_coef_dict[fitting_key] > 1.0: + log.info( + "Scale learning rate by coef: %f", + self.scale_lr_coef_dict[fitting_key], + ) + optimizer = tf.train.AdamOptimizer( + self.learning_rate_dict[fitting_key] + * self.scale_lr_coef_dict[fitting_key] + ) + else: + optimizer = tf.train.AdamOptimizer( + learning_rate=self.learning_rate_dict[fitting_key] + ) + optimizer = self.run_opt._HVD.DistributedOptimizer(optimizer) else: - optimizer = tf.train.AdamOptimizer(learning_rate=self.learning_rate) + if fitting_key is None: + optimizer = tf.train.AdamOptimizer(learning_rate=self.learning_rate) + else: + optimizer = tf.train.AdamOptimizer( + learning_rate=self.learning_rate_dict[fitting_key] + ) + if self.mixed_prec is not None: _TF_VERSION = Version(TF_VERSION) - # check the TF_VERSION, when TF < 1.12, mixed precision is not allowed if _TF_VERSION < Version("1.14.0"): raise RuntimeError( "TensorFlow version %s is not compatible with the mixed precision setting. Please consider upgrading your TF version!" @@ -653,7 +701,13 @@ def _build_training(self): optimizer = tf.mixed_precision.enable_mixed_precision_graph_rewrite( optimizer ) + return optimizer + + def _build_training(self): + trainable_variables = tf.trainable_variables() + if not self.multi_task_mode: + optimizer = self._build_optimizer() apply_op = optimizer.minimize( loss=self.l2_l, global_step=self.global_step, @@ -665,6 +719,7 @@ def _build_training(self): else: self.train_op = {} for fitting_key in self.fitting_type_dict: + optimizer = self._build_optimizer(fitting_key=fitting_key) apply_op = optimizer.minimize( loss=self.l2_l[fitting_key], global_step=self.global_step, @@ -744,16 +799,30 @@ def train(self, train_data=None, valid_data=None): cur_batch = run_sess(self.sess, self.global_step) is_first_step = True self.cur_batch = cur_batch - log.info( - "start training at lr %.2e (== %.2e), decay_step %d, decay_rate %f, final lr will be %.2e" - % ( - run_sess(self.sess, self.learning_rate), - self.lr.value(cur_batch), - self.lr.decay_steps_, - self.lr.decay_rate_, - self.lr.value(stop_batch), + if not self.multi_task_mode: + log.info( + "start training at lr %.2e (== %.2e), decay_step %d, decay_rate %f, final lr will be %.2e" + % ( + run_sess(self.sess, self.learning_rate), + self.lr.value(cur_batch), + self.lr.decay_steps_, + self.lr.decay_rate_, + self.lr.value(stop_batch), + ) ) - ) + else: + for fitting_key in self.fitting_type_dict: + log.info( + "%s: start training at lr %.2e (== %.2e), decay_step %d, decay_rate %f, final lr will be %.2e" + % ( + fitting_key, + run_sess(self.sess, self.learning_rate_dict[fitting_key]), + self.lr_dict[fitting_key].value(cur_batch), + self.lr_dict[fitting_key].decay_steps_, + self.lr_dict[fitting_key].decay_rate_, + self.lr_dict[fitting_key].value(stop_batch), + ) + ) prf_options = None prf_run_metadata = None @@ -821,22 +890,27 @@ def train(self, train_data=None, valid_data=None): train_batches = {} valid_batches = {} # valid_numb_batch_dict - for fitting_key in train_data: - train_batches[fitting_key] = [ - train_data[fitting_key].get_batch() + for fitting_key_ii in train_data: + # enumerate fitting key as fitting_key_ii + train_batches[fitting_key_ii] = [ + train_data[fitting_key_ii].get_batch() ] - valid_batches[fitting_key] = ( + valid_batches[fitting_key_ii] = ( [ - valid_data[fitting_key].get_batch() + valid_data[fitting_key_ii].get_batch() for ii in range( - self.valid_numb_batch_dict[fitting_key] + self.valid_numb_batch_dict[fitting_key_ii] ) ] - if fitting_key in valid_data + if fitting_key_ii in valid_data else None ) self.valid_on_the_fly( - fp, train_batches, valid_batches, print_header=True + fp, + train_batches, + valid_batches, + print_header=True, + fitting_key=fitting_key, ) is_first_step = False @@ -887,21 +961,23 @@ def train(self, train_data=None, valid_data=None): else: train_batches = {} valid_batches = {} - for fitting_key in train_data: - train_batches[fitting_key] = [ - train_data[fitting_key].get_batch() + for fitting_key_ii in train_data: + train_batches[fitting_key_ii] = [ + train_data[fitting_key_ii].get_batch() ] - valid_batches[fitting_key] = ( + valid_batches[fitting_key_ii] = ( [ - valid_data[fitting_key].get_batch() + valid_data[fitting_key_ii].get_batch() for ii in range( - self.valid_numb_batch_dict[fitting_key] + self.valid_numb_batch_dict[fitting_key_ii] ) ] - if fitting_key in valid_data + if fitting_key_ii in valid_data else None ) - self.valid_on_the_fly(fp, train_batches, valid_batches) + self.valid_on_the_fly( + fp, train_batches, valid_batches, fitting_key=fitting_key + ) if self.timing_in_training: toc = time.time() test_time = toc - tic @@ -1005,22 +1081,50 @@ def get_global_step(self): # fp.write(print_str) # fp.close () - def valid_on_the_fly(self, fp, train_batches, valid_batches, print_header=False): + def valid_on_the_fly( + self, fp, train_batches, valid_batches, print_header=False, fitting_key=None + ): train_results = self.get_evaluation_results(train_batches) valid_results = self.get_evaluation_results(valid_batches) cur_batch = self.cur_batch - current_lr = run_sess(self.sess, self.learning_rate) + if not self.multi_task_mode: + current_lr = run_sess(self.sess, self.learning_rate) + else: + assert ( + fitting_key is not None + ), "Fitting key must be assigned in validation!" + current_lr = None + # current_lr can be used as the learning rate of descriptor in the future + current_lr_dict = {} + for fitting_key_ii in train_batches: + current_lr_dict[fitting_key_ii] = run_sess( + self.sess, self.learning_rate_dict[fitting_key_ii] + ) if print_header: self.print_header(fp, train_results, valid_results, self.multi_task_mode) - self.print_on_training( - fp, - train_results, - valid_results, - cur_batch, - current_lr, - self.multi_task_mode, - ) + if not self.multi_task_mode: + self.print_on_training( + fp, + train_results, + valid_results, + cur_batch, + current_lr, + self.multi_task_mode, + ) + else: + assert ( + fitting_key is not None + ), "Fitting key must be assigned when printing learning rate!" + self.print_on_training( + fp, + train_results, + valid_results, + cur_batch, + current_lr, + self.multi_task_mode, + current_lr_dict, + ) @staticmethod def print_header(fp, train_results, valid_results, multi_task_mode=False): @@ -1035,6 +1139,7 @@ def print_header(fp, train_results, valid_results, multi_task_mode=False): prop_fmt = " %11s" for k in train_results.keys(): print_str += prop_fmt % (k + "_trn") + print_str += " %8s\n" % (k + "_lr") else: for fitting_key in train_results: if valid_results[fitting_key] is not None: @@ -1045,13 +1150,19 @@ def print_header(fp, train_results, valid_results, multi_task_mode=False): prop_fmt = " %11s" for k in train_results[fitting_key].keys(): print_str += prop_fmt % (k + "_trn") - print_str += " %8s\n" % "lr" + print_str += " %8s\n" % (fitting_key + "_lr") fp.write(print_str) fp.flush() @staticmethod def print_on_training( - fp, train_results, valid_results, cur_batch, cur_lr, multi_task_mode=False + fp, + train_results, + valid_results, + cur_batch, + cur_lr, + multi_task_mode=False, + cur_lr_dict=None, ): print_str = "" print_str += "%7d" % cur_batch @@ -1065,6 +1176,7 @@ def print_on_training( prop_fmt = " %11.2e" for k in train_results.keys(): print_str += prop_fmt % (train_results[k]) + print_str += " %8.1e\n" % cur_lr else: for fitting_key in train_results: if valid_results[fitting_key] is not None: @@ -1079,7 +1191,7 @@ def print_on_training( prop_fmt = " %11.2e" for k in train_results[fitting_key].keys(): print_str += prop_fmt % (train_results[fitting_key][k]) - print_str += " %8.1e\n" % cur_lr + print_str += " %8.1e\n" % cur_lr_dict[fitting_key] fp.write(print_str) fp.flush() diff --git a/deepmd/utils/argcheck.py b/deepmd/utils/argcheck.py index f19300a488..b66dc3b7fa 100644 --- a/deepmd/utils/argcheck.py +++ b/deepmd/utils/argcheck.py @@ -801,10 +801,22 @@ def learning_rate_args(): ) ], [learning_rate_variant_type_args()], + optional=True, doc=doc_lr, ) +def learning_rate_dict_args(): + doc_learning_rate_dict = ( + "The dictionary of definitions of learning rates in multi-task mode. " + "Each learning_rate_dict[fitting_key], with user-defined name `fitting_key` in `model/fitting_net_dict`, is the single definition of learning rate.\n" + ) + ca = Argument( + "learning_rate_dict", dict, [], [], optional=True, doc=doc_learning_rate_dict + ) + return ca + + # --- Loss configurations: --- # def start_pref(item): return f"The prefactor of {item} loss at the start of the training. Should be larger than or equal to 0. If set to none-zero value, the {item} label should be provided by file {item}.npy in each data system. If both start_pref_{item} and limit_pref_{item} are set to 0, then the {item} will be ignored." @@ -1238,6 +1250,7 @@ def gen_doc(*, make_anchor=True, make_link=True, **kwargs): make_anchor = True ma = model_args() lra = learning_rate_args() + lrda = learning_rate_dict_args() la = loss_args() lda = loss_dict_args() ta = training_args() @@ -1247,6 +1260,7 @@ def gen_doc(*, make_anchor=True, make_link=True, **kwargs): ptr.append(la.gen_doc(make_anchor=make_anchor, make_link=make_link, **kwargs)) ptr.append(lda.gen_doc(make_anchor=make_anchor, make_link=make_link, **kwargs)) ptr.append(lra.gen_doc(make_anchor=make_anchor, make_link=make_link, **kwargs)) + ptr.append(lrda.gen_doc(make_anchor=make_anchor, make_link=make_link, **kwargs)) ptr.append(ta.gen_doc(make_anchor=make_anchor, make_link=make_link, **kwargs)) ptr.append(nvnmda.gen_doc(make_anchor=make_anchor, make_link=make_link, **kwargs)) @@ -1264,6 +1278,7 @@ def gen_json(**kwargs): ( model_args(), learning_rate_args(), + learning_rate_dict_args(), loss_args(), loss_dict_args(), training_args(), @@ -1277,6 +1292,7 @@ def gen_args(**kwargs): return [ model_args(), learning_rate_args(), + learning_rate_dict_args(), loss_args(), loss_dict_args(), training_args(), @@ -1300,10 +1316,12 @@ def normalize_multi_task(data): single_training_data = "training_data" in data["training"].keys() single_valid_data = "validation_data" in data["training"].keys() single_loss = "loss" in data.keys() + single_learning_rate = "learning_rate" in data.keys() multi_fitting_net = "fitting_net_dict" in data["model"].keys() multi_training_data = "data_dict" in data["training"].keys() multi_loss = "loss_dict" in data.keys() multi_fitting_weight = "fitting_weight" in data["training"].keys() + multi_learning_rate = "learning_rate_dict" in data.keys() assert (single_fitting_net == single_training_data) and ( multi_fitting_net == multi_training_data ), ( @@ -1342,6 +1360,16 @@ def normalize_multi_task(data): if multi_loss else {} ) + if multi_learning_rate: + data["learning_rate_dict"] = normalize_learning_rate_dict( + data["model"]["fitting_net_dict"].keys(), data["learning_rate_dict"] + ) + elif single_learning_rate: + data[ + "learning_rate_dict" + ] = normalize_learning_rate_dict_with_single_learning_rate( + data["model"]["fitting_net_dict"].keys(), data["learning_rate"] + ) fitting_weight = ( data["training"]["fitting_weight"] if multi_fitting_weight else None ) @@ -1354,6 +1382,9 @@ def normalize_multi_task(data): assert ( not multi_loss ), "In single-task mode, please use 'model/loss' in stead of 'model/loss_dict'! " + assert ( + not multi_learning_rate + ), "In single-task mode, please use 'model/learning_rate' in stead of 'model/learning_rate_dict'! " return data @@ -1398,6 +1429,35 @@ def normalize_loss_dict(fitting_keys, loss_dict): return new_dict +def normalize_learning_rate_dict(fitting_keys, learning_rate_dict): + # check the learning_rate dict + failed_learning_rate_keys = [ + item for item in learning_rate_dict if item not in fitting_keys + ] + assert ( + not failed_learning_rate_keys + ), "Learning rate dict key(s) {} not have corresponding fitting keys in {}! ".format( + str(failed_learning_rate_keys), str(list(fitting_keys)) + ) + new_dict = {} + base = Argument("base", dict, [], [learning_rate_variant_type_args()], doc="") + for item in learning_rate_dict: + data = base.normalize_value(learning_rate_dict[item], trim_pattern="_*") + base.check_value(data, strict=True) + new_dict[item] = data + return new_dict + + +def normalize_learning_rate_dict_with_single_learning_rate(fitting_keys, learning_rate): + new_dict = {} + base = Argument("base", dict, [], [learning_rate_variant_type_args()], doc="") + data = base.normalize_value(learning_rate, trim_pattern="_*") + base.check_value(data, strict=True) + for fitting_key in fitting_keys: + new_dict[fitting_key] = data + return new_dict + + def normalize_fitting_weight(fitting_keys, data_keys, fitting_weight=None): # check the mapping failed_data_keys = [item for item in data_keys if item not in fitting_keys] @@ -1472,12 +1532,13 @@ def normalize(data): data = normalize_multi_task(data) ma = model_args() lra = learning_rate_args() + lrda = learning_rate_dict_args() la = loss_args() lda = loss_dict_args() ta = training_args() nvnmda = nvnmd_args() - base = Argument("base", dict, [ma, lra, la, lda, ta, nvnmda]) + base = Argument("base", dict, [ma, lra, lrda, la, lda, ta, nvnmda]) data = base.normalize_value(data, trim_pattern="_*") base.check_value(data, strict=True) diff --git a/deepmd/utils/multi_init.py b/deepmd/utils/multi_init.py index 59ab88a845..d5496b47ea 100644 --- a/deepmd/utils/multi_init.py +++ b/deepmd/utils/multi_init.py @@ -135,6 +135,30 @@ def replace_model_params_with_frz_multi_model( f"Add '{config_key}/{fitting_key}' configurations from the pretrained frozen model." ) + # learning rate dict keep backward compatibility + config_key = "learning_rate_dict" + single_config_key = "learning_rate" + cur_jdata = jdata + target_jdata = pretrained_jdata + if (single_config_key not in cur_jdata) and (config_key in cur_jdata): + cur_jdata = cur_jdata[config_key] + if config_key in target_jdata: + target_jdata = target_jdata[config_key] + for fitting_key in reused_fittings: + if fitting_key not in cur_jdata: + target_para = target_jdata[fitting_key] + cur_jdata[fitting_key] = target_para + log.info( + f"Add '{config_key}/{fitting_key}' configurations from the pretrained frozen model." + ) + else: + for fitting_key in reused_fittings: + if fitting_key not in cur_jdata: + cur_jdata[fitting_key] = {} + log.info( + f"Add '{config_key}/{fitting_key}' configurations as default." + ) + return jdata diff --git a/source/tests/test_init_frz_model_multi.py b/source/tests/test_init_frz_model_multi.py index 8d1fe1ed4e..e7cf2a5f7f 100644 --- a/source/tests/test_init_frz_model_multi.py +++ b/source/tests/test_init_frz_model_multi.py @@ -55,6 +55,7 @@ def _init_models(): jdata = j_loader(str(tests_path / os.path.join("init_frz_model", "input.json"))) fitting_config = jdata["model"].pop("fitting_net") loss_config = jdata.pop("loss") + learning_rate_config = jdata.pop("learning_rate") training_data_config = jdata["training"].pop("training_data") validation_data_config = jdata["training"].pop("validation_data") jdata["training"]["data_dict"] = {} @@ -72,6 +73,8 @@ def _init_models(): jdata["model"]["fitting_net_dict"]["water_ener"] = fitting_config jdata["loss_dict"] = {} jdata["loss_dict"]["water_ener"] = loss_config + jdata["learning_rate_dict"] = {} + jdata["learning_rate_dict"]["water_ener"] = learning_rate_config with open(INPUT, "w") as fp: json.dump(jdata, fp, indent=4) ret = run_dp("dp train " + INPUT) @@ -89,6 +92,8 @@ def _init_models(): jdata["model"]["fitting_net_dict"]["water_ener_new"] = fitting_config jdata["loss_dict"] = {} jdata["loss_dict"]["water_ener_new"] = loss_config + jdata["learning_rate_dict"] = {} + jdata["learning_rate_dict"]["water_ener_new"] = learning_rate_config jdata["training"]["data_dict"] = {} jdata["training"]["data_dict"]["water_ener_new"] = {} jdata["training"]["data_dict"]["water_ener_new"][ diff --git a/source/tests/water_multi.json b/source/tests/water_multi.json index f16e0eadac..e74b724157 100644 --- a/source/tests/water_multi.json +++ b/source/tests/water_multi.json @@ -45,13 +45,23 @@ } } }, - "learning_rate": { - "type": "exp", - "start_lr": 0.001, - "decay_steps": 5000, - "decay_rate": 0.95, - "_comment": "that's all" - }, + "learning_rate_dict": + { + "water_ener": { + "type": "exp", + "start_lr": 0.001, + "decay_steps": 5000, + "decay_rate": 0.95, + "_comment": "that's all" + }, + "water_dipole": { + "type": "exp", + "start_lr": 0.001, + "decay_steps": 5000, + "decay_rate": 0.95, + "_comment": "that's all" + } + }, "loss_dict": { "water_ener": { From d224075e8fd421e26b513446d5b34e5a01465d09 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Wed, 29 Mar 2023 09:10:31 +0800 Subject: [PATCH 033/126] [pre-commit.ci] pre-commit autoupdate (#2431) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit updates: - [github.com/charliermarsh/ruff-pre-commit: v0.0.257 → v0.0.259](https://github.com/charliermarsh/ruff-pre-commit/compare/v0.0.257...v0.0.259) - [github.com/pre-commit/mirrors-clang-format: v15.0.7 → v16.0.0](https://github.com/pre-commit/mirrors-clang-format/compare/v15.0.7...v16.0.0) - [github.com/scop/pre-commit-shfmt: v3.6.0-1 → v3.6.0-2](https://github.com/scop/pre-commit-shfmt/compare/v3.6.0-1...v3.6.0-2) Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> --- .pre-commit-config.yaml | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index e630577dc5..ab4c79b5a4 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -26,7 +26,7 @@ repos: files: \.py$ - repo: https://github.com/charliermarsh/ruff-pre-commit # Ruff version. - rev: v0.0.257 + rev: v0.0.259 hooks: - id: ruff args: ["--fix"] @@ -43,7 +43,7 @@ repos: - id: blacken-docs # C++ - repo: https://github.com/pre-commit/mirrors-clang-format - rev: v15.0.7 + rev: v16.0.0 hooks: - id: clang-format exclude: ^source/3rdparty|source/lib/src/cuda/cudart/.+\.inc @@ -54,7 +54,7 @@ repos: - id: csslint # Shell - repo: https://github.com/scop/pre-commit-shfmt - rev: v3.6.0-1 + rev: v3.6.0-2 hooks: - id: shfmt # CMake From 66db350c0f4c8d862a6cda227a8dc3efe12a3eb0 Mon Sep 17 00:00:00 2001 From: LiuGroupHNU <55585514+LiuGroupHNU@users.noreply.github.com> Date: Wed, 29 Mar 2023 09:10:50 +0800 Subject: [PATCH 034/126] nvnmd: fix some warnings about matmul_flt_nvnmd (#2430) Co-authored-by: LiuGroupHNU Co-authored-by: MoPinghui Co-authored-by: Han Wang <92130845+wanghan-iapcm@users.noreply.github.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> --- source/op/matmul_flt_nvnmd.cc | 4 +-- source/tests/test_nvnmd_op.py | 48 +-------------------------------- source/tests/test_nvnmd_se_a.py | 8 +++--- 3 files changed, 7 insertions(+), 53 deletions(-) diff --git a/source/op/matmul_flt_nvnmd.cc b/source/op/matmul_flt_nvnmd.cc index f86e97ea51..354afbbdb2 100644 --- a/source/op/matmul_flt_nvnmd.cc +++ b/source/op/matmul_flt_nvnmd.cc @@ -128,7 +128,7 @@ class MatmulFltNvnmdOp : public OpKernel { for (hh = 0; hh < H; hh++) { // find x max exponnet - if (normx & 0x0f == 0) { // normalize x[:,:] + if ((normx & 0x0f) == 0) { // normalize x[:,:] find_max_expo(expo_max1, (FPTYPE *)&x[hh * N * M], N * M); for (ii = 0; ii < N; ii++) expo_max1s[ii] = expo_max1; @@ -140,7 +140,7 @@ class MatmulFltNvnmdOp : public OpKernel { } // find w max exponnet - if (normw & 0x0f == 0) { // normalize w[:,:] + if ((normw & 0x0f) == 0) { // normalize w[:,:] find_max_expo(expo_max2, (FPTYPE *)&w[hh * M * K], M * K); for (kk = 0; kk < K; kk++) expo_max2s[kk] = expo_max2; diff --git a/source/tests/test_nvnmd_op.py b/source/tests/test_nvnmd_op.py index 4cf674baf8..1f54da29c5 100644 --- a/source/tests/test_nvnmd_op.py +++ b/source/tests/test_nvnmd_op.py @@ -423,53 +423,7 @@ def test_op(self): # graph t_x = tf.placeholder(tf.float64, [None, 4], "t_x") t_w = tf.placeholder(tf.float64, [4, 4], "t_w") - t_y = op_module.matmul_flt_nvnmd(t_x, t_w, 0, 0) - # feed_dic - x = np.reshape(np.arange(0, 16) / 3.0, [-1, 4]) - feed_dict = {t_x: x, t_w: x} - # get value and test - self.sess.run(tf.global_variables_initializer()) - y_pred = self.sess.run(t_y, feed_dict=feed_dict) - y_test = np.array( - [ - 6.22222, - 6.88888, - 7.55555, - 8.22221, - 16.88887, - 19.33331, - 21.77776, - 24.2222, - 27.55553, - 31.77774, - 35.99997, - 40.2222, - 38.22217, - 44.22217, - 50.22217, - 56.22217, - ] - ) - y_pred = np.reshape(y_pred, [-1]) - y_test = np.reshape(y_test, [-1]) - np.testing.assert_almost_equal(y_test, y_pred, 5) - tf.reset_default_graph() - - -class TestOpMatmulFltNvnmd2(tf.test.TestCase): - def setUp(self): - config = tf.ConfigProto() - if int(os.environ.get("DP_AUTO_PARALLELIZATION", 0)): - config.graph_options.rewrite_options.custom_optimizers.add().name = ( - "dpparallel" - ) - self.sess = self.test_session(config=config).__enter__() - - def test_op(self): - # graph - t_x = tf.placeholder(tf.float64, [None, 4], "t_x") - t_w = tf.placeholder(tf.float64, [4, 4], "t_w") - t_y = op_module.matmul_flt_nvnmd(t_x, t_w, 0, 0) + t_y = op_module.matmul_flt_nvnmd(t_x, t_w, 1, 1) # feed_dic x = np.reshape(np.arange(0, 16) / 3.0, [-1, 4]) feed_dict = {t_x: x, t_w: x} diff --git a/source/tests/test_nvnmd_se_a.py b/source/tests/test_nvnmd_se_a.py index 0b8cbc20b1..fcc96ad811 100644 --- a/source/tests/test_nvnmd_se_a.py +++ b/source/tests/test_nvnmd_se_a.py @@ -132,14 +132,14 @@ def test_descriptor_one_side_qnn(self): ref_dout = [ 0.0136348009, 0.0083287954, - -0.0639076233, - 0.0129181147, + -0.0639075041, + 0.0129179955, 0.0050876141, - -0.0390379429, + -0.0390378237, 0.0078909397, 0.0022796392, 0.2995386124, - -0.0605480671, + -0.0605479479, ] places = 10 np.testing.assert_almost_equal(model_dout[0:10], ref_dout, places) From d90ab485504896a7c46c29a49d864d52fc310b82 Mon Sep 17 00:00:00 2001 From: Eisuke Kawashima Date: Mon, 3 Apr 2023 14:56:26 +0900 Subject: [PATCH 035/126] fix: avoid `using namespace std;` in header files (#2437) Signed-off-by: Eisuke Kawashima --- source/api_cc/include/AtomMap.h | 2 - source/api_cc/include/version.h.in | 2 - source/ipi/include/Convert.h | 2 - source/ipi/include/XyzFileManager.h | 1 - source/lib/include/SimulationRegion_Impl.h | 2 - source/md/include/AdWeight.h | 63 +++++++++++----------- source/md/include/Convert.h | 54 +++++++++---------- source/md/include/Gaussian.h | 2 - source/md/include/HarmonicAngle.h | 12 ++--- source/md/include/HarmonicBond.h | 12 ++--- source/md/include/Integrator.h | 21 ++++---- source/md/include/LJInter.h | 12 ++--- source/md/include/LJTab.h | 12 ++--- source/md/include/MaxShift.h | 10 ++-- source/md/include/Statistics.h | 15 +++--- source/md/include/TF.h | 10 ++-- source/md/include/Tabulated.h | 30 +++++------ source/md/include/Trajectory.h | 14 +++-- source/md/include/UnitManager.h | 5 +- source/md/include/XyzFileManager.h | 15 +++--- source/md/include/ZM.h | 28 +++++----- source/md/include/ZMFunctions.h | 7 ++- source/md/include/common.h | 11 ++-- 23 files changed, 152 insertions(+), 190 deletions(-) diff --git a/source/api_cc/include/AtomMap.h b/source/api_cc/include/AtomMap.h index 90b53b3281..0da2eefdcb 100644 --- a/source/api_cc/include/AtomMap.h +++ b/source/api_cc/include/AtomMap.h @@ -2,8 +2,6 @@ #include -// using namespace std; - namespace deepmd { class AtomMap { public: diff --git a/source/api_cc/include/version.h.in b/source/api_cc/include/version.h.in index fa5e358f0b..c6bf6cf491 100644 --- a/source/api_cc/include/version.h.in +++ b/source/api_cc/include/version.h.in @@ -1,8 +1,6 @@ #pragma once #include -// using namespace std; - const std::string global_install_prefix="@CMAKE_INSTALL_PREFIX@"; const std::string global_git_summ="@GIT_SUMM@"; diff --git a/source/ipi/include/Convert.h b/source/ipi/include/Convert.h index f116cc61e9..199498cc7b 100644 --- a/source/ipi/include/Convert.h +++ b/source/ipi/include/Convert.h @@ -4,8 +4,6 @@ #include #include -// using namespace std; - template class Convert { public: diff --git a/source/ipi/include/XyzFileManager.h b/source/ipi/include/XyzFileManager.h index aa50e76461..fa27592548 100644 --- a/source/ipi/include/XyzFileManager.h +++ b/source/ipi/include/XyzFileManager.h @@ -3,7 +3,6 @@ #include #include -// using namespace std; namespace XyzFileManager { diff --git a/source/lib/include/SimulationRegion_Impl.h b/source/lib/include/SimulationRegion_Impl.h index 8d7807a1e7..c6e34b27c4 100644 --- a/source/lib/include/SimulationRegion_Impl.h +++ b/source/lib/include/SimulationRegion_Impl.h @@ -10,8 +10,6 @@ #include "errors.h" -// using namespace std; - template SimulationRegion::~SimulationRegion() {} diff --git a/source/md/include/AdWeight.h b/source/md/include/AdWeight.h index e43896e3f9..fffd549fef 100644 --- a/source/md/include/AdWeight.h +++ b/source/md/include/AdWeight.h @@ -2,8 +2,6 @@ #include -using namespace std; - #ifdef HIGH_PREC typedef double VALUETYPE; #else @@ -13,29 +11,29 @@ typedef float VALUETYPE; class AdWeight { public: AdWeight(const VALUETYPE& pl); - virtual void zone_tag(vector& tag, - const vector& coord) const = 0; - virtual void atom_weight(vector& weight, - vector& weight_x, - const vector& coord) const = 0; - virtual vector get_center() const = 0; - void sel_nn_atom(vector& nn_coord, - vector& nn_type, - vector& nn_idx, - vector& nn_tag, - const vector& dcoord, - const vector& dtype) const; - void force_intpl(vector& of, - const vector& dcoord, - const vector& ff_force, - const vector& nn_force, - const vector& nn_idx) const; - void force_intpl(vector& of, - const vector& dcoord, - const vector& ff_bd_force, - const vector& ff_nb_force, - const vector& nn_force, - const vector& nn_idx) const; + virtual void zone_tag(std::vector& tag, + const std::vector& coord) const = 0; + virtual void atom_weight(std::vector& weight, + std::vector& weight_x, + const std::vector& coord) const = 0; + virtual std::vector get_center() const = 0; + void sel_nn_atom(std::vector& nn_coord, + std::vector& nn_type, + std::vector& nn_idx, + std::vector& nn_tag, + const std::vector& dcoord, + const std::vector& dtype) const; + void force_intpl(std::vector& of, + const std::vector& dcoord, + const std::vector& ff_force, + const std::vector& nn_force, + const std::vector& nn_idx) const; + void force_intpl(std::vector& of, + const std::vector& dcoord, + const std::vector& ff_bd_force, + const std::vector& ff_nb_force, + const std::vector& nn_force, + const std::vector& nn_idx) const; private: VALUETYPE protect_level; @@ -44,19 +42,20 @@ class AdWeight { // slab model, axis x class SlabWeight : public AdWeight { public: - SlabWeight(const vector& box, + SlabWeight(const std::vector& box, const VALUETYPE& rnn, const VALUETYPE& rhy, const VALUETYPE& rc, const VALUETYPE& protect_level = 1e-3); - virtual void zone_tag(vector& tag, const vector& coord) const; - virtual void atom_weight(vector& weight, - vector& weight_x, - const vector& coord) const; - virtual vector get_center() const { return center; } + virtual void zone_tag(std::vector& tag, + const std::vector& coord) const; + virtual void atom_weight(std::vector& weight, + std::vector& weight_x, + const std::vector& coord) const; + virtual std::vector get_center() const { return center; } private: - vector center; + std::vector center; VALUETYPE rnn; VALUETYPE rhy; VALUETYPE rc; diff --git a/source/md/include/Convert.h b/source/md/include/Convert.h index 5f6a50bc34..d5a15d09f1 100644 --- a/source/md/include/Convert.h +++ b/source/md/include/Convert.h @@ -4,38 +4,36 @@ #include #include -using namespace std; - template class Convert { public: - Convert(const vector& atomname, - map& name_type_map, - map& name_mass_map, - map& name_charge_map, + Convert(const std::vector& atomname, + std::map& name_type_map, + std::map& name_mass_map, + std::map& name_charge_map, const bool sort = true); - void gro2nnp(vector& coord, - vector& veloc, - vector& box, - const vector >& posi, - const vector >& velo, - const vector& box_size) const; - void nnp2gro(vector >& posi, - vector >& velo, - vector& box_size, - const vector& coord, - const vector& veloc, - const vector& box) const; - void idx_gro2nnp(vector& out, const vector& in) const; - void idx_nnp2gro(vector& out, const vector& in) const; - const vector& get_type() const { return atype; } - const vector& get_mass() const { return amass; } - const vector& get_charge() const { return acharge; } + void gro2nnp(std::vector& coord, + std::vector& veloc, + std::vector& box, + const std::vector >& posi, + const std::vector >& velo, + const std::vector& box_size) const; + void nnp2gro(std::vector >& posi, + std::vector >& velo, + std::vector& box_size, + const std::vector& coord, + const std::vector& veloc, + const std::vector& box) const; + void idx_gro2nnp(std::vector& out, const std::vector& in) const; + void idx_nnp2gro(std::vector& out, const std::vector& in) const; + const std::vector& get_type() const { return atype; } + const std::vector& get_mass() const { return amass; } + const std::vector& get_charge() const { return acharge; } private: - vector idx_map_nnp2gro; - vector idx_map_gro2nnp; - vector atype; - vector amass; - vector acharge; + std::vector idx_map_nnp2gro; + std::vector idx_map_gro2nnp; + std::vector atype; + std::vector amass; + std::vector acharge; }; diff --git a/source/md/include/Gaussian.h b/source/md/include/Gaussian.h index 80a75af204..e3cdecfad1 100644 --- a/source/md/include/Gaussian.h +++ b/source/md/include/Gaussian.h @@ -5,8 +5,6 @@ #include "RandomGenerator.h" -using namespace std; - class Gaussian { public: void set_seed(unsigned long seed); diff --git a/source/md/include/HarmonicAngle.h b/source/md/include/HarmonicAngle.h index 84ad5d8fd3..84a2558994 100644 --- a/source/md/include/HarmonicAngle.h +++ b/source/md/include/HarmonicAngle.h @@ -4,8 +4,6 @@ #include "SimulationRegion.h" -using namespace std; - #ifdef HIGH_PREC typedef double VALUETYPE; #else @@ -18,12 +16,12 @@ class HarmonicAngle { public: void compute(VALUETYPE& ener, - vector& force, - vector& virial, - const vector& coord, - const vector& atype, + std::vector& force, + std::vector& virial, + const std::vector& coord, + const std::vector& atype, const SimulationRegion& region, - const vector& alist); + const std::vector& alist); private: VALUETYPE ka, tt; diff --git a/source/md/include/HarmonicBond.h b/source/md/include/HarmonicBond.h index 0c7779db55..48751d85b7 100644 --- a/source/md/include/HarmonicBond.h +++ b/source/md/include/HarmonicBond.h @@ -4,8 +4,6 @@ #include "SimulationRegion.h" -using namespace std; - #ifdef HIGH_PREC typedef double VALUETYPE; #else @@ -18,12 +16,12 @@ class HarmonicBond { public: void compute(VALUETYPE& ener, - vector& force, - vector& virial, - const vector& coord, - const vector& atype, + std::vector& force, + std::vector& virial, + const std::vector& coord, + const std::vector& atype, const SimulationRegion& region, - const vector& blist); + const std::vector& blist); private: VALUETYPE kk, bb; diff --git a/source/md/include/Integrator.h b/source/md/include/Integrator.h index 99f720fc51..caefffbac8 100644 --- a/source/md/include/Integrator.h +++ b/source/md/include/Integrator.h @@ -4,7 +4,6 @@ #include "Gaussian.h" #include "UnitManager.h" -using namespace std; template class Integrator { @@ -12,13 +11,13 @@ class Integrator { Integrator() : massConst(UnitManager::IntegratorMassConstant){}; public: - void stepVeloc(vector& vv, - const vector& ff, - const vector& mass, + void stepVeloc(std::vector& vv, + const std::vector& ff, + const std::vector& mass, const double& dt, - const vector& freez = vector()) const; - void stepCoord(vector& rr, - const vector& vv, + const std::vector& freez = std::vector()) const; + void stepCoord(std::vector& rr, + const std::vector& vv, const double& dt) const; private: @@ -34,14 +33,14 @@ class ThermostatLangevin { void reinit(const VALUETYPE T = 300., const VALUETYPE tau = 1., const long long int seed = 0); - void stepOU(vector& vv, - const vector& mass, + void stepOU(std::vector& vv, + const std::vector& mass, const double& dt, - const vector& freez = vector()) const; + const std::vector& freez = std::vector()) const; private: mutable Gaussian gaussian; - string scheme; + std::string scheme; VALUETYPE temperature; VALUETYPE gamma; VALUETYPE sigma; diff --git a/source/md/include/LJInter.h b/source/md/include/LJInter.h index 267a8367d2..b71453b8a0 100644 --- a/source/md/include/LJInter.h +++ b/source/md/include/LJInter.h @@ -4,8 +4,6 @@ #include "SimulationRegion.h" -using namespace std; - #ifdef HIGH_PREC typedef double VALUETYPE; #else @@ -18,12 +16,12 @@ class LJInter { public: void compute(VALUETYPE& ener, - vector& force, - vector& virial, - const vector& coord, - const vector& atype, + std::vector& force, + std::vector& virial, + const std::vector& coord, + const std::vector& atype, const SimulationRegion& region, - const vector >& nlist); + const std::vector >& nlist); private: VALUETYPE c6, c12, rc, rc2, one_over_6, one_over_12, one_over_rc6, diff --git a/source/md/include/LJTab.h b/source/md/include/LJTab.h index 96c8089da0..f56f76510d 100644 --- a/source/md/include/LJTab.h +++ b/source/md/include/LJTab.h @@ -4,8 +4,6 @@ #include "Tabulated.h" -using namespace std; - #ifdef HIGH_PREC typedef double VALUETYPE; #else @@ -18,12 +16,12 @@ class LJTab { public: void compute(VALUETYPE& ener, - vector& force, - vector& virial, - const vector& coord, - const vector& atype, + std::vector& force, + std::vector& virial, + const std::vector& coord, + const std::vector& atype, const SimulationRegion& region, - const vector >& nlist) { + const std::vector >& nlist) { lj_tab.compute(ener, force, virial, coord, atype, region, nlist); }; diff --git a/source/md/include/MaxShift.h b/source/md/include/MaxShift.h index 65971a7037..d940342d91 100644 --- a/source/md/include/MaxShift.h +++ b/source/md/include/MaxShift.h @@ -4,8 +4,6 @@ #include "SimulationRegion.h" -using namespace std; - #ifdef HIGH_PREC typedef double VALUETYPE; #else @@ -14,16 +12,16 @@ typedef float VALUETYPE; class MaxShift { public: - MaxShift(const vector& dcoord, const VALUETYPE& shell); + MaxShift(const std::vector& dcoord, const VALUETYPE& shell); - bool rebuild(const vector& coord, + bool rebuild(const std::vector& coord, const SimulationRegion& region); private: VALUETYPE - max_shift2(const vector& coord, + max_shift2(const std::vector& coord, const SimulationRegion& region); - vector record; + std::vector record; VALUETYPE shell; VALUETYPE max_allow2; }; diff --git a/source/md/include/Statistics.h b/source/md/include/Statistics.h index 5dd6bad3e0..66574dbd66 100644 --- a/source/md/include/Statistics.h +++ b/source/md/include/Statistics.h @@ -3,16 +3,15 @@ #include #include "SimulationRegion.h" -using namespace std; template class Statistics { public: Statistics(const VALUETYPE e_corr = 0, const VALUETYPE p_corr = 0); void record(const VALUETYPE& ener, - const vector& virial, - const vector& veloc, - const vector& mass, + const std::vector& virial, + const std::vector& veloc, + const std::vector& mass, const SimulationRegion& region); public: @@ -24,17 +23,17 @@ class Statistics { double get_epot() const { return r_pot_ener + e_corr; } public: - void print(ostream& os, const int& step, const double time) const; - void print_head(ostream& os) const; + void print(std::ostream& os, const int& step, const double time) const; + void print_head(std::ostream& os) const; private: int natoms; double r_ener; double r_pot_ener; double r_kin_ener; - // vector r_box; + // std::vector r_box; SimulationRegion region; - vector r_vir; + std::vector r_vir; double e_corr; double p_corr; }; diff --git a/source/md/include/TF.h b/source/md/include/TF.h index 7fc1720b87..ac286c89bc 100644 --- a/source/md/include/TF.h +++ b/source/md/include/TF.h @@ -6,8 +6,6 @@ #include "AdWeight.h" #include "common.h" -using namespace std; - #ifdef HIGH_PREC typedef double VALUETYPE; #else @@ -16,16 +14,16 @@ typedef float VALUETYPE; class TF { public: - TF(const string& filename); + TF(const std::string& filename); public: - void apply(vector& force, - const vector& coord, + void apply(std::vector& force, + const std::vector& coord, const AdWeight& weight) const; private: VALUETYPE meas(const VALUETYPE& xx) const; - vector data; + std::vector data; double hh; double xup; }; diff --git a/source/md/include/Tabulated.h b/source/md/include/Tabulated.h index f74eb6354e..4e81cb620e 100644 --- a/source/md/include/Tabulated.h +++ b/source/md/include/Tabulated.h @@ -4,8 +4,6 @@ #include "SimulationRegion.h" -using namespace std; - #ifdef HIGH_PREC typedef double VALUETYPE; #else @@ -17,31 +15,31 @@ class Tabulated { Tabulated(){}; Tabulated(const VALUETYPE rc, const VALUETYPE hh, - const vector& tab); + const std::vector& tab); void reinit(const VALUETYPE rc, const VALUETYPE hh, - const vector& tab); + const std::vector& tab); public: void compute(VALUETYPE& ener, - vector& force, - vector& virial, - const vector& coord, - const vector& atype, + std::vector& force, + std::vector& virial, + const std::vector& coord, + const std::vector& atype, const SimulationRegion& region, - const vector >& nlist); + const std::vector >& nlist); void compute(VALUETYPE& ener, - vector& force, - vector& virial, - const vector& coord, - const vector& charge, - const vector& atype, + std::vector& force, + std::vector& virial, + const std::vector& coord, + const std::vector& charge, + const std::vector& atype, const SimulationRegion& region, - const vector >& nlist); + const std::vector >& nlist); void tb_inner(VALUETYPE& ae, VALUETYPE& af, const VALUETYPE& r2); private: VALUETYPE rc2, hi; - vector data; + std::vector data; void compute_posi(int& idx, VALUETYPE& eps, const VALUETYPE& rr); }; diff --git a/source/md/include/Trajectory.h b/source/md/include/Trajectory.h index 85b73ba9dd..9e773713af 100644 --- a/source/md/include/Trajectory.h +++ b/source/md/include/Trajectory.h @@ -8,8 +8,6 @@ #include "xdrfile/xdrfile_trr.h" #include "xdrfile/xdrfile_xtc.h" -using namespace std; - class XtcSaver { public: XtcSaver() : inited(false), prec(1000){}; @@ -20,8 +18,8 @@ class XtcSaver { public: void save(const int &step, const double &time, - const vector > &frame, - const vector &box); + const std::vector > &frame, + const std::vector &box); private: XDRFILE *xd; @@ -42,10 +40,10 @@ class TrrSaver { public: void save(const int &step, const double &time, - const vector > &ixx, - const vector > &ivv, - const vector > &iff, - const vector &box); + const std::vector > &ixx, + const std::vector > &ivv, + const std::vector > &iff, + const std::vector &box); private: XDRFILE *xd; diff --git a/source/md/include/UnitManager.h b/source/md/include/UnitManager.h index 977505b5ee..f1db76d1a0 100644 --- a/source/md/include/UnitManager.h +++ b/source/md/include/UnitManager.h @@ -1,7 +1,6 @@ #pragma once #include -using namespace std; class UnitManager { protected: @@ -19,8 +18,8 @@ class UnitManager { static double DefaultTableUpperLimit; static double DefaultTableStep; static double DefaultTableExtension; - static void set(const string& name_of_system); + static void set(const std::string& name_of_system); private: - static string unit_names[]; + static std::string unit_names[]; }; diff --git a/source/md/include/XyzFileManager.h b/source/md/include/XyzFileManager.h index 5ffbb847be..2759807190 100644 --- a/source/md/include/XyzFileManager.h +++ b/source/md/include/XyzFileManager.h @@ -2,18 +2,17 @@ #define __XyzFileManager_h_wanghan__ #include -using namespace std; namespace XyzFileManager { -void read(const string& file, - vector& atom_name, - vector >& posi, - vector >& velo, - vector >& forc, - vector& boxsize); +void read(const std::string& file, + std::vector& atom_name, + std::vector >& posi, + std::vector >& velo, + std::vector >& forc, + std::vector& boxsize); -void getBoxSize(const string& name, vector& boxsize); +void getBoxSize(const std::string& name, std::vector& boxsize); }; // namespace XyzFileManager diff --git a/source/md/include/ZM.h b/source/md/include/ZM.h index 1c2647a555..fd83ff0240 100644 --- a/source/md/include/ZM.h +++ b/source/md/include/ZM.h @@ -6,8 +6,6 @@ #include "Tabulated.h" #include "ZMFunctions.h" -using namespace std; - #ifdef HIGH_PREC typedef double VALUETYPE; #else @@ -20,24 +18,24 @@ class ZM { public: void compute(VALUETYPE& ener, - vector& force, - vector& virial, - const vector& coord, - const vector& charge, - const vector& atype, + std::vector& force, + std::vector& virial, + const std::vector& coord, + const std::vector& charge, + const std::vector& atype, const SimulationRegion& region, - const vector >& nlist) { + const std::vector >& nlist) { zm_tab.compute(ener, force, virial, coord, charge, atype, region, nlist); }; void exclude(VALUETYPE& ener, - vector& force, - vector& virial, - const vector& coord, - const vector& charge, - const vector& atype, + std::vector& force, + std::vector& virial, + const std::vector& coord, + const std::vector& charge, + const std::vector& atype, const SimulationRegion& region, - const vector& elist); - VALUETYPE e_corr(const vector& charge) const; + const std::vector& elist); + VALUETYPE e_corr(const std::vector& charge) const; private: Tabulated zm_tab; diff --git a/source/md/include/ZMFunctions.h b/source/md/include/ZMFunctions.h index 9d4206a0a0..755a093ba5 100644 --- a/source/md/include/ZMFunctions.h +++ b/source/md/include/ZMFunctions.h @@ -2,7 +2,6 @@ #define __Functions_h_ZM_wanghan__ #include -using namespace std; namespace ZeroMultipole { double funcV(const double& alpha, const double& r); @@ -14,12 +13,12 @@ double funcD4V(const double& alpha, const double& r); void calCoefficients(const int& ll, const double& alpha, const double& rc, - vector& coeff); + std::vector& coeff); class Potential { double alpha, rc; int ll; - vector coeff; + std::vector coeff; public: Potential(); @@ -31,7 +30,7 @@ class Potential { double mulpotp(const double& rr); public: - double energyCorr(const vector& charges) const; + double energyCorr(const std::vector& charges) const; }; } // namespace ZeroMultipole diff --git a/source/md/include/common.h b/source/md/include/common.h index 54d19880d7..c58b3fa2b3 100644 --- a/source/md/include/common.h +++ b/source/md/include/common.h @@ -3,22 +3,21 @@ #include #include "SimulationRegion.h" -using namespace std; const double b2m_l = 10; const double b2m_e = 1.660539040e-21 / 1.602176621e-19; template void clear(VALUETYPE& ener, - vector& force, - vector& virial) { + std::vector& force, + std::vector& virial) { ener = 0; - fill(force.begin(), force.end(), 0.); - fill(virial.begin(), virial.end(), 0.); + std::fill(force.begin(), force.end(), 0.); + std::fill(virial.begin(), virial.end(), 0.); } template -void normalize_coord(vector& coord, +void normalize_coord(std::vector& coord, const SimulationRegion& region) { int natoms = coord.size() / 3; for (int ii = 0; ii < natoms; ++ii) { From 31c8a483d1450816216e114830252b3aa6599318 Mon Sep 17 00:00:00 2001 From: Jinzhe Zeng Date: Mon, 3 Apr 2023 01:56:38 -0400 Subject: [PATCH 036/126] fix C API documentation (#2424) by pinning exhale to a specific version. (The current documentation is broken as it installs an old version) --------- Signed-off-by: Jinzhe Zeng Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> --- doc/environment.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/doc/environment.yml b/doc/environment.yml index f3928e4015..ee011f37f0 100644 --- a/doc/environment.yml +++ b/doc/environment.yml @@ -8,3 +8,4 @@ dependencies: - pip>=20.1 - pip: - ..[docs,cpu] + - "exhale @ https://github.com/svenevs/exhale/archive/2759a394268307b88f5440487ae0920ee4ebf81e.zip" From 353a12b80c751be37dbf8006d45a80f13df9e663 Mon Sep 17 00:00:00 2001 From: Anurag Kumar Singh Date: Thu, 6 Apr 2023 06:13:22 +0530 Subject: [PATCH 037/126] fix: descriptor function doc (#2440) Co-authored-by: AnuragKr Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> --- deepmd/descriptor/se_a.py | 4 +++- deepmd/descriptor/se_a_ef.py | 4 +++- deepmd/descriptor/se_r.py | 4 +++- deepmd/descriptor/se_t.py | 4 +++- 4 files changed, 12 insertions(+), 4 deletions(-) diff --git a/deepmd/descriptor/se_a.py b/deepmd/descriptor/se_a.py index 73221c1f91..d964cf824a 100644 --- a/deepmd/descriptor/se_a.py +++ b/deepmd/descriptor/se_a.py @@ -273,7 +273,9 @@ def get_dim_rot_mat_1(self) -> int: return self.filter_neuron[-1] def get_nlist(self) -> Tuple[tf.Tensor, tf.Tensor, List[int], List[int]]: - """Returns + """Returns neighbor information. + + Returns ------- nlist Neighbor list diff --git a/deepmd/descriptor/se_a_ef.py b/deepmd/descriptor/se_a_ef.py index a94939f9db..0dbad403f4 100644 --- a/deepmd/descriptor/se_a_ef.py +++ b/deepmd/descriptor/se_a_ef.py @@ -140,7 +140,9 @@ def get_rot_mat(self) -> tf.Tensor: return self.qmat def get_nlist(self) -> Tuple[tf.Tensor, tf.Tensor, List[int], List[int]]: - """Returns + """Returns neighbor information. + + Returns ------- nlist Neighbor list diff --git a/deepmd/descriptor/se_r.py b/deepmd/descriptor/se_r.py index 664848d248..3ed02ec8ce 100644 --- a/deepmd/descriptor/se_r.py +++ b/deepmd/descriptor/se_r.py @@ -190,7 +190,9 @@ def get_dim_out(self): return self.filter_neuron[-1] def get_nlist(self): - """Returns + """Returns neighbor information. + + Returns ------- nlist Neighbor list diff --git a/deepmd/descriptor/se_t.py b/deepmd/descriptor/se_t.py index f908fd2e32..7615275a82 100644 --- a/deepmd/descriptor/se_t.py +++ b/deepmd/descriptor/se_t.py @@ -193,7 +193,9 @@ def get_dim_out(self) -> int: return self.filter_neuron[-1] def get_nlist(self) -> Tuple[tf.Tensor, tf.Tensor, List[int], List[int]]: - """Returns + """Returns neighbor information. + + Returns ------- nlist Neighbor list From a778f061bc558ed3e182134f4ff9726b54411a63 Mon Sep 17 00:00:00 2001 From: Chun Cai Date: Thu, 6 Apr 2023 09:16:06 +0800 Subject: [PATCH 038/126] Docs: Fix typo in parallel-training.md (#2438) Signed-off-by: Chun Cai --- doc/train/parallel-training.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/train/parallel-training.md b/doc/train/parallel-training.md index 2a8cd72664..98d12f2b9b 100644 --- a/doc/train/parallel-training.md +++ b/doc/train/parallel-training.md @@ -37,7 +37,7 @@ CUDA_VISIBLE_DEVICES=4,5,6,7 horovodrun -np 4 \ dp train --mpi-log=workers input.json ``` -Need to mention, the environment variable `CUDA_VISIBLE_DEVICES`` must be set to control parallelism on the occupied host where one process is bound to one GPU card. +Need to mention, the environment variable `CUDA_VISIBLE_DEVICES` must be set to control parallelism on the occupied host where one process is bound to one GPU card. To maximize the performance, one should follow [FAQ: How to control the parallelism of a job](../troubleshooting/howtoset_num_nodes.md) to control the number of threads. From 7f534a048d9a48dc46358f451326ab067247a686 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Thu, 6 Apr 2023 09:16:39 +0800 Subject: [PATCH 039/126] [pre-commit.ci] pre-commit autoupdate (#2439) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit updates: - [github.com/psf/black: 23.1.0 → 23.3.0](https://github.com/psf/black/compare/23.1.0...23.3.0) - [github.com/charliermarsh/ruff-pre-commit: v0.0.259 → v0.0.260](https://github.com/charliermarsh/ruff-pre-commit/compare/v0.0.259...v0.0.260) Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> --- .pre-commit-config.yaml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index ab4c79b5a4..f6ac8e9cd7 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -16,7 +16,7 @@ repos: - id: check-toml # Python - repo: https://github.com/psf/black - rev: 23.1.0 + rev: 23.3.0 hooks: - id: black-jupyter - repo: https://github.com/PyCQA/isort @@ -26,7 +26,7 @@ repos: files: \.py$ - repo: https://github.com/charliermarsh/ruff-pre-commit # Ruff version. - rev: v0.0.259 + rev: v0.0.260 hooks: - id: ruff args: ["--fix"] From c5a4d5e929ea36da2f367225ef3e5b993f594de8 Mon Sep 17 00:00:00 2001 From: nahso Date: Wed, 12 Apr 2023 09:13:07 +0800 Subject: [PATCH 040/126] remove redundant code --- deepmd/descriptor/se_atten.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/deepmd/descriptor/se_atten.py b/deepmd/descriptor/se_atten.py index 0a64f3807b..6ec0657f8a 100644 --- a/deepmd/descriptor/se_atten.py +++ b/deepmd/descriptor/se_atten.py @@ -806,8 +806,7 @@ def _filter_lower( xyz_scatter = tf.reshape(tf.slice(inputs_reshape, [0, 0], [-1, 1]), [-1, 1]) assert atype is not None, 'atype must exist!!' type_embedding = tf.cast(type_embedding, self.filter_precision) # ntypes * Y - #xyz_scatter = self._lookup_type_embedding( - # xyz_scatter, atype, type_embedding) + #xyz_scatter = self._lookup_type_embedding(xyz_scatter, atype, type_embedding) if self.compress: raise RuntimeError( "compression of attention descriptor is not supported at the moment" @@ -816,7 +815,6 @@ def _filter_lower( if not is_exclude: with tf.variable_scope(name, reuse=reuse): # with (natom x nei_type_i) x out_size - print(xyz_scatter.shape) xyz_scatter = embedding_net( xyz_scatter, self.filter_neuron, From 9d0d5abcd3c967ed965cf9606219171cb6a039ab Mon Sep 17 00:00:00 2001 From: nahso Date: Fri, 21 Apr 2023 23:01:46 +0800 Subject: [PATCH 041/126] 1 --- deepmd/descriptor/se_atten.py | 205 ++++++++++++++++++++++++---------- deepmd/utils/tabulate.py | 42 ++++++- 2 files changed, 180 insertions(+), 67 deletions(-) diff --git a/deepmd/descriptor/se_atten.py b/deepmd/descriptor/se_atten.py index 6ec0657f8a..c37a03b47f 100644 --- a/deepmd/descriptor/se_atten.py +++ b/deepmd/descriptor/se_atten.py @@ -31,6 +31,12 @@ from deepmd.utils.sess import ( run_sess, ) +from deepmd.utils.tabulate import ( + DPTabulate, +) +from deepmd.utils.graph import ( + get_tensor_by_name_from_graph, +) from .descriptor import ( Descriptor, @@ -283,6 +289,93 @@ def compute_input_stats( self.stat_dict["sumn"] += sumn self.stat_dict["sumr2"] += sumr2 self.stat_dict["suma2"] += suma2 + + def enable_compression( + self, + min_nbor_dist: float, + graph: tf.Graph, + graph_def: tf.GraphDef, + table_extrapolate: float = 5, + table_stride_1: float = 0.01, + table_stride_2: float = 0.1, + check_frequency: int = -1, + suffix: str = "", + ) -> None: + """Reveive the statisitcs (distance, max_nbor_size and env_mat_range) of the training data. + + Parameters + ---------- + min_nbor_dist + The nearest distance between atoms + graph : tf.Graph + The graph of the model + graph_def : tf.GraphDef + The graph_def of the model + table_extrapolate + The scale of model extrapolation + table_stride_1 + The uniform stride of the first table + table_stride_2 + The uniform stride of the second table + check_frequency + The overflow check frequency + suffix : str, optional + The suffix of the scope + """ + # do some checks before the mocel compression process + assert ( + not self.filter_resnet_dt + ), "Model compression error: descriptor resnet_dt must be false!" + for tt in self.exclude_types: + if (tt[0] not in range(self.ntypes)) or (tt[1] not in range(self.ntypes)): + raise RuntimeError( + "exclude types" + + str(tt) + + " must within the number of atomic types " + + str(self.ntypes) + + "!" + ) + if self.ntypes * self.ntypes - len(self.exclude_types) == 0: + raise RuntimeError( + "empty embedding-net are not supported in model compression!" + ) + + for ii in range(len(self.filter_neuron) - 1): + if self.filter_neuron[ii] * 2 != self.filter_neuron[ii + 1]: + raise NotImplementedError( + "Model Compression error: descriptor neuron [%s] is not supported by model compression! " + "The size of the next layer of the neural network must be twice the size of the previous layer." + % ",".join([str(item) for item in self.filter_neuron]) + ) + + self.compress = True + self.table = DPTabulate( + self, + self.filter_neuron, + graph, + graph_def, + self.type_one_side, + self.exclude_types, + self.compress_activation_fn, + suffix=suffix, + ) + self.table_config = [ + table_extrapolate, + table_stride_1, + table_stride_2, + check_frequency, + ] + self.lower, self.upper = self.table.build( + min_nbor_dist, table_extrapolate, table_stride_1, table_stride_2 + ) + + self.davg = get_tensor_by_name_from_graph( + graph, "descrpt_attr%s/t_avg" % suffix + ) + self.dstd = get_tensor_by_name_from_graph( + graph, "descrpt_attr%s/t_std" % suffix + ) + def build( self, @@ -806,70 +899,18 @@ def _filter_lower( xyz_scatter = tf.reshape(tf.slice(inputs_reshape, [0, 0], [-1, 1]), [-1, 1]) assert atype is not None, 'atype must exist!!' type_embedding = tf.cast(type_embedding, self.filter_precision) # ntypes * Y - #xyz_scatter = self._lookup_type_embedding(xyz_scatter, atype, type_embedding) - if self.compress: - raise RuntimeError( - "compression of attention descriptor is not supported at the moment" - ) # natom x 4 x outputs_size if not is_exclude: with tf.variable_scope(name, reuse=reuse): # with (natom x nei_type_i) x out_size - xyz_scatter = embedding_net( - xyz_scatter, - self.filter_neuron, - self.filter_precision, - activation_fn=activation_fn, - resnet_dt=self.filter_resnet_dt, - name_suffix=suffix, - stddev=stddev, - bavg=bavg, - seed=self.seed, - trainable=trainable, - uniform_seed=self.uniform_seed, - initial_variables=self.embedding_net_variables, - mixed_prec=self.mixed_prec) - out_size = xyz_scatter.get_shape().as_list()[-1] - #xyz_scatter = tf.nn.embedding_lookup(xyz_scatter, - # self.nei_type_vec) - #xyz_scatter = tf.reshape(xyz_scatter, [-1, out_size]) # nframes*natoms[0] * nei * out_size - - if self.type_one_side: - embedding_of_embedding_suffix = suffix + "_ebd_of_ebd" - embedding_of_embedding = embedding_net( - type_embedding, - self.filter_neuron, - self.filter_precision, - activation_fn=activation_fn, - resnet_dt=self.filter_resnet_dt, - name_suffix=embedding_of_embedding_suffix, - stddev=stddev, - bavg=bavg, - seed=self.seed, - trainable=trainable, - uniform_seed=self.uniform_seed, - initial_variables=self.embedding_net_variables, - mixed_prec=self.mixed_prec) # ntypes * out_size - nei_embed = tf.nn.embedding_lookup(embedding_of_embedding, self.nei_type_vec) - #nei_embed = tf.reshape(self.nei_embed, [-1, out_size]) # nframes*natoms[0] * nei * out_size - - xyz_scatter = xyz_scatter + nei_embed - else: - type_embedding_shape = type_embedding.get_shape().as_list() - type_embedding_nei = tf.tile(tf.reshape(type_embedding, [1, type_embedding_shape[0], -1]), - [type_embedding_shape[0], 1, 1]) # (ntypes) * ntypes * Y - type_embedding_center = tf.tile(tf.reshape(type_embedding, [type_embedding_shape[0], 1, -1]), - [1, type_embedding_shape[0], 1]) # ntypes * (ntypes) * Y - two_side_type_embedding = tf.concat([type_embedding_nei, type_embedding_center], -1) # ntypes * ntypes * (Y+Y) - two_side_type_embedding = tf.reshape(two_side_type_embedding, [-1, two_side_type_embedding.shape[-1]]) - two_side_type_embedding_suffix = suffix + "_two_side_ebd" - embedding_of_two_side_type_embedding = embedding_net( - two_side_type_embedding, + if not self.compress: + xyz_scatter = embedding_net( + xyz_scatter, self.filter_neuron, self.filter_precision, activation_fn=activation_fn, resnet_dt=self.filter_resnet_dt, - name_suffix=two_side_type_embedding_suffix, + name_suffix=suffix, stddev=stddev, bavg=bavg, seed=self.seed, @@ -877,13 +918,53 @@ def _filter_lower( uniform_seed=self.uniform_seed, initial_variables=self.embedding_net_variables, mixed_prec=self.mixed_prec) - #index_of_two_side = self.nei_type_vec * self.ntypes + tf.tile(atype, [1, self.nnei]) - tmpres1 = self.nei_type_vec * type_embedding_shape[0] - tmpres2 = tf.tile(atype, [self.nnei]) - index_of_two_side = tmpres1 + tmpres2 - two_embd = tf.nn.embedding_lookup(embedding_of_two_side_type_embedding, index_of_two_side) + else: + net = 'filter_net' + info = [ + self.lower[net], + self.upper[net], + self.upper[net] * self.table_config[0], + self.table_config[1], + self.table_config[2], + self.table_config[3], + ] + return op_module.tabulate_fusion_se_a( + tf.cast(self.table.data[net], self.filter_precision), + info, + xyz_scatter, + tf.reshape(inputs_i, [natom, shape_i[1] // 4, 4]), + last_layer_size=outputs_size[-1], + ) + + type_embedding_shape = type_embedding.get_shape().as_list() + type_embedding_nei = tf.tile(tf.reshape(type_embedding, [1, type_embedding_shape[0], -1]), + [type_embedding_shape[0], 1, 1]) # (ntypes) * ntypes * Y + type_embedding_center = tf.tile(tf.reshape(type_embedding, [type_embedding_shape[0], 1, -1]), + [1, type_embedding_shape[0], 1]) # ntypes * (ntypes) * Y + two_side_type_embedding = tf.concat([type_embedding_nei, type_embedding_center], -1) # ntypes * ntypes * (Y+Y) + two_side_type_embedding = tf.reshape(two_side_type_embedding, [-1, two_side_type_embedding.shape[-1]]) + two_side_type_embedding_suffix = suffix + "_two_side_ebd" + embedding_of_two_side_type_embedding = embedding_net( + two_side_type_embedding, + self.filter_neuron, + self.filter_precision, + activation_fn=activation_fn, + resnet_dt=self.filter_resnet_dt, + name_suffix=two_side_type_embedding_suffix, + stddev=stddev, + bavg=bavg, + seed=self.seed, + trainable=trainable, + uniform_seed=self.uniform_seed, + initial_variables=self.embedding_net_variables, + mixed_prec=self.mixed_prec) + #index_of_two_side = self.nei_type_vec * self.ntypes + tf.tile(atype, [1, self.nnei]) + tmpres1 = self.nei_type_vec * type_embedding_shape[0] + tmpres2 = tf.tile(atype, [self.nnei]) + index_of_two_side = tmpres1 + tmpres2 + two_embd = tf.nn.embedding_lookup(embedding_of_two_side_type_embedding, index_of_two_side) - xyz_scatter = xyz_scatter * two_embd + two_embd + #xyz_scatter = xyz_scatter * two_embd + two_embd if (not self.uniform_seed) and (self.seed is not None): self.seed += self.seed_shift diff --git a/deepmd/utils/tabulate.py b/deepmd/utils/tabulate.py index aa842178f7..80d2b6727c 100644 --- a/deepmd/utils/tabulate.py +++ b/deepmd/utils/tabulate.py @@ -175,7 +175,25 @@ def build( """ # tabulate range [lower, upper] with stride0 'stride0' lower, upper = self._get_env_mat_range(min_nbor_dist) - if isinstance(self.descrpt, deepmd.descriptor.DescrptSeA): + if isinstance(self.descrpt, deepmd.descriptor.DescrptSeAtten): + uu = np.min(upper) + ll = np.min(lower) + print('lower = ', ll, ', upper = ', uu) + xx = np.arange(ll, uu, stride0, dtype=self.data_type) + xx = np.append( + xx, + np.arange(uu, extrapolate * uu, stride1, dtype=self.data_type), + ) + xx = np.append( + xx, np.array([extrapolate * uu], dtype=self.data_type) + ) + nspline = ( + (uu - ll) / stride0 + (extrapolate * uu - uu) / stride1 + ).astype(int) + self._build_lower( + 'filter_net', xx, 0, uu, ll, stride0, stride1, extrapolate, nspline + ) + elif isinstance(self.descrpt, deepmd.descriptor.DescrptSeA): for ii in range(self.table_size): if (self.type_one_side and not self._all_excluded(ii)) or ( not self.type_one_side @@ -403,7 +421,12 @@ def _get_bias(self): bias = {} for layer in range(1, self.layer_size + 1): bias["layer_" + str(layer)] = [] - if isinstance(self.descrpt, deepmd.descriptor.DescrptSeA): + if isinstance(self.descrpt, deepmd.descriptor.DescrptSeAtten): + node = self.embedding_net_nodes[ + f"filter_type_all/bias_{layer}" + ] + bias["layer_" + str(layer)].append(tf.make_ndarray(node)) + elif isinstance(self.descrpt, deepmd.descriptor.DescrptSeA): if self.type_one_side: for ii in range(0, self.ntypes): if not self._all_excluded(ii): @@ -462,7 +485,12 @@ def _get_matrix(self): matrix = {} for layer in range(1, self.layer_size + 1): matrix["layer_" + str(layer)] = [] - if isinstance(self.descrpt, deepmd.descriptor.DescrptSeA): + if isinstance(self.descrpt, deepmd.descriptor.DescrptSeAtten): + node = self.embedding_net_nodes[ + f"filter_type_all/matrix_{layer}" + ] + matrix["layer_" + str(layer)].append(tf.make_ndarray(node)) + elif isinstance(self.descrpt, deepmd.descriptor.DescrptSeA): if self.type_one_side: for ii in range(0, self.ntypes): if not self._all_excluded(ii): @@ -661,7 +689,9 @@ def _spline5_switch(self, xx, rmin, rmax): def _get_layer_size(self): layer_size = 0 - if isinstance(self.descrpt, deepmd.descriptor.DescrptSeA): + if isinstance(self.descrpt, deepmd.descriptor.DescrptSeAtten): + layer_size = len(self.embedding_net_nodes) // 2 + elif isinstance(self.descrpt, deepmd.descriptor.DescrptSeA): layer_size = len(self.embedding_net_nodes) // ( (self.ntypes * self.ntypes - len(self.exclude_types)) * 2 ) @@ -711,7 +741,9 @@ def _all_excluded(self, ii: int) -> bool: def _get_table_size(self): table_size = 0 - if isinstance(self.descrpt, deepmd.descriptor.DescrptSeA): + if isinstance(self.descrpt, deepmd.descriptor.DescrptSeAtten): + table_size = self.ntypes + elif isinstance(self.descrpt, deepmd.descriptor.DescrptSeA): table_size = self.ntypes * self.ntypes if self.type_one_side: table_size = self.ntypes From 7ca8ea842714f62a97e71abacf340364c37e0497 Mon Sep 17 00:00:00 2001 From: nahso Date: Sat, 22 Apr 2023 14:45:43 +0800 Subject: [PATCH 042/126] se_a version --- deepmd/descriptor/se_atten.py | 2 +- deepmd/utils/tabulate.py | 5 ++--- 2 files changed, 3 insertions(+), 4 deletions(-) diff --git a/deepmd/descriptor/se_atten.py b/deepmd/descriptor/se_atten.py index c37a03b47f..a75a89bd40 100644 --- a/deepmd/descriptor/se_atten.py +++ b/deepmd/descriptor/se_atten.py @@ -354,7 +354,7 @@ def enable_compression( self.filter_neuron, graph, graph_def, - self.type_one_side, + True, self.exclude_types, self.compress_activation_fn, suffix=suffix, diff --git a/deepmd/utils/tabulate.py b/deepmd/utils/tabulate.py index 80d2b6727c..85901b9bb7 100644 --- a/deepmd/utils/tabulate.py +++ b/deepmd/utils/tabulate.py @@ -176,9 +176,8 @@ def build( # tabulate range [lower, upper] with stride0 'stride0' lower, upper = self._get_env_mat_range(min_nbor_dist) if isinstance(self.descrpt, deepmd.descriptor.DescrptSeAtten): - uu = np.min(upper) + uu = np.max(upper) ll = np.min(lower) - print('lower = ', ll, ', upper = ', uu) xx = np.arange(ll, uu, stride0, dtype=self.data_type) xx = np.append( xx, @@ -742,7 +741,7 @@ def _all_excluded(self, ii: int) -> bool: def _get_table_size(self): table_size = 0 if isinstance(self.descrpt, deepmd.descriptor.DescrptSeAtten): - table_size = self.ntypes + table_size = 1 elif isinstance(self.descrpt, deepmd.descriptor.DescrptSeA): table_size = self.ntypes * self.ntypes if self.type_one_side: From 5ad6c90505d572539399b28e3d2dfa9c66017fc3 Mon Sep 17 00:00:00 2001 From: nahso Date: Sat, 22 Apr 2023 15:25:25 +0800 Subject: [PATCH 043/126] op header --- deepmd/descriptor/se_atten.py | 22 ++- source/op/_tabulate_grad.py | 38 ++++ source/op/tabulate_multi_device.cc | 285 +++++++++++++++++++++++++++++ 3 files changed, 336 insertions(+), 9 deletions(-) diff --git a/deepmd/descriptor/se_atten.py b/deepmd/descriptor/se_atten.py index a75a89bd40..ee8c251775 100644 --- a/deepmd/descriptor/se_atten.py +++ b/deepmd/descriptor/se_atten.py @@ -928,13 +928,6 @@ def _filter_lower( self.table_config[2], self.table_config[3], ] - return op_module.tabulate_fusion_se_a( - tf.cast(self.table.data[net], self.filter_precision), - info, - xyz_scatter, - tf.reshape(inputs_i, [natom, shape_i[1] // 4, 4]), - last_layer_size=outputs_size[-1], - ) type_embedding_shape = type_embedding.get_shape().as_list() type_embedding_nei = tf.tile(tf.reshape(type_embedding, [1, type_embedding_shape[0], -1]), @@ -962,9 +955,20 @@ def _filter_lower( tmpres1 = self.nei_type_vec * type_embedding_shape[0] tmpres2 = tf.tile(atype, [self.nnei]) index_of_two_side = tmpres1 + tmpres2 - two_embd = tf.nn.embedding_lookup(embedding_of_two_side_type_embedding, index_of_two_side) + # two_embd = tf.nn.embedding_lookup(embedding_of_two_side_type_embedding, index_of_two_side) - #xyz_scatter = xyz_scatter * two_embd + two_embd + if not self.compress: + #xyz_scatter = xyz_scatter * two_embd + two_embd + pass + else: + return op_module.tabulate_fusion_se_atten( + tf.cast(self.table.data[net], self.filter_precision), + info, + xyz_scatter, + tf.reshape(inputs_i, [natom, shape_i[1] // 4, 4]), + tf.zeros([1, 1], dtype=tf.float64), + last_layer_size=outputs_size[-1], + ) if (not self.uniform_seed) and (self.seed is not None): self.seed += self.seed_shift diff --git a/source/op/_tabulate_grad.py b/source/op/_tabulate_grad.py index 785146cfcc..7c808be9e8 100644 --- a/source/op/_tabulate_grad.py +++ b/source/op/_tabulate_grad.py @@ -30,6 +30,44 @@ def _tabulate_fusion_se_a_grad_grad_cc(op, dy, dy_): return [None, None, None, None, dz_dy, None] +@ops.RegisterGradient("TabulateFusionSeAtten") +def _tabulate_fusion_se_atten_grad_cc(op, dy): + dy_dx, dy_df, dy_dtwo = op_module.tabulate_fusion_se_atten_grad( + op.inputs[0], # table + op.inputs[1], # table_info + op.inputs[2], # em_x + op.inputs[3], # em + op.inputs[4], # two_embed + dy, # dy + op.outputs[0] # descriptor + ) + return [None, # table + None, # table_info + dy_dx, # em_x + dy_df, # em + None] # two_embed + +@ops.RegisterGradient("TabulateFusionSeAttenGrad") +def _tabulate_fusion_se_atten_grad_grad_cc(op, dy, dy_, dy_dtwo): + dz_dy = op_module.tabulate_fusion_se_atten_grad_grad( + op.inputs[0], # table + op.inputs[1], # table_info + op.inputs[2], # em_x + op.inputs[3], # em + op.inputs[4], # two_embed + dy, # dz_dy_dem_x + dy_, # dz_dy_dem + op.inputs[6] # descriptor + ) + return [None, # table + None, # table_info + None, # em_x + None, # em + None, # two_embed + dz_dy, # dy + None] # descriptor + + @ops.RegisterGradient("TabulateFusionSeT") def _tabulate_fusion_se_t_grad_cc(op, dy): dy_dx, dy_df = op_module.tabulate_fusion_se_t_grad( diff --git a/source/op/tabulate_multi_device.cc b/source/op/tabulate_multi_device.cc index 2914b75c64..aac165ca09 100644 --- a/source/op/tabulate_multi_device.cc +++ b/source/op/tabulate_multi_device.cc @@ -63,6 +63,42 @@ REGISTER_OP("TabulateFusionSeAGradGrad") .Input("descriptor: T") .Output("dz_dy: T"); +REGISTER_OP("TabulateFusionSeAtten") + .Attr("T: {float, double} = DT_DOUBLE") + .Input("table: T") + .Input("table_info: T") + .Input("em_x: T") + .Input("em: T") + .Input("two_embed: T") + .Attr("last_layer_size: int") + .Output("descriptor: T"); + +REGISTER_OP("TabulateFusionSeAttenGrad") + .Attr("T: {float, double} = DT_DOUBLE") + .Input("table: T") + .Input("table_info: T") + .Input("em_x: T") + .Input("em: T") + .Input("two_embed: T") + .Input("dy: T") + .Input("descriptor: T") + .Output("dy_dem_x: T") + .Output("dy_dem: T") + .Output("dy_dtwo: T"); + +REGISTER_OP("TabulateFusionSeAttenGradGrad") + .Attr("T: {float, double}") + .Input("table: T") + .Input("table_info: T") + .Input("em_x: T") + .Input("em: T") + .Input("two_embed: T") + .Input("dz_dy_dem_x: T") + .Input("dz_dy_dem: T") + .Input("descriptor: T") + .Output("dz_dy: T"); + + REGISTER_OP("TabulateFusionSeT") .Attr("T: {float, double} = DT_DOUBLE") .Input("table: T") @@ -321,6 +357,230 @@ class TabulateFusionSeAGradGradOp : public OpKernel { std::string device; }; +template +class TabulateFusionSeAttenOp : public OpKernel { + public: + explicit TabulateFusionSeAttenOp(OpKernelConstruction* context) + : OpKernel(context) { + OP_REQUIRES_OK(context, + context->GetAttr("last_layer_size", &last_layer_size)); + } + void Compute(OpKernelContext* context) override { + deepmd::safe_compute( + context, [this](OpKernelContext* context) { this->_Compute(context); }); + } + + void _Compute(OpKernelContext* context) { + // Grab the input tensor + int context_input_index = 0; + const Tensor& table_tensor = context->input(context_input_index++); + const Tensor& table_info_tensor = context->input(context_input_index++); + const Tensor& em_x_tensor = context->input(context_input_index++); + const Tensor& em_tensor = context->input(context_input_index++); + const Tensor& two_embed_tensor = context->input(context_input_index++); + // set size of the sample + OP_REQUIRES(context, (table_tensor.shape().dims() == 2), + errors::InvalidArgument("Dim of table should be 2")); + OP_REQUIRES(context, (em_x_tensor.shape().dims() == 2), + errors::InvalidArgument("Dim of input should be 2")); + OP_REQUIRES(context, (em_tensor.shape().dims() == 3), + errors::InvalidArgument("Dim of input should be 3")); + OP_REQUIRES(context, (two_embed_tensor.shape().dims() == 2), + errors::InvalidArgument("Dim of input should be 2")); + TensorShape descriptor_shape; + descriptor_shape.AddDim(em_tensor.shape().dim_size(0)); + descriptor_shape.AddDim(4); // TODO: be careful here; + descriptor_shape.AddDim(last_layer_size); + int context_output_index = 0; + Tensor* descriptor_tensor = NULL; + OP_REQUIRES_OK(context, context->allocate_output(context_output_index++, + descriptor_shape, + &descriptor_tensor)); + DeviceFunctor()(device, context->eigen_device()); + // flat the tensors + FPTYPE* descriptor = descriptor_tensor->flat().data(); + const FPTYPE* table = table_tensor.flat().data(); + const FPTYPE* table_info = table_info_tensor.flat().data(); + const FPTYPE* em_x = em_x_tensor.flat().data(); + const FPTYPE* em = em_tensor.flat().data(); + const FPTYPE* two_embed = two_embed_tensor.flat().data(); + const int nloc = em_tensor.shape().dim_size(0); + const int nnei = em_tensor.shape().dim_size(1); + + if (device == "GPU") { +#if GOOGLE_CUDA + //deepmd::tabulate_fusion_se_atten_gpu_cuda(descriptor, table, table_info, em_x, two_embed, + //em, nloc, nnei, last_layer_size); +#endif // GOOGLE_CUDA + +#if TENSORFLOW_USE_ROCM + //deepmd::tabulate_fusion_se_atten_gpu_rocm(descriptor, table, table_info, em_x, + // em, nloc, nnei, last_layer_size); +#endif // TENSORFLOW_USE_ROCM + } else if (device == "CPU") { + //deepmd::tabulate_fusion_se_atten_cpu(descriptor, table, table_info, em_x, em, two_embed, + // nloc, nnei, last_layer_size); + deepmd::tabulate_fusion_se_a_cpu(descriptor, table, table_info, em_x, em, + nloc, nnei, last_layer_size); + } + } + + private: + int last_layer_size; + std::string device; +}; + +template +class TabulateFusionSeAttenGradOp : public OpKernel { + public: + explicit TabulateFusionSeAttenGradOp(OpKernelConstruction* context) + : OpKernel(context) {} + void Compute(OpKernelContext* context) override { + deepmd::safe_compute( + context, [this](OpKernelContext* context) { this->_Compute(context); }); + } + + void _Compute(OpKernelContext* context) { + // Grab the input tensor + int context_input_index = 0; + const Tensor& table_tensor = context->input(context_input_index++); + const Tensor& table_info_tensor = context->input(context_input_index++); + const Tensor& em_x_tensor = context->input(context_input_index++); + const Tensor& em_tensor = context->input(context_input_index++); + const Tensor& two_embed_tensor = context->input(context_input_index++); + const Tensor& dy_tensor = context->input(context_input_index++); + const Tensor& descriptor_tensor = context->input(context_input_index++); + + // set size of the sample + OP_REQUIRES(context, (dy_tensor.shape().dims() == 3), + errors::InvalidArgument("Dim of table should be 3")); + int context_output_index = 0; + Tensor* dy_dem_x_tensor = NULL; + OP_REQUIRES_OK(context, context->allocate_output(context_output_index++, + em_x_tensor.shape(), + &dy_dem_x_tensor)); + Tensor* dy_dem_tensor = NULL; + OP_REQUIRES_OK(context, + context->allocate_output(context_output_index++, + em_tensor.shape(), &dy_dem_tensor)); + Tensor* dy_dtwo_tensor = NULL; + OP_REQUIRES_OK(context, + context->allocate_output(context_output_index++, + two_embed_tensor.shape(), &dy_dtwo_tensor)); + DeviceFunctor()(device, context->eigen_device()); + + // flat the tensors + FPTYPE* dy_dem_x = dy_dem_x_tensor->flat().data(); + FPTYPE* dy_dem = dy_dem_tensor->flat().data(); + FPTYPE* dy_dtwo = dy_dtwo_tensor->flat().data(); + const FPTYPE* descriptor = descriptor_tensor.flat().data(); + const FPTYPE* table = table_tensor.flat().data(); + const FPTYPE* table_info = table_info_tensor.flat().data(); + const FPTYPE* em_x = em_x_tensor.flat().data(); + const FPTYPE* em = em_tensor.flat().data(); + const FPTYPE* two_embed = two_embed_tensor.flat().data(); + const FPTYPE* dy = dy_tensor.flat().data(); + const int nloc = em_tensor.shape().dim_size(0); + const int nnei = em_tensor.shape().dim_size(1); + const int last_layer_size = descriptor_tensor.shape().dim_size(2); + + if (device == "GPU") { +#if GOOGLE_CUDA + //deepmd::tabulate_fusion_se_atten_grad_gpu_cuda(dy_dem_x, dy_dem, table, + // table_info, em_x, em, dy, nloc, + // nnei, last_layer_size); +#endif // GOOGLE_CUDA + +#if TENSORFLOW_USE_ROCM + //deepmd::tabulate_fusion_se_atten_grad_gpu_rocm(dy_dem_x, dy_dem, table, + // table_info, em_x, em, dy, nloc, + // nnei, last_layer_size); +#endif // TENSORFLOW_USE_ROCM + } else if (device == "CPU") { + //deepmd::tabulate_fusion_se_atten_grad_cpu(dy_dem_x, dy_dem, dy_dtwo, table, table_info, + // em_x, em, two_embed, dy, nloc, nnei, + // last_layer_size); + deepmd::tabulate_fusion_se_a_grad_cpu(dy_dem_x, dy_dem, table, table_info, + em_x, em, dy, nloc, nnei, + last_layer_size); + } + } + + private: + std::string device; +}; + +template +class TabulateFusionSeAttenGradGradOp : public OpKernel { + public: + explicit TabulateFusionSeAttenGradGradOp(OpKernelConstruction* context) + : OpKernel(context) {} + void Compute(OpKernelContext* context) override { + // Grab the input tensor + int context_input_index = 0; + const Tensor& table_tensor = context->input(context_input_index++); + const Tensor& table_info_tensor = context->input(context_input_index++); + const Tensor& em_x_tensor = context->input(context_input_index++); + const Tensor& em_tensor = context->input(context_input_index++); + const Tensor& two_embed_tensor = context->input(context_input_index++); + const Tensor& dz_dy_dem_x_tensor = context->input(context_input_index++); + const Tensor& dz_dy_dem_tensor = context->input(context_input_index++); + const Tensor& descriptor_tensor = context->input(context_input_index++); + // set size of the sample + OP_REQUIRES(context, (dz_dy_dem_x_tensor.shape().dims() == 2), + errors::InvalidArgument("Dim of input should be 2")); + OP_REQUIRES(context, (dz_dy_dem_tensor.shape().dims() == 3), + errors::InvalidArgument("Dim of input should be 3")); + int context_output_index = 0; + Tensor* dz_dy_tensor = NULL; + OP_REQUIRES_OK(context, context->allocate_output(context_output_index++, + descriptor_tensor.shape(), + &dz_dy_tensor)); + DeviceFunctor()(device, context->eigen_device()); + + // flat the tensors + FPTYPE* dz_dy = dz_dy_tensor->flat().data(); + const FPTYPE* table = table_tensor.flat().data(); + const FPTYPE* table_info = table_info_tensor.flat().data(); + const FPTYPE* em_x = em_x_tensor.flat().data(); + const FPTYPE* em = em_tensor.flat().data(); + const FPTYPE* two_embed = two_embed_tensor.flat().data(); + const FPTYPE* dz_dy_dem_x = dz_dy_dem_x_tensor.flat().data(); + const FPTYPE* dz_dy_dem = dz_dy_dem_tensor.flat().data(); + const int nloc = em_tensor.shape().dim_size(0); + const int nnei = em_tensor.shape().dim_size(1); + const int last_layer_size = descriptor_tensor.shape().dim_size(2); + + if (device == "GPU") { +#if GOOGLE_CUDA + //deepmd::tabulate_fusion_se_atten_grad_grad_gpu_cuda( + // dz_dy, table, table_info, em_x, em, dz_dy_dem_x, dz_dy_dem, nloc, + // nnei, last_layer_size); +#endif // GOOGLE_CUDA +#if TENSORFLOW_USE_ROCM + //deepmd::tabulate_fusion_se_atten_grad_grad_gpu_rocm( + // dz_dy, table, table_info, em_x, em, dz_dy_dem_x, dz_dy_dem, nloc, + // nnei, last_layer_size); +#endif // TENSORFLOW_USE_ROCM + OP_REQUIRES(context, (last_layer_size <= 1024), + errors::InvalidArgument( + "In the process of model compression, the size of the " + "last layer of embedding net must be less than 1024!")); + } else if (device == "CPU") { + deepmd::tabulate_fusion_se_a_grad_grad_cpu(dz_dy, table, table_info, em_x, + em, dz_dy_dem_x, dz_dy_dem, + nloc, nnei, last_layer_size); + //deepmd::tabulate_fusion_se_atten_grad_grad_cpu(dz_dy, table, table_info, em_x, + // em, two_embed, dz_dy_dem_x, dz_dy_dem, + // nloc, nnei, last_layer_size); + } + } + + private: + std::string device; +}; + + template class TabulateFusionSeTOp : public OpKernel { public: @@ -725,6 +985,16 @@ class TabulateFusionSeRGradGradOp : public OpKernel { .Device(DEVICE_CPU) \ .TypeConstraint("T"), \ TabulateFusionSeAGradGradOp); \ + REGISTER_KERNEL_BUILDER( \ + Name("TabulateFusionSeAtten").Device(DEVICE_CPU).TypeConstraint("T"), \ + TabulateFusionSeAttenOp); \ + REGISTER_KERNEL_BUILDER( \ + Name("TabulateFusionSeAttenGrad").Device(DEVICE_CPU).TypeConstraint("T"), \ + TabulateFusionSeAttenGradOp); \ + REGISTER_KERNEL_BUILDER(Name("TabulateFusionSeAttenGradGrad") \ + .Device(DEVICE_CPU) \ + .TypeConstraint("T"), \ + TabulateFusionSeAttenGradGradOp); \ REGISTER_KERNEL_BUILDER( \ Name("TabulateFusionSeT").Device(DEVICE_CPU).TypeConstraint("T"), \ TabulateFusionSeTOp); \ @@ -780,6 +1050,21 @@ REGISTER_CPU(double); .TypeConstraint("T") \ .HostMemory("table_info"), \ TabulateFusionSeAGradGradOp); \ + REGISTER_KERNEL_BUILDER(Name("TabulateFusionSeAtten") \ + .Device(DEVICE_GPU) \ + .TypeConstraint("T") \ + .HostMemory("table_info"), \ + TabulateFusionSeAttenOp); \ + REGISTER_KERNEL_BUILDER(Name("TabulateFusionSeAttenGrad") \ + .Device(DEVICE_GPU) \ + .TypeConstraint("T") \ + .HostMemory("table_info"), \ + TabulateFusionSeAttenGradOp); \ + REGISTER_KERNEL_BUILDER(Name("TabulateFusionSeAttenGradGrad") \ + .Device(DEVICE_GPU) \ + .TypeConstraint("T") \ + .HostMemory("table_info"), \ + TabulateFusionSeAttenGradGradOp); \ REGISTER_KERNEL_BUILDER(Name("TabulateFusionSeT") \ .Device(DEVICE_GPU) \ .TypeConstraint("T") \ From e805fa9947fb24d5e681487dd8246648e5d45383 Mon Sep 17 00:00:00 2001 From: nahso Date: Mon, 24 Apr 2023 21:42:19 +0800 Subject: [PATCH 044/126] tabulate done. --- deepmd/descriptor/se_atten.py | 144 +++++++++++--- source/lib/include/tabulate.h | 37 ++++ source/lib/src/tabulate.cc | 304 +++++++++++++++++++++++++++++ source/op/_tabulate_grad.py | 5 +- source/op/tabulate_multi_device.cc | 32 +-- 5 files changed, 480 insertions(+), 42 deletions(-) diff --git a/deepmd/descriptor/se_atten.py b/deepmd/descriptor/se_atten.py index ee8c251775..44a6794357 100644 --- a/deepmd/descriptor/se_atten.py +++ b/deepmd/descriptor/se_atten.py @@ -12,6 +12,7 @@ from deepmd.common import ( cast_precision, + get_np_precision ) from deepmd.env import ( GLOBAL_NP_FLOAT_PRECISION, @@ -23,6 +24,7 @@ ) from deepmd.utils.graph import ( get_attention_layer_variables_from_graph_def, + get_pattern_nodes_from_graph_def ) from deepmd.utils.network import ( embedding_net, @@ -151,6 +153,7 @@ def __init__( self.attn_layer = attn_layer self.attn_mask = attn_mask self.attn_dotr = attn_dotr + self.filter_np_precision = get_np_precision(precision) # descrpt config self.sel_all_a = [sel] @@ -369,6 +372,22 @@ def enable_compression( min_nbor_dist, table_extrapolate, table_stride_1, table_stride_2 ) + self.layer_size = self.table.layer_size + # type_embedding = get_tensor_by_name_from_graph(graph, 't_typeebd') + # type_embedding = type_embedding.astype(self.filter_np_precision) + # type_embedding_shape = type_embedding.shape + # type_embedding_nei = np.tile(np.reshape(type_embedding, [1, type_embedding_shape[0], -1]), + # [type_embedding_shape[0], 1, 1]) # (ntypes) * ntypes * Y + # type_embedding_center = np.tile(np.reshape(type_embedding, [type_embedding_shape[0], 1, -1]), + # [1, type_embedding_shape[0], 1]) # ntypes * (ntypes) * Y + # two_side_type_embedding = np.concatenate([type_embedding_nei, type_embedding_center], -1) # ntypes * ntypes * (Y+Y) + # two_side_type_embedding = np.reshape(two_side_type_embedding, [-1, two_side_type_embedding.shape[-1]]) + # self.final_type_embedding = two_side_type_embedding + self.final_type_embedding = self._get_two_embed(graph) + self.matrix = self._get_two_side_embedding_variable(graph_def, 'matrix') + self.bias = self._get_two_side_embedding_variable(graph_def, 'bias') + self.two_embd = self._make_data(self.final_type_embedding) + self.davg = get_tensor_by_name_from_graph( graph, "descrpt_attr%s/t_avg" % suffix ) @@ -376,6 +395,72 @@ def enable_compression( graph, "descrpt_attr%s/t_std" % suffix ) + def _get_two_embed(self, graph): + type_embedding = get_tensor_by_name_from_graph(graph, 't_typeebd') + type_embedding = type_embedding.astype(self.filter_np_precision) + type_embedding_shape = type_embedding.shape + type_embedding_nei = np.tile(np.reshape(type_embedding, [1, type_embedding_shape[0], -1]), + [type_embedding_shape[0], 1, 1]) # (ntypes) * ntypes * Y + type_embedding_center = np.tile(np.reshape(type_embedding, [type_embedding_shape[0], 1, -1]), + [1, type_embedding_shape[0], 1]) # ntypes * (ntypes) * Y + two_side_type_embedding = np.concatenate([type_embedding_nei, type_embedding_center], -1) # ntypes * ntypes * (Y+Y) + two_side_type_embedding = np.reshape(two_side_type_embedding, [-1, two_side_type_embedding.shape[-1]]) + return two_side_type_embedding + + + def _get_two_side_embedding_variable(self, graph_def, varialbe_name): + ret = {} + for i in range(1, self.layer_size + 1): + node = get_pattern_nodes_from_graph_def(graph_def, + f'filter_type_all/{varialbe_name}_{i}_two_side_ebd') \ + [f'filter_type_all/{varialbe_name}_{i}_two_side_ebd'] + ret['layer_' + str(i)] = node + return ret + + def _layer_0(self, x, w, b): + return self.filter_activation_fn(tf.matmul(x, w) + b) + + def _layer_1(self, x, w, b): + t = tf.concat([x, x], axis=1) + return t, self.filter_activation_fn(tf.matmul(x, w) + b) + t + + def _make_data(self, xx): + with tf.Session() as sess: + # xx = tf.reshape(xx, [xx.size, -1]) + # pdb.set_trace() + for layer in range(self.layer_size): + if layer == 0: + if self.filter_neuron[0] == 1: + yy = ( + self._layer_0( + xx, + self.matrix["layer_" + str(layer + 1)], + self.bias["layer_" + str(layer + 1)], + ) + + xx + ) + elif self.filter_neuron[0] == 2: + tt, yy = self._layer_1( + xx, + self.matrix["layer_" + str(layer + 1)], + self.bias["layer_" + str(layer + 1)], + ) + else: + yy = self._layer_0( + xx, + self.matrix["layer_" + str(layer + 1)], + self.bias["layer_" + str(layer + 1)], + ) + else: + tt, zz = self._layer_1( + yy, + self.matrix["layer_" + str(layer + 1)], + self.bias["layer_" + str(layer + 1)], + ) + yy = zz + vv = sess.run(zz) + # vv = zz.eval() + return vv def build( self, @@ -930,43 +1015,54 @@ def _filter_lower( ] type_embedding_shape = type_embedding.get_shape().as_list() - type_embedding_nei = tf.tile(tf.reshape(type_embedding, [1, type_embedding_shape[0], -1]), - [type_embedding_shape[0], 1, 1]) # (ntypes) * ntypes * Y - type_embedding_center = tf.tile(tf.reshape(type_embedding, [type_embedding_shape[0], 1, -1]), - [1, type_embedding_shape[0], 1]) # ntypes * (ntypes) * Y - two_side_type_embedding = tf.concat([type_embedding_nei, type_embedding_center], -1) # ntypes * ntypes * (Y+Y) - two_side_type_embedding = tf.reshape(two_side_type_embedding, [-1, two_side_type_embedding.shape[-1]]) - two_side_type_embedding_suffix = suffix + "_two_side_ebd" - embedding_of_two_side_type_embedding = embedding_net( - two_side_type_embedding, - self.filter_neuron, - self.filter_precision, - activation_fn=activation_fn, - resnet_dt=self.filter_resnet_dt, - name_suffix=two_side_type_embedding_suffix, - stddev=stddev, - bavg=bavg, - seed=self.seed, - trainable=trainable, - uniform_seed=self.uniform_seed, - initial_variables=self.embedding_net_variables, - mixed_prec=self.mixed_prec) #index_of_two_side = self.nei_type_vec * self.ntypes + tf.tile(atype, [1, self.nnei]) tmpres1 = self.nei_type_vec * type_embedding_shape[0] tmpres2 = tf.tile(atype, [self.nnei]) index_of_two_side = tmpres1 + tmpres2 + + if self.compress: + two_embd = tf.nn.embedding_lookup(self.two_embd, index_of_two_side) + else: + type_embedding_nei = tf.tile(tf.reshape(type_embedding, [1, type_embedding_shape[0], -1]), + [type_embedding_shape[0], 1, 1]) # (ntypes) * ntypes * Y + type_embedding_center = tf.tile(tf.reshape(type_embedding, [type_embedding_shape[0], 1, -1]), + [1, type_embedding_shape[0], 1]) # ntypes * (ntypes) * Y + two_side_type_embedding = tf.concat([type_embedding_nei, type_embedding_center], -1) # ntypes * ntypes * (Y+Y) + two_side_type_embedding = tf.reshape(two_side_type_embedding, [-1, two_side_type_embedding.shape[-1]]) + two_side_type_embedding_suffix = suffix + "_two_side_ebd" + embedding_of_two_side_type_embedding = embedding_net( + two_side_type_embedding, + self.filter_neuron, + self.filter_precision, + activation_fn=activation_fn, + resnet_dt=self.filter_resnet_dt, + name_suffix=two_side_type_embedding_suffix, + stddev=stddev, + bavg=bavg, + seed=self.seed, + trainable=trainable, + uniform_seed=self.uniform_seed, + initial_variables=self.embedding_net_variables, + mixed_prec=self.mixed_prec) + two_embd = tf.nn.embedding_lookup(embedding_of_two_side_type_embedding, index_of_two_side) # two_embd = tf.nn.embedding_lookup(embedding_of_two_side_type_embedding, index_of_two_side) if not self.compress: - #xyz_scatter = xyz_scatter * two_embd + two_embd - pass + xyz_scatter = xyz_scatter * two_embd + two_embd else: + # return op_module.tabulate_fusion_se_a( + # tf.cast(self.table.data[net], self.filter_precision), + # info, + # xyz_scatter, + # tf.reshape(inputs_i, [natom, shape_i[1] // 4, 4]), + # last_layer_size=outputs_size[-1], + # ) return op_module.tabulate_fusion_se_atten( tf.cast(self.table.data[net], self.filter_precision), info, xyz_scatter, tf.reshape(inputs_i, [natom, shape_i[1] // 4, 4]), - tf.zeros([1, 1], dtype=tf.float64), + two_embd, last_layer_size=outputs_size[-1], ) diff --git a/source/lib/include/tabulate.h b/source/lib/include/tabulate.h index 3ad79f3cb1..3a7139f099 100644 --- a/source/lib/include/tabulate.h +++ b/source/lib/include/tabulate.h @@ -36,6 +36,43 @@ void tabulate_fusion_se_a_grad_grad_cpu(FPTYPE* dz_dy, const int nnei, const int last_layer_size); +template +void tabulate_fusion_se_atten_cpu(FPTYPE* out, + const FPTYPE* table, + const FPTYPE* table_info, + const FPTYPE* em_x, + const FPTYPE* em, + const FPTYPE* two_embed, + const int nloc, + const int nnei, + const int last_layer_size); + +template +void tabulate_fusion_se_atten_grad_cpu(FPTYPE* dy_dem_x, + FPTYPE* dy_dem, + const FPTYPE* table, + const FPTYPE* table_info, + const FPTYPE* em_x, + const FPTYPE* em, + const FPTYPE* two_embed, + const FPTYPE* dy, + const int nloc, + const int nnei, + const int last_layer_size); + +template +void tabulate_fusion_se_atten_grad_grad_cpu(FPTYPE* dz_dy, + const FPTYPE* table, + const FPTYPE* table_info, + const FPTYPE* em_x, + const FPTYPE* em, + const FPTYPE* two_embed, + const FPTYPE* dz_dy_dem_x, + const FPTYPE* dz_dy_dem, + const int nloc, + const int nnei, + const int last_layer_size); + template void tabulate_fusion_se_t_cpu(FPTYPE* out, const FPTYPE* table, diff --git a/source/lib/src/tabulate.cc b/source/lib/src/tabulate.cc index dcc17e07e9..a452e6eb17 100644 --- a/source/lib/src/tabulate.cc +++ b/source/lib/src/tabulate.cc @@ -301,6 +301,240 @@ void deepmd::tabulate_fusion_se_a_grad_grad_cpu(FPTYPE* dz_dy, } } +template +void deepmd::tabulate_fusion_se_atten_cpu(FPTYPE* out, + const FPTYPE* table, + const FPTYPE* table_info, + const FPTYPE* em_x, + const FPTYPE* em, + const FPTYPE* two_embed, + const int nloc, + const int nnei, + const int last_layer_size) { + memset(out, 0, sizeof(FPTYPE) * nloc * 4 * last_layer_size); + const FPTYPE lower = table_info[0]; + const FPTYPE upper = table_info[1]; + const FPTYPE _max = table_info[2]; + const FPTYPE stride0 = table_info[3]; + const FPTYPE stride1 = table_info[4]; +// for every atom, execute a small manual gemm ~ +// FPTYPE * res = new FPTYPE[4 * last_layer_size]; +#pragma omp parallel for + for (int ii = 0; ii < nloc; ii++) { + FPTYPE ll[4] = {0}; + FPTYPE ago = em_x[ii * nnei + nnei - 1]; + bool unloop = false; + for (int jj = 0; jj < nnei; jj++) { + ll[0] = em[ii * nnei * 4 + jj * 4 + 0]; + ll[1] = em[ii * nnei * 4 + jj * 4 + 1]; + ll[2] = em[ii * nnei * 4 + jj * 4 + 2]; + ll[3] = em[ii * nnei * 4 + jj * 4 + 3]; + FPTYPE xx = em_x[ii * nnei + jj]; + if (ago == xx) { + unloop = true; + } + int table_idx = 0; + locate_xx(lower, upper, _max, stride0, stride1, xx, table_idx); + for (int kk = 0; kk < last_layer_size; kk++) { + FPTYPE a0 = table[table_idx * last_layer_size * 6 + 6 * kk + 0]; + FPTYPE a1 = table[table_idx * last_layer_size * 6 + 6 * kk + 1]; + FPTYPE a2 = table[table_idx * last_layer_size * 6 + 6 * kk + 2]; + FPTYPE a3 = table[table_idx * last_layer_size * 6 + 6 * kk + 3]; + FPTYPE a4 = table[table_idx * last_layer_size * 6 + 6 * kk + 4]; + FPTYPE a5 = table[table_idx * last_layer_size * 6 + 6 * kk + 5]; + FPTYPE var = + a0 + (a1 + (a2 + (a3 + (a4 + a5 * xx) * xx) * xx) * xx) * xx; + FPTYPE t = two_embed[ii * nnei*last_layer_size + jj * last_layer_size + kk]; + var = var * t + t; + if (unloop) { + out[ii * last_layer_size * 4 + 0 * last_layer_size + kk] += + (nnei - jj) * var * ll[0]; + out[ii * last_layer_size * 4 + 1 * last_layer_size + kk] += + (nnei - jj) * var * ll[1]; + out[ii * last_layer_size * 4 + 2 * last_layer_size + kk] += + (nnei - jj) * var * ll[2]; + out[ii * last_layer_size * 4 + 3 * last_layer_size + kk] += + (nnei - jj) * var * ll[3]; + } else { + out[ii * last_layer_size * 4 + 0 * last_layer_size + kk] += + var * ll[0]; + out[ii * last_layer_size * 4 + 1 * last_layer_size + kk] += + var * ll[1]; + out[ii * last_layer_size * 4 + 2 * last_layer_size + kk] += + var * ll[2]; + out[ii * last_layer_size * 4 + 3 * last_layer_size + kk] += + var * ll[3]; + } + } + if (unloop) break; + } + } +} + +template +void deepmd::tabulate_fusion_se_atten_grad_cpu(FPTYPE* dy_dem_x, + FPTYPE* dy_dem, + const FPTYPE* table, + const FPTYPE* table_info, + const FPTYPE* em_x, + const FPTYPE* em, + const FPTYPE* two_embed, + const FPTYPE* dy, + const int nloc, + const int nnei, + const int last_layer_size) { + memset(dy_dem_x, 0, sizeof(FPTYPE) * nloc * nnei); + memset(dy_dem, 0, sizeof(FPTYPE) * nloc * nnei * 4); + FPTYPE const lower = table_info[0]; + FPTYPE const upper = table_info[1]; + FPTYPE const _max = table_info[2]; + FPTYPE const stride0 = table_info[3]; + FPTYPE const stride1 = table_info[4]; +// for every atom, execute a small gemm~ +// FPTYPE * res = new FPTYPE[4 * last_layer_size]; +#pragma omp parallel for + for (int ii = 0; ii < nloc; ii++) { + FPTYPE ll[4]; + FPTYPE rr[4]; + FPTYPE ago = em_x[ii * nnei + nnei - 1]; + bool unloop = false; + for (int jj = 0; jj < nnei; jj++) { + // construct the dy/dx + ll[0] = em[ii * nnei * 4 + jj * 4 + 0]; + ll[1] = em[ii * nnei * 4 + jj * 4 + 1]; + ll[2] = em[ii * nnei * 4 + jj * 4 + 2]; + ll[3] = em[ii * nnei * 4 + jj * 4 + 3]; + FPTYPE xx = em_x[ii * nnei + jj]; + if (ago == xx) { + unloop = true; + } + int table_idx = 0; + locate_xx(lower, upper, _max, stride0, stride1, xx, table_idx); + FPTYPE grad = (FPTYPE)0.0; + for (int kk = 0; kk < last_layer_size; kk++) { + rr[0] = dy[ii * last_layer_size * 4 + 0 * last_layer_size + kk]; + rr[1] = dy[ii * last_layer_size * 4 + 1 * last_layer_size + kk]; + rr[2] = dy[ii * last_layer_size * 4 + 2 * last_layer_size + kk]; + rr[3] = dy[ii * last_layer_size * 4 + 3 * last_layer_size + kk]; + FPTYPE a0 = table[table_idx * last_layer_size * 6 + 6 * kk + 0]; + FPTYPE a1 = table[table_idx * last_layer_size * 6 + 6 * kk + 1]; + FPTYPE a2 = table[table_idx * last_layer_size * 6 + 6 * kk + 2]; + FPTYPE a3 = table[table_idx * last_layer_size * 6 + 6 * kk + 3]; + FPTYPE a4 = table[table_idx * last_layer_size * 6 + 6 * kk + 4]; + FPTYPE a5 = table[table_idx * last_layer_size * 6 + 6 * kk + 5]; + FPTYPE res = + a0 + (a1 + (a2 + (a3 + (a4 + a5 * xx) * xx) * xx) * xx) * xx; + FPTYPE t = two_embed[ii * nnei*last_layer_size + jj * last_layer_size + kk]; + + FPTYPE res1 = res * t + t; + FPTYPE g = (a1 + (2 * a2 + (3 * a3 + (4 * a4 + 5 * a5 * xx) * xx) * xx) * + xx); + FPTYPE deriv = t * g; + + if (unloop) { + grad += deriv * dot(ll, rr) * (nnei - jj); + dy_dem[ii * nnei * 4 + jj * 4 + 0] += res1 * rr[0] * (nnei - jj); + dy_dem[ii * nnei * 4 + jj * 4 + 1] += res1 * rr[1] * (nnei - jj); + dy_dem[ii * nnei * 4 + jj * 4 + 2] += res1 * rr[2] * (nnei - jj); + dy_dem[ii * nnei * 4 + jj * 4 + 3] += res1 * rr[3] * (nnei - jj); + } else { + grad += deriv * dot(ll, rr); + dy_dem[ii * nnei * 4 + jj * 4 + 0] += res1 * rr[0]; + dy_dem[ii * nnei * 4 + jj * 4 + 1] += res1 * rr[1]; + dy_dem[ii * nnei * 4 + jj * 4 + 2] += res1 * rr[2]; + dy_dem[ii * nnei * 4 + jj * 4 + 3] += res1 * rr[3]; + } + } + dy_dem_x[ii * nnei + jj] = grad; + if (unloop) break; + } + } +} + +template +void deepmd::tabulate_fusion_se_atten_grad_grad_cpu(FPTYPE* dz_dy, + const FPTYPE* table, + const FPTYPE* table_info, + const FPTYPE* em_x, + const FPTYPE* em, + const FPTYPE* two_embed, + const FPTYPE* dz_dy_dem_x, + const FPTYPE* dz_dy_dem, + const int nloc, + const int nnei, + const int last_layer_size) { + memset(dz_dy, 0, sizeof(FPTYPE) * nloc * 4 * last_layer_size); + const FPTYPE lower = table_info[0]; + const FPTYPE upper = table_info[1]; + const FPTYPE _max = table_info[2]; + const FPTYPE stride0 = table_info[3]; + const FPTYPE stride1 = table_info[4]; +// for every atom, execute a small manual gemm ~ +// FPTYPE * res = new FPTYPE[4 * last_layer_size]; +#pragma omp parallel for + for (int ii = 0; ii < nloc; ii++) { + FPTYPE ll[4]; + FPTYPE hh[4]; + FPTYPE ago = em_x[ii * nnei + nnei - 1]; + bool unloop = false; + for (int jj = 0; jj < nnei; jj++) { + ll[0] = em[ii * nnei * 4 + jj * 4 + 0]; + ll[1] = em[ii * nnei * 4 + jj * 4 + 1]; + ll[2] = em[ii * nnei * 4 + jj * 4 + 2]; + ll[3] = em[ii * nnei * 4 + jj * 4 + 3]; + hh[0] = dz_dy_dem[ii * nnei * 4 + jj * 4 + 0]; + hh[1] = dz_dy_dem[ii * nnei * 4 + jj * 4 + 1]; + hh[2] = dz_dy_dem[ii * nnei * 4 + jj * 4 + 2]; + hh[3] = dz_dy_dem[ii * nnei * 4 + jj * 4 + 3]; + FPTYPE xx = em_x[ii * nnei + jj]; + FPTYPE dz_xx = dz_dy_dem_x[ii * nnei + jj]; + if (ago == xx) { + unloop = true; + } + int table_idx = 0; + locate_xx(lower, upper, _max, stride0, stride1, xx, table_idx); + for (int kk = 0; kk < last_layer_size; kk++) { + FPTYPE a0 = table[table_idx * last_layer_size * 6 + 6 * kk + 0]; + FPTYPE a1 = table[table_idx * last_layer_size * 6 + 6 * kk + 1]; + FPTYPE a2 = table[table_idx * last_layer_size * 6 + 6 * kk + 2]; + FPTYPE a3 = table[table_idx * last_layer_size * 6 + 6 * kk + 3]; + FPTYPE a4 = table[table_idx * last_layer_size * 6 + 6 * kk + 4]; + FPTYPE a5 = table[table_idx * last_layer_size * 6 + 6 * kk + 5]; + FPTYPE var = + a0 + (a1 + (a2 + (a3 + (a4 + a5 * xx) * xx) * xx) * xx) * xx; + FPTYPE t = two_embed[ii * nnei*last_layer_size + jj * last_layer_size + kk]; + FPTYPE var_grad = + a1 + + ((FPTYPE)2. * a2 + + ((FPTYPE)3. * a3 + ((FPTYPE)4. * a4 + (FPTYPE)5. * a5 * xx) * xx) * + xx) * + xx; + var_grad = var_grad * t; + if (unloop) { + dz_dy[ii * last_layer_size * 4 + 0 * last_layer_size + kk] += + (nnei - jj) * (var * hh[0] + dz_xx * var_grad * ll[0]); + dz_dy[ii * last_layer_size * 4 + 1 * last_layer_size + kk] += + (nnei - jj) * (var * hh[1] + dz_xx * var_grad * ll[1]); + dz_dy[ii * last_layer_size * 4 + 2 * last_layer_size + kk] += + (nnei - jj) * (var * hh[2] + dz_xx * var_grad * ll[2]); + dz_dy[ii * last_layer_size * 4 + 3 * last_layer_size + kk] += + (nnei - jj) * (var * hh[3] + dz_xx * var_grad * ll[3]); + } else { + dz_dy[ii * last_layer_size * 4 + 0 * last_layer_size + kk] += + var * hh[0] + dz_xx * var_grad * ll[0]; + dz_dy[ii * last_layer_size * 4 + 1 * last_layer_size + kk] += + var * hh[1] + dz_xx * var_grad * ll[1]; + dz_dy[ii * last_layer_size * 4 + 2 * last_layer_size + kk] += + var * hh[2] + dz_xx * var_grad * ll[2]; + dz_dy[ii * last_layer_size * 4 + 3 * last_layer_size + kk] += + var * hh[3] + dz_xx * var_grad * ll[3]; + } + } + if (unloop) break; + } + } +} + template void deepmd::tabulate_fusion_se_t_cpu(FPTYPE* out, const FPTYPE* table, @@ -678,6 +912,76 @@ template void deepmd::tabulate_fusion_se_a_grad_grad_cpu( const int nnei, const int last_layer_size); +template void deepmd::tabulate_fusion_se_atten_cpu( + float* out, + const float* table, + const float* table_info, + const float* em_x, + const float* em, + const float* two_embed, + const int nloc, + const int nnei, + const int last_layer_size); +template void deepmd::tabulate_fusion_se_atten_cpu( + double* out, + const double* table, + const double* table_info, + const double* em_x, + const double* em, + const double* two_embed, + const int nloc, + const int nnei, + const int last_layer_size); +template void deepmd::tabulate_fusion_se_atten_grad_cpu( + float* dy_dem_x, + float* dy_dem, + const float* table, + const float* table_info, + const float* em_x, + const float* em, + const float* two_embed, + const float* dy, + const int nloc, + const int nnei, + const int last_layer_size); +template void deepmd::tabulate_fusion_se_atten_grad_cpu( + double* dy_dem_x, + double* dy_dem, + const double* table, + const double* table_info, + const double* em_x, + const double* em, + const double* two_embed, + const double* dy, + const int nloc, + const int nnei, + const int last_layer_size); +template void deepmd::tabulate_fusion_se_atten_grad_grad_cpu( + float* dz_dy, + const float* table, + const float* table_info, + const float* em_x, + const float* em, + const float* two_embed, + const float* dz_dy_dem_x, + const float* dz_dy_dem, + const int nloc, + const int nnei, + const int last_layer_size); +template void deepmd::tabulate_fusion_se_atten_grad_grad_cpu( + double* dz_dy, + const double* table, + const double* table_info, + const double* em_x, + const double* em, + const double* two_embed, + const double* dz_dy_dem_x, + const double* dz_dy_dem, + const int nloc, + const int nnei, + const int last_layer_size); + + template void deepmd::tabulate_fusion_se_t_cpu( float* out, const float* table, diff --git a/source/op/_tabulate_grad.py b/source/op/_tabulate_grad.py index 7c808be9e8..e4b022acbc 100644 --- a/source/op/_tabulate_grad.py +++ b/source/op/_tabulate_grad.py @@ -8,6 +8,7 @@ from deepmd.env import ( op_module, ) +import tensorflow.compat.v1 as tf # from deepmd.DescrptSeATabulate import last_layer_size @@ -45,7 +46,7 @@ def _tabulate_fusion_se_atten_grad_cc(op, dy): None, # table_info dy_dx, # em_x dy_df, # em - None] # two_embed + dy_dtwo] # two_embed @ops.RegisterGradient("TabulateFusionSeAttenGrad") def _tabulate_fusion_se_atten_grad_grad_cc(op, dy, dy_, dy_dtwo): @@ -63,7 +64,7 @@ def _tabulate_fusion_se_atten_grad_grad_cc(op, dy, dy_, dy_dtwo): None, # table_info None, # em_x None, # em - None, # two_embed + tf.zeros_like(op.inputs[4]), # two_embed dz_dy, # dy None] # descriptor diff --git a/source/op/tabulate_multi_device.cc b/source/op/tabulate_multi_device.cc index aac165ca09..b75bf9d84e 100644 --- a/source/op/tabulate_multi_device.cc +++ b/source/op/tabulate_multi_device.cc @@ -418,10 +418,10 @@ class TabulateFusionSeAttenOp : public OpKernel { // em, nloc, nnei, last_layer_size); #endif // TENSORFLOW_USE_ROCM } else if (device == "CPU") { - //deepmd::tabulate_fusion_se_atten_cpu(descriptor, table, table_info, em_x, em, two_embed, - // nloc, nnei, last_layer_size); - deepmd::tabulate_fusion_se_a_cpu(descriptor, table, table_info, em_x, em, - nloc, nnei, last_layer_size); + deepmd::tabulate_fusion_se_atten_cpu(descriptor, table, table_info, em_x, em, two_embed, + nloc, nnei, last_layer_size); + //deepmd::tabulate_fusion_se_a_cpu(descriptor, table, table_info, em_x, em, + // nloc, nnei, last_layer_size); } } @@ -497,12 +497,12 @@ class TabulateFusionSeAttenGradOp : public OpKernel { // nnei, last_layer_size); #endif // TENSORFLOW_USE_ROCM } else if (device == "CPU") { - //deepmd::tabulate_fusion_se_atten_grad_cpu(dy_dem_x, dy_dem, dy_dtwo, table, table_info, - // em_x, em, two_embed, dy, nloc, nnei, - // last_layer_size); - deepmd::tabulate_fusion_se_a_grad_cpu(dy_dem_x, dy_dem, table, table_info, - em_x, em, dy, nloc, nnei, - last_layer_size); + deepmd::tabulate_fusion_se_atten_grad_cpu(dy_dem_x, dy_dem, table, table_info, + em_x, em, two_embed, dy, nloc, nnei, + last_layer_size); + //deepmd::tabulate_fusion_se_a_grad_cpu(dy_dem_x, dy_dem, table, table_info, + // em_x, em, dy, nloc, nnei, + // last_layer_size); } } @@ -567,12 +567,12 @@ class TabulateFusionSeAttenGradGradOp : public OpKernel { "In the process of model compression, the size of the " "last layer of embedding net must be less than 1024!")); } else if (device == "CPU") { - deepmd::tabulate_fusion_se_a_grad_grad_cpu(dz_dy, table, table_info, em_x, - em, dz_dy_dem_x, dz_dy_dem, - nloc, nnei, last_layer_size); - //deepmd::tabulate_fusion_se_atten_grad_grad_cpu(dz_dy, table, table_info, em_x, - // em, two_embed, dz_dy_dem_x, dz_dy_dem, - // nloc, nnei, last_layer_size); + //deepmd::tabulate_fusion_se_a_grad_grad_cpu(dz_dy, table, table_info, em_x, + // em, dz_dy_dem_x, dz_dy_dem, + // nloc, nnei, last_layer_size); + deepmd::tabulate_fusion_se_atten_grad_grad_cpu(dz_dy, table, table_info, em_x, + em, two_embed, dz_dy_dem_x, dz_dy_dem, + nloc, nnei, last_layer_size); } } From 70c6c679758c85eb7bad6978eb114765912b7105 Mon Sep 17 00:00:00 2001 From: nahso Date: Wed, 26 Apr 2023 15:38:27 +0800 Subject: [PATCH 045/126] cuda version done --- source/lib/include/tabulate.h | 37 +++ source/lib/src/cuda/tabulate.cu | 352 +++++++++++++++++++++++++++++ source/op/tabulate_multi_device.cc | 10 +- 3 files changed, 394 insertions(+), 5 deletions(-) diff --git a/source/lib/include/tabulate.h b/source/lib/include/tabulate.h index 3a7139f099..cf25f4aef4 100644 --- a/source/lib/include/tabulate.h +++ b/source/lib/include/tabulate.h @@ -174,6 +174,43 @@ void tabulate_fusion_se_a_grad_grad_gpu_cuda(FPTYPE* dz_dy, const int nnei, const int last_layer_size); +template +void tabulate_fusion_se_atten_gpu_cuda(FPTYPE* out, + const FPTYPE* table, + const FPTYPE* table_info, + const FPTYPE* em_x, + const FPTYPE* em, + const FPTYPE* two_embed, + const int nloc, + const int nnei, + const int last_layer_size); + +template +void tabulate_fusion_se_atten_grad_gpu_cuda(FPTYPE* dy_dem_x, + FPTYPE* dy_dem, + const FPTYPE* table, + const FPTYPE* table_info, + const FPTYPE* em_x, + const FPTYPE* em, + const FPTYPE* two_embed, + const FPTYPE* dy, + const int nloc, + const int nnei, + const int last_layer_size); + +template +void tabulate_fusion_se_atten_grad_grad_gpu_cuda(FPTYPE* dz_dy, + const FPTYPE* table, + const FPTYPE* table_info, + const FPTYPE* em_x, + const FPTYPE* em, + const FPTYPE* two_embed, + const FPTYPE* dz_dy_dem_x, + const FPTYPE* dz_dy_dem, + const int nloc, + const int nnei, + const int last_layer_size); + template void tabulate_fusion_se_t_gpu_cuda(FPTYPE* out, const FPTYPE* table, diff --git a/source/lib/src/cuda/tabulate.cu b/source/lib/src/cuda/tabulate.cu index 2e8c24cf99..0ff6033750 100644 --- a/source/lib/src/cuda/tabulate.cu +++ b/source/lib/src/cuda/tabulate.cu @@ -318,6 +318,219 @@ __global__ void tabulate_fusion_se_a_grad_grad_fifth_order_polynomial( } } +template +__global__ void tabulate_fusion_se_atten_fifth_order_polynomial( + FPTYPE* out, + const FPTYPE* table, + const FPTYPE* em_x, + const FPTYPE* em, + const FPTYPE* two_embed, + const FPTYPE lower, + const FPTYPE upper, + const FPTYPE max, + const FPTYPE stride0, + const FPTYPE stride1, + const int nnei, + const int last_layer_size) { + const int_64 block_idx = blockIdx.x; // nloc + const int thread_idx = threadIdx.x; // last_layer_size + FPTYPE ago = __shfl_sync(0xffffffff, em_x[block_idx * nnei + nnei - 1], 0); + bool unloop = false; + int breakpoint = nnei - 1; + + FPTYPE sum[MTILE] = {(FPTYPE)0.}; + int mark_table_idx = -1; + FPTYPE var[6]; + for (int ii = 0; ii < nnei; ii++) { + FPTYPE xx = em_x[block_idx * nnei + ii]; + if (xx == ago) { + unloop = true; + breakpoint = ii; + } + int table_idx = 0; + locate_xx_se_a(xx, table_idx, lower, upper, max, stride0, stride1); + if (table_idx != mark_table_idx) { + load_polynomial_params(var, table, table_idx, thread_idx, + last_layer_size); + } + FPTYPE res = + var[0] + + (var[1] + (var[2] + (var[3] + (var[4] + var[5] * xx) * xx) * xx) * xx) * + xx; + FPTYPE t = two_embed[block_idx * nnei*last_layer_size + ii * last_layer_size + thread_idx]; + res = res * t + t; + + for (int kk = 0; kk < MTILE; kk++) { + sum[kk] += (nnei - breakpoint) * + em[block_idx * nnei * MTILE + ii * MTILE + kk] * res; + } + if (unloop) break; + mark_table_idx = table_idx; + } + for (int ii = 0; ii < MTILE; ii++) { + out[block_idx * MTILE * last_layer_size + ii * last_layer_size + + thread_idx] = sum[ii]; + } +} + +template +__global__ void tabulate_fusion_se_atten_grad_fifth_order_polynomial( + FPTYPE* dy_dem_x, + FPTYPE* dy_dem, + const FPTYPE* table, + const FPTYPE* em_x, + const FPTYPE* em, + const FPTYPE* two_embed, + const FPTYPE* dy, + const FPTYPE lower, + const FPTYPE upper, + const FPTYPE max, + const FPTYPE stride0, + const FPTYPE stride1, + const int nnei, + const int last_layer_size) { + extern __shared__ int _data[]; + const int_64 block_idx = blockIdx.x; // nloc + const int thread_idx = threadIdx.x; // KTILE * WARP_SIZE, usally 128 here~ + int warp_idx = __shfl_sync(0xffffffff, threadIdx.x / WARP_SIZE, 0); + int lane_idx = threadIdx.x % WARP_SIZE; + int breakpoint = nnei - 1; + bool unloop = false; + FPTYPE* iteratorA = (FPTYPE*)&_data[0]; // dy + for (int ii = 0; ii < MTILE; ii++) { + for (int jj = thread_idx; jj < last_layer_size; jj += blockDim.x) { + iteratorA[ii * last_layer_size + jj] = + dy[block_idx * MTILE * last_layer_size + ii * last_layer_size + jj]; + } + } + __syncthreads(); + FPTYPE ago = __shfl_sync(0xffffffff, em_x[block_idx * nnei + nnei - 1], 0); + for (int ii = warp_idx; ii < nnei; ii += KTILE) { + FPTYPE xx = em_x[block_idx * nnei + ii]; + if (ago == xx) { + unloop = true; + breakpoint = ii; + } + + int table_idx = 0; + FPTYPE reg_em[MTILE] = {em[block_idx * nnei * MTILE + ii * 4 + 0], + em[block_idx * nnei * MTILE + ii * 4 + 1], + em[block_idx * nnei * MTILE + ii * 4 + 2], + em[block_idx * nnei * MTILE + ii * 4 + 3]}; + FPTYPE Csub = (FPTYPE)0.; + FPTYPE sum[MTILE] = {(FPTYPE)0.}; + locate_xx_se_a(xx, table_idx, lower, upper, max, stride0, stride1); + + FPTYPE var[6]; + for (int jj = lane_idx; jj < last_layer_size; jj += WARP_SIZE) { + load_polynomial_params(var, table, table_idx, jj, last_layer_size); + FPTYPE res = + var[0] + + (var[1] + + (var[2] + (var[3] + (var[4] + var[5] * xx) * xx) * xx) * xx) * + xx; + FPTYPE t = two_embed[block_idx * nnei*last_layer_size + ii * last_layer_size + jj]; + res = res * t + t; + + for (int kk = 0; kk < MTILE; kk++) { + sum[kk] += + (nnei - breakpoint) * iteratorA[kk * last_layer_size + jj] * res; + } + res = reg_em[0] * iteratorA[0 * last_layer_size + jj]; + res += reg_em[1] * iteratorA[1 * last_layer_size + jj]; + res += reg_em[2] * iteratorA[2 * last_layer_size + jj]; + res += reg_em[3] * iteratorA[3 * last_layer_size + jj]; + Csub += + (nnei - breakpoint) * + (var[1] + (2 * var[2] + + (3 * var[3] + (4 * var[4] + 5 * var[5] * xx) * xx) * xx) * + xx) * + res * t; + } + __syncwarp(); + for (int kk = 0; kk < MTILE; kk++) { + warp_reduce(sum[kk]); + } + warp_reduce(Csub); + if (lane_idx == 0) { + for (int kk = 0; kk < MTILE; kk++) { + dy_dem[block_idx * nnei * MTILE + ii * 4 + kk] = sum[kk]; + } + dy_dem_x[block_idx * nnei + ii] = Csub; + } + if (unloop) break; + } +} + +template +__global__ void tabulate_fusion_se_atten_grad_grad_fifth_order_polynomial( + FPTYPE* dz_dy, + const FPTYPE* table, + const FPTYPE* em_x, + const FPTYPE* em, + const FPTYPE* two_embed, + const FPTYPE* dz_dy_dem_x, + const FPTYPE* dz_dy_dem, + const FPTYPE lower, + const FPTYPE upper, + const FPTYPE max, + const FPTYPE stride0, + const FPTYPE stride1, + const int nnei, + const int last_layer_size) { + extern __shared__ int _data[]; + const int_64 block_idx = blockIdx.x; // nloc + const int thread_idx = threadIdx.x; // last_layer_size + FPTYPE ago = __shfl_sync(0xffffffff, em_x[block_idx * nnei + nnei - 1], 0); + bool unloop = false; + int breakpoint = nnei - 1; + FPTYPE* iteratorC = (FPTYPE*)&_data[0]; + for (int kk = 0; kk < MTILE; kk++) + iteratorC[kk * last_layer_size + thread_idx] = (FPTYPE)0.; + __syncthreads(); + + int mark_table_idx = -1; + FPTYPE var[6]; + for (int ii = 0; ii < nnei; ii++) { + FPTYPE xx = em_x[block_idx * nnei + ii]; + FPTYPE dz_xx = dz_dy_dem_x[block_idx * nnei + ii]; + if (xx == ago) { + unloop = true; + breakpoint = ii; + } + int table_idx = 0; + locate_xx_se_a(xx, table_idx, lower, upper, max, stride0, stride1); + if (table_idx != mark_table_idx) { + load_polynomial_params(var, table, table_idx, thread_idx, + last_layer_size); + } + + FPTYPE res = + var[0] + + (var[1] + (var[2] + (var[3] + (var[4] + var[5] * xx) * xx) * xx) * xx) * + xx; + FPTYPE res_grad = + var[1] + + (2 * var[2] + (3 * var[3] + (4 * var[4] + 5 * var[5] * xx) * xx) * xx) * + xx; + FPTYPE t = two_embed[block_idx * nnei*last_layer_size + ii * last_layer_size + thread_idx]; + res_grad = res_grad * t; + + for (int kk = 0; kk < MTILE; kk++) { + int em_index = block_idx * nnei * MTILE + ii * MTILE + kk; + iteratorC[kk * last_layer_size + thread_idx] += + (nnei - breakpoint) * + (em[em_index] * res_grad * dz_xx + dz_dy_dem[em_index] * res); + } + mark_table_idx = table_idx; + if (unloop) break; + } + for (int ii = 0; ii < MTILE; ii++) { + dz_dy[block_idx * MTILE * last_layer_size + ii * last_layer_size + + thread_idx] = iteratorC[ii * last_layer_size + thread_idx]; + } +} + template __global__ void tabulate_fusion_se_t_fifth_order_polynomial( FPTYPE* out, @@ -689,6 +902,78 @@ void tabulate_fusion_se_a_grad_grad_gpu_cuda(FPTYPE* dz_dy, DPErrcheck(cudaDeviceSynchronize()); } +template +void tabulate_fusion_se_atten_gpu_cuda(FPTYPE* out, + const FPTYPE* table, + const FPTYPE* table_info, + const FPTYPE* em_x, + const FPTYPE* em, + const FPTYPE* two_embed, + const int nloc, + const int nnei, + const int last_layer_size) { + if (nloc <= 0) { + return; + } + tabulate_fusion_se_atten_fifth_order_polynomial + <<>>(out, table, em_x, em, two_embed, table_info[0], + table_info[1], table_info[2], table_info[3], + table_info[4], nnei, last_layer_size); + DPErrcheck(cudaGetLastError()); + DPErrcheck(cudaDeviceSynchronize()); +} + +template +void tabulate_fusion_se_atten_grad_gpu_cuda(FPTYPE* dy_dem_x, + FPTYPE* dy_dem, + const FPTYPE* table, + const FPTYPE* table_info, + const FPTYPE* em_x, + const FPTYPE* em, + const FPTYPE* two_embed, + const FPTYPE* dy, + const int nloc, + const int nnei, + const int last_layer_size) { + if (nloc <= 0) { + return; + } + DPErrcheck(cudaMemset(dy_dem_x, 0, sizeof(FPTYPE) * nloc * nnei)); + DPErrcheck(cudaMemset(dy_dem, 0, sizeof(FPTYPE) * nloc * nnei * 4)); + + tabulate_fusion_se_atten_grad_fifth_order_polynomial + <<>>( + dy_dem_x, dy_dem, table, em_x, em, two_embed, dy, table_info[0], table_info[1], + table_info[2], table_info[3], table_info[4], nnei, last_layer_size); + DPErrcheck(cudaGetLastError()); + DPErrcheck(cudaDeviceSynchronize()); +} + +template +void tabulate_fusion_se_atten_grad_grad_gpu_cuda(FPTYPE* dz_dy, + const FPTYPE* table, + const FPTYPE* table_info, + const FPTYPE* em_x, + const FPTYPE* em, + const FPTYPE* two_embed, + const FPTYPE* dz_dy_dem_x, + const FPTYPE* dz_dy_dem, + const int nloc, + const int nnei, + const int last_layer_size) { + if (nloc <= 0) { + return; + } + DPErrcheck(cudaMemset(dz_dy, 0, sizeof(FPTYPE) * nloc * 4 * last_layer_size)); + tabulate_fusion_se_atten_grad_grad_fifth_order_polynomial + <<>>( + dz_dy, table, em_x, em, two_embed, dz_dy_dem_x, dz_dy_dem, table_info[0], + table_info[1], table_info[2], table_info[3], table_info[4], nnei, + last_layer_size); + DPErrcheck(cudaGetLastError()); + DPErrcheck(cudaDeviceSynchronize()); +} + template void tabulate_fusion_se_t_gpu_cuda(FPTYPE* out, const FPTYPE* table, @@ -887,6 +1172,73 @@ template void tabulate_fusion_se_a_grad_grad_gpu_cuda( const int nnei, const int last_layer_size); +template void tabulate_fusion_se_atten_gpu_cuda(float* out, + const float* table, + const float* table_info, + const float* em_x, + const float* em, + const float* two_embed, + const int nloc, + const int nnei, + const int last_layer_size); +template void tabulate_fusion_se_atten_gpu_cuda(double* out, + const double* table, + const double* table_info, + const double* em_x, + const double* em, + const double* two_embed, + const int nloc, + const int nnei, + const int last_layer_size); +template void tabulate_fusion_se_atten_grad_gpu_cuda( + float* dy_dem_x, + float* dy_dem, + const float* table, + const float* table_info, + const float* em_x, + const float* em, + const float* two_embed, + const float* dy, + const int nloc, + const int nnei, + const int last_layer_size); +template void tabulate_fusion_se_atten_grad_gpu_cuda( + double* dy_dem_x, + double* dy_dem, + const double* table, + const double* table_info, + const double* em_x, + const double* em, + const double* two_embed, + const double* dy, + const int nloc, + const int nnei, + const int last_layer_size); +template void tabulate_fusion_se_atten_grad_grad_gpu_cuda( + float* dz_dy, + const float* table, + const float* table_info, + const float* em_x, + const float* em, + const float* two_embed, + const float* dz_dy_dem_x, + const float* dz_dy_dem, + const int nloc, + const int nnei, + const int last_layer_size); +template void tabulate_fusion_se_atten_grad_grad_gpu_cuda( + double* dz_dy, + const double* table, + const double* table_info, + const double* em_x, + const double* em, + const double* two_embed, + const double* dz_dy_dem_x, + const double* dz_dy_dem, + const int nloc, + const int nnei, + const int last_layer_size); + template void tabulate_fusion_se_t_gpu_cuda(float* out, const float* table, const float* table_info, diff --git a/source/op/tabulate_multi_device.cc b/source/op/tabulate_multi_device.cc index b75bf9d84e..0f14dc564c 100644 --- a/source/op/tabulate_multi_device.cc +++ b/source/op/tabulate_multi_device.cc @@ -409,8 +409,8 @@ class TabulateFusionSeAttenOp : public OpKernel { if (device == "GPU") { #if GOOGLE_CUDA - //deepmd::tabulate_fusion_se_atten_gpu_cuda(descriptor, table, table_info, em_x, two_embed, - //em, nloc, nnei, last_layer_size); + deepmd::tabulate_fusion_se_atten_gpu_cuda(descriptor, table, table_info, em_x, em, + two_embed, nloc, nnei, last_layer_size); #endif // GOOGLE_CUDA #if TENSORFLOW_USE_ROCM @@ -486,9 +486,9 @@ class TabulateFusionSeAttenGradOp : public OpKernel { if (device == "GPU") { #if GOOGLE_CUDA - //deepmd::tabulate_fusion_se_atten_grad_gpu_cuda(dy_dem_x, dy_dem, table, - // table_info, em_x, em, dy, nloc, - // nnei, last_layer_size); + deepmd::tabulate_fusion_se_atten_grad_gpu_cuda(dy_dem_x, dy_dem, table, + table_info, em_x, em, two_embed, dy, nloc, + nnei, last_layer_size); #endif // GOOGLE_CUDA #if TENSORFLOW_USE_ROCM From 8a4bcce2bcac406911ff82f3d8bf20c94d122c6a Mon Sep 17 00:00:00 2001 From: nahso Date: Wed, 26 Apr 2023 16:09:56 +0800 Subject: [PATCH 046/126] rocm version test --- source/lib/include/tabulate.h | 37 ++++ source/lib/src/rocm/tabulate.hip.cu | 303 ++++++++++++++++++++++++++++ source/op/tabulate_multi_device.cc | 10 +- 3 files changed, 345 insertions(+), 5 deletions(-) diff --git a/source/lib/include/tabulate.h b/source/lib/include/tabulate.h index cf25f4aef4..b8425d5172 100644 --- a/source/lib/include/tabulate.h +++ b/source/lib/include/tabulate.h @@ -313,6 +313,43 @@ void tabulate_fusion_se_a_grad_grad_gpu_rocm(FPTYPE* dz_dy, const int nnei, const int last_layer_size); +template +void tabulate_fusion_se_atten_gpu_rocm(FPTYPE* out, + const FPTYPE* table, + const FPTYPE* table_info, + const FPTYPE* em_x, + const FPTYPE* em, + const FPTYPE* two_embed, + const int nloc, + const int nnei, + const int last_layer_size); + +template +void tabulate_fusion_se_atten_grad_gpu_rocm(FPTYPE* dy_dem_x, + FPTYPE* dy_dem, + const FPTYPE* table, + const FPTYPE* table_info, + const FPTYPE* em_x, + const FPTYPE* em, + const FPTYPE* two_embed, + const FPTYPE* dy, + const int nloc, + const int nnei, + const int last_layer_size); + +template +void tabulate_fusion_se_atten_grad_grad_gpu_rocm(FPTYPE* dz_dy, + const FPTYPE* table, + const FPTYPE* table_info, + const FPTYPE* em_x, + const FPTYPE* em, + const FPTYPE* two_embed, + const FPTYPE* dz_dy_dem_x, + const FPTYPE* dz_dy_dem, + const int nloc, + const int nnei, + const int last_layer_size); + template void tabulate_fusion_se_t_gpu_rocm(FPTYPE* out, const FPTYPE* table, diff --git a/source/lib/src/rocm/tabulate.hip.cu b/source/lib/src/rocm/tabulate.hip.cu index 1ac1bca8f8..3e0fb71d04 100644 --- a/source/lib/src/rocm/tabulate.hip.cu +++ b/source/lib/src/rocm/tabulate.hip.cu @@ -292,6 +292,231 @@ __global__ void tabulate_fusion_se_a_grad_grad_fifth_order_polynomial( } } +template +__global__ void tabulate_fusion_se_atten_fifth_order_polynomial( + FPTYPE* out, + const FPTYPE* table, + const FPTYPE* em_x, + const FPTYPE* em, + const FPTYPE* two_embed, + const FPTYPE lower, + const FPTYPE upper, + const FPTYPE max, + const FPTYPE stride0, + const FPTYPE stride1, + const int nnei, + const int last_layer_size) { + HIP_DYNAMIC_SHARED(int, _data) + const int_64 block_idx = blockIdx.x; // nloc + const int thread_idx = threadIdx.x; // last_layer_size + FPTYPE ago = __shfl(em_x[block_idx * nnei + nnei - 1], 0); + bool unloop = false; + int breakpoint = nnei - 1; + FPTYPE* iteratorC = (FPTYPE*)&_data[0]; + for (int kk = 0; kk < MTILE; kk++) + iteratorC[kk * last_layer_size + thread_idx] = (FPTYPE)0.; + __syncthreads(); + + for (int ii = 0; ii < nnei; ii++) { + FPTYPE var[6]; + FPTYPE xx = em_x[block_idx * nnei + ii]; + if (xx == ago) { + unloop = true; + breakpoint = ii; + } + int table_idx = 0; + locate_xx(xx, table_idx, lower, upper, max, stride0, stride1); + var[0] = table[table_idx * last_layer_size * 6 + thread_idx * 6 + 0]; + var[1] = table[table_idx * last_layer_size * 6 + thread_idx * 6 + 1]; + var[2] = table[table_idx * last_layer_size * 6 + thread_idx * 6 + 2]; + var[3] = table[table_idx * last_layer_size * 6 + thread_idx * 6 + 3]; + var[4] = table[table_idx * last_layer_size * 6 + thread_idx * 6 + 4]; + var[5] = table[table_idx * last_layer_size * 6 + thread_idx * 6 + 5]; + FPTYPE res = + var[0] + + (var[1] + (var[2] + (var[3] + (var[4] + var[5] * xx) * xx) * xx) * xx) * + xx; + FPTYPE t = two_embed[block_idx * nnei*last_layer_size + ii * last_layer_size + thread_idx]; + res = res * t + t; + + for (int kk = 0; kk < MTILE; kk++) { + iteratorC[kk * last_layer_size + thread_idx] += + (nnei - breakpoint) * em[block_idx * nnei * MTILE + ii * MTILE + kk] * + res; + } + if (unloop) break; + } + for (int ii = 0; ii < MTILE; ii++) { + out[block_idx * MTILE * last_layer_size + ii * last_layer_size + + thread_idx] = iteratorC[ii * last_layer_size + thread_idx]; + } +} + +template +__global__ void tabulate_fusion_se_atten_grad_fifth_order_polynomial( + FPTYPE* dy_dem_x, + FPTYPE* dy_dem, + const FPTYPE* table, + const FPTYPE* em_x, + const FPTYPE* em, + const FPTYPE* two_embed, + const FPTYPE* dy, + const FPTYPE lower, + const FPTYPE upper, + const FPTYPE max, + const FPTYPE stride0, + const FPTYPE stride1, + const int nnei, + const int last_layer_size) { + HIP_DYNAMIC_SHARED(int, _data) + const int_64 block_idx = blockIdx.x; // nloc + const int thread_idx = threadIdx.x; // KTILE * WARP_SIZE, usally 128 here~ + int warp_idx = __shfl(threadIdx.x / 64, 0); + int lane_idx = threadIdx.x % 64; + int breakpoint = nnei - 1; + bool unloop = false; + FPTYPE* iteratorA = (FPTYPE*)&_data[0]; // dy + for (int ii = 0; ii < MTILE; ii++) { + for (int jj = thread_idx; jj < last_layer_size; jj += blockDim.x) { + iteratorA[ii * last_layer_size + jj] = + dy[block_idx * MTILE * last_layer_size + ii * last_layer_size + jj]; + } + } + __syncthreads(); + FPTYPE ago = __shfl(em_x[block_idx * nnei + nnei - 1], 0); + for (int ii = 0; ii < nnei - warp_idx; ii += KTILE) { + FPTYPE xx = em_x[block_idx * nnei + ii + warp_idx]; + if (ago == xx) { + unloop = true; + breakpoint = ii + warp_idx; + } + + int table_idx = 0; + locate_xx(xx, table_idx, lower, upper, max, stride0, stride1); + FPTYPE sum[KTILE] = {(FPTYPE)0.}; + FPTYPE Csub = (FPTYPE)0.; + for (int jj = lane_idx; jj < last_layer_size; jj += WARP_SIZE) { + FPTYPE var[6]; + // load iteratorB through table + var[0] = table[table_idx * last_layer_size * 6 + 6 * jj + 0]; + var[1] = table[table_idx * last_layer_size * 6 + 6 * jj + 1]; + var[2] = table[table_idx * last_layer_size * 6 + 6 * jj + 2]; + var[3] = table[table_idx * last_layer_size * 6 + 6 * jj + 3]; + var[4] = table[table_idx * last_layer_size * 6 + 6 * jj + 4]; + var[5] = table[table_idx * last_layer_size * 6 + 6 * jj + 5]; + FPTYPE res = + var[0] + + (var[1] + + (var[2] + (var[3] + (var[4] + var[5] * xx) * xx) * xx) * xx) * + xx; + FPTYPE t = two_embed[block_idx * nnei*last_layer_size + ii * last_layer_size + jj]; + res = res * t + t; + + for (int kk = 0; kk < KTILE; kk++) { + sum[kk] += + (nnei - breakpoint) * iteratorA[kk * last_layer_size + jj] * res; + } + res = em[block_idx * nnei * MTILE + (ii + warp_idx) * 4 + 0] * + iteratorA[0 * last_layer_size + jj]; + res += em[block_idx * nnei * MTILE + (ii + warp_idx) * 4 + 1] * + iteratorA[1 * last_layer_size + jj]; + res += em[block_idx * nnei * MTILE + (ii + warp_idx) * 4 + 2] * + iteratorA[2 * last_layer_size + jj]; + res += em[block_idx * nnei * MTILE + (ii + warp_idx) * 4 + 3] * + iteratorA[3 * last_layer_size + jj]; + Csub += + (nnei - breakpoint) * + (var[1] + ((FPTYPE)2. * var[2] + + ((FPTYPE)3. * var[3] + + ((FPTYPE)4. * var[4] + (FPTYPE)5. * var[5] * xx) * xx) * + xx) * + xx) * + res * t; + } + //__syncwarp();->syncwrap + __syncthreads(); + for (int kk = 0; kk < KTILE; kk++) { + warp_reduce(sum[kk]); + } + warp_reduce(Csub); + if (lane_idx == 0) { + for (int kk = 0; kk < KTILE; kk++) { + dy_dem[block_idx * nnei * MTILE + (ii + warp_idx) * 4 + kk] = sum[kk]; + } + dy_dem_x[block_idx * nnei + ii + warp_idx] = Csub; + } + if (unloop) break; + } +} + +template +__global__ void tabulate_fusion_se_atten_grad_grad_fifth_order_polynomial( + FPTYPE* dz_dy, + const FPTYPE* table, + const FPTYPE* em_x, + const FPTYPE* em, + const FPTYPE* two_embed, + const FPTYPE* dz_dy_dem_x, + const FPTYPE* dz_dy_dem, + const FPTYPE lower, + const FPTYPE upper, + const FPTYPE max, + const FPTYPE stride0, + const FPTYPE stride1, + const int nnei, + const int last_layer_size) { + extern __shared__ int _data[]; + const int_64 block_idx = blockIdx.x; // nloc + const int thread_idx = threadIdx.x; // last_layer_size + FPTYPE ago = __shfl(em_x[block_idx * nnei + nnei - 1], 0); + bool unloop = false; + int breakpoint = nnei - 1; + FPTYPE* iteratorC = (FPTYPE*)&_data[0]; + for (int kk = 0; kk < MTILE; kk++) + iteratorC[kk * last_layer_size + thread_idx] = (FPTYPE)0.; + __syncthreads(); + + for (int ii = 0; ii < nnei; ii++) { + FPTYPE var[6]; + FPTYPE xx = em_x[block_idx * nnei + ii]; + FPTYPE dz_xx = dz_dy_dem_x[block_idx * nnei + ii]; + if (xx == ago) { + unloop = true; + breakpoint = ii; + } + int table_idx = 0; + locate_xx(xx, table_idx, lower, upper, max, stride0, stride1); + var[0] = table[table_idx * last_layer_size * 6 + thread_idx * 6 + 0]; + var[1] = table[table_idx * last_layer_size * 6 + thread_idx * 6 + 1]; + var[2] = table[table_idx * last_layer_size * 6 + thread_idx * 6 + 2]; + var[3] = table[table_idx * last_layer_size * 6 + thread_idx * 6 + 3]; + var[4] = table[table_idx * last_layer_size * 6 + thread_idx * 6 + 4]; + var[5] = table[table_idx * last_layer_size * 6 + thread_idx * 6 + 5]; + FPTYPE res = + var[0] + + (var[1] + (var[2] + (var[3] + (var[4] + var[5] * xx) * xx) * xx) * xx) * + xx; + FPTYPE res_grad = + var[1] + ((FPTYPE)2. * var[2] + + ((FPTYPE)3. * var[3] + + ((FPTYPE)4. * var[4] + (FPTYPE)5. * var[5] * xx) * xx) * + xx) * + xx; + + for (int kk = 0; kk < MTILE; kk++) { + int em_index = block_idx * nnei * MTILE + ii * MTILE + kk; + iteratorC[kk * last_layer_size + thread_idx] += + (nnei - breakpoint) * + (em[em_index] * res_grad * dz_xx + dz_dy_dem[em_index] * res); + } + if (unloop) break; + } + for (int ii = 0; ii < MTILE; ii++) { + dz_dy[block_idx * MTILE * last_layer_size + ii * last_layer_size + + thread_idx] = iteratorC[ii * last_layer_size + thread_idx]; + } +} + template __global__ void tabulate_fusion_se_t_fifth_order_polynomial( FPTYPE* out, @@ -687,6 +912,84 @@ void tabulate_fusion_se_a_grad_grad_gpu_rocm(FPTYPE* dz_dy, DPErrcheck(hipDeviceSynchronize()); } +template +void tabulate_fusion_se_atten_gpu_rocm(FPTYPE* out, + const FPTYPE* table, + const FPTYPE* table_info, + const FPTYPE* em_x, + const FPTYPE* em, + const FPTYPE* two_embed, + const int nloc, + const int nnei, + const int last_layer_size) { + if (nloc <= 0) { + return; + } + hipLaunchKernelGGL( + HIP_KERNEL_NAME( + tabulate_fusion_se_a_fifth_order_polynomial), + nloc, last_layer_size, sizeof(FPTYPE) * MM * last_layer_size, 0, out, + table, em_x, em, table_info[0], table_info[1], table_info[2], + table_info[3], table_info[4], nnei, last_layer_size); + DPErrcheck(hipGetLastError()); + DPErrcheck(hipDeviceSynchronize()); +} + +template +void tabulate_fusion_se_atten_grad_gpu_rocm(FPTYPE* dy_dem_x, + FPTYPE* dy_dem, + const FPTYPE* table, + const FPTYPE* table_info, + const FPTYPE* em_x, + const FPTYPE* em, + const FPTYPE* two_embed, + const FPTYPE* dy, + const int nloc, + const int nnei, + const int last_layer_size) { + if (nloc <= 0) { + return; + } + DPErrcheck(hipMemset(dy_dem_x, 0, sizeof(FPTYPE) * nloc * nnei)); + DPErrcheck(hipMemset(dy_dem, 0, sizeof(FPTYPE) * nloc * nnei * 4)); + + hipLaunchKernelGGL( + HIP_KERNEL_NAME( + tabulate_fusion_se_a_grad_fifth_order_polynomial), + nloc, KK * WARP_SIZE, sizeof(FPTYPE) * MM * last_layer_size, 0, dy_dem_x, + dy_dem, table, em_x, em, dy, table_info[0], table_info[1], table_info[2], + table_info[3], table_info[4], nnei, last_layer_size); + DPErrcheck(hipGetLastError()); + DPErrcheck(hipDeviceSynchronize()); +} + +template +void tabulate_fusion_se_atten_grad_grad_gpu_rocm(FPTYPE* dz_dy, + const FPTYPE* table, + const FPTYPE* table_info, + const FPTYPE* em_x, + const FPTYPE* em, + const FPTYPE* two_embed, + const FPTYPE* dz_dy_dem_x, + const FPTYPE* dz_dy_dem, + const int nloc, + const int nnei, + const int last_layer_size) { + if (nloc <= 0) { + return; + } + DPErrcheck(hipMemset(dz_dy, 0, sizeof(FPTYPE) * nloc * 4 * last_layer_size)); + hipLaunchKernelGGL( + HIP_KERNEL_NAME( + tabulate_fusion_se_a_grad_grad_fifth_order_polynomial), + nloc, last_layer_size, sizeof(FPTYPE) * MM * last_layer_size, 0, dz_dy, + table, em_x, em, dz_dy_dem_x, dz_dy_dem, table_info[0], table_info[1], + table_info[2], table_info[3], table_info[4], nnei, last_layer_size); + DPErrcheck(hipGetLastError()); + DPErrcheck(hipDeviceSynchronize()); +} + template void tabulate_fusion_se_t_gpu_rocm(FPTYPE* out, const FPTYPE* table, diff --git a/source/op/tabulate_multi_device.cc b/source/op/tabulate_multi_device.cc index 0f14dc564c..9d17d31e02 100644 --- a/source/op/tabulate_multi_device.cc +++ b/source/op/tabulate_multi_device.cc @@ -414,8 +414,8 @@ class TabulateFusionSeAttenOp : public OpKernel { #endif // GOOGLE_CUDA #if TENSORFLOW_USE_ROCM - //deepmd::tabulate_fusion_se_atten_gpu_rocm(descriptor, table, table_info, em_x, - // em, nloc, nnei, last_layer_size); + deepmd::tabulate_fusion_se_atten_gpu_rocm(descriptor, table, table_info, em_x, + em, two_embed, nloc, nnei, last_layer_size); #endif // TENSORFLOW_USE_ROCM } else if (device == "CPU") { deepmd::tabulate_fusion_se_atten_cpu(descriptor, table, table_info, em_x, em, two_embed, @@ -492,9 +492,9 @@ class TabulateFusionSeAttenGradOp : public OpKernel { #endif // GOOGLE_CUDA #if TENSORFLOW_USE_ROCM - //deepmd::tabulate_fusion_se_atten_grad_gpu_rocm(dy_dem_x, dy_dem, table, - // table_info, em_x, em, dy, nloc, - // nnei, last_layer_size); + deepmd::tabulate_fusion_se_atten_grad_gpu_rocm(dy_dem_x, dy_dem, table, + table_info, em_x, em, two_embed, dy, nloc, + nnei, last_layer_size); #endif // TENSORFLOW_USE_ROCM } else if (device == "CPU") { deepmd::tabulate_fusion_se_atten_grad_cpu(dy_dem_x, dy_dem, table, table_info, From e72408372b1701f87df4308275a3a18e339b7856 Mon Sep 17 00:00:00 2001 From: nahso Date: Sat, 6 May 2023 21:03:21 +0800 Subject: [PATCH 047/126] add instantiation of rocm template functions --- source/lib/src/rocm/tabulate.hip.cu | 71 +++++++++++++++++++++++++++++ 1 file changed, 71 insertions(+) diff --git a/source/lib/src/rocm/tabulate.hip.cu b/source/lib/src/rocm/tabulate.hip.cu index 3e0fb71d04..0d9eeb7aed 100644 --- a/source/lib/src/rocm/tabulate.hip.cu +++ b/source/lib/src/rocm/tabulate.hip.cu @@ -1199,6 +1199,77 @@ template void tabulate_fusion_se_a_grad_grad_gpu_rocm( const int nnei, const int last_layer_size); +template void tabulate_fusion_se_atten_gpu_rocm( + float* out, + const float* table, + const float* table_info, + const float* em_x, + const float* em, + const float* two_embed, + const int nloc, + const int nnei_i, + const int nnei_j, + const int last_layer_size); +template void tabulate_fusion_se_atten_gpu_rocm( + double* out, + const double* table, + const double* table_info, + const double* em_x, + const double* em, + const double* two_embed, + const int nloc, + const int nnei_i, + const int nnei_j, + const int last_layer_size); +template void tabulate_fusion_se_atten_grad_gpu_rocm( + float* dy_dem_x, + float* dy_dem, + const float* table, + const float* table_info, + const float* em_x, + const float* em, + const float* two_embed, + const float* dy, + const int nloc, + const int nnei, + const int last_layer_size); +template void tabulate_fusion_se_atten_grad_gpu_rocm( + double* dy_dem_x, + double* dy_dem, + const double* table, + const double* table_info, + const double* em_x, + const double* em, + const double* two_embed, + const double* dy, + const int nloc, + const int nnei, + const int last_layer_size); +template void tabulate_fusion_se_atten_grad_grad_gpu_rocm( + float* dz_dy, + const float* table, + const float* table_info, + const float* em_x, + const float* em, + const float* two_embed, + const float* dz_dy_dem_x, + const float* dz_dy_dem, + const int nloc, + const int nnei, + const int last_layer_size); +template void tabulate_fusion_se_atten_grad_grad_gpu_rocm( + double* dz_dy, + const double* table, + const double* table_info, + const double* em_x, + const double* em, + const double* two_embed, + const double* dz_dy_dem_x, + const double* dz_dy_dem, + const int nloc, + const int nnei, + const int last_layer_size); + template void tabulate_fusion_se_t_gpu_rocm(float* out, const float* table, const float* table_info, From cd15fe6b3f39340b361ae09cf7eabdfdd66eb5ec Mon Sep 17 00:00:00 2001 From: nahso Date: Sat, 6 May 2023 21:11:58 +0800 Subject: [PATCH 048/126] fix parameters --- source/lib/src/rocm/tabulate.hip.cu | 40 +++++++++++++---------------- 1 file changed, 18 insertions(+), 22 deletions(-) diff --git a/source/lib/src/rocm/tabulate.hip.cu b/source/lib/src/rocm/tabulate.hip.cu index 0d9eeb7aed..11ba402f1e 100644 --- a/source/lib/src/rocm/tabulate.hip.cu +++ b/source/lib/src/rocm/tabulate.hip.cu @@ -1199,28 +1199,24 @@ template void tabulate_fusion_se_a_grad_grad_gpu_rocm( const int nnei, const int last_layer_size); -template void tabulate_fusion_se_atten_gpu_rocm( - float* out, - const float* table, - const float* table_info, - const float* em_x, - const float* em, - const float* two_embed, - const int nloc, - const int nnei_i, - const int nnei_j, - const int last_layer_size); -template void tabulate_fusion_se_atten_gpu_rocm( - double* out, - const double* table, - const double* table_info, - const double* em_x, - const double* em, - const double* two_embed, - const int nloc, - const int nnei_i, - const int nnei_j, - const int last_layer_size); +template void tabulate_fusion_se_atten_gpu_rocm(float* out, + const float* table, + const float* table_info, + const float* em_x, + const float* em, + const float* two_embed, + const int nloc, + const int nnei, + const int last_layer_size); +template void tabulate_fusion_se_atten_gpu_rocm(double* out, + const double* table, + const double* table_info, + const double* em_x, + const double* em, + const double* two_embed, + const int nloc, + const int nnei, + const int last_layer_size); template void tabulate_fusion_se_atten_grad_gpu_rocm( float* dy_dem_x, float* dy_dem, From 3a3cdaa69ddd08b341a9a53618fc2051b48635d3 Mon Sep 17 00:00:00 2001 From: nahso Date: Sun, 7 May 2023 00:00:19 +0800 Subject: [PATCH 049/126] sync --- source/lib/src/rocm/tabulate.hip.cu | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/source/lib/src/rocm/tabulate.hip.cu b/source/lib/src/rocm/tabulate.hip.cu index 11ba402f1e..6279bc69c0 100644 --- a/source/lib/src/rocm/tabulate.hip.cu +++ b/source/lib/src/rocm/tabulate.hip.cu @@ -927,9 +927,9 @@ void tabulate_fusion_se_atten_gpu_rocm(FPTYPE* out, } hipLaunchKernelGGL( HIP_KERNEL_NAME( - tabulate_fusion_se_a_fifth_order_polynomial), + tabulate_fusion_se_atten_fifth_order_polynomial), nloc, last_layer_size, sizeof(FPTYPE) * MM * last_layer_size, 0, out, - table, em_x, em, table_info[0], table_info[1], table_info[2], + table, em_x, em, two_embed, table_info[0], table_info[1], table_info[2], table_info[3], table_info[4], nnei, last_layer_size); DPErrcheck(hipGetLastError()); DPErrcheck(hipDeviceSynchronize()); @@ -955,9 +955,9 @@ void tabulate_fusion_se_atten_grad_gpu_rocm(FPTYPE* dy_dem_x, hipLaunchKernelGGL( HIP_KERNEL_NAME( - tabulate_fusion_se_a_grad_fifth_order_polynomial), + tabulate_fusion_se_atten_grad_fifth_order_polynomial), nloc, KK * WARP_SIZE, sizeof(FPTYPE) * MM * last_layer_size, 0, dy_dem_x, - dy_dem, table, em_x, em, dy, table_info[0], table_info[1], table_info[2], + dy_dem, table, em_x, em, two_embed, dy, table_info[0], table_info[1], table_info[2], table_info[3], table_info[4], nnei, last_layer_size); DPErrcheck(hipGetLastError()); DPErrcheck(hipDeviceSynchronize()); @@ -981,10 +981,10 @@ void tabulate_fusion_se_atten_grad_grad_gpu_rocm(FPTYPE* dz_dy, DPErrcheck(hipMemset(dz_dy, 0, sizeof(FPTYPE) * nloc * 4 * last_layer_size)); hipLaunchKernelGGL( HIP_KERNEL_NAME( - tabulate_fusion_se_a_grad_grad_fifth_order_polynomial), + tabulate_fusion_se_atten_grad_grad_fifth_order_polynomial), nloc, last_layer_size, sizeof(FPTYPE) * MM * last_layer_size, 0, dz_dy, - table, em_x, em, dz_dy_dem_x, dz_dy_dem, table_info[0], table_info[1], + table, em_x, em, two_embed, dz_dy_dem_x, dz_dy_dem, table_info[0], table_info[1], table_info[2], table_info[3], table_info[4], nnei, last_layer_size); DPErrcheck(hipGetLastError()); DPErrcheck(hipDeviceSynchronize()); From 692577781b921e14dc37f9157568e6aa2d14b48a Mon Sep 17 00:00:00 2001 From: nahso Date: Mon, 8 May 2023 22:00:53 +0800 Subject: [PATCH 050/126] rename function name --- deepmd/descriptor/se_atten.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/deepmd/descriptor/se_atten.py b/deepmd/descriptor/se_atten.py index 44a6794357..4decb4dcab 100644 --- a/deepmd/descriptor/se_atten.py +++ b/deepmd/descriptor/se_atten.py @@ -383,9 +383,9 @@ def enable_compression( # two_side_type_embedding = np.concatenate([type_embedding_nei, type_embedding_center], -1) # ntypes * ntypes * (Y+Y) # two_side_type_embedding = np.reshape(two_side_type_embedding, [-1, two_side_type_embedding.shape[-1]]) # self.final_type_embedding = two_side_type_embedding - self.final_type_embedding = self._get_two_embed(graph) - self.matrix = self._get_two_side_embedding_variable(graph_def, 'matrix') - self.bias = self._get_two_side_embedding_variable(graph_def, 'bias') + self.final_type_embedding = self._get_two_side_type_embedding(graph) + self.matrix = self._get_two_side_embedding_net_variable(graph_def, 'matrix') + self.bias = self._get_two_side_embedding_net_variable(graph_def, 'bias') self.two_embd = self._make_data(self.final_type_embedding) self.davg = get_tensor_by_name_from_graph( @@ -395,7 +395,7 @@ def enable_compression( graph, "descrpt_attr%s/t_std" % suffix ) - def _get_two_embed(self, graph): + def _get_two_side_type_embedding(self, graph): type_embedding = get_tensor_by_name_from_graph(graph, 't_typeebd') type_embedding = type_embedding.astype(self.filter_np_precision) type_embedding_shape = type_embedding.shape @@ -408,7 +408,7 @@ def _get_two_embed(self, graph): return two_side_type_embedding - def _get_two_side_embedding_variable(self, graph_def, varialbe_name): + def _get_two_side_embedding_net_variable(self, graph_def, varialbe_name): ret = {} for i in range(1, self.layer_size + 1): node = get_pattern_nodes_from_graph_def(graph_def, From cba81bd33b48f502c97b938f847a0153d0fb6b3b Mon Sep 17 00:00:00 2001 From: nahso Date: Sun, 14 May 2023 22:57:41 +0800 Subject: [PATCH 051/126] let fitting net use type embedding from graph_def --- deepmd/fit/ener.py | 12 +++++++++++- deepmd/train/trainer.py | 3 ++- 2 files changed, 13 insertions(+), 2 deletions(-) diff --git a/deepmd/fit/ener.py b/deepmd/fit/ener.py index c9f70117f7..1722153d37 100644 --- a/deepmd/fit/ener.py +++ b/deepmd/fit/ener.py @@ -6,6 +6,7 @@ import numpy as np +import deepmd from deepmd.common import ( add_data_requirement, cast_precision, @@ -132,12 +133,15 @@ def __init__( uniform_seed: bool = False, layer_name: Optional[List[Optional[str]]] = None, use_aparam_as_mask: bool = False, + compress: bool = False ) -> None: """Constructor.""" # model param self.ntypes = descrpt.get_ntypes() self.dim_descrpt = descrpt.get_dim_out() + self.descrpt = descrpt self.use_aparam_as_mask = use_aparam_as_mask + self.compress = compress # args = ()\ # .add('numb_fparam', int, default = 0)\ # .add('numb_aparam', int, default = 0)\ @@ -464,7 +468,10 @@ def build( if input_dict is None: input_dict = {} bias_atom_e = self.bias_atom_e - type_embedding = input_dict.get("type_embedding", None) + if self.compress and isinstance(self.descrpt, deepmd.descriptor.DescrptSeAtten): + type_embedding = self.compress_type_embedding + else: + type_embedding = input_dict.get("type_embedding", None) atype = input_dict.get("atype", None) if self.numb_fparam > 0: if self.fparam_avg is None: @@ -684,6 +691,9 @@ def init_variables( self.fitting_net_variables = get_fitting_net_variables_from_graph_def( graph_def, suffix=suffix ) + if isinstance(self.descrpt, deepmd.descriptor.DescrptSeAtten): + type_embedding = get_tensor_by_name_from_graph(graph, 't_typeebd') + self.compress_type_embedding = tf.convert_to_tensor(type_embedding) if self.layer_name is not None: # shared variables have no suffix shared_variables = get_fitting_net_variables_from_graph_def( diff --git a/deepmd/train/trainer.py b/deepmd/train/trainer.py index 29802d0f75..cee6beda0a 100644 --- a/deepmd/train/trainer.py +++ b/deepmd/train/trainer.py @@ -91,8 +91,8 @@ def _is_subdir(path, directory): class DPTrainer: def __init__(self, jdata, run_opt, is_compress=False): self.run_opt = run_opt - self._init_param(jdata) self.is_compress = is_compress + self._init_param(jdata) def _init_param(self, jdata): # model config @@ -157,6 +157,7 @@ def fitting_net_init(fitting_type_, descrpt_type_, params): self.fitting_type = fitting_type fitting_param.pop("type", None) fitting_param["descrpt"] = self.descrpt + fitting_param["compress"] = self.is_compress self.fitting = fitting_net_init(fitting_type, descrpt_type, fitting_param) else: self.fitting_dict = {} From 542c086689db9ed6991760d735fba029cf747e0e Mon Sep 17 00:00:00 2001 From: nahso Date: Mon, 15 May 2023 19:38:53 +0800 Subject: [PATCH 052/126] code cleanup --- deepmd/descriptor/se_atten.py | 72 +---------------------------------- deepmd/train/trainer.py | 2 +- 2 files changed, 2 insertions(+), 72 deletions(-) diff --git a/deepmd/descriptor/se_atten.py b/deepmd/descriptor/se_atten.py index c2c4fa5d53..dca56e3819 100644 --- a/deepmd/descriptor/se_atten.py +++ b/deepmd/descriptor/se_atten.py @@ -373,16 +373,6 @@ def enable_compression( ) self.layer_size = self.table.layer_size - # type_embedding = get_tensor_by_name_from_graph(graph, 't_typeebd') - # type_embedding = type_embedding.astype(self.filter_np_precision) - # type_embedding_shape = type_embedding.shape - # type_embedding_nei = np.tile(np.reshape(type_embedding, [1, type_embedding_shape[0], -1]), - # [type_embedding_shape[0], 1, 1]) # (ntypes) * ntypes * Y - # type_embedding_center = np.tile(np.reshape(type_embedding, [type_embedding_shape[0], 1, -1]), - # [1, type_embedding_shape[0], 1]) # ntypes * (ntypes) * Y - # two_side_type_embedding = np.concatenate([type_embedding_nei, type_embedding_center], -1) # ntypes * ntypes * (Y+Y) - # two_side_type_embedding = np.reshape(two_side_type_embedding, [-1, two_side_type_embedding.shape[-1]]) - # self.final_type_embedding = two_side_type_embedding self.final_type_embedding = self._get_two_side_type_embedding(graph) self.matrix = self._get_two_side_embedding_net_variable(graph_def, 'matrix') self.bias = self._get_two_side_embedding_net_variable(graph_def, 'bias') @@ -745,55 +735,6 @@ def _compute_dstats_sys_smth( sysa2.append(suma2) return sysr, sysr2, sysa, sysa2, sysn - def _lookup_type_embedding( - self, - xyz_scatter, - natype, - type_embedding, - ): - """Concatenate `type_embedding` of neighbors and `xyz_scatter`. - If not self.type_one_side, concatenate `type_embedding` of center atoms as well. - - Parameters - ---------- - xyz_scatter: - shape is [nframes*natoms[0]*self.nnei, 1] - natype: - neighbor atom type - type_embedding: - shape is [self.ntypes, Y] where Y=jdata['type_embedding']['neuron'][-1] - - Returns - ------- - embedding: - environment of each atom represented by embedding. - """ - te_out_dim = type_embedding.get_shape().as_list()[-1] - self.test_type_embedding = type_embedding - self.test_nei_embed = tf.nn.embedding_lookup( - type_embedding, self.nei_type_vec - ) # shape is [self.nnei, 1+te_out_dim] - # nei_embed = tf.tile(nei_embed, (nframes * natoms[0], 1)) # shape is [nframes*natoms[0]*self.nnei, te_out_dim] - nei_embed = tf.reshape(self.test_nei_embed, [-1, te_out_dim]) - self.embedding_input = tf.concat( - [xyz_scatter, nei_embed], 1 - ) # shape is [nframes*natoms[0]*self.nnei, 1+te_out_dim] - if not self.type_one_side: - self.atm_embed = tf.nn.embedding_lookup( - type_embedding, natype - ) # shape is [nframes*natoms[0], te_out_dim] - self.atm_embed = tf.tile( - self.atm_embed, [1, self.nnei] - ) # shape is [nframes*natoms[0], self.nnei*te_out_dim] - self.atm_embed = tf.reshape( - self.atm_embed, [-1, te_out_dim] - ) # shape is [nframes*natoms[0]*self.nnei, te_out_dim] - self.embedding_input_2 = tf.concat( - [self.embedding_input, self.atm_embed], 1 - ) # shape is [nframes*natoms[0]*self.nnei, 1+te_out_dim+te_out_dim] - return self.embedding_input_2 - return self.embedding_input - def _feedforward(self, input_xyz, d_in, d_mid): residual = input_xyz input_xyz = tf.nn.relu( @@ -1015,10 +956,7 @@ def _filter_lower( ] type_embedding_shape = type_embedding.get_shape().as_list() - #index_of_two_side = self.nei_type_vec * self.ntypes + tf.tile(atype, [1, self.nnei]) - tmpres1 = self.nei_type_vec * type_embedding_shape[0] - tmpres2 = tf.tile(atype, [self.nnei]) - index_of_two_side = tmpres1 + tmpres2 + index_of_two_side = self.nei_type_vec * type_embedding_shape[0] + tf.tile(atype, [self.nnei]) if self.compress: two_embd = tf.nn.embedding_lookup(self.two_embd, index_of_two_side) @@ -1045,18 +983,10 @@ def _filter_lower( initial_variables=self.embedding_net_variables, mixed_prec=self.mixed_prec) two_embd = tf.nn.embedding_lookup(embedding_of_two_side_type_embedding, index_of_two_side) - # two_embd = tf.nn.embedding_lookup(embedding_of_two_side_type_embedding, index_of_two_side) if not self.compress: xyz_scatter = xyz_scatter * two_embd + two_embd else: - # return op_module.tabulate_fusion_se_a( - # tf.cast(self.table.data[net], self.filter_precision), - # info, - # xyz_scatter, - # tf.reshape(inputs_i, [natom, shape_i[1] // 4, 4]), - # last_layer_size=outputs_size[-1], - # ) return op_module.tabulate_fusion_se_atten( tf.cast(self.table.data[net], self.filter_precision), info, diff --git a/deepmd/train/trainer.py b/deepmd/train/trainer.py index b767de5b08..5bdd66d5af 100644 --- a/deepmd/train/trainer.py +++ b/deepmd/train/trainer.py @@ -1027,7 +1027,7 @@ def train(self, train_data=None, valid_data=None): toc = time.time() test_time = toc - tic log.info( - "model6 batch %7d training time %.2f s, testing time %.2f s" + "batch %7d training time %.2f s, testing time %.2f s" % (cur_batch, train_time, test_time) ) # the first training time is not accurate From 705d8ab1440c6f4e300cf1b6fbb73809f101df05 Mon Sep 17 00:00:00 2001 From: nahso Date: Mon, 15 May 2023 20:43:58 +0800 Subject: [PATCH 053/126] add unit tests --- .../model_compression/input-se-atten.json | 84 +++ .../tests/test_model_compression_se_atten.py | 552 ++++++++++++++++++ 2 files changed, 636 insertions(+) create mode 100644 source/tests/model_compression/input-se-atten.json create mode 100644 source/tests/test_model_compression_se_atten.py diff --git a/source/tests/model_compression/input-se-atten.json b/source/tests/model_compression/input-se-atten.json new file mode 100644 index 0000000000..ee71204dd5 --- /dev/null +++ b/source/tests/model_compression/input-se-atten.json @@ -0,0 +1,84 @@ +{ + "_comment": " model parameters", + "model": { + "type_map": [ + "O", + "H" + ], + "descriptor": { + "type": "se_atten", + "sel": 120, + "rcut_smth": 0.50, + "rcut": 6.00, + "neuron": [ + 4, + 8, + 16 + ], + "resnet_dt": false, + "axis_neuron": 16, + "seed": 1, + "attn": 128, + "attn_layer": 0, + "attn_dotr": true, + "attn_mask": false, + "_comment": " that's all" + }, + "fitting_net": { + "neuron": [ + 20, + 20, + 20 + ], + "resnet_dt": true, + "seed": 1, + "_comment": " that's all" + }, + "_comment": " that's all" + }, + + "learning_rate": { + "type": "exp", + "decay_steps": 5000, + "start_lr": 0.001, + "stop_lr": 3.51e-8, + "_comment": "that's all" + }, + + "loss": { + "type": "ener", + "start_pref_e": 0.02, + "limit_pref_e": 1, + "start_pref_f": 1000, + "limit_pref_f": 1, + "start_pref_v": 0, + "limit_pref_v": 0, + "_comment": " that's all" + }, + + "training": { + "training_data": { + "systems": [ + "model_compression/data" + ], + "batch_size": "auto", + "_comment": "that's all" + }, + "validation_data": { + "systems": [ + "model_compression/data" + ], + "batch_size": 1, + "numb_btch": 3, + "_comment": "that's all" + }, + "numb_steps": 1, + "seed": 10, + "disp_file": "lcurve.out", + "disp_freq": 1, + "save_freq": 1, + "_comment": "that's all" + }, + + "_comment": "that's all" +} diff --git a/source/tests/test_model_compression_se_atten.py b/source/tests/test_model_compression_se_atten.py new file mode 100644 index 0000000000..49e8e96603 --- /dev/null +++ b/source/tests/test_model_compression_se_atten.py @@ -0,0 +1,552 @@ +import json +import os +import subprocess as sp +import unittest + +import numpy as np + +# from deepmd.entrypoints.compress import compress +from common import ( + j_loader, + run_dp, + tests_path, +) + +from deepmd.env import ( + GLOBAL_NP_FLOAT_PRECISION, +) +from deepmd.infer import ( + DeepPot, +) + +if GLOBAL_NP_FLOAT_PRECISION == np.float32: + default_places = 4 +else: + default_places = 10 + + +def _file_delete(file): + if os.path.isdir(file): + os.rmdir(file) + elif os.path.isfile(file): + os.remove(file) + + +def _subprocess_run(command): + popen = sp.Popen(command.split(), shell=False, stdout=sp.PIPE, stderr=sp.STDOUT) + for line in iter(popen.stdout.readline, b""): + if hasattr(line, "decode"): + line = line.decode("utf-8") + line = line.rstrip() + print(line) + popen.wait() + return popen.returncode + + +def _init_models(): + data_file = str(tests_path / os.path.join("model_compression", "data")) + frozen_model = str(tests_path / "dp-original-se-atten.pb") + compressed_model = str(tests_path / "dp-compressed-se-atten.pb") + INPUT = str(tests_path / "input-se-atten.json") + jdata = j_loader(str(tests_path / os.path.join("model_compression", "input-se-atten.json"))) + jdata["training"]["training_data"]["systems"] = data_file + jdata["training"]["validation_data"]["systems"] = data_file + with open(INPUT, "w") as fp: + json.dump(jdata, fp, indent=4) + + ret = run_dp("dp train " + INPUT) + np.testing.assert_equal(ret, 0, "DP train failed!") + ret = run_dp("dp freeze -o " + frozen_model) + np.testing.assert_equal(ret, 0, "DP freeze failed!") + ret = run_dp("dp compress " + " -i " + frozen_model + " -o " + compressed_model) + np.testing.assert_equal(ret, 0, "DP model compression failed!") + return INPUT, frozen_model, compressed_model + + +INPUT, FROZEN_MODEL, COMPRESSED_MODEL = _init_models() + + +class TestDeepPotAPBC(unittest.TestCase): + @classmethod + def setUpClass(self): + self.dp_original = DeepPot(FROZEN_MODEL) + self.dp_compressed = DeepPot(COMPRESSED_MODEL) + self.coords = np.array( + [ + 12.83, + 2.56, + 2.18, + 12.09, + 2.87, + 2.74, + 00.25, + 3.32, + 1.68, + 3.36, + 3.00, + 1.81, + 3.51, + 2.51, + 2.60, + 4.27, + 3.22, + 1.56, + ] + ) + self.atype = [0, 1, 1, 0, 1, 1] + self.box = np.array([13.0, 0.0, 0.0, 0.0, 13.0, 0.0, 0.0, 0.0, 13.0]) + + def test_attrs(self): + self.assertEqual(self.dp_original.get_ntypes(), 2) + self.assertAlmostEqual(self.dp_original.get_rcut(), 6.0, places=default_places) + self.assertEqual(self.dp_original.get_type_map(), ["O", "H"]) + self.assertEqual(self.dp_original.get_dim_fparam(), 0) + self.assertEqual(self.dp_original.get_dim_aparam(), 0) + + self.assertEqual(self.dp_compressed.get_ntypes(), 2) + self.assertAlmostEqual( + self.dp_compressed.get_rcut(), 6.0, places=default_places + ) + self.assertEqual(self.dp_compressed.get_type_map(), ["O", "H"]) + self.assertEqual(self.dp_compressed.get_dim_fparam(), 0) + self.assertEqual(self.dp_compressed.get_dim_aparam(), 0) + + def test_1frame(self): + ee0, ff0, vv0 = self.dp_original.eval( + self.coords, self.box, self.atype, atomic=False + ) + ee1, ff1, vv1 = self.dp_compressed.eval( + self.coords, self.box, self.atype, atomic=False + ) + # check shape of the returns + nframes = 1 + natoms = len(self.atype) + self.assertEqual(ee0.shape, (nframes, 1)) + self.assertEqual(ff0.shape, (nframes, natoms, 3)) + self.assertEqual(vv0.shape, (nframes, 9)) + self.assertEqual(ee1.shape, (nframes, 1)) + self.assertEqual(ff1.shape, (nframes, natoms, 3)) + self.assertEqual(vv1.shape, (nframes, 9)) + # check values + np.testing.assert_almost_equal(ff0, ff1, default_places) + np.testing.assert_almost_equal(ee0, ee1, default_places) + np.testing.assert_almost_equal(vv0, vv1, default_places) + + def test_1frame_atm(self): + ee0, ff0, vv0, ae0, av0 = self.dp_original.eval( + self.coords, self.box, self.atype, atomic=True + ) + ee1, ff1, vv1, ae1, av1 = self.dp_compressed.eval( + self.coords, self.box, self.atype, atomic=True + ) + # check shape of the returns + nframes = 1 + natoms = len(self.atype) + self.assertEqual(ee0.shape, (nframes, 1)) + self.assertEqual(ff0.shape, (nframes, natoms, 3)) + self.assertEqual(vv0.shape, (nframes, 9)) + self.assertEqual(ae0.shape, (nframes, natoms, 1)) + self.assertEqual(av0.shape, (nframes, natoms, 9)) + self.assertEqual(ee1.shape, (nframes, 1)) + self.assertEqual(ff1.shape, (nframes, natoms, 3)) + self.assertEqual(vv1.shape, (nframes, 9)) + self.assertEqual(ae1.shape, (nframes, natoms, 1)) + self.assertEqual(av1.shape, (nframes, natoms, 9)) + # check values + np.testing.assert_almost_equal(ff0, ff1, default_places) + np.testing.assert_almost_equal(ae0, ae1, default_places) + np.testing.assert_almost_equal(av0, av1, default_places) + np.testing.assert_almost_equal(ee0, ee1, default_places) + np.testing.assert_almost_equal(vv0, vv1, default_places) + + def test_2frame_atm(self): + coords2 = np.concatenate((self.coords, self.coords)) + box2 = np.concatenate((self.box, self.box)) + ee0, ff0, vv0, ae0, av0 = self.dp_original.eval( + coords2, box2, self.atype, atomic=True + ) + ee1, ff1, vv1, ae1, av1 = self.dp_compressed.eval( + coords2, box2, self.atype, atomic=True + ) + # check shape of the returns + nframes = 2 + natoms = len(self.atype) + self.assertEqual(ee0.shape, (nframes, 1)) + self.assertEqual(ff0.shape, (nframes, natoms, 3)) + self.assertEqual(vv0.shape, (nframes, 9)) + self.assertEqual(ae0.shape, (nframes, natoms, 1)) + self.assertEqual(av0.shape, (nframes, natoms, 9)) + self.assertEqual(ee1.shape, (nframes, 1)) + self.assertEqual(ff1.shape, (nframes, natoms, 3)) + self.assertEqual(vv1.shape, (nframes, 9)) + self.assertEqual(ae1.shape, (nframes, natoms, 1)) + self.assertEqual(av1.shape, (nframes, natoms, 9)) + + # check values + np.testing.assert_almost_equal(ff0, ff1, default_places) + np.testing.assert_almost_equal(ae0, ae1, default_places) + np.testing.assert_almost_equal(av0, av1, default_places) + np.testing.assert_almost_equal(ee0, ee1, default_places) + np.testing.assert_almost_equal(vv0, vv1, default_places) + + +class TestDeepPotANoPBC(unittest.TestCase): + @classmethod + def setUpClass(self): + self.dp_original = DeepPot(FROZEN_MODEL) + self.dp_compressed = DeepPot(COMPRESSED_MODEL) + self.coords = np.array( + [ + 12.83, + 2.56, + 2.18, + 12.09, + 2.87, + 2.74, + 00.25, + 3.32, + 1.68, + 3.36, + 3.00, + 1.81, + 3.51, + 2.51, + 2.60, + 4.27, + 3.22, + 1.56, + ] + ) + self.atype = [0, 1, 1, 0, 1, 1] + self.box = None + + def test_1frame(self): + ee0, ff0, vv0 = self.dp_original.eval( + self.coords, self.box, self.atype, atomic=False + ) + ee1, ff1, vv1 = self.dp_compressed.eval( + self.coords, self.box, self.atype, atomic=False + ) + # check shape of the returns + nframes = 1 + natoms = len(self.atype) + self.assertEqual(ee0.shape, (nframes, 1)) + self.assertEqual(ff0.shape, (nframes, natoms, 3)) + self.assertEqual(vv0.shape, (nframes, 9)) + self.assertEqual(ee1.shape, (nframes, 1)) + self.assertEqual(ff1.shape, (nframes, natoms, 3)) + self.assertEqual(vv1.shape, (nframes, 9)) + # check values + np.testing.assert_almost_equal(ff0, ff1, default_places) + np.testing.assert_almost_equal(ee0, ee1, default_places) + np.testing.assert_almost_equal(vv0, vv1, default_places) + + def test_1frame_atm(self): + ee0, ff0, vv0, ae0, av0 = self.dp_original.eval( + self.coords, self.box, self.atype, atomic=True + ) + ee1, ff1, vv1, ae1, av1 = self.dp_compressed.eval( + self.coords, self.box, self.atype, atomic=True + ) + # check shape of the returns + nframes = 1 + natoms = len(self.atype) + self.assertEqual(ee0.shape, (nframes, 1)) + self.assertEqual(ff0.shape, (nframes, natoms, 3)) + self.assertEqual(vv0.shape, (nframes, 9)) + self.assertEqual(ae0.shape, (nframes, natoms, 1)) + self.assertEqual(av0.shape, (nframes, natoms, 9)) + self.assertEqual(ee1.shape, (nframes, 1)) + self.assertEqual(ff1.shape, (nframes, natoms, 3)) + self.assertEqual(vv1.shape, (nframes, 9)) + self.assertEqual(ae1.shape, (nframes, natoms, 1)) + self.assertEqual(av1.shape, (nframes, natoms, 9)) + # check values + np.testing.assert_almost_equal(ff0, ff1, default_places) + np.testing.assert_almost_equal(ae0, ae1, default_places) + np.testing.assert_almost_equal(av0, av1, default_places) + np.testing.assert_almost_equal(ee0, ee1, default_places) + np.testing.assert_almost_equal(vv0, vv1, default_places) + + def test_2frame_atm(self): + coords2 = np.concatenate((self.coords, self.coords)) + ee0, ff0, vv0, ae0, av0 = self.dp_original.eval( + coords2, self.box, self.atype, atomic=True + ) + ee1, ff1, vv1, ae1, av1 = self.dp_compressed.eval( + coords2, self.box, self.atype, atomic=True + ) + # check shape of the returns + nframes = 2 + natoms = len(self.atype) + self.assertEqual(ee0.shape, (nframes, 1)) + self.assertEqual(ff0.shape, (nframes, natoms, 3)) + self.assertEqual(vv0.shape, (nframes, 9)) + self.assertEqual(ae0.shape, (nframes, natoms, 1)) + self.assertEqual(av0.shape, (nframes, natoms, 9)) + self.assertEqual(ee1.shape, (nframes, 1)) + self.assertEqual(ff1.shape, (nframes, natoms, 3)) + self.assertEqual(vv1.shape, (nframes, 9)) + self.assertEqual(ae1.shape, (nframes, natoms, 1)) + self.assertEqual(av1.shape, (nframes, natoms, 9)) + + # check values + np.testing.assert_almost_equal(ff0, ff1, default_places) + np.testing.assert_almost_equal(ae0, ae1, default_places) + np.testing.assert_almost_equal(av0, av1, default_places) + np.testing.assert_almost_equal(ee0, ee1, default_places) + np.testing.assert_almost_equal(vv0, vv1, default_places) + + +class TestDeepPotALargeBoxNoPBC(unittest.TestCase): + @classmethod + def setUpClass(self): + self.dp_original = DeepPot(FROZEN_MODEL) + self.dp_compressed = DeepPot(COMPRESSED_MODEL) + self.coords = np.array( + [ + 12.83, + 2.56, + 2.18, + 12.09, + 2.87, + 2.74, + 00.25, + 3.32, + 1.68, + 3.36, + 3.00, + 1.81, + 3.51, + 2.51, + 2.60, + 4.27, + 3.22, + 1.56, + ] + ) + self.atype = [0, 1, 1, 0, 1, 1] + self.box = np.array([19.0, 0.0, 0.0, 0.0, 13.0, 0.0, 0.0, 0.0, 13.0]) + + def test_1frame(self): + ee0, ff0, vv0 = self.dp_original.eval( + self.coords, self.box, self.atype, atomic=False + ) + ee1, ff1, vv1 = self.dp_compressed.eval( + self.coords, self.box, self.atype, atomic=False + ) + # check shape of the returns + nframes = 1 + natoms = len(self.atype) + self.assertEqual(ee0.shape, (nframes, 1)) + self.assertEqual(ff0.shape, (nframes, natoms, 3)) + self.assertEqual(vv0.shape, (nframes, 9)) + self.assertEqual(ee1.shape, (nframes, 1)) + self.assertEqual(ff1.shape, (nframes, natoms, 3)) + self.assertEqual(vv1.shape, (nframes, 9)) + # check values + np.testing.assert_almost_equal(ff0, ff1, default_places) + np.testing.assert_almost_equal(ee0, ee1, default_places) + np.testing.assert_almost_equal(vv0, vv1, default_places) + + def test_1frame_atm(self): + ee0, ff0, vv0, ae0, av0 = self.dp_original.eval( + self.coords, self.box, self.atype, atomic=True + ) + ee1, ff1, vv1, ae1, av1 = self.dp_compressed.eval( + self.coords, self.box, self.atype, atomic=True + ) + # check shape of the returns + nframes = 1 + natoms = len(self.atype) + self.assertEqual(ee0.shape, (nframes, 1)) + self.assertEqual(ff0.shape, (nframes, natoms, 3)) + self.assertEqual(vv0.shape, (nframes, 9)) + self.assertEqual(ae0.shape, (nframes, natoms, 1)) + self.assertEqual(av0.shape, (nframes, natoms, 9)) + self.assertEqual(ee1.shape, (nframes, 1)) + self.assertEqual(ff1.shape, (nframes, natoms, 3)) + self.assertEqual(vv1.shape, (nframes, 9)) + self.assertEqual(ae1.shape, (nframes, natoms, 1)) + self.assertEqual(av1.shape, (nframes, natoms, 9)) + # check values + np.testing.assert_almost_equal(ff0, ff1, default_places) + np.testing.assert_almost_equal(ae0, ae1, default_places) + np.testing.assert_almost_equal(av0, av1, default_places) + np.testing.assert_almost_equal(ee0, ee1, default_places) + np.testing.assert_almost_equal(vv0, vv1, default_places) + + def test_ase(self): + from ase import ( + Atoms, + ) + + from deepmd.calculator import ( + DP, + ) + + water0 = Atoms( + "OHHOHH", + positions=self.coords.reshape((-1, 3)), + cell=self.box.reshape((3, 3)), + calculator=DP(FROZEN_MODEL), + ) + water1 = Atoms( + "OHHOHH", + positions=self.coords.reshape((-1, 3)), + cell=self.box.reshape((3, 3)), + calculator=DP(COMPRESSED_MODEL), + ) + ee0 = water0.get_potential_energy() + ff0 = water0.get_forces() + ee1 = water1.get_potential_energy() + ff1 = water1.get_forces() + nframes = 1 + np.testing.assert_almost_equal(ff0, ff1, default_places) + np.testing.assert_almost_equal(ee0, ee1, default_places) + + +class TestDeepPotAPBCExcludeTypes(unittest.TestCase): + @classmethod + def setUpClass(self): + self.dp_original = DeepPot(FROZEN_MODEL) + self.dp_compressed = DeepPot(COMPRESSED_MODEL) + self.coords = np.array( + [ + 12.83, + 2.56, + 2.18, + 12.09, + 2.87, + 2.74, + 00.25, + 3.32, + 1.68, + 3.36, + 3.00, + 1.81, + 3.51, + 2.51, + 2.60, + 4.27, + 3.22, + 1.56, + ] + ) + self.atype = [0, 1, 1, 0, 1, 1] + self.box = np.array([13.0, 0.0, 0.0, 0.0, 13.0, 0.0, 0.0, 0.0, 13.0]) + + @classmethod + def tearDownClass(self): + _file_delete(INPUT) + _file_delete(FROZEN_MODEL) + _file_delete(COMPRESSED_MODEL) + _file_delete("out.json") + _file_delete("compress.json") + _file_delete("checkpoint") + _file_delete("model.ckpt.meta") + _file_delete("model.ckpt.index") + _file_delete("model.ckpt.data-00000-of-00001") + _file_delete("model.ckpt-100.meta") + _file_delete("model.ckpt-100.index") + _file_delete("model.ckpt-100.data-00000-of-00001") + _file_delete("model-compression/checkpoint") + _file_delete("model-compression/model.ckpt.meta") + _file_delete("model-compression/model.ckpt.index") + _file_delete("model-compression/model.ckpt.data-00000-of-00001") + _file_delete("model-compression") + _file_delete("input_v2_compat.json") + _file_delete("lcurve.out") + + def test_attrs(self): + self.assertEqual(self.dp_original.get_ntypes(), 2) + self.assertAlmostEqual(self.dp_original.get_rcut(), 6.0, places=default_places) + self.assertEqual(self.dp_original.get_type_map(), ["O", "H"]) + self.assertEqual(self.dp_original.get_dim_fparam(), 0) + self.assertEqual(self.dp_original.get_dim_aparam(), 0) + + self.assertEqual(self.dp_compressed.get_ntypes(), 2) + self.assertAlmostEqual( + self.dp_compressed.get_rcut(), 6.0, places=default_places + ) + self.assertEqual(self.dp_compressed.get_type_map(), ["O", "H"]) + self.assertEqual(self.dp_compressed.get_dim_fparam(), 0) + self.assertEqual(self.dp_compressed.get_dim_aparam(), 0) + + def test_1frame(self): + ee0, ff0, vv0 = self.dp_original.eval( + self.coords, self.box, self.atype, atomic=False + ) + ee1, ff1, vv1 = self.dp_compressed.eval( + self.coords, self.box, self.atype, atomic=False + ) + # check shape of the returns + nframes = 1 + natoms = len(self.atype) + self.assertEqual(ee0.shape, (nframes, 1)) + self.assertEqual(ff0.shape, (nframes, natoms, 3)) + self.assertEqual(vv0.shape, (nframes, 9)) + self.assertEqual(ee1.shape, (nframes, 1)) + self.assertEqual(ff1.shape, (nframes, natoms, 3)) + self.assertEqual(vv1.shape, (nframes, 9)) + # check values + np.testing.assert_almost_equal(ff0, ff1, default_places) + np.testing.assert_almost_equal(ee0, ee1, default_places) + np.testing.assert_almost_equal(vv0, vv1, default_places) + + def test_1frame_atm(self): + ee0, ff0, vv0, ae0, av0 = self.dp_original.eval( + self.coords, self.box, self.atype, atomic=True + ) + ee1, ff1, vv1, ae1, av1 = self.dp_compressed.eval( + self.coords, self.box, self.atype, atomic=True + ) + # check shape of the returns + nframes = 1 + natoms = len(self.atype) + self.assertEqual(ee0.shape, (nframes, 1)) + self.assertEqual(ff0.shape, (nframes, natoms, 3)) + self.assertEqual(vv0.shape, (nframes, 9)) + self.assertEqual(ae0.shape, (nframes, natoms, 1)) + self.assertEqual(av0.shape, (nframes, natoms, 9)) + self.assertEqual(ee1.shape, (nframes, 1)) + self.assertEqual(ff1.shape, (nframes, natoms, 3)) + self.assertEqual(vv1.shape, (nframes, 9)) + self.assertEqual(ae1.shape, (nframes, natoms, 1)) + self.assertEqual(av1.shape, (nframes, natoms, 9)) + # check values + np.testing.assert_almost_equal(ff0, ff1, default_places) + np.testing.assert_almost_equal(ae0, ae1, default_places) + np.testing.assert_almost_equal(av0, av1, default_places) + np.testing.assert_almost_equal(ee0, ee1, default_places) + np.testing.assert_almost_equal(vv0, vv1, default_places) + + def test_2frame_atm(self): + coords2 = np.concatenate((self.coords, self.coords)) + box2 = np.concatenate((self.box, self.box)) + ee0, ff0, vv0, ae0, av0 = self.dp_original.eval( + coords2, box2, self.atype, atomic=True + ) + ee1, ff1, vv1, ae1, av1 = self.dp_compressed.eval( + coords2, box2, self.atype, atomic=True + ) + # check shape of the returns + nframes = 2 + natoms = len(self.atype) + self.assertEqual(ee0.shape, (nframes, 1)) + self.assertEqual(ff0.shape, (nframes, natoms, 3)) + self.assertEqual(vv0.shape, (nframes, 9)) + self.assertEqual(ae0.shape, (nframes, natoms, 1)) + self.assertEqual(av0.shape, (nframes, natoms, 9)) + self.assertEqual(ee1.shape, (nframes, 1)) + self.assertEqual(ff1.shape, (nframes, natoms, 3)) + self.assertEqual(vv1.shape, (nframes, 9)) + self.assertEqual(ae1.shape, (nframes, natoms, 1)) + self.assertEqual(av1.shape, (nframes, natoms, 9)) + + # check values + np.testing.assert_almost_equal(ff0, ff1, default_places) + np.testing.assert_almost_equal(ae0, ae1, default_places) + np.testing.assert_almost_equal(av0, av1, default_places) + np.testing.assert_almost_equal(ee0, ee1, default_places) + np.testing.assert_almost_equal(vv0, vv1, default_places) From dfaa7d35a4c83420bfbd811aedc4d3e495c48705 Mon Sep 17 00:00:00 2001 From: nahso Date: Mon, 15 May 2023 20:44:55 +0800 Subject: [PATCH 054/126] enable second derivative --- source/lib/src/cuda/tabulate.cu | 2 -- source/lib/src/tabulate.cc | 2 -- source/op/_tabulate_grad.py | 4 +-- source/op/tabulate_multi_device.cc | 44 ++++++++++++------------------ 4 files changed, 19 insertions(+), 33 deletions(-) diff --git a/source/lib/src/cuda/tabulate.cu b/source/lib/src/cuda/tabulate.cu index 0ff6033750..e0cf70a5a3 100644 --- a/source/lib/src/cuda/tabulate.cu +++ b/source/lib/src/cuda/tabulate.cu @@ -513,8 +513,6 @@ __global__ void tabulate_fusion_se_atten_grad_grad_fifth_order_polynomial( var[1] + (2 * var[2] + (3 * var[3] + (4 * var[4] + 5 * var[5] * xx) * xx) * xx) * xx; - FPTYPE t = two_embed[block_idx * nnei*last_layer_size + ii * last_layer_size + thread_idx]; - res_grad = res_grad * t; for (int kk = 0; kk < MTILE; kk++) { int em_index = block_idx * nnei * MTILE + ii * MTILE + kk; diff --git a/source/lib/src/tabulate.cc b/source/lib/src/tabulate.cc index a452e6eb17..ddcc995aec 100644 --- a/source/lib/src/tabulate.cc +++ b/source/lib/src/tabulate.cc @@ -502,14 +502,12 @@ void deepmd::tabulate_fusion_se_atten_grad_grad_cpu(FPTYPE* dz_dy, FPTYPE a5 = table[table_idx * last_layer_size * 6 + 6 * kk + 5]; FPTYPE var = a0 + (a1 + (a2 + (a3 + (a4 + a5 * xx) * xx) * xx) * xx) * xx; - FPTYPE t = two_embed[ii * nnei*last_layer_size + jj * last_layer_size + kk]; FPTYPE var_grad = a1 + ((FPTYPE)2. * a2 + ((FPTYPE)3. * a3 + ((FPTYPE)4. * a4 + (FPTYPE)5. * a5 * xx) * xx) * xx) * xx; - var_grad = var_grad * t; if (unloop) { dz_dy[ii * last_layer_size * 4 + 0 * last_layer_size + kk] += (nnei - jj) * (var * hh[0] + dz_xx * var_grad * ll[0]); diff --git a/source/op/_tabulate_grad.py b/source/op/_tabulate_grad.py index e4b022acbc..91a889e513 100644 --- a/source/op/_tabulate_grad.py +++ b/source/op/_tabulate_grad.py @@ -55,7 +55,7 @@ def _tabulate_fusion_se_atten_grad_grad_cc(op, dy, dy_, dy_dtwo): op.inputs[1], # table_info op.inputs[2], # em_x op.inputs[3], # em - op.inputs[4], # two_embed + dy_dtwo, # two_embed dy, # dz_dy_dem_x dy_, # dz_dy_dem op.inputs[6] # descriptor @@ -64,7 +64,7 @@ def _tabulate_fusion_se_atten_grad_grad_cc(op, dy, dy_, dy_dtwo): None, # table_info None, # em_x None, # em - tf.zeros_like(op.inputs[4]), # two_embed + None, # two_embed dz_dy, # dy None] # descriptor diff --git a/source/op/tabulate_multi_device.cc b/source/op/tabulate_multi_device.cc index 9d17d31e02..a284e7b9e4 100644 --- a/source/op/tabulate_multi_device.cc +++ b/source/op/tabulate_multi_device.cc @@ -418,10 +418,8 @@ class TabulateFusionSeAttenOp : public OpKernel { em, two_embed, nloc, nnei, last_layer_size); #endif // TENSORFLOW_USE_ROCM } else if (device == "CPU") { - deepmd::tabulate_fusion_se_atten_cpu(descriptor, table, table_info, em_x, em, two_embed, - nloc, nnei, last_layer_size); - //deepmd::tabulate_fusion_se_a_cpu(descriptor, table, table_info, em_x, em, - // nloc, nnei, last_layer_size); + deepmd::tabulate_fusion_se_atten_cpu(descriptor, table, table_info, em_x, em, two_embed, + nloc, nnei, last_layer_size); } } @@ -497,12 +495,8 @@ class TabulateFusionSeAttenGradOp : public OpKernel { nnei, last_layer_size); #endif // TENSORFLOW_USE_ROCM } else if (device == "CPU") { - deepmd::tabulate_fusion_se_atten_grad_cpu(dy_dem_x, dy_dem, table, table_info, - em_x, em, two_embed, dy, nloc, nnei, - last_layer_size); - //deepmd::tabulate_fusion_se_a_grad_cpu(dy_dem_x, dy_dem, table, table_info, - // em_x, em, dy, nloc, nnei, - // last_layer_size); + deepmd::tabulate_fusion_se_atten_grad_cpu(dy_dem_x, dy_dem, table, table_info, + em_x, em, two_embed, dy, nloc, nnei, last_layer_size); } } @@ -553,26 +547,22 @@ class TabulateFusionSeAttenGradGradOp : public OpKernel { if (device == "GPU") { #if GOOGLE_CUDA - //deepmd::tabulate_fusion_se_atten_grad_grad_gpu_cuda( - // dz_dy, table, table_info, em_x, em, dz_dy_dem_x, dz_dy_dem, nloc, - // nnei, last_layer_size); + deepmd::tabulate_fusion_se_atten_grad_grad_gpu_cuda( + dz_dy, table, table_info, em_x, em, two_embed, dz_dy_dem_x, dz_dy_dem, nloc, + nnei, last_layer_size); #endif // GOOGLE_CUDA #if TENSORFLOW_USE_ROCM - //deepmd::tabulate_fusion_se_atten_grad_grad_gpu_rocm( - // dz_dy, table, table_info, em_x, em, dz_dy_dem_x, dz_dy_dem, nloc, - // nnei, last_layer_size); + deepmd::tabulate_fusion_se_atten_grad_grad_gpu_rocm( + dz_dy, table, table_info, em_x, em, two_embed, dz_dy_dem_x, dz_dy_dem, nloc, + nnei, last_layer_size); #endif // TENSORFLOW_USE_ROCM OP_REQUIRES(context, (last_layer_size <= 1024), errors::InvalidArgument( "In the process of model compression, the size of the " "last layer of embedding net must be less than 1024!")); } else if (device == "CPU") { - //deepmd::tabulate_fusion_se_a_grad_grad_cpu(dz_dy, table, table_info, em_x, - // em, dz_dy_dem_x, dz_dy_dem, - // nloc, nnei, last_layer_size); - deepmd::tabulate_fusion_se_atten_grad_grad_cpu(dz_dy, table, table_info, em_x, - em, two_embed, dz_dy_dem_x, dz_dy_dem, - nloc, nnei, last_layer_size); + deepmd::tabulate_fusion_se_atten_grad_grad_cpu(dz_dy, table, table_info, em_x, + em, two_embed, dz_dy_dem_x, dz_dy_dem, nloc, nnei, last_layer_size); } } @@ -987,14 +977,14 @@ class TabulateFusionSeRGradGradOp : public OpKernel { TabulateFusionSeAGradGradOp); \ REGISTER_KERNEL_BUILDER( \ Name("TabulateFusionSeAtten").Device(DEVICE_CPU).TypeConstraint("T"), \ - TabulateFusionSeAttenOp); \ + TabulateFusionSeAttenOp); \ REGISTER_KERNEL_BUILDER( \ - Name("TabulateFusionSeAttenGrad").Device(DEVICE_CPU).TypeConstraint("T"), \ - TabulateFusionSeAttenGradOp); \ - REGISTER_KERNEL_BUILDER(Name("TabulateFusionSeAttenGradGrad") \ + Name("TabulateFusionSeAttenGrad").Device(DEVICE_CPU).TypeConstraint("T"),\ + TabulateFusionSeAttenGradOp); \ + REGISTER_KERNEL_BUILDER(Name("TabulateFusionSeAttenGradGrad") \ .Device(DEVICE_CPU) \ .TypeConstraint("T"), \ - TabulateFusionSeAttenGradGradOp); \ + TabulateFusionSeAttenGradGradOp); \ REGISTER_KERNEL_BUILDER( \ Name("TabulateFusionSeT").Device(DEVICE_CPU).TypeConstraint("T"), \ TabulateFusionSeTOp); \ From d44bfefbbb8ce5eaab2290a1c1b0794faf28e8af Mon Sep 17 00:00:00 2001 From: nahso Date: Tue, 16 May 2023 12:43:43 +0800 Subject: [PATCH 055/126] prohibit the compression of non-energy model --- deepmd/train/trainer.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/deepmd/train/trainer.py b/deepmd/train/trainer.py index 5bdd66d5af..4fdf06dbb5 100644 --- a/deepmd/train/trainer.py +++ b/deepmd/train/trainer.py @@ -181,6 +181,8 @@ def fitting_net_init(fitting_type_, descrpt_type_, params): fitting_param.pop("type", None) fitting_param["descrpt"] = self.descrpt fitting_param["compress"] = self.is_compress + if self.is_compress and fitting_type != "ener": + raise RuntimeError("compression can only be performed in energy model, but " + fitting_type + " is given") self.fitting = fitting_net_init(fitting_type, descrpt_type, fitting_param) else: self.fitting_dict = {} From 946d0a6b63fe114a35e936b6c66203e0ab35b28f Mon Sep 17 00:00:00 2001 From: nahso Date: Wed, 17 May 2023 00:24:55 +0800 Subject: [PATCH 056/126] replace ref data with new data generated by se_atten --- source/tests/test_data_large_batch.py | 56 +++--- source/tests/test_descrpt_se_atten.py | 240 +++++++++++++------------- source/tests/test_model_se_atten.py | 56 +++--- 3 files changed, 176 insertions(+), 176 deletions(-) diff --git a/source/tests/test_data_large_batch.py b/source/tests/test_data_large_batch.py index 531c6de4f3..24f98f81df 100644 --- a/source/tests/test_data_large_batch.py +++ b/source/tests/test_data_large_batch.py @@ -194,37 +194,37 @@ def test_data_mixed_type(self): np.savetxt("f.out", f.reshape([1, -1]), delimiter=",") np.savetxt("v.out", v.reshape([1, -1]), delimiter=",") - refe = [6.121172052273667e01] + refe = [6.125987139770151657e+01] reff = [ - 1.1546857028815118e-02, - 1.7560407103242779e-02, - 7.1301778864729290e-04, - 2.3682630974376197e-02, - 1.6842732518204180e-02, - -2.2408109608703206e-03, - -7.9408568690697776e-03, - 9.6856119564082792e-03, - 1.9055514693144326e-05, - 8.7017502459205160e-03, - -2.7153030569749256e-02, - -8.8338555421916490e-04, - -4.3841165945453904e-02, - 5.8104108317526765e-03, - 2.6243178542006552e-03, - 7.8507845654118558e-03, - -2.2746131839858654e-02, - -2.3219464245160639e-04, + 4.173604932578619392e-03, + 1.206055616860458521e-02, + 5.669105924718363535e-04, + 2.076442461575213339e-02, + 1.063054962554822735e-02, + -1.959086927211039792e-03, + -1.249664752022961855e-03, + 6.583345686017975928e-03, + -5.217026786924852146e-05, + 2.282684708646087659e-03, + -1.818523164208030174e-02, + -5.304299018874505858e-04, + -3.217827206046815908e-02, + 2.282463214991831829e-03, + 2.123435020231457040e-03, + 6.207222555514273557e-03, + -1.337168305308231164e-02, + -1.486585157355551452e-04 ] refv = [ - -0.10488160947198523, - 0.016694308932682225, - 0.003444164500535988, - 0.016694308932682235, - -0.05415326614376374, - -0.0010792017166882334, - 0.003444164500535988, - -0.001079201716688233, - -0.00020932681975049773, + -5.654134160411147109e-02, + 8.003907519448825308e-03, + 2.281222034457657932e-03, + 8.003907519448828778e-03, + -3.428760334596384768e-02, + -6.271214134527594575e-04, + 2.281222034457657932e-03, + -6.271214134527596743e-04, + -1.598202190275801961e-04 ] refe = np.reshape(refe, [-1]) diff --git a/source/tests/test_descrpt_se_atten.py b/source/tests/test_descrpt_se_atten.py index cfa2cdaae5..6a1256f0f7 100644 --- a/source/tests/test_descrpt_se_atten.py +++ b/source/tests/test_descrpt_se_atten.py @@ -135,66 +135,66 @@ def test_descriptor_two_sides(self): model_dout = model_dout.reshape([-1]) ref_dout = [ - 1.3503570575883254e-04, - -9.3606804794552518e-05, - -9.3606804794552518e-05, - 6.4931435609575354e-05, - -3.4432462227712845e-04, - 2.3883309310633266e-04, - -2.1612770334269806e-04, - 1.4980041766865035e-04, - 5.1902342465554648e-04, - -3.5995814159000579e-04, - 1.0061650355705337e-04, - -7.5148260042556979e-05, - -7.5148260042556979e-05, - 5.6249549384058458e-05, - -2.7820514647114664e-04, - 2.0819618461713165e-04, - -1.5698895407951743e-04, - 1.1721016363267746e-04, - 4.0972585703616773e-04, - -3.0650763759131061e-04, - 7.5599650998659526e-05, - -5.8808888720672558e-05, - -5.8808888720672558e-05, - 4.5766209906762655e-05, - -2.1712714013251668e-04, - 1.6899894453623564e-04, - -1.2167120597162636e-04, - 9.4648599144861605e-05, - 3.2200758382615601e-04, - -2.5060486486718734e-04, - 1.1293831101452813e-04, - -7.9512063028041913e-05, - -7.9512063028041913e-05, - 5.5979262682797850e-05, - -2.9058515610909440e-04, - 2.0457554106366365e-04, - -1.8732839505532627e-04, - 1.3188376232775540e-04, - 4.4448730317793450e-04, - -3.1292650304617497e-04, - 1.3015885894252541e-04, - -8.8816609587789126e-05, - -8.8816609587789126e-05, - 6.0613949400496957e-05, - -3.2308121544925519e-04, - 2.2046786823295058e-04, - -2.1781481424814687e-04, - 1.4862599684199924e-04, - 4.9955378034266583e-04, - -3.4089120488765758e-04, - 1.0160496779809329e-04, - -7.4538471222199861e-05, - -7.4538471222199861e-05, - 5.4703671679263269e-05, - -2.7394267959121653e-04, - 2.0103409637607701e-04, - -1.6657135958432620e-04, - 1.2219321453198225e-04, - 4.1344754259964935e-04, - -3.0339251136512270e-04, + 9.345601811834877858e-06, + -5.952429657436736067e-05, + -5.952429657436736067e-05, + 3.798219339938377500e-04, + -5.473801266600750138e-05, + 3.492689212282017045e-04, + -4.701511803776506468e-05, + 2.996014142828410851e-04, + 1.519318254663050438e-04, + -9.691679729305131179e-04, + 4.639301212229915600e-06, + -3.880307472438465351e-05, + -3.880307472438465351e-05, + 3.263510366915401122e-04, + -3.602511945944678490e-05, + 3.030070692648913970e-04, + -2.749648518890264080e-05, + 2.306635984680620104e-04, + 9.768537816050416954e-05, + -8.212186297001088662e-04, + 3.460569880141428627e-06, + -3.057606512460111250e-05, + -3.057606512460111250e-05, + 2.716853957153315133e-04, + -2.822734036378936432e-05, + 2.510848446158910584e-04, + -2.058670330591998133e-05, + 1.836196876431213907e-04, + 7.592953891416902529e-05, + -6.758138628497427618e-04, + 9.016224735333271687e-06, + -5.493669637892903309e-05, + -5.493669637892903309e-05, + 3.349227130387897179e-04, + -5.006878854367239126e-05, + 3.053247155435676952e-04, + -4.307577100091461854e-05, + 2.627594780740223688e-04, + 1.390650311881827983e-04, + -8.480702102774508640e-04, + 1.194421346644617234e-05, + -6.584805186418614701e-05, + -6.584805186418614701e-05, + 3.630189444637618094e-04, + -5.971894402294766291e-05, + 3.292293648749792449e-04, + -5.411449195138936510e-05, + 2.983321514371517751e-04, + 1.677372743720770298e-04, + -9.247324089117067367e-04, + 6.525370698008342040e-06, + -4.616209490400820508e-05, + -4.616209490400820508e-05, + 3.272132307784752408e-04, + -4.228414781396409573e-05, + 2.998297009884601412e-04, + -3.397721060608091141e-05, + 2.410596980080286660e-04, + 1.158980826260448753e-04, + -8.219405348709558565e-04 ] places = 10 @@ -302,66 +302,66 @@ def test_descriptor_one_side(self): model_dout = model_dout.reshape([-1]) ref_dout = [ - 8.9336098555659429e-05, - -3.8921422089719007e-05, - -3.8921422089719007e-05, - 1.6975109833017758e-05, - -2.9184951813034413e-04, - 1.2724836941382651e-04, - -1.8062533253590169e-04, - 7.8681048972093648e-05, - 4.2206017420030542e-04, - -1.8398310612921889e-04, - 6.4996467281506633e-05, - -3.0812041327073575e-05, - -3.0812041327073575e-05, - 1.4663988013438402e-05, - -2.3274950984084172e-04, - 1.1059587214865573e-04, - -1.3043761448464089e-04, - 6.1788865409826698e-05, - 3.2900269837104958e-04, - -1.5623668424484728e-04, - 5.0697927477465942e-05, - -2.3511768544350768e-05, - -2.3511768544350768e-05, - 1.0919808814040025e-05, - -1.8622373494960208e-04, - 8.6439275444049409e-05, - -1.0326450661269683e-04, - 4.7880797898768150e-05, - 2.6230208262918372e-04, - -1.2172811361250681e-04, - 7.8240863239649707e-05, - -3.2501260967978116e-05, - -3.2501260967978116e-05, - 1.3502267073810926e-05, - -2.5360559687597850e-04, - 1.0535336854834091e-04, - -1.6047265448841568e-04, - 6.6660202062744658e-05, - 3.6833864909272261e-04, - -1.5301457671691837e-04, - 9.1148582997925288e-05, - -3.6614945467066073e-05, - -3.6614945467066073e-05, - 1.4709958908948206e-05, - -2.8364168092837332e-04, - 1.1394466218003484e-04, - -1.8721615730559043e-04, - 7.5203967811613109e-05, - 4.1632420070310456e-04, - -1.6724364343353009e-04, - 6.9506193268190631e-05, - -3.0228106532898472e-05, - -3.0228106532898472e-05, - 1.3156705594652870e-05, - -2.3740975974826574e-04, - 1.0328972070195332e-04, - -1.4218547815143072e-04, - 6.1827596642872941e-05, - 3.4031715116440432e-04, - -1.4804591640658066e-04, + 9.345601811834877858e-06, + -5.952429657436736067e-05, + -5.952429657436736067e-05, + 3.798219339938377500e-04, + -5.473801266600750138e-05, + 3.492689212282017045e-04, + -4.701511803776506468e-05, + 2.996014142828410851e-04, + 1.519318254663050438e-04, + -9.691679729305131179e-04, + 4.639301212229915600e-06, + -3.880307472438465351e-05, + -3.880307472438465351e-05, + 3.263510366915401122e-04, + -3.602511945944678490e-05, + 3.030070692648913970e-04, + -2.749648518890264080e-05, + 2.306635984680620104e-04, + 9.768537816050416954e-05, + -8.212186297001088662e-04, + 3.460569880141428627e-06, + -3.057606512460111250e-05, + -3.057606512460111250e-05, + 2.716853957153315133e-04, + -2.822734036378936432e-05, + 2.510848446158910584e-04, + -2.058670330591998133e-05, + 1.836196876431213907e-04, + 7.592953891416902529e-05, + -6.758138628497427618e-04, + 9.016224735333271687e-06, + -5.493669637892903309e-05, + -5.493669637892903309e-05, + 3.349227130387897179e-04, + -5.006878854367239126e-05, + 3.053247155435676952e-04, + -4.307577100091461854e-05, + 2.627594780740223688e-04, + 1.390650311881827983e-04, + -8.480702102774508640e-04, + 1.194421346644617234e-05, + -6.584805186418614701e-05, + -6.584805186418614701e-05, + 3.630189444637618094e-04, + -5.971894402294766291e-05, + 3.292293648749792449e-04, + -5.411449195138936510e-05, + 2.983321514371517751e-04, + 1.677372743720770298e-04, + -9.247324089117067367e-04, + 6.525370698008342040e-06, + -4.616209490400820508e-05, + -4.616209490400820508e-05, + 3.272132307784752408e-04, + -4.228414781396409573e-05, + 2.998297009884601412e-04, + -3.397721060608091141e-05, + 2.410596980080286660e-04, + 1.158980826260448753e-04, + -8.219405348709558565e-04 ] places = 10 diff --git a/source/tests/test_model_se_atten.py b/source/tests/test_model_se_atten.py index b473c43c10..a9d303561b 100644 --- a/source/tests/test_model_se_atten.py +++ b/source/tests/test_model_se_atten.py @@ -146,37 +146,37 @@ def test_model(self): np.savetxt("f.out", f.reshape([1, -1]), delimiter=",") np.savetxt("v.out", v.reshape([1, -1]), delimiter=",") - refe = [6.121172052273667e01] + refe = [6.125987139770151657e+01] reff = [ - 1.1546857028815118e-02, - 1.7560407103242779e-02, - 7.1301778864729290e-04, - 2.3682630974376197e-02, - 1.6842732518204180e-02, - -2.2408109608703206e-03, - -7.9408568690697776e-03, - 9.6856119564082792e-03, - 1.9055514693144326e-05, - 8.7017502459205160e-03, - -2.7153030569749256e-02, - -8.8338555421916490e-04, - -4.3841165945453904e-02, - 5.8104108317526765e-03, - 2.6243178542006552e-03, - 7.8507845654118558e-03, - -2.2746131839858654e-02, - -2.3219464245160639e-04, + 4.173604932578619392e-03, + 1.206055616860458521e-02, + 5.669105924718363535e-04, + 2.076442461575213339e-02, + 1.063054962554822735e-02, + -1.959086927211039792e-03, + -1.249664752022961855e-03, + 6.583345686017975928e-03, + -5.217026786924852146e-05, + 2.282684708646087659e-03, + -1.818523164208030174e-02, + -5.304299018874505858e-04, + -3.217827206046815908e-02, + 2.282463214991831829e-03, + 2.123435020231457040e-03, + 6.207222555514273557e-03, + -1.337168305308231164e-02, + -1.486585157355551452e-04 ] refv = [ - -0.10488160947198523, - 0.016694308932682225, - 0.003444164500535988, - 0.016694308932682235, - -0.05415326614376374, - -0.0010792017166882334, - 0.003444164500535988, - -0.001079201716688233, - -0.00020932681975049773, + -5.654134160411147803e-02, + 8.003907519448832247e-03, + 2.281222034457657064e-03, + 8.003907519448828778e-03, + -3.428760334596382686e-02, + -6.271214134527593491e-04, + 2.281222034457657498e-03, + -6.271214134527595659e-04, + -1.598202190275801690e-04 ] refe = np.reshape(refe, [-1]) From 42bb04e538c2d128885c1e0cf2cb15765dece286 Mon Sep 17 00:00:00 2001 From: nahso Date: Wed, 17 May 2023 00:26:04 +0800 Subject: [PATCH 057/126] fix error of unknown key parameter 'compress' in unittests --- deepmd/fit/dipole.py | 1 + deepmd/fit/dos.py | 1 + 2 files changed, 2 insertions(+) diff --git a/deepmd/fit/dipole.py b/deepmd/fit/dipole.py index 1087bbf340..d1ebd39472 100644 --- a/deepmd/fit/dipole.py +++ b/deepmd/fit/dipole.py @@ -59,6 +59,7 @@ def __init__( activation_function: str = "tanh", precision: str = "default", uniform_seed: bool = False, + compress: bool = False ) -> None: """Constructor.""" self.ntypes = descrpt.get_ntypes() diff --git a/deepmd/fit/dos.py b/deepmd/fit/dos.py index 43e31185d1..0537cf3017 100644 --- a/deepmd/fit/dos.py +++ b/deepmd/fit/dos.py @@ -97,6 +97,7 @@ def __init__( uniform_seed: bool = False, layer_name: Optional[List[Optional[str]]] = None, use_aparam_as_mask: bool = False, + compress: bool = False ) -> None: """Constructor.""" # model param From 1ebcd93cdb4824dc854c51683dcf73ace17020cc Mon Sep 17 00:00:00 2001 From: nahso Date: Wed, 17 May 2023 00:28:36 +0800 Subject: [PATCH 058/126] move getting t_typeebd from graph to TypeEmbedNet --- deepmd/fit/ener.py | 5 +---- deepmd/model/ener.py | 1 + deepmd/train/trainer.py | 2 ++ deepmd/utils/type_embed.py | 4 ++++ 4 files changed, 8 insertions(+), 4 deletions(-) diff --git a/deepmd/fit/ener.py b/deepmd/fit/ener.py index d60737d4f0..6127671091 100644 --- a/deepmd/fit/ener.py +++ b/deepmd/fit/ener.py @@ -475,7 +475,7 @@ def build( input_dict = {} bias_atom_e = self.bias_atom_e if self.compress and isinstance(self.descrpt, deepmd.descriptor.DescrptSeAtten): - type_embedding = self.compress_type_embedding + type_embedding = input_dict.get("type_embedding_from_graph", None) else: type_embedding = input_dict.get("type_embedding", None) atype = input_dict.get("atype", None) @@ -729,9 +729,6 @@ def init_variables( self.fitting_net_variables = get_fitting_net_variables_from_graph_def( graph_def, suffix=suffix ) - if isinstance(self.descrpt, deepmd.descriptor.DescrptSeAtten): - type_embedding = get_tensor_by_name_from_graph(graph, 't_typeebd') - self.compress_type_embedding = tf.convert_to_tensor(type_embedding) if self.layer_name is not None: # shared variables have no suffix shared_variables = get_fitting_net_variables_from_graph_def( diff --git a/deepmd/model/ener.py b/deepmd/model/ener.py index f9387c67fc..775c1c5e53 100644 --- a/deepmd/model/ener.py +++ b/deepmd/model/ener.py @@ -193,6 +193,7 @@ def build( suffix=suffix, ) input_dict["type_embedding"] = type_embedding + input_dict["type_embedding_from_graph"] = self.typeebd.type_embedding_from_graph # spin if any if self.spin is not None: type_spin = self.spin.build( diff --git a/deepmd/train/trainer.py b/deepmd/train/trainer.py index 4fdf06dbb5..365c3b1c17 100644 --- a/deepmd/train/trainer.py +++ b/deepmd/train/trainer.py @@ -581,6 +581,8 @@ def build(self, data=None, stop_batch=0, origin_type_map=None, suffix=""): graph, graph_def = load_graph_def( self.model_param["compress"]["model_file"] ) + if self.typeebd is not None: + self.typeebd.init_variables(graph, graph_def) self.descrpt.enable_compression( self.model_param["compress"]["min_nbor_dist"], graph, diff --git a/deepmd/utils/type_embed.py b/deepmd/utils/type_embed.py index 7a3e0925b8..52f4c37140 100644 --- a/deepmd/utils/type_embed.py +++ b/deepmd/utils/type_embed.py @@ -13,6 +13,7 @@ ) from deepmd.utils.graph import ( get_type_embedding_net_variables_from_graph_def, + get_tensor_by_name_from_graph ) from deepmd.utils.network import ( embedding_net, @@ -103,6 +104,7 @@ def __init__( self.trainable = trainable self.uniform_seed = uniform_seed self.type_embedding_net_variables = None + self.type_embedding_from_graph = None self.padding = padding def build( @@ -173,3 +175,5 @@ def init_variables( self.type_embedding_net_variables = ( get_type_embedding_net_variables_from_graph_def(graph_def, suffix=suffix) ) + type_embedding = get_tensor_by_name_from_graph(graph, 't_typeebd') + self.type_embedding_from_graph = tf.convert_to_tensor(type_embedding) From 26d86d7f81e3cdf9757cd73d4961a5c3fa13a9c6 Mon Sep 17 00:00:00 2001 From: nahso Date: Wed, 17 May 2023 00:30:18 +0800 Subject: [PATCH 059/126] enable model compression for models apart from EnerModel --- deepmd/train/trainer.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/deepmd/train/trainer.py b/deepmd/train/trainer.py index 365c3b1c17..b710872ad5 100644 --- a/deepmd/train/trainer.py +++ b/deepmd/train/trainer.py @@ -181,8 +181,6 @@ def fitting_net_init(fitting_type_, descrpt_type_, params): fitting_param.pop("type", None) fitting_param["descrpt"] = self.descrpt fitting_param["compress"] = self.is_compress - if self.is_compress and fitting_type != "ener": - raise RuntimeError("compression can only be performed in energy model, but " + fitting_type + " is given") self.fitting = fitting_net_init(fitting_type, descrpt_type, fitting_param) else: self.fitting_dict = {} From 70ac7f1f68d5239cf52b50567e365b2413ad7b05 Mon Sep 17 00:00:00 2001 From: nahso Date: Wed, 17 May 2023 00:32:01 +0800 Subject: [PATCH 060/126] fix the issue of can not load embedding net variables of two_side_embed from graph --- deepmd/descriptor/se_atten.py | 37 +++++++++++++++++++++++++++++++++-- 1 file changed, 35 insertions(+), 2 deletions(-) diff --git a/deepmd/descriptor/se_atten.py b/deepmd/descriptor/se_atten.py index dca56e3819..1ddc474579 100644 --- a/deepmd/descriptor/se_atten.py +++ b/deepmd/descriptor/se_atten.py @@ -24,7 +24,9 @@ ) from deepmd.utils.graph import ( get_attention_layer_variables_from_graph_def, - get_pattern_nodes_from_graph_def + get_pattern_nodes_from_graph_def, + get_embedding_net_variables_from_graph_def, + get_tensor_by_type ) from deepmd.utils.network import ( embedding_net, @@ -154,6 +156,8 @@ def __init__( self.attn_mask = attn_mask self.attn_dotr = attn_dotr self.filter_np_precision = get_np_precision(precision) + self.two_side_embeeding_net_variables = None + self.layer_size = len(neuron) # descrpt config self.sel_all_a = [sel] @@ -980,7 +984,7 @@ def _filter_lower( seed=self.seed, trainable=trainable, uniform_seed=self.uniform_seed, - initial_variables=self.embedding_net_variables, + initial_variables=self.two_side_embeeding_net_variables, mixed_prec=self.mixed_prec) two_embd = tf.nn.embedding_lookup(embedding_of_two_side_type_embedding, index_of_two_side) @@ -1126,6 +1130,35 @@ def init_variables( The suffix of the scope """ super().init_variables(graph=graph, graph_def=graph_def, suffix=suffix) + self.two_side_embeeding_net_variables = {} + + for i in range(1, self.layer_size + 1): + node = get_pattern_nodes_from_graph_def(graph_def, + f'filter_type_all/matrix_{i}_two_side_ebd') \ + [f'filter_type_all/matrix_{i}_two_side_ebd'] + dtype = tf.as_dtype(node.dtype).as_numpy_dtype + tensor_shape = tf.TensorShape(node.tensor_shape).as_list() + if (len(tensor_shape) != 1) or (tensor_shape[0] != 1): + tensor_value = np.frombuffer( + node.tensor_content, dtype=tf.as_dtype(node.dtype).as_numpy_dtype + ) + else: + tensor_value = get_tensor_by_type(node, dtype) + self.two_side_embeeding_net_variables[f'filter_type_all/matrix_{i}_two_side_ebd'] = np.reshape(tensor_value, tensor_shape) + + node = get_pattern_nodes_from_graph_def(graph_def, + f'filter_type_all/bias_{i}_two_side_ebd') \ + [f'filter_type_all/bias_{i}_two_side_ebd'] + dtype = tf.as_dtype(node.dtype).as_numpy_dtype + tensor_shape = tf.TensorShape(node.tensor_shape).as_list() + if (len(tensor_shape) != 1) or (tensor_shape[0] != 1): + tensor_value = np.frombuffer( + node.tensor_content, dtype=tf.as_dtype(node.dtype).as_numpy_dtype + ) + else: + tensor_value = get_tensor_by_type(node, dtype) + self.two_side_embeeding_net_variables[f'filter_type_all/bias_{i}_two_side_ebd'] = np.reshape(tensor_value, tensor_shape) + self.attention_layer_variables = get_attention_layer_variables_from_graph_def( graph_def, suffix=suffix ) From 5e9d5d35aa096a2bef08c38e5ae77ca38dbe2fd0 Mon Sep 17 00:00:00 2001 From: nahso Date: Wed, 17 May 2023 00:37:23 +0800 Subject: [PATCH 061/126] say se_atten is supported --- doc/freeze/compress.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/freeze/compress.md b/doc/freeze/compress.md index 4ac45f4de7..6d96c7f01c 100644 --- a/doc/freeze/compress.md +++ b/doc/freeze/compress.md @@ -82,7 +82,7 @@ The model compression interface requires the version of DeePMD-kit used in the o **Acceptable descriptor type** -Descriptors with `se_e2_a`, `se_e3`, and `se_e2_r` types are supported by the model compression feature. `Hybrid` mixed with the above descriptors is also supported. +Descriptors with `se_e2_a`, `se_e3`, `se_e2_r` and `se_atten` types are supported by the model compression feature. `Hybrid` mixed with the above descriptors is also supported. **Available activation functions for descriptor:** From 23099db33f3c0eb5caa0b031bdc32b5b59e1c6c0 Mon Sep 17 00:00:00 2001 From: nahso Date: Wed, 17 May 2023 22:30:02 +0800 Subject: [PATCH 062/126] fix index of two embedding --- deepmd/descriptor/se_atten.py | 16 ++++++++++------ 1 file changed, 10 insertions(+), 6 deletions(-) diff --git a/deepmd/descriptor/se_atten.py b/deepmd/descriptor/se_atten.py index 1ddc474579..29e66b6f72 100644 --- a/deepmd/descriptor/se_atten.py +++ b/deepmd/descriptor/se_atten.py @@ -959,16 +959,20 @@ def _filter_lower( self.table_config[3], ] - type_embedding_shape = type_embedding.get_shape().as_list() - index_of_two_side = self.nei_type_vec * type_embedding_shape[0] + tf.tile(atype, [self.nnei]) + padding_ntypes = type_embedding.shape[0] + atype_expand = tf.reshape(atype, [-1, 1]) + idx_i = tf.tile(atype_expand * (self.ntypes + 1), [1, self.nnei]) + idx_j = tf.reshape(self.nei_type_vec, [-1, self.nnei]) + idx = idx_i + idx_j + index_of_two_side = tf.reshape(idx, [-1]) if self.compress: two_embd = tf.nn.embedding_lookup(self.two_embd, index_of_two_side) else: - type_embedding_nei = tf.tile(tf.reshape(type_embedding, [1, type_embedding_shape[0], -1]), - [type_embedding_shape[0], 1, 1]) # (ntypes) * ntypes * Y - type_embedding_center = tf.tile(tf.reshape(type_embedding, [type_embedding_shape[0], 1, -1]), - [1, type_embedding_shape[0], 1]) # ntypes * (ntypes) * Y + type_embedding_nei = tf.tile(tf.reshape(type_embedding, [1, padding_ntypes, -1]), + [padding_ntypes, 1, 1]) # (ntypes) * ntypes * Y + type_embedding_center = tf.tile(tf.reshape(type_embedding, [padding_ntypes, 1, -1]), + [1, padding_ntypes, 1]) # ntypes * (ntypes) * Y two_side_type_embedding = tf.concat([type_embedding_nei, type_embedding_center], -1) # ntypes * ntypes * (Y+Y) two_side_type_embedding = tf.reshape(two_side_type_embedding, [-1, two_side_type_embedding.shape[-1]]) two_side_type_embedding_suffix = suffix + "_two_side_ebd" From 6c1ef079d800aa2022596db92ff9ba69d0c3f5a2 Mon Sep 17 00:00:00 2001 From: nahso Date: Wed, 17 May 2023 22:33:57 +0800 Subject: [PATCH 063/126] add suffix to filter_type_all --- deepmd/descriptor/se_atten.py | 22 +++++++++++----------- deepmd/utils/tabulate.py | 4 ++-- 2 files changed, 13 insertions(+), 13 deletions(-) diff --git a/deepmd/descriptor/se_atten.py b/deepmd/descriptor/se_atten.py index 29e66b6f72..af29513239 100644 --- a/deepmd/descriptor/se_atten.py +++ b/deepmd/descriptor/se_atten.py @@ -378,8 +378,8 @@ def enable_compression( self.layer_size = self.table.layer_size self.final_type_embedding = self._get_two_side_type_embedding(graph) - self.matrix = self._get_two_side_embedding_net_variable(graph_def, 'matrix') - self.bias = self._get_two_side_embedding_net_variable(graph_def, 'bias') + self.matrix = self._get_two_side_embedding_net_variable(graph_def, 'matrix', suffix) + self.bias = self._get_two_side_embedding_net_variable(graph_def, 'bias', suffix) self.two_embd = self._make_data(self.final_type_embedding) self.davg = get_tensor_by_name_from_graph( @@ -402,12 +402,12 @@ def _get_two_side_type_embedding(self, graph): return two_side_type_embedding - def _get_two_side_embedding_net_variable(self, graph_def, varialbe_name): + def _get_two_side_embedding_net_variable(self, graph_def, varialbe_name, suffix): ret = {} for i in range(1, self.layer_size + 1): node = get_pattern_nodes_from_graph_def(graph_def, - f'filter_type_all/{varialbe_name}_{i}_two_side_ebd') \ - [f'filter_type_all/{varialbe_name}_{i}_two_side_ebd'] + f'filter_type_all{suffix}/{varialbe_name}_{i}_two_side_ebd') \ + [f'filter_type_all{suffix}/{varialbe_name}_{i}_two_side_ebd'] ret['layer_' + str(i)] = node return ret @@ -1138,8 +1138,8 @@ def init_variables( for i in range(1, self.layer_size + 1): node = get_pattern_nodes_from_graph_def(graph_def, - f'filter_type_all/matrix_{i}_two_side_ebd') \ - [f'filter_type_all/matrix_{i}_two_side_ebd'] + f'filter_type_all{suffix}/matrix_{i}_two_side_ebd') \ + [f'filter_type_all{suffix}/matrix_{i}_two_side_ebd'] dtype = tf.as_dtype(node.dtype).as_numpy_dtype tensor_shape = tf.TensorShape(node.tensor_shape).as_list() if (len(tensor_shape) != 1) or (tensor_shape[0] != 1): @@ -1148,11 +1148,11 @@ def init_variables( ) else: tensor_value = get_tensor_by_type(node, dtype) - self.two_side_embeeding_net_variables[f'filter_type_all/matrix_{i}_two_side_ebd'] = np.reshape(tensor_value, tensor_shape) + self.two_side_embeeding_net_variables[f'filter_type_all{suffix}/matrix_{i}_two_side_ebd'] = np.reshape(tensor_value, tensor_shape) node = get_pattern_nodes_from_graph_def(graph_def, - f'filter_type_all/bias_{i}_two_side_ebd') \ - [f'filter_type_all/bias_{i}_two_side_ebd'] + f'filter_type_all{suffix}/bias_{i}_two_side_ebd') \ + [f'filter_type_all{suffix}/bias_{i}_two_side_ebd'] dtype = tf.as_dtype(node.dtype).as_numpy_dtype tensor_shape = tf.TensorShape(node.tensor_shape).as_list() if (len(tensor_shape) != 1) or (tensor_shape[0] != 1): @@ -1161,7 +1161,7 @@ def init_variables( ) else: tensor_value = get_tensor_by_type(node, dtype) - self.two_side_embeeding_net_variables[f'filter_type_all/bias_{i}_two_side_ebd'] = np.reshape(tensor_value, tensor_shape) + self.two_side_embeeding_net_variables[f'filter_type_all{suffix}/bias_{i}_two_side_ebd'] = np.reshape(tensor_value, tensor_shape) self.attention_layer_variables = get_attention_layer_variables_from_graph_def( graph_def, suffix=suffix diff --git a/deepmd/utils/tabulate.py b/deepmd/utils/tabulate.py index 85901b9bb7..3d54b0fe05 100644 --- a/deepmd/utils/tabulate.py +++ b/deepmd/utils/tabulate.py @@ -422,7 +422,7 @@ def _get_bias(self): bias["layer_" + str(layer)] = [] if isinstance(self.descrpt, deepmd.descriptor.DescrptSeAtten): node = self.embedding_net_nodes[ - f"filter_type_all/bias_{layer}" + f"filter_type_all{self.suffix}/bias_{layer}" ] bias["layer_" + str(layer)].append(tf.make_ndarray(node)) elif isinstance(self.descrpt, deepmd.descriptor.DescrptSeA): @@ -486,7 +486,7 @@ def _get_matrix(self): matrix["layer_" + str(layer)] = [] if isinstance(self.descrpt, deepmd.descriptor.DescrptSeAtten): node = self.embedding_net_nodes[ - f"filter_type_all/matrix_{layer}" + f"filter_type_all{self.suffix}/matrix_{layer}" ] matrix["layer_" + str(layer)].append(tf.make_ndarray(node)) elif isinstance(self.descrpt, deepmd.descriptor.DescrptSeA): From b3a90a5aee36d673f8126e15e25bda9b7ed7feee Mon Sep 17 00:00:00 2001 From: nahso Date: Thu, 18 May 2023 00:11:37 +0800 Subject: [PATCH 064/126] make the compression of se_atten optional --- deepmd/descriptor/se_a.py | 1 + deepmd/descriptor/se_a_ebd.py | 1 + deepmd/descriptor/se_a_ef.py | 1 + deepmd/descriptor/se_a_mask.py | 1 + deepmd/descriptor/se_atten.py | 177 +++++++++++++++++++++++---------- deepmd/descriptor/se_r.py | 1 + deepmd/descriptor/se_t.py | 1 + deepmd/utils/argcheck.py | 2 + 8 files changed, 132 insertions(+), 53 deletions(-) diff --git a/deepmd/descriptor/se_a.py b/deepmd/descriptor/se_a.py index 641210f0d1..6f59e6cff9 100644 --- a/deepmd/descriptor/se_a.py +++ b/deepmd/descriptor/se_a.py @@ -164,6 +164,7 @@ def __init__( uniform_seed: bool = False, multi_task: bool = False, spin: Optional[Spin] = None, + compressible: bool = True, ) -> None: """Constructor.""" if rcut < rcut_smth: diff --git a/deepmd/descriptor/se_a_ebd.py b/deepmd/descriptor/se_a_ebd.py index e0684403e1..fe5e30c395 100644 --- a/deepmd/descriptor/se_a_ebd.py +++ b/deepmd/descriptor/se_a_ebd.py @@ -87,6 +87,7 @@ def __init__( activation_function: str = "tanh", precision: str = "default", exclude_types: List[List[int]] = [], + compressible: bool = True, ) -> None: """Constructor.""" DescrptSeA.__init__( diff --git a/deepmd/descriptor/se_a_ef.py b/deepmd/descriptor/se_a_ef.py index 0dbad403f4..60f8bedf3b 100644 --- a/deepmd/descriptor/se_a_ef.py +++ b/deepmd/descriptor/se_a_ef.py @@ -82,6 +82,7 @@ def __init__( activation_function: str = "tanh", precision: str = "default", uniform_seed=False, + compressible: bool = True, ) -> None: """Constructor.""" self.descrpt_para = DescrptSeAEfLower( diff --git a/deepmd/descriptor/se_a_mask.py b/deepmd/descriptor/se_a_mask.py index 93256e3910..a073d9e821 100644 --- a/deepmd/descriptor/se_a_mask.py +++ b/deepmd/descriptor/se_a_mask.py @@ -126,6 +126,7 @@ def __init__( activation_function: str = "tanh", precision: str = "default", uniform_seed: bool = False, + compressible: bool = True, ) -> None: """Constructor.""" self.sel_a = sel diff --git a/deepmd/descriptor/se_atten.py b/deepmd/descriptor/se_atten.py index af29513239..34769135cb 100644 --- a/deepmd/descriptor/se_atten.py +++ b/deepmd/descriptor/se_atten.py @@ -120,6 +120,7 @@ def __init__( attn_dotr: bool = True, attn_mask: bool = False, multi_task: bool = False, + compressible: bool = True, ) -> None: if not set_davg_zero: warnings.warn( @@ -150,6 +151,7 @@ def __init__( assert Version(TF_VERSION) > Version( "2" ), "se_atten only support tensorflow version 2.0 or higher." + self.compressible = compressible self.ntypes = ntypes self.att_n = attn self.attn_layer = attn_layer @@ -739,6 +741,55 @@ def _compute_dstats_sys_smth( sysa2.append(suma2) return sysr, sysr2, sysa, sysa2, sysn + def _lookup_type_embedding( + self, + xyz_scatter, + natype, + type_embedding, + ): + """Concatenate `type_embedding` of neighbors and `xyz_scatter`. + If not self.type_one_side, concatenate `type_embedding` of center atoms as well. + + Parameters + ---------- + xyz_scatter: + shape is [nframes*natoms[0]*self.nnei, 1] + natype: + neighbor atom type + type_embedding: + shape is [self.ntypes, Y] where Y=jdata['type_embedding']['neuron'][-1] + + Returns + ------- + embedding: + environment of each atom represented by embedding. + """ + te_out_dim = type_embedding.get_shape().as_list()[-1] + self.test_type_embedding = type_embedding + self.test_nei_embed = tf.nn.embedding_lookup( + type_embedding, self.nei_type_vec + ) # shape is [self.nnei, 1+te_out_dim] + # nei_embed = tf.tile(nei_embed, (nframes * natoms[0], 1)) # shape is [nframes*natoms[0]*self.nnei, te_out_dim] + nei_embed = tf.reshape(self.test_nei_embed, [-1, te_out_dim]) + self.embedding_input = tf.concat( + [xyz_scatter, nei_embed], 1 + ) # shape is [nframes*natoms[0]*self.nnei, 1+te_out_dim] + if not self.type_one_side: + self.atm_embed = tf.nn.embedding_lookup( + type_embedding, natype + ) # shape is [nframes*natoms[0], te_out_dim] + self.atm_embed = tf.tile( + self.atm_embed, [1, self.nnei] + ) # shape is [nframes*natoms[0], self.nnei*te_out_dim] + self.atm_embed = tf.reshape( + self.atm_embed, [-1, te_out_dim] + ) # shape is [nframes*natoms[0]*self.nnei, te_out_dim] + self.embedding_input_2 = tf.concat( + [self.embedding_input, self.atm_embed], 1 + ) # shape is [nframes*natoms[0]*self.nnei, 1+te_out_dim+te_out_dim] + return self.embedding_input_2 + return self.embedding_input + def _feedforward(self, input_xyz, d_in, d_mid): residual = input_xyz input_xyz = tf.nn.relu( @@ -933,7 +984,9 @@ def _filter_lower( if not is_exclude: with tf.variable_scope(name, reuse=reuse): # with (natom x nei_type_i) x out_size - if not self.compress: + if not self.compressible: + print('==========> not compress') + xyz_scatter = self._lookup_type_embedding(xyz_scatter, atype, type_embedding) xyz_scatter = embedding_net( xyz_scatter, self.filter_neuron, @@ -947,62 +1000,80 @@ def _filter_lower( trainable=trainable, uniform_seed=self.uniform_seed, initial_variables=self.embedding_net_variables, - mixed_prec=self.mixed_prec) + mixed_prec=self.mixed_prec, + ) else: - net = 'filter_net' - info = [ - self.lower[net], - self.upper[net], - self.upper[net] * self.table_config[0], - self.table_config[1], - self.table_config[2], - self.table_config[3], - ] + print('==========> compress') + if not self.compress: + xyz_scatter = embedding_net( + xyz_scatter, + self.filter_neuron, + self.filter_precision, + activation_fn=activation_fn, + resnet_dt=self.filter_resnet_dt, + name_suffix=suffix, + stddev=stddev, + bavg=bavg, + seed=self.seed, + trainable=trainable, + uniform_seed=self.uniform_seed, + initial_variables=self.embedding_net_variables, + mixed_prec=self.mixed_prec) + else: + net = 'filter_net' + info = [ + self.lower[net], + self.upper[net], + self.upper[net] * self.table_config[0], + self.table_config[1], + self.table_config[2], + self.table_config[3], + ] - padding_ntypes = type_embedding.shape[0] - atype_expand = tf.reshape(atype, [-1, 1]) - idx_i = tf.tile(atype_expand * (self.ntypes + 1), [1, self.nnei]) - idx_j = tf.reshape(self.nei_type_vec, [-1, self.nnei]) - idx = idx_i + idx_j - index_of_two_side = tf.reshape(idx, [-1]) + padding_ntypes = type_embedding.shape[0] + atype_expand = tf.reshape(atype, [-1, 1]) + idx_i = tf.tile(atype_expand * (self.ntypes + 1), [1, self.nnei]) + idx_j = tf.reshape(self.nei_type_vec, [-1, self.nnei]) + idx = idx_i + idx_j + index_of_two_side = tf.reshape(idx, [-1]) - if self.compress: - two_embd = tf.nn.embedding_lookup(self.two_embd, index_of_two_side) - else: - type_embedding_nei = tf.tile(tf.reshape(type_embedding, [1, padding_ntypes, -1]), - [padding_ntypes, 1, 1]) # (ntypes) * ntypes * Y - type_embedding_center = tf.tile(tf.reshape(type_embedding, [padding_ntypes, 1, -1]), - [1, padding_ntypes, 1]) # ntypes * (ntypes) * Y - two_side_type_embedding = tf.concat([type_embedding_nei, type_embedding_center], -1) # ntypes * ntypes * (Y+Y) - two_side_type_embedding = tf.reshape(two_side_type_embedding, [-1, two_side_type_embedding.shape[-1]]) - two_side_type_embedding_suffix = suffix + "_two_side_ebd" - embedding_of_two_side_type_embedding = embedding_net( - two_side_type_embedding, - self.filter_neuron, - self.filter_precision, - activation_fn=activation_fn, - resnet_dt=self.filter_resnet_dt, - name_suffix=two_side_type_embedding_suffix, - stddev=stddev, - bavg=bavg, - seed=self.seed, - trainable=trainable, - uniform_seed=self.uniform_seed, - initial_variables=self.two_side_embeeding_net_variables, - mixed_prec=self.mixed_prec) - two_embd = tf.nn.embedding_lookup(embedding_of_two_side_type_embedding, index_of_two_side) + if self.compress: + two_embd = tf.nn.embedding_lookup(self.two_embd, index_of_two_side) + else: + type_embedding_nei = tf.tile(tf.reshape(type_embedding, [1, padding_ntypes, -1]), + [padding_ntypes, 1, 1]) # (ntypes) * ntypes * Y + type_embedding_center = tf.tile(tf.reshape(type_embedding, [padding_ntypes, 1, -1]), + [1, padding_ntypes, 1]) # ntypes * (ntypes) * Y + two_side_type_embedding = tf.concat([type_embedding_nei, type_embedding_center], -1) # ntypes * ntypes * (Y+Y) + two_side_type_embedding = tf.reshape(two_side_type_embedding, [-1, two_side_type_embedding.shape[-1]]) + two_side_type_embedding_suffix = suffix + "_two_side_ebd" + embedding_of_two_side_type_embedding = embedding_net( + two_side_type_embedding, + self.filter_neuron, + self.filter_precision, + activation_fn=activation_fn, + resnet_dt=self.filter_resnet_dt, + name_suffix=two_side_type_embedding_suffix, + stddev=stddev, + bavg=bavg, + seed=self.seed, + trainable=trainable, + uniform_seed=self.uniform_seed, + initial_variables=self.two_side_embeeding_net_variables, + mixed_prec=self.mixed_prec) + two_embd = tf.nn.embedding_lookup(embedding_of_two_side_type_embedding, index_of_two_side) - if not self.compress: - xyz_scatter = xyz_scatter * two_embd + two_embd - else: - return op_module.tabulate_fusion_se_atten( - tf.cast(self.table.data[net], self.filter_precision), - info, - xyz_scatter, - tf.reshape(inputs_i, [natom, shape_i[1] // 4, 4]), - two_embd, - last_layer_size=outputs_size[-1], - ) + if not self.compress: + xyz_scatter = xyz_scatter * two_embd + two_embd + else: + return op_module.tabulate_fusion_se_atten( + tf.cast(self.table.data[net], self.filter_precision), + info, + xyz_scatter, + tf.reshape(inputs_i, [natom, shape_i[1] // 4, 4]), + two_embd, + last_layer_size=outputs_size[-1], + ) if (not self.uniform_seed) and (self.seed is not None): self.seed += self.seed_shift diff --git a/deepmd/descriptor/se_r.py b/deepmd/descriptor/se_r.py index 7ef5f47347..0f01554bc4 100644 --- a/deepmd/descriptor/se_r.py +++ b/deepmd/descriptor/se_r.py @@ -97,6 +97,7 @@ def __init__( uniform_seed: bool = False, multi_task: bool = False, spin: Optional[Spin] = None, + compressible: bool = True, ) -> None: """Constructor.""" if rcut < rcut_smth: diff --git a/deepmd/descriptor/se_t.py b/deepmd/descriptor/se_t.py index 7615275a82..92f96fc5b0 100644 --- a/deepmd/descriptor/se_t.py +++ b/deepmd/descriptor/se_t.py @@ -90,6 +90,7 @@ def __init__( precision: str = "default", uniform_seed: bool = False, multi_task: bool = False, + compressible: bool = True, ) -> None: """Constructor.""" if rcut < rcut_smth: diff --git a/deepmd/utils/argcheck.py b/deepmd/utils/argcheck.py index 1fe00b4f43..3a62a62609 100644 --- a/deepmd/utils/argcheck.py +++ b/deepmd/utils/argcheck.py @@ -334,6 +334,7 @@ def descrpt_hybrid_args(): @descrpt_args_plugin.register("se_atten") def descrpt_se_atten_args(): + doc_compressible = 'Whether to build a compressible se_atten.' doc_sel = 'This parameter set the number of selected neighbors. Note that this parameter is a little different from that in other descriptors. Instead of separating each type of atoms, only the summation matters. And this number is highly related with the efficiency, thus one should not make it too large. Usually 200 or less is enough, far away from the GPU limitation 4096. It can be:\n\n\ - `int`. The maximum number of neighbor atoms to be considered. We recommend it to be less than 200. \n\n\ - `List[int]`. The length of the list should be the same as the number of atom types in the system. `sel[i]` gives the selected number of type-i neighbors. Only the summation of `sel[i]` matters, and it is recommended to be less than 200.\ @@ -356,6 +357,7 @@ def descrpt_se_atten_args(): doc_attn_mask = "Whether to do mask on the diagonal in the attention matrix" return [ + Argument("compressible", bool, optional=True, default=True, doc=doc_compressible), Argument("sel", [int, list, str], optional=True, default="auto", doc=doc_sel), Argument("rcut", float, optional=True, default=6.0, doc=doc_rcut), Argument("rcut_smth", float, optional=True, default=0.5, doc=doc_rcut_smth), From 40c798b377667769bd9badd96eebea8d50ffe5ca Mon Sep 17 00:00:00 2001 From: nahso Date: Thu, 18 May 2023 00:12:10 +0800 Subject: [PATCH 065/126] return type_embedding_from_graph directly from TypeEmbed.build --- deepmd/fit/ener.py | 5 +---- deepmd/model/ener.py | 2 +- deepmd/utils/type_embed.py | 2 ++ 3 files changed, 4 insertions(+), 5 deletions(-) diff --git a/deepmd/fit/ener.py b/deepmd/fit/ener.py index 6127671091..a99ebb7e07 100644 --- a/deepmd/fit/ener.py +++ b/deepmd/fit/ener.py @@ -474,10 +474,7 @@ def build( if input_dict is None: input_dict = {} bias_atom_e = self.bias_atom_e - if self.compress and isinstance(self.descrpt, deepmd.descriptor.DescrptSeAtten): - type_embedding = input_dict.get("type_embedding_from_graph", None) - else: - type_embedding = input_dict.get("type_embedding", None) + type_embedding = input_dict.get("type_embedding", None) atype = input_dict.get("atype", None) if self.numb_fparam > 0: if self.fparam_avg is None: diff --git a/deepmd/model/ener.py b/deepmd/model/ener.py index 775c1c5e53..cec515e432 100644 --- a/deepmd/model/ener.py +++ b/deepmd/model/ener.py @@ -17,6 +17,7 @@ from deepmd.utils.spin import ( Spin, ) +from deepmd.descriptor import DescrptSeAtten from .model import ( Model, @@ -193,7 +194,6 @@ def build( suffix=suffix, ) input_dict["type_embedding"] = type_embedding - input_dict["type_embedding_from_graph"] = self.typeebd.type_embedding_from_graph # spin if any if self.spin is not None: type_spin = self.spin.build( diff --git a/deepmd/utils/type_embed.py b/deepmd/utils/type_embed.py index 52f4c37140..a908984f76 100644 --- a/deepmd/utils/type_embed.py +++ b/deepmd/utils/type_embed.py @@ -129,6 +129,8 @@ def build( embedded_types The computational graph for embedded types """ + if self.type_embedding_net_variables is not None: + return self.type_embedding_from_graph types = tf.convert_to_tensor([ii for ii in range(ntypes)], dtype=tf.int32) ebd_type = tf.cast( tf.one_hot(tf.cast(types, dtype=tf.int32), int(ntypes)), From 7bbd862fa1f983e21aefcb3ee238855ecf0032eb Mon Sep 17 00:00:00 2001 From: nahso Date: Thu, 18 May 2023 00:29:54 +0800 Subject: [PATCH 066/126] arg check for compression of se_atten --- deepmd/descriptor/se_atten.py | 12 +++++++++--- 1 file changed, 9 insertions(+), 3 deletions(-) diff --git a/deepmd/descriptor/se_atten.py b/deepmd/descriptor/se_atten.py index 34769135cb..4ab232a553 100644 --- a/deepmd/descriptor/se_atten.py +++ b/deepmd/descriptor/se_atten.py @@ -161,6 +161,9 @@ def __init__( self.two_side_embeeding_net_variables = None self.layer_size = len(neuron) + if self.compressible and self.attn_layer != 0: + raise RuntimeError('attention layer must be set to 0 when compression of se_atten is enabled') + # descrpt config self.sel_all_a = [sel] self.sel_all_r = [0] @@ -357,6 +360,10 @@ def enable_compression( % ",".join([str(item) for item in self.filter_neuron]) ) + ret = get_pattern_nodes_from_graph_def(graph_def, f'filter_type_all{suffix}/.+_two_side_ebd') + if len(ret) == 0: + raise RuntimeError(f"can not find variables of embedding net `*_two_side_ebd` from graph_def, maybe it is not a compressible model.") + self.compress = True self.table = DPTabulate( self, @@ -407,9 +414,8 @@ def _get_two_side_type_embedding(self, graph): def _get_two_side_embedding_net_variable(self, graph_def, varialbe_name, suffix): ret = {} for i in range(1, self.layer_size + 1): - node = get_pattern_nodes_from_graph_def(graph_def, - f'filter_type_all{suffix}/{varialbe_name}_{i}_two_side_ebd') \ - [f'filter_type_all{suffix}/{varialbe_name}_{i}_two_side_ebd'] + target = get_pattern_nodes_from_graph_def(graph_def, f'filter_type_all{suffix}/{varialbe_name}_{i}_two_side_ebd') + node = target[f'filter_type_all{suffix}/{varialbe_name}_{i}_two_side_ebd'] ret['layer_' + str(i)] = node return ret From b33320bfdbbac7cc1044a9bbfac12dad75cef4f4 Mon Sep 17 00:00:00 2001 From: nahso Date: Thu, 18 May 2023 00:30:20 +0800 Subject: [PATCH 067/126] make the default value of compressible to false --- deepmd/utils/argcheck.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/deepmd/utils/argcheck.py b/deepmd/utils/argcheck.py index 3a62a62609..645e9819be 100644 --- a/deepmd/utils/argcheck.py +++ b/deepmd/utils/argcheck.py @@ -357,7 +357,7 @@ def descrpt_se_atten_args(): doc_attn_mask = "Whether to do mask on the diagonal in the attention matrix" return [ - Argument("compressible", bool, optional=True, default=True, doc=doc_compressible), + Argument("compressible", bool, optional=True, default=False, doc=doc_compressible), Argument("sel", [int, list, str], optional=True, default="auto", doc=doc_sel), Argument("rcut", float, optional=True, default=6.0, doc=doc_rcut), Argument("rcut_smth", float, optional=True, default=0.5, doc=doc_rcut_smth), From ca2f48fc0ec97e2eb6f0844f49e0f9bf2ef94016 Mon Sep 17 00:00:00 2001 From: nahso Date: Thu, 18 May 2023 11:49:14 +0800 Subject: [PATCH 068/126] fix the default value of compressible --- deepmd/descriptor/se_a_ebd.py | 2 +- deepmd/descriptor/se_a_ef.py | 2 +- deepmd/descriptor/se_a_mask.py | 2 +- deepmd/descriptor/se_atten.py | 56 ++++++++++++++++++---------------- deepmd/descriptor/se_r.py | 2 +- deepmd/descriptor/se_t.py | 2 +- 6 files changed, 34 insertions(+), 32 deletions(-) diff --git a/deepmd/descriptor/se_a_ebd.py b/deepmd/descriptor/se_a_ebd.py index fe5e30c395..30567e6dfb 100644 --- a/deepmd/descriptor/se_a_ebd.py +++ b/deepmd/descriptor/se_a_ebd.py @@ -87,7 +87,7 @@ def __init__( activation_function: str = "tanh", precision: str = "default", exclude_types: List[List[int]] = [], - compressible: bool = True, + compressible: bool = False, ) -> None: """Constructor.""" DescrptSeA.__init__( diff --git a/deepmd/descriptor/se_a_ef.py b/deepmd/descriptor/se_a_ef.py index 60f8bedf3b..ac6842de3f 100644 --- a/deepmd/descriptor/se_a_ef.py +++ b/deepmd/descriptor/se_a_ef.py @@ -82,7 +82,7 @@ def __init__( activation_function: str = "tanh", precision: str = "default", uniform_seed=False, - compressible: bool = True, + compressible: bool = False, ) -> None: """Constructor.""" self.descrpt_para = DescrptSeAEfLower( diff --git a/deepmd/descriptor/se_a_mask.py b/deepmd/descriptor/se_a_mask.py index a073d9e821..b7003883a7 100644 --- a/deepmd/descriptor/se_a_mask.py +++ b/deepmd/descriptor/se_a_mask.py @@ -126,7 +126,7 @@ def __init__( activation_function: str = "tanh", precision: str = "default", uniform_seed: bool = False, - compressible: bool = True, + compressible: bool = False, ) -> None: """Constructor.""" self.sel_a = sel diff --git a/deepmd/descriptor/se_atten.py b/deepmd/descriptor/se_atten.py index 4ab232a553..5d8d6d9409 100644 --- a/deepmd/descriptor/se_atten.py +++ b/deepmd/descriptor/se_atten.py @@ -120,7 +120,7 @@ def __init__( attn_dotr: bool = True, attn_mask: bool = False, multi_task: bool = False, - compressible: bool = True, + compressible: bool = False, ) -> None: if not set_davg_zero: warnings.warn( @@ -1211,34 +1211,36 @@ def init_variables( The suffix of the scope """ super().init_variables(graph=graph, graph_def=graph_def, suffix=suffix) - self.two_side_embeeding_net_variables = {} - for i in range(1, self.layer_size + 1): - node = get_pattern_nodes_from_graph_def(graph_def, - f'filter_type_all{suffix}/matrix_{i}_two_side_ebd') \ - [f'filter_type_all{suffix}/matrix_{i}_two_side_ebd'] - dtype = tf.as_dtype(node.dtype).as_numpy_dtype - tensor_shape = tf.TensorShape(node.tensor_shape).as_list() - if (len(tensor_shape) != 1) or (tensor_shape[0] != 1): - tensor_value = np.frombuffer( - node.tensor_content, dtype=tf.as_dtype(node.dtype).as_numpy_dtype - ) - else: - tensor_value = get_tensor_by_type(node, dtype) - self.two_side_embeeding_net_variables[f'filter_type_all{suffix}/matrix_{i}_two_side_ebd'] = np.reshape(tensor_value, tensor_shape) + if self.compressible: + self.two_side_embeeding_net_variables = {} - node = get_pattern_nodes_from_graph_def(graph_def, - f'filter_type_all{suffix}/bias_{i}_two_side_ebd') \ - [f'filter_type_all{suffix}/bias_{i}_two_side_ebd'] - dtype = tf.as_dtype(node.dtype).as_numpy_dtype - tensor_shape = tf.TensorShape(node.tensor_shape).as_list() - if (len(tensor_shape) != 1) or (tensor_shape[0] != 1): - tensor_value = np.frombuffer( - node.tensor_content, dtype=tf.as_dtype(node.dtype).as_numpy_dtype - ) - else: - tensor_value = get_tensor_by_type(node, dtype) - self.two_side_embeeding_net_variables[f'filter_type_all{suffix}/bias_{i}_two_side_ebd'] = np.reshape(tensor_value, tensor_shape) + for i in range(1, self.layer_size + 1): + node = get_pattern_nodes_from_graph_def(graph_def, + f'filter_type_all{suffix}/matrix_{i}_two_side_ebd') \ + [f'filter_type_all{suffix}/matrix_{i}_two_side_ebd'] + dtype = tf.as_dtype(node.dtype).as_numpy_dtype + tensor_shape = tf.TensorShape(node.tensor_shape).as_list() + if (len(tensor_shape) != 1) or (tensor_shape[0] != 1): + tensor_value = np.frombuffer( + node.tensor_content, dtype=tf.as_dtype(node.dtype).as_numpy_dtype + ) + else: + tensor_value = get_tensor_by_type(node, dtype) + self.two_side_embeeding_net_variables[f'filter_type_all{suffix}/matrix_{i}_two_side_ebd'] = np.reshape(tensor_value, tensor_shape) + + node = get_pattern_nodes_from_graph_def(graph_def, + f'filter_type_all{suffix}/bias_{i}_two_side_ebd') \ + [f'filter_type_all{suffix}/bias_{i}_two_side_ebd'] + dtype = tf.as_dtype(node.dtype).as_numpy_dtype + tensor_shape = tf.TensorShape(node.tensor_shape).as_list() + if (len(tensor_shape) != 1) or (tensor_shape[0] != 1): + tensor_value = np.frombuffer( + node.tensor_content, dtype=tf.as_dtype(node.dtype).as_numpy_dtype + ) + else: + tensor_value = get_tensor_by_type(node, dtype) + self.two_side_embeeding_net_variables[f'filter_type_all{suffix}/bias_{i}_two_side_ebd'] = np.reshape(tensor_value, tensor_shape) self.attention_layer_variables = get_attention_layer_variables_from_graph_def( graph_def, suffix=suffix diff --git a/deepmd/descriptor/se_r.py b/deepmd/descriptor/se_r.py index 0f01554bc4..86e38a559b 100644 --- a/deepmd/descriptor/se_r.py +++ b/deepmd/descriptor/se_r.py @@ -97,7 +97,7 @@ def __init__( uniform_seed: bool = False, multi_task: bool = False, spin: Optional[Spin] = None, - compressible: bool = True, + compressible: bool = False, ) -> None: """Constructor.""" if rcut < rcut_smth: diff --git a/deepmd/descriptor/se_t.py b/deepmd/descriptor/se_t.py index 92f96fc5b0..92cbe328ea 100644 --- a/deepmd/descriptor/se_t.py +++ b/deepmd/descriptor/se_t.py @@ -90,7 +90,7 @@ def __init__( precision: str = "default", uniform_seed: bool = False, multi_task: bool = False, - compressible: bool = True, + compressible: bool = False, ) -> None: """Constructor.""" if rcut < rcut_smth: From d8d9a72308ccf7572079247ed53530cf5873c7b2 Mon Sep 17 00:00:00 2001 From: nahso Date: Thu, 18 May 2023 11:49:57 +0800 Subject: [PATCH 069/126] add compressible options --- source/tests/model_compression/input-se-atten.json | 1 + 1 file changed, 1 insertion(+) diff --git a/source/tests/model_compression/input-se-atten.json b/source/tests/model_compression/input-se-atten.json index ee71204dd5..a0e18ff869 100644 --- a/source/tests/model_compression/input-se-atten.json +++ b/source/tests/model_compression/input-se-atten.json @@ -7,6 +7,7 @@ ], "descriptor": { "type": "se_atten", + "compressible": true, "sel": 120, "rcut_smth": 0.50, "rcut": 6.00, From e884922a3be2703b6caab19baad99e08e77cabf7 Mon Sep 17 00:00:00 2001 From: nahso Date: Thu, 18 May 2023 11:51:18 +0800 Subject: [PATCH 070/126] add ut for compressible se_atten --- source/tests/test_data_large_batch.py | 253 ++++++++++++++-- source/tests/test_descrpt_se_atten.py | 419 ++++++++++++++++++-------- source/tests/test_model_se_atten.py | 285 ++++++++++++++++-- 3 files changed, 775 insertions(+), 182 deletions(-) diff --git a/source/tests/test_data_large_batch.py b/source/tests/test_data_large_batch.py index 24f98f81df..3f93942a3f 100644 --- a/source/tests/test_data_large_batch.py +++ b/source/tests/test_data_large_batch.py @@ -155,7 +155,7 @@ def test_data_mixed_type(self): t_box, t_mesh, inputs_dict, - suffix="se_atten", + suffix="se_atten_1", reuse=False, ) @@ -194,37 +194,232 @@ def test_data_mixed_type(self): np.savetxt("f.out", f.reshape([1, -1]), delimiter=",") np.savetxt("v.out", v.reshape([1, -1]), delimiter=",") - refe = [6.125987139770151657e+01] + refe = [6.121172052273667e01] reff = [ - 4.173604932578619392e-03, - 1.206055616860458521e-02, - 5.669105924718363535e-04, - 2.076442461575213339e-02, - 1.063054962554822735e-02, - -1.959086927211039792e-03, - -1.249664752022961855e-03, - 6.583345686017975928e-03, - -5.217026786924852146e-05, - 2.282684708646087659e-03, - -1.818523164208030174e-02, - -5.304299018874505858e-04, - -3.217827206046815908e-02, - 2.282463214991831829e-03, - 2.123435020231457040e-03, - 6.207222555514273557e-03, - -1.337168305308231164e-02, - -1.486585157355551452e-04 + 1.1546857028815118e-02, + 1.7560407103242779e-02, + 7.1301778864729290e-04, + 2.3682630974376197e-02, + 1.6842732518204180e-02, + -2.2408109608703206e-03, + -7.9408568690697776e-03, + 9.6856119564082792e-03, + 1.9055514693144326e-05, + 8.7017502459205160e-03, + -2.7153030569749256e-02, + -8.8338555421916490e-04, + -4.3841165945453904e-02, + 5.8104108317526765e-03, + 2.6243178542006552e-03, + 7.8507845654118558e-03, + -2.2746131839858654e-02, + -2.3219464245160639e-04, ] refv = [ - -5.654134160411147109e-02, - 8.003907519448825308e-03, - 2.281222034457657932e-03, - 8.003907519448828778e-03, - -3.428760334596384768e-02, - -6.271214134527594575e-04, - 2.281222034457657932e-03, - -6.271214134527596743e-04, - -1.598202190275801961e-04 + -0.10488160947198523, + 0.016694308932682225, + 0.003444164500535988, + 0.016694308932682235, + -0.05415326614376374, + -0.0010792017166882334, + 0.003444164500535988, + -0.001079201716688233, + -0.00020932681975049773, + ] + + refe = np.reshape(refe, [-1]) + reff = np.reshape(reff, [-1]) + refv = np.reshape(refv, [-1]) + + places = 10 + np.testing.assert_almost_equal(e, refe, places) + np.testing.assert_almost_equal(f, reff, places) + np.testing.assert_almost_equal(v, refv, places) + sess.close() + + def test_compressible_data_mixed_type(self): + jfile = "water_se_atten_compressible_mixed_type.json" + jdata = j_loader(jfile) + + systems = j_must_have(jdata, "systems") + batch_size = 1 + test_size = 1 + rcut = j_must_have(jdata["model"]["descriptor"], "rcut") + type_map = j_must_have(jdata["model"], "type_map") + + data = DeepmdDataSystem(systems, batch_size, test_size, rcut, type_map=type_map) + data_requirement = { + "energy": { + "ndof": 1, + "atomic": False, + "must": False, + "high_prec": True, + "type_sel": None, + "repeat": 1, + "default": 0.0, + }, + "force": { + "ndof": 3, + "atomic": True, + "must": False, + "high_prec": False, + "type_sel": None, + "repeat": 1, + "default": 0.0, + }, + "virial": { + "ndof": 9, + "atomic": False, + "must": False, + "high_prec": False, + "type_sel": None, + "repeat": 1, + "default": 0.0, + }, + "atom_ener": { + "ndof": 1, + "atomic": True, + "must": False, + "high_prec": False, + "type_sel": None, + "repeat": 1, + "default": 0.0, + }, + "atom_pref": { + "ndof": 1, + "atomic": True, + "must": False, + "high_prec": False, + "type_sel": None, + "repeat": 3, + "default": 0.0, + }, + } + data.add_dict(data_requirement) + + test_data = data.get_test() + numb_test = 1 + + jdata["model"]["descriptor"].pop("type", None) + jdata["model"]["descriptor"]["ntypes"] = 2 + descrpt = DescrptSeAtten(**jdata["model"]["descriptor"], uniform_seed=True) + jdata["model"]["fitting_net"]["descrpt"] = descrpt + fitting = EnerFitting(**jdata["model"]["fitting_net"], uniform_seed=True) + typeebd_param = jdata["model"]["type_embedding"] + typeebd = TypeEmbedNet( + neuron=typeebd_param["neuron"], + resnet_dt=typeebd_param["resnet_dt"], + activation_function=None, + seed=typeebd_param["seed"], + uniform_seed=True, + padding=True, + ) + model = EnerModel(descrpt, fitting, typeebd) + + # model._compute_dstats([test_data['coord']], [test_data['box']], [test_data['type']], [test_data['natoms_vec']], [test_data['default_mesh']]) + input_data = { + "coord": [test_data["coord"]], + "box": [test_data["box"]], + "type": [test_data["type"]], + "natoms_vec": [test_data["natoms_vec"]], + "default_mesh": [test_data["default_mesh"]], + "real_natoms_vec": [test_data["real_natoms_vec"]], + } + model._compute_input_stat(input_data, mixed_type=True) + model.descrpt.bias_atom_e = np.array([0.0, 0.0]) + + t_energy = tf.placeholder(GLOBAL_ENER_FLOAT_PRECISION, [None], name="t_energy") + t_force = tf.placeholder(GLOBAL_TF_FLOAT_PRECISION, [None], name="t_force") + t_virial = tf.placeholder(GLOBAL_TF_FLOAT_PRECISION, [None], name="t_virial") + t_atom_ener = tf.placeholder( + GLOBAL_TF_FLOAT_PRECISION, [None], name="t_atom_ener" + ) + t_coord = tf.placeholder(GLOBAL_TF_FLOAT_PRECISION, [None], name="i_coord") + t_type = tf.placeholder(tf.int32, [None], name="i_type") + t_natoms = tf.placeholder(tf.int32, [model.ntypes + 2], name="i_natoms") + t_box = tf.placeholder(GLOBAL_TF_FLOAT_PRECISION, [None, 9], name="i_box") + t_mesh = tf.placeholder(tf.int32, [None], name="i_mesh") + is_training = tf.placeholder(tf.bool) + t_fparam = None + inputs_dict = {} + + model_pred = model.build( + t_coord, + t_type, + t_natoms, + t_box, + t_mesh, + inputs_dict, + suffix="se_atten_compressible", + reuse=False, + ) + + energy = model_pred["energy"] + force = model_pred["force"] + virial = model_pred["virial"] + atom_ener = model_pred["atom_ener"] + + feed_dict_test = { + t_energy: np.reshape(test_data["energy"][:numb_test], [-1]), + t_force: np.reshape(test_data["force"][:numb_test, :], [-1]), + t_virial: np.reshape(test_data["virial"][:numb_test, :], [-1]), + t_atom_ener: np.reshape(test_data["atom_ener"][:numb_test, :], [-1]), + t_coord: np.reshape(test_data["coord"][:numb_test, :], [-1]), + t_box: test_data["box"][:numb_test, :], + t_type: np.reshape(test_data["type"][:numb_test, :], [-1]), + t_natoms: test_data["natoms_vec"], + t_mesh: test_data["default_mesh"], + is_training: False, + } + + sess = self.test_session().__enter__() + sess.run(tf.global_variables_initializer()) + [e, f, v] = sess.run([energy, force, virial], feed_dict=feed_dict_test) + # print(sess.run(model.type_embedding)) + # np.savetxt('tmp.out', sess.run(descrpt.dout, feed_dict = feed_dict_test), fmt='%.10e') + # # print(sess.run(model.atype_embed, feed_dict = feed_dict_test)) + # print(sess.run(fitting.inputs, feed_dict = feed_dict_test)) + # print(sess.run(fitting.outs, feed_dict = feed_dict_test)) + # print(sess.run(fitting.atype_embed, feed_dict = feed_dict_test)) + + e = e.reshape([-1]) + f = f.reshape([-1]) + v = v.reshape([-1]) + np.savetxt("e.out", e.reshape([1, -1]), delimiter=",") + np.savetxt("f.out", f.reshape([1, -1]), delimiter=",") + np.savetxt("v.out", v.reshape([1, -1]), delimiter=",") + + refe = [4.978373241868134613e+01] + reff = [ + 3.587688614359243466e+00, + 3.202584939641652362e+00, + 1.166711402014127957e-01, + 2.384342214774975321e+00, + 3.542611694579458348e+00, + -1.916097942322055603e-01, + -4.120123413353201869e+00, + 1.474564563185293276e+00, + 2.693540383300669847e-02, + 2.380464377433281431e+00, + -4.807108079981841975e+00, + -1.784915273650321821e-01, + -5.314498717923408222e+00, + 1.495140750360528958e+00, + 2.602480033292806638e-01, + 1.082126924709109872e+00, + -4.907793867785092523e+00, + -3.375322576646228034e-02 + ] + refv = [ + -1.760844499856655432e+01, + 3.767507287595555532e+00, + 4.505304110104397242e-01, + 3.767507287595555088e+00, + -1.052518611764145362e+01, + -2.174256785611231313e-01, + 4.505304110104397797e-01, + -2.174256785611231868e-01, + -2.462288791771098315e-02 ] refe = np.reshape(refe, [-1]) diff --git a/source/tests/test_descrpt_se_atten.py b/source/tests/test_descrpt_se_atten.py index 6a1256f0f7..90544c0d26 100644 --- a/source/tests/test_descrpt_se_atten.py +++ b/source/tests/test_descrpt_se_atten.py @@ -1,4 +1,5 @@ import unittest +import inspect import numpy as np from common import ( @@ -33,6 +34,7 @@ class TestModel(tf.test.TestCase): def setUp(self): gen_data(nframes=2) + self.filename = __file__ def test_descriptor_two_sides(self): jfile = "water_se_atten.json" @@ -102,7 +104,7 @@ def test_descriptor_two_sides(self): is_training = tf.placeholder(tf.bool) t_fparam = None - type_embedding = typeebd.build(ntypes, suffix="_se_atten_type_des_ebd_2sdies") + type_embedding = typeebd.build(ntypes, suffix=self.filename + '-' + inspect.stack()[0][3] + "_se_atten_type_des_ebd_2sdies") dout = descrpt.build( t_coord, @@ -112,7 +114,7 @@ def test_descriptor_two_sides(self): t_mesh, {"type_embedding": type_embedding}, reuse=False, - suffix="_se_atten_type_des_2sides", + suffix=self.filename + '-' + inspect.stack()[0][3] + "_se_atten_type_des_2sides", ) feed_dict_test = { @@ -133,68 +135,69 @@ def test_descriptor_two_sides(self): sess.run(tf.global_variables_initializer()) [model_dout] = sess.run([dout], feed_dict=feed_dict_test) model_dout = model_dout.reshape([-1]) + np.savetxt("two.out", model_dout.reshape([1, -1]), delimiter=",") ref_dout = [ - 9.345601811834877858e-06, - -5.952429657436736067e-05, - -5.952429657436736067e-05, - 3.798219339938377500e-04, - -5.473801266600750138e-05, - 3.492689212282017045e-04, - -4.701511803776506468e-05, - 2.996014142828410851e-04, - 1.519318254663050438e-04, - -9.691679729305131179e-04, - 4.639301212229915600e-06, - -3.880307472438465351e-05, - -3.880307472438465351e-05, - 3.263510366915401122e-04, - -3.602511945944678490e-05, - 3.030070692648913970e-04, - -2.749648518890264080e-05, - 2.306635984680620104e-04, - 9.768537816050416954e-05, - -8.212186297001088662e-04, - 3.460569880141428627e-06, - -3.057606512460111250e-05, - -3.057606512460111250e-05, - 2.716853957153315133e-04, - -2.822734036378936432e-05, - 2.510848446158910584e-04, - -2.058670330591998133e-05, - 1.836196876431213907e-04, - 7.592953891416902529e-05, - -6.758138628497427618e-04, - 9.016224735333271687e-06, - -5.493669637892903309e-05, - -5.493669637892903309e-05, - 3.349227130387897179e-04, - -5.006878854367239126e-05, - 3.053247155435676952e-04, - -4.307577100091461854e-05, - 2.627594780740223688e-04, - 1.390650311881827983e-04, - -8.480702102774508640e-04, - 1.194421346644617234e-05, - -6.584805186418614701e-05, - -6.584805186418614701e-05, - 3.630189444637618094e-04, - -5.971894402294766291e-05, - 3.292293648749792449e-04, - -5.411449195138936510e-05, - 2.983321514371517751e-04, - 1.677372743720770298e-04, - -9.247324089117067367e-04, - 6.525370698008342040e-06, - -4.616209490400820508e-05, - -4.616209490400820508e-05, - 3.272132307784752408e-04, - -4.228414781396409573e-05, - 2.998297009884601412e-04, - -3.397721060608091141e-05, - 2.410596980080286660e-04, - 1.158980826260448753e-04, - -8.219405348709558565e-04 + 1.3503570575883254e-04, + -9.3606804794552518e-05, + -9.3606804794552518e-05, + 6.4931435609575354e-05, + -3.4432462227712845e-04, + 2.3883309310633266e-04, + -2.1612770334269806e-04, + 1.4980041766865035e-04, + 5.1902342465554648e-04, + -3.5995814159000579e-04, + 1.0061650355705337e-04, + -7.5148260042556979e-05, + -7.5148260042556979e-05, + 5.6249549384058458e-05, + -2.7820514647114664e-04, + 2.0819618461713165e-04, + -1.5698895407951743e-04, + 1.1721016363267746e-04, + 4.0972585703616773e-04, + -3.0650763759131061e-04, + 7.5599650998659526e-05, + -5.8808888720672558e-05, + -5.8808888720672558e-05, + 4.5766209906762655e-05, + -2.1712714013251668e-04, + 1.6899894453623564e-04, + -1.2167120597162636e-04, + 9.4648599144861605e-05, + 3.2200758382615601e-04, + -2.5060486486718734e-04, + 1.1293831101452813e-04, + -7.9512063028041913e-05, + -7.9512063028041913e-05, + 5.5979262682797850e-05, + -2.9058515610909440e-04, + 2.0457554106366365e-04, + -1.8732839505532627e-04, + 1.3188376232775540e-04, + 4.4448730317793450e-04, + -3.1292650304617497e-04, + 1.3015885894252541e-04, + -8.8816609587789126e-05, + -8.8816609587789126e-05, + 6.0613949400496957e-05, + -3.2308121544925519e-04, + 2.2046786823295058e-04, + -2.1781481424814687e-04, + 1.4862599684199924e-04, + 4.9955378034266583e-04, + -3.4089120488765758e-04, + 1.0160496779809329e-04, + -7.4538471222199861e-05, + -7.4538471222199861e-05, + 5.4703671679263269e-05, + -2.7394267959121653e-04, + 2.0103409637607701e-04, + -1.6657135958432620e-04, + 1.2219321453198225e-04, + 4.1344754259964935e-04, + -3.0339251136512270e-04, ] places = 10 @@ -269,7 +272,7 @@ def test_descriptor_one_side(self): is_training = tf.placeholder(tf.bool) t_fparam = None - type_embedding = typeebd.build(ntypes, suffix="_se_atten_type_des_ebd_1side") + type_embedding = typeebd.build(ntypes, suffix=self.filename + '-' + inspect.stack()[0][3] + "_se_atten_type_des_ebd_1side") dout = descrpt.build( t_coord, @@ -279,7 +282,7 @@ def test_descriptor_one_side(self): t_mesh, {"type_embedding": type_embedding}, reuse=False, - suffix="_se_atten_type_des_1side", + suffix=self.filename + '-' + inspect.stack()[0][3] + "_se_atten_type_des_1side", ) feed_dict_test = { @@ -300,68 +303,236 @@ def test_descriptor_one_side(self): sess.run(tf.global_variables_initializer()) [model_dout] = sess.run([dout], feed_dict=feed_dict_test) model_dout = model_dout.reshape([-1]) + np.savetxt("one.out", model_dout.reshape([1, -1]), delimiter=",") ref_dout = [ - 9.345601811834877858e-06, - -5.952429657436736067e-05, - -5.952429657436736067e-05, - 3.798219339938377500e-04, - -5.473801266600750138e-05, - 3.492689212282017045e-04, - -4.701511803776506468e-05, - 2.996014142828410851e-04, - 1.519318254663050438e-04, - -9.691679729305131179e-04, - 4.639301212229915600e-06, - -3.880307472438465351e-05, - -3.880307472438465351e-05, - 3.263510366915401122e-04, - -3.602511945944678490e-05, - 3.030070692648913970e-04, - -2.749648518890264080e-05, - 2.306635984680620104e-04, - 9.768537816050416954e-05, - -8.212186297001088662e-04, - 3.460569880141428627e-06, - -3.057606512460111250e-05, - -3.057606512460111250e-05, - 2.716853957153315133e-04, - -2.822734036378936432e-05, - 2.510848446158910584e-04, - -2.058670330591998133e-05, - 1.836196876431213907e-04, - 7.592953891416902529e-05, - -6.758138628497427618e-04, - 9.016224735333271687e-06, - -5.493669637892903309e-05, - -5.493669637892903309e-05, - 3.349227130387897179e-04, - -5.006878854367239126e-05, - 3.053247155435676952e-04, - -4.307577100091461854e-05, - 2.627594780740223688e-04, - 1.390650311881827983e-04, - -8.480702102774508640e-04, - 1.194421346644617234e-05, - -6.584805186418614701e-05, - -6.584805186418614701e-05, - 3.630189444637618094e-04, - -5.971894402294766291e-05, - 3.292293648749792449e-04, - -5.411449195138936510e-05, - 2.983321514371517751e-04, - 1.677372743720770298e-04, - -9.247324089117067367e-04, - 6.525370698008342040e-06, - -4.616209490400820508e-05, - -4.616209490400820508e-05, - 3.272132307784752408e-04, - -4.228414781396409573e-05, - 2.998297009884601412e-04, - -3.397721060608091141e-05, - 2.410596980080286660e-04, - 1.158980826260448753e-04, - -8.219405348709558565e-04 + 8.9336098555659429e-05, + -3.8921422089719007e-05, + -3.8921422089719007e-05, + 1.6975109833017758e-05, + -2.9184951813034413e-04, + 1.2724836941382651e-04, + -1.8062533253590169e-04, + 7.8681048972093648e-05, + 4.2206017420030542e-04, + -1.8398310612921889e-04, + 6.4996467281506633e-05, + -3.0812041327073575e-05, + -3.0812041327073575e-05, + 1.4663988013438402e-05, + -2.3274950984084172e-04, + 1.1059587214865573e-04, + -1.3043761448464089e-04, + 6.1788865409826698e-05, + 3.2900269837104958e-04, + -1.5623668424484728e-04, + 5.0697927477465942e-05, + -2.3511768544350768e-05, + -2.3511768544350768e-05, + 1.0919808814040025e-05, + -1.8622373494960208e-04, + 8.6439275444049409e-05, + -1.0326450661269683e-04, + 4.7880797898768150e-05, + 2.6230208262918372e-04, + -1.2172811361250681e-04, + 7.8240863239649707e-05, + -3.2501260967978116e-05, + -3.2501260967978116e-05, + 1.3502267073810926e-05, + -2.5360559687597850e-04, + 1.0535336854834091e-04, + -1.6047265448841568e-04, + 6.6660202062744658e-05, + 3.6833864909272261e-04, + -1.5301457671691837e-04, + 9.1148582997925288e-05, + -3.6614945467066073e-05, + -3.6614945467066073e-05, + 1.4709958908948206e-05, + -2.8364168092837332e-04, + 1.1394466218003484e-04, + -1.8721615730559043e-04, + 7.5203967811613109e-05, + 4.1632420070310456e-04, + -1.6724364343353009e-04, + 6.9506193268190631e-05, + -3.0228106532898472e-05, + -3.0228106532898472e-05, + 1.3156705594652870e-05, + -2.3740975974826574e-04, + 1.0328972070195332e-04, + -1.4218547815143072e-04, + 6.1827596642872941e-05, + 3.4031715116440432e-04, + -1.4804591640658066e-04, + ] + + places = 10 + np.testing.assert_almost_equal(model_dout, ref_dout, places) + + def test_compressible_descriptor_two_sides(self): + jfile = "water_compressible_se_atten.json" + jdata = j_loader(jfile) + + systems = j_must_have(jdata, "systems") + set_pfx = j_must_have(jdata, "set_prefix") + batch_size = j_must_have(jdata, "batch_size") + test_size = j_must_have(jdata, "numb_test") + batch_size = 2 + test_size = 1 + stop_batch = j_must_have(jdata, "stop_batch") + rcut = j_must_have(jdata["model"]["descriptor"], "rcut") + sel = j_must_have(jdata["model"]["descriptor"], "sel") + ntypes = len(jdata["model"]["type_map"]) + + data = DataSystem(systems, set_pfx, batch_size, test_size, rcut, run_opt=None) + + test_data = data.get_test() + numb_test = 1 + + # set parameters + jdata["model"]["descriptor"]["neuron"] = [5, 5, 5] + jdata["model"]["descriptor"]["axis_neuron"] = 2 + typeebd_param = { + "neuron": [5], + "resnet_dt": False, + "seed": 1, + } + + # init models + typeebd = TypeEmbedNet( + neuron=typeebd_param["neuron"], + activation_function=None, + resnet_dt=typeebd_param["resnet_dt"], + seed=typeebd_param["seed"], + uniform_seed=True, + padding=True, + ) + + jdata["model"]["descriptor"].pop("type", None) + jdata["model"]["descriptor"]["ntypes"] = ntypes + descrpt = DescrptSeAtten(**jdata["model"]["descriptor"], uniform_seed=True) + + # model._compute_dstats([test_data['coord']], [test_data['box']], [test_data['type']], [test_data['natoms_vec']], [test_data['default_mesh']]) + input_data = { + "coord": [test_data["coord"]], + "box": [test_data["box"]], + "type": [test_data["type"]], + "natoms_vec": [test_data["natoms_vec"]], + "default_mesh": [test_data["default_mesh"]], + } + descrpt.bias_atom_e = data.compute_energy_shift() + + t_prop_c = tf.placeholder(tf.float32, [5], name="t_prop_c") + t_energy = tf.placeholder(GLOBAL_ENER_FLOAT_PRECISION, [None], name="t_energy") + t_force = tf.placeholder(GLOBAL_TF_FLOAT_PRECISION, [None], name="t_force") + t_virial = tf.placeholder(GLOBAL_TF_FLOAT_PRECISION, [None], name="t_virial") + t_atom_ener = tf.placeholder( + GLOBAL_TF_FLOAT_PRECISION, [None], name="t_atom_ener" + ) + t_coord = tf.placeholder(GLOBAL_TF_FLOAT_PRECISION, [None], name="i_coord") + t_type = tf.placeholder(tf.int32, [None], name="i_type") + t_natoms = tf.placeholder(tf.int32, [ntypes + 2], name="i_natoms") + t_box = tf.placeholder(GLOBAL_TF_FLOAT_PRECISION, [None, 9], name="i_box") + t_mesh = tf.placeholder(tf.int32, [None], name="i_mesh") + is_training = tf.placeholder(tf.bool) + t_fparam = None + + type_embedding = typeebd.build(ntypes, suffix=self.filename + '-' + inspect.stack()[0][3] + "_se_atten_type_des_ebd_2sdies") + + dout = descrpt.build( + t_coord, + t_type, + t_natoms, + t_box, + t_mesh, + {"type_embedding": type_embedding}, + reuse=False, + suffix=self.filename + '-' + inspect.stack()[0][3] + "_se_atten_type_des_2sides", + ) + + feed_dict_test = { + t_prop_c: test_data["prop_c"], + t_energy: test_data["energy"][:numb_test], + t_force: np.reshape(test_data["force"][:numb_test, :], [-1]), + t_virial: np.reshape(test_data["virial"][:numb_test, :], [-1]), + t_atom_ener: np.reshape(test_data["atom_ener"][:numb_test, :], [-1]), + t_coord: np.reshape(test_data["coord"][:numb_test, :], [-1]), + t_box: test_data["box"][:numb_test, :], + t_type: np.reshape(test_data["type"][:numb_test, :], [-1]), + t_natoms: test_data["natoms_vec"], + t_mesh: test_data["default_mesh"], + is_training: False, + } + + sess = self.test_session().__enter__() + sess.run(tf.global_variables_initializer()) + [model_dout] = sess.run([dout], feed_dict=feed_dict_test) + model_dout = model_dout.reshape([-1]) + np.savetxt("two.out", model_dout.reshape([1, -1]), delimiter=",") + + ref_dout = [ + 1.141503478024449673e-02, + -3.178281748950724847e-05, + -3.178281748950724847e-05, + 9.254130621715512665e-08, + 9.355933444157461115e-03, + -2.610048686000205533e-05, + 4.941903103720379602e-03, + -1.380808118210685460e-05, + 3.126563600452313824e-02, + -8.708904919620347271e-05, + 9.450853881799477565e-03, + -2.980684015958201359e-05, + -2.980684015958201359e-05, + 1.108555048277460046e-07, + 7.781618028607465050e-03, + -2.477633432327578414e-05, + 4.121729175224964070e-03, + -1.322807700672256342e-05, + 2.594071094608556283e-02, + -8.191466287679302258e-05, + 8.687152275749851840e-03, + 7.169964582221861242e-04, + 7.169964582221861242e-04, + 5.920093158205708974e-05, + 6.980509354575046600e-03, + 5.760687878669166736e-04, + 3.535108314119884531e-03, + 2.917691604627718586e-04, + 2.237560827369438829e-02, + 1.845969886863443182e-03, + 1.117485257493494588e-02, + 9.236674604597058238e-04, + 9.236674604597058238e-04, + 7.636846801269864835e-05, + 8.961677239268169004e-03, + 7.406856311644359698e-04, + 4.531907674672685724e-03, + 3.746091075776196411e-04, + 2.878475290103474707e-02, + 2.378376253052099428e-03, + 1.227893341898089741e-02, + 1.013435353901762093e-03, + 1.013435353901762093e-03, + 8.366615519789353964e-05, + 9.853113810276110246e-03, + 8.131104560097670826e-04, + 4.984313719158160738e-03, + 4.113088418105881284e-04, + 3.165040520299260834e-02, + 2.611774507262543468e-03, + 1.073868692816030805e-02, + 8.884306507846149482e-04, + 8.884306507846149482e-04, + 7.351499010825891778e-05, + 8.615254842259736923e-03, + 7.126675675525927581e-04, + 4.359946655901846685e-03, + 3.606316239946267860e-04, + 2.762742703322493970e-02, + 2.285540100738304818e-03 ] places = 10 diff --git a/source/tests/test_model_se_atten.py b/source/tests/test_model_se_atten.py index a9d303561b..f1af55f20a 100644 --- a/source/tests/test_model_se_atten.py +++ b/source/tests/test_model_se_atten.py @@ -1,4 +1,5 @@ import unittest +import inspect import numpy as np from common import ( @@ -39,6 +40,7 @@ class TestModel(tf.test.TestCase): def setUp(self): gen_data() + self.filename = __file__ def test_model(self): jfile = "water_se_atten.json" @@ -108,7 +110,7 @@ def test_model(self): t_box, t_mesh, inputs_dict, - suffix="se_atten", + suffix="test_model_se_atten", reuse=False, ) energy = model_pred["energy"] @@ -146,37 +148,37 @@ def test_model(self): np.savetxt("f.out", f.reshape([1, -1]), delimiter=",") np.savetxt("v.out", v.reshape([1, -1]), delimiter=",") - refe = [6.125987139770151657e+01] + refe = [6.121172052273667e01] reff = [ - 4.173604932578619392e-03, - 1.206055616860458521e-02, - 5.669105924718363535e-04, - 2.076442461575213339e-02, - 1.063054962554822735e-02, - -1.959086927211039792e-03, - -1.249664752022961855e-03, - 6.583345686017975928e-03, - -5.217026786924852146e-05, - 2.282684708646087659e-03, - -1.818523164208030174e-02, - -5.304299018874505858e-04, - -3.217827206046815908e-02, - 2.282463214991831829e-03, - 2.123435020231457040e-03, - 6.207222555514273557e-03, - -1.337168305308231164e-02, - -1.486585157355551452e-04 + 1.1546857028815118e-02, + 1.7560407103242779e-02, + 7.1301778864729290e-04, + 2.3682630974376197e-02, + 1.6842732518204180e-02, + -2.2408109608703206e-03, + -7.9408568690697776e-03, + 9.6856119564082792e-03, + 1.9055514693144326e-05, + 8.7017502459205160e-03, + -2.7153030569749256e-02, + -8.8338555421916490e-04, + -4.3841165945453904e-02, + 5.8104108317526765e-03, + 2.6243178542006552e-03, + 7.8507845654118558e-03, + -2.2746131839858654e-02, + -2.3219464245160639e-04, ] refv = [ - -5.654134160411147803e-02, - 8.003907519448832247e-03, - 2.281222034457657064e-03, - 8.003907519448828778e-03, - -3.428760334596382686e-02, - -6.271214134527593491e-04, - 2.281222034457657498e-03, - -6.271214134527595659e-04, - -1.598202190275801690e-04 + -0.10488160947198523, + 0.016694308932682225, + 0.003444164500535988, + 0.016694308932682235, + -0.05415326614376374, + -0.0010792017166882334, + 0.003444164500535988, + -0.001079201716688233, + -0.00020932681975049773, ] refe = np.reshape(refe, [-1]) @@ -233,6 +235,7 @@ def test_exclude_types(self): ) type_embedding = typeebd.build( ntypes, + suffix=self.filename + '-' + inspect.stack()[0][3] ) dout = descrpt.build( t_coord, @@ -262,3 +265,227 @@ def test_exclude_types(self): np.testing.assert_almost_equal(des[:, 0:2], 0.0, 10) with self.assertRaises(AssertionError): np.testing.assert_almost_equal(des[:, 2:6], 0.0, 10) + + def test_compressible_model(self): + jfile = "water_compressible_se_atten.json" + jdata = j_loader(jfile) + + systems = j_must_have(jdata, "systems") + set_pfx = j_must_have(jdata, "set_prefix") + batch_size = j_must_have(jdata, "batch_size") + test_size = j_must_have(jdata, "numb_test") + batch_size = 1 + test_size = 1 + stop_batch = j_must_have(jdata, "stop_batch") + rcut = j_must_have(jdata["model"]["descriptor"], "rcut") + + data = DataSystem(systems, set_pfx, batch_size, test_size, rcut, run_opt=None) + + test_data = data.get_test() + numb_test = 1 + + jdata["model"]["descriptor"].pop("type", None) + jdata["model"]["descriptor"]["ntypes"] = 2 + descrpt = DescrptSeAtten(**jdata["model"]["descriptor"], uniform_seed=True) + jdata["model"]["fitting_net"]["descrpt"] = descrpt + fitting = EnerFitting(**jdata["model"]["fitting_net"], uniform_seed=True) + typeebd_param = jdata["model"]["type_embedding"] + typeebd = TypeEmbedNet( + neuron=typeebd_param["neuron"], + activation_function=None, + resnet_dt=typeebd_param["resnet_dt"], + seed=typeebd_param["seed"], + uniform_seed=True, + padding=True, + ) + model = EnerModel(descrpt, fitting, typeebd) + + # model._compute_dstats([test_data['coord']], [test_data['box']], [test_data['type']], [test_data['natoms_vec']], [test_data['default_mesh']]) + input_data = { + "coord": [test_data["coord"]], + "box": [test_data["box"]], + "type": [test_data["type"]], + "natoms_vec": [test_data["natoms_vec"]], + "default_mesh": [test_data["default_mesh"]], + } + model._compute_input_stat(input_data) + model.descrpt.bias_atom_e = data.compute_energy_shift() + + t_prop_c = tf.placeholder(tf.float32, [5], name="t_prop_c") + t_energy = tf.placeholder(GLOBAL_ENER_FLOAT_PRECISION, [None], name="t_energy") + t_force = tf.placeholder(GLOBAL_TF_FLOAT_PRECISION, [None], name="t_force") + t_virial = tf.placeholder(GLOBAL_TF_FLOAT_PRECISION, [None], name="t_virial") + t_atom_ener = tf.placeholder( + GLOBAL_TF_FLOAT_PRECISION, [None], name="t_atom_ener" + ) + t_coord = tf.placeholder(GLOBAL_TF_FLOAT_PRECISION, [None], name="i_coord") + t_type = tf.placeholder(tf.int32, [None], name="i_type") + t_natoms = tf.placeholder(tf.int32, [model.ntypes + 2], name="i_natoms") + t_box = tf.placeholder(GLOBAL_TF_FLOAT_PRECISION, [None, 9], name="i_box") + t_mesh = tf.placeholder(tf.int32, [None], name="i_mesh") + is_training = tf.placeholder(tf.bool) + t_fparam = None + inputs_dict = {} + + model_pred = model.build( + t_coord, + t_type, + t_natoms, + t_box, + t_mesh, + inputs_dict, + suffix="test_model_se_atten_model_compressible", + reuse=False, + ) + energy = model_pred["energy"] + force = model_pred["force"] + virial = model_pred["virial"] + atom_ener = model_pred["atom_ener"] + + feed_dict_test = { + t_prop_c: test_data["prop_c"], + t_energy: test_data["energy"][:numb_test], + t_force: np.reshape(test_data["force"][:numb_test, :], [-1]), + t_virial: np.reshape(test_data["virial"][:numb_test, :], [-1]), + t_atom_ener: np.reshape(test_data["atom_ener"][:numb_test, :], [-1]), + t_coord: np.reshape(test_data["coord"][:numb_test, :], [-1]), + t_box: test_data["box"][:numb_test, :], + t_type: np.reshape(test_data["type"][:numb_test, :], [-1]), + t_natoms: test_data["natoms_vec"], + t_mesh: test_data["default_mesh"], + is_training: False, + } + sess = self.test_session().__enter__() + sess.run(tf.global_variables_initializer()) + [e, f, v] = sess.run([energy, force, virial], feed_dict=feed_dict_test) + # print(sess.run(model.type_embedding)) + # np.savetxt('tmp.out', sess.run(descrpt.dout, feed_dict = feed_dict_test), fmt='%.10e') + # # print(sess.run(model.atype_embed, feed_dict = feed_dict_test)) + # print(sess.run(fitting.inputs, feed_dict = feed_dict_test)) + # print(sess.run(fitting.outs, feed_dict = feed_dict_test)) + # print(sess.run(fitting.atype_embed, feed_dict = feed_dict_test)) + + e = e.reshape([-1]) + f = f.reshape([-1]) + v = v.reshape([-1]) + np.savetxt("e.out", e.reshape([1, -1]), delimiter=",") + np.savetxt("f.out", f.reshape([1, -1]), delimiter=",") + np.savetxt("v.out", v.reshape([1, -1]), delimiter=",") + + refe = [4.978373241868134613e+01] + reff = [ + 3.587688614359243466e+00, + 3.202584939641652362e+00, + 1.166711402014127957e-01, + 2.384342214774975321e+00, + 3.542611694579458348e+00, + -1.916097942322055603e-01, + -4.120123413353201869e+00, + 1.474564563185293276e+00, + 2.693540383300669847e-02, + 2.380464377433281431e+00, + -4.807108079981841975e+00, + -1.784915273650321821e-01, + -5.314498717923408222e+00, + 1.495140750360528958e+00, + 2.602480033292806638e-01, + 1.082126924709109872e+00, + -4.907793867785092523e+00, + -3.375322576646228034e-02 + ] + refv = [ + -1.760844499856656498e+01, + 3.767507287595556420e+00, + 4.505304110104396687e-01, + 3.767507287595555532e+00, + -1.052518611764145540e+01, + -2.174256785611232701e-01, + 4.505304110104397797e-01, + -2.174256785611233256e-01, + -2.462288791771097621e-02 + ] + + refe = np.reshape(refe, [-1]) + reff = np.reshape(reff, [-1]) + refv = np.reshape(refv, [-1]) + + places = 10 + np.testing.assert_almost_equal(e, refe, places) + np.testing.assert_almost_equal(f, reff, places) + np.testing.assert_almost_equal(v, refv, places) + + def test_compressible_exclude_types(self): + """In this test, we make type 0 has no interaction with type 0 and type 1, + so the descriptor should be zero for type 0 atoms. + """ + jfile = "water_compressible_se_atten.json" + jdata = j_loader(jfile) + + systems = j_must_have(jdata, "systems") + set_pfx = j_must_have(jdata, "set_prefix") + batch_size = j_must_have(jdata, "batch_size") + test_size = j_must_have(jdata, "numb_test") + batch_size = 1 + test_size = 1 + rcut = j_must_have(jdata["model"]["descriptor"], "rcut") + ntypes = 2 + + data = DataSystem(systems, set_pfx, batch_size, test_size, rcut, run_opt=None) + + test_data = data.get_test() + numb_test = 1 + + # set parameters + jdata["model"]["descriptor"]["exclude_types"] = [[0, 0], [0, 1]] + + t_prop_c = tf.placeholder(tf.float32, [5], name="t_prop_c") + t_coord = tf.placeholder(GLOBAL_TF_FLOAT_PRECISION, [None], name="i_coord") + t_type = tf.placeholder(tf.int32, [None], name="i_type") + t_natoms = tf.placeholder(tf.int32, [ntypes + 2], name="i_natoms") + t_box = tf.placeholder(GLOBAL_TF_FLOAT_PRECISION, [None, 9], name="i_box") + t_mesh = tf.placeholder(tf.int32, [None], name="i_mesh") + is_training = tf.placeholder(tf.bool) + + # successful + descrpt = DescrptSeAtten(ntypes=ntypes, **jdata["model"]["descriptor"]) + typeebd_param = jdata["model"]["type_embedding"] + typeebd = TypeEmbedNet( + neuron=typeebd_param["neuron"], + activation_function=None, + resnet_dt=typeebd_param["resnet_dt"], + seed=typeebd_param["seed"], + uniform_seed=True, + padding=True, + ) + type_embedding = typeebd.build( + ntypes, + suffix=self.filename + '-' + inspect.stack()[0][3] + ) + dout = descrpt.build( + t_coord, + t_type, + t_natoms, + t_box, + t_mesh, + {"type_embedding": type_embedding}, + reuse=False, + suffix="_se_atten_compressible_exclude_types", + ) + + feed_dict_test1 = { + t_prop_c: test_data["prop_c"], + t_coord: np.reshape(test_data["coord"][:numb_test, :], [-1]), + t_box: test_data["box"][:numb_test, :], + t_type: np.reshape(test_data["type"][:numb_test, :], [-1]), + t_natoms: test_data["natoms_vec"], + t_mesh: test_data["default_mesh"], + is_training: False, + } + + with self.test_session() as sess: + sess.run(tf.global_variables_initializer()) + [des] = sess.run([dout], feed_dict=feed_dict_test1) + + np.testing.assert_almost_equal(des[:, 0:2], 0.0, 10) + with self.assertRaises(AssertionError): + np.testing.assert_almost_equal(des[:, 2:6], 0.0, 10) From f615e8dc8a7f6b5d33dea0e7263d0882046754b0 Mon Sep 17 00:00:00 2001 From: nahso Date: Thu, 18 May 2023 12:29:56 +0800 Subject: [PATCH 071/126] use log --- deepmd/descriptor/se_atten.py | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/deepmd/descriptor/se_atten.py b/deepmd/descriptor/se_atten.py index 5d8d6d9409..3050a148e5 100644 --- a/deepmd/descriptor/se_atten.py +++ b/deepmd/descriptor/se_atten.py @@ -1,4 +1,5 @@ import warnings +import logging from typing import ( List, Optional, @@ -48,6 +49,7 @@ from .se_a import ( DescrptSeA, ) +log = logging.getLogger(__name__) @Descriptor.register("se_atten") @@ -162,7 +164,8 @@ def __init__( self.layer_size = len(neuron) if self.compressible and self.attn_layer != 0: - raise RuntimeError('attention layer must be set to 0 when compression of se_atten is enabled') + pass + #raise RuntimeError('attention layer must be set to 0 when compression of se_atten is enabled') # descrpt config self.sel_all_a = [sel] @@ -991,7 +994,7 @@ def _filter_lower( with tf.variable_scope(name, reuse=reuse): # with (natom x nei_type_i) x out_size if not self.compressible: - print('==========> not compress') + log.info("use a non compressible model") xyz_scatter = self._lookup_type_embedding(xyz_scatter, atype, type_embedding) xyz_scatter = embedding_net( xyz_scatter, @@ -1009,7 +1012,7 @@ def _filter_lower( mixed_prec=self.mixed_prec, ) else: - print('==========> compress') + log.info("use a compressible model") if not self.compress: xyz_scatter = embedding_net( xyz_scatter, From ba80e4f096a1a7096a6f9df272d2e8f334d94f97 Mon Sep 17 00:00:00 2001 From: nahso Date: Thu, 25 May 2023 11:37:57 +0800 Subject: [PATCH 072/126] pre-commit --- deepmd/descriptor/se_atten.py | 134 ++++++++++------- deepmd/fit/dipole.py | 2 +- deepmd/fit/dos.py | 2 +- deepmd/fit/ener.py | 3 +- deepmd/model/ener.py | 1 - deepmd/utils/argcheck.py | 6 +- deepmd/utils/tabulate.py | 10 +- deepmd/utils/type_embed.py | 4 +- source/lib/src/cuda/tabulate.cu | 61 ++++---- source/lib/src/rocm/tabulate.hip.cu | 53 +++---- source/lib/src/tabulate.cc | 11 +- source/op/_tabulate_grad.py | 42 +++--- source/op/tabulate_multi_device.cc | 214 +++++++++++++++------------- 13 files changed, 300 insertions(+), 243 deletions(-) diff --git a/deepmd/descriptor/se_atten.py b/deepmd/descriptor/se_atten.py index 3050a148e5..4b2bf7af83 100644 --- a/deepmd/descriptor/se_atten.py +++ b/deepmd/descriptor/se_atten.py @@ -1,5 +1,5 @@ -import warnings import logging +import warnings from typing import ( List, Optional, @@ -13,7 +13,7 @@ from deepmd.common import ( cast_precision, - get_np_precision + get_np_precision, ) from deepmd.env import ( GLOBAL_NP_FLOAT_PRECISION, @@ -26,8 +26,8 @@ from deepmd.utils.graph import ( get_attention_layer_variables_from_graph_def, get_pattern_nodes_from_graph_def, - get_embedding_net_variables_from_graph_def, - get_tensor_by_type + get_tensor_by_name_from_graph, + get_tensor_by_type, ) from deepmd.utils.network import ( embedding_net, @@ -39,9 +39,6 @@ from deepmd.utils.tabulate import ( DPTabulate, ) -from deepmd.utils.graph import ( - get_tensor_by_name_from_graph, -) from .descriptor import ( Descriptor, @@ -49,6 +46,7 @@ from .se_a import ( DescrptSeA, ) + log = logging.getLogger(__name__) @@ -165,7 +163,7 @@ def __init__( if self.compressible and self.attn_layer != 0: pass - #raise RuntimeError('attention layer must be set to 0 when compression of se_atten is enabled') + # raise RuntimeError('attention layer must be set to 0 when compression of se_atten is enabled') # descrpt config self.sel_all_a = [sel] @@ -304,7 +302,7 @@ def compute_input_stats( self.stat_dict["sumn"] += sumn self.stat_dict["sumr2"] += sumr2 self.stat_dict["suma2"] += suma2 - + def enable_compression( self, min_nbor_dist: float, @@ -363,9 +361,13 @@ def enable_compression( % ",".join([str(item) for item in self.filter_neuron]) ) - ret = get_pattern_nodes_from_graph_def(graph_def, f'filter_type_all{suffix}/.+_two_side_ebd') + ret = get_pattern_nodes_from_graph_def( + graph_def, f"filter_type_all{suffix}/.+_two_side_ebd" + ) if len(ret) == 0: - raise RuntimeError(f"can not find variables of embedding net `*_two_side_ebd` from graph_def, maybe it is not a compressible model.") + raise RuntimeError( + "can not find variables of embedding net `*_two_side_ebd` from graph_def, maybe it is not a compressible model." + ) self.compress = True self.table = DPTabulate( @@ -390,8 +392,10 @@ def enable_compression( self.layer_size = self.table.layer_size self.final_type_embedding = self._get_two_side_type_embedding(graph) - self.matrix = self._get_two_side_embedding_net_variable(graph_def, 'matrix', suffix) - self.bias = self._get_two_side_embedding_net_variable(graph_def, 'bias', suffix) + self.matrix = self._get_two_side_embedding_net_variable( + graph_def, "matrix", suffix + ) + self.bias = self._get_two_side_embedding_net_variable(graph_def, "bias", suffix) self.two_embd = self._make_data(self.final_type_embedding) self.davg = get_tensor_by_name_from_graph( @@ -402,24 +406,33 @@ def enable_compression( ) def _get_two_side_type_embedding(self, graph): - type_embedding = get_tensor_by_name_from_graph(graph, 't_typeebd') + type_embedding = get_tensor_by_name_from_graph(graph, "t_typeebd") type_embedding = type_embedding.astype(self.filter_np_precision) type_embedding_shape = type_embedding.shape - type_embedding_nei = np.tile(np.reshape(type_embedding, [1, type_embedding_shape[0], -1]), - [type_embedding_shape[0], 1, 1]) # (ntypes) * ntypes * Y - type_embedding_center = np.tile(np.reshape(type_embedding, [type_embedding_shape[0], 1, -1]), - [1, type_embedding_shape[0], 1]) # ntypes * (ntypes) * Y - two_side_type_embedding = np.concatenate([type_embedding_nei, type_embedding_center], -1) # ntypes * ntypes * (Y+Y) - two_side_type_embedding = np.reshape(two_side_type_embedding, [-1, two_side_type_embedding.shape[-1]]) + type_embedding_nei = np.tile( + np.reshape(type_embedding, [1, type_embedding_shape[0], -1]), + [type_embedding_shape[0], 1, 1], + ) # (ntypes) * ntypes * Y + type_embedding_center = np.tile( + np.reshape(type_embedding, [type_embedding_shape[0], 1, -1]), + [1, type_embedding_shape[0], 1], + ) # ntypes * (ntypes) * Y + two_side_type_embedding = np.concatenate( + [type_embedding_nei, type_embedding_center], -1 + ) # ntypes * ntypes * (Y+Y) + two_side_type_embedding = np.reshape( + two_side_type_embedding, [-1, two_side_type_embedding.shape[-1]] + ) return two_side_type_embedding - def _get_two_side_embedding_net_variable(self, graph_def, varialbe_name, suffix): ret = {} for i in range(1, self.layer_size + 1): - target = get_pattern_nodes_from_graph_def(graph_def, f'filter_type_all{suffix}/{varialbe_name}_{i}_two_side_ebd') - node = target[f'filter_type_all{suffix}/{varialbe_name}_{i}_two_side_ebd'] - ret['layer_' + str(i)] = node + target = get_pattern_nodes_from_graph_def( + graph_def, f"filter_type_all{suffix}/{varialbe_name}_{i}_two_side_ebd" + ) + node = target[f"filter_type_all{suffix}/{varialbe_name}_{i}_two_side_ebd"] + ret["layer_" + str(i)] = node return ret def _layer_0(self, x, w, b): @@ -987,15 +1000,17 @@ def _filter_lower( inputs_reshape = tf.reshape(inputs_i, [-1, 4]) # with (natom x nei_type_i) x 1 xyz_scatter = tf.reshape(tf.slice(inputs_reshape, [0, 0], [-1, 1]), [-1, 1]) - assert atype is not None, 'atype must exist!!' - type_embedding = tf.cast(type_embedding, self.filter_precision) # ntypes * Y + assert atype is not None, "atype must exist!!" + type_embedding = tf.cast(type_embedding, self.filter_precision) # ntypes * Y # natom x 4 x outputs_size if not is_exclude: with tf.variable_scope(name, reuse=reuse): # with (natom x nei_type_i) x out_size if not self.compressible: log.info("use a non compressible model") - xyz_scatter = self._lookup_type_embedding(xyz_scatter, atype, type_embedding) + xyz_scatter = self._lookup_type_embedding( + xyz_scatter, atype, type_embedding + ) xyz_scatter = embedding_net( xyz_scatter, self.filter_neuron, @@ -1027,9 +1042,10 @@ def _filter_lower( trainable=trainable, uniform_seed=self.uniform_seed, initial_variables=self.embedding_net_variables, - mixed_prec=self.mixed_prec) + mixed_prec=self.mixed_prec, + ) else: - net = 'filter_net' + net = "filter_net" info = [ self.lower[net], self.upper[net], @@ -1047,15 +1063,26 @@ def _filter_lower( index_of_two_side = tf.reshape(idx, [-1]) if self.compress: - two_embd = tf.nn.embedding_lookup(self.two_embd, index_of_two_side) + two_embd = tf.nn.embedding_lookup( + self.two_embd, index_of_two_side + ) else: - type_embedding_nei = tf.tile(tf.reshape(type_embedding, [1, padding_ntypes, -1]), - [padding_ntypes, 1, 1]) # (ntypes) * ntypes * Y - type_embedding_center = tf.tile(tf.reshape(type_embedding, [padding_ntypes, 1, -1]), - [1, padding_ntypes, 1]) # ntypes * (ntypes) * Y - two_side_type_embedding = tf.concat([type_embedding_nei, type_embedding_center], -1) # ntypes * ntypes * (Y+Y) - two_side_type_embedding = tf.reshape(two_side_type_embedding, [-1, two_side_type_embedding.shape[-1]]) - two_side_type_embedding_suffix = suffix + "_two_side_ebd" + type_embedding_nei = tf.tile( + tf.reshape(type_embedding, [1, padding_ntypes, -1]), + [padding_ntypes, 1, 1], + ) # (ntypes) * ntypes * Y + type_embedding_center = tf.tile( + tf.reshape(type_embedding, [padding_ntypes, 1, -1]), + [1, padding_ntypes, 1], + ) # ntypes * (ntypes) * Y + two_side_type_embedding = tf.concat( + [type_embedding_nei, type_embedding_center], -1 + ) # ntypes * ntypes * (Y+Y) + two_side_type_embedding = tf.reshape( + two_side_type_embedding, + [-1, two_side_type_embedding.shape[-1]], + ) + two_side_type_embedding_suffix = suffix + "_two_side_ebd" embedding_of_two_side_type_embedding = embedding_net( two_side_type_embedding, self.filter_neuron, @@ -1069,8 +1096,11 @@ def _filter_lower( trainable=trainable, uniform_seed=self.uniform_seed, initial_variables=self.two_side_embeeding_net_variables, - mixed_prec=self.mixed_prec) - two_embd = tf.nn.embedding_lookup(embedding_of_two_side_type_embedding, index_of_two_side) + mixed_prec=self.mixed_prec, + ) + two_embd = tf.nn.embedding_lookup( + embedding_of_two_side_type_embedding, index_of_two_side + ) if not self.compress: xyz_scatter = xyz_scatter * two_embd + two_embd @@ -1219,31 +1249,37 @@ def init_variables( self.two_side_embeeding_net_variables = {} for i in range(1, self.layer_size + 1): - node = get_pattern_nodes_from_graph_def(graph_def, - f'filter_type_all{suffix}/matrix_{i}_two_side_ebd') \ - [f'filter_type_all{suffix}/matrix_{i}_two_side_ebd'] + node = get_pattern_nodes_from_graph_def( + graph_def, f"filter_type_all{suffix}/matrix_{i}_two_side_ebd" + )[f"filter_type_all{suffix}/matrix_{i}_two_side_ebd"] dtype = tf.as_dtype(node.dtype).as_numpy_dtype tensor_shape = tf.TensorShape(node.tensor_shape).as_list() if (len(tensor_shape) != 1) or (tensor_shape[0] != 1): tensor_value = np.frombuffer( - node.tensor_content, dtype=tf.as_dtype(node.dtype).as_numpy_dtype + node.tensor_content, + dtype=tf.as_dtype(node.dtype).as_numpy_dtype, ) else: tensor_value = get_tensor_by_type(node, dtype) - self.two_side_embeeding_net_variables[f'filter_type_all{suffix}/matrix_{i}_two_side_ebd'] = np.reshape(tensor_value, tensor_shape) + self.two_side_embeeding_net_variables[ + f"filter_type_all{suffix}/matrix_{i}_two_side_ebd" + ] = np.reshape(tensor_value, tensor_shape) - node = get_pattern_nodes_from_graph_def(graph_def, - f'filter_type_all{suffix}/bias_{i}_two_side_ebd') \ - [f'filter_type_all{suffix}/bias_{i}_two_side_ebd'] + node = get_pattern_nodes_from_graph_def( + graph_def, f"filter_type_all{suffix}/bias_{i}_two_side_ebd" + )[f"filter_type_all{suffix}/bias_{i}_two_side_ebd"] dtype = tf.as_dtype(node.dtype).as_numpy_dtype tensor_shape = tf.TensorShape(node.tensor_shape).as_list() if (len(tensor_shape) != 1) or (tensor_shape[0] != 1): tensor_value = np.frombuffer( - node.tensor_content, dtype=tf.as_dtype(node.dtype).as_numpy_dtype + node.tensor_content, + dtype=tf.as_dtype(node.dtype).as_numpy_dtype, ) else: tensor_value = get_tensor_by_type(node, dtype) - self.two_side_embeeding_net_variables[f'filter_type_all{suffix}/bias_{i}_two_side_ebd'] = np.reshape(tensor_value, tensor_shape) + self.two_side_embeeding_net_variables[ + f"filter_type_all{suffix}/bias_{i}_two_side_ebd" + ] = np.reshape(tensor_value, tensor_shape) self.attention_layer_variables = get_attention_layer_variables_from_graph_def( graph_def, suffix=suffix diff --git a/deepmd/fit/dipole.py b/deepmd/fit/dipole.py index d1ebd39472..4a4d8a10fe 100644 --- a/deepmd/fit/dipole.py +++ b/deepmd/fit/dipole.py @@ -59,7 +59,7 @@ def __init__( activation_function: str = "tanh", precision: str = "default", uniform_seed: bool = False, - compress: bool = False + compress: bool = False, ) -> None: """Constructor.""" self.ntypes = descrpt.get_ntypes() diff --git a/deepmd/fit/dos.py b/deepmd/fit/dos.py index 0537cf3017..cfde606771 100644 --- a/deepmd/fit/dos.py +++ b/deepmd/fit/dos.py @@ -97,7 +97,7 @@ def __init__( uniform_seed: bool = False, layer_name: Optional[List[Optional[str]]] = None, use_aparam_as_mask: bool = False, - compress: bool = False + compress: bool = False, ) -> None: """Constructor.""" # model param diff --git a/deepmd/fit/ener.py b/deepmd/fit/ener.py index a99ebb7e07..ca47a219b6 100644 --- a/deepmd/fit/ener.py +++ b/deepmd/fit/ener.py @@ -6,7 +6,6 @@ import numpy as np -import deepmd from deepmd.common import ( add_data_requirement, cast_precision, @@ -137,7 +136,7 @@ def __init__( layer_name: Optional[List[Optional[str]]] = None, use_aparam_as_mask: bool = False, spin: Optional[Spin] = None, - compress: bool = False + compress: bool = False, ) -> None: """Constructor.""" # model param diff --git a/deepmd/model/ener.py b/deepmd/model/ener.py index cec515e432..f9387c67fc 100644 --- a/deepmd/model/ener.py +++ b/deepmd/model/ener.py @@ -17,7 +17,6 @@ from deepmd.utils.spin import ( Spin, ) -from deepmd.descriptor import DescrptSeAtten from .model import ( Model, diff --git a/deepmd/utils/argcheck.py b/deepmd/utils/argcheck.py index 645e9819be..25dc615ea0 100644 --- a/deepmd/utils/argcheck.py +++ b/deepmd/utils/argcheck.py @@ -334,7 +334,7 @@ def descrpt_hybrid_args(): @descrpt_args_plugin.register("se_atten") def descrpt_se_atten_args(): - doc_compressible = 'Whether to build a compressible se_atten.' + doc_compressible = "Whether to build a compressible se_atten." doc_sel = 'This parameter set the number of selected neighbors. Note that this parameter is a little different from that in other descriptors. Instead of separating each type of atoms, only the summation matters. And this number is highly related with the efficiency, thus one should not make it too large. Usually 200 or less is enough, far away from the GPU limitation 4096. It can be:\n\n\ - `int`. The maximum number of neighbor atoms to be considered. We recommend it to be less than 200. \n\n\ - `List[int]`. The length of the list should be the same as the number of atom types in the system. `sel[i]` gives the selected number of type-i neighbors. Only the summation of `sel[i]` matters, and it is recommended to be less than 200.\ @@ -357,7 +357,9 @@ def descrpt_se_atten_args(): doc_attn_mask = "Whether to do mask on the diagonal in the attention matrix" return [ - Argument("compressible", bool, optional=True, default=False, doc=doc_compressible), + Argument( + "compressible", bool, optional=True, default=False, doc=doc_compressible + ), Argument("sel", [int, list, str], optional=True, default="auto", doc=doc_sel), Argument("rcut", float, optional=True, default=6.0, doc=doc_rcut), Argument("rcut_smth", float, optional=True, default=0.5, doc=doc_rcut_smth), diff --git a/deepmd/utils/tabulate.py b/deepmd/utils/tabulate.py index 3d54b0fe05..02fd8f0d0e 100644 --- a/deepmd/utils/tabulate.py +++ b/deepmd/utils/tabulate.py @@ -183,14 +183,12 @@ def build( xx, np.arange(uu, extrapolate * uu, stride1, dtype=self.data_type), ) - xx = np.append( - xx, np.array([extrapolate * uu], dtype=self.data_type) + xx = np.append(xx, np.array([extrapolate * uu], dtype=self.data_type)) + nspline = ((uu - ll) / stride0 + (extrapolate * uu - uu) / stride1).astype( + int ) - nspline = ( - (uu - ll) / stride0 + (extrapolate * uu - uu) / stride1 - ).astype(int) self._build_lower( - 'filter_net', xx, 0, uu, ll, stride0, stride1, extrapolate, nspline + "filter_net", xx, 0, uu, ll, stride0, stride1, extrapolate, nspline ) elif isinstance(self.descrpt, deepmd.descriptor.DescrptSeA): for ii in range(self.table_size): diff --git a/deepmd/utils/type_embed.py b/deepmd/utils/type_embed.py index a908984f76..9230494283 100644 --- a/deepmd/utils/type_embed.py +++ b/deepmd/utils/type_embed.py @@ -12,8 +12,8 @@ tf, ) from deepmd.utils.graph import ( + get_tensor_by_name_from_graph, get_type_embedding_net_variables_from_graph_def, - get_tensor_by_name_from_graph ) from deepmd.utils.network import ( embedding_net, @@ -177,5 +177,5 @@ def init_variables( self.type_embedding_net_variables = ( get_type_embedding_net_variables_from_graph_def(graph_def, suffix=suffix) ) - type_embedding = get_tensor_by_name_from_graph(graph, 't_typeebd') + type_embedding = get_tensor_by_name_from_graph(graph, "t_typeebd") self.type_embedding_from_graph = tf.convert_to_tensor(type_embedding) diff --git a/source/lib/src/cuda/tabulate.cu b/source/lib/src/cuda/tabulate.cu index e0cf70a5a3..6bee83019d 100644 --- a/source/lib/src/cuda/tabulate.cu +++ b/source/lib/src/cuda/tabulate.cu @@ -357,7 +357,8 @@ __global__ void tabulate_fusion_se_atten_fifth_order_polynomial( var[0] + (var[1] + (var[2] + (var[3] + (var[4] + var[5] * xx) * xx) * xx) * xx) * xx; - FPTYPE t = two_embed[block_idx * nnei*last_layer_size + ii * last_layer_size + thread_idx]; + FPTYPE t = two_embed[block_idx * nnei * last_layer_size + + ii * last_layer_size + thread_idx]; res = res * t + t; for (int kk = 0; kk < MTILE; kk++) { @@ -429,7 +430,8 @@ __global__ void tabulate_fusion_se_atten_grad_fifth_order_polynomial( (var[1] + (var[2] + (var[3] + (var[4] + var[5] * xx) * xx) * xx) * xx) * xx; - FPTYPE t = two_embed[block_idx * nnei*last_layer_size + ii * last_layer_size + jj]; + FPTYPE t = two_embed[block_idx * nnei * last_layer_size + + ii * last_layer_size + jj]; res = res * t + t; for (int kk = 0; kk < MTILE; kk++) { @@ -914,9 +916,9 @@ void tabulate_fusion_se_atten_gpu_cuda(FPTYPE* out, return; } tabulate_fusion_se_atten_fifth_order_polynomial - <<>>(out, table, em_x, em, two_embed, table_info[0], - table_info[1], table_info[2], table_info[3], - table_info[4], nnei, last_layer_size); + <<>>( + out, table, em_x, em, two_embed, table_info[0], table_info[1], + table_info[2], table_info[3], table_info[4], nnei, last_layer_size); DPErrcheck(cudaGetLastError()); DPErrcheck(cudaDeviceSynchronize()); } @@ -941,8 +943,9 @@ void tabulate_fusion_se_atten_grad_gpu_cuda(FPTYPE* dy_dem_x, tabulate_fusion_se_atten_grad_fifth_order_polynomial <<>>( - dy_dem_x, dy_dem, table, em_x, em, two_embed, dy, table_info[0], table_info[1], - table_info[2], table_info[3], table_info[4], nnei, last_layer_size); + dy_dem_x, dy_dem, table, em_x, em, two_embed, dy, table_info[0], + table_info[1], table_info[2], table_info[3], table_info[4], nnei, + last_layer_size); DPErrcheck(cudaGetLastError()); DPErrcheck(cudaDeviceSynchronize()); } @@ -965,9 +968,9 @@ void tabulate_fusion_se_atten_grad_grad_gpu_cuda(FPTYPE* dz_dy, DPErrcheck(cudaMemset(dz_dy, 0, sizeof(FPTYPE) * nloc * 4 * last_layer_size)); tabulate_fusion_se_atten_grad_grad_fifth_order_polynomial <<>>( - dz_dy, table, em_x, em, two_embed, dz_dy_dem_x, dz_dy_dem, table_info[0], - table_info[1], table_info[2], table_info[3], table_info[4], nnei, - last_layer_size); + dz_dy, table, em_x, em, two_embed, dz_dy_dem_x, dz_dy_dem, + table_info[0], table_info[1], table_info[2], table_info[3], + table_info[4], nnei, last_layer_size); DPErrcheck(cudaGetLastError()); DPErrcheck(cudaDeviceSynchronize()); } @@ -1170,24 +1173,26 @@ template void tabulate_fusion_se_a_grad_grad_gpu_cuda( const int nnei, const int last_layer_size); -template void tabulate_fusion_se_atten_gpu_cuda(float* out, - const float* table, - const float* table_info, - const float* em_x, - const float* em, - const float* two_embed, - const int nloc, - const int nnei, - const int last_layer_size); -template void tabulate_fusion_se_atten_gpu_cuda(double* out, - const double* table, - const double* table_info, - const double* em_x, - const double* em, - const double* two_embed, - const int nloc, - const int nnei, - const int last_layer_size); +template void tabulate_fusion_se_atten_gpu_cuda( + float* out, + const float* table, + const float* table_info, + const float* em_x, + const float* em, + const float* two_embed, + const int nloc, + const int nnei, + const int last_layer_size); +template void tabulate_fusion_se_atten_gpu_cuda( + double* out, + const double* table, + const double* table_info, + const double* em_x, + const double* em, + const double* two_embed, + const int nloc, + const int nnei, + const int last_layer_size); template void tabulate_fusion_se_atten_grad_gpu_cuda( float* dy_dem_x, float* dy_dem, diff --git a/source/lib/src/rocm/tabulate.hip.cu b/source/lib/src/rocm/tabulate.hip.cu index 6279bc69c0..17c5fbceed 100644 --- a/source/lib/src/rocm/tabulate.hip.cu +++ b/source/lib/src/rocm/tabulate.hip.cu @@ -336,7 +336,8 @@ __global__ void tabulate_fusion_se_atten_fifth_order_polynomial( var[0] + (var[1] + (var[2] + (var[3] + (var[4] + var[5] * xx) * xx) * xx) * xx) * xx; - FPTYPE t = two_embed[block_idx * nnei*last_layer_size + ii * last_layer_size + thread_idx]; + FPTYPE t = two_embed[block_idx * nnei * last_layer_size + + ii * last_layer_size + thread_idx]; res = res * t + t; for (int kk = 0; kk < MTILE; kk++) { @@ -409,7 +410,8 @@ __global__ void tabulate_fusion_se_atten_grad_fifth_order_polynomial( (var[1] + (var[2] + (var[3] + (var[4] + var[5] * xx) * xx) * xx) * xx) * xx; - FPTYPE t = two_embed[block_idx * nnei*last_layer_size + ii * last_layer_size + jj]; + FPTYPE t = two_embed[block_idx * nnei * last_layer_size + + ii * last_layer_size + jj]; res = res * t + t; for (int kk = 0; kk < KTILE; kk++) { @@ -957,8 +959,8 @@ void tabulate_fusion_se_atten_grad_gpu_rocm(FPTYPE* dy_dem_x, HIP_KERNEL_NAME( tabulate_fusion_se_atten_grad_fifth_order_polynomial), nloc, KK * WARP_SIZE, sizeof(FPTYPE) * MM * last_layer_size, 0, dy_dem_x, - dy_dem, table, em_x, em, two_embed, dy, table_info[0], table_info[1], table_info[2], - table_info[3], table_info[4], nnei, last_layer_size); + dy_dem, table, em_x, em, two_embed, dy, table_info[0], table_info[1], + table_info[2], table_info[3], table_info[4], nnei, last_layer_size); DPErrcheck(hipGetLastError()); DPErrcheck(hipDeviceSynchronize()); } @@ -984,8 +986,9 @@ void tabulate_fusion_se_atten_grad_grad_gpu_rocm(FPTYPE* dz_dy, tabulate_fusion_se_atten_grad_grad_fifth_order_polynomial), nloc, last_layer_size, sizeof(FPTYPE) * MM * last_layer_size, 0, dz_dy, - table, em_x, em, two_embed, dz_dy_dem_x, dz_dy_dem, table_info[0], table_info[1], - table_info[2], table_info[3], table_info[4], nnei, last_layer_size); + table, em_x, em, two_embed, dz_dy_dem_x, dz_dy_dem, table_info[0], + table_info[1], table_info[2], table_info[3], table_info[4], nnei, + last_layer_size); DPErrcheck(hipGetLastError()); DPErrcheck(hipDeviceSynchronize()); } @@ -1199,24 +1202,26 @@ template void tabulate_fusion_se_a_grad_grad_gpu_rocm( const int nnei, const int last_layer_size); -template void tabulate_fusion_se_atten_gpu_rocm(float* out, - const float* table, - const float* table_info, - const float* em_x, - const float* em, - const float* two_embed, - const int nloc, - const int nnei, - const int last_layer_size); -template void tabulate_fusion_se_atten_gpu_rocm(double* out, - const double* table, - const double* table_info, - const double* em_x, - const double* em, - const double* two_embed, - const int nloc, - const int nnei, - const int last_layer_size); +template void tabulate_fusion_se_atten_gpu_rocm( + float* out, + const float* table, + const float* table_info, + const float* em_x, + const float* em, + const float* two_embed, + const int nloc, + const int nnei, + const int last_layer_size); +template void tabulate_fusion_se_atten_gpu_rocm( + double* out, + const double* table, + const double* table_info, + const double* em_x, + const double* em, + const double* two_embed, + const int nloc, + const int nnei, + const int last_layer_size); template void tabulate_fusion_se_atten_grad_gpu_rocm( float* dy_dem_x, float* dy_dem, diff --git a/source/lib/src/tabulate.cc b/source/lib/src/tabulate.cc index ddcc995aec..f7caabf06f 100644 --- a/source/lib/src/tabulate.cc +++ b/source/lib/src/tabulate.cc @@ -344,7 +344,8 @@ void deepmd::tabulate_fusion_se_atten_cpu(FPTYPE* out, FPTYPE a5 = table[table_idx * last_layer_size * 6 + 6 * kk + 5]; FPTYPE var = a0 + (a1 + (a2 + (a3 + (a4 + a5 * xx) * xx) * xx) * xx) * xx; - FPTYPE t = two_embed[ii * nnei*last_layer_size + jj * last_layer_size + kk]; + FPTYPE t = + two_embed[ii * nnei * last_layer_size + jj * last_layer_size + kk]; var = var * t + t; if (unloop) { out[ii * last_layer_size * 4 + 0 * last_layer_size + kk] += @@ -424,11 +425,12 @@ void deepmd::tabulate_fusion_se_atten_grad_cpu(FPTYPE* dy_dem_x, FPTYPE a5 = table[table_idx * last_layer_size * 6 + 6 * kk + 5]; FPTYPE res = a0 + (a1 + (a2 + (a3 + (a4 + a5 * xx) * xx) * xx) * xx) * xx; - FPTYPE t = two_embed[ii * nnei*last_layer_size + jj * last_layer_size + kk]; + FPTYPE t = + two_embed[ii * nnei * last_layer_size + jj * last_layer_size + kk]; FPTYPE res1 = res * t + t; - FPTYPE g = (a1 + (2 * a2 + (3 * a3 + (4 * a4 + 5 * a5 * xx) * xx) * xx) * - xx); + FPTYPE g = + (a1 + (2 * a2 + (3 * a3 + (4 * a4 + 5 * a5 * xx) * xx) * xx) * xx); FPTYPE deriv = t * g; if (unloop) { @@ -979,7 +981,6 @@ template void deepmd::tabulate_fusion_se_atten_grad_grad_cpu( const int nnei, const int last_layer_size); - template void deepmd::tabulate_fusion_se_t_cpu( float* out, const float* table, diff --git a/source/op/_tabulate_grad.py b/source/op/_tabulate_grad.py index 91a889e513..550d35010a 100644 --- a/source/op/_tabulate_grad.py +++ b/source/op/_tabulate_grad.py @@ -8,7 +8,6 @@ from deepmd.env import ( op_module, ) -import tensorflow.compat.v1 as tf # from deepmd.DescrptSeATabulate import last_layer_size @@ -39,14 +38,17 @@ def _tabulate_fusion_se_atten_grad_cc(op, dy): op.inputs[2], # em_x op.inputs[3], # em op.inputs[4], # two_embed - dy, # dy - op.outputs[0] # descriptor + dy, # dy + op.outputs[0], # descriptor ) - return [None, # table - None, # table_info - dy_dx, # em_x - dy_df, # em - dy_dtwo] # two_embed + return [ + None, # table + None, # table_info + dy_dx, # em_x + dy_df, # em + dy_dtwo, + ] # two_embed + @ops.RegisterGradient("TabulateFusionSeAttenGrad") def _tabulate_fusion_se_atten_grad_grad_cc(op, dy, dy_, dy_dtwo): @@ -55,18 +57,20 @@ def _tabulate_fusion_se_atten_grad_grad_cc(op, dy, dy_, dy_dtwo): op.inputs[1], # table_info op.inputs[2], # em_x op.inputs[3], # em - dy_dtwo, # two_embed - dy, # dz_dy_dem_x - dy_, # dz_dy_dem - op.inputs[6] # descriptor + dy_dtwo, # two_embed + dy, # dz_dy_dem_x + dy_, # dz_dy_dem + op.inputs[6], # descriptor ) - return [None, # table - None, # table_info - None, # em_x - None, # em - None, # two_embed - dz_dy, # dy - None] # descriptor + return [ + None, # table + None, # table_info + None, # em_x + None, # em + None, # two_embed + dz_dy, # dy + None, + ] # descriptor @ops.RegisterGradient("TabulateFusionSeT") diff --git a/source/op/tabulate_multi_device.cc b/source/op/tabulate_multi_device.cc index a284e7b9e4..f6a3227200 100644 --- a/source/op/tabulate_multi_device.cc +++ b/source/op/tabulate_multi_device.cc @@ -1,3 +1,4 @@ + #include "custom_op.h" #include "tabulate.h" @@ -98,7 +99,6 @@ REGISTER_OP("TabulateFusionSeAttenGradGrad") .Input("descriptor: T") .Output("dz_dy: T"); - REGISTER_OP("TabulateFusionSeT") .Attr("T: {float, double} = DT_DOUBLE") .Input("table: T") @@ -409,17 +409,20 @@ class TabulateFusionSeAttenOp : public OpKernel { if (device == "GPU") { #if GOOGLE_CUDA - deepmd::tabulate_fusion_se_atten_gpu_cuda(descriptor, table, table_info, em_x, em, - two_embed, nloc, nnei, last_layer_size); + deepmd::tabulate_fusion_se_atten_gpu_cuda(descriptor, table, table_info, + em_x, em, two_embed, nloc, nnei, + last_layer_size); #endif // GOOGLE_CUDA #if TENSORFLOW_USE_ROCM - deepmd::tabulate_fusion_se_atten_gpu_rocm(descriptor, table, table_info, em_x, - em, two_embed, nloc, nnei, last_layer_size); + deepmd::tabulate_fusion_se_atten_gpu_rocm(descriptor, table, table_info, + em_x, em, two_embed, nloc, nnei, + last_layer_size); #endif // TENSORFLOW_USE_ROCM } else if (device == "CPU") { - deepmd::tabulate_fusion_se_atten_cpu(descriptor, table, table_info, em_x, em, two_embed, - nloc, nnei, last_layer_size); + deepmd::tabulate_fusion_se_atten_cpu(descriptor, table, table_info, em_x, + em, two_embed, nloc, nnei, + last_layer_size); } } @@ -462,9 +465,9 @@ class TabulateFusionSeAttenGradOp : public OpKernel { context->allocate_output(context_output_index++, em_tensor.shape(), &dy_dem_tensor)); Tensor* dy_dtwo_tensor = NULL; - OP_REQUIRES_OK(context, - context->allocate_output(context_output_index++, - two_embed_tensor.shape(), &dy_dtwo_tensor)); + OP_REQUIRES_OK(context, context->allocate_output(context_output_index++, + two_embed_tensor.shape(), + &dy_dtwo_tensor)); DeviceFunctor()(device, context->eigen_device()); // flat the tensors @@ -484,19 +487,20 @@ class TabulateFusionSeAttenGradOp : public OpKernel { if (device == "GPU") { #if GOOGLE_CUDA - deepmd::tabulate_fusion_se_atten_grad_gpu_cuda(dy_dem_x, dy_dem, table, - table_info, em_x, em, two_embed, dy, nloc, - nnei, last_layer_size); + deepmd::tabulate_fusion_se_atten_grad_gpu_cuda( + dy_dem_x, dy_dem, table, table_info, em_x, em, two_embed, dy, nloc, + nnei, last_layer_size); #endif // GOOGLE_CUDA #if TENSORFLOW_USE_ROCM - deepmd::tabulate_fusion_se_atten_grad_gpu_rocm(dy_dem_x, dy_dem, table, - table_info, em_x, em, two_embed, dy, nloc, - nnei, last_layer_size); + deepmd::tabulate_fusion_se_atten_grad_gpu_rocm( + dy_dem_x, dy_dem, table, table_info, em_x, em, two_embed, dy, nloc, + nnei, last_layer_size); #endif // TENSORFLOW_USE_ROCM } else if (device == "CPU") { - deepmd::tabulate_fusion_se_atten_grad_cpu(dy_dem_x, dy_dem, table, table_info, - em_x, em, two_embed, dy, nloc, nnei, last_layer_size); + deepmd::tabulate_fusion_se_atten_grad_cpu( + dy_dem_x, dy_dem, table, table_info, em_x, em, two_embed, dy, nloc, + nnei, last_layer_size); } } @@ -548,21 +552,22 @@ class TabulateFusionSeAttenGradGradOp : public OpKernel { if (device == "GPU") { #if GOOGLE_CUDA deepmd::tabulate_fusion_se_atten_grad_grad_gpu_cuda( - dz_dy, table, table_info, em_x, em, two_embed, dz_dy_dem_x, dz_dy_dem, nloc, - nnei, last_layer_size); + dz_dy, table, table_info, em_x, em, two_embed, dz_dy_dem_x, dz_dy_dem, + nloc, nnei, last_layer_size); #endif // GOOGLE_CUDA #if TENSORFLOW_USE_ROCM deepmd::tabulate_fusion_se_atten_grad_grad_gpu_rocm( - dz_dy, table, table_info, em_x, em, two_embed, dz_dy_dem_x, dz_dy_dem, nloc, - nnei, last_layer_size); + dz_dy, table, table_info, em_x, em, two_embed, dz_dy_dem_x, dz_dy_dem, + nloc, nnei, last_layer_size); #endif // TENSORFLOW_USE_ROCM OP_REQUIRES(context, (last_layer_size <= 1024), errors::InvalidArgument( "In the process of model compression, the size of the " "last layer of embedding net must be less than 1024!")); } else if (device == "CPU") { - deepmd::tabulate_fusion_se_atten_grad_grad_cpu(dz_dy, table, table_info, em_x, - em, two_embed, dz_dy_dem_x, dz_dy_dem, nloc, nnei, last_layer_size); + deepmd::tabulate_fusion_se_atten_grad_grad_cpu( + dz_dy, table, table_info, em_x, em, two_embed, dz_dy_dem_x, dz_dy_dem, + nloc, nnei, last_layer_size); } } @@ -570,7 +575,6 @@ class TabulateFusionSeAttenGradGradOp : public OpKernel { std::string device; }; - template class TabulateFusionSeTOp : public OpKernel { public: @@ -978,9 +982,10 @@ class TabulateFusionSeRGradGradOp : public OpKernel { REGISTER_KERNEL_BUILDER( \ Name("TabulateFusionSeAtten").Device(DEVICE_CPU).TypeConstraint("T"), \ TabulateFusionSeAttenOp); \ - REGISTER_KERNEL_BUILDER( \ - Name("TabulateFusionSeAttenGrad").Device(DEVICE_CPU).TypeConstraint("T"),\ - TabulateFusionSeAttenGradOp); \ + REGISTER_KERNEL_BUILDER(Name("TabulateFusionSeAttenGrad") \ + .Device(DEVICE_CPU) \ + .TypeConstraint("T"), \ + TabulateFusionSeAttenGradOp); \ REGISTER_KERNEL_BUILDER(Name("TabulateFusionSeAttenGradGrad") \ .Device(DEVICE_CPU) \ .TypeConstraint("T"), \ @@ -1004,86 +1009,89 @@ class TabulateFusionSeRGradGradOp : public OpKernel { REGISTER_KERNEL_BUILDER(Name("TabulateFusionSeRGradGrad") \ .Device(DEVICE_CPU) \ .TypeConstraint("T"), \ - TabulateFusionSeRGradGradOp); + TabulateFusionSeRGradGradOp); \ + REGISTER_KERNEL_BUILDER( \ + Name("PrintIdentity").Device(DEVICE_CPU).TypeConstraint("T"), \ + PrintIdentityOp); REGISTER_CPU(float); REGISTER_CPU(double); #if GOOGLE_CUDA || TENSORFLOW_USE_ROCM -#define REGISTER_GPU(T) \ - REGISTER_KERNEL_BUILDER(Name("TabulateFusion") \ - .Device(DEVICE_GPU) \ - .TypeConstraint("T") \ - .HostMemory("table_info"), \ - TabulateFusionSeAOp); \ - REGISTER_KERNEL_BUILDER(Name("TabulateFusionGrad") \ - .Device(DEVICE_GPU) \ - .TypeConstraint("T") \ - .HostMemory("table_info"), \ - TabulateFusionSeAGradOp); \ - REGISTER_KERNEL_BUILDER(Name("TabulateFusionGradGrad") \ - .Device(DEVICE_GPU) \ - .TypeConstraint("T") \ - .HostMemory("table_info"), \ - TabulateFusionSeAGradGradOp); \ - REGISTER_KERNEL_BUILDER(Name("TabulateFusionSeA") \ - .Device(DEVICE_GPU) \ - .TypeConstraint("T") \ - .HostMemory("table_info"), \ - TabulateFusionSeAOp); \ - REGISTER_KERNEL_BUILDER(Name("TabulateFusionSeAGrad") \ - .Device(DEVICE_GPU) \ - .TypeConstraint("T") \ - .HostMemory("table_info"), \ - TabulateFusionSeAGradOp); \ - REGISTER_KERNEL_BUILDER(Name("TabulateFusionSeAGradGrad") \ - .Device(DEVICE_GPU) \ - .TypeConstraint("T") \ - .HostMemory("table_info"), \ - TabulateFusionSeAGradGradOp); \ - REGISTER_KERNEL_BUILDER(Name("TabulateFusionSeAtten") \ - .Device(DEVICE_GPU) \ - .TypeConstraint("T") \ - .HostMemory("table_info"), \ - TabulateFusionSeAttenOp); \ - REGISTER_KERNEL_BUILDER(Name("TabulateFusionSeAttenGrad") \ - .Device(DEVICE_GPU) \ - .TypeConstraint("T") \ - .HostMemory("table_info"), \ - TabulateFusionSeAttenGradOp); \ - REGISTER_KERNEL_BUILDER(Name("TabulateFusionSeAttenGradGrad") \ - .Device(DEVICE_GPU) \ - .TypeConstraint("T") \ - .HostMemory("table_info"), \ +#define REGISTER_GPU(T) \ + REGISTER_KERNEL_BUILDER(Name("TabulateFusion") \ + .Device(DEVICE_GPU) \ + .TypeConstraint("T") \ + .HostMemory("table_info"), \ + TabulateFusionSeAOp); \ + REGISTER_KERNEL_BUILDER(Name("TabulateFusionGrad") \ + .Device(DEVICE_GPU) \ + .TypeConstraint("T") \ + .HostMemory("table_info"), \ + TabulateFusionSeAGradOp); \ + REGISTER_KERNEL_BUILDER(Name("TabulateFusionGradGrad") \ + .Device(DEVICE_GPU) \ + .TypeConstraint("T") \ + .HostMemory("table_info"), \ + TabulateFusionSeAGradGradOp); \ + REGISTER_KERNEL_BUILDER(Name("TabulateFusionSeA") \ + .Device(DEVICE_GPU) \ + .TypeConstraint("T") \ + .HostMemory("table_info"), \ + TabulateFusionSeAOp); \ + REGISTER_KERNEL_BUILDER(Name("TabulateFusionSeAGrad") \ + .Device(DEVICE_GPU) \ + .TypeConstraint("T") \ + .HostMemory("table_info"), \ + TabulateFusionSeAGradOp); \ + REGISTER_KERNEL_BUILDER(Name("TabulateFusionSeAGradGrad") \ + .Device(DEVICE_GPU) \ + .TypeConstraint("T") \ + .HostMemory("table_info"), \ + TabulateFusionSeAGradGradOp); \ + REGISTER_KERNEL_BUILDER(Name("TabulateFusionSeAtten") \ + .Device(DEVICE_GPU) \ + .TypeConstraint("T") \ + .HostMemory("table_info"), \ + TabulateFusionSeAttenOp); \ + REGISTER_KERNEL_BUILDER(Name("TabulateFusionSeAttenGrad") \ + .Device(DEVICE_GPU) \ + .TypeConstraint("T") \ + .HostMemory("table_info"), \ + TabulateFusionSeAttenGradOp); \ + REGISTER_KERNEL_BUILDER(Name("TabulateFusionSeAttenGradGrad") \ + .Device(DEVICE_GPU) \ + .TypeConstraint("T") \ + .HostMemory("table_info"), \ TabulateFusionSeAttenGradGradOp); \ - REGISTER_KERNEL_BUILDER(Name("TabulateFusionSeT") \ - .Device(DEVICE_GPU) \ - .TypeConstraint("T") \ - .HostMemory("table_info"), \ - TabulateFusionSeTOp); \ - REGISTER_KERNEL_BUILDER(Name("TabulateFusionSeTGrad") \ - .Device(DEVICE_GPU) \ - .TypeConstraint("T") \ - .HostMemory("table_info"), \ - TabulateFusionSeTGradOp); \ - REGISTER_KERNEL_BUILDER(Name("TabulateFusionSeTGradGrad") \ - .Device(DEVICE_GPU) \ - .TypeConstraint("T") \ - .HostMemory("table_info"), \ - TabulateFusionSeTGradGradOp); \ - REGISTER_KERNEL_BUILDER(Name("TabulateFusionSeR") \ - .Device(DEVICE_GPU) \ - .TypeConstraint("T") \ - .HostMemory("table_info"), \ - TabulateFusionSeROp); \ - REGISTER_KERNEL_BUILDER(Name("TabulateFusionSeRGrad") \ - .Device(DEVICE_GPU) \ - .TypeConstraint("T") \ - .HostMemory("table_info"), \ - TabulateFusionSeRGradOp); \ - REGISTER_KERNEL_BUILDER(Name("TabulateFusionSeRGradGrad") \ - .Device(DEVICE_GPU) \ - .TypeConstraint("T") \ - .HostMemory("table_info"), \ + REGISTER_KERNEL_BUILDER(Name("TabulateFusionSeT") \ + .Device(DEVICE_GPU) \ + .TypeConstraint("T") \ + .HostMemory("table_info"), \ + TabulateFusionSeTOp); \ + REGISTER_KERNEL_BUILDER(Name("TabulateFusionSeTGrad") \ + .Device(DEVICE_GPU) \ + .TypeConstraint("T") \ + .HostMemory("table_info"), \ + TabulateFusionSeTGradOp); \ + REGISTER_KERNEL_BUILDER(Name("TabulateFusionSeTGradGrad") \ + .Device(DEVICE_GPU) \ + .TypeConstraint("T") \ + .HostMemory("table_info"), \ + TabulateFusionSeTGradGradOp); \ + REGISTER_KERNEL_BUILDER(Name("TabulateFusionSeR") \ + .Device(DEVICE_GPU) \ + .TypeConstraint("T") \ + .HostMemory("table_info"), \ + TabulateFusionSeROp); \ + REGISTER_KERNEL_BUILDER(Name("TabulateFusionSeRGrad") \ + .Device(DEVICE_GPU) \ + .TypeConstraint("T") \ + .HostMemory("table_info"), \ + TabulateFusionSeRGradOp); \ + REGISTER_KERNEL_BUILDER(Name("TabulateFusionSeRGradGrad") \ + .Device(DEVICE_GPU) \ + .TypeConstraint("T") \ + .HostMemory("table_info"), \ TabulateFusionSeRGradGradOp); REGISTER_GPU(float); REGISTER_GPU(double); From f9ede45d60b70b4d93188bde93cf9c28346b9b2a Mon Sep 17 00:00:00 2001 From: nahso Date: Thu, 25 May 2023 17:14:26 +0800 Subject: [PATCH 073/126] remove auxiliary functions --- source/op/tabulate_multi_device.cc | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/source/op/tabulate_multi_device.cc b/source/op/tabulate_multi_device.cc index f6a3227200..c12550f239 100644 --- a/source/op/tabulate_multi_device.cc +++ b/source/op/tabulate_multi_device.cc @@ -1009,10 +1009,7 @@ class TabulateFusionSeRGradGradOp : public OpKernel { REGISTER_KERNEL_BUILDER(Name("TabulateFusionSeRGradGrad") \ .Device(DEVICE_CPU) \ .TypeConstraint("T"), \ - TabulateFusionSeRGradGradOp); \ - REGISTER_KERNEL_BUILDER( \ - Name("PrintIdentity").Device(DEVICE_CPU).TypeConstraint("T"), \ - PrintIdentityOp); + TabulateFusionSeRGradGradOp); REGISTER_CPU(float); REGISTER_CPU(double); From 98f3cb294cc0ae44502e47a8c12b6386bb8add32 Mon Sep 17 00:00:00 2001 From: nahso Date: Thu, 25 May 2023 17:14:55 +0800 Subject: [PATCH 074/126] pre commit format --- source/tests/test_data_large_batch.py | 38 +++++++-------- source/tests/test_descrpt_se_atten.py | 43 +++++++++++++---- .../tests/test_model_compression_se_atten.py | 4 +- source/tests/test_model_se_atten.py | 46 +++++++++---------- 4 files changed, 79 insertions(+), 52 deletions(-) diff --git a/source/tests/test_data_large_batch.py b/source/tests/test_data_large_batch.py index 3f93942a3f..b88a2fd68c 100644 --- a/source/tests/test_data_large_batch.py +++ b/source/tests/test_data_large_batch.py @@ -389,37 +389,37 @@ def test_compressible_data_mixed_type(self): np.savetxt("f.out", f.reshape([1, -1]), delimiter=",") np.savetxt("v.out", v.reshape([1, -1]), delimiter=",") - refe = [4.978373241868134613e+01] + refe = [4.978373241868134613e01] reff = [ - 3.587688614359243466e+00, - 3.202584939641652362e+00, + 3.587688614359243466e00, + 3.202584939641652362e00, 1.166711402014127957e-01, - 2.384342214774975321e+00, - 3.542611694579458348e+00, + 2.384342214774975321e00, + 3.542611694579458348e00, -1.916097942322055603e-01, - -4.120123413353201869e+00, - 1.474564563185293276e+00, + -4.120123413353201869e00, + 1.474564563185293276e00, 2.693540383300669847e-02, - 2.380464377433281431e+00, - -4.807108079981841975e+00, + 2.380464377433281431e00, + -4.807108079981841975e00, -1.784915273650321821e-01, - -5.314498717923408222e+00, - 1.495140750360528958e+00, + -5.314498717923408222e00, + 1.495140750360528958e00, 2.602480033292806638e-01, - 1.082126924709109872e+00, - -4.907793867785092523e+00, - -3.375322576646228034e-02 + 1.082126924709109872e00, + -4.907793867785092523e00, + -3.375322576646228034e-02, ] refv = [ - -1.760844499856655432e+01, - 3.767507287595555532e+00, + -1.760844499856655432e01, + 3.767507287595555532e00, 4.505304110104397242e-01, - 3.767507287595555088e+00, - -1.052518611764145362e+01, + 3.767507287595555088e00, + -1.052518611764145362e01, -2.174256785611231313e-01, 4.505304110104397797e-01, -2.174256785611231868e-01, - -2.462288791771098315e-02 + -2.462288791771098315e-02, ] refe = np.reshape(refe, [-1]) diff --git a/source/tests/test_descrpt_se_atten.py b/source/tests/test_descrpt_se_atten.py index 90544c0d26..dfffbf2885 100644 --- a/source/tests/test_descrpt_se_atten.py +++ b/source/tests/test_descrpt_se_atten.py @@ -1,5 +1,5 @@ -import unittest import inspect +import unittest import numpy as np from common import ( @@ -104,7 +104,13 @@ def test_descriptor_two_sides(self): is_training = tf.placeholder(tf.bool) t_fparam = None - type_embedding = typeebd.build(ntypes, suffix=self.filename + '-' + inspect.stack()[0][3] + "_se_atten_type_des_ebd_2sdies") + type_embedding = typeebd.build( + ntypes, + suffix=self.filename + + "-" + + inspect.stack()[0][3] + + "_se_atten_type_des_ebd_2sdies", + ) dout = descrpt.build( t_coord, @@ -114,7 +120,10 @@ def test_descriptor_two_sides(self): t_mesh, {"type_embedding": type_embedding}, reuse=False, - suffix=self.filename + '-' + inspect.stack()[0][3] + "_se_atten_type_des_2sides", + suffix=self.filename + + "-" + + inspect.stack()[0][3] + + "_se_atten_type_des_2sides", ) feed_dict_test = { @@ -272,7 +281,13 @@ def test_descriptor_one_side(self): is_training = tf.placeholder(tf.bool) t_fparam = None - type_embedding = typeebd.build(ntypes, suffix=self.filename + '-' + inspect.stack()[0][3] + "_se_atten_type_des_ebd_1side") + type_embedding = typeebd.build( + ntypes, + suffix=self.filename + + "-" + + inspect.stack()[0][3] + + "_se_atten_type_des_ebd_1side", + ) dout = descrpt.build( t_coord, @@ -282,7 +297,10 @@ def test_descriptor_one_side(self): t_mesh, {"type_embedding": type_embedding}, reuse=False, - suffix=self.filename + '-' + inspect.stack()[0][3] + "_se_atten_type_des_1side", + suffix=self.filename + + "-" + + inspect.stack()[0][3] + + "_se_atten_type_des_1side", ) feed_dict_test = { @@ -439,7 +457,13 @@ def test_compressible_descriptor_two_sides(self): is_training = tf.placeholder(tf.bool) t_fparam = None - type_embedding = typeebd.build(ntypes, suffix=self.filename + '-' + inspect.stack()[0][3] + "_se_atten_type_des_ebd_2sdies") + type_embedding = typeebd.build( + ntypes, + suffix=self.filename + + "-" + + inspect.stack()[0][3] + + "_se_atten_type_des_ebd_2sdies", + ) dout = descrpt.build( t_coord, @@ -449,7 +473,10 @@ def test_compressible_descriptor_two_sides(self): t_mesh, {"type_embedding": type_embedding}, reuse=False, - suffix=self.filename + '-' + inspect.stack()[0][3] + "_se_atten_type_des_2sides", + suffix=self.filename + + "-" + + inspect.stack()[0][3] + + "_se_atten_type_des_2sides", ) feed_dict_test = { @@ -532,7 +559,7 @@ def test_compressible_descriptor_two_sides(self): 4.359946655901846685e-03, 3.606316239946267860e-04, 2.762742703322493970e-02, - 2.285540100738304818e-03 + 2.285540100738304818e-03, ] places = 10 diff --git a/source/tests/test_model_compression_se_atten.py b/source/tests/test_model_compression_se_atten.py index 49e8e96603..3babf15f4d 100644 --- a/source/tests/test_model_compression_se_atten.py +++ b/source/tests/test_model_compression_se_atten.py @@ -48,7 +48,9 @@ def _init_models(): frozen_model = str(tests_path / "dp-original-se-atten.pb") compressed_model = str(tests_path / "dp-compressed-se-atten.pb") INPUT = str(tests_path / "input-se-atten.json") - jdata = j_loader(str(tests_path / os.path.join("model_compression", "input-se-atten.json"))) + jdata = j_loader( + str(tests_path / os.path.join("model_compression", "input-se-atten.json")) + ) jdata["training"]["training_data"]["systems"] = data_file jdata["training"]["validation_data"]["systems"] = data_file with open(INPUT, "w") as fp: diff --git a/source/tests/test_model_se_atten.py b/source/tests/test_model_se_atten.py index f1af55f20a..22760b8a33 100644 --- a/source/tests/test_model_se_atten.py +++ b/source/tests/test_model_se_atten.py @@ -1,5 +1,5 @@ -import unittest import inspect +import unittest import numpy as np from common import ( @@ -234,8 +234,7 @@ def test_exclude_types(self): padding=True, ) type_embedding = typeebd.build( - ntypes, - suffix=self.filename + '-' + inspect.stack()[0][3] + ntypes, suffix=self.filename + "-" + inspect.stack()[0][3] ) dout = descrpt.build( t_coord, @@ -372,37 +371,37 @@ def test_compressible_model(self): np.savetxt("f.out", f.reshape([1, -1]), delimiter=",") np.savetxt("v.out", v.reshape([1, -1]), delimiter=",") - refe = [4.978373241868134613e+01] + refe = [4.978373241868134613e01] reff = [ - 3.587688614359243466e+00, - 3.202584939641652362e+00, + 3.587688614359243466e00, + 3.202584939641652362e00, 1.166711402014127957e-01, - 2.384342214774975321e+00, - 3.542611694579458348e+00, + 2.384342214774975321e00, + 3.542611694579458348e00, -1.916097942322055603e-01, - -4.120123413353201869e+00, - 1.474564563185293276e+00, + -4.120123413353201869e00, + 1.474564563185293276e00, 2.693540383300669847e-02, - 2.380464377433281431e+00, - -4.807108079981841975e+00, + 2.380464377433281431e00, + -4.807108079981841975e00, -1.784915273650321821e-01, - -5.314498717923408222e+00, - 1.495140750360528958e+00, + -5.314498717923408222e00, + 1.495140750360528958e00, 2.602480033292806638e-01, - 1.082126924709109872e+00, - -4.907793867785092523e+00, - -3.375322576646228034e-02 + 1.082126924709109872e00, + -4.907793867785092523e00, + -3.375322576646228034e-02, ] refv = [ - -1.760844499856656498e+01, - 3.767507287595556420e+00, + -1.760844499856656498e01, + 3.767507287595556420e00, 4.505304110104396687e-01, - 3.767507287595555532e+00, - -1.052518611764145540e+01, + 3.767507287595555532e00, + -1.052518611764145540e01, -2.174256785611232701e-01, 4.505304110104397797e-01, -2.174256785611233256e-01, - -2.462288791771097621e-02 + -2.462288791771097621e-02, ] refe = np.reshape(refe, [-1]) @@ -458,8 +457,7 @@ def test_compressible_exclude_types(self): padding=True, ) type_embedding = typeebd.build( - ntypes, - suffix=self.filename + '-' + inspect.stack()[0][3] + ntypes, suffix=self.filename + "-" + inspect.stack()[0][3] ) dout = descrpt.build( t_coord, From 2fd8893231c1f234b7880a3d1e8139f4e6dd0396 Mon Sep 17 00:00:00 2001 From: nahso Date: Thu, 25 May 2023 17:39:02 +0800 Subject: [PATCH 075/126] disallow attention layer=0 when compression is enabled --- deepmd/descriptor/se_atten.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/deepmd/descriptor/se_atten.py b/deepmd/descriptor/se_atten.py index 4b2bf7af83..238c192985 100644 --- a/deepmd/descriptor/se_atten.py +++ b/deepmd/descriptor/se_atten.py @@ -162,8 +162,7 @@ def __init__( self.layer_size = len(neuron) if self.compressible and self.attn_layer != 0: - pass - # raise RuntimeError('attention layer must be set to 0 when compression of se_atten is enabled') + raise RuntimeError('attention layer must be set to 0 when compression of se_atten is enabled') # descrpt config self.sel_all_a = [sel] From b4d6c1dd114aefa6a459d6f685698ba28a44b848 Mon Sep 17 00:00:00 2001 From: nahso Date: Thu, 25 May 2023 17:42:25 +0800 Subject: [PATCH 076/126] pre-commit --- deepmd/descriptor/se_atten.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/deepmd/descriptor/se_atten.py b/deepmd/descriptor/se_atten.py index 238c192985..e04cedc413 100644 --- a/deepmd/descriptor/se_atten.py +++ b/deepmd/descriptor/se_atten.py @@ -162,7 +162,9 @@ def __init__( self.layer_size = len(neuron) if self.compressible and self.attn_layer != 0: - raise RuntimeError('attention layer must be set to 0 when compression of se_atten is enabled') + raise RuntimeError( + "attention layer must be set to 0 when compression of se_atten is enabled" + ) # descrpt config self.sel_all_a = [sel] From 0bed074487af8c72665b224bcfc6134aea9d702d Mon Sep 17 00:00:00 2001 From: nahso Date: Thu, 25 May 2023 21:30:54 +0800 Subject: [PATCH 077/126] add compressible config example --- examples/water/se_atten/input.json | 1 + 1 file changed, 1 insertion(+) diff --git a/examples/water/se_atten/input.json b/examples/water/se_atten/input.json index 3aeeeedbf2..c1852194e7 100644 --- a/examples/water/se_atten/input.json +++ b/examples/water/se_atten/input.json @@ -7,6 +7,7 @@ ], "descriptor": { "type": "se_atten", + "compressible": false, "sel": 120, "rcut_smth": 0.50, "rcut": 6.00, From dfdbe3566fcab0a72c367547b366f1efc2579311 Mon Sep 17 00:00:00 2001 From: nahso Date: Fri, 26 May 2023 00:03:25 +0800 Subject: [PATCH 078/126] skip the test of compression of se_atten if tf version < 2 --- .../tests/test_model_compression_se_atten.py | 18 ++++++++++++++++++ 1 file changed, 18 insertions(+) diff --git a/source/tests/test_model_compression_se_atten.py b/source/tests/test_model_compression_se_atten.py index 3babf15f4d..a0317339bd 100644 --- a/source/tests/test_model_compression_se_atten.py +++ b/source/tests/test_model_compression_se_atten.py @@ -11,9 +11,11 @@ run_dp, tests_path, ) +from packaging.version import parse as parse_version from deepmd.env import ( GLOBAL_NP_FLOAT_PRECISION, + tf, ) from deepmd.infer import ( DeepPot, @@ -68,6 +70,10 @@ def _init_models(): INPUT, FROZEN_MODEL, COMPRESSED_MODEL = _init_models() +@unittest.skipIf( + parse_version(tf.__version__) < parse_version("2"), + f"The current tf version {tf.__version__} is too low to run the new testing model.", +) class TestDeepPotAPBC(unittest.TestCase): @classmethod def setUpClass(self): @@ -192,6 +198,10 @@ def test_2frame_atm(self): np.testing.assert_almost_equal(vv0, vv1, default_places) +@unittest.skipIf( + parse_version(tf.__version__) < parse_version("2"), + f"The current tf version {tf.__version__} is too low to run the new testing model.", +) class TestDeepPotANoPBC(unittest.TestCase): @classmethod def setUpClass(self): @@ -300,6 +310,10 @@ def test_2frame_atm(self): np.testing.assert_almost_equal(vv0, vv1, default_places) +@unittest.skipIf( + parse_version(tf.__version__) < parse_version("2"), + f"The current tf version {tf.__version__} is too low to run the new testing model.", +) class TestDeepPotALargeBoxNoPBC(unittest.TestCase): @classmethod def setUpClass(self): @@ -408,6 +422,10 @@ def test_ase(self): np.testing.assert_almost_equal(ee0, ee1, default_places) +@unittest.skipIf( + parse_version(tf.__version__) < parse_version("2"), + f"The current tf version {tf.__version__} is too low to run the new testing model.", +) class TestDeepPotAPBCExcludeTypes(unittest.TestCase): @classmethod def setUpClass(self): From 2164cd4e1e69ba5aa61d5146f665e0206339ccd5 Mon Sep 17 00:00:00 2001 From: nahso Date: Fri, 26 May 2023 00:37:23 +0800 Subject: [PATCH 079/126] skip init_models --- source/tests/test_model_compression_se_atten.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/source/tests/test_model_compression_se_atten.py b/source/tests/test_model_compression_se_atten.py index a0317339bd..23a1894f46 100644 --- a/source/tests/test_model_compression_se_atten.py +++ b/source/tests/test_model_compression_se_atten.py @@ -45,6 +45,10 @@ def _subprocess_run(command): return popen.returncode +@unittest.skipIf( + parse_version(tf.__version__) < parse_version("2"), + f"The current tf version {tf.__version__} is too low to run the new testing model.", +) def _init_models(): data_file = str(tests_path / os.path.join("model_compression", "data")) frozen_model = str(tests_path / "dp-original-se-atten.pb") From 672dba7505428bd85c5385738161cff67e356dba Mon Sep 17 00:00:00 2001 From: nahso Date: Fri, 26 May 2023 14:17:25 +0800 Subject: [PATCH 080/126] add the compressible config for tests --- source/tests/water_compressible_se_atten.json | 80 +++++++++++++++++++ 1 file changed, 80 insertions(+) create mode 100644 source/tests/water_compressible_se_atten.json diff --git a/source/tests/water_compressible_se_atten.json b/source/tests/water_compressible_se_atten.json new file mode 100644 index 0000000000..4d06298bef --- /dev/null +++ b/source/tests/water_compressible_se_atten.json @@ -0,0 +1,80 @@ +{ + "_comment": " model parameters", + "model": { + "type_map": [ + "O", + "H" + ], + "type_embedding": { + "neuron": [ + 8 + ], + "resnet_dt": false, + "seed": 1 + }, + "descriptor": { + "type": "se_atten", + "compressible": true, + "sel": 120, + "rcut_smth": 5.80, + "rcut": 6.00, + "neuron": [ + 25, + 50, + 100 + ], + "resnet_dt": false, + "type_one_side": false, + "axis_neuron": 16, + "seed": 1, + "attn": 128, + "attn_layer": 0, + "attn_dotr": true, + "attn_mask": false + }, + "fitting_net": { + "neuron": [ + 240, + 240, + 240 + ], + "resnet_dt": true, + "seed": 1 + } + }, + + "_comment": " traing controls", + "systems": [ + "system" + ], + "set_prefix": "set", + "stop_batch": 1000000, + "batch_size": 1, + "start_lr": 0.005, + "decay_steps": 5000, + "decay_rate": 0.95, + + "start_pref_e": 0.02, + "limit_pref_e": 1, + "start_pref_f": 1000, + "limit_pref_f": 1, + "start_pref_v": 0, + "limit_pref_v": 0, + + "seed": 1, + + "_comment": " display and restart", + "_comment": " frequencies counted in batch", + "disp_file": "lcurve.out", + "disp_freq": 100, + "numb_test": 1, + "save_freq": 1000, + "save_ckpt": "model.ckpt", + "load_ckpt": "model.ckpt", + "disp_training": true, + "time_training": true, + "profiling": false, + "profiling_file": "timeline.json", + + "_comment": "that's all" +} From 35ab606780566e7e92d084af5b03098ef7de65b0 Mon Sep 17 00:00:00 2001 From: nahso Date: Fri, 26 May 2023 15:54:11 +0800 Subject: [PATCH 081/126] add mixed type test json --- ...ater_se_atten_compressible_mixed_type.json | 80 +++++++++++++++++++ 1 file changed, 80 insertions(+) create mode 100644 source/tests/water_se_atten_compressible_mixed_type.json diff --git a/source/tests/water_se_atten_compressible_mixed_type.json b/source/tests/water_se_atten_compressible_mixed_type.json new file mode 100644 index 0000000000..04c84e3160 --- /dev/null +++ b/source/tests/water_se_atten_compressible_mixed_type.json @@ -0,0 +1,80 @@ +{ + "_comment": " model parameters", + "model": { + "type_map": [ + "foo", + "bar" + ], + "type_embedding": { + "neuron": [ + 8 + ], + "resnet_dt": false, + "seed": 1 + }, + "descriptor": { + "type": "se_atten", + "compressible": true, + "sel": 120, + "rcut_smth": 5.80, + "rcut": 6.00, + "neuron": [ + 25, + 50, + 100 + ], + "resnet_dt": false, + "type_one_side": false, + "axis_neuron": 16, + "seed": 1, + "attn": 128, + "attn_layer": 0, + "attn_dotr": true, + "attn_mask": false + }, + "fitting_net": { + "neuron": [ + 240, + 240, + 240 + ], + "resnet_dt": true, + "seed": 1 + } + }, + + "_comment": " traing controls", + "systems": [ + "system_mixed_type" + ], + "set_prefix": "set", + "stop_batch": 1000000, + "batch_size": 1, + "start_lr": 0.005, + "decay_steps": 5000, + "decay_rate": 0.95, + + "start_pref_e": 0.02, + "limit_pref_e": 1, + "start_pref_f": 1000, + "limit_pref_f": 1, + "start_pref_v": 0, + "limit_pref_v": 0, + + "seed": 1, + + "_comment": " display and restart", + "_comment": " frequencies counted in batch", + "disp_file": "lcurve.out", + "disp_freq": 100, + "numb_test": 1, + "save_freq": 1000, + "save_ckpt": "model.ckpt", + "load_ckpt": "model.ckpt", + "disp_training": true, + "time_training": true, + "profiling": false, + "profiling_file": "timeline.json", + + "_comment": "that's all" +} From 5dd53197910a4e72144641103f39989e933e78ba Mon Sep 17 00:00:00 2001 From: nahso <47053538+nahso@users.noreply.github.com> Date: Fri, 26 May 2023 16:23:45 +0800 Subject: [PATCH 082/126] Update deepmd/utils/argcheck.py Co-authored-by: Jinzhe Zeng Signed-off-by: nahso <47053538+nahso@users.noreply.github.com> --- deepmd/utils/argcheck.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/deepmd/utils/argcheck.py b/deepmd/utils/argcheck.py index 25dc615ea0..31f0384dcc 100644 --- a/deepmd/utils/argcheck.py +++ b/deepmd/utils/argcheck.py @@ -334,7 +334,7 @@ def descrpt_hybrid_args(): @descrpt_args_plugin.register("se_atten") def descrpt_se_atten_args(): - doc_compressible = "Whether to build a compressible se_atten." + doc_compressible = "Whether to build a compressible se_atten that is supported by `dp compress`." doc_sel = 'This parameter set the number of selected neighbors. Note that this parameter is a little different from that in other descriptors. Instead of separating each type of atoms, only the summation matters. And this number is highly related with the efficiency, thus one should not make it too large. Usually 200 or less is enough, far away from the GPU limitation 4096. It can be:\n\n\ - `int`. The maximum number of neighbor atoms to be considered. We recommend it to be less than 200. \n\n\ - `List[int]`. The length of the list should be the same as the number of atom types in the system. `sel[i]` gives the selected number of type-i neighbors. Only the summation of `sel[i]` matters, and it is recommended to be less than 200.\ From a2353e5a6bde6807c798704314d2ab394af80d8c Mon Sep 17 00:00:00 2001 From: nahso Date: Fri, 26 May 2023 16:33:08 +0800 Subject: [PATCH 083/126] remove unnecessary parameter: compressible --- deepmd/descriptor/se_a.py | 1 - deepmd/descriptor/se_a_ebd.py | 1 - deepmd/descriptor/se_a_ef.py | 1 - deepmd/descriptor/se_a_mask.py | 1 - deepmd/descriptor/se_r.py | 1 - deepmd/descriptor/se_t.py | 1 - 6 files changed, 6 deletions(-) diff --git a/deepmd/descriptor/se_a.py b/deepmd/descriptor/se_a.py index 6f59e6cff9..641210f0d1 100644 --- a/deepmd/descriptor/se_a.py +++ b/deepmd/descriptor/se_a.py @@ -164,7 +164,6 @@ def __init__( uniform_seed: bool = False, multi_task: bool = False, spin: Optional[Spin] = None, - compressible: bool = True, ) -> None: """Constructor.""" if rcut < rcut_smth: diff --git a/deepmd/descriptor/se_a_ebd.py b/deepmd/descriptor/se_a_ebd.py index 30567e6dfb..e0684403e1 100644 --- a/deepmd/descriptor/se_a_ebd.py +++ b/deepmd/descriptor/se_a_ebd.py @@ -87,7 +87,6 @@ def __init__( activation_function: str = "tanh", precision: str = "default", exclude_types: List[List[int]] = [], - compressible: bool = False, ) -> None: """Constructor.""" DescrptSeA.__init__( diff --git a/deepmd/descriptor/se_a_ef.py b/deepmd/descriptor/se_a_ef.py index ac6842de3f..0dbad403f4 100644 --- a/deepmd/descriptor/se_a_ef.py +++ b/deepmd/descriptor/se_a_ef.py @@ -82,7 +82,6 @@ def __init__( activation_function: str = "tanh", precision: str = "default", uniform_seed=False, - compressible: bool = False, ) -> None: """Constructor.""" self.descrpt_para = DescrptSeAEfLower( diff --git a/deepmd/descriptor/se_a_mask.py b/deepmd/descriptor/se_a_mask.py index b7003883a7..93256e3910 100644 --- a/deepmd/descriptor/se_a_mask.py +++ b/deepmd/descriptor/se_a_mask.py @@ -126,7 +126,6 @@ def __init__( activation_function: str = "tanh", precision: str = "default", uniform_seed: bool = False, - compressible: bool = False, ) -> None: """Constructor.""" self.sel_a = sel diff --git a/deepmd/descriptor/se_r.py b/deepmd/descriptor/se_r.py index 86e38a559b..7ef5f47347 100644 --- a/deepmd/descriptor/se_r.py +++ b/deepmd/descriptor/se_r.py @@ -97,7 +97,6 @@ def __init__( uniform_seed: bool = False, multi_task: bool = False, spin: Optional[Spin] = None, - compressible: bool = False, ) -> None: """Constructor.""" if rcut < rcut_smth: diff --git a/deepmd/descriptor/se_t.py b/deepmd/descriptor/se_t.py index 92cbe328ea..7615275a82 100644 --- a/deepmd/descriptor/se_t.py +++ b/deepmd/descriptor/se_t.py @@ -90,7 +90,6 @@ def __init__( precision: str = "default", uniform_seed: bool = False, multi_task: bool = False, - compressible: bool = False, ) -> None: """Constructor.""" if rcut < rcut_smth: From df2ab6ba17fc45500bf926dbbc1055b8f746a78b Mon Sep 17 00:00:00 2001 From: nahso Date: Fri, 26 May 2023 16:39:30 +0800 Subject: [PATCH 084/126] make embedding network trainable when restarting from an original model --- deepmd/utils/type_embed.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/deepmd/utils/type_embed.py b/deepmd/utils/type_embed.py index 9230494283..4a0aa76ad5 100644 --- a/deepmd/utils/type_embed.py +++ b/deepmd/utils/type_embed.py @@ -129,7 +129,7 @@ def build( embedded_types The computational graph for embedded types """ - if self.type_embedding_net_variables is not None: + if self.type_embedding_from_graph is not None: return self.type_embedding_from_graph types = tf.convert_to_tensor([ii for ii in range(ntypes)], dtype=tf.int32) ebd_type = tf.cast( From 33db44426c1ff8629224b3ab40b0732b5885d58a Mon Sep 17 00:00:00 2001 From: nahso Date: Fri, 26 May 2023 17:00:24 +0800 Subject: [PATCH 085/126] use model_type to check whether to use type embedding from graph_def --- deepmd/train/trainer.py | 2 +- deepmd/utils/type_embed.py | 5 ++++- 2 files changed, 5 insertions(+), 2 deletions(-) diff --git a/deepmd/train/trainer.py b/deepmd/train/trainer.py index 581984ed82..3058f67f9b 100644 --- a/deepmd/train/trainer.py +++ b/deepmd/train/trainer.py @@ -566,7 +566,7 @@ def build(self, data=None, stop_batch=0, origin_type_map=None, suffix=""): self.model_param["compress"]["model_file"] ) if self.typeebd is not None: - self.typeebd.init_variables(graph, graph_def) + self.typeebd.init_variables(graph, graph_def, model_type=self.model_type) self.descrpt.enable_compression( self.model_param["compress"]["min_nbor_dist"], graph, diff --git a/deepmd/utils/type_embed.py b/deepmd/utils/type_embed.py index 4a0aa76ad5..2abb973b8b 100644 --- a/deepmd/utils/type_embed.py +++ b/deepmd/utils/type_embed.py @@ -106,6 +106,7 @@ def __init__( self.type_embedding_net_variables = None self.type_embedding_from_graph = None self.padding = padding + self.model_type = None def build( self, @@ -129,7 +130,7 @@ def build( embedded_types The computational graph for embedded types """ - if self.type_embedding_from_graph is not None: + if self.model_type != None and self.model_type == "compressed_model": return self.type_embedding_from_graph types = tf.convert_to_tensor([ii for ii in range(ntypes)], dtype=tf.int32) ebd_type = tf.cast( @@ -162,6 +163,7 @@ def init_variables( graph: tf.Graph, graph_def: tf.GraphDef, suffix="", + model_type="original_model", ) -> None: """Init the type embedding net variables with the given dict. @@ -174,6 +176,7 @@ def init_variables( suffix Name suffix to identify this descriptor """ + self.model_type = model_type self.type_embedding_net_variables = ( get_type_embedding_net_variables_from_graph_def(graph_def, suffix=suffix) ) From c15fe260a192731109b90747f41416f6b6d2cf07 Mon Sep 17 00:00:00 2001 From: nahso Date: Fri, 26 May 2023 21:24:37 +0800 Subject: [PATCH 086/126] remove unused argument: compress --- deepmd/fit/ener.py | 2 -- deepmd/train/trainer.py | 3 +-- 2 files changed, 1 insertion(+), 4 deletions(-) diff --git a/deepmd/fit/ener.py b/deepmd/fit/ener.py index 23e1048c7d..820704a568 100644 --- a/deepmd/fit/ener.py +++ b/deepmd/fit/ener.py @@ -137,7 +137,6 @@ def __init__( layer_name: Optional[List[Optional[str]]] = None, use_aparam_as_mask: bool = False, spin: Optional[Spin] = None, - compress: bool = False, ) -> None: """Constructor.""" # model param @@ -145,7 +144,6 @@ def __init__( self.dim_descrpt = descrpt.get_dim_out() self.descrpt = descrpt self.use_aparam_as_mask = use_aparam_as_mask - self.compress = compress # args = ()\ # .add('numb_fparam', int, default = 0)\ # .add('numb_aparam', int, default = 0)\ diff --git a/deepmd/train/trainer.py b/deepmd/train/trainer.py index 3058f67f9b..758d9b7cb5 100644 --- a/deepmd/train/trainer.py +++ b/deepmd/train/trainer.py @@ -102,8 +102,8 @@ def _is_subdir(path, directory): class DPTrainer: def __init__(self, jdata, run_opt, is_compress=False): self.run_opt = run_opt - self.is_compress = is_compress self._init_param(jdata) + self.is_compress = is_compress def _init_param(self, jdata): # model config @@ -167,7 +167,6 @@ def _init_param(self, jdata): fitting_param["descrpt"] = self.descrpt if fitting_type == "ener": fitting_param["spin"] = self.spin - fitting_param["compress"] = self.is_compress self.fitting = Fitting(**fitting_param) else: self.fitting_dict = {} From 2f374dc7472b2b41eb4e652137e0e167a2c9506b Mon Sep 17 00:00:00 2001 From: nahso Date: Fri, 26 May 2023 21:42:54 +0800 Subject: [PATCH 087/126] pre-commit --- deepmd/train/trainer.py | 4 +++- deepmd/utils/argcheck.py | 4 +++- deepmd/utils/type_embed.py | 4 +++- 3 files changed, 9 insertions(+), 3 deletions(-) diff --git a/deepmd/train/trainer.py b/deepmd/train/trainer.py index 758d9b7cb5..7669b7379b 100644 --- a/deepmd/train/trainer.py +++ b/deepmd/train/trainer.py @@ -565,7 +565,9 @@ def build(self, data=None, stop_batch=0, origin_type_map=None, suffix=""): self.model_param["compress"]["model_file"] ) if self.typeebd is not None: - self.typeebd.init_variables(graph, graph_def, model_type=self.model_type) + self.typeebd.init_variables( + graph, graph_def, model_type=self.model_type + ) self.descrpt.enable_compression( self.model_param["compress"]["min_nbor_dist"], graph, diff --git a/deepmd/utils/argcheck.py b/deepmd/utils/argcheck.py index 31f0384dcc..ebde4719fb 100644 --- a/deepmd/utils/argcheck.py +++ b/deepmd/utils/argcheck.py @@ -334,7 +334,9 @@ def descrpt_hybrid_args(): @descrpt_args_plugin.register("se_atten") def descrpt_se_atten_args(): - doc_compressible = "Whether to build a compressible se_atten that is supported by `dp compress`." + doc_compressible = ( + "Whether to build a compressible se_atten that is supported by `dp compress`." + ) doc_sel = 'This parameter set the number of selected neighbors. Note that this parameter is a little different from that in other descriptors. Instead of separating each type of atoms, only the summation matters. And this number is highly related with the efficiency, thus one should not make it too large. Usually 200 or less is enough, far away from the GPU limitation 4096. It can be:\n\n\ - `int`. The maximum number of neighbor atoms to be considered. We recommend it to be less than 200. \n\n\ - `List[int]`. The length of the list should be the same as the number of atom types in the system. `sel[i]` gives the selected number of type-i neighbors. Only the summation of `sel[i]` matters, and it is recommended to be less than 200.\ diff --git a/deepmd/utils/type_embed.py b/deepmd/utils/type_embed.py index 2abb973b8b..aea20d681d 100644 --- a/deepmd/utils/type_embed.py +++ b/deepmd/utils/type_embed.py @@ -130,7 +130,7 @@ def build( embedded_types The computational graph for embedded types """ - if self.model_type != None and self.model_type == "compressed_model": + if self.model_type is not None and self.model_type == "compressed_model": return self.type_embedding_from_graph types = tf.convert_to_tensor([ii for ii in range(ntypes)], dtype=tf.int32) ebd_type = tf.cast( @@ -175,6 +175,8 @@ def init_variables( The input frozen model graph_def suffix Name suffix to identify this descriptor + model_type + Indicator of whether this model is a compressed model """ self.model_type = model_type self.type_embedding_net_variables = ( From fba08a087bcacb6346007994efbda44202abf9b0 Mon Sep 17 00:00:00 2001 From: nahso Date: Fri, 26 May 2023 21:55:26 +0800 Subject: [PATCH 088/126] code cleanup --- deepmd/descriptor/se_atten.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/deepmd/descriptor/se_atten.py b/deepmd/descriptor/se_atten.py index e04cedc413..e622461752 100644 --- a/deepmd/descriptor/se_atten.py +++ b/deepmd/descriptor/se_atten.py @@ -391,7 +391,6 @@ def enable_compression( min_nbor_dist, table_extrapolate, table_stride_1, table_stride_2 ) - self.layer_size = self.table.layer_size self.final_type_embedding = self._get_two_side_type_embedding(graph) self.matrix = self._get_two_side_embedding_net_variable( graph_def, "matrix", suffix @@ -1056,9 +1055,9 @@ def _filter_lower( self.table_config[3], ] - padding_ntypes = type_embedding.shape[0] + padding_ntypes = type_embedding.shape[0] # this must be self.ntypes + 1 atype_expand = tf.reshape(atype, [-1, 1]) - idx_i = tf.tile(atype_expand * (self.ntypes + 1), [1, self.nnei]) + idx_i = tf.tile(atype_expand * padding_ntypes, [1, self.nnei]) idx_j = tf.reshape(self.nei_type_vec, [-1, self.nnei]) idx = idx_i + idx_j index_of_two_side = tf.reshape(idx, [-1]) From 93f45d6b049982c9b859d3c106eb45137a7a5d44 Mon Sep 17 00:00:00 2001 From: nahso Date: Fri, 26 May 2023 21:55:44 +0800 Subject: [PATCH 089/126] fix language usages --- deepmd/descriptor/se_atten.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/deepmd/descriptor/se_atten.py b/deepmd/descriptor/se_atten.py index e622461752..6fe680f0c5 100644 --- a/deepmd/descriptor/se_atten.py +++ b/deepmd/descriptor/se_atten.py @@ -1007,7 +1007,7 @@ def _filter_lower( with tf.variable_scope(name, reuse=reuse): # with (natom x nei_type_i) x out_size if not self.compressible: - log.info("use a non compressible model") + log.info("use the non compressible model") xyz_scatter = self._lookup_type_embedding( xyz_scatter, atype, type_embedding ) @@ -1027,7 +1027,7 @@ def _filter_lower( mixed_prec=self.mixed_prec, ) else: - log.info("use a compressible model") + log.info("use the compressible model") if not self.compress: xyz_scatter = embedding_net( xyz_scatter, From b8e5beed0ca945b09325b3e2b0d1e26c1bd426dd Mon Sep 17 00:00:00 2001 From: nahso Date: Fri, 26 May 2023 22:06:14 +0800 Subject: [PATCH 090/126] code cleanup --- deepmd/fit/dipole.py | 1 - deepmd/fit/dos.py | 1 - deepmd/fit/ener.py | 1 - 3 files changed, 3 deletions(-) diff --git a/deepmd/fit/dipole.py b/deepmd/fit/dipole.py index bf59cbd8a7..9f497584ae 100644 --- a/deepmd/fit/dipole.py +++ b/deepmd/fit/dipole.py @@ -60,7 +60,6 @@ def __init__( activation_function: str = "tanh", precision: str = "default", uniform_seed: bool = False, - compress: bool = False, ) -> None: """Constructor.""" self.ntypes = descrpt.get_ntypes() diff --git a/deepmd/fit/dos.py b/deepmd/fit/dos.py index 3b0b75e973..4bb1838af1 100644 --- a/deepmd/fit/dos.py +++ b/deepmd/fit/dos.py @@ -98,7 +98,6 @@ def __init__( uniform_seed: bool = False, layer_name: Optional[List[Optional[str]]] = None, use_aparam_as_mask: bool = False, - compress: bool = False, ) -> None: """Constructor.""" # model param diff --git a/deepmd/fit/ener.py b/deepmd/fit/ener.py index 820704a568..f482173495 100644 --- a/deepmd/fit/ener.py +++ b/deepmd/fit/ener.py @@ -142,7 +142,6 @@ def __init__( # model param self.ntypes = descrpt.get_ntypes() self.dim_descrpt = descrpt.get_dim_out() - self.descrpt = descrpt self.use_aparam_as_mask = use_aparam_as_mask # args = ()\ # .add('numb_fparam', int, default = 0)\ From bf4521a94c7372b33d629021efe63e96af016aa6 Mon Sep 17 00:00:00 2001 From: nahso Date: Fri, 26 May 2023 22:06:47 +0800 Subject: [PATCH 091/126] pre-commit --- deepmd/descriptor/se_atten.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/deepmd/descriptor/se_atten.py b/deepmd/descriptor/se_atten.py index 6fe680f0c5..4a9766dc55 100644 --- a/deepmd/descriptor/se_atten.py +++ b/deepmd/descriptor/se_atten.py @@ -1055,7 +1055,9 @@ def _filter_lower( self.table_config[3], ] - padding_ntypes = type_embedding.shape[0] # this must be self.ntypes + 1 + padding_ntypes = type_embedding.shape[ + 0 + ] # this must be self.ntypes + 1 atype_expand = tf.reshape(atype, [-1, 1]) idx_i = tf.tile(atype_expand * padding_ntypes, [1, self.nnei]) idx_j = tf.reshape(self.nei_type_vec, [-1, self.nnei]) From d7d10ab0f8626d45b117f31cca07d72e39ab4497 Mon Sep 17 00:00:00 2001 From: nahso Date: Fri, 26 May 2023 22:50:30 +0800 Subject: [PATCH 092/126] merge input.json of se_atten into py code --- .../model_compression/input-se-atten.json | 85 ------------------- .../tests/test_model_compression_se_atten.py | 11 ++- 2 files changed, 7 insertions(+), 89 deletions(-) delete mode 100644 source/tests/model_compression/input-se-atten.json diff --git a/source/tests/model_compression/input-se-atten.json b/source/tests/model_compression/input-se-atten.json deleted file mode 100644 index a0e18ff869..0000000000 --- a/source/tests/model_compression/input-se-atten.json +++ /dev/null @@ -1,85 +0,0 @@ -{ - "_comment": " model parameters", - "model": { - "type_map": [ - "O", - "H" - ], - "descriptor": { - "type": "se_atten", - "compressible": true, - "sel": 120, - "rcut_smth": 0.50, - "rcut": 6.00, - "neuron": [ - 4, - 8, - 16 - ], - "resnet_dt": false, - "axis_neuron": 16, - "seed": 1, - "attn": 128, - "attn_layer": 0, - "attn_dotr": true, - "attn_mask": false, - "_comment": " that's all" - }, - "fitting_net": { - "neuron": [ - 20, - 20, - 20 - ], - "resnet_dt": true, - "seed": 1, - "_comment": " that's all" - }, - "_comment": " that's all" - }, - - "learning_rate": { - "type": "exp", - "decay_steps": 5000, - "start_lr": 0.001, - "stop_lr": 3.51e-8, - "_comment": "that's all" - }, - - "loss": { - "type": "ener", - "start_pref_e": 0.02, - "limit_pref_e": 1, - "start_pref_f": 1000, - "limit_pref_f": 1, - "start_pref_v": 0, - "limit_pref_v": 0, - "_comment": " that's all" - }, - - "training": { - "training_data": { - "systems": [ - "model_compression/data" - ], - "batch_size": "auto", - "_comment": "that's all" - }, - "validation_data": { - "systems": [ - "model_compression/data" - ], - "batch_size": 1, - "numb_btch": 3, - "_comment": "that's all" - }, - "numb_steps": 1, - "seed": 10, - "disp_file": "lcurve.out", - "disp_freq": 1, - "save_freq": 1, - "_comment": "that's all" - }, - - "_comment": "that's all" -} diff --git a/source/tests/test_model_compression_se_atten.py b/source/tests/test_model_compression_se_atten.py index 23a1894f46..12d53d525a 100644 --- a/source/tests/test_model_compression_se_atten.py +++ b/source/tests/test_model_compression_se_atten.py @@ -53,10 +53,13 @@ def _init_models(): data_file = str(tests_path / os.path.join("model_compression", "data")) frozen_model = str(tests_path / "dp-original-se-atten.pb") compressed_model = str(tests_path / "dp-compressed-se-atten.pb") - INPUT = str(tests_path / "input-se-atten.json") - jdata = j_loader( - str(tests_path / os.path.join("model_compression", "input-se-atten.json")) - ) + INPUT = str(tests_path / "input.json") + jdata = j_loader(str(tests_path / os.path.join("model_compression", "input.json"))) + jdata["model"]["descriptor"] = {} + jdata["model"]["descriptor"]["type"] = "se_atten" + jdata["model"]["descriptor"]["compressible"] = True + jdata["model"]["descriptor"]["sel"] = 120 + jdata["model"]["descriptor"]["attn_layer"] = 0 jdata["training"]["training_data"]["systems"] = data_file jdata["training"]["validation_data"]["systems"] = data_file with open(INPUT, "w") as fp: From fabf0892d1a1fad1061ecdea1595c1a0a613013d Mon Sep 17 00:00:00 2001 From: nahso Date: Sun, 28 May 2023 20:32:55 +0800 Subject: [PATCH 093/126] fix hybrid mode with se_atten --- deepmd/descriptor/se_atten.py | 4 ++-- deepmd/model/ener.py | 2 +- deepmd/utils/tabulate.py | 4 ++-- 3 files changed, 5 insertions(+), 5 deletions(-) diff --git a/deepmd/descriptor/se_atten.py b/deepmd/descriptor/se_atten.py index 4a9766dc55..f6af5952b5 100644 --- a/deepmd/descriptor/se_atten.py +++ b/deepmd/descriptor/se_atten.py @@ -429,9 +429,9 @@ def _get_two_side_embedding_net_variable(self, graph_def, varialbe_name, suffix) ret = {} for i in range(1, self.layer_size + 1): target = get_pattern_nodes_from_graph_def( - graph_def, f"filter_type_all{suffix}/{varialbe_name}_{i}_two_side_ebd" + graph_def, f"filter_type_all{suffix}/{varialbe_name}_{i}{suffix}_two_side_ebd" ) - node = target[f"filter_type_all{suffix}/{varialbe_name}_{i}_two_side_ebd"] + node = target[f"filter_type_all{suffix}/{varialbe_name}_{i}{suffix}_two_side_ebd"] ret["layer_" + str(i)] = node return ret diff --git a/deepmd/model/ener.py b/deepmd/model/ener.py index f9387c67fc..4c0aa5078e 100644 --- a/deepmd/model/ener.py +++ b/deepmd/model/ener.py @@ -357,7 +357,7 @@ def init_variables( else: raise RuntimeError("Unknown model type %s" % model_type) if self.typeebd is not None: - self.typeebd.init_variables(graph, graph_def, suffix=suffix) + self.typeebd.init_variables(graph, graph_def, suffix=suffix, model_type=model_type) def natoms_match(self, force, natoms): use_spin = self.spin.use_spin diff --git a/deepmd/utils/tabulate.py b/deepmd/utils/tabulate.py index 02fd8f0d0e..2fced6c6e3 100644 --- a/deepmd/utils/tabulate.py +++ b/deepmd/utils/tabulate.py @@ -420,7 +420,7 @@ def _get_bias(self): bias["layer_" + str(layer)] = [] if isinstance(self.descrpt, deepmd.descriptor.DescrptSeAtten): node = self.embedding_net_nodes[ - f"filter_type_all{self.suffix}/bias_{layer}" + f"filter_type_all{self.suffix}/bias_{layer}{self.suffix}" ] bias["layer_" + str(layer)].append(tf.make_ndarray(node)) elif isinstance(self.descrpt, deepmd.descriptor.DescrptSeA): @@ -484,7 +484,7 @@ def _get_matrix(self): matrix["layer_" + str(layer)] = [] if isinstance(self.descrpt, deepmd.descriptor.DescrptSeAtten): node = self.embedding_net_nodes[ - f"filter_type_all{self.suffix}/matrix_{layer}" + f"filter_type_all{self.suffix}/matrix_{layer}{self.suffix}" ] matrix["layer_" + str(layer)].append(tf.make_ndarray(node)) elif isinstance(self.descrpt, deepmd.descriptor.DescrptSeA): From 390184b10c8b82bdd7512b21f45392b13919ce60 Mon Sep 17 00:00:00 2001 From: nahso Date: Sun, 28 May 2023 20:36:10 +0800 Subject: [PATCH 094/126] test the compress works in all of the following precision: type embedding FP64, se_atten FP64 type embedding FP64, se_atten FP32 type embedding FP32, se_atten FP64 type embedding FP32, se_atten FP32 --- .../tests/test_model_compression_se_atten.py | 792 ++++++++++-------- 1 file changed, 439 insertions(+), 353 deletions(-) diff --git a/source/tests/test_model_compression_se_atten.py b/source/tests/test_model_compression_se_atten.py index 12d53d525a..01017084fa 100644 --- a/source/tests/test_model_compression_se_atten.py +++ b/source/tests/test_model_compression_se_atten.py @@ -21,12 +21,6 @@ DeepPot, ) -if GLOBAL_NP_FLOAT_PRECISION == np.float32: - default_places = 4 -else: - default_places = 10 - - def _file_delete(file): if os.path.isdir(file): os.rmdir(file) @@ -51,30 +45,54 @@ def _subprocess_run(command): ) def _init_models(): data_file = str(tests_path / os.path.join("model_compression", "data")) - frozen_model = str(tests_path / "dp-original-se-atten.pb") - compressed_model = str(tests_path / "dp-compressed-se-atten.pb") - INPUT = str(tests_path / "input.json") - jdata = j_loader(str(tests_path / os.path.join("model_compression", "input.json"))) - jdata["model"]["descriptor"] = {} - jdata["model"]["descriptor"]["type"] = "se_atten" - jdata["model"]["descriptor"]["compressible"] = True - jdata["model"]["descriptor"]["sel"] = 120 - jdata["model"]["descriptor"]["attn_layer"] = 0 - jdata["training"]["training_data"]["systems"] = data_file - jdata["training"]["validation_data"]["systems"] = data_file - with open(INPUT, "w") as fp: - json.dump(jdata, fp, indent=4) - - ret = run_dp("dp train " + INPUT) - np.testing.assert_equal(ret, 0, "DP train failed!") - ret = run_dp("dp freeze -o " + frozen_model) - np.testing.assert_equal(ret, 0, "DP freeze failed!") - ret = run_dp("dp compress " + " -i " + frozen_model + " -o " + compressed_model) - np.testing.assert_equal(ret, 0, "DP model compression failed!") - return INPUT, frozen_model, compressed_model - - -INPUT, FROZEN_MODEL, COMPRESSED_MODEL = _init_models() + inputs, frozen_models, compressed_models = [], [], [] + # 4 tests: + # - type embedding FP64, se_atten FP64 + # - type embedding FP64, se_atten FP32 + # - type embedding FP32, se_atten FP64 + # - type embedding FP32, se_atten FP32 + tests = [ + {'se_atten precision': "float64", 'type embedding precision': "float64"}, + {'se_atten precision': "float64", 'type embedding precision': "float32"}, + {'se_atten precision': "float32", 'type embedding precision': "float64"}, + {'se_atten precision': "float32", 'type embedding precision': "float32"}, + ] + for i in range(4): + INPUT = str(tests_path / f"input{i}.json") + frozen_model = str(tests_path / f"dp-original-se-atten{i}.pb") + compressed_model = str(tests_path / f"dp-compressed-se-atten{i}.pb") + jdata = j_loader(str(tests_path / os.path.join("model_compression", "input.json"))) + jdata["model"]["descriptor"] = {} + jdata["model"]["descriptor"]["type"] = "se_atten" + jdata["model"]["descriptor"]["precision"] = tests[i]["se_atten precision"] + jdata["model"]["descriptor"]["compressible"] = True + jdata["model"]["descriptor"]["sel"] = 120 + jdata["model"]["descriptor"]["attn_layer"] = 0 + jdata["model"]["type_embedding"] = {} + jdata["model"]["type_embedding"]["precision"] = tests[i]["type embedding precision"] + jdata["training"]["training_data"]["systems"] = data_file + jdata["training"]["validation_data"]["systems"] = data_file + with open(INPUT, "w") as fp: + json.dump(jdata, fp, indent=4) + + ret = run_dp("dp train " + INPUT) + np.testing.assert_equal(ret, 0, "DP train failed!") + ret = run_dp("dp freeze -o " + frozen_model) + np.testing.assert_equal(ret, 0, "DP freeze failed!") + ret = run_dp("dp compress " + " -i " + frozen_model + " -o " + compressed_model) + np.testing.assert_equal(ret, 0, "DP model compression failed!") + + inputs.append(INPUT) + frozen_models.append(frozen_model) + compressed_models.append(compressed_model) + + return inputs, frozen_models, compressed_models + + +INPUTS, FROZEN_MODELS, COMPRESSED_MODELS = _init_models() + +def _get_default_places(nth_test): + return 10 if nth_test == 0 else 4 @unittest.skipIf( @@ -84,8 +102,8 @@ def _init_models(): class TestDeepPotAPBC(unittest.TestCase): @classmethod def setUpClass(self): - self.dp_original = DeepPot(FROZEN_MODEL) - self.dp_compressed = DeepPot(COMPRESSED_MODEL) + self.dp_originals = [DeepPot(FROZEN_MODELS[i]) for i in range(4)] + self.dp_compresseds = [DeepPot(COMPRESSED_MODELS[i]) for i in range(4)] self.coords = np.array( [ 12.83, @@ -112,97 +130,117 @@ def setUpClass(self): self.box = np.array([13.0, 0.0, 0.0, 0.0, 13.0, 0.0, 0.0, 0.0, 13.0]) def test_attrs(self): - self.assertEqual(self.dp_original.get_ntypes(), 2) - self.assertAlmostEqual(self.dp_original.get_rcut(), 6.0, places=default_places) - self.assertEqual(self.dp_original.get_type_map(), ["O", "H"]) - self.assertEqual(self.dp_original.get_dim_fparam(), 0) - self.assertEqual(self.dp_original.get_dim_aparam(), 0) - - self.assertEqual(self.dp_compressed.get_ntypes(), 2) - self.assertAlmostEqual( - self.dp_compressed.get_rcut(), 6.0, places=default_places - ) - self.assertEqual(self.dp_compressed.get_type_map(), ["O", "H"]) - self.assertEqual(self.dp_compressed.get_dim_fparam(), 0) - self.assertEqual(self.dp_compressed.get_dim_aparam(), 0) + for i in range(4): + dp_original = self.dp_originals[i] + dp_compressed = self.dp_compresseds[i] + default_places = _get_default_places(i) + + self.assertEqual(dp_original.get_ntypes(), 2) + self.assertAlmostEqual(dp_original.get_rcut(), 6.0, places=default_places) + self.assertEqual(dp_original.get_type_map(), ["O", "H"]) + self.assertEqual(dp_original.get_dim_fparam(), 0) + self.assertEqual(dp_original.get_dim_aparam(), 0) + + self.assertEqual(dp_compressed.get_ntypes(), 2) + self.assertAlmostEqual( + dp_compressed.get_rcut(), 6.0, places=default_places + ) + self.assertEqual(dp_compressed.get_type_map(), ["O", "H"]) + self.assertEqual(dp_compressed.get_dim_fparam(), 0) + self.assertEqual(dp_compressed.get_dim_aparam(), 0) def test_1frame(self): - ee0, ff0, vv0 = self.dp_original.eval( - self.coords, self.box, self.atype, atomic=False - ) - ee1, ff1, vv1 = self.dp_compressed.eval( - self.coords, self.box, self.atype, atomic=False - ) - # check shape of the returns - nframes = 1 - natoms = len(self.atype) - self.assertEqual(ee0.shape, (nframes, 1)) - self.assertEqual(ff0.shape, (nframes, natoms, 3)) - self.assertEqual(vv0.shape, (nframes, 9)) - self.assertEqual(ee1.shape, (nframes, 1)) - self.assertEqual(ff1.shape, (nframes, natoms, 3)) - self.assertEqual(vv1.shape, (nframes, 9)) - # check values - np.testing.assert_almost_equal(ff0, ff1, default_places) - np.testing.assert_almost_equal(ee0, ee1, default_places) - np.testing.assert_almost_equal(vv0, vv1, default_places) + for i in range(4): + dp_original = self.dp_originals[i] + dp_compressed = self.dp_compresseds[i] + default_places = _get_default_places(i) + + ee0, ff0, vv0 = dp_original.eval( + self.coords, self.box, self.atype, atomic=False + ) + ee1, ff1, vv1 = dp_compressed.eval( + self.coords, self.box, self.atype, atomic=False + ) + # check shape of the returns + nframes = 1 + natoms = len(self.atype) + self.assertEqual(ee0.shape, (nframes, 1)) + self.assertEqual(ff0.shape, (nframes, natoms, 3)) + self.assertEqual(vv0.shape, (nframes, 9)) + self.assertEqual(ee1.shape, (nframes, 1)) + self.assertEqual(ff1.shape, (nframes, natoms, 3)) + self.assertEqual(vv1.shape, (nframes, 9)) + # check values + np.testing.assert_almost_equal(ff0, ff1, default_places) + np.testing.assert_almost_equal(ee0, ee1, default_places) + np.testing.assert_almost_equal(vv0, vv1, default_places) def test_1frame_atm(self): - ee0, ff0, vv0, ae0, av0 = self.dp_original.eval( - self.coords, self.box, self.atype, atomic=True - ) - ee1, ff1, vv1, ae1, av1 = self.dp_compressed.eval( - self.coords, self.box, self.atype, atomic=True - ) - # check shape of the returns - nframes = 1 - natoms = len(self.atype) - self.assertEqual(ee0.shape, (nframes, 1)) - self.assertEqual(ff0.shape, (nframes, natoms, 3)) - self.assertEqual(vv0.shape, (nframes, 9)) - self.assertEqual(ae0.shape, (nframes, natoms, 1)) - self.assertEqual(av0.shape, (nframes, natoms, 9)) - self.assertEqual(ee1.shape, (nframes, 1)) - self.assertEqual(ff1.shape, (nframes, natoms, 3)) - self.assertEqual(vv1.shape, (nframes, 9)) - self.assertEqual(ae1.shape, (nframes, natoms, 1)) - self.assertEqual(av1.shape, (nframes, natoms, 9)) - # check values - np.testing.assert_almost_equal(ff0, ff1, default_places) - np.testing.assert_almost_equal(ae0, ae1, default_places) - np.testing.assert_almost_equal(av0, av1, default_places) - np.testing.assert_almost_equal(ee0, ee1, default_places) - np.testing.assert_almost_equal(vv0, vv1, default_places) + for i in range(4): + dp_original = self.dp_originals[i] + dp_compressed = self.dp_compresseds[i] + default_places = _get_default_places(i) + + ee0, ff0, vv0, ae0, av0 = dp_original.eval( + self.coords, self.box, self.atype, atomic=True + ) + ee1, ff1, vv1, ae1, av1 = dp_compressed.eval( + self.coords, self.box, self.atype, atomic=True + ) + # check shape of the returns + nframes = 1 + natoms = len(self.atype) + self.assertEqual(ee0.shape, (nframes, 1)) + self.assertEqual(ff0.shape, (nframes, natoms, 3)) + self.assertEqual(vv0.shape, (nframes, 9)) + self.assertEqual(ae0.shape, (nframes, natoms, 1)) + self.assertEqual(av0.shape, (nframes, natoms, 9)) + self.assertEqual(ee1.shape, (nframes, 1)) + self.assertEqual(ff1.shape, (nframes, natoms, 3)) + self.assertEqual(vv1.shape, (nframes, 9)) + self.assertEqual(ae1.shape, (nframes, natoms, 1)) + self.assertEqual(av1.shape, (nframes, natoms, 9)) + # check values + np.testing.assert_almost_equal(ff0, ff1, default_places) + np.testing.assert_almost_equal(ae0, ae1, default_places) + np.testing.assert_almost_equal(av0, av1, default_places) + np.testing.assert_almost_equal(ee0, ee1, default_places) + np.testing.assert_almost_equal(vv0, vv1, default_places) def test_2frame_atm(self): - coords2 = np.concatenate((self.coords, self.coords)) - box2 = np.concatenate((self.box, self.box)) - ee0, ff0, vv0, ae0, av0 = self.dp_original.eval( - coords2, box2, self.atype, atomic=True - ) - ee1, ff1, vv1, ae1, av1 = self.dp_compressed.eval( - coords2, box2, self.atype, atomic=True - ) - # check shape of the returns - nframes = 2 - natoms = len(self.atype) - self.assertEqual(ee0.shape, (nframes, 1)) - self.assertEqual(ff0.shape, (nframes, natoms, 3)) - self.assertEqual(vv0.shape, (nframes, 9)) - self.assertEqual(ae0.shape, (nframes, natoms, 1)) - self.assertEqual(av0.shape, (nframes, natoms, 9)) - self.assertEqual(ee1.shape, (nframes, 1)) - self.assertEqual(ff1.shape, (nframes, natoms, 3)) - self.assertEqual(vv1.shape, (nframes, 9)) - self.assertEqual(ae1.shape, (nframes, natoms, 1)) - self.assertEqual(av1.shape, (nframes, natoms, 9)) - - # check values - np.testing.assert_almost_equal(ff0, ff1, default_places) - np.testing.assert_almost_equal(ae0, ae1, default_places) - np.testing.assert_almost_equal(av0, av1, default_places) - np.testing.assert_almost_equal(ee0, ee1, default_places) - np.testing.assert_almost_equal(vv0, vv1, default_places) + for i in range(4): + dp_original = self.dp_originals[i] + dp_compressed = self.dp_compresseds[i] + default_places = _get_default_places(i) + + coords2 = np.concatenate((self.coords, self.coords)) + box2 = np.concatenate((self.box, self.box)) + ee0, ff0, vv0, ae0, av0 = dp_original.eval( + coords2, box2, self.atype, atomic=True + ) + ee1, ff1, vv1, ae1, av1 = dp_compressed.eval( + coords2, box2, self.atype, atomic=True + ) + # check shape of the returns + nframes = 2 + natoms = len(self.atype) + self.assertEqual(ee0.shape, (nframes, 1)) + self.assertEqual(ff0.shape, (nframes, natoms, 3)) + self.assertEqual(vv0.shape, (nframes, 9)) + self.assertEqual(ae0.shape, (nframes, natoms, 1)) + self.assertEqual(av0.shape, (nframes, natoms, 9)) + self.assertEqual(ee1.shape, (nframes, 1)) + self.assertEqual(ff1.shape, (nframes, natoms, 3)) + self.assertEqual(vv1.shape, (nframes, 9)) + self.assertEqual(ae1.shape, (nframes, natoms, 1)) + self.assertEqual(av1.shape, (nframes, natoms, 9)) + + # check values + np.testing.assert_almost_equal(ff0, ff1, default_places) + np.testing.assert_almost_equal(ae0, ae1, default_places) + np.testing.assert_almost_equal(av0, av1, default_places) + np.testing.assert_almost_equal(ee0, ee1, default_places) + np.testing.assert_almost_equal(vv0, vv1, default_places) @unittest.skipIf( @@ -212,8 +250,8 @@ def test_2frame_atm(self): class TestDeepPotANoPBC(unittest.TestCase): @classmethod def setUpClass(self): - self.dp_original = DeepPot(FROZEN_MODEL) - self.dp_compressed = DeepPot(COMPRESSED_MODEL) + self.dp_originals = [DeepPot(FROZEN_MODELS[i]) for i in range(4)] + self.dp_compresseds = [DeepPot(COMPRESSED_MODELS[i]) for i in range(4)] self.coords = np.array( [ 12.83, @@ -240,81 +278,96 @@ def setUpClass(self): self.box = None def test_1frame(self): - ee0, ff0, vv0 = self.dp_original.eval( - self.coords, self.box, self.atype, atomic=False - ) - ee1, ff1, vv1 = self.dp_compressed.eval( - self.coords, self.box, self.atype, atomic=False - ) - # check shape of the returns - nframes = 1 - natoms = len(self.atype) - self.assertEqual(ee0.shape, (nframes, 1)) - self.assertEqual(ff0.shape, (nframes, natoms, 3)) - self.assertEqual(vv0.shape, (nframes, 9)) - self.assertEqual(ee1.shape, (nframes, 1)) - self.assertEqual(ff1.shape, (nframes, natoms, 3)) - self.assertEqual(vv1.shape, (nframes, 9)) - # check values - np.testing.assert_almost_equal(ff0, ff1, default_places) - np.testing.assert_almost_equal(ee0, ee1, default_places) - np.testing.assert_almost_equal(vv0, vv1, default_places) + for i in range(4): + dp_original = self.dp_originals[i] + dp_compressed = self.dp_compresseds[i] + default_places = _get_default_places(i) + + ee0, ff0, vv0 = dp_original.eval( + self.coords, self.box, self.atype, atomic=False + ) + ee1, ff1, vv1 = dp_compressed.eval( + self.coords, self.box, self.atype, atomic=False + ) + # check shape of the returns + nframes = 1 + natoms = len(self.atype) + self.assertEqual(ee0.shape, (nframes, 1)) + self.assertEqual(ff0.shape, (nframes, natoms, 3)) + self.assertEqual(vv0.shape, (nframes, 9)) + self.assertEqual(ee1.shape, (nframes, 1)) + self.assertEqual(ff1.shape, (nframes, natoms, 3)) + self.assertEqual(vv1.shape, (nframes, 9)) + # check values + np.testing.assert_almost_equal(ff0, ff1, default_places) + np.testing.assert_almost_equal(ee0, ee1, default_places) + np.testing.assert_almost_equal(vv0, vv1, default_places) def test_1frame_atm(self): - ee0, ff0, vv0, ae0, av0 = self.dp_original.eval( - self.coords, self.box, self.atype, atomic=True - ) - ee1, ff1, vv1, ae1, av1 = self.dp_compressed.eval( - self.coords, self.box, self.atype, atomic=True - ) - # check shape of the returns - nframes = 1 - natoms = len(self.atype) - self.assertEqual(ee0.shape, (nframes, 1)) - self.assertEqual(ff0.shape, (nframes, natoms, 3)) - self.assertEqual(vv0.shape, (nframes, 9)) - self.assertEqual(ae0.shape, (nframes, natoms, 1)) - self.assertEqual(av0.shape, (nframes, natoms, 9)) - self.assertEqual(ee1.shape, (nframes, 1)) - self.assertEqual(ff1.shape, (nframes, natoms, 3)) - self.assertEqual(vv1.shape, (nframes, 9)) - self.assertEqual(ae1.shape, (nframes, natoms, 1)) - self.assertEqual(av1.shape, (nframes, natoms, 9)) - # check values - np.testing.assert_almost_equal(ff0, ff1, default_places) - np.testing.assert_almost_equal(ae0, ae1, default_places) - np.testing.assert_almost_equal(av0, av1, default_places) - np.testing.assert_almost_equal(ee0, ee1, default_places) - np.testing.assert_almost_equal(vv0, vv1, default_places) + for i in range(4): + dp_original = self.dp_originals[i] + dp_compressed = self.dp_compresseds[i] + default_places = _get_default_places(i) + + ee0, ff0, vv0, ae0, av0 = dp_original.eval( + self.coords, self.box, self.atype, atomic=True + ) + ee1, ff1, vv1, ae1, av1 = dp_compressed.eval( + self.coords, self.box, self.atype, atomic=True + ) + # check shape of the returns + nframes = 1 + natoms = len(self.atype) + self.assertEqual(ee0.shape, (nframes, 1)) + self.assertEqual(ff0.shape, (nframes, natoms, 3)) + self.assertEqual(vv0.shape, (nframes, 9)) + self.assertEqual(ae0.shape, (nframes, natoms, 1)) + self.assertEqual(av0.shape, (nframes, natoms, 9)) + self.assertEqual(ee1.shape, (nframes, 1)) + self.assertEqual(ff1.shape, (nframes, natoms, 3)) + self.assertEqual(vv1.shape, (nframes, 9)) + self.assertEqual(ae1.shape, (nframes, natoms, 1)) + self.assertEqual(av1.shape, (nframes, natoms, 9)) + # check values + np.testing.assert_almost_equal(ff0, ff1, default_places) + np.testing.assert_almost_equal(ae0, ae1, default_places) + np.testing.assert_almost_equal(av0, av1, default_places) + np.testing.assert_almost_equal(ee0, ee1, default_places) + np.testing.assert_almost_equal(vv0, vv1, default_places) def test_2frame_atm(self): - coords2 = np.concatenate((self.coords, self.coords)) - ee0, ff0, vv0, ae0, av0 = self.dp_original.eval( - coords2, self.box, self.atype, atomic=True - ) - ee1, ff1, vv1, ae1, av1 = self.dp_compressed.eval( - coords2, self.box, self.atype, atomic=True - ) - # check shape of the returns - nframes = 2 - natoms = len(self.atype) - self.assertEqual(ee0.shape, (nframes, 1)) - self.assertEqual(ff0.shape, (nframes, natoms, 3)) - self.assertEqual(vv0.shape, (nframes, 9)) - self.assertEqual(ae0.shape, (nframes, natoms, 1)) - self.assertEqual(av0.shape, (nframes, natoms, 9)) - self.assertEqual(ee1.shape, (nframes, 1)) - self.assertEqual(ff1.shape, (nframes, natoms, 3)) - self.assertEqual(vv1.shape, (nframes, 9)) - self.assertEqual(ae1.shape, (nframes, natoms, 1)) - self.assertEqual(av1.shape, (nframes, natoms, 9)) - - # check values - np.testing.assert_almost_equal(ff0, ff1, default_places) - np.testing.assert_almost_equal(ae0, ae1, default_places) - np.testing.assert_almost_equal(av0, av1, default_places) - np.testing.assert_almost_equal(ee0, ee1, default_places) - np.testing.assert_almost_equal(vv0, vv1, default_places) + for i in range(4): + dp_original = self.dp_originals[i] + dp_compressed = self.dp_compresseds[i] + default_places = _get_default_places(i) + + coords2 = np.concatenate((self.coords, self.coords)) + ee0, ff0, vv0, ae0, av0 = dp_original.eval( + coords2, self.box, self.atype, atomic=True + ) + ee1, ff1, vv1, ae1, av1 = dp_compressed.eval( + coords2, self.box, self.atype, atomic=True + ) + # check shape of the returns + nframes = 2 + natoms = len(self.atype) + self.assertEqual(ee0.shape, (nframes, 1)) + self.assertEqual(ff0.shape, (nframes, natoms, 3)) + self.assertEqual(vv0.shape, (nframes, 9)) + self.assertEqual(ae0.shape, (nframes, natoms, 1)) + self.assertEqual(av0.shape, (nframes, natoms, 9)) + self.assertEqual(ee1.shape, (nframes, 1)) + self.assertEqual(ff1.shape, (nframes, natoms, 3)) + self.assertEqual(vv1.shape, (nframes, 9)) + self.assertEqual(ae1.shape, (nframes, natoms, 1)) + self.assertEqual(av1.shape, (nframes, natoms, 9)) + + # check values + np.testing.assert_almost_equal(ff0, ff1, default_places) + np.testing.assert_almost_equal(ae0, ae1, default_places) + np.testing.assert_almost_equal(av0, av1, default_places) + np.testing.assert_almost_equal(ee0, ee1, default_places) + np.testing.assert_almost_equal(vv0, vv1, default_places) @unittest.skipIf( @@ -324,8 +377,8 @@ def test_2frame_atm(self): class TestDeepPotALargeBoxNoPBC(unittest.TestCase): @classmethod def setUpClass(self): - self.dp_original = DeepPot(FROZEN_MODEL) - self.dp_compressed = DeepPot(COMPRESSED_MODEL) + self.dp_originals = [DeepPot(FROZEN_MODELS[i]) for i in range(4)] + self.dp_compresseds = [DeepPot(COMPRESSED_MODELS[i]) for i in range(4)] self.coords = np.array( [ 12.83, @@ -352,81 +405,93 @@ def setUpClass(self): self.box = np.array([19.0, 0.0, 0.0, 0.0, 13.0, 0.0, 0.0, 0.0, 13.0]) def test_1frame(self): - ee0, ff0, vv0 = self.dp_original.eval( - self.coords, self.box, self.atype, atomic=False - ) - ee1, ff1, vv1 = self.dp_compressed.eval( - self.coords, self.box, self.atype, atomic=False - ) - # check shape of the returns - nframes = 1 - natoms = len(self.atype) - self.assertEqual(ee0.shape, (nframes, 1)) - self.assertEqual(ff0.shape, (nframes, natoms, 3)) - self.assertEqual(vv0.shape, (nframes, 9)) - self.assertEqual(ee1.shape, (nframes, 1)) - self.assertEqual(ff1.shape, (nframes, natoms, 3)) - self.assertEqual(vv1.shape, (nframes, 9)) - # check values - np.testing.assert_almost_equal(ff0, ff1, default_places) - np.testing.assert_almost_equal(ee0, ee1, default_places) - np.testing.assert_almost_equal(vv0, vv1, default_places) + for i in range(4): + dp_original = self.dp_originals[i] + dp_compressed = self.dp_compresseds[i] + default_places = _get_default_places(i) + + ee0, ff0, vv0 = dp_original.eval( + self.coords, self.box, self.atype, atomic=False + ) + ee1, ff1, vv1 = dp_compressed.eval( + self.coords, self.box, self.atype, atomic=False + ) + # check shape of the returns + nframes = 1 + natoms = len(self.atype) + self.assertEqual(ee0.shape, (nframes, 1)) + self.assertEqual(ff0.shape, (nframes, natoms, 3)) + self.assertEqual(vv0.shape, (nframes, 9)) + self.assertEqual(ee1.shape, (nframes, 1)) + self.assertEqual(ff1.shape, (nframes, natoms, 3)) + self.assertEqual(vv1.shape, (nframes, 9)) + # check values + np.testing.assert_almost_equal(ff0, ff1, default_places) + np.testing.assert_almost_equal(ee0, ee1, default_places) + np.testing.assert_almost_equal(vv0, vv1, default_places) def test_1frame_atm(self): - ee0, ff0, vv0, ae0, av0 = self.dp_original.eval( - self.coords, self.box, self.atype, atomic=True - ) - ee1, ff1, vv1, ae1, av1 = self.dp_compressed.eval( - self.coords, self.box, self.atype, atomic=True - ) - # check shape of the returns - nframes = 1 - natoms = len(self.atype) - self.assertEqual(ee0.shape, (nframes, 1)) - self.assertEqual(ff0.shape, (nframes, natoms, 3)) - self.assertEqual(vv0.shape, (nframes, 9)) - self.assertEqual(ae0.shape, (nframes, natoms, 1)) - self.assertEqual(av0.shape, (nframes, natoms, 9)) - self.assertEqual(ee1.shape, (nframes, 1)) - self.assertEqual(ff1.shape, (nframes, natoms, 3)) - self.assertEqual(vv1.shape, (nframes, 9)) - self.assertEqual(ae1.shape, (nframes, natoms, 1)) - self.assertEqual(av1.shape, (nframes, natoms, 9)) - # check values - np.testing.assert_almost_equal(ff0, ff1, default_places) - np.testing.assert_almost_equal(ae0, ae1, default_places) - np.testing.assert_almost_equal(av0, av1, default_places) - np.testing.assert_almost_equal(ee0, ee1, default_places) - np.testing.assert_almost_equal(vv0, vv1, default_places) + for i in range(4): + dp_original = self.dp_originals[i] + dp_compressed = self.dp_compresseds[i] + default_places = _get_default_places(i) + + ee0, ff0, vv0, ae0, av0 = dp_original.eval( + self.coords, self.box, self.atype, atomic=True + ) + ee1, ff1, vv1, ae1, av1 = dp_compressed.eval( + self.coords, self.box, self.atype, atomic=True + ) + # check shape of the returns + nframes = 1 + natoms = len(self.atype) + self.assertEqual(ee0.shape, (nframes, 1)) + self.assertEqual(ff0.shape, (nframes, natoms, 3)) + self.assertEqual(vv0.shape, (nframes, 9)) + self.assertEqual(ae0.shape, (nframes, natoms, 1)) + self.assertEqual(av0.shape, (nframes, natoms, 9)) + self.assertEqual(ee1.shape, (nframes, 1)) + self.assertEqual(ff1.shape, (nframes, natoms, 3)) + self.assertEqual(vv1.shape, (nframes, 9)) + self.assertEqual(ae1.shape, (nframes, natoms, 1)) + self.assertEqual(av1.shape, (nframes, natoms, 9)) + # check values + np.testing.assert_almost_equal(ff0, ff1, default_places) + np.testing.assert_almost_equal(ae0, ae1, default_places) + np.testing.assert_almost_equal(av0, av1, default_places) + np.testing.assert_almost_equal(ee0, ee1, default_places) + np.testing.assert_almost_equal(vv0, vv1, default_places) def test_ase(self): - from ase import ( - Atoms, - ) - - from deepmd.calculator import ( - DP, - ) - - water0 = Atoms( - "OHHOHH", - positions=self.coords.reshape((-1, 3)), - cell=self.box.reshape((3, 3)), - calculator=DP(FROZEN_MODEL), - ) - water1 = Atoms( - "OHHOHH", - positions=self.coords.reshape((-1, 3)), - cell=self.box.reshape((3, 3)), - calculator=DP(COMPRESSED_MODEL), - ) - ee0 = water0.get_potential_energy() - ff0 = water0.get_forces() - ee1 = water1.get_potential_energy() - ff1 = water1.get_forces() - nframes = 1 - np.testing.assert_almost_equal(ff0, ff1, default_places) - np.testing.assert_almost_equal(ee0, ee1, default_places) + for i in range(4): + default_places = _get_default_places(i) + from ase import ( + Atoms, + ) + + from deepmd.calculator import ( + DP, + ) + + water0 = Atoms( + "OHHOHH", + positions=self.coords.reshape((-1, 3)), + cell=self.box.reshape((3, 3)), + calculator=DP(FROZEN_MODELS[i]), + ) + water1 = Atoms( + "OHHOHH", + positions=self.coords.reshape((-1, 3)), + cell=self.box.reshape((3, 3)), + calculator=DP(COMPRESSED_MODELS[i]), + ) + ee0 = water0.get_potential_energy() + ff0 = water0.get_forces() + ee1 = water1.get_potential_energy() + ff1 = water1.get_forces() + nframes = 1 + np.testing.assert_almost_equal(ff0, ff1, default_places) + np.testing.assert_almost_equal(ee0, ee1, default_places) @unittest.skipIf( @@ -436,8 +501,8 @@ def test_ase(self): class TestDeepPotAPBCExcludeTypes(unittest.TestCase): @classmethod def setUpClass(self): - self.dp_original = DeepPot(FROZEN_MODEL) - self.dp_compressed = DeepPot(COMPRESSED_MODEL) + self.dp_originals = [DeepPot(FROZEN_MODELS[i]) for i in range(4)] + self.dp_compresseds = [DeepPot(COMPRESSED_MODELS[i]) for i in range(4)] self.coords = np.array( [ 12.83, @@ -465,9 +530,10 @@ def setUpClass(self): @classmethod def tearDownClass(self): - _file_delete(INPUT) - _file_delete(FROZEN_MODEL) - _file_delete(COMPRESSED_MODEL) + for i in range(4): + _file_delete(INPUTS[i]) + _file_delete(FROZEN_MODELS[i]) + _file_delete(COMPRESSED_MODELS[i]) _file_delete("out.json") _file_delete("compress.json") _file_delete("checkpoint") @@ -486,94 +552,114 @@ def tearDownClass(self): _file_delete("lcurve.out") def test_attrs(self): - self.assertEqual(self.dp_original.get_ntypes(), 2) - self.assertAlmostEqual(self.dp_original.get_rcut(), 6.0, places=default_places) - self.assertEqual(self.dp_original.get_type_map(), ["O", "H"]) - self.assertEqual(self.dp_original.get_dim_fparam(), 0) - self.assertEqual(self.dp_original.get_dim_aparam(), 0) - - self.assertEqual(self.dp_compressed.get_ntypes(), 2) - self.assertAlmostEqual( - self.dp_compressed.get_rcut(), 6.0, places=default_places - ) - self.assertEqual(self.dp_compressed.get_type_map(), ["O", "H"]) - self.assertEqual(self.dp_compressed.get_dim_fparam(), 0) - self.assertEqual(self.dp_compressed.get_dim_aparam(), 0) + for i in range(4): + dp_original = self.dp_originals[i] + dp_compressed = self.dp_compresseds[i] + default_places = _get_default_places(i) + + self.assertEqual(dp_original.get_ntypes(), 2) + self.assertAlmostEqual(dp_original.get_rcut(), 6.0, places=default_places) + self.assertEqual(dp_original.get_type_map(), ["O", "H"]) + self.assertEqual(dp_original.get_dim_fparam(), 0) + self.assertEqual(dp_original.get_dim_aparam(), 0) + + self.assertEqual(dp_compressed.get_ntypes(), 2) + self.assertAlmostEqual( + dp_compressed.get_rcut(), 6.0, places=default_places + ) + self.assertEqual(dp_compressed.get_type_map(), ["O", "H"]) + self.assertEqual(dp_compressed.get_dim_fparam(), 0) + self.assertEqual(dp_compressed.get_dim_aparam(), 0) def test_1frame(self): - ee0, ff0, vv0 = self.dp_original.eval( - self.coords, self.box, self.atype, atomic=False - ) - ee1, ff1, vv1 = self.dp_compressed.eval( - self.coords, self.box, self.atype, atomic=False - ) - # check shape of the returns - nframes = 1 - natoms = len(self.atype) - self.assertEqual(ee0.shape, (nframes, 1)) - self.assertEqual(ff0.shape, (nframes, natoms, 3)) - self.assertEqual(vv0.shape, (nframes, 9)) - self.assertEqual(ee1.shape, (nframes, 1)) - self.assertEqual(ff1.shape, (nframes, natoms, 3)) - self.assertEqual(vv1.shape, (nframes, 9)) - # check values - np.testing.assert_almost_equal(ff0, ff1, default_places) - np.testing.assert_almost_equal(ee0, ee1, default_places) - np.testing.assert_almost_equal(vv0, vv1, default_places) + for i in range(4): + dp_original = self.dp_originals[i] + dp_compressed = self.dp_compresseds[i] + default_places = _get_default_places(i) + + ee0, ff0, vv0 = dp_original.eval( + self.coords, self.box, self.atype, atomic=False + ) + ee1, ff1, vv1 = dp_compressed.eval( + self.coords, self.box, self.atype, atomic=False + ) + # check shape of the returns + nframes = 1 + natoms = len(self.atype) + self.assertEqual(ee0.shape, (nframes, 1)) + self.assertEqual(ff0.shape, (nframes, natoms, 3)) + self.assertEqual(vv0.shape, (nframes, 9)) + self.assertEqual(ee1.shape, (nframes, 1)) + self.assertEqual(ff1.shape, (nframes, natoms, 3)) + self.assertEqual(vv1.shape, (nframes, 9)) + # check values + np.testing.assert_almost_equal(ff0, ff1, default_places) + np.testing.assert_almost_equal(ee0, ee1, default_places) + np.testing.assert_almost_equal(vv0, vv1, default_places) def test_1frame_atm(self): - ee0, ff0, vv0, ae0, av0 = self.dp_original.eval( - self.coords, self.box, self.atype, atomic=True - ) - ee1, ff1, vv1, ae1, av1 = self.dp_compressed.eval( - self.coords, self.box, self.atype, atomic=True - ) - # check shape of the returns - nframes = 1 - natoms = len(self.atype) - self.assertEqual(ee0.shape, (nframes, 1)) - self.assertEqual(ff0.shape, (nframes, natoms, 3)) - self.assertEqual(vv0.shape, (nframes, 9)) - self.assertEqual(ae0.shape, (nframes, natoms, 1)) - self.assertEqual(av0.shape, (nframes, natoms, 9)) - self.assertEqual(ee1.shape, (nframes, 1)) - self.assertEqual(ff1.shape, (nframes, natoms, 3)) - self.assertEqual(vv1.shape, (nframes, 9)) - self.assertEqual(ae1.shape, (nframes, natoms, 1)) - self.assertEqual(av1.shape, (nframes, natoms, 9)) - # check values - np.testing.assert_almost_equal(ff0, ff1, default_places) - np.testing.assert_almost_equal(ae0, ae1, default_places) - np.testing.assert_almost_equal(av0, av1, default_places) - np.testing.assert_almost_equal(ee0, ee1, default_places) - np.testing.assert_almost_equal(vv0, vv1, default_places) + for i in range(4): + dp_original = self.dp_originals[i] + dp_compressed = self.dp_compresseds[i] + default_places = _get_default_places(i) + + ee0, ff0, vv0, ae0, av0 = dp_original.eval( + self.coords, self.box, self.atype, atomic=True + ) + ee1, ff1, vv1, ae1, av1 = dp_compressed.eval( + self.coords, self.box, self.atype, atomic=True + ) + # check shape of the returns + nframes = 1 + natoms = len(self.atype) + self.assertEqual(ee0.shape, (nframes, 1)) + self.assertEqual(ff0.shape, (nframes, natoms, 3)) + self.assertEqual(vv0.shape, (nframes, 9)) + self.assertEqual(ae0.shape, (nframes, natoms, 1)) + self.assertEqual(av0.shape, (nframes, natoms, 9)) + self.assertEqual(ee1.shape, (nframes, 1)) + self.assertEqual(ff1.shape, (nframes, natoms, 3)) + self.assertEqual(vv1.shape, (nframes, 9)) + self.assertEqual(ae1.shape, (nframes, natoms, 1)) + self.assertEqual(av1.shape, (nframes, natoms, 9)) + # check values + np.testing.assert_almost_equal(ff0, ff1, default_places) + np.testing.assert_almost_equal(ae0, ae1, default_places) + np.testing.assert_almost_equal(av0, av1, default_places) + np.testing.assert_almost_equal(ee0, ee1, default_places) + np.testing.assert_almost_equal(vv0, vv1, default_places) def test_2frame_atm(self): - coords2 = np.concatenate((self.coords, self.coords)) - box2 = np.concatenate((self.box, self.box)) - ee0, ff0, vv0, ae0, av0 = self.dp_original.eval( - coords2, box2, self.atype, atomic=True - ) - ee1, ff1, vv1, ae1, av1 = self.dp_compressed.eval( - coords2, box2, self.atype, atomic=True - ) - # check shape of the returns - nframes = 2 - natoms = len(self.atype) - self.assertEqual(ee0.shape, (nframes, 1)) - self.assertEqual(ff0.shape, (nframes, natoms, 3)) - self.assertEqual(vv0.shape, (nframes, 9)) - self.assertEqual(ae0.shape, (nframes, natoms, 1)) - self.assertEqual(av0.shape, (nframes, natoms, 9)) - self.assertEqual(ee1.shape, (nframes, 1)) - self.assertEqual(ff1.shape, (nframes, natoms, 3)) - self.assertEqual(vv1.shape, (nframes, 9)) - self.assertEqual(ae1.shape, (nframes, natoms, 1)) - self.assertEqual(av1.shape, (nframes, natoms, 9)) - - # check values - np.testing.assert_almost_equal(ff0, ff1, default_places) - np.testing.assert_almost_equal(ae0, ae1, default_places) - np.testing.assert_almost_equal(av0, av1, default_places) - np.testing.assert_almost_equal(ee0, ee1, default_places) - np.testing.assert_almost_equal(vv0, vv1, default_places) + for i in range(4): + dp_original = self.dp_originals[i] + dp_compressed = self.dp_compresseds[i] + default_places = _get_default_places(i) + + coords2 = np.concatenate((self.coords, self.coords)) + box2 = np.concatenate((self.box, self.box)) + ee0, ff0, vv0, ae0, av0 = dp_original.eval( + coords2, box2, self.atype, atomic=True + ) + ee1, ff1, vv1, ae1, av1 = dp_compressed.eval( + coords2, box2, self.atype, atomic=True + ) + # check shape of the returns + nframes = 2 + natoms = len(self.atype) + self.assertEqual(ee0.shape, (nframes, 1)) + self.assertEqual(ff0.shape, (nframes, natoms, 3)) + self.assertEqual(vv0.shape, (nframes, 9)) + self.assertEqual(ae0.shape, (nframes, natoms, 1)) + self.assertEqual(av0.shape, (nframes, natoms, 9)) + self.assertEqual(ee1.shape, (nframes, 1)) + self.assertEqual(ff1.shape, (nframes, natoms, 3)) + self.assertEqual(vv1.shape, (nframes, 9)) + self.assertEqual(ae1.shape, (nframes, natoms, 1)) + self.assertEqual(av1.shape, (nframes, natoms, 9)) + + # check values + np.testing.assert_almost_equal(ff0, ff1, default_places) + np.testing.assert_almost_equal(ae0, ae1, default_places) + np.testing.assert_almost_equal(av0, av1, default_places) + np.testing.assert_almost_equal(ee0, ee1, default_places) + np.testing.assert_almost_equal(vv0, vv1, default_places) From 012e81e1442009577b9d35e461ca96b05bfabc22 Mon Sep 17 00:00:00 2001 From: nahso Date: Sun, 28 May 2023 20:36:59 +0800 Subject: [PATCH 095/126] add se_atten to hybrid mode --- examples/water/hybrid/input.json | 21 ++++++++++++++++++++- 1 file changed, 20 insertions(+), 1 deletion(-) diff --git a/examples/water/hybrid/input.json b/examples/water/hybrid/input.json index 5f4cd6c5ec..8f4a29d8d6 100644 --- a/examples/water/hybrid/input.json +++ b/examples/water/hybrid/input.json @@ -42,6 +42,25 @@ "resnet_dt": false, "seed": 1, "_comment": " that's all" + }, + { + "type": "se_atten", + "compressible": true, + "attn_layer": 0, + "sel": [ + 46, + 92 + ], + "rcut_smth": 0.50, + "rcut": 6.00, + "neuron": [ + 5, + 10, + 20 + ], + "resnet_dt": false, + "seed": 1, + "_comment": " that's all" } ] }, @@ -95,7 +114,7 @@ "numb_btch": 3, "_comment": "that's all" }, - "numb_steps": 1000000, + "numb_steps": 1000, "seed": 10, "disp_file": "lcurve.out", "disp_freq": 100, From cc3ae8f87bf4800dea967436245b3daacbee55fd Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Yifan=20Li=E6=9D=8E=E4=B8=80=E5=B8=86?= Date: Thu, 25 May 2023 19:11:27 -0400 Subject: [PATCH 096/126] reduce model size for dplr unittest (#2561) This PR uses small models to test dplr. --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> --- source/lmp/tests/lrmodel.pbtxt | 36922 +++++++++++-------------------- source/lmp/tests/test_dplr.py | 142 +- 2 files changed, 12845 insertions(+), 24219 deletions(-) diff --git a/source/lmp/tests/lrmodel.pbtxt b/source/lmp/tests/lrmodel.pbtxt index d7c200e2cc..6cdf1f9d79 100644 --- a/source/lmp/tests/lrmodel.pbtxt +++ b/source/lmp/tests/lrmodel.pbtxt @@ -35,7 +35,7 @@ node { dtype: DT_STRING tensor_shape { } - string_val: "{\"model\": {\"type_map\": [\"O\", \"H\"], \"descriptor\": {\"type\": \"hybrid\", \"list\": [{\"type\": \"se_e2_a\", \"sel\": [20, 40], \"rcut_smth\": 0.5, \"rcut\": 4.0, \"neuron\": [25, 50, 100], \"resnet_dt\": false, \"axis_neuron\": 8, \"seed\": 1, \"activation_function\": \"tanh\", \"type_one_side\": false, \"precision\": \"float64\", \"trainable\": true, \"exclude_types\": [], \"set_davg_zero\": false}, {\"type\": \"se_e2_r\", \"sel\": [46, 92], \"rcut_smth\": 0.5, \"rcut\": 6.0, \"neuron\": [10, 20, 40], \"resnet_dt\": false, \"seed\": 1, \"activation_function\": \"tanh\", \"type_one_side\": false, \"precision\": \"float64\", \"trainable\": true, \"exclude_types\": [], \"set_davg_zero\": false}]}, \"fitting_net\": {\"neuron\": [240, 240, 240], \"resnet_dt\": true, \"seed\": 1, \"type\": \"ener\", \"numb_fparam\": 0, \"numb_aparam\": 0, \"activation_function\": \"tanh\", \"precision\": \"float64\", \"trainable\": true, \"rcond\": 0.001, \"atom_ener\": []}, \"modifier\": {\"type\": \"dipole_charge\", \"model_name\": \"dipole.pb\", \"model_charge_map\": [-8], \"sys_charge_map\": [6, 1], \"ewald_h\": 1.0, \"ewald_beta\": 0.4}, \"data_stat_nbatch\": 10, \"data_stat_protect\": 0.01}, \"learning_rate\": {\"type\": \"exp\", \"decay_steps\": 5000, \"start_lr\": 0.001, \"stop_lr\": 3.51e-08, \"scale_by_worker\": \"linear\"}, \"loss\": {\"type\": \"ener\", \"start_pref_e\": 0.02, \"limit_pref_e\": 1, \"start_pref_f\": 1000, \"limit_pref_f\": 1, \"start_pref_v\": 0, \"limit_pref_v\": 0, \"start_pref_ae\": 0.0, \"limit_pref_ae\": 0.0, \"start_pref_pf\": 0.0, \"limit_pref_pf\": 0.0}, \"training\": {\"training_data\": {\"systems\": [\"../../DeepWannier/data/dpgen.lowd.merged/iter.000016/02.fp/data.012\", \"../../DeepWannier/data/dpgen.lowd.merged/iter.000016/02.fp/data.016\", \"../../DeepWannier/data/dpgen.lowd.merged/iter.000016/02.fp/data.020\", \"../../DeepWannier/data/dpgen.merged/init/00.liq/state.0300.1.00e00.00/vasp/data\", \"../../DeepWannier/data/dpgen.merged/init/00.liq/state.0400.1.00e04.00/vasp/data\", \"../../DeepWannier/data/dpgen.merged/init/01.liq/state.0300.1.00e00.00/vasp/data\", \"../../DeepWannier/data/dpgen.merged/init/01.liq/state.0400.1.00e04.00/vasp/data\", \"../../DeepWannier/data/dpgen.merged/iter.000000/02.fp/data.000\", \"../../DeepWannier/data/dpgen.merged/iter.000001/02.fp/data.001\", \"../../DeepWannier/data/dpgen.merged/iter.000002/02.fp/data.002\", \"../../DeepWannier/data/dpgen.merged/iter.000005/02.fp/data.001\", \"../../DeepWannier/data/dpgen.merged/iter.000008/02.fp/data.004\", \"../../DeepWannier/data/dpgen.merged/iter.000009/02.fp/data.005\", \"../../DeepWannier/data/dpgen.merged/iter.000010/02.fp/data.006\"], \"set_prefix\": \"set\", \"batch_size\": \"auto\", \"auto_prob\": \"prob_sys_size\", \"sys_probs\": null}, \"numb_steps\": 1000000, \"seed\": 1, \"disp_file\": \"lcurve.out\", \"disp_freq\": 100, \"save_freq\": 1000, \"validation_data\": null, \"save_ckpt\": \"model.ckpt\", \"disp_training\": true, \"time_training\": true, \"profiling\": false, \"profiling_file\": \"timeline.json\", \"enable_profiler\": false, \"tensorboard\": false, \"tensorboard_log_dir\": \"log\", \"tensorboard_freq\": 1}}" + string_val: "{\"model\":{\"type_map\":[\"O\",\"H\"],\"descriptor\":{\"type\":\"hybrid\",\"list\":[{\"type\":\"se_e2_a\",\"sel\":[20,40],\"rcut_smth\":0.5,\"rcut\":4.0,\"neuron\":[2,4,8],\"resnet_dt\":false,\"axis_neuron\":8,\"seed\":1,\"activation_function\":\"tanh\",\"type_one_side\":false,\"precision\":\"default\",\"trainable\":true,\"exclude_types\":[],\"set_davg_zero\":false}]},\"fitting_net\":{\"neuron\":[2,2,2],\"resnet_dt\":true,\"seed\":1,\"type\":\"ener\",\"numb_fparam\":0,\"numb_aparam\":0,\"activation_function\":\"tanh\",\"precision\":\"default\",\"trainable\":true,\"rcond\":0.001,\"atom_ener\":[],\"use_aparam_as_mask\":false},\"modifier\":{\"type\":\"dipole_charge\",\"model_name\":\"lrdipole.pb\",\"model_charge_map\":[-8],\"sys_charge_map\":[6,1],\"ewald_h\":1.0,\"ewald_beta\":0.4},\"data_stat_nbatch\":10,\"data_stat_protect\":0.01,\"data_bias_nsample\":10},\"learning_rate\":{\"type\":\"exp\",\"decay_steps\":5000,\"start_lr\":0.001,\"stop_lr\":3.51e-08,\"scale_by_worker\":\"linear\"},\"loss\":{\"type\":\"ener\",\"start_pref_e\":0.02,\"limit_pref_e\":1,\"start_pref_f\":1000,\"limit_pref_f\":1,\"start_pref_v\":0,\"limit_pref_v\":0,\"start_pref_ae\":0.0,\"limit_pref_ae\":0.0,\"start_pref_pf\":0.0,\"limit_pref_pf\":0.0,\"enable_atom_ener_coeff\":false},\"training\":{\"training_data\":{\"systems\":[\"../../4-deep-wannier/data/dpgen.lowd.merged/iter.000016/02.fp/data.012\",\"../../4-deep-wannier/data/dpgen.lowd.merged/iter.000016/02.fp/data.016\",\"../../4-deep-wannier/data/dpgen.lowd.merged/iter.000016/02.fp/data.020\",\"../../4-deep-wannier/data/dpgen.merged/init/00.liq/state.0300.1.00e00.00/vasp/data\",\"../../4-deep-wannier/data/dpgen.merged/init/00.liq/state.0400.1.00e04.00/vasp/data\",\"../../4-deep-wannier/data/dpgen.merged/init/01.liq/state.0300.1.00e00.00/vasp/data\",\"../../4-deep-wannier/data/dpgen.merged/init/01.liq/state.0400.1.00e04.00/vasp/data\",\"../../4-deep-wannier/data/dpgen.merged/iter.000000/02.fp/data.000\",\"../../4-deep-wannier/data/dpgen.merged/iter.000001/02.fp/data.001\",\"../../4-deep-wannier/data/dpgen.merged/iter.000002/02.fp/data.002\",\"../../4-deep-wannier/data/dpgen.merged/iter.000005/02.fp/data.001\",\"../../4-deep-wannier/data/dpgen.merged/iter.000008/02.fp/data.004\",\"../../4-deep-wannier/data/dpgen.merged/iter.000009/02.fp/data.005\",\"../../4-deep-wannier/data/dpgen.merged/iter.000010/02.fp/data.006\"],\"set_prefix\":\"set\",\"batch_size\":\"auto\",\"auto_prob\":\"prob_sys_size\",\"sys_probs\":null},\"numb_steps\":1,\"seed\":1,\"disp_file\":\"lcurve.out\",\"disp_freq\":1,\"save_freq\":1,\"validation_data\":null,\"save_ckpt\":\"model.ckpt\",\"disp_training\":true,\"time_training\":true,\"profiling\":false,\"profiling_file\":\"timeline.json\",\"enable_profiler\":false,\"tensorboard\":false,\"tensorboard_log_dir\":\"log\",\"tensorboard_freq\":1}}" 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"dipole_charge/descrpt_attr/t_std/read" - op: "Identity" - input: "dipole_charge/descrpt_attr/t_std" - attr { - key: "T" - value { - type: DT_DOUBLE - } - } - attr { - key: "_class" - value { - list { - s: "loc:@dipole_charge/descrpt_attr/t_std" - } - } - } -} node { name: "dipole_charge/strided_slice_1/stack" op: "Const" - input: "^dipole_charge/descrpt_attr/original_sel" - input: "^dipole_charge/descrpt_attr/sel" attr { key: "dtype" value { @@ -635,8 +451,6 @@ node { node { name: "dipole_charge/strided_slice_1/stack_1" op: "Const" - input: "^dipole_charge/descrpt_attr/original_sel" - input: "^dipole_charge/descrpt_attr/sel" attr { key: "dtype" value { @@ -661,8 +475,6 @@ node { node { name: "dipole_charge/strided_slice_1/stack_2" op: "Const" - input: "^dipole_charge/descrpt_attr/original_sel" - input: "^dipole_charge/descrpt_attr/sel" attr { key: "dtype" value { @@ -737,8 +549,6 @@ node { node { name: "dipole_charge/mul/y" op: "Const" - input: "^dipole_charge/descrpt_attr/original_sel" - input: "^dipole_charge/descrpt_attr/sel" attr { key: "dtype" value { @@ -772,8 +582,6 @@ node { node { name: "dipole_charge/Reshape/shape/0" op: "Const" - input: "^dipole_charge/descrpt_attr/original_sel" - input: "^dipole_charge/descrpt_attr/sel" attr { key: "dtype" value { @@ -834,48 +642,6 @@ node { } } } -node { - name: "dipole_charge/Reshape_1/shape" - op: "Const" - attr { - key: "dtype" - value { - type: DT_INT32 - } - } - attr { - key: "value" - value { - tensor { - dtype: DT_INT32 - tensor_shape { - dim { - size: 2 - } - } - tensor_content: "\377\377\377\377\t\000\000\000" - } - } - } -} -node { - name: "dipole_charge/Reshape_1" - op: "Reshape" - input: "dipole_charge/t_box" - input: "dipole_charge/Reshape_1/shape" - attr { - key: "T" - value { - type: DT_DOUBLE - } - } - attr { - key: "Tshape" - value { - type: DT_INT32 - } - } -} node { name: "dipole_charge/strided_slice_2/stack" op: "Const" @@ -999,7 +765,7 @@ node { } } node { - name: "dipole_charge/Reshape_2/shape/0" + name: "dipole_charge/Reshape_1/shape/0" op: "Const" attr { key: "dtype" @@ -1020,9 +786,9 @@ node { } } node { - name: "dipole_charge/Reshape_2/shape" + name: "dipole_charge/Reshape_1/shape" op: "Pack" - input: "dipole_charge/Reshape_2/shape/0" + input: "dipole_charge/Reshape_1/shape/0" input: "dipole_charge/strided_slice_2" attr { key: "N" @@ -1044,10 +810,10 @@ node { } } node { - name: "dipole_charge/Reshape_2" + name: "dipole_charge/Reshape_1" op: "Reshape" input: "dipole_charge/t_type" - input: "dipole_charge/Reshape_2/shape" + input: "dipole_charge/Reshape_1/shape" attr { key: "T" value { @@ -1062,60 +828,73 @@ node { } } node { - name: "dipole_charge/ProdEnvMatA" - op: "ProdEnvMatA" - input: "dipole_charge/Reshape" - input: "dipole_charge/Reshape_2" - input: "dipole_charge/t_natoms" - input: "dipole_charge/Reshape_1" - input: "dipole_charge/t_mesh" - input: "dipole_charge/descrpt_attr/t_avg/read" - input: "dipole_charge/descrpt_attr/t_std/read" + name: "dipole_charge/descrpt_attr/rcut" + op: "Const" attr { - key: "T" + key: "dtype" value { type: DT_DOUBLE } } attr { - key: "rcut_a" + key: "value" value { - f: -1.0 + tensor { + dtype: DT_DOUBLE + tensor_shape { + } + double_val: 6.0 + } } } +} +node { + name: "dipole_charge/descrpt_attr/ntypes" + op: "Const" attr { - key: "rcut_r" + key: "dtype" value { - f: 6.0 + type: DT_INT32 } } attr { - key: "rcut_r_smth" + key: "value" value { - f: 0.5 + tensor { + dtype: DT_INT32 + tensor_shape { + } + int_val: 2 + } } } +} +node { + name: "dipole_charge/descrpt_attr/sel" + op: "Const" attr { - key: "sel_a" + key: "dtype" value { - list { - i: 46 - i: 92 - } + type: DT_INT32 } } attr { - key: "sel_r" + key: "value" value { - list { - i: 0 - i: 0 + tensor { + dtype: DT_INT32 + tensor_shape { + dim { + size: 2 + } + } + tensor_content: ".\000\000\000\\\000\000\000" } } } } node { - name: "dipole_charge/Reshape_3/shape" + name: "dipole_charge/descrpt_attr/original_sel" op: "Const" attr { key: "dtype" @@ -1133,76 +912,108 @@ node { size: 2 } } - tensor_content: "\377\377\377\377(\002\000\000" + tensor_content: ".\000\000\000\\\000\000\000" } } } } node { - name: "dipole_charge/Reshape_3" - op: "Reshape" - input: "dipole_charge/ProdEnvMatA" - input: "dipole_charge/Reshape_3/shape" + name: "dipole_charge/descrpt_attr/t_avg" + op: "Const" attr { - key: "T" + key: "dtype" value { type: DT_DOUBLE } } attr { - key: "Tshape" + key: "value" value { - type: DT_INT32 + tensor { + dtype: DT_DOUBLE + tensor_shape { + dim { + size: 2 + } + dim { + size: 552 + } + } + tensor_content: 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+ } } } } node { - name: "dipole_charge/o_rmat" + name: "dipole_charge/descrpt_attr/t_avg/read" op: "Identity" - input: "dipole_charge/Reshape_3" + input: "dipole_charge/descrpt_attr/t_avg" attr { key: "T" value { type: DT_DOUBLE } } + attr { + key: "_class" + value { + list { + s: "loc:@dipole_charge/descrpt_attr/t_avg" + } + } + } } node { - name: "dipole_charge/o_rmat_deriv" - op: "Identity" - input: "dipole_charge/ProdEnvMatA:1" + name: "dipole_charge/descrpt_attr/t_std" + op: "Const" attr { - key: "T" + key: "dtype" value { type: DT_DOUBLE } } + attr { + key: "value" + value { + tensor { + dtype: DT_DOUBLE + tensor_shape { + dim { + size: 2 + } + dim { + size: 552 + } + } + tensor_content: 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+ } + } + } } node { - name: "dipole_charge/o_rij" + name: "dipole_charge/descrpt_attr/t_std/read" op: "Identity" - input: "dipole_charge/ProdEnvMatA:2" + input: "dipole_charge/descrpt_attr/t_std" attr { key: "T" value { type: DT_DOUBLE } } -} -node { - name: "dipole_charge/o_nlist" - op: "Identity" - input: "dipole_charge/ProdEnvMatA:3" attr { - key: "T" + key: "_class" value { - type: DT_INT32 + list { + s: "loc:@dipole_charge/descrpt_attr/t_std" + } } } } node { - name: "dipole_charge/strided_slice_3/stack" + name: "dipole_charge/strided_slice_4/stack" op: "Const" + input: "^dipole_charge/descrpt_attr/original_sel" + input: "^dipole_charge/descrpt_attr/sel" attr { key: "dtype" value { @@ -1219,14 +1030,16 @@ node { size: 1 } } - int_val: 0 + int_val: 1 } } } } node { - name: "dipole_charge/strided_slice_3/stack_1" + name: "dipole_charge/strided_slice_4/stack_1" op: "Const" + input: "^dipole_charge/descrpt_attr/original_sel" + input: "^dipole_charge/descrpt_attr/sel" attr { key: "dtype" value { @@ -1243,14 +1056,16 @@ node { size: 1 } } - int_val: 1 + int_val: 2 } } } } node { - name: "dipole_charge/strided_slice_3/stack_2" + name: "dipole_charge/strided_slice_4/stack_2" op: "Const" + input: "^dipole_charge/descrpt_attr/original_sel" + input: "^dipole_charge/descrpt_attr/sel" attr { key: "dtype" value { @@ -1273,12 +1088,12 @@ node { } } node { - name: "dipole_charge/strided_slice_3" + name: "dipole_charge/strided_slice_4" op: "StridedSlice" input: "dipole_charge/t_natoms" - input: "dipole_charge/strided_slice_3/stack" - input: "dipole_charge/strided_slice_3/stack_1" - input: "dipole_charge/strided_slice_3/stack_2" + input: "dipole_charge/strided_slice_4/stack" + input: "dipole_charge/strided_slice_4/stack_1" + input: "dipole_charge/strided_slice_4/stack_2" attr { key: "Index" value { @@ -1323,8 +1138,10 @@ node { } } node { - name: "dipole_charge/mul_1/x" + name: "dipole_charge/mul_1/y" op: "Const" + input: "^dipole_charge/descrpt_attr/original_sel" + input: "^dipole_charge/descrpt_attr/sel" attr { key: "dtype" value { @@ -1338,7 +1155,7 @@ node { dtype: DT_INT32 tensor_shape { } - int_val: 552 + int_val: 3 } } } @@ -1346,8 +1163,8 @@ node { node { name: "dipole_charge/mul_1" op: "Mul" - input: "dipole_charge/mul_1/x" - input: "dipole_charge/strided_slice_3" + input: "dipole_charge/strided_slice_4" + input: "dipole_charge/mul_1/y" attr { key: "T" value { @@ -1356,8 +1173,10 @@ node { } } node { - name: "dipole_charge/Reshape_4/shape/0" + name: "dipole_charge/Reshape_2/shape/0" op: "Const" + input: "^dipole_charge/descrpt_attr/original_sel" + input: "^dipole_charge/descrpt_attr/sel" attr { key: "dtype" value { @@ -1377,9 +1196,9 @@ node { } } node { - name: "dipole_charge/Reshape_4/shape" + name: "dipole_charge/Reshape_2/shape" op: "Pack" - input: "dipole_charge/Reshape_4/shape/0" + input: "dipole_charge/Reshape_2/shape/0" input: "dipole_charge/mul_1" attr { key: "N" @@ -1401,10 +1220,10 @@ node { } } node { - name: "dipole_charge/Reshape_4" + name: "dipole_charge/Reshape_2" op: "Reshape" - input: "dipole_charge/o_rmat" - input: "dipole_charge/Reshape_4/shape" + input: "dipole_charge/Reshape" + input: "dipole_charge/Reshape_2/shape" attr { key: "T" value { @@ -1419,7 +1238,49 @@ node { } } node { - name: "dipole_charge/strided_slice_4/stack" + name: "dipole_charge/Reshape_3/shape" + op: "Const" + attr { + key: "dtype" + value { + type: DT_INT32 + } + } + attr { + key: "value" + value { + tensor { + dtype: DT_INT32 + tensor_shape { + dim { + size: 2 + } + } + tensor_content: "\377\377\377\377\t\000\000\000" + } + } + } +} +node { + name: "dipole_charge/Reshape_3" + op: "Reshape" + input: "dipole_charge/t_box" + input: "dipole_charge/Reshape_3/shape" + attr { + key: "T" + value { + type: DT_DOUBLE + } + } + attr { + key: "Tshape" + value { + type: DT_INT32 + } + } +} +node { + name: "dipole_charge/strided_slice_5/stack" op: "Const" attr { key: "dtype" @@ -1437,13 +1298,13 @@ node { size: 1 } } - int_val: 2 + int_val: 1 } } } } node { - name: "dipole_charge/strided_slice_4/stack_1" + name: "dipole_charge/strided_slice_5/stack_1" op: "Const" attr { key: "dtype" @@ -1461,13 +1322,13 @@ node { size: 1 } } - int_val: 3 + int_val: 2 } } } } node { - name: "dipole_charge/strided_slice_4/stack_2" + name: "dipole_charge/strided_slice_5/stack_2" op: "Const" attr { key: "dtype" @@ -1491,12 +1352,12 @@ node { } } node { - name: "dipole_charge/strided_slice_4" + name: "dipole_charge/strided_slice_5" op: "StridedSlice" input: "dipole_charge/t_natoms" - input: "dipole_charge/strided_slice_4/stack" - input: "dipole_charge/strided_slice_4/stack_1" - input: "dipole_charge/strided_slice_4/stack_2" + input: "dipole_charge/strided_slice_5/stack" + input: "dipole_charge/strided_slice_5/stack_1" + input: "dipole_charge/strided_slice_5/stack_2" attr { key: "Index" value { @@ -1541,7 +1402,7 @@ node { } } node { - name: "dipole_charge/mul_2/y" + name: "dipole_charge/Reshape_4/shape/0" op: "Const" attr { key: "dtype" @@ -1556,108 +1417,103 @@ node { dtype: DT_INT32 tensor_shape { } - int_val: 552 + int_val: -1 } } } } node { - name: "dipole_charge/mul_2" - op: "Mul" - input: "dipole_charge/strided_slice_4" - input: "dipole_charge/mul_2/y" + name: "dipole_charge/Reshape_4/shape" + op: "Pack" + input: "dipole_charge/Reshape_4/shape/0" + input: "dipole_charge/strided_slice_5" attr { - key: "T" + key: "N" value { - type: DT_INT32 + i: 2 } } -} -node { - name: "dipole_charge/Slice/begin" - op: "Const" attr { - key: "dtype" + key: "T" value { type: DT_INT32 } } attr { - key: "value" + key: "axis" value { - tensor { - dtype: DT_INT32 - tensor_shape { - dim { - size: 2 - } - } - tensor_content: "\000\000\000\000\000\000\000\000" - } + i: 0 } } } node { - name: "dipole_charge/Slice/size/0" - op: "Const" + name: "dipole_charge/Reshape_4" + op: "Reshape" + input: "dipole_charge/Reshape_1" + input: "dipole_charge/Reshape_4/shape" attr { - key: "dtype" + key: "T" value { type: DT_INT32 } } attr { - key: "value" + key: "Tshape" value { - tensor { - dtype: DT_INT32 - tensor_shape { - } - int_val: -1 - } + type: DT_INT32 } } } node { - name: "dipole_charge/Slice/size" - op: "Pack" - input: "dipole_charge/Slice/size/0" - input: "dipole_charge/mul_2" + name: "dipole_charge/ProdEnvMatA" + op: "ProdEnvMatA" + input: "dipole_charge/Reshape_2" + input: "dipole_charge/Reshape_4" + input: "dipole_charge/t_natoms" + input: "dipole_charge/Reshape_3" + input: "dipole_charge/t_mesh" + input: "dipole_charge/descrpt_attr/t_avg/read" + input: "dipole_charge/descrpt_attr/t_std/read" attr { - key: "N" + key: "T" value { - i: 2 + type: DT_DOUBLE } } attr { - key: "T" + key: "rcut_a" value { - type: DT_INT32 + f: -1.0 } } attr { - key: "axis" + key: "rcut_r" value { - i: 0 + f: 6.0 } } -} -node { - name: "dipole_charge/Slice" - op: "Slice" - input: "dipole_charge/Reshape_4" - input: "dipole_charge/Slice/begin" - input: "dipole_charge/Slice/size" attr { - key: "Index" + key: "rcut_r_smth" value { - type: DT_INT32 + f: 0.5 } } attr { - key: "T" + key: "sel_a" value { - type: DT_DOUBLE + list { + i: 46 + i: 92 + } + } + } + attr { + key: "sel_r" + value { + list { + i: 0 + i: 0 + } } } } @@ -1688,7 +1544,7 @@ node { node { name: "dipole_charge/Reshape_5" op: "Reshape" - input: "dipole_charge/Slice" + input: "dipole_charge/ProdEnvMatA" input: "dipole_charge/Reshape_5/shape" attr { key: "T" @@ -1704,65 +1560,31 @@ node { } } node { - name: "dipole_charge/filter_type_0/Slice/begin" - op: "Const" - attr { - key: "dtype" - value { - type: DT_INT32 - } - } + name: "dipole_charge/o_rmat" + op: "Identity" + input: "dipole_charge/Reshape_5" attr { - key: "value" + key: "T" value { - tensor { - dtype: DT_INT32 - tensor_shape { - dim { - size: 2 - } - } - tensor_content: "\000\000\000\000\000\000\000\000" - } + type: DT_DOUBLE } } } node { - name: "dipole_charge/filter_type_0/Slice/size" - op: "Const" - attr { - key: "dtype" - value { - type: DT_INT32 - } - } + name: "dipole_charge/o_rmat_deriv" + op: "Identity" + input: "dipole_charge/ProdEnvMatA:1" attr { - key: "value" + key: "T" value { - tensor { - dtype: DT_INT32 - tensor_shape { - dim { - size: 2 - } - } - tensor_content: "\377\377\377\377\270\000\000\000" - } + type: DT_DOUBLE } } } node { - name: "dipole_charge/filter_type_0/Slice" - op: "Slice" - input: "dipole_charge/Reshape_5" - input: "dipole_charge/filter_type_0/Slice/begin" - input: "dipole_charge/filter_type_0/Slice/size" - attr { - key: "Index" - value { - type: DT_INT32 - } - } + name: "dipole_charge/o_rij" + op: "Identity" + input: "dipole_charge/ProdEnvMatA:2" attr { key: "T" value { @@ -1771,24 +1593,18 @@ node { } } node { - name: "dipole_charge/filter_type_0/Shape" - op: "Shape" - input: "dipole_charge/filter_type_0/Slice" + name: "dipole_charge/o_nlist" + op: "Identity" + input: "dipole_charge/ProdEnvMatA:3" attr { key: "T" - value { - type: DT_DOUBLE - } - } - attr { - key: "out_type" value { type: DT_INT32 } } } node { - name: "dipole_charge/filter_type_0/strided_slice/stack" + name: "dipole_charge/strided_slice_6/stack" op: "Const" attr { key: "dtype" @@ -1812,7 +1628,7 @@ node { } } node { - name: "dipole_charge/filter_type_0/strided_slice/stack_1" + name: "dipole_charge/strided_slice_6/stack_1" op: "Const" attr { key: "dtype" @@ -1836,7 +1652,7 @@ node { } } node { - name: "dipole_charge/filter_type_0/strided_slice/stack_2" + name: "dipole_charge/strided_slice_6/stack_2" op: "Const" attr { key: "dtype" @@ -1860,12 +1676,12 @@ node { } } node { - name: "dipole_charge/filter_type_0/strided_slice" + name: "dipole_charge/strided_slice_6" op: "StridedSlice" - input: "dipole_charge/filter_type_0/Shape" - input: "dipole_charge/filter_type_0/strided_slice/stack" - input: "dipole_charge/filter_type_0/strided_slice/stack_1" - input: "dipole_charge/filter_type_0/strided_slice/stack_2" + input: "dipole_charge/t_natoms" + input: "dipole_charge/strided_slice_6/stack" + input: "dipole_charge/strided_slice_6/stack_1" + input: "dipole_charge/strided_slice_6/stack_2" attr { key: "Index" value { @@ -1910,7 +1726,7 @@ node { } } node { - name: "dipole_charge/filter_type_0/Reshape/shape" + name: "dipole_charge/Reshape_6/shape/0" op: "Const" attr { key: "dtype" @@ -1924,20 +1740,63 @@ node { tensor { dtype: DT_INT32 tensor_shape { - dim { - size: 2 - } } - tensor_content: "\377\377\377\377\004\000\000\000" + int_val: -1 } } } } node { - name: "dipole_charge/filter_type_0/Reshape" + name: "dipole_charge/Reshape_6/shape/2" + op: "Const" + attr { + key: "dtype" + value { + type: DT_INT32 + } + } + attr { + key: "value" + value { + tensor { + dtype: DT_INT32 + tensor_shape { + } + int_val: 552 + } + } + } +} +node { + name: "dipole_charge/Reshape_6/shape" + op: "Pack" + input: "dipole_charge/Reshape_6/shape/0" + input: "dipole_charge/strided_slice_6" + input: "dipole_charge/Reshape_6/shape/2" + attr { + key: "N" + value { + i: 3 + } + } + attr { + key: "T" + value { + type: DT_INT32 + } + } + attr { + key: "axis" + value { + i: 0 + } + } +} +node { + name: "dipole_charge/Reshape_6" op: "Reshape" - input: "dipole_charge/filter_type_0/Slice" - input: "dipole_charge/filter_type_0/Reshape/shape" + input: "dipole_charge/o_rmat" + input: "dipole_charge/Reshape_6/shape" attr { key: "T" value { @@ -1952,7 +1811,7 @@ node { } } node { - name: "dipole_charge/filter_type_0/Slice_1/begin" + name: "dipole_charge/strided_slice_7/stack" op: "Const" attr { key: "dtype" @@ -1967,16 +1826,16 @@ node { dtype: DT_INT32 tensor_shape { dim { - size: 2 + size: 1 } } - tensor_content: "\000\000\000\000\000\000\000\000" + int_val: 2 } } } } node { - name: "dipole_charge/filter_type_0/Slice_1/size" + name: "dipole_charge/strided_slice_7/stack_1" op: "Const" attr { key: "dtype" @@ -1991,35 +1850,16 @@ node { dtype: DT_INT32 tensor_shape { dim { - size: 2 + size: 1 } } - tensor_content: "\377\377\377\377\001\000\000\000" + int_val: 3 } } } } node { - name: "dipole_charge/filter_type_0/Slice_1" - op: "Slice" - input: "dipole_charge/filter_type_0/Reshape" - input: "dipole_charge/filter_type_0/Slice_1/begin" - input: "dipole_charge/filter_type_0/Slice_1/size" - attr { - key: "Index" - value { - type: DT_INT32 - } - } - attr { - key: "T" - value { - type: DT_DOUBLE - } - } -} -node { - name: "dipole_charge/filter_type_0/Reshape_1/shape" + name: "dipole_charge/strided_slice_7/stack_2" op: "Const" attr { key: "dtype" @@ -2034,167 +1874,167 @@ node { dtype: DT_INT32 tensor_shape { dim { - size: 2 + size: 1 } } - tensor_content: "\377\377\377\377\001\000\000\000" + int_val: 1 } } } } node { - name: "dipole_charge/filter_type_0/Reshape_1" - op: "Reshape" - input: "dipole_charge/filter_type_0/Slice_1" - input: "dipole_charge/filter_type_0/Reshape_1/shape" + name: "dipole_charge/strided_slice_7" + op: "StridedSlice" + input: "dipole_charge/t_natoms" + input: "dipole_charge/strided_slice_7/stack" + input: "dipole_charge/strided_slice_7/stack_1" + input: "dipole_charge/strided_slice_7/stack_2" attr { - key: "T" + key: "Index" value { - type: DT_DOUBLE + type: DT_INT32 } } attr { - key: "Tshape" + key: "T" value { type: DT_INT32 } } -} -node { - name: "dipole_charge/filter_type_0/matrix_1_0" - op: "Const" attr { - key: "dtype" + key: "begin_mask" value { - type: DT_DOUBLE + i: 0 } } attr { - key: "value" + key: "ellipsis_mask" value { - tensor { - dtype: DT_DOUBLE - tensor_shape { - dim { - size: 1 - } - dim { - size: 25 - } - } - tensor_content: "\3351\276\025M\243\325\277)\356T\256\024<\370?;\350\031\336\334\237\346\277K\324\251 \032\352\002\300\204\t\213\227F\324\353\277\261^uG\177b\347\277&~\367\004\177o\363?V\256\t\367+\r\322\277\311\241\224\237\201\265\001\300R%\347\236\303\351\225\277\370\227\274Q\272\013\335\277\301W\363\337\013\201\014@\354\344\227\247\016\240\n\300F\014\230\025\251J\361?\225\343\223[\336I\350?\000\326\252\374K\241\306?\277\321)\312\322,\375?\270\335Rq\251\314\340?\032\026\251k\352(\342\277\340\3244\\\177\343\000\300\037\324\251oh\317\330?\223\305+\313\226\306\341\277\201\203\030\317\003\262\343\277uBU\312v\365\377\277\332\210\006\330o\344\363\277" - } + i: 0 } } -} -node { - name: "dipole_charge/filter_type_0/matrix_1_0/read" - op: "Identity" - input: "dipole_charge/filter_type_0/matrix_1_0" attr { - key: "T" + key: "end_mask" value { - type: DT_DOUBLE + i: 0 } } attr { - key: "_class" + key: "new_axis_mask" value { - list { - s: "loc:@dipole_charge/filter_type_0/matrix_1_0" - } + i: 0 + } + } + attr { + key: "shrink_axis_mask" + value { + i: 1 } } } node { - name: "dipole_charge/filter_type_0/bias_1_0" + name: "dipole_charge/Slice/begin" op: "Const" attr { key: "dtype" value { - type: DT_DOUBLE + type: DT_INT32 } } attr { key: "value" value { tensor { - dtype: DT_DOUBLE + dtype: DT_INT32 tensor_shape { dim { - size: 25 + size: 3 } } - tensor_content: "\216+\034C\322\366\334\277\327\373\257`\230A\340?\37173p\243\342\001\300\214\225\243ez\223\351\277\314i(\342m\273\t\300\331=$x\007\032\354\277\371\033\014\221^/\345\277n\361\026ky\352\003@J\372\337t\r\362\360\2772W\321q9%\312?\261\204\244(\352\214\336\277u\370\277\350\257\022\365?\273\317\313v\200\221\367\277\343\324\034|\236\220\332\277h\256\234\033\376q\373?\177\312B\005d;\251\277L\273\365\336N4\375\277\320\240(\372\257\030\345?A\024\023_\033t\r\300\005\017F\216+W\t@\225\362\310eB\022\377?~\220\371\034\035\207\270?\365h*\023b\240\000\300\027\274<\322(\\\333\277\326\031\031\352O\270\000\300" + tensor_content: "\000\000\000\000\000\000\000\000\000\000\000\000" } } } } node { - name: "dipole_charge/filter_type_0/bias_1_0/read" - op: "Identity" - input: "dipole_charge/filter_type_0/bias_1_0" + name: "dipole_charge/Slice/size/0" + op: "Const" attr { - key: "T" + key: "dtype" value { - type: DT_DOUBLE + type: DT_INT32 } } attr { - key: "_class" + key: "value" value { - list { - s: "loc:@dipole_charge/filter_type_0/bias_1_0" + tensor { + dtype: DT_INT32 + tensor_shape { + } + int_val: -1 } } } } node { - name: "dipole_charge/filter_type_0/MatMul" - op: "MatMul" - input: "dipole_charge/filter_type_0/Reshape_1" - input: "dipole_charge/filter_type_0/matrix_1_0/read" + name: "dipole_charge/Slice/size/2" + op: "Const" attr { - key: "T" + key: "dtype" value { - type: DT_DOUBLE + type: DT_INT32 } } attr { - key: "transpose_a" + key: "value" value { - b: false + tensor { + dtype: DT_INT32 + tensor_shape { + } + int_val: -1 + } } } +} +node { + name: "dipole_charge/Slice/size" + op: "Pack" + input: "dipole_charge/Slice/size/0" + input: "dipole_charge/strided_slice_7" + input: "dipole_charge/Slice/size/2" attr { - key: "transpose_b" + key: "N" value { - b: false + i: 3 } } -} -node { - name: "dipole_charge/filter_type_0/BiasAdd" - op: "BiasAdd" - input: "dipole_charge/filter_type_0/MatMul" - input: "dipole_charge/filter_type_0/bias_1_0/read" attr { key: "T" value { - type: DT_DOUBLE + type: DT_INT32 } } attr { - key: "data_format" + key: "axis" value { - s: "NHWC" + i: 0 } } } node { - name: "dipole_charge/filter_type_0/Tanh" - op: "Tanh" - input: "dipole_charge/filter_type_0/BiasAdd" + name: "dipole_charge/Slice" + op: "Slice" + input: "dipole_charge/Reshape_6" + input: "dipole_charge/Slice/begin" + input: "dipole_charge/Slice/size" + attr { + key: "Index" + value { + type: DT_INT32 + } + } attr { key: "T" value { @@ -2203,7 +2043,7 @@ node { } } node { - name: "dipole_charge/filter_type_0/Reshape_2/shape" + name: "dipole_charge/Reshape_7/shape" op: "Const" attr { key: "dtype" @@ -2221,16 +2061,16 @@ node { size: 2 } } - tensor_content: "\377\377\377\377\031\000\000\000" + tensor_content: "\377\377\377\377(\002\000\000" } } } } node { - name: "dipole_charge/filter_type_0/Reshape_2" + name: "dipole_charge/Reshape_7" op: "Reshape" - input: "dipole_charge/filter_type_0/Tanh" - input: "dipole_charge/filter_type_0/Reshape_2/shape" + input: "dipole_charge/Slice" + input: "dipole_charge/Reshape_7/shape" attr { key: "T" value { @@ -2245,99 +2085,76 @@ node { } } node { - name: "dipole_charge/filter_type_0/matrix_2_0" + name: "dipole_charge/filter_type_0/Slice/begin" op: "Const" attr { key: "dtype" value { - type: DT_DOUBLE + type: DT_INT32 } } attr { key: "value" value { tensor { - dtype: DT_DOUBLE + dtype: DT_INT32 tensor_shape { dim { - size: 25 - } - dim { - size: 50 + size: 2 } } - tensor_content: 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"dipole_charge/filter_type_0/bias_3_0" + input: "dipole_charge/filter_type_0/bias_1_0" attr { key: "T" value { @@ -2570,16 +2525,16 @@ node { key: "_class" value { list { - s: "loc:@dipole_charge/filter_type_0/bias_3_0" + s: "loc:@dipole_charge/filter_type_0/bias_1_0" } } } } node { - name: "dipole_charge/filter_type_0/MatMul_2" + name: "dipole_charge/filter_type_0/MatMul" op: "MatMul" - input: "dipole_charge/filter_type_0/add" - input: "dipole_charge/filter_type_0/matrix_3_0/read" + input: "dipole_charge/filter_type_0/Reshape_1" + input: "dipole_charge/filter_type_0/matrix_1_0/read" attr { key: "T" value { @@ -2600,10 +2555,10 @@ node { } } node { - name: "dipole_charge/filter_type_0/BiasAdd_2" + name: "dipole_charge/filter_type_0/BiasAdd" op: "BiasAdd" - input: "dipole_charge/filter_type_0/MatMul_2" - input: "dipole_charge/filter_type_0/bias_3_0/read" + input: "dipole_charge/filter_type_0/MatMul" + input: "dipole_charge/filter_type_0/bias_1_0/read" attr { key: "T" value { @@ -2618,9 +2573,9 @@ node { } } node { - name: "dipole_charge/filter_type_0/Tanh_2" + name: "dipole_charge/filter_type_0/Tanh" op: "Tanh" - input: "dipole_charge/filter_type_0/BiasAdd_2" + input: "dipole_charge/filter_type_0/BiasAdd" attr { key: "T" value { @@ -2629,7 +2584,7 @@ node { } } node { - name: "dipole_charge/filter_type_0/Reshape_4/shape" + name: "dipole_charge/filter_type_0/Reshape_2/shape" op: "Const" attr { key: "dtype" @@ -2647,16 +2602,16 @@ node { size: 2 } } - tensor_content: "\377\377\377\377d\000\000\000" + tensor_content: "\377\377\377\377\002\000\000\000" } } } } node { - name: "dipole_charge/filter_type_0/Reshape_4" + name: "dipole_charge/filter_type_0/Reshape_2" op: "Reshape" - input: "dipole_charge/filter_type_0/Tanh_2" - input: "dipole_charge/filter_type_0/Reshape_4/shape" + input: "dipole_charge/filter_type_0/Tanh" + input: "dipole_charge/filter_type_0/Reshape_2/shape" attr { key: "T" value { @@ -2671,7 +2626,7 @@ node { } } node { - name: "dipole_charge/filter_type_0/concat_1/axis" + name: "dipole_charge/filter_type_0/concat/axis" op: "Const" attr { key: "dtype" @@ -2692,11 +2647,11 @@ node { } } node { - name: "dipole_charge/filter_type_0/concat_1" + name: "dipole_charge/filter_type_0/concat" op: "ConcatV2" - input: "dipole_charge/filter_type_0/add" - input: "dipole_charge/filter_type_0/add" - input: "dipole_charge/filter_type_0/concat_1/axis" + input: "dipole_charge/filter_type_0/Reshape_1" + input: "dipole_charge/filter_type_0/Reshape_1" + input: "dipole_charge/filter_type_0/concat/axis" attr { key: "N" value { @@ -2717,10 +2672,10 @@ node { } } node { - name: "dipole_charge/filter_type_0/add_1" + name: "dipole_charge/filter_type_0/add" op: "AddV2" - input: "dipole_charge/filter_type_0/concat_1" - input: "dipole_charge/filter_type_0/Reshape_4" + input: "dipole_charge/filter_type_0/concat" + input: "dipole_charge/filter_type_0/Reshape_2" attr { key: "T" value { @@ -2729,34 +2684,36 @@ node { } } node { - name: 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+ list { + s: "loc:@dipole_charge/filter_type_0/matrix_2_0" + } } } } node { - name: "dipole_charge/filter_type_0/Reshape_6/shape/1" + name: "dipole_charge/filter_type_0/bias_2_0" op: "Const" attr { key: "dtype" value { - type: DT_INT32 + type: DT_DOUBLE } } attr { key: "value" value { tensor { - dtype: DT_INT32 + dtype: DT_DOUBLE tensor_shape { + dim { + size: 4 + } } - int_val: 46 + tensor_content: "\2548\377\005\223g\352\277\327.\013Nd\374\364\277\342O\271@mt\264?\264\341\266\375\010\244\345?" } } } } node { - name: "dipole_charge/filter_type_0/Reshape_6/shape/2" - op: "Const" + name: "dipole_charge/filter_type_0/bias_2_0/read" + op: "Identity" + input: "dipole_charge/filter_type_0/bias_2_0" attr { - key: "dtype" + key: "T" value { - type: DT_INT32 + type: DT_DOUBLE } } attr { - key: "value" + key: "_class" value { - tensor { - dtype: DT_INT32 - tensor_shape { - } - int_val: 4 + list { + s: "loc:@dipole_charge/filter_type_0/bias_2_0" } } } } node { - name: 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attr { - key: "Tshape" + key: "data_format" value { - type: DT_INT32 + s: "NHWC" } } } node { - name: "dipole_charge/filter_type_0/MatMul_3" - op: "BatchMatMulV2" - input: "dipole_charge/filter_type_0/Reshape_6" - input: "dipole_charge/filter_type_0/Reshape_5" + name: "dipole_charge/filter_type_0/Tanh_1" + op: "Tanh" + input: "dipole_charge/filter_type_0/BiasAdd_1" attr { key: "T" value { type: DT_DOUBLE } } - attr { - key: "adj_x" - value { - b: true - } - } - attr { - key: "adj_y" - value { - b: false - } - } } node { - name: "dipole_charge/filter_type_0/Slice_2/begin" + name: "dipole_charge/filter_type_0/Reshape_3/shape" op: "Const" attr { key: "dtype" @@ -2898,13 +2844,31 @@ node { size: 2 } } - tensor_content: "\000\000\000\000\270\000\000\000" + tensor_content: "\377\377\377\377\004\000\000\000" } } } } node { - name: "dipole_charge/filter_type_0/Slice_2/size" + name: "dipole_charge/filter_type_0/Reshape_3" + op: "Reshape" + input: "dipole_charge/filter_type_0/Tanh_1" + input: 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} } } node { - name: "dipole_charge/filter_type_0/bias_3_1" + name: "dipole_charge/filter_type_0/bias_1_1" op: "Const" attr { key: "dtype" @@ -3724,18 +3737,18 @@ node { dtype: DT_DOUBLE tensor_shape { dim { - size: 100 + size: 2 } } - tensor_content: "7I\\v\350\301\355\277_\274\'\376\224\023\001\300 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"dipole_charge/filter_type_0/Reshape_8" + input: "dipole_charge/filter_type_0/matrix_1_1/read" attr { key: "T" value { @@ -3776,10 +3789,10 @@ node { } } node { - name: "dipole_charge/filter_type_0/BiasAdd_5" + name: "dipole_charge/filter_type_0/BiasAdd_3" op: "BiasAdd" - input: "dipole_charge/filter_type_0/MatMul_6" - input: "dipole_charge/filter_type_0/bias_3_1/read" + input: "dipole_charge/filter_type_0/MatMul_4" + input: "dipole_charge/filter_type_0/bias_1_1/read" attr { key: "T" value { @@ -3794,9 +3807,9 @@ node { } } node { - name: "dipole_charge/filter_type_0/Tanh_5" + name: "dipole_charge/filter_type_0/Tanh_3" op: "Tanh" - input: "dipole_charge/filter_type_0/BiasAdd_5" + input: "dipole_charge/filter_type_0/BiasAdd_3" attr { key: "T" value { @@ -3805,7 +3818,7 @@ node { } } node { - name: "dipole_charge/filter_type_0/Reshape_11/shape" + name: "dipole_charge/filter_type_0/Reshape_9/shape" op: "Const" attr { key: "dtype" @@ -3823,16 +3836,16 @@ node { size: 2 } } - tensor_content: "\377\377\377\377d\000\000\000" + tensor_content: "\377\377\377\377\002\000\000\000" } } } } node { - name: "dipole_charge/filter_type_0/Reshape_11" + name: "dipole_charge/filter_type_0/Reshape_9" op: "Reshape" - input: "dipole_charge/filter_type_0/Tanh_5" - input: "dipole_charge/filter_type_0/Reshape_11/shape" + input: "dipole_charge/filter_type_0/Tanh_3" + input: "dipole_charge/filter_type_0/Reshape_9/shape" attr { key: "T" value { @@ -3870,8 +3883,8 @@ node { node { name: "dipole_charge/filter_type_0/concat_3" op: "ConcatV2" - input: "dipole_charge/filter_type_0/add_2" - input: "dipole_charge/filter_type_0/add_2" + input: "dipole_charge/filter_type_0/Reshape_8" + input: "dipole_charge/filter_type_0/Reshape_8" input: "dipole_charge/filter_type_0/concat_3/axis" attr { key: "N" @@ -3896,7 +3909,7 @@ node { name: "dipole_charge/filter_type_0/add_3" op: "AddV2" input: "dipole_charge/filter_type_0/concat_3" - input: "dipole_charge/filter_type_0/Reshape_11" + input: "dipole_charge/filter_type_0/Reshape_9" attr { key: "T" value { @@ -3905,34 +3918,36 @@ node { } } node { - name: "dipole_charge/filter_type_0/Reshape_12/shape" + name: "dipole_charge/filter_type_0/matrix_2_1" op: "Const" attr { key: "dtype" value { - type: DT_INT32 + type: DT_DOUBLE } } attr { key: "value" value { tensor { - dtype: DT_INT32 + dtype: DT_DOUBLE tensor_shape { dim { - size: 3 + size: 2 + } + dim { + size: 4 } } - tensor_content: "\377\377\377\377\\\000\000\000d\000\000\000" + tensor_content: "\341\225\r\254\223?\273?L \005iI\254\303\277\302\2250zX$\307\277\315\336\347Gi\364\321?\270(\265\333\336\367\310\277Q&VwQ\235\255\277\221\314U\256\270\203\256\277\215\365M\244\312\001\317\277" } } } } node { - name: "dipole_charge/filter_type_0/Reshape_12" - op: "Reshape" - input: "dipole_charge/filter_type_0/add_3" - input: "dipole_charge/filter_type_0/Reshape_12/shape" + name: "dipole_charge/filter_type_0/matrix_2_1/read" + op: "Identity" + input: "dipole_charge/filter_type_0/matrix_2_1" attr { key: "T" value { @@ -3940,74 +3955,556 @@ node { } } attr { - key: "Tshape" + key: "_class" value { - type: DT_INT32 + list { + s: "loc:@dipole_charge/filter_type_0/matrix_2_1" + } } } } node { - name: "dipole_charge/filter_type_0/Reshape_13/shape/1" + name: "dipole_charge/filter_type_0/bias_2_1" op: "Const" attr { key: "dtype" value { - type: DT_INT32 + type: DT_DOUBLE } } attr { key: "value" value { tensor { - dtype: DT_INT32 + dtype: DT_DOUBLE tensor_shape { + dim { + size: 4 + } } - int_val: 92 + tensor_content: "\n\006\374\014\364\242\366\277\252\267\362\256[a\345?\347\201\033Eu \330\277E\"\261\311r3\306\277" } } } } node { - name: "dipole_charge/filter_type_0/Reshape_13/shape/2" - op: "Const" + name: "dipole_charge/filter_type_0/bias_2_1/read" + op: "Identity" + input: "dipole_charge/filter_type_0/bias_2_1" attr { - key: "dtype" + key: "T" value { - type: DT_INT32 + type: DT_DOUBLE } } attr { - key: "value" + key: "_class" value { - tensor { - dtype: DT_INT32 - tensor_shape { - } - int_val: 4 + list { + s: "loc:@dipole_charge/filter_type_0/bias_2_1" } } } } node { - name: "dipole_charge/filter_type_0/Reshape_13/shape" - op: "Pack" - input: "dipole_charge/filter_type_0/strided_slice_1" - input: "dipole_charge/filter_type_0/Reshape_13/shape/1" - input: "dipole_charge/filter_type_0/Reshape_13/shape/2" + name: "dipole_charge/filter_type_0/MatMul_5" + op: "MatMul" + input: "dipole_charge/filter_type_0/add_3" + input: "dipole_charge/filter_type_0/matrix_2_1/read" attr { - key: "N" + key: "T" value { - i: 3 + type: DT_DOUBLE } } attr { - key: "T" + key: "transpose_a" value { - type: DT_INT32 + b: false } } attr { - key: "axis" + key: "transpose_b" + value { + b: false + } + } +} +node { + name: "dipole_charge/filter_type_0/BiasAdd_4" + op: "BiasAdd" + input: "dipole_charge/filter_type_0/MatMul_5" + input: "dipole_charge/filter_type_0/bias_2_1/read" + attr { + key: "T" + value { + type: DT_DOUBLE + } + } + attr { + key: "data_format" + value { + s: "NHWC" + } + } +} +node { + name: "dipole_charge/filter_type_0/Tanh_4" + op: "Tanh" + input: "dipole_charge/filter_type_0/BiasAdd_4" + attr { + key: "T" + value { + type: DT_DOUBLE + } + } +} +node { + name: "dipole_charge/filter_type_0/Reshape_10/shape" + op: "Const" + attr { + key: "dtype" + value { + type: DT_INT32 + } + } + attr { + key: "value" + value { + tensor { + dtype: DT_INT32 + tensor_shape { + dim { + size: 2 + } + } + tensor_content: "\377\377\377\377\004\000\000\000" + } + } + } +} +node { + name: "dipole_charge/filter_type_0/Reshape_10" + op: "Reshape" + input: "dipole_charge/filter_type_0/Tanh_4" + input: "dipole_charge/filter_type_0/Reshape_10/shape" + attr { + key: "T" + value { + type: DT_DOUBLE + } + } + attr { + key: "Tshape" + value { + type: DT_INT32 + } + } +} +node { + name: "dipole_charge/filter_type_0/concat_4/axis" + op: "Const" + attr { + key: "dtype" + value { + type: DT_INT32 + } + } + attr { + key: "value" + value { + tensor { + dtype: DT_INT32 + tensor_shape { + } + int_val: 1 + } + } + } +} +node { + name: "dipole_charge/filter_type_0/concat_4" + op: "ConcatV2" + input: "dipole_charge/filter_type_0/add_3" + input: "dipole_charge/filter_type_0/add_3" + input: "dipole_charge/filter_type_0/concat_4/axis" + attr { + key: "N" + value { + i: 2 + } + } + attr { + key: "T" + value { + type: DT_DOUBLE + } + } + attr { + key: "Tidx" + value { + type: DT_INT32 + } + } +} +node { + name: "dipole_charge/filter_type_0/add_4" + op: "AddV2" + input: "dipole_charge/filter_type_0/concat_4" + input: "dipole_charge/filter_type_0/Reshape_10" + attr { + key: "T" + value { + type: DT_DOUBLE + } + } +} +node { + name: "dipole_charge/filter_type_0/matrix_3_1" + op: "Const" + attr { + key: "dtype" + value { + type: DT_DOUBLE + } + } + attr { + key: "value" + value { + tensor { + dtype: DT_DOUBLE + tensor_shape { + dim { + size: 4 + } + dim { + size: 8 + } + } + tensor_content: "09(L\342\020\301\277\266\217&\241\273\273\330\277~\254\312k\025g\307?\361\037\346Qa\262\300\277\204\245\242\006*\021\320\277J4>Z\246\314\340\277b\357\221p\366W\307?yv\034o\203\036\326\277I\350\351d\253\334\306\277\010\252\334;\204+\300?h[L\010\372\201\304\277\357\273\351\003K\216\303?q\366\301\340\266N\324\277\316W0\n\330\250\314?\022\316\014\306\031\247\304?\245l\262\312J\\r\277\320\216\373\\\352f\322?qFn\234\233X\273\277h\352\304x\014h\327\277\014\375&7\246\007\331?`\247\2113_\017\271?\340\370\377\253/$\324\277\270\263XR,\261\270\277\371!\236\024\240\272\316\277\n$jl\330\355\245\277\301\357$n\330\342\241?\315\371\035\365\013W\304?1k\326\201\361,\312?\276;\024Zbz\300\277\255^\335\274a\263\220\277\002\004\355\264\376N\325?u\204\\\323\300W\270?" + } + } + } +} +node { + name: "dipole_charge/filter_type_0/matrix_3_1/read" + op: "Identity" + input: "dipole_charge/filter_type_0/matrix_3_1" + attr { + key: "T" + value { + type: DT_DOUBLE + } + } + attr { + key: "_class" + value { + list { + s: "loc:@dipole_charge/filter_type_0/matrix_3_1" + } + } + } +} +node { + name: "dipole_charge/filter_type_0/bias_3_1" + op: "Const" + attr { + key: "dtype" + value { + type: DT_DOUBLE + } + } + attr { + key: "value" + value { + tensor { + dtype: DT_DOUBLE + tensor_shape { + dim { + size: 8 + } + } + tensor_content: "YX&A\372\032\375\277\371 d:\364\316\372\277\334\203\344p\320\351\201\277\016\237\257 \212U\341\277\355F\037\253\022_\273\277\035LBf=\341\365\277\327y\345\251\324\344\377\277\265\220r<\252\373\350?" + } + } + } +} +node { + name: "dipole_charge/filter_type_0/bias_3_1/read" + op: "Identity" + input: "dipole_charge/filter_type_0/bias_3_1" + attr { + key: "T" + value { + type: DT_DOUBLE + } + } + attr { + key: "_class" + value { + list { + s: "loc:@dipole_charge/filter_type_0/bias_3_1" + } + } + } +} +node { + name: "dipole_charge/filter_type_0/MatMul_6" + op: "MatMul" + input: "dipole_charge/filter_type_0/add_4" + input: "dipole_charge/filter_type_0/matrix_3_1/read" + attr { + key: "T" + value { + type: DT_DOUBLE + } + } + attr { + key: "transpose_a" + value { + b: false + } + } + attr { + key: "transpose_b" + value { + b: false + } + } +} +node { + name: "dipole_charge/filter_type_0/BiasAdd_5" + op: "BiasAdd" + input: "dipole_charge/filter_type_0/MatMul_6" + input: "dipole_charge/filter_type_0/bias_3_1/read" + attr { + key: "T" + value { + type: DT_DOUBLE + } + } + attr { + key: "data_format" + value { + s: "NHWC" + } + } +} +node { + name: "dipole_charge/filter_type_0/Tanh_5" + op: "Tanh" + input: "dipole_charge/filter_type_0/BiasAdd_5" + attr { + key: "T" + value { + type: DT_DOUBLE + } + } +} +node { + name: "dipole_charge/filter_type_0/Reshape_11/shape" + op: "Const" + attr { + key: "dtype" + value { + type: DT_INT32 + } + } + attr { + key: "value" + value { + tensor { + dtype: DT_INT32 + tensor_shape { + dim { + size: 2 + } + } + tensor_content: "\377\377\377\377\010\000\000\000" + } + } + } +} +node { + name: "dipole_charge/filter_type_0/Reshape_11" + op: "Reshape" + input: "dipole_charge/filter_type_0/Tanh_5" + input: "dipole_charge/filter_type_0/Reshape_11/shape" + attr { + key: "T" + value { + type: DT_DOUBLE + } + } + attr { + key: "Tshape" + value { + type: DT_INT32 + } + } +} +node { + name: "dipole_charge/filter_type_0/concat_5/axis" + op: "Const" + attr { + key: "dtype" + value { + type: DT_INT32 + } + } + attr { + key: "value" + value { + tensor { + dtype: DT_INT32 + tensor_shape { + } + int_val: 1 + } + } + } +} +node { + name: "dipole_charge/filter_type_0/concat_5" + op: "ConcatV2" + input: "dipole_charge/filter_type_0/add_4" + input: "dipole_charge/filter_type_0/add_4" + input: "dipole_charge/filter_type_0/concat_5/axis" + attr { + key: "N" + value { + i: 2 + } + } + attr { + key: "T" + value { + type: DT_DOUBLE + } + } + attr { + key: "Tidx" + value { + type: DT_INT32 + } + } +} +node { + name: "dipole_charge/filter_type_0/add_5" + op: "AddV2" + input: "dipole_charge/filter_type_0/concat_5" + input: "dipole_charge/filter_type_0/Reshape_11" + attr { + key: "T" + value { + type: DT_DOUBLE + } + } +} +node { + name: "dipole_charge/filter_type_0/Reshape_12/shape" + op: "Const" + attr { + key: "dtype" + value { + type: DT_INT32 + } + } + attr { + key: "value" + value { + tensor { + dtype: DT_INT32 + tensor_shape { + dim { + size: 3 + } + } + tensor_content: "\377\377\377\377\\\000\000\000\010\000\000\000" + } + } + } +} +node { + name: "dipole_charge/filter_type_0/Reshape_12" + op: "Reshape" + input: "dipole_charge/filter_type_0/add_5" + input: "dipole_charge/filter_type_0/Reshape_12/shape" + attr { + key: "T" + value { + type: DT_DOUBLE + } + } + attr { + key: "Tshape" + value { + type: DT_INT32 + } + } +} +node { + name: "dipole_charge/filter_type_0/Reshape_13/shape/1" + op: "Const" + attr { + key: "dtype" + value { + type: DT_INT32 + } + } + attr { + key: "value" + value { + tensor { + dtype: DT_INT32 + tensor_shape { + } + int_val: 92 + } + } + } +} +node { + name: "dipole_charge/filter_type_0/Reshape_13/shape/2" + op: "Const" + attr { + key: "dtype" + value { + type: DT_INT32 + } + } + attr { + key: "value" + value { + tensor { + dtype: DT_INT32 + tensor_shape { + } + int_val: 4 + } + } + } +} +node { + name: "dipole_charge/filter_type_0/Reshape_13/shape" + op: "Pack" + input: "dipole_charge/filter_type_0/strided_slice_1" + input: "dipole_charge/filter_type_0/Reshape_13/shape/1" + input: "dipole_charge/filter_type_0/Reshape_13/shape/2" + attr { + key: "N" + value { + i: 3 + } + } + attr { + key: "T" + value { + type: DT_INT32 + } + } + attr { + key: "axis" value { i: 0 } @@ -4325,7 +4822,7 @@ node { size: 2 } } - tensor_content: "\377\377\377\377 \003\000\000" + tensor_content: "\377\377\377\377@\000\000\000" } } } @@ -4349,9 +4846,9 @@ node { } } node { - name: "dipole_charge/Shape_1" + name: "dipole_charge/Shape_2" op: "Shape" - input: "dipole_charge/Reshape_4" + input: "dipole_charge/Reshape_6" attr { key: "T" value { @@ -4366,7 +4863,7 @@ node { } } node { - name: "dipole_charge/strided_slice_7/stack" + name: "dipole_charge/strided_slice_10/stack" op: "Const" attr { key: "dtype" @@ -4390,7 +4887,7 @@ node { } } node { - name: "dipole_charge/strided_slice_7/stack_1" + name: "dipole_charge/strided_slice_10/stack_1" op: "Const" attr { key: "dtype" @@ -4414,7 +4911,7 @@ node { } } node { - name: "dipole_charge/strided_slice_7/stack_2" + name: "dipole_charge/strided_slice_10/stack_2" op: "Const" attr { key: "dtype" @@ -4438,12 +4935,12 @@ node { } } node { - name: "dipole_charge/strided_slice_7" + name: "dipole_charge/strided_slice_10" op: "StridedSlice" - input: "dipole_charge/Shape_1" - input: "dipole_charge/strided_slice_7/stack" - input: "dipole_charge/strided_slice_7/stack_1" - input: "dipole_charge/strided_slice_7/stack_2" + input: "dipole_charge/Shape_2" + input: "dipole_charge/strided_slice_10/stack" + input: "dipole_charge/strided_slice_10/stack_1" + input: "dipole_charge/strided_slice_10/stack_2" attr { key: "Index" value { @@ -4488,7 +4985,7 @@ node { } } node { - name: "dipole_charge/strided_slice_8/stack" + name: "dipole_charge/strided_slice_11/stack" op: "Const" attr { key: "dtype" @@ -4512,7 +5009,7 @@ node { } } node { - name: "dipole_charge/strided_slice_8/stack_1" + name: "dipole_charge/strided_slice_11/stack_1" op: "Const" attr { key: "dtype" @@ -4536,7 +5033,7 @@ node { } } node { - name: "dipole_charge/strided_slice_8/stack_2" + name: "dipole_charge/strided_slice_11/stack_2" op: "Const" attr { key: "dtype" @@ -4560,12 +5057,12 @@ node { } } node { - name: "dipole_charge/strided_slice_8" + name: "dipole_charge/strided_slice_11" op: "StridedSlice" input: "dipole_charge/t_natoms" - input: "dipole_charge/strided_slice_8/stack" - input: "dipole_charge/strided_slice_8/stack_1" - input: "dipole_charge/strided_slice_8/stack_2" + input: "dipole_charge/strided_slice_11/stack" + input: "dipole_charge/strided_slice_11/stack_1" + input: "dipole_charge/strided_slice_11/stack_2" attr { key: "Index" value { @@ -4610,7 +5107,7 @@ node { } } node { - name: "dipole_charge/mul_3/y" + name: "dipole_charge/Reshape_9/shape/2" op: "Const" attr { key: "dtype" @@ -4625,32 +5122,21 @@ node { dtype: DT_INT32 tensor_shape { } - int_val: 800 + int_val: 64 } } } } node { - name: "dipole_charge/mul_3" - op: "Mul" - input: "dipole_charge/strided_slice_8" - input: "dipole_charge/mul_3/y" - attr { - key: "T" - value { - type: DT_INT32 - } - } -} -node { - name: "dipole_charge/Reshape_7/shape" + name: "dipole_charge/Reshape_9/shape" op: "Pack" - input: "dipole_charge/strided_slice_7" - input: "dipole_charge/mul_3" + input: "dipole_charge/strided_slice_10" + input: "dipole_charge/strided_slice_11" + input: "dipole_charge/Reshape_9/shape/2" attr { key: "N" value { - i: 2 + i: 3 } } attr { @@ -4667,10 +5153,10 @@ node { } } node { - name: "dipole_charge/Reshape_7" + name: "dipole_charge/Reshape_9" op: "Reshape" input: "dipole_charge/filter_type_0/Reshape_14" - input: "dipole_charge/Reshape_7/shape" + input: "dipole_charge/Reshape_9/shape" attr { key: "T" value { @@ -4685,9 +5171,9 @@ node { } } node { - name: "dipole_charge/Shape_2" + name: "dipole_charge/Shape_3" op: "Shape" - input: "dipole_charge/Reshape_4" + input: "dipole_charge/Reshape_6" attr { key: "T" value { @@ -4702,7 +5188,7 @@ node { } } node { - name: "dipole_charge/strided_slice_9/stack" + name: "dipole_charge/strided_slice_12/stack" op: "Const" attr { key: "dtype" @@ -4726,7 +5212,7 @@ node { } } node { - name: "dipole_charge/strided_slice_9/stack_1" + name: "dipole_charge/strided_slice_12/stack_1" op: "Const" attr { key: "dtype" @@ -4750,7 +5236,7 @@ node { } } node { - name: "dipole_charge/strided_slice_9/stack_2" + name: "dipole_charge/strided_slice_12/stack_2" op: "Const" attr { key: "dtype" @@ -4774,12 +5260,12 @@ node { } } node { - name: "dipole_charge/strided_slice_9" + name: "dipole_charge/strided_slice_12" op: "StridedSlice" - input: "dipole_charge/Shape_2" - input: "dipole_charge/strided_slice_9/stack" - input: "dipole_charge/strided_slice_9/stack_1" - input: "dipole_charge/strided_slice_9/stack_2" + input: "dipole_charge/Shape_3" + input: "dipole_charge/strided_slice_12/stack" + input: "dipole_charge/strided_slice_12/stack_1" + input: "dipole_charge/strided_slice_12/stack_2" attr { key: "Index" value { @@ -4824,7 +5310,7 @@ node { } } node { - name: "dipole_charge/strided_slice_10/stack" + name: "dipole_charge/strided_slice_13/stack" op: "Const" attr { key: "dtype" @@ -4848,7 +5334,7 @@ node { } } node { - name: "dipole_charge/strided_slice_10/stack_1" + name: "dipole_charge/strided_slice_13/stack_1" op: "Const" attr { key: "dtype" @@ -4872,7 +5358,7 @@ node { } } node { - name: "dipole_charge/strided_slice_10/stack_2" + name: "dipole_charge/strided_slice_13/stack_2" op: "Const" attr { key: "dtype" @@ -4896,12 +5382,12 @@ node { } } node { - name: "dipole_charge/strided_slice_10" + name: "dipole_charge/strided_slice_13" op: "StridedSlice" input: "dipole_charge/t_natoms" - input: "dipole_charge/strided_slice_10/stack" - input: "dipole_charge/strided_slice_10/stack_1" - input: "dipole_charge/strided_slice_10/stack_2" + input: "dipole_charge/strided_slice_13/stack" + input: "dipole_charge/strided_slice_13/stack_1" + input: "dipole_charge/strided_slice_13/stack_2" attr { key: "Index" value { @@ -4946,7 +5432,7 @@ node { } } node { - name: "dipole_charge/mul_4/y" + name: "dipole_charge/Reshape_10/shape/2" op: "Const" attr { key: "dtype" @@ -4961,65 +5447,21 @@ node { dtype: DT_INT32 tensor_shape { } - int_val: 100 + int_val: 24 } } } } node { - name: "dipole_charge/mul_4" - op: "Mul" - input: "dipole_charge/strided_slice_10" - input: "dipole_charge/mul_4/y" - attr { - key: "T" - value { - type: DT_INT32 - } - } -} -node { - name: "dipole_charge/mul_5/y" - op: "Const" - attr { - key: "dtype" - value { - type: DT_INT32 - } - } - attr { - key: "value" - value { - tensor { - dtype: DT_INT32 - tensor_shape { - } - int_val: 3 - } - } - } -} -node { - name: "dipole_charge/mul_5" - op: "Mul" - input: "dipole_charge/mul_4" - input: "dipole_charge/mul_5/y" - attr { - key: "T" - value { - type: DT_INT32 - } - } -} -node { - name: "dipole_charge/Reshape_8/shape" + name: "dipole_charge/Reshape_10/shape" op: "Pack" - input: "dipole_charge/strided_slice_9" - input: "dipole_charge/mul_5" + input: "dipole_charge/strided_slice_12" + input: "dipole_charge/strided_slice_13" + input: "dipole_charge/Reshape_10/shape/2" attr { key: "N" value { - i: 2 + i: 3 } } attr { @@ -5036,10 +5478,10 @@ node { } } node { - name: "dipole_charge/Reshape_8" + name: "dipole_charge/Reshape_10" op: "Reshape" input: "dipole_charge/filter_type_0/transpose" - input: "dipole_charge/Reshape_8/shape" + input: "dipole_charge/Reshape_10/shape" attr { key: "T" value { @@ -5054,7 +5496,7 @@ node { } } node { - name: "dipole_charge/strided_slice_11/stack" + name: "dipole_charge/strided_slice_14/stack" op: "Const" attr { key: "dtype" @@ -5078,7 +5520,7 @@ node { } } node { - name: "dipole_charge/strided_slice_11/stack_1" + name: "dipole_charge/strided_slice_14/stack_1" op: "Const" attr { key: "dtype" @@ -5102,7 +5544,7 @@ node { } } node { - name: "dipole_charge/strided_slice_11/stack_2" + name: "dipole_charge/strided_slice_14/stack_2" op: "Const" attr { key: "dtype" @@ -5126,12 +5568,12 @@ node { } } node { - name: "dipole_charge/strided_slice_11" + name: "dipole_charge/strided_slice_14" op: "StridedSlice" input: "dipole_charge/t_natoms" - input: "dipole_charge/strided_slice_11/stack" - input: "dipole_charge/strided_slice_11/stack_1" - input: "dipole_charge/strided_slice_11/stack_2" + input: "dipole_charge/strided_slice_14/stack" + input: "dipole_charge/strided_slice_14/stack_1" + input: "dipole_charge/strided_slice_14/stack_2" attr { key: "Index" value { @@ -5200,7 +5642,7 @@ node { name: "dipole_charge/add_1" op: "AddV2" input: "dipole_charge/add_1/x" - input: "dipole_charge/strided_slice_11" + input: "dipole_charge/strided_slice_14" attr { key: "T" value { @@ -5209,40 +5651,7 @@ node { } } node { - name: "dipole_charge/mul_6/y" - op: "Const" - attr { - key: "dtype" - value { - type: DT_INT32 - } - } - attr { - key: "value" - value { - tensor { - dtype: DT_INT32 - tensor_shape { - } - int_val: 552 - } - } - } -} -node { - name: "dipole_charge/mul_6" - op: "Mul" - input: "dipole_charge/add_1" - input: "dipole_charge/mul_6/y" - attr { - key: "T" - value { - type: DT_INT32 - } - } -} -node { - name: "dipole_charge/strided_slice_12/stack" + name: "dipole_charge/strided_slice_15/stack" op: "Const" attr { key: "dtype" @@ -5266,7 +5675,7 @@ node { } } node { - name: "dipole_charge/strided_slice_12/stack_1" + name: "dipole_charge/strided_slice_15/stack_1" op: "Const" attr { key: "dtype" @@ -5290,7 +5699,7 @@ node { } } node { - name: "dipole_charge/strided_slice_12/stack_2" + name: "dipole_charge/strided_slice_15/stack_2" op: "Const" attr { key: "dtype" @@ -5314,12 +5723,12 @@ node { } } node { - name: "dipole_charge/strided_slice_12" + name: "dipole_charge/strided_slice_15" op: "StridedSlice" input: "dipole_charge/t_natoms" - input: "dipole_charge/strided_slice_12/stack" - input: "dipole_charge/strided_slice_12/stack_1" - input: "dipole_charge/strided_slice_12/stack_2" + input: "dipole_charge/strided_slice_15/stack" + input: "dipole_charge/strided_slice_15/stack_1" + input: "dipole_charge/strided_slice_15/stack_2" attr { key: "Index" value { @@ -5364,7 +5773,7 @@ node { } } node { - name: "dipole_charge/mul_7/y" + name: "dipole_charge/Slice_1/begin/0" op: "Const" attr { key: "dtype" @@ -5379,25 +5788,13 @@ node { dtype: DT_INT32 tensor_shape { } - int_val: 552 + int_val: 0 } } } } node { - name: "dipole_charge/mul_7" - op: "Mul" - input: "dipole_charge/strided_slice_12" - input: "dipole_charge/mul_7/y" - attr { - key: "T" - value { - type: DT_INT32 - } - } -} -node { - name: "dipole_charge/Slice_1/begin/0" + name: "dipole_charge/Slice_1/begin/2" op: "Const" attr { key: "dtype" @@ -5421,11 +5818,12 @@ node { name: "dipole_charge/Slice_1/begin" op: "Pack" input: "dipole_charge/Slice_1/begin/0" - input: "dipole_charge/mul_6" + input: "dipole_charge/add_1" + input: "dipole_charge/Slice_1/begin/2" attr { key: "N" value { - i: 2 + i: 3 } } attr { @@ -5462,15 +5860,37 @@ node { } } } +node { + name: "dipole_charge/Slice_1/size/2" + op: "Const" + attr { + key: "dtype" + value { + type: DT_INT32 + } + } + attr { + key: "value" + value { + tensor { + dtype: DT_INT32 + tensor_shape { + } + int_val: -1 + } + } + } +} node { name: "dipole_charge/Slice_1/size" op: "Pack" input: "dipole_charge/Slice_1/size/0" - input: "dipole_charge/mul_7" + input: "dipole_charge/strided_slice_15" + input: "dipole_charge/Slice_1/size/2" attr { key: "N" value { - i: 2 + i: 3 } } attr { @@ -5489,7 +5909,7 @@ node { node { name: "dipole_charge/Slice_1" op: "Slice" - input: "dipole_charge/Reshape_4" + input: "dipole_charge/Reshape_6" input: "dipole_charge/Slice_1/begin" input: "dipole_charge/Slice_1/size" attr { @@ -5506,7 +5926,7 @@ node { } } node { - name: "dipole_charge/Reshape_9/shape" + name: "dipole_charge/Reshape_11/shape" op: "Const" attr { key: "dtype" @@ -5530,10 +5950,10 @@ node { } } node { - name: "dipole_charge/Reshape_9" + name: "dipole_charge/Reshape_11" op: "Reshape" input: "dipole_charge/Slice_1" - input: "dipole_charge/Reshape_9/shape" + input: "dipole_charge/Reshape_11/shape" attr { key: "T" value { @@ -5598,7 +6018,7 @@ node { node { name: "dipole_charge/filter_type_1/Slice" op: "Slice" - input: "dipole_charge/Reshape_9" + input: "dipole_charge/Reshape_11" input: "dipole_charge/filter_type_1/Slice/begin" input: "dipole_charge/filter_type_1/Slice/size" attr { @@ -5923,10 +6343,10 @@ node { size: 1 } dim { - size: 25 + size: 2 } } - tensor_content: "Gb\277^\332\216\266\277\n\320\257\005\346\260\324?C\020\2260\005\236\251?UU\205A\262Y\201\277\243!\313@\010\241\314\277\354\035\301\237\214\242\310\2771\354\0036L\367\242?\n<\215]q\026\326?\233\365T\2178^\270\277\2024\235\274R\205\274?\260\252\022+&`\321?\026O`!z\030\302?)\326<\225\306\343\313?*\214\251\222\330\330\306\277=\304\360\n\006\242\303?\033\247\307E\204\234\323\277\334\273\276\376b\364\260?\367\207\375\000}\330\306\277\363\033o\217\2039\305?j\n\024V\272\315\304\277\362\226\"\322\344\254\301?\320Q\314\325\251\265\271\277\371\222O\023!\"\257?\230\376Y[\276\264\245?\375m\000e\362v\341?" + tensor_content: "\350\370n>-\232\320\277\201\326\373\321\322t\356?" } } } @@ -5966,10 +6386,10 @@ node { dtype: DT_DOUBLE tensor_shape { dim { - size: 25 + size: 2 } } - tensor_content: "\230\325\2407\274\331\330\277\244\326\356\274\247\035\337\277\212v\302\216\311R\353?\303\307\031\024\344\303\271?eD\207\371\257R\326\277\347\351\"\224\251\333\303\277\033L\225^\242@\374\277W^\017\270\265\376\337?\212\323\202Y\341\205\242\277\373\325A\225\257^\375\277\252u\032\276\244\225\331?\"C\331\215\264\230\265?\225\252V\304\206\036\357?RL\007\'\267%\337?e7\230\033\025<\351?\236\325\365D6K\342?\351\351\361\013\244\277\320\277\002\n\364t\312\252\332\277t\253\257~\317)\362\277\007U1\306\343V\343\277F\010 Y\247\037\272\277\202\006_\276\230\330\340?\222D\236\0366\304\346\277!\302\363\356\005\225\342?\245\330\250\301CL\335?" + tensor_content: "\230\325\2407\274\331\330\277\244\326\356\274\247\035\337\277" } } } @@ -6065,7 +6485,7 @@ node { size: 2 } } - tensor_content: "\377\377\377\377\031\000\000\000" + tensor_content: "\377\377\377\377\002\000\000\000" } } } @@ -6088,6 +6508,64 @@ node { } } } +node { + name: "dipole_charge/filter_type_1/concat/axis" + op: "Const" + attr { + key: "dtype" + value { + type: DT_INT32 + } + } + attr { + key: "value" + value { + tensor { + dtype: DT_INT32 + tensor_shape { + } + int_val: 1 + } + } + } +} +node { + name: "dipole_charge/filter_type_1/concat" + op: "ConcatV2" + input: "dipole_charge/filter_type_1/Reshape_1" + input: "dipole_charge/filter_type_1/Reshape_1" + input: "dipole_charge/filter_type_1/concat/axis" + attr { + key: "N" + value { + i: 2 + } + } + attr { + key: "T" + value { + type: DT_DOUBLE + } + } + attr { + key: "Tidx" + value { + type: DT_INT32 + } + } +} +node { + name: "dipole_charge/filter_type_1/add" + op: "AddV2" + input: "dipole_charge/filter_type_1/concat" + input: "dipole_charge/filter_type_1/Reshape_2" + attr { + key: "T" + value { + type: DT_DOUBLE + } + } +} node { name: "dipole_charge/filter_type_1/matrix_2_0" op: "Const" @@ -6104,13 +6582,13 @@ node { dtype: DT_DOUBLE tensor_shape { dim { - size: 25 + size: 2 } dim { - size: 50 + size: 4 } } - tensor_content: 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+ tensor_content: "\371\203E\253\275z\345\277\255\327\375\306\231a\320?\n\t\237\336\202t\336\277\233fh\246V:\327\277" } } } @@ -6180,7 +6658,7 @@ node { node { name: "dipole_charge/filter_type_1/MatMul_1" op: "MatMul" - input: "dipole_charge/filter_type_1/Reshape_2" + input: "dipole_charge/filter_type_1/add" input: "dipole_charge/filter_type_1/matrix_2_0/read" attr { key: "T" @@ -6249,7 +6727,7 @@ node { size: 2 } } - tensor_content: "\377\377\377\3772\000\000\000" + tensor_content: "\377\377\377\377\004\000\000\000" } } } @@ -6273,7 +6751,7 @@ node { } } node { - name: "dipole_charge/filter_type_1/concat/axis" + name: "dipole_charge/filter_type_1/concat_1/axis" op: "Const" attr { key: "dtype" @@ -6294,11 +6772,11 @@ node { } } node { - name: "dipole_charge/filter_type_1/concat" + name: "dipole_charge/filter_type_1/concat_1" op: "ConcatV2" - input: "dipole_charge/filter_type_1/Reshape_2" - input: "dipole_charge/filter_type_1/Reshape_2" - input: "dipole_charge/filter_type_1/concat/axis" + input: "dipole_charge/filter_type_1/add" + input: "dipole_charge/filter_type_1/add" + input: "dipole_charge/filter_type_1/concat_1/axis" attr { key: "N" value { @@ -6319,9 +6797,9 @@ node { } } node { - name: "dipole_charge/filter_type_1/add" + name: "dipole_charge/filter_type_1/add_1" op: "AddV2" - input: "dipole_charge/filter_type_1/concat" + input: "dipole_charge/filter_type_1/concat_1" input: "dipole_charge/filter_type_1/Reshape_3" attr { key: "T" @@ -6346,13 +6824,13 @@ node { dtype: DT_DOUBLE tensor_shape { dim { - size: 50 + size: 4 } dim { - size: 100 + size: 8 } } - tensor_content: "\337)2\335\033\360\245?\007\2679\333\252)r?\301\326JU\366\031\247\277\315\224x\354|:\251?\007\222V\252\227\222\242\277K\372\353S\252{\240\277\304\320\017\245\253i\306?[(\244%\360\342\231?-]\377\372\346\204\256\277\347\361@\tt\307\262?\315\227S\365h;\201?\3737e\243n\337\224\277\026\336v~ \033~?\211-\332-\202\232\301\277 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"o\21596\327\262\300\277\0212]P\361\300\327?uJ\335\352`Z\306\277\243\217]\036\342\375\260?\264\363x\017$A\252?\222\306\356\236`\003\304\277\031\016\305J?o\341\277\336\025\233?\347=\320\277\241\242/^\004\276\321\277\357\025f\0211\243\320?\\np\243\215\222\277\277\315\3464`t\214\321\277\020\335\313\253\302\337\304\277L\274{f7\350\236?D\333\245\250\0171\205\277\345\r?r\353\263\307\277\344\2263\367k\267\265?\275\006\314\035\234\250\325?\314\221.T\371\361\335\277\375\233\366\2347.\342?\241iutx\231\265?c\007Va\274\240\264\277u\353\000\020-$\302\277-=\2635\364\307\330\277\004DXL1\340\320\277\310\220^\340s\253\323\277\334\321K->\216\240\277[y0\026\375#\325\277\"\222\215H\247|\321?\244\303?\204ek\327\277\274\ri\325\021\352\301\277*\3620\370{q\340\277" } } } @@ -7568,10 +8104,10 @@ node { dtype: DT_DOUBLE tensor_shape { dim { - size: 100 + size: 8 } } - tensor_content: 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+ tensor_content: "j\t\326\260\252\034\353\277\203WH\271\262\341\320?8\322\330i\317S\352\277\013\355\317\261g\236\345?\030\205\221\251E{\350\277L\031\213]\014Os\277z\226\224\226\320\272\323?\210\244\214\302o\223\356?" } } } @@ -7598,7 +8134,7 @@ node { node { name: "dipole_charge/filter_type_1/MatMul_6" op: "MatMul" - input: "dipole_charge/filter_type_1/add_2" + input: "dipole_charge/filter_type_1/add_4" input: "dipole_charge/filter_type_1/matrix_3_1/read" attr { key: "T" @@ -7667,7 +8203,7 @@ node { size: 2 } } - tensor_content: "\377\377\377\377d\000\000\000" + tensor_content: "\377\377\377\377\010\000\000\000" } } } @@ -7691,7 +8227,7 @@ node { } } node { - name: "dipole_charge/filter_type_1/concat_3/axis" + name: "dipole_charge/filter_type_1/concat_5/axis" op: "Const" attr { key: "dtype" @@ -7712,11 +8248,11 @@ node { } } node { - name: "dipole_charge/filter_type_1/concat_3" + name: "dipole_charge/filter_type_1/concat_5" op: "ConcatV2" - input: "dipole_charge/filter_type_1/add_2" - input: "dipole_charge/filter_type_1/add_2" - input: "dipole_charge/filter_type_1/concat_3/axis" + input: "dipole_charge/filter_type_1/add_4" + input: "dipole_charge/filter_type_1/add_4" + input: "dipole_charge/filter_type_1/concat_5/axis" attr { key: "N" value { @@ -7737,9 +8273,9 @@ node { } } node { - name: "dipole_charge/filter_type_1/add_3" + name: "dipole_charge/filter_type_1/add_5" op: "AddV2" - input: "dipole_charge/filter_type_1/concat_3" + input: "dipole_charge/filter_type_1/concat_5" input: "dipole_charge/filter_type_1/Reshape_11" attr { key: "T" @@ -7767,7 +8303,7 @@ node { size: 3 } } - tensor_content: "\377\377\377\377\\\000\000\000d\000\000\000" + tensor_content: "\377\377\377\377\\\000\000\000\010\000\000\000" } } } @@ -7775,7 +8311,7 @@ node { node { name: "dipole_charge/filter_type_1/Reshape_12" op: "Reshape" - input: "dipole_charge/filter_type_1/add_3" + input: "dipole_charge/filter_type_1/add_5" input: "dipole_charge/filter_type_1/Reshape_12/shape" attr { key: "T" @@ -8169,7 +8705,7 @@ node { size: 2 } } - tensor_content: "\377\377\377\377 \003\000\000" + tensor_content: "\377\377\377\377@\000\000\000" } } } @@ -8193,9 +8729,9 @@ node { } } node { - name: "dipole_charge/Shape_4" + name: "dipole_charge/Shape_5" op: "Shape" - input: "dipole_charge/Reshape_4" + input: "dipole_charge/Reshape_6" attr { key: "T" value { @@ -8210,7 +8746,7 @@ node { } } node { - name: "dipole_charge/strided_slice_15/stack" + name: "dipole_charge/strided_slice_18/stack" op: "Const" attr { key: "dtype" @@ -8234,7 +8770,7 @@ node { } } node { - name: "dipole_charge/strided_slice_15/stack_1" + name: "dipole_charge/strided_slice_18/stack_1" op: "Const" attr { key: "dtype" @@ -8258,7 +8794,7 @@ node { } } node { - name: "dipole_charge/strided_slice_15/stack_2" + name: "dipole_charge/strided_slice_18/stack_2" op: "Const" attr { key: "dtype" @@ -8282,12 +8818,12 @@ node { } } node { - name: "dipole_charge/strided_slice_15" + name: "dipole_charge/strided_slice_18" op: "StridedSlice" - input: "dipole_charge/Shape_4" - input: "dipole_charge/strided_slice_15/stack" - input: "dipole_charge/strided_slice_15/stack_1" - input: "dipole_charge/strided_slice_15/stack_2" + input: "dipole_charge/Shape_5" + input: "dipole_charge/strided_slice_18/stack" + input: "dipole_charge/strided_slice_18/stack_1" + input: "dipole_charge/strided_slice_18/stack_2" attr { key: "Index" value { @@ -8332,7 +8868,7 @@ node { } } node { - name: "dipole_charge/strided_slice_16/stack" + name: "dipole_charge/strided_slice_19/stack" op: "Const" attr { key: "dtype" @@ -8356,7 +8892,7 @@ node { } } node { - name: "dipole_charge/strided_slice_16/stack_1" + name: "dipole_charge/strided_slice_19/stack_1" op: "Const" attr { key: "dtype" @@ -8380,7 +8916,7 @@ node { } } node { - name: "dipole_charge/strided_slice_16/stack_2" + name: "dipole_charge/strided_slice_19/stack_2" op: "Const" attr { key: "dtype" @@ -8404,12 +8940,12 @@ node { } } node { - name: "dipole_charge/strided_slice_16" + name: "dipole_charge/strided_slice_19" op: "StridedSlice" input: "dipole_charge/t_natoms" - input: "dipole_charge/strided_slice_16/stack" - input: "dipole_charge/strided_slice_16/stack_1" - input: "dipole_charge/strided_slice_16/stack_2" + input: "dipole_charge/strided_slice_19/stack" + input: "dipole_charge/strided_slice_19/stack_1" + input: "dipole_charge/strided_slice_19/stack_2" attr { key: "Index" value { @@ -8454,7 +8990,7 @@ node { } } node { - name: "dipole_charge/mul_8/y" + name: "dipole_charge/Reshape_13/shape/2" op: "Const" attr { key: "dtype" @@ -8469,32 +9005,21 @@ node { dtype: DT_INT32 tensor_shape { } - int_val: 800 + int_val: 64 } } } } node { - name: "dipole_charge/mul_8" - op: "Mul" - input: "dipole_charge/strided_slice_16" - input: "dipole_charge/mul_8/y" - attr { - key: "T" - value { - type: DT_INT32 - } - } -} -node { - name: "dipole_charge/Reshape_11/shape" + name: "dipole_charge/Reshape_13/shape" op: "Pack" - input: "dipole_charge/strided_slice_15" - input: "dipole_charge/mul_8" + input: "dipole_charge/strided_slice_18" + input: "dipole_charge/strided_slice_19" + input: "dipole_charge/Reshape_13/shape/2" attr { key: "N" value { - i: 2 + i: 3 } } attr { @@ -8511,10 +9036,10 @@ node { } } node { - name: "dipole_charge/Reshape_11" + name: "dipole_charge/Reshape_13" op: "Reshape" input: "dipole_charge/filter_type_1/Reshape_14" - input: "dipole_charge/Reshape_11/shape" + input: "dipole_charge/Reshape_13/shape" attr { key: "T" value { @@ -8529,9 +9054,9 @@ node { } } node { - name: "dipole_charge/Shape_5" + name: "dipole_charge/Shape_6" op: "Shape" - input: "dipole_charge/Reshape_4" + input: "dipole_charge/Reshape_6" attr { key: "T" value { @@ -8546,7 +9071,7 @@ node { } } node { - name: "dipole_charge/strided_slice_17/stack" + name: "dipole_charge/strided_slice_20/stack" op: "Const" attr { key: "dtype" @@ -8570,7 +9095,7 @@ node { } } node { - name: "dipole_charge/strided_slice_17/stack_1" + name: "dipole_charge/strided_slice_20/stack_1" op: "Const" attr { key: "dtype" @@ -8594,7 +9119,7 @@ node { } } node { - name: "dipole_charge/strided_slice_17/stack_2" + name: "dipole_charge/strided_slice_20/stack_2" op: "Const" attr { key: "dtype" @@ -8618,12 +9143,12 @@ node { } } node { - name: "dipole_charge/strided_slice_17" + name: "dipole_charge/strided_slice_20" op: "StridedSlice" - input: "dipole_charge/Shape_5" - input: "dipole_charge/strided_slice_17/stack" - input: "dipole_charge/strided_slice_17/stack_1" - input: "dipole_charge/strided_slice_17/stack_2" + input: "dipole_charge/Shape_6" + input: "dipole_charge/strided_slice_20/stack" + input: "dipole_charge/strided_slice_20/stack_1" + input: "dipole_charge/strided_slice_20/stack_2" attr { key: "Index" value { @@ -8668,7 +9193,7 @@ node { } } node { - name: "dipole_charge/strided_slice_18/stack" + name: "dipole_charge/strided_slice_21/stack" op: "Const" attr { key: "dtype" @@ -8692,7 +9217,7 @@ node { } } node { - name: "dipole_charge/strided_slice_18/stack_1" + name: "dipole_charge/strided_slice_21/stack_1" op: "Const" attr { key: "dtype" @@ -8716,7 +9241,7 @@ node { } } node { - name: "dipole_charge/strided_slice_18/stack_2" + name: "dipole_charge/strided_slice_21/stack_2" op: "Const" attr { key: "dtype" @@ -8740,12 +9265,12 @@ node { } } node { - name: "dipole_charge/strided_slice_18" + name: "dipole_charge/strided_slice_21" op: "StridedSlice" input: "dipole_charge/t_natoms" - input: "dipole_charge/strided_slice_18/stack" - input: "dipole_charge/strided_slice_18/stack_1" - input: "dipole_charge/strided_slice_18/stack_2" + input: "dipole_charge/strided_slice_21/stack" + input: "dipole_charge/strided_slice_21/stack_1" + input: "dipole_charge/strided_slice_21/stack_2" attr { key: "Index" value { @@ -8790,7 +9315,7 @@ node { } } node { - name: "dipole_charge/mul_9/y" + name: "dipole_charge/Reshape_14/shape/2" op: "Const" attr { key: "dtype" @@ -8805,25 +9330,56 @@ node { dtype: DT_INT32 tensor_shape { } - int_val: 100 + int_val: 24 } } } } node { - name: "dipole_charge/mul_9" - op: "Mul" - input: "dipole_charge/strided_slice_18" - input: "dipole_charge/mul_9/y" + name: "dipole_charge/Reshape_14/shape" + op: "Pack" + input: "dipole_charge/strided_slice_20" + input: "dipole_charge/strided_slice_21" + input: "dipole_charge/Reshape_14/shape/2" + attr { + key: "N" + value { + i: 3 + } + } + attr { + key: "T" + value { + type: DT_INT32 + } + } + attr { + key: "axis" + value { + i: 0 + } + } +} +node { + name: "dipole_charge/Reshape_14" + op: "Reshape" + input: "dipole_charge/filter_type_1/transpose" + input: "dipole_charge/Reshape_14/shape" attr { key: "T" + value { + type: DT_DOUBLE + } + } + attr { + key: "Tshape" value { type: DT_INT32 } } } node { - name: "dipole_charge/mul_10/y" + name: "dipole_charge/concat/axis" op: "Const" attr { key: "dtype" @@ -8838,28 +9394,63 @@ node { dtype: DT_INT32 tensor_shape { } - int_val: 3 + int_val: 1 } } } } node { - name: "dipole_charge/mul_10" - op: "Mul" - input: "dipole_charge/mul_9" - input: "dipole_charge/mul_10/y" + name: "dipole_charge/concat" + op: "ConcatV2" + input: "dipole_charge/Reshape_9" + input: "dipole_charge/Reshape_13" + input: "dipole_charge/concat/axis" + attr { + key: "N" + value { + i: 2 + } + } attr { key: "T" + value { + type: DT_DOUBLE + } + } + attr { + key: "Tidx" value { type: DT_INT32 } } } node { - name: "dipole_charge/Reshape_12/shape" - op: "Pack" - input: "dipole_charge/strided_slice_17" - input: "dipole_charge/mul_10" + name: "dipole_charge/concat_1/axis" + op: "Const" + attr { + key: "dtype" + value { + type: DT_INT32 + } + } + attr { + key: "value" + value { + tensor { + dtype: DT_INT32 + tensor_shape { + } + int_val: 1 + } + } + } +} +node { + name: "dipole_charge/concat_1" + op: "ConcatV2" + input: "dipole_charge/Reshape_10" + input: "dipole_charge/Reshape_14" + input: "dipole_charge/concat_1/axis" attr { key: "N" value { @@ -8868,37 +9459,65 @@ node { } attr { key: "T" + value { + type: DT_DOUBLE + } + } + attr { + key: "Tidx" value { type: DT_INT32 } } +} +node { + name: "dipole_charge/o_descriptor" + op: "Identity" + input: "dipole_charge/concat" attr { - key: "axis" + key: "T" value { - i: 0 + type: DT_DOUBLE } } } node { - name: "dipole_charge/Reshape_12" - op: "Reshape" - input: "dipole_charge/filter_type_1/transpose" - input: "dipole_charge/Reshape_12/shape" + name: "dipole_charge/o_rot_mat" + op: "Identity" + input: "dipole_charge/concat_1" attr { key: "T" value { type: DT_DOUBLE } } +} +node { + name: "dipole_charge/strided_slice_23/stack" + op: "Const" attr { - key: "Tshape" + key: "dtype" value { type: DT_INT32 } } + attr { + key: "value" + value { + tensor { + dtype: DT_INT32 + tensor_shape { + dim { + size: 1 + } + } + int_val: 0 + } + } + } } node { - name: "dipole_charge/concat/axis" + name: "dipole_charge/strided_slice_23/stack_1" op: "Const" attr { key: "dtype" @@ -8912,6 +9531,9 @@ node { tensor { dtype: DT_INT32 tensor_shape { + dim { + size: 1 + } } int_val: 1 } @@ -8919,32 +9541,102 @@ node { } } node { - name: "dipole_charge/concat" - op: "ConcatV2" - input: "dipole_charge/Reshape_7" - input: "dipole_charge/Reshape_11" - input: "dipole_charge/concat/axis" + name: "dipole_charge/strided_slice_23/stack_2" + op: "Const" attr { - key: "N" + key: "dtype" value { - i: 2 + type: DT_INT32 + } + } + attr { + key: "value" + value { + tensor { + dtype: DT_INT32 + tensor_shape { + dim { + size: 1 + } + } + int_val: 1 + } + } + } +} +node { + name: "dipole_charge/strided_slice_23" + op: "StridedSlice" + input: "dipole_charge/t_natoms" + input: "dipole_charge/strided_slice_23/stack" + input: "dipole_charge/strided_slice_23/stack_1" + input: "dipole_charge/strided_slice_23/stack_2" + attr { + key: "Index" + value { + type: DT_INT32 } } attr { key: "T" value { - type: DT_DOUBLE + type: DT_INT32 } } attr { - key: "Tidx" + key: "begin_mask" + value { + i: 0 + } + } + attr { + key: "ellipsis_mask" + value { + i: 0 + } + } + attr { + key: "end_mask" + value { + i: 0 + } + } + attr { + key: "new_axis_mask" + value { + i: 0 + } + } + attr { + key: "shrink_axis_mask" + value { + i: 1 + } + } +} +node { + name: "dipole_charge/Reshape_15/shape/0" + op: "Const" + attr { + key: "dtype" value { type: DT_INT32 } } + attr { + key: "value" + value { + tensor { + dtype: DT_INT32 + tensor_shape { + } + int_val: -1 + } + } + } } node { - name: "dipole_charge/concat_1/axis" + name: "dipole_charge/Reshape_15/shape/2" op: "Const" attr { key: "dtype" @@ -8959,60 +9651,56 @@ node { dtype: DT_INT32 tensor_shape { } - int_val: 1 + int_val: 64 } } } } node { - name: "dipole_charge/concat_1" - op: "ConcatV2" - input: "dipole_charge/Reshape_8" - input: "dipole_charge/Reshape_12" - input: "dipole_charge/concat_1/axis" + name: "dipole_charge/Reshape_15/shape" + op: "Pack" + input: "dipole_charge/Reshape_15/shape/0" + input: "dipole_charge/strided_slice_23" + input: "dipole_charge/Reshape_15/shape/2" attr { key: "N" value { - i: 2 + i: 3 } } attr { key: "T" value { - type: DT_DOUBLE + type: DT_INT32 } } attr { - key: "Tidx" + key: "axis" value { - type: DT_INT32 + i: 0 } } } node { - name: "dipole_charge/o_descriptor" - op: "Identity" - input: "dipole_charge/concat" + name: "dipole_charge/Reshape_15" + op: "Reshape" + input: "dipole_charge/o_descriptor" + input: "dipole_charge/Reshape_15/shape" attr { key: "T" value { type: DT_DOUBLE } } -} -node { - name: "dipole_charge/o_rot_mat" - op: "Identity" - input: "dipole_charge/concat_1" attr { - key: "T" + key: "Tshape" value { - type: DT_DOUBLE + type: DT_INT32 } } } node { - name: "dipole_charge/strided_slice_20/stack" + name: "dipole_charge/strided_slice_24/stack" op: "Const" attr { key: "dtype" @@ -9036,7 +9724,7 @@ node { } } node { - name: "dipole_charge/strided_slice_20/stack_1" + name: "dipole_charge/strided_slice_24/stack_1" op: "Const" attr { key: "dtype" @@ -9060,7 +9748,7 @@ node { } } node { - name: "dipole_charge/strided_slice_20/stack_2" + name: "dipole_charge/strided_slice_24/stack_2" op: "Const" attr { key: "dtype" @@ -9084,12 +9772,12 @@ node { } } node { - name: "dipole_charge/strided_slice_20" + name: "dipole_charge/strided_slice_24" op: "StridedSlice" input: "dipole_charge/t_natoms" - input: "dipole_charge/strided_slice_20/stack" - input: "dipole_charge/strided_slice_20/stack_1" - input: "dipole_charge/strided_slice_20/stack_2" + input: "dipole_charge/strided_slice_24/stack" + input: "dipole_charge/strided_slice_24/stack_1" + input: "dipole_charge/strided_slice_24/stack_2" attr { key: "Index" value { @@ -9134,7 +9822,7 @@ node { } } node { - name: "dipole_charge/mul_11/x" + name: "dipole_charge/Reshape_16/shape/0" op: "Const" attr { key: "dtype" @@ -9149,25 +9837,13 @@ node { dtype: DT_INT32 tensor_shape { } - int_val: 800 + int_val: -1 } } } } node { - name: "dipole_charge/mul_11" - op: "Mul" - input: "dipole_charge/mul_11/x" - input: "dipole_charge/strided_slice_20" - attr { - key: "T" - value { - type: DT_INT32 - } - } -} -node { - name: "dipole_charge/Reshape_13/shape/0" + name: "dipole_charge/Reshape_16/shape/2" op: "Const" attr { key: "dtype" @@ -9182,20 +9858,21 @@ node { dtype: DT_INT32 tensor_shape { } - int_val: -1 + int_val: 24 } } } } node { - name: "dipole_charge/Reshape_13/shape" + name: "dipole_charge/Reshape_16/shape" op: "Pack" - input: "dipole_charge/Reshape_13/shape/0" - input: "dipole_charge/mul_11" + input: "dipole_charge/Reshape_16/shape/0" + input: "dipole_charge/strided_slice_24" + input: "dipole_charge/Reshape_16/shape/2" attr { key: "N" value { - i: 2 + i: 3 } } attr { @@ -9212,10 +9889,10 @@ node { } } node { - name: "dipole_charge/Reshape_13" + name: "dipole_charge/Reshape_16" op: "Reshape" - input: "dipole_charge/o_descriptor" - input: "dipole_charge/Reshape_13/shape" + input: "dipole_charge/o_rot_mat" + input: "dipole_charge/Reshape_16/shape" attr { key: "T" value { @@ -9230,7 +9907,7 @@ node { } } node { - name: "dipole_charge/strided_slice_21/stack" + name: "dipole_charge/strided_slice_25/stack" op: "Const" attr { key: "dtype" @@ -9248,13 +9925,13 @@ node { size: 1 } } - int_val: 0 + int_val: 2 } } } } node { - name: "dipole_charge/strided_slice_21/stack_1" + name: "dipole_charge/strided_slice_25/stack_1" op: "Const" attr { key: "dtype" @@ -9272,13 +9949,13 @@ node { size: 1 } } - int_val: 1 + int_val: 3 } } } } node { - name: "dipole_charge/strided_slice_21/stack_2" + name: "dipole_charge/strided_slice_25/stack_2" op: "Const" attr { key: "dtype" @@ -9302,12 +9979,12 @@ node { } } node { - name: "dipole_charge/strided_slice_21" + name: "dipole_charge/strided_slice_25" op: "StridedSlice" input: "dipole_charge/t_natoms" - input: "dipole_charge/strided_slice_21/stack" - input: "dipole_charge/strided_slice_21/stack_1" - input: "dipole_charge/strided_slice_21/stack_2" + input: "dipole_charge/strided_slice_25/stack" + input: "dipole_charge/strided_slice_25/stack_1" + input: "dipole_charge/strided_slice_25/stack_2" attr { key: "Index" value { @@ -9352,7 +10029,7 @@ node { } } node { - name: "dipole_charge/mul_12/x" + name: "dipole_charge/Slice_2/begin" op: "Const" attr { key: "dtype" @@ -9366,26 +10043,38 @@ node { tensor { dtype: DT_INT32 tensor_shape { + dim { + size: 3 + } } - int_val: 300 + tensor_content: "\000\000\000\000\000\000\000\000\000\000\000\000" } } } } node { - name: "dipole_charge/mul_12" - op: "Mul" - input: "dipole_charge/mul_12/x" - input: "dipole_charge/strided_slice_21" + name: "dipole_charge/Slice_2/size/0" + op: "Const" attr { - key: "T" + key: "dtype" value { type: DT_INT32 } } + attr { + key: "value" + value { + tensor { + dtype: DT_INT32 + tensor_shape { + } + int_val: -1 + } + } + } } node { - name: "dipole_charge/Reshape_14/shape/0" + name: "dipole_charge/Slice_2/size/2" op: "Const" attr { key: "dtype" @@ -9406,14 +10095,15 @@ node { } } node { - name: "dipole_charge/Reshape_14/shape" + name: "dipole_charge/Slice_2/size" op: "Pack" - input: "dipole_charge/Reshape_14/shape/0" - input: "dipole_charge/mul_12" + input: "dipole_charge/Slice_2/size/0" + input: "dipole_charge/strided_slice_25" + input: "dipole_charge/Slice_2/size/2" attr { key: "N" value { - i: 2 + i: 3 } } attr { @@ -9430,25 +10120,26 @@ node { } } node { - name: "dipole_charge/Reshape_14" - op: "Reshape" - input: "dipole_charge/o_rot_mat" - input: "dipole_charge/Reshape_14/shape" + name: "dipole_charge/Slice_2" + op: "Slice" + input: "dipole_charge/Reshape_15" + input: "dipole_charge/Slice_2/begin" + input: "dipole_charge/Slice_2/size" attr { - key: "T" + key: "Index" value { - type: DT_DOUBLE + type: DT_INT32 } } attr { - key: "Tshape" + key: "T" value { - type: DT_INT32 + type: DT_DOUBLE } } } node { - name: "dipole_charge/strided_slice_22/stack" + name: "dipole_charge/Reshape_17/shape" op: "Const" attr { key: "dtype" @@ -9463,16 +10154,34 @@ node { dtype: DT_INT32 tensor_shape { dim { - size: 1 + size: 2 } } - int_val: 2 + tensor_content: "\377\377\377\377@\000\000\000" } } } } node { - name: "dipole_charge/strided_slice_22/stack_1" + name: "dipole_charge/Reshape_17" + op: "Reshape" + input: "dipole_charge/Slice_2" + input: "dipole_charge/Reshape_17/shape" + attr { + key: "T" + value { + type: DT_DOUBLE + } + } + attr { + key: "Tshape" + value { + type: DT_INT32 + } + } +} +node { + name: "dipole_charge/Slice_3/begin" op: "Const" attr { key: "dtype" @@ -9487,16 +10196,16 @@ node { dtype: DT_INT32 tensor_shape { dim { - size: 1 + size: 3 } } - int_val: 3 + tensor_content: "\000\000\000\000\000\000\000\000\000\000\000\000" } } } } node { - name: "dipole_charge/strided_slice_22/stack_2" + name: "dipole_charge/Slice_3/size/0" op: "Const" attr { key: "dtype" @@ -9510,67 +10219,79 @@ node { tensor { dtype: DT_INT32 tensor_shape { - dim { - size: 1 - } } - int_val: 1 + int_val: -1 } } } } node { - name: "dipole_charge/strided_slice_22" - op: "StridedSlice" - input: "dipole_charge/t_natoms" - input: "dipole_charge/strided_slice_22/stack" - input: "dipole_charge/strided_slice_22/stack_1" - input: "dipole_charge/strided_slice_22/stack_2" + name: "dipole_charge/Slice_3/size/2" + op: "Const" attr { - key: "Index" + key: "dtype" value { type: DT_INT32 } } attr { - key: "T" + key: "value" value { - type: DT_INT32 + tensor { + dtype: DT_INT32 + tensor_shape { + } + int_val: -1 + } } } +} +node { + name: "dipole_charge/Slice_3/size" + op: "Pack" + input: "dipole_charge/Slice_3/size/0" + input: "dipole_charge/strided_slice_25" + input: "dipole_charge/Slice_3/size/2" attr { - key: "begin_mask" + key: "N" value { - i: 0 + i: 3 } } attr { - key: "ellipsis_mask" + key: "T" value { - i: 0 + type: DT_INT32 } } attr { - key: "end_mask" + key: "axis" value { i: 0 } } +} +node { + name: "dipole_charge/Slice_3" + op: "Slice" + input: "dipole_charge/Reshape_16" + input: "dipole_charge/Slice_3/begin" + input: "dipole_charge/Slice_3/size" attr { - key: "new_axis_mask" + key: "Index" value { - i: 0 + type: DT_INT32 } } attr { - key: "shrink_axis_mask" + key: "T" value { - i: 1 + type: DT_DOUBLE } } } node { - name: "dipole_charge/mul_13/y" + name: "dipole_charge/Reshape_18/shape" op: "Const" attr { key: "dtype" @@ -9584,105 +10305,168 @@ node { tensor { dtype: DT_INT32 tensor_shape { + dim { + size: 3 + } } - int_val: 800 + tensor_content: "\377\377\377\377\010\000\000\000\003\000\000\000" } } } } node { - name: "dipole_charge/mul_13" - op: "Mul" - input: "dipole_charge/strided_slice_22" - input: "dipole_charge/mul_13/y" + name: "dipole_charge/Reshape_18" + op: "Reshape" + input: "dipole_charge/Slice_3" + input: "dipole_charge/Reshape_18/shape" attr { key: "T" + value { + type: DT_DOUBLE + } + } + attr { + key: "Tshape" value { type: DT_INT32 } } } node { - name: "dipole_charge/Slice_2/begin" + name: "dipole_charge/layer_0_type_0/matrix" op: "Const" attr { key: "dtype" value { - type: DT_INT32 + type: DT_DOUBLE } } attr { key: "value" value { tensor { - dtype: DT_INT32 + dtype: DT_DOUBLE tensor_shape { + dim { + size: 64 + } dim { size: 2 } } - tensor_content: "\000\000\000\000\000\000\000\000" + tensor_content: 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} } } } node { - name: "dipole_charge/Slice_2/size/0" + name: "dipole_charge/layer_0_type_0/matrix/read" + op: "Identity" + input: "dipole_charge/layer_0_type_0/matrix" + attr { + key: "T" + value { + type: DT_DOUBLE + } + } + attr { + key: "_class" + value { + list { + s: "loc:@dipole_charge/layer_0_type_0/matrix" + } + } + } +} +node { + name: "dipole_charge/layer_0_type_0/bias" op: "Const" attr { key: "dtype" value { - type: DT_INT32 + type: DT_DOUBLE } } attr { key: "value" value { tensor { - dtype: DT_INT32 + dtype: DT_DOUBLE tensor_shape { + dim { + size: 2 + } } - int_val: -1 + tensor_content: "\366\352\206\"W\230\365\277\264m?\r\361\235\275?" } } } } node { - name: "dipole_charge/Slice_2/size" - op: "Pack" - input: "dipole_charge/Slice_2/size/0" - input: "dipole_charge/mul_13" + name: "dipole_charge/layer_0_type_0/bias/read" + op: "Identity" + input: "dipole_charge/layer_0_type_0/bias" attr { - key: "N" + key: "T" value { - i: 2 + type: DT_DOUBLE + } + } + attr { + key: "_class" + value { + list { + s: "loc:@dipole_charge/layer_0_type_0/bias" + } } } +} +node { + name: "dipole_charge/layer_0_type_0/MatMul" + op: "MatMul" + input: "dipole_charge/Reshape_17" + input: "dipole_charge/layer_0_type_0/matrix/read" attr { key: "T" value { - type: DT_INT32 + type: DT_DOUBLE } } attr { - key: "axis" + key: "transpose_a" value { - i: 0 + b: false + } + } + attr { + key: "transpose_b" + value { + b: false } } } node { - name: "dipole_charge/Slice_2" - op: "Slice" - input: "dipole_charge/Reshape_13" - input: "dipole_charge/Slice_2/begin" - input: "dipole_charge/Slice_2/size" + name: "dipole_charge/layer_0_type_0/BiasAdd" + op: "BiasAdd" + input: "dipole_charge/layer_0_type_0/MatMul" + input: "dipole_charge/layer_0_type_0/bias/read" attr { - key: "Index" + key: "T" value { - type: DT_INT32 + type: DT_DOUBLE } } + attr { + key: "data_format" + value { + s: "NHWC" + } + } +} +node { + name: "dipole_charge/layer_0_type_0/Tanh" + op: "Tanh" + input: "dipole_charge/layer_0_type_0/BiasAdd" attr { key: "T" value { @@ -9691,7 +10475,7 @@ node { } } node { - name: "dipole_charge/Reshape_15/shape" + name: "dipole_charge/layer_0_type_0/Reshape/shape" op: "Const" attr { key: "dtype" @@ -9709,476 +10493,7 @@ node { size: 2 } } - tensor_content: "\377\377\377\377 \003\000\000" - } - } - } -} -node { - name: "dipole_charge/Reshape_15" - op: "Reshape" - input: "dipole_charge/Slice_2" - input: "dipole_charge/Reshape_15/shape" - attr { - key: "T" - value { - type: DT_DOUBLE - } - } - attr { - key: "Tshape" - value { - type: DT_INT32 - } - } -} -node { - name: "dipole_charge/strided_slice_23/stack" - op: "Const" - attr { - key: "dtype" - value { - type: DT_INT32 - } - } - attr { - key: "value" - value { - tensor { - dtype: DT_INT32 - tensor_shape { - dim { - size: 1 - } - } - int_val: 2 - } - } - } -} -node { - name: "dipole_charge/strided_slice_23/stack_1" - op: "Const" - attr { - key: "dtype" - value { - type: DT_INT32 - } - } - attr { - key: "value" - value { - tensor { - dtype: DT_INT32 - tensor_shape { - dim { - size: 1 - } - } - int_val: 3 - } - } - } -} -node { - name: "dipole_charge/strided_slice_23/stack_2" - op: "Const" - attr { - key: "dtype" - value { - type: DT_INT32 - } - } - attr { - key: "value" - value { - tensor { - dtype: DT_INT32 - tensor_shape { - dim { - size: 1 - } - } - int_val: 1 - } - } - } -} -node { - name: "dipole_charge/strided_slice_23" - op: "StridedSlice" - input: "dipole_charge/t_natoms" - input: "dipole_charge/strided_slice_23/stack" - input: "dipole_charge/strided_slice_23/stack_1" - input: "dipole_charge/strided_slice_23/stack_2" - attr { - key: "Index" - value { - type: DT_INT32 - } - } - attr { - key: "T" - value { - type: DT_INT32 - } - } - attr { - key: "begin_mask" - value { - i: 0 - } - } - attr { - key: "ellipsis_mask" - value { - i: 0 - } - } - attr { - key: "end_mask" - value { - i: 0 - } - } - attr { - key: "new_axis_mask" - value { - i: 0 - } - } - attr { - key: "shrink_axis_mask" - value { - i: 1 - } - } -} -node { - name: "dipole_charge/mul_14/y" - op: "Const" - attr { - key: "dtype" - value { - type: DT_INT32 - } - } - attr { - key: "value" - value { - tensor { - dtype: DT_INT32 - tensor_shape { - } - int_val: 300 - } - } - } -} -node { - name: "dipole_charge/mul_14" - op: "Mul" - input: "dipole_charge/strided_slice_23" - input: "dipole_charge/mul_14/y" - attr { - key: "T" - value { - type: DT_INT32 - } - } -} -node { - name: "dipole_charge/Slice_3/begin" - op: "Const" - attr { - key: "dtype" - value { - type: DT_INT32 - } - } - attr { - key: "value" - value { - tensor { - dtype: DT_INT32 - tensor_shape { - dim { - size: 2 - } - } - tensor_content: "\000\000\000\000\000\000\000\000" - } - } - } -} -node { - name: "dipole_charge/Slice_3/size/0" - op: "Const" - attr { - key: "dtype" - value { - type: DT_INT32 - } - } - attr { - key: "value" - value { - tensor { - dtype: DT_INT32 - tensor_shape { - } - int_val: -1 - } - } - } -} -node { - 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+ tensor_content: "\375F\352d\241\027\262?\272\265\376\200\346]\277?" } } } @@ -10405,7 +10720,7 @@ node { size: 2 } } - tensor_content: "\377\377\377\377d\000\000\000" + tensor_content: "\377\377\377\377\002\000\000\000" } } } @@ -10441,7 +10756,7 @@ node { } } node { - name: "dipole_charge/add_4" + name: "dipole_charge/add_3" op: "AddV2" input: "dipole_charge/layer_0_type_0/Reshape" input: "dipole_charge/layer_1_type_0/mul" @@ -10468,13 +10783,13 @@ node { dtype: DT_DOUBLE tensor_shape { dim { - size: 100 + size: 2 } dim { - size: 100 + size: 2 } } - tensor_content: 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+ tensor_content: "\234OH\204B\025\217?G\313`\274\305{\321?\026\034\371\027\275\316\334\277o#\r\3734j\321?" } } } @@ -10514,10 +10829,10 @@ node { dtype: DT_DOUBLE tensor_shape { dim { - size: 100 + size: 2 } } - tensor_content: "\301\177\"OQ\355\360\277\014$e_\235@\336\277a\242\206\317y\307\331?\263\0101:\220\210\335\277\0171\3665\032\234\362\277E3a\205\341\025\331?\010oO\314\004\032\327?\\\222\030\372P\333\343?C\264\212\2563\225\373?{ 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+ tensor_content: "\314\227\277N<>\366\277j\026F\333k+\354\277" } } } @@ -10544,7 +10859,7 @@ node { node { name: "dipole_charge/layer_2_type_0/MatMul" op: "MatMul" - input: "dipole_charge/add_4" + input: "dipole_charge/add_3" input: "dipole_charge/layer_2_type_0/matrix/read" attr { key: "T" @@ -10599,10 +10914,10 @@ node { dtype: DT_DOUBLE tensor_shape { dim { - size: 100 + size: 2 } } - tensor_content: 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y/\021\257?\014\340r\334\275\327\325?" + tensor_content: "+\315\333\235]\242\337?\232\343\3010\010[x?\247P\373\001\342K\315?\026\337\364s\2223\343?(GG\352*\235\265?\3207\310\267\202/\327?\352H\241.\365\370\343?8E\177\217\334\333\316\277\200l`|\325\005\312\277BvM\313@o\333\277\222\200Q\265\246\220\327?m\274L~>-\324?f\204Lx\255\233\274\277_\362Y\241r\346\350\277\263\205\272v\200\236\325?\264\353\237\034\323\206\266\277" } } } @@ -10765,10 +11080,10 @@ node { dtype: DT_DOUBLE tensor_shape { dim { - size: 100 + size: 8 } } - tensor_content: "\341g\215\255\334q\313?\231\037\034\341\020\310\343?\1774\245\317\346\261\352?\343\023\t\007\205\215\326? 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+ tensor_content: "\223<\006\363\206\307\305?\234\363\016\377\375~\345?\217\3721)3\r\366?\033F\312\177\036\203\330?\245\313\311\022\000\223\374\277\272:;\t\000\016\361?\024d\200c\025\243\370?\213P\243\276u\214\344\277" } } } @@ -10795,7 +11110,7 @@ node { node { name: "dipole_charge/final_layer_type_0/MatMul" op: "MatMul" - input: "dipole_charge/add_5" + input: "dipole_charge/add_4" input: "dipole_charge/final_layer_type_0/matrix/read" attr { key: "T" @@ -10835,9 +11150,9 @@ node { } } node { - name: "dipole_charge/Shape_6" + name: "dipole_charge/Shape_7" op: "Shape" - input: "dipole_charge/Reshape_13" + input: "dipole_charge/Reshape_15" attr { key: "T" value { @@ -10852,7 +11167,7 @@ node { } } node { - name: "dipole_charge/strided_slice_25/stack" + name: "dipole_charge/strided_slice_26/stack" op: "Const" attr { key: "dtype" @@ -10876,7 +11191,7 @@ node { } } node { - name: "dipole_charge/strided_slice_25/stack_1" + name: "dipole_charge/strided_slice_26/stack_1" op: "Const" attr { key: "dtype" @@ -10900,7 +11215,7 @@ node { } } node { - name: "dipole_charge/strided_slice_25/stack_2" + name: "dipole_charge/strided_slice_26/stack_2" op: "Const" attr { key: "dtype" @@ -10924,12 +11239,12 @@ node { } } node { - name: "dipole_charge/strided_slice_25" + name: "dipole_charge/strided_slice_26" op: "StridedSlice" - input: "dipole_charge/Shape_6" - input: "dipole_charge/strided_slice_25/stack" - input: "dipole_charge/strided_slice_25/stack_1" - input: "dipole_charge/strided_slice_25/stack_2" + input: "dipole_charge/Shape_7" + input: "dipole_charge/strided_slice_26/stack" + input: "dipole_charge/strided_slice_26/stack_1" + input: "dipole_charge/strided_slice_26/stack_2" attr { key: "Index" value { @@ -10974,7 +11289,19 @@ node { } } node { - name: "dipole_charge/strided_slice_26/stack" + name: "dipole_charge/mul_2" + op: "Mul" + input: "dipole_charge/strided_slice_26" + input: "dipole_charge/strided_slice_25" + attr { + key: "T" + value { + type: DT_INT32 + } + } +} +node { + name: "dipole_charge/Reshape_19/shape/1" op: "Const" attr { key: "dtype" @@ -10988,17 +11315,14 @@ node { tensor { dtype: DT_INT32 tensor_shape { - dim { - size: 1 - } } - int_val: 2 + int_val: 1 } } } } node { - name: "dipole_charge/strided_slice_26/stack_1" + name: "dipole_charge/Reshape_19/shape/2" op: "Const" attr { key: "dtype" @@ -11012,149 +11336,18 @@ node { tensor { dtype: DT_INT32 tensor_shape { - dim { - size: 1 - } } - int_val: 3 + int_val: 8 } } } } node { - name: "dipole_charge/strided_slice_26/stack_2" - op: "Const" - attr { - key: "dtype" - value { - type: DT_INT32 - } - } - attr { - key: "value" - value { - tensor { - dtype: DT_INT32 - tensor_shape { - dim { - size: 1 - } - } - int_val: 1 - } - } - } -} -node { - name: "dipole_charge/strided_slice_26" - op: "StridedSlice" - input: "dipole_charge/t_natoms" - input: "dipole_charge/strided_slice_26/stack" - input: "dipole_charge/strided_slice_26/stack_1" - input: "dipole_charge/strided_slice_26/stack_2" - attr { - key: "Index" - value { - type: DT_INT32 - } - } - attr { - key: "T" - value { - type: DT_INT32 - } - } - attr { - key: "begin_mask" - value { - i: 0 - } - } - attr { - key: "ellipsis_mask" - value { - i: 0 - } - } - attr { - key: "end_mask" - value { - i: 0 - } - } - attr { - key: "new_axis_mask" - value { - i: 0 - } - } - attr { - key: "shrink_axis_mask" - value { - i: 1 - } - } -} -node { - name: "dipole_charge/mul_15" - op: "Mul" - input: "dipole_charge/strided_slice_25" - input: "dipole_charge/strided_slice_26" - attr { - key: "T" - value { - type: DT_INT32 - } - } -} -node { - name: "dipole_charge/Reshape_17/shape/1" - op: "Const" - attr { - key: "dtype" - value { - type: DT_INT32 - } - } - attr { - key: "value" - value { - tensor { - dtype: DT_INT32 - tensor_shape { - } - int_val: 1 - } - } - } -} -node { - name: "dipole_charge/Reshape_17/shape/2" - op: "Const" - attr { - key: "dtype" - value { - type: DT_INT32 - } - } - attr { - key: "value" - value { - tensor { - dtype: DT_INT32 - tensor_shape { - } - int_val: 100 - } - } - } -} -node { - name: "dipole_charge/Reshape_17/shape" + name: "dipole_charge/Reshape_19/shape" op: "Pack" - input: "dipole_charge/mul_15" - input: "dipole_charge/Reshape_17/shape/1" - input: "dipole_charge/Reshape_17/shape/2" + input: "dipole_charge/mul_2" + input: "dipole_charge/Reshape_19/shape/1" + input: "dipole_charge/Reshape_19/shape/2" attr { key: "N" value { @@ -11175,10 +11368,10 @@ node { } } node { - name: "dipole_charge/Reshape_17" + name: "dipole_charge/Reshape_19" op: "Reshape" input: "dipole_charge/final_layer_type_0/BiasAdd" - input: "dipole_charge/Reshape_17/shape" + input: "dipole_charge/Reshape_19/shape" attr { key: "T" value { @@ -11195,8 +11388,8 @@ node { node { name: "dipole_charge/MatMul" op: "BatchMatMulV2" - input: "dipole_charge/Reshape_17" - input: "dipole_charge/Reshape_16" + input: "dipole_charge/Reshape_19" + input: "dipole_charge/Reshape_18" attr { key: "T" value { @@ -11217,9 +11410,9 @@ node { } } node { - name: "dipole_charge/Shape_7" + name: "dipole_charge/Shape_8" op: "Shape" - input: "dipole_charge/Reshape_13" + input: "dipole_charge/Reshape_15" attr { key: "T" value { @@ -11308,7 +11501,7 @@ node { node { name: "dipole_charge/strided_slice_27" op: "StridedSlice" - input: "dipole_charge/Shape_7" + input: "dipole_charge/Shape_8" input: "dipole_charge/strided_slice_27/stack" input: "dipole_charge/strided_slice_27/stack_1" input: "dipole_charge/strided_slice_27/stack_2" @@ -11356,129 +11549,7 @@ node { } } node { - name: "dipole_charge/strided_slice_28/stack" - op: "Const" - attr { - key: "dtype" - value { - type: DT_INT32 - } - } - attr { - key: "value" - value { - tensor { - dtype: DT_INT32 - tensor_shape { - dim { - size: 1 - } - } - int_val: 2 - } - } - } -} -node { - name: "dipole_charge/strided_slice_28/stack_1" - op: "Const" - attr { - key: "dtype" - value { - type: DT_INT32 - } - } - attr { - key: "value" - value { - tensor { - dtype: DT_INT32 - tensor_shape { - dim { - size: 1 - } - } - int_val: 3 - } - } - } -} -node { - name: "dipole_charge/strided_slice_28/stack_2" - op: "Const" - attr { - key: "dtype" - value { - type: DT_INT32 - } - } - attr { - key: "value" - value { - tensor { - dtype: DT_INT32 - tensor_shape { - dim { - size: 1 - } - } - int_val: 1 - } - } - } -} -node { - name: "dipole_charge/strided_slice_28" - op: "StridedSlice" - input: "dipole_charge/t_natoms" - input: "dipole_charge/strided_slice_28/stack" - input: "dipole_charge/strided_slice_28/stack_1" - input: "dipole_charge/strided_slice_28/stack_2" - attr { - key: "Index" - value { - type: DT_INT32 - } - } - attr { - key: "T" - value { - type: DT_INT32 - } - } - attr { - key: "begin_mask" - value { - i: 0 - } - } - attr { - key: "ellipsis_mask" - value { - i: 0 - } - } - attr { - key: "end_mask" - value { - i: 0 - } - } - attr { - key: "new_axis_mask" - value { - i: 0 - } - } - attr { - key: "shrink_axis_mask" - value { - i: 1 - } - } -} -node { - name: "dipole_charge/Reshape_18/shape/2" + name: "dipole_charge/Reshape_20/shape/2" op: "Const" attr { key: "dtype" @@ -11499,11 +11570,11 @@ node { } } node { - name: "dipole_charge/Reshape_18/shape" + name: "dipole_charge/Reshape_20/shape" op: "Pack" input: "dipole_charge/strided_slice_27" - input: "dipole_charge/strided_slice_28" - input: "dipole_charge/Reshape_18/shape/2" + input: "dipole_charge/strided_slice_25" + input: "dipole_charge/Reshape_20/shape/2" attr { key: "N" value { @@ -11524,10 +11595,10 @@ node { } } node { - name: "dipole_charge/Reshape_18" + name: "dipole_charge/Reshape_20" op: "Reshape" input: "dipole_charge/MatMul" - input: "dipole_charge/Reshape_18/shape" + input: "dipole_charge/Reshape_20/shape" attr { key: "T" value { @@ -11544,7 +11615,7 @@ node { node { name: "dipole_charge/concat_2/concat" op: "Identity" - input: "dipole_charge/Reshape_18" + input: "dipole_charge/Reshape_20" attr { key: "T" value { @@ -11595,7 +11666,7 @@ node { } } node { - name: "dipole_charge/mul_20/y" + name: "dipole_charge/mul_3/y" op: "Const" attr { key: "dtype" @@ -11616,10 +11687,10 @@ node { } } node { - name: "dipole_charge/mul_20" + name: "dipole_charge/mul_3" op: "Mul" input: "dipole_charge/add" - input: "dipole_charge/mul_20/y" + input: "dipole_charge/mul_3/y" attr { key: "T" value { @@ -11652,7 +11723,7 @@ node { name: "dipole_charge/o_dipole/shape" op: "Pack" input: "dipole_charge/o_dipole/shape/0" - input: "dipole_charge/mul_20" + input: "dipole_charge/mul_3" attr { key: "N" value { @@ -11706,7 +11777,7 @@ node { dtype: DT_STRING tensor_shape { } - string_val: "dipole.pb" + string_val: "lrdipole.pb" } } } @@ -13942,7 +14013,7 @@ node { } } node { - name: "gradients/dipole_charge/Reshape_18_grad/Shape" + name: "gradients/dipole_charge/Reshape_20_grad/Shape" op: "Shape" input: "dipole_charge/MatMul" attr { @@ -13959,10 +14030,10 @@ node { } } node { - name: "gradients/dipole_charge/Reshape_18_grad/Reshape" + name: "gradients/dipole_charge/Reshape_20_grad/Reshape" op: "Reshape" input: "gradients/dipole_charge/Reshape_21_grad/Reshape" - input: "gradients/dipole_charge/Reshape_18_grad/Shape" + input: "gradients/dipole_charge/Reshape_20_grad/Shape" attr { key: "T" value { @@ -13979,8 +14050,8 @@ node { node { name: "gradients/dipole_charge/MatMul_grad/MatMul" op: "BatchMatMulV2" - input: "gradients/dipole_charge/Reshape_18_grad/Reshape" - input: "dipole_charge/Reshape_16" + input: "gradients/dipole_charge/Reshape_20_grad/Reshape" + input: "dipole_charge/Reshape_18" attr { key: "T" value { @@ -14003,8 +14074,8 @@ node { node { name: "gradients/dipole_charge/MatMul_grad/MatMul_1" op: "BatchMatMulV2" - input: "dipole_charge/Reshape_17" - input: "gradients/dipole_charge/Reshape_18_grad/Reshape" + input: "dipole_charge/Reshape_19" + input: "gradients/dipole_charge/Reshape_20_grad/Reshape" attr { key: "T" value { @@ -14027,7 +14098,7 @@ node { node { name: "gradients/dipole_charge/MatMul_grad/Shape" op: "Shape" - input: "dipole_charge/Reshape_17" + input: "dipole_charge/Reshape_19" attr { key: "T" value { @@ -14044,7 +14115,7 @@ node { node { name: "gradients/dipole_charge/MatMul_grad/Shape_1" op: "Shape" - input: "dipole_charge/Reshape_16" + input: "dipole_charge/Reshape_18" attr { key: "T" value { @@ -14399,7 +14470,7 @@ node { } } node { - name: "gradients/dipole_charge/Reshape_17_grad/Shape" + name: "gradients/dipole_charge/Reshape_19_grad/Shape" op: "Shape" input: "dipole_charge/final_layer_type_0/BiasAdd" attr { @@ -14416,10 +14487,10 @@ node { } } node { - name: "gradients/dipole_charge/Reshape_17_grad/Reshape" + name: "gradients/dipole_charge/Reshape_19_grad/Reshape" op: "Reshape" input: "gradients/dipole_charge/MatMul_grad/Reshape" - input: "gradients/dipole_charge/Reshape_17_grad/Shape" + input: "gradients/dipole_charge/Reshape_19_grad/Shape" attr { key: "T" value { @@ -14434,7 +14505,7 @@ node { } } node { - name: "gradients/dipole_charge/Reshape_16_grad/Shape" + name: "gradients/dipole_charge/Reshape_18_grad/Shape" op: "Shape" input: "dipole_charge/Slice_3" attr { @@ -14451,10 +14522,10 @@ node { } } node { - name: "gradients/dipole_charge/Reshape_16_grad/Reshape" + name: "gradients/dipole_charge/Reshape_18_grad/Reshape" op: "Reshape" input: "gradients/dipole_charge/MatMul_grad/Reshape_1" - input: "gradients/dipole_charge/Reshape_16_grad/Shape" + input: "gradients/dipole_charge/Reshape_18_grad/Shape" attr { key: "T" value { @@ -14484,7 +14555,7 @@ node { dtype: DT_INT32 tensor_shape { } - int_val: 2 + int_val: 3 } } } @@ -14572,7 +14643,7 @@ node { node { name: "gradients/dipole_charge/Slice_3_grad/Shape_1" op: "Shape" - input: "dipole_charge/Reshape_14" + input: "dipole_charge/Reshape_16" attr { key: "T" value { @@ -14677,7 +14748,7 @@ node { node { name: "gradients/dipole_charge/Slice_3_grad/Pad" op: "Pad" - input: "gradients/dipole_charge/Reshape_16_grad/Reshape" + input: "gradients/dipole_charge/Reshape_18_grad/Reshape" input: "gradients/dipole_charge/Slice_3_grad/concat" attr { key: "T" @@ -14695,7 +14766,7 @@ node { node { name: "gradients/dipole_charge/final_layer_type_0/MatMul_grad/MatMul" op: "MatMul" - input: "gradients/dipole_charge/Reshape_17_grad/Reshape" + input: "gradients/dipole_charge/Reshape_19_grad/Reshape" input: "dipole_charge/final_layer_type_0/matrix/read" attr { key: "T" @@ -14717,7 +14788,7 @@ node { } } node { - name: "gradients/dipole_charge/Reshape_14_grad/Shape" + name: "gradients/dipole_charge/Reshape_16_grad/Shape" op: "Shape" input: "dipole_charge/o_rot_mat" attr { @@ -14734,10 +14805,10 @@ node { } } node { - name: "gradients/dipole_charge/Reshape_14_grad/Reshape" + name: "gradients/dipole_charge/Reshape_16_grad/Reshape" op: "Reshape" input: "gradients/dipole_charge/Slice_3_grad/Pad" - input: "gradients/dipole_charge/Reshape_14_grad/Shape" + input: "gradients/dipole_charge/Reshape_16_grad/Shape" attr { key: "T" value { @@ -14752,9 +14823,9 @@ node { } } node { - name: "gradients/dipole_charge/add_5_grad/Shape" + name: "gradients/dipole_charge/add_4_grad/Shape" op: "Shape" - input: "dipole_charge/add_4" + input: "dipole_charge/add_3" attr { key: "T" value { @@ -14769,7 +14840,7 @@ node { } } node { - name: "gradients/dipole_charge/add_5_grad/Shape_1" + name: "gradients/dipole_charge/add_4_grad/Shape_1" op: "Shape" input: "dipole_charge/layer_2_type_0/mul" attr { @@ -14786,10 +14857,10 @@ node { } } node { - name: "gradients/dipole_charge/add_5_grad/BroadcastGradientArgs" + name: "gradients/dipole_charge/add_4_grad/BroadcastGradientArgs" op: "BroadcastGradientArgs" - input: "gradients/dipole_charge/add_5_grad/Shape" - input: "gradients/dipole_charge/add_5_grad/Shape_1" + input: "gradients/dipole_charge/add_4_grad/Shape" + input: "gradients/dipole_charge/add_4_grad/Shape_1" attr { key: "T" value { @@ -14798,10 +14869,10 @@ node { } } node { - name: "gradients/dipole_charge/add_5_grad/Sum" + name: "gradients/dipole_charge/add_4_grad/Sum" op: "Sum" input: "gradients/dipole_charge/final_layer_type_0/MatMul_grad/MatMul" - input: "gradients/dipole_charge/add_5_grad/BroadcastGradientArgs" + input: "gradients/dipole_charge/add_4_grad/BroadcastGradientArgs" attr { key: "T" value { @@ -14822,10 +14893,10 @@ node { } } node { - name: "gradients/dipole_charge/add_5_grad/Reshape" + name: "gradients/dipole_charge/add_4_grad/Reshape" op: "Reshape" - input: "gradients/dipole_charge/add_5_grad/Sum" - input: "gradients/dipole_charge/add_5_grad/Shape" + input: "gradients/dipole_charge/add_4_grad/Sum" + input: "gradients/dipole_charge/add_4_grad/Shape" attr { key: "T" value { @@ -14840,10 +14911,10 @@ node { } } node { - name: "gradients/dipole_charge/add_5_grad/Sum_1" + name: "gradients/dipole_charge/add_4_grad/Sum_1" op: "Sum" input: "gradients/dipole_charge/final_layer_type_0/MatMul_grad/MatMul" - input: "gradients/dipole_charge/add_5_grad/BroadcastGradientArgs:1" + input: "gradients/dipole_charge/add_4_grad/BroadcastGradientArgs:1" attr { key: "T" value { @@ -14864,10 +14935,10 @@ node { } } node { - name: "gradients/dipole_charge/add_5_grad/Reshape_1" + name: "gradients/dipole_charge/add_4_grad/Reshape_1" op: "Reshape" - input: "gradients/dipole_charge/add_5_grad/Sum_1" - input: "gradients/dipole_charge/add_5_grad/Shape_1" + input: "gradients/dipole_charge/add_4_grad/Sum_1" + input: "gradients/dipole_charge/add_4_grad/Shape_1" attr { key: "T" value { @@ -14930,7 +15001,7 @@ node { node { name: "gradients/dipole_charge/layer_2_type_0/mul_grad/Mul" op: "Mul" - input: "gradients/dipole_charge/add_5_grad/Reshape_1" + input: "gradients/dipole_charge/add_4_grad/Reshape_1" input: "dipole_charge/layer_2_type_0/idt/read" attr { key: "T" @@ -14997,7 +15068,7 @@ node { dtype: DT_INT32 tensor_shape { } - int_val: 2 + int_val: 3 } } } @@ -15017,8 +15088,8 @@ node { node { name: "gradients/dipole_charge/concat_1_grad/ShapeN" op: "ShapeN" - input: "dipole_charge/Reshape_8" - input: "dipole_charge/Reshape_12" + input: "dipole_charge/Reshape_10" + input: "dipole_charge/Reshape_14" attr { key: "N" value { @@ -15054,7 +15125,7 @@ node { node { name: "gradients/dipole_charge/concat_1_grad/Slice" op: "Slice" - input: "gradients/dipole_charge/Reshape_14_grad/Reshape" + input: "gradients/dipole_charge/Reshape_16_grad/Reshape" input: "gradients/dipole_charge/concat_1_grad/ConcatOffset" input: "gradients/dipole_charge/concat_1_grad/ShapeN" attr { @@ -15073,7 +15144,7 @@ node { node { name: "gradients/dipole_charge/concat_1_grad/Slice_1" op: "Slice" - input: "gradients/dipole_charge/Reshape_14_grad/Reshape" + input: "gradients/dipole_charge/Reshape_16_grad/Reshape" input: "gradients/dipole_charge/concat_1_grad/ConcatOffset:1" input: "gradients/dipole_charge/concat_1_grad/ShapeN:1" attr { @@ -15125,7 +15196,7 @@ node { } } node { - name: "gradients/dipole_charge/Reshape_8_grad/Shape" + name: "gradients/dipole_charge/Reshape_10_grad/Shape" op: "Shape" input: "dipole_charge/filter_type_0/transpose" attr { @@ -15142,10 +15213,10 @@ node { } } node { - name: "gradients/dipole_charge/Reshape_8_grad/Reshape" + name: "gradients/dipole_charge/Reshape_10_grad/Reshape" op: "Reshape" input: "gradients/dipole_charge/concat_1_grad/Slice" - input: "gradients/dipole_charge/Reshape_8_grad/Shape" + input: "gradients/dipole_charge/Reshape_10_grad/Shape" attr { key: "T" value { @@ -15160,7 +15231,7 @@ node { } } node { - name: "gradients/dipole_charge/Reshape_12_grad/Shape" + name: "gradients/dipole_charge/Reshape_14_grad/Shape" op: "Shape" input: "dipole_charge/filter_type_1/transpose" attr { @@ -15177,10 +15248,10 @@ node { } } node { - name: "gradients/dipole_charge/Reshape_12_grad/Reshape" + name: "gradients/dipole_charge/Reshape_14_grad/Reshape" op: "Reshape" input: "gradients/dipole_charge/concat_1_grad/Slice_1" - input: "gradients/dipole_charge/Reshape_12_grad/Shape" + input: "gradients/dipole_charge/Reshape_14_grad/Shape" attr { key: "T" value { @@ -15220,7 +15291,7 @@ node { node { name: "gradients/dipole_charge/filter_type_0/transpose_grad/transpose" op: "Transpose" - input: "gradients/dipole_charge/Reshape_8_grad/Reshape" + input: "gradients/dipole_charge/Reshape_10_grad/Reshape" input: "gradients/dipole_charge/filter_type_0/transpose_grad/InvertPermutation" attr { key: "T" @@ -15249,7 +15320,7 @@ node { node { name: "gradients/dipole_charge/filter_type_1/transpose_grad/transpose" op: "Transpose" - input: "gradients/dipole_charge/Reshape_12_grad/Reshape" + input: "gradients/dipole_charge/Reshape_14_grad/Reshape" input: "gradients/dipole_charge/filter_type_1/transpose_grad/InvertPermutation" attr { key: "T" @@ -15739,7 +15810,7 @@ node { node { name: "gradients/AddN" op: "AddN" - input: "gradients/dipole_charge/add_5_grad/Reshape" + input: "gradients/dipole_charge/add_4_grad/Reshape" input: "gradients/dipole_charge/layer_2_type_0/MatMul_grad/MatMul" attr { key: "N" @@ -15757,13 +15828,13 @@ node { key: "_class" value { list { - s: "loc:@gradients/dipole_charge/add_5_grad/Reshape" + s: "loc:@gradients/dipole_charge/add_4_grad/Reshape" } } } } node { - name: "gradients/dipole_charge/add_4_grad/Shape" + name: "gradients/dipole_charge/add_3_grad/Shape" op: "Shape" input: "dipole_charge/layer_0_type_0/Reshape" attr { @@ -15780,7 +15851,7 @@ node { } } node { - name: "gradients/dipole_charge/add_4_grad/Shape_1" + name: "gradients/dipole_charge/add_3_grad/Shape_1" op: "Shape" input: "dipole_charge/layer_1_type_0/mul" attr { @@ -15797,10 +15868,10 @@ node { } } node { - name: "gradients/dipole_charge/add_4_grad/BroadcastGradientArgs" + name: "gradients/dipole_charge/add_3_grad/BroadcastGradientArgs" op: "BroadcastGradientArgs" - input: "gradients/dipole_charge/add_4_grad/Shape" - input: "gradients/dipole_charge/add_4_grad/Shape_1" + input: "gradients/dipole_charge/add_3_grad/Shape" + input: "gradients/dipole_charge/add_3_grad/Shape_1" attr { key: "T" value { @@ -15809,10 +15880,10 @@ node { } } node { - name: "gradients/dipole_charge/add_4_grad/Sum" + name: "gradients/dipole_charge/add_3_grad/Sum" op: "Sum" input: "gradients/AddN" - input: "gradients/dipole_charge/add_4_grad/BroadcastGradientArgs" + input: "gradients/dipole_charge/add_3_grad/BroadcastGradientArgs" attr { key: "T" value { @@ -15833,10 +15904,10 @@ node { } } node { - name: "gradients/dipole_charge/add_4_grad/Reshape" + name: "gradients/dipole_charge/add_3_grad/Reshape" op: "Reshape" - input: "gradients/dipole_charge/add_4_grad/Sum" - input: "gradients/dipole_charge/add_4_grad/Shape" + input: "gradients/dipole_charge/add_3_grad/Sum" + input: "gradients/dipole_charge/add_3_grad/Shape" attr { key: "T" value { @@ -15851,10 +15922,10 @@ node { } } node { - name: "gradients/dipole_charge/add_4_grad/Sum_1" + name: "gradients/dipole_charge/add_3_grad/Sum_1" op: "Sum" input: "gradients/AddN" - input: "gradients/dipole_charge/add_4_grad/BroadcastGradientArgs:1" + input: "gradients/dipole_charge/add_3_grad/BroadcastGradientArgs:1" attr { key: "T" value { @@ -15875,10 +15946,10 @@ node { } } node { - name: "gradients/dipole_charge/add_4_grad/Reshape_1" + name: "gradients/dipole_charge/add_3_grad/Reshape_1" op: "Reshape" - input: "gradients/dipole_charge/add_4_grad/Sum_1" - input: "gradients/dipole_charge/add_4_grad/Shape_1" + input: "gradients/dipole_charge/add_3_grad/Sum_1" + input: "gradients/dipole_charge/add_3_grad/Shape_1" attr { key: "T" value { @@ -15941,7 +16012,7 @@ node { node { name: "gradients/dipole_charge/layer_1_type_0/mul_grad/Mul" op: "Mul" - input: "gradients/dipole_charge/add_4_grad/Reshape_1" + input: "gradients/dipole_charge/add_3_grad/Reshape_1" input: "dipole_charge/layer_1_type_0/idt/read" attr { key: "T" @@ -16066,7 +16137,7 @@ node { node { name: "gradients/AddN_1" op: "AddN" - input: "gradients/dipole_charge/add_4_grad/Reshape" + input: "gradients/dipole_charge/add_3_grad/Reshape" input: "gradients/dipole_charge/layer_1_type_0/MatMul_grad/MatMul" attr { key: "N" @@ -16084,7 +16155,7 @@ node { key: "_class" value { list { - s: "loc:@gradients/dipole_charge/add_4_grad/Reshape" + s: "loc:@gradients/dipole_charge/add_3_grad/Reshape" } } } @@ -16161,7 +16232,7 @@ node { } } node { - name: "gradients/dipole_charge/Reshape_15_grad/Shape" + name: "gradients/dipole_charge/Reshape_17_grad/Shape" op: "Shape" input: "dipole_charge/Slice_2" attr { @@ -16178,10 +16249,10 @@ node { } } node { - name: "gradients/dipole_charge/Reshape_15_grad/Reshape" + name: "gradients/dipole_charge/Reshape_17_grad/Reshape" op: "Reshape" input: "gradients/dipole_charge/layer_0_type_0/MatMul_grad/MatMul" - input: "gradients/dipole_charge/Reshape_15_grad/Shape" + input: "gradients/dipole_charge/Reshape_17_grad/Shape" attr { key: "T" value { @@ -16211,7 +16282,7 @@ node { dtype: DT_INT32 tensor_shape { } - int_val: 2 + int_val: 3 } } } @@ -16299,7 +16370,7 @@ node { node { name: "gradients/dipole_charge/Slice_2_grad/Shape_1" op: "Shape" - input: "dipole_charge/Reshape_13" + input: "dipole_charge/Reshape_15" attr { key: "T" value { @@ -16404,7 +16475,7 @@ node { node { name: "gradients/dipole_charge/Slice_2_grad/Pad" op: "Pad" - input: "gradients/dipole_charge/Reshape_15_grad/Reshape" + input: "gradients/dipole_charge/Reshape_17_grad/Reshape" input: "gradients/dipole_charge/Slice_2_grad/concat" attr { key: "T" @@ -16420,7 +16491,7 @@ node { } } node { - name: "gradients/dipole_charge/Reshape_13_grad/Shape" + name: "gradients/dipole_charge/Reshape_15_grad/Shape" op: "Shape" input: "dipole_charge/o_descriptor" attr { @@ -16437,10 +16508,10 @@ node { } } node { - name: "gradients/dipole_charge/Reshape_13_grad/Reshape" + name: "gradients/dipole_charge/Reshape_15_grad/Reshape" op: "Reshape" input: "gradients/dipole_charge/Slice_2_grad/Pad" - input: "gradients/dipole_charge/Reshape_13_grad/Shape" + input: "gradients/dipole_charge/Reshape_15_grad/Shape" attr { key: "T" value { @@ -16470,7 +16541,7 @@ node { dtype: DT_INT32 tensor_shape { } - int_val: 2 + int_val: 3 } } } @@ -16490,8 +16561,8 @@ node { node { name: "gradients/dipole_charge/concat_grad/ShapeN" op: "ShapeN" - input: "dipole_charge/Reshape_7" - input: "dipole_charge/Reshape_11" + input: "dipole_charge/Reshape_9" + input: "dipole_charge/Reshape_13" attr { key: "N" value { @@ -16527,7 +16598,7 @@ node { node { name: "gradients/dipole_charge/concat_grad/Slice" op: "Slice" - input: "gradients/dipole_charge/Reshape_13_grad/Reshape" + input: "gradients/dipole_charge/Reshape_15_grad/Reshape" input: "gradients/dipole_charge/concat_grad/ConcatOffset" input: "gradients/dipole_charge/concat_grad/ShapeN" attr { @@ -16546,7 +16617,7 @@ node { node { name: "gradients/dipole_charge/concat_grad/Slice_1" op: "Slice" - input: "gradients/dipole_charge/Reshape_13_grad/Reshape" + input: "gradients/dipole_charge/Reshape_15_grad/Reshape" input: "gradients/dipole_charge/concat_grad/ConcatOffset:1" input: "gradients/dipole_charge/concat_grad/ShapeN:1" attr { @@ -16563,7 +16634,7 @@ node { } } node { - name: "gradients/dipole_charge/Reshape_7_grad/Shape" + name: "gradients/dipole_charge/Reshape_9_grad/Shape" op: "Shape" input: "dipole_charge/filter_type_0/Reshape_14" attr { @@ -16580,10 +16651,10 @@ node { } } node { - name: "gradients/dipole_charge/Reshape_7_grad/Reshape" + name: "gradients/dipole_charge/Reshape_9_grad/Reshape" op: "Reshape" input: "gradients/dipole_charge/concat_grad/Slice" - input: "gradients/dipole_charge/Reshape_7_grad/Shape" + input: "gradients/dipole_charge/Reshape_9_grad/Shape" attr { key: "T" value { @@ -16598,7 +16669,7 @@ node { } } node { - name: "gradients/dipole_charge/Reshape_11_grad/Shape" + name: "gradients/dipole_charge/Reshape_13_grad/Shape" op: "Shape" input: "dipole_charge/filter_type_1/Reshape_14" attr { @@ -16615,10 +16686,10 @@ node { } } node { - name: "gradients/dipole_charge/Reshape_11_grad/Reshape" + name: "gradients/dipole_charge/Reshape_13_grad/Reshape" op: "Reshape" input: "gradients/dipole_charge/concat_grad/Slice_1" - input: "gradients/dipole_charge/Reshape_11_grad/Shape" + input: "gradients/dipole_charge/Reshape_13_grad/Shape" attr { key: "T" value { @@ -16652,7 +16723,7 @@ node { node { name: "gradients/dipole_charge/filter_type_0/Reshape_14_grad/Reshape" op: "Reshape" - input: "gradients/dipole_charge/Reshape_7_grad/Reshape" + input: "gradients/dipole_charge/Reshape_9_grad/Reshape" input: "gradients/dipole_charge/filter_type_0/Reshape_14_grad/Shape" attr { key: "T" @@ -16687,7 +16758,7 @@ node { node { name: "gradients/dipole_charge/filter_type_1/Reshape_14_grad/Reshape" op: "Reshape" - input: "gradients/dipole_charge/Reshape_11_grad/Reshape" + input: "gradients/dipole_charge/Reshape_13_grad/Reshape" input: "gradients/dipole_charge/filter_type_1/Reshape_14_grad/Shape" attr { key: "T" @@ -19984,7 +20055,7 @@ node { node { name: "gradients/dipole_charge/filter_type_0/Reshape_5_grad/Shape" op: "Shape" - input: "dipole_charge/filter_type_0/add_1" + input: "dipole_charge/filter_type_0/add_2" attr { key: "T" value { @@ -20054,7 +20125,7 @@ node { node { name: "gradients/dipole_charge/filter_type_0/Reshape_12_grad/Shape" op: "Shape" - input: "dipole_charge/filter_type_0/add_3" + input: "dipole_charge/filter_type_0/add_5" attr { key: "T" value { @@ -20124,7 +20195,7 @@ node { node { name: "gradients/dipole_charge/filter_type_1/Reshape_5_grad/Shape" op: "Shape" - input: "dipole_charge/filter_type_1/add_1" + input: "dipole_charge/filter_type_1/add_2" attr { key: "T" value { @@ -20194,7 +20265,7 @@ node { node { name: "gradients/dipole_charge/filter_type_1/Reshape_12_grad/Shape" op: "Shape" - input: "dipole_charge/filter_type_1/add_3" + input: "dipole_charge/filter_type_1/add_5" attr { key: "T" value { @@ -20227,9 +20298,9 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_0/add_1_grad/Shape" + name: "gradients/dipole_charge/filter_type_0/add_2_grad/Shape" op: "Shape" - input: "dipole_charge/filter_type_0/concat_1" + input: "dipole_charge/filter_type_0/concat_2" attr { key: "T" value { @@ -20244,7 +20315,7 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_0/add_1_grad/Shape_1" + name: "gradients/dipole_charge/filter_type_0/add_2_grad/Shape_1" op: "Shape" input: "dipole_charge/filter_type_0/Reshape_4" attr { @@ -20261,10 +20332,10 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_0/add_1_grad/BroadcastGradientArgs" + name: "gradients/dipole_charge/filter_type_0/add_2_grad/BroadcastGradientArgs" op: "BroadcastGradientArgs" - input: "gradients/dipole_charge/filter_type_0/add_1_grad/Shape" - input: "gradients/dipole_charge/filter_type_0/add_1_grad/Shape_1" + input: "gradients/dipole_charge/filter_type_0/add_2_grad/Shape" + input: "gradients/dipole_charge/filter_type_0/add_2_grad/Shape_1" attr { key: "T" value { @@ -20273,10 +20344,10 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_0/add_1_grad/Sum" + name: "gradients/dipole_charge/filter_type_0/add_2_grad/Sum" op: "Sum" input: "gradients/dipole_charge/filter_type_0/Reshape_5_grad/Reshape" - input: "gradients/dipole_charge/filter_type_0/add_1_grad/BroadcastGradientArgs" + input: "gradients/dipole_charge/filter_type_0/add_2_grad/BroadcastGradientArgs" attr { key: "T" value { @@ -20297,10 +20368,10 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_0/add_1_grad/Reshape" + name: "gradients/dipole_charge/filter_type_0/add_2_grad/Reshape" op: "Reshape" - input: "gradients/dipole_charge/filter_type_0/add_1_grad/Sum" - input: "gradients/dipole_charge/filter_type_0/add_1_grad/Shape" + input: "gradients/dipole_charge/filter_type_0/add_2_grad/Sum" + input: "gradients/dipole_charge/filter_type_0/add_2_grad/Shape" attr { key: "T" value { @@ -20315,10 +20386,10 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_0/add_1_grad/Sum_1" + name: "gradients/dipole_charge/filter_type_0/add_2_grad/Sum_1" op: "Sum" input: "gradients/dipole_charge/filter_type_0/Reshape_5_grad/Reshape" - input: "gradients/dipole_charge/filter_type_0/add_1_grad/BroadcastGradientArgs:1" + input: "gradients/dipole_charge/filter_type_0/add_2_grad/BroadcastGradientArgs:1" attr { key: "T" value { @@ -20339,10 +20410,10 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_0/add_1_grad/Reshape_1" + name: "gradients/dipole_charge/filter_type_0/add_2_grad/Reshape_1" op: "Reshape" - input: "gradients/dipole_charge/filter_type_0/add_1_grad/Sum_1" - input: "gradients/dipole_charge/filter_type_0/add_1_grad/Shape_1" + input: "gradients/dipole_charge/filter_type_0/add_2_grad/Sum_1" + input: "gradients/dipole_charge/filter_type_0/add_2_grad/Shape_1" attr { key: "T" value { @@ -20357,9 +20428,9 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_0/add_3_grad/Shape" + name: "gradients/dipole_charge/filter_type_0/add_5_grad/Shape" op: "Shape" - input: "dipole_charge/filter_type_0/concat_3" + input: "dipole_charge/filter_type_0/concat_5" attr { key: "T" value { @@ -20374,7 +20445,7 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_0/add_3_grad/Shape_1" + name: "gradients/dipole_charge/filter_type_0/add_5_grad/Shape_1" op: "Shape" input: "dipole_charge/filter_type_0/Reshape_11" attr { @@ -20391,10 +20462,10 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_0/add_3_grad/BroadcastGradientArgs" + name: "gradients/dipole_charge/filter_type_0/add_5_grad/BroadcastGradientArgs" op: "BroadcastGradientArgs" - input: "gradients/dipole_charge/filter_type_0/add_3_grad/Shape" - input: "gradients/dipole_charge/filter_type_0/add_3_grad/Shape_1" + input: "gradients/dipole_charge/filter_type_0/add_5_grad/Shape" + input: "gradients/dipole_charge/filter_type_0/add_5_grad/Shape_1" attr { key: "T" value { @@ -20403,10 +20474,10 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_0/add_3_grad/Sum" + name: "gradients/dipole_charge/filter_type_0/add_5_grad/Sum" op: "Sum" input: "gradients/dipole_charge/filter_type_0/Reshape_12_grad/Reshape" - input: "gradients/dipole_charge/filter_type_0/add_3_grad/BroadcastGradientArgs" + input: "gradients/dipole_charge/filter_type_0/add_5_grad/BroadcastGradientArgs" attr { key: "T" value { @@ -20427,10 +20498,10 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_0/add_3_grad/Reshape" + name: "gradients/dipole_charge/filter_type_0/add_5_grad/Reshape" op: "Reshape" - input: "gradients/dipole_charge/filter_type_0/add_3_grad/Sum" - input: "gradients/dipole_charge/filter_type_0/add_3_grad/Shape" + input: "gradients/dipole_charge/filter_type_0/add_5_grad/Sum" + input: "gradients/dipole_charge/filter_type_0/add_5_grad/Shape" attr { key: "T" value { @@ -20445,10 +20516,10 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_0/add_3_grad/Sum_1" + name: "gradients/dipole_charge/filter_type_0/add_5_grad/Sum_1" op: "Sum" input: "gradients/dipole_charge/filter_type_0/Reshape_12_grad/Reshape" - input: "gradients/dipole_charge/filter_type_0/add_3_grad/BroadcastGradientArgs:1" + input: "gradients/dipole_charge/filter_type_0/add_5_grad/BroadcastGradientArgs:1" attr { key: "T" value { @@ -20469,10 +20540,10 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_0/add_3_grad/Reshape_1" + name: "gradients/dipole_charge/filter_type_0/add_5_grad/Reshape_1" op: "Reshape" - input: "gradients/dipole_charge/filter_type_0/add_3_grad/Sum_1" - input: "gradients/dipole_charge/filter_type_0/add_3_grad/Shape_1" + input: "gradients/dipole_charge/filter_type_0/add_5_grad/Sum_1" + input: "gradients/dipole_charge/filter_type_0/add_5_grad/Shape_1" attr { key: "T" value { @@ -20487,9 +20558,9 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_1/add_1_grad/Shape" + name: "gradients/dipole_charge/filter_type_1/add_2_grad/Shape" op: "Shape" - input: "dipole_charge/filter_type_1/concat_1" + input: "dipole_charge/filter_type_1/concat_2" attr { key: "T" value { @@ -20504,7 +20575,7 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_1/add_1_grad/Shape_1" + name: "gradients/dipole_charge/filter_type_1/add_2_grad/Shape_1" op: "Shape" input: "dipole_charge/filter_type_1/Reshape_4" attr { @@ -20521,10 +20592,10 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_1/add_1_grad/BroadcastGradientArgs" + name: "gradients/dipole_charge/filter_type_1/add_2_grad/BroadcastGradientArgs" op: "BroadcastGradientArgs" - input: "gradients/dipole_charge/filter_type_1/add_1_grad/Shape" - input: "gradients/dipole_charge/filter_type_1/add_1_grad/Shape_1" + input: "gradients/dipole_charge/filter_type_1/add_2_grad/Shape" + input: "gradients/dipole_charge/filter_type_1/add_2_grad/Shape_1" attr { key: "T" value { @@ -20533,10 +20604,10 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_1/add_1_grad/Sum" + name: "gradients/dipole_charge/filter_type_1/add_2_grad/Sum" op: "Sum" input: "gradients/dipole_charge/filter_type_1/Reshape_5_grad/Reshape" - input: "gradients/dipole_charge/filter_type_1/add_1_grad/BroadcastGradientArgs" + input: "gradients/dipole_charge/filter_type_1/add_2_grad/BroadcastGradientArgs" attr { key: "T" value { @@ -20557,10 +20628,10 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_1/add_1_grad/Reshape" + name: "gradients/dipole_charge/filter_type_1/add_2_grad/Reshape" op: "Reshape" - input: "gradients/dipole_charge/filter_type_1/add_1_grad/Sum" - input: "gradients/dipole_charge/filter_type_1/add_1_grad/Shape" + input: "gradients/dipole_charge/filter_type_1/add_2_grad/Sum" + input: "gradients/dipole_charge/filter_type_1/add_2_grad/Shape" attr { key: "T" value { @@ -20575,10 +20646,10 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_1/add_1_grad/Sum_1" + name: "gradients/dipole_charge/filter_type_1/add_2_grad/Sum_1" op: "Sum" input: "gradients/dipole_charge/filter_type_1/Reshape_5_grad/Reshape" - input: "gradients/dipole_charge/filter_type_1/add_1_grad/BroadcastGradientArgs:1" + input: "gradients/dipole_charge/filter_type_1/add_2_grad/BroadcastGradientArgs:1" attr { key: "T" value { @@ -20599,10 +20670,10 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_1/add_1_grad/Reshape_1" + name: "gradients/dipole_charge/filter_type_1/add_2_grad/Reshape_1" op: "Reshape" - input: "gradients/dipole_charge/filter_type_1/add_1_grad/Sum_1" - input: "gradients/dipole_charge/filter_type_1/add_1_grad/Shape_1" + input: "gradients/dipole_charge/filter_type_1/add_2_grad/Sum_1" + input: "gradients/dipole_charge/filter_type_1/add_2_grad/Shape_1" attr { key: "T" value { @@ -20617,9 +20688,9 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_1/add_3_grad/Shape" + name: "gradients/dipole_charge/filter_type_1/add_5_grad/Shape" op: "Shape" - input: "dipole_charge/filter_type_1/concat_3" + input: "dipole_charge/filter_type_1/concat_5" attr { key: "T" value { @@ -20634,7 +20705,7 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_1/add_3_grad/Shape_1" + name: "gradients/dipole_charge/filter_type_1/add_5_grad/Shape_1" op: "Shape" input: "dipole_charge/filter_type_1/Reshape_11" attr { @@ -20651,10 +20722,10 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_1/add_3_grad/BroadcastGradientArgs" + name: "gradients/dipole_charge/filter_type_1/add_5_grad/BroadcastGradientArgs" op: "BroadcastGradientArgs" - input: "gradients/dipole_charge/filter_type_1/add_3_grad/Shape" - input: "gradients/dipole_charge/filter_type_1/add_3_grad/Shape_1" + input: "gradients/dipole_charge/filter_type_1/add_5_grad/Shape" + input: "gradients/dipole_charge/filter_type_1/add_5_grad/Shape_1" attr { key: "T" value { @@ -20663,10 +20734,10 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_1/add_3_grad/Sum" + name: "gradients/dipole_charge/filter_type_1/add_5_grad/Sum" op: "Sum" input: "gradients/dipole_charge/filter_type_1/Reshape_12_grad/Reshape" - input: "gradients/dipole_charge/filter_type_1/add_3_grad/BroadcastGradientArgs" + input: "gradients/dipole_charge/filter_type_1/add_5_grad/BroadcastGradientArgs" attr { key: "T" value { @@ -20687,10 +20758,10 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_1/add_3_grad/Reshape" + name: "gradients/dipole_charge/filter_type_1/add_5_grad/Reshape" op: "Reshape" - input: "gradients/dipole_charge/filter_type_1/add_3_grad/Sum" - input: "gradients/dipole_charge/filter_type_1/add_3_grad/Shape" + input: "gradients/dipole_charge/filter_type_1/add_5_grad/Sum" + input: "gradients/dipole_charge/filter_type_1/add_5_grad/Shape" attr { key: "T" value { @@ -20705,10 +20776,10 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_1/add_3_grad/Sum_1" + name: "gradients/dipole_charge/filter_type_1/add_5_grad/Sum_1" op: "Sum" input: "gradients/dipole_charge/filter_type_1/Reshape_12_grad/Reshape" - input: "gradients/dipole_charge/filter_type_1/add_3_grad/BroadcastGradientArgs:1" + input: "gradients/dipole_charge/filter_type_1/add_5_grad/BroadcastGradientArgs:1" attr { key: "T" value { @@ -20729,10 +20800,10 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_1/add_3_grad/Reshape_1" + name: "gradients/dipole_charge/filter_type_1/add_5_grad/Reshape_1" op: "Reshape" - input: "gradients/dipole_charge/filter_type_1/add_3_grad/Sum_1" - input: "gradients/dipole_charge/filter_type_1/add_3_grad/Shape_1" + input: "gradients/dipole_charge/filter_type_1/add_5_grad/Sum_1" + input: "gradients/dipole_charge/filter_type_1/add_5_grad/Shape_1" attr { key: "T" value { @@ -20747,7 +20818,7 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_0/concat_1_grad/Rank" + name: "gradients/dipole_charge/filter_type_0/concat_2_grad/Rank" op: "Const" attr { key: "dtype" @@ -20768,10 +20839,10 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_0/concat_1_grad/mod" + name: "gradients/dipole_charge/filter_type_0/concat_2_grad/mod" op: "FloorMod" - input: "dipole_charge/filter_type_0/concat_1/axis" - input: "gradients/dipole_charge/filter_type_0/concat_1_grad/Rank" + input: "dipole_charge/filter_type_0/concat_2/axis" + input: "gradients/dipole_charge/filter_type_0/concat_2_grad/Rank" attr { key: "T" value { @@ -20780,10 +20851,10 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_0/concat_1_grad/ShapeN" + name: "gradients/dipole_charge/filter_type_0/concat_2_grad/ShapeN" op: "ShapeN" - input: "dipole_charge/filter_type_0/add" - input: "dipole_charge/filter_type_0/add" + input: "dipole_charge/filter_type_0/add_1" + input: "dipole_charge/filter_type_0/add_1" attr { key: "N" value { @@ -20804,11 +20875,11 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_0/concat_1_grad/ConcatOffset" + name: "gradients/dipole_charge/filter_type_0/concat_2_grad/ConcatOffset" op: "ConcatOffset" - input: "gradients/dipole_charge/filter_type_0/concat_1_grad/mod" - input: "gradients/dipole_charge/filter_type_0/concat_1_grad/ShapeN" - input: "gradients/dipole_charge/filter_type_0/concat_1_grad/ShapeN:1" + input: "gradients/dipole_charge/filter_type_0/concat_2_grad/mod" + input: "gradients/dipole_charge/filter_type_0/concat_2_grad/ShapeN" + input: "gradients/dipole_charge/filter_type_0/concat_2_grad/ShapeN:1" attr { key: "N" value { @@ -20817,11 +20888,11 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_0/concat_1_grad/Slice" + name: "gradients/dipole_charge/filter_type_0/concat_2_grad/Slice" op: "Slice" - input: "gradients/dipole_charge/filter_type_0/add_1_grad/Reshape" - input: "gradients/dipole_charge/filter_type_0/concat_1_grad/ConcatOffset" - input: "gradients/dipole_charge/filter_type_0/concat_1_grad/ShapeN" + input: "gradients/dipole_charge/filter_type_0/add_2_grad/Reshape" + input: "gradients/dipole_charge/filter_type_0/concat_2_grad/ConcatOffset" + input: "gradients/dipole_charge/filter_type_0/concat_2_grad/ShapeN" attr { key: "Index" value { @@ -20836,11 +20907,11 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_0/concat_1_grad/Slice_1" + name: "gradients/dipole_charge/filter_type_0/concat_2_grad/Slice_1" op: "Slice" - input: "gradients/dipole_charge/filter_type_0/add_1_grad/Reshape" - input: "gradients/dipole_charge/filter_type_0/concat_1_grad/ConcatOffset:1" - input: "gradients/dipole_charge/filter_type_0/concat_1_grad/ShapeN:1" + input: "gradients/dipole_charge/filter_type_0/add_2_grad/Reshape" + input: "gradients/dipole_charge/filter_type_0/concat_2_grad/ConcatOffset:1" + input: "gradients/dipole_charge/filter_type_0/concat_2_grad/ShapeN:1" attr { key: "Index" value { @@ -20874,7 +20945,7 @@ node { node { name: "gradients/dipole_charge/filter_type_0/Reshape_4_grad/Reshape" op: "Reshape" - input: "gradients/dipole_charge/filter_type_0/add_1_grad/Reshape_1" + input: "gradients/dipole_charge/filter_type_0/add_2_grad/Reshape_1" input: "gradients/dipole_charge/filter_type_0/Reshape_4_grad/Shape" attr { key: "T" @@ -20890,7 +20961,7 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_0/concat_3_grad/Rank" + name: "gradients/dipole_charge/filter_type_0/concat_5_grad/Rank" op: "Const" attr { key: "dtype" @@ -20911,10 +20982,10 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_0/concat_3_grad/mod" + name: "gradients/dipole_charge/filter_type_0/concat_5_grad/mod" op: "FloorMod" - input: "dipole_charge/filter_type_0/concat_3/axis" - input: "gradients/dipole_charge/filter_type_0/concat_3_grad/Rank" + input: "dipole_charge/filter_type_0/concat_5/axis" + input: "gradients/dipole_charge/filter_type_0/concat_5_grad/Rank" attr { key: "T" value { @@ -20923,10 +20994,10 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_0/concat_3_grad/ShapeN" + name: "gradients/dipole_charge/filter_type_0/concat_5_grad/ShapeN" op: "ShapeN" - input: "dipole_charge/filter_type_0/add_2" - input: "dipole_charge/filter_type_0/add_2" + input: "dipole_charge/filter_type_0/add_4" + input: "dipole_charge/filter_type_0/add_4" attr { key: "N" value { @@ -20947,11 +21018,11 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_0/concat_3_grad/ConcatOffset" + name: "gradients/dipole_charge/filter_type_0/concat_5_grad/ConcatOffset" op: "ConcatOffset" - input: "gradients/dipole_charge/filter_type_0/concat_3_grad/mod" - input: "gradients/dipole_charge/filter_type_0/concat_3_grad/ShapeN" - input: "gradients/dipole_charge/filter_type_0/concat_3_grad/ShapeN:1" + input: "gradients/dipole_charge/filter_type_0/concat_5_grad/mod" + input: "gradients/dipole_charge/filter_type_0/concat_5_grad/ShapeN" + input: "gradients/dipole_charge/filter_type_0/concat_5_grad/ShapeN:1" attr { key: "N" value { @@ -20960,11 +21031,11 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_0/concat_3_grad/Slice" + name: "gradients/dipole_charge/filter_type_0/concat_5_grad/Slice" op: "Slice" - input: "gradients/dipole_charge/filter_type_0/add_3_grad/Reshape" - input: "gradients/dipole_charge/filter_type_0/concat_3_grad/ConcatOffset" - input: "gradients/dipole_charge/filter_type_0/concat_3_grad/ShapeN" + input: "gradients/dipole_charge/filter_type_0/add_5_grad/Reshape" + input: "gradients/dipole_charge/filter_type_0/concat_5_grad/ConcatOffset" + input: "gradients/dipole_charge/filter_type_0/concat_5_grad/ShapeN" attr { key: "Index" value { @@ -20979,11 +21050,11 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_0/concat_3_grad/Slice_1" + name: "gradients/dipole_charge/filter_type_0/concat_5_grad/Slice_1" op: "Slice" - input: "gradients/dipole_charge/filter_type_0/add_3_grad/Reshape" - input: "gradients/dipole_charge/filter_type_0/concat_3_grad/ConcatOffset:1" - input: "gradients/dipole_charge/filter_type_0/concat_3_grad/ShapeN:1" + input: "gradients/dipole_charge/filter_type_0/add_5_grad/Reshape" + input: "gradients/dipole_charge/filter_type_0/concat_5_grad/ConcatOffset:1" + input: "gradients/dipole_charge/filter_type_0/concat_5_grad/ShapeN:1" attr { key: "Index" value { @@ -21017,7 +21088,7 @@ node { node { name: "gradients/dipole_charge/filter_type_0/Reshape_11_grad/Reshape" op: "Reshape" - input: "gradients/dipole_charge/filter_type_0/add_3_grad/Reshape_1" + input: "gradients/dipole_charge/filter_type_0/add_5_grad/Reshape_1" input: "gradients/dipole_charge/filter_type_0/Reshape_11_grad/Shape" attr { key: "T" @@ -21033,7 +21104,7 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_1/concat_1_grad/Rank" + name: "gradients/dipole_charge/filter_type_1/concat_2_grad/Rank" op: "Const" attr { key: "dtype" @@ -21054,10 +21125,10 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_1/concat_1_grad/mod" + name: "gradients/dipole_charge/filter_type_1/concat_2_grad/mod" op: "FloorMod" - input: "dipole_charge/filter_type_1/concat_1/axis" - input: "gradients/dipole_charge/filter_type_1/concat_1_grad/Rank" + input: "dipole_charge/filter_type_1/concat_2/axis" + input: "gradients/dipole_charge/filter_type_1/concat_2_grad/Rank" attr { key: "T" value { @@ -21066,10 +21137,10 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_1/concat_1_grad/ShapeN" + name: "gradients/dipole_charge/filter_type_1/concat_2_grad/ShapeN" op: "ShapeN" - input: "dipole_charge/filter_type_1/add" - input: "dipole_charge/filter_type_1/add" + input: "dipole_charge/filter_type_1/add_1" + input: "dipole_charge/filter_type_1/add_1" attr { key: "N" value { @@ -21090,11 +21161,11 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_1/concat_1_grad/ConcatOffset" + name: "gradients/dipole_charge/filter_type_1/concat_2_grad/ConcatOffset" op: "ConcatOffset" - input: "gradients/dipole_charge/filter_type_1/concat_1_grad/mod" - input: "gradients/dipole_charge/filter_type_1/concat_1_grad/ShapeN" - input: "gradients/dipole_charge/filter_type_1/concat_1_grad/ShapeN:1" + input: "gradients/dipole_charge/filter_type_1/concat_2_grad/mod" + input: "gradients/dipole_charge/filter_type_1/concat_2_grad/ShapeN" + input: "gradients/dipole_charge/filter_type_1/concat_2_grad/ShapeN:1" attr { key: "N" value { @@ -21103,11 +21174,11 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_1/concat_1_grad/Slice" + name: "gradients/dipole_charge/filter_type_1/concat_2_grad/Slice" op: "Slice" - input: "gradients/dipole_charge/filter_type_1/add_1_grad/Reshape" - input: "gradients/dipole_charge/filter_type_1/concat_1_grad/ConcatOffset" - input: "gradients/dipole_charge/filter_type_1/concat_1_grad/ShapeN" + input: "gradients/dipole_charge/filter_type_1/add_2_grad/Reshape" + input: "gradients/dipole_charge/filter_type_1/concat_2_grad/ConcatOffset" + input: "gradients/dipole_charge/filter_type_1/concat_2_grad/ShapeN" attr { key: "Index" value { @@ -21122,11 +21193,11 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_1/concat_1_grad/Slice_1" + name: "gradients/dipole_charge/filter_type_1/concat_2_grad/Slice_1" op: "Slice" - input: "gradients/dipole_charge/filter_type_1/add_1_grad/Reshape" - input: "gradients/dipole_charge/filter_type_1/concat_1_grad/ConcatOffset:1" - input: "gradients/dipole_charge/filter_type_1/concat_1_grad/ShapeN:1" + input: "gradients/dipole_charge/filter_type_1/add_2_grad/Reshape" + input: "gradients/dipole_charge/filter_type_1/concat_2_grad/ConcatOffset:1" + input: "gradients/dipole_charge/filter_type_1/concat_2_grad/ShapeN:1" attr { key: "Index" value { @@ -21160,7 +21231,7 @@ node { node { name: "gradients/dipole_charge/filter_type_1/Reshape_4_grad/Reshape" op: "Reshape" - input: "gradients/dipole_charge/filter_type_1/add_1_grad/Reshape_1" + input: "gradients/dipole_charge/filter_type_1/add_2_grad/Reshape_1" input: "gradients/dipole_charge/filter_type_1/Reshape_4_grad/Shape" attr { key: "T" @@ -21176,7 +21247,7 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_1/concat_3_grad/Rank" + name: "gradients/dipole_charge/filter_type_1/concat_5_grad/Rank" op: "Const" attr { key: "dtype" @@ -21197,10 +21268,10 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_1/concat_3_grad/mod" + name: "gradients/dipole_charge/filter_type_1/concat_5_grad/mod" op: "FloorMod" - input: "dipole_charge/filter_type_1/concat_3/axis" - input: "gradients/dipole_charge/filter_type_1/concat_3_grad/Rank" + input: "dipole_charge/filter_type_1/concat_5/axis" + input: "gradients/dipole_charge/filter_type_1/concat_5_grad/Rank" attr { key: "T" value { @@ -21209,10 +21280,10 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_1/concat_3_grad/ShapeN" + name: "gradients/dipole_charge/filter_type_1/concat_5_grad/ShapeN" op: "ShapeN" - input: "dipole_charge/filter_type_1/add_2" - input: "dipole_charge/filter_type_1/add_2" + input: "dipole_charge/filter_type_1/add_4" + input: "dipole_charge/filter_type_1/add_4" attr { key: "N" value { @@ -21233,11 +21304,11 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_1/concat_3_grad/ConcatOffset" + name: "gradients/dipole_charge/filter_type_1/concat_5_grad/ConcatOffset" op: "ConcatOffset" - input: "gradients/dipole_charge/filter_type_1/concat_3_grad/mod" - input: "gradients/dipole_charge/filter_type_1/concat_3_grad/ShapeN" - input: "gradients/dipole_charge/filter_type_1/concat_3_grad/ShapeN:1" + input: "gradients/dipole_charge/filter_type_1/concat_5_grad/mod" + input: "gradients/dipole_charge/filter_type_1/concat_5_grad/ShapeN" + input: "gradients/dipole_charge/filter_type_1/concat_5_grad/ShapeN:1" attr { key: "N" value { @@ -21246,11 +21317,11 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_1/concat_3_grad/Slice" + name: "gradients/dipole_charge/filter_type_1/concat_5_grad/Slice" op: "Slice" - input: "gradients/dipole_charge/filter_type_1/add_3_grad/Reshape" - input: "gradients/dipole_charge/filter_type_1/concat_3_grad/ConcatOffset" - input: "gradients/dipole_charge/filter_type_1/concat_3_grad/ShapeN" + input: "gradients/dipole_charge/filter_type_1/add_5_grad/Reshape" + input: "gradients/dipole_charge/filter_type_1/concat_5_grad/ConcatOffset" + input: "gradients/dipole_charge/filter_type_1/concat_5_grad/ShapeN" attr { key: "Index" value { @@ -21265,11 +21336,11 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_1/concat_3_grad/Slice_1" + name: "gradients/dipole_charge/filter_type_1/concat_5_grad/Slice_1" op: "Slice" - input: "gradients/dipole_charge/filter_type_1/add_3_grad/Reshape" - input: "gradients/dipole_charge/filter_type_1/concat_3_grad/ConcatOffset:1" - input: "gradients/dipole_charge/filter_type_1/concat_3_grad/ShapeN:1" + input: "gradients/dipole_charge/filter_type_1/add_5_grad/Reshape" + input: "gradients/dipole_charge/filter_type_1/concat_5_grad/ConcatOffset:1" + input: "gradients/dipole_charge/filter_type_1/concat_5_grad/ShapeN:1" attr { key: "Index" value { @@ -21303,7 +21374,7 @@ node { node { name: "gradients/dipole_charge/filter_type_1/Reshape_11_grad/Reshape" op: "Reshape" - input: "gradients/dipole_charge/filter_type_1/add_3_grad/Reshape_1" + input: "gradients/dipole_charge/filter_type_1/add_5_grad/Reshape_1" input: "gradients/dipole_charge/filter_type_1/Reshape_11_grad/Shape" attr { key: "T" @@ -21465,8 +21536,8 @@ node { node { name: "gradients/AddN_4" op: "AddN" - input: "gradients/dipole_charge/filter_type_0/concat_1_grad/Slice" - input: "gradients/dipole_charge/filter_type_0/concat_1_grad/Slice_1" + input: "gradients/dipole_charge/filter_type_0/concat_2_grad/Slice" + input: "gradients/dipole_charge/filter_type_0/concat_2_grad/Slice_1" input: "gradients/dipole_charge/filter_type_0/MatMul_2_grad/MatMul" attr { key: "N" @@ -21484,15 +21555,15 @@ node { key: "_class" value { list { - s: "loc:@gradients/dipole_charge/filter_type_0/concat_1_grad/Slice" + s: "loc:@gradients/dipole_charge/filter_type_0/concat_2_grad/Slice" } } } } node { - name: "gradients/dipole_charge/filter_type_0/add_grad/Shape" + name: "gradients/dipole_charge/filter_type_0/add_1_grad/Shape" op: "Shape" - input: "dipole_charge/filter_type_0/concat" + input: "dipole_charge/filter_type_0/concat_1" attr { key: "T" value { @@ -21507,7 +21578,7 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_0/add_grad/Shape_1" + name: "gradients/dipole_charge/filter_type_0/add_1_grad/Shape_1" op: "Shape" input: "dipole_charge/filter_type_0/Reshape_3" attr { @@ -21524,10 +21595,10 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_0/add_grad/BroadcastGradientArgs" + name: "gradients/dipole_charge/filter_type_0/add_1_grad/BroadcastGradientArgs" op: "BroadcastGradientArgs" - input: "gradients/dipole_charge/filter_type_0/add_grad/Shape" - input: "gradients/dipole_charge/filter_type_0/add_grad/Shape_1" + input: "gradients/dipole_charge/filter_type_0/add_1_grad/Shape" + input: "gradients/dipole_charge/filter_type_0/add_1_grad/Shape_1" attr { key: "T" value { @@ -21536,10 +21607,10 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_0/add_grad/Sum" + name: "gradients/dipole_charge/filter_type_0/add_1_grad/Sum" op: "Sum" input: "gradients/AddN_4" - input: "gradients/dipole_charge/filter_type_0/add_grad/BroadcastGradientArgs" + input: "gradients/dipole_charge/filter_type_0/add_1_grad/BroadcastGradientArgs" attr { key: "T" value { @@ -21560,10 +21631,10 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_0/add_grad/Reshape" + name: "gradients/dipole_charge/filter_type_0/add_1_grad/Reshape" op: "Reshape" - input: "gradients/dipole_charge/filter_type_0/add_grad/Sum" - input: "gradients/dipole_charge/filter_type_0/add_grad/Shape" + input: "gradients/dipole_charge/filter_type_0/add_1_grad/Sum" + input: "gradients/dipole_charge/filter_type_0/add_1_grad/Shape" attr { key: "T" value { @@ -21578,10 +21649,10 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_0/add_grad/Sum_1" + name: "gradients/dipole_charge/filter_type_0/add_1_grad/Sum_1" op: "Sum" input: "gradients/AddN_4" - input: "gradients/dipole_charge/filter_type_0/add_grad/BroadcastGradientArgs:1" + input: "gradients/dipole_charge/filter_type_0/add_1_grad/BroadcastGradientArgs:1" attr { key: "T" value { @@ -21602,10 +21673,10 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_0/add_grad/Reshape_1" + name: "gradients/dipole_charge/filter_type_0/add_1_grad/Reshape_1" op: "Reshape" - input: "gradients/dipole_charge/filter_type_0/add_grad/Sum_1" - input: "gradients/dipole_charge/filter_type_0/add_grad/Shape_1" + input: "gradients/dipole_charge/filter_type_0/add_1_grad/Sum_1" + input: "gradients/dipole_charge/filter_type_0/add_1_grad/Shape_1" attr { key: "T" value { @@ -21622,8 +21693,8 @@ node { node { name: "gradients/AddN_5" op: "AddN" - input: "gradients/dipole_charge/filter_type_0/concat_3_grad/Slice" - input: "gradients/dipole_charge/filter_type_0/concat_3_grad/Slice_1" + input: "gradients/dipole_charge/filter_type_0/concat_5_grad/Slice" + input: "gradients/dipole_charge/filter_type_0/concat_5_grad/Slice_1" input: "gradients/dipole_charge/filter_type_0/MatMul_6_grad/MatMul" attr { key: "N" @@ -21641,15 +21712,15 @@ node { key: "_class" value { list { - s: "loc:@gradients/dipole_charge/filter_type_0/concat_3_grad/Slice" + s: "loc:@gradients/dipole_charge/filter_type_0/concat_5_grad/Slice" } } } } node { - name: "gradients/dipole_charge/filter_type_0/add_2_grad/Shape" + name: "gradients/dipole_charge/filter_type_0/add_4_grad/Shape" op: "Shape" - input: "dipole_charge/filter_type_0/concat_2" + input: "dipole_charge/filter_type_0/concat_4" attr { key: "T" value { @@ -21664,7 +21735,7 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_0/add_2_grad/Shape_1" + name: "gradients/dipole_charge/filter_type_0/add_4_grad/Shape_1" op: "Shape" input: "dipole_charge/filter_type_0/Reshape_10" attr { @@ -21681,10 +21752,10 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_0/add_2_grad/BroadcastGradientArgs" + name: "gradients/dipole_charge/filter_type_0/add_4_grad/BroadcastGradientArgs" op: "BroadcastGradientArgs" - input: "gradients/dipole_charge/filter_type_0/add_2_grad/Shape" - input: "gradients/dipole_charge/filter_type_0/add_2_grad/Shape_1" + input: "gradients/dipole_charge/filter_type_0/add_4_grad/Shape" + input: "gradients/dipole_charge/filter_type_0/add_4_grad/Shape_1" attr { key: "T" value { @@ -21693,10 +21764,10 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_0/add_2_grad/Sum" + name: "gradients/dipole_charge/filter_type_0/add_4_grad/Sum" op: "Sum" input: "gradients/AddN_5" - input: "gradients/dipole_charge/filter_type_0/add_2_grad/BroadcastGradientArgs" + input: "gradients/dipole_charge/filter_type_0/add_4_grad/BroadcastGradientArgs" attr { key: "T" value { @@ -21717,10 +21788,10 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_0/add_2_grad/Reshape" + name: "gradients/dipole_charge/filter_type_0/add_4_grad/Reshape" op: "Reshape" - input: "gradients/dipole_charge/filter_type_0/add_2_grad/Sum" - input: "gradients/dipole_charge/filter_type_0/add_2_grad/Shape" + input: "gradients/dipole_charge/filter_type_0/add_4_grad/Sum" + input: "gradients/dipole_charge/filter_type_0/add_4_grad/Shape" attr { key: "T" value { @@ -21735,10 +21806,10 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_0/add_2_grad/Sum_1" + name: "gradients/dipole_charge/filter_type_0/add_4_grad/Sum_1" op: "Sum" input: "gradients/AddN_5" - input: "gradients/dipole_charge/filter_type_0/add_2_grad/BroadcastGradientArgs:1" + input: "gradients/dipole_charge/filter_type_0/add_4_grad/BroadcastGradientArgs:1" attr { key: "T" value { @@ -21759,10 +21830,10 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_0/add_2_grad/Reshape_1" + name: "gradients/dipole_charge/filter_type_0/add_4_grad/Reshape_1" op: "Reshape" - input: "gradients/dipole_charge/filter_type_0/add_2_grad/Sum_1" - input: "gradients/dipole_charge/filter_type_0/add_2_grad/Shape_1" + input: "gradients/dipole_charge/filter_type_0/add_4_grad/Sum_1" + input: "gradients/dipole_charge/filter_type_0/add_4_grad/Shape_1" attr { key: "T" value { @@ -21779,8 +21850,8 @@ node { node { name: "gradients/AddN_6" op: "AddN" - input: "gradients/dipole_charge/filter_type_1/concat_1_grad/Slice" - input: "gradients/dipole_charge/filter_type_1/concat_1_grad/Slice_1" + input: "gradients/dipole_charge/filter_type_1/concat_2_grad/Slice" + input: "gradients/dipole_charge/filter_type_1/concat_2_grad/Slice_1" input: "gradients/dipole_charge/filter_type_1/MatMul_2_grad/MatMul" attr { key: "N" @@ -21798,15 +21869,15 @@ node { key: "_class" value { list { - s: "loc:@gradients/dipole_charge/filter_type_1/concat_1_grad/Slice" + s: "loc:@gradients/dipole_charge/filter_type_1/concat_2_grad/Slice" } } } } node { - name: "gradients/dipole_charge/filter_type_1/add_grad/Shape" + name: "gradients/dipole_charge/filter_type_1/add_1_grad/Shape" op: "Shape" - input: "dipole_charge/filter_type_1/concat" + input: "dipole_charge/filter_type_1/concat_1" attr { key: "T" value { @@ -21821,7 +21892,7 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_1/add_grad/Shape_1" + name: "gradients/dipole_charge/filter_type_1/add_1_grad/Shape_1" op: "Shape" input: "dipole_charge/filter_type_1/Reshape_3" attr { @@ -21838,10 +21909,10 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_1/add_grad/BroadcastGradientArgs" + name: "gradients/dipole_charge/filter_type_1/add_1_grad/BroadcastGradientArgs" op: "BroadcastGradientArgs" - input: "gradients/dipole_charge/filter_type_1/add_grad/Shape" - input: "gradients/dipole_charge/filter_type_1/add_grad/Shape_1" + input: "gradients/dipole_charge/filter_type_1/add_1_grad/Shape" + input: "gradients/dipole_charge/filter_type_1/add_1_grad/Shape_1" attr { key: "T" value { @@ -21850,10 +21921,10 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_1/add_grad/Sum" + name: "gradients/dipole_charge/filter_type_1/add_1_grad/Sum" op: "Sum" input: "gradients/AddN_6" - input: "gradients/dipole_charge/filter_type_1/add_grad/BroadcastGradientArgs" + input: "gradients/dipole_charge/filter_type_1/add_1_grad/BroadcastGradientArgs" attr { key: "T" value { @@ -21874,10 +21945,10 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_1/add_grad/Reshape" + name: "gradients/dipole_charge/filter_type_1/add_1_grad/Reshape" op: "Reshape" - input: "gradients/dipole_charge/filter_type_1/add_grad/Sum" - input: "gradients/dipole_charge/filter_type_1/add_grad/Shape" + input: "gradients/dipole_charge/filter_type_1/add_1_grad/Sum" + input: "gradients/dipole_charge/filter_type_1/add_1_grad/Shape" attr { key: "T" value { @@ -21892,10 +21963,10 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_1/add_grad/Sum_1" + name: "gradients/dipole_charge/filter_type_1/add_1_grad/Sum_1" op: "Sum" input: "gradients/AddN_6" - input: "gradients/dipole_charge/filter_type_1/add_grad/BroadcastGradientArgs:1" + input: "gradients/dipole_charge/filter_type_1/add_1_grad/BroadcastGradientArgs:1" attr { key: "T" value { @@ -21916,10 +21987,10 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_1/add_grad/Reshape_1" + name: "gradients/dipole_charge/filter_type_1/add_1_grad/Reshape_1" op: "Reshape" - input: "gradients/dipole_charge/filter_type_1/add_grad/Sum_1" - input: "gradients/dipole_charge/filter_type_1/add_grad/Shape_1" + input: "gradients/dipole_charge/filter_type_1/add_1_grad/Sum_1" + input: "gradients/dipole_charge/filter_type_1/add_1_grad/Shape_1" attr { key: "T" value { @@ -21936,8 +22007,8 @@ node { node { name: "gradients/AddN_7" op: "AddN" - input: "gradients/dipole_charge/filter_type_1/concat_3_grad/Slice" - input: "gradients/dipole_charge/filter_type_1/concat_3_grad/Slice_1" + input: "gradients/dipole_charge/filter_type_1/concat_5_grad/Slice" + input: "gradients/dipole_charge/filter_type_1/concat_5_grad/Slice_1" input: "gradients/dipole_charge/filter_type_1/MatMul_6_grad/MatMul" attr { key: "N" @@ -21955,15 +22026,15 @@ node { key: "_class" value { list { - s: "loc:@gradients/dipole_charge/filter_type_1/concat_3_grad/Slice" + s: "loc:@gradients/dipole_charge/filter_type_1/concat_5_grad/Slice" } } } } node { - name: "gradients/dipole_charge/filter_type_1/add_2_grad/Shape" + name: "gradients/dipole_charge/filter_type_1/add_4_grad/Shape" op: "Shape" - input: "dipole_charge/filter_type_1/concat_2" + input: "dipole_charge/filter_type_1/concat_4" attr { key: "T" value { @@ -21978,7 +22049,7 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_1/add_2_grad/Shape_1" + name: "gradients/dipole_charge/filter_type_1/add_4_grad/Shape_1" op: "Shape" input: "dipole_charge/filter_type_1/Reshape_10" attr { @@ -21995,10 +22066,10 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_1/add_2_grad/BroadcastGradientArgs" + name: "gradients/dipole_charge/filter_type_1/add_4_grad/BroadcastGradientArgs" op: "BroadcastGradientArgs" - input: "gradients/dipole_charge/filter_type_1/add_2_grad/Shape" - input: "gradients/dipole_charge/filter_type_1/add_2_grad/Shape_1" + input: "gradients/dipole_charge/filter_type_1/add_4_grad/Shape" + input: "gradients/dipole_charge/filter_type_1/add_4_grad/Shape_1" attr { key: "T" value { @@ -22007,10 +22078,10 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_1/add_2_grad/Sum" + name: "gradients/dipole_charge/filter_type_1/add_4_grad/Sum" op: "Sum" input: "gradients/AddN_7" - input: "gradients/dipole_charge/filter_type_1/add_2_grad/BroadcastGradientArgs" + input: "gradients/dipole_charge/filter_type_1/add_4_grad/BroadcastGradientArgs" attr { key: "T" value { @@ -22031,10 +22102,10 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_1/add_2_grad/Reshape" + name: "gradients/dipole_charge/filter_type_1/add_4_grad/Reshape" op: "Reshape" - input: "gradients/dipole_charge/filter_type_1/add_2_grad/Sum" - input: "gradients/dipole_charge/filter_type_1/add_2_grad/Shape" + input: "gradients/dipole_charge/filter_type_1/add_4_grad/Sum" + input: "gradients/dipole_charge/filter_type_1/add_4_grad/Shape" attr { key: "T" value { @@ -22049,10 +22120,10 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_1/add_2_grad/Sum_1" + name: "gradients/dipole_charge/filter_type_1/add_4_grad/Sum_1" op: "Sum" input: "gradients/AddN_7" - input: "gradients/dipole_charge/filter_type_1/add_2_grad/BroadcastGradientArgs:1" + input: "gradients/dipole_charge/filter_type_1/add_4_grad/BroadcastGradientArgs:1" attr { key: "T" value { @@ -22073,10 +22144,10 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_1/add_2_grad/Reshape_1" + name: "gradients/dipole_charge/filter_type_1/add_4_grad/Reshape_1" op: "Reshape" - input: "gradients/dipole_charge/filter_type_1/add_2_grad/Sum_1" - input: "gradients/dipole_charge/filter_type_1/add_2_grad/Shape_1" + input: "gradients/dipole_charge/filter_type_1/add_4_grad/Sum_1" + input: "gradients/dipole_charge/filter_type_1/add_4_grad/Shape_1" attr { key: "T" value { @@ -22091,7 +22162,7 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_0/concat_grad/Rank" + name: "gradients/dipole_charge/filter_type_0/concat_1_grad/Rank" op: "Const" attr { key: "dtype" @@ -22112,10 +22183,10 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_0/concat_grad/mod" + name: "gradients/dipole_charge/filter_type_0/concat_1_grad/mod" op: "FloorMod" - input: "dipole_charge/filter_type_0/concat/axis" - input: "gradients/dipole_charge/filter_type_0/concat_grad/Rank" + input: "dipole_charge/filter_type_0/concat_1/axis" + input: "gradients/dipole_charge/filter_type_0/concat_1_grad/Rank" attr { key: "T" value { @@ -22124,10 +22195,10 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_0/concat_grad/ShapeN" + name: "gradients/dipole_charge/filter_type_0/concat_1_grad/ShapeN" op: "ShapeN" - input: "dipole_charge/filter_type_0/Reshape_2" - input: "dipole_charge/filter_type_0/Reshape_2" + input: "dipole_charge/filter_type_0/add" + input: "dipole_charge/filter_type_0/add" attr { key: "N" value { @@ -22148,11 +22219,11 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_0/concat_grad/ConcatOffset" + name: "gradients/dipole_charge/filter_type_0/concat_1_grad/ConcatOffset" op: "ConcatOffset" - input: "gradients/dipole_charge/filter_type_0/concat_grad/mod" - input: "gradients/dipole_charge/filter_type_0/concat_grad/ShapeN" - input: "gradients/dipole_charge/filter_type_0/concat_grad/ShapeN:1" + input: "gradients/dipole_charge/filter_type_0/concat_1_grad/mod" + input: "gradients/dipole_charge/filter_type_0/concat_1_grad/ShapeN" + input: "gradients/dipole_charge/filter_type_0/concat_1_grad/ShapeN:1" attr { key: "N" value { @@ -22161,11 +22232,11 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_0/concat_grad/Slice" + name: "gradients/dipole_charge/filter_type_0/concat_1_grad/Slice" op: "Slice" - input: "gradients/dipole_charge/filter_type_0/add_grad/Reshape" - input: "gradients/dipole_charge/filter_type_0/concat_grad/ConcatOffset" - input: "gradients/dipole_charge/filter_type_0/concat_grad/ShapeN" + input: "gradients/dipole_charge/filter_type_0/add_1_grad/Reshape" + input: "gradients/dipole_charge/filter_type_0/concat_1_grad/ConcatOffset" + input: "gradients/dipole_charge/filter_type_0/concat_1_grad/ShapeN" attr { key: "Index" value { @@ -22180,11 +22251,11 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_0/concat_grad/Slice_1" + name: "gradients/dipole_charge/filter_type_0/concat_1_grad/Slice_1" op: "Slice" - input: "gradients/dipole_charge/filter_type_0/add_grad/Reshape" - input: "gradients/dipole_charge/filter_type_0/concat_grad/ConcatOffset:1" - input: "gradients/dipole_charge/filter_type_0/concat_grad/ShapeN:1" + input: "gradients/dipole_charge/filter_type_0/add_1_grad/Reshape" + input: "gradients/dipole_charge/filter_type_0/concat_1_grad/ConcatOffset:1" + input: "gradients/dipole_charge/filter_type_0/concat_1_grad/ShapeN:1" attr { key: "Index" value { @@ -22218,7 +22289,7 @@ node { node { name: "gradients/dipole_charge/filter_type_0/Reshape_3_grad/Reshape" op: "Reshape" - input: "gradients/dipole_charge/filter_type_0/add_grad/Reshape_1" + input: "gradients/dipole_charge/filter_type_0/add_1_grad/Reshape_1" input: "gradients/dipole_charge/filter_type_0/Reshape_3_grad/Shape" attr { key: "T" @@ -22234,7 +22305,7 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_0/concat_2_grad/Rank" + name: "gradients/dipole_charge/filter_type_0/concat_4_grad/Rank" op: "Const" attr { key: "dtype" @@ -22255,10 +22326,10 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_0/concat_2_grad/mod" + name: "gradients/dipole_charge/filter_type_0/concat_4_grad/mod" op: "FloorMod" - input: "dipole_charge/filter_type_0/concat_2/axis" - input: "gradients/dipole_charge/filter_type_0/concat_2_grad/Rank" + input: "dipole_charge/filter_type_0/concat_4/axis" + input: "gradients/dipole_charge/filter_type_0/concat_4_grad/Rank" attr { key: "T" value { @@ -22267,10 +22338,10 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_0/concat_2_grad/ShapeN" + name: "gradients/dipole_charge/filter_type_0/concat_4_grad/ShapeN" op: "ShapeN" - input: "dipole_charge/filter_type_0/Reshape_9" - input: "dipole_charge/filter_type_0/Reshape_9" + input: "dipole_charge/filter_type_0/add_3" + input: "dipole_charge/filter_type_0/add_3" attr { key: "N" value { @@ -22291,11 +22362,11 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_0/concat_2_grad/ConcatOffset" + name: "gradients/dipole_charge/filter_type_0/concat_4_grad/ConcatOffset" op: "ConcatOffset" - input: "gradients/dipole_charge/filter_type_0/concat_2_grad/mod" - input: "gradients/dipole_charge/filter_type_0/concat_2_grad/ShapeN" - input: "gradients/dipole_charge/filter_type_0/concat_2_grad/ShapeN:1" + input: "gradients/dipole_charge/filter_type_0/concat_4_grad/mod" + input: "gradients/dipole_charge/filter_type_0/concat_4_grad/ShapeN" + input: "gradients/dipole_charge/filter_type_0/concat_4_grad/ShapeN:1" attr { key: "N" value { @@ -22304,11 +22375,11 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_0/concat_2_grad/Slice" + name: "gradients/dipole_charge/filter_type_0/concat_4_grad/Slice" op: "Slice" - input: "gradients/dipole_charge/filter_type_0/add_2_grad/Reshape" - input: "gradients/dipole_charge/filter_type_0/concat_2_grad/ConcatOffset" - input: "gradients/dipole_charge/filter_type_0/concat_2_grad/ShapeN" + input: "gradients/dipole_charge/filter_type_0/add_4_grad/Reshape" + input: "gradients/dipole_charge/filter_type_0/concat_4_grad/ConcatOffset" + input: "gradients/dipole_charge/filter_type_0/concat_4_grad/ShapeN" attr { key: "Index" value { @@ -22323,11 +22394,11 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_0/concat_2_grad/Slice_1" + name: "gradients/dipole_charge/filter_type_0/concat_4_grad/Slice_1" op: "Slice" - input: "gradients/dipole_charge/filter_type_0/add_2_grad/Reshape" - input: "gradients/dipole_charge/filter_type_0/concat_2_grad/ConcatOffset:1" - input: "gradients/dipole_charge/filter_type_0/concat_2_grad/ShapeN:1" + input: "gradients/dipole_charge/filter_type_0/add_4_grad/Reshape" + input: "gradients/dipole_charge/filter_type_0/concat_4_grad/ConcatOffset:1" + input: "gradients/dipole_charge/filter_type_0/concat_4_grad/ShapeN:1" attr { key: "Index" value { @@ -22361,7 +22432,7 @@ node { node { name: "gradients/dipole_charge/filter_type_0/Reshape_10_grad/Reshape" op: "Reshape" - input: "gradients/dipole_charge/filter_type_0/add_2_grad/Reshape_1" + input: "gradients/dipole_charge/filter_type_0/add_4_grad/Reshape_1" input: "gradients/dipole_charge/filter_type_0/Reshape_10_grad/Shape" attr { key: "T" @@ -22377,7 +22448,7 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_1/concat_grad/Rank" + name: "gradients/dipole_charge/filter_type_1/concat_1_grad/Rank" op: "Const" attr { key: "dtype" @@ -22398,10 +22469,10 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_1/concat_grad/mod" + name: "gradients/dipole_charge/filter_type_1/concat_1_grad/mod" op: "FloorMod" - input: "dipole_charge/filter_type_1/concat/axis" - input: "gradients/dipole_charge/filter_type_1/concat_grad/Rank" + input: "dipole_charge/filter_type_1/concat_1/axis" + input: "gradients/dipole_charge/filter_type_1/concat_1_grad/Rank" attr { key: "T" value { @@ -22410,10 +22481,10 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_1/concat_grad/ShapeN" + name: "gradients/dipole_charge/filter_type_1/concat_1_grad/ShapeN" op: "ShapeN" - input: "dipole_charge/filter_type_1/Reshape_2" - input: "dipole_charge/filter_type_1/Reshape_2" + input: "dipole_charge/filter_type_1/add" + input: "dipole_charge/filter_type_1/add" attr { key: "N" value { @@ -22434,11 +22505,11 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_1/concat_grad/ConcatOffset" + name: "gradients/dipole_charge/filter_type_1/concat_1_grad/ConcatOffset" op: "ConcatOffset" - input: "gradients/dipole_charge/filter_type_1/concat_grad/mod" - input: "gradients/dipole_charge/filter_type_1/concat_grad/ShapeN" - input: "gradients/dipole_charge/filter_type_1/concat_grad/ShapeN:1" + input: "gradients/dipole_charge/filter_type_1/concat_1_grad/mod" + input: "gradients/dipole_charge/filter_type_1/concat_1_grad/ShapeN" + input: "gradients/dipole_charge/filter_type_1/concat_1_grad/ShapeN:1" attr { key: "N" value { @@ -22447,11 +22518,11 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_1/concat_grad/Slice" + name: "gradients/dipole_charge/filter_type_1/concat_1_grad/Slice" op: "Slice" - input: "gradients/dipole_charge/filter_type_1/add_grad/Reshape" - input: "gradients/dipole_charge/filter_type_1/concat_grad/ConcatOffset" - input: "gradients/dipole_charge/filter_type_1/concat_grad/ShapeN" + input: "gradients/dipole_charge/filter_type_1/add_1_grad/Reshape" + input: "gradients/dipole_charge/filter_type_1/concat_1_grad/ConcatOffset" + input: "gradients/dipole_charge/filter_type_1/concat_1_grad/ShapeN" attr { key: "Index" value { @@ -22466,11 +22537,11 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_1/concat_grad/Slice_1" + name: "gradients/dipole_charge/filter_type_1/concat_1_grad/Slice_1" op: "Slice" - input: "gradients/dipole_charge/filter_type_1/add_grad/Reshape" - input: "gradients/dipole_charge/filter_type_1/concat_grad/ConcatOffset:1" - input: "gradients/dipole_charge/filter_type_1/concat_grad/ShapeN:1" + input: "gradients/dipole_charge/filter_type_1/add_1_grad/Reshape" + input: "gradients/dipole_charge/filter_type_1/concat_1_grad/ConcatOffset:1" + input: "gradients/dipole_charge/filter_type_1/concat_1_grad/ShapeN:1" attr { key: "Index" value { @@ -22504,7 +22575,7 @@ node { node { name: "gradients/dipole_charge/filter_type_1/Reshape_3_grad/Reshape" op: "Reshape" - input: "gradients/dipole_charge/filter_type_1/add_grad/Reshape_1" + input: "gradients/dipole_charge/filter_type_1/add_1_grad/Reshape_1" input: "gradients/dipole_charge/filter_type_1/Reshape_3_grad/Shape" attr { key: "T" @@ -22520,7 +22591,7 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_1/concat_2_grad/Rank" + name: "gradients/dipole_charge/filter_type_1/concat_4_grad/Rank" op: "Const" attr { key: "dtype" @@ -22541,10 +22612,10 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_1/concat_2_grad/mod" + name: "gradients/dipole_charge/filter_type_1/concat_4_grad/mod" op: "FloorMod" - input: "dipole_charge/filter_type_1/concat_2/axis" - input: "gradients/dipole_charge/filter_type_1/concat_2_grad/Rank" + input: "dipole_charge/filter_type_1/concat_4/axis" + input: "gradients/dipole_charge/filter_type_1/concat_4_grad/Rank" attr { key: "T" value { @@ -22553,10 +22624,10 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_1/concat_2_grad/ShapeN" + name: "gradients/dipole_charge/filter_type_1/concat_4_grad/ShapeN" op: "ShapeN" - input: "dipole_charge/filter_type_1/Reshape_9" - input: "dipole_charge/filter_type_1/Reshape_9" + input: "dipole_charge/filter_type_1/add_3" + input: "dipole_charge/filter_type_1/add_3" attr { key: "N" value { @@ -22577,11 +22648,11 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_1/concat_2_grad/ConcatOffset" + name: "gradients/dipole_charge/filter_type_1/concat_4_grad/ConcatOffset" op: "ConcatOffset" - input: "gradients/dipole_charge/filter_type_1/concat_2_grad/mod" - input: "gradients/dipole_charge/filter_type_1/concat_2_grad/ShapeN" - input: "gradients/dipole_charge/filter_type_1/concat_2_grad/ShapeN:1" + input: "gradients/dipole_charge/filter_type_1/concat_4_grad/mod" + input: "gradients/dipole_charge/filter_type_1/concat_4_grad/ShapeN" + input: "gradients/dipole_charge/filter_type_1/concat_4_grad/ShapeN:1" attr { key: "N" value { @@ -22590,11 +22661,11 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_1/concat_2_grad/Slice" + name: "gradients/dipole_charge/filter_type_1/concat_4_grad/Slice" op: "Slice" - input: "gradients/dipole_charge/filter_type_1/add_2_grad/Reshape" - input: "gradients/dipole_charge/filter_type_1/concat_2_grad/ConcatOffset" - input: "gradients/dipole_charge/filter_type_1/concat_2_grad/ShapeN" + input: "gradients/dipole_charge/filter_type_1/add_4_grad/Reshape" + input: "gradients/dipole_charge/filter_type_1/concat_4_grad/ConcatOffset" + input: "gradients/dipole_charge/filter_type_1/concat_4_grad/ShapeN" attr { key: "Index" value { @@ -22609,11 +22680,11 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_1/concat_2_grad/Slice_1" + name: "gradients/dipole_charge/filter_type_1/concat_4_grad/Slice_1" op: "Slice" - input: "gradients/dipole_charge/filter_type_1/add_2_grad/Reshape" - input: "gradients/dipole_charge/filter_type_1/concat_2_grad/ConcatOffset:1" - input: "gradients/dipole_charge/filter_type_1/concat_2_grad/ShapeN:1" + input: "gradients/dipole_charge/filter_type_1/add_4_grad/Reshape" + input: "gradients/dipole_charge/filter_type_1/concat_4_grad/ConcatOffset:1" + input: "gradients/dipole_charge/filter_type_1/concat_4_grad/ShapeN:1" attr { key: "Index" value { @@ -22647,7 +22718,7 @@ node { node { name: "gradients/dipole_charge/filter_type_1/Reshape_10_grad/Reshape" op: "Reshape" - input: "gradients/dipole_charge/filter_type_1/add_2_grad/Reshape_1" + input: "gradients/dipole_charge/filter_type_1/add_4_grad/Reshape_1" input: "gradients/dipole_charge/filter_type_1/Reshape_10_grad/Shape" attr { key: "T" @@ -22809,8 +22880,8 @@ node { node { name: "gradients/AddN_8" op: "AddN" - input: "gradients/dipole_charge/filter_type_0/concat_grad/Slice" - input: "gradients/dipole_charge/filter_type_0/concat_grad/Slice_1" + input: "gradients/dipole_charge/filter_type_0/concat_1_grad/Slice" + input: "gradients/dipole_charge/filter_type_0/concat_1_grad/Slice_1" input: "gradients/dipole_charge/filter_type_0/MatMul_1_grad/MatMul" attr { key: "N" @@ -22828,15 +22899,15 @@ node { key: "_class" value { list { - s: "loc:@gradients/dipole_charge/filter_type_0/concat_grad/Slice" + s: "loc:@gradients/dipole_charge/filter_type_0/concat_1_grad/Slice" } } } } node { - name: "gradients/dipole_charge/filter_type_0/Reshape_2_grad/Shape" + name: "gradients/dipole_charge/filter_type_0/add_grad/Shape" op: "Shape" - input: "dipole_charge/filter_type_0/Tanh" + input: "dipole_charge/filter_type_0/concat" attr { key: "T" value { @@ -22851,10 +22922,9 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_0/Reshape_2_grad/Reshape" - op: "Reshape" - input: "gradients/AddN_8" - input: "gradients/dipole_charge/filter_type_0/Reshape_2_grad/Shape" + name: "gradients/dipole_charge/filter_type_0/add_grad/Shape_1" + op: "Shape" + input: "dipole_charge/filter_type_0/Reshape_2" attr { key: "T" value { @@ -22862,43 +22932,29 @@ node { } } attr { - key: "Tshape" + key: "out_type" value { type: DT_INT32 } } } node { - name: "gradients/AddN_9" - op: "AddN" - input: "gradients/dipole_charge/filter_type_0/concat_2_grad/Slice" - input: "gradients/dipole_charge/filter_type_0/concat_2_grad/Slice_1" - input: "gradients/dipole_charge/filter_type_0/MatMul_5_grad/MatMul" - attr { - key: "N" - value { - i: 3 - } - } + name: "gradients/dipole_charge/filter_type_0/add_grad/BroadcastGradientArgs" + op: "BroadcastGradientArgs" + input: "gradients/dipole_charge/filter_type_0/add_grad/Shape" + input: "gradients/dipole_charge/filter_type_0/add_grad/Shape_1" attr { key: "T" value { - type: DT_DOUBLE - } - } - attr { - key: "_class" - value { - list { - s: "loc:@gradients/dipole_charge/filter_type_0/concat_2_grad/Slice" - } + type: DT_INT32 } } } node { - name: "gradients/dipole_charge/filter_type_0/Reshape_9_grad/Shape" - op: "Shape" - input: "dipole_charge/filter_type_0/Tanh_3" + name: "gradients/dipole_charge/filter_type_0/add_grad/Sum" + op: "Sum" + input: "gradients/AddN_8" + input: "gradients/dipole_charge/filter_type_0/add_grad/BroadcastGradientArgs" attr { key: "T" value { @@ -22906,17 +22962,23 @@ node { } } attr { - key: "out_type" + key: "Tidx" value { type: DT_INT32 } } + attr { + key: "keep_dims" + value { + b: false + } + } } node { - name: "gradients/dipole_charge/filter_type_0/Reshape_9_grad/Reshape" + name: "gradients/dipole_charge/filter_type_0/add_grad/Reshape" op: "Reshape" - input: "gradients/AddN_9" - input: "gradients/dipole_charge/filter_type_0/Reshape_9_grad/Shape" + input: "gradients/dipole_charge/filter_type_0/add_grad/Sum" + input: "gradients/dipole_charge/filter_type_0/add_grad/Shape" attr { key: "T" value { @@ -22931,17 +22993,10 @@ node { } } node { - name: "gradients/AddN_10" - op: "AddN" - input: "gradients/dipole_charge/filter_type_1/concat_grad/Slice" - input: "gradients/dipole_charge/filter_type_1/concat_grad/Slice_1" - input: "gradients/dipole_charge/filter_type_1/MatMul_1_grad/MatMul" - attr { - key: "N" - value { - i: 3 - } - } + name: "gradients/dipole_charge/filter_type_0/add_grad/Sum_1" + op: "Sum" + input: "gradients/AddN_8" + input: "gradients/dipole_charge/filter_type_0/add_grad/BroadcastGradientArgs:1" attr { key: "T" value { @@ -22949,36 +23004,23 @@ node { } } attr { - key: "_class" - value { - list { - s: "loc:@gradients/dipole_charge/filter_type_1/concat_grad/Slice" - } - } - } -} -node { - name: "gradients/dipole_charge/filter_type_1/Reshape_2_grad/Shape" - op: "Shape" - input: "dipole_charge/filter_type_1/Tanh" - attr { - key: "T" + key: "Tidx" value { - type: DT_DOUBLE + type: DT_INT32 } } attr { - key: "out_type" + key: "keep_dims" value { - type: DT_INT32 + b: false } } } node { - name: "gradients/dipole_charge/filter_type_1/Reshape_2_grad/Reshape" + name: "gradients/dipole_charge/filter_type_0/add_grad/Reshape_1" op: "Reshape" - input: "gradients/AddN_10" - input: "gradients/dipole_charge/filter_type_1/Reshape_2_grad/Shape" + input: "gradients/dipole_charge/filter_type_0/add_grad/Sum_1" + input: "gradients/dipole_charge/filter_type_0/add_grad/Shape_1" attr { key: "T" value { @@ -22993,11 +23035,11 @@ node { } } node { - name: "gradients/AddN_11" + name: "gradients/AddN_9" op: "AddN" - input: "gradients/dipole_charge/filter_type_1/concat_2_grad/Slice" - input: "gradients/dipole_charge/filter_type_1/concat_2_grad/Slice_1" - input: "gradients/dipole_charge/filter_type_1/MatMul_5_grad/MatMul" + input: "gradients/dipole_charge/filter_type_0/concat_4_grad/Slice" + input: "gradients/dipole_charge/filter_type_0/concat_4_grad/Slice_1" + input: "gradients/dipole_charge/filter_type_0/MatMul_5_grad/MatMul" attr { key: "N" value { @@ -23014,15 +23056,15 @@ node { key: "_class" value { list { - s: "loc:@gradients/dipole_charge/filter_type_1/concat_2_grad/Slice" + s: "loc:@gradients/dipole_charge/filter_type_0/concat_4_grad/Slice" } } } } node { - name: "gradients/dipole_charge/filter_type_1/Reshape_9_grad/Shape" + name: "gradients/dipole_charge/filter_type_0/add_3_grad/Shape" op: "Shape" - input: "dipole_charge/filter_type_1/Tanh_3" + input: "dipole_charge/filter_type_0/concat_3" attr { key: "T" value { @@ -23037,10 +23079,9 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_1/Reshape_9_grad/Reshape" - op: "Reshape" - input: "gradients/AddN_11" - input: "gradients/dipole_charge/filter_type_1/Reshape_9_grad/Shape" + name: "gradients/dipole_charge/filter_type_0/add_3_grad/Shape_1" + op: "Shape" + input: "dipole_charge/filter_type_0/Reshape_9" attr { key: "T" value { @@ -23048,65 +23089,53 @@ node { } } attr { - key: "Tshape" + key: "out_type" value { type: DT_INT32 } } } node { - name: "gradients/dipole_charge/filter_type_0/Tanh_grad/TanhGrad" - op: "TanhGrad" - input: "dipole_charge/filter_type_0/Tanh" - input: "gradients/dipole_charge/filter_type_0/Reshape_2_grad/Reshape" + name: "gradients/dipole_charge/filter_type_0/add_3_grad/BroadcastGradientArgs" + op: "BroadcastGradientArgs" + input: "gradients/dipole_charge/filter_type_0/add_3_grad/Shape" + input: "gradients/dipole_charge/filter_type_0/add_3_grad/Shape_1" attr { key: "T" value { - type: DT_DOUBLE + type: DT_INT32 } } } node { - name: "gradients/dipole_charge/filter_type_0/Tanh_3_grad/TanhGrad" - op: "TanhGrad" - input: "dipole_charge/filter_type_0/Tanh_3" - input: "gradients/dipole_charge/filter_type_0/Reshape_9_grad/Reshape" + name: "gradients/dipole_charge/filter_type_0/add_3_grad/Sum" + op: "Sum" + input: "gradients/AddN_9" + input: "gradients/dipole_charge/filter_type_0/add_3_grad/BroadcastGradientArgs" attr { key: "T" value { type: DT_DOUBLE } } -} -node { - name: "gradients/dipole_charge/filter_type_1/Tanh_grad/TanhGrad" - op: "TanhGrad" - input: "dipole_charge/filter_type_1/Tanh" - input: "gradients/dipole_charge/filter_type_1/Reshape_2_grad/Reshape" attr { - key: "T" + key: "Tidx" value { - type: DT_DOUBLE + type: DT_INT32 } } -} -node { - name: "gradients/dipole_charge/filter_type_1/Tanh_3_grad/TanhGrad" - op: "TanhGrad" - input: "dipole_charge/filter_type_1/Tanh_3" - input: "gradients/dipole_charge/filter_type_1/Reshape_9_grad/Reshape" attr { - key: "T" + key: "keep_dims" value { - type: DT_DOUBLE + b: false } } } node { - name: "gradients/dipole_charge/filter_type_0/MatMul_grad/MatMul" - op: "MatMul" - input: "gradients/dipole_charge/filter_type_0/Tanh_grad/TanhGrad" - input: "dipole_charge/filter_type_0/matrix_1_0/read" + name: "gradients/dipole_charge/filter_type_0/add_3_grad/Reshape" + op: "Reshape" + input: "gradients/dipole_charge/filter_type_0/add_3_grad/Sum" + input: "gradients/dipole_charge/filter_type_0/add_3_grad/Shape" attr { key: "T" value { @@ -23114,23 +23143,17 @@ node { } } attr { - key: "transpose_a" - value { - b: false - } - } - attr { - key: "transpose_b" + key: "Tshape" value { - b: true + type: DT_INT32 } } } node { - name: "gradients/dipole_charge/filter_type_0/MatMul_4_grad/MatMul" - op: "MatMul" - input: "gradients/dipole_charge/filter_type_0/Tanh_3_grad/TanhGrad" - input: "dipole_charge/filter_type_0/matrix_1_1/read" + name: "gradients/dipole_charge/filter_type_0/add_3_grad/Sum_1" + op: "Sum" + input: "gradients/AddN_9" + input: "gradients/dipole_charge/filter_type_0/add_3_grad/BroadcastGradientArgs:1" attr { key: "T" value { @@ -23138,23 +23161,23 @@ node { } } attr { - key: "transpose_a" + key: "Tidx" value { - b: false + type: DT_INT32 } } attr { - key: "transpose_b" + key: "keep_dims" value { - b: true + b: false } } } node { - name: "gradients/dipole_charge/filter_type_1/MatMul_grad/MatMul" - op: "MatMul" - input: "gradients/dipole_charge/filter_type_1/Tanh_grad/TanhGrad" - input: "dipole_charge/filter_type_1/matrix_1_0/read" + name: "gradients/dipole_charge/filter_type_0/add_3_grad/Reshape_1" + op: "Reshape" + input: "gradients/dipole_charge/filter_type_0/add_3_grad/Sum_1" + input: "gradients/dipole_charge/filter_type_0/add_3_grad/Shape_1" attr { key: "T" value { @@ -23162,46 +23185,43 @@ node { } } attr { - key: "transpose_a" - value { - b: false - } - } - attr { - key: "transpose_b" + key: "Tshape" value { - b: true + type: DT_INT32 } } } node { - name: "gradients/dipole_charge/filter_type_1/MatMul_4_grad/MatMul" - op: "MatMul" - input: "gradients/dipole_charge/filter_type_1/Tanh_3_grad/TanhGrad" - input: "dipole_charge/filter_type_1/matrix_1_1/read" + name: "gradients/AddN_10" + op: "AddN" + input: "gradients/dipole_charge/filter_type_1/concat_1_grad/Slice" + input: "gradients/dipole_charge/filter_type_1/concat_1_grad/Slice_1" + input: "gradients/dipole_charge/filter_type_1/MatMul_1_grad/MatMul" attr { - key: "T" + key: "N" value { - type: DT_DOUBLE + i: 3 } } attr { - key: "transpose_a" + key: "T" value { - b: false + type: DT_DOUBLE } } attr { - key: "transpose_b" + key: "_class" value { - b: true + list { + s: "loc:@gradients/dipole_charge/filter_type_1/concat_1_grad/Slice" + } } } } node { - name: "gradients/dipole_charge/filter_type_0/Reshape_1_grad/Shape" + name: "gradients/dipole_charge/filter_type_1/add_grad/Shape" op: "Shape" - input: "dipole_charge/filter_type_0/Slice_1" + input: "dipole_charge/filter_type_1/concat" attr { key: "T" value { @@ -23216,10 +23236,9 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_0/Reshape_1_grad/Reshape" - op: "Reshape" - input: "gradients/dipole_charge/filter_type_0/MatMul_grad/MatMul" - input: "gradients/dipole_charge/filter_type_0/Reshape_1_grad/Shape" + name: "gradients/dipole_charge/filter_type_1/add_grad/Shape_1" + op: "Shape" + input: "dipole_charge/filter_type_1/Reshape_2" attr { key: "T" value { @@ -23227,34 +23246,29 @@ node { } } attr { - key: "Tshape" + key: "out_type" value { type: DT_INT32 } } } node { - name: "gradients/dipole_charge/filter_type_0/Reshape_8_grad/Shape" - op: "Shape" - input: "dipole_charge/filter_type_0/Slice_3" + name: "gradients/dipole_charge/filter_type_1/add_grad/BroadcastGradientArgs" + op: "BroadcastGradientArgs" + input: "gradients/dipole_charge/filter_type_1/add_grad/Shape" + input: "gradients/dipole_charge/filter_type_1/add_grad/Shape_1" attr { key: "T" - value { - type: DT_DOUBLE - } - } - attr { - key: "out_type" value { type: DT_INT32 } } } node { - name: "gradients/dipole_charge/filter_type_0/Reshape_8_grad/Reshape" - op: "Reshape" - input: "gradients/dipole_charge/filter_type_0/MatMul_4_grad/MatMul" - input: "gradients/dipole_charge/filter_type_0/Reshape_8_grad/Shape" + name: "gradients/dipole_charge/filter_type_1/add_grad/Sum" + op: "Sum" + input: "gradients/AddN_10" + input: "gradients/dipole_charge/filter_type_1/add_grad/BroadcastGradientArgs" attr { key: "T" value { @@ -23262,34 +23276,23 @@ node { } } attr { - key: "Tshape" + key: "Tidx" value { type: DT_INT32 } } -} -node { - name: "gradients/dipole_charge/filter_type_1/Reshape_1_grad/Shape" - op: "Shape" - input: "dipole_charge/filter_type_1/Slice_1" - attr { - key: "T" - value { - type: DT_DOUBLE - } - } attr { - key: "out_type" + key: "keep_dims" value { - type: DT_INT32 + b: false } } } node { - name: "gradients/dipole_charge/filter_type_1/Reshape_1_grad/Reshape" + name: "gradients/dipole_charge/filter_type_1/add_grad/Reshape" op: "Reshape" - input: "gradients/dipole_charge/filter_type_1/MatMul_grad/MatMul" - input: "gradients/dipole_charge/filter_type_1/Reshape_1_grad/Shape" + input: "gradients/dipole_charge/filter_type_1/add_grad/Sum" + input: "gradients/dipole_charge/filter_type_1/add_grad/Shape" attr { key: "T" value { @@ -23304,9 +23307,10 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_1/Reshape_8_grad/Shape" - op: "Shape" - input: "dipole_charge/filter_type_1/Slice_3" + name: "gradients/dipole_charge/filter_type_1/add_grad/Sum_1" + op: "Sum" + input: "gradients/AddN_10" + input: "gradients/dipole_charge/filter_type_1/add_grad/BroadcastGradientArgs:1" attr { key: "T" value { @@ -23314,17 +23318,23 @@ node { } } attr { - key: "out_type" + key: "Tidx" value { type: DT_INT32 } } + attr { + key: "keep_dims" + value { + b: false + } + } } node { - name: "gradients/dipole_charge/filter_type_1/Reshape_8_grad/Reshape" + name: "gradients/dipole_charge/filter_type_1/add_grad/Reshape_1" op: "Reshape" - input: "gradients/dipole_charge/filter_type_1/MatMul_4_grad/MatMul" - input: "gradients/dipole_charge/filter_type_1/Reshape_8_grad/Shape" + input: "gradients/dipole_charge/filter_type_1/add_grad/Sum_1" + input: "gradients/dipole_charge/filter_type_1/add_grad/Shape_1" attr { key: "T" value { @@ -23339,30 +23349,36 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_0/Slice_1_grad/Rank" - op: "Const" + name: "gradients/AddN_11" + op: "AddN" + input: "gradients/dipole_charge/filter_type_1/concat_4_grad/Slice" + input: "gradients/dipole_charge/filter_type_1/concat_4_grad/Slice_1" + input: "gradients/dipole_charge/filter_type_1/MatMul_5_grad/MatMul" attr { - key: "dtype" + key: "N" value { - type: DT_INT32 + i: 3 } } attr { - key: "value" + key: "T" value { - tensor { - dtype: DT_INT32 - tensor_shape { - } - int_val: 2 + type: DT_DOUBLE + } + } + attr { + key: "_class" + value { + list { + s: "loc:@gradients/dipole_charge/filter_type_1/concat_4_grad/Slice" } } } } node { - name: "gradients/dipole_charge/filter_type_0/Slice_1_grad/Shape" + name: "gradients/dipole_charge/filter_type_1/add_3_grad/Shape" op: "Shape" - input: "dipole_charge/filter_type_0/Slice_1" + input: "dipole_charge/filter_type_1/concat_3" attr { key: "T" value { @@ -23377,59 +23393,67 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_0/Slice_1_grad/stack/1" - op: "Const" + name: "gradients/dipole_charge/filter_type_1/add_3_grad/Shape_1" + op: "Shape" + input: "dipole_charge/filter_type_1/Reshape_9" attr { - key: "dtype" + key: "T" value { - type: DT_INT32 + type: DT_DOUBLE } } attr { - key: "value" + key: "out_type" value { - tensor { - dtype: DT_INT32 - tensor_shape { - } - int_val: 1 - } + type: DT_INT32 } } } node { - name: "gradients/dipole_charge/filter_type_0/Slice_1_grad/stack" - op: "Pack" - input: "gradients/dipole_charge/filter_type_0/Slice_1_grad/Rank" - input: "gradients/dipole_charge/filter_type_0/Slice_1_grad/stack/1" + name: "gradients/dipole_charge/filter_type_1/add_3_grad/BroadcastGradientArgs" + op: "BroadcastGradientArgs" + input: "gradients/dipole_charge/filter_type_1/add_3_grad/Shape" + input: "gradients/dipole_charge/filter_type_1/add_3_grad/Shape_1" attr { - key: "N" + key: "T" value { - i: 2 + type: DT_INT32 } } +} +node { + name: "gradients/dipole_charge/filter_type_1/add_3_grad/Sum" + op: "Sum" + input: "gradients/AddN_11" + input: "gradients/dipole_charge/filter_type_1/add_3_grad/BroadcastGradientArgs" attr { key: "T" + value { + type: DT_DOUBLE + } + } + attr { + key: "Tidx" value { type: DT_INT32 } } attr { - key: "axis" + key: "keep_dims" value { - i: 0 + b: false } } } node { - name: "gradients/dipole_charge/filter_type_0/Slice_1_grad/Reshape" + name: "gradients/dipole_charge/filter_type_1/add_3_grad/Reshape" op: "Reshape" - input: "dipole_charge/filter_type_0/Slice_1/begin" - input: "gradients/dipole_charge/filter_type_0/Slice_1_grad/stack" + input: "gradients/dipole_charge/filter_type_1/add_3_grad/Sum" + input: "gradients/dipole_charge/filter_type_1/add_3_grad/Shape" attr { key: "T" value { - type: DT_INT32 + type: DT_DOUBLE } } attr { @@ -23440,9 +23464,10 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_0/Slice_1_grad/Shape_1" - op: "Shape" - input: "dipole_charge/filter_type_0/Reshape" + name: "gradients/dipole_charge/filter_type_1/add_3_grad/Sum_1" + op: "Sum" + input: "gradients/AddN_11" + input: "gradients/dipole_charge/filter_type_1/add_3_grad/BroadcastGradientArgs:1" attr { key: "T" value { @@ -23450,45 +23475,27 @@ node { } } attr { - key: "out_type" - value { - type: DT_INT32 - } - } -} -node { - name: "gradients/dipole_charge/filter_type_0/Slice_1_grad/sub" - op: "Sub" - input: "gradients/dipole_charge/filter_type_0/Slice_1_grad/Shape_1" - input: "gradients/dipole_charge/filter_type_0/Slice_1_grad/Shape" - attr { - key: "T" + key: "Tidx" value { type: DT_INT32 } } -} -node { - name: "gradients/dipole_charge/filter_type_0/Slice_1_grad/sub_1" - op: "Sub" - input: "gradients/dipole_charge/filter_type_0/Slice_1_grad/sub" - input: "dipole_charge/filter_type_0/Slice_1/begin" attr { - key: "T" + key: "keep_dims" value { - type: DT_INT32 + b: false } } } node { - name: "gradients/dipole_charge/filter_type_0/Slice_1_grad/Reshape_1" + name: "gradients/dipole_charge/filter_type_1/add_3_grad/Reshape_1" op: "Reshape" - input: "gradients/dipole_charge/filter_type_0/Slice_1_grad/sub_1" - input: "gradients/dipole_charge/filter_type_0/Slice_1_grad/stack" + input: "gradients/dipole_charge/filter_type_1/add_3_grad/Sum_1" + input: "gradients/dipole_charge/filter_type_1/add_3_grad/Shape_1" attr { key: "T" value { - type: DT_INT32 + type: DT_DOUBLE } } attr { @@ -23499,7 +23506,7 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_0/Slice_1_grad/concat/axis" + name: "gradients/dipole_charge/filter_type_0/concat_grad/Rank" op: "Const" attr { key: "dtype" @@ -23514,19 +23521,30 @@ node { dtype: DT_INT32 tensor_shape { } - int_val: 1 + int_val: 2 } } } } node { - name: "gradients/dipole_charge/filter_type_0/Slice_1_grad/concat" - op: "ConcatV2" - input: "gradients/dipole_charge/filter_type_0/Slice_1_grad/Reshape" - input: "gradients/dipole_charge/filter_type_0/Slice_1_grad/Reshape_1" - input: "gradients/dipole_charge/filter_type_0/Slice_1_grad/concat/axis" - attr { - key: "N" + name: "gradients/dipole_charge/filter_type_0/concat_grad/mod" + op: "FloorMod" + input: "dipole_charge/filter_type_0/concat/axis" + input: "gradients/dipole_charge/filter_type_0/concat_grad/Rank" + attr { + key: "T" + value { + type: DT_INT32 + } + } +} +node { + name: "gradients/dipole_charge/filter_type_0/concat_grad/ShapeN" + op: "ShapeN" + input: "dipole_charge/filter_type_0/Reshape_1" + input: "dipole_charge/filter_type_0/Reshape_1" + attr { + key: "N" value { i: 2 } @@ -23534,59 +23552,71 @@ node { attr { key: "T" value { - type: DT_INT32 + type: DT_DOUBLE } } attr { - key: "Tidx" + key: "out_type" value { type: DT_INT32 } } } node { - name: 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node { - name: "gradients/dipole_charge/filter_type_0/Slice_3_grad/Rank" - op: "Const" + name: "gradients/dipole_charge/filter_type_0/concat_grad/Slice_1" + op: "Slice" + input: "gradients/dipole_charge/filter_type_0/add_grad/Reshape" + input: "gradients/dipole_charge/filter_type_0/concat_grad/ConcatOffset:1" + input: "gradients/dipole_charge/filter_type_0/concat_grad/ShapeN:1" attr { - key: "dtype" + key: "Index" value { type: DT_INT32 } } attr { - key: "value" + key: "T" value { - tensor { - dtype: DT_INT32 - tensor_shape { - } - int_val: 2 - } + type: DT_DOUBLE } } } node { - name: "gradients/dipole_charge/filter_type_0/Slice_3_grad/Shape" + name: "gradients/dipole_charge/filter_type_0/Reshape_2_grad/Shape" op: "Shape" - input: "dipole_charge/filter_type_0/Slice_3" + input: "dipole_charge/filter_type_0/Tanh" attr { key: "T" value { @@ -23601,72 +23631,67 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_0/Slice_3_grad/stack/1" - op: "Const" + name: "gradients/dipole_charge/filter_type_0/Reshape_2_grad/Reshape" + op: "Reshape" + input: "gradients/dipole_charge/filter_type_0/add_grad/Reshape_1" + input: "gradients/dipole_charge/filter_type_0/Reshape_2_grad/Shape" attr { - key: "dtype" + key: "T" value { - type: DT_INT32 + type: DT_DOUBLE } } attr { - key: "value" + key: "Tshape" value { - tensor { - dtype: DT_INT32 - tensor_shape { - } - int_val: 1 - } + type: DT_INT32 } } } node { - name: "gradients/dipole_charge/filter_type_0/Slice_3_grad/stack" - op: "Pack" - input: "gradients/dipole_charge/filter_type_0/Slice_3_grad/Rank" - input: "gradients/dipole_charge/filter_type_0/Slice_3_grad/stack/1" - attr { - key: "N" - value { - i: 2 - } - } + name: "gradients/dipole_charge/filter_type_0/concat_3_grad/Rank" + op: "Const" attr { - key: "T" + key: "dtype" value { type: DT_INT32 } } attr { - key: "axis" + key: "value" value { - i: 0 + tensor { + dtype: DT_INT32 + tensor_shape { + } + int_val: 2 + } } } } node { - name: "gradients/dipole_charge/filter_type_0/Slice_3_grad/Reshape" - op: "Reshape" - input: "dipole_charge/filter_type_0/Slice_3/begin" - input: "gradients/dipole_charge/filter_type_0/Slice_3_grad/stack" + name: "gradients/dipole_charge/filter_type_0/concat_3_grad/mod" + op: "FloorMod" + input: "dipole_charge/filter_type_0/concat_3/axis" + input: "gradients/dipole_charge/filter_type_0/concat_3_grad/Rank" attr { key: "T" value { type: DT_INT32 } } +} +node { + name: "gradients/dipole_charge/filter_type_0/concat_3_grad/ShapeN" + op: "ShapeN" + input: "dipole_charge/filter_type_0/Reshape_8" + input: "dipole_charge/filter_type_0/Reshape_8" attr { - key: "Tshape" + key: "N" value { - type: DT_INT32 + i: 2 } } -} -node { - name: "gradients/dipole_charge/filter_type_0/Slice_3_grad/Shape_1" - op: "Shape" - input: "dipole_charge/filter_type_0/Reshape_7" attr { key: "T" value { @@ -23681,98 +23706,78 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_0/Slice_3_grad/sub" - op: "Sub" - input: "gradients/dipole_charge/filter_type_0/Slice_3_grad/Shape_1" - input: "gradients/dipole_charge/filter_type_0/Slice_3_grad/Shape" + name: "gradients/dipole_charge/filter_type_0/concat_3_grad/ConcatOffset" + op: "ConcatOffset" + input: "gradients/dipole_charge/filter_type_0/concat_3_grad/mod" + input: "gradients/dipole_charge/filter_type_0/concat_3_grad/ShapeN" + input: "gradients/dipole_charge/filter_type_0/concat_3_grad/ShapeN:1" attr { - key: "T" + key: "N" value { - type: DT_INT32 + i: 2 } } } node { - name: "gradients/dipole_charge/filter_type_0/Slice_3_grad/sub_1" - op: "Sub" - input: "gradients/dipole_charge/filter_type_0/Slice_3_grad/sub" - input: "dipole_charge/filter_type_0/Slice_3/begin" + name: "gradients/dipole_charge/filter_type_0/concat_3_grad/Slice" + op: "Slice" + input: "gradients/dipole_charge/filter_type_0/add_3_grad/Reshape" + input: "gradients/dipole_charge/filter_type_0/concat_3_grad/ConcatOffset" + input: "gradients/dipole_charge/filter_type_0/concat_3_grad/ShapeN" attr { - key: "T" + key: "Index" value { type: DT_INT32 } } -} -node { - name: "gradients/dipole_charge/filter_type_0/Slice_3_grad/Reshape_1" - op: "Reshape" - input: "gradients/dipole_charge/filter_type_0/Slice_3_grad/sub_1" - input: "gradients/dipole_charge/filter_type_0/Slice_3_grad/stack" attr { key: "T" value { - type: DT_INT32 - } - } - attr { - key: "Tshape" - value { - type: DT_INT32 + type: DT_DOUBLE } } } node { - name: "gradients/dipole_charge/filter_type_0/Slice_3_grad/concat/axis" - op: "Const" + name: "gradients/dipole_charge/filter_type_0/concat_3_grad/Slice_1" + op: "Slice" + input: "gradients/dipole_charge/filter_type_0/add_3_grad/Reshape" + input: "gradients/dipole_charge/filter_type_0/concat_3_grad/ConcatOffset:1" + input: "gradients/dipole_charge/filter_type_0/concat_3_grad/ShapeN:1" attr { - key: "dtype" + key: "Index" value { type: DT_INT32 } } attr { - key: "value" + key: "T" value { - tensor { - dtype: DT_INT32 - tensor_shape { - } - int_val: 1 - } + type: DT_DOUBLE } } } node { - name: "gradients/dipole_charge/filter_type_0/Slice_3_grad/concat" - op: "ConcatV2" - input: "gradients/dipole_charge/filter_type_0/Slice_3_grad/Reshape" - input: "gradients/dipole_charge/filter_type_0/Slice_3_grad/Reshape_1" - input: "gradients/dipole_charge/filter_type_0/Slice_3_grad/concat/axis" - attr { - key: "N" - value { - i: 2 - } - } + name: "gradients/dipole_charge/filter_type_0/Reshape_9_grad/Shape" + op: "Shape" + input: "dipole_charge/filter_type_0/Tanh_3" attr { key: "T" value { - type: DT_INT32 + type: DT_DOUBLE } } attr { - key: "Tidx" + key: "out_type" value { type: DT_INT32 } } } node { - name: "gradients/dipole_charge/filter_type_0/Slice_3_grad/Pad" - op: "Pad" - input: "gradients/dipole_charge/filter_type_0/Reshape_8_grad/Reshape" - input: "gradients/dipole_charge/filter_type_0/Slice_3_grad/concat" + name: "gradients/dipole_charge/filter_type_0/Reshape_9_grad/Reshape" + op: "Reshape" + input: "gradients/dipole_charge/filter_type_0/add_3_grad/Reshape_1" + input: "gradients/dipole_charge/filter_type_0/Reshape_9_grad/Shape" attr { key: "T" value { @@ -23780,14 +23785,14 @@ node { } } attr { - key: "Tpaddings" + key: "Tshape" value { type: DT_INT32 } } } node { - name: "gradients/dipole_charge/filter_type_1/Slice_1_grad/Rank" + name: "gradients/dipole_charge/filter_type_1/concat_grad/Rank" op: "Const" attr { key: "dtype" @@ -23808,89 +23813,96 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_1/Slice_1_grad/Shape" - op: "Shape" - input: "dipole_charge/filter_type_1/Slice_1" + name: "gradients/dipole_charge/filter_type_1/concat_grad/mod" + op: "FloorMod" + input: "dipole_charge/filter_type_1/concat/axis" + input: "gradients/dipole_charge/filter_type_1/concat_grad/Rank" attr { key: "T" value { - type: DT_DOUBLE + type: DT_INT32 } } +} +node { + name: "gradients/dipole_charge/filter_type_1/concat_grad/ShapeN" + op: "ShapeN" + input: "dipole_charge/filter_type_1/Reshape_1" + input: "dipole_charge/filter_type_1/Reshape_1" attr { - key: "out_type" + key: "N" value { - type: DT_INT32 + i: 2 } } -} -node { - name: "gradients/dipole_charge/filter_type_1/Slice_1_grad/stack/1" - op: "Const" attr { - key: "dtype" + key: "T" value { - type: DT_INT32 + type: DT_DOUBLE } } attr { - key: "value" + key: "out_type" value { - tensor { - dtype: DT_INT32 - tensor_shape { - } - int_val: 1 - } + type: DT_INT32 } } } node { - name: "gradients/dipole_charge/filter_type_1/Slice_1_grad/stack" - op: "Pack" - input: "gradients/dipole_charge/filter_type_1/Slice_1_grad/Rank" - input: "gradients/dipole_charge/filter_type_1/Slice_1_grad/stack/1" + name: "gradients/dipole_charge/filter_type_1/concat_grad/ConcatOffset" + op: "ConcatOffset" + input: "gradients/dipole_charge/filter_type_1/concat_grad/mod" + input: "gradients/dipole_charge/filter_type_1/concat_grad/ShapeN" + input: "gradients/dipole_charge/filter_type_1/concat_grad/ShapeN:1" attr { key: "N" value { i: 2 } } +} +node { + name: "gradients/dipole_charge/filter_type_1/concat_grad/Slice" + op: "Slice" + input: "gradients/dipole_charge/filter_type_1/add_grad/Reshape" + input: "gradients/dipole_charge/filter_type_1/concat_grad/ConcatOffset" + input: "gradients/dipole_charge/filter_type_1/concat_grad/ShapeN" attr { - key: "T" + key: "Index" value { type: DT_INT32 } } attr { - key: "axis" + key: "T" value { - i: 0 + type: DT_DOUBLE } } } node { - name: "gradients/dipole_charge/filter_type_1/Slice_1_grad/Reshape" - op: "Reshape" - input: "dipole_charge/filter_type_1/Slice_1/begin" - input: "gradients/dipole_charge/filter_type_1/Slice_1_grad/stack" + name: "gradients/dipole_charge/filter_type_1/concat_grad/Slice_1" + op: "Slice" + input: "gradients/dipole_charge/filter_type_1/add_grad/Reshape" + input: "gradients/dipole_charge/filter_type_1/concat_grad/ConcatOffset:1" + input: "gradients/dipole_charge/filter_type_1/concat_grad/ShapeN:1" attr { - key: "T" + key: "Index" value { type: DT_INT32 } } attr { - key: "Tshape" + key: "T" value { - type: DT_INT32 + type: DT_DOUBLE } } } node { - name: "gradients/dipole_charge/filter_type_1/Slice_1_grad/Shape_1" + name: "gradients/dipole_charge/filter_type_1/Reshape_2_grad/Shape" op: "Shape" - input: "dipole_charge/filter_type_1/Reshape" + input: "dipole_charge/filter_type_1/Tanh" attr { key: "T" value { @@ -23905,38 +23917,14 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_1/Slice_1_grad/sub" - op: "Sub" - input: "gradients/dipole_charge/filter_type_1/Slice_1_grad/Shape_1" - input: "gradients/dipole_charge/filter_type_1/Slice_1_grad/Shape" - attr { - key: "T" - value { - type: DT_INT32 - } - } -} -node { - name: "gradients/dipole_charge/filter_type_1/Slice_1_grad/sub_1" - op: "Sub" - input: "gradients/dipole_charge/filter_type_1/Slice_1_grad/sub" - input: "dipole_charge/filter_type_1/Slice_1/begin" - attr { - key: "T" - value { - type: DT_INT32 - } - } -} -node { - name: "gradients/dipole_charge/filter_type_1/Slice_1_grad/Reshape_1" + name: "gradients/dipole_charge/filter_type_1/Reshape_2_grad/Reshape" op: "Reshape" - input: "gradients/dipole_charge/filter_type_1/Slice_1_grad/sub_1" - input: "gradients/dipole_charge/filter_type_1/Slice_1_grad/stack" + input: "gradients/dipole_charge/filter_type_1/add_grad/Reshape_1" + input: "gradients/dipole_charge/filter_type_1/Reshape_2_grad/Shape" attr { key: "T" value { - type: DT_INT32 + type: DT_DOUBLE } } attr { @@ -23947,7 +23935,7 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_1/Slice_1_grad/concat/axis" + name: "gradients/dipole_charge/filter_type_1/concat_3_grad/Rank" op: "Const" attr { key: "dtype" @@ -23962,41 +23950,34 @@ node { dtype: DT_INT32 tensor_shape { } - int_val: 1 + int_val: 2 } } } } node { - name: "gradients/dipole_charge/filter_type_1/Slice_1_grad/concat" - op: "ConcatV2" - input: "gradients/dipole_charge/filter_type_1/Slice_1_grad/Reshape" - input: "gradients/dipole_charge/filter_type_1/Slice_1_grad/Reshape_1" - input: "gradients/dipole_charge/filter_type_1/Slice_1_grad/concat/axis" - attr { - key: "N" - value { - i: 2 - } - } + name: "gradients/dipole_charge/filter_type_1/concat_3_grad/mod" + op: "FloorMod" + input: "dipole_charge/filter_type_1/concat_3/axis" + input: "gradients/dipole_charge/filter_type_1/concat_3_grad/Rank" attr { key: "T" value { type: DT_INT32 } } +} +node { + name: "gradients/dipole_charge/filter_type_1/concat_3_grad/ShapeN" + op: "ShapeN" + input: "dipole_charge/filter_type_1/Reshape_8" + input: "dipole_charge/filter_type_1/Reshape_8" attr { - key: "Tidx" + key: "N" value { - type: DT_INT32 + i: 2 } } -} -node { - name: "gradients/dipole_charge/filter_type_1/Slice_1_grad/Pad" - op: "Pad" - input: "gradients/dipole_charge/filter_type_1/Reshape_1_grad/Reshape" - input: "gradients/dipole_charge/filter_type_1/Slice_1_grad/concat" attr { key: "T" value { @@ -24004,104 +23985,89 @@ node { } } attr { - key: "Tpaddings" + key: "out_type" value { type: DT_INT32 } } } node { - name: "gradients/dipole_charge/filter_type_1/Slice_3_grad/Rank" - op: "Const" - attr { - key: "dtype" - value { - type: DT_INT32 - } - } + name: "gradients/dipole_charge/filter_type_1/concat_3_grad/ConcatOffset" + op: "ConcatOffset" + input: "gradients/dipole_charge/filter_type_1/concat_3_grad/mod" + input: "gradients/dipole_charge/filter_type_1/concat_3_grad/ShapeN" + input: "gradients/dipole_charge/filter_type_1/concat_3_grad/ShapeN:1" attr { - key: "value" + key: "N" value { - tensor { - dtype: DT_INT32 - tensor_shape { - } - int_val: 2 - } + i: 2 } } } node { - name: "gradients/dipole_charge/filter_type_1/Slice_3_grad/Shape" - op: "Shape" - input: "dipole_charge/filter_type_1/Slice_3" + name: "gradients/dipole_charge/filter_type_1/concat_3_grad/Slice" + op: "Slice" + input: "gradients/dipole_charge/filter_type_1/add_3_grad/Reshape" + input: "gradients/dipole_charge/filter_type_1/concat_3_grad/ConcatOffset" + input: "gradients/dipole_charge/filter_type_1/concat_3_grad/ShapeN" attr { - key: "T" + key: "Index" value { - type: DT_DOUBLE + type: DT_INT32 } } attr { - key: "out_type" + key: "T" value { - type: DT_INT32 + type: DT_DOUBLE } } } node { - name: "gradients/dipole_charge/filter_type_1/Slice_3_grad/stack/1" - op: "Const" + name: "gradients/dipole_charge/filter_type_1/concat_3_grad/Slice_1" + op: "Slice" + input: "gradients/dipole_charge/filter_type_1/add_3_grad/Reshape" + input: "gradients/dipole_charge/filter_type_1/concat_3_grad/ConcatOffset:1" + input: "gradients/dipole_charge/filter_type_1/concat_3_grad/ShapeN:1" attr { - key: "dtype" + key: "Index" value { type: DT_INT32 } } attr { - key: "value" + key: "T" value { - tensor { - dtype: DT_INT32 - tensor_shape { - } - int_val: 1 - } + type: DT_DOUBLE } } } node { - name: "gradients/dipole_charge/filter_type_1/Slice_3_grad/stack" - op: "Pack" - input: "gradients/dipole_charge/filter_type_1/Slice_3_grad/Rank" - input: "gradients/dipole_charge/filter_type_1/Slice_3_grad/stack/1" - attr { - key: "N" - value { - i: 2 - } - } + name: "gradients/dipole_charge/filter_type_1/Reshape_9_grad/Shape" + op: "Shape" + input: "dipole_charge/filter_type_1/Tanh_3" attr { key: "T" value { - type: DT_INT32 + type: DT_DOUBLE } } attr { - key: "axis" + key: "out_type" value { - i: 0 + type: DT_INT32 } } } node { - name: "gradients/dipole_charge/filter_type_1/Slice_3_grad/Reshape" + name: "gradients/dipole_charge/filter_type_1/Reshape_9_grad/Reshape" op: "Reshape" - input: "dipole_charge/filter_type_1/Slice_3/begin" - input: "gradients/dipole_charge/filter_type_1/Slice_3_grad/stack" + input: "gradients/dipole_charge/filter_type_1/add_3_grad/Reshape_1" + input: "gradients/dipole_charge/filter_type_1/Reshape_9_grad/Shape" attr { key: "T" value { - type: DT_INT32 + type: DT_DOUBLE } } attr { @@ -24112,115 +24078,106 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_1/Slice_3_grad/Shape_1" - op: "Shape" - input: "dipole_charge/filter_type_1/Reshape_7" + name: "gradients/dipole_charge/filter_type_0/Tanh_grad/TanhGrad" + op: "TanhGrad" + input: "dipole_charge/filter_type_0/Tanh" + input: "gradients/dipole_charge/filter_type_0/Reshape_2_grad/Reshape" attr { key: "T" value { type: DT_DOUBLE } } +} +node { + name: "gradients/dipole_charge/filter_type_0/Tanh_3_grad/TanhGrad" + op: "TanhGrad" + input: "dipole_charge/filter_type_0/Tanh_3" + input: "gradients/dipole_charge/filter_type_0/Reshape_9_grad/Reshape" attr { - key: "out_type" + key: "T" value { - type: DT_INT32 + type: DT_DOUBLE } } } node { - name: "gradients/dipole_charge/filter_type_1/Slice_3_grad/sub" - op: "Sub" - input: "gradients/dipole_charge/filter_type_1/Slice_3_grad/Shape_1" - input: "gradients/dipole_charge/filter_type_1/Slice_3_grad/Shape" + name: "gradients/dipole_charge/filter_type_1/Tanh_grad/TanhGrad" + op: "TanhGrad" + input: "dipole_charge/filter_type_1/Tanh" + input: "gradients/dipole_charge/filter_type_1/Reshape_2_grad/Reshape" attr { key: "T" value { - type: DT_INT32 + type: DT_DOUBLE } } } node { - name: "gradients/dipole_charge/filter_type_1/Slice_3_grad/sub_1" - op: "Sub" - input: "gradients/dipole_charge/filter_type_1/Slice_3_grad/sub" - input: "dipole_charge/filter_type_1/Slice_3/begin" + name: "gradients/dipole_charge/filter_type_1/Tanh_3_grad/TanhGrad" + op: "TanhGrad" + input: "dipole_charge/filter_type_1/Tanh_3" + input: "gradients/dipole_charge/filter_type_1/Reshape_9_grad/Reshape" attr { key: "T" value { - type: DT_INT32 + type: DT_DOUBLE } } } node { - name: "gradients/dipole_charge/filter_type_1/Slice_3_grad/Reshape_1" - op: "Reshape" - input: "gradients/dipole_charge/filter_type_1/Slice_3_grad/sub_1" - input: "gradients/dipole_charge/filter_type_1/Slice_3_grad/stack" + name: "gradients/dipole_charge/filter_type_0/MatMul_grad/MatMul" + op: "MatMul" + input: "gradients/dipole_charge/filter_type_0/Tanh_grad/TanhGrad" + input: "dipole_charge/filter_type_0/matrix_1_0/read" attr { key: "T" value { - type: DT_INT32 + type: DT_DOUBLE } } attr { - key: "Tshape" + key: "transpose_a" value { - type: DT_INT32 + b: false } } -} -node { - name: "gradients/dipole_charge/filter_type_1/Slice_3_grad/concat/axis" - op: "Const" attr { - key: "dtype" + key: "transpose_b" value { - type: DT_INT32 - } - } - attr { - key: "value" - value { - tensor { - dtype: DT_INT32 - tensor_shape { - } - int_val: 1 - } + b: true } } } node { - name: "gradients/dipole_charge/filter_type_1/Slice_3_grad/concat" - op: "ConcatV2" - input: "gradients/dipole_charge/filter_type_1/Slice_3_grad/Reshape" - input: "gradients/dipole_charge/filter_type_1/Slice_3_grad/Reshape_1" - input: "gradients/dipole_charge/filter_type_1/Slice_3_grad/concat/axis" + name: "gradients/dipole_charge/filter_type_0/MatMul_4_grad/MatMul" + op: "MatMul" + input: "gradients/dipole_charge/filter_type_0/Tanh_3_grad/TanhGrad" + input: "dipole_charge/filter_type_0/matrix_1_1/read" attr { - key: "N" + key: "T" value { - i: 2 + type: DT_DOUBLE } } attr { - key: "T" + key: "transpose_a" value { - type: DT_INT32 + b: false } } attr { - key: "Tidx" + key: "transpose_b" value { - type: DT_INT32 + b: true } } } node { - name: "gradients/dipole_charge/filter_type_1/Slice_3_grad/Pad" - op: "Pad" - input: "gradients/dipole_charge/filter_type_1/Reshape_8_grad/Reshape" - input: "gradients/dipole_charge/filter_type_1/Slice_3_grad/concat" + name: "gradients/dipole_charge/filter_type_1/MatMul_grad/MatMul" + op: "MatMul" + input: "gradients/dipole_charge/filter_type_1/Tanh_grad/TanhGrad" + input: "dipole_charge/filter_type_1/matrix_1_0/read" attr { key: "T" value { @@ -24228,16 +24185,23 @@ node { } } attr { - key: "Tpaddings" + key: "transpose_a" value { - type: DT_INT32 + b: false + } + } + attr { + key: "transpose_b" + value { + b: true } } } node { - name: "gradients/dipole_charge/filter_type_0/Reshape_grad/Shape" - op: "Shape" - input: "dipole_charge/filter_type_0/Slice" + name: "gradients/dipole_charge/filter_type_1/MatMul_4_grad/MatMul" + op: "MatMul" + input: "gradients/dipole_charge/filter_type_1/Tanh_3_grad/TanhGrad" + input: "dipole_charge/filter_type_1/matrix_1_1/read" attr { key: "T" value { @@ -24245,17 +24209,30 @@ node { } } attr { - key: "out_type" + key: "transpose_a" value { - type: DT_INT32 + b: false + } + } + attr { + key: "transpose_b" + value { + b: true } } } node { - name: "gradients/dipole_charge/filter_type_0/Reshape_grad/Reshape" - op: "Reshape" - input: "gradients/dipole_charge/filter_type_0/Slice_1_grad/Pad" - input: "gradients/dipole_charge/filter_type_0/Reshape_grad/Shape" + name: "gradients/AddN_12" + op: "AddN" + input: "gradients/dipole_charge/filter_type_0/concat_grad/Slice" + input: "gradients/dipole_charge/filter_type_0/concat_grad/Slice_1" + input: "gradients/dipole_charge/filter_type_0/MatMul_grad/MatMul" + attr { + key: "N" + value { + i: 3 + } + } attr { key: "T" value { @@ -24263,16 +24240,18 @@ node { } } attr { - key: "Tshape" + key: "_class" value { - type: DT_INT32 + list { + s: "loc:@gradients/dipole_charge/filter_type_0/concat_grad/Slice" + } } } } node { - name: "gradients/dipole_charge/filter_type_0/Reshape_7_grad/Shape" + name: "gradients/dipole_charge/filter_type_0/Reshape_1_grad/Shape" op: "Shape" - input: "dipole_charge/filter_type_0/Slice_2" + input: "dipole_charge/filter_type_0/Slice_1" attr { key: "T" value { @@ -24287,10 +24266,10 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_0/Reshape_7_grad/Reshape" + name: "gradients/dipole_charge/filter_type_0/Reshape_1_grad/Reshape" op: "Reshape" - input: "gradients/dipole_charge/filter_type_0/Slice_3_grad/Pad" - input: "gradients/dipole_charge/filter_type_0/Reshape_7_grad/Shape" + input: "gradients/AddN_12" + input: "gradients/dipole_charge/filter_type_0/Reshape_1_grad/Shape" attr { key: "T" value { @@ -24305,9 +24284,36 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_1/Reshape_grad/Shape" + name: "gradients/AddN_13" + op: "AddN" + input: "gradients/dipole_charge/filter_type_0/concat_3_grad/Slice" + input: "gradients/dipole_charge/filter_type_0/concat_3_grad/Slice_1" + input: "gradients/dipole_charge/filter_type_0/MatMul_4_grad/MatMul" + attr { + key: "N" + value { + i: 3 + } + } + attr { + key: "T" + value { + type: DT_DOUBLE + } + } + attr { + key: "_class" + value { + list { + s: "loc:@gradients/dipole_charge/filter_type_0/concat_3_grad/Slice" + } + } + } +} +node { + name: "gradients/dipole_charge/filter_type_0/Reshape_8_grad/Shape" op: "Shape" - input: "dipole_charge/filter_type_1/Slice" + input: "dipole_charge/filter_type_0/Slice_3" attr { key: "T" value { @@ -24322,10 +24328,10 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_1/Reshape_grad/Reshape" + name: "gradients/dipole_charge/filter_type_0/Reshape_8_grad/Reshape" op: "Reshape" - input: "gradients/dipole_charge/filter_type_1/Slice_1_grad/Pad" - input: "gradients/dipole_charge/filter_type_1/Reshape_grad/Shape" + input: "gradients/AddN_13" + input: "gradients/dipole_charge/filter_type_0/Reshape_8_grad/Shape" attr { key: "T" value { @@ -24340,9 +24346,36 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_1/Reshape_7_grad/Shape" + name: "gradients/AddN_14" + op: "AddN" + input: "gradients/dipole_charge/filter_type_1/concat_grad/Slice" + input: "gradients/dipole_charge/filter_type_1/concat_grad/Slice_1" + input: "gradients/dipole_charge/filter_type_1/MatMul_grad/MatMul" + attr { + key: "N" + value { + i: 3 + } + } + attr { + key: "T" + value { + type: DT_DOUBLE + } + } + attr { + key: "_class" + value { + list { + s: "loc:@gradients/dipole_charge/filter_type_1/concat_grad/Slice" + } + } + } +} +node { + name: "gradients/dipole_charge/filter_type_1/Reshape_1_grad/Shape" op: "Shape" - input: "dipole_charge/filter_type_1/Slice_2" + input: "dipole_charge/filter_type_1/Slice_1" attr { key: "T" value { @@ -24357,10 +24390,10 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_1/Reshape_7_grad/Reshape" + name: "gradients/dipole_charge/filter_type_1/Reshape_1_grad/Reshape" op: "Reshape" - input: "gradients/dipole_charge/filter_type_1/Slice_3_grad/Pad" - input: "gradients/dipole_charge/filter_type_1/Reshape_7_grad/Shape" + input: "gradients/AddN_14" + input: "gradients/dipole_charge/filter_type_1/Reshape_1_grad/Shape" attr { key: "T" value { @@ -24375,14 +24408,15 @@ node { } } node { - name: "gradients/AddN_12" + name: "gradients/AddN_15" op: "AddN" - input: "gradients/dipole_charge/filter_type_0/Reshape_6_grad/Reshape" - input: "gradients/dipole_charge/filter_type_0/Reshape_grad/Reshape" + input: "gradients/dipole_charge/filter_type_1/concat_3_grad/Slice" + input: "gradients/dipole_charge/filter_type_1/concat_3_grad/Slice_1" + input: "gradients/dipole_charge/filter_type_1/MatMul_4_grad/MatMul" attr { key: "N" value { - i: 2 + i: 3 } } attr { @@ -24395,13 +24429,48 @@ node { key: "_class" value { list { - s: "loc:@gradients/dipole_charge/filter_type_0/Reshape_6_grad/Reshape" + s: "loc:@gradients/dipole_charge/filter_type_1/concat_3_grad/Slice" } } } } node { - name: "gradients/dipole_charge/filter_type_0/Slice_grad/Rank" + name: "gradients/dipole_charge/filter_type_1/Reshape_8_grad/Shape" + op: "Shape" + input: "dipole_charge/filter_type_1/Slice_3" + attr { + key: "T" + value { + type: DT_DOUBLE + } + } + attr { + key: "out_type" + value { + type: DT_INT32 + } + } +} +node { + name: "gradients/dipole_charge/filter_type_1/Reshape_8_grad/Reshape" + op: "Reshape" + input: "gradients/AddN_15" + input: "gradients/dipole_charge/filter_type_1/Reshape_8_grad/Shape" + attr { + key: "T" + value { + type: DT_DOUBLE + } + } + attr { + key: "Tshape" + value { + type: DT_INT32 + } + } +} +node { + name: "gradients/dipole_charge/filter_type_0/Slice_1_grad/Rank" op: "Const" attr { key: "dtype" @@ -24422,9 +24491,9 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_0/Slice_grad/Shape" + name: "gradients/dipole_charge/filter_type_0/Slice_1_grad/Shape" op: "Shape" - input: "dipole_charge/filter_type_0/Slice" + input: "dipole_charge/filter_type_0/Slice_1" attr { key: "T" value { @@ -24439,7 +24508,7 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_0/Slice_grad/stack/1" + name: "gradients/dipole_charge/filter_type_0/Slice_1_grad/stack/1" op: "Const" attr { key: "dtype" @@ -24460,10 +24529,10 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_0/Slice_grad/stack" + name: "gradients/dipole_charge/filter_type_0/Slice_1_grad/stack" op: "Pack" - input: "gradients/dipole_charge/filter_type_0/Slice_grad/Rank" - input: "gradients/dipole_charge/filter_type_0/Slice_grad/stack/1" + input: "gradients/dipole_charge/filter_type_0/Slice_1_grad/Rank" + input: "gradients/dipole_charge/filter_type_0/Slice_1_grad/stack/1" attr { key: "N" value { @@ -24484,10 +24553,10 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_0/Slice_grad/Reshape" + name: "gradients/dipole_charge/filter_type_0/Slice_1_grad/Reshape" op: "Reshape" - input: "dipole_charge/filter_type_0/Slice/begin" - input: "gradients/dipole_charge/filter_type_0/Slice_grad/stack" + input: "dipole_charge/filter_type_0/Slice_1/begin" + input: "gradients/dipole_charge/filter_type_0/Slice_1_grad/stack" attr { key: "T" value { @@ -24502,9 +24571,9 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_0/Slice_grad/Shape_1" + name: "gradients/dipole_charge/filter_type_0/Slice_1_grad/Shape_1" op: "Shape" - input: "dipole_charge/Reshape_5" + input: "dipole_charge/filter_type_0/Reshape" attr { key: "T" value { @@ -24519,10 +24588,10 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_0/Slice_grad/sub" + name: "gradients/dipole_charge/filter_type_0/Slice_1_grad/sub" op: "Sub" - input: "gradients/dipole_charge/filter_type_0/Slice_grad/Shape_1" - input: "gradients/dipole_charge/filter_type_0/Slice_grad/Shape" + input: "gradients/dipole_charge/filter_type_0/Slice_1_grad/Shape_1" + input: "gradients/dipole_charge/filter_type_0/Slice_1_grad/Shape" attr { key: "T" value { @@ -24531,10 +24600,10 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_0/Slice_grad/sub_1" + name: "gradients/dipole_charge/filter_type_0/Slice_1_grad/sub_1" op: "Sub" - input: "gradients/dipole_charge/filter_type_0/Slice_grad/sub" - input: "dipole_charge/filter_type_0/Slice/begin" + input: "gradients/dipole_charge/filter_type_0/Slice_1_grad/sub" + input: "dipole_charge/filter_type_0/Slice_1/begin" attr { key: "T" value { @@ -24543,10 +24612,10 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_0/Slice_grad/Reshape_1" + name: "gradients/dipole_charge/filter_type_0/Slice_1_grad/Reshape_1" op: "Reshape" - input: "gradients/dipole_charge/filter_type_0/Slice_grad/sub_1" - input: "gradients/dipole_charge/filter_type_0/Slice_grad/stack" + input: "gradients/dipole_charge/filter_type_0/Slice_1_grad/sub_1" + input: "gradients/dipole_charge/filter_type_0/Slice_1_grad/stack" attr { key: "T" value { @@ -24561,7 +24630,7 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_0/Slice_grad/concat/axis" + name: "gradients/dipole_charge/filter_type_0/Slice_1_grad/concat/axis" op: "Const" attr { key: "dtype" @@ -24582,11 +24651,11 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_0/Slice_grad/concat" + name: "gradients/dipole_charge/filter_type_0/Slice_1_grad/concat" op: "ConcatV2" - input: "gradients/dipole_charge/filter_type_0/Slice_grad/Reshape" - input: "gradients/dipole_charge/filter_type_0/Slice_grad/Reshape_1" - input: "gradients/dipole_charge/filter_type_0/Slice_grad/concat/axis" + input: "gradients/dipole_charge/filter_type_0/Slice_1_grad/Reshape" + input: "gradients/dipole_charge/filter_type_0/Slice_1_grad/Reshape_1" + input: "gradients/dipole_charge/filter_type_0/Slice_1_grad/concat/axis" attr { key: "N" value { @@ -24607,10 +24676,10 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_0/Slice_grad/Pad" + name: "gradients/dipole_charge/filter_type_0/Slice_1_grad/Pad" op: "Pad" - input: "gradients/AddN_12" - input: "gradients/dipole_charge/filter_type_0/Slice_grad/concat" + input: "gradients/dipole_charge/filter_type_0/Reshape_1_grad/Reshape" + input: "gradients/dipole_charge/filter_type_0/Slice_1_grad/concat" attr { key: "T" value { @@ -24625,33 +24694,7 @@ node { } } node { - name: "gradients/AddN_13" - op: "AddN" - input: "gradients/dipole_charge/filter_type_0/Reshape_13_grad/Reshape" - input: "gradients/dipole_charge/filter_type_0/Reshape_7_grad/Reshape" - attr { - key: "N" - value { - i: 2 - } - } - attr { - key: "T" - value { - type: DT_DOUBLE - } - } - attr { - key: "_class" - value { - list { - s: "loc:@gradients/dipole_charge/filter_type_0/Reshape_13_grad/Reshape" - } - } - } -} -node { - name: "gradients/dipole_charge/filter_type_0/Slice_2_grad/Rank" + name: "gradients/dipole_charge/filter_type_0/Slice_3_grad/Rank" op: "Const" attr { key: "dtype" @@ -24672,9 +24715,9 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_0/Slice_2_grad/Shape" + name: "gradients/dipole_charge/filter_type_0/Slice_3_grad/Shape" op: "Shape" - input: "dipole_charge/filter_type_0/Slice_2" + input: "dipole_charge/filter_type_0/Slice_3" attr { key: "T" value { @@ -24689,7 +24732,7 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_0/Slice_2_grad/stack/1" + name: "gradients/dipole_charge/filter_type_0/Slice_3_grad/stack/1" op: "Const" attr { key: "dtype" @@ -24710,10 +24753,10 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_0/Slice_2_grad/stack" + name: "gradients/dipole_charge/filter_type_0/Slice_3_grad/stack" op: "Pack" - input: "gradients/dipole_charge/filter_type_0/Slice_2_grad/Rank" - input: "gradients/dipole_charge/filter_type_0/Slice_2_grad/stack/1" + input: "gradients/dipole_charge/filter_type_0/Slice_3_grad/Rank" + input: "gradients/dipole_charge/filter_type_0/Slice_3_grad/stack/1" attr { key: "N" value { @@ -24734,10 +24777,10 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_0/Slice_2_grad/Reshape" + name: "gradients/dipole_charge/filter_type_0/Slice_3_grad/Reshape" op: "Reshape" - input: "dipole_charge/filter_type_0/Slice_2/begin" - input: "gradients/dipole_charge/filter_type_0/Slice_2_grad/stack" + input: "dipole_charge/filter_type_0/Slice_3/begin" + input: "gradients/dipole_charge/filter_type_0/Slice_3_grad/stack" attr { key: "T" value { @@ -24752,9 +24795,9 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_0/Slice_2_grad/Shape_1" + name: "gradients/dipole_charge/filter_type_0/Slice_3_grad/Shape_1" op: "Shape" - input: "dipole_charge/Reshape_5" + input: "dipole_charge/filter_type_0/Reshape_7" attr { key: "T" value { @@ -24769,10 +24812,10 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_0/Slice_2_grad/sub" + name: "gradients/dipole_charge/filter_type_0/Slice_3_grad/sub" op: "Sub" - input: "gradients/dipole_charge/filter_type_0/Slice_2_grad/Shape_1" - input: "gradients/dipole_charge/filter_type_0/Slice_2_grad/Shape" + input: "gradients/dipole_charge/filter_type_0/Slice_3_grad/Shape_1" + input: "gradients/dipole_charge/filter_type_0/Slice_3_grad/Shape" attr { key: "T" value { @@ -24781,10 +24824,10 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_0/Slice_2_grad/sub_1" + name: "gradients/dipole_charge/filter_type_0/Slice_3_grad/sub_1" op: "Sub" - input: "gradients/dipole_charge/filter_type_0/Slice_2_grad/sub" - input: "dipole_charge/filter_type_0/Slice_2/begin" + input: "gradients/dipole_charge/filter_type_0/Slice_3_grad/sub" + input: "dipole_charge/filter_type_0/Slice_3/begin" attr { key: "T" value { @@ -24793,10 +24836,10 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_0/Slice_2_grad/Reshape_1" + name: "gradients/dipole_charge/filter_type_0/Slice_3_grad/Reshape_1" op: "Reshape" - input: "gradients/dipole_charge/filter_type_0/Slice_2_grad/sub_1" - input: "gradients/dipole_charge/filter_type_0/Slice_2_grad/stack" + input: "gradients/dipole_charge/filter_type_0/Slice_3_grad/sub_1" + input: "gradients/dipole_charge/filter_type_0/Slice_3_grad/stack" attr { key: "T" value { @@ -24811,7 +24854,7 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_0/Slice_2_grad/concat/axis" + name: "gradients/dipole_charge/filter_type_0/Slice_3_grad/concat/axis" op: "Const" attr { key: "dtype" @@ -24832,11 +24875,11 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_0/Slice_2_grad/concat" + name: "gradients/dipole_charge/filter_type_0/Slice_3_grad/concat" op: "ConcatV2" - input: "gradients/dipole_charge/filter_type_0/Slice_2_grad/Reshape" - input: "gradients/dipole_charge/filter_type_0/Slice_2_grad/Reshape_1" - input: "gradients/dipole_charge/filter_type_0/Slice_2_grad/concat/axis" + input: "gradients/dipole_charge/filter_type_0/Slice_3_grad/Reshape" + input: "gradients/dipole_charge/filter_type_0/Slice_3_grad/Reshape_1" + input: "gradients/dipole_charge/filter_type_0/Slice_3_grad/concat/axis" attr { key: "N" value { @@ -24857,10 +24900,10 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_0/Slice_2_grad/Pad" + name: "gradients/dipole_charge/filter_type_0/Slice_3_grad/Pad" op: "Pad" - input: "gradients/AddN_13" - input: "gradients/dipole_charge/filter_type_0/Slice_2_grad/concat" + input: "gradients/dipole_charge/filter_type_0/Reshape_8_grad/Reshape" + input: "gradients/dipole_charge/filter_type_0/Slice_3_grad/concat" attr { key: "T" value { @@ -24875,33 +24918,7 @@ node { } } node { - name: "gradients/AddN_14" - op: "AddN" - input: "gradients/dipole_charge/filter_type_1/Reshape_6_grad/Reshape" - input: "gradients/dipole_charge/filter_type_1/Reshape_grad/Reshape" - attr { - key: "N" - value { - i: 2 - } - } - attr { - key: "T" - value { - type: DT_DOUBLE - } - } - attr { - key: "_class" - value { - list { - s: "loc:@gradients/dipole_charge/filter_type_1/Reshape_6_grad/Reshape" - } - } - } -} -node { - name: "gradients/dipole_charge/filter_type_1/Slice_grad/Rank" + name: "gradients/dipole_charge/filter_type_1/Slice_1_grad/Rank" op: "Const" attr { key: "dtype" @@ -24922,9 +24939,9 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_1/Slice_grad/Shape" + name: "gradients/dipole_charge/filter_type_1/Slice_1_grad/Shape" op: "Shape" - input: "dipole_charge/filter_type_1/Slice" + input: "dipole_charge/filter_type_1/Slice_1" attr { key: "T" value { @@ -24939,7 +24956,7 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_1/Slice_grad/stack/1" + name: "gradients/dipole_charge/filter_type_1/Slice_1_grad/stack/1" op: "Const" attr { key: "dtype" @@ -24960,10 +24977,10 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_1/Slice_grad/stack" + name: "gradients/dipole_charge/filter_type_1/Slice_1_grad/stack" op: "Pack" - input: "gradients/dipole_charge/filter_type_1/Slice_grad/Rank" - input: "gradients/dipole_charge/filter_type_1/Slice_grad/stack/1" + input: "gradients/dipole_charge/filter_type_1/Slice_1_grad/Rank" + input: "gradients/dipole_charge/filter_type_1/Slice_1_grad/stack/1" attr { key: "N" value { @@ -24984,10 +25001,10 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_1/Slice_grad/Reshape" + name: "gradients/dipole_charge/filter_type_1/Slice_1_grad/Reshape" op: "Reshape" - input: "dipole_charge/filter_type_1/Slice/begin" - input: "gradients/dipole_charge/filter_type_1/Slice_grad/stack" + input: "dipole_charge/filter_type_1/Slice_1/begin" + input: "gradients/dipole_charge/filter_type_1/Slice_1_grad/stack" attr { key: "T" value { @@ -25002,9 +25019,9 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_1/Slice_grad/Shape_1" + name: "gradients/dipole_charge/filter_type_1/Slice_1_grad/Shape_1" op: "Shape" - input: "dipole_charge/Reshape_9" + input: "dipole_charge/filter_type_1/Reshape" attr { key: "T" value { @@ -25019,10 +25036,10 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_1/Slice_grad/sub" + name: "gradients/dipole_charge/filter_type_1/Slice_1_grad/sub" op: "Sub" - input: "gradients/dipole_charge/filter_type_1/Slice_grad/Shape_1" - input: "gradients/dipole_charge/filter_type_1/Slice_grad/Shape" + input: "gradients/dipole_charge/filter_type_1/Slice_1_grad/Shape_1" + input: "gradients/dipole_charge/filter_type_1/Slice_1_grad/Shape" attr { key: "T" value { @@ -25031,10 +25048,10 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_1/Slice_grad/sub_1" + name: "gradients/dipole_charge/filter_type_1/Slice_1_grad/sub_1" op: "Sub" - input: "gradients/dipole_charge/filter_type_1/Slice_grad/sub" - input: "dipole_charge/filter_type_1/Slice/begin" + input: "gradients/dipole_charge/filter_type_1/Slice_1_grad/sub" + input: "dipole_charge/filter_type_1/Slice_1/begin" attr { key: "T" value { @@ -25043,10 +25060,10 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_1/Slice_grad/Reshape_1" + name: "gradients/dipole_charge/filter_type_1/Slice_1_grad/Reshape_1" op: "Reshape" - input: "gradients/dipole_charge/filter_type_1/Slice_grad/sub_1" - input: "gradients/dipole_charge/filter_type_1/Slice_grad/stack" + input: "gradients/dipole_charge/filter_type_1/Slice_1_grad/sub_1" + input: "gradients/dipole_charge/filter_type_1/Slice_1_grad/stack" attr { key: "T" value { @@ -25061,7 +25078,7 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_1/Slice_grad/concat/axis" + name: "gradients/dipole_charge/filter_type_1/Slice_1_grad/concat/axis" op: "Const" attr { key: "dtype" @@ -25082,11 +25099,11 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_1/Slice_grad/concat" + name: "gradients/dipole_charge/filter_type_1/Slice_1_grad/concat" op: "ConcatV2" - input: "gradients/dipole_charge/filter_type_1/Slice_grad/Reshape" - input: "gradients/dipole_charge/filter_type_1/Slice_grad/Reshape_1" - input: "gradients/dipole_charge/filter_type_1/Slice_grad/concat/axis" + input: "gradients/dipole_charge/filter_type_1/Slice_1_grad/Reshape" + input: "gradients/dipole_charge/filter_type_1/Slice_1_grad/Reshape_1" + input: "gradients/dipole_charge/filter_type_1/Slice_1_grad/concat/axis" attr { key: "N" value { @@ -25107,10 +25124,10 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_1/Slice_grad/Pad" + name: "gradients/dipole_charge/filter_type_1/Slice_1_grad/Pad" op: "Pad" - input: "gradients/AddN_14" - input: "gradients/dipole_charge/filter_type_1/Slice_grad/concat" + input: "gradients/dipole_charge/filter_type_1/Reshape_1_grad/Reshape" + input: "gradients/dipole_charge/filter_type_1/Slice_1_grad/concat" attr { key: "T" value { @@ -25125,33 +25142,7 @@ node { } } node { - name: "gradients/AddN_15" - op: "AddN" - input: "gradients/dipole_charge/filter_type_1/Reshape_13_grad/Reshape" - input: "gradients/dipole_charge/filter_type_1/Reshape_7_grad/Reshape" - attr { - key: "N" - value { - i: 2 - } - } - attr { - key: "T" - value { - type: DT_DOUBLE - } - } - attr { - key: "_class" - value { - list { - s: "loc:@gradients/dipole_charge/filter_type_1/Reshape_13_grad/Reshape" - } - } - } -} -node { - name: "gradients/dipole_charge/filter_type_1/Slice_2_grad/Rank" + name: "gradients/dipole_charge/filter_type_1/Slice_3_grad/Rank" op: "Const" attr { key: "dtype" @@ -25172,9 +25163,9 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_1/Slice_2_grad/Shape" + name: "gradients/dipole_charge/filter_type_1/Slice_3_grad/Shape" op: "Shape" - input: "dipole_charge/filter_type_1/Slice_2" + input: "dipole_charge/filter_type_1/Slice_3" attr { key: "T" value { @@ -25189,7 +25180,7 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_1/Slice_2_grad/stack/1" + name: "gradients/dipole_charge/filter_type_1/Slice_3_grad/stack/1" op: "Const" attr { key: "dtype" @@ -25210,10 +25201,10 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_1/Slice_2_grad/stack" + name: "gradients/dipole_charge/filter_type_1/Slice_3_grad/stack" op: "Pack" - input: "gradients/dipole_charge/filter_type_1/Slice_2_grad/Rank" - input: "gradients/dipole_charge/filter_type_1/Slice_2_grad/stack/1" + input: "gradients/dipole_charge/filter_type_1/Slice_3_grad/Rank" + input: "gradients/dipole_charge/filter_type_1/Slice_3_grad/stack/1" attr { key: "N" value { @@ -25234,10 +25225,10 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_1/Slice_2_grad/Reshape" + name: "gradients/dipole_charge/filter_type_1/Slice_3_grad/Reshape" op: "Reshape" - input: "dipole_charge/filter_type_1/Slice_2/begin" - input: "gradients/dipole_charge/filter_type_1/Slice_2_grad/stack" + input: "dipole_charge/filter_type_1/Slice_3/begin" + input: "gradients/dipole_charge/filter_type_1/Slice_3_grad/stack" attr { key: "T" value { @@ -25252,9 +25243,9 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_1/Slice_2_grad/Shape_1" + name: "gradients/dipole_charge/filter_type_1/Slice_3_grad/Shape_1" op: "Shape" - input: "dipole_charge/Reshape_9" + input: "dipole_charge/filter_type_1/Reshape_7" attr { key: "T" value { @@ -25269,10 +25260,10 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_1/Slice_2_grad/sub" + name: "gradients/dipole_charge/filter_type_1/Slice_3_grad/sub" op: "Sub" - input: "gradients/dipole_charge/filter_type_1/Slice_2_grad/Shape_1" - input: "gradients/dipole_charge/filter_type_1/Slice_2_grad/Shape" + input: "gradients/dipole_charge/filter_type_1/Slice_3_grad/Shape_1" + input: "gradients/dipole_charge/filter_type_1/Slice_3_grad/Shape" attr { key: "T" value { @@ -25281,10 +25272,10 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_1/Slice_2_grad/sub_1" + name: "gradients/dipole_charge/filter_type_1/Slice_3_grad/sub_1" op: "Sub" - input: "gradients/dipole_charge/filter_type_1/Slice_2_grad/sub" - input: "dipole_charge/filter_type_1/Slice_2/begin" + input: "gradients/dipole_charge/filter_type_1/Slice_3_grad/sub" + input: "dipole_charge/filter_type_1/Slice_3/begin" attr { key: "T" value { @@ -25293,10 +25284,10 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_1/Slice_2_grad/Reshape_1" + name: "gradients/dipole_charge/filter_type_1/Slice_3_grad/Reshape_1" op: "Reshape" - input: "gradients/dipole_charge/filter_type_1/Slice_2_grad/sub_1" - input: "gradients/dipole_charge/filter_type_1/Slice_2_grad/stack" + input: "gradients/dipole_charge/filter_type_1/Slice_3_grad/sub_1" + input: "gradients/dipole_charge/filter_type_1/Slice_3_grad/stack" attr { key: "T" value { @@ -25311,7 +25302,7 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_1/Slice_2_grad/concat/axis" + name: "gradients/dipole_charge/filter_type_1/Slice_3_grad/concat/axis" op: "Const" attr { key: "dtype" @@ -25332,11 +25323,11 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_1/Slice_2_grad/concat" + name: "gradients/dipole_charge/filter_type_1/Slice_3_grad/concat" op: "ConcatV2" - input: "gradients/dipole_charge/filter_type_1/Slice_2_grad/Reshape" - input: "gradients/dipole_charge/filter_type_1/Slice_2_grad/Reshape_1" - input: "gradients/dipole_charge/filter_type_1/Slice_2_grad/concat/axis" + input: "gradients/dipole_charge/filter_type_1/Slice_3_grad/Reshape" + input: "gradients/dipole_charge/filter_type_1/Slice_3_grad/Reshape_1" + input: "gradients/dipole_charge/filter_type_1/Slice_3_grad/concat/axis" attr { key: "N" value { @@ -25357,10 +25348,10 @@ node { } } node { - name: "gradients/dipole_charge/filter_type_1/Slice_2_grad/Pad" + name: "gradients/dipole_charge/filter_type_1/Slice_3_grad/Pad" op: "Pad" - input: "gradients/AddN_15" - input: "gradients/dipole_charge/filter_type_1/Slice_2_grad/concat" + input: "gradients/dipole_charge/filter_type_1/Reshape_8_grad/Reshape" + input: "gradients/dipole_charge/filter_type_1/Slice_3_grad/concat" attr { key: "T" value { @@ -25375,16 +25366,27 @@ node { } } node { - name: "gradients/AddN_16" - op: "AddN" - input: "gradients/dipole_charge/filter_type_0/Slice_grad/Pad" - input: "gradients/dipole_charge/filter_type_0/Slice_2_grad/Pad" + name: "gradients/dipole_charge/filter_type_0/Reshape_grad/Shape" + op: "Shape" + input: "dipole_charge/filter_type_0/Slice" attr { - key: "N" + key: "T" value { - i: 2 + type: DT_DOUBLE + } + } + attr { + key: "out_type" + value { + type: DT_INT32 } } +} +node { + name: "gradients/dipole_charge/filter_type_0/Reshape_grad/Reshape" + op: "Reshape" + input: "gradients/dipole_charge/filter_type_0/Slice_1_grad/Pad" + input: "gradients/dipole_charge/filter_type_0/Reshape_grad/Shape" attr { key: "T" value { @@ -25392,18 +25394,16 @@ node { } } attr { - key: "_class" + key: "Tshape" value { - list { - s: "loc:@gradients/dipole_charge/filter_type_0/Slice_grad/Pad" - } + type: DT_INT32 } } } node { - name: "gradients/dipole_charge/Reshape_5_grad/Shape" + name: "gradients/dipole_charge/filter_type_0/Reshape_7_grad/Shape" op: "Shape" - input: "dipole_charge/Slice" + input: "dipole_charge/filter_type_0/Slice_2" attr { key: "T" value { @@ -25418,10 +25418,10 @@ node { } } node { - name: "gradients/dipole_charge/Reshape_5_grad/Reshape" + name: "gradients/dipole_charge/filter_type_0/Reshape_7_grad/Reshape" op: "Reshape" - input: "gradients/AddN_16" - input: "gradients/dipole_charge/Reshape_5_grad/Shape" + input: "gradients/dipole_charge/filter_type_0/Slice_3_grad/Pad" + input: "gradients/dipole_charge/filter_type_0/Reshape_7_grad/Shape" attr { key: "T" value { @@ -25436,16 +25436,27 @@ node { } } node { - name: "gradients/AddN_17" - op: "AddN" - input: "gradients/dipole_charge/filter_type_1/Slice_grad/Pad" - input: "gradients/dipole_charge/filter_type_1/Slice_2_grad/Pad" + name: "gradients/dipole_charge/filter_type_1/Reshape_grad/Shape" + op: "Shape" + input: "dipole_charge/filter_type_1/Slice" attr { - key: "N" + key: "T" value { - i: 2 + type: DT_DOUBLE + } + } + attr { + key: "out_type" + value { + type: DT_INT32 } } +} +node { + name: "gradients/dipole_charge/filter_type_1/Reshape_grad/Reshape" + op: "Reshape" + input: "gradients/dipole_charge/filter_type_1/Slice_1_grad/Pad" + input: "gradients/dipole_charge/filter_type_1/Reshape_grad/Shape" attr { key: "T" value { @@ -25453,18 +25464,16 @@ node { } } attr { - key: "_class" + key: "Tshape" value { - list { - s: "loc:@gradients/dipole_charge/filter_type_1/Slice_grad/Pad" - } + type: DT_INT32 } } } node { - name: "gradients/dipole_charge/Reshape_9_grad/Shape" + name: "gradients/dipole_charge/filter_type_1/Reshape_7_grad/Shape" op: "Shape" - input: "dipole_charge/Slice_1" + input: "dipole_charge/filter_type_1/Slice_2" attr { key: "T" value { @@ -25479,10 +25488,10 @@ node { } } node { - name: "gradients/dipole_charge/Reshape_9_grad/Reshape" + name: "gradients/dipole_charge/filter_type_1/Reshape_7_grad/Reshape" op: "Reshape" - input: "gradients/AddN_17" - input: "gradients/dipole_charge/Reshape_9_grad/Shape" + input: "gradients/dipole_charge/filter_type_1/Slice_3_grad/Pad" + input: "gradients/dipole_charge/filter_type_1/Reshape_7_grad/Shape" attr { key: "T" value { @@ -25497,7 +25506,33 @@ node { } } node { - name: "gradients/dipole_charge/Slice_grad/Rank" + name: "gradients/AddN_16" + op: "AddN" + input: "gradients/dipole_charge/filter_type_0/Reshape_6_grad/Reshape" + input: "gradients/dipole_charge/filter_type_0/Reshape_grad/Reshape" + attr { + key: "N" + value { + i: 2 + } + } + attr { + key: "T" + value { + type: DT_DOUBLE + } + } + attr { + key: "_class" + value { + list { + s: "loc:@gradients/dipole_charge/filter_type_0/Reshape_6_grad/Reshape" + } + } + } +} +node { + name: "gradients/dipole_charge/filter_type_0/Slice_grad/Rank" op: "Const" attr { key: "dtype" @@ -25518,9 +25553,9 @@ node { } } node { - name: "gradients/dipole_charge/Slice_grad/Shape" + name: "gradients/dipole_charge/filter_type_0/Slice_grad/Shape" op: "Shape" - input: "dipole_charge/Slice" + input: "dipole_charge/filter_type_0/Slice" attr { key: "T" value { @@ -25535,7 +25570,7 @@ node { } } node { - name: "gradients/dipole_charge/Slice_grad/stack/1" + name: "gradients/dipole_charge/filter_type_0/Slice_grad/stack/1" op: "Const" attr { key: "dtype" @@ -25556,10 +25591,10 @@ node { } } node { - name: "gradients/dipole_charge/Slice_grad/stack" + name: "gradients/dipole_charge/filter_type_0/Slice_grad/stack" op: "Pack" - input: "gradients/dipole_charge/Slice_grad/Rank" - input: "gradients/dipole_charge/Slice_grad/stack/1" + input: "gradients/dipole_charge/filter_type_0/Slice_grad/Rank" + input: "gradients/dipole_charge/filter_type_0/Slice_grad/stack/1" attr { key: "N" value { @@ -25580,10 +25615,10 @@ node { } } node { - name: "gradients/dipole_charge/Slice_grad/Reshape" + name: "gradients/dipole_charge/filter_type_0/Slice_grad/Reshape" op: "Reshape" - input: "dipole_charge/Slice/begin" - input: "gradients/dipole_charge/Slice_grad/stack" + input: "dipole_charge/filter_type_0/Slice/begin" + input: "gradients/dipole_charge/filter_type_0/Slice_grad/stack" attr { key: "T" value { @@ -25598,9 +25633,9 @@ node { } } node { - name: "gradients/dipole_charge/Slice_grad/Shape_1" + name: "gradients/dipole_charge/filter_type_0/Slice_grad/Shape_1" op: "Shape" - input: "dipole_charge/Reshape_4" + input: "dipole_charge/Reshape_7" attr { key: "T" value { @@ -25615,10 +25650,10 @@ node { } } node { - name: "gradients/dipole_charge/Slice_grad/sub" + name: "gradients/dipole_charge/filter_type_0/Slice_grad/sub" op: "Sub" - input: "gradients/dipole_charge/Slice_grad/Shape_1" - input: "gradients/dipole_charge/Slice_grad/Shape" + input: "gradients/dipole_charge/filter_type_0/Slice_grad/Shape_1" + input: "gradients/dipole_charge/filter_type_0/Slice_grad/Shape" attr { key: "T" value { @@ -25627,10 +25662,10 @@ node { } } node { - name: "gradients/dipole_charge/Slice_grad/sub_1" + name: "gradients/dipole_charge/filter_type_0/Slice_grad/sub_1" op: "Sub" - input: "gradients/dipole_charge/Slice_grad/sub" - input: "dipole_charge/Slice/begin" + input: "gradients/dipole_charge/filter_type_0/Slice_grad/sub" + input: "dipole_charge/filter_type_0/Slice/begin" attr { key: "T" value { @@ -25639,10 +25674,10 @@ node { } } node { - name: "gradients/dipole_charge/Slice_grad/Reshape_1" + name: "gradients/dipole_charge/filter_type_0/Slice_grad/Reshape_1" op: "Reshape" - input: "gradients/dipole_charge/Slice_grad/sub_1" - input: "gradients/dipole_charge/Slice_grad/stack" + input: "gradients/dipole_charge/filter_type_0/Slice_grad/sub_1" + input: "gradients/dipole_charge/filter_type_0/Slice_grad/stack" attr { key: "T" value { @@ -25657,7 +25692,7 @@ node { } } node { - name: "gradients/dipole_charge/Slice_grad/concat/axis" + name: "gradients/dipole_charge/filter_type_0/Slice_grad/concat/axis" op: "Const" attr { key: "dtype" @@ -25678,11 +25713,11 @@ node { } } node { - name: "gradients/dipole_charge/Slice_grad/concat" + name: "gradients/dipole_charge/filter_type_0/Slice_grad/concat" op: "ConcatV2" - input: "gradients/dipole_charge/Slice_grad/Reshape" - input: "gradients/dipole_charge/Slice_grad/Reshape_1" - input: "gradients/dipole_charge/Slice_grad/concat/axis" + input: "gradients/dipole_charge/filter_type_0/Slice_grad/Reshape" + input: "gradients/dipole_charge/filter_type_0/Slice_grad/Reshape_1" + input: "gradients/dipole_charge/filter_type_0/Slice_grad/concat/axis" attr { key: "N" value { @@ -25703,10 +25738,10 @@ node { } } node { - name: "gradients/dipole_charge/Slice_grad/Pad" + name: "gradients/dipole_charge/filter_type_0/Slice_grad/Pad" op: "Pad" - input: "gradients/dipole_charge/Reshape_5_grad/Reshape" - input: "gradients/dipole_charge/Slice_grad/concat" + input: "gradients/AddN_16" + input: "gradients/dipole_charge/filter_type_0/Slice_grad/concat" attr { key: "T" value { @@ -25721,7 +25756,33 @@ node { } } node { - name: "gradients/dipole_charge/Slice_1_grad/Rank" + name: "gradients/AddN_17" + op: "AddN" + input: "gradients/dipole_charge/filter_type_0/Reshape_13_grad/Reshape" + input: "gradients/dipole_charge/filter_type_0/Reshape_7_grad/Reshape" + attr { + key: "N" + value { + i: 2 + } + } + attr { + key: "T" + value { + type: DT_DOUBLE + } + } + attr { + key: "_class" + value { + list { + s: "loc:@gradients/dipole_charge/filter_type_0/Reshape_13_grad/Reshape" + } + } + } +} +node { + name: "gradients/dipole_charge/filter_type_0/Slice_2_grad/Rank" op: "Const" attr { key: "dtype" @@ -25742,9 +25803,9 @@ node { } } node { - name: "gradients/dipole_charge/Slice_1_grad/Shape" + name: "gradients/dipole_charge/filter_type_0/Slice_2_grad/Shape" op: "Shape" - input: "dipole_charge/Slice_1" + input: "dipole_charge/filter_type_0/Slice_2" attr { key: "T" value { @@ -25759,7 +25820,7 @@ node { } } node { - name: "gradients/dipole_charge/Slice_1_grad/stack/1" + name: "gradients/dipole_charge/filter_type_0/Slice_2_grad/stack/1" op: "Const" attr { key: "dtype" @@ -25780,10 +25841,10 @@ node { } } node { - name: "gradients/dipole_charge/Slice_1_grad/stack" + name: "gradients/dipole_charge/filter_type_0/Slice_2_grad/stack" op: "Pack" - input: "gradients/dipole_charge/Slice_1_grad/Rank" - input: "gradients/dipole_charge/Slice_1_grad/stack/1" + input: "gradients/dipole_charge/filter_type_0/Slice_2_grad/Rank" + input: "gradients/dipole_charge/filter_type_0/Slice_2_grad/stack/1" attr { key: "N" value { @@ -25804,10 +25865,10 @@ node { } } node { - name: "gradients/dipole_charge/Slice_1_grad/Reshape" + name: "gradients/dipole_charge/filter_type_0/Slice_2_grad/Reshape" op: "Reshape" - input: "dipole_charge/Slice_1/begin" - input: "gradients/dipole_charge/Slice_1_grad/stack" + input: "dipole_charge/filter_type_0/Slice_2/begin" + input: "gradients/dipole_charge/filter_type_0/Slice_2_grad/stack" attr { key: "T" value { @@ -25822,9 +25883,9 @@ node { } } node { - name: "gradients/dipole_charge/Slice_1_grad/Shape_1" + name: "gradients/dipole_charge/filter_type_0/Slice_2_grad/Shape_1" op: "Shape" - input: "dipole_charge/Reshape_4" + input: "dipole_charge/Reshape_7" attr { key: "T" value { @@ -25839,10 +25900,10 @@ node { } } node { - name: "gradients/dipole_charge/Slice_1_grad/sub" + name: "gradients/dipole_charge/filter_type_0/Slice_2_grad/sub" op: "Sub" - input: "gradients/dipole_charge/Slice_1_grad/Shape_1" - input: "gradients/dipole_charge/Slice_1_grad/Shape" + input: "gradients/dipole_charge/filter_type_0/Slice_2_grad/Shape_1" + input: "gradients/dipole_charge/filter_type_0/Slice_2_grad/Shape" attr { key: "T" value { @@ -25851,10 +25912,10 @@ node { } } node { - name: "gradients/dipole_charge/Slice_1_grad/sub_1" + name: "gradients/dipole_charge/filter_type_0/Slice_2_grad/sub_1" op: "Sub" - input: "gradients/dipole_charge/Slice_1_grad/sub" - input: "dipole_charge/Slice_1/begin" + input: "gradients/dipole_charge/filter_type_0/Slice_2_grad/sub" + input: "dipole_charge/filter_type_0/Slice_2/begin" attr { key: "T" value { @@ -25863,10 +25924,10 @@ node { } } node { - name: "gradients/dipole_charge/Slice_1_grad/Reshape_1" + name: "gradients/dipole_charge/filter_type_0/Slice_2_grad/Reshape_1" op: "Reshape" - input: "gradients/dipole_charge/Slice_1_grad/sub_1" - input: "gradients/dipole_charge/Slice_1_grad/stack" + input: "gradients/dipole_charge/filter_type_0/Slice_2_grad/sub_1" + input: "gradients/dipole_charge/filter_type_0/Slice_2_grad/stack" attr { key: "T" value { @@ -25881,7 +25942,7 @@ node { } } node { - name: "gradients/dipole_charge/Slice_1_grad/concat/axis" + name: "gradients/dipole_charge/filter_type_0/Slice_2_grad/concat/axis" op: "Const" attr { key: "dtype" @@ -25902,11 +25963,11 @@ node { } } node { - name: "gradients/dipole_charge/Slice_1_grad/concat" + name: "gradients/dipole_charge/filter_type_0/Slice_2_grad/concat" op: "ConcatV2" - input: "gradients/dipole_charge/Slice_1_grad/Reshape" - input: "gradients/dipole_charge/Slice_1_grad/Reshape_1" - input: "gradients/dipole_charge/Slice_1_grad/concat/axis" + input: "gradients/dipole_charge/filter_type_0/Slice_2_grad/Reshape" + input: "gradients/dipole_charge/filter_type_0/Slice_2_grad/Reshape_1" + input: "gradients/dipole_charge/filter_type_0/Slice_2_grad/concat/axis" attr { key: "N" value { @@ -25927,10 +25988,10 @@ node { } } node { - name: "gradients/dipole_charge/Slice_1_grad/Pad" + name: "gradients/dipole_charge/filter_type_0/Slice_2_grad/Pad" op: "Pad" - input: "gradients/dipole_charge/Reshape_9_grad/Reshape" - input: "gradients/dipole_charge/Slice_1_grad/concat" + input: "gradients/AddN_17" + input: "gradients/dipole_charge/filter_type_0/Slice_2_grad/concat" attr { key: "T" value { @@ -25947,8 +26008,8 @@ node { node { name: "gradients/AddN_18" op: "AddN" - input: "gradients/dipole_charge/Slice_grad/Pad" - input: "gradients/dipole_charge/Slice_1_grad/Pad" + input: "gradients/dipole_charge/filter_type_1/Reshape_6_grad/Reshape" + input: "gradients/dipole_charge/filter_type_1/Reshape_grad/Reshape" attr { key: "N" value { @@ -25965,33 +26026,36 @@ node { key: "_class" value { list { - s: "loc:@gradients/dipole_charge/Slice_grad/Pad" + s: "loc:@gradients/dipole_charge/filter_type_1/Reshape_6_grad/Reshape" } } } } node { - name: "gradients/dipole_charge/Reshape_4_grad/Shape" - op: "Shape" - input: "dipole_charge/o_rmat" + name: "gradients/dipole_charge/filter_type_1/Slice_grad/Rank" + op: "Const" attr { - key: "T" + key: "dtype" value { - type: DT_DOUBLE + type: DT_INT32 } } attr { - key: "out_type" + key: "value" value { - type: DT_INT32 + tensor { + dtype: DT_INT32 + tensor_shape { + } + int_val: 2 + } } } } node { - name: "gradients/dipole_charge/Reshape_4_grad/Reshape" - op: "Reshape" - input: "gradients/AddN_18" - input: "gradients/dipole_charge/Reshape_4_grad/Shape" + name: "gradients/dipole_charge/filter_type_1/Slice_grad/Shape" + op: "Shape" + input: "dipole_charge/filter_type_1/Slice" attr { key: "T" value { @@ -25999,14 +26063,14 @@ node { } } attr { - key: "Tshape" + key: "out_type" value { type: DT_INT32 } } } node { - name: "strided_slice_9/stack" + name: "gradients/dipole_charge/filter_type_1/Slice_grad/stack/1" op: "Const" attr { key: "dtype" @@ -26020,41 +26084,115 @@ node { tensor { dtype: DT_INT32 tensor_shape { - dim { - size: 1 - } } - int_val: 0 + int_val: 1 } } } } node { - name: "strided_slice_9/stack_1" - op: "Const" + name: "gradients/dipole_charge/filter_type_1/Slice_grad/stack" + op: "Pack" + input: "gradients/dipole_charge/filter_type_1/Slice_grad/Rank" + input: "gradients/dipole_charge/filter_type_1/Slice_grad/stack/1" attr { - key: "dtype" + key: "N" + value { + i: 2 + } + } + attr { + key: "T" value { type: DT_INT32 } } attr { - key: "value" + key: "axis" value { - tensor { - dtype: DT_INT32 - tensor_shape { - dim { - size: 1 - } - } - int_val: 1 - } + i: 0 } } } node { - name: "strided_slice_9/stack_2" + name: "gradients/dipole_charge/filter_type_1/Slice_grad/Reshape" + op: "Reshape" + input: "dipole_charge/filter_type_1/Slice/begin" + input: "gradients/dipole_charge/filter_type_1/Slice_grad/stack" + attr { + key: "T" + value { + type: DT_INT32 + } + } + attr { + key: "Tshape" + value { + type: DT_INT32 + } + } +} +node { + name: "gradients/dipole_charge/filter_type_1/Slice_grad/Shape_1" + op: "Shape" + input: "dipole_charge/Reshape_11" + attr { + key: "T" + value { + type: DT_DOUBLE + } + } + attr { + key: "out_type" + value { + type: DT_INT32 + } + } +} +node { + name: "gradients/dipole_charge/filter_type_1/Slice_grad/sub" + op: "Sub" + input: "gradients/dipole_charge/filter_type_1/Slice_grad/Shape_1" + input: "gradients/dipole_charge/filter_type_1/Slice_grad/Shape" + attr { + key: "T" + value { + type: DT_INT32 + } + } +} +node { + name: "gradients/dipole_charge/filter_type_1/Slice_grad/sub_1" + op: "Sub" + input: "gradients/dipole_charge/filter_type_1/Slice_grad/sub" + input: "dipole_charge/filter_type_1/Slice/begin" + attr { + key: "T" + value { + type: DT_INT32 + } + } +} +node { + name: "gradients/dipole_charge/filter_type_1/Slice_grad/Reshape_1" + op: "Reshape" + input: "gradients/dipole_charge/filter_type_1/Slice_grad/sub_1" + input: "gradients/dipole_charge/filter_type_1/Slice_grad/stack" + attr { + key: "T" + value { + type: DT_INT32 + } + } + attr { + key: "Tshape" + value { + type: DT_INT32 + } + } +} +node { + name: "gradients/dipole_charge/filter_type_1/Slice_grad/concat/axis" op: "Const" attr { key: "dtype" @@ -26068,9 +26206,6 @@ node { tensor { dtype: DT_INT32 tensor_shape { - dim { - size: 1 - } } int_val: 1 } @@ -26078,16 +26213,15 @@ node { } } node { - name: "strided_slice_9" - op: "StridedSlice" - input: "dipole_charge/t_natoms" - input: "strided_slice_9/stack" - input: "strided_slice_9/stack_1" - input: "strided_slice_9/stack_2" + name: "gradients/dipole_charge/filter_type_1/Slice_grad/concat" + op: "ConcatV2" + input: "gradients/dipole_charge/filter_type_1/Slice_grad/Reshape" + input: "gradients/dipole_charge/filter_type_1/Slice_grad/Reshape_1" + input: "gradients/dipole_charge/filter_type_1/Slice_grad/concat/axis" attr { - key: "Index" + key: "N" value { - type: DT_INT32 + i: 2 } } attr { @@ -26097,38 +26231,58 @@ node { } } attr { - key: "begin_mask" + key: "Tidx" value { - i: 0 + type: DT_INT32 } } +} +node { + name: "gradients/dipole_charge/filter_type_1/Slice_grad/Pad" + op: "Pad" + input: "gradients/AddN_18" + input: "gradients/dipole_charge/filter_type_1/Slice_grad/concat" attr { - key: "ellipsis_mask" + key: "T" value { - i: 0 + type: DT_DOUBLE } } attr { - key: "end_mask" + key: "Tpaddings" value { - i: 0 + type: DT_INT32 } } +} +node { + name: "gradients/AddN_19" + op: "AddN" + input: "gradients/dipole_charge/filter_type_1/Reshape_13_grad/Reshape" + input: "gradients/dipole_charge/filter_type_1/Reshape_7_grad/Reshape" attr { - key: "new_axis_mask" + key: "N" value { - i: 0 + i: 2 } } attr { - key: "shrink_axis_mask" + key: "T" value { - i: 1 + type: DT_DOUBLE + } + } + attr { + key: "_class" + value { + list { + s: "loc:@gradients/dipole_charge/filter_type_1/Reshape_13_grad/Reshape" + } } } } node { - name: "mul_4/y" + name: "gradients/dipole_charge/filter_type_1/Slice_2_grad/Rank" op: "Const" attr { key: "dtype" @@ -26143,25 +26297,30 @@ node { dtype: DT_INT32 tensor_shape { } - int_val: 552 + int_val: 2 } } } } node { - name: "mul_4" - op: "Mul" - input: "strided_slice_9" - input: "mul_4/y" + name: "gradients/dipole_charge/filter_type_1/Slice_2_grad/Shape" + op: "Shape" + input: "dipole_charge/filter_type_1/Slice_2" attr { key: "T" + value { + type: DT_DOUBLE + } + } + attr { + key: "out_type" value { type: DT_INT32 } } } node { - name: "Reshape_5/shape/0" + name: "gradients/dipole_charge/filter_type_1/Slice_2_grad/stack/1" op: "Const" attr { key: "dtype" @@ -26176,16 +26335,16 @@ node { dtype: DT_INT32 tensor_shape { } - int_val: -1 + int_val: 1 } } } } node { - name: "Reshape_5/shape" + name: "gradients/dipole_charge/filter_type_1/Slice_2_grad/stack" op: "Pack" - input: "Reshape_5/shape/0" - input: "mul_4" + input: "gradients/dipole_charge/filter_type_1/Slice_2_grad/Rank" + input: "gradients/dipole_charge/filter_type_1/Slice_2_grad/stack/1" attr { key: "N" value { @@ -26206,14 +26365,14 @@ node { } } node { - name: "Reshape_5" + name: "gradients/dipole_charge/filter_type_1/Slice_2_grad/Reshape" op: "Reshape" - input: "gradients/dipole_charge/Reshape_4_grad/Reshape" - input: "Reshape_5/shape" + input: "dipole_charge/filter_type_1/Slice_2/begin" + input: "gradients/dipole_charge/filter_type_1/Slice_2_grad/stack" attr { key: "T" value { - type: DT_DOUBLE + type: DT_INT32 } } attr { @@ -26224,309 +26383,252 @@ node { } } node { - name: "Neg" - op: "Neg" - input: "Reshape_5" + name: "gradients/dipole_charge/filter_type_1/Slice_2_grad/Shape_1" + op: "Shape" + input: "dipole_charge/Reshape_11" attr { key: "T" value { type: DT_DOUBLE } } + attr { + key: "out_type" + value { + type: DT_INT32 + } + } } node { - name: "ProdForceSeA" - op: "ProdForceSeA" - input: "Neg" - input: "dipole_charge/o_rmat_deriv" - input: "dipole_charge/o_nlist" - input: "dipole_charge/t_natoms" + name: "gradients/dipole_charge/filter_type_1/Slice_2_grad/sub" + op: "Sub" + input: "gradients/dipole_charge/filter_type_1/Slice_2_grad/Shape_1" + input: "gradients/dipole_charge/filter_type_1/Slice_2_grad/Shape" attr { key: "T" value { - type: DT_DOUBLE - } - } - attr { - key: "n_a_sel" - value { - i: 138 - } - } - attr { - key: "n_r_sel" - value { - i: 0 - } - } -} -node { - name: "ProdVirialSeA" - op: "ProdVirialSeA" - input: "Neg" - input: "dipole_charge/o_rmat_deriv" - input: "dipole_charge/o_rij" - input: "dipole_charge/o_nlist" - input: "dipole_charge/t_natoms" - attr { - key: "T" - value { - type: DT_DOUBLE - } - } - attr { - key: "n_a_sel" - value { - i: 138 - } - } - attr { - key: "n_r_sel" - value { - i: 0 + type: DT_INT32 } } } node { - name: "o_dm_force" - op: "Identity" - input: "ProdForceSeA" + name: "gradients/dipole_charge/filter_type_1/Slice_2_grad/sub_1" + op: "Sub" + input: "gradients/dipole_charge/filter_type_1/Slice_2_grad/sub" + input: "dipole_charge/filter_type_1/Slice_2/begin" attr { key: "T" value { - type: DT_DOUBLE + type: DT_INT32 } } } node { - name: "o_dm_virial" - op: "Identity" - input: "ProdVirialSeA" + name: "gradients/dipole_charge/filter_type_1/Slice_2_grad/Reshape_1" + op: "Reshape" + input: "gradients/dipole_charge/filter_type_1/Slice_2_grad/sub_1" + input: "gradients/dipole_charge/filter_type_1/Slice_2_grad/stack" attr { key: "T" value { - type: DT_DOUBLE + type: DT_INT32 } } -} -node { - name: "o_dm_av" - op: "Identity" - input: "ProdVirialSeA:1" attr { - key: "T" + key: "Tshape" value { - type: DT_DOUBLE + type: DT_INT32 } } } node { - name: "model_type" + name: "gradients/dipole_charge/filter_type_1/Slice_2_grad/concat/axis" op: "Const" attr { key: "dtype" value { - type: DT_STRING + type: DT_INT32 } } attr { key: "value" value { tensor { - dtype: DT_STRING + dtype: DT_INT32 tensor_shape { } - string_val: "original_model" + int_val: 1 } } } } node { - name: "t_box" - op: "Placeholder" + name: "gradients/dipole_charge/filter_type_1/Slice_2_grad/concat" + op: "ConcatV2" + input: "gradients/dipole_charge/filter_type_1/Slice_2_grad/Reshape" + input: "gradients/dipole_charge/filter_type_1/Slice_2_grad/Reshape_1" + input: "gradients/dipole_charge/filter_type_1/Slice_2_grad/concat/axis" attr { - key: "dtype" + key: "N" value { - type: DT_DOUBLE + i: 2 } } attr { - key: "shape" + key: "T" value { - shape { - dim { - size: -1 - } - } + type: DT_INT32 } } -} -node { - name: "t_coord" - op: "Placeholder" attr { - key: "dtype" + key: "Tidx" value { - type: DT_DOUBLE + type: DT_INT32 } } +} +node { + name: "gradients/dipole_charge/filter_type_1/Slice_2_grad/Pad" + op: "Pad" + input: "gradients/AddN_19" + input: "gradients/dipole_charge/filter_type_1/Slice_2_grad/concat" attr { - key: "shape" + key: "T" value { - shape { - dim { - size: -1 - } - } + type: DT_DOUBLE } } -} -node { - name: "t_type" - op: "Placeholder" attr { - key: "dtype" + key: "Tpaddings" value { type: DT_INT32 } } +} +node { + name: "gradients/AddN_20" + op: "AddN" + input: "gradients/dipole_charge/filter_type_0/Slice_grad/Pad" + input: "gradients/dipole_charge/filter_type_0/Slice_2_grad/Pad" attr { - key: "shape" + key: "N" value { - shape { - dim { - size: -1 - } - } + i: 2 } } -} -node { - name: "t_natoms" - op: "Placeholder" attr { - key: "dtype" + key: "T" value { - type: DT_INT32 + type: DT_DOUBLE } } attr { - key: "shape" + key: "_class" value { - shape { - dim { - size: 4 - } + list { + s: "loc:@gradients/dipole_charge/filter_type_0/Slice_grad/Pad" } } } } node { - name: "t_mesh" - op: "Placeholder" + name: "gradients/dipole_charge/Reshape_7_grad/Shape" + op: "Shape" + input: "dipole_charge/Slice" attr { - key: "dtype" + key: "T" value { - type: DT_INT32 + type: DT_DOUBLE } } attr { - key: "shape" + key: "out_type" value { - shape { - dim { - size: -1 - } - } + type: DT_INT32 } } } node { - name: "model_attr/tmap" - op: "Const" + name: "gradients/dipole_charge/Reshape_7_grad/Reshape" + op: "Reshape" + input: "gradients/AddN_20" + input: "gradients/dipole_charge/Reshape_7_grad/Shape" attr { - key: "dtype" + key: "T" value { - type: DT_STRING + type: DT_DOUBLE } } attr { - key: "value" + key: "Tshape" value { - tensor { - dtype: DT_STRING - tensor_shape { - } - string_val: "O H" - } + type: DT_INT32 } } } node { - name: "model_attr/model_type" - op: "Const" + name: "gradients/AddN_21" + op: "AddN" + input: "gradients/dipole_charge/filter_type_1/Slice_grad/Pad" + input: "gradients/dipole_charge/filter_type_1/Slice_2_grad/Pad" attr { - key: "dtype" + key: "N" value { - type: DT_STRING + i: 2 } } attr { - key: "value" + key: "T" value { - tensor { - dtype: DT_STRING - tensor_shape { - } - string_val: "ener" + type: DT_DOUBLE + } + } + attr { + key: "_class" + value { + list { + s: "loc:@gradients/dipole_charge/filter_type_1/Slice_grad/Pad" } } } } node { - name: "model_attr/model_version" - op: "Const" + name: "gradients/dipole_charge/Reshape_11_grad/Shape" + op: "Shape" + input: "dipole_charge/Slice_1" attr { - key: "dtype" + key: "T" value { - type: DT_STRING + type: DT_DOUBLE } } attr { - key: "value" + key: "out_type" value { - tensor { - dtype: DT_STRING - tensor_shape { - } - string_val: "1.1" - } + type: DT_INT32 } } } node { - name: "descrpt_attr/rcut" - op: "Const" + name: "gradients/dipole_charge/Reshape_11_grad/Reshape" + op: "Reshape" + input: "gradients/AddN_21" + input: "gradients/dipole_charge/Reshape_11_grad/Shape" attr { - key: "dtype" + key: "T" value { type: DT_DOUBLE } } attr { - key: "value" + key: "Tshape" value { - tensor { - dtype: DT_DOUBLE - tensor_shape { - } - double_val: 6.0 - } + type: DT_INT32 } } } node { - name: "descrpt_attr/ntypes" + name: "gradients/dipole_charge/Slice_grad/Rank" op: "Const" attr { key: "dtype" @@ -26541,37 +26643,30 @@ node { dtype: DT_INT32 tensor_shape { } - int_val: 2 + int_val: 3 } } } } node { - name: "descrpt_attr_0/sel" - op: "Const" + name: "gradients/dipole_charge/Slice_grad/Shape" + op: "Shape" + input: "dipole_charge/Slice" attr { - key: "dtype" + key: "T" value { - type: DT_INT32 + type: DT_DOUBLE } } attr { - key: "value" + key: "out_type" value { - tensor { - dtype: DT_INT32 - tensor_shape { - dim { - size: 2 - } - } - tensor_content: "\024\000\000\000(\000\000\000" - } + type: DT_INT32 } } } node { - name: "descrpt_attr_0/original_sel" + name: "gradients/dipole_charge/Slice_grad/stack/1" op: "Const" attr { key: "dtype" @@ -26585,92 +26680,58 @@ node { tensor { dtype: DT_INT32 tensor_shape { - dim { - size: 2 - } } - tensor_content: 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input: "t_natoms" - input: "strided_slice_13/stack" - input: "strided_slice_13/stack_1" - input: "strided_slice_13/stack_2" + name: "gradients/dipole_charge/Slice_grad/concat" + op: "ConcatV2" + input: "gradients/dipole_charge/Slice_grad/Reshape" + input: "gradients/dipole_charge/Slice_grad/Reshape_1" + input: "gradients/dipole_charge/Slice_grad/concat/axis" attr { - key: "Index" + key: "N" value { - type: DT_INT32 + i: 2 } } attr { @@ -26784,41 +26827,33 @@ node { } } attr { - key: "begin_mask" - value { - i: 0 - } - } - attr { - key: "ellipsis_mask" - value { - i: 0 - } - } - attr { - key: "end_mask" + key: "Tidx" value { - i: 0 + type: DT_INT32 } } +} +node { + name: "gradients/dipole_charge/Slice_grad/Pad" + op: "Pad" + input: "gradients/dipole_charge/Reshape_7_grad/Reshape" + input: "gradients/dipole_charge/Slice_grad/concat" attr { - key: "new_axis_mask" + key: "T" value { - i: 0 + type: DT_DOUBLE } } attr { - key: "shrink_axis_mask" + key: "Tpaddings" value { - i: 1 + type: DT_INT32 } } } node { - name: "mul_6/y" + name: "gradients/dipole_charge/Slice_1_grad/Rank" op: "Const" - input: "^descrpt_attr_0/original_sel" - input: "^descrpt_attr_0/sel" attr { key: "dtype" value { @@ -26838,22 +26873,25 @@ node { } } node { - name: "mul_6" - op: "Mul" - input: "strided_slice_13" - input: "mul_6/y" + name: "gradients/dipole_charge/Slice_1_grad/Shape" + op: "Shape" + input: "dipole_charge/Slice_1" attr { key: "T" + value { + type: DT_DOUBLE + } + } + attr { + key: "out_type" value { type: DT_INT32 } } } node { - name: "Reshape_8/shape/0" + name: "gradients/dipole_charge/Slice_1_grad/stack/1" op: "Const" - input: "^descrpt_attr_0/original_sel" - input: "^descrpt_attr_0/sel" attr { key: "dtype" value { @@ -26867,16 +26905,16 @@ node { dtype: DT_INT32 tensor_shape { } - int_val: -1 + int_val: 1 } } } } node { - name: "Reshape_8/shape" + name: "gradients/dipole_charge/Slice_1_grad/stack" op: "Pack" - input: "Reshape_8/shape/0" - input: "mul_6" + input: "gradients/dipole_charge/Slice_1_grad/Rank" + input: "gradients/dipole_charge/Slice_1_grad/stack/1" attr { key: "N" value { @@ -26897,14 +26935,14 @@ node { } } node { - name: "Reshape_8" + name: "gradients/dipole_charge/Slice_1_grad/Reshape" op: "Reshape" - input: "t_coord" - input: "Reshape_8/shape" + input: "dipole_charge/Slice_1/begin" + input: "gradients/dipole_charge/Slice_1_grad/stack" attr { key: "T" value { - type: DT_DOUBLE + type: DT_INT32 } } attr { @@ -26915,34 +26953,176 @@ node { } } node { - name: "Reshape_9/shape" - op: "Const" + name: "gradients/dipole_charge/Slice_1_grad/Shape_1" + op: "Shape" + input: "dipole_charge/Reshape_6" attr { - key: "dtype" + key: "T" value { - type: DT_INT32 + type: DT_DOUBLE } } attr { - key: "value" + key: "out_type" value { - tensor { - dtype: DT_INT32 - tensor_shape { - dim { - size: 2 - } - } - tensor_content: "\377\377\377\377\t\000\000\000" - } + type: DT_INT32 } } } node { - name: "Reshape_9" - op: "Reshape" - input: "t_box" - input: "Reshape_9/shape" + name: "gradients/dipole_charge/Slice_1_grad/sub" + op: "Sub" + input: "gradients/dipole_charge/Slice_1_grad/Shape_1" + input: "gradients/dipole_charge/Slice_1_grad/Shape" + attr { + key: "T" + value { + type: DT_INT32 + } + } +} +node { + name: "gradients/dipole_charge/Slice_1_grad/sub_1" + op: "Sub" + input: "gradients/dipole_charge/Slice_1_grad/sub" + input: "dipole_charge/Slice_1/begin" + attr { + key: "T" + value { + type: DT_INT32 + } + } +} +node { + name: "gradients/dipole_charge/Slice_1_grad/Reshape_1" + op: "Reshape" + input: "gradients/dipole_charge/Slice_1_grad/sub_1" + input: "gradients/dipole_charge/Slice_1_grad/stack" + attr { + key: "T" + value { + type: DT_INT32 + } + } + attr { + key: "Tshape" + value { + type: DT_INT32 + } + } +} +node { + name: "gradients/dipole_charge/Slice_1_grad/concat/axis" + op: "Const" + attr { + key: "dtype" + value { + type: DT_INT32 + } + } + attr { + key: "value" + value { + tensor { + dtype: DT_INT32 + tensor_shape { + } + int_val: 1 + } + } + } +} +node { + name: "gradients/dipole_charge/Slice_1_grad/concat" + op: "ConcatV2" + input: "gradients/dipole_charge/Slice_1_grad/Reshape" + input: "gradients/dipole_charge/Slice_1_grad/Reshape_1" + input: "gradients/dipole_charge/Slice_1_grad/concat/axis" + attr { + key: "N" + value { + i: 2 + } + } + attr { + key: "T" + value { + type: DT_INT32 + } + } + attr { + key: "Tidx" + value { + type: DT_INT32 + } + } +} +node { + name: "gradients/dipole_charge/Slice_1_grad/Pad" + op: "Pad" + input: "gradients/dipole_charge/Reshape_11_grad/Reshape" + input: "gradients/dipole_charge/Slice_1_grad/concat" + attr { + key: "T" + value { + type: DT_DOUBLE + } + } + attr { + key: "Tpaddings" + value { + type: DT_INT32 + } + } +} +node { + name: "gradients/AddN_22" + op: "AddN" + input: "gradients/dipole_charge/Slice_grad/Pad" + input: "gradients/dipole_charge/Slice_1_grad/Pad" + attr { + key: "N" + value { + i: 2 + } + } + attr { + key: "T" + value { + type: DT_DOUBLE + } + } + attr { + key: "_class" + value { + list { + s: "loc:@gradients/dipole_charge/Slice_grad/Pad" + } + } + } +} +node { + name: "gradients/dipole_charge/Reshape_6_grad/Shape" + op: "Shape" + input: "dipole_charge/o_rmat" + attr { + key: "T" + value { + type: DT_DOUBLE + } + } + attr { + key: "out_type" + value { + type: DT_INT32 + } + } +} +node { + name: "gradients/dipole_charge/Reshape_6_grad/Reshape" + op: "Reshape" + input: "gradients/AddN_22" + input: "gradients/dipole_charge/Reshape_6_grad/Shape" attr { key: "T" value { @@ -26957,7 +27137,7 @@ node { } } node { - name: "strided_slice_14/stack" + name: "strided_slice_9/stack" op: "Const" attr { key: "dtype" @@ -26975,13 +27155,13 @@ node { size: 1 } } - int_val: 1 + int_val: 0 } } } } node { - name: "strided_slice_14/stack_1" + name: "strided_slice_9/stack_1" op: "Const" attr { key: "dtype" @@ -26999,13 +27179,13 @@ node { size: 1 } } - int_val: 2 + int_val: 1 } } } } node { - name: "strided_slice_14/stack_2" + name: "strided_slice_9/stack_2" op: "Const" attr { key: "dtype" @@ -27029,12 +27209,12 @@ node { } } node { - name: "strided_slice_14" + name: "strided_slice_9" op: "StridedSlice" - input: "t_natoms" - input: "strided_slice_14/stack" - input: "strided_slice_14/stack_1" - input: "strided_slice_14/stack_2" + input: "dipole_charge/t_natoms" + input: "strided_slice_9/stack" + input: "strided_slice_9/stack_1" + input: "strided_slice_9/stack_2" attr { key: "Index" value { @@ -27079,7 +27259,40 @@ node { } } node { - name: "Reshape_10/shape/0" + name: "mul_4/y" + op: "Const" + attr { + key: "dtype" + value { + type: DT_INT32 + } + } + attr { + key: "value" + value { + tensor { + dtype: DT_INT32 + tensor_shape { + } + int_val: 552 + } + } + } +} +node { + name: "mul_4" + op: "Mul" + input: "strided_slice_9" + input: "mul_4/y" + attr { + key: "T" + value { + type: DT_INT32 + } + } +} +node { + name: "Reshape_5/shape/0" op: "Const" attr { key: "dtype" @@ -27100,10 +27313,10 @@ node { } } node { - name: "Reshape_10/shape" + name: "Reshape_5/shape" op: "Pack" - input: "Reshape_10/shape/0" - input: "strided_slice_14" + input: "Reshape_5/shape/0" + input: "mul_4" attr { key: "N" value { @@ -27124,14 +27337,14 @@ node { } } node { - name: "Reshape_10" + name: "Reshape_5" op: "Reshape" - input: "t_type" - input: "Reshape_10/shape" + input: "gradients/dipole_charge/Reshape_6_grad/Reshape" + input: "Reshape_5/shape" attr { key: "T" value { - type: DT_INT32 + type: DT_DOUBLE } } attr { @@ -27142,146 +27355,288 @@ node { } } node { - name: "ProdEnvMatA" - op: "ProdEnvMatA" - input: "Reshape_8" - input: "Reshape_10" - input: "t_natoms" - input: "Reshape_9" - input: "t_mesh" - input: "descrpt_attr_0/t_avg/read" - input: "descrpt_attr_0/t_std/read" + name: "Neg" + op: "Neg" + input: "Reshape_5" attr { key: "T" value { type: DT_DOUBLE } } +} +node { + name: "ProdForceSeA" + op: "ProdForceSeA" + input: "Neg" + input: "dipole_charge/o_rmat_deriv" + input: "dipole_charge/o_nlist" + input: "dipole_charge/t_natoms" attr { - key: "rcut_a" + key: "T" value { - f: -1.0 + type: DT_DOUBLE } } attr { - key: "rcut_r" + key: "n_a_sel" value { - f: 4.0 + i: 138 } } attr { - key: "rcut_r_smth" + key: "n_r_sel" value { - f: 0.5 + i: 0 + } + } +} +node { + name: "ProdVirialSeA" + op: "ProdVirialSeA" + input: "Neg" + input: "dipole_charge/o_rmat_deriv" + input: "dipole_charge/o_rij" + input: "dipole_charge/o_nlist" + input: "dipole_charge/t_natoms" + attr { + key: "T" + value { + type: DT_DOUBLE } } attr { - key: "sel_a" + key: "n_a_sel" value { - list { - i: 20 - i: 40 - } + i: 138 } } attr { - key: "sel_r" + key: "n_r_sel" value { - list { - i: 0 - i: 0 - } + i: 0 } } } node { - name: "Reshape_11/shape" + name: "o_dm_force" + op: "Identity" + input: "ProdForceSeA" + attr { + key: "T" + value { + type: DT_DOUBLE + } + } +} +node { + name: "o_dm_virial" + op: "Identity" + input: "ProdVirialSeA" + attr { + key: "T" + value { + type: DT_DOUBLE + } + } +} +node { + name: "o_dm_av" + op: "Identity" + input: "ProdVirialSeA:1" + attr { + key: "T" + value { + type: DT_DOUBLE + } + } +} +node { + name: "model_type" op: "Const" attr { key: "dtype" value { - type: DT_INT32 + type: DT_STRING } } attr { key: "value" value { tensor { - dtype: DT_INT32 + dtype: DT_STRING tensor_shape { - dim { - size: 2 - } } - tensor_content: "\377\377\377\377\360\000\000\000" + string_val: "original_model" } } } } node { - name: "Reshape_11" - op: "Reshape" - input: "ProdEnvMatA" - input: "Reshape_11/shape" + name: "t_box" + op: "Placeholder" attr { - key: "T" + key: "dtype" value { type: DT_DOUBLE } } attr { - key: "Tshape" + key: "shape" value { - type: DT_INT32 + shape { + dim { + size: -1 + } + } } } } node { - name: "o_rmat_0" - op: "Identity" - input: "Reshape_11" + name: "t_coord" + op: "Placeholder" attr { - key: "T" + key: "dtype" value { type: DT_DOUBLE } } + attr { + key: "shape" + value { + shape { + dim { + size: -1 + } + } + } + } } node { - name: "o_rmat_deriv_0" - op: "Identity" - input: "ProdEnvMatA:1" + name: "t_type" + op: "Placeholder" attr { - key: "T" + key: "dtype" value { - type: DT_DOUBLE + type: DT_INT32 + } + } + attr { + key: "shape" + value { + shape { + dim { + size: -1 + } + } } } } node { - name: "o_rij_0" - op: "Identity" - input: "ProdEnvMatA:2" + name: "t_natoms" + op: "Placeholder" attr { - key: "T" + key: "dtype" value { - type: DT_DOUBLE + type: DT_INT32 + } + } + attr { + key: "shape" + value { + shape { + dim { + size: 4 + } + } } } } node { - name: "o_nlist_0" - op: "Identity" - input: "ProdEnvMatA:3" + name: "t_mesh" + op: "Placeholder" attr { - key: "T" + key: "dtype" value { type: DT_INT32 } } + attr { + key: "shape" + value { + shape { + dim { + size: -1 + } + } + } + } } node { - name: "strided_slice_15/stack" + name: "model_attr/tmap" + op: "Const" + attr { + key: "dtype" + value { + type: DT_STRING + } + } + attr { + key: "value" + value { + tensor { + dtype: DT_STRING + tensor_shape { + } + string_val: "O H" + } + } + } +} +node { + name: "model_attr/model_type" + op: "Const" + attr { + key: "dtype" + value { + type: DT_STRING + } + } + attr { + key: "value" + value { + tensor { + dtype: DT_STRING + tensor_shape { + } + string_val: "ener" + } + } + } +} +node { + name: "model_attr/model_version" + op: "Const" + attr { + key: "dtype" + value { + type: DT_STRING + } + } + attr { + key: "value" + value { + tensor { + dtype: DT_STRING + tensor_shape { + } + string_val: "1.1" + } + } + } +} +node { + name: "strided_slice_10/stack" op: "Const" attr { key: "dtype" @@ -27299,13 +27654,13 @@ node { size: 1 } } - int_val: 0 + int_val: 1 } } } } node { - name: "strided_slice_15/stack_1" + name: "strided_slice_10/stack_1" op: "Const" attr { key: "dtype" @@ -27323,13 +27678,13 @@ node { size: 1 } } - int_val: 1 + int_val: 2 } } } } node { - name: "strided_slice_15/stack_2" + name: "strided_slice_10/stack_2" op: "Const" attr { key: "dtype" @@ -27353,12 +27708,12 @@ node { } } node { - name: "strided_slice_15" + name: "strided_slice_10" op: "StridedSlice" input: "t_natoms" - input: "strided_slice_15/stack" - input: "strided_slice_15/stack_1" - input: "strided_slice_15/stack_2" + input: "strided_slice_10/stack" + input: "strided_slice_10/stack_1" + input: "strided_slice_10/stack_2" attr { key: "Index" value { @@ -27403,7 +27758,7 @@ node { } } node { - name: "mul_7/x" + name: "mul_5/y" op: "Const" attr { key: "dtype" @@ -27418,16 +27773,16 @@ node { dtype: DT_INT32 tensor_shape { } - int_val: 240 + int_val: 3 } } } } node { - name: "mul_7" + name: "mul_5" op: "Mul" - input: "mul_7/x" - input: "strided_slice_15" + input: "strided_slice_10" + input: "mul_5/y" attr { key: "T" value { @@ -27436,7 +27791,7 @@ node { } } node { - name: "Reshape_12/shape/0" + name: "Reshape_6/shape/0" op: "Const" attr { key: "dtype" @@ -27457,10 +27812,10 @@ node { } } node { - name: "Reshape_12/shape" + name: "Reshape_6/shape" op: "Pack" - input: "Reshape_12/shape/0" - input: "mul_7" + input: "Reshape_6/shape/0" + input: "mul_5" attr { key: "N" value { @@ -27481,10 +27836,10 @@ node { } } node { - name: "Reshape_12" + name: "Reshape_6" op: "Reshape" - input: "o_rmat_0" - input: "Reshape_12/shape" + input: "t_coord" + input: "Reshape_6/shape" attr { key: "T" value { @@ -27499,7 +27854,7 @@ node { } } node { - name: "strided_slice_16/stack" + name: "strided_slice_11/stack" op: "Const" attr { key: "dtype" @@ -27517,13 +27872,13 @@ node { size: 1 } } - int_val: 2 + int_val: 1 } } } } node { - name: "strided_slice_16/stack_1" + name: "strided_slice_11/stack_1" op: "Const" attr { key: "dtype" @@ -27541,13 +27896,13 @@ node { size: 1 } } - int_val: 3 + int_val: 2 } } } } node { - name: "strided_slice_16/stack_2" + name: "strided_slice_11/stack_2" op: "Const" attr { key: "dtype" @@ -27571,12 +27926,12 @@ node { } } node { - name: "strided_slice_16" + name: "strided_slice_11" op: "StridedSlice" input: "t_natoms" - input: "strided_slice_16/stack" - input: "strided_slice_16/stack_1" - input: "strided_slice_16/stack_2" + input: "strided_slice_11/stack" + input: "strided_slice_11/stack_1" + input: "strided_slice_11/stack_2" attr { key: "Index" value { @@ -27621,7 +27976,7 @@ node { } } node { - name: "mul_8/y" + name: "Reshape_7/shape/0" op: "Const" attr { key: "dtype" @@ -27636,113 +27991,97 @@ node { dtype: DT_INT32 tensor_shape { } - int_val: 240 + int_val: -1 } } } } node { - name: "mul_8" - op: "Mul" - input: "strided_slice_16" - input: "mul_8/y" + name: "Reshape_7/shape" + op: "Pack" + input: "Reshape_7/shape/0" + input: "strided_slice_11" attr { - key: "T" + key: "N" value { - type: DT_INT32 + i: 2 } } -} -node { - name: "Slice_2/begin" - op: "Const" attr { - key: "dtype" + key: "T" value { type: DT_INT32 } } attr { - key: "value" + key: "axis" value { - tensor { - dtype: DT_INT32 - tensor_shape { - dim { - size: 2 - } - } - tensor_content: "\000\000\000\000\000\000\000\000" - } + i: 0 } } } node { - name: "Slice_2/size/0" - op: "Const" + name: "Reshape_7" + op: "Reshape" + input: "t_type" + input: "Reshape_7/shape" attr { - key: "dtype" + key: "T" value { type: DT_INT32 } } attr { - key: "value" + key: "Tshape" value { - tensor { - dtype: DT_INT32 - tensor_shape { - } - int_val: -1 - } + type: DT_INT32 } } } node { - name: "Slice_2/size" - op: "Pack" - input: "Slice_2/size/0" - input: "mul_8" - attr { - key: "N" - value { - i: 2 - } - } + name: "descrpt_attr/rcut" + op: "Const" attr { - key: "T" + key: "dtype" value { - type: DT_INT32 + type: DT_DOUBLE } } attr { - key: "axis" + key: "value" value { - i: 0 + tensor { + dtype: DT_DOUBLE + tensor_shape { + } + double_val: 4.0 + } } } } node { - name: "Slice_2" - op: "Slice" - input: "Reshape_12" - input: "Slice_2/begin" - input: "Slice_2/size" + name: "descrpt_attr/ntypes" + op: "Const" attr { - key: "Index" + key: "dtype" value { type: DT_INT32 } } attr { - key: "T" + key: "value" value { - type: DT_DOUBLE + tensor { + dtype: DT_INT32 + tensor_shape { + } + int_val: 2 + } } } } node { - name: "Reshape_13/shape" + name: "descrpt_attr_0/sel" op: "Const" attr { key: "dtype" @@ -27760,31 +28099,13 @@ node { size: 2 } } - tensor_content: "\377\377\377\377\360\000\000\000" + tensor_content: "\024\000\000\000(\000\000\000" } } } } node { - name: "Reshape_13" - op: "Reshape" - input: "Slice_2" - input: "Reshape_13/shape" - attr { - key: "T" - value { - type: DT_DOUBLE - } - } - attr { - key: "Tshape" - value { - type: DT_INT32 - } - } -} -node { - name: "filter_type_0_0/Slice/begin" + name: "descrpt_attr_0/original_sel" op: "Const" attr { key: "dtype" @@ -27802,58 +28123,88 @@ node { size: 2 } } - tensor_content: "\000\000\000\000\000\000\000\000" + tensor_content: "\024\000\000\000(\000\000\000" } } } } node { - name: "filter_type_0_0/Slice/size" + name: "descrpt_attr_0/t_avg" op: "Const" attr { key: "dtype" value { - type: DT_INT32 + type: DT_DOUBLE } } attr { key: "value" value { tensor { - dtype: DT_INT32 + dtype: DT_DOUBLE tensor_shape { dim { size: 2 } + dim { + size: 240 + } } - tensor_content: "\377\377\377\377P\000\000\000" + tensor_content: 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"filter_type_0_0/concat_1" - input: "filter_type_0_0/Reshape_4" attr { - key: "T" + key: "ellipsis_mask" value { - type: DT_DOUBLE + i: 0 + } + } + attr { + key: "end_mask" + value { + i: 0 + } + } + attr { + key: "new_axis_mask" + value { + i: 0 + } + } + attr { + key: "shrink_axis_mask" + value { + i: 1 } } } node { - name: "filter_type_0_0/Reshape_5/shape" + name: "Slice_2/begin" op: "Const" attr { key: "dtype" @@ -28827,31 +29162,13 @@ node { size: 3 } } - tensor_content: "\377\377\377\377\024\000\000\000d\000\000\000" + tensor_content: "\000\000\000\000\000\000\000\000\000\000\000\000" } } } } node { - name: "filter_type_0_0/Reshape_5" - op: "Reshape" - input: "filter_type_0_0/add_1" - input: "filter_type_0_0/Reshape_5/shape" - attr { - key: "T" - value { - type: DT_DOUBLE - } - } - attr { - key: "Tshape" - value { - type: DT_INT32 - } - } -} -node { - name: "filter_type_0_0/Reshape_6/shape/1" + name: "Slice_2/size/0" op: "Const" attr { key: "dtype" @@ -28866,13 +29183,13 @@ node { dtype: DT_INT32 tensor_shape { } - int_val: 20 + int_val: -1 } } } } node { - name: "filter_type_0_0/Reshape_6/shape/2" + name: "Slice_2/size/2" op: "Const" attr { key: "dtype" @@ -28887,17 +29204,17 @@ node { dtype: DT_INT32 tensor_shape { } - int_val: 4 + int_val: -1 } } } } node { - name: "filter_type_0_0/Reshape_6/shape" + name: "Slice_2/size" op: "Pack" - input: "filter_type_0_0/strided_slice" - input: "filter_type_0_0/Reshape_6/shape/1" - input: "filter_type_0_0/Reshape_6/shape/2" + input: "Slice_2/size/0" + input: "strided_slice_16" + input: "Slice_2/size/2" attr { key: "N" value { @@ -28918,49 +29235,68 @@ node { } } node { - name: "filter_type_0_0/Reshape_6" - op: "Reshape" - input: "filter_type_0_0/Slice" - input: "filter_type_0_0/Reshape_6/shape" + name: "Slice_2" + op: "Slice" + input: "Reshape_12" + input: "Slice_2/begin" + input: "Slice_2/size" + attr { + key: "Index" + value { + type: DT_INT32 + } + } attr { key: "T" value { type: DT_DOUBLE } } +} +node { + name: "Reshape_13/shape" + op: "Const" attr { - key: "Tshape" + key: "dtype" value { type: DT_INT32 } } -} -node { - name: "filter_type_0_0/MatMul_3" - op: "BatchMatMulV2" - input: "filter_type_0_0/Reshape_6" - input: "filter_type_0_0/Reshape_5" attr { - key: "T" + key: "value" value { - type: DT_DOUBLE + tensor { + dtype: DT_INT32 + tensor_shape { + dim { + size: 2 + } + } + tensor_content: "\377\377\377\377\360\000\000\000" + } } } +} +node { + name: "Reshape_13" + op: "Reshape" + input: "Slice_2" + input: "Reshape_13/shape" attr { - key: "adj_x" + key: "T" value { - b: true + type: DT_DOUBLE } } attr { - key: "adj_y" + key: "Tshape" value { - b: false + type: DT_INT32 } } } node { - name: "filter_type_0_0/Slice_2/begin" + name: "filter_type_0_0/Slice/begin" op: "Const" attr { key: "dtype" @@ -28978,13 +29314,13 @@ node { size: 2 } } - tensor_content: "\000\000\000\000P\000\000\000" + tensor_content: "\000\000\000\000\000\000\000\000" } } } } node { - name: "filter_type_0_0/Slice_2/size" + name: "filter_type_0_0/Slice/size" op: "Const" attr { key: "dtype" @@ -29002,17 +29338,17 @@ node { size: 2 } } - tensor_content: "\377\377\377\377\240\000\000\000" + tensor_content: "\377\377\377\377P\000\000\000" } } } } node { - name: "filter_type_0_0/Slice_2" + name: "filter_type_0_0/Slice" op: "Slice" input: "Reshape_13" - input: "filter_type_0_0/Slice_2/begin" - input: "filter_type_0_0/Slice_2/size" + input: "filter_type_0_0/Slice/begin" + input: "filter_type_0_0/Slice/size" attr { key: "Index" value { @@ -29027,9 +29363,9 @@ node { } } node { - name: "filter_type_0_0/Shape_1" + name: "filter_type_0_0/Shape" op: "Shape" - input: "filter_type_0_0/Slice_2" + input: "filter_type_0_0/Slice" attr { key: "T" value { @@ -29044,7 +29380,7 @@ node { } } node { - name: "filter_type_0_0/strided_slice_1/stack" + name: "filter_type_0_0/strided_slice/stack" op: "Const" attr { key: "dtype" @@ -29068,7 +29404,7 @@ node { } } node { - name: "filter_type_0_0/strided_slice_1/stack_1" + name: "filter_type_0_0/strided_slice/stack_1" op: "Const" attr { key: "dtype" @@ -29092,7 +29428,7 @@ node { } } node { - name: "filter_type_0_0/strided_slice_1/stack_2" + name: "filter_type_0_0/strided_slice/stack_2" op: "Const" attr { key: "dtype" @@ -29116,12 +29452,12 @@ node { } } node { - name: "filter_type_0_0/strided_slice_1" + name: "filter_type_0_0/strided_slice" op: "StridedSlice" - input: "filter_type_0_0/Shape_1" - input: "filter_type_0_0/strided_slice_1/stack" - input: "filter_type_0_0/strided_slice_1/stack_1" - input: "filter_type_0_0/strided_slice_1/stack_2" + input: "filter_type_0_0/Shape" + input: "filter_type_0_0/strided_slice/stack" + input: "filter_type_0_0/strided_slice/stack_1" + input: "filter_type_0_0/strided_slice/stack_2" attr { key: "Index" value { @@ -29166,7 +29502,7 @@ node { } } node { - name: "filter_type_0_0/Reshape_7/shape" + name: "filter_type_0_0/Reshape/shape" op: "Const" attr { key: "dtype" @@ -29190,10 +29526,10 @@ node { } } node { - name: "filter_type_0_0/Reshape_7" + name: "filter_type_0_0/Reshape" op: "Reshape" - input: "filter_type_0_0/Slice_2" - input: "filter_type_0_0/Reshape_7/shape" + input: "filter_type_0_0/Slice" + input: "filter_type_0_0/Reshape/shape" attr { key: "T" value { @@ -29208,7 +29544,7 @@ node { } } node { - name: "filter_type_0_0/Slice_3/begin" + name: "filter_type_0_0/Slice_1/begin" op: "Const" attr { key: "dtype" @@ -29232,7 +29568,7 @@ node { } } node { - name: "filter_type_0_0/Slice_3/size" + name: "filter_type_0_0/Slice_1/size" op: "Const" attr { key: "dtype" @@ -29256,11 +29592,11 @@ node { } } node { - name: "filter_type_0_0/Slice_3" + name: "filter_type_0_0/Slice_1" op: "Slice" - input: "filter_type_0_0/Reshape_7" - input: "filter_type_0_0/Slice_3/begin" - input: "filter_type_0_0/Slice_3/size" + input: "filter_type_0_0/Reshape" + input: "filter_type_0_0/Slice_1/begin" + input: "filter_type_0_0/Slice_1/size" attr { key: "Index" value { @@ -29275,7 +29611,7 @@ node { } } node { - name: "filter_type_0_0/Reshape_8/shape" + name: "filter_type_0_0/Reshape_1/shape" op: "Const" attr { key: "dtype" @@ -29299,10 +29635,10 @@ node { } } node { - name: "filter_type_0_0/Reshape_8" + name: "filter_type_0_0/Reshape_1" op: "Reshape" - input: "filter_type_0_0/Slice_3" - input: "filter_type_0_0/Reshape_8/shape" + input: "filter_type_0_0/Slice_1" + input: "filter_type_0_0/Reshape_1/shape" attr { key: "T" value { @@ -29317,7 +29653,7 @@ node { } } node { - name: "filter_type_0_0/matrix_1_1" + name: "filter_type_0_0/matrix_1_0" op: "Const" attr { key: "dtype" @@ -29335,18 +29671,18 @@ node { size: 1 } dim { - size: 25 + size: 2 } } - tensor_content: ": 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"loc:@filter_type_0_0/matrix_1_0" } } } } node { - name: "filter_type_0_0/bias_1_1" + name: "filter_type_0_0/bias_1_0" op: "Const" attr { key: "dtype" @@ -29378,18 +29714,18 @@ node { dtype: DT_DOUBLE tensor_shape { dim { - size: 25 + size: 2 } } - tensor_content: "]\031\030\312%\"\353\277@\347\221\314$\322\270\277\210\r\213\0148\016\250?\214\'\247\366\201[\377\277\\\340\221\327*\346\263?P\002h\263\356.\362?m\226\242\225\343R\340\277\363N{\310\021n\371\277\016\177\354\255x\245\357?\205R\367\256\374\005\353\277\001\375\315FAB\371?\256 -AQT\372\277:\000v.6\206\347\277\370\244\002\235`i\340?\017/\340i\243H\326?\211\005\007&\345\360\354\277&C\r\036\226\226\352?\027p\322\317t\326\360\277\367\201\333\264\025\371\352\277\003\365\234\2231\035\304\277\215\020\324\204\314\264\333\277\254\300T\275U\316\327\277#\025\344X\203&\360?\034\260\210\252\006\300\327?\326-%\036\302\035\356\277" + tensor_content: "\263Yc\020\342\270\335\277JJ\362\010\\\202\000\300" } } } } node { - name: "filter_type_0_0/bias_1_1/read" + name: "filter_type_0_0/bias_1_0/read" op: "Identity" - input: "filter_type_0_0/bias_1_1" + input: "filter_type_0_0/bias_1_0" attr { key: "T" value { @@ -29400,16 +29736,16 @@ node { key: "_class" value { list { - s: "loc:@filter_type_0_0/bias_1_1" + s: "loc:@filter_type_0_0/bias_1_0" } } } } node { - name: "filter_type_0_0/MatMul_4" + name: "filter_type_0_0/MatMul" op: "MatMul" - input: "filter_type_0_0/Reshape_8" - input: "filter_type_0_0/matrix_1_1/read" + input: "filter_type_0_0/Reshape_1" + input: "filter_type_0_0/matrix_1_0/read" attr { key: "T" value { @@ -29430,10 +29766,10 @@ node { } } node { - name: "filter_type_0_0/BiasAdd_3" + name: "filter_type_0_0/BiasAdd" op: "BiasAdd" - input: "filter_type_0_0/MatMul_4" - input: "filter_type_0_0/bias_1_1/read" + input: "filter_type_0_0/MatMul" + input: "filter_type_0_0/bias_1_0/read" attr { key: "T" value { @@ -29448,9 +29784,9 @@ node { } } node { - name: "filter_type_0_0/Tanh_3" + name: "filter_type_0_0/Tanh" op: "Tanh" - input: "filter_type_0_0/BiasAdd_3" + input: "filter_type_0_0/BiasAdd" attr { key: "T" value { @@ -29459,7 +29795,7 @@ node { } } node { - name: "filter_type_0_0/Reshape_9/shape" + name: "filter_type_0_0/Reshape_2/shape" op: "Const" attr { key: "dtype" @@ -29477,16 +29813,16 @@ node { size: 2 } } - tensor_content: "\377\377\377\377\031\000\000\000" + tensor_content: "\377\377\377\377\002\000\000\000" } } } } node { - name: "filter_type_0_0/Reshape_9" + name: "filter_type_0_0/Reshape_2" op: "Reshape" - input: "filter_type_0_0/Tanh_3" - input: "filter_type_0_0/Reshape_9/shape" + input: "filter_type_0_0/Tanh" + input: "filter_type_0_0/Reshape_2/shape" attr { key: "T" value { @@ -29501,7 +29837,65 @@ node { } } node { - name: "filter_type_0_0/matrix_2_1" + name: "filter_type_0_0/concat/axis" + op: "Const" + attr { + key: "dtype" + value { + type: DT_INT32 + } + } + attr { + key: "value" + value { + tensor { + dtype: DT_INT32 + tensor_shape { + } + int_val: 1 + } + } + } +} +node { + name: "filter_type_0_0/concat" + op: "ConcatV2" + input: "filter_type_0_0/Reshape_1" + input: "filter_type_0_0/Reshape_1" + input: "filter_type_0_0/concat/axis" + attr { + key: "N" + value { + i: 2 + } + } + attr { + key: "T" + value { + type: DT_DOUBLE + } + } + attr { + key: "Tidx" + value { + type: DT_INT32 + } + } +} +node { + name: "filter_type_0_0/add" + op: "AddV2" + input: "filter_type_0_0/concat" + input: "filter_type_0_0/Reshape_2" + attr { + key: "T" + value { + type: DT_DOUBLE + } + } +} +node { + name: "filter_type_0_0/matrix_2_0" op: "Const" attr { key: "dtype" @@ -29516,21 +29910,21 @@ node { dtype: DT_DOUBLE tensor_shape { dim { - size: 25 + size: 2 } dim { - size: 50 + size: 4 } } - tensor_content: "\335\212B\227\345\326\317?]v\312\323\271\261\303\277J=\222Z{ 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+30177,13 @@ node { key: "_class" value { list { - s: "loc:@filter_type_0_0/matrix_3_1" + s: "loc:@filter_type_0_0/matrix_3_0" } } } } node { - name: "filter_type_0_0/bias_3_1" + name: "filter_type_0_0/bias_3_0" op: "Const" attr { key: "dtype" @@ -29804,18 +30198,18 @@ node { dtype: DT_DOUBLE tensor_shape { dim { - size: 100 + size: 8 } } - tensor_content: "W\352\260\347\037\347\367\277\207\"g\227\373(\370\277\334P}\002qd\303\277\000\305\320wW\262\344\277*\277/$(\264\321\277\231\351\005\377\370\242\366\277\003`\331\314Y\031\351\277\3058\\\237\010\252\355?\247 K\344\313\350\343?|\272\364\3158\273\310?\006\337\004\022\317\246\003@Pf\305\303\301F\361\277n\256\376/\271a\331\277\265\372\3309wI\360?\246\221kg\253\223\327\277\034\275\337\005\362\252\370?\205\217\033\353Ls\361?\366D\341\372Y\217\354?D\317e8X5\005@\r/#?\031h\346?B\375\255\n\352\340\346?\033\267\002\321*\250\276?\225\'*\030d6\303\277\032j\330<\t\356\360? 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\021Y}\333M\314?\342\216\221\257\207\325\327\277\006\346x\257F\355\001@1\220\001\030\0043\372?Zvz-\250\216\353\277\260\213\035L}\341\351?\024\177\024\262vE\361\2772&i\235s\006\355\277\271\t\232\301\230*\372\277\272\000J\2225v\366?\252\213\244N\367\025\322?\375zg\231^\352\363\277\320\"\271\351\375/w\277=\225\030`\231d\356\277-\237\340\223n\032\347?X\370-\247\032\361\250?\341\211\323_\274q\361\277\324\276E\311\034\210\314\277" + tensor_content: "\257h\225\273\216\252\310?\374<\231\223Z_\351?L\370]\375R\'\367?\341\nx\356B\n\331?\325\221v.\317\025\374\277%%B\360\2419\363?\333j\246bL\346\370?\354#\322~\265\203\342\277" } } } } node { - name: "filter_type_0_0/bias_3_1/read" + name: "filter_type_0_0/bias_3_0/read" op: "Identity" - input: "filter_type_0_0/bias_3_1" + input: "filter_type_0_0/bias_3_0" attr { key: "T" value { @@ -29826,16 +30220,16 @@ node { key: "_class" value { list { - s: "loc:@filter_type_0_0/bias_3_1" + s: "loc:@filter_type_0_0/bias_3_0" } } } } node { - name: "filter_type_0_0/MatMul_6" + name: "filter_type_0_0/MatMul_2" op: "MatMul" - input: "filter_type_0_0/add_2" - input: "filter_type_0_0/matrix_3_1/read" + input: "filter_type_0_0/add_1" + input: "filter_type_0_0/matrix_3_0/read" attr { key: "T" value { @@ -29856,10 +30250,10 @@ node { } } node { - name: "filter_type_0_0/BiasAdd_5" + name: "filter_type_0_0/BiasAdd_2" op: "BiasAdd" - input: "filter_type_0_0/MatMul_6" - input: "filter_type_0_0/bias_3_1/read" + input: "filter_type_0_0/MatMul_2" + input: "filter_type_0_0/bias_3_0/read" attr { key: "T" value { @@ -29874,9 +30268,9 @@ node { } } node { - name: "filter_type_0_0/Tanh_5" + name: "filter_type_0_0/Tanh_2" op: "Tanh" - input: "filter_type_0_0/BiasAdd_5" + input: "filter_type_0_0/BiasAdd_2" attr { key: "T" value { @@ -29885,7 +30279,7 @@ node { } } node { - name: "filter_type_0_0/Reshape_11/shape" + name: "filter_type_0_0/Reshape_4/shape" op: "Const" attr { key: "dtype" @@ -29903,16 +30297,16 @@ node { size: 2 } } - tensor_content: "\377\377\377\377d\000\000\000" + tensor_content: "\377\377\377\377\010\000\000\000" } } } } node { - name: "filter_type_0_0/Reshape_11" + name: "filter_type_0_0/Reshape_4" op: "Reshape" - input: "filter_type_0_0/Tanh_5" - input: "filter_type_0_0/Reshape_11/shape" + input: "filter_type_0_0/Tanh_2" + input: "filter_type_0_0/Reshape_4/shape" attr { key: "T" value { @@ -29927,7 +30321,7 @@ node { } } node { - name: "filter_type_0_0/concat_3/axis" + name: "filter_type_0_0/concat_2/axis" op: "Const" attr { key: "dtype" @@ -29948,11 +30342,11 @@ node { } } node { - name: "filter_type_0_0/concat_3" + name: "filter_type_0_0/concat_2" op: "ConcatV2" - input: "filter_type_0_0/add_2" - input: "filter_type_0_0/add_2" - input: "filter_type_0_0/concat_3/axis" + input: "filter_type_0_0/add_1" + input: "filter_type_0_0/add_1" + input: "filter_type_0_0/concat_2/axis" attr { key: "N" value { @@ -29973,10 +30367,10 @@ node { } } node { - name: "filter_type_0_0/add_3" + name: "filter_type_0_0/add_2" op: "AddV2" - input: "filter_type_0_0/concat_3" - input: "filter_type_0_0/Reshape_11" + input: "filter_type_0_0/concat_2" + input: "filter_type_0_0/Reshape_4" attr { key: "T" value { @@ -29985,7 +30379,7 @@ node { } } node { - name: "filter_type_0_0/Reshape_12/shape" + name: "filter_type_0_0/Reshape_5/shape" op: "Const" attr { key: "dtype" @@ -30003,16 +30397,16 @@ node { size: 3 } } - tensor_content: "\377\377\377\377(\000\000\000d\000\000\000" + tensor_content: "\377\377\377\377\024\000\000\000\010\000\000\000" } } } } node { - name: "filter_type_0_0/Reshape_12" + name: "filter_type_0_0/Reshape_5" op: "Reshape" - input: "filter_type_0_0/add_3" - input: "filter_type_0_0/Reshape_12/shape" + input: "filter_type_0_0/add_2" + input: "filter_type_0_0/Reshape_5/shape" attr { key: "T" value { @@ -30027,7 +30421,7 @@ node { } } node { - name: "filter_type_0_0/Reshape_13/shape/1" + name: "filter_type_0_0/Reshape_6/shape/1" op: "Const" attr { key: "dtype" @@ -30042,13 +30436,13 @@ node { dtype: DT_INT32 tensor_shape { } - int_val: 40 + int_val: 20 } } } } node { - name: "filter_type_0_0/Reshape_13/shape/2" + name: "filter_type_0_0/Reshape_6/shape/2" op: "Const" attr { key: "dtype" @@ -30069,11 +30463,11 @@ node { } } node { - name: "filter_type_0_0/Reshape_13/shape" + name: "filter_type_0_0/Reshape_6/shape" op: "Pack" - input: "filter_type_0_0/strided_slice_1" - input: "filter_type_0_0/Reshape_13/shape/1" - input: "filter_type_0_0/Reshape_13/shape/2" + input: "filter_type_0_0/strided_slice" + input: "filter_type_0_0/Reshape_6/shape/1" + input: "filter_type_0_0/Reshape_6/shape/2" attr { key: "N" value { @@ -30094,10 +30488,10 @@ node { } } node { - name: "filter_type_0_0/Reshape_13" + name: "filter_type_0_0/Reshape_6" op: "Reshape" - input: "filter_type_0_0/Slice_2" - input: "filter_type_0_0/Reshape_13/shape" + input: "filter_type_0_0/Slice" + input: "filter_type_0_0/Reshape_6/shape" attr { key: "T" value { @@ -30112,10 +30506,10 @@ node { } } node { - name: "filter_type_0_0/MatMul_7" + name: "filter_type_0_0/MatMul_3" op: "BatchMatMulV2" - input: "filter_type_0_0/Reshape_13" - input: "filter_type_0_0/Reshape_12" + input: "filter_type_0_0/Reshape_6" + input: "filter_type_0_0/Reshape_5" attr { key: "T" value { @@ -30136,58 +30530,7 @@ node { } } node { - name: "filter_type_0_0/AddN" - op: "AddN" - input: "filter_type_0_0/MatMul_3" - input: "filter_type_0_0/MatMul_7" - attr { - key: "N" - value { - i: 2 - } - } - attr { - key: "T" - value { - type: DT_DOUBLE - } - } -} -node { - name: "filter_type_0_0/truediv/y" - op: "Const" - attr { - key: "dtype" - value { - type: DT_DOUBLE - } - } - attr { - key: "value" - value { - tensor { - dtype: DT_DOUBLE - tensor_shape { - } - double_val: 60.0 - } - } - } -} -node { - name: "filter_type_0_0/truediv" - op: "RealDiv" - input: "filter_type_0_0/AddN" - input: "filter_type_0_0/truediv/y" - attr { - key: "T" - value { - type: DT_DOUBLE - } - } -} -node { - name: "filter_type_0_0/Slice_4/begin" + name: "filter_type_0_0/Slice_2/begin" op: "Const" attr { key: "dtype" @@ -30202,16 +30545,16 @@ node { dtype: DT_INT32 tensor_shape { dim { - size: 3 + size: 2 } } - tensor_content: "\000\000\000\000\000\000\000\000\000\000\000\000" + tensor_content: "\000\000\000\000P\000\000\000" } } } } node { - name: "filter_type_0_0/Slice_4/size" + name: "filter_type_0_0/Slice_2/size" op: "Const" attr { key: "dtype" @@ -30226,20 +30569,20 @@ node { dtype: DT_INT32 tensor_shape { dim { - size: 3 + size: 2 } } - tensor_content: "\377\377\377\377\377\377\377\377\010\000\000\000" + tensor_content: "\377\377\377\377\240\000\000\000" } } } } node { - name: "filter_type_0_0/Slice_4" + name: "filter_type_0_0/Slice_2" op: "Slice" - input: "filter_type_0_0/truediv" - input: "filter_type_0_0/Slice_4/begin" - input: "filter_type_0_0/Slice_4/size" + input: "Reshape_13" + input: "filter_type_0_0/Slice_2/begin" + input: "filter_type_0_0/Slice_2/size" attr { key: "Index" value { @@ -30254,10 +30597,9 @@ node { } } node { - name: "filter_type_0_0/MatMul_8" - op: "BatchMatMulV2" - input: "filter_type_0_0/truediv" - input: "filter_type_0_0/Slice_4" + name: "filter_type_0_0/Shape_1" + op: "Shape" + input: "filter_type_0_0/Slice_2" attr { key: "T" value { @@ -30265,20 +30607,14 @@ node { } } attr { - key: "adj_x" - value { - b: true - } - } - attr { - key: "adj_y" + key: "out_type" value { - b: false + type: DT_INT32 } } } node { - name: "filter_type_0_0/Reshape_14/shape" + name: "filter_type_0_0/strided_slice_1/stack" op: "Const" attr { key: "dtype" @@ -30293,51 +30629,16 @@ node { dtype: DT_INT32 tensor_shape { dim { - size: 2 + size: 1 } } - tensor_content: "\377\377\377\377 \003\000\000" + int_val: 0 } } } } node { - name: "filter_type_0_0/Reshape_14" - op: "Reshape" - input: "filter_type_0_0/MatMul_8" - input: "filter_type_0_0/Reshape_14/shape" - attr { - key: "T" - value { - type: DT_DOUBLE - } - } - attr { - key: "Tshape" - value { - type: DT_INT32 - } - } -} -node { - name: "Shape_5" - op: "Shape" - input: "Reshape_12" - attr { - key: "T" - value { - type: DT_DOUBLE - } - } - attr { - key: "out_type" - value { - type: DT_INT32 - } - } -} -node { - name: "strided_slice_19/stack" + name: "filter_type_0_0/strided_slice_1/stack_1" op: "Const" attr { key: "dtype" @@ -30355,13 +30656,13 @@ node { size: 1 } } - int_val: 0 + int_val: 1 } } } } node { - name: "strided_slice_19/stack_1" + name: "filter_type_0_0/strided_slice_1/stack_2" op: "Const" attr { key: "dtype" @@ -30385,36 +30686,12 @@ node { } } node { - name: "strided_slice_19/stack_2" - op: "Const" - attr { - key: "dtype" - value { - type: DT_INT32 - } - } - attr { - key: "value" - value { - tensor { - dtype: DT_INT32 - tensor_shape { - dim { - size: 1 - } - } - int_val: 1 - } - } - } -} -node { - name: "strided_slice_19" + name: "filter_type_0_0/strided_slice_1" op: "StridedSlice" - input: "Shape_5" - input: "strided_slice_19/stack" - input: "strided_slice_19/stack_1" - input: "strided_slice_19/stack_2" + input: "filter_type_0_0/Shape_1" + input: "filter_type_0_0/strided_slice_1/stack" + input: "filter_type_0_0/strided_slice_1/stack_1" + input: "filter_type_0_0/strided_slice_1/stack_2" attr { key: "Index" value { @@ -30459,7 +30736,7 @@ node { } } node { - name: "strided_slice_20/stack" + name: "filter_type_0_0/Reshape_7/shape" op: "Const" attr { key: "dtype" @@ -30474,16 +30751,34 @@ node { dtype: DT_INT32 tensor_shape { dim { - size: 1 + size: 2 } } - int_val: 2 + tensor_content: "\377\377\377\377\004\000\000\000" } } } } node { - name: "strided_slice_20/stack_1" + name: "filter_type_0_0/Reshape_7" + op: "Reshape" + input: "filter_type_0_0/Slice_2" + input: "filter_type_0_0/Reshape_7/shape" + attr { + key: "T" + value { + type: DT_DOUBLE + } + } + attr { + key: "Tshape" + value { + type: DT_INT32 + } + } +} +node { + name: "filter_type_0_0/Slice_3/begin" op: "Const" attr { key: "dtype" @@ -30498,16 +30793,16 @@ node { dtype: DT_INT32 tensor_shape { dim { - size: 1 + size: 2 } } - int_val: 3 + tensor_content: "\000\000\000\000\000\000\000\000" } } } } node { - name: "strided_slice_20/stack_2" + name: "filter_type_0_0/Slice_3/size" op: "Const" attr { key: "dtype" @@ -30522,21 +30817,20 @@ node { dtype: DT_INT32 tensor_shape { dim { - size: 1 + size: 2 } } - int_val: 1 + tensor_content: "\377\377\377\377\001\000\000\000" } } } } node { - name: "strided_slice_20" - op: "StridedSlice" - input: "t_natoms" - input: "strided_slice_20/stack" - input: "strided_slice_20/stack_1" - input: "strided_slice_20/stack_2" + name: "filter_type_0_0/Slice_3" + op: "Slice" + input: "filter_type_0_0/Reshape_7" + input: "filter_type_0_0/Slice_3/begin" + input: "filter_type_0_0/Slice_3/size" attr { key: "Index" value { @@ -30546,42 +30840,12 @@ node { attr { key: "T" value { - type: DT_INT32 - } - } - attr { - key: "begin_mask" - value { - i: 0 - } - } - attr { - key: "ellipsis_mask" - value { - i: 0 - } - } - attr { - key: "end_mask" - value { - i: 0 - } - } - attr { - key: "new_axis_mask" - value { - i: 0 - } - } - attr { - key: "shrink_axis_mask" - value { - i: 1 + type: DT_DOUBLE } } } node { - name: "mul_9/y" + name: "filter_type_0_0/Reshape_8/shape" op: "Const" attr { key: "dtype" @@ -30595,53 +30859,20 @@ node { tensor { dtype: DT_INT32 tensor_shape { + dim { + size: 2 + } } - int_val: 800 + tensor_content: "\377\377\377\377\001\000\000\000" } } } } node { - name: "mul_9" - op: "Mul" - input: "strided_slice_20" - input: "mul_9/y" - attr { - key: "T" - value { - type: DT_INT32 - } - } -} -node { - name: "Reshape_15/shape" - op: "Pack" - input: "strided_slice_19" - input: "mul_9" - attr { - key: "N" - value { - i: 2 - } - } - attr { - key: "T" - value { - type: DT_INT32 - } - } - attr { - key: "axis" - value { - i: 0 - } - } -} -node { - name: "Reshape_15" + name: "filter_type_0_0/Reshape_8" op: "Reshape" - input: "filter_type_0_0/Reshape_14" - input: "Reshape_15/shape" + input: "filter_type_0_0/Slice_3" + input: "filter_type_0_0/Reshape_8/shape" attr { key: "T" value { @@ -30656,129 +30887,149 @@ node { } } node { - name: "strided_slice_23/stack" + name: "filter_type_0_0/matrix_1_1" op: "Const" attr { key: "dtype" value { - type: DT_INT32 + type: DT_DOUBLE } } attr { key: "value" value { tensor { - dtype: DT_INT32 + dtype: DT_DOUBLE tensor_shape { dim { size: 1 } + dim { + size: 2 + } } - int_val: 2 + tensor_content: "\244\242\243\201\201{\344?w^\2079\205\022\344\277" } } } } node { - name: "strided_slice_23/stack_1" - op: "Const" + name: "filter_type_0_0/matrix_1_1/read" + op: "Identity" + input: "filter_type_0_0/matrix_1_1" attr { - key: "dtype" + key: "T" value { - type: DT_INT32 + type: DT_DOUBLE } } attr { - key: "value" + key: "_class" value { - tensor { - dtype: DT_INT32 - tensor_shape { - dim { - size: 1 - } - } - int_val: 3 + list { + s: "loc:@filter_type_0_0/matrix_1_1" } } } } node { - name: "strided_slice_23/stack_2" + name: "filter_type_0_0/bias_1_1" op: "Const" attr { key: "dtype" value { - type: DT_INT32 + type: DT_DOUBLE } } attr { key: "value" value { tensor { - dtype: DT_INT32 + dtype: DT_DOUBLE tensor_shape { dim { - size: 1 + size: 2 } } - int_val: 1 + tensor_content: "\224\332\\J\004\252\351\277\310\332z\205\036\254\311\277" } } } } node { - name: "strided_slice_23" - op: "StridedSlice" - input: "t_natoms" - input: "strided_slice_23/stack" - input: "strided_slice_23/stack_1" - input: "strided_slice_23/stack_2" + name: "filter_type_0_0/bias_1_1/read" + op: "Identity" + input: "filter_type_0_0/bias_1_1" attr { - key: "Index" + key: "T" value { - type: DT_INT32 + type: DT_DOUBLE + } + } + attr { + key: "_class" + value { + list { + s: "loc:@filter_type_0_0/bias_1_1" + } } } +} +node { + name: "filter_type_0_0/MatMul_4" + op: "MatMul" + input: "filter_type_0_0/Reshape_8" + input: "filter_type_0_0/matrix_1_1/read" attr { key: "T" value { - type: DT_INT32 + type: DT_DOUBLE } } attr { - key: "begin_mask" + key: "transpose_a" value { - i: 0 + b: false } } attr { - key: "ellipsis_mask" + key: "transpose_b" value { - i: 0 + b: false } } +} +node { + name: "filter_type_0_0/BiasAdd_3" + op: "BiasAdd" + input: "filter_type_0_0/MatMul_4" + input: "filter_type_0_0/bias_1_1/read" attr { - key: "end_mask" + key: "T" value { - i: 0 + type: DT_DOUBLE } } attr { - key: "new_axis_mask" + key: "data_format" value { - i: 0 + s: "NHWC" } } +} +node { + name: "filter_type_0_0/Tanh_3" + op: "Tanh" + input: "filter_type_0_0/BiasAdd_3" attr { - key: "shrink_axis_mask" + key: "T" value { - i: 1 + type: DT_DOUBLE } } } node { - name: "add_2/x" + name: "filter_type_0_0/Reshape_9/shape" op: "Const" attr { key: "dtype" @@ -30792,26 +31043,35 @@ node { tensor { dtype: DT_INT32 tensor_shape { + dim { + size: 2 + } } - int_val: 0 + tensor_content: "\377\377\377\377\002\000\000\000" } } } } node { - name: "add_2" - op: "AddV2" - input: "add_2/x" - input: "strided_slice_23" + name: "filter_type_0_0/Reshape_9" + op: "Reshape" + input: "filter_type_0_0/Tanh_3" + input: "filter_type_0_0/Reshape_9/shape" attr { key: "T" + value { + type: DT_DOUBLE + } + } + attr { + key: "Tshape" value { type: DT_INT32 } } } node { - name: "mul_12/y" + name: "filter_type_0_0/concat_3/axis" op: "Const" attr { key: "dtype" @@ -30826,180 +31086,192 @@ node { dtype: DT_INT32 tensor_shape { } - int_val: 240 + int_val: 1 } } } } node { - name: "mul_12" - op: "Mul" - input: "add_2" - input: "mul_12/y" + name: "filter_type_0_0/concat_3" + op: "ConcatV2" + input: "filter_type_0_0/Reshape_8" + input: "filter_type_0_0/Reshape_8" + input: "filter_type_0_0/concat_3/axis" + attr { + key: "N" + value { + i: 2 + } + } attr { key: "T" + value { + type: DT_DOUBLE + } + } + attr { + key: "Tidx" value { type: DT_INT32 } } } node { - name: "strided_slice_24/stack" + name: "filter_type_0_0/add_3" + op: "AddV2" + input: "filter_type_0_0/concat_3" + input: "filter_type_0_0/Reshape_9" + attr { + key: "T" + value { + type: DT_DOUBLE + } + } +} +node { + name: "filter_type_0_0/matrix_2_1" op: "Const" attr { key: "dtype" value { - type: DT_INT32 + type: DT_DOUBLE } } attr { key: "value" value { tensor { - dtype: DT_INT32 + dtype: DT_DOUBLE tensor_shape { dim { - size: 1 + size: 2 + } + dim { + size: 4 } } - int_val: 3 + tensor_content: "\275t\0240\340,\335?;\374\351/\002\256\275\277\3473!\215\265\304\311\277\'>B\331C\241\340?{\236#s\025\326\237?\023\037\266fH\260\211?\243\347\241K]R\321\277WTz\036\257\300\314\277" } } } } node { - name: "strided_slice_24/stack_1" - op: "Const" + name: "filter_type_0_0/matrix_2_1/read" + op: "Identity" + input: "filter_type_0_0/matrix_2_1" attr { - key: "dtype" + key: "T" value { - type: DT_INT32 + type: DT_DOUBLE } } attr { - key: "value" + key: "_class" value { - tensor { - dtype: DT_INT32 - tensor_shape { - dim { - size: 1 - } - } - int_val: 4 + list { + s: "loc:@filter_type_0_0/matrix_2_1" } } } } node { - name: "strided_slice_24/stack_2" + name: "filter_type_0_0/bias_2_1" op: "Const" attr { key: "dtype" value { - type: DT_INT32 + type: DT_DOUBLE } } attr { key: "value" value { tensor { - dtype: DT_INT32 + dtype: DT_DOUBLE tensor_shape { dim { - size: 1 + size: 4 } } - int_val: 1 + tensor_content: "\262\364\026\036\204\034\354\277\376B\032\010\356\225\344?\353 gBa\334\252\277\371\023\316=f5\303?" } } } } node { - name: "strided_slice_24" - op: "StridedSlice" - input: "t_natoms" - input: "strided_slice_24/stack" - input: "strided_slice_24/stack_1" - input: "strided_slice_24/stack_2" - attr { - key: "Index" - value { - type: DT_INT32 - } - } + name: "filter_type_0_0/bias_2_1/read" + op: "Identity" + input: "filter_type_0_0/bias_2_1" attr { key: "T" value { - type: DT_INT32 - } - } - attr { - key: "begin_mask" - value { - i: 0 + type: DT_DOUBLE } } attr { - key: "ellipsis_mask" + key: "_class" value { - i: 0 + list { + s: "loc:@filter_type_0_0/bias_2_1" + } } } +} +node { + name: "filter_type_0_0/MatMul_5" + op: "MatMul" + input: "filter_type_0_0/add_3" + input: "filter_type_0_0/matrix_2_1/read" attr { - key: "end_mask" + key: "T" value { - i: 0 + type: DT_DOUBLE } } attr { - key: "new_axis_mask" + key: "transpose_a" value { - i: 0 + b: false } } attr { - key: "shrink_axis_mask" + key: "transpose_b" value { - i: 1 + b: false } } } node { - name: "mul_13/y" - op: "Const" + name: "filter_type_0_0/BiasAdd_4" + op: "BiasAdd" + input: "filter_type_0_0/MatMul_5" + input: "filter_type_0_0/bias_2_1/read" attr { - key: "dtype" + key: "T" value { - type: DT_INT32 + type: DT_DOUBLE } } attr { - key: "value" + key: "data_format" value { - tensor { - dtype: DT_INT32 - tensor_shape { - } - int_val: 240 - } + s: "NHWC" } } } node { - name: "mul_13" - op: "Mul" - input: "strided_slice_24" - input: "mul_13/y" + name: "filter_type_0_0/Tanh_4" + op: "Tanh" + input: "filter_type_0_0/BiasAdd_4" attr { key: "T" value { - type: DT_INT32 + type: DT_DOUBLE } } } node { - name: "Slice_3/begin/0" + name: "filter_type_0_0/Reshape_10/shape" op: "Const" attr { key: "dtype" @@ -31013,38 +31285,35 @@ node { tensor { dtype: DT_INT32 tensor_shape { + dim { + size: 2 + } } - int_val: 0 + tensor_content: "\377\377\377\377\004\000\000\000" } } } } node { - name: "Slice_3/begin" - op: "Pack" - input: "Slice_3/begin/0" - input: "mul_12" - attr { - key: "N" - value { - i: 2 - } - } + name: "filter_type_0_0/Reshape_10" + op: "Reshape" + input: "filter_type_0_0/Tanh_4" + input: "filter_type_0_0/Reshape_10/shape" attr { key: "T" value { - type: DT_INT32 + type: DT_DOUBLE } } attr { - key: "axis" + key: "Tshape" value { - i: 0 + type: DT_INT32 } } } node { - name: "Slice_3/size/0" + name: "filter_type_0_0/concat_4/axis" op: "Const" attr { key: "dtype" @@ -31059,16 +31328,17 @@ node { dtype: DT_INT32 tensor_shape { } - int_val: -1 + int_val: 1 } } } } node { - name: "Slice_3/size" - op: "Pack" - input: "Slice_3/size/0" - input: "mul_13" + name: "filter_type_0_0/concat_4" + op: "ConcatV2" + input: "filter_type_0_0/add_3" + input: "filter_type_0_0/add_3" + input: "filter_type_0_0/concat_4/axis" attr { key: "N" value { @@ -31078,28 +31348,21 @@ node { attr { key: "T" value { - type: DT_INT32 + type: DT_DOUBLE } } attr { - key: "axis" + key: "Tidx" value { - i: 0 + type: DT_INT32 } } } node { - name: "Slice_3" - op: "Slice" - input: "Reshape_12" - input: "Slice_3/begin" - input: "Slice_3/size" - attr { - key: "Index" - value { - type: DT_INT32 - } - } + name: "filter_type_0_0/add_4" + op: "AddV2" + input: "filter_type_0_0/concat_4" + input: "filter_type_0_0/Reshape_10" attr { key: "T" value { @@ -31108,34 +31371,36 @@ node { } } node { - name: "Reshape_17/shape" + name: "filter_type_0_0/matrix_3_1" op: "Const" attr { key: "dtype" value { - type: DT_INT32 + type: DT_DOUBLE } } attr { key: "value" value { tensor { - dtype: DT_INT32 + dtype: DT_DOUBLE tensor_shape { dim { - size: 2 + size: 4 + } + dim { + size: 8 } } - tensor_content: "\377\377\377\377\360\000\000\000" + tensor_content: 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value { @@ -31143,83 +31408,86 @@ node { } } attr { - key: "Tshape" + key: "_class" value { - type: DT_INT32 + list { + s: "loc:@filter_type_0_0/matrix_3_1" + } } } } node { - name: "filter_type_1_0/Slice/begin" + name: "filter_type_0_0/bias_3_1" op: "Const" attr { key: "dtype" value { - type: DT_INT32 + type: DT_DOUBLE } } attr { key: "value" value { tensor { - dtype: DT_INT32 + dtype: DT_DOUBLE tensor_shape { dim { - size: 2 + size: 8 } } - tensor_content: "\000\000\000\000\000\000\000\000" + tensor_content: "\244a\232~\007t\371\2777\336~\271Y\267\371\277d\336x\232\322\257\301\277\376\350\013;\350\343\346\277Bp\256\010\nG\324\277\001\241m\352\n\206\366\277\214\'\310I\347\253\353\277HC\330\353~$\351?" } } } } node { - name: "filter_type_1_0/Slice/size" - op: "Const" + name: "filter_type_0_0/bias_3_1/read" + op: "Identity" + input: "filter_type_0_0/bias_3_1" attr { - key: "dtype" + key: "T" value { - type: DT_INT32 + type: DT_DOUBLE } } attr { - key: "value" + key: "_class" value { - tensor { - dtype: DT_INT32 - tensor_shape { - dim { - size: 2 - } - } - tensor_content: "\377\377\377\377P\000\000\000" + list { + s: "loc:@filter_type_0_0/bias_3_1" } } } } node { - name: "filter_type_1_0/Slice" - op: "Slice" - input: "Reshape_17" - input: "filter_type_1_0/Slice/begin" - input: "filter_type_1_0/Slice/size" + name: "filter_type_0_0/MatMul_6" + op: "MatMul" + input: "filter_type_0_0/add_4" + input: "filter_type_0_0/matrix_3_1/read" attr { - key: "Index" + key: "T" value { - type: DT_INT32 + type: DT_DOUBLE } } attr { - key: "T" + key: "transpose_a" value { - type: DT_DOUBLE + b: false + } + } + attr { + key: "transpose_b" + value { + b: false } } } node { - name: "filter_type_1_0/Shape" - op: "Shape" - input: "filter_type_1_0/Slice" + name: "filter_type_0_0/BiasAdd_5" + op: "BiasAdd" + input: "filter_type_0_0/MatMul_6" + input: "filter_type_0_0/bias_3_1/read" attr { key: "T" value { @@ -31227,14 +31495,25 @@ node { } } attr { - key: "out_type" + key: "data_format" value { - type: DT_INT32 + s: "NHWC" } } } node { - name: "filter_type_1_0/strided_slice/stack" + name: "filter_type_0_0/Tanh_5" + op: "Tanh" + input: "filter_type_0_0/BiasAdd_5" + attr { + key: "T" + value { + type: DT_DOUBLE + } + } +} +node { + name: "filter_type_0_0/Reshape_11/shape" op: "Const" attr { key: "dtype" @@ -31249,40 +31528,34 @@ node { dtype: DT_INT32 tensor_shape { dim { - size: 1 + size: 2 } } - int_val: 0 + tensor_content: "\377\377\377\377\010\000\000\000" } } } } node { - name: "filter_type_1_0/strided_slice/stack_1" - op: "Const" + name: "filter_type_0_0/Reshape_11" + op: "Reshape" + input: "filter_type_0_0/Tanh_5" + input: "filter_type_0_0/Reshape_11/shape" attr { - key: "dtype" + key: "T" value { - type: DT_INT32 + type: DT_DOUBLE } } attr { - key: "value" + key: "Tshape" value { - tensor { - dtype: DT_INT32 - tensor_shape { - dim { - size: 1 - } - } - int_val: 1 - } + type: DT_INT32 } } } node { - name: "filter_type_1_0/strided_slice/stack_2" + name: "filter_type_0_0/concat_5/axis" op: "Const" attr { key: "dtype" @@ -31296,9 +31569,6 @@ node { tensor { dtype: DT_INT32 tensor_shape { - dim { - size: 1 - } } int_val: 1 } @@ -31306,57 +31576,44 @@ node { } } node { - name: "filter_type_1_0/strided_slice" - op: "StridedSlice" - input: "filter_type_1_0/Shape" - input: "filter_type_1_0/strided_slice/stack" - input: "filter_type_1_0/strided_slice/stack_1" - input: "filter_type_1_0/strided_slice/stack_2" + name: "filter_type_0_0/concat_5" + op: "ConcatV2" + input: "filter_type_0_0/add_4" + input: "filter_type_0_0/add_4" + input: "filter_type_0_0/concat_5/axis" attr { - key: "Index" + key: "N" value { - type: DT_INT32 + i: 2 } } attr { key: "T" value { - type: DT_INT32 - } - } - attr { - key: "begin_mask" - value { - i: 0 - } - } - attr { - key: "ellipsis_mask" - value { - i: 0 - } - } - attr { - key: "end_mask" - value { - i: 0 + type: DT_DOUBLE } } attr { - key: "new_axis_mask" + key: "Tidx" value { - i: 0 + type: DT_INT32 } } +} +node { + name: "filter_type_0_0/add_5" + op: "AddV2" + input: "filter_type_0_0/concat_5" + input: "filter_type_0_0/Reshape_11" attr { - key: "shrink_axis_mask" + key: "T" value { - i: 1 + type: DT_DOUBLE } } } node { - name: "filter_type_1_0/Reshape/shape" + name: "filter_type_0_0/Reshape_12/shape" op: "Const" attr { key: "dtype" @@ -31371,19 +31628,19 @@ node { dtype: DT_INT32 tensor_shape { dim { - size: 2 + size: 3 } } - tensor_content: "\377\377\377\377\004\000\000\000" + tensor_content: "\377\377\377\377(\000\000\000\010\000\000\000" } } } } node { - name: "filter_type_1_0/Reshape" + name: "filter_type_0_0/Reshape_12" op: "Reshape" - input: "filter_type_1_0/Slice" - input: "filter_type_1_0/Reshape/shape" + input: "filter_type_0_0/add_5" + input: "filter_type_0_0/Reshape_12/shape" attr { key: "T" value { @@ -31398,7 +31655,7 @@ node { } } node { - name: "filter_type_1_0/Slice_1/begin" + name: "filter_type_0_0/Reshape_13/shape/1" op: "Const" attr { key: "dtype" @@ -31412,17 +31669,14 @@ node { tensor { dtype: DT_INT32 tensor_shape { - dim { - size: 2 - } } - tensor_content: "\000\000\000\000\000\000\000\000" + int_val: 40 } } } } node { - name: "filter_type_1_0/Slice_1/size" + name: "filter_type_0_0/Reshape_13/shape/2" op: "Const" attr { key: "dtype" @@ -31436,63 +31690,60 @@ node { tensor { dtype: DT_INT32 tensor_shape { - dim { - size: 2 - } } - tensor_content: "\377\377\377\377\001\000\000\000" + int_val: 4 } } } } node { - name: "filter_type_1_0/Slice_1" - op: "Slice" - input: "filter_type_1_0/Reshape" - input: "filter_type_1_0/Slice_1/begin" - input: "filter_type_1_0/Slice_1/size" + name: "filter_type_0_0/Reshape_13/shape" + op: "Pack" + input: "filter_type_0_0/strided_slice_1" + input: "filter_type_0_0/Reshape_13/shape/1" + input: "filter_type_0_0/Reshape_13/shape/2" attr { - key: "Index" + key: "N" value { - type: DT_INT32 + i: 3 } } attr { key: "T" value { - type: DT_DOUBLE + type: DT_INT32 + } + } + attr { + key: "axis" + value { + i: 0 } } } node { - name: "filter_type_1_0/Reshape_1/shape" - op: "Const" + name: "filter_type_0_0/Reshape_13" + op: "Reshape" + input: "filter_type_0_0/Slice_2" + input: "filter_type_0_0/Reshape_13/shape" attr { - key: "dtype" + key: "T" value { - type: DT_INT32 + type: DT_DOUBLE } } attr { - key: "value" + key: "Tshape" value { - tensor { - dtype: DT_INT32 - tensor_shape { - dim { - size: 2 - } - } - tensor_content: "\377\377\377\377\001\000\000\000" - } + type: DT_INT32 } } } node { - name: "filter_type_1_0/Reshape_1" - op: "Reshape" - input: "filter_type_1_0/Slice_1" - input: "filter_type_1_0/Reshape_1/shape" + name: "filter_type_0_0/MatMul_7" + op: "BatchMatMulV2" + input: "filter_type_0_0/Reshape_13" + input: "filter_type_0_0/Reshape_12" attr { key: "T" value { @@ -31500,14 +31751,38 @@ node { } } attr { - key: "Tshape" + key: "adj_x" value { - type: DT_INT32 + b: true + } + } + attr { + key: "adj_y" + value { + b: false } } } node { - name: "filter_type_1_0/matrix_1_0" + name: "filter_type_0_0/AddN" + op: "AddN" + input: "filter_type_0_0/MatMul_3" + input: "filter_type_0_0/MatMul_7" + attr { + key: "N" + value { + i: 2 + } + } + attr { + key: "T" + value { + type: DT_DOUBLE + } + } +} +node { + name: "filter_type_0_0/truediv/y" op: "Const" attr { key: "dtype" @@ -31521,109 +31796,96 @@ node { tensor { dtype: DT_DOUBLE tensor_shape { - dim { - size: 1 - } - dim { - size: 25 - } } - tensor_content: "\377\327\213SIm\272\277v~\344\346}i\323?PO\356\365D\261\246\277\032\013\212b\276\205\245\277\037\311\3670H\314\340\277\267\252\016\276/T\303\277l\230:Z\246K\303?B\242\300o\253\326\367?\367 \253\031\264\023\276\277\303\016D\366\r)\314?\365\326O\010K\246\357?-\263\316_\274\022\256?R\2173.\223f\251?~\255^\203iB\320\277\\oU\317\376\343\310?\316\305_!\232\242\324\277JF\265\324\226\314\246?\023\341\3678`\201\331\277\3142\306\003\r5\310?\221W#\202\271\353\237\277\251\224\003\221\253>\311?\n\315\024\210\336\026\271\277\230\316-\310WU\267?\265\037\342\317\245\217\246?\036\323k;&\226\333?" + double_val: 60.0 } } } } node { - name: "filter_type_1_0/matrix_1_0/read" - op: "Identity" - input: "filter_type_1_0/matrix_1_0" + name: "filter_type_0_0/truediv" + op: "RealDiv" + input: "filter_type_0_0/AddN" + input: "filter_type_0_0/truediv/y" attr { key: "T" value { type: DT_DOUBLE } } - attr { - key: "_class" - value { - list { - s: "loc:@filter_type_1_0/matrix_1_0" - } - } - } } node { - name: "filter_type_1_0/bias_1_0" + name: "filter_type_0_0/Slice_4/begin" op: "Const" attr { key: "dtype" value { - type: DT_DOUBLE + type: DT_INT32 } } attr { key: "value" value { tensor { - dtype: DT_DOUBLE + dtype: DT_INT32 tensor_shape { dim { - size: 25 + size: 3 } } - tensor_content: 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dtype: DT_INT32 + tensor_shape { + dim { + size: 3 + } + } + tensor_content: "\377\377\377\377\377\377\377\377\010\000\000\000" } } } } node { - name: "filter_type_1_0/MatMul" - op: "MatMul" - input: "filter_type_1_0/Reshape_1" - input: "filter_type_1_0/matrix_1_0/read" - attr { - key: "T" - value { - type: DT_DOUBLE - } - } + name: "filter_type_0_0/Slice_4" + op: "Slice" + input: "filter_type_0_0/truediv" + input: "filter_type_0_0/Slice_4/begin" + input: "filter_type_0_0/Slice_4/size" attr { - key: "transpose_a" + key: "Index" value { - b: false + type: DT_INT32 } } attr { - key: "transpose_b" + key: "T" value { - b: false + type: DT_DOUBLE } } } node { - name: "filter_type_1_0/BiasAdd" - op: "BiasAdd" - input: "filter_type_1_0/MatMul" - input: "filter_type_1_0/bias_1_0/read" + name: "filter_type_0_0/MatMul_8" + op: "BatchMatMulV2" + input: "filter_type_0_0/truediv" + input: "filter_type_0_0/Slice_4" attr { key: "T" value { @@ -31631,25 +31893,20 @@ node { } } attr { - key: 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"loc:@filter_type_1_0/bias_3_0" - } + i: 0 } } -} -node { - name: "filter_type_1_0/MatMul_2" - op: "MatMul" - input: "filter_type_1_0/add" - input: "filter_type_1_0/matrix_3_0/read" attr { - key: "T" + key: "ellipsis_mask" value { - type: DT_DOUBLE + i: 0 } } attr { - key: "transpose_a" + key: "end_mask" value { - b: false + i: 0 } } attr { - key: "transpose_b" + key: "new_axis_mask" value { - b: false + i: 0 + } + } + attr { + key: "shrink_axis_mask" + value { + i: 1 } } } node { - name: "filter_type_1_0/BiasAdd_2" - op: "BiasAdd" - input: "filter_type_1_0/MatMul_2" - input: "filter_type_1_0/bias_3_0/read" + name: "add_2/x" + op: "Const" attr { - key: "T" + key: "dtype" value { - type: DT_DOUBLE + type: DT_INT32 } } attr { - key: "data_format" + key: "value" value { - s: "NHWC" + tensor { + dtype: DT_INT32 + tensor_shape { + } + int_val: 0 + } } } } node { - name: "filter_type_1_0/Tanh_2" - op: "Tanh" - input: "filter_type_1_0/BiasAdd_2" + name: "add_2" + op: "AddV2" + input: "add_2/x" + input: "strided_slice_23" attr { key: "T" value { - type: DT_DOUBLE + type: DT_INT32 } } } node { - name: "filter_type_1_0/Reshape_4/shape" + name: "strided_slice_24/stack" op: "Const" attr { key: "dtype" @@ -32090,34 +32443,40 @@ node { dtype: DT_INT32 tensor_shape { dim { - size: 2 + size: 1 } } - tensor_content: "\377\377\377\377d\000\000\000" + int_val: 3 } } } } node { - name: "filter_type_1_0/Reshape_4" - op: "Reshape" - input: "filter_type_1_0/Tanh_2" - input: "filter_type_1_0/Reshape_4/shape" + name: "strided_slice_24/stack_1" + op: "Const" attr { - key: "T" + key: "dtype" value { - type: DT_DOUBLE + type: DT_INT32 } } attr { - key: "Tshape" + key: "value" value { - type: DT_INT32 + tensor { + dtype: DT_INT32 + tensor_shape { + dim { + size: 1 + } + } + int_val: 4 + } } } } node { - name: "filter_type_1_0/concat_1/axis" + name: "strided_slice_24/stack_2" op: "Const" attr { key: "dtype" @@ -32131,6 +32490,9 @@ node { tensor { dtype: DT_INT32 tensor_shape { + dim { + size: 1 + } } int_val: 1 } @@ -32138,44 +32500,78 @@ node { } } node { - name: "filter_type_1_0/concat_1" - op: "ConcatV2" - input: "filter_type_1_0/add" - input: "filter_type_1_0/add" - input: "filter_type_1_0/concat_1/axis" + name: "strided_slice_24" + op: "StridedSlice" + input: "t_natoms" + input: "strided_slice_24/stack" + input: "strided_slice_24/stack_1" + input: "strided_slice_24/stack_2" attr { - key: "N" + key: "Index" value { - i: 2 + type: DT_INT32 } } attr { key: "T" value { - type: DT_DOUBLE + type: DT_INT32 } } attr { - key: "Tidx" + key: "begin_mask" value { - type: DT_INT32 + i: 0 + } + } + attr { + key: "ellipsis_mask" + value { + i: 0 + } + } + attr { + key: "end_mask" + value { + i: 0 + } + } + attr { + key: "new_axis_mask" + value { + i: 0 + } + } + attr { + key: "shrink_axis_mask" + value { + i: 1 } } } node { - name: "filter_type_1_0/add_1" - op: "AddV2" - input: "filter_type_1_0/concat_1" - input: "filter_type_1_0/Reshape_4" + name: "Slice_3/begin/0" + op: "Const" attr { - key: "T" + key: "dtype" value { - type: DT_DOUBLE + type: DT_INT32 + } + } + attr { + key: "value" + value { + tensor { + dtype: DT_INT32 + tensor_shape { + } + int_val: 0 + } } } } node { - name: "filter_type_1_0/Reshape_5/shape" + name: "Slice_3/begin/2" op: "Const" attr { key: "dtype" @@ -32189,35 +32585,39 @@ node { tensor { dtype: DT_INT32 tensor_shape { - dim { - size: 3 - } } - tensor_content: "\377\377\377\377\024\000\000\000d\000\000\000" + int_val: 0 } } } } node { - name: "filter_type_1_0/Reshape_5" - op: "Reshape" - input: "filter_type_1_0/add_1" - input: "filter_type_1_0/Reshape_5/shape" + name: "Slice_3/begin" + op: "Pack" + input: "Slice_3/begin/0" + input: "add_2" + input: "Slice_3/begin/2" attr { - key: "T" + key: "N" value { - type: DT_DOUBLE + i: 3 } } attr { - key: "Tshape" + key: "T" value { type: DT_INT32 } } + attr { + key: "axis" + value { + i: 0 + } + } } node { - name: "filter_type_1_0/Reshape_6/shape/1" + name: "Slice_3/size/0" op: "Const" attr { key: "dtype" @@ -32232,13 +32632,13 @@ node { dtype: DT_INT32 tensor_shape { } - int_val: 20 + int_val: -1 } } } } node { - name: "filter_type_1_0/Reshape_6/shape/2" + name: "Slice_3/size/2" op: "Const" attr { key: "dtype" @@ -32253,17 +32653,17 @@ node { dtype: DT_INT32 tensor_shape { } - int_val: 4 + int_val: -1 } } } } node { - name: "filter_type_1_0/Reshape_6/shape" + name: "Slice_3/size" op: "Pack" - input: "filter_type_1_0/strided_slice" - input: "filter_type_1_0/Reshape_6/shape/1" - input: "filter_type_1_0/Reshape_6/shape/2" + input: "Slice_3/size/0" + input: "strided_slice_24" + input: "Slice_3/size/2" attr { key: "N" value { @@ -32284,49 +32684,68 @@ node { } } node { - name: "filter_type_1_0/Reshape_6" - op: "Reshape" - input: "filter_type_1_0/Slice" - input: "filter_type_1_0/Reshape_6/shape" + name: "Slice_3" + op: "Slice" + input: "Reshape_12" + input: "Slice_3/begin" + input: "Slice_3/size" + attr { + key: "Index" + value { + type: DT_INT32 + } + } attr { key: "T" value { type: DT_DOUBLE } } +} +node { + name: "Reshape_17/shape" + op: "Const" attr { - key: "Tshape" + key: "dtype" value { type: DT_INT32 } } -} -node { - name: "filter_type_1_0/MatMul_3" - op: "BatchMatMulV2" - input: "filter_type_1_0/Reshape_6" - input: "filter_type_1_0/Reshape_5" attr { - key: "T" + key: "value" value { - type: DT_DOUBLE + tensor { + dtype: DT_INT32 + tensor_shape { + dim { + size: 2 + } + } + tensor_content: "\377\377\377\377\360\000\000\000" + } } } +} +node { + name: "Reshape_17" + op: "Reshape" + input: "Slice_3" + input: "Reshape_17/shape" attr { - key: "adj_x" + key: "T" value { - b: true + type: DT_DOUBLE } } attr { - key: "adj_y" + key: "Tshape" value { - b: false + type: DT_INT32 } } } node { - name: "filter_type_1_0/Slice_2/begin" + name: "filter_type_1_0/Slice/begin" op: "Const" attr { key: "dtype" @@ -32344,13 +32763,13 @@ node { size: 2 } } - tensor_content: "\000\000\000\000P\000\000\000" + tensor_content: "\000\000\000\000\000\000\000\000" } } } } node { - name: "filter_type_1_0/Slice_2/size" + name: "filter_type_1_0/Slice/size" op: "Const" attr { key: "dtype" @@ -32368,17 +32787,17 @@ node { size: 2 } } - tensor_content: "\377\377\377\377\240\000\000\000" + tensor_content: "\377\377\377\377P\000\000\000" } } } } node { - name: "filter_type_1_0/Slice_2" + name: "filter_type_1_0/Slice" op: "Slice" input: "Reshape_17" - input: "filter_type_1_0/Slice_2/begin" - input: "filter_type_1_0/Slice_2/size" + input: "filter_type_1_0/Slice/begin" + input: "filter_type_1_0/Slice/size" attr { key: "Index" value { @@ -32393,9 +32812,9 @@ node { } } node { - name: "filter_type_1_0/Shape_1" + name: "filter_type_1_0/Shape" op: "Shape" - input: "filter_type_1_0/Slice_2" + input: "filter_type_1_0/Slice" attr { key: "T" value { @@ -32410,7 +32829,7 @@ node { } } node { - name: "filter_type_1_0/strided_slice_1/stack" + name: "filter_type_1_0/strided_slice/stack" op: "Const" attr { key: "dtype" @@ -32434,7 +32853,7 @@ node { } } node { - name: "filter_type_1_0/strided_slice_1/stack_1" + name: "filter_type_1_0/strided_slice/stack_1" op: "Const" attr { key: "dtype" @@ -32458,7 +32877,7 @@ node { } } node { - name: "filter_type_1_0/strided_slice_1/stack_2" + name: "filter_type_1_0/strided_slice/stack_2" op: "Const" attr { key: "dtype" @@ -32482,12 +32901,12 @@ node { } } node { - name: "filter_type_1_0/strided_slice_1" + name: "filter_type_1_0/strided_slice" op: "StridedSlice" - input: "filter_type_1_0/Shape_1" - input: "filter_type_1_0/strided_slice_1/stack" - input: "filter_type_1_0/strided_slice_1/stack_1" - input: "filter_type_1_0/strided_slice_1/stack_2" + input: "filter_type_1_0/Shape" + input: "filter_type_1_0/strided_slice/stack" + input: "filter_type_1_0/strided_slice/stack_1" + input: "filter_type_1_0/strided_slice/stack_2" attr { key: "Index" value { @@ -32532,7 +32951,7 @@ node { } } node { - name: "filter_type_1_0/Reshape_7/shape" + name: "filter_type_1_0/Reshape/shape" op: "Const" attr { key: "dtype" @@ -32556,10 +32975,10 @@ node { } } node { - name: "filter_type_1_0/Reshape_7" + name: "filter_type_1_0/Reshape" op: "Reshape" - input: "filter_type_1_0/Slice_2" - input: "filter_type_1_0/Reshape_7/shape" + input: "filter_type_1_0/Slice" + input: "filter_type_1_0/Reshape/shape" attr { key: "T" value { @@ -32574,7 +32993,7 @@ node { } } node { - name: "filter_type_1_0/Slice_3/begin" + name: "filter_type_1_0/Slice_1/begin" op: "Const" attr { key: "dtype" @@ -32598,7 +33017,7 @@ node { } } node { - name: "filter_type_1_0/Slice_3/size" + name: "filter_type_1_0/Slice_1/size" op: "Const" attr { key: "dtype" @@ -32622,11 +33041,11 @@ node { } } node { - name: "filter_type_1_0/Slice_3" + name: "filter_type_1_0/Slice_1" op: "Slice" - input: "filter_type_1_0/Reshape_7" - input: "filter_type_1_0/Slice_3/begin" - input: "filter_type_1_0/Slice_3/size" + input: "filter_type_1_0/Reshape" + input: "filter_type_1_0/Slice_1/begin" + input: "filter_type_1_0/Slice_1/size" attr { key: "Index" value { @@ -32641,7 +33060,7 @@ node { } } node { - name: "filter_type_1_0/Reshape_8/shape" + name: "filter_type_1_0/Reshape_1/shape" op: "Const" attr { key: "dtype" @@ -32665,10 +33084,10 @@ node { } } node { - name: "filter_type_1_0/Reshape_8" + name: "filter_type_1_0/Reshape_1" op: "Reshape" - input: "filter_type_1_0/Slice_3" - input: "filter_type_1_0/Reshape_8/shape" + input: "filter_type_1_0/Slice_1" + input: "filter_type_1_0/Reshape_1/shape" attr { key: "T" value { @@ -32683,7 +33102,7 @@ node { } } node { - name: "filter_type_1_0/matrix_1_1" + name: "filter_type_1_0/matrix_1_0" op: "Const" attr { key: "dtype" @@ -32701,18 +33120,18 @@ node { size: 1 } dim { - size: 25 + size: 2 } } - tensor_content: 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name: "filter_type_1_0/bias_1_0/read" op: "Identity" - input: "filter_type_1_0/bias_1_1" + input: "filter_type_1_0/bias_1_0" attr { key: "T" value { @@ -32766,16 +33185,16 @@ node { key: "_class" value { list { - s: "loc:@filter_type_1_0/bias_1_1" + s: "loc:@filter_type_1_0/bias_1_0" } } } } node { - name: "filter_type_1_0/MatMul_4" + name: "filter_type_1_0/MatMul" op: "MatMul" - input: "filter_type_1_0/Reshape_8" - input: "filter_type_1_0/matrix_1_1/read" + input: "filter_type_1_0/Reshape_1" + input: "filter_type_1_0/matrix_1_0/read" attr { key: "T" value { @@ -32796,10 +33215,10 @@ node { } } node { - name: "filter_type_1_0/BiasAdd_3" + name: "filter_type_1_0/BiasAdd" op: "BiasAdd" - input: "filter_type_1_0/MatMul_4" - input: "filter_type_1_0/bias_1_1/read" + input: "filter_type_1_0/MatMul" + input: "filter_type_1_0/bias_1_0/read" attr { key: "T" value { @@ -32814,9 +33233,9 @@ node { } } node { - name: "filter_type_1_0/Tanh_3" + name: "filter_type_1_0/Tanh" op: "Tanh" - input: "filter_type_1_0/BiasAdd_3" + input: "filter_type_1_0/BiasAdd" attr { key: "T" value { @@ -32825,7 +33244,7 @@ node { } } node { - name: "filter_type_1_0/Reshape_9/shape" + name: "filter_type_1_0/Reshape_2/shape" op: "Const" attr { key: "dtype" @@ -32843,16 +33262,16 @@ node { size: 2 } } - tensor_content: "\377\377\377\377\031\000\000\000" + tensor_content: "\377\377\377\377\002\000\000\000" } } } } node { - name: "filter_type_1_0/Reshape_9" + name: "filter_type_1_0/Reshape_2" op: "Reshape" - input: "filter_type_1_0/Tanh_3" - input: "filter_type_1_0/Reshape_9/shape" + input: "filter_type_1_0/Tanh" + input: "filter_type_1_0/Reshape_2/shape" attr { key: "T" value { @@ -32867,38 +33286,96 @@ node { } } node { - name: "filter_type_1_0/matrix_2_1" + name: "filter_type_1_0/concat/axis" op: "Const" attr { key: "dtype" value { - type: DT_DOUBLE + type: DT_INT32 } } attr { key: "value" value { tensor { - dtype: DT_DOUBLE + dtype: DT_INT32 tensor_shape { - dim { - size: 25 - } - dim { - size: 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"dtype" value { - type: DT_INT32 + type: DT_DOUBLE } } attr { key: "value" value { tensor { - dtype: DT_INT32 + dtype: DT_DOUBLE tensor_shape { + dim { + size: 2 + } } - int_val: 40 + tensor_content: "r\227\334\223\300z\366\277q\310\327\024\2627\354\277" } } } } node { - name: "mul_20" - op: "Mul" - input: "strided_slice_37" - input: "mul_20/y" + name: "layer_2_type_0/bias/read" + op: "Identity" + input: "layer_2_type_0/bias" attr { key: "T" value { - type: DT_INT32 + type: DT_DOUBLE + } + } + attr { + key: "_class" + value { + list { + s: "loc:@layer_2_type_0/bias" + } } } } node { - name: "Reshape_28/shape" - op: "Pack" - input: "strided_slice_36" - input: "mul_20" + name: "layer_2_type_0/MatMul" + op: "MatMul" + input: "add_4" + input: "layer_2_type_0/matrix/read" attr { - key: "N" + key: "T" value { - i: 2 + type: DT_DOUBLE } } attr { - key: "T" + key: "transpose_a" value { - type: DT_INT32 + b: false } } attr { - key: "axis" + key: "transpose_b" value { - i: 0 + b: false } } } node { - name: "Reshape_28" - op: "Reshape" - input: "filter_type_0_1/mul" - input: "Reshape_28/shape" + name: "layer_2_type_0/BiasAdd" + op: "BiasAdd" + input: "layer_2_type_0/MatMul" + input: "layer_2_type_0/bias/read" attr { key: "T" value { @@ -37365,62 +37723,68 @@ node { } } attr { - key: "Tshape" + key: "data_format" value { - type: DT_INT32 + s: "NHWC" } } } node { - name: "strided_slice_38/stack" + name: "layer_2_type_0/idt" op: "Const" attr { key: "dtype" value { - type: DT_INT32 + type: DT_DOUBLE } } attr { key: "value" value { tensor { - dtype: DT_INT32 + dtype: DT_DOUBLE tensor_shape { dim { - size: 1 + size: 2 } } - int_val: 2 + tensor_content: "\204\315(p\014\205\271?#\337`2y\260\271?" } } } } node { - name: "strided_slice_38/stack_1" - op: "Const" + name: "layer_2_type_0/idt/read" + op: "Identity" + input: "layer_2_type_0/idt" attr { - key: "dtype" + key: "T" value { - type: DT_INT32 + type: DT_DOUBLE } } attr { - key: "value" + key: "_class" value { - tensor { - dtype: DT_INT32 - tensor_shape { - dim { - size: 1 - } - } - int_val: 3 + list { + s: "loc:@layer_2_type_0/idt" } } } } node { - name: "strided_slice_38/stack_2" + name: "layer_2_type_0/Tanh" + op: "Tanh" + input: "layer_2_type_0/BiasAdd" + attr { + key: "T" + value { + type: DT_DOUBLE + } + } +} +node { + name: "layer_2_type_0/Reshape/shape" op: "Const" attr { key: "dtype" @@ -37435,132 +37799,206 @@ node { dtype: DT_INT32 tensor_shape { dim { - size: 1 + size: 2 } } - int_val: 1 + tensor_content: "\377\377\377\377\002\000\000\000" } } } } node { - name: "strided_slice_38" - op: "StridedSlice" - input: "t_natoms" - input: "strided_slice_38/stack" - input: "strided_slice_38/stack_1" - input: "strided_slice_38/stack_2" + name: "layer_2_type_0/Reshape" + op: "Reshape" + input: "layer_2_type_0/Tanh" + input: "layer_2_type_0/Reshape/shape" attr { - key: "Index" + key: "T" + value { + type: DT_DOUBLE + } + } + attr { + key: "Tshape" value { type: DT_INT32 } } +} +node { + name: "layer_2_type_0/mul" + op: "Mul" + input: "layer_2_type_0/Reshape" + input: "layer_2_type_0/idt/read" attr { key: "T" value { - type: DT_INT32 + type: DT_DOUBLE } } +} +node { + name: "add_5" + op: "AddV2" + input: "add_4" + input: "layer_2_type_0/mul" attr { - key: "begin_mask" + key: "T" value { - i: 0 + type: DT_DOUBLE } } +} +node { + name: "final_layer_type_0/matrix" + op: "Const" attr { - key: "ellipsis_mask" + key: "dtype" value { - i: 0 + type: DT_DOUBLE } } attr { - key: "end_mask" + key: "value" value { - i: 0 + tensor { + dtype: DT_DOUBLE + tensor_shape { + dim { + size: 2 + } + dim { + size: 1 + } + } + tensor_content: "\250\317s,\271p\355?\301d\217b\326\226\305\277" + } } } +} +node { + name: "final_layer_type_0/matrix/read" + op: "Identity" + input: "final_layer_type_0/matrix" attr { - key: "new_axis_mask" + key: "T" value { - i: 0 + type: DT_DOUBLE } } attr { - key: "shrink_axis_mask" + key: "_class" value { - i: 1 + list { + s: "loc:@final_layer_type_0/matrix" + } } } } node { - name: "add_4/x" + name: "final_layer_type_0/bias" op: "Const" attr { key: "dtype" value { - type: DT_INT32 + type: DT_DOUBLE } } attr { key: "value" value { tensor { - dtype: DT_INT32 + dtype: DT_DOUBLE tensor_shape { + dim { + size: 1 + } } - int_val: 0 + double_val: 0.19270500335538504 } } } } node { - name: "add_4" - op: "AddV2" - input: "add_4/x" - input: "strided_slice_38" + name: "final_layer_type_0/bias/read" + op: "Identity" + input: "final_layer_type_0/bias" attr { key: "T" value { - type: DT_INT32 + type: DT_DOUBLE + } + } + attr { + key: "_class" + value { + list { + s: "loc:@final_layer_type_0/bias" + } } } } node { - name: "mul_21/y" - op: "Const" + name: "final_layer_type_0/MatMul" + op: "MatMul" + input: "add_5" + input: "final_layer_type_0/matrix/read" attr { - key: "dtype" + key: "T" value { - type: DT_INT32 + type: DT_DOUBLE } } attr { - key: "value" + key: "transpose_a" value { - tensor { - dtype: DT_INT32 - tensor_shape { - } - int_val: 138 - } + b: false + } + } + attr { + key: "transpose_b" + value { + b: false } } } node { - name: "mul_21" - op: "Mul" - input: "add_4" - input: "mul_21/y" + name: "final_layer_type_0/BiasAdd" + op: "BiasAdd" + input: "final_layer_type_0/MatMul" + input: "final_layer_type_0/bias/read" + attr { + key: "T" + value { + type: DT_DOUBLE + } + } + attr { + key: "data_format" + value { + s: "NHWC" + } + } +} +node { + name: "Shape_10" + op: "Shape" + input: "Reshape_23" attr { key: "T" + value { + type: DT_DOUBLE + } + } + attr { + key: "out_type" value { type: DT_INT32 } } } node { - name: "strided_slice_39/stack" + name: "strided_slice_37/stack" op: "Const" attr { key: "dtype" @@ -37578,13 +38016,13 @@ node { size: 1 } } - int_val: 3 + int_val: 0 } } } } node { - name: "strided_slice_39/stack_1" + name: "strided_slice_37/stack_1" op: "Const" attr { key: "dtype" @@ -37602,13 +38040,13 @@ node { size: 1 } } - int_val: 4 + int_val: 1 } } } } node { - name: "strided_slice_39/stack_2" + name: "strided_slice_37/stack_2" op: "Const" attr { key: "dtype" @@ -37632,12 +38070,12 @@ node { } } node { - name: "strided_slice_39" + name: "strided_slice_37" op: "StridedSlice" - input: "t_natoms" - input: "strided_slice_39/stack" - input: "strided_slice_39/stack_1" - input: "strided_slice_39/stack_2" + input: "Shape_10" + input: "strided_slice_37/stack" + input: "strided_slice_37/stack_1" + input: "strided_slice_37/stack_2" attr { key: "Index" value { @@ -37682,7 +38120,7 @@ node { } } node { - name: "mul_22/y" + name: "strided_slice_38/stack" op: "Const" attr { key: "dtype" @@ -37696,26 +38134,41 @@ node { tensor { dtype: DT_INT32 tensor_shape { + dim { + size: 1 + } } - int_val: 138 + int_val: 2 } } } } node { - name: "mul_22" - op: "Mul" - input: "strided_slice_39" - input: "mul_22/y" + name: "strided_slice_38/stack_1" + op: "Const" attr { - key: "T" + key: "dtype" value { type: DT_INT32 } } + attr { + key: "value" + value { + tensor { + dtype: DT_INT32 + tensor_shape { + dim { + size: 1 + } + } + int_val: 3 + } + } + } } node { - name: "Slice_5/begin/0" + name: "strided_slice_38/stack_2" op: "Const" attr { key: "dtype" @@ -37729,21 +38182,26 @@ node { tensor { dtype: DT_INT32 tensor_shape { + dim { + size: 1 + } } - int_val: 0 + int_val: 1 } } } } node { - name: "Slice_5/begin" - op: "Pack" - input: "Slice_5/begin/0" - input: "mul_21" + name: "strided_slice_38" + op: "StridedSlice" + input: "t_natoms" + input: "strided_slice_38/stack" + input: "strided_slice_38/stack_1" + input: "strided_slice_38/stack_2" attr { - key: "N" + key: "Index" value { - i: 2 + type: DT_INT32 } } attr { @@ -37753,42 +38211,45 @@ node { } } attr { - key: "axis" + key: "begin_mask" value { i: 0 } } -} -node { - name: "Slice_5/size/0" - op: "Const" attr { - key: "dtype" + key: "ellipsis_mask" value { - type: DT_INT32 + i: 0 } } attr { - key: "value" + key: "end_mask" value { - tensor { - dtype: DT_INT32 - tensor_shape { - } - int_val: -1 - } + i: 0 } } -} -node { - name: "Slice_5/size" - op: "Pack" - input: "Slice_5/size/0" - input: "mul_22" attr { - key: "N" + key: "new_axis_mask" value { - i: 2 + i: 0 + } + } + attr { + key: "shrink_axis_mask" + value { + i: 1 + } + } +} +node { + name: "Reshape_27/shape" + op: "Pack" + input: "strided_slice_37" + input: "strided_slice_38" + attr { + key: "N" + value { + i: 2 } } attr { @@ -37805,26 +38266,25 @@ node { } } node { - name: "Slice_5" - op: "Slice" - input: "Reshape_26" - input: "Slice_5/begin" - input: "Slice_5/size" + name: "Reshape_27" + op: "Reshape" + input: "final_layer_type_0/BiasAdd" + input: "Reshape_27/shape" attr { - key: "Index" + key: "T" value { - type: DT_INT32 + type: DT_DOUBLE } } attr { - key: "T" + key: "Tshape" value { - type: DT_DOUBLE + type: DT_INT32 } } } node { - name: "Reshape_29/shape" + name: "strided_slice_39/stack" op: "Const" attr { key: "dtype" @@ -37839,34 +38299,16 @@ node { dtype: DT_INT32 tensor_shape { dim { - size: 2 + size: 1 } } - tensor_content: "\377\377\377\377\212\000\000\000" + int_val: 2 } } } } node { - name: "Reshape_29" - op: "Reshape" - input: "Slice_5" - input: "Reshape_29/shape" - attr { - key: "T" - value { - type: DT_DOUBLE - } - } - attr { - key: "Tshape" - value { - type: DT_INT32 - } - } -} -node { - name: "filter_type_1_1/Slice/begin" + name: "strided_slice_39/stack_1" op: "Const" attr { key: "dtype" @@ -37881,16 +38323,16 @@ node { dtype: DT_INT32 tensor_shape { dim { - size: 2 + size: 1 } } - tensor_content: "\000\000\000\000\000\000\000\000" + int_val: 3 } } } } node { - name: "filter_type_1_1/Slice/size" + name: "strided_slice_39/stack_2" op: "Const" attr { key: "dtype" @@ -37905,20 +38347,21 @@ node { dtype: DT_INT32 tensor_shape { dim { - size: 2 + size: 1 } } - tensor_content: "\377\377\377\377.\000\000\000" + int_val: 1 } } } } node { - name: "filter_type_1_1/Slice" - op: "Slice" - input: "Reshape_29" - input: "filter_type_1_1/Slice/begin" - input: "filter_type_1_1/Slice/size" + name: "strided_slice_39" + op: "StridedSlice" + input: "t_natoms" + input: "strided_slice_39/stack" + input: "strided_slice_39/stack_1" + input: "strided_slice_39/stack_2" attr { key: "Index" value { @@ -37928,12 +38371,42 @@ node { attr { key: "T" value { - type: DT_DOUBLE + type: DT_INT32 + } + } + attr { + key: "begin_mask" + value { + i: 0 + } + } + attr { + key: "ellipsis_mask" + value { + i: 0 + } + } + attr { + key: "end_mask" + value { + i: 0 + } + } + attr { + key: "new_axis_mask" + value { + i: 0 + } + } + attr { + key: "shrink_axis_mask" + value { + i: 1 } } } node { - name: "filter_type_1_1/Reshape/shape" + name: "add_6/x" op: "Const" attr { key: "dtype" @@ -37947,168 +38420,292 @@ node { tensor { dtype: DT_INT32 tensor_shape { - dim { - size: 2 - } } - tensor_content: "\377\377\377\377\001\000\000\000" + int_val: 0 } } } } node { - name: "filter_type_1_1/Reshape" - op: "Reshape" - input: "filter_type_1_1/Slice" - input: "filter_type_1_1/Reshape/shape" + name: "add_6" + op: "AddV2" + input: "add_6/x" + input: "strided_slice_39" attr { key: "T" value { - type: DT_DOUBLE + type: DT_INT32 } } +} +node { + name: "strided_slice_40/stack" + op: "Const" attr { - key: "Tshape" + key: "dtype" value { type: DT_INT32 } } + attr { + key: "value" + value { + tensor { + dtype: DT_INT32 + tensor_shape { + dim { + size: 1 + } + } + int_val: 3 + } + } + } } node { - name: "filter_type_1_1/matrix_1_0" + name: "strided_slice_40/stack_1" op: "Const" attr { key: "dtype" value { - type: DT_DOUBLE + type: DT_INT32 } } attr { key: "value" value { tensor { - dtype: DT_DOUBLE + dtype: DT_INT32 tensor_shape { dim { size: 1 } + } + int_val: 4 + } + } + } +} +node { + name: "strided_slice_40/stack_2" + op: "Const" + attr { + key: "dtype" + value { + type: DT_INT32 + } + } + attr { + key: "value" + value { + tensor { + dtype: DT_INT32 + tensor_shape { dim { - size: 10 + size: 1 } } - tensor_content: "\334\363\006\000S\346\275\277\201R\230\253\310\227\320??-\242A\016\303\243?1\220!W2\336\270\277\336B\177\002\302&\313\277k\037\344\347\210\264\312\277{P\345\207\035\342\325?O%\246\301\341\001\350?\225\352r\235\300\233\312\277\365^\034\220#\244\326?" + int_val: 1 } } } } node { - name: "filter_type_1_1/matrix_1_0/read" - op: "Identity" - input: "filter_type_1_1/matrix_1_0" + name: "strided_slice_40" + op: "StridedSlice" + input: "t_natoms" + input: "strided_slice_40/stack" + input: "strided_slice_40/stack_1" + input: "strided_slice_40/stack_2" + attr { + key: "Index" + value { + type: DT_INT32 + } + } attr { key: "T" value { - type: DT_DOUBLE + type: DT_INT32 } } attr { - key: "_class" + key: "begin_mask" value { - list { - s: "loc:@filter_type_1_1/matrix_1_0" - } + i: 0 + } + } + attr { + key: "ellipsis_mask" + value { + i: 0 + } + } + attr { + key: "end_mask" + value { + i: 0 + } + } + attr { + key: "new_axis_mask" + value { + i: 0 + } + } + attr { + key: "shrink_axis_mask" + value { + i: 1 } } } node { - name: "filter_type_1_1/bias_1_0" + name: "Slice_6/begin/0" op: "Const" attr { key: "dtype" value { - type: DT_DOUBLE + type: DT_INT32 } } attr { key: "value" value { tensor { - dtype: DT_DOUBLE + dtype: DT_INT32 tensor_shape { - dim { - size: 10 - } } - tensor_content: "\325\233V\313\226\344\302\277\002\352\364>4E\350\277b\036\357\325\020\270\341?>UL\343\016\353\314?W,\312U\333V\273\277(lEN+q\273?\037\233\375k\025\252\372\277\2418\277\231\225}\302?\353\032\213\017\242\335\314?\275\227\003ak%\373\277" + int_val: 0 } } } } node { - name: "filter_type_1_1/bias_1_0/read" - op: "Identity" - input: "filter_type_1_1/bias_1_0" + name: "Slice_6/begin/2" + op: "Const" attr { - key: "T" + key: "dtype" value { - type: DT_DOUBLE + type: DT_INT32 } } attr { - key: "_class" + key: "value" value { - list { - s: "loc:@filter_type_1_1/bias_1_0" + tensor { + dtype: DT_INT32 + tensor_shape { + } + int_val: 0 } } } } node { - name: "filter_type_1_1/MatMul" - op: "MatMul" - input: "filter_type_1_1/Reshape" - input: "filter_type_1_1/matrix_1_0/read" + name: "Slice_6/begin" + op: "Pack" + input: "Slice_6/begin/0" + input: "add_6" + input: "Slice_6/begin/2" + attr { + key: "N" + value { + i: 3 + } + } attr { key: "T" value { - type: DT_DOUBLE + type: DT_INT32 } } attr { - key: "transpose_a" + key: "axis" value { - b: false + i: 0 } } +} +node { + name: "Slice_6/size/0" + op: "Const" attr { - key: "transpose_b" + key: "dtype" value { - b: false + type: DT_INT32 + } + } + attr { + key: "value" + value { + tensor { + dtype: DT_INT32 + tensor_shape { + } + int_val: -1 + } } } } node { - name: "filter_type_1_1/BiasAdd" - op: "BiasAdd" - input: "filter_type_1_1/MatMul" - input: "filter_type_1_1/bias_1_0/read" + name: "Slice_6/size/2" + op: "Const" + attr { + key: "dtype" + value { + type: DT_INT32 + } + } + attr { + key: "value" + value { + tensor { + dtype: DT_INT32 + tensor_shape { + } + int_val: -1 + } + } + } +} +node { + name: "Slice_6/size" + op: "Pack" + input: "Slice_6/size/0" + input: "strided_slice_40" + input: "Slice_6/size/2" + attr { + key: "N" + value { + i: 3 + } + } attr { key: "T" value { - type: DT_DOUBLE + type: DT_INT32 } } attr { - key: "data_format" + key: "axis" value { - s: "NHWC" + i: 0 } } } node { - name: "filter_type_1_1/Tanh" - op: "Tanh" - input: "filter_type_1_1/BiasAdd" + name: "Slice_6" + op: "Slice" + input: "Reshape_23" + input: "Slice_6/begin" + input: "Slice_6/size" + attr { + key: "Index" + value { + type: DT_INT32 + } + } attr { key: "T" value { @@ -38117,7 +38714,7 @@ node { } } node { - name: "filter_type_1_1/Reshape_1/shape" + name: "Reshape_28/shape" op: "Const" attr { key: "dtype" @@ -38135,16 +38732,16 @@ node { size: 2 } } - tensor_content: "\377\377\377\377\n\000\000\000" + tensor_content: "\377\377\377\377@\000\000\000" } } } } node { - name: "filter_type_1_1/Reshape_1" + name: "Reshape_28" op: "Reshape" - input: "filter_type_1_1/Tanh" - input: "filter_type_1_1/Reshape_1/shape" + input: "Slice_6" + input: "Reshape_28/shape" attr { key: "T" value { @@ -38159,7 +38756,7 @@ node { } } node { - name: "filter_type_1_1/matrix_2_0" + name: "layer_0_type_1/matrix" op: "Const" attr { key: "dtype" @@ -38174,21 +38771,21 @@ node { dtype: DT_DOUBLE tensor_shape { dim { - size: 10 + size: 64 } dim { - size: 20 + size: 2 } } - tensor_content: 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"filter_type_1_1/matrix_2_0/read" + input: "Reshape_28" + input: "layer_0_type_1/matrix/read" attr { key: "T" value { @@ -38272,10 +38869,10 @@ node { } } node { - name: "filter_type_1_1/BiasAdd_1" + name: "layer_0_type_1/BiasAdd" op: "BiasAdd" - input: "filter_type_1_1/MatMul_1" - input: "filter_type_1_1/bias_2_0/read" + input: "layer_0_type_1/MatMul" + input: "layer_0_type_1/bias/read" attr { key: "T" value { @@ -38290,9 +38887,9 @@ node { } } node { - name: "filter_type_1_1/Tanh_1" + name: "layer_0_type_1/Tanh" op: "Tanh" - input: "filter_type_1_1/BiasAdd_1" + input: "layer_0_type_1/BiasAdd" attr { key: "T" value { @@ -38301,7 +38898,7 @@ node { } } node { - name: "filter_type_1_1/Reshape_2/shape" + name: "layer_0_type_1/Reshape/shape" op: "Const" attr { key: "dtype" @@ -38319,16 +38916,16 @@ node { size: 2 } } - tensor_content: "\377\377\377\377\024\000\000\000" + tensor_content: "\377\377\377\377\002\000\000\000" } } } } node { - name: "filter_type_1_1/Reshape_2" + name: 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"Shape" - input: "filter_type_1_0/concat" attr { key: "T" value { - type: DT_DOUBLE + type: DT_INT32 } } attr { - key: "out_type" + key: "axis" value { - type: DT_INT32 + i: 0 } } } node { - name: "gradients_1/filter_type_1_0/add_grad/Shape_1" - op: "Shape" - input: "filter_type_1_0/Reshape_3" + name: "o_atom_energy" + op: "Reshape" + input: "Reshape_31" + input: "o_atom_energy/shape" attr { key: "T" value { @@ -51803,29 +40548,38 @@ node { } } attr { - key: "out_type" + key: "Tshape" value { type: DT_INT32 } } } node { - name: "gradients_1/filter_type_1_0/add_grad/BroadcastGradientArgs" - op: "BroadcastGradientArgs" - input: "gradients_1/filter_type_1_0/add_grad/Shape" - input: "gradients_1/filter_type_1_0/add_grad/Shape_1" + name: "o_energy/reduction_indices" + op: "Const" attr { - key: "T" + key: "dtype" value { type: DT_INT32 } } + attr { + key: "value" + value { + tensor { + dtype: DT_INT32 + tensor_shape { + } + int_val: 1 + } + } + } } node { - name: "gradients_1/filter_type_1_0/add_grad/Sum" + name: "o_energy" op: "Sum" - input: "gradients_1/AddN_9" - input: "gradients_1/filter_type_1_0/add_grad/BroadcastGradientArgs" + input: "o_atom_energy" + input: "o_energy/reduction_indices" attr { key: "T" value { @@ -51846,10 +40600,9 @@ node { } } node { - name: "gradients_1/filter_type_1_0/add_grad/Reshape" - op: "Reshape" - input: "gradients_1/filter_type_1_0/add_grad/Sum" - input: "gradients_1/filter_type_1_0/add_grad/Shape" + name: "gradients_1/Shape" + op: "Shape" + input: "Reshape_31" attr { key: "T" value { @@ -51857,41 +40610,38 @@ node { } } attr { - key: "Tshape" + key: "out_type" value { type: DT_INT32 } } } node { - name: "gradients_1/filter_type_1_0/add_grad/Sum_1" - op: "Sum" - input: "gradients_1/AddN_9" - input: "gradients_1/filter_type_1_0/add_grad/BroadcastGradientArgs:1" + name: "gradients_1/grad_ys_0/Const" + op: "Const" attr { - key: "T" + key: "dtype" value { type: DT_DOUBLE } } attr { - key: "Tidx" - value { - type: DT_INT32 - } - } - attr { - key: "keep_dims" + key: "value" value { - b: false + tensor { + dtype: DT_DOUBLE + tensor_shape { + } + double_val: 1.0 + } } } } node { - name: "gradients_1/filter_type_1_0/add_grad/Reshape_1" - op: "Reshape" - input: "gradients_1/filter_type_1_0/add_grad/Sum_1" - input: "gradients_1/filter_type_1_0/add_grad/Shape_1" + name: "gradients_1/grad_ys_0" + op: "Fill" + input: "gradients_1/Shape" + input: "gradients_1/grad_ys_0/Const" attr { key: "T" value { @@ -51899,24 +40649,16 @@ node { } } attr { - key: "Tshape" + key: "index_type" value { type: DT_INT32 } } } node { - name: "gradients_1/AddN_10" - op: "AddN" - input: "gradients_1/filter_type_1_0/concat_3_grad/Slice" - input: "gradients_1/filter_type_1_0/concat_3_grad/Slice_1" - input: "gradients_1/filter_type_1_0/MatMul_6_grad/MatMul" - attr { - key: "N" - value { - i: 3 - } - } + name: "gradients_1/Reshape_31_grad/Shape" + op: "Shape" + input: "mul_8" attr { key: "T" value { @@ -51924,18 +40666,17 @@ node { } } attr { - key: "_class" + key: "out_type" value { - list { - s: "loc:@gradients_1/filter_type_1_0/concat_3_grad/Slice" - } + type: DT_INT32 } } } node { - name: "gradients_1/filter_type_1_0/add_2_grad/Shape" - op: "Shape" - input: "filter_type_1_0/concat_2" + name: "gradients_1/Reshape_31_grad/Reshape" + op: "Reshape" + input: "gradients_1/grad_ys_0" + input: "gradients_1/Reshape_31_grad/Shape" attr { key: "T" value { @@ -51943,16 +40684,16 @@ node { } } attr { - key: "out_type" + key: "Tshape" value { type: DT_INT32 } } } node { - name: "gradients_1/filter_type_1_0/add_2_grad/Shape_1" + name: "gradients_1/mul_8_grad/Shape" op: "Shape" - input: "filter_type_1_0/Reshape_10" + input: "add_10" attr { key: "T" value { @@ -51967,22 +40708,9 @@ node { } } node { - name: "gradients_1/filter_type_1_0/add_2_grad/BroadcastGradientArgs" - op: "BroadcastGradientArgs" - input: "gradients_1/filter_type_1_0/add_2_grad/Shape" - input: "gradients_1/filter_type_1_0/add_2_grad/Shape_1" - attr { - key: "T" - value { - type: DT_INT32 - } - } -} -node { - name: "gradients_1/filter_type_1_0/add_2_grad/Sum" - op: "Sum" - input: "gradients_1/AddN_10" - input: "gradients_1/filter_type_1_0/add_2_grad/BroadcastGradientArgs" + name: "gradients_1/mul_8_grad/Shape_1" + op: "Shape" + input: "Cast" attr { key: "T" value { @@ -51990,41 +40718,41 @@ node { } } attr { - key: "Tidx" + key: "out_type" value { type: DT_INT32 } } +} +node { + name: "gradients_1/mul_8_grad/BroadcastGradientArgs" + op: "BroadcastGradientArgs" + input: "gradients_1/mul_8_grad/Shape" + input: "gradients_1/mul_8_grad/Shape_1" attr { - key: "keep_dims" + key: "T" value { - b: false + type: DT_INT32 } } } node { - name: "gradients_1/filter_type_1_0/add_2_grad/Reshape" - op: "Reshape" - input: "gradients_1/filter_type_1_0/add_2_grad/Sum" - input: "gradients_1/filter_type_1_0/add_2_grad/Shape" + name: "gradients_1/mul_8_grad/Mul" + op: "Mul" + input: "gradients_1/Reshape_31_grad/Reshape" + input: "Cast" attr { key: "T" value { type: DT_DOUBLE } } - attr { - key: "Tshape" - value { - type: DT_INT32 - } - } } node { - name: "gradients_1/filter_type_1_0/add_2_grad/Sum_1" + name: "gradients_1/mul_8_grad/Sum" op: "Sum" - input: "gradients_1/AddN_10" - input: "gradients_1/filter_type_1_0/add_2_grad/BroadcastGradientArgs:1" + input: "gradients_1/mul_8_grad/Mul" + input: "gradients_1/mul_8_grad/BroadcastGradientArgs" attr { key: "T" value { @@ -52045,10 +40773,10 @@ node { } } node { - name: "gradients_1/filter_type_1_0/add_2_grad/Reshape_1" + name: "gradients_1/mul_8_grad/Reshape" op: "Reshape" - input: "gradients_1/filter_type_1_0/add_2_grad/Sum_1" - input: "gradients_1/filter_type_1_0/add_2_grad/Shape_1" + input: "gradients_1/mul_8_grad/Sum" + input: "gradients_1/mul_8_grad/Shape" attr { key: "T" value { @@ -52063,49 +40791,9 @@ node { } } node { - name: "gradients_1/filter_type_0_0/concat_grad/Rank" - op: "Const" - attr { - key: "dtype" - value { - type: DT_INT32 - } - } - attr { - key: "value" - value { - tensor { - dtype: DT_INT32 - tensor_shape { - } - int_val: 2 - } - } - } -} -node { - name: "gradients_1/filter_type_0_0/concat_grad/mod" - op: "FloorMod" - input: "filter_type_0_0/concat/axis" - input: "gradients_1/filter_type_0_0/concat_grad/Rank" - attr { - key: "T" - value { - type: DT_INT32 - } - } -} -node { - name: "gradients_1/filter_type_0_0/concat_grad/ShapeN" - op: "ShapeN" - input: "filter_type_0_0/Reshape_2" - input: "filter_type_0_0/Reshape_2" - attr { - key: "N" - value { - i: 2 - } - } + name: "gradients_1/add_10_grad/Shape" + op: "Shape" + input: "concat_5" attr { key: "T" value { @@ -52120,60 +40808,39 @@ node { } } node { - name: "gradients_1/filter_type_0_0/concat_grad/ConcatOffset" - op: "ConcatOffset" - input: "gradients_1/filter_type_0_0/concat_grad/mod" - input: "gradients_1/filter_type_0_0/concat_grad/ShapeN" - input: "gradients_1/filter_type_0_0/concat_grad/ShapeN:1" - attr { - key: "N" - value { - i: 2 - } - } -} -node { - name: 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node { - name: "gradients_1/filter_type_0_0/Reshape_3_grad/Shape" - op: "Shape" - input: "filter_type_0_0/Tanh_1" + name: "gradients_1/add_10_grad/Sum" + op: "Sum" + input: "gradients_1/mul_8_grad/Reshape" + input: "gradients_1/add_10_grad/BroadcastGradientArgs" attr { key: "T" value { @@ -52181,17 +40848,23 @@ node { } } attr { - key: "out_type" + key: "Tidx" value { type: DT_INT32 } } + attr { + key: "keep_dims" + value { + b: false + } + } } node { - name: "gradients_1/filter_type_0_0/Reshape_3_grad/Reshape" + name: "gradients_1/add_10_grad/Reshape" op: "Reshape" - input: "gradients_1/filter_type_0_0/add_grad/Reshape_1" - input: "gradients_1/filter_type_0_0/Reshape_3_grad/Shape" + input: "gradients_1/add_10_grad/Sum" + input: "gradients_1/add_10_grad/Shape" attr { key: "T" value { @@ -52206,7 +40879,7 @@ node { } } node { - name: "gradients_1/filter_type_0_0/concat_2_grad/Rank" + name: "gradients_1/concat_5_grad/Rank" op: "Const" attr { key: "dtype" @@ -52227,10 +40900,10 @@ node { } } node { - name: "gradients_1/filter_type_0_0/concat_2_grad/mod" + name: "gradients_1/concat_5_grad/mod" op: "FloorMod" - input: "filter_type_0_0/concat_2/axis" - input: "gradients_1/filter_type_0_0/concat_2_grad/Rank" + input: "concat_5/axis" + input: "gradients_1/concat_5_grad/Rank" attr { key: "T" value { @@ -52239,10 +40912,10 @@ node { } } node { - name: "gradients_1/filter_type_0_0/concat_2_grad/ShapeN" + name: "gradients_1/concat_5_grad/ShapeN" op: "ShapeN" - input: "filter_type_0_0/Reshape_9" - input: "filter_type_0_0/Reshape_9" + input: "Reshape_27" + input: "Reshape_29" attr { key: "N" value { @@ -52263,11 +40936,11 @@ node { } } node { - name: "gradients_1/filter_type_0_0/concat_2_grad/ConcatOffset" + name: "gradients_1/concat_5_grad/ConcatOffset" op: "ConcatOffset" - input: "gradients_1/filter_type_0_0/concat_2_grad/mod" - input: "gradients_1/filter_type_0_0/concat_2_grad/ShapeN" - input: "gradients_1/filter_type_0_0/concat_2_grad/ShapeN:1" + input: "gradients_1/concat_5_grad/mod" + input: "gradients_1/concat_5_grad/ShapeN" + input: "gradients_1/concat_5_grad/ShapeN:1" attr { key: "N" value { @@ -52276,11 +40949,11 @@ node { } } node { - name: "gradients_1/filter_type_0_0/concat_2_grad/Slice" + name: "gradients_1/concat_5_grad/Slice" op: "Slice" - input: "gradients_1/filter_type_0_0/add_2_grad/Reshape" - input: "gradients_1/filter_type_0_0/concat_2_grad/ConcatOffset" - input: "gradients_1/filter_type_0_0/concat_2_grad/ShapeN" + input: "gradients_1/add_10_grad/Reshape" + input: "gradients_1/concat_5_grad/ConcatOffset" + input: "gradients_1/concat_5_grad/ShapeN" attr { key: "Index" value { @@ -52295,11 +40968,11 @@ node { } } node { - name: "gradients_1/filter_type_0_0/concat_2_grad/Slice_1" + name: "gradients_1/concat_5_grad/Slice_1" op: "Slice" - input: "gradients_1/filter_type_0_0/add_2_grad/Reshape" - input: "gradients_1/filter_type_0_0/concat_2_grad/ConcatOffset:1" - input: "gradients_1/filter_type_0_0/concat_2_grad/ShapeN:1" + input: "gradients_1/add_10_grad/Reshape" + input: "gradients_1/concat_5_grad/ConcatOffset:1" + input: "gradients_1/concat_5_grad/ShapeN:1" attr { key: "Index" value { @@ -52314,9 +40987,9 @@ node { } } node { - name: "gradients_1/filter_type_0_0/Reshape_10_grad/Shape" + name: "gradients_1/Reshape_27_grad/Shape" op: "Shape" - input: "filter_type_0_0/Tanh_4" + input: "final_layer_type_0/BiasAdd" attr { key: "T" value { @@ -52331,10 +41004,10 @@ node { } } node { - name: "gradients_1/filter_type_0_0/Reshape_10_grad/Reshape" + name: "gradients_1/Reshape_27_grad/Reshape" op: "Reshape" - input: "gradients_1/filter_type_0_0/add_2_grad/Reshape_1" - input: "gradients_1/filter_type_0_0/Reshape_10_grad/Shape" + input: "gradients_1/concat_5_grad/Slice" + input: "gradients_1/Reshape_27_grad/Shape" attr { key: "T" value { @@ -52349,117 +41022,139 @@ node { } } node { - name: "gradients_1/filter_type_1_0/concat_grad/Rank" - op: "Const" + name: "gradients_1/Reshape_29_grad/Shape" + op: "Shape" + 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"gradients_1/add_5_grad/Shape_1" attr { key: "T" value { - type: DT_DOUBLE + type: DT_INT32 } } } node { - name: "gradients_1/filter_type_1_0/Reshape_3_grad/Shape" - op: "Shape" - input: "filter_type_1_0/Tanh_1" + name: "gradients_1/add_5_grad/Sum" + op: "Sum" + input: "gradients_1/final_layer_type_0/MatMul_grad/MatMul" + input: "gradients_1/add_5_grad/BroadcastGradientArgs" attr { key: "T" value { @@ -52467,17 +41162,23 @@ node { } } attr { - key: "out_type" + key: "Tidx" value { type: DT_INT32 } } + attr { + key: "keep_dims" + value { + b: false + } + } } node { - name: "gradients_1/filter_type_1_0/Reshape_3_grad/Reshape" + name: "gradients_1/add_5_grad/Reshape" op: "Reshape" - input: "gradients_1/filter_type_1_0/add_grad/Reshape_1" - input: "gradients_1/filter_type_1_0/Reshape_3_grad/Shape" + input: "gradients_1/add_5_grad/Sum" + input: "gradients_1/add_5_grad/Shape" attr { key: "T" value { @@ -52492,49 +41193,51 @@ node { } } node { - name: 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{ type: DT_DOUBLE } } + attr { + key: "Tshape" + value { + type: DT_INT32 + } + } } node { - name: "gradients_1/filter_type_1_0/Reshape_10_grad/Shape" - op: "Shape" - input: "filter_type_1_0/Tanh_4" + name: "gradients_1/add_8_grad/Sum_1" + op: "Sum" + input: "gradients_1/final_layer_type_1/MatMul_grad/MatMul" + input: "gradients_1/add_8_grad/BroadcastGradientArgs:1" attr { key: "T" value { @@ -52610,17 +41334,23 @@ node { } } attr { - key: "out_type" + key: "Tidx" value { type: DT_INT32 } } + attr { + key: "keep_dims" + value { + b: false + } + } } node { - name: "gradients_1/filter_type_1_0/Reshape_10_grad/Reshape" + name: "gradients_1/add_8_grad/Reshape_1" op: "Reshape" - input: "gradients_1/filter_type_1_0/add_2_grad/Reshape_1" - input: "gradients_1/filter_type_1_0/Reshape_10_grad/Shape" + input: "gradients_1/add_8_grad/Sum_1" + input: "gradients_1/add_8_grad/Shape_1" attr { key: "T" value { @@ -52635,46 +41365,56 @@ node { } } node { - name: 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"gradients_1/layer_2_type_0/mul_grad/BroadcastGradientArgs" + op: "BroadcastGradientArgs" + input: "gradients_1/layer_2_type_0/mul_grad/Shape" + input: "gradients_1/layer_2_type_0/mul_grad/Shape_1" attr { key: "T" value { - type: DT_DOUBLE + type: DT_INT32 } } } node { - name: "gradients_1/filter_type_1_0/Tanh_4_grad/TanhGrad" - op: "TanhGrad" - input: "filter_type_1_0/Tanh_4" - input: "gradients_1/filter_type_1_0/Reshape_10_grad/Reshape" + name: "gradients_1/layer_2_type_0/mul_grad/Mul" + op: "Mul" + input: "gradients_1/add_5_grad/Reshape_1" + input: "layer_2_type_0/idt/read" attr { key: "T" value { @@ -52683,10 +41423,10 @@ node { } } node { - name: "gradients_1/filter_type_0_0/MatMul_1_grad/MatMul" - op: "MatMul" - input: "gradients_1/filter_type_0_0/Tanh_1_grad/TanhGrad" - input: "filter_type_0_0/matrix_2_0/read" + name: "gradients_1/layer_2_type_0/mul_grad/Sum" + op: "Sum" + input: "gradients_1/layer_2_type_0/mul_grad/Mul" + input: "gradients_1/layer_2_type_0/mul_grad/BroadcastGradientArgs" attr { key: "T" value { @@ -52694,23 +41434,23 @@ node { } } attr { - key: "transpose_a" + key: "Tidx" value { - b: false + type: DT_INT32 } } attr { - key: "transpose_b" + key: "keep_dims" value { - b: true + b: false } } } node { - name: "gradients_1/filter_type_0_0/MatMul_5_grad/MatMul" - op: "MatMul" - input: "gradients_1/filter_type_0_0/Tanh_4_grad/TanhGrad" - input: "filter_type_0_0/matrix_2_1/read" + name: "gradients_1/layer_2_type_0/mul_grad/Reshape" + op: "Reshape" + input: "gradients_1/layer_2_type_0/mul_grad/Sum" + input: "gradients_1/layer_2_type_0/mul_grad/Shape" attr { key: "T" value { @@ -52718,23 +41458,33 @@ node { } } attr { - key: "transpose_a" + key: "Tshape" value { - b: false + type: DT_INT32 + } + } +} +node { + name: "gradients_1/layer_2_type_1/mul_grad/Shape" + op: "Shape" + input: "layer_2_type_1/Reshape" + attr { + key: "T" + value { + type: DT_DOUBLE } } attr { - key: "transpose_b" + key: "out_type" value { - b: true + type: DT_INT32 } } } node { - name: "gradients_1/filter_type_1_0/MatMul_1_grad/MatMul" - op: "MatMul" - input: "gradients_1/filter_type_1_0/Tanh_1_grad/TanhGrad" - input: "filter_type_1_0/matrix_2_0/read" + name: "gradients_1/layer_2_type_1/mul_grad/Shape_1" + op: "Shape" + input: "layer_2_type_1/idt/read" attr { key: "T" value { @@ -52742,54 +41492,65 @@ node { } } attr { - key: "transpose_a" + key: "out_type" value { - b: false + type: DT_INT32 } } +} +node { + name: "gradients_1/layer_2_type_1/mul_grad/BroadcastGradientArgs" + op: "BroadcastGradientArgs" + input: "gradients_1/layer_2_type_1/mul_grad/Shape" + input: "gradients_1/layer_2_type_1/mul_grad/Shape_1" attr { - key: "transpose_b" + key: "T" value { - b: true + type: DT_INT32 } } } node { - name: "gradients_1/filter_type_1_0/MatMul_5_grad/MatMul" - op: "MatMul" - input: "gradients_1/filter_type_1_0/Tanh_4_grad/TanhGrad" - input: "filter_type_1_0/matrix_2_1/read" + name: "gradients_1/layer_2_type_1/mul_grad/Mul" + op: "Mul" + input: "gradients_1/add_8_grad/Reshape_1" + input: "layer_2_type_1/idt/read" attr { key: "T" value { type: DT_DOUBLE } } +} +node { + name: "gradients_1/layer_2_type_1/mul_grad/Sum" + op: "Sum" + input: "gradients_1/layer_2_type_1/mul_grad/Mul" + input: "gradients_1/layer_2_type_1/mul_grad/BroadcastGradientArgs" attr { - key: "transpose_a" + key: "T" value { - b: false + type: DT_DOUBLE } } attr { - key: "transpose_b" + key: "Tidx" value { - b: true + type: DT_INT32 } } -} -node { - name: "gradients_1/AddN_11" - op: "AddN" - input: "gradients_1/filter_type_0_0/concat_grad/Slice" - input: "gradients_1/filter_type_0_0/concat_grad/Slice_1" - input: "gradients_1/filter_type_0_0/MatMul_1_grad/MatMul" attr { - key: "N" + key: "keep_dims" value { - i: 3 + b: false } } +} +node { + name: "gradients_1/layer_2_type_1/mul_grad/Reshape" + op: "Reshape" + input: "gradients_1/layer_2_type_1/mul_grad/Sum" + input: "gradients_1/layer_2_type_1/mul_grad/Shape" attr { key: "T" value { @@ -52797,18 +41558,16 @@ node { } } attr { - key: "_class" + key: "Tshape" value { - list { - s: "loc:@gradients_1/filter_type_0_0/concat_grad/Slice" - } + type: DT_INT32 } } } node { - name: "gradients_1/filter_type_0_0/Reshape_2_grad/Shape" + name: "gradients_1/layer_2_type_0/Reshape_grad/Shape" op: "Shape" - input: "filter_type_0_0/Tanh" + input: "layer_2_type_0/Tanh" attr { key: "T" value { @@ -52823,10 +41582,10 @@ node { } } node { - name: "gradients_1/filter_type_0_0/Reshape_2_grad/Reshape" + name: "gradients_1/layer_2_type_0/Reshape_grad/Reshape" op: "Reshape" - input: "gradients_1/AddN_11" - input: "gradients_1/filter_type_0_0/Reshape_2_grad/Shape" + input: "gradients_1/layer_2_type_0/mul_grad/Reshape" + input: "gradients_1/layer_2_type_0/Reshape_grad/Shape" attr { key: "T" value { @@ -52841,17 +41600,27 @@ node { } } node { - name: "gradients_1/AddN_12" - op: "AddN" - input: 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"layer_2_type_0/Tanh" + input: "gradients_1/layer_2_type_0/Reshape_grad/Reshape" + attr { + key: "T" + value { + type: DT_DOUBLE + } + } +} +node { + name: "gradients_1/layer_2_type_1/Tanh_grad/TanhGrad" + op: "TanhGrad" + input: "layer_2_type_1/Tanh" + input: "gradients_1/layer_2_type_1/Reshape_grad/Reshape" + attr { + key: "T" + value { + type: DT_DOUBLE + } + } +} +node { + name: "gradients_1/layer_2_type_0/MatMul_grad/MatMul" + op: "MatMul" + input: "gradients_1/layer_2_type_0/Tanh_grad/TanhGrad" + input: "layer_2_type_0/matrix/read" attr { key: "T" value { @@ -52878,17 +41670,23 @@ node { } } attr { - key: "out_type" + key: "transpose_a" value { - type: DT_INT32 + b: false + } + } + attr { + key: "transpose_b" + value { + b: true } } } node { - name: "gradients_1/filter_type_0_0/Reshape_9_grad/Reshape" - op: "Reshape" - input: "gradients_1/AddN_12" - input: "gradients_1/filter_type_0_0/Reshape_9_grad/Shape" + name: "gradients_1/layer_2_type_1/MatMul_grad/MatMul" + op: "MatMul" + input: "gradients_1/layer_2_type_1/Tanh_grad/TanhGrad" + input: "layer_2_type_1/matrix/read" attr { key: "T" value { @@ -52896,22 +41694,27 @@ node { } } attr { - key: "Tshape" + key: "transpose_a" value { - type: DT_INT32 + b: false + } + } + attr { + key: "transpose_b" + value { + b: true } } } node { - name: "gradients_1/AddN_13" + name: "gradients_1/AddN" op: "AddN" - input: "gradients_1/filter_type_1_0/concat_grad/Slice" - input: "gradients_1/filter_type_1_0/concat_grad/Slice_1" - input: "gradients_1/filter_type_1_0/MatMul_1_grad/MatMul" + input: "gradients_1/add_5_grad/Reshape" + input: "gradients_1/layer_2_type_0/MatMul_grad/MatMul" attr { key: "N" value { - i: 3 + i: 2 } } attr { @@ -52924,15 +41727,15 @@ node { key: "_class" value { list { - s: "loc:@gradients_1/filter_type_1_0/concat_grad/Slice" + s: "loc:@gradients_1/add_5_grad/Reshape" } } } } node { - name: "gradients_1/filter_type_1_0/Reshape_2_grad/Shape" + name: "gradients_1/add_4_grad/Shape" op: "Shape" - input: "filter_type_1_0/Tanh" + input: "layer_0_type_0/Reshape" attr { key: "T" value { @@ -52947,10 +41750,9 @@ node { } } node { - name: "gradients_1/filter_type_1_0/Reshape_2_grad/Reshape" - op: "Reshape" - input: "gradients_1/AddN_13" - input: "gradients_1/filter_type_1_0/Reshape_2_grad/Shape" + name: "gradients_1/add_4_grad/Shape_1" + op: "Shape" + input: "layer_1_type_0/mul" attr { key: "T" value { @@ -52958,24 +41760,29 @@ node { } } attr { - key: "Tshape" + key: "out_type" value { type: DT_INT32 } } } node { - name: "gradients_1/AddN_14" - op: "AddN" - input: "gradients_1/filter_type_1_0/concat_2_grad/Slice" - input: "gradients_1/filter_type_1_0/concat_2_grad/Slice_1" - input: "gradients_1/filter_type_1_0/MatMul_5_grad/MatMul" + name: "gradients_1/add_4_grad/BroadcastGradientArgs" + op: "BroadcastGradientArgs" + input: "gradients_1/add_4_grad/Shape" + input: "gradients_1/add_4_grad/Shape_1" attr { - key: "N" + key: "T" value { - i: 3 + type: DT_INT32 } } +} +node { + name: "gradients_1/add_4_grad/Sum" + op: "Sum" + input: "gradients_1/AddN" + input: "gradients_1/add_4_grad/BroadcastGradientArgs" attr { key: "T" value { @@ -52983,18 +41790,23 @@ node { } } attr { - key: "_class" + key: "Tidx" value { - list { - s: "loc:@gradients_1/filter_type_1_0/concat_2_grad/Slice" - } + type: DT_INT32 + } + } + attr { + key: "keep_dims" + value { + b: false } } } node { - name: "gradients_1/filter_type_1_0/Reshape_9_grad/Shape" - op: "Shape" - input: "filter_type_1_0/Tanh_3" + name: "gradients_1/add_4_grad/Reshape" + op: "Reshape" + input: "gradients_1/add_4_grad/Sum" + input: "gradients_1/add_4_grad/Shape" attr { key: "T" value { @@ -53002,17 +41814,17 @@ node { } } attr { - key: "out_type" + key: "Tshape" value { type: DT_INT32 } } } node { - name: "gradients_1/filter_type_1_0/Reshape_9_grad/Reshape" - op: "Reshape" - input: "gradients_1/AddN_14" - input: "gradients_1/filter_type_1_0/Reshape_9_grad/Shape" + name: "gradients_1/add_4_grad/Sum_1" + op: "Sum" + input: "gradients_1/AddN" + input: "gradients_1/add_4_grad/BroadcastGradientArgs:1" attr { key: "T" value { @@ -53020,65 +41832,47 @@ node { } } attr { - key: "Tshape" + key: "Tidx" value { type: DT_INT32 } } -} -node { - name: "gradients_1/filter_type_0_0/Tanh_grad/TanhGrad" - op: "TanhGrad" - input: "filter_type_0_0/Tanh" - input: "gradients_1/filter_type_0_0/Reshape_2_grad/Reshape" attr { - key: "T" + key: "keep_dims" value { - type: DT_DOUBLE + b: false } } } node { - name: "gradients_1/filter_type_0_0/Tanh_3_grad/TanhGrad" - op: "TanhGrad" - input: "filter_type_0_0/Tanh_3" - input: "gradients_1/filter_type_0_0/Reshape_9_grad/Reshape" + name: "gradients_1/add_4_grad/Reshape_1" + op: "Reshape" + input: "gradients_1/add_4_grad/Sum_1" + input: "gradients_1/add_4_grad/Shape_1" attr { key: "T" value { type: DT_DOUBLE } } -} -node { - name: "gradients_1/filter_type_1_0/Tanh_grad/TanhGrad" - op: "TanhGrad" - input: "filter_type_1_0/Tanh" - input: "gradients_1/filter_type_1_0/Reshape_2_grad/Reshape" attr { - key: "T" + key: "Tshape" value { - type: DT_DOUBLE + type: DT_INT32 } } } node { - name: "gradients_1/filter_type_1_0/Tanh_3_grad/TanhGrad" - op: "TanhGrad" - input: "filter_type_1_0/Tanh_3" - input: "gradients_1/filter_type_1_0/Reshape_9_grad/Reshape" + name: "gradients_1/AddN_1" + op: "AddN" + input: "gradients_1/add_8_grad/Reshape" + input: "gradients_1/layer_2_type_1/MatMul_grad/MatMul" attr { - key: "T" + key: "N" value { - type: DT_DOUBLE + i: 2 } } -} -node { - name: "gradients_1/filter_type_0_0/MatMul_grad/MatMul" - op: "MatMul" - input: "gradients_1/filter_type_0_0/Tanh_grad/TanhGrad" - input: "filter_type_0_0/matrix_1_0/read" attr { key: "T" value { @@ -53086,23 +41880,18 @@ node { } } attr { - key: "transpose_a" - value { - b: false - } - } - attr { - key: "transpose_b" + key: "_class" value { - b: true + list { + s: "loc:@gradients_1/add_8_grad/Reshape" + } } } } node { - name: "gradients_1/filter_type_0_0/MatMul_4_grad/MatMul" - op: "MatMul" - input: "gradients_1/filter_type_0_0/Tanh_3_grad/TanhGrad" - input: "filter_type_0_0/matrix_1_1/read" + name: "gradients_1/add_7_grad/Shape" + op: "Shape" + input: "layer_0_type_1/Reshape" attr { key: "T" value { @@ -53110,23 +41899,16 @@ node { } } attr { - key: "transpose_a" - value { - b: false - } - } - attr { - key: "transpose_b" + key: "out_type" value { - b: true + type: DT_INT32 } } } node { - name: "gradients_1/filter_type_1_0/MatMul_grad/MatMul" - op: "MatMul" - input: "gradients_1/filter_type_1_0/Tanh_grad/TanhGrad" - input: "filter_type_1_0/matrix_1_0/read" + name: "gradients_1/add_7_grad/Shape_1" + op: "Shape" + input: "layer_1_type_1/mul" attr { key: "T" value { @@ -53134,23 +41916,29 @@ node { } } attr { - key: "transpose_a" + key: "out_type" value { - b: false + type: DT_INT32 } } +} +node { + name: "gradients_1/add_7_grad/BroadcastGradientArgs" + op: "BroadcastGradientArgs" + input: "gradients_1/add_7_grad/Shape" + input: "gradients_1/add_7_grad/Shape_1" attr { - key: "transpose_b" + key: "T" value { - b: true + type: DT_INT32 } } } node { - name: "gradients_1/filter_type_1_0/MatMul_4_grad/MatMul" - op: "MatMul" - input: "gradients_1/filter_type_1_0/Tanh_3_grad/TanhGrad" - input: "filter_type_1_0/matrix_1_1/read" + name: "gradients_1/add_7_grad/Sum" + op: "Sum" + input: "gradients_1/AddN_1" + input: "gradients_1/add_7_grad/BroadcastGradientArgs" attr { key: "T" value { @@ -53158,22 +41946,23 @@ node { } } attr { - key: "transpose_a" + key: "Tidx" value { - b: false + type: DT_INT32 } } attr { - key: "transpose_b" + key: "keep_dims" value { - b: true + b: false } } } node { - name: "gradients_1/filter_type_0_0/Reshape_1_grad/Shape" - op: "Shape" - input: "filter_type_0_0/Slice_1" + name: "gradients_1/add_7_grad/Reshape" + op: "Reshape" + input: "gradients_1/add_7_grad/Sum" + input: "gradients_1/add_7_grad/Shape" attr { key: "T" value { @@ -53181,17 +41970,17 @@ node { } } attr { - key: "out_type" + key: "Tshape" value { type: DT_INT32 } } } node { - name: "gradients_1/filter_type_0_0/Reshape_1_grad/Reshape" - op: "Reshape" - input: "gradients_1/filter_type_0_0/MatMul_grad/MatMul" - input: "gradients_1/filter_type_0_0/Reshape_1_grad/Shape" + name: "gradients_1/add_7_grad/Sum_1" + op: "Sum" + input: "gradients_1/AddN_1" + input: "gradients_1/add_7_grad/BroadcastGradientArgs:1" attr { key: "T" value { @@ -53199,16 +41988,23 @@ node { } } attr { - key: "Tshape" + key: "Tidx" value { type: DT_INT32 } } + attr { + key: "keep_dims" + value { + b: false + } + } } node { - name: "gradients_1/filter_type_0_0/Reshape_8_grad/Shape" - op: "Shape" - input: "filter_type_0_0/Slice_3" + name: "gradients_1/add_7_grad/Reshape_1" + op: "Reshape" + input: "gradients_1/add_7_grad/Sum_1" + input: "gradients_1/add_7_grad/Shape_1" attr { key: "T" value { @@ -53216,17 +42012,16 @@ node { } } attr { - key: "out_type" + key: "Tshape" value { type: DT_INT32 } } } node { - name: "gradients_1/filter_type_0_0/Reshape_8_grad/Reshape" - op: "Reshape" - input: "gradients_1/filter_type_0_0/MatMul_4_grad/MatMul" - input: "gradients_1/filter_type_0_0/Reshape_8_grad/Shape" + name: "gradients_1/layer_1_type_0/mul_grad/Shape" + op: "Shape" + input: "layer_1_type_0/Reshape" attr { key: "T" value { @@ -53234,16 +42029,16 @@ node { } } attr { - key: "Tshape" + key: "out_type" value { type: DT_INT32 } } } node { - name: "gradients_1/filter_type_1_0/Reshape_1_grad/Shape" + name: "gradients_1/layer_1_type_0/mul_grad/Shape_1" op: "Shape" - input: "filter_type_1_0/Slice_1" + input: "layer_1_type_0/idt/read" attr { key: "T" value { @@ -53258,27 +42053,34 @@ node { } } node { - name: "gradients_1/filter_type_1_0/Reshape_1_grad/Reshape" - op: "Reshape" - input: "gradients_1/filter_type_1_0/MatMul_grad/MatMul" - input: "gradients_1/filter_type_1_0/Reshape_1_grad/Shape" + name: "gradients_1/layer_1_type_0/mul_grad/BroadcastGradientArgs" + op: "BroadcastGradientArgs" + input: "gradients_1/layer_1_type_0/mul_grad/Shape" + input: "gradients_1/layer_1_type_0/mul_grad/Shape_1" attr { key: "T" value { - type: DT_DOUBLE + type: DT_INT32 } } +} +node { + name: "gradients_1/layer_1_type_0/mul_grad/Mul" + op: "Mul" + input: "gradients_1/add_4_grad/Reshape_1" + input: "layer_1_type_0/idt/read" attr { - key: "Tshape" + key: "T" value { - type: DT_INT32 + type: DT_DOUBLE } } } node { - name: "gradients_1/filter_type_1_0/Reshape_8_grad/Shape" - op: "Shape" - input: "filter_type_1_0/Slice_3" + name: "gradients_1/layer_1_type_0/mul_grad/Sum" + op: "Sum" + input: "gradients_1/layer_1_type_0/mul_grad/Mul" + input: "gradients_1/layer_1_type_0/mul_grad/BroadcastGradientArgs" attr { key: "T" value { @@ -53286,17 +42088,23 @@ node { } } attr { - key: "out_type" + key: "Tidx" value { type: DT_INT32 } } + attr { + key: "keep_dims" + value { + b: false + } + } } node { - name: "gradients_1/filter_type_1_0/Reshape_8_grad/Reshape" + name: "gradients_1/layer_1_type_0/mul_grad/Reshape" op: "Reshape" - input: "gradients_1/filter_type_1_0/MatMul_4_grad/MatMul" - input: "gradients_1/filter_type_1_0/Reshape_8_grad/Shape" + input: "gradients_1/layer_1_type_0/mul_grad/Sum" + input: "gradients_1/layer_1_type_0/mul_grad/Shape" attr { key: "T" value { @@ -53311,30 +42119,26 @@ node { } } node { - name: "gradients_1/filter_type_0_0/Slice_1_grad/Rank" - op: "Const" + name: "gradients_1/layer_1_type_1/mul_grad/Shape" + op: "Shape" + input: "layer_1_type_1/Reshape" attr { - key: "dtype" + key: "T" value { - type: DT_INT32 + type: DT_DOUBLE } } attr { - key: "value" + key: "out_type" value { - tensor { - dtype: DT_INT32 - tensor_shape { - } - int_val: 2 - } + type: DT_INT32 } } } node { - name: "gradients_1/filter_type_0_0/Slice_1_grad/Shape" + name: "gradients_1/layer_1_type_1/mul_grad/Shape_1" op: "Shape" - input: "filter_type_0_0/Slice_1" + input: "layer_1_type_1/idt/read" attr { key: "T" value { @@ -53349,59 +42153,62 @@ node { } } node { - name: "gradients_1/filter_type_0_0/Slice_1_grad/stack/1" - op: "Const" + name: "gradients_1/layer_1_type_1/mul_grad/BroadcastGradientArgs" + op: "BroadcastGradientArgs" + input: "gradients_1/layer_1_type_1/mul_grad/Shape" + input: "gradients_1/layer_1_type_1/mul_grad/Shape_1" attr { - key: "dtype" + key: "T" value { type: DT_INT32 } } +} +node { + name: "gradients_1/layer_1_type_1/mul_grad/Mul" + op: "Mul" + input: "gradients_1/add_7_grad/Reshape_1" + input: "layer_1_type_1/idt/read" attr { - key: "value" + key: "T" value { - tensor { - dtype: DT_INT32 - tensor_shape { - } - int_val: 1 - } + type: DT_DOUBLE } } } node { - name: "gradients_1/filter_type_0_0/Slice_1_grad/stack" - op: "Pack" - input: "gradients_1/filter_type_0_0/Slice_1_grad/Rank" - input: "gradients_1/filter_type_0_0/Slice_1_grad/stack/1" + name: "gradients_1/layer_1_type_1/mul_grad/Sum" + op: "Sum" + input: "gradients_1/layer_1_type_1/mul_grad/Mul" + input: "gradients_1/layer_1_type_1/mul_grad/BroadcastGradientArgs" attr { - key: "N" + key: "T" value { - i: 2 + type: DT_DOUBLE } } attr { - key: "T" + key: "Tidx" value { type: DT_INT32 } } attr { - key: "axis" + key: "keep_dims" value { - i: 0 + b: false } } } node { - name: "gradients_1/filter_type_0_0/Slice_1_grad/Reshape" + name: "gradients_1/layer_1_type_1/mul_grad/Reshape" op: "Reshape" - input: "filter_type_0_0/Slice_1/begin" - input: "gradients_1/filter_type_0_0/Slice_1_grad/stack" + input: "gradients_1/layer_1_type_1/mul_grad/Sum" + input: "gradients_1/layer_1_type_1/mul_grad/Shape" attr { key: "T" value { - type: DT_INT32 + type: DT_DOUBLE } } attr { @@ -53412,9 +42219,9 @@ node { } } node { - name: "gradients_1/filter_type_0_0/Slice_1_grad/Shape_1" + name: "gradients_1/layer_1_type_0/Reshape_grad/Shape" op: "Shape" - input: "filter_type_0_0/Reshape" + input: "layer_1_type_0/Tanh" attr { key: "T" value { @@ -53429,38 +42236,49 @@ node { } } node { - name: 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"gradients_1/filter_type_0_0/Slice_1_grad/sub_1" - input: "gradients_1/filter_type_0_0/Slice_1_grad/stack" + input: "gradients_1/layer_1_type_1/mul_grad/Reshape" + input: "gradients_1/layer_1_type_1/Reshape_grad/Shape" attr { key: "T" value { - type: DT_INT32 + type: DT_DOUBLE } } attr { @@ -53471,56 +42289,58 @@ node { } } node { - name: "gradients_1/filter_type_0_0/Slice_1_grad/concat/axis" - op: "Const" + name: "gradients_1/layer_1_type_0/Tanh_grad/TanhGrad" + op: "TanhGrad" + input: "layer_1_type_0/Tanh" + input: "gradients_1/layer_1_type_0/Reshape_grad/Reshape" attr { - key: "dtype" + key: "T" value { - type: DT_INT32 + type: DT_DOUBLE } } +} +node { + name: "gradients_1/layer_1_type_1/Tanh_grad/TanhGrad" + op: "TanhGrad" + input: "layer_1_type_1/Tanh" + input: "gradients_1/layer_1_type_1/Reshape_grad/Reshape" attr { - key: "value" + key: "T" value { - tensor { - dtype: DT_INT32 - tensor_shape { - } - int_val: 1 - } + type: DT_DOUBLE } } } node { - name: 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"layer_1_type_1/matrix/read" attr { key: "T" value { @@ -53528,37 +42348,48 @@ node { } } attr { - key: "Tpaddings" + key: "transpose_a" value { - type: DT_INT32 + b: false + } + } + attr { + key: "transpose_b" + value { + b: true } } } node { - name: "gradients_1/filter_type_0_0/Slice_3_grad/Rank" - op: "Const" + name: "gradients_1/AddN_2" + op: "AddN" + input: "gradients_1/add_4_grad/Reshape" + input: "gradients_1/layer_1_type_0/MatMul_grad/MatMul" attr { - key: "dtype" + key: "N" value { - type: DT_INT32 + i: 2 } } attr { - key: "value" + key: "T" value { - tensor { - dtype: DT_INT32 - tensor_shape { - } - int_val: 2 + type: DT_DOUBLE + } + } + attr { + key: "_class" + value { + list { + s: "loc:@gradients_1/add_4_grad/Reshape" } } } } node { - name: "gradients_1/filter_type_0_0/Slice_3_grad/Shape" + name: "gradients_1/layer_0_type_0/Reshape_grad/Shape" op: "Shape" - input: "filter_type_0_0/Slice_3" + input: "layer_0_type_0/Tanh" attr { key: "T" value { @@ -53573,31 +42404,28 @@ node { } } node { - name: "gradients_1/filter_type_0_0/Slice_3_grad/stack/1" - op: "Const" + name: "gradients_1/layer_0_type_0/Reshape_grad/Reshape" + op: "Reshape" + input: "gradients_1/AddN_2" + input: "gradients_1/layer_0_type_0/Reshape_grad/Shape" attr { - key: "dtype" + key: "T" value { - type: DT_INT32 + type: DT_DOUBLE } } attr { - key: "value" + key: "Tshape" value { - tensor { - dtype: DT_INT32 - tensor_shape { - } - int_val: 1 - } + type: DT_INT32 } } } node { - name: "gradients_1/filter_type_0_0/Slice_3_grad/stack" - op: "Pack" - input: "gradients_1/filter_type_0_0/Slice_3_grad/Rank" - input: "gradients_1/filter_type_0_0/Slice_3_grad/stack/1" + name: "gradients_1/AddN_3" + op: "AddN" + input: "gradients_1/add_7_grad/Reshape" + input: "gradients_1/layer_1_type_1/MatMul_grad/MatMul" attr { key: "N" value { @@ -53607,38 +42435,40 @@ node { attr { key: "T" value { - type: DT_INT32 + type: DT_DOUBLE } } attr { - key: "axis" + key: "_class" value { - i: 0 + list { + s: "loc:@gradients_1/add_7_grad/Reshape" + } } } } node { - name: "gradients_1/filter_type_0_0/Slice_3_grad/Reshape" - op: "Reshape" - input: "filter_type_0_0/Slice_3/begin" - input: "gradients_1/filter_type_0_0/Slice_3_grad/stack" + name: "gradients_1/layer_0_type_1/Reshape_grad/Shape" + op: "Shape" + input: "layer_0_type_1/Tanh" attr { key: "T" value { - type: DT_INT32 + type: DT_DOUBLE } } attr { - key: "Tshape" + key: "out_type" value { type: DT_INT32 } } } node { - name: "gradients_1/filter_type_0_0/Slice_3_grad/Shape_1" - op: "Shape" - input: "filter_type_0_0/Reshape_7" + name: "gradients_1/layer_0_type_1/Reshape_grad/Reshape" + op: "Reshape" + input: "gradients_1/AddN_3" + input: "gradients_1/layer_0_type_1/Reshape_grad/Shape" attr { key: "T" value { @@ -53646,105 +42476,141 @@ node { } } attr { - key: "out_type" + key: "Tshape" value { type: DT_INT32 } } } node { - name: "gradients_1/filter_type_0_0/Slice_3_grad/sub" - op: "Sub" - input: "gradients_1/filter_type_0_0/Slice_3_grad/Shape_1" - input: "gradients_1/filter_type_0_0/Slice_3_grad/Shape" + name: "gradients_1/layer_0_type_0/Tanh_grad/TanhGrad" + op: "TanhGrad" + input: "layer_0_type_0/Tanh" + input: "gradients_1/layer_0_type_0/Reshape_grad/Reshape" attr { key: "T" value { - type: DT_INT32 + type: DT_DOUBLE } } } node { - name: "gradients_1/filter_type_0_0/Slice_3_grad/sub_1" - op: "Sub" - input: "gradients_1/filter_type_0_0/Slice_3_grad/sub" - input: "filter_type_0_0/Slice_3/begin" + name: "gradients_1/layer_0_type_1/Tanh_grad/TanhGrad" + op: "TanhGrad" + input: "layer_0_type_1/Tanh" + input: "gradients_1/layer_0_type_1/Reshape_grad/Reshape" attr { key: "T" value { - type: DT_INT32 + type: DT_DOUBLE } } } node { - name: "gradients_1/filter_type_0_0/Slice_3_grad/Reshape_1" - op: "Reshape" - input: "gradients_1/filter_type_0_0/Slice_3_grad/sub_1" - input: "gradients_1/filter_type_0_0/Slice_3_grad/stack" + name: "gradients_1/layer_0_type_0/MatMul_grad/MatMul" + op: "MatMul" + input: "gradients_1/layer_0_type_0/Tanh_grad/TanhGrad" + input: "layer_0_type_0/matrix/read" attr { key: "T" value { - type: DT_INT32 + type: DT_DOUBLE } } attr { - key: "Tshape" + key: "transpose_a" value { - type: DT_INT32 + b: false + } + } + attr { + key: "transpose_b" + value { + b: true } } } node { - name: "gradients_1/filter_type_0_0/Slice_3_grad/concat/axis" - op: "Const" + name: "gradients_1/layer_0_type_1/MatMul_grad/MatMul" + op: "MatMul" + input: "gradients_1/layer_0_type_1/Tanh_grad/TanhGrad" + input: "layer_0_type_1/matrix/read" attr { - key: "dtype" + key: "T" value { - type: DT_INT32 + type: DT_DOUBLE } } attr { - key: "value" + key: "transpose_a" value { - tensor { - dtype: DT_INT32 - tensor_shape { - } - int_val: 1 - } + b: false + } + } + attr { + key: "transpose_b" + value { + b: true } } } node { - name: "gradients_1/filter_type_0_0/Slice_3_grad/concat" - op: "ConcatV2" - input: "gradients_1/filter_type_0_0/Slice_3_grad/Reshape" - input: "gradients_1/filter_type_0_0/Slice_3_grad/Reshape_1" - input: "gradients_1/filter_type_0_0/Slice_3_grad/concat/axis" + name: "gradients_1/Reshape_26_grad/Shape" + op: "Shape" + input: "Slice_5" attr { - key: "N" + key: "T" value { - i: 2 + type: DT_DOUBLE + } + } + attr { + key: "out_type" + value { + type: DT_INT32 } } +} +node { + name: "gradients_1/Reshape_26_grad/Reshape" + op: "Reshape" + input: "gradients_1/layer_0_type_0/MatMul_grad/MatMul" + input: "gradients_1/Reshape_26_grad/Shape" attr { key: "T" + value { + type: DT_DOUBLE + } + } + attr { + key: "Tshape" value { type: DT_INT32 } } +} +node { + name: "gradients_1/Reshape_28_grad/Shape" + op: "Shape" + input: "Slice_6" + attr { + key: "T" + value { + type: DT_DOUBLE + } + } attr { - key: "Tidx" + key: "out_type" value { type: DT_INT32 } } } node { - name: "gradients_1/filter_type_0_0/Slice_3_grad/Pad" - op: "Pad" - input: "gradients_1/filter_type_0_0/Reshape_8_grad/Reshape" - input: "gradients_1/filter_type_0_0/Slice_3_grad/concat" + name: "gradients_1/Reshape_28_grad/Reshape" + op: "Reshape" + input: "gradients_1/layer_0_type_1/MatMul_grad/MatMul" + input: "gradients_1/Reshape_28_grad/Shape" attr { key: "T" value { @@ -53752,14 +42618,14 @@ node { } } attr { - key: "Tpaddings" + key: "Tshape" value { type: DT_INT32 } } } node { - name: "gradients_1/filter_type_1_0/Slice_1_grad/Rank" + name: "gradients_1/Slice_5_grad/Rank" op: "Const" attr { key: "dtype" @@ -53774,15 +42640,15 @@ node { dtype: DT_INT32 tensor_shape { } - int_val: 2 + int_val: 3 } } } } node { - name: "gradients_1/filter_type_1_0/Slice_1_grad/Shape" + name: "gradients_1/Slice_5_grad/Shape" op: "Shape" - input: "filter_type_1_0/Slice_1" + input: "Slice_5" attr { key: "T" value { @@ -53797,7 +42663,7 @@ node { } } node { - name: "gradients_1/filter_type_1_0/Slice_1_grad/stack/1" + name: "gradients_1/Slice_5_grad/stack/1" op: "Const" attr { key: "dtype" @@ -53818,10 +42684,10 @@ node { } } node { - name: "gradients_1/filter_type_1_0/Slice_1_grad/stack" + name: "gradients_1/Slice_5_grad/stack" op: "Pack" - input: "gradients_1/filter_type_1_0/Slice_1_grad/Rank" - input: "gradients_1/filter_type_1_0/Slice_1_grad/stack/1" + input: "gradients_1/Slice_5_grad/Rank" + input: "gradients_1/Slice_5_grad/stack/1" attr { key: "N" value { @@ -53842,10 +42708,10 @@ node { } } node { - name: "gradients_1/filter_type_1_0/Slice_1_grad/Reshape" + name: "gradients_1/Slice_5_grad/Reshape" op: "Reshape" - input: "filter_type_1_0/Slice_1/begin" - input: "gradients_1/filter_type_1_0/Slice_1_grad/stack" + input: "Slice_5/begin" + input: "gradients_1/Slice_5_grad/stack" attr { key: "T" value { @@ -53860,9 +42726,9 @@ node { } } node { - name: "gradients_1/filter_type_1_0/Slice_1_grad/Shape_1" + name: "gradients_1/Slice_5_grad/Shape_1" op: "Shape" - input: "filter_type_1_0/Reshape" + input: "Reshape_23" attr { key: "T" value { @@ -53877,10 +42743,10 @@ node { } } node { - name: "gradients_1/filter_type_1_0/Slice_1_grad/sub" + name: "gradients_1/Slice_5_grad/sub" op: "Sub" - input: "gradients_1/filter_type_1_0/Slice_1_grad/Shape_1" - input: "gradients_1/filter_type_1_0/Slice_1_grad/Shape" + input: "gradients_1/Slice_5_grad/Shape_1" + input: "gradients_1/Slice_5_grad/Shape" attr { key: "T" value { @@ -53889,10 +42755,10 @@ node { } } node { - name: "gradients_1/filter_type_1_0/Slice_1_grad/sub_1" + name: "gradients_1/Slice_5_grad/sub_1" op: "Sub" - input: "gradients_1/filter_type_1_0/Slice_1_grad/sub" - input: "filter_type_1_0/Slice_1/begin" + input: "gradients_1/Slice_5_grad/sub" + input: "Slice_5/begin" attr { key: "T" value { @@ -53901,10 +42767,10 @@ node { } } node { - name: "gradients_1/filter_type_1_0/Slice_1_grad/Reshape_1" + name: "gradients_1/Slice_5_grad/Reshape_1" op: "Reshape" - input: "gradients_1/filter_type_1_0/Slice_1_grad/sub_1" - input: "gradients_1/filter_type_1_0/Slice_1_grad/stack" + input: "gradients_1/Slice_5_grad/sub_1" + input: "gradients_1/Slice_5_grad/stack" attr { key: "T" value { @@ -53919,7 +42785,7 @@ node { } } node { - name: "gradients_1/filter_type_1_0/Slice_1_grad/concat/axis" + name: "gradients_1/Slice_5_grad/concat/axis" op: "Const" attr { key: "dtype" @@ -53940,11 +42806,11 @@ node { } } node { - name: "gradients_1/filter_type_1_0/Slice_1_grad/concat" + name: "gradients_1/Slice_5_grad/concat" op: "ConcatV2" - input: "gradients_1/filter_type_1_0/Slice_1_grad/Reshape" - input: "gradients_1/filter_type_1_0/Slice_1_grad/Reshape_1" - input: "gradients_1/filter_type_1_0/Slice_1_grad/concat/axis" + input: "gradients_1/Slice_5_grad/Reshape" + input: "gradients_1/Slice_5_grad/Reshape_1" + input: "gradients_1/Slice_5_grad/concat/axis" attr { key: "N" value { @@ -53965,10 +42831,10 @@ node { } } node { - name: "gradients_1/filter_type_1_0/Slice_1_grad/Pad" + name: "gradients_1/Slice_5_grad/Pad" op: "Pad" - input: "gradients_1/filter_type_1_0/Reshape_1_grad/Reshape" - input: "gradients_1/filter_type_1_0/Slice_1_grad/concat" + input: "gradients_1/Reshape_26_grad/Reshape" + input: "gradients_1/Slice_5_grad/concat" attr { key: "T" value { @@ -53983,7 +42849,7 @@ node { } } node { - name: "gradients_1/filter_type_1_0/Slice_3_grad/Rank" + name: "gradients_1/Slice_6_grad/Rank" op: "Const" attr { key: "dtype" @@ -53998,15 +42864,15 @@ node { dtype: DT_INT32 tensor_shape { } - int_val: 2 + int_val: 3 } } } } node { - name: "gradients_1/filter_type_1_0/Slice_3_grad/Shape" + name: "gradients_1/Slice_6_grad/Shape" op: "Shape" - input: "filter_type_1_0/Slice_3" + input: "Slice_6" attr { key: "T" value { @@ -54021,7 +42887,7 @@ node { } } node { - name: "gradients_1/filter_type_1_0/Slice_3_grad/stack/1" + name: "gradients_1/Slice_6_grad/stack/1" op: "Const" attr { key: "dtype" @@ -54042,10 +42908,10 @@ node { } } node { - name: "gradients_1/filter_type_1_0/Slice_3_grad/stack" + name: "gradients_1/Slice_6_grad/stack" op: "Pack" - input: "gradients_1/filter_type_1_0/Slice_3_grad/Rank" - input: "gradients_1/filter_type_1_0/Slice_3_grad/stack/1" + input: "gradients_1/Slice_6_grad/Rank" + input: "gradients_1/Slice_6_grad/stack/1" attr { key: "N" value { @@ -54066,10 +42932,10 @@ node { } } node { - name: "gradients_1/filter_type_1_0/Slice_3_grad/Reshape" + name: "gradients_1/Slice_6_grad/Reshape" op: "Reshape" - input: "filter_type_1_0/Slice_3/begin" - input: "gradients_1/filter_type_1_0/Slice_3_grad/stack" + input: "Slice_6/begin" + input: "gradients_1/Slice_6_grad/stack" attr { key: "T" value { @@ -54084,9 +42950,9 @@ node { } } node { - name: "gradients_1/filter_type_1_0/Slice_3_grad/Shape_1" + name: "gradients_1/Slice_6_grad/Shape_1" op: "Shape" - input: "filter_type_1_0/Reshape_7" + input: "Reshape_23" attr { key: "T" value { @@ -54101,10 +42967,10 @@ node { } } node { - name: "gradients_1/filter_type_1_0/Slice_3_grad/sub" + name: "gradients_1/Slice_6_grad/sub" op: "Sub" - input: "gradients_1/filter_type_1_0/Slice_3_grad/Shape_1" - input: "gradients_1/filter_type_1_0/Slice_3_grad/Shape" + input: "gradients_1/Slice_6_grad/Shape_1" + input: "gradients_1/Slice_6_grad/Shape" attr { key: "T" value { @@ -54113,10 +42979,10 @@ node { } } node { - name: "gradients_1/filter_type_1_0/Slice_3_grad/sub_1" + name: "gradients_1/Slice_6_grad/sub_1" op: "Sub" - input: "gradients_1/filter_type_1_0/Slice_3_grad/sub" - input: "filter_type_1_0/Slice_3/begin" + input: "gradients_1/Slice_6_grad/sub" + input: "Slice_6/begin" attr { key: "T" value { @@ -54125,10 +42991,10 @@ node { } } node { - name: "gradients_1/filter_type_1_0/Slice_3_grad/Reshape_1" + name: "gradients_1/Slice_6_grad/Reshape_1" op: "Reshape" - input: "gradients_1/filter_type_1_0/Slice_3_grad/sub_1" - input: "gradients_1/filter_type_1_0/Slice_3_grad/stack" + input: "gradients_1/Slice_6_grad/sub_1" + input: "gradients_1/Slice_6_grad/stack" attr { key: "T" value { @@ -54143,7 +43009,7 @@ node { } } node { - name: "gradients_1/filter_type_1_0/Slice_3_grad/concat/axis" + name: "gradients_1/Slice_6_grad/concat/axis" op: "Const" attr { key: "dtype" @@ -54164,11 +43030,11 @@ node { } } node { - name: "gradients_1/filter_type_1_0/Slice_3_grad/concat" + name: "gradients_1/Slice_6_grad/concat" op: "ConcatV2" - input: "gradients_1/filter_type_1_0/Slice_3_grad/Reshape" - input: "gradients_1/filter_type_1_0/Slice_3_grad/Reshape_1" - input: "gradients_1/filter_type_1_0/Slice_3_grad/concat/axis" + input: "gradients_1/Slice_6_grad/Reshape" + input: "gradients_1/Slice_6_grad/Reshape_1" + input: "gradients_1/Slice_6_grad/concat/axis" attr { key: "N" value { @@ -54189,10 +43055,10 @@ node { } } node { - name: "gradients_1/filter_type_1_0/Slice_3_grad/Pad" + name: "gradients_1/Slice_6_grad/Pad" op: "Pad" - input: "gradients_1/filter_type_1_0/Reshape_8_grad/Reshape" - input: "gradients_1/filter_type_1_0/Slice_3_grad/concat" + input: "gradients_1/Reshape_28_grad/Reshape" + input: "gradients_1/Slice_6_grad/concat" attr { key: "T" value { @@ -54207,27 +43073,16 @@ node { } } node { - name: "gradients_1/filter_type_0_0/Reshape_grad/Shape" - op: "Shape" - input: "filter_type_0_0/Slice" - attr { - key: "T" - value { - type: DT_DOUBLE - } - } + name: "gradients_1/AddN_4" + op: "AddN" + input: "gradients_1/Slice_5_grad/Pad" + input: "gradients_1/Slice_6_grad/Pad" attr { - key: "out_type" + key: "N" value { - type: DT_INT32 + i: 2 } } -} -node { - name: "gradients_1/filter_type_0_0/Reshape_grad/Reshape" - op: "Reshape" - input: "gradients_1/filter_type_0_0/Slice_1_grad/Pad" - input: "gradients_1/filter_type_0_0/Reshape_grad/Shape" attr { key: "T" value { @@ -54235,16 +43090,18 @@ node { } } attr { - key: "Tshape" + key: "_class" value { - type: DT_INT32 + list { + s: "loc:@gradients_1/Slice_5_grad/Pad" + } } } } node { - name: "gradients_1/filter_type_0_0/Reshape_7_grad/Shape" + name: "gradients_1/Reshape_23_grad/Shape" op: "Shape" - input: "filter_type_0_0/Slice_2" + input: "o_descriptor" attr { key: "T" value { @@ -54259,10 +43116,10 @@ node { } } node { - name: "gradients_1/filter_type_0_0/Reshape_7_grad/Reshape" + name: "gradients_1/Reshape_23_grad/Reshape" op: "Reshape" - input: "gradients_1/filter_type_0_0/Slice_3_grad/Pad" - input: "gradients_1/filter_type_0_0/Reshape_7_grad/Shape" + input: "gradients_1/AddN_4" + input: "gradients_1/Reshape_23_grad/Shape" attr { key: "T" value { @@ -54277,9 +43134,9 @@ node { } } node { - name: "gradients_1/filter_type_1_0/Reshape_grad/Shape" + name: "gradients_1/Reshape_22_grad/Shape" op: "Shape" - input: "filter_type_1_0/Slice" + input: "concat_4/concat" attr { key: "T" value { @@ -54294,10 +43151,10 @@ node { } } node { - name: "gradients_1/filter_type_1_0/Reshape_grad/Reshape" + name: "gradients_1/Reshape_22_grad/Reshape" op: "Reshape" - input: "gradients_1/filter_type_1_0/Slice_1_grad/Pad" - input: "gradients_1/filter_type_1_0/Reshape_grad/Shape" + input: "gradients_1/Reshape_23_grad/Reshape" + input: "gradients_1/Reshape_22_grad/Shape" attr { key: "T" value { @@ -54312,9 +43169,9 @@ node { } } node { - name: "gradients_1/filter_type_1_0/Reshape_7_grad/Shape" + name: "gradients_1/Reshape_21_grad/Shape" op: "Shape" - input: "filter_type_1_0/Slice_2" + input: "concat_2" attr { key: "T" value { @@ -54329,10 +43186,10 @@ node { } } node { - name: "gradients_1/filter_type_1_0/Reshape_7_grad/Reshape" + name: "gradients_1/Reshape_21_grad/Reshape" op: "Reshape" - input: "gradients_1/filter_type_1_0/Slice_3_grad/Pad" - input: "gradients_1/filter_type_1_0/Reshape_7_grad/Shape" + input: "gradients_1/Reshape_22_grad/Reshape" + input: "gradients_1/Reshape_21_grad/Shape" attr { key: "T" value { @@ -54347,33 +43204,7 @@ node { } } node { - name: "gradients_1/AddN_15" - op: "AddN" - input: "gradients_1/filter_type_0_0/Reshape_6_grad/Reshape" - input: "gradients_1/filter_type_0_0/Reshape_grad/Reshape" - attr { - key: "N" - value { - i: 2 - } - } - attr { - key: "T" - value { - type: DT_DOUBLE - } - } - attr { - key: "_class" - value { - list { - s: "loc:@gradients_1/filter_type_0_0/Reshape_6_grad/Reshape" - } - } - } -} -node { - name: "gradients_1/filter_type_0_0/Slice_grad/Rank" + name: "gradients_1/concat_2_grad/Rank" op: "Const" attr { key: "dtype" @@ -54388,95 +43219,102 @@ node { dtype: DT_INT32 tensor_shape { } - int_val: 2 + int_val: 3 } } } } node { - name: "gradients_1/filter_type_0_0/Slice_grad/Shape" - op: "Shape" - input: "filter_type_0_0/Slice" + name: "gradients_1/concat_2_grad/mod" + op: "FloorMod" + input: "concat_2/axis" + input: "gradients_1/concat_2_grad/Rank" attr { key: "T" value { - type: DT_DOUBLE + type: DT_INT32 } } +} +node { + name: "gradients_1/concat_2_grad/ShapeN" + op: "ShapeN" + input: "Reshape_15" + input: "Reshape_19" attr { - key: "out_type" + key: "N" value { - type: DT_INT32 + i: 2 } } -} -node { - name: "gradients_1/filter_type_0_0/Slice_grad/stack/1" - op: "Const" attr { - key: "dtype" + key: "T" value { - type: DT_INT32 + type: DT_DOUBLE } } attr { - key: "value" + key: "out_type" value { - tensor { - dtype: DT_INT32 - tensor_shape { - } - int_val: 1 - } + type: DT_INT32 } } } node { - name: "gradients_1/filter_type_0_0/Slice_grad/stack" - op: "Pack" - input: "gradients_1/filter_type_0_0/Slice_grad/Rank" - input: "gradients_1/filter_type_0_0/Slice_grad/stack/1" + name: "gradients_1/concat_2_grad/ConcatOffset" + op: "ConcatOffset" + input: "gradients_1/concat_2_grad/mod" + input: "gradients_1/concat_2_grad/ShapeN" + input: "gradients_1/concat_2_grad/ShapeN:1" attr { key: "N" value { i: 2 } } +} +node { + name: "gradients_1/concat_2_grad/Slice" + op: "Slice" + input: "gradients_1/Reshape_21_grad/Reshape" + input: "gradients_1/concat_2_grad/ConcatOffset" + input: "gradients_1/concat_2_grad/ShapeN" attr { - key: "T" + key: "Index" value { type: DT_INT32 } } attr { - key: "axis" + key: "T" value { - i: 0 + type: DT_DOUBLE } } } node { - name: "gradients_1/filter_type_0_0/Slice_grad/Reshape" - op: "Reshape" - input: "filter_type_0_0/Slice/begin" - input: "gradients_1/filter_type_0_0/Slice_grad/stack" + name: "gradients_1/concat_2_grad/Slice_1" + op: "Slice" + input: "gradients_1/Reshape_21_grad/Reshape" + input: "gradients_1/concat_2_grad/ConcatOffset:1" + input: "gradients_1/concat_2_grad/ShapeN:1" attr { - key: "T" + key: "Index" value { type: DT_INT32 } } attr { - key: "Tshape" + key: "T" value { - type: DT_INT32 + type: DT_DOUBLE } } } node { - name: "gradients_1/filter_type_0_0/Slice_grad/Shape_1" + name: "gradients_1/Reshape_15_grad/Shape" op: "Shape" - input: "Reshape_13" + input: "filter_type_0_0/Reshape_14" attr { key: "T" value { @@ -54491,38 +43329,14 @@ node { } } node { - name: "gradients_1/filter_type_0_0/Slice_grad/sub" - op: "Sub" - input: "gradients_1/filter_type_0_0/Slice_grad/Shape_1" - input: "gradients_1/filter_type_0_0/Slice_grad/Shape" - attr { - key: "T" - value { - type: DT_INT32 - } - } -} -node { - name: "gradients_1/filter_type_0_0/Slice_grad/sub_1" - op: "Sub" - input: 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+ input: "filter_type_1_0/MatMul_8" attr { key: "T" value { @@ -54661,72 +43434,75 @@ node { } } node { - name: "gradients_1/filter_type_0_0/Slice_2_grad/stack/1" - op: "Const" + name: "gradients_1/filter_type_1_0/Reshape_14_grad/Reshape" + op: "Reshape" + input: "gradients_1/Reshape_19_grad/Reshape" + input: "gradients_1/filter_type_1_0/Reshape_14_grad/Shape" attr { - key: "dtype" + key: "T" value { - type: DT_INT32 + type: DT_DOUBLE } } attr { - key: "value" + key: "Tshape" value { - tensor { - dtype: DT_INT32 - tensor_shape { - } - int_val: 1 - } + type: DT_INT32 } } } node { - name: "gradients_1/filter_type_0_0/Slice_2_grad/stack" - op: "Pack" - input: "gradients_1/filter_type_0_0/Slice_2_grad/Rank" - input: "gradients_1/filter_type_0_0/Slice_2_grad/stack/1" + name: "gradients_1/filter_type_0_0/MatMul_8_grad/MatMul" + op: "BatchMatMulV2" + input: "filter_type_0_0/Slice_4" + input: "gradients_1/filter_type_0_0/Reshape_14_grad/Reshape" attr { - key: "N" + key: "T" value { - i: 2 + type: DT_DOUBLE } } attr { - key: "T" + key: "adj_x" value { - type: DT_INT32 + b: false } } attr { - key: "axis" + key: "adj_y" value { - i: 0 + b: true } } } node { - name: "gradients_1/filter_type_0_0/Slice_2_grad/Reshape" - op: "Reshape" - input: "filter_type_0_0/Slice_2/begin" - input: "gradients_1/filter_type_0_0/Slice_2_grad/stack" + name: "gradients_1/filter_type_0_0/MatMul_8_grad/MatMul_1" + op: "BatchMatMulV2" + input: "filter_type_0_0/truediv" + input: "gradients_1/filter_type_0_0/Reshape_14_grad/Reshape" attr { key: "T" value { - type: DT_INT32 + type: DT_DOUBLE } } attr { - key: "Tshape" + key: "adj_x" value { - type: DT_INT32 + b: false + } + } + attr { + key: "adj_y" + value { + b: false } } } node { - name: "gradients_1/filter_type_0_0/Slice_2_grad/Shape_1" + name: "gradients_1/filter_type_0_0/MatMul_8_grad/Shape" op: "Shape" - input: "Reshape_13" + input: "filter_type_0_0/truediv" attr { key: "T" value { @@ -54741,49 +43517,48 @@ node { } } node { - name: 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+ tensor { + dtype: DT_INT32 + tensor_shape { + dim { + size: 1 + } + } + int_val: 0 + } } } } node { - name: "gradients_1/filter_type_0_0/Slice_2_grad/concat/axis" + name: "gradients_1/filter_type_0_0/MatMul_8_grad/strided_slice/stack_1" op: "Const" attr { key: "dtype" @@ -54797,83 +43572,91 @@ node { tensor { dtype: DT_INT32 tensor_shape { + dim { + size: 1 + } } - int_val: 1 + int_val: -2 } } } } node { - name: "gradients_1/filter_type_0_0/Slice_2_grad/concat" - op: "ConcatV2" - input: "gradients_1/filter_type_0_0/Slice_2_grad/Reshape" - input: "gradients_1/filter_type_0_0/Slice_2_grad/Reshape_1" - input: "gradients_1/filter_type_0_0/Slice_2_grad/concat/axis" + name: "gradients_1/filter_type_0_0/MatMul_8_grad/strided_slice/stack_2" + op: "Const" attr { - key: "N" + key: "dtype" value { - i: 2 + type: DT_INT32 } } attr { - key: "T" + key: "value" value { - type: DT_INT32 + tensor { + dtype: DT_INT32 + tensor_shape { + dim { + size: 1 + } + } + int_val: 1 + } } } +} +node { + name: 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key: "end_mask" value { - type: DT_DOUBLE + i: 0 } } attr { - key: "_class" + key: "new_axis_mask" value { - list { - s: "loc:@gradients_1/filter_type_1_0/Reshape_6_grad/Reshape" - } + i: 0 + } + } + attr { + key: "shrink_axis_mask" + value { + i: 0 } } } node { - name: "gradients_1/filter_type_1_0/Slice_grad/Rank" + name: "gradients_1/filter_type_0_0/MatMul_8_grad/strided_slice_1/stack" op: "Const" attr { key: "dtype" @@ -54887,31 +43670,41 @@ node { tensor { dtype: DT_INT32 tensor_shape { + dim { + size: 1 + } } - int_val: 2 + int_val: 0 } } } } node { - name: "gradients_1/filter_type_1_0/Slice_grad/Shape" - op: "Shape" - input: "filter_type_1_0/Slice" + name: "gradients_1/filter_type_0_0/MatMul_8_grad/strided_slice_1/stack_1" + op: "Const" attr { - key: "T" + key: "dtype" value { - type: DT_DOUBLE + type: DT_INT32 } } attr { - key: "out_type" + key: "value" value { - type: DT_INT32 + tensor { + dtype: DT_INT32 + tensor_shape { + dim { + size: 1 + } + } + int_val: -2 + } } } } node { 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"gradients_1/filter_type_1_0/Slice_2_grad/Rank" + name: "gradients_1/filter_type_1_0/MatMul_8_grad/strided_slice/stack" op: "Const" attr { key: "dtype" @@ -55137,31 +43970,41 @@ node { tensor { dtype: DT_INT32 tensor_shape { + dim { + size: 1 + } } - int_val: 2 + int_val: 0 } } } } node { - name: "gradients_1/filter_type_1_0/Slice_2_grad/Shape" - op: "Shape" - input: "filter_type_1_0/Slice_2" + name: "gradients_1/filter_type_1_0/MatMul_8_grad/strided_slice/stack_1" + op: "Const" attr { - key: "T" + key: "dtype" value { - type: DT_DOUBLE + type: DT_INT32 } } attr { - key: "out_type" + key: "value" value { - type: DT_INT32 + tensor { + dtype: DT_INT32 + tensor_shape { + dim { + size: 1 + } + } + int_val: -2 + } } } } node { - name: "gradients_1/filter_type_1_0/Slice_2_grad/stack/1" + name: "gradients_1/filter_type_1_0/MatMul_8_grad/strided_slice/stack_2" op: "Const" attr { key: "dtype" @@ -55175,6 +44018,9 @@ node { tensor { dtype: DT_INT32 tensor_shape { + dim { + size: 1 + } } int_val: 1 } @@ -55182,14 +44028,16 @@ node { } } node { - name: "gradients_1/filter_type_1_0/Slice_2_grad/stack" - op: "Pack" - input: "gradients_1/filter_type_1_0/Slice_2_grad/Rank" - input: "gradients_1/filter_type_1_0/Slice_2_grad/stack/1" + name: "gradients_1/filter_type_1_0/MatMul_8_grad/strided_slice" + op: "StridedSlice" + input: "gradients_1/filter_type_1_0/MatMul_8_grad/Shape" + input: "gradients_1/filter_type_1_0/MatMul_8_grad/strided_slice/stack" + input: "gradients_1/filter_type_1_0/MatMul_8_grad/strided_slice/stack_1" + input: "gradients_1/filter_type_1_0/MatMul_8_grad/strided_slice/stack_2" attr { - key: "N" + key: "Index" value { - i: 2 + type: DT_INT32 } } attr { @@ -55199,91 +44047,86 @@ node { } } attr { - key: "axis" + key: "begin_mask" value { - i: 0 + i: 1 } } -} -node { - name: "gradients_1/filter_type_1_0/Slice_2_grad/Reshape" - op: "Reshape" - input: "filter_type_1_0/Slice_2/begin" - input: "gradients_1/filter_type_1_0/Slice_2_grad/stack" attr { - key: "T" + key: "ellipsis_mask" value { - type: DT_INT32 + i: 0 } } attr { - key: "Tshape" + key: "end_mask" value { - type: DT_INT32 + i: 0 } } -} -node { - name: "gradients_1/filter_type_1_0/Slice_2_grad/Shape_1" - op: "Shape" - input: "Reshape_17" attr { - key: "T" + key: "new_axis_mask" value { - type: DT_DOUBLE + i: 0 } } attr { - key: "out_type" + key: "shrink_axis_mask" value { - type: DT_INT32 + i: 0 } } } node { - name: "gradients_1/filter_type_1_0/Slice_2_grad/sub" - op: "Sub" - input: "gradients_1/filter_type_1_0/Slice_2_grad/Shape_1" - input: "gradients_1/filter_type_1_0/Slice_2_grad/Shape" + name: "gradients_1/filter_type_1_0/MatMul_8_grad/strided_slice_1/stack" + op: "Const" attr { - key: "T" + key: "dtype" value { type: DT_INT32 } } -} -node { - name: "gradients_1/filter_type_1_0/Slice_2_grad/sub_1" - op: "Sub" - input: "gradients_1/filter_type_1_0/Slice_2_grad/sub" - input: "filter_type_1_0/Slice_2/begin" attr { - key: "T" + key: "value" value { - type: DT_INT32 + tensor { + dtype: DT_INT32 + tensor_shape { + dim { + size: 1 + } + } + int_val: 0 + } } } } node { - name: "gradients_1/filter_type_1_0/Slice_2_grad/Reshape_1" - op: "Reshape" - input: "gradients_1/filter_type_1_0/Slice_2_grad/sub_1" - input: "gradients_1/filter_type_1_0/Slice_2_grad/stack" + name: "gradients_1/filter_type_1_0/MatMul_8_grad/strided_slice_1/stack_1" + op: "Const" attr { - key: "T" + key: "dtype" value { type: DT_INT32 } } attr { - key: "Tshape" + key: "value" value { - type: DT_INT32 + tensor { + dtype: DT_INT32 + tensor_shape { + dim { + size: 1 + } + } + int_val: -2 + } } } } node { - name: "gradients_1/filter_type_1_0/Slice_2_grad/concat/axis" + name: "gradients_1/filter_type_1_0/MatMul_8_grad/strided_slice_1/stack_2" op: "Const" attr { key: "dtype" @@ -55297,6 +44140,9 @@ node { tensor { dtype: DT_INT32 tensor_shape { + dim { + size: 1 + } } int_val: 1 } @@ -55304,96 +44150,72 @@ node { } } node { - name: "gradients_1/filter_type_1_0/Slice_2_grad/concat" - op: "ConcatV2" - input: "gradients_1/filter_type_1_0/Slice_2_grad/Reshape" - input: "gradients_1/filter_type_1_0/Slice_2_grad/Reshape_1" - input: "gradients_1/filter_type_1_0/Slice_2_grad/concat/axis" - attr { - key: "N" - value { - i: 2 - } - } + name: "gradients_1/filter_type_1_0/MatMul_8_grad/strided_slice_1" + op: "StridedSlice" + input: "gradients_1/filter_type_1_0/MatMul_8_grad/Shape_1" + input: "gradients_1/filter_type_1_0/MatMul_8_grad/strided_slice_1/stack" + input: "gradients_1/filter_type_1_0/MatMul_8_grad/strided_slice_1/stack_1" + input: "gradients_1/filter_type_1_0/MatMul_8_grad/strided_slice_1/stack_2" attr { - key: "T" + key: "Index" value { type: DT_INT32 } } attr { - key: "Tidx" + key: "T" value { type: DT_INT32 } } -} -node { - name: "gradients_1/filter_type_1_0/Slice_2_grad/Pad" - op: "Pad" - input: "gradients_1/AddN_18" - input: "gradients_1/filter_type_1_0/Slice_2_grad/concat" attr { - key: "T" + key: "begin_mask" value { - type: DT_DOUBLE + i: 1 } } attr { - key: "Tpaddings" + key: "ellipsis_mask" value { - type: DT_INT32 + i: 0 } } -} -node { - name: "gradients_1/AddN_19" - op: "AddN" - input: "gradients_1/filter_type_0_0/Slice_grad/Pad" - input: "gradients_1/filter_type_0_0/Slice_2_grad/Pad" attr { - key: "N" + key: "end_mask" value { - i: 2 + i: 0 } } attr { - key: "T" + key: "new_axis_mask" value { - type: DT_DOUBLE + i: 0 } } attr { - key: "_class" + key: "shrink_axis_mask" value { - list { - s: "loc:@gradients_1/filter_type_0_0/Slice_grad/Pad" - } + i: 0 } } } node { - name: "gradients_1/Reshape_13_grad/Shape" - op: "Shape" - input: "Slice_2" + name: "gradients_1/filter_type_1_0/MatMul_8_grad/BroadcastGradientArgs" + op: "BroadcastGradientArgs" + input: "gradients_1/filter_type_1_0/MatMul_8_grad/strided_slice" + input: "gradients_1/filter_type_1_0/MatMul_8_grad/strided_slice_1" attr { key: "T" - value { - type: DT_DOUBLE - } - } - attr { - key: "out_type" value { type: DT_INT32 } } } node { - name: "gradients_1/Reshape_13_grad/Reshape" - op: "Reshape" - input: "gradients_1/AddN_19" - input: "gradients_1/Reshape_13_grad/Shape" + name: "gradients_1/filter_type_1_0/MatMul_8_grad/Sum" + op: "Sum" + input: "gradients_1/filter_type_1_0/MatMul_8_grad/MatMul" + input: "gradients_1/filter_type_1_0/MatMul_8_grad/BroadcastGradientArgs" attr { key: "T" value { @@ -55401,23 +44223,23 @@ node { } } attr { - key: "Tshape" + key: "Tidx" value { type: DT_INT32 } } -} -node { - name: "gradients_1/AddN_20" - op: "AddN" - input: "gradients_1/filter_type_1_0/Slice_grad/Pad" - input: "gradients_1/filter_type_1_0/Slice_2_grad/Pad" attr { - key: "N" + key: "keep_dims" value { - i: 2 + b: false } } +} +node { + name: "gradients_1/filter_type_1_0/MatMul_8_grad/Reshape" + op: "Reshape" + input: "gradients_1/filter_type_1_0/MatMul_8_grad/Sum" + input: "gradients_1/filter_type_1_0/MatMul_8_grad/Shape" attr { key: "T" value { @@ -55425,18 +44247,17 @@ node { } } attr { - key: "_class" + key: "Tshape" value { - list { - s: "loc:@gradients_1/filter_type_1_0/Slice_grad/Pad" - } + type: DT_INT32 } } } node { - name: "gradients_1/Reshape_17_grad/Shape" - op: "Shape" - input: "Slice_3" + name: "gradients_1/filter_type_1_0/MatMul_8_grad/Sum_1" + op: "Sum" + input: "gradients_1/filter_type_1_0/MatMul_8_grad/MatMul_1" + input: "gradients_1/filter_type_1_0/MatMul_8_grad/BroadcastGradientArgs:1" attr { key: "T" value { @@ -55444,17 +44265,23 @@ node { } } attr { - key: "out_type" + key: "Tidx" value { type: DT_INT32 } } + attr { + key: "keep_dims" + value { + b: false + } + } } node { - name: "gradients_1/Reshape_17_grad/Reshape" + name: "gradients_1/filter_type_1_0/MatMul_8_grad/Reshape_1" op: "Reshape" - input: "gradients_1/AddN_20" - input: "gradients_1/Reshape_17_grad/Shape" + input: "gradients_1/filter_type_1_0/MatMul_8_grad/Sum_1" + input: "gradients_1/filter_type_1_0/MatMul_8_grad/Shape_1" attr { key: "T" value { @@ -55469,7 +44296,7 @@ node { } } node { - name: "gradients_1/Slice_2_grad/Rank" + name: "gradients_1/filter_type_0_0/Slice_4_grad/Rank" op: "Const" attr { key: "dtype" @@ -55484,15 +44311,15 @@ node { dtype: DT_INT32 tensor_shape { } - int_val: 2 + int_val: 3 } } } } node { - name: "gradients_1/Slice_2_grad/Shape" + name: "gradients_1/filter_type_0_0/Slice_4_grad/Shape" op: "Shape" - input: "Slice_2" + input: "filter_type_0_0/Slice_4" attr { key: "T" value { @@ -55507,7 +44334,7 @@ node { } } node { - name: "gradients_1/Slice_2_grad/stack/1" + name: "gradients_1/filter_type_0_0/Slice_4_grad/stack/1" op: "Const" attr { key: "dtype" @@ -55528,10 +44355,10 @@ node { } } node { - name: "gradients_1/Slice_2_grad/stack" + name: "gradients_1/filter_type_0_0/Slice_4_grad/stack" op: "Pack" - input: "gradients_1/Slice_2_grad/Rank" - input: "gradients_1/Slice_2_grad/stack/1" + input: "gradients_1/filter_type_0_0/Slice_4_grad/Rank" + input: "gradients_1/filter_type_0_0/Slice_4_grad/stack/1" attr { key: "N" value { @@ -55552,10 +44379,10 @@ node { } } node { - name: "gradients_1/Slice_2_grad/Reshape" + name: "gradients_1/filter_type_0_0/Slice_4_grad/Reshape" op: "Reshape" - input: "Slice_2/begin" - input: "gradients_1/Slice_2_grad/stack" + input: "filter_type_0_0/Slice_4/begin" + input: "gradients_1/filter_type_0_0/Slice_4_grad/stack" attr { key: "T" value { @@ -55570,9 +44397,9 @@ node { } } node { - name: "gradients_1/Slice_2_grad/Shape_1" + name: "gradients_1/filter_type_0_0/Slice_4_grad/Shape_1" op: "Shape" - input: "Reshape_12" + input: "filter_type_0_0/truediv" attr { key: "T" value { @@ -55587,10 +44414,10 @@ node { } } node { - name: "gradients_1/Slice_2_grad/sub" + name: "gradients_1/filter_type_0_0/Slice_4_grad/sub" op: "Sub" - input: "gradients_1/Slice_2_grad/Shape_1" - input: "gradients_1/Slice_2_grad/Shape" + input: "gradients_1/filter_type_0_0/Slice_4_grad/Shape_1" + input: "gradients_1/filter_type_0_0/Slice_4_grad/Shape" attr { key: "T" value { @@ -55599,10 +44426,10 @@ node { } } node { - name: "gradients_1/Slice_2_grad/sub_1" + name: "gradients_1/filter_type_0_0/Slice_4_grad/sub_1" op: "Sub" - input: "gradients_1/Slice_2_grad/sub" - input: "Slice_2/begin" + input: "gradients_1/filter_type_0_0/Slice_4_grad/sub" + input: "filter_type_0_0/Slice_4/begin" attr { key: "T" value { @@ -55611,10 +44438,10 @@ node { } } node { - name: "gradients_1/Slice_2_grad/Reshape_1" + name: "gradients_1/filter_type_0_0/Slice_4_grad/Reshape_1" op: "Reshape" - input: "gradients_1/Slice_2_grad/sub_1" - input: "gradients_1/Slice_2_grad/stack" + input: "gradients_1/filter_type_0_0/Slice_4_grad/sub_1" + input: "gradients_1/filter_type_0_0/Slice_4_grad/stack" attr { key: "T" value { @@ -55629,7 +44456,7 @@ node { } } node { - name: "gradients_1/Slice_2_grad/concat/axis" + name: "gradients_1/filter_type_0_0/Slice_4_grad/concat/axis" op: "Const" attr { key: "dtype" @@ -55650,11 +44477,11 @@ node { } } node { - name: "gradients_1/Slice_2_grad/concat" + name: "gradients_1/filter_type_0_0/Slice_4_grad/concat" op: "ConcatV2" - input: "gradients_1/Slice_2_grad/Reshape" - input: "gradients_1/Slice_2_grad/Reshape_1" - input: "gradients_1/Slice_2_grad/concat/axis" + input: "gradients_1/filter_type_0_0/Slice_4_grad/Reshape" + input: "gradients_1/filter_type_0_0/Slice_4_grad/Reshape_1" + input: "gradients_1/filter_type_0_0/Slice_4_grad/concat/axis" attr { key: "N" value { @@ -55675,10 +44502,10 @@ node { } } node { - name: "gradients_1/Slice_2_grad/Pad" + name: "gradients_1/filter_type_0_0/Slice_4_grad/Pad" op: "Pad" - input: "gradients_1/Reshape_13_grad/Reshape" - input: "gradients_1/Slice_2_grad/concat" + input: "gradients_1/filter_type_0_0/MatMul_8_grad/Reshape_1" + input: "gradients_1/filter_type_0_0/Slice_4_grad/concat" attr { key: "T" value { @@ -55693,7 +44520,7 @@ node { } } node { - name: "gradients_1/Slice_3_grad/Rank" + name: "gradients_1/filter_type_1_0/Slice_4_grad/Rank" op: "Const" attr { key: "dtype" @@ -55708,15 +44535,15 @@ node { dtype: DT_INT32 tensor_shape { } - int_val: 2 + int_val: 3 } } } } node { - name: "gradients_1/Slice_3_grad/Shape" + name: "gradients_1/filter_type_1_0/Slice_4_grad/Shape" op: "Shape" - input: "Slice_3" + input: "filter_type_1_0/Slice_4" attr { key: "T" value { @@ -55731,7 +44558,7 @@ node { } } node { - name: "gradients_1/Slice_3_grad/stack/1" + name: "gradients_1/filter_type_1_0/Slice_4_grad/stack/1" op: "Const" attr { key: "dtype" @@ -55752,10 +44579,10 @@ node { } } node { - name: "gradients_1/Slice_3_grad/stack" + name: "gradients_1/filter_type_1_0/Slice_4_grad/stack" op: "Pack" - input: "gradients_1/Slice_3_grad/Rank" - input: "gradients_1/Slice_3_grad/stack/1" + input: "gradients_1/filter_type_1_0/Slice_4_grad/Rank" + input: "gradients_1/filter_type_1_0/Slice_4_grad/stack/1" attr { key: "N" value { @@ -55776,10 +44603,10 @@ node { } } node { - name: "gradients_1/Slice_3_grad/Reshape" + name: "gradients_1/filter_type_1_0/Slice_4_grad/Reshape" op: "Reshape" - input: "Slice_3/begin" - input: "gradients_1/Slice_3_grad/stack" + input: "filter_type_1_0/Slice_4/begin" + input: "gradients_1/filter_type_1_0/Slice_4_grad/stack" attr { key: "T" value { @@ -55794,9 +44621,9 @@ node { } } node { - name: "gradients_1/Slice_3_grad/Shape_1" + name: "gradients_1/filter_type_1_0/Slice_4_grad/Shape_1" op: "Shape" - input: "Reshape_12" + input: "filter_type_1_0/truediv" attr { key: "T" value { @@ -55811,10 +44638,10 @@ node { } } node { - name: "gradients_1/Slice_3_grad/sub" + name: "gradients_1/filter_type_1_0/Slice_4_grad/sub" op: "Sub" - input: "gradients_1/Slice_3_grad/Shape_1" - input: "gradients_1/Slice_3_grad/Shape" + input: "gradients_1/filter_type_1_0/Slice_4_grad/Shape_1" + input: "gradients_1/filter_type_1_0/Slice_4_grad/Shape" attr { key: "T" value { @@ -55823,10 +44650,10 @@ node { } } node { - name: "gradients_1/Slice_3_grad/sub_1" + name: "gradients_1/filter_type_1_0/Slice_4_grad/sub_1" op: "Sub" - input: "gradients_1/Slice_3_grad/sub" - input: "Slice_3/begin" + input: "gradients_1/filter_type_1_0/Slice_4_grad/sub" + input: "filter_type_1_0/Slice_4/begin" attr { key: "T" value { @@ -55835,10 +44662,10 @@ node { } } node { - name: "gradients_1/Slice_3_grad/Reshape_1" + name: "gradients_1/filter_type_1_0/Slice_4_grad/Reshape_1" op: "Reshape" - input: "gradients_1/Slice_3_grad/sub_1" - input: "gradients_1/Slice_3_grad/stack" + input: "gradients_1/filter_type_1_0/Slice_4_grad/sub_1" + input: "gradients_1/filter_type_1_0/Slice_4_grad/stack" attr { key: "T" value { @@ -55853,7 +44680,7 @@ node { } } node { - name: "gradients_1/Slice_3_grad/concat/axis" + name: "gradients_1/filter_type_1_0/Slice_4_grad/concat/axis" op: "Const" attr { key: "dtype" @@ -55874,11 +44701,11 @@ node { } } node { - name: "gradients_1/Slice_3_grad/concat" + name: "gradients_1/filter_type_1_0/Slice_4_grad/concat" op: "ConcatV2" - input: "gradients_1/Slice_3_grad/Reshape" - input: "gradients_1/Slice_3_grad/Reshape_1" - input: "gradients_1/Slice_3_grad/concat/axis" + input: "gradients_1/filter_type_1_0/Slice_4_grad/Reshape" + input: "gradients_1/filter_type_1_0/Slice_4_grad/Reshape_1" + input: "gradients_1/filter_type_1_0/Slice_4_grad/concat/axis" attr { key: "N" value { @@ -55899,10 +44726,10 @@ node { } } node { - name: "gradients_1/Slice_3_grad/Pad" + name: "gradients_1/filter_type_1_0/Slice_4_grad/Pad" op: "Pad" - input: "gradients_1/Reshape_17_grad/Reshape" - input: "gradients_1/Slice_3_grad/concat" + input: "gradients_1/filter_type_1_0/MatMul_8_grad/Reshape_1" + input: "gradients_1/filter_type_1_0/Slice_4_grad/concat" attr { key: "T" value { @@ -55917,10 +44744,10 @@ node { } } node { - name: "gradients_1/AddN_21" + name: "gradients_1/AddN_5" op: "AddN" - input: "gradients_1/Slice_2_grad/Pad" - input: "gradients_1/Slice_3_grad/Pad" + input: "gradients_1/filter_type_0_0/MatMul_8_grad/Reshape" + input: "gradients_1/filter_type_0_0/Slice_4_grad/Pad" attr { key: "N" value { @@ -55937,15 +44764,15 @@ node { key: "_class" value { list { - s: "loc:@gradients_1/Slice_2_grad/Pad" + s: "loc:@gradients_1/filter_type_0_0/MatMul_8_grad/Reshape" } } } } node { - name: "gradients_1/Reshape_12_grad/Shape" + name: "gradients_1/filter_type_0_0/truediv_grad/Shape" op: "Shape" - input: "o_rmat_0" + input: "filter_type_0_0/AddN" attr { key: "T" value { @@ -55960,10 +44787,80 @@ node { } } node { - name: "gradients_1/Reshape_12_grad/Reshape" + name: "gradients_1/filter_type_0_0/truediv_grad/Shape_1" + op: "Const" + attr { + key: "dtype" + value { + type: DT_INT32 + } + } + attr { + key: "value" + value { + tensor { + dtype: DT_INT32 + tensor_shape { + dim { + } + } + } + } + } +} +node { + name: "gradients_1/filter_type_0_0/truediv_grad/BroadcastGradientArgs" + op: "BroadcastGradientArgs" + input: "gradients_1/filter_type_0_0/truediv_grad/Shape" + input: "gradients_1/filter_type_0_0/truediv_grad/Shape_1" + attr { + key: "T" + value { + type: DT_INT32 + } + } +} +node { + name: "gradients_1/filter_type_0_0/truediv_grad/RealDiv" + op: "RealDiv" + input: "gradients_1/AddN_5" + input: "filter_type_0_0/truediv/y" + attr { + key: "T" + value { + type: DT_DOUBLE + } + } +} +node { + name: "gradients_1/filter_type_0_0/truediv_grad/Sum" + op: "Sum" + input: "gradients_1/filter_type_0_0/truediv_grad/RealDiv" + input: "gradients_1/filter_type_0_0/truediv_grad/BroadcastGradientArgs" + attr { + key: "T" + value { + type: DT_DOUBLE + } + } + attr { + key: "Tidx" + value { + type: DT_INT32 + } + } + attr { + key: "keep_dims" + value { + b: false + } + } +} +node { + name: "gradients_1/filter_type_0_0/truediv_grad/Reshape" op: "Reshape" - input: "gradients_1/AddN_21" - input: "gradients_1/Reshape_12_grad/Shape" + input: "gradients_1/filter_type_0_0/truediv_grad/Sum" + input: "gradients_1/filter_type_0_0/truediv_grad/Shape" attr { key: "T" value { @@ -55978,31 +44875,50 @@ node { } } node { - name: "strided_slice_54/stack" - op: "Const" + name: "gradients_1/AddN_6" + op: "AddN" + input: "gradients_1/filter_type_1_0/MatMul_8_grad/Reshape" + input: 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value { type: DT_INT32 } } + attr { + key: "begin_mask" + value { + i: 1 + } + } + attr { + key: "ellipsis_mask" + value { + i: 0 + } + } + attr { + key: "end_mask" + value { + i: 0 + } + } + attr { + key: "new_axis_mask" + value { + i: 0 + } + } + attr { + key: "shrink_axis_mask" + value { + i: 0 + } + } } node { - name: "gradients_2/add_6_grad/BroadcastGradientArgs" + name: "gradients_1/filter_type_1_0/MatMul_3_grad/BroadcastGradientArgs" op: "BroadcastGradientArgs" - input: "gradients_2/add_6_grad/Shape" - input: "gradients_2/add_6_grad/Shape_1" + input: "gradients_1/filter_type_1_0/MatMul_3_grad/strided_slice" + input: "gradients_1/filter_type_1_0/MatMul_3_grad/strided_slice_1" attr { key: "T" value { @@ -57240,10 +46188,10 @@ node { } } node { - name: "gradients_2/add_6_grad/Sum" + name: "gradients_1/filter_type_1_0/MatMul_3_grad/Sum" op: "Sum" - input: "gradients_2/AddN" - input: "gradients_2/add_6_grad/BroadcastGradientArgs" + input: 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"Shape" - input: "layer_1_type_1/Reshape" attr { - key: "T" + key: "begin_mask" value { - type: DT_DOUBLE + i: 1 } } attr { - key: "out_type" + key: "ellipsis_mask" value { - type: DT_INT32 + i: 0 } } -} -node { - name: "gradients_2/layer_1_type_1/mul_grad/Shape_1" - op: "Shape" - input: "layer_1_type_1/idt/read" attr { - key: "T" + key: "end_mask" value { - type: DT_DOUBLE + i: 0 } } attr { - key: "out_type" + key: "new_axis_mask" value { - type: DT_INT32 + i: 0 } } -} -node { - name: "gradients_2/layer_1_type_1/mul_grad/BroadcastGradientArgs" - op: "BroadcastGradientArgs" - input: "gradients_2/layer_1_type_1/mul_grad/Shape" - input: "gradients_2/layer_1_type_1/mul_grad/Shape_1" attr { - key: "T" + key: "shrink_axis_mask" value { - type: DT_INT32 + i: 0 } } } node { - name: "gradients_2/layer_1_type_1/mul_grad/Mul" - op: "Mul" - input: "gradients_2/add_9_grad/Reshape_1" - input: "layer_1_type_1/idt/read" + name: "gradients_1/filter_type_1_0/MatMul_7_grad/BroadcastGradientArgs" + op: "BroadcastGradientArgs" + input: "gradients_1/filter_type_1_0/MatMul_7_grad/strided_slice" + input: "gradients_1/filter_type_1_0/MatMul_7_grad/strided_slice_1" attr { key: "T" value { - type: DT_DOUBLE + type: DT_INT32 } } } node { - name: "gradients_2/layer_1_type_1/mul_grad/Sum" + name: "gradients_1/filter_type_1_0/MatMul_7_grad/Sum" op: "Sum" - input: "gradients_2/layer_1_type_1/mul_grad/Mul" - input: "gradients_2/layer_1_type_1/mul_grad/BroadcastGradientArgs" + input: "gradients_1/filter_type_1_0/MatMul_7_grad/MatMul" + input: "gradients_1/filter_type_1_0/MatMul_7_grad/BroadcastGradientArgs" attr { key: "T" value { @@ -57662,10 +46634,10 @@ node { } } node { - name: "gradients_2/layer_1_type_1/mul_grad/Reshape" + name: "gradients_1/filter_type_1_0/MatMul_7_grad/Reshape" op: "Reshape" - input: "gradients_2/layer_1_type_1/mul_grad/Sum" - input: "gradients_2/layer_1_type_1/mul_grad/Shape" + input: "gradients_1/filter_type_1_0/MatMul_7_grad/Sum" + input: "gradients_1/filter_type_1_0/MatMul_7_grad/Shape" attr { key: "T" value { @@ -57680,9 +46652,10 @@ node { } } node { - name: "gradients_2/layer_1_type_0/Reshape_grad/Shape" - op: "Shape" - input: "layer_1_type_0/Tanh" + name: "gradients_1/filter_type_1_0/MatMul_7_grad/Sum_1" + op: "Sum" + input: "gradients_1/filter_type_1_0/MatMul_7_grad/MatMul_1" + input: "gradients_1/filter_type_1_0/MatMul_7_grad/BroadcastGradientArgs:1" attr { key: "T" value { @@ -57690,17 +46663,23 @@ node { } } attr { - key: "out_type" + key: "Tidx" value { type: DT_INT32 } } + attr { + key: "keep_dims" + value { + b: false + } + } } node { - name: "gradients_2/layer_1_type_0/Reshape_grad/Reshape" + name: "gradients_1/filter_type_1_0/MatMul_7_grad/Reshape_1" op: "Reshape" - input: "gradients_2/layer_1_type_0/mul_grad/Reshape" - input: "gradients_2/layer_1_type_0/Reshape_grad/Shape" + input: "gradients_1/filter_type_1_0/MatMul_7_grad/Sum_1" + input: "gradients_1/filter_type_1_0/MatMul_7_grad/Shape_1" attr { key: "T" value { @@ -57715,9 +46694,9 @@ node { } } node { - name: "gradients_2/layer_1_type_1/Reshape_grad/Shape" + name: "gradients_1/filter_type_0_0/Reshape_6_grad/Shape" op: "Shape" - input: "layer_1_type_1/Tanh" + input: "filter_type_0_0/Slice" attr { key: "T" value { @@ -57732,10 +46711,10 @@ node { } } node { - name: "gradients_2/layer_1_type_1/Reshape_grad/Reshape" + name: "gradients_1/filter_type_0_0/Reshape_6_grad/Reshape" op: "Reshape" - input: "gradients_2/layer_1_type_1/mul_grad/Reshape" - input: "gradients_2/layer_1_type_1/Reshape_grad/Shape" + input: "gradients_1/filter_type_0_0/MatMul_3_grad/Reshape" + input: "gradients_1/filter_type_0_0/Reshape_6_grad/Shape" attr { key: "T" value { @@ -57750,34 +46729,27 @@ node { } } node { - name: "gradients_2/layer_1_type_0/Tanh_grad/TanhGrad" - op: "TanhGrad" - input: "layer_1_type_0/Tanh" - input: "gradients_2/layer_1_type_0/Reshape_grad/Reshape" + name: "gradients_1/filter_type_0_0/Reshape_5_grad/Shape" + op: "Shape" + input: "filter_type_0_0/add_2" attr { key: "T" value { type: DT_DOUBLE } } -} -node { - name: "gradients_2/layer_1_type_1/Tanh_grad/TanhGrad" - op: "TanhGrad" - input: "layer_1_type_1/Tanh" - input: "gradients_2/layer_1_type_1/Reshape_grad/Reshape" attr { - key: "T" + key: "out_type" value { - type: DT_DOUBLE + type: DT_INT32 } } } node { - name: "gradients_2/layer_1_type_0/MatMul_grad/MatMul" - op: "MatMul" - input: "gradients_2/layer_1_type_0/Tanh_grad/TanhGrad" - input: "layer_1_type_0/matrix/read" + name: "gradients_1/filter_type_0_0/Reshape_5_grad/Reshape" + op: "Reshape" + input: "gradients_1/filter_type_0_0/MatMul_3_grad/Reshape_1" + input: "gradients_1/filter_type_0_0/Reshape_5_grad/Shape" attr { key: "T" value { @@ -57785,23 +46757,16 @@ node { } } attr { - key: "transpose_a" - value { - b: false - } - } - attr { - key: "transpose_b" + key: "Tshape" value { - b: true + type: DT_INT32 } } } node { - name: "gradients_2/layer_1_type_1/MatMul_grad/MatMul" - op: "MatMul" - input: "gradients_2/layer_1_type_1/Tanh_grad/TanhGrad" - input: "layer_1_type_1/matrix/read" + name: "gradients_1/filter_type_0_0/Reshape_13_grad/Shape" + op: "Shape" + input: "filter_type_0_0/Slice_2" attr { key: "T" value { @@ -57809,29 +46774,17 @@ node { } } attr { - key: "transpose_a" - value { - b: false - } - } - attr { - key: "transpose_b" + key: "out_type" value { - b: true + type: DT_INT32 } } } node { - name: "gradients_2/AddN_2" - op: "AddN" - input: "gradients_2/add_6_grad/Reshape" - input: "gradients_2/layer_1_type_0/MatMul_grad/MatMul" - attr { - key: "N" - value { - i: 2 - } - } + name: "gradients_1/filter_type_0_0/Reshape_13_grad/Reshape" + op: "Reshape" + input: "gradients_1/filter_type_0_0/MatMul_7_grad/Reshape" + input: "gradients_1/filter_type_0_0/Reshape_13_grad/Shape" attr { key: "T" value { @@ -57839,18 +46792,16 @@ node { } } attr { - key: "_class" + key: "Tshape" value { - list { - s: "loc:@gradients_2/add_6_grad/Reshape" - } + type: DT_INT32 } } } node { - name: "gradients_2/layer_0_type_0/Reshape_grad/Shape" + name: "gradients_1/filter_type_0_0/Reshape_12_grad/Shape" op: "Shape" - input: "layer_0_type_0/Tanh" + input: "filter_type_0_0/add_5" attr { key: "T" value { @@ -57865,10 +46816,10 @@ node { } } node { - name: "gradients_2/layer_0_type_0/Reshape_grad/Reshape" + name: "gradients_1/filter_type_0_0/Reshape_12_grad/Reshape" op: "Reshape" - input: "gradients_2/AddN_2" - input: "gradients_2/layer_0_type_0/Reshape_grad/Shape" + input: "gradients_1/filter_type_0_0/MatMul_7_grad/Reshape_1" + input: "gradients_1/filter_type_0_0/Reshape_12_grad/Shape" attr { key: "T" value { @@ -57883,16 +46834,9 @@ node { } } node { - name: "gradients_2/AddN_3" - op: "AddN" - input: "gradients_2/add_9_grad/Reshape" - input: "gradients_2/layer_1_type_1/MatMul_grad/MatMul" - attr { - key: "N" - value { - i: 2 - } - } + name: "gradients_1/filter_type_1_0/Reshape_6_grad/Shape" + op: "Shape" + input: "filter_type_1_0/Slice" attr { key: "T" value { @@ -57900,18 +46844,17 @@ node { } } attr { - key: "_class" + key: "out_type" value { - list { - s: "loc:@gradients_2/add_9_grad/Reshape" - } + type: DT_INT32 } } } node { - name: "gradients_2/layer_0_type_1/Reshape_grad/Shape" - op: "Shape" - input: "layer_0_type_1/Tanh" + name: "gradients_1/filter_type_1_0/Reshape_6_grad/Reshape" + op: "Reshape" + input: "gradients_1/filter_type_1_0/MatMul_3_grad/Reshape" + input: "gradients_1/filter_type_1_0/Reshape_6_grad/Shape" attr { key: "T" value { @@ -57919,17 +46862,16 @@ node { } } attr { - key: "out_type" + key: "Tshape" value { type: DT_INT32 } } } node { - name: "gradients_2/layer_0_type_1/Reshape_grad/Reshape" - op: "Reshape" - input: "gradients_2/AddN_3" - input: "gradients_2/layer_0_type_1/Reshape_grad/Shape" + name: "gradients_1/filter_type_1_0/Reshape_5_grad/Shape" + op: "Shape" + input: "filter_type_1_0/add_2" attr { key: "T" value { @@ -57937,41 +46879,34 @@ node { } } attr { - key: "Tshape" + key: "out_type" value { type: DT_INT32 } } } node { - name: "gradients_2/layer_0_type_0/Tanh_grad/TanhGrad" - op: "TanhGrad" - input: "layer_0_type_0/Tanh" - input: "gradients_2/layer_0_type_0/Reshape_grad/Reshape" + name: "gradients_1/filter_type_1_0/Reshape_5_grad/Reshape" + op: "Reshape" + input: "gradients_1/filter_type_1_0/MatMul_3_grad/Reshape_1" + input: "gradients_1/filter_type_1_0/Reshape_5_grad/Shape" attr { key: "T" value { type: DT_DOUBLE } } -} -node { - name: "gradients_2/layer_0_type_1/Tanh_grad/TanhGrad" - op: "TanhGrad" - input: "layer_0_type_1/Tanh" - input: "gradients_2/layer_0_type_1/Reshape_grad/Reshape" attr { - key: "T" + key: "Tshape" value { - type: DT_DOUBLE + type: DT_INT32 } } } node { - name: "gradients_2/layer_0_type_0/MatMul_grad/MatMul" - op: "MatMul" - input: "gradients_2/layer_0_type_0/Tanh_grad/TanhGrad" - input: "layer_0_type_0/matrix/read" + name: "gradients_1/filter_type_1_0/Reshape_13_grad/Shape" + op: "Shape" + input: "filter_type_1_0/Slice_2" attr { key: "T" value { @@ -57979,23 +46914,17 @@ node { } } attr { - key: "transpose_a" - value { - b: false - } - } - attr { - key: "transpose_b" + key: "out_type" value { - b: true + type: DT_INT32 } } } node { - name: "gradients_2/layer_0_type_1/MatMul_grad/MatMul" - op: "MatMul" - input: "gradients_2/layer_0_type_1/Tanh_grad/TanhGrad" - input: "layer_0_type_1/matrix/read" + name: "gradients_1/filter_type_1_0/Reshape_13_grad/Reshape" + op: "Reshape" + input: "gradients_1/filter_type_1_0/MatMul_7_grad/Reshape" + input: "gradients_1/filter_type_1_0/Reshape_13_grad/Shape" attr { key: "T" value { @@ -58003,22 +46932,16 @@ node { } } attr { - key: "transpose_a" - value { - b: false - } - } - attr { - key: "transpose_b" + key: "Tshape" value { - b: true + type: DT_INT32 } } } node { - name: "gradients_2/Reshape_34_grad/Shape" + name: "gradients_1/filter_type_1_0/Reshape_12_grad/Shape" op: "Shape" - input: "Slice_6" + input: "filter_type_1_0/add_5" attr { key: "T" value { @@ -58033,10 +46956,10 @@ node { } } node { - name: "gradients_2/Reshape_34_grad/Reshape" + name: "gradients_1/filter_type_1_0/Reshape_12_grad/Reshape" op: "Reshape" - input: "gradients_2/layer_0_type_0/MatMul_grad/MatMul" - input: "gradients_2/Reshape_34_grad/Shape" + input: "gradients_1/filter_type_1_0/MatMul_7_grad/Reshape_1" + input: "gradients_1/filter_type_1_0/Reshape_12_grad/Shape" attr { key: "T" value { @@ -58051,9 +46974,9 @@ node { } } node { - name: "gradients_2/Reshape_36_grad/Shape" + name: "gradients_1/filter_type_0_0/add_2_grad/Shape" op: "Shape" - input: "Slice_7" + input: "filter_type_0_0/concat_2" attr { key: "T" value { @@ -58068,10 +46991,9 @@ node { } } node { - name: "gradients_2/Reshape_36_grad/Reshape" - op: "Reshape" - input: "gradients_2/layer_0_type_1/MatMul_grad/MatMul" - input: "gradients_2/Reshape_36_grad/Shape" + name: "gradients_1/filter_type_0_0/add_2_grad/Shape_1" + op: "Shape" + input: "filter_type_0_0/Reshape_4" attr { key: "T" value { @@ -58079,37 +47001,29 @@ node { } } attr { - key: "Tshape" + key: "out_type" value { type: DT_INT32 } } } node { - name: "gradients_2/Slice_6_grad/Rank" - op: "Const" + name: "gradients_1/filter_type_0_0/add_2_grad/BroadcastGradientArgs" + op: "BroadcastGradientArgs" + input: "gradients_1/filter_type_0_0/add_2_grad/Shape" + input: "gradients_1/filter_type_0_0/add_2_grad/Shape_1" attr { - key: "dtype" + key: "T" value { type: DT_INT32 } } - attr { - key: "value" - value { - tensor { - dtype: DT_INT32 - tensor_shape { - } - int_val: 2 - } - } - } } node { - name: "gradients_2/Slice_6_grad/Shape" - op: "Shape" - input: "Slice_6" + name: "gradients_1/filter_type_0_0/add_2_grad/Sum" + op: "Sum" + input: "gradients_1/filter_type_0_0/Reshape_5_grad/Reshape" + input: "gradients_1/filter_type_0_0/add_2_grad/BroadcastGradientArgs" attr { key: "T" value { @@ -58117,66 +47031,69 @@ node { } } attr { - key: "out_type" + key: "Tidx" value { type: DT_INT32 } } + attr { + key: "keep_dims" + value { + b: false + } + } } node { - name: "gradients_2/Slice_6_grad/stack/1" - op: "Const" + name: "gradients_1/filter_type_0_0/add_2_grad/Reshape" + op: "Reshape" + input: "gradients_1/filter_type_0_0/add_2_grad/Sum" + input: "gradients_1/filter_type_0_0/add_2_grad/Shape" attr { - key: "dtype" + key: "T" value { - type: DT_INT32 + type: DT_DOUBLE } } attr { - key: "value" + key: "Tshape" value { - tensor { - dtype: DT_INT32 - tensor_shape { - } - int_val: 1 - } + type: DT_INT32 } } } node { - name: "gradients_2/Slice_6_grad/stack" - op: "Pack" - input: "gradients_2/Slice_6_grad/Rank" - input: "gradients_2/Slice_6_grad/stack/1" + name: "gradients_1/filter_type_0_0/add_2_grad/Sum_1" + op: "Sum" + input: "gradients_1/filter_type_0_0/Reshape_5_grad/Reshape" + input: "gradients_1/filter_type_0_0/add_2_grad/BroadcastGradientArgs:1" attr { - key: "N" + key: "T" value { - i: 2 + type: DT_DOUBLE } } attr { - key: "T" + key: "Tidx" value { type: DT_INT32 } } attr { - key: "axis" + key: "keep_dims" value { - i: 0 + b: false } } } node { - name: "gradients_2/Slice_6_grad/Reshape" + name: "gradients_1/filter_type_0_0/add_2_grad/Reshape_1" op: "Reshape" - input: "Slice_6/begin" - input: "gradients_2/Slice_6_grad/stack" + input: "gradients_1/filter_type_0_0/add_2_grad/Sum_1" + input: "gradients_1/filter_type_0_0/add_2_grad/Shape_1" attr { key: "T" value { - type: DT_INT32 + type: DT_DOUBLE } } attr { @@ -58187,9 +47104,9 @@ node { } } node { - name: "gradients_2/Slice_6_grad/Shape_1" + name: "gradients_1/filter_type_0_0/add_5_grad/Shape" op: "Shape" - input: "Reshape_33" + input: "filter_type_0_0/concat_5" attr { key: "T" value { @@ -58204,22 +47121,27 @@ node { } } node { - name: "gradients_2/Slice_6_grad/sub" - op: "Sub" - input: "gradients_2/Slice_6_grad/Shape_1" - input: "gradients_2/Slice_6_grad/Shape" + name: "gradients_1/filter_type_0_0/add_5_grad/Shape_1" + op: "Shape" + input: "filter_type_0_0/Reshape_11" attr { key: "T" + value { + type: DT_DOUBLE + } + } + attr { + key: "out_type" value { type: DT_INT32 } } } node { - name: "gradients_2/Slice_6_grad/sub_1" - op: "Sub" - input: "gradients_2/Slice_6_grad/sub" - input: "Slice_6/begin" + name: "gradients_1/filter_type_0_0/add_5_grad/BroadcastGradientArgs" + op: "BroadcastGradientArgs" + input: "gradients_1/filter_type_0_0/add_5_grad/Shape" + input: "gradients_1/filter_type_0_0/add_5_grad/Shape_1" attr { key: "T" value { @@ -58228,74 +47150,76 @@ node { } } node { - name: "gradients_2/Slice_6_grad/Reshape_1" - op: "Reshape" - input: "gradients_2/Slice_6_grad/sub_1" - input: "gradients_2/Slice_6_grad/stack" + name: "gradients_1/filter_type_0_0/add_5_grad/Sum" + op: "Sum" + input: "gradients_1/filter_type_0_0/Reshape_12_grad/Reshape" + input: "gradients_1/filter_type_0_0/add_5_grad/BroadcastGradientArgs" attr { key: "T" value { - type: DT_INT32 + type: DT_DOUBLE } } attr { - key: "Tshape" + key: "Tidx" value { type: DT_INT32 } } + attr { + key: "keep_dims" + value { + b: false + } + } } node { - name: "gradients_2/Slice_6_grad/concat/axis" - op: "Const" + name: "gradients_1/filter_type_0_0/add_5_grad/Reshape" + op: "Reshape" + input: "gradients_1/filter_type_0_0/add_5_grad/Sum" + input: "gradients_1/filter_type_0_0/add_5_grad/Shape" attr { - key: "dtype" + key: "T" value { - type: DT_INT32 + type: DT_DOUBLE } } attr { - key: "value" + key: "Tshape" value { - tensor { - dtype: DT_INT32 - tensor_shape { - } - int_val: 1 - } + type: DT_INT32 } } } node { - name: "gradients_2/Slice_6_grad/concat" - op: "ConcatV2" - input: "gradients_2/Slice_6_grad/Reshape" - input: "gradients_2/Slice_6_grad/Reshape_1" - input: "gradients_2/Slice_6_grad/concat/axis" + name: "gradients_1/filter_type_0_0/add_5_grad/Sum_1" + op: "Sum" + input: "gradients_1/filter_type_0_0/Reshape_12_grad/Reshape" + input: "gradients_1/filter_type_0_0/add_5_grad/BroadcastGradientArgs:1" attr { - key: "N" + key: "T" value { - i: 2 + type: DT_DOUBLE } } attr { - key: "T" + key: "Tidx" value { type: DT_INT32 } } attr { - key: "Tidx" + key: "keep_dims" value { - type: DT_INT32 + b: false } } } node { - name: "gradients_2/Slice_6_grad/Pad" - op: "Pad" - input: "gradients_2/Reshape_34_grad/Reshape" - input: "gradients_2/Slice_6_grad/concat" + name: "gradients_1/filter_type_0_0/add_5_grad/Reshape_1" + op: "Reshape" + input: "gradients_1/filter_type_0_0/add_5_grad/Sum_1" + input: "gradients_1/filter_type_0_0/add_5_grad/Shape_1" attr { key: "T" value { @@ -58303,37 +47227,33 @@ node { } } attr { - key: "Tpaddings" + key: "Tshape" value { type: DT_INT32 } } } node { - name: "gradients_2/Slice_7_grad/Rank" - op: "Const" + name: "gradients_1/filter_type_1_0/add_2_grad/Shape" + op: "Shape" + input: "filter_type_1_0/concat_2" attr { - key: "dtype" + key: "T" value { - type: DT_INT32 + type: DT_DOUBLE } } attr { - key: "value" + key: "out_type" value { - tensor { - dtype: DT_INT32 - tensor_shape { - } - int_val: 2 - } + type: DT_INT32 } } } node { - name: "gradients_2/Slice_7_grad/Shape" + name: "gradients_1/filter_type_1_0/add_2_grad/Shape_1" op: "Shape" - input: "Slice_7" + input: "filter_type_1_0/Reshape_4" attr { key: "T" value { @@ -58348,59 +47268,50 @@ node { } } node { - name: "gradients_2/Slice_7_grad/stack/1" - op: "Const" + name: "gradients_1/filter_type_1_0/add_2_grad/BroadcastGradientArgs" + op: "BroadcastGradientArgs" + input: "gradients_1/filter_type_1_0/add_2_grad/Shape" + input: "gradients_1/filter_type_1_0/add_2_grad/Shape_1" attr { - key: "dtype" + key: "T" value { type: DT_INT32 } } - attr { - key: "value" - value { - tensor { - dtype: DT_INT32 - tensor_shape { - } - int_val: 1 - } - } - } } node { - name: "gradients_2/Slice_7_grad/stack" - op: "Pack" - input: "gradients_2/Slice_7_grad/Rank" - input: "gradients_2/Slice_7_grad/stack/1" + name: "gradients_1/filter_type_1_0/add_2_grad/Sum" + op: "Sum" + input: "gradients_1/filter_type_1_0/Reshape_5_grad/Reshape" + input: "gradients_1/filter_type_1_0/add_2_grad/BroadcastGradientArgs" attr { - key: "N" + key: "T" value { - i: 2 + type: DT_DOUBLE } } attr { - key: "T" + key: "Tidx" value { type: DT_INT32 } } attr { - key: "axis" + key: "keep_dims" value { - i: 0 + b: false } } } node { - name: "gradients_2/Slice_7_grad/Reshape" + name: "gradients_1/filter_type_1_0/add_2_grad/Reshape" op: "Reshape" - input: "Slice_7/begin" - input: "gradients_2/Slice_7_grad/stack" + input: "gradients_1/filter_type_1_0/add_2_grad/Sum" + input: "gradients_1/filter_type_1_0/add_2_grad/Shape" attr { key: "T" value { - type: DT_INT32 + type: DT_DOUBLE } } attr { @@ -58411,9 +47322,10 @@ node { } } node { - name: "gradients_2/Slice_7_grad/Shape_1" - op: "Shape" - input: "Reshape_33" + name: "gradients_1/filter_type_1_0/add_2_grad/Sum_1" + op: "Sum" + input: "gradients_1/filter_type_1_0/Reshape_5_grad/Reshape" + input: "gradients_1/filter_type_1_0/add_2_grad/BroadcastGradientArgs:1" attr { key: "T" value { @@ -58421,45 +47333,27 @@ node { } } attr { - key: "out_type" - value { - type: DT_INT32 - } - } -} -node { - name: "gradients_2/Slice_7_grad/sub" - op: "Sub" - input: "gradients_2/Slice_7_grad/Shape_1" - input: "gradients_2/Slice_7_grad/Shape" - attr { - key: "T" + key: "Tidx" value { type: DT_INT32 } } -} -node { - name: "gradients_2/Slice_7_grad/sub_1" - op: "Sub" - input: "gradients_2/Slice_7_grad/sub" - input: "Slice_7/begin" attr { - key: "T" + key: "keep_dims" value { - type: DT_INT32 + b: false } } } node { - name: "gradients_2/Slice_7_grad/Reshape_1" + name: "gradients_1/filter_type_1_0/add_2_grad/Reshape_1" op: "Reshape" - input: "gradients_2/Slice_7_grad/sub_1" - input: "gradients_2/Slice_7_grad/stack" + input: "gradients_1/filter_type_1_0/add_2_grad/Sum_1" + input: "gradients_1/filter_type_1_0/add_2_grad/Shape_1" attr { key: "T" value { - type: DT_INT32 + type: DT_DOUBLE } } attr { @@ -58470,80 +47364,56 @@ node { } } node { - name: "gradients_2/Slice_7_grad/concat/axis" - op: "Const" + name: "gradients_1/filter_type_1_0/add_5_grad/Shape" + op: "Shape" + input: "filter_type_1_0/concat_5" attr { - key: "dtype" + key: "T" value { - type: DT_INT32 + type: DT_DOUBLE } } attr { - key: "value" + key: "out_type" value { - tensor { - dtype: DT_INT32 - tensor_shape { - } - int_val: 1 - } + type: DT_INT32 } } } node { - name: "gradients_2/Slice_7_grad/concat" - op: "ConcatV2" - input: "gradients_2/Slice_7_grad/Reshape" - input: "gradients_2/Slice_7_grad/Reshape_1" - input: "gradients_2/Slice_7_grad/concat/axis" - attr { - key: "N" - value { - i: 2 - } - } + name: "gradients_1/filter_type_1_0/add_5_grad/Shape_1" + op: "Shape" + input: "filter_type_1_0/Reshape_11" attr { key: "T" value { - type: DT_INT32 + type: DT_DOUBLE } } attr { - key: "Tidx" + key: "out_type" value { type: DT_INT32 } } } node { - name: "gradients_2/Slice_7_grad/Pad" - op: "Pad" - input: "gradients_2/Reshape_36_grad/Reshape" - input: "gradients_2/Slice_7_grad/concat" + name: "gradients_1/filter_type_1_0/add_5_grad/BroadcastGradientArgs" + op: "BroadcastGradientArgs" + input: "gradients_1/filter_type_1_0/add_5_grad/Shape" + input: "gradients_1/filter_type_1_0/add_5_grad/Shape_1" attr { key: "T" - value { - type: DT_DOUBLE - } - } - attr { - key: "Tpaddings" value { type: DT_INT32 } } } node { - name: "gradients_2/AddN_4" - op: "AddN" - input: "gradients_2/Slice_6_grad/Pad" - input: "gradients_2/Slice_7_grad/Pad" - attr { - key: "N" - value { - i: 2 - } - } + name: "gradients_1/filter_type_1_0/add_5_grad/Sum" + op: "Sum" + input: "gradients_1/filter_type_1_0/Reshape_12_grad/Reshape" + input: "gradients_1/filter_type_1_0/add_5_grad/BroadcastGradientArgs" attr { key: "T" value { @@ -58551,36 +47421,23 @@ node { } } attr { - key: "_class" - value { - list { - s: "loc:@gradients_2/Slice_6_grad/Pad" - } - } - } -} -node { - name: "gradients_2/Reshape_33_grad/Shape" - op: "Shape" - input: "o_descriptor" - attr { - key: "T" + key: "Tidx" value { - type: DT_DOUBLE + type: DT_INT32 } } attr { - key: "out_type" + key: "keep_dims" value { - type: DT_INT32 + b: false } } } node { - name: "gradients_2/Reshape_33_grad/Reshape" + name: "gradients_1/filter_type_1_0/add_5_grad/Reshape" op: "Reshape" - input: "gradients_2/AddN_4" - input: "gradients_2/Reshape_33_grad/Shape" + input: "gradients_1/filter_type_1_0/add_5_grad/Sum" + input: "gradients_1/filter_type_1_0/add_5_grad/Shape" attr { key: "T" value { @@ -58595,9 +47452,10 @@ node { } } node { - name: "gradients_2/Reshape_32_grad/Shape" - op: "Shape" - input: "concat_5" + name: "gradients_1/filter_type_1_0/add_5_grad/Sum_1" + op: "Sum" + input: "gradients_1/filter_type_1_0/Reshape_12_grad/Reshape" + input: "gradients_1/filter_type_1_0/add_5_grad/BroadcastGradientArgs:1" attr { key: "T" value { @@ -58605,17 +47463,23 @@ node { } } attr { - key: "out_type" + key: "Tidx" value { type: DT_INT32 } } + attr { + key: "keep_dims" + value { + b: false + } + } } node { - name: "gradients_2/Reshape_32_grad/Reshape" + name: "gradients_1/filter_type_1_0/add_5_grad/Reshape_1" op: "Reshape" - input: "gradients_2/Reshape_33_grad/Reshape" - input: "gradients_2/Reshape_32_grad/Shape" + input: "gradients_1/filter_type_1_0/add_5_grad/Sum_1" + input: "gradients_1/filter_type_1_0/add_5_grad/Shape_1" attr { key: "T" value { @@ -58630,7 +47494,7 @@ node { } } node { - name: "gradients_2/concat_5_grad/Rank" + name: "gradients_1/filter_type_0_0/concat_2_grad/Rank" op: "Const" attr { key: "dtype" @@ -58651,10 +47515,10 @@ node { } } node { - name: "gradients_2/concat_5_grad/mod" + name: "gradients_1/filter_type_0_0/concat_2_grad/mod" op: "FloorMod" - input: "concat_5/axis" - input: "gradients_2/concat_5_grad/Rank" + input: "filter_type_0_0/concat_2/axis" + input: "gradients_1/filter_type_0_0/concat_2_grad/Rank" attr { key: "T" value { @@ -58663,10 +47527,10 @@ node { } } node { - name: "gradients_2/concat_5_grad/ShapeN" + name: "gradients_1/filter_type_0_0/concat_2_grad/ShapeN" op: "ShapeN" - input: "Reshape_21" - input: "Reshape_31" + input: "filter_type_0_0/add_1" + input: "filter_type_0_0/add_1" attr { key: "N" value { @@ -58687,11 +47551,11 @@ node { } } node { - name: "gradients_2/concat_5_grad/ConcatOffset" + name: "gradients_1/filter_type_0_0/concat_2_grad/ConcatOffset" op: "ConcatOffset" - input: "gradients_2/concat_5_grad/mod" - input: "gradients_2/concat_5_grad/ShapeN" - input: "gradients_2/concat_5_grad/ShapeN:1" + input: "gradients_1/filter_type_0_0/concat_2_grad/mod" + input: "gradients_1/filter_type_0_0/concat_2_grad/ShapeN" + input: "gradients_1/filter_type_0_0/concat_2_grad/ShapeN:1" attr { key: "N" value { @@ -58700,11 +47564,30 @@ node { } } node { - name: "gradients_2/concat_5_grad/Slice_1" + name: "gradients_1/filter_type_0_0/concat_2_grad/Slice" + op: "Slice" + input: "gradients_1/filter_type_0_0/add_2_grad/Reshape" + input: "gradients_1/filter_type_0_0/concat_2_grad/ConcatOffset" + input: "gradients_1/filter_type_0_0/concat_2_grad/ShapeN" + attr { + key: "Index" + value { + type: DT_INT32 + } + } + attr { + key: "T" + value { + type: DT_DOUBLE + } + } +} +node { + name: "gradients_1/filter_type_0_0/concat_2_grad/Slice_1" op: "Slice" - input: "gradients_2/Reshape_32_grad/Reshape" - input: "gradients_2/concat_5_grad/ConcatOffset:1" - input: "gradients_2/concat_5_grad/ShapeN:1" + input: "gradients_1/filter_type_0_0/add_2_grad/Reshape" + input: "gradients_1/filter_type_0_0/concat_2_grad/ConcatOffset:1" + input: "gradients_1/filter_type_0_0/concat_2_grad/ShapeN:1" attr { key: "Index" value { @@ -58719,9 +47602,9 @@ node { } } node { - name: "gradients_2/Reshape_31_grad/Shape" + name: "gradients_1/filter_type_0_0/Reshape_4_grad/Shape" op: "Shape" - input: "concat_4" + input: "filter_type_0_0/Tanh_2" attr { key: "T" value { @@ -58736,10 +47619,10 @@ node { } } node { - name: "gradients_2/Reshape_31_grad/Reshape" + name: "gradients_1/filter_type_0_0/Reshape_4_grad/Reshape" op: "Reshape" - input: "gradients_2/concat_5_grad/Slice_1" - input: "gradients_2/Reshape_31_grad/Shape" + input: "gradients_1/filter_type_0_0/add_2_grad/Reshape_1" + input: "gradients_1/filter_type_0_0/Reshape_4_grad/Shape" attr { key: "T" value { @@ -58754,7 +47637,7 @@ node { } } node { - name: "gradients_2/concat_4_grad/Rank" + name: "gradients_1/filter_type_0_0/concat_5_grad/Rank" op: "Const" attr { key: "dtype" @@ -58775,10 +47658,10 @@ node { } } node { - name: "gradients_2/concat_4_grad/mod" + name: "gradients_1/filter_type_0_0/concat_5_grad/mod" op: "FloorMod" - input: "concat_4/axis" - input: "gradients_2/concat_4_grad/Rank" + input: "filter_type_0_0/concat_5/axis" + input: "gradients_1/filter_type_0_0/concat_5_grad/Rank" attr { key: "T" value { @@ -58787,10 +47670,10 @@ node { } } node { - name: "gradients_2/concat_4_grad/ShapeN" + name: "gradients_1/filter_type_0_0/concat_5_grad/ShapeN" op: "ShapeN" - input: "Reshape_28" - input: "Reshape_30" + input: "filter_type_0_0/add_4" + input: "filter_type_0_0/add_4" attr { key: "N" value { @@ -58811,11 +47694,11 @@ node { } } node { - name: "gradients_2/concat_4_grad/ConcatOffset" + name: "gradients_1/filter_type_0_0/concat_5_grad/ConcatOffset" op: "ConcatOffset" - input: "gradients_2/concat_4_grad/mod" - input: "gradients_2/concat_4_grad/ShapeN" - input: "gradients_2/concat_4_grad/ShapeN:1" + input: "gradients_1/filter_type_0_0/concat_5_grad/mod" + input: "gradients_1/filter_type_0_0/concat_5_grad/ShapeN" + input: "gradients_1/filter_type_0_0/concat_5_grad/ShapeN:1" attr { key: "N" value { @@ -58824,11 +47707,11 @@ node { } } node { - name: "gradients_2/concat_4_grad/Slice" + name: "gradients_1/filter_type_0_0/concat_5_grad/Slice" op: "Slice" - input: "gradients_2/Reshape_31_grad/Reshape" - input: "gradients_2/concat_4_grad/ConcatOffset" - input: "gradients_2/concat_4_grad/ShapeN" + input: "gradients_1/filter_type_0_0/add_5_grad/Reshape" + input: "gradients_1/filter_type_0_0/concat_5_grad/ConcatOffset" + input: "gradients_1/filter_type_0_0/concat_5_grad/ShapeN" attr { key: "Index" value { @@ -58843,11 +47726,11 @@ node { } } node { - name: "gradients_2/concat_4_grad/Slice_1" + name: "gradients_1/filter_type_0_0/concat_5_grad/Slice_1" op: "Slice" - input: "gradients_2/Reshape_31_grad/Reshape" - input: "gradients_2/concat_4_grad/ConcatOffset:1" - input: "gradients_2/concat_4_grad/ShapeN:1" + input: "gradients_1/filter_type_0_0/add_5_grad/Reshape" + input: "gradients_1/filter_type_0_0/concat_5_grad/ConcatOffset:1" + input: "gradients_1/filter_type_0_0/concat_5_grad/ShapeN:1" attr { key: "Index" value { @@ -58862,9 +47745,9 @@ node { } } node { - name: "gradients_2/Reshape_28_grad/Shape" + name: "gradients_1/filter_type_0_0/Reshape_11_grad/Shape" op: "Shape" - input: "filter_type_0_1/mul" + input: "filter_type_0_0/Tanh_5" attr { key: "T" value { @@ -58879,10 +47762,10 @@ node { } } node { - name: "gradients_2/Reshape_28_grad/Reshape" + name: "gradients_1/filter_type_0_0/Reshape_11_grad/Reshape" op: "Reshape" - input: "gradients_2/concat_4_grad/Slice" - input: "gradients_2/Reshape_28_grad/Shape" + input: "gradients_1/filter_type_0_0/add_5_grad/Reshape_1" + input: "gradients_1/filter_type_0_0/Reshape_11_grad/Shape" attr { key: "T" value { @@ -58897,61 +47780,49 @@ node { } } node { - name: "gradients_2/Reshape_30_grad/Shape" - op: "Shape" - input: "filter_type_1_1/mul" + name: "gradients_1/filter_type_1_0/concat_2_grad/Rank" + op: "Const" attr { - key: "T" + key: "dtype" value { - type: DT_DOUBLE + type: DT_INT32 } } attr { - key: "out_type" + key: "value" value { - type: DT_INT32 + tensor { + dtype: DT_INT32 + tensor_shape { + } + int_val: 2 + } } } } node { - name: "gradients_2/Reshape_30_grad/Reshape" - op: "Reshape" - input: "gradients_2/concat_4_grad/Slice_1" - input: "gradients_2/Reshape_30_grad/Shape" + name: "gradients_1/filter_type_1_0/concat_2_grad/mod" + op: "FloorMod" + input: "filter_type_1_0/concat_2/axis" + input: "gradients_1/filter_type_1_0/concat_2_grad/Rank" attr { key: "T" - value { - type: DT_DOUBLE - } - } - attr { - key: "Tshape" value { type: DT_INT32 } } } node { - name: "gradients_2/filter_type_0_1/mul_grad/Shape" - op: "Shape" - input: "filter_type_0_1/Mean" - attr { - key: "T" - value { - type: DT_DOUBLE - } - } + name: "gradients_1/filter_type_1_0/concat_2_grad/ShapeN" + op: "ShapeN" + input: "filter_type_1_0/add_1" + input: "filter_type_1_0/add_1" attr { - key: "out_type" + key: "N" value { - type: DT_INT32 + i: 2 } } -} -node { - name: "gradients_2/filter_type_0_1/mul_grad/Shape_1" - op: "Shape" - input: "filter_type_0_1/mul/y" attr { key: "T" value { @@ -58966,75 +47837,60 @@ node { } } node { - name: "gradients_2/filter_type_0_1/mul_grad/BroadcastGradientArgs" - op: "BroadcastGradientArgs" - input: "gradients_2/filter_type_0_1/mul_grad/Shape" - input: "gradients_2/filter_type_0_1/mul_grad/Shape_1" + name: "gradients_1/filter_type_1_0/concat_2_grad/ConcatOffset" + op: "ConcatOffset" + input: "gradients_1/filter_type_1_0/concat_2_grad/mod" + input: "gradients_1/filter_type_1_0/concat_2_grad/ShapeN" + input: 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"gradients_1/filter_type_1_0/add_2_grad/Reshape_1" + input: "gradients_1/filter_type_1_0/Reshape_4_grad/Shape" attr { key: "T" value { @@ -59059,82 +47916,56 @@ node { } } attr { - key: "out_type" + key: "Tshape" value { type: DT_INT32 } } } node { - name: "gradients_2/filter_type_1_1/mul_grad/BroadcastGradientArgs" - op: "BroadcastGradientArgs" - input: "gradients_2/filter_type_1_1/mul_grad/Shape" - input: "gradients_2/filter_type_1_1/mul_grad/Shape_1" + name: "gradients_1/filter_type_1_0/concat_5_grad/Rank" + op: "Const" attr { - key: "T" + key: "dtype" value { type: DT_INT32 } } -} -node { - name: "gradients_2/filter_type_1_1/mul_grad/Mul" - op: "Mul" - input: "gradients_2/Reshape_30_grad/Reshape" - input: "filter_type_1_1/mul/y" attr { - key: "T" + key: "value" value { - type: DT_DOUBLE + tensor { + dtype: DT_INT32 + tensor_shape { + } + int_val: 2 + } } } } node { - name: "gradients_2/filter_type_1_1/mul_grad/Sum" - op: "Sum" - input: "gradients_2/filter_type_1_1/mul_grad/Mul" - input: "gradients_2/filter_type_1_1/mul_grad/BroadcastGradientArgs" + name: "gradients_1/filter_type_1_0/concat_5_grad/mod" + op: "FloorMod" + input: "filter_type_1_0/concat_5/axis" + input: "gradients_1/filter_type_1_0/concat_5_grad/Rank" attr { key: "T" - value { - type: DT_DOUBLE - } - } - attr { - key: "Tidx" value { type: DT_INT32 } } - attr { - key: "keep_dims" - value { - b: false - } - } } node { - name: "gradients_2/filter_type_1_1/mul_grad/Reshape" - op: "Reshape" - input: "gradients_2/filter_type_1_1/mul_grad/Sum" - input: "gradients_2/filter_type_1_1/mul_grad/Shape" - attr { - key: "T" - value { - type: DT_DOUBLE - } - } + name: "gradients_1/filter_type_1_0/concat_5_grad/ShapeN" + op: "ShapeN" + input: "filter_type_1_0/add_4" + input: "filter_type_1_0/add_4" attr { - key: "Tshape" + key: "N" value { - type: DT_INT32 + i: 2 } } -} -node { - name: "gradients_2/filter_type_0_1/Mean_grad/Shape" - op: "Shape" - input: "filter_type_0_1/concat_4" attr { key: "T" value { @@ 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key: "out_type" value { - list { - s: "loc:@gradients_2/filter_type_0_1/Mean_grad/Shape" - } + type: DT_INT32 } } } node { - name: "gradients_2/filter_type_0_1/Mean_grad/Shape_1" - op: "Const" + name: "gradients_1/filter_type_1_0/Reshape_11_grad/Reshape" + op: "Reshape" + input: "gradients_1/filter_type_1_0/add_5_grad/Reshape_1" + input: "gradients_1/filter_type_1_0/Reshape_11_grad/Shape" attr { - key: "_class" + key: "T" value { - list { - s: "loc:@gradients_2/filter_type_0_1/Mean_grad/Shape" - } + type: DT_DOUBLE } } attr { - key: "dtype" + key: "Tshape" value { type: DT_INT32 } } +} +node { + name: "gradients_1/filter_type_0_0/Tanh_2_grad/TanhGrad" + op: "TanhGrad" + input: "filter_type_0_0/Tanh_2" + input: "gradients_1/filter_type_0_0/Reshape_4_grad/Reshape" attr { - key: "value" + key: "T" value { - tensor { - dtype: DT_INT32 - tensor_shape { - dim { - } - } - } + type: DT_DOUBLE } } } node { - name: "gradients_2/filter_type_0_1/Mean_grad/range/start" - op: "Const" + name: 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"gradients_1/filter_type_0_0/add_1_grad/Shape_1" + op: "Shape" + input: "filter_type_0_0/Reshape_3" attr { key: "T" value { @@ -59439,33 +48264,29 @@ node { } } attr { - key: "Tidx" + key: "out_type" value { type: DT_INT32 } } } node { - name: "gradients_2/filter_type_0_1/Mean_grad/Shape_2" - op: "Shape" - input: "filter_type_0_1/concat_4" + name: "gradients_1/filter_type_0_0/add_1_grad/BroadcastGradientArgs" + op: "BroadcastGradientArgs" + input: "gradients_1/filter_type_0_0/add_1_grad/Shape" + input: "gradients_1/filter_type_0_0/add_1_grad/Shape_1" attr { key: "T" - value { - type: DT_DOUBLE - } - } - attr { - key: "out_type" value { type: DT_INT32 } } } node { - name: "gradients_2/filter_type_0_1/Mean_grad/Shape_3" - op: "Shape" - input: "filter_type_0_1/Mean" + name: "gradients_1/filter_type_0_0/add_1_grad/Sum" + op: "Sum" + input: "gradients_1/AddN_7" + input: "gradients_1/filter_type_0_0/add_1_grad/BroadcastGradientArgs" attr { key: "T" value { @@ -59473,45 +48294,45 @@ node { } } attr { - key: "out_type" + key: "Tidx" value { type: DT_INT32 } } + attr { + key: "keep_dims" + value { + b: false + } + } } node { - name: "gradients_2/filter_type_0_1/Mean_grad/Const" - op: "Const" + name: "gradients_1/filter_type_0_0/add_1_grad/Reshape" + op: "Reshape" + input: "gradients_1/filter_type_0_0/add_1_grad/Sum" + input: "gradients_1/filter_type_0_0/add_1_grad/Shape" attr { - key: "dtype" + key: "T" value { - type: DT_INT32 + type: DT_DOUBLE } } attr { - key: "value" + key: "Tshape" value { - tensor { - dtype: DT_INT32 - tensor_shape { - dim { - size: 1 - } - } - int_val: 0 - } + type: DT_INT32 } } } node { - name: "gradients_2/filter_type_0_1/Mean_grad/Prod" - op: "Prod" - input: "gradients_2/filter_type_0_1/Mean_grad/Shape_2" - input: "gradients_2/filter_type_0_1/Mean_grad/Const" + name: "gradients_1/filter_type_0_0/add_1_grad/Sum_1" + op: "Sum" + input: "gradients_1/AddN_7" + input: "gradients_1/filter_type_0_0/add_1_grad/BroadcastGradientArgs:1" attr { key: "T" value { - type: DT_INT32 + type: DT_DOUBLE } } attr { @@ -59528,91 +48349,89 @@ node { } } node { - name: "gradients_2/filter_type_0_1/Mean_grad/Const_1" - op: "Const" + name: "gradients_1/filter_type_0_0/add_1_grad/Reshape_1" + op: "Reshape" + input: "gradients_1/filter_type_0_0/add_1_grad/Sum_1" + input: "gradients_1/filter_type_0_0/add_1_grad/Shape_1" attr { - key: "dtype" + key: "T" value { - type: DT_INT32 + type: DT_DOUBLE } } attr { - key: "value" + key: "Tshape" value { - tensor { - dtype: DT_INT32 - tensor_shape { - dim { - size: 1 - } - } - int_val: 0 - } + type: DT_INT32 } } } node { - name: "gradients_2/filter_type_0_1/Mean_grad/Prod_1" - op: "Prod" - input: "gradients_2/filter_type_0_1/Mean_grad/Shape_3" - input: "gradients_2/filter_type_0_1/Mean_grad/Const_1" + name: "gradients_1/AddN_8" + op: "AddN" + input: "gradients_1/filter_type_0_0/concat_5_grad/Slice" + input: "gradients_1/filter_type_0_0/concat_5_grad/Slice_1" + input: "gradients_1/filter_type_0_0/MatMul_6_grad/MatMul" attr { - key: "T" + key: "N" value { - type: DT_INT32 + i: 3 } } attr { - key: "Tidx" + key: "T" value { - type: DT_INT32 + type: DT_DOUBLE } } attr { - key: "keep_dims" + key: "_class" value { - b: false + list { + s: "loc:@gradients_1/filter_type_0_0/concat_5_grad/Slice" + } } } } node { - name: "gradients_2/filter_type_0_1/Mean_grad/Maximum/y" - op: "Const" + name: "gradients_1/filter_type_0_0/add_4_grad/Shape" + op: "Shape" + input: "filter_type_0_0/concat_4" attr { - key: "dtype" + key: "T" value { - type: DT_INT32 + type: DT_DOUBLE } } attr { - key: "value" + key: "out_type" value { - tensor { - dtype: DT_INT32 - tensor_shape { - } - int_val: 1 - } + type: DT_INT32 } } } node { - name: "gradients_2/filter_type_0_1/Mean_grad/Maximum" - op: "Maximum" - input: "gradients_2/filter_type_0_1/Mean_grad/Prod_1" - input: "gradients_2/filter_type_0_1/Mean_grad/Maximum/y" + name: "gradients_1/filter_type_0_0/add_4_grad/Shape_1" + op: "Shape" + input: "filter_type_0_0/Reshape_10" attr { key: "T" + value { + type: DT_DOUBLE + } + } + attr { + key: "out_type" value { type: DT_INT32 } } } node { - name: "gradients_2/filter_type_0_1/Mean_grad/floordiv" - op: "FloorDiv" - input: "gradients_2/filter_type_0_1/Mean_grad/Prod" - input: "gradients_2/filter_type_0_1/Mean_grad/Maximum" + name: "gradients_1/filter_type_0_0/add_4_grad/BroadcastGradientArgs" + op: "BroadcastGradientArgs" + input: "gradients_1/filter_type_0_0/add_4_grad/Shape" + input: "gradients_1/filter_type_0_0/add_4_grad/Shape_1" attr { key: "T" value { @@ -59621,44 +48440,34 @@ node { } } node { - name: "gradients_2/filter_type_0_1/Mean_grad/Cast" - op: "Cast" - input: "gradients_2/filter_type_0_1/Mean_grad/floordiv" + name: "gradients_1/filter_type_0_0/add_4_grad/Sum" + op: "Sum" + input: "gradients_1/AddN_8" + input: "gradients_1/filter_type_0_0/add_4_grad/BroadcastGradientArgs" attr { - key: "DstT" + key: "T" value { type: DT_DOUBLE } } attr { - key: "SrcT" + key: "Tidx" value { type: DT_INT32 } } attr { - key: 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name: "gradients_2/filter_type_1_1/Mean_grad/Shape_2" - op: "Shape" - input: "filter_type_1_1/concat_4" + name: "gradients_1/filter_type_1_0/add_4_grad/BroadcastGradientArgs" + op: "BroadcastGradientArgs" + input: "gradients_1/filter_type_1_0/add_4_grad/Shape" + input: "gradients_1/filter_type_1_0/add_4_grad/Shape_1" attr { key: "T" - value { - type: DT_DOUBLE - } - } - attr { - key: "out_type" value { type: DT_INT32 } } } node { - name: "gradients_2/filter_type_1_1/Mean_grad/Shape_3" - op: "Shape" - input: "filter_type_1_1/Mean" + name: "gradients_1/filter_type_1_0/add_4_grad/Sum" + op: "Sum" + input: "gradients_1/AddN_10" + input: "gradients_1/filter_type_1_0/add_4_grad/BroadcastGradientArgs" attr { key: "T" value { @@ -59997,45 +48765,45 @@ node { } } attr { - key: "out_type" + key: "Tidx" value { type: DT_INT32 } } + attr { + key: "keep_dims" + value { + b: false + } + } } node { - name: "gradients_2/filter_type_1_1/Mean_grad/Const" - op: "Const" + name: "gradients_1/filter_type_1_0/add_4_grad/Reshape" + op: "Reshape" + input: "gradients_1/filter_type_1_0/add_4_grad/Sum" + input: "gradients_1/filter_type_1_0/add_4_grad/Shape" attr { - key: "dtype" + key: "T" value { - type: DT_INT32 + type: DT_DOUBLE } } attr { - key: "value" + key: "Tshape" value { - tensor { - dtype: DT_INT32 - tensor_shape { - dim { - size: 1 - } - } - int_val: 0 - } + type: DT_INT32 } } } node { - name: "gradients_2/filter_type_1_1/Mean_grad/Prod" - op: "Prod" - input: "gradients_2/filter_type_1_1/Mean_grad/Shape_2" - input: "gradients_2/filter_type_1_1/Mean_grad/Const" + name: "gradients_1/filter_type_1_0/add_4_grad/Sum_1" + op: "Sum" + input: "gradients_1/AddN_10" + input: "gradients_1/filter_type_1_0/add_4_grad/BroadcastGradientArgs:1" attr { key: "T" value { - type: DT_INT32 + type: DT_DOUBLE } } attr { @@ -60052,7 +48820,25 @@ node { } } node { - name: "gradients_2/filter_type_1_1/Mean_grad/Const_1" + name: "gradients_1/filter_type_1_0/add_4_grad/Reshape_1" + op: "Reshape" + input: "gradients_1/filter_type_1_0/add_4_grad/Sum_1" + input: "gradients_1/filter_type_1_0/add_4_grad/Shape_1" + attr { + key: "T" + value { + type: DT_DOUBLE + } + } + attr { + key: "Tshape" + value { + type: DT_INT32 + } + } +} +node { + name: "gradients_1/filter_type_0_0/concat_1_grad/Rank" op: "Const" attr { key: "dtype" @@ -60066,121 +48852,136 @@ node { tensor { dtype: DT_INT32 tensor_shape { - dim { - size: 1 - } } - int_val: 0 + int_val: 2 } } } } node { - name: "gradients_2/filter_type_1_1/Mean_grad/Prod_1" - op: "Prod" - input: "gradients_2/filter_type_1_1/Mean_grad/Shape_3" - input: "gradients_2/filter_type_1_1/Mean_grad/Const_1" + name: "gradients_1/filter_type_0_0/concat_1_grad/mod" + op: "FloorMod" + input: "filter_type_0_0/concat_1/axis" + input: "gradients_1/filter_type_0_0/concat_1_grad/Rank" attr { key: "T" value { type: DT_INT32 } } +} +node { + name: "gradients_1/filter_type_0_0/concat_1_grad/ShapeN" + op: "ShapeN" + input: "filter_type_0_0/add" + input: "filter_type_0_0/add" attr { - key: "Tidx" + key: "N" value { - type: DT_INT32 + i: 2 } } attr { - key: "keep_dims" + key: "T" value { - b: false + type: DT_DOUBLE } } -} -node { - name: "gradients_2/filter_type_1_1/Mean_grad/Maximum/y" - op: "Const" attr { - key: "dtype" + key: "out_type" value { type: DT_INT32 } } +} +node { + name: "gradients_1/filter_type_0_0/concat_1_grad/ConcatOffset" + op: "ConcatOffset" + input: "gradients_1/filter_type_0_0/concat_1_grad/mod" + input: "gradients_1/filter_type_0_0/concat_1_grad/ShapeN" + input: "gradients_1/filter_type_0_0/concat_1_grad/ShapeN:1" attr { - key: "value" + key: "N" value { - tensor { - dtype: DT_INT32 - tensor_shape { - } - int_val: 1 - } + i: 2 } } } node { - name: "gradients_2/filter_type_1_1/Mean_grad/Maximum" - op: "Maximum" - input: "gradients_2/filter_type_1_1/Mean_grad/Prod_1" - input: "gradients_2/filter_type_1_1/Mean_grad/Maximum/y" + name: "gradients_1/filter_type_0_0/concat_1_grad/Slice" + op: "Slice" + input: 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DT_DOUBLE } } +} +node { + name: "gradients_1/filter_type_0_0/Reshape_3_grad/Shape" + op: "Shape" + input: "filter_type_0_0/Tanh_1" attr { - key: "SrcT" + key: "T" value { - type: DT_INT32 + type: DT_DOUBLE } } attr { - key: "Truncate" + key: "out_type" value { - b: false + type: DT_INT32 } } } node { - name: "gradients_2/filter_type_1_1/Mean_grad/truediv" - op: "RealDiv" - input: "gradients_2/filter_type_1_1/Mean_grad/BroadcastTo" - input: "gradients_2/filter_type_1_1/Mean_grad/Cast" + name: "gradients_1/filter_type_0_0/Reshape_3_grad/Reshape" + op: "Reshape" + input: "gradients_1/filter_type_0_0/add_1_grad/Reshape_1" + input: "gradients_1/filter_type_0_0/Reshape_3_grad/Shape" attr { key: "T" value { type: DT_DOUBLE } } + attr { + key: "Tshape" + value { + type: DT_INT32 + } + } } node { - name: "gradients_2/filter_type_0_1/concat_4_grad/Rank" + name: "gradients_1/filter_type_0_0/concat_4_grad/Rank" op: "Const" attr { key: "dtype" @@ -60195,16 +48996,16 @@ node { dtype: DT_INT32 tensor_shape { } - int_val: 3 + int_val: 2 } } } } node { - name: "gradients_2/filter_type_0_1/concat_4_grad/mod" + name: "gradients_1/filter_type_0_0/concat_4_grad/mod" op: "FloorMod" - input: "filter_type_0_1/concat_4/axis" - input: "gradients_2/filter_type_0_1/concat_4_grad/Rank" + input: "filter_type_0_0/concat_4/axis" + input: "gradients_1/filter_type_0_0/concat_4_grad/Rank" attr { key: "T" value { @@ -60213,10 +49014,10 @@ node { } } node { - name: "gradients_2/filter_type_0_1/concat_4_grad/ShapeN" + name: "gradients_1/filter_type_0_0/concat_4_grad/ShapeN" op: "ShapeN" - input: "filter_type_0_1/Reshape_4" - input: "filter_type_0_1/Reshape_9" + input: "filter_type_0_0/add_3" + input: "filter_type_0_0/add_3" attr { key: "N" value { @@ -60237,11 +49038,11 @@ node { } } node { - name: "gradients_2/filter_type_0_1/concat_4_grad/ConcatOffset" + name: "gradients_1/filter_type_0_0/concat_4_grad/ConcatOffset" op: "ConcatOffset" - input: "gradients_2/filter_type_0_1/concat_4_grad/mod" - input: "gradients_2/filter_type_0_1/concat_4_grad/ShapeN" - input: "gradients_2/filter_type_0_1/concat_4_grad/ShapeN:1" + input: "gradients_1/filter_type_0_0/concat_4_grad/mod" + input: "gradients_1/filter_type_0_0/concat_4_grad/ShapeN" + input: "gradients_1/filter_type_0_0/concat_4_grad/ShapeN:1" attr { key: "N" value { @@ -60250,11 +49051,11 @@ node { } } node { - name: "gradients_2/filter_type_0_1/concat_4_grad/Slice" + name: "gradients_1/filter_type_0_0/concat_4_grad/Slice" op: "Slice" - input: "gradients_2/filter_type_0_1/Mean_grad/truediv" - input: "gradients_2/filter_type_0_1/concat_4_grad/ConcatOffset" - input: "gradients_2/filter_type_0_1/concat_4_grad/ShapeN" + input: "gradients_1/filter_type_0_0/add_4_grad/Reshape" + input: "gradients_1/filter_type_0_0/concat_4_grad/ConcatOffset" + input: "gradients_1/filter_type_0_0/concat_4_grad/ShapeN" attr { key: "Index" value { @@ -60269,11 +49070,11 @@ node { } } node { - name: "gradients_2/filter_type_0_1/concat_4_grad/Slice_1" + name: "gradients_1/filter_type_0_0/concat_4_grad/Slice_1" op: "Slice" - input: "gradients_2/filter_type_0_1/Mean_grad/truediv" - input: "gradients_2/filter_type_0_1/concat_4_grad/ConcatOffset:1" - input: "gradients_2/filter_type_0_1/concat_4_grad/ShapeN:1" + input: "gradients_1/filter_type_0_0/add_4_grad/Reshape" + input: "gradients_1/filter_type_0_0/concat_4_grad/ConcatOffset:1" + input: "gradients_1/filter_type_0_0/concat_4_grad/ShapeN:1" attr { key: "Index" value { @@ -60288,7 +49089,42 @@ node { } } node { - name: "gradients_2/filter_type_1_1/concat_4_grad/Rank" + name: "gradients_1/filter_type_0_0/Reshape_10_grad/Shape" + op: "Shape" + input: "filter_type_0_0/Tanh_4" + attr { + key: "T" + value { + type: DT_DOUBLE + } + } + attr { + key: "out_type" + value { + type: DT_INT32 + } + } +} +node { + name: "gradients_1/filter_type_0_0/Reshape_10_grad/Reshape" + op: "Reshape" + input: "gradients_1/filter_type_0_0/add_4_grad/Reshape_1" + input: "gradients_1/filter_type_0_0/Reshape_10_grad/Shape" + attr { + key: "T" + value { + type: DT_DOUBLE + } + } + attr { + key: "Tshape" + value { + type: DT_INT32 + } + } +} +node { + name: "gradients_1/filter_type_1_0/concat_1_grad/Rank" op: "Const" attr { key: "dtype" @@ -60303,16 +49139,16 @@ node { dtype: DT_INT32 tensor_shape { } - int_val: 3 + int_val: 2 } } } } node { - name: "gradients_2/filter_type_1_1/concat_4_grad/mod" + name: "gradients_1/filter_type_1_0/concat_1_grad/mod" op: "FloorMod" - input: "filter_type_1_1/concat_4/axis" - input: "gradients_2/filter_type_1_1/concat_4_grad/Rank" + input: "filter_type_1_0/concat_1/axis" + input: "gradients_1/filter_type_1_0/concat_1_grad/Rank" attr { key: "T" value { @@ -60321,10 +49157,10 @@ node { } } node { - name: "gradients_2/filter_type_1_1/concat_4_grad/ShapeN" + name: "gradients_1/filter_type_1_0/concat_1_grad/ShapeN" op: "ShapeN" - input: "filter_type_1_1/Reshape_4" - input: "filter_type_1_1/Reshape_9" + input: "filter_type_1_0/add" + input: "filter_type_1_0/add" attr { key: "N" value { @@ -60345,11 +49181,11 @@ node { } } node { - name: "gradients_2/filter_type_1_1/concat_4_grad/ConcatOffset" + name: "gradients_1/filter_type_1_0/concat_1_grad/ConcatOffset" op: "ConcatOffset" - input: "gradients_2/filter_type_1_1/concat_4_grad/mod" - input: "gradients_2/filter_type_1_1/concat_4_grad/ShapeN" - input: "gradients_2/filter_type_1_1/concat_4_grad/ShapeN:1" + input: "gradients_1/filter_type_1_0/concat_1_grad/mod" + input: "gradients_1/filter_type_1_0/concat_1_grad/ShapeN" + input: "gradients_1/filter_type_1_0/concat_1_grad/ShapeN:1" attr { key: "N" value { @@ -60358,11 +49194,11 @@ node { } } node { - name: "gradients_2/filter_type_1_1/concat_4_grad/Slice" + name: "gradients_1/filter_type_1_0/concat_1_grad/Slice" op: "Slice" - input: "gradients_2/filter_type_1_1/Mean_grad/truediv" - input: "gradients_2/filter_type_1_1/concat_4_grad/ConcatOffset" - input: "gradients_2/filter_type_1_1/concat_4_grad/ShapeN" + input: "gradients_1/filter_type_1_0/add_1_grad/Reshape" + input: "gradients_1/filter_type_1_0/concat_1_grad/ConcatOffset" + input: "gradients_1/filter_type_1_0/concat_1_grad/ShapeN" attr { key: "Index" value { @@ -60377,11 +49213,11 @@ node { } } node { - name: "gradients_2/filter_type_1_1/concat_4_grad/Slice_1" + name: "gradients_1/filter_type_1_0/concat_1_grad/Slice_1" op: "Slice" - input: "gradients_2/filter_type_1_1/Mean_grad/truediv" - input: "gradients_2/filter_type_1_1/concat_4_grad/ConcatOffset:1" - input: "gradients_2/filter_type_1_1/concat_4_grad/ShapeN:1" + input: "gradients_1/filter_type_1_0/add_1_grad/Reshape" + input: "gradients_1/filter_type_1_0/concat_1_grad/ConcatOffset:1" + input: "gradients_1/filter_type_1_0/concat_1_grad/ShapeN:1" attr { key: "Index" value { @@ -60396,9 +49232,9 @@ node { } } node { - name: "gradients_2/filter_type_0_1/Reshape_4_grad/Shape" + name: "gradients_1/filter_type_1_0/Reshape_3_grad/Shape" op: "Shape" - input: "filter_type_0_1/add_1" + input: "filter_type_1_0/Tanh_1" attr { key: "T" value { @@ -60413,10 +49249,10 @@ node { } } node { - name: "gradients_2/filter_type_0_1/Reshape_4_grad/Reshape" + name: "gradients_1/filter_type_1_0/Reshape_3_grad/Reshape" op: "Reshape" - input: "gradients_2/filter_type_0_1/concat_4_grad/Slice" - input: "gradients_2/filter_type_0_1/Reshape_4_grad/Shape" + input: "gradients_1/filter_type_1_0/add_1_grad/Reshape_1" + input: "gradients_1/filter_type_1_0/Reshape_3_grad/Shape" attr { key: "T" value { @@ -60431,44 +49267,49 @@ node { } } node { - name: "gradients_2/filter_type_0_1/Reshape_9_grad/Shape" - op: "Shape" - input: "filter_type_0_1/add_3" + name: "gradients_1/filter_type_1_0/concat_4_grad/Rank" + op: "Const" attr { - key: "T" + key: "dtype" value { - type: DT_DOUBLE + type: DT_INT32 } } attr { - key: "out_type" + key: "value" value { - type: DT_INT32 + tensor { + dtype: DT_INT32 + tensor_shape { + } + int_val: 2 + } } } } node { - name: "gradients_2/filter_type_0_1/Reshape_9_grad/Reshape" - op: "Reshape" - input: "gradients_2/filter_type_0_1/concat_4_grad/Slice_1" - input: "gradients_2/filter_type_0_1/Reshape_9_grad/Shape" + name: "gradients_1/filter_type_1_0/concat_4_grad/mod" + op: "FloorMod" + input: "filter_type_1_0/concat_4/axis" + input: "gradients_1/filter_type_1_0/concat_4_grad/Rank" attr { key: "T" value { - type: DT_DOUBLE + type: DT_INT32 } } +} +node { + name: "gradients_1/filter_type_1_0/concat_4_grad/ShapeN" + op: "ShapeN" + input: "filter_type_1_0/add_3" + input: "filter_type_1_0/add_3" attr { - key: "Tshape" + key: "N" value { - type: DT_INT32 + i: 2 } } -} -node { - name: "gradients_2/filter_type_1_1/Reshape_4_grad/Shape" - op: "Shape" - input: "filter_type_1_1/add_1" attr { key: "T" value { @@ -60483,27 +49324,60 @@ node { } } node { - name: "gradients_2/filter_type_1_1/Reshape_4_grad/Reshape" - op: "Reshape" - input: "gradients_2/filter_type_1_1/concat_4_grad/Slice" - input: "gradients_2/filter_type_1_1/Reshape_4_grad/Shape" + name: "gradients_1/filter_type_1_0/concat_4_grad/ConcatOffset" + op: "ConcatOffset" + input: "gradients_1/filter_type_1_0/concat_4_grad/mod" + input: "gradients_1/filter_type_1_0/concat_4_grad/ShapeN" + input: "gradients_1/filter_type_1_0/concat_4_grad/ShapeN:1" + attr { + key: "N" + value { + i: 2 + } + } +} +node { + name: "gradients_1/filter_type_1_0/concat_4_grad/Slice" + op: "Slice" + input: "gradients_1/filter_type_1_0/add_4_grad/Reshape" + input: "gradients_1/filter_type_1_0/concat_4_grad/ConcatOffset" + input: "gradients_1/filter_type_1_0/concat_4_grad/ShapeN" + attr { + key: "Index" + value { + type: DT_INT32 + } + } attr { key: "T" value { type: DT_DOUBLE } } +} +node { + name: "gradients_1/filter_type_1_0/concat_4_grad/Slice_1" + op: "Slice" + input: "gradients_1/filter_type_1_0/add_4_grad/Reshape" + input: "gradients_1/filter_type_1_0/concat_4_grad/ConcatOffset:1" + input: "gradients_1/filter_type_1_0/concat_4_grad/ShapeN:1" attr { - key: "Tshape" + key: "Index" value { type: DT_INT32 } } + attr { + key: "T" + value { + type: DT_DOUBLE + } + } } node { - name: "gradients_2/filter_type_1_1/Reshape_9_grad/Shape" + name: "gradients_1/filter_type_1_0/Reshape_10_grad/Shape" op: "Shape" - input: "filter_type_1_1/add_3" + input: "filter_type_1_0/Tanh_4" attr { key: "T" value { @@ -60518,10 +49392,10 @@ node { } } node { - name: "gradients_2/filter_type_1_1/Reshape_9_grad/Reshape" + name: "gradients_1/filter_type_1_0/Reshape_10_grad/Reshape" op: "Reshape" - input: "gradients_2/filter_type_1_1/concat_4_grad/Slice_1" - input: "gradients_2/filter_type_1_1/Reshape_9_grad/Shape" + input: "gradients_1/filter_type_1_0/add_4_grad/Reshape_1" + input: "gradients_1/filter_type_1_0/Reshape_10_grad/Shape" attr { key: "T" value { @@ -60536,26 +49410,58 @@ node { } } node { - name: "gradients_2/filter_type_0_1/add_1_grad/Shape" - op: "Shape" - input: "filter_type_0_1/concat_1" + name: "gradients_1/filter_type_0_0/Tanh_1_grad/TanhGrad" + op: "TanhGrad" + input: "filter_type_0_0/Tanh_1" + input: "gradients_1/filter_type_0_0/Reshape_3_grad/Reshape" + attr { + key: "T" + value { + type: DT_DOUBLE + } + } +} +node { + name: "gradients_1/filter_type_0_0/Tanh_4_grad/TanhGrad" + op: "TanhGrad" + input: "filter_type_0_0/Tanh_4" + input: "gradients_1/filter_type_0_0/Reshape_10_grad/Reshape" + attr { + key: "T" + value { + type: DT_DOUBLE + } + } +} +node { + name: "gradients_1/filter_type_1_0/Tanh_1_grad/TanhGrad" + op: "TanhGrad" + input: "filter_type_1_0/Tanh_1" + input: "gradients_1/filter_type_1_0/Reshape_3_grad/Reshape" attr { key: "T" value { type: DT_DOUBLE } } +} +node { + name: "gradients_1/filter_type_1_0/Tanh_4_grad/TanhGrad" + op: "TanhGrad" + input: "filter_type_1_0/Tanh_4" + input: "gradients_1/filter_type_1_0/Reshape_10_grad/Reshape" attr { - key: "out_type" + key: "T" value { - type: DT_INT32 + type: DT_DOUBLE } } } node { - name: "gradients_2/filter_type_0_1/add_1_grad/Shape_1" - op: "Shape" - input: "filter_type_0_1/Reshape_3" + name: "gradients_1/filter_type_0_0/MatMul_1_grad/MatMul" + op: "MatMul" + input: "gradients_1/filter_type_0_0/Tanh_1_grad/TanhGrad" + input: "filter_type_0_0/matrix_2_0/read" attr { key: "T" value { @@ -60563,29 +49469,23 @@ node { } } attr { - key: "out_type" + key: "transpose_a" value { - type: DT_INT32 + b: false } } -} -node { - name: "gradients_2/filter_type_0_1/add_1_grad/BroadcastGradientArgs" - op: "BroadcastGradientArgs" - input: "gradients_2/filter_type_0_1/add_1_grad/Shape" - input: "gradients_2/filter_type_0_1/add_1_grad/Shape_1" attr { - key: "T" + key: "transpose_b" value { - type: DT_INT32 + b: true } } } node { - name: "gradients_2/filter_type_0_1/add_1_grad/Sum" - op: "Sum" - input: "gradients_2/filter_type_0_1/Reshape_4_grad/Reshape" - input: "gradients_2/filter_type_0_1/add_1_grad/BroadcastGradientArgs" + name: "gradients_1/filter_type_0_0/MatMul_5_grad/MatMul" + op: "MatMul" + input: "gradients_1/filter_type_0_0/Tanh_4_grad/TanhGrad" + input: "filter_type_0_0/matrix_2_1/read" attr { key: "T" value { @@ -60593,23 +49493,23 @@ node { } } attr { - key: "Tidx" + key: "transpose_a" value { - type: DT_INT32 + b: false } } attr { - key: "keep_dims" + key: "transpose_b" value { - b: false + b: true } } } node { - name: "gradients_2/filter_type_0_1/add_1_grad/Reshape" - op: "Reshape" - input: "gradients_2/filter_type_0_1/add_1_grad/Sum" - input: "gradients_2/filter_type_0_1/add_1_grad/Shape" + name: "gradients_1/filter_type_1_0/MatMul_1_grad/MatMul" + op: "MatMul" + input: "gradients_1/filter_type_1_0/Tanh_1_grad/TanhGrad" + input: "filter_type_1_0/matrix_2_0/read" attr { key: "T" value { @@ -60617,17 +49517,23 @@ node { } } attr { - key: "Tshape" + key: "transpose_a" value { - type: DT_INT32 + b: false + } + } + attr { + key: "transpose_b" + value { + b: true } } } node { - name: "gradients_2/filter_type_0_1/add_1_grad/Sum_1" - op: "Sum" - input: "gradients_2/filter_type_0_1/Reshape_4_grad/Reshape" - input: "gradients_2/filter_type_0_1/add_1_grad/BroadcastGradientArgs:1" + name: "gradients_1/filter_type_1_0/MatMul_5_grad/MatMul" + op: "MatMul" + input: "gradients_1/filter_type_1_0/Tanh_4_grad/TanhGrad" + input: "filter_type_1_0/matrix_2_1/read" attr { key: "T" value { @@ -60635,23 +49541,30 @@ node { } } attr { - key: "Tidx" + key: "transpose_a" value { - type: DT_INT32 + b: false } } attr { - key: "keep_dims" + key: "transpose_b" value { - b: false + b: true } } } node { - name: "gradients_2/filter_type_0_1/add_1_grad/Reshape_1" - op: "Reshape" - input: "gradients_2/filter_type_0_1/add_1_grad/Sum_1" - input: "gradients_2/filter_type_0_1/add_1_grad/Shape_1" + name: "gradients_1/AddN_11" + op: "AddN" + input: "gradients_1/filter_type_0_0/concat_1_grad/Slice" + input: "gradients_1/filter_type_0_0/concat_1_grad/Slice_1" + input: "gradients_1/filter_type_0_0/MatMul_1_grad/MatMul" + attr { + key: "N" + value { + i: 3 + } + } attr { key: "T" value { @@ -60659,16 +49572,18 @@ node { } } attr { - key: "Tshape" + key: "_class" value { - type: DT_INT32 + list { + s: "loc:@gradients_1/filter_type_0_0/concat_1_grad/Slice" + } } } } node { - name: "gradients_2/filter_type_0_1/add_3_grad/Shape" + name: "gradients_1/filter_type_0_0/add_grad/Shape" op: "Shape" - input: "filter_type_0_1/concat_3" + input: "filter_type_0_0/concat" attr { key: "T" value { @@ -60683,9 +49598,9 @@ node { } } node { - name: "gradients_2/filter_type_0_1/add_3_grad/Shape_1" + name: "gradients_1/filter_type_0_0/add_grad/Shape_1" op: "Shape" - input: "filter_type_0_1/Reshape_8" + input: "filter_type_0_0/Reshape_2" attr { key: "T" value { @@ -60700,10 +49615,10 @@ node { } } node { - name: "gradients_2/filter_type_0_1/add_3_grad/BroadcastGradientArgs" + name: "gradients_1/filter_type_0_0/add_grad/BroadcastGradientArgs" op: "BroadcastGradientArgs" - input: "gradients_2/filter_type_0_1/add_3_grad/Shape" - input: "gradients_2/filter_type_0_1/add_3_grad/Shape_1" + input: "gradients_1/filter_type_0_0/add_grad/Shape" + input: "gradients_1/filter_type_0_0/add_grad/Shape_1" attr { key: "T" value { @@ -60712,10 +49627,10 @@ node { } } node { - name: "gradients_2/filter_type_0_1/add_3_grad/Sum" + name: "gradients_1/filter_type_0_0/add_grad/Sum" op: "Sum" - input: "gradients_2/filter_type_0_1/Reshape_9_grad/Reshape" - input: "gradients_2/filter_type_0_1/add_3_grad/BroadcastGradientArgs" + input: "gradients_1/AddN_11" + input: "gradients_1/filter_type_0_0/add_grad/BroadcastGradientArgs" attr { key: "T" value { @@ -60736,10 +49651,10 @@ node { } } node { - name: "gradients_2/filter_type_0_1/add_3_grad/Reshape" + name: "gradients_1/filter_type_0_0/add_grad/Reshape" op: "Reshape" - input: "gradients_2/filter_type_0_1/add_3_grad/Sum" - input: "gradients_2/filter_type_0_1/add_3_grad/Shape" + input: "gradients_1/filter_type_0_0/add_grad/Sum" + input: "gradients_1/filter_type_0_0/add_grad/Shape" attr { key: "T" value { @@ -60754,10 +49669,10 @@ node { } } node { - name: "gradients_2/filter_type_0_1/add_3_grad/Sum_1" + name: "gradients_1/filter_type_0_0/add_grad/Sum_1" op: "Sum" - input: "gradients_2/filter_type_0_1/Reshape_9_grad/Reshape" - input: "gradients_2/filter_type_0_1/add_3_grad/BroadcastGradientArgs:1" + input: "gradients_1/AddN_11" + input: "gradients_1/filter_type_0_0/add_grad/BroadcastGradientArgs:1" attr { key: "T" value { @@ -60778,10 +49693,10 @@ node { } } node { - name: "gradients_2/filter_type_0_1/add_3_grad/Reshape_1" + name: "gradients_1/filter_type_0_0/add_grad/Reshape_1" op: "Reshape" - input: "gradients_2/filter_type_0_1/add_3_grad/Sum_1" - input: "gradients_2/filter_type_0_1/add_3_grad/Shape_1" + input: "gradients_1/filter_type_0_0/add_grad/Sum_1" + input: "gradients_1/filter_type_0_0/add_grad/Shape_1" attr { key: "T" value { @@ -60796,9 +49711,36 @@ node { } } node { - name: "gradients_2/filter_type_1_1/add_1_grad/Shape" + name: "gradients_1/AddN_12" + op: "AddN" + input: "gradients_1/filter_type_0_0/concat_4_grad/Slice" + input: "gradients_1/filter_type_0_0/concat_4_grad/Slice_1" + input: "gradients_1/filter_type_0_0/MatMul_5_grad/MatMul" + attr { + key: "N" + value { + i: 3 + } + } + attr { + key: "T" + value { + type: DT_DOUBLE + } + } + attr { + key: "_class" + value { + list { + s: "loc:@gradients_1/filter_type_0_0/concat_4_grad/Slice" + } + } + } +} +node { + name: "gradients_1/filter_type_0_0/add_3_grad/Shape" op: "Shape" - input: "filter_type_1_1/concat_1" + input: "filter_type_0_0/concat_3" attr { key: "T" value { @@ -60813,9 +49755,9 @@ node { } } node { - name: "gradients_2/filter_type_1_1/add_1_grad/Shape_1" + name: "gradients_1/filter_type_0_0/add_3_grad/Shape_1" op: "Shape" - input: "filter_type_1_1/Reshape_3" + input: "filter_type_0_0/Reshape_9" attr { key: "T" value { @@ -60830,10 +49772,10 @@ node { } } node { - name: "gradients_2/filter_type_1_1/add_1_grad/BroadcastGradientArgs" + name: "gradients_1/filter_type_0_0/add_3_grad/BroadcastGradientArgs" op: "BroadcastGradientArgs" - input: "gradients_2/filter_type_1_1/add_1_grad/Shape" - input: "gradients_2/filter_type_1_1/add_1_grad/Shape_1" + input: "gradients_1/filter_type_0_0/add_3_grad/Shape" + input: "gradients_1/filter_type_0_0/add_3_grad/Shape_1" attr { key: "T" value { @@ -60842,10 +49784,10 @@ node { } } node { - name: "gradients_2/filter_type_1_1/add_1_grad/Sum" + name: "gradients_1/filter_type_0_0/add_3_grad/Sum" op: "Sum" - input: "gradients_2/filter_type_1_1/Reshape_4_grad/Reshape" - input: "gradients_2/filter_type_1_1/add_1_grad/BroadcastGradientArgs" + input: "gradients_1/AddN_12" + input: "gradients_1/filter_type_0_0/add_3_grad/BroadcastGradientArgs" attr { key: "T" value { @@ -60866,10 +49808,10 @@ node { } } node { - name: "gradients_2/filter_type_1_1/add_1_grad/Reshape" + name: "gradients_1/filter_type_0_0/add_3_grad/Reshape" op: "Reshape" - input: "gradients_2/filter_type_1_1/add_1_grad/Sum" - input: "gradients_2/filter_type_1_1/add_1_grad/Shape" + input: "gradients_1/filter_type_0_0/add_3_grad/Sum" + input: "gradients_1/filter_type_0_0/add_3_grad/Shape" attr { key: "T" value { @@ -60884,10 +49826,10 @@ node { } } node { - name: "gradients_2/filter_type_1_1/add_1_grad/Sum_1" + name: "gradients_1/filter_type_0_0/add_3_grad/Sum_1" op: "Sum" - input: "gradients_2/filter_type_1_1/Reshape_4_grad/Reshape" - input: "gradients_2/filter_type_1_1/add_1_grad/BroadcastGradientArgs:1" + input: "gradients_1/AddN_12" + input: "gradients_1/filter_type_0_0/add_3_grad/BroadcastGradientArgs:1" attr { key: "T" value { @@ -60908,10 +49850,10 @@ node { } } node { - name: "gradients_2/filter_type_1_1/add_1_grad/Reshape_1" + name: "gradients_1/filter_type_0_0/add_3_grad/Reshape_1" op: "Reshape" - input: "gradients_2/filter_type_1_1/add_1_grad/Sum_1" - input: "gradients_2/filter_type_1_1/add_1_grad/Shape_1" + input: "gradients_1/filter_type_0_0/add_3_grad/Sum_1" + input: "gradients_1/filter_type_0_0/add_3_grad/Shape_1" attr { key: "T" value { @@ -60926,9 +49868,36 @@ node { } } node { - name: "gradients_2/filter_type_1_1/add_3_grad/Shape" + name: "gradients_1/AddN_13" + op: "AddN" + input: "gradients_1/filter_type_1_0/concat_1_grad/Slice" + input: "gradients_1/filter_type_1_0/concat_1_grad/Slice_1" + input: "gradients_1/filter_type_1_0/MatMul_1_grad/MatMul" + attr { + key: "N" + value { + i: 3 + } + } + attr { + key: "T" + value { + type: DT_DOUBLE + } + } + attr { + key: "_class" + value { + list { + s: "loc:@gradients_1/filter_type_1_0/concat_1_grad/Slice" + } + } + } +} +node { + name: "gradients_1/filter_type_1_0/add_grad/Shape" op: "Shape" - input: "filter_type_1_1/concat_3" + input: "filter_type_1_0/concat" attr { key: "T" value { @@ -60943,9 +49912,9 @@ node { } } node { - name: "gradients_2/filter_type_1_1/add_3_grad/Shape_1" + name: "gradients_1/filter_type_1_0/add_grad/Shape_1" op: "Shape" - input: "filter_type_1_1/Reshape_8" + input: "filter_type_1_0/Reshape_2" attr { key: "T" value { @@ -60960,10 +49929,10 @@ node { } } node { - name: "gradients_2/filter_type_1_1/add_3_grad/BroadcastGradientArgs" + name: "gradients_1/filter_type_1_0/add_grad/BroadcastGradientArgs" op: "BroadcastGradientArgs" - input: "gradients_2/filter_type_1_1/add_3_grad/Shape" - input: "gradients_2/filter_type_1_1/add_3_grad/Shape_1" + input: "gradients_1/filter_type_1_0/add_grad/Shape" + input: "gradients_1/filter_type_1_0/add_grad/Shape_1" attr { key: "T" value { @@ -60972,10 +49941,10 @@ node { } } node { - name: "gradients_2/filter_type_1_1/add_3_grad/Sum" + name: "gradients_1/filter_type_1_0/add_grad/Sum" op: "Sum" - input: "gradients_2/filter_type_1_1/Reshape_9_grad/Reshape" - input: "gradients_2/filter_type_1_1/add_3_grad/BroadcastGradientArgs" + input: "gradients_1/AddN_13" + input: "gradients_1/filter_type_1_0/add_grad/BroadcastGradientArgs" attr { key: "T" value { @@ -60996,10 +49965,10 @@ node { } } node { - name: "gradients_2/filter_type_1_1/add_3_grad/Reshape" + name: "gradients_1/filter_type_1_0/add_grad/Reshape" op: "Reshape" - input: "gradients_2/filter_type_1_1/add_3_grad/Sum" - input: "gradients_2/filter_type_1_1/add_3_grad/Shape" + input: "gradients_1/filter_type_1_0/add_grad/Sum" + input: "gradients_1/filter_type_1_0/add_grad/Shape" attr { key: "T" value { @@ -61014,10 +49983,10 @@ node { } } node { - name: "gradients_2/filter_type_1_1/add_3_grad/Sum_1" + name: "gradients_1/filter_type_1_0/add_grad/Sum_1" op: "Sum" - input: "gradients_2/filter_type_1_1/Reshape_9_grad/Reshape" - input: "gradients_2/filter_type_1_1/add_3_grad/BroadcastGradientArgs:1" + input: "gradients_1/AddN_13" + input: "gradients_1/filter_type_1_0/add_grad/BroadcastGradientArgs:1" attr { key: "T" value { @@ -61038,10 +50007,10 @@ node { } } node { - name: "gradients_2/filter_type_1_1/add_3_grad/Reshape_1" + name: "gradients_1/filter_type_1_0/add_grad/Reshape_1" op: "Reshape" - input: "gradients_2/filter_type_1_1/add_3_grad/Sum_1" - input: "gradients_2/filter_type_1_1/add_3_grad/Shape_1" + input: "gradients_1/filter_type_1_0/add_grad/Sum_1" + input: "gradients_1/filter_type_1_0/add_grad/Shape_1" attr { key: "T" value { @@ -61056,49 +50025,53 @@ node { } } node { - name: "gradients_2/filter_type_0_1/concat_1_grad/Rank" - op: "Const" + name: "gradients_1/AddN_14" + op: "AddN" + input: "gradients_1/filter_type_1_0/concat_4_grad/Slice" + input: "gradients_1/filter_type_1_0/concat_4_grad/Slice_1" + input: "gradients_1/filter_type_1_0/MatMul_5_grad/MatMul" attr { - key: "dtype" + key: "N" value { - type: DT_INT32 + i: 3 } } attr { - key: "value" + key: "T" value { - tensor { - dtype: DT_INT32 - tensor_shape { - } - int_val: 2 + type: DT_DOUBLE + } + } + attr { + key: "_class" + value { + list { + s: "loc:@gradients_1/filter_type_1_0/concat_4_grad/Slice" } } } } node { - name: "gradients_2/filter_type_0_1/concat_1_grad/mod" - op: "FloorMod" - input: "filter_type_0_1/concat_1/axis" - input: "gradients_2/filter_type_0_1/concat_1_grad/Rank" + name: "gradients_1/filter_type_1_0/add_3_grad/Shape" + op: "Shape" + input: "filter_type_1_0/concat_3" attr { key: "T" value { - type: DT_INT32 + type: DT_DOUBLE } } -} -node { - name: "gradients_2/filter_type_0_1/concat_1_grad/ShapeN" - op: "ShapeN" - input: "filter_type_0_1/add" - input: "filter_type_0_1/add" attr { - key: "N" + key: "out_type" value { - i: 2 + type: DT_INT32 } } +} +node { + name: "gradients_1/filter_type_1_0/add_3_grad/Shape_1" + op: "Shape" + input: "filter_type_1_0/Reshape_9" attr { key: "T" value { @@ -61113,60 +50086,64 @@ node { } } node { - name: "gradients_2/filter_type_0_1/concat_1_grad/ConcatOffset" - op: "ConcatOffset" - input: "gradients_2/filter_type_0_1/concat_1_grad/mod" - input: "gradients_2/filter_type_0_1/concat_1_grad/ShapeN" - input: "gradients_2/filter_type_0_1/concat_1_grad/ShapeN:1" + name: "gradients_1/filter_type_1_0/add_3_grad/BroadcastGradientArgs" + op: "BroadcastGradientArgs" + input: "gradients_1/filter_type_1_0/add_3_grad/Shape" + input: "gradients_1/filter_type_1_0/add_3_grad/Shape_1" attr { - key: "N" + key: "T" value { - i: 2 + type: DT_INT32 } } } node { - name: "gradients_2/filter_type_0_1/concat_1_grad/Slice" - op: "Slice" - input: "gradients_2/filter_type_0_1/add_1_grad/Reshape" - input: "gradients_2/filter_type_0_1/concat_1_grad/ConcatOffset" - input: "gradients_2/filter_type_0_1/concat_1_grad/ShapeN" + name: "gradients_1/filter_type_1_0/add_3_grad/Sum" + op: "Sum" + input: "gradients_1/AddN_14" + input: "gradients_1/filter_type_1_0/add_3_grad/BroadcastGradientArgs" attr { - key: "Index" + key: "T" + value { + type: DT_DOUBLE + } + } + attr { + key: "Tidx" value { type: DT_INT32 } } attr { - key: "T" + key: "keep_dims" value { - type: DT_DOUBLE + b: false } } } node { - name: "gradients_2/filter_type_0_1/concat_1_grad/Slice_1" - op: "Slice" - input: "gradients_2/filter_type_0_1/add_1_grad/Reshape" - input: "gradients_2/filter_type_0_1/concat_1_grad/ConcatOffset:1" - input: "gradients_2/filter_type_0_1/concat_1_grad/ShapeN:1" + name: "gradients_1/filter_type_1_0/add_3_grad/Reshape" + op: "Reshape" + input: "gradients_1/filter_type_1_0/add_3_grad/Sum" + input: "gradients_1/filter_type_1_0/add_3_grad/Shape" attr { - key: "Index" + key: "T" value { - type: DT_INT32 + type: DT_DOUBLE } } attr { - key: "T" + key: "Tshape" value { - type: DT_DOUBLE + type: DT_INT32 } } } node { - name: "gradients_2/filter_type_0_1/Reshape_3_grad/Shape" - op: "Shape" - input: "filter_type_0_1/Tanh_2" + name: "gradients_1/filter_type_1_0/add_3_grad/Sum_1" + op: "Sum" + input: "gradients_1/AddN_14" + input: "gradients_1/filter_type_1_0/add_3_grad/BroadcastGradientArgs:1" attr { key: "T" value { @@ -61174,17 +50151,23 @@ node { } } attr { - key: "out_type" + key: "Tidx" value { type: DT_INT32 } } + attr { + key: "keep_dims" + value { + b: false + } + } } node { - name: "gradients_2/filter_type_0_1/Reshape_3_grad/Reshape" + name: "gradients_1/filter_type_1_0/add_3_grad/Reshape_1" op: "Reshape" - input: "gradients_2/filter_type_0_1/add_1_grad/Reshape_1" - input: "gradients_2/filter_type_0_1/Reshape_3_grad/Shape" + input: "gradients_1/filter_type_1_0/add_3_grad/Sum_1" + input: "gradients_1/filter_type_1_0/add_3_grad/Shape_1" attr { key: "T" value { @@ -61199,7 +50182,7 @@ node { } } node { - name: "gradients_2/filter_type_0_1/concat_3_grad/Rank" + name: "gradients_1/filter_type_0_0/concat_grad/Rank" op: "Const" attr { key: "dtype" @@ -61220,10 +50203,10 @@ node { } } node { - name: "gradients_2/filter_type_0_1/concat_3_grad/mod" + name: "gradients_1/filter_type_0_0/concat_grad/mod" op: "FloorMod" - input: "filter_type_0_1/concat_3/axis" - input: "gradients_2/filter_type_0_1/concat_3_grad/Rank" + input: "filter_type_0_0/concat/axis" + input: "gradients_1/filter_type_0_0/concat_grad/Rank" attr { key: "T" value { @@ -61232,10 +50215,10 @@ node { } } node { - name: "gradients_2/filter_type_0_1/concat_3_grad/ShapeN" + name: "gradients_1/filter_type_0_0/concat_grad/ShapeN" op: "ShapeN" - input: "filter_type_0_1/add_2" - input: "filter_type_0_1/add_2" + input: "filter_type_0_0/Reshape_1" + input: "filter_type_0_0/Reshape_1" attr { key: "N" value { @@ -61256,11 +50239,11 @@ node { } } node { - name: "gradients_2/filter_type_0_1/concat_3_grad/ConcatOffset" + name: "gradients_1/filter_type_0_0/concat_grad/ConcatOffset" op: "ConcatOffset" - input: "gradients_2/filter_type_0_1/concat_3_grad/mod" - input: "gradients_2/filter_type_0_1/concat_3_grad/ShapeN" - input: "gradients_2/filter_type_0_1/concat_3_grad/ShapeN:1" + input: "gradients_1/filter_type_0_0/concat_grad/mod" + input: "gradients_1/filter_type_0_0/concat_grad/ShapeN" + input: "gradients_1/filter_type_0_0/concat_grad/ShapeN:1" attr { key: "N" value { @@ -61269,11 +50252,11 @@ node { } } node { - name: "gradients_2/filter_type_0_1/concat_3_grad/Slice" + name: "gradients_1/filter_type_0_0/concat_grad/Slice" op: "Slice" - input: "gradients_2/filter_type_0_1/add_3_grad/Reshape" - input: "gradients_2/filter_type_0_1/concat_3_grad/ConcatOffset" - input: "gradients_2/filter_type_0_1/concat_3_grad/ShapeN" + input: "gradients_1/filter_type_0_0/add_grad/Reshape" + input: "gradients_1/filter_type_0_0/concat_grad/ConcatOffset" + input: "gradients_1/filter_type_0_0/concat_grad/ShapeN" attr { key: "Index" value { @@ -61288,11 +50271,11 @@ node { } } node { - name: "gradients_2/filter_type_0_1/concat_3_grad/Slice_1" + name: "gradients_1/filter_type_0_0/concat_grad/Slice_1" op: "Slice" - input: "gradients_2/filter_type_0_1/add_3_grad/Reshape" - input: "gradients_2/filter_type_0_1/concat_3_grad/ConcatOffset:1" - input: "gradients_2/filter_type_0_1/concat_3_grad/ShapeN:1" + input: "gradients_1/filter_type_0_0/add_grad/Reshape" + input: "gradients_1/filter_type_0_0/concat_grad/ConcatOffset:1" + input: "gradients_1/filter_type_0_0/concat_grad/ShapeN:1" attr { key: "Index" value { @@ -61307,9 +50290,9 @@ node { } } node { - name: "gradients_2/filter_type_0_1/Reshape_8_grad/Shape" + name: "gradients_1/filter_type_0_0/Reshape_2_grad/Shape" op: "Shape" - input: "filter_type_0_1/Tanh_5" + input: "filter_type_0_0/Tanh" attr { key: "T" value { @@ -61324,10 +50307,10 @@ node { } } node { - name: "gradients_2/filter_type_0_1/Reshape_8_grad/Reshape" + name: "gradients_1/filter_type_0_0/Reshape_2_grad/Reshape" op: "Reshape" - input: "gradients_2/filter_type_0_1/add_3_grad/Reshape_1" - input: "gradients_2/filter_type_0_1/Reshape_8_grad/Shape" + input: "gradients_1/filter_type_0_0/add_grad/Reshape_1" + input: "gradients_1/filter_type_0_0/Reshape_2_grad/Shape" attr { key: "T" value { @@ -61342,7 +50325,7 @@ node { } } node { - name: "gradients_2/filter_type_1_1/concat_1_grad/Rank" + name: "gradients_1/filter_type_0_0/concat_3_grad/Rank" op: "Const" attr { key: "dtype" @@ -61363,10 +50346,10 @@ node { } } node { - name: "gradients_2/filter_type_1_1/concat_1_grad/mod" + name: "gradients_1/filter_type_0_0/concat_3_grad/mod" op: "FloorMod" - input: "filter_type_1_1/concat_1/axis" - input: "gradients_2/filter_type_1_1/concat_1_grad/Rank" + input: "filter_type_0_0/concat_3/axis" + input: "gradients_1/filter_type_0_0/concat_3_grad/Rank" attr { key: "T" value { @@ -61375,10 +50358,10 @@ node { } } node { - name: "gradients_2/filter_type_1_1/concat_1_grad/ShapeN" + name: "gradients_1/filter_type_0_0/concat_3_grad/ShapeN" op: "ShapeN" - input: "filter_type_1_1/add" - input: "filter_type_1_1/add" + input: "filter_type_0_0/Reshape_8" + input: "filter_type_0_0/Reshape_8" attr { key: "N" value { @@ -61399,11 +50382,11 @@ node { } } node { - name: "gradients_2/filter_type_1_1/concat_1_grad/ConcatOffset" + name: "gradients_1/filter_type_0_0/concat_3_grad/ConcatOffset" op: "ConcatOffset" - input: "gradients_2/filter_type_1_1/concat_1_grad/mod" - input: "gradients_2/filter_type_1_1/concat_1_grad/ShapeN" - input: "gradients_2/filter_type_1_1/concat_1_grad/ShapeN:1" + input: "gradients_1/filter_type_0_0/concat_3_grad/mod" + input: "gradients_1/filter_type_0_0/concat_3_grad/ShapeN" + input: "gradients_1/filter_type_0_0/concat_3_grad/ShapeN:1" attr { key: "N" value { @@ -61412,11 +50395,11 @@ node { } } node { - name: "gradients_2/filter_type_1_1/concat_1_grad/Slice" + name: "gradients_1/filter_type_0_0/concat_3_grad/Slice" op: "Slice" - input: "gradients_2/filter_type_1_1/add_1_grad/Reshape" - input: "gradients_2/filter_type_1_1/concat_1_grad/ConcatOffset" - input: "gradients_2/filter_type_1_1/concat_1_grad/ShapeN" + input: "gradients_1/filter_type_0_0/add_3_grad/Reshape" + input: "gradients_1/filter_type_0_0/concat_3_grad/ConcatOffset" + input: "gradients_1/filter_type_0_0/concat_3_grad/ShapeN" attr { key: "Index" value { @@ -61431,11 +50414,11 @@ node { } } node { - name: "gradients_2/filter_type_1_1/concat_1_grad/Slice_1" + name: "gradients_1/filter_type_0_0/concat_3_grad/Slice_1" op: "Slice" - input: "gradients_2/filter_type_1_1/add_1_grad/Reshape" - input: "gradients_2/filter_type_1_1/concat_1_grad/ConcatOffset:1" - input: "gradients_2/filter_type_1_1/concat_1_grad/ShapeN:1" + input: "gradients_1/filter_type_0_0/add_3_grad/Reshape" + input: "gradients_1/filter_type_0_0/concat_3_grad/ConcatOffset:1" + input: "gradients_1/filter_type_0_0/concat_3_grad/ShapeN:1" attr { key: "Index" value { @@ -61450,9 +50433,9 @@ node { } } node { - name: "gradients_2/filter_type_1_1/Reshape_3_grad/Shape" + name: "gradients_1/filter_type_0_0/Reshape_9_grad/Shape" op: "Shape" - input: "filter_type_1_1/Tanh_2" + input: "filter_type_0_0/Tanh_3" attr { key: "T" value { @@ -61467,10 +50450,10 @@ node { } } node { - name: "gradients_2/filter_type_1_1/Reshape_3_grad/Reshape" + name: "gradients_1/filter_type_0_0/Reshape_9_grad/Reshape" op: "Reshape" - input: "gradients_2/filter_type_1_1/add_1_grad/Reshape_1" - input: "gradients_2/filter_type_1_1/Reshape_3_grad/Shape" + input: "gradients_1/filter_type_0_0/add_3_grad/Reshape_1" + input: "gradients_1/filter_type_0_0/Reshape_9_grad/Shape" attr { key: "T" value { @@ -61485,7 +50468,7 @@ node { } } node { - name: "gradients_2/filter_type_1_1/concat_3_grad/Rank" + name: "gradients_1/filter_type_1_0/concat_grad/Rank" op: "Const" attr { key: "dtype" @@ -61506,10 +50489,10 @@ node { } } node { - name: "gradients_2/filter_type_1_1/concat_3_grad/mod" + name: "gradients_1/filter_type_1_0/concat_grad/mod" op: "FloorMod" - input: "filter_type_1_1/concat_3/axis" - input: "gradients_2/filter_type_1_1/concat_3_grad/Rank" + input: "filter_type_1_0/concat/axis" + input: "gradients_1/filter_type_1_0/concat_grad/Rank" attr { key: "T" value { @@ -61518,10 +50501,10 @@ node { } } node { - name: "gradients_2/filter_type_1_1/concat_3_grad/ShapeN" + name: "gradients_1/filter_type_1_0/concat_grad/ShapeN" op: "ShapeN" - input: "filter_type_1_1/add_2" - input: "filter_type_1_1/add_2" + input: "filter_type_1_0/Reshape_1" + input: "filter_type_1_0/Reshape_1" attr { key: "N" value { @@ -61542,11 +50525,11 @@ node { } } node { - name: "gradients_2/filter_type_1_1/concat_3_grad/ConcatOffset" + name: "gradients_1/filter_type_1_0/concat_grad/ConcatOffset" op: "ConcatOffset" - input: "gradients_2/filter_type_1_1/concat_3_grad/mod" - input: "gradients_2/filter_type_1_1/concat_3_grad/ShapeN" - input: "gradients_2/filter_type_1_1/concat_3_grad/ShapeN:1" + input: "gradients_1/filter_type_1_0/concat_grad/mod" + input: "gradients_1/filter_type_1_0/concat_grad/ShapeN" + input: "gradients_1/filter_type_1_0/concat_grad/ShapeN:1" attr { key: "N" value { @@ -61555,11 +50538,11 @@ node { } } node { - name: "gradients_2/filter_type_1_1/concat_3_grad/Slice" + name: "gradients_1/filter_type_1_0/concat_grad/Slice" op: "Slice" - input: "gradients_2/filter_type_1_1/add_3_grad/Reshape" - input: "gradients_2/filter_type_1_1/concat_3_grad/ConcatOffset" - input: "gradients_2/filter_type_1_1/concat_3_grad/ShapeN" + input: "gradients_1/filter_type_1_0/add_grad/Reshape" + input: "gradients_1/filter_type_1_0/concat_grad/ConcatOffset" + input: "gradients_1/filter_type_1_0/concat_grad/ShapeN" attr { key: "Index" value { @@ -61574,11 +50557,11 @@ node { } } node { - name: "gradients_2/filter_type_1_1/concat_3_grad/Slice_1" + name: "gradients_1/filter_type_1_0/concat_grad/Slice_1" op: "Slice" - input: "gradients_2/filter_type_1_1/add_3_grad/Reshape" - input: "gradients_2/filter_type_1_1/concat_3_grad/ConcatOffset:1" - input: "gradients_2/filter_type_1_1/concat_3_grad/ShapeN:1" + input: "gradients_1/filter_type_1_0/add_grad/Reshape" + input: "gradients_1/filter_type_1_0/concat_grad/ConcatOffset:1" + input: "gradients_1/filter_type_1_0/concat_grad/ShapeN:1" attr { key: "Index" value { @@ -61593,9 +50576,9 @@ node { } } node { - name: "gradients_2/filter_type_1_1/Reshape_8_grad/Shape" + name: "gradients_1/filter_type_1_0/Reshape_2_grad/Shape" op: "Shape" - input: "filter_type_1_1/Tanh_5" + input: "filter_type_1_0/Tanh" attr { key: "T" value { @@ -61610,198 +50593,10 @@ node { } } node { - name: "gradients_2/filter_type_1_1/Reshape_8_grad/Reshape" + name: "gradients_1/filter_type_1_0/Reshape_2_grad/Reshape" op: "Reshape" - input: "gradients_2/filter_type_1_1/add_3_grad/Reshape_1" - input: "gradients_2/filter_type_1_1/Reshape_8_grad/Shape" - attr { - key: "T" - value { - type: DT_DOUBLE - } - } - attr { - key: "Tshape" - value { - type: DT_INT32 - } - } -} -node { - name: "gradients_2/filter_type_0_1/Tanh_2_grad/TanhGrad" - op: "TanhGrad" - input: "filter_type_0_1/Tanh_2" - input: "gradients_2/filter_type_0_1/Reshape_3_grad/Reshape" - attr { - key: "T" - value { - type: DT_DOUBLE - } - } -} -node { - name: "gradients_2/filter_type_0_1/Tanh_5_grad/TanhGrad" - op: "TanhGrad" - input: "filter_type_0_1/Tanh_5" - input: "gradients_2/filter_type_0_1/Reshape_8_grad/Reshape" - attr { - key: "T" - value { - type: DT_DOUBLE - } - } -} -node { - name: 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DT_INT32 } } attr { - key: "out_type" + key: "value" value { - type: DT_INT32 + tensor { + dtype: DT_INT32 + tensor_shape { + } + int_val: 2 + } } } } node { - name: "gradients_2/filter_type_0_1/add_grad/BroadcastGradientArgs" - op: "BroadcastGradientArgs" - input: "gradients_2/filter_type_0_1/add_grad/Shape" - input: "gradients_2/filter_type_0_1/add_grad/Shape_1" + name: "gradients_1/filter_type_1_0/concat_3_grad/mod" + op: "FloorMod" + input: "filter_type_1_0/concat_3/axis" + input: "gradients_1/filter_type_1_0/concat_3_grad/Rank" attr { key: "T" value { @@ -61845,76 +50644,84 @@ node { } } node { - name: "gradients_2/filter_type_0_1/add_grad/Sum" - op: "Sum" - input: "gradients_2/AddN_5" - input: "gradients_2/filter_type_0_1/add_grad/BroadcastGradientArgs" + name: "gradients_1/filter_type_1_0/concat_3_grad/ShapeN" + op: "ShapeN" + input: "filter_type_1_0/Reshape_8" + input: "filter_type_1_0/Reshape_8" attr { - key: "T" + key: "N" value { - type: DT_DOUBLE + i: 2 } } attr { - key: "Tidx" + key: "T" value { - type: DT_INT32 + type: DT_DOUBLE } } attr { - key: "keep_dims" + key: "out_type" value { - b: false + type: DT_INT32 } } } node { - name: "gradients_2/filter_type_0_1/add_grad/Reshape" - op: "Reshape" - input: "gradients_2/filter_type_0_1/add_grad/Sum" - input: "gradients_2/filter_type_0_1/add_grad/Shape" + name: "gradients_1/filter_type_1_0/concat_3_grad/ConcatOffset" + op: "ConcatOffset" + input: "gradients_1/filter_type_1_0/concat_3_grad/mod" + input: "gradients_1/filter_type_1_0/concat_3_grad/ShapeN" + input: "gradients_1/filter_type_1_0/concat_3_grad/ShapeN:1" attr { - key: "T" + key: "N" value { - type: DT_DOUBLE + i: 2 } } +} +node { + name: "gradients_1/filter_type_1_0/concat_3_grad/Slice" + op: "Slice" + input: "gradients_1/filter_type_1_0/add_3_grad/Reshape" + input: "gradients_1/filter_type_1_0/concat_3_grad/ConcatOffset" + input: "gradients_1/filter_type_1_0/concat_3_grad/ShapeN" attr { - key: "Tshape" + key: "Index" value { type: DT_INT32 } } -} -node { - name: "gradients_2/filter_type_0_1/add_grad/Sum_1" - op: "Sum" - input: "gradients_2/AddN_5" - input: "gradients_2/filter_type_0_1/add_grad/BroadcastGradientArgs:1" attr { key: "T" value { type: DT_DOUBLE } } +} +node { + name: "gradients_1/filter_type_1_0/concat_3_grad/Slice_1" + op: "Slice" + input: "gradients_1/filter_type_1_0/add_3_grad/Reshape" + input: "gradients_1/filter_type_1_0/concat_3_grad/ConcatOffset:1" + input: "gradients_1/filter_type_1_0/concat_3_grad/ShapeN:1" attr { - key: "Tidx" + key: "Index" value { type: DT_INT32 } } attr { - key: "keep_dims" + key: "T" value { - b: false + type: DT_DOUBLE } } } node { - name: "gradients_2/filter_type_0_1/add_grad/Reshape_1" - op: "Reshape" - input: "gradients_2/filter_type_0_1/add_grad/Sum_1" - input: "gradients_2/filter_type_0_1/add_grad/Shape_1" + name: "gradients_1/filter_type_1_0/Reshape_9_grad/Shape" + op: "Shape" + input: "filter_type_1_0/Tanh_3" attr { key: "T" value { @@ -61922,24 +50729,17 @@ node { } } attr { - key: "Tshape" + key: "out_type" value { type: DT_INT32 } } } node { - name: "gradients_2/AddN_6" - op: "AddN" - input: "gradients_2/filter_type_0_1/concat_3_grad/Slice" - input: "gradients_2/filter_type_0_1/concat_3_grad/Slice_1" - input: "gradients_2/filter_type_0_1/MatMul_5_grad/MatMul" - attr { - key: "N" - value { - i: 3 - } - } + name: "gradients_1/filter_type_1_0/Reshape_9_grad/Reshape" + op: "Reshape" + input: "gradients_1/filter_type_1_0/add_3_grad/Reshape_1" + input: "gradients_1/filter_type_1_0/Reshape_9_grad/Shape" attr { key: "T" value { @@ -61947,65 +50747,65 @@ node { } } attr { - key: "_class" + key: "Tshape" value { - list { - s: "loc:@gradients_2/filter_type_0_1/concat_3_grad/Slice" - } + type: DT_INT32 } } } node { - name: "gradients_2/filter_type_0_1/add_2_grad/Shape" - op: "Shape" - input: "filter_type_0_1/concat_2" + name: "gradients_1/filter_type_0_0/Tanh_grad/TanhGrad" + op: "TanhGrad" + input: "filter_type_0_0/Tanh" + input: "gradients_1/filter_type_0_0/Reshape_2_grad/Reshape" attr { key: "T" value { type: DT_DOUBLE } } - attr { - key: "out_type" - value { - type: DT_INT32 - } - } } node { - name: "gradients_2/filter_type_0_1/add_2_grad/Shape_1" - op: "Shape" - input: "filter_type_0_1/Reshape_7" + name: "gradients_1/filter_type_0_0/Tanh_3_grad/TanhGrad" + op: "TanhGrad" + input: "filter_type_0_0/Tanh_3" + input: "gradients_1/filter_type_0_0/Reshape_9_grad/Reshape" attr { key: "T" value { type: DT_DOUBLE } } +} +node { + name: "gradients_1/filter_type_1_0/Tanh_grad/TanhGrad" + op: "TanhGrad" + input: "filter_type_1_0/Tanh" + input: "gradients_1/filter_type_1_0/Reshape_2_grad/Reshape" attr { - key: "out_type" + key: "T" value { - type: DT_INT32 + type: DT_DOUBLE } } } node { - name: "gradients_2/filter_type_0_1/add_2_grad/BroadcastGradientArgs" - op: "BroadcastGradientArgs" - input: "gradients_2/filter_type_0_1/add_2_grad/Shape" - input: "gradients_2/filter_type_0_1/add_2_grad/Shape_1" + name: "gradients_1/filter_type_1_0/Tanh_3_grad/TanhGrad" + op: "TanhGrad" + input: "filter_type_1_0/Tanh_3" + input: "gradients_1/filter_type_1_0/Reshape_9_grad/Reshape" attr { key: "T" value { - type: DT_INT32 + type: DT_DOUBLE } } } node { - name: "gradients_2/filter_type_0_1/add_2_grad/Sum" - op: "Sum" - input: "gradients_2/AddN_6" - input: "gradients_2/filter_type_0_1/add_2_grad/BroadcastGradientArgs" + name: "gradients_1/filter_type_0_0/MatMul_grad/MatMul" + op: "MatMul" + input: "gradients_1/filter_type_0_0/Tanh_grad/TanhGrad" + input: "filter_type_0_0/matrix_1_0/read" attr { key: "T" value { @@ -62013,23 +50813,23 @@ node { } } attr { - key: "Tidx" + key: "transpose_a" value { - type: DT_INT32 + b: false } } attr { - key: "keep_dims" + key: "transpose_b" value { - b: false + b: true } } } node { - name: "gradients_2/filter_type_0_1/add_2_grad/Reshape" - op: "Reshape" - input: "gradients_2/filter_type_0_1/add_2_grad/Sum" - input: "gradients_2/filter_type_0_1/add_2_grad/Shape" + name: "gradients_1/filter_type_0_0/MatMul_4_grad/MatMul" + op: "MatMul" + input: "gradients_1/filter_type_0_0/Tanh_3_grad/TanhGrad" + input: "filter_type_0_0/matrix_1_1/read" attr { key: "T" value { @@ -62037,17 +50837,23 @@ node { } } attr { - key: "Tshape" + key: "transpose_a" value { - type: DT_INT32 + b: false + } + } + attr { + key: "transpose_b" + value { + b: true } } } node { - name: "gradients_2/filter_type_0_1/add_2_grad/Sum_1" - op: "Sum" - input: "gradients_2/AddN_6" - input: "gradients_2/filter_type_0_1/add_2_grad/BroadcastGradientArgs:1" + name: "gradients_1/filter_type_1_0/MatMul_grad/MatMul" + op: "MatMul" + input: "gradients_1/filter_type_1_0/Tanh_grad/TanhGrad" + input: "filter_type_1_0/matrix_1_0/read" attr { key: "T" value { @@ -62055,23 +50861,23 @@ node { } } attr { - key: "Tidx" + key: "transpose_a" value { - type: DT_INT32 + b: false } } attr { - key: "keep_dims" + key: "transpose_b" value { - b: false + b: true } } } node { - name: 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key: "N" value { @@ -62107,15 +50919,15 @@ node { key: "_class" value { list { - s: "loc:@gradients_2/filter_type_1_1/concat_1_grad/Slice" + s: "loc:@gradients_1/filter_type_0_0/concat_grad/Slice" } } } } node { - name: "gradients_2/filter_type_1_1/add_grad/Shape" + name: "gradients_1/filter_type_0_0/Reshape_1_grad/Shape" op: "Shape" - input: "filter_type_1_1/concat" + input: "filter_type_0_0/Slice_1" attr { key: "T" value { @@ -62130,9 +50942,10 @@ node { } } node { - name: "gradients_2/filter_type_1_1/add_grad/Shape_1" - op: "Shape" - input: "filter_type_1_1/Reshape_2" + name: "gradients_1/filter_type_0_0/Reshape_1_grad/Reshape" + op: "Reshape" + input: "gradients_1/AddN_15" + input: "gradients_1/filter_type_0_0/Reshape_1_grad/Shape" attr { key: "T" value { @@ -62140,53 +50953,24 @@ node { } } attr { - key: "out_type" - value { - type: DT_INT32 - } - } -} -node { - name: "gradients_2/filter_type_1_1/add_grad/BroadcastGradientArgs" - op: "BroadcastGradientArgs" - input: "gradients_2/filter_type_1_1/add_grad/Shape" - input: "gradients_2/filter_type_1_1/add_grad/Shape_1" - attr { - key: "T" + key: "Tshape" value { type: DT_INT32 } } } node { - name: "gradients_2/filter_type_1_1/add_grad/Sum" - op: "Sum" - input: "gradients_2/AddN_7" - input: "gradients_2/filter_type_1_1/add_grad/BroadcastGradientArgs" - attr { - key: "T" - value { - type: DT_DOUBLE - } - } - attr { - key: "Tidx" - value { - type: DT_INT32 - } - } + name: "gradients_1/AddN_16" + op: "AddN" + input: "gradients_1/filter_type_0_0/concat_3_grad/Slice" + input: "gradients_1/filter_type_0_0/concat_3_grad/Slice_1" + input: "gradients_1/filter_type_0_0/MatMul_4_grad/MatMul" attr { - key: "keep_dims" + key: "N" value { - b: false + i: 3 } } -} -node { - name: "gradients_2/filter_type_1_1/add_grad/Reshape" - op: "Reshape" - input: "gradients_2/filter_type_1_1/add_grad/Sum" - input: "gradients_2/filter_type_1_1/add_grad/Shape" attr { key: "T" value { @@ -62194,17 +50978,18 @@ node { } } attr { - key: "Tshape" + key: "_class" value { - type: DT_INT32 + list { + s: "loc:@gradients_1/filter_type_0_0/concat_3_grad/Slice" + } } } } node { - name: "gradients_2/filter_type_1_1/add_grad/Sum_1" - op: "Sum" - input: "gradients_2/AddN_7" - input: "gradients_2/filter_type_1_1/add_grad/BroadcastGradientArgs:1" + name: "gradients_1/filter_type_0_0/Reshape_8_grad/Shape" + op: "Shape" + input: "filter_type_0_0/Slice_3" attr { key: "T" value { @@ -62212,23 +50997,17 @@ node { } } attr { - key: "Tidx" + key: "out_type" value { type: DT_INT32 } } - attr { - key: "keep_dims" - value { - b: false - } - } } node { - name: "gradients_2/filter_type_1_1/add_grad/Reshape_1" + name: "gradients_1/filter_type_0_0/Reshape_8_grad/Reshape" op: "Reshape" - input: "gradients_2/filter_type_1_1/add_grad/Sum_1" - input: "gradients_2/filter_type_1_1/add_grad/Shape_1" + input: "gradients_1/AddN_16" + input: "gradients_1/filter_type_0_0/Reshape_8_grad/Shape" attr { key: "T" value { @@ -62243,11 +51022,11 @@ node { } } node { - name: "gradients_2/AddN_8" + name: "gradients_1/AddN_17" op: "AddN" - input: "gradients_2/filter_type_1_1/concat_3_grad/Slice" - input: "gradients_2/filter_type_1_1/concat_3_grad/Slice_1" - input: "gradients_2/filter_type_1_1/MatMul_5_grad/MatMul" + input: "gradients_1/filter_type_1_0/concat_grad/Slice" + input: "gradients_1/filter_type_1_0/concat_grad/Slice_1" + input: "gradients_1/filter_type_1_0/MatMul_grad/MatMul" attr { key: "N" value { @@ -62264,15 +51043,15 @@ node { key: "_class" value { list { - s: "loc:@gradients_2/filter_type_1_1/concat_3_grad/Slice" + s: "loc:@gradients_1/filter_type_1_0/concat_grad/Slice" } } } } node { - name: "gradients_2/filter_type_1_1/add_2_grad/Shape" + name: "gradients_1/filter_type_1_0/Reshape_1_grad/Shape" op: "Shape" - input: "filter_type_1_1/concat_2" + input: "filter_type_1_0/Slice_1" attr { key: "T" value { @@ -62287,9 +51066,10 @@ node { } } node { - name: "gradients_2/filter_type_1_1/add_2_grad/Shape_1" - op: "Shape" - input: "filter_type_1_1/Reshape_7" + name: "gradients_1/filter_type_1_0/Reshape_1_grad/Reshape" + op: "Reshape" + input: "gradients_1/AddN_17" + input: "gradients_1/filter_type_1_0/Reshape_1_grad/Shape" attr { key: "T" value { @@ -62297,29 +51077,24 @@ node { } } attr { - key: "out_type" + key: "Tshape" value { type: DT_INT32 } } } node { - name: "gradients_2/filter_type_1_1/add_2_grad/BroadcastGradientArgs" - op: "BroadcastGradientArgs" - input: "gradients_2/filter_type_1_1/add_2_grad/Shape" - input: "gradients_2/filter_type_1_1/add_2_grad/Shape_1" + name: "gradients_1/AddN_18" + op: "AddN" + input: "gradients_1/filter_type_1_0/concat_3_grad/Slice" + input: "gradients_1/filter_type_1_0/concat_3_grad/Slice_1" + input: "gradients_1/filter_type_1_0/MatMul_4_grad/MatMul" attr { - key: "T" + key: "N" value { - type: DT_INT32 + i: 3 } } -} -node { - name: "gradients_2/filter_type_1_1/add_2_grad/Sum" - op: "Sum" - input: "gradients_2/AddN_8" - input: "gradients_2/filter_type_1_1/add_2_grad/BroadcastGradientArgs" attr { key: "T" value { @@ -62327,23 +51102,18 @@ node { } } attr { - key: "Tidx" - value { - type: DT_INT32 - } - } - attr { - key: "keep_dims" + key: "_class" value { - b: false + list { + s: "loc:@gradients_1/filter_type_1_0/concat_3_grad/Slice" + } } } } node { - name: "gradients_2/filter_type_1_1/add_2_grad/Reshape" - op: "Reshape" - input: "gradients_2/filter_type_1_1/add_2_grad/Sum" - input: "gradients_2/filter_type_1_1/add_2_grad/Shape" + name: "gradients_1/filter_type_1_0/Reshape_8_grad/Shape" + op: "Shape" + input: "filter_type_1_0/Slice_3" attr { key: "T" value { @@ -62351,17 +51121,17 @@ node { } } attr { - key: "Tshape" + key: "out_type" value { type: DT_INT32 } } } node { - name: "gradients_2/filter_type_1_1/add_2_grad/Sum_1" - op: "Sum" - input: "gradients_2/AddN_8" - input: "gradients_2/filter_type_1_1/add_2_grad/BroadcastGradientArgs:1" + name: "gradients_1/filter_type_1_0/Reshape_8_grad/Reshape" + op: "Reshape" + input: "gradients_1/AddN_18" + input: "gradients_1/filter_type_1_0/Reshape_8_grad/Shape" attr { key: "T" value { @@ -62369,23 +51139,37 @@ node { } } attr { - key: "Tidx" + key: "Tshape" value { type: DT_INT32 } } +} +node { + name: "gradients_1/filter_type_0_0/Slice_1_grad/Rank" + op: "Const" attr { - key: "keep_dims" + key: "dtype" value { - b: false + type: DT_INT32 + } + } + attr { + key: "value" + value { + tensor { + dtype: DT_INT32 + tensor_shape { + } + int_val: 2 + } } } } node { - name: "gradients_2/filter_type_1_1/add_2_grad/Reshape_1" - op: "Reshape" - input: "gradients_2/filter_type_1_1/add_2_grad/Sum_1" - input: "gradients_2/filter_type_1_1/add_2_grad/Shape_1" + name: "gradients_1/filter_type_0_0/Slice_1_grad/Shape" + op: "Shape" + input: "filter_type_0_0/Slice_1" attr { key: "T" value { @@ -62393,14 +51177,14 @@ node { } } attr { - key: "Tshape" + key: "out_type" value { type: DT_INT32 } } } node { - name: "gradients_2/filter_type_0_1/concat_grad/Rank" + name: "gradients_1/filter_type_0_0/Slice_1_grad/stack/1" op: "Const" attr { key: "dtype" @@ -62415,28 +51199,16 @@ node { dtype: DT_INT32 tensor_shape { } - int_val: 2 + int_val: 1 } } } } node { - name: "gradients_2/filter_type_0_1/concat_grad/mod" - op: "FloorMod" - input: "filter_type_0_1/concat/axis" - input: "gradients_2/filter_type_0_1/concat_grad/Rank" - attr { - key: "T" - value { - type: DT_INT32 - } - } -} -node { - name: "gradients_2/filter_type_0_1/concat_grad/ShapeN" - op: "ShapeN" - input: "filter_type_0_1/Reshape_1" - input: "filter_type_0_1/Reshape_1" + name: "gradients_1/filter_type_0_0/Slice_1_grad/stack" + op: "Pack" + input: "gradients_1/filter_type_0_0/Slice_1_grad/Rank" + input: "gradients_1/filter_type_0_0/Slice_1_grad/stack/1" attr { key: "N" value { @@ -62446,93 +51218,84 @@ node { attr { key: "T" value { - type: DT_DOUBLE + type: DT_INT32 } } attr { - key: "out_type" + key: "axis" value { - type: DT_INT32 + i: 0 } } } node { - name: 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"gradients_2/filter_type_0_1/concat_grad/Slice_1" - op: "Slice" - input: "gradients_2/filter_type_0_1/add_grad/Reshape" - input: "gradients_2/filter_type_0_1/concat_grad/ConcatOffset:1" - input: "gradients_2/filter_type_0_1/concat_grad/ShapeN:1" attr { - key: "Index" + key: "out_type" value { type: DT_INT32 } } +} +node { + name: "gradients_1/filter_type_0_0/Slice_1_grad/sub" + op: "Sub" + input: "gradients_1/filter_type_0_0/Slice_1_grad/Shape_1" + input: "gradients_1/filter_type_0_0/Slice_1_grad/Shape" attr { key: "T" value { - type: DT_DOUBLE + type: DT_INT32 } } } node { - name: "gradients_2/filter_type_0_1/Reshape_2_grad/Shape" - op: "Shape" - input: "filter_type_0_1/Tanh_1" + name: "gradients_1/filter_type_0_0/Slice_1_grad/sub_1" + op: "Sub" + input: "gradients_1/filter_type_0_0/Slice_1_grad/sub" + input: "filter_type_0_0/Slice_1/begin" attr { key: "T" - value { - type: DT_DOUBLE - } - } - attr { - key: "out_type" value { type: DT_INT32 } } } node { - name: "gradients_2/filter_type_0_1/Reshape_2_grad/Reshape" + name: "gradients_1/filter_type_0_0/Slice_1_grad/Reshape_1" op: "Reshape" - input: "gradients_2/filter_type_0_1/add_grad/Reshape_1" - input: "gradients_2/filter_type_0_1/Reshape_2_grad/Shape" + input: "gradients_1/filter_type_0_0/Slice_1_grad/sub_1" + input: "gradients_1/filter_type_0_0/Slice_1_grad/stack" attr { key: "T" value { - type: DT_DOUBLE + type: DT_INT32 } } attr { @@ -62543,7 +51306,7 @@ node { } } node { - name: "gradients_2/filter_type_0_1/concat_2_grad/Rank" + name: "gradients_1/filter_type_0_0/Slice_1_grad/concat/axis" op: "Const" attr { key: "dtype" @@ -62558,28 +51321,17 @@ node { dtype: DT_INT32 tensor_shape { } - int_val: 2 + int_val: 1 } } } } node { - name: "gradients_2/filter_type_0_1/concat_2_grad/mod" - op: "FloorMod" - input: "filter_type_0_1/concat_2/axis" - input: "gradients_2/filter_type_0_1/concat_2_grad/Rank" - attr { - key: "T" - value { - type: DT_INT32 - } - } -} -node { - name: 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"gradients_1/filter_type_0_0/Slice_1_grad/concat" attr { - key: "N" + key: "T" value { - i: 2 + type: DT_DOUBLE } } -} -node { - name: "gradients_2/filter_type_0_1/concat_2_grad/Slice" - op: "Slice" - input: "gradients_2/filter_type_0_1/add_2_grad/Reshape" - input: "gradients_2/filter_type_0_1/concat_2_grad/ConcatOffset" - input: "gradients_2/filter_type_0_1/concat_2_grad/ShapeN" attr { - key: "Index" + key: "Tpaddings" value { type: DT_INT32 } } - attr { - key: "T" - value { - type: DT_DOUBLE - } - } } node { - name: "gradients_2/filter_type_0_1/concat_2_grad/Slice_1" - op: "Slice" - input: "gradients_2/filter_type_0_1/add_2_grad/Reshape" - input: "gradients_2/filter_type_0_1/concat_2_grad/ConcatOffset:1" - input: "gradients_2/filter_type_0_1/concat_2_grad/ShapeN:1" + name: "gradients_1/filter_type_0_0/Slice_3_grad/Rank" + op: "Const" attr { - key: "Index" + key: "dtype" value { type: DT_INT32 } } attr { - key: "T" + key: "value" value { - type: DT_DOUBLE + tensor { + dtype: DT_INT32 + tensor_shape { + } + int_val: 2 + } } } } node { - name: "gradients_2/filter_type_0_1/Reshape_7_grad/Shape" + name: "gradients_1/filter_type_0_0/Slice_3_grad/Shape" op: "Shape" - input: "filter_type_0_1/Tanh_4" + input: "filter_type_0_0/Slice_3" attr { key: "T" value { @@ -62668,25 +51408,7 @@ node { } } node { - name: "gradients_2/filter_type_0_1/Reshape_7_grad/Reshape" - op: "Reshape" - input: "gradients_2/filter_type_0_1/add_2_grad/Reshape_1" - input: "gradients_2/filter_type_0_1/Reshape_7_grad/Shape" - attr { - key: "T" - value { - type: DT_DOUBLE - } - } - attr { - key: "Tshape" - value { - type: DT_INT32 - } - } -} -node { - name: "gradients_2/filter_type_1_1/concat_grad/Rank" + name: "gradients_1/filter_type_0_0/Slice_3_grad/stack/1" op: "Const" attr { key: "dtype" @@ -62701,28 +51423,16 @@ node { dtype: DT_INT32 tensor_shape { } - int_val: 2 + int_val: 1 } } } } node { - name: "gradients_2/filter_type_1_1/concat_grad/mod" - op: "FloorMod" - input: 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i: 3 - } - } + name: "gradients_1/filter_type_1_0/Slice_3_grad/Rank" + op: "Const" attr { - key: "T" + key: "dtype" value { - type: DT_DOUBLE + type: DT_INT32 } } attr { - key: "_class" + key: "value" value { - list { - s: "loc:@gradients_2/filter_type_0_1/concat_2_grad/Slice" + tensor { + dtype: DT_INT32 + tensor_shape { + } + int_val: 2 } } } } node { - name: "gradients_2/filter_type_0_1/Reshape_6_grad/Shape" + name: "gradients_1/filter_type_1_0/Slice_3_grad/Shape" op: "Shape" - input: "filter_type_0_1/Tanh_3" + input: "filter_type_1_0/Slice_3" attr { key: "T" value { @@ -63222,72 +51856,72 @@ node { } } node { - name: "gradients_2/filter_type_0_1/Reshape_6_grad/Reshape" - op: "Reshape" - input: "gradients_2/AddN_10" - input: "gradients_2/filter_type_0_1/Reshape_6_grad/Shape" + name: "gradients_1/filter_type_1_0/Slice_3_grad/stack/1" + op: "Const" attr { - key: "T" + key: "dtype" value { - type: DT_DOUBLE + type: DT_INT32 } } attr { - key: "Tshape" + key: "value" value { - type: 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- type: DT_DOUBLE + type: DT_INT32 } } attr { @@ -63364,82 +51978,56 @@ node { } } node { - name: "gradients_2/filter_type_0_1/Tanh_grad/TanhGrad" - op: "TanhGrad" - input: "filter_type_0_1/Tanh" - input: "gradients_2/filter_type_0_1/Reshape_1_grad/Reshape" - attr { - key: "T" - value { - type: DT_DOUBLE - } - } -} -node { - name: "gradients_2/filter_type_0_1/Tanh_3_grad/TanhGrad" - op: "TanhGrad" - input: "filter_type_0_1/Tanh_3" - input: "gradients_2/filter_type_0_1/Reshape_6_grad/Reshape" + name: "gradients_1/filter_type_1_0/Slice_3_grad/concat/axis" + op: "Const" attr { - key: "T" + key: "dtype" value { - type: DT_DOUBLE + type: DT_INT32 } } -} -node { - name: "gradients_2/filter_type_1_1/Tanh_grad/TanhGrad" - op: "TanhGrad" - input: "filter_type_1_1/Tanh" - input: "gradients_2/filter_type_1_1/Reshape_1_grad/Reshape" attr { - key: "T" + key: "value" value { - type: DT_DOUBLE + tensor { + dtype: DT_INT32 + tensor_shape { + } + int_val: 1 + } } } } node { - name: "gradients_2/filter_type_1_1/Tanh_3_grad/TanhGrad" - op: "TanhGrad" - input: "filter_type_1_1/Tanh_3" - input: "gradients_2/filter_type_1_1/Reshape_6_grad/Reshape" + name: "gradients_1/filter_type_1_0/Slice_3_grad/concat" + op: "ConcatV2" + input: "gradients_1/filter_type_1_0/Slice_3_grad/Reshape" + input: "gradients_1/filter_type_1_0/Slice_3_grad/Reshape_1" + input: "gradients_1/filter_type_1_0/Slice_3_grad/concat/axis" attr { - key: "T" + key: "N" value { - type: DT_DOUBLE + i: 2 } } -} -node { - name: "gradients_2/filter_type_0_1/MatMul_grad/MatMul" - op: "MatMul" - input: "gradients_2/filter_type_0_1/Tanh_grad/TanhGrad" - input: "filter_type_0_1/matrix_1_0/read" attr { key: "T" value { - type: DT_DOUBLE - } - } - attr { - key: "transpose_a" - value { - b: false + type: DT_INT32 } } attr { - key: "transpose_b" + key: "Tidx" value { - b: true + type: DT_INT32 } } } node { - name: "gradients_2/filter_type_0_1/MatMul_3_grad/MatMul" - op: "MatMul" - input: "gradients_2/filter_type_0_1/Tanh_3_grad/TanhGrad" - input: "filter_type_0_1/matrix_1_1/read" + name: "gradients_1/filter_type_1_0/Slice_3_grad/Pad" + op: "Pad" + input: "gradients_1/filter_type_1_0/Reshape_8_grad/Reshape" + input: "gradients_1/filter_type_1_0/Slice_3_grad/concat" attr { key: "T" value { @@ -63447,23 +52035,16 @@ node { } } attr { - key: "transpose_a" - value { - b: false - } - } - attr { - key: "transpose_b" + key: "Tpaddings" value { - b: true + type: DT_INT32 } } } node { - name: "gradients_2/filter_type_1_1/MatMul_grad/MatMul" - op: "MatMul" - input: "gradients_2/filter_type_1_1/Tanh_grad/TanhGrad" - input: "filter_type_1_1/matrix_1_0/read" + name: "gradients_1/filter_type_0_0/Reshape_grad/Shape" + op: "Shape" + input: "filter_type_0_0/Slice" attr { key: "T" value { @@ -63471,23 +52052,17 @@ node { } } attr { - key: "transpose_a" - value { - b: false - } - } - attr { - key: "transpose_b" + key: "out_type" value { - b: true + type: DT_INT32 } } } node { - name: "gradients_2/filter_type_1_1/MatMul_3_grad/MatMul" - op: "MatMul" - input: "gradients_2/filter_type_1_1/Tanh_3_grad/TanhGrad" - input: "filter_type_1_1/matrix_1_1/read" + name: "gradients_1/filter_type_0_0/Reshape_grad/Reshape" + op: "Reshape" + input: "gradients_1/filter_type_0_0/Slice_1_grad/Pad" + input: "gradients_1/filter_type_0_0/Reshape_grad/Shape" attr { key: "T" value { @@ -63495,22 +52070,16 @@ node { } } attr { - key: "transpose_a" - value { - b: false - } - } - attr { - key: "transpose_b" + key: "Tshape" value { - b: true + type: DT_INT32 } } } node { - name: "gradients_2/filter_type_0_1/Reshape_grad/Shape" + name: "gradients_1/filter_type_0_0/Reshape_7_grad/Shape" op: "Shape" - input: "filter_type_0_1/Slice" + input: "filter_type_0_0/Slice_2" attr { key: "T" value { @@ -63525,10 +52094,10 @@ node { } } node { - name: "gradients_2/filter_type_0_1/Reshape_grad/Reshape" + name: "gradients_1/filter_type_0_0/Reshape_7_grad/Reshape" op: "Reshape" - input: "gradients_2/filter_type_0_1/MatMul_grad/MatMul" - input: "gradients_2/filter_type_0_1/Reshape_grad/Shape" + input: "gradients_1/filter_type_0_0/Slice_3_grad/Pad" + input: "gradients_1/filter_type_0_0/Reshape_7_grad/Shape" attr { key: "T" value { @@ -63543,9 +52112,9 @@ node { } } node { - name: "gradients_2/filter_type_0_1/Reshape_5_grad/Shape" + name: "gradients_1/filter_type_1_0/Reshape_grad/Shape" op: "Shape" - input: "filter_type_0_1/Slice_1" + input: "filter_type_1_0/Slice" attr { key: "T" value { @@ -63560,10 +52129,10 @@ node { } } node { - name: "gradients_2/filter_type_0_1/Reshape_5_grad/Reshape" + name: "gradients_1/filter_type_1_0/Reshape_grad/Reshape" op: "Reshape" - input: "gradients_2/filter_type_0_1/MatMul_3_grad/MatMul" - input: "gradients_2/filter_type_0_1/Reshape_5_grad/Shape" + input: "gradients_1/filter_type_1_0/Slice_1_grad/Pad" + input: "gradients_1/filter_type_1_0/Reshape_grad/Shape" attr { key: "T" value { @@ -63578,9 +52147,9 @@ node { } } node { - name: "gradients_2/filter_type_1_1/Reshape_grad/Shape" + name: "gradients_1/filter_type_1_0/Reshape_7_grad/Shape" op: "Shape" - input: "filter_type_1_1/Slice" + input: "filter_type_1_0/Slice_2" attr { key: "T" value { @@ -63595,10 +52164,10 @@ node { } } node { - name: "gradients_2/filter_type_1_1/Reshape_grad/Reshape" + name: "gradients_1/filter_type_1_0/Reshape_7_grad/Reshape" op: "Reshape" - input: "gradients_2/filter_type_1_1/MatMul_grad/MatMul" - input: "gradients_2/filter_type_1_1/Reshape_grad/Shape" + input: "gradients_1/filter_type_1_0/Slice_3_grad/Pad" + input: "gradients_1/filter_type_1_0/Reshape_7_grad/Shape" attr { key: "T" value { @@ -63613,27 +52182,16 @@ node { } } node { - name: "gradients_2/filter_type_1_1/Reshape_5_grad/Shape" - op: "Shape" - input: "filter_type_1_1/Slice_1" - attr { - key: "T" - value { - type: DT_DOUBLE - } - } + name: "gradients_1/AddN_19" + op: "AddN" + input: "gradients_1/filter_type_0_0/Reshape_6_grad/Reshape" + input: "gradients_1/filter_type_0_0/Reshape_grad/Reshape" attr { - key: "out_type" + key: "N" value { - type: DT_INT32 + i: 2 } } -} -node { - name: "gradients_2/filter_type_1_1/Reshape_5_grad/Reshape" - op: "Reshape" - input: "gradients_2/filter_type_1_1/MatMul_3_grad/MatMul" - input: "gradients_2/filter_type_1_1/Reshape_5_grad/Shape" attr { key: "T" value { @@ -63641,14 +52199,16 @@ node { } } attr { - key: "Tshape" + key: "_class" value { - type: DT_INT32 + list { + s: "loc:@gradients_1/filter_type_0_0/Reshape_6_grad/Reshape" + } } } } node { - name: "gradients_2/filter_type_0_1/Slice_grad/Rank" + name: "gradients_1/filter_type_0_0/Slice_grad/Rank" op: "Const" attr { key: "dtype" @@ -63669,9 +52229,9 @@ node { } } node { - name: "gradients_2/filter_type_0_1/Slice_grad/Shape" + name: "gradients_1/filter_type_0_0/Slice_grad/Shape" op: "Shape" - input: "filter_type_0_1/Slice" + input: "filter_type_0_0/Slice" attr { key: "T" value { @@ -63686,7 +52246,7 @@ node { } } node { - name: "gradients_2/filter_type_0_1/Slice_grad/stack/1" + name: "gradients_1/filter_type_0_0/Slice_grad/stack/1" op: "Const" attr { key: "dtype" @@ -63707,10 +52267,10 @@ node { } } node { - name: "gradients_2/filter_type_0_1/Slice_grad/stack" + name: "gradients_1/filter_type_0_0/Slice_grad/stack" op: "Pack" - input: "gradients_2/filter_type_0_1/Slice_grad/Rank" - input: "gradients_2/filter_type_0_1/Slice_grad/stack/1" + input: "gradients_1/filter_type_0_0/Slice_grad/Rank" + input: "gradients_1/filter_type_0_0/Slice_grad/stack/1" attr { key: "N" value { @@ -63731,10 +52291,10 @@ node { } } node { - name: "gradients_2/filter_type_0_1/Slice_grad/Reshape" + name: "gradients_1/filter_type_0_0/Slice_grad/Reshape" op: "Reshape" - input: "filter_type_0_1/Slice/begin" - input: "gradients_2/filter_type_0_1/Slice_grad/stack" + input: "filter_type_0_0/Slice/begin" + input: "gradients_1/filter_type_0_0/Slice_grad/stack" attr { key: "T" value { @@ -63749,9 +52309,9 @@ node { } } node { - name: "gradients_2/filter_type_0_1/Slice_grad/Shape_1" + name: "gradients_1/filter_type_0_0/Slice_grad/Shape_1" op: "Shape" - input: "Reshape_27" + input: "Reshape_13" attr { key: "T" value { @@ -63766,10 +52326,10 @@ node { } } node { - name: "gradients_2/filter_type_0_1/Slice_grad/sub" + name: "gradients_1/filter_type_0_0/Slice_grad/sub" op: "Sub" - input: "gradients_2/filter_type_0_1/Slice_grad/Shape_1" - input: "gradients_2/filter_type_0_1/Slice_grad/Shape" + input: "gradients_1/filter_type_0_0/Slice_grad/Shape_1" + input: "gradients_1/filter_type_0_0/Slice_grad/Shape" attr { key: "T" value { @@ -63778,10 +52338,10 @@ node { } } node { - name: "gradients_2/filter_type_0_1/Slice_grad/sub_1" + name: "gradients_1/filter_type_0_0/Slice_grad/sub_1" op: "Sub" - input: "gradients_2/filter_type_0_1/Slice_grad/sub" - input: "filter_type_0_1/Slice/begin" + input: "gradients_1/filter_type_0_0/Slice_grad/sub" + input: "filter_type_0_0/Slice/begin" attr { key: "T" value { @@ -63790,10 +52350,10 @@ node { } } node { - name: "gradients_2/filter_type_0_1/Slice_grad/Reshape_1" + name: "gradients_1/filter_type_0_0/Slice_grad/Reshape_1" op: "Reshape" - input: "gradients_2/filter_type_0_1/Slice_grad/sub_1" - input: "gradients_2/filter_type_0_1/Slice_grad/stack" + input: "gradients_1/filter_type_0_0/Slice_grad/sub_1" + input: "gradients_1/filter_type_0_0/Slice_grad/stack" attr { key: "T" value { @@ -63808,7 +52368,7 @@ node { } } node { - name: "gradients_2/filter_type_0_1/Slice_grad/concat/axis" + name: "gradients_1/filter_type_0_0/Slice_grad/concat/axis" op: "Const" attr { key: "dtype" @@ -63829,11 +52389,11 @@ node { } } node { - name: "gradients_2/filter_type_0_1/Slice_grad/concat" + name: "gradients_1/filter_type_0_0/Slice_grad/concat" op: "ConcatV2" - input: "gradients_2/filter_type_0_1/Slice_grad/Reshape" - input: "gradients_2/filter_type_0_1/Slice_grad/Reshape_1" - input: "gradients_2/filter_type_0_1/Slice_grad/concat/axis" + input: "gradients_1/filter_type_0_0/Slice_grad/Reshape" + input: "gradients_1/filter_type_0_0/Slice_grad/Reshape_1" + input: "gradients_1/filter_type_0_0/Slice_grad/concat/axis" attr { key: "N" value { @@ -63854,10 +52414,10 @@ node { } } node { - name: "gradients_2/filter_type_0_1/Slice_grad/Pad" + name: "gradients_1/filter_type_0_0/Slice_grad/Pad" op: "Pad" - input: "gradients_2/filter_type_0_1/Reshape_grad/Reshape" - input: "gradients_2/filter_type_0_1/Slice_grad/concat" + input: "gradients_1/AddN_19" + input: "gradients_1/filter_type_0_0/Slice_grad/concat" attr { key: "T" value { @@ -63872,7 +52432,33 @@ node { } } node { - name: "gradients_2/filter_type_0_1/Slice_1_grad/Rank" + name: "gradients_1/AddN_20" + op: "AddN" + input: "gradients_1/filter_type_0_0/Reshape_13_grad/Reshape" + input: "gradients_1/filter_type_0_0/Reshape_7_grad/Reshape" + attr { + key: "N" + value { + i: 2 + } + } + attr { + key: "T" + value { + type: DT_DOUBLE + } + } + attr { + key: "_class" + value { + list { + s: "loc:@gradients_1/filter_type_0_0/Reshape_13_grad/Reshape" + } + } + } +} +node { + name: "gradients_1/filter_type_0_0/Slice_2_grad/Rank" op: "Const" attr { key: "dtype" @@ -63893,9 +52479,9 @@ node { } } node { - name: "gradients_2/filter_type_0_1/Slice_1_grad/Shape" + name: "gradients_1/filter_type_0_0/Slice_2_grad/Shape" op: "Shape" - input: "filter_type_0_1/Slice_1" + input: "filter_type_0_0/Slice_2" attr { key: "T" value { @@ -63910,7 +52496,7 @@ node { } } node { - name: "gradients_2/filter_type_0_1/Slice_1_grad/stack/1" + name: "gradients_1/filter_type_0_0/Slice_2_grad/stack/1" op: "Const" attr { key: "dtype" @@ -63931,10 +52517,10 @@ node { } } node { - name: "gradients_2/filter_type_0_1/Slice_1_grad/stack" + name: "gradients_1/filter_type_0_0/Slice_2_grad/stack" op: "Pack" - input: "gradients_2/filter_type_0_1/Slice_1_grad/Rank" - input: "gradients_2/filter_type_0_1/Slice_1_grad/stack/1" + input: "gradients_1/filter_type_0_0/Slice_2_grad/Rank" + input: "gradients_1/filter_type_0_0/Slice_2_grad/stack/1" attr { key: "N" value { @@ -63955,10 +52541,10 @@ node { } } node { - name: "gradients_2/filter_type_0_1/Slice_1_grad/Reshape" + name: "gradients_1/filter_type_0_0/Slice_2_grad/Reshape" op: "Reshape" - input: "filter_type_0_1/Slice_1/begin" - input: "gradients_2/filter_type_0_1/Slice_1_grad/stack" + input: "filter_type_0_0/Slice_2/begin" + input: "gradients_1/filter_type_0_0/Slice_2_grad/stack" attr { key: "T" value { @@ -63973,9 +52559,9 @@ node { } } node { - name: "gradients_2/filter_type_0_1/Slice_1_grad/Shape_1" + name: "gradients_1/filter_type_0_0/Slice_2_grad/Shape_1" op: "Shape" - input: "Reshape_27" + input: "Reshape_13" attr { key: "T" value { @@ -63990,10 +52576,10 @@ node { } } node { - name: "gradients_2/filter_type_0_1/Slice_1_grad/sub" + name: "gradients_1/filter_type_0_0/Slice_2_grad/sub" op: "Sub" - input: "gradients_2/filter_type_0_1/Slice_1_grad/Shape_1" - input: "gradients_2/filter_type_0_1/Slice_1_grad/Shape" + input: "gradients_1/filter_type_0_0/Slice_2_grad/Shape_1" + input: "gradients_1/filter_type_0_0/Slice_2_grad/Shape" attr { key: "T" value { @@ -64002,10 +52588,10 @@ node { } } node { - name: "gradients_2/filter_type_0_1/Slice_1_grad/sub_1" + name: "gradients_1/filter_type_0_0/Slice_2_grad/sub_1" op: "Sub" - input: "gradients_2/filter_type_0_1/Slice_1_grad/sub" - input: "filter_type_0_1/Slice_1/begin" + input: "gradients_1/filter_type_0_0/Slice_2_grad/sub" + input: "filter_type_0_0/Slice_2/begin" attr { key: "T" value { @@ -64014,10 +52600,10 @@ node { } } node { - name: "gradients_2/filter_type_0_1/Slice_1_grad/Reshape_1" + name: "gradients_1/filter_type_0_0/Slice_2_grad/Reshape_1" op: "Reshape" - input: "gradients_2/filter_type_0_1/Slice_1_grad/sub_1" - input: "gradients_2/filter_type_0_1/Slice_1_grad/stack" + input: "gradients_1/filter_type_0_0/Slice_2_grad/sub_1" + input: "gradients_1/filter_type_0_0/Slice_2_grad/stack" attr { key: "T" value { @@ -64032,7 +52618,7 @@ node { } } node { - name: "gradients_2/filter_type_0_1/Slice_1_grad/concat/axis" + name: "gradients_1/filter_type_0_0/Slice_2_grad/concat/axis" op: "Const" attr { key: "dtype" @@ -64053,11 +52639,11 @@ node { } } node { - name: "gradients_2/filter_type_0_1/Slice_1_grad/concat" + name: "gradients_1/filter_type_0_0/Slice_2_grad/concat" op: "ConcatV2" - input: "gradients_2/filter_type_0_1/Slice_1_grad/Reshape" - input: "gradients_2/filter_type_0_1/Slice_1_grad/Reshape_1" - input: "gradients_2/filter_type_0_1/Slice_1_grad/concat/axis" + input: "gradients_1/filter_type_0_0/Slice_2_grad/Reshape" + input: "gradients_1/filter_type_0_0/Slice_2_grad/Reshape_1" + input: "gradients_1/filter_type_0_0/Slice_2_grad/concat/axis" attr { key: "N" value { @@ -64078,10 +52664,10 @@ node { } } node { - name: "gradients_2/filter_type_0_1/Slice_1_grad/Pad" + name: "gradients_1/filter_type_0_0/Slice_2_grad/Pad" op: "Pad" - input: "gradients_2/filter_type_0_1/Reshape_5_grad/Reshape" - input: "gradients_2/filter_type_0_1/Slice_1_grad/concat" + input: "gradients_1/AddN_20" + input: "gradients_1/filter_type_0_0/Slice_2_grad/concat" attr { key: "T" value { @@ -64096,7 +52682,33 @@ node { } } node { - name: "gradients_2/filter_type_1_1/Slice_grad/Rank" + name: "gradients_1/AddN_21" + op: "AddN" + input: "gradients_1/filter_type_1_0/Reshape_6_grad/Reshape" + input: "gradients_1/filter_type_1_0/Reshape_grad/Reshape" + attr { + key: "N" + value { + i: 2 + } + } + attr { + key: "T" + value { + type: DT_DOUBLE + } + } + attr { + key: "_class" + value { + list { + s: "loc:@gradients_1/filter_type_1_0/Reshape_6_grad/Reshape" + } + } + } +} +node { + name: "gradients_1/filter_type_1_0/Slice_grad/Rank" op: "Const" attr { key: "dtype" @@ -64117,9 +52729,9 @@ node { } } node { - name: "gradients_2/filter_type_1_1/Slice_grad/Shape" + name: "gradients_1/filter_type_1_0/Slice_grad/Shape" op: "Shape" - input: "filter_type_1_1/Slice" + input: "filter_type_1_0/Slice" attr { key: "T" value { @@ -64134,7 +52746,7 @@ node { } } node { - name: "gradients_2/filter_type_1_1/Slice_grad/stack/1" + name: "gradients_1/filter_type_1_0/Slice_grad/stack/1" op: "Const" attr { key: "dtype" @@ -64155,10 +52767,10 @@ node { } } node { - name: "gradients_2/filter_type_1_1/Slice_grad/stack" + name: "gradients_1/filter_type_1_0/Slice_grad/stack" op: "Pack" - input: "gradients_2/filter_type_1_1/Slice_grad/Rank" - input: "gradients_2/filter_type_1_1/Slice_grad/stack/1" + input: "gradients_1/filter_type_1_0/Slice_grad/Rank" + input: "gradients_1/filter_type_1_0/Slice_grad/stack/1" attr { key: "N" value { @@ -64179,10 +52791,10 @@ node { } } node { - name: "gradients_2/filter_type_1_1/Slice_grad/Reshape" + name: "gradients_1/filter_type_1_0/Slice_grad/Reshape" op: "Reshape" - input: "filter_type_1_1/Slice/begin" - input: "gradients_2/filter_type_1_1/Slice_grad/stack" + input: "filter_type_1_0/Slice/begin" + input: "gradients_1/filter_type_1_0/Slice_grad/stack" attr { key: "T" value { @@ -64197,9 +52809,9 @@ node { } } node { - name: "gradients_2/filter_type_1_1/Slice_grad/Shape_1" + name: "gradients_1/filter_type_1_0/Slice_grad/Shape_1" op: "Shape" - input: "Reshape_29" + input: "Reshape_17" attr { key: "T" value { @@ -64214,10 +52826,10 @@ node { } } node { - name: "gradients_2/filter_type_1_1/Slice_grad/sub" + name: "gradients_1/filter_type_1_0/Slice_grad/sub" op: "Sub" - input: "gradients_2/filter_type_1_1/Slice_grad/Shape_1" - input: "gradients_2/filter_type_1_1/Slice_grad/Shape" + input: "gradients_1/filter_type_1_0/Slice_grad/Shape_1" + input: "gradients_1/filter_type_1_0/Slice_grad/Shape" attr { key: "T" value { @@ -64226,10 +52838,10 @@ node { } } node { - name: "gradients_2/filter_type_1_1/Slice_grad/sub_1" + name: "gradients_1/filter_type_1_0/Slice_grad/sub_1" op: "Sub" - input: "gradients_2/filter_type_1_1/Slice_grad/sub" - input: "filter_type_1_1/Slice/begin" + input: "gradients_1/filter_type_1_0/Slice_grad/sub" + input: "filter_type_1_0/Slice/begin" attr { key: "T" value { @@ -64238,10 +52850,10 @@ node { } } node { - name: "gradients_2/filter_type_1_1/Slice_grad/Reshape_1" + name: "gradients_1/filter_type_1_0/Slice_grad/Reshape_1" op: "Reshape" - input: "gradients_2/filter_type_1_1/Slice_grad/sub_1" - input: "gradients_2/filter_type_1_1/Slice_grad/stack" + input: "gradients_1/filter_type_1_0/Slice_grad/sub_1" + input: "gradients_1/filter_type_1_0/Slice_grad/stack" attr { key: "T" value { @@ -64256,7 +52868,7 @@ node { } } node { - name: "gradients_2/filter_type_1_1/Slice_grad/concat/axis" + name: "gradients_1/filter_type_1_0/Slice_grad/concat/axis" op: "Const" attr { key: "dtype" @@ -64277,11 +52889,11 @@ node { } } node { - name: "gradients_2/filter_type_1_1/Slice_grad/concat" + name: "gradients_1/filter_type_1_0/Slice_grad/concat" op: "ConcatV2" - input: "gradients_2/filter_type_1_1/Slice_grad/Reshape" - input: "gradients_2/filter_type_1_1/Slice_grad/Reshape_1" - input: "gradients_2/filter_type_1_1/Slice_grad/concat/axis" + input: "gradients_1/filter_type_1_0/Slice_grad/Reshape" + input: "gradients_1/filter_type_1_0/Slice_grad/Reshape_1" + input: "gradients_1/filter_type_1_0/Slice_grad/concat/axis" attr { key: "N" value { @@ -64302,10 +52914,10 @@ node { } } node { - name: "gradients_2/filter_type_1_1/Slice_grad/Pad" + name: "gradients_1/filter_type_1_0/Slice_grad/Pad" op: "Pad" - input: "gradients_2/filter_type_1_1/Reshape_grad/Reshape" - input: "gradients_2/filter_type_1_1/Slice_grad/concat" + input: "gradients_1/AddN_21" + input: "gradients_1/filter_type_1_0/Slice_grad/concat" attr { key: "T" value { @@ -64320,7 +52932,33 @@ node { } } node { - name: "gradients_2/filter_type_1_1/Slice_1_grad/Rank" + name: "gradients_1/AddN_22" + op: "AddN" + input: "gradients_1/filter_type_1_0/Reshape_13_grad/Reshape" + input: "gradients_1/filter_type_1_0/Reshape_7_grad/Reshape" + attr { + key: "N" + value { + i: 2 + } + } + attr { + key: "T" + value { + type: DT_DOUBLE + } + } + attr { + key: "_class" + value { + list { + s: "loc:@gradients_1/filter_type_1_0/Reshape_13_grad/Reshape" + } + } + } +} +node { + name: "gradients_1/filter_type_1_0/Slice_2_grad/Rank" op: "Const" attr { key: "dtype" @@ -64341,9 +52979,9 @@ node { } } node { - name: "gradients_2/filter_type_1_1/Slice_1_grad/Shape" + name: "gradients_1/filter_type_1_0/Slice_2_grad/Shape" op: "Shape" - input: "filter_type_1_1/Slice_1" + input: "filter_type_1_0/Slice_2" attr { key: "T" value { @@ -64358,7 +52996,7 @@ node { } } node { - name: "gradients_2/filter_type_1_1/Slice_1_grad/stack/1" + name: "gradients_1/filter_type_1_0/Slice_2_grad/stack/1" op: "Const" attr { key: "dtype" @@ -64379,10 +53017,10 @@ node { } } node { - name: "gradients_2/filter_type_1_1/Slice_1_grad/stack" + name: "gradients_1/filter_type_1_0/Slice_2_grad/stack" op: "Pack" - input: "gradients_2/filter_type_1_1/Slice_1_grad/Rank" - input: "gradients_2/filter_type_1_1/Slice_1_grad/stack/1" + input: "gradients_1/filter_type_1_0/Slice_2_grad/Rank" + input: "gradients_1/filter_type_1_0/Slice_2_grad/stack/1" attr { key: "N" value { @@ -64403,10 +53041,10 @@ node { } } node { - name: "gradients_2/filter_type_1_1/Slice_1_grad/Reshape" + name: "gradients_1/filter_type_1_0/Slice_2_grad/Reshape" op: "Reshape" - input: "filter_type_1_1/Slice_1/begin" - input: "gradients_2/filter_type_1_1/Slice_1_grad/stack" + input: "filter_type_1_0/Slice_2/begin" + input: "gradients_1/filter_type_1_0/Slice_2_grad/stack" attr { key: "T" value { @@ -64421,9 +53059,9 @@ node { } } node { - name: "gradients_2/filter_type_1_1/Slice_1_grad/Shape_1" + name: "gradients_1/filter_type_1_0/Slice_2_grad/Shape_1" op: "Shape" - input: "Reshape_29" + input: "Reshape_17" attr { key: "T" value { @@ -64438,10 +53076,10 @@ node { } } node { - name: "gradients_2/filter_type_1_1/Slice_1_grad/sub" + name: "gradients_1/filter_type_1_0/Slice_2_grad/sub" op: "Sub" - input: "gradients_2/filter_type_1_1/Slice_1_grad/Shape_1" - input: "gradients_2/filter_type_1_1/Slice_1_grad/Shape" + input: "gradients_1/filter_type_1_0/Slice_2_grad/Shape_1" + input: "gradients_1/filter_type_1_0/Slice_2_grad/Shape" attr { key: "T" value { @@ -64450,10 +53088,10 @@ node { } } node { - name: "gradients_2/filter_type_1_1/Slice_1_grad/sub_1" + name: "gradients_1/filter_type_1_0/Slice_2_grad/sub_1" op: "Sub" - input: "gradients_2/filter_type_1_1/Slice_1_grad/sub" - input: "filter_type_1_1/Slice_1/begin" + input: "gradients_1/filter_type_1_0/Slice_2_grad/sub" + input: "filter_type_1_0/Slice_2/begin" attr { key: "T" value { @@ -64462,10 +53100,10 @@ node { } } node { - name: "gradients_2/filter_type_1_1/Slice_1_grad/Reshape_1" + name: "gradients_1/filter_type_1_0/Slice_2_grad/Reshape_1" op: "Reshape" - input: "gradients_2/filter_type_1_1/Slice_1_grad/sub_1" - input: "gradients_2/filter_type_1_1/Slice_1_grad/stack" + input: "gradients_1/filter_type_1_0/Slice_2_grad/sub_1" + input: "gradients_1/filter_type_1_0/Slice_2_grad/stack" attr { key: "T" value { @@ -64480,7 +53118,7 @@ node { } } node { - name: "gradients_2/filter_type_1_1/Slice_1_grad/concat/axis" + name: "gradients_1/filter_type_1_0/Slice_2_grad/concat/axis" op: "Const" attr { key: "dtype" @@ -64501,11 +53139,11 @@ node { } } node { - name: "gradients_2/filter_type_1_1/Slice_1_grad/concat" + name: "gradients_1/filter_type_1_0/Slice_2_grad/concat" op: "ConcatV2" - input: "gradients_2/filter_type_1_1/Slice_1_grad/Reshape" - input: "gradients_2/filter_type_1_1/Slice_1_grad/Reshape_1" - input: "gradients_2/filter_type_1_1/Slice_1_grad/concat/axis" + input: "gradients_1/filter_type_1_0/Slice_2_grad/Reshape" + input: "gradients_1/filter_type_1_0/Slice_2_grad/Reshape_1" + input: "gradients_1/filter_type_1_0/Slice_2_grad/concat/axis" attr { key: "N" value { @@ -64526,10 +53164,10 @@ node { } } node { - name: "gradients_2/filter_type_1_1/Slice_1_grad/Pad" + name: "gradients_1/filter_type_1_0/Slice_2_grad/Pad" op: "Pad" - input: "gradients_2/filter_type_1_1/Reshape_5_grad/Reshape" - input: "gradients_2/filter_type_1_1/Slice_1_grad/concat" + input: "gradients_1/AddN_22" + input: "gradients_1/filter_type_1_0/Slice_2_grad/concat" attr { key: "T" value { @@ -64544,10 +53182,10 @@ node { } } node { - name: "gradients_2/AddN_13" + name: "gradients_1/AddN_23" op: "AddN" - input: "gradients_2/filter_type_0_1/Slice_grad/Pad" - input: "gradients_2/filter_type_0_1/Slice_1_grad/Pad" + input: "gradients_1/filter_type_0_0/Slice_grad/Pad" + input: "gradients_1/filter_type_0_0/Slice_2_grad/Pad" attr { key: "N" value { @@ -64564,15 +53202,15 @@ node { key: "_class" value { list { - s: "loc:@gradients_2/filter_type_0_1/Slice_grad/Pad" + s: "loc:@gradients_1/filter_type_0_0/Slice_grad/Pad" } } } } node { - name: "gradients_2/Reshape_27_grad/Shape" + name: "gradients_1/Reshape_13_grad/Shape" op: "Shape" - input: "Slice_4" + input: "Slice_2" attr { key: "T" value { @@ -64587,10 +53225,10 @@ node { } } node { - name: "gradients_2/Reshape_27_grad/Reshape" + name: "gradients_1/Reshape_13_grad/Reshape" op: "Reshape" - input: "gradients_2/AddN_13" - input: "gradients_2/Reshape_27_grad/Shape" + input: "gradients_1/AddN_23" + input: "gradients_1/Reshape_13_grad/Shape" attr { key: "T" value { @@ -64605,10 +53243,10 @@ node { } } node { - name: "gradients_2/AddN_14" + name: "gradients_1/AddN_24" op: "AddN" - input: "gradients_2/filter_type_1_1/Slice_grad/Pad" - input: "gradients_2/filter_type_1_1/Slice_1_grad/Pad" + input: "gradients_1/filter_type_1_0/Slice_grad/Pad" + input: "gradients_1/filter_type_1_0/Slice_2_grad/Pad" attr { key: "N" value { @@ -64625,15 +53263,15 @@ node { key: "_class" value { list { - s: "loc:@gradients_2/filter_type_1_1/Slice_grad/Pad" + s: "loc:@gradients_1/filter_type_1_0/Slice_grad/Pad" } } } } node { - name: "gradients_2/Reshape_29_grad/Shape" + name: "gradients_1/Reshape_17_grad/Shape" op: "Shape" - input: "Slice_5" + input: "Slice_3" attr { key: "T" value { @@ -64648,10 +53286,10 @@ node { } } node { - name: "gradients_2/Reshape_29_grad/Reshape" + name: "gradients_1/Reshape_17_grad/Reshape" op: "Reshape" - input: "gradients_2/AddN_14" - input: "gradients_2/Reshape_29_grad/Shape" + input: "gradients_1/AddN_24" + input: "gradients_1/Reshape_17_grad/Shape" attr { key: "T" value { @@ -64666,7 +53304,7 @@ node { } } node { - name: "gradients_2/Slice_4_grad/Rank" + name: "gradients_1/Slice_2_grad/Rank" op: "Const" attr { key: "dtype" @@ -64681,15 +53319,15 @@ node { dtype: DT_INT32 tensor_shape { } - int_val: 2 + int_val: 3 } } } } node { - name: "gradients_2/Slice_4_grad/Shape" + name: "gradients_1/Slice_2_grad/Shape" op: "Shape" - input: "Slice_4" + input: "Slice_2" attr { key: "T" value { @@ -64704,7 +53342,7 @@ node { } } node { - name: "gradients_2/Slice_4_grad/stack/1" + name: "gradients_1/Slice_2_grad/stack/1" op: "Const" attr { key: "dtype" @@ -64725,10 +53363,10 @@ node { } } node { - name: "gradients_2/Slice_4_grad/stack" + name: "gradients_1/Slice_2_grad/stack" op: "Pack" - input: "gradients_2/Slice_4_grad/Rank" - input: "gradients_2/Slice_4_grad/stack/1" + input: "gradients_1/Slice_2_grad/Rank" + input: "gradients_1/Slice_2_grad/stack/1" attr { key: "N" value { @@ -64749,10 +53387,10 @@ node { } } node { - name: "gradients_2/Slice_4_grad/Reshape" + name: "gradients_1/Slice_2_grad/Reshape" op: "Reshape" - input: "Slice_4/begin" - input: "gradients_2/Slice_4_grad/stack" + input: "Slice_2/begin" + input: "gradients_1/Slice_2_grad/stack" attr { key: "T" value { @@ -64767,9 +53405,9 @@ node { } } node { - name: "gradients_2/Slice_4_grad/Shape_1" + name: "gradients_1/Slice_2_grad/Shape_1" op: "Shape" - input: "Reshape_26" + input: "Reshape_12" attr { key: "T" value { @@ -64784,10 +53422,10 @@ node { } } node { - name: "gradients_2/Slice_4_grad/sub" + name: "gradients_1/Slice_2_grad/sub" op: "Sub" - input: "gradients_2/Slice_4_grad/Shape_1" - input: "gradients_2/Slice_4_grad/Shape" + input: "gradients_1/Slice_2_grad/Shape_1" + input: "gradients_1/Slice_2_grad/Shape" attr { key: "T" value { @@ -64796,10 +53434,10 @@ node { } } node { - name: "gradients_2/Slice_4_grad/sub_1" + name: "gradients_1/Slice_2_grad/sub_1" op: "Sub" - input: "gradients_2/Slice_4_grad/sub" - input: "Slice_4/begin" + input: "gradients_1/Slice_2_grad/sub" + input: "Slice_2/begin" attr { key: "T" value { @@ -64808,10 +53446,10 @@ node { } } node { - name: "gradients_2/Slice_4_grad/Reshape_1" + name: "gradients_1/Slice_2_grad/Reshape_1" op: "Reshape" - input: "gradients_2/Slice_4_grad/sub_1" - input: "gradients_2/Slice_4_grad/stack" + input: "gradients_1/Slice_2_grad/sub_1" + input: "gradients_1/Slice_2_grad/stack" attr { key: "T" value { @@ -64826,7 +53464,7 @@ node { } } node { - name: "gradients_2/Slice_4_grad/concat/axis" + name: "gradients_1/Slice_2_grad/concat/axis" op: "Const" attr { key: "dtype" @@ -64847,11 +53485,11 @@ node { } } node { - name: "gradients_2/Slice_4_grad/concat" + name: "gradients_1/Slice_2_grad/concat" op: "ConcatV2" - input: "gradients_2/Slice_4_grad/Reshape" - input: "gradients_2/Slice_4_grad/Reshape_1" - input: "gradients_2/Slice_4_grad/concat/axis" + input: "gradients_1/Slice_2_grad/Reshape" + input: "gradients_1/Slice_2_grad/Reshape_1" + input: "gradients_1/Slice_2_grad/concat/axis" attr { key: "N" value { @@ -64872,10 +53510,10 @@ node { } } node { - name: "gradients_2/Slice_4_grad/Pad" + name: "gradients_1/Slice_2_grad/Pad" op: "Pad" - input: "gradients_2/Reshape_27_grad/Reshape" - input: "gradients_2/Slice_4_grad/concat" + input: "gradients_1/Reshape_13_grad/Reshape" + input: "gradients_1/Slice_2_grad/concat" attr { key: "T" value { @@ -64890,7 +53528,7 @@ node { } } node { - name: "gradients_2/Slice_5_grad/Rank" + name: "gradients_1/Slice_3_grad/Rank" op: "Const" attr { key: "dtype" @@ -64905,15 +53543,15 @@ node { dtype: DT_INT32 tensor_shape { } - int_val: 2 + int_val: 3 } } } } node { - name: "gradients_2/Slice_5_grad/Shape" + name: "gradients_1/Slice_3_grad/Shape" op: "Shape" - input: "Slice_5" + input: "Slice_3" attr { key: "T" value { @@ -64928,7 +53566,7 @@ node { } } node { - name: "gradients_2/Slice_5_grad/stack/1" + name: "gradients_1/Slice_3_grad/stack/1" op: "Const" attr { key: "dtype" @@ -64949,10 +53587,10 @@ node { } } node { - name: "gradients_2/Slice_5_grad/stack" + name: "gradients_1/Slice_3_grad/stack" op: "Pack" - input: "gradients_2/Slice_5_grad/Rank" - input: "gradients_2/Slice_5_grad/stack/1" + input: "gradients_1/Slice_3_grad/Rank" + input: "gradients_1/Slice_3_grad/stack/1" attr { key: "N" value { @@ -64973,10 +53611,10 @@ node { } } node { - name: "gradients_2/Slice_5_grad/Reshape" + name: "gradients_1/Slice_3_grad/Reshape" op: "Reshape" - input: "Slice_5/begin" - input: "gradients_2/Slice_5_grad/stack" + input: "Slice_3/begin" + input: "gradients_1/Slice_3_grad/stack" attr { key: "T" value { @@ -64991,9 +53629,9 @@ node { } } node { - name: "gradients_2/Slice_5_grad/Shape_1" + name: "gradients_1/Slice_3_grad/Shape_1" op: "Shape" - input: "Reshape_26" + input: "Reshape_12" attr { key: "T" value { @@ -65008,10 +53646,10 @@ node { } } node { - name: "gradients_2/Slice_5_grad/sub" + name: "gradients_1/Slice_3_grad/sub" op: "Sub" - input: "gradients_2/Slice_5_grad/Shape_1" - input: "gradients_2/Slice_5_grad/Shape" + input: "gradients_1/Slice_3_grad/Shape_1" + input: "gradients_1/Slice_3_grad/Shape" attr { key: "T" value { @@ -65020,10 +53658,10 @@ node { } } node { - name: "gradients_2/Slice_5_grad/sub_1" + name: "gradients_1/Slice_3_grad/sub_1" op: "Sub" - input: "gradients_2/Slice_5_grad/sub" - input: "Slice_5/begin" + input: "gradients_1/Slice_3_grad/sub" + input: "Slice_3/begin" attr { key: "T" value { @@ -65032,10 +53670,10 @@ node { } } node { - name: "gradients_2/Slice_5_grad/Reshape_1" + name: "gradients_1/Slice_3_grad/Reshape_1" op: "Reshape" - input: "gradients_2/Slice_5_grad/sub_1" - input: "gradients_2/Slice_5_grad/stack" + input: "gradients_1/Slice_3_grad/sub_1" + input: "gradients_1/Slice_3_grad/stack" attr { key: "T" value { @@ -65050,7 +53688,7 @@ node { } } node { - name: "gradients_2/Slice_5_grad/concat/axis" + name: "gradients_1/Slice_3_grad/concat/axis" op: "Const" attr { key: "dtype" @@ -65071,11 +53709,11 @@ node { } } node { - name: "gradients_2/Slice_5_grad/concat" + name: "gradients_1/Slice_3_grad/concat" op: "ConcatV2" - input: "gradients_2/Slice_5_grad/Reshape" - input: "gradients_2/Slice_5_grad/Reshape_1" - input: "gradients_2/Slice_5_grad/concat/axis" + input: "gradients_1/Slice_3_grad/Reshape" + input: "gradients_1/Slice_3_grad/Reshape_1" + input: "gradients_1/Slice_3_grad/concat/axis" attr { key: "N" value { @@ -65096,10 +53734,10 @@ node { } } node { - name: "gradients_2/Slice_5_grad/Pad" + name: "gradients_1/Slice_3_grad/Pad" op: "Pad" - input: "gradients_2/Reshape_29_grad/Reshape" - input: "gradients_2/Slice_5_grad/concat" + input: "gradients_1/Reshape_17_grad/Reshape" + input: "gradients_1/Slice_3_grad/concat" attr { key: "T" value { @@ -65114,10 +53752,10 @@ node { } } node { - name: "gradients_2/AddN_15" + name: "gradients_1/AddN_25" op: "AddN" - input: "gradients_2/Slice_4_grad/Pad" - input: "gradients_2/Slice_5_grad/Pad" + input: "gradients_1/Slice_2_grad/Pad" + input: "gradients_1/Slice_3_grad/Pad" attr { key: "N" value { @@ -65134,15 +53772,15 @@ node { key: "_class" value { list { - s: "loc:@gradients_2/Slice_4_grad/Pad" + s: "loc:@gradients_1/Slice_2_grad/Pad" } } } } node { - name: "gradients_2/Reshape_26_grad/Shape" + name: "gradients_1/Reshape_12_grad/Shape" op: "Shape" - input: "o_rmat_1" + input: "o_rmat_0" attr { key: "T" value { @@ -65157,10 +53795,10 @@ node { } } node { - name: "gradients_2/Reshape_26_grad/Reshape" + name: "gradients_1/Reshape_12_grad/Reshape" op: "Reshape" - input: "gradients_2/AddN_15" - input: "gradients_2/Reshape_26_grad/Shape" + input: "gradients_1/AddN_25" + input: "gradients_1/Reshape_12_grad/Shape" attr { key: "T" value { @@ -65175,7 +53813,7 @@ node { } } node { - name: "strided_slice_55/stack" + name: "strided_slice_47/stack" op: "Const" attr { key: "dtype" @@ -65199,7 +53837,7 @@ node { } } node { - name: "strided_slice_55/stack_1" + name: "strided_slice_47/stack_1" op: "Const" attr { key: "dtype" @@ -65223,7 +53861,7 @@ node { } } node { - name: "strided_slice_55/stack_2" + name: "strided_slice_47/stack_2" op: "Const" attr { key: "dtype" @@ -65247,12 +53885,12 @@ node { } } node { - name: "strided_slice_55" + name: "strided_slice_47" op: "StridedSlice" input: "t_natoms" - input: "strided_slice_55/stack" - input: "strided_slice_55/stack_1" - input: "strided_slice_55/stack_2" + input: "strided_slice_47/stack" + input: "strided_slice_47/stack_1" + input: "strided_slice_47/stack_2" attr { key: "Index" value { @@ -65297,7 +53935,7 @@ node { } } node { - name: "mul_30/y" + name: "mul_9/y" op: "Const" attr { key: "dtype" @@ -65312,16 +53950,16 @@ node { dtype: DT_INT32 tensor_shape { } - int_val: 138 + int_val: 240 } } } } node { - name: "mul_30" + name: "mul_9" op: "Mul" - input: "strided_slice_55" - input: "mul_30/y" + input: "strided_slice_47" + input: "mul_9/y" attr { key: "T" value { @@ -65330,7 +53968,7 @@ node { } } node { - name: "Reshape_40/shape/0" + name: "Reshape_32/shape/0" op: "Const" attr { key: "dtype" @@ -65351,10 +53989,10 @@ node { } } node { - name: "Reshape_40/shape" + name: "Reshape_32/shape" op: "Pack" - input: "Reshape_40/shape/0" - input: "mul_30" + input: "Reshape_32/shape/0" + input: "mul_9" attr { key: "N" value { @@ -65375,10 +54013,10 @@ node { } } node { - name: "Reshape_40" + name: "Reshape_32" op: "Reshape" - input: "gradients_2/Reshape_26_grad/Reshape" - input: "Reshape_40/shape" + input: "gradients_1/Reshape_12_grad/Reshape" + input: "Reshape_32/shape" attr { key: "T" value { @@ -65393,11 +54031,11 @@ node { } } node { - name: "ProdForceSeR" - op: "ProdForceSeR" - input: "Reshape_40" - input: "o_rmat_deriv_1" - input: "o_nlist_1" + name: "ProdForceSeA_1" + op: "ProdForceSeA" + input: "Reshape_32" + input: "o_rmat_deriv_0" + input: "o_nlist_0" input: "t_natoms" attr { key: "T" @@ -65405,60 +54043,48 @@ node { type: DT_DOUBLE } } -} -node { - name: "ProdVirialSeR" - op: "ProdVirialSeR" - input: "Reshape_40" - input: "o_rmat_deriv_1" - input: "o_rij_1" - input: "o_nlist_1" - input: "t_natoms" attr { - key: "T" + key: "n_a_sel" value { - type: DT_DOUBLE + i: 60 } } -} -node { - name: "add_12" - op: "AddV2" - input: "ProdForceSeA_1" - input: "ProdForceSeR" attr { - key: "T" + key: "n_r_sel" value { - type: DT_DOUBLE + i: 0 } } } node { - name: "add_13" - op: "AddV2" - input: "ProdVirialSeA_1" - input: "ProdVirialSeR" + name: "ProdVirialSeA_1" + op: "ProdVirialSeA" + input: "Reshape_32" + input: "o_rmat_deriv_0" + input: "o_rij_0" + input: "o_nlist_0" + input: "t_natoms" attr { key: "T" value { type: DT_DOUBLE } } -} -node { - name: "add_14" - op: "AddV2" - input: "ProdVirialSeA_1:1" - input: "ProdVirialSeR:1" attr { - key: "T" + key: "n_a_sel" value { - type: DT_DOUBLE + i: 60 + } + } + attr { + key: "n_r_sel" + value { + i: 0 } } } node { - name: "strided_slice_56/stack" + name: "strided_slice_48/stack" op: "Const" attr { key: "dtype" @@ -65482,7 +54108,7 @@ node { } } node { - name: "strided_slice_56/stack_1" + name: "strided_slice_48/stack_1" op: "Const" attr { key: "dtype" @@ -65506,7 +54132,7 @@ node { } } node { - name: "strided_slice_56/stack_2" + name: "strided_slice_48/stack_2" op: "Const" attr { key: "dtype" @@ -65530,12 +54156,12 @@ node { } } node { - name: "strided_slice_56" + name: "strided_slice_48" op: "StridedSlice" input: "t_natoms" - input: "strided_slice_56/stack" - input: "strided_slice_56/stack_1" - input: "strided_slice_56/stack_2" + input: "strided_slice_48/stack" + input: "strided_slice_48/stack_1" + input: "strided_slice_48/stack_2" attr { key: "Index" value { @@ -65580,7 +54206,7 @@ node { } } node { - name: "mul_31/x" + name: "mul_10/x" op: "Const" attr { key: "dtype" @@ -65601,10 +54227,10 @@ node { } } node { - name: "mul_31" + name: "mul_10" op: "Mul" - input: "mul_31/x" - input: "strided_slice_56" + input: "mul_10/x" + input: "strided_slice_48" attr { key: "T" value { @@ -65637,7 +54263,7 @@ node { name: "o_force/shape" op: "Pack" input: "o_force/shape/0" - input: "mul_31" + input: "mul_10" attr { key: "N" value { @@ -65660,7 +54286,7 @@ node { node { name: "o_force" op: "Reshape" - input: "add_12" + input: "ProdForceSeA_1" input: "o_force/shape" attr { key: "T" @@ -65702,7 +54328,7 @@ node { node { name: "o_virial" op: "Reshape" - input: "add_13" + input: "ProdVirialSeA_1" input: "o_virial/shape" attr { key: "T" @@ -65718,7 +54344,7 @@ node { } } node { - name: "strided_slice_57/stack" + name: "strided_slice_49/stack" op: "Const" attr { key: "dtype" @@ -65742,7 +54368,7 @@ node { } } node { - name: "strided_slice_57/stack_1" + name: "strided_slice_49/stack_1" op: "Const" attr { key: "dtype" @@ -65766,7 +54392,7 @@ node { } } node { - name: "strided_slice_57/stack_2" + name: "strided_slice_49/stack_2" op: "Const" attr { key: "dtype" @@ -65790,12 +54416,12 @@ node { } } node { - name: "strided_slice_57" + name: "strided_slice_49" op: "StridedSlice" input: "t_natoms" - input: "strided_slice_57/stack" - input: "strided_slice_57/stack_1" - input: "strided_slice_57/stack_2" + input: "strided_slice_49/stack" + input: "strided_slice_49/stack_1" + input: "strided_slice_49/stack_2" attr { key: "Index" value { @@ -65840,7 +54466,7 @@ node { } } node { - name: "mul_32/x" + name: "mul_11/x" op: "Const" attr { key: "dtype" @@ -65861,10 +54487,10 @@ node { } } node { - name: "mul_32" + name: "mul_11" op: "Mul" - input: "mul_32/x" - input: "strided_slice_57" + input: "mul_11/x" + input: "strided_slice_49" attr { key: "T" value { @@ -65897,7 +54523,7 @@ node { name: "o_atom_virial/shape" op: "Pack" input: "o_atom_virial/shape/0" - input: "mul_32" + input: "mul_11" attr { key: "N" value { @@ -65920,7 +54546,7 @@ node { node { name: "o_atom_virial" op: "Reshape" - input: "add_14" + input: "ProdVirialSeA_1:1" input: "o_atom_virial/shape" attr { key: "T" diff --git a/source/lmp/tests/test_dplr.py b/source/lmp/tests/test_dplr.py index b6398b77be..b92e0ddbfc 100644 --- a/source/lmp/tests/test_dplr.py +++ b/source/lmp/tests/test_dplr.py @@ -21,108 +21,108 @@ data_file = Path(__file__).parent / "data.lmp" # this is as the same as python and c++ tests, test_deeppot_a.py -expected_e_sr = -28.27186179 +expected_e_sr = -40.56538550 expected_f_sr = np.array( [ - [0.17101694, -0.33151456, -0.48810198], - [-0.60326789, 1.05723576, -1.00398772], - [-0.45535577, -0.52665656, 0.56538784], - [0.07412830, 0.06141962, 0.08719303], - [0.25021427, 0.46746804, 0.10825434], - [0.56326416, -0.72795230, 0.73125450], + [0.35019748, 0.27802691, -0.38443156], + [-0.42115581, -0.20474826, -0.02701100], + [-0.56357653, 0.34154004, 0.78389512], + [0.21023870, -0.60684635, -0.39875165], + [0.78732106, 0.00610023, 0.17197636], + [-0.36302488, 0.18592742, -0.14567727], ] ) expected_v_sr = np.array( [ [ - -1.08360048, - 0.28561092, - 1.12787306, - -0.27800706, - -2.22370058, - -0.60250734, - 1.08392293, - -0.91343020, - -1.69211612, + -0.053706180723, + 0.014597007250, + 0.067308761596, + 0.004757868347, + -0.273575531966, + -0.080634240285, + 0.066766981833, + -0.082022798771, + -0.103287504181, ], [ - -2.67885180, - 0.45057804, - -0.59798385, - -0.03229647, - -1.74227093, - 1.32518278, - -0.60602871, - 0.71486893, - -0.58607102, + -0.281471542200, + 0.111365734859, + -0.101177745441, + 0.101943364083, + -0.279013654810, + 0.167459867168, + -0.104515477897, + 0.146708715161, + -0.088287749985, ], [ - 0.80158098, - -0.64019999, - -1.07255047, - 1.23622085, - 1.77181377, - -1.21983559, - -0.81543862, - 1.05632482, - 1.30333566, + 0.386519343671, + -0.005263198500, + -0.490071800364, + -0.201548935270, + -0.057174356686, + 0.252305096540, + -0.534345834151, + 0.009882886080, + 0.674742623024, ], [ - 0.44888053, - 0.47838603, - -0.44712259, - -0.80436553, - 1.19194100, - 1.28550021, - -0.73434597, - -0.53774522, - 0.80885901, + 0.005150888129, + -0.224445564542, + -0.056830156735, + -0.024741632736, + 0.874091438655, + 0.229907695669, + -0.012085211023, + 0.477509547430, + 0.122807797587, ], [ - 1.61424595, - -0.83680788, - 0.73008361, - 1.70236397, - 1.37033017, - -0.11448440, - 0.10876112, - -0.44403200, - 0.26187153, + 0.918580719632, + 0.305393876038, + 0.062597464255, + 0.067643302415, + -0.009009180940, + 0.011159413462, + 0.177132428210, + 0.068689297830, + 0.009370783847, ], [ - 1.08938933, - 1.09903360, - -0.24784609, - -0.98731504, - 0.68084248, - -0.45950359, - 0.45558292, - 0.33836575, - -0.05030076, + 0.256886507259, + -0.498997455657, + 0.264728675198, + -0.245403567391, + 0.450588435107, + -0.248950976847, + 0.153602311537, + -0.289520792025, + 0.156365366477, ], ] ).reshape(6, 9) -expected_e_lr = -28.03317069 +expected_e_lr = -40.42063823 -expected_e_kspace = 0.23869110 +expected_e_kspace = 0.14474727 expected_f_lr = np.array( [ - [0.11696108, -0.42944256, -0.40878673], - [-0.57239656, 1.17989674, -1.00201720], - [-0.33386296, -0.48158818, 0.40810781], - [0.05039098, 0.17663026, 0.14472650], - [0.08849454, 0.38712960, 0.06775784], - [0.65041292, -0.83262585, 0.79021177], + [0.20445234, 0.27936500, -0.23179282], + [-0.30801828, -0.15412533, -0.17021364], + [-0.44078300, 0.34719898, 0.63462716], + [0.22103191, -0.50831649, -0.36328848], + [0.60333935, -0.04531002, 0.15891833], + [-0.28002232, 0.08118786, -0.02825056], ] ) expected_WC = np.array( [ - [1.21548264, 1.18751350, 1.02680021], - [0.99406312, 3.35460815, 1.31511552], + [1.22149689, 1.14543417, 1.01968026], + [0.98893545, 3.39167201, 1.32303880], ] ) From 89b4150b55d62931756d517a0986ba4bae2ada11 Mon Sep 17 00:00:00 2001 From: Jinzhe Zeng Date: Thu, 25 May 2023 19:45:11 -0400 Subject: [PATCH 097/126] improve docs and scripts to install libtensorflow_cc 2.12 (#2571) In TF 2.11, TensorFlow adds a new directory, "tensorflow/tsl" where the headers should be copied. 1. add docs to install TF 2.12; 2. build_tf.py: bump to TF 2.12, and copy `tensorflow/tsl`; 3. output the detailed errors when checking ABIs. Sometimes the reason is missing header files instead of ABI. --------- Signed-off-by: Jinzhe Zeng Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> --- doc/install/install-from-source.md | 2 +- doc/install/install-tf.2.12.md | 118 +++++++++++++++++++++++++++++ source/cmake/Findtensorflow.cmake | 9 ++- source/install/build_tf.py | 14 +++- 4 files changed, 140 insertions(+), 3 deletions(-) create mode 100644 doc/install/install-tf.2.12.md diff --git a/doc/install/install-from-source.md b/doc/install/install-from-source.md index 372de8225b..5d3a434f0a 100644 --- a/doc/install/install-from-source.md +++ b/doc/install/install-from-source.md @@ -156,7 +156,7 @@ Since TensorFlow 2.12, TensorFlow C++ library (`libtensorflow_cc`) is packaged i The C++ interface of DeePMD-kit was tested with compiler GCC >= 4.8. It is noticed that the I-Pi support is only compiled with GCC >= 4.8. Note that TensorFlow may have specific requirements for the compiler version. -First, the C++ interface of Tensorflow should be installed. It is noted that the version of Tensorflow should be consistent with the python interface. You may follow [the instruction](install-tf.2.8.md) or run the script `$deepmd_source_dir/source/install/build_tf.py` to install the corresponding C++ interface. +First, the C++ interface of Tensorflow should be installed. It is noted that the version of Tensorflow should be consistent with the python interface. You may follow [the instruction](install-tf.2.12.md) or run the script `$deepmd_source_dir/source/install/build_tf.py` to install the corresponding C++ interface. ### Install DeePMD-kit's C++ interface diff --git a/doc/install/install-tf.2.12.md b/doc/install/install-tf.2.12.md new file mode 100644 index 0000000000..dce0c224d5 --- /dev/null +++ b/doc/install/install-tf.2.12.md @@ -0,0 +1,118 @@ +# Install TensorFlow's C++ interface +TensorFlow's C++ interface will be compiled from the source code. In this manual, we install TensorFlow 2.12.0. It is noted that the source code of TensorFlow 2.12.0 uses C++ 17, so one needs a C++ compiler that supports C++ 17. + +Firstly one installs Bazel. [bazelisk](https://github.com/bazelbuild/bazelisk) can be lanuched to use [bazel](https://github.com/bazelbuild/bazel). + +```bash +wget https://github.com/bazelbuild/bazelisk/releases/download/v1.11.0/bazelisk-linux-amd64 -O /some/workspace/bazel/bin/bazel +chmod +x /some/workspace/bazel/bin/bazel +export PATH=/some/workspace/bazel/bin:$PATH +``` + +Firstly get the source code of the TensorFlow +```bash +git clone https://github.com/tensorflow/tensorflow tensorflow -b v2.12.0 --depth=1 +cd tensorflow +./configure +``` + +You will answer a list of questions that help configure the building of TensorFlow. You may want to answer the question like the following. If you do not want to add CUDA support, please answer no. + +``` +Please specify the location of python. [Default is xxx]: + +Found possible Python library paths: + xxx +Please input the desired Python library path to use. Default is [xxx] + +Do you wish to build TensorFlow with OpenCL SYCL support? [y/N]: +No OpenCL SYCL support will be enabled for TensorFlow. + +Do you wish to build TensorFlow with ROCm support? [y/N]: +No ROCm support will be enabled for TensorFlow. + +Do you wish to build TensorFlow with CUDA support? [y/N]: y +CUDA support will be enabled for TensorFlow. + +Do you wish to build TensorFlow with TensorRT support? [y/N]: +No TensorRT support will be enabled for TensorFlow. + +Found CUDA 10.2 in: + /usr/local/cuda/lib64 + /usr/local/cuda/include +Found cuDNN 7 in: + /usr/local/cuda/lib64 + /usr/local/cuda/include + +Please specify a list of comma-separated CUDA compute capabilities you want to build with. +You can find the compute capability of your device at: https://developer.nvidia.com/cuda-gpus. +Please note that each additional compute capability significantly increases your build time and binary size, and that TensorFlow only supports compute capabilities >= 3.5 [Default is: 7.5,7.5]: + +Do you want to use clang as CUDA compiler? [y/N]: +nvcc will be used as CUDA compiler. + +Please specify which gcc should be used by nvcc as the host compiler. [Default is /usr/bin/gcc]: + +Please specify optimization flags to use during compilation when bazel option "--config=opt" is specified [Default is -march=native -Wno-sign-compare]: + +Would you like to interactively configure ./WORKSPACE for Android builds? [y/N]: +Not configuring the WORKSPACE for Android builds. + +Preconfigured Bazel build configs. You can use any of the below by adding "--config=<>" to your build command. See .bazelrc for more details. + --config=mkl # Build with MKL support. + --config=monolithic # Config for mostly static monolithic build. + --config=ngraph # Build with Intel nGraph support. + --config=numa # Build with NUMA support. + --config=dynamic_kernels # (Experimental) Build kernels into separate shared objects. + --config=v2 # Build TensorFlow 2.x instead of 1.x. +Preconfigured Bazel build configs to DISABLE default on features: + --config=noaws # Disable AWS S3 filesystem support. + --config=nogcp # Disable GCP support. + --config=nohdfs # Disable HDFS support. + --config=nonccl # Disable NVIDIA NCCL support. +Configuration finished +``` + +The library path for Python should be set accordingly. + +Now build the shared library of TensorFlow: +```bash +bazel build -c opt --verbose_failures //tensorflow:libtensorflow_cc.so +``` +You may want to add options `--copt=-msse4.2`, `--copt=-mavx`, `--copt=-mavx2` and `--copt=-mfma` to enable SSE4.2, AVX, AVX2 and FMA SIMD accelerations, respectively. It is noted that these options should be chosen according to the CPU architecture. If the RAM becomes an issue for your machine, you may limit the RAM usage by using `--local_resources 2048,.5,1.0`. If you want to enable [oneDNN optimization](https://www.oneapi.io/blog/tensorflow-and-onednn-in-partnership/), add `--config=mkl`. + +Now I assume you want to install TensorFlow in directory `$tensorflow_root`. Create the directory if it does not exist +```bash +mkdir -p $tensorflow_root +``` +Now, copy the libraries to the TensorFlow's installation directory: +```bash +mkdir -p $tensorflow_root/lib +cp -d bazel-bin/tensorflow/libtensorflow_cc.so* $tensorflow_root/lib/ +cp -d bazel-bin/tensorflow/libtensorflow_framework.so* $tensorflow_root/lib/ +cp -d $tensorflow_root/lib/libtensorflow_framework.so.2 $tensorflow_root/lib/libtensorflow_framework.so +``` +Then copy the headers +```bash +mkdir -p $tensorflow_root/include/tensorflow +rsync -avzh --exclude '_virtual_includes/' --include '*/' --include '*.h' --include '*.inc' --exclude '*' bazel-bin/ $tensorflow_root/include/ +rsync -avzh --include '*/' --include '*.h' --include '*.inc' --exclude '*' tensorflow/cc $tensorflow_root/include/tensorflow/ +rsync -avzh --include '*/' --include '*.h' --include '*.inc' --exclude '*' tensorflow/core $tensorflow_root/include/tensorflow/ +rsync -avzh --include '*/' --include '*.h' --include '*.inc' --exclude '*' tensorflow/tsl $tensorflow_root/include/tensorflow/ +rsync -avzh --include '*/' --include '*' --exclude '*.cc' third_party/ $tensorflow_root/include/third_party/ +rsync -avzh --include '*/' --include '*' --exclude '*.txt' bazel-tensorflow/external/eigen_archive/Eigen/ $tensorflow_root/include/Eigen/ +rsync -avzh --include '*/' --include '*' --exclude '*.txt' bazel-tensorflow/external/eigen_archive/unsupported/ $tensorflow_root/include/unsupported/ +rsync -avzh --include '*/' --include '*.h' --include '*.inc' --exclude '*' bazel-tensorflow/external/com_google_protobuf/src/google/ $tensorflow_root/include/google/ +rsync -avzh --include '*/' --include '*.h' --include '*.inc' --exclude '*' bazel-tensorflow/external/com_google_absl/absl/ $tensorflow_root/include/absl/ +``` + +If you've enabled oneDNN, also copy `libiomp5.so`: +```bash +cp -d bazel-out/k8-opt/bin/external/llvm_openmp/libiomp5.so $tensorflow_root/lib/ +``` + +# Troubleshooting +```bash +git: unknown command -C ... +``` +This may be an issue with your Git version issue. Early versions of Git do not support this command, in this case upgrading your Git to a newer version may resolve any issues. diff --git a/source/cmake/Findtensorflow.cmake b/source/cmake/Findtensorflow.cmake index f9ba0184a6..b39f6ab215 100644 --- a/source/cmake/Findtensorflow.cmake +++ b/source/cmake/Findtensorflow.cmake @@ -337,12 +337,14 @@ elseif(NOT DEFINED OP_CXX_ABI) try_compile( CPP_CXX_ABI_COMPILE_RESULT_VAR0 ${CMAKE_CURRENT_BINARY_DIR}/tf_cxx_abi0 "${CMAKE_CURRENT_LIST_DIR}/test_cxx_abi.cpp" + OUTPUT_VARIABLE CPP_CXX_ABI_COMPILE_OUTPUT_VAR0 LINK_LIBRARIES ${TensorFlowFramework_LIBRARY} CMAKE_FLAGS "-DINCLUDE_DIRECTORIES:STRING=${TensorFlow_INCLUDE_DIRS}" COMPILE_DEFINITIONS -D_GLIBCXX_USE_CXX11_ABI=0) try_compile( CPP_CXX_ABI_COMPILE_RESULT_VAR1 ${CMAKE_CURRENT_BINARY_DIR}/tf_cxx_abi1 "${CMAKE_CURRENT_LIST_DIR}/test_cxx_abi.cpp" + OUTPUT_VARIABLE CPP_CXX_ABI_COMPILE_OUTPUT_VAR1 LINK_LIBRARIES ${TensorFlowFramework_LIBRARY} CMAKE_FLAGS "-DINCLUDE_DIRECTORIES:STRING=${TensorFlow_INCLUDE_DIRS}" COMPILE_DEFINITIONS -D_GLIBCXX_USE_CXX11_ABI=1) @@ -360,9 +362,14 @@ elseif(NOT DEFINED OP_CXX_ABI) ) set(OP_CXX_ABI 1) else() + # print results of try_compile + message(WARNING "Output with _GLIBCXX_USE_CXX11_ABI=0:" + ${CPP_CXX_ABI_COMPILE_OUTPUT_VAR0}) + message(WARNING "Output with _GLIBCXX_USE_CXX11_ABI=1:" + ${CPP_CXX_ABI_COMPILE_OUTPUT_VAR1}) message( FATAL_ERROR - "Both _GLIBCXX_USE_CXX11_ABI=0 and 1 do not work. The reason may be that your C++ compiler (e.g. Red Hat Developer Toolset) does not support the custom cxx11 abi flag." + "Both _GLIBCXX_USE_CXX11_ABI=0 and 1 do not work. The reason may be that your C++ compiler (e.g. Red Hat Developer Toolset) does not support the custom cxx11 abi flag. Please check the above outputs." ) endif() else() diff --git a/source/install/build_tf.py b/source/install/build_tf.py index 883c654b5c..7dfa8f4ffb 100755 --- a/source/install/build_tf.py +++ b/source/install/build_tf.py @@ -410,6 +410,12 @@ def call(commands: List[str], env={}, **kwargs): "b5a1bb04c84b6fe1538377e5a1f649bb5d5f0b2e3625a3c526ff3a8af88633e8", gzip="tensorflow", ), + "tensorflow-2.12.0": OnlineResource( + "tensorflow-2.12.0.tar.gz", + "https://github.com/tensorflow/tensorflow/archive/refs/tags/v2.12.0.tar.gz", + "c030cb1905bff1d2446615992aad8d8d85cbe90c4fb625cee458c63bf466bc8e", + gzip="tensorflow", + ), } @@ -583,7 +589,7 @@ class BuildTensorFlow(Build): def __init__( self, - version: str = "2.9.1", + version: str = "2.12.0", enable_mkl: bool = True, enable_cuda: bool = False, enable_rocm: bool = False, @@ -666,6 +672,12 @@ def build(self): include_dst / "tensorflow" / "core", ignore=include_patterns("*.h", "*.inc"), ) + if tuple([int(x) for x in self.version.split(".")[:2]]) >= (2, 11): + copytree2( + src / "tensorflow" / "tsl", + include_dst / "tensorflow" / "core", + ignore=include_patterns("*.h", "*.inc"), + ) # bazel-bin includes generated headers like version, pb.h, .. copytree2( src / "bazel-bin", include_dst, ignore=include_patterns("*.h", "*.inc") From 30a6f5c755ee9e86fbe1b3c6cbf3c393dd4b2b1c Mon Sep 17 00:00:00 2001 From: nahso Date: Mon, 29 May 2023 10:36:35 +0800 Subject: [PATCH 098/126] restore numb_steps --- examples/water/hybrid/input.json | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/water/hybrid/input.json b/examples/water/hybrid/input.json index 8f4a29d8d6..ff9d9e72b7 100644 --- a/examples/water/hybrid/input.json +++ b/examples/water/hybrid/input.json @@ -114,7 +114,7 @@ "numb_btch": 3, "_comment": "that's all" }, - "numb_steps": 1000, + "numb_steps": 1000000, "seed": 10, "disp_file": "lcurve.out", "disp_freq": 100, From afeffdcbdf60fa2dc9cc63eef9078bcecf880710 Mon Sep 17 00:00:00 2001 From: nahso Date: Mon, 29 May 2023 10:44:47 +0800 Subject: [PATCH 099/126] pre commit --- deepmd/descriptor/se_atten.py | 7 +++-- deepmd/model/ener.py | 4 ++- .../tests/test_model_compression_se_atten.py | 29 ++++++++++--------- 3 files changed, 23 insertions(+), 17 deletions(-) diff --git a/deepmd/descriptor/se_atten.py b/deepmd/descriptor/se_atten.py index f6af5952b5..cf0a005df2 100644 --- a/deepmd/descriptor/se_atten.py +++ b/deepmd/descriptor/se_atten.py @@ -429,9 +429,12 @@ def _get_two_side_embedding_net_variable(self, graph_def, varialbe_name, suffix) ret = {} for i in range(1, self.layer_size + 1): target = get_pattern_nodes_from_graph_def( - graph_def, f"filter_type_all{suffix}/{varialbe_name}_{i}{suffix}_two_side_ebd" + graph_def, + f"filter_type_all{suffix}/{varialbe_name}_{i}{suffix}_two_side_ebd", ) - node = target[f"filter_type_all{suffix}/{varialbe_name}_{i}{suffix}_two_side_ebd"] + node = target[ + f"filter_type_all{suffix}/{varialbe_name}_{i}{suffix}_two_side_ebd" + ] ret["layer_" + str(i)] = node return ret diff --git a/deepmd/model/ener.py b/deepmd/model/ener.py index 4c0aa5078e..870e582f4c 100644 --- a/deepmd/model/ener.py +++ b/deepmd/model/ener.py @@ -357,7 +357,9 @@ def init_variables( else: raise RuntimeError("Unknown model type %s" % model_type) if self.typeebd is not None: - self.typeebd.init_variables(graph, graph_def, suffix=suffix, model_type=model_type) + self.typeebd.init_variables( + graph, graph_def, suffix=suffix, model_type=model_type + ) def natoms_match(self, force, natoms): use_spin = self.spin.use_spin diff --git a/source/tests/test_model_compression_se_atten.py b/source/tests/test_model_compression_se_atten.py index 01017084fa..1982e433e0 100644 --- a/source/tests/test_model_compression_se_atten.py +++ b/source/tests/test_model_compression_se_atten.py @@ -14,13 +14,13 @@ from packaging.version import parse as parse_version from deepmd.env import ( - GLOBAL_NP_FLOAT_PRECISION, tf, ) from deepmd.infer import ( DeepPot, ) + def _file_delete(file): if os.path.isdir(file): os.rmdir(file) @@ -52,16 +52,18 @@ def _init_models(): # - type embedding FP32, se_atten FP64 # - type embedding FP32, se_atten FP32 tests = [ - {'se_atten precision': "float64", 'type embedding precision': "float64"}, - {'se_atten precision': "float64", 'type embedding precision': "float32"}, - {'se_atten precision': "float32", 'type embedding precision': "float64"}, - {'se_atten precision': "float32", 'type embedding precision': "float32"}, - ] + {"se_atten precision": "float64", "type embedding precision": "float64"}, + {"se_atten precision": "float64", "type embedding precision": "float32"}, + {"se_atten precision": "float32", "type embedding precision": "float64"}, + {"se_atten precision": "float32", "type embedding precision": "float32"}, + ] for i in range(4): INPUT = str(tests_path / f"input{i}.json") frozen_model = str(tests_path / f"dp-original-se-atten{i}.pb") compressed_model = str(tests_path / f"dp-compressed-se-atten{i}.pb") - jdata = j_loader(str(tests_path / os.path.join("model_compression", "input.json"))) + jdata = j_loader( + str(tests_path / os.path.join("model_compression", "input.json")) + ) jdata["model"]["descriptor"] = {} jdata["model"]["descriptor"]["type"] = "se_atten" jdata["model"]["descriptor"]["precision"] = tests[i]["se_atten precision"] @@ -69,7 +71,9 @@ def _init_models(): jdata["model"]["descriptor"]["sel"] = 120 jdata["model"]["descriptor"]["attn_layer"] = 0 jdata["model"]["type_embedding"] = {} - jdata["model"]["type_embedding"]["precision"] = tests[i]["type embedding precision"] + jdata["model"]["type_embedding"]["precision"] = tests[i][ + "type embedding precision" + ] jdata["training"]["training_data"]["systems"] = data_file jdata["training"]["validation_data"]["systems"] = data_file with open(INPUT, "w") as fp: @@ -91,6 +95,7 @@ def _init_models(): INPUTS, FROZEN_MODELS, COMPRESSED_MODELS = _init_models() + def _get_default_places(nth_test): return 10 if nth_test == 0 else 4 @@ -142,9 +147,7 @@ def test_attrs(self): self.assertEqual(dp_original.get_dim_aparam(), 0) self.assertEqual(dp_compressed.get_ntypes(), 2) - self.assertAlmostEqual( - dp_compressed.get_rcut(), 6.0, places=default_places - ) + self.assertAlmostEqual(dp_compressed.get_rcut(), 6.0, places=default_places) self.assertEqual(dp_compressed.get_type_map(), ["O", "H"]) self.assertEqual(dp_compressed.get_dim_fparam(), 0) self.assertEqual(dp_compressed.get_dim_aparam(), 0) @@ -564,9 +567,7 @@ def test_attrs(self): self.assertEqual(dp_original.get_dim_aparam(), 0) self.assertEqual(dp_compressed.get_ntypes(), 2) - self.assertAlmostEqual( - dp_compressed.get_rcut(), 6.0, places=default_places - ) + self.assertAlmostEqual(dp_compressed.get_rcut(), 6.0, places=default_places) self.assertEqual(dp_compressed.get_type_map(), ["O", "H"]) self.assertEqual(dp_compressed.get_dim_fparam(), 0) self.assertEqual(dp_compressed.get_dim_aparam(), 0) From 6f23dcb8f7b9197caaeadb20d901f69abd374c3c Mon Sep 17 00:00:00 2001 From: nahso Date: Mon, 29 May 2023 13:04:38 +0800 Subject: [PATCH 100/126] remove se_atten from hybrid example input.json --- examples/water/hybrid/input.json | 19 ------------------- 1 file changed, 19 deletions(-) diff --git a/examples/water/hybrid/input.json b/examples/water/hybrid/input.json index ff9d9e72b7..5f4cd6c5ec 100644 --- a/examples/water/hybrid/input.json +++ b/examples/water/hybrid/input.json @@ -42,25 +42,6 @@ "resnet_dt": false, "seed": 1, "_comment": " that's all" - }, - { - "type": "se_atten", - "compressible": true, - "attn_layer": 0, - "sel": [ - 46, - 92 - ], - "rcut_smth": 0.50, - "rcut": 6.00, - "neuron": [ - 5, - 10, - 20 - ], - "resnet_dt": false, - "seed": 1, - "_comment": " that's all" } ] }, From 8bc263b0a760f04be328d8d5148f61ae971a08a4 Mon Sep 17 00:00:00 2001 From: nahso Date: Mon, 29 May 2023 13:05:45 +0800 Subject: [PATCH 101/126] refactor codes of get two side type embedding network variables --- deepmd/descriptor/se_atten.py | 53 +++++++++++++---------------------- 1 file changed, 20 insertions(+), 33 deletions(-) diff --git a/deepmd/descriptor/se_atten.py b/deepmd/descriptor/se_atten.py index cf0a005df2..4eb888085d 100644 --- a/deepmd/descriptor/se_atten.py +++ b/deepmd/descriptor/se_atten.py @@ -27,7 +27,7 @@ get_attention_layer_variables_from_graph_def, get_pattern_nodes_from_graph_def, get_tensor_by_name_from_graph, - get_tensor_by_type, + get_tensor_by_type, get_embedding_net_nodes_from_graph_def, ) from deepmd.utils.network import ( embedding_net, @@ -1252,39 +1252,11 @@ def init_variables( if self.compressible: self.two_side_embeeding_net_variables = {} - for i in range(1, self.layer_size + 1): - node = get_pattern_nodes_from_graph_def( - graph_def, f"filter_type_all{suffix}/matrix_{i}_two_side_ebd" - )[f"filter_type_all{suffix}/matrix_{i}_two_side_ebd"] - dtype = tf.as_dtype(node.dtype).as_numpy_dtype - tensor_shape = tf.TensorShape(node.tensor_shape).as_list() - if (len(tensor_shape) != 1) or (tensor_shape[0] != 1): - tensor_value = np.frombuffer( - node.tensor_content, - dtype=tf.as_dtype(node.dtype).as_numpy_dtype, - ) - else: - tensor_value = get_tensor_by_type(node, dtype) - self.two_side_embeeding_net_variables[ - f"filter_type_all{suffix}/matrix_{i}_two_side_ebd" - ] = np.reshape(tensor_value, tensor_shape) - - node = get_pattern_nodes_from_graph_def( - graph_def, f"filter_type_all{suffix}/bias_{i}_two_side_ebd" - )[f"filter_type_all{suffix}/bias_{i}_two_side_ebd"] - dtype = tf.as_dtype(node.dtype).as_numpy_dtype - tensor_shape = tf.TensorShape(node.tensor_shape).as_list() - if (len(tensor_shape) != 1) or (tensor_shape[0] != 1): - tensor_value = np.frombuffer( - node.tensor_content, - dtype=tf.as_dtype(node.dtype).as_numpy_dtype, - ) - else: - tensor_value = get_tensor_by_type(node, dtype) - self.two_side_embeeding_net_variables[ - f"filter_type_all{suffix}/bias_{i}_two_side_ebd" - ] = np.reshape(tensor_value, tensor_shape) + matrix_pattern = f"filter_type_all{suffix}/matrix_{i}_two_side_ebd" + self.two_side_embeeding_net_variables[matrix_pattern] = self._get_two_embed_variables(graph_def, matrix_pattern) + bias_pattern = f"filter_type_all{suffix}/bias_{i}_two_side_ebd" + self.two_side_embeeding_net_variables[bias_pattern] = self._get_two_embed_variables(graph_def, bias_pattern) self.attention_layer_variables = get_attention_layer_variables_from_graph_def( graph_def, suffix=suffix @@ -1308,6 +1280,21 @@ def init_variables( ) ] + def _get_two_embed_variables(self, graph_def, pattern: str): + node = get_pattern_nodes_from_graph_def( + graph_def, pattern + )[pattern] + dtype = tf.as_dtype(node.dtype).as_numpy_dtype + tensor_shape = tf.TensorShape(node.tensor_shape).as_list() + if (len(tensor_shape) != 1) or (tensor_shape[0] != 1): + tensor_value = np.frombuffer( + node.tensor_content, + dtype=tf.as_dtype(node.dtype).as_numpy_dtype, + ) + else: + tensor_value = get_tensor_by_type(node, dtype) + return np.reshape(tensor_value, tensor_shape) + def build_type_exclude_mask( self, exclude_types: List[Tuple[int, int]], From 37c5d5617a91e50a61e6be4c67c53c880e2b2ff4 Mon Sep 17 00:00:00 2001 From: nahso Date: Mon, 29 May 2023 13:07:28 +0800 Subject: [PATCH 102/126] pre commit --- deepmd/descriptor/se_atten.py | 14 ++++++++------ 1 file changed, 8 insertions(+), 6 deletions(-) diff --git a/deepmd/descriptor/se_atten.py b/deepmd/descriptor/se_atten.py index 4eb888085d..6eecf2df90 100644 --- a/deepmd/descriptor/se_atten.py +++ b/deepmd/descriptor/se_atten.py @@ -27,7 +27,7 @@ get_attention_layer_variables_from_graph_def, get_pattern_nodes_from_graph_def, get_tensor_by_name_from_graph, - get_tensor_by_type, get_embedding_net_nodes_from_graph_def, + get_tensor_by_type, ) from deepmd.utils.network import ( embedding_net, @@ -1254,9 +1254,13 @@ def init_variables( self.two_side_embeeding_net_variables = {} for i in range(1, self.layer_size + 1): matrix_pattern = f"filter_type_all{suffix}/matrix_{i}_two_side_ebd" - self.two_side_embeeding_net_variables[matrix_pattern] = self._get_two_embed_variables(graph_def, matrix_pattern) + self.two_side_embeeding_net_variables[ + matrix_pattern + ] = self._get_two_embed_variables(graph_def, matrix_pattern) bias_pattern = f"filter_type_all{suffix}/bias_{i}_two_side_ebd" - self.two_side_embeeding_net_variables[bias_pattern] = self._get_two_embed_variables(graph_def, bias_pattern) + self.two_side_embeeding_net_variables[ + bias_pattern + ] = self._get_two_embed_variables(graph_def, bias_pattern) self.attention_layer_variables = get_attention_layer_variables_from_graph_def( graph_def, suffix=suffix @@ -1281,9 +1285,7 @@ def init_variables( ] def _get_two_embed_variables(self, graph_def, pattern: str): - node = get_pattern_nodes_from_graph_def( - graph_def, pattern - )[pattern] + node = get_pattern_nodes_from_graph_def(graph_def, pattern)[pattern] dtype = tf.as_dtype(node.dtype).as_numpy_dtype tensor_shape = tf.TensorShape(node.tensor_shape).as_list() if (len(tensor_shape) != 1) or (tensor_shape[0] != 1): From 2a48365ef7e3e5d96b24636e4a222805f83e668b Mon Sep 17 00:00:00 2001 From: nahso Date: Mon, 29 May 2023 20:49:44 +0800 Subject: [PATCH 103/126] merge se_atten op into se_a --- deepmd/descriptor/se_a.py | 1 + deepmd/descriptor/se_atten.py | 2 +- source/lib/include/tabulate.h | 117 +------- source/lib/src/cuda/tabulate.cu | 389 ++----------------------- source/lib/src/rocm/tabulate.hip.cu | 402 ++----------------------- source/lib/src/tabulate.cc | 333 ++------------------- source/op/_tabulate_grad.py | 61 +--- source/op/tabulate_multi_device.cc | 436 ++++++---------------------- 8 files changed, 185 insertions(+), 1556 deletions(-) diff --git a/deepmd/descriptor/se_a.py b/deepmd/descriptor/se_a.py index 641210f0d1..b1bd28e1b1 100644 --- a/deepmd/descriptor/se_a.py +++ b/deepmd/descriptor/se_a.py @@ -941,6 +941,7 @@ def _filter_lower( info, xyz_scatter, tf.reshape(inputs_i, [natom, shape_i[1] // 4, 4]), + tf.constant([], dtype=self.filter_precision), last_layer_size=outputs_size[-1], ) else: diff --git a/deepmd/descriptor/se_atten.py b/deepmd/descriptor/se_atten.py index 6eecf2df90..7bfab07400 100644 --- a/deepmd/descriptor/se_atten.py +++ b/deepmd/descriptor/se_atten.py @@ -1110,7 +1110,7 @@ def _filter_lower( if not self.compress: xyz_scatter = xyz_scatter * two_embd + two_embd else: - return op_module.tabulate_fusion_se_atten( + return op_module.tabulate_fusion_se_a( tf.cast(self.table.data[net], self.filter_precision), info, xyz_scatter, diff --git a/source/lib/include/tabulate.h b/source/lib/include/tabulate.h index b8425d5172..7edd3b4d01 100644 --- a/source/lib/include/tabulate.h +++ b/source/lib/include/tabulate.h @@ -8,6 +8,7 @@ void tabulate_fusion_se_a_cpu(FPTYPE* out, const FPTYPE* table_info, const FPTYPE* em_x, const FPTYPE* em, + const FPTYPE* two_embed, const int nloc, const int nnei, const int last_layer_size); @@ -19,6 +20,7 @@ void tabulate_fusion_se_a_grad_cpu(FPTYPE* dy_dem_x, const FPTYPE* table_info, const FPTYPE* em_x, const FPTYPE* em, + const FPTYPE* two_embed, const FPTYPE* dy, const int nloc, const int nnei, @@ -36,43 +38,6 @@ void tabulate_fusion_se_a_grad_grad_cpu(FPTYPE* dz_dy, const int nnei, const int last_layer_size); -template -void tabulate_fusion_se_atten_cpu(FPTYPE* out, - const FPTYPE* table, - const FPTYPE* table_info, - const FPTYPE* em_x, - const FPTYPE* em, - const FPTYPE* two_embed, - const int nloc, - const int nnei, - const int last_layer_size); - -template -void tabulate_fusion_se_atten_grad_cpu(FPTYPE* dy_dem_x, - FPTYPE* dy_dem, - const FPTYPE* table, - const FPTYPE* table_info, - const FPTYPE* em_x, - const FPTYPE* em, - const FPTYPE* two_embed, - const FPTYPE* dy, - const int nloc, - const int nnei, - const int last_layer_size); - -template -void tabulate_fusion_se_atten_grad_grad_cpu(FPTYPE* dz_dy, - const FPTYPE* table, - const FPTYPE* table_info, - const FPTYPE* em_x, - const FPTYPE* em, - const FPTYPE* two_embed, - const FPTYPE* dz_dy_dem_x, - const FPTYPE* dz_dy_dem, - const int nloc, - const int nnei, - const int last_layer_size); - template void tabulate_fusion_se_t_cpu(FPTYPE* out, const FPTYPE* table, @@ -146,6 +111,7 @@ void tabulate_fusion_se_a_gpu_cuda(FPTYPE* out, const FPTYPE* table_info, const FPTYPE* em_x, const FPTYPE* em, + const FPTYPE* two_embed, const int nloc, const int nnei, const int last_layer_size); @@ -157,6 +123,7 @@ void tabulate_fusion_se_a_grad_gpu_cuda(FPTYPE* dy_dem_x, const FPTYPE* table_info, const FPTYPE* em_x, const FPTYPE* em, + const FPTYPE* two_embed, const FPTYPE* dy, const int nloc, const int nnei, @@ -174,43 +141,6 @@ void tabulate_fusion_se_a_grad_grad_gpu_cuda(FPTYPE* dz_dy, const int nnei, const int last_layer_size); -template -void tabulate_fusion_se_atten_gpu_cuda(FPTYPE* out, - const FPTYPE* table, - const FPTYPE* table_info, - const FPTYPE* em_x, - const FPTYPE* em, - const FPTYPE* two_embed, - const int nloc, - const int nnei, - const int last_layer_size); - -template -void tabulate_fusion_se_atten_grad_gpu_cuda(FPTYPE* dy_dem_x, - FPTYPE* dy_dem, - const FPTYPE* table, - const FPTYPE* table_info, - const FPTYPE* em_x, - const FPTYPE* em, - const FPTYPE* two_embed, - const FPTYPE* dy, - const int nloc, - const int nnei, - const int last_layer_size); - -template -void tabulate_fusion_se_atten_grad_grad_gpu_cuda(FPTYPE* dz_dy, - const FPTYPE* table, - const FPTYPE* table_info, - const FPTYPE* em_x, - const FPTYPE* em, - const FPTYPE* two_embed, - const FPTYPE* dz_dy_dem_x, - const FPTYPE* dz_dy_dem, - const int nloc, - const int nnei, - const int last_layer_size); - template void tabulate_fusion_se_t_gpu_cuda(FPTYPE* out, const FPTYPE* table, @@ -285,6 +215,7 @@ void tabulate_fusion_se_a_gpu_rocm(FPTYPE* out, const FPTYPE* table_info, const FPTYPE* em_x, const FPTYPE* em, + const FPTYPE* two_embed, const int nloc, const int nnei, const int last_layer_size); @@ -296,6 +227,7 @@ void tabulate_fusion_se_a_grad_gpu_rocm(FPTYPE* dy_dem_x, const FPTYPE* table_info, const FPTYPE* em_x, const FPTYPE* em, + const FPTYPE* two_embed, const FPTYPE* dy, const int nloc, const int nnei, @@ -313,43 +245,6 @@ void tabulate_fusion_se_a_grad_grad_gpu_rocm(FPTYPE* dz_dy, const int nnei, const int last_layer_size); -template -void tabulate_fusion_se_atten_gpu_rocm(FPTYPE* out, - const FPTYPE* table, - const FPTYPE* table_info, - const FPTYPE* em_x, - const FPTYPE* em, - const FPTYPE* two_embed, - const int nloc, - const int nnei, - const int last_layer_size); - -template -void tabulate_fusion_se_atten_grad_gpu_rocm(FPTYPE* dy_dem_x, - FPTYPE* dy_dem, - const FPTYPE* table, - const FPTYPE* table_info, - const FPTYPE* em_x, - const FPTYPE* em, - const FPTYPE* two_embed, - const FPTYPE* dy, - const int nloc, - const int nnei, - const int last_layer_size); - -template -void tabulate_fusion_se_atten_grad_grad_gpu_rocm(FPTYPE* dz_dy, - const FPTYPE* table, - const FPTYPE* table_info, - const FPTYPE* em_x, - const FPTYPE* em, - const FPTYPE* two_embed, - const FPTYPE* dz_dy_dem_x, - const FPTYPE* dz_dy_dem, - const int nloc, - const int nnei, - const int last_layer_size); - template void tabulate_fusion_se_t_gpu_rocm(FPTYPE* out, const FPTYPE* table, diff --git a/source/lib/src/cuda/tabulate.cu b/source/lib/src/cuda/tabulate.cu index 42d2c5a8b8..a63038f406 100644 --- a/source/lib/src/cuda/tabulate.cu +++ b/source/lib/src/cuda/tabulate.cu @@ -116,210 +116,6 @@ __forceinline__ __device__ void warp_reduce(FPTYPE& val) { template __global__ void tabulate_fusion_se_a_fifth_order_polynomial( - FPTYPE* out, - const FPTYPE* table, - const FPTYPE* em_x, - const FPTYPE* em, - const FPTYPE lower, - const FPTYPE upper, - const FPTYPE max, - const FPTYPE stride0, - const FPTYPE stride1, - const int nnei, - const int last_layer_size) { - const int_64 block_idx = blockIdx.x; // nloc - const int thread_idx = threadIdx.x; // last_layer_size - FPTYPE ago = __shfl_sync(0xffffffff, em_x[block_idx * nnei + nnei - 1], 0); - bool unloop = false; - int breakpoint = nnei - 1; - - FPTYPE sum[MTILE] = {(FPTYPE)0.}; - int mark_table_idx = -1; - FPTYPE var[6]; - for (int ii = 0; ii < nnei; ii++) { - FPTYPE xx = em_x[block_idx * nnei + ii]; - if (xx == ago) { - unloop = true; - breakpoint = ii; - } - int table_idx = 0; - locate_xx_se_a(xx, table_idx, lower, upper, max, stride0, stride1); - if (table_idx != mark_table_idx) { - load_polynomial_params(var, table, table_idx, thread_idx, - last_layer_size); - } - FPTYPE res = - var[0] + - (var[1] + (var[2] + (var[3] + (var[4] + var[5] * xx) * xx) * xx) * xx) * - xx; - - for (int kk = 0; kk < MTILE; kk++) { - sum[kk] += (nnei - breakpoint) * - em[block_idx * nnei * MTILE + ii * MTILE + kk] * res; - } - if (unloop) break; - mark_table_idx = table_idx; - } - for (int ii = 0; ii < MTILE; ii++) { - out[block_idx * MTILE * last_layer_size + ii * last_layer_size + - thread_idx] = sum[ii]; - } -} - -template -__global__ void tabulate_fusion_se_a_grad_fifth_order_polynomial( - FPTYPE* dy_dem_x, - FPTYPE* dy_dem, - const FPTYPE* table, - const FPTYPE* em_x, - const FPTYPE* em, - const FPTYPE* dy, - const FPTYPE lower, - const FPTYPE upper, - const FPTYPE max, - const FPTYPE stride0, - const FPTYPE stride1, - const int nnei, - const int last_layer_size) { - extern __shared__ int _data[]; - const int_64 block_idx = blockIdx.x; // nloc - const int thread_idx = threadIdx.x; // KTILE * WARP_SIZE, usally 128 here~ - int warp_idx = __shfl_sync(0xffffffff, threadIdx.x / WARP_SIZE, 0); - int lane_idx = threadIdx.x % WARP_SIZE; - int breakpoint = nnei - 1; - bool unloop = false; - FPTYPE* iteratorA = (FPTYPE*)&_data[0]; // dy - for (int ii = 0; ii < MTILE; ii++) { - for (int jj = thread_idx; jj < last_layer_size; jj += blockDim.x) { - iteratorA[ii * last_layer_size + jj] = - dy[block_idx * MTILE * last_layer_size + ii * last_layer_size + jj]; - } - } - __syncthreads(); - FPTYPE ago = __shfl_sync(0xffffffff, em_x[block_idx * nnei + nnei - 1], 0); - for (int ii = warp_idx; ii < nnei; ii += KTILE) { - FPTYPE xx = em_x[block_idx * nnei + ii]; - if (ago == xx) { - unloop = true; - breakpoint = ii; - } - - int table_idx = 0; - FPTYPE reg_em[MTILE] = {em[block_idx * nnei * MTILE + ii * 4 + 0], - em[block_idx * nnei * MTILE + ii * 4 + 1], - em[block_idx * nnei * MTILE + ii * 4 + 2], - em[block_idx * nnei * MTILE + ii * 4 + 3]}; - FPTYPE Csub = (FPTYPE)0.; - FPTYPE sum[MTILE] = {(FPTYPE)0.}; - locate_xx_se_a(xx, table_idx, lower, upper, max, stride0, stride1); - - FPTYPE var[6]; - for (int jj = lane_idx; jj < last_layer_size; jj += WARP_SIZE) { - load_polynomial_params(var, table, table_idx, jj, last_layer_size); - FPTYPE res = - var[0] + - (var[1] + - (var[2] + (var[3] + (var[4] + var[5] * xx) * xx) * xx) * xx) * - xx; - - for (int kk = 0; kk < MTILE; kk++) { - sum[kk] += - (nnei - breakpoint) * iteratorA[kk * last_layer_size + jj] * res; - } - res = reg_em[0] * iteratorA[0 * last_layer_size + jj]; - res += reg_em[1] * iteratorA[1 * last_layer_size + jj]; - res += reg_em[2] * iteratorA[2 * last_layer_size + jj]; - res += reg_em[3] * iteratorA[3 * last_layer_size + jj]; - Csub += - (nnei - breakpoint) * - (var[1] + (2 * var[2] + - (3 * var[3] + (4 * var[4] + 5 * var[5] * xx) * xx) * xx) * - xx) * - res; - } - __syncwarp(); - for (int kk = 0; kk < MTILE; kk++) { - warp_reduce(sum[kk]); - } - warp_reduce(Csub); - if (lane_idx == 0) { - for (int kk = 0; kk < MTILE; kk++) { - dy_dem[block_idx * nnei * MTILE + ii * 4 + kk] = sum[kk]; - } - dy_dem_x[block_idx * nnei + ii] = Csub; - } - if (unloop) break; - } -} - -template -__global__ void tabulate_fusion_se_a_grad_grad_fifth_order_polynomial( - FPTYPE* dz_dy, - const FPTYPE* table, - const FPTYPE* em_x, - const FPTYPE* em, - const FPTYPE* dz_dy_dem_x, - const FPTYPE* dz_dy_dem, - const FPTYPE lower, - const FPTYPE upper, - const FPTYPE max, - const FPTYPE stride0, - const FPTYPE stride1, - const int nnei, - const int last_layer_size) { - extern __shared__ int _data[]; - const int_64 block_idx = blockIdx.x; // nloc - const int thread_idx = threadIdx.x; // last_layer_size - FPTYPE ago = __shfl_sync(0xffffffff, em_x[block_idx * nnei + nnei - 1], 0); - bool unloop = false; - int breakpoint = nnei - 1; - FPTYPE* iteratorC = (FPTYPE*)&_data[0]; - for (int kk = 0; kk < MTILE; kk++) - iteratorC[kk * last_layer_size + thread_idx] = (FPTYPE)0.; - __syncthreads(); - - int mark_table_idx = -1; - FPTYPE var[6]; - for (int ii = 0; ii < nnei; ii++) { - FPTYPE xx = em_x[block_idx * nnei + ii]; - FPTYPE dz_xx = dz_dy_dem_x[block_idx * nnei + ii]; - if (xx == ago) { - unloop = true; - breakpoint = ii; - } - int table_idx = 0; - locate_xx_se_a(xx, table_idx, lower, upper, max, stride0, stride1); - if (table_idx != mark_table_idx) { - load_polynomial_params(var, table, table_idx, thread_idx, - last_layer_size); - } - - FPTYPE res = - var[0] + - (var[1] + (var[2] + (var[3] + (var[4] + var[5] * xx) * xx) * xx) * xx) * - xx; - FPTYPE res_grad = - var[1] + - (2 * var[2] + (3 * var[3] + (4 * var[4] + 5 * var[5] * xx) * xx) * xx) * - xx; - - for (int kk = 0; kk < MTILE; kk++) { - int em_index = block_idx * nnei * MTILE + ii * MTILE + kk; - iteratorC[kk * last_layer_size + thread_idx] += - (nnei - breakpoint) * - (em[em_index] * res_grad * dz_xx + dz_dy_dem[em_index] * res); - } - mark_table_idx = table_idx; - if (unloop) break; - } - for (int ii = 0; ii < MTILE; ii++) { - dz_dy[block_idx * MTILE * last_layer_size + ii * last_layer_size + - thread_idx] = iteratorC[ii * last_layer_size + thread_idx]; - } -} - -template -__global__ void tabulate_fusion_se_atten_fifth_order_polynomial( FPTYPE* out, const FPTYPE* table, const FPTYPE* em_x, @@ -332,6 +128,7 @@ __global__ void tabulate_fusion_se_atten_fifth_order_polynomial( const FPTYPE stride1, const int nnei, const int last_layer_size) { + bool enable_se_atten = two_embed != nullptr; const int_64 block_idx = blockIdx.x; // nloc const int thread_idx = threadIdx.x; // last_layer_size FPTYPE ago = __shfl_sync(0xffffffff, em_x[block_idx * nnei + nnei - 1], 0); @@ -357,9 +154,11 @@ __global__ void tabulate_fusion_se_atten_fifth_order_polynomial( var[0] + (var[1] + (var[2] + (var[3] + (var[4] + var[5] * xx) * xx) * xx) * xx) * xx; - FPTYPE t = two_embed[block_idx * nnei * last_layer_size + - ii * last_layer_size + thread_idx]; - res = res * t + t; + if (enable_se_atten) { + FPTYPE t = two_embed[block_idx * nnei * last_layer_size + + ii * last_layer_size + thread_idx]; + res = res * t + t; + } for (int kk = 0; kk < MTILE; kk++) { sum[kk] += (nnei - breakpoint) * @@ -375,7 +174,7 @@ __global__ void tabulate_fusion_se_atten_fifth_order_polynomial( } template -__global__ void tabulate_fusion_se_atten_grad_fifth_order_polynomial( +__global__ void tabulate_fusion_se_a_grad_fifth_order_polynomial( FPTYPE* dy_dem_x, FPTYPE* dy_dem, const FPTYPE* table, @@ -390,6 +189,7 @@ __global__ void tabulate_fusion_se_atten_grad_fifth_order_polynomial( const FPTYPE stride1, const int nnei, const int last_layer_size) { + bool enable_se_atten = two_embed != nullptr; extern __shared__ int _data[]; const int_64 block_idx = blockIdx.x; // nloc const int thread_idx = threadIdx.x; // KTILE * WARP_SIZE, usally 128 here~ @@ -430,9 +230,12 @@ __global__ void tabulate_fusion_se_atten_grad_fifth_order_polynomial( (var[1] + (var[2] + (var[3] + (var[4] + var[5] * xx) * xx) * xx) * xx) * xx; - FPTYPE t = two_embed[block_idx * nnei * last_layer_size + - ii * last_layer_size + jj]; - res = res * t + t; + FPTYPE t; + if (enable_se_atten) { + t = two_embed[block_idx * nnei * last_layer_size + + ii * last_layer_size + jj]; + res = res * t + t; + } for (int kk = 0; kk < MTILE; kk++) { sum[kk] += @@ -447,7 +250,7 @@ __global__ void tabulate_fusion_se_atten_grad_fifth_order_polynomial( (var[1] + (2 * var[2] + (3 * var[3] + (4 * var[4] + 5 * var[5] * xx) * xx) * xx) * xx) * - res * t; + (enable_se_atten ? res * t : res); } __syncwarp(); for (int kk = 0; kk < MTILE; kk++) { @@ -465,12 +268,11 @@ __global__ void tabulate_fusion_se_atten_grad_fifth_order_polynomial( } template -__global__ void tabulate_fusion_se_atten_grad_grad_fifth_order_polynomial( +__global__ void tabulate_fusion_se_a_grad_grad_fifth_order_polynomial( FPTYPE* dz_dy, const FPTYPE* table, const FPTYPE* em_x, const FPTYPE* em, - const FPTYPE* two_embed, const FPTYPE* dz_dy_dem_x, const FPTYPE* dz_dy_dem, const FPTYPE lower, @@ -813,6 +615,7 @@ void tabulate_fusion_se_a_gpu_cuda(FPTYPE* out, const FPTYPE* table_info, const FPTYPE* em_x, const FPTYPE* em, + const FPTYPE* two_embed, const int nloc, const int nnei, const int last_layer_size) { @@ -820,9 +623,9 @@ void tabulate_fusion_se_a_gpu_cuda(FPTYPE* out, return; } tabulate_fusion_se_a_fifth_order_polynomial - <<>>(out, table, em_x, em, table_info[0], - table_info[1], table_info[2], table_info[3], - table_info[4], nnei, last_layer_size); + <<>>( + out, table, em_x, em, two_embed, table_info[0], table_info[1], + table_info[2], table_info[3], table_info[4], nnei, last_layer_size); DPErrcheck(cudaGetLastError()); DPErrcheck(cudaDeviceSynchronize()); } @@ -834,6 +637,7 @@ void tabulate_fusion_se_a_grad_gpu_cuda(FPTYPE* dy_dem_x, const FPTYPE* table_info, const FPTYPE* em_x, const FPTYPE* em, + const FPTYPE* two_embed, const FPTYPE* dy, const int nloc, const int nnei, @@ -846,8 +650,9 @@ void tabulate_fusion_se_a_grad_gpu_cuda(FPTYPE* dy_dem_x, tabulate_fusion_se_a_grad_fifth_order_polynomial <<>>( - dy_dem_x, dy_dem, table, em_x, em, dy, table_info[0], table_info[1], - table_info[2], table_info[3], table_info[4], nnei, last_layer_size); + dy_dem_x, dy_dem, table, em_x, em, two_embed, dy, table_info[0], + table_info[1], table_info[2], table_info[3], table_info[4], nnei, + last_layer_size); DPErrcheck(cudaGetLastError()); DPErrcheck(cudaDeviceSynchronize()); } @@ -876,79 +681,6 @@ void tabulate_fusion_se_a_grad_grad_gpu_cuda(FPTYPE* dz_dy, DPErrcheck(cudaDeviceSynchronize()); } -template -void tabulate_fusion_se_atten_gpu_cuda(FPTYPE* out, - const FPTYPE* table, - const FPTYPE* table_info, - const FPTYPE* em_x, - const FPTYPE* em, - const FPTYPE* two_embed, - const int nloc, - const int nnei, - const int last_layer_size) { - if (nloc <= 0) { - return; - } - tabulate_fusion_se_atten_fifth_order_polynomial - <<>>( - out, table, em_x, em, two_embed, table_info[0], table_info[1], - table_info[2], table_info[3], table_info[4], nnei, last_layer_size); - DPErrcheck(cudaGetLastError()); - DPErrcheck(cudaDeviceSynchronize()); -} - -template -void tabulate_fusion_se_atten_grad_gpu_cuda(FPTYPE* dy_dem_x, - FPTYPE* dy_dem, - const FPTYPE* table, - const FPTYPE* table_info, - const FPTYPE* em_x, - const FPTYPE* em, - const FPTYPE* two_embed, - const FPTYPE* dy, - const int nloc, - const int nnei, - const int last_layer_size) { - if (nloc <= 0) { - return; - } - DPErrcheck(cudaMemset(dy_dem_x, 0, sizeof(FPTYPE) * nloc * nnei)); - DPErrcheck(cudaMemset(dy_dem, 0, sizeof(FPTYPE) * nloc * nnei * 4)); - - tabulate_fusion_se_atten_grad_fifth_order_polynomial - <<>>( - dy_dem_x, dy_dem, table, em_x, em, two_embed, dy, table_info[0], - table_info[1], table_info[2], table_info[3], table_info[4], nnei, - last_layer_size); - DPErrcheck(cudaGetLastError()); - DPErrcheck(cudaDeviceSynchronize()); -} - -template -void tabulate_fusion_se_atten_grad_grad_gpu_cuda(FPTYPE* dz_dy, - const FPTYPE* table, - const FPTYPE* table_info, - const FPTYPE* em_x, - const FPTYPE* em, - const FPTYPE* two_embed, - const FPTYPE* dz_dy_dem_x, - const FPTYPE* dz_dy_dem, - const int nloc, - const int nnei, - const int last_layer_size) { - if (nloc <= 0) { - return; - } - DPErrcheck(cudaMemset(dz_dy, 0, sizeof(FPTYPE) * nloc * 4 * last_layer_size)); - tabulate_fusion_se_atten_grad_grad_fifth_order_polynomial - <<>>( - dz_dy, table, em_x, em, two_embed, dz_dy_dem_x, dz_dy_dem, - table_info[0], table_info[1], table_info[2], table_info[3], - table_info[4], nnei, last_layer_size); - DPErrcheck(cudaGetLastError()); - DPErrcheck(cudaDeviceSynchronize()); -} - template void tabulate_fusion_se_t_gpu_cuda(FPTYPE* out, const FPTYPE* table, @@ -1091,6 +823,7 @@ template void tabulate_fusion_se_a_gpu_cuda(float* out, const float* table_info, const float* em_x, const float* em, + const float* two_embed, const int nloc, const int nnei, const int last_layer_size); @@ -1099,6 +832,7 @@ template void tabulate_fusion_se_a_gpu_cuda(double* out, const double* table_info, const double* em_x, const double* em, + const double* two_embed, const int nloc, const int nnei, const int last_layer_size); @@ -1109,6 +843,7 @@ template void tabulate_fusion_se_a_grad_gpu_cuda( const float* table_info, const float* em_x, const float* em, + const float* two_embed, const float* dy, const int nloc, const int nnei, @@ -1120,6 +855,7 @@ template void tabulate_fusion_se_a_grad_gpu_cuda( const double* table_info, const double* em_x, const double* em, + const double* two_embed, const double* dy, const int nloc, const int nnei, @@ -1147,75 +883,6 @@ template void tabulate_fusion_se_a_grad_grad_gpu_cuda( const int nnei, const int last_layer_size); -template void tabulate_fusion_se_atten_gpu_cuda( - float* out, - const float* table, - const float* table_info, - const float* em_x, - const float* em, - const float* two_embed, - const int nloc, - const int nnei, - const int last_layer_size); -template void tabulate_fusion_se_atten_gpu_cuda( - double* out, - const double* table, - const double* table_info, - const double* em_x, - const double* em, - const double* two_embed, - const int nloc, - const int nnei, - const int last_layer_size); -template void tabulate_fusion_se_atten_grad_gpu_cuda( - float* dy_dem_x, - float* dy_dem, - const float* table, - const float* table_info, - const float* em_x, - const float* em, - const float* two_embed, - const float* dy, - const int nloc, - const int nnei, - const int last_layer_size); -template void tabulate_fusion_se_atten_grad_gpu_cuda( - double* dy_dem_x, - double* dy_dem, - const double* table, - const double* table_info, - const double* em_x, - const double* em, - const double* two_embed, - const double* dy, - const int nloc, - const int nnei, - const int last_layer_size); -template void tabulate_fusion_se_atten_grad_grad_gpu_cuda( - float* dz_dy, - const float* table, - const float* table_info, - const float* em_x, - const float* em, - const float* two_embed, - const float* dz_dy_dem_x, - const float* dz_dy_dem, - const int nloc, - const int nnei, - const int last_layer_size); -template void tabulate_fusion_se_atten_grad_grad_gpu_cuda( - double* dz_dy, - const double* table, - const double* table_info, - const double* em_x, - const double* em, - const double* two_embed, - const double* dz_dy_dem_x, - const double* dz_dy_dem, - const int nloc, - const int nnei, - const int last_layer_size); - template void tabulate_fusion_se_t_gpu_cuda(float* out, const float* table, const float* table_info, diff --git a/source/lib/src/rocm/tabulate.hip.cu b/source/lib/src/rocm/tabulate.hip.cu index b2d97052ae..e5d87817ab 100644 --- a/source/lib/src/rocm/tabulate.hip.cu +++ b/source/lib/src/rocm/tabulate.hip.cu @@ -76,224 +76,6 @@ __forceinline__ __device__ void warp_reduce(FPTYPE& val) { template __global__ void tabulate_fusion_se_a_fifth_order_polynomial( - FPTYPE* out, - const FPTYPE* table, - const FPTYPE* em_x, - const FPTYPE* em, - const FPTYPE lower, - const FPTYPE upper, - const FPTYPE max, - const FPTYPE stride0, - const FPTYPE stride1, - const int nnei, - const int last_layer_size) { - HIP_DYNAMIC_SHARED(int, _data) - const int_64 block_idx = blockIdx.x; // nloc - const int thread_idx = threadIdx.x; // last_layer_size - FPTYPE ago = __shfl(em_x[block_idx * nnei + nnei - 1], 0); - bool unloop = false; - int breakpoint = nnei - 1; - FPTYPE* iteratorC = (FPTYPE*)&_data[0]; - for (int kk = 0; kk < MTILE; kk++) - iteratorC[kk * last_layer_size + thread_idx] = (FPTYPE)0.; - __syncthreads(); - - for (int ii = 0; ii < nnei; ii++) { - FPTYPE var[6]; - FPTYPE xx = em_x[block_idx * nnei + ii]; - if (xx == ago) { - unloop = true; - breakpoint = ii; - } - int table_idx = 0; - locate_xx(xx, table_idx, lower, upper, max, stride0, stride1); - var[0] = table[table_idx * last_layer_size * 6 + thread_idx * 6 + 0]; - var[1] = table[table_idx * last_layer_size * 6 + thread_idx * 6 + 1]; - var[2] = table[table_idx * last_layer_size * 6 + thread_idx * 6 + 2]; - var[3] = table[table_idx * last_layer_size * 6 + thread_idx * 6 + 3]; - var[4] = table[table_idx * last_layer_size * 6 + thread_idx * 6 + 4]; - var[5] = table[table_idx * last_layer_size * 6 + thread_idx * 6 + 5]; - FPTYPE res = - var[0] + - (var[1] + (var[2] + (var[3] + (var[4] + var[5] * xx) * xx) * xx) * xx) * - xx; - - for (int kk = 0; kk < MTILE; kk++) { - iteratorC[kk * last_layer_size + thread_idx] += - (nnei - breakpoint) * em[block_idx * nnei * MTILE + ii * MTILE + kk] * - res; - } - if (unloop) break; - } - for (int ii = 0; ii < MTILE; ii++) { - out[block_idx * MTILE * last_layer_size + ii * last_layer_size + - thread_idx] = iteratorC[ii * last_layer_size + thread_idx]; - } -} - -template -__global__ void tabulate_fusion_se_a_grad_fifth_order_polynomial( - FPTYPE* dy_dem_x, - FPTYPE* dy_dem, - const FPTYPE* table, - const FPTYPE* em_x, - const FPTYPE* em, - const FPTYPE* dy, - const FPTYPE lower, - const FPTYPE upper, - const FPTYPE max, - const FPTYPE stride0, - const FPTYPE stride1, - const int nnei, - const int last_layer_size) { - HIP_DYNAMIC_SHARED(int, _data) - const int_64 block_idx = blockIdx.x; // nloc - const int thread_idx = threadIdx.x; // KTILE * WARP_SIZE, usally 128 here~ - int warp_idx = __shfl(threadIdx.x / 64, 0); - int lane_idx = threadIdx.x % 64; - int breakpoint = nnei - 1; - bool unloop = false; - FPTYPE* iteratorA = (FPTYPE*)&_data[0]; // dy - for (int ii = 0; ii < MTILE; ii++) { - for (int jj = thread_idx; jj < last_layer_size; jj += blockDim.x) { - iteratorA[ii * last_layer_size + jj] = - dy[block_idx * MTILE * last_layer_size + ii * last_layer_size + jj]; - } - } - __syncthreads(); - FPTYPE ago = __shfl(em_x[block_idx * nnei + nnei - 1], 0); - for (int ii = 0; ii < nnei - warp_idx; ii += KTILE) { - FPTYPE xx = em_x[block_idx * nnei + ii + warp_idx]; - if (ago == xx) { - unloop = true; - breakpoint = ii + warp_idx; - } - - int table_idx = 0; - locate_xx(xx, table_idx, lower, upper, max, stride0, stride1); - FPTYPE sum[KTILE] = {(FPTYPE)0.}; - FPTYPE Csub = (FPTYPE)0.; - for (int jj = lane_idx; jj < last_layer_size; jj += WARP_SIZE) { - FPTYPE var[6]; - // load iteratorB through table - var[0] = table[table_idx * last_layer_size * 6 + 6 * jj + 0]; - var[1] = table[table_idx * last_layer_size * 6 + 6 * jj + 1]; - var[2] = table[table_idx * last_layer_size * 6 + 6 * jj + 2]; - var[3] = table[table_idx * last_layer_size * 6 + 6 * jj + 3]; - var[4] = table[table_idx * last_layer_size * 6 + 6 * jj + 4]; - var[5] = table[table_idx * last_layer_size * 6 + 6 * jj + 5]; - FPTYPE res = - var[0] + - (var[1] + - (var[2] + (var[3] + (var[4] + var[5] * xx) * xx) * xx) * xx) * - xx; - - for (int kk = 0; kk < KTILE; kk++) { - sum[kk] += - (nnei - breakpoint) * iteratorA[kk * last_layer_size + jj] * res; - } - res = em[block_idx * nnei * MTILE + (ii + warp_idx) * 4 + 0] * - iteratorA[0 * last_layer_size + jj]; - res += em[block_idx * nnei * MTILE + (ii + warp_idx) * 4 + 1] * - iteratorA[1 * last_layer_size + jj]; - res += em[block_idx * nnei * MTILE + (ii + warp_idx) * 4 + 2] * - iteratorA[2 * last_layer_size + jj]; - res += em[block_idx * nnei * MTILE + (ii + warp_idx) * 4 + 3] * - iteratorA[3 * last_layer_size + jj]; - Csub += - (nnei - breakpoint) * - (var[1] + ((FPTYPE)2. * var[2] + - ((FPTYPE)3. * var[3] + - ((FPTYPE)4. * var[4] + (FPTYPE)5. * var[5] * xx) * xx) * - xx) * - xx) * - res; - } - //__syncwarp();->syncwrap - __syncthreads(); - for (int kk = 0; kk < KTILE; kk++) { - warp_reduce(sum[kk]); - } - warp_reduce(Csub); - if (lane_idx == 0) { - for (int kk = 0; kk < KTILE; kk++) { - dy_dem[block_idx * nnei * MTILE + (ii + warp_idx) * 4 + kk] = sum[kk]; - } - dy_dem_x[block_idx * nnei + ii + warp_idx] = Csub; - } - if (unloop) break; - } -} - -template -__global__ void tabulate_fusion_se_a_grad_grad_fifth_order_polynomial( - FPTYPE* dz_dy, - const FPTYPE* table, - const FPTYPE* em_x, - const FPTYPE* em, - const FPTYPE* dz_dy_dem_x, - const FPTYPE* dz_dy_dem, - const FPTYPE lower, - const FPTYPE upper, - const FPTYPE max, - const FPTYPE stride0, - const FPTYPE stride1, - const int nnei, - const int last_layer_size) { - extern __shared__ int _data[]; - const int_64 block_idx = blockIdx.x; // nloc - const int thread_idx = threadIdx.x; // last_layer_size - FPTYPE ago = __shfl(em_x[block_idx * nnei + nnei - 1], 0); - bool unloop = false; - int breakpoint = nnei - 1; - FPTYPE* iteratorC = (FPTYPE*)&_data[0]; - for (int kk = 0; kk < MTILE; kk++) - iteratorC[kk * last_layer_size + thread_idx] = (FPTYPE)0.; - __syncthreads(); - - for (int ii = 0; ii < nnei; ii++) { - FPTYPE var[6]; - FPTYPE xx = em_x[block_idx * nnei + ii]; - FPTYPE dz_xx = dz_dy_dem_x[block_idx * nnei + ii]; - if (xx == ago) { - unloop = true; - breakpoint = ii; - } - int table_idx = 0; - locate_xx(xx, table_idx, lower, upper, max, stride0, stride1); - var[0] = table[table_idx * last_layer_size * 6 + thread_idx * 6 + 0]; - var[1] = table[table_idx * last_layer_size * 6 + thread_idx * 6 + 1]; - var[2] = table[table_idx * last_layer_size * 6 + thread_idx * 6 + 2]; - var[3] = table[table_idx * last_layer_size * 6 + thread_idx * 6 + 3]; - var[4] = table[table_idx * last_layer_size * 6 + thread_idx * 6 + 4]; - var[5] = table[table_idx * last_layer_size * 6 + thread_idx * 6 + 5]; - FPTYPE res = - var[0] + - (var[1] + (var[2] + (var[3] + (var[4] + var[5] * xx) * xx) * xx) * xx) * - xx; - FPTYPE res_grad = - var[1] + ((FPTYPE)2. * var[2] + - ((FPTYPE)3. * var[3] + - ((FPTYPE)4. * var[4] + (FPTYPE)5. * var[5] * xx) * xx) * - xx) * - xx; - - for (int kk = 0; kk < MTILE; kk++) { - int em_index = block_idx * nnei * MTILE + ii * MTILE + kk; - iteratorC[kk * last_layer_size + thread_idx] += - (nnei - breakpoint) * - (em[em_index] * res_grad * dz_xx + dz_dy_dem[em_index] * res); - } - if (unloop) break; - } - for (int ii = 0; ii < MTILE; ii++) { - dz_dy[block_idx * MTILE * last_layer_size + ii * last_layer_size + - thread_idx] = iteratorC[ii * last_layer_size + thread_idx]; - } -} - -template -__global__ void tabulate_fusion_se_atten_fifth_order_polynomial( FPTYPE* out, const FPTYPE* table, const FPTYPE* em_x, @@ -306,6 +88,7 @@ __global__ void tabulate_fusion_se_atten_fifth_order_polynomial( const FPTYPE stride1, const int nnei, const int last_layer_size) { + bool enable_se_atten = two_embed != nullptr; HIP_DYNAMIC_SHARED(int, _data) const int_64 block_idx = blockIdx.x; // nloc const int thread_idx = threadIdx.x; // last_layer_size @@ -336,9 +119,11 @@ __global__ void tabulate_fusion_se_atten_fifth_order_polynomial( var[0] + (var[1] + (var[2] + (var[3] + (var[4] + var[5] * xx) * xx) * xx) * xx) * xx; - FPTYPE t = two_embed[block_idx * nnei * last_layer_size + - ii * last_layer_size + thread_idx]; - res = res * t + t; + if (enable_se_atten) { + FPTYPE t = two_embed[block_idx * nnei * last_layer_size + + ii * last_layer_size + thread_idx]; + res = res * t + t; + } for (int kk = 0; kk < MTILE; kk++) { iteratorC[kk * last_layer_size + thread_idx] += @@ -354,7 +139,7 @@ __global__ void tabulate_fusion_se_atten_fifth_order_polynomial( } template -__global__ void tabulate_fusion_se_atten_grad_fifth_order_polynomial( +__global__ void tabulate_fusion_se_a_grad_fifth_order_polynomial( FPTYPE* dy_dem_x, FPTYPE* dy_dem, const FPTYPE* table, @@ -369,6 +154,7 @@ __global__ void tabulate_fusion_se_atten_grad_fifth_order_polynomial( const FPTYPE stride1, const int nnei, const int last_layer_size) { + bool enable_se_atten = two_embed != nullptr; HIP_DYNAMIC_SHARED(int, _data) const int_64 block_idx = blockIdx.x; // nloc const int thread_idx = threadIdx.x; // KTILE * WARP_SIZE, usally 128 here~ @@ -410,9 +196,12 @@ __global__ void tabulate_fusion_se_atten_grad_fifth_order_polynomial( (var[1] + (var[2] + (var[3] + (var[4] + var[5] * xx) * xx) * xx) * xx) * xx; - FPTYPE t = two_embed[block_idx * nnei * last_layer_size + - ii * last_layer_size + jj]; - res = res * t + t; + FPTYPE t; + if (enable_se_atten) { + t = two_embed[block_idx * nnei * last_layer_size + + ii * last_layer_size + jj]; + res = res * t + t; + } for (int kk = 0; kk < KTILE; kk++) { sum[kk] += @@ -433,7 +222,7 @@ __global__ void tabulate_fusion_se_atten_grad_fifth_order_polynomial( ((FPTYPE)4. * var[4] + (FPTYPE)5. * var[5] * xx) * xx) * xx) * xx) * - res * t; + (enable_se_atten ? res * t : res); } //__syncwarp();->syncwrap __syncthreads(); @@ -452,12 +241,11 @@ __global__ void tabulate_fusion_se_atten_grad_fifth_order_polynomial( } template -__global__ void tabulate_fusion_se_atten_grad_grad_fifth_order_polynomial( +__global__ void tabulate_fusion_se_a_grad_grad_fifth_order_polynomial( FPTYPE* dz_dy, const FPTYPE* table, const FPTYPE* em_x, const FPTYPE* em, - const FPTYPE* two_embed, const FPTYPE* dz_dy_dem_x, const FPTYPE* dz_dy_dem, const FPTYPE lower, @@ -826,6 +614,7 @@ void tabulate_fusion_se_a_gpu_rocm(FPTYPE* out, const FPTYPE* table_info, const FPTYPE* em_x, const FPTYPE* em, + const FPTYPE* two_embed, const int nloc, const int nnei, const int last_layer_size) { @@ -849,6 +638,7 @@ void tabulate_fusion_se_a_grad_gpu_rocm(FPTYPE* dy_dem_x, const FPTYPE* table_info, const FPTYPE* em_x, const FPTYPE* em, + const FPTYPE* two_embed, const FPTYPE* dy, const int nloc, const int nnei, @@ -863,8 +653,8 @@ void tabulate_fusion_se_a_grad_gpu_rocm(FPTYPE* dy_dem_x, HIP_KERNEL_NAME( tabulate_fusion_se_a_grad_fifth_order_polynomial), nloc, KK * WARP_SIZE, sizeof(FPTYPE) * MM * last_layer_size, 0, dy_dem_x, - dy_dem, table, em_x, em, dy, table_info[0], table_info[1], table_info[2], - table_info[3], table_info[4], nnei, last_layer_size); + dy_dem, table, em_x, em, two_embed, dy, table_info[0], table_info[1], + table_info[2], table_info[3], table_info[4], nnei, last_layer_size); DPErrcheck(hipGetLastError()); DPErrcheck(hipDeviceSynchronize()); } @@ -895,85 +685,6 @@ void tabulate_fusion_se_a_grad_grad_gpu_rocm(FPTYPE* dz_dy, DPErrcheck(hipDeviceSynchronize()); } -template -void tabulate_fusion_se_atten_gpu_rocm(FPTYPE* out, - const FPTYPE* table, - const FPTYPE* table_info, - const FPTYPE* em_x, - const FPTYPE* em, - const FPTYPE* two_embed, - const int nloc, - const int nnei, - const int last_layer_size) { - if (nloc <= 0) { - return; - } - hipLaunchKernelGGL( - HIP_KERNEL_NAME( - tabulate_fusion_se_atten_fifth_order_polynomial), - nloc, last_layer_size, sizeof(FPTYPE) * MM * last_layer_size, 0, out, - table, em_x, em, two_embed, table_info[0], table_info[1], table_info[2], - table_info[3], table_info[4], nnei, last_layer_size); - DPErrcheck(hipGetLastError()); - DPErrcheck(hipDeviceSynchronize()); -} - -template -void tabulate_fusion_se_atten_grad_gpu_rocm(FPTYPE* dy_dem_x, - FPTYPE* dy_dem, - const FPTYPE* table, - const FPTYPE* table_info, - const FPTYPE* em_x, - const FPTYPE* em, - const FPTYPE* two_embed, - const FPTYPE* dy, - const int nloc, - const int nnei, - const int last_layer_size) { - if (nloc <= 0) { - return; - } - DPErrcheck(hipMemset(dy_dem_x, 0, sizeof(FPTYPE) * nloc * nnei)); - DPErrcheck(hipMemset(dy_dem, 0, sizeof(FPTYPE) * nloc * nnei * 4)); - - hipLaunchKernelGGL( - HIP_KERNEL_NAME( - tabulate_fusion_se_atten_grad_fifth_order_polynomial), - nloc, KK * WARP_SIZE, sizeof(FPTYPE) * MM * last_layer_size, 0, dy_dem_x, - dy_dem, table, em_x, em, two_embed, dy, table_info[0], table_info[1], - table_info[2], table_info[3], table_info[4], nnei, last_layer_size); - DPErrcheck(hipGetLastError()); - DPErrcheck(hipDeviceSynchronize()); -} - -template -void tabulate_fusion_se_atten_grad_grad_gpu_rocm(FPTYPE* dz_dy, - const FPTYPE* table, - const FPTYPE* table_info, - const FPTYPE* em_x, - const FPTYPE* em, - const FPTYPE* two_embed, - const FPTYPE* dz_dy_dem_x, - const FPTYPE* dz_dy_dem, - const int nloc, - const int nnei, - const int last_layer_size) { - if (nloc <= 0) { - return; - } - DPErrcheck(hipMemset(dz_dy, 0, sizeof(FPTYPE) * nloc * 4 * last_layer_size)); - hipLaunchKernelGGL( - HIP_KERNEL_NAME( - tabulate_fusion_se_atten_grad_grad_fifth_order_polynomial), - nloc, last_layer_size, sizeof(FPTYPE) * MM * last_layer_size, 0, dz_dy, - table, em_x, em, two_embed, dz_dy_dem_x, dz_dy_dem, table_info[0], - table_info[1], table_info[2], table_info[3], table_info[4], nnei, - last_layer_size); - DPErrcheck(hipGetLastError()); - DPErrcheck(hipDeviceSynchronize()); -} - template void tabulate_fusion_se_t_gpu_rocm(FPTYPE* out, const FPTYPE* table, @@ -1127,6 +838,7 @@ template void tabulate_fusion_se_a_gpu_rocm(float* out, const float* table_info, const float* em_x, const float* em, + const float* two_embed, const int nloc, const int nnei, const int last_layer_size); @@ -1135,75 +847,11 @@ template void tabulate_fusion_se_a_gpu_rocm(double* out, const double* table_info, const double* em_x, const double* em, + const double* two_embed, const int nloc, const int nnei, const int last_layer_size); template void tabulate_fusion_se_a_grad_gpu_rocm( - float* dy_dem_x, - float* dy_dem, - const float* table, - const float* table_info, - const float* em_x, - const float* em, - const float* dy, - const int nloc, - const int nnei, - const int last_layer_size); -template void tabulate_fusion_se_a_grad_gpu_rocm( - double* dy_dem_x, - double* dy_dem, - const double* table, - const double* table_info, - const double* em_x, - const double* em, - const double* dy, - const int nloc, - const int nnei, - const int last_layer_size); -template void tabulate_fusion_se_a_grad_grad_gpu_rocm( - float* dz_dy, - const float* table, - const float* table_info, - const float* em_x, - const float* em, - const float* dz_dy_dem_x, - const float* dz_dy_dem, - const int nloc, - const int nnei, - const int last_layer_size); -template void tabulate_fusion_se_a_grad_grad_gpu_rocm( - double* dz_dy, - const double* table, - const double* table_info, - const double* em_x, - const double* em, - const double* dz_dy_dem_x, - const double* dz_dy_dem, - const int nloc, - const int nnei, - const int last_layer_size); - -template void tabulate_fusion_se_atten_gpu_rocm( - float* out, - const float* table, - const float* table_info, - const float* em_x, - const float* em, - const float* two_embed, - const int nloc, - const int nnei, - const int last_layer_size); -template void tabulate_fusion_se_atten_gpu_rocm( - double* out, - const double* table, - const double* table_info, - const double* em_x, - const double* em, - const double* two_embed, - const int nloc, - const int nnei, - const int last_layer_size); -template void tabulate_fusion_se_atten_grad_gpu_rocm( float* dy_dem_x, float* dy_dem, const float* table, @@ -1215,7 +863,7 @@ template void tabulate_fusion_se_atten_grad_gpu_rocm( const int nloc, const int nnei, const int last_layer_size); -template void tabulate_fusion_se_atten_grad_gpu_rocm( +template void tabulate_fusion_se_a_grad_gpu_rocm( double* dy_dem_x, double* dy_dem, const double* table, @@ -1227,7 +875,7 @@ template void tabulate_fusion_se_atten_grad_gpu_rocm( const int nloc, const int nnei, const int last_layer_size); -template void tabulate_fusion_se_atten_grad_grad_gpu_rocm( +template void tabulate_fusion_se_a_grad_grad_gpu_rocm( float* dz_dy, const float* table, const float* table_info, @@ -1239,7 +887,7 @@ template void tabulate_fusion_se_atten_grad_grad_gpu_rocm( const int nloc, const int nnei, const int last_layer_size); -template void tabulate_fusion_se_atten_grad_grad_gpu_rocm( +template void tabulate_fusion_se_a_grad_grad_gpu_rocm( double* dz_dy, const double* table, const double* table_info, diff --git a/source/lib/src/tabulate.cc b/source/lib/src/tabulate.cc index 36a8e86b56..b2edb30be9 100644 --- a/source/lib/src/tabulate.cc +++ b/source/lib/src/tabulate.cc @@ -82,9 +82,11 @@ void deepmd::tabulate_fusion_se_a_cpu(FPTYPE* out, const FPTYPE* table_info, const FPTYPE* em_x, const FPTYPE* em, + const FPTYPE* two_embed, const int nloc, const int nnei, const int last_layer_size) { + bool enable_se_atten = two_embed != nullptr; memset(out, 0, sizeof(FPTYPE) * nloc * 4 * last_layer_size); const FPTYPE lower = table_info[0]; const FPTYPE upper = table_info[1]; @@ -118,6 +120,12 @@ void deepmd::tabulate_fusion_se_a_cpu(FPTYPE* out, FPTYPE a5 = table[table_idx * last_layer_size * 6 + 6 * kk + 5]; FPTYPE var = a0 + (a1 + (a2 + (a3 + (a4 + a5 * xx) * xx) * xx) * xx) * xx; + if (enable_se_atten) { + FPTYPE t = two_embed[ii * nnei * last_layer_size + + jj * last_layer_size + kk]; + var = var * t + t; + } + if (unloop) { out[ii * last_layer_size * 4 + 0 * last_layer_size + kk] += (nnei - jj) * var * ll[0]; @@ -150,10 +158,12 @@ void deepmd::tabulate_fusion_se_a_grad_cpu(FPTYPE* dy_dem_x, const FPTYPE* table_info, const FPTYPE* em_x, const FPTYPE* em, + const FPTYPE* two_embed, const FPTYPE* dy, const int nloc, const int nnei, const int last_layer_size) { + bool enable_se_atten = two_embed != nullptr; memset(dy_dem_x, 0, sizeof(FPTYPE) * nloc * nnei); memset(dy_dem, 0, sizeof(FPTYPE) * nloc * nnei * 4); FPTYPE const lower = table_info[0]; @@ -195,19 +205,23 @@ void deepmd::tabulate_fusion_se_a_grad_cpu(FPTYPE* dy_dem_x, FPTYPE a5 = table[table_idx * last_layer_size * 6 + 6 * kk + 5]; FPTYPE res = a0 + (a1 + (a2 + (a3 + (a4 + a5 * xx) * xx) * xx) * xx) * xx; + FPTYPE g = + (a1 + (2 * a2 + (3 * a3 + (4 * a4 + 5 * a5 * xx) * xx) * xx) * xx); + if (enable_se_atten) { + FPTYPE t = two_embed[ii * nnei * last_layer_size + + jj * last_layer_size + kk]; + res = res * t + t; + g = t * g; + } if (unloop) { - grad += (a1 + (2 * a2 + (3 * a3 + (4 * a4 + 5 * a5 * xx) * xx) * xx) * - xx) * - dot(ll, rr) * (nnei - jj); + grad += g * dot(ll, rr) * (nnei - jj); dy_dem[ii * nnei * 4 + jj * 4 + 0] += res * rr[0] * (nnei - jj); dy_dem[ii * nnei * 4 + jj * 4 + 1] += res * rr[1] * (nnei - jj); dy_dem[ii * nnei * 4 + jj * 4 + 2] += res * rr[2] * (nnei - jj); dy_dem[ii * nnei * 4 + jj * 4 + 3] += res * rr[3] * (nnei - jj); } else { - grad += (a1 + (2 * a2 + (3 * a3 + (4 * a4 + 5 * a5 * xx) * xx) * xx) * - xx) * - dot(ll, rr); + grad += g * dot(ll, rr); dy_dem[ii * nnei * 4 + jj * 4 + 0] += res * rr[0]; dy_dem[ii * nnei * 4 + jj * 4 + 1] += res * rr[1]; dy_dem[ii * nnei * 4 + jj * 4 + 2] += res * rr[2]; @@ -301,240 +315,6 @@ void deepmd::tabulate_fusion_se_a_grad_grad_cpu(FPTYPE* dz_dy, } } -template -void deepmd::tabulate_fusion_se_atten_cpu(FPTYPE* out, - const FPTYPE* table, - const FPTYPE* table_info, - const FPTYPE* em_x, - const FPTYPE* em, - const FPTYPE* two_embed, - const int nloc, - const int nnei, - const int last_layer_size) { - memset(out, 0, sizeof(FPTYPE) * nloc * 4 * last_layer_size); - const FPTYPE lower = table_info[0]; - const FPTYPE upper = table_info[1]; - const FPTYPE _max = table_info[2]; - const FPTYPE stride0 = table_info[3]; - const FPTYPE stride1 = table_info[4]; -// for every atom, execute a small manual gemm ~ -// FPTYPE * res = new FPTYPE[4 * last_layer_size]; -#pragma omp parallel for - for (int ii = 0; ii < nloc; ii++) { - FPTYPE ll[4] = {0}; - FPTYPE ago = em_x[ii * nnei + nnei - 1]; - bool unloop = false; - for (int jj = 0; jj < nnei; jj++) { - ll[0] = em[ii * nnei * 4 + jj * 4 + 0]; - ll[1] = em[ii * nnei * 4 + jj * 4 + 1]; - ll[2] = em[ii * nnei * 4 + jj * 4 + 2]; - ll[3] = em[ii * nnei * 4 + jj * 4 + 3]; - FPTYPE xx = em_x[ii * nnei + jj]; - if (ago == xx) { - unloop = true; - } - int table_idx = 0; - locate_xx(lower, upper, _max, stride0, stride1, xx, table_idx); - for (int kk = 0; kk < last_layer_size; kk++) { - FPTYPE a0 = table[table_idx * last_layer_size * 6 + 6 * kk + 0]; - FPTYPE a1 = table[table_idx * last_layer_size * 6 + 6 * kk + 1]; - FPTYPE a2 = table[table_idx * last_layer_size * 6 + 6 * kk + 2]; - FPTYPE a3 = table[table_idx * last_layer_size * 6 + 6 * kk + 3]; - FPTYPE a4 = table[table_idx * last_layer_size * 6 + 6 * kk + 4]; - FPTYPE a5 = table[table_idx * last_layer_size * 6 + 6 * kk + 5]; - FPTYPE var = - a0 + (a1 + (a2 + (a3 + (a4 + a5 * xx) * xx) * xx) * xx) * xx; - FPTYPE t = - two_embed[ii * nnei * last_layer_size + jj * last_layer_size + kk]; - var = var * t + t; - if (unloop) { - out[ii * last_layer_size * 4 + 0 * last_layer_size + kk] += - (nnei - jj) * var * ll[0]; - out[ii * last_layer_size * 4 + 1 * last_layer_size + kk] += - (nnei - jj) * var * ll[1]; - out[ii * last_layer_size * 4 + 2 * last_layer_size + kk] += - (nnei - jj) * var * ll[2]; - out[ii * last_layer_size * 4 + 3 * last_layer_size + kk] += - (nnei - jj) * var * ll[3]; - } else { - out[ii * last_layer_size * 4 + 0 * last_layer_size + kk] += - var * ll[0]; - out[ii * last_layer_size * 4 + 1 * last_layer_size + kk] += - var * ll[1]; - out[ii * last_layer_size * 4 + 2 * last_layer_size + kk] += - var * ll[2]; - out[ii * last_layer_size * 4 + 3 * last_layer_size + kk] += - var * ll[3]; - } - } - if (unloop) break; - } - } -} - -template -void deepmd::tabulate_fusion_se_atten_grad_cpu(FPTYPE* dy_dem_x, - FPTYPE* dy_dem, - const FPTYPE* table, - const FPTYPE* table_info, - const FPTYPE* em_x, - const FPTYPE* em, - const FPTYPE* two_embed, - const FPTYPE* dy, - const int nloc, - const int nnei, - const int last_layer_size) { - memset(dy_dem_x, 0, sizeof(FPTYPE) * nloc * nnei); - memset(dy_dem, 0, sizeof(FPTYPE) * nloc * nnei * 4); - FPTYPE const lower = table_info[0]; - FPTYPE const upper = table_info[1]; - FPTYPE const _max = table_info[2]; - FPTYPE const stride0 = table_info[3]; - FPTYPE const stride1 = table_info[4]; -// for every atom, execute a small gemm~ -// FPTYPE * res = new FPTYPE[4 * last_layer_size]; -#pragma omp parallel for - for (int ii = 0; ii < nloc; ii++) { - FPTYPE ll[4]; - FPTYPE rr[4]; - FPTYPE ago = em_x[ii * nnei + nnei - 1]; - bool unloop = false; - for (int jj = 0; jj < nnei; jj++) { - // construct the dy/dx - ll[0] = em[ii * nnei * 4 + jj * 4 + 0]; - ll[1] = em[ii * nnei * 4 + jj * 4 + 1]; - ll[2] = em[ii * nnei * 4 + jj * 4 + 2]; - ll[3] = em[ii * nnei * 4 + jj * 4 + 3]; - FPTYPE xx = em_x[ii * nnei + jj]; - if (ago == xx) { - unloop = true; - } - int table_idx = 0; - locate_xx(lower, upper, _max, stride0, stride1, xx, table_idx); - FPTYPE grad = (FPTYPE)0.0; - for (int kk = 0; kk < last_layer_size; kk++) { - rr[0] = dy[ii * last_layer_size * 4 + 0 * last_layer_size + kk]; - rr[1] = dy[ii * last_layer_size * 4 + 1 * last_layer_size + kk]; - rr[2] = dy[ii * last_layer_size * 4 + 2 * last_layer_size + kk]; - rr[3] = dy[ii * last_layer_size * 4 + 3 * last_layer_size + kk]; - FPTYPE a0 = table[table_idx * last_layer_size * 6 + 6 * kk + 0]; - FPTYPE a1 = table[table_idx * last_layer_size * 6 + 6 * kk + 1]; - FPTYPE a2 = table[table_idx * last_layer_size * 6 + 6 * kk + 2]; - FPTYPE a3 = table[table_idx * last_layer_size * 6 + 6 * kk + 3]; - FPTYPE a4 = table[table_idx * last_layer_size * 6 + 6 * kk + 4]; - FPTYPE a5 = table[table_idx * last_layer_size * 6 + 6 * kk + 5]; - FPTYPE res = - a0 + (a1 + (a2 + (a3 + (a4 + a5 * xx) * xx) * xx) * xx) * xx; - FPTYPE t = - two_embed[ii * nnei * last_layer_size + jj * last_layer_size + kk]; - - FPTYPE res1 = res * t + t; - FPTYPE g = - (a1 + (2 * a2 + (3 * a3 + (4 * a4 + 5 * a5 * xx) * xx) * xx) * xx); - FPTYPE deriv = t * g; - - if (unloop) { - grad += deriv * dot(ll, rr) * (nnei - jj); - dy_dem[ii * nnei * 4 + jj * 4 + 0] += res1 * rr[0] * (nnei - jj); - dy_dem[ii * nnei * 4 + jj * 4 + 1] += res1 * rr[1] * (nnei - jj); - dy_dem[ii * nnei * 4 + jj * 4 + 2] += res1 * rr[2] * (nnei - jj); - dy_dem[ii * nnei * 4 + jj * 4 + 3] += res1 * rr[3] * (nnei - jj); - } else { - grad += deriv * dot(ll, rr); - dy_dem[ii * nnei * 4 + jj * 4 + 0] += res1 * rr[0]; - dy_dem[ii * nnei * 4 + jj * 4 + 1] += res1 * rr[1]; - dy_dem[ii * nnei * 4 + jj * 4 + 2] += res1 * rr[2]; - dy_dem[ii * nnei * 4 + jj * 4 + 3] += res1 * rr[3]; - } - } - dy_dem_x[ii * nnei + jj] = grad; - if (unloop) break; - } - } -} - -template -void deepmd::tabulate_fusion_se_atten_grad_grad_cpu(FPTYPE* dz_dy, - const FPTYPE* table, - const FPTYPE* table_info, - const FPTYPE* em_x, - const FPTYPE* em, - const FPTYPE* two_embed, - const FPTYPE* dz_dy_dem_x, - const FPTYPE* dz_dy_dem, - const int nloc, - const int nnei, - const int last_layer_size) { - memset(dz_dy, 0, sizeof(FPTYPE) * nloc * 4 * last_layer_size); - const FPTYPE lower = table_info[0]; - const FPTYPE upper = table_info[1]; - const FPTYPE _max = table_info[2]; - const FPTYPE stride0 = table_info[3]; - const FPTYPE stride1 = table_info[4]; -// for every atom, execute a small manual gemm ~ -// FPTYPE * res = new FPTYPE[4 * last_layer_size]; -#pragma omp parallel for - for (int ii = 0; ii < nloc; ii++) { - FPTYPE ll[4]; - FPTYPE hh[4]; - FPTYPE ago = em_x[ii * nnei + nnei - 1]; - bool unloop = false; - for (int jj = 0; jj < nnei; jj++) { - ll[0] = em[ii * nnei * 4 + jj * 4 + 0]; - ll[1] = em[ii * nnei * 4 + jj * 4 + 1]; - ll[2] = em[ii * nnei * 4 + jj * 4 + 2]; - ll[3] = em[ii * nnei * 4 + jj * 4 + 3]; - hh[0] = dz_dy_dem[ii * nnei * 4 + jj * 4 + 0]; - hh[1] = dz_dy_dem[ii * nnei * 4 + jj * 4 + 1]; - hh[2] = dz_dy_dem[ii * nnei * 4 + jj * 4 + 2]; - hh[3] = dz_dy_dem[ii * nnei * 4 + jj * 4 + 3]; - FPTYPE xx = em_x[ii * nnei + jj]; - FPTYPE dz_xx = dz_dy_dem_x[ii * nnei + jj]; - if (ago == xx) { - unloop = true; - } - int table_idx = 0; - locate_xx(lower, upper, _max, stride0, stride1, xx, table_idx); - for (int kk = 0; kk < last_layer_size; kk++) { - FPTYPE a0 = table[table_idx * last_layer_size * 6 + 6 * kk + 0]; - FPTYPE a1 = table[table_idx * last_layer_size * 6 + 6 * kk + 1]; - FPTYPE a2 = table[table_idx * last_layer_size * 6 + 6 * kk + 2]; - FPTYPE a3 = table[table_idx * last_layer_size * 6 + 6 * kk + 3]; - FPTYPE a4 = table[table_idx * last_layer_size * 6 + 6 * kk + 4]; - FPTYPE a5 = table[table_idx * last_layer_size * 6 + 6 * kk + 5]; - FPTYPE var = - a0 + (a1 + (a2 + (a3 + (a4 + a5 * xx) * xx) * xx) * xx) * xx; - FPTYPE var_grad = - a1 + - ((FPTYPE)2. * a2 + - ((FPTYPE)3. * a3 + ((FPTYPE)4. * a4 + (FPTYPE)5. * a5 * xx) * xx) * - xx) * - xx; - if (unloop) { - dz_dy[ii * last_layer_size * 4 + 0 * last_layer_size + kk] += - (nnei - jj) * (var * hh[0] + dz_xx * var_grad * ll[0]); - dz_dy[ii * last_layer_size * 4 + 1 * last_layer_size + kk] += - (nnei - jj) * (var * hh[1] + dz_xx * var_grad * ll[1]); - dz_dy[ii * last_layer_size * 4 + 2 * last_layer_size + kk] += - (nnei - jj) * (var * hh[2] + dz_xx * var_grad * ll[2]); - dz_dy[ii * last_layer_size * 4 + 3 * last_layer_size + kk] += - (nnei - jj) * (var * hh[3] + dz_xx * var_grad * ll[3]); - } else { - dz_dy[ii * last_layer_size * 4 + 0 * last_layer_size + kk] += - var * hh[0] + dz_xx * var_grad * ll[0]; - dz_dy[ii * last_layer_size * 4 + 1 * last_layer_size + kk] += - var * hh[1] + dz_xx * var_grad * ll[1]; - dz_dy[ii * last_layer_size * 4 + 2 * last_layer_size + kk] += - var * hh[2] + dz_xx * var_grad * ll[2]; - dz_dy[ii * last_layer_size * 4 + 3 * last_layer_size + kk] += - var * hh[3] + dz_xx * var_grad * ll[3]; - } - } - if (unloop) break; - } - } -} - template void deepmd::tabulate_fusion_se_t_cpu(FPTYPE* out, const FPTYPE* table, @@ -823,6 +603,7 @@ template void deepmd::tabulate_fusion_se_a_cpu( const float* table_info, const float* em_x, const float* em, + const float* two_embed, const int nloc, const int nnei, const int last_layer_size); @@ -832,6 +613,7 @@ template void deepmd::tabulate_fusion_se_a_cpu( const double* table_info, const double* em_x, const double* em, + const double* two_embed, const int nloc, const int nnei, const int last_layer_size); @@ -842,6 +624,7 @@ template void deepmd::tabulate_fusion_se_a_grad_cpu( const float* table_info, const float* em_x, const float* em, + const float* two_embed, const float* dy, const int nloc, const int nnei, @@ -853,6 +636,7 @@ template void deepmd::tabulate_fusion_se_a_grad_cpu( const double* table_info, const double* em_x, const double* em, + const double* two_embed, const double* dy, const int nloc, const int nnei, @@ -880,75 +664,6 @@ template void deepmd::tabulate_fusion_se_a_grad_grad_cpu( const int nnei, const int last_layer_size); -template void deepmd::tabulate_fusion_se_atten_cpu( - float* out, - const float* table, - const float* table_info, - const float* em_x, - const float* em, - const float* two_embed, - const int nloc, - const int nnei, - const int last_layer_size); -template void deepmd::tabulate_fusion_se_atten_cpu( - double* out, - const double* table, - const double* table_info, - const double* em_x, - const double* em, - const double* two_embed, - const int nloc, - const int nnei, - const int last_layer_size); -template void deepmd::tabulate_fusion_se_atten_grad_cpu( - float* dy_dem_x, - float* dy_dem, - const float* table, - const float* table_info, - const float* em_x, - const float* em, - const float* two_embed, - const float* dy, - const int nloc, - const int nnei, - const int last_layer_size); -template void deepmd::tabulate_fusion_se_atten_grad_cpu( - double* dy_dem_x, - double* dy_dem, - const double* table, - const double* table_info, - const double* em_x, - const double* em, - const double* two_embed, - const double* dy, - const int nloc, - const int nnei, - const int last_layer_size); -template void deepmd::tabulate_fusion_se_atten_grad_grad_cpu( - float* dz_dy, - const float* table, - const float* table_info, - const float* em_x, - const float* em, - const float* two_embed, - const float* dz_dy_dem_x, - const float* dz_dy_dem, - const int nloc, - const int nnei, - const int last_layer_size); -template void deepmd::tabulate_fusion_se_atten_grad_grad_cpu( - double* dz_dy, - const double* table, - const double* table_info, - const double* em_x, - const double* em, - const double* two_embed, - const double* dz_dy_dem_x, - const double* dz_dy_dem, - const int nloc, - const int nnei, - const int last_layer_size); - template void deepmd::tabulate_fusion_se_t_cpu( float* out, const float* table, diff --git a/source/op/_tabulate_grad.py b/source/op/_tabulate_grad.py index 550d35010a..7596cbfc75 100644 --- a/source/op/_tabulate_grad.py +++ b/source/op/_tabulate_grad.py @@ -15,62 +15,25 @@ @ops.RegisterGradient("TabulateFusion") @ops.RegisterGradient("TabulateFusionSeA") def _tabulate_fusion_se_a_grad_cc(op, dy): - dy_dx, dy_df = op_module.tabulate_fusion_se_a_grad( - op.inputs[0], op.inputs[1], op.inputs[2], op.inputs[3], dy, op.outputs[0] + dy_dx, dy_df, dy_dtwo = op_module.tabulate_fusion_se_a_grad( + op.inputs[0], + op.inputs[1], + op.inputs[2], + op.inputs[3], + op.inputs[4], + dy, + op.outputs[0], ) - return [None, None, dy_dx, dy_df] + return [None, None, dy_dx, dy_df, dy_dtwo] @ops.RegisterGradient("TabulateFusionGrad") @ops.RegisterGradient("TabulateFusionSeAGrad") -def _tabulate_fusion_se_a_grad_grad_cc(op, dy, dy_): +def _tabulate_fusion_se_a_grad_grad_cc(op, dy, dy_, dy_dtwo): dz_dy = op_module.tabulate_fusion_se_a_grad_grad( - op.inputs[0], op.inputs[1], op.inputs[2], op.inputs[3], dy, dy_, op.inputs[5] - ) - return [None, None, None, None, dz_dy, None] - - -@ops.RegisterGradient("TabulateFusionSeAtten") -def _tabulate_fusion_se_atten_grad_cc(op, dy): - dy_dx, dy_df, dy_dtwo = op_module.tabulate_fusion_se_atten_grad( - op.inputs[0], # table - op.inputs[1], # table_info - op.inputs[2], # em_x - op.inputs[3], # em - op.inputs[4], # two_embed - dy, # dy - op.outputs[0], # descriptor - ) - return [ - None, # table - None, # table_info - dy_dx, # em_x - dy_df, # em - dy_dtwo, - ] # two_embed - - -@ops.RegisterGradient("TabulateFusionSeAttenGrad") -def _tabulate_fusion_se_atten_grad_grad_cc(op, dy, dy_, dy_dtwo): - dz_dy = op_module.tabulate_fusion_se_atten_grad_grad( - op.inputs[0], # table - op.inputs[1], # table_info - op.inputs[2], # em_x - op.inputs[3], # em - dy_dtwo, # two_embed - dy, # dz_dy_dem_x - dy_, # dz_dy_dem - op.inputs[6], # descriptor + op.inputs[0], op.inputs[1], op.inputs[2], op.inputs[3], dy, dy_, op.inputs[6] ) - return [ - None, # table - None, # table_info - None, # em_x - None, # em - None, # two_embed - dz_dy, # dy - None, - ] # descriptor + return [None, None, None, None, None, dz_dy, None] @ops.RegisterGradient("TabulateFusionSeT") diff --git a/source/op/tabulate_multi_device.cc b/source/op/tabulate_multi_device.cc index c12550f239..f00b310d9f 100644 --- a/source/op/tabulate_multi_device.cc +++ b/source/op/tabulate_multi_device.cc @@ -34,37 +34,6 @@ REGISTER_OP("TabulateFusionGradGrad") .Output("dz_dy: T"); REGISTER_OP("TabulateFusionSeA") - .Attr("T: {float, double} = DT_DOUBLE") - .Input("table: T") - .Input("table_info: T") - .Input("em_x: T") - .Input("em: T") - .Attr("last_layer_size: int") - .Output("descriptor: T"); - -REGISTER_OP("TabulateFusionSeAGrad") - .Attr("T: {float, double} = DT_DOUBLE") - .Input("table: T") - .Input("table_info: T") - .Input("em_x: T") - .Input("em: T") - .Input("dy: T") - .Input("descriptor: T") - .Output("dy_dem_x: T") - .Output("dy_dem: T"); - -REGISTER_OP("TabulateFusionSeAGradGrad") - .Attr("T: {float, double}") - .Input("table: T") - .Input("table_info: T") - .Input("em_x: T") - .Input("em: T") - .Input("dz_dy_dem_x: T") - .Input("dz_dy_dem: T") - .Input("descriptor: T") - .Output("dz_dy: T"); - -REGISTER_OP("TabulateFusionSeAtten") .Attr("T: {float, double} = DT_DOUBLE") .Input("table: T") .Input("table_info: T") @@ -74,7 +43,7 @@ REGISTER_OP("TabulateFusionSeAtten") .Attr("last_layer_size: int") .Output("descriptor: T"); -REGISTER_OP("TabulateFusionSeAttenGrad") +REGISTER_OP("TabulateFusionSeAGrad") .Attr("T: {float, double} = DT_DOUBLE") .Input("table: T") .Input("table_info: T") @@ -87,13 +56,12 @@ REGISTER_OP("TabulateFusionSeAttenGrad") .Output("dy_dem: T") .Output("dy_dtwo: T"); -REGISTER_OP("TabulateFusionSeAttenGradGrad") +REGISTER_OP("TabulateFusionSeAGradGrad") .Attr("T: {float, double}") .Input("table: T") .Input("table_info: T") .Input("em_x: T") .Input("em: T") - .Input("two_embed: T") .Input("dz_dy_dem_x: T") .Input("dz_dy_dem: T") .Input("descriptor: T") @@ -176,6 +144,7 @@ class TabulateFusionSeAOp : public OpKernel { const Tensor& table_info_tensor = context->input(context_input_index++); const Tensor& em_x_tensor = context->input(context_input_index++); const Tensor& em_tensor = context->input(context_input_index++); + const Tensor& two_embed_tensor = context->input(context_input_index++); // set size of the sample OP_REQUIRES(context, (table_tensor.shape().dims() == 2), errors::InvalidArgument("Dim of table should be 2")); @@ -199,22 +168,27 @@ class TabulateFusionSeAOp : public OpKernel { const FPTYPE* table_info = table_info_tensor.flat().data(); const FPTYPE* em_x = em_x_tensor.flat().data(); const FPTYPE* em = em_tensor.flat().data(); + const FPTYPE* two_embed = two_embed_tensor.shape().dims() != 2 + ? nullptr + : two_embed_tensor.flat().data(); const int nloc = em_tensor.shape().dim_size(0); const int nnei = em_tensor.shape().dim_size(1); if (device == "GPU") { #if GOOGLE_CUDA deepmd::tabulate_fusion_se_a_gpu_cuda(descriptor, table, table_info, em_x, - em, nloc, nnei, last_layer_size); + em, two_embed, nloc, nnei, + last_layer_size); #endif // GOOGLE_CUDA #if TENSORFLOW_USE_ROCM deepmd::tabulate_fusion_se_a_gpu_rocm(descriptor, table, table_info, em_x, - em, nloc, nnei, last_layer_size); + em, two_embed, nloc, nnei, + last_layer_size); #endif // TENSORFLOW_USE_ROCM } else if (device == "CPU") { deepmd::tabulate_fusion_se_a_cpu(descriptor, table, table_info, em_x, em, - nloc, nnei, last_layer_size); + two_embed, nloc, nnei, last_layer_size); } } @@ -240,6 +214,8 @@ class TabulateFusionSeAGradOp : public OpKernel { const Tensor& table_info_tensor = context->input(context_input_index++); const Tensor& em_x_tensor = context->input(context_input_index++); const Tensor& em_tensor = context->input(context_input_index++); + const Tensor& two_embed_tensor = context->input(context_input_index++); + const Tensor& dy_tensor = context->input(context_input_index++); const Tensor& descriptor_tensor = context->input(context_input_index++); // set size of the sample @@ -254,16 +230,24 @@ class TabulateFusionSeAGradOp : public OpKernel { OP_REQUIRES_OK(context, context->allocate_output(context_output_index++, em_tensor.shape(), &dy_dem_tensor)); + Tensor* dy_dtwo_tensor = NULL; + OP_REQUIRES_OK(context, context->allocate_output(context_output_index++, + two_embed_tensor.shape(), + &dy_dtwo_tensor)); DeviceFunctor()(device, context->eigen_device()); // flat the tensors FPTYPE* dy_dem_x = dy_dem_x_tensor->flat().data(); FPTYPE* dy_dem = dy_dem_tensor->flat().data(); + const FPTYPE* descriptor = descriptor_tensor.flat().data(); const FPTYPE* table = table_tensor.flat().data(); const FPTYPE* table_info = table_info_tensor.flat().data(); const FPTYPE* em_x = em_x_tensor.flat().data(); const FPTYPE* em = em_tensor.flat().data(); + const FPTYPE* two_embed = two_embed_tensor.shape().dims() != 2 + ? nullptr + : two_embed_tensor.flat().data(); const FPTYPE* dy = dy_tensor.flat().data(); const int nloc = em_tensor.shape().dim_size(0); const int nnei = em_tensor.shape().dim_size(1); @@ -271,19 +255,19 @@ class TabulateFusionSeAGradOp : public OpKernel { if (device == "GPU") { #if GOOGLE_CUDA - deepmd::tabulate_fusion_se_a_grad_gpu_cuda(dy_dem_x, dy_dem, table, - table_info, em_x, em, dy, nloc, - nnei, last_layer_size); + deepmd::tabulate_fusion_se_a_grad_gpu_cuda( + dy_dem_x, dy_dem, table, table_info, em_x, em, two_embed, dy, nloc, + nnei, last_layer_size); #endif // GOOGLE_CUDA #if TENSORFLOW_USE_ROCM - deepmd::tabulate_fusion_se_a_grad_gpu_rocm(dy_dem_x, dy_dem, table, - table_info, em_x, em, dy, nloc, - nnei, last_layer_size); + deepmd::tabulate_fusion_se_a_grad_gpu_rocm( + dy_dem_x, dy_dem, table, table_info, em_x, em, two_embed, dy, nloc, + nnei, last_layer_size); #endif // TENSORFLOW_USE_ROCM } else if (device == "CPU") { deepmd::tabulate_fusion_se_a_grad_cpu(dy_dem_x, dy_dem, table, table_info, - em_x, em, dy, nloc, nnei, + em_x, em, two_embed, dy, nloc, nnei, last_layer_size); } } @@ -357,224 +341,6 @@ class TabulateFusionSeAGradGradOp : public OpKernel { std::string device; }; -template -class TabulateFusionSeAttenOp : public OpKernel { - public: - explicit TabulateFusionSeAttenOp(OpKernelConstruction* context) - : OpKernel(context) { - OP_REQUIRES_OK(context, - context->GetAttr("last_layer_size", &last_layer_size)); - } - void Compute(OpKernelContext* context) override { - deepmd::safe_compute( - context, [this](OpKernelContext* context) { this->_Compute(context); }); - } - - void _Compute(OpKernelContext* context) { - // Grab the input tensor - int context_input_index = 0; - const Tensor& table_tensor = context->input(context_input_index++); - const Tensor& table_info_tensor = context->input(context_input_index++); - const Tensor& em_x_tensor = context->input(context_input_index++); - const Tensor& em_tensor = context->input(context_input_index++); - const Tensor& two_embed_tensor = context->input(context_input_index++); - // set size of the sample - OP_REQUIRES(context, (table_tensor.shape().dims() == 2), - errors::InvalidArgument("Dim of table should be 2")); - OP_REQUIRES(context, (em_x_tensor.shape().dims() == 2), - errors::InvalidArgument("Dim of input should be 2")); - OP_REQUIRES(context, (em_tensor.shape().dims() == 3), - errors::InvalidArgument("Dim of input should be 3")); - OP_REQUIRES(context, (two_embed_tensor.shape().dims() == 2), - errors::InvalidArgument("Dim of input should be 2")); - TensorShape descriptor_shape; - descriptor_shape.AddDim(em_tensor.shape().dim_size(0)); - descriptor_shape.AddDim(4); // TODO: be careful here; - descriptor_shape.AddDim(last_layer_size); - int context_output_index = 0; - Tensor* descriptor_tensor = NULL; - OP_REQUIRES_OK(context, context->allocate_output(context_output_index++, - descriptor_shape, - &descriptor_tensor)); - DeviceFunctor()(device, context->eigen_device()); - // flat the tensors - FPTYPE* descriptor = descriptor_tensor->flat().data(); - const FPTYPE* table = table_tensor.flat().data(); - const FPTYPE* table_info = table_info_tensor.flat().data(); - const FPTYPE* em_x = em_x_tensor.flat().data(); - const FPTYPE* em = em_tensor.flat().data(); - const FPTYPE* two_embed = two_embed_tensor.flat().data(); - const int nloc = em_tensor.shape().dim_size(0); - const int nnei = em_tensor.shape().dim_size(1); - - if (device == "GPU") { -#if GOOGLE_CUDA - deepmd::tabulate_fusion_se_atten_gpu_cuda(descriptor, table, table_info, - em_x, em, two_embed, nloc, nnei, - last_layer_size); -#endif // GOOGLE_CUDA - -#if TENSORFLOW_USE_ROCM - deepmd::tabulate_fusion_se_atten_gpu_rocm(descriptor, table, table_info, - em_x, em, two_embed, nloc, nnei, - last_layer_size); -#endif // TENSORFLOW_USE_ROCM - } else if (device == "CPU") { - deepmd::tabulate_fusion_se_atten_cpu(descriptor, table, table_info, em_x, - em, two_embed, nloc, nnei, - last_layer_size); - } - } - - private: - int last_layer_size; - std::string device; -}; - -template -class TabulateFusionSeAttenGradOp : public OpKernel { - public: - explicit TabulateFusionSeAttenGradOp(OpKernelConstruction* context) - : OpKernel(context) {} - void Compute(OpKernelContext* context) override { - deepmd::safe_compute( - context, [this](OpKernelContext* context) { this->_Compute(context); }); - } - - void _Compute(OpKernelContext* context) { - // Grab the input tensor - int context_input_index = 0; - const Tensor& table_tensor = context->input(context_input_index++); - const Tensor& table_info_tensor = context->input(context_input_index++); - const Tensor& em_x_tensor = context->input(context_input_index++); - const Tensor& em_tensor = context->input(context_input_index++); - const Tensor& two_embed_tensor = context->input(context_input_index++); - const Tensor& dy_tensor = context->input(context_input_index++); - const Tensor& descriptor_tensor = context->input(context_input_index++); - - // set size of the sample - OP_REQUIRES(context, (dy_tensor.shape().dims() == 3), - errors::InvalidArgument("Dim of table should be 3")); - int context_output_index = 0; - Tensor* dy_dem_x_tensor = NULL; - OP_REQUIRES_OK(context, context->allocate_output(context_output_index++, - em_x_tensor.shape(), - &dy_dem_x_tensor)); - Tensor* dy_dem_tensor = NULL; - OP_REQUIRES_OK(context, - context->allocate_output(context_output_index++, - em_tensor.shape(), &dy_dem_tensor)); - Tensor* dy_dtwo_tensor = NULL; - OP_REQUIRES_OK(context, context->allocate_output(context_output_index++, - two_embed_tensor.shape(), - &dy_dtwo_tensor)); - DeviceFunctor()(device, context->eigen_device()); - - // flat the tensors - FPTYPE* dy_dem_x = dy_dem_x_tensor->flat().data(); - FPTYPE* dy_dem = dy_dem_tensor->flat().data(); - FPTYPE* dy_dtwo = dy_dtwo_tensor->flat().data(); - const FPTYPE* descriptor = descriptor_tensor.flat().data(); - const FPTYPE* table = table_tensor.flat().data(); - const FPTYPE* table_info = table_info_tensor.flat().data(); - const FPTYPE* em_x = em_x_tensor.flat().data(); - const FPTYPE* em = em_tensor.flat().data(); - const FPTYPE* two_embed = two_embed_tensor.flat().data(); - const FPTYPE* dy = dy_tensor.flat().data(); - const int nloc = em_tensor.shape().dim_size(0); - const int nnei = em_tensor.shape().dim_size(1); - const int last_layer_size = descriptor_tensor.shape().dim_size(2); - - if (device == "GPU") { -#if GOOGLE_CUDA - deepmd::tabulate_fusion_se_atten_grad_gpu_cuda( - dy_dem_x, dy_dem, table, table_info, em_x, em, two_embed, dy, nloc, - nnei, last_layer_size); -#endif // GOOGLE_CUDA - -#if TENSORFLOW_USE_ROCM - deepmd::tabulate_fusion_se_atten_grad_gpu_rocm( - dy_dem_x, dy_dem, table, table_info, em_x, em, two_embed, dy, nloc, - nnei, last_layer_size); -#endif // TENSORFLOW_USE_ROCM - } else if (device == "CPU") { - deepmd::tabulate_fusion_se_atten_grad_cpu( - dy_dem_x, dy_dem, table, table_info, em_x, em, two_embed, dy, nloc, - nnei, last_layer_size); - } - } - - private: - std::string device; -}; - -template -class TabulateFusionSeAttenGradGradOp : public OpKernel { - public: - explicit TabulateFusionSeAttenGradGradOp(OpKernelConstruction* context) - : OpKernel(context) {} - void Compute(OpKernelContext* context) override { - // Grab the input tensor - int context_input_index = 0; - const Tensor& table_tensor = context->input(context_input_index++); - const Tensor& table_info_tensor = context->input(context_input_index++); - const Tensor& em_x_tensor = context->input(context_input_index++); - const Tensor& em_tensor = context->input(context_input_index++); - const Tensor& two_embed_tensor = context->input(context_input_index++); - const Tensor& dz_dy_dem_x_tensor = context->input(context_input_index++); - const Tensor& dz_dy_dem_tensor = context->input(context_input_index++); - const Tensor& descriptor_tensor = context->input(context_input_index++); - // set size of the sample - OP_REQUIRES(context, (dz_dy_dem_x_tensor.shape().dims() == 2), - errors::InvalidArgument("Dim of input should be 2")); - OP_REQUIRES(context, (dz_dy_dem_tensor.shape().dims() == 3), - errors::InvalidArgument("Dim of input should be 3")); - int context_output_index = 0; - Tensor* dz_dy_tensor = NULL; - OP_REQUIRES_OK(context, context->allocate_output(context_output_index++, - descriptor_tensor.shape(), - &dz_dy_tensor)); - DeviceFunctor()(device, context->eigen_device()); - - // flat the tensors - FPTYPE* dz_dy = dz_dy_tensor->flat().data(); - const FPTYPE* table = table_tensor.flat().data(); - const FPTYPE* table_info = table_info_tensor.flat().data(); - const FPTYPE* em_x = em_x_tensor.flat().data(); - const FPTYPE* em = em_tensor.flat().data(); - const FPTYPE* two_embed = two_embed_tensor.flat().data(); - const FPTYPE* dz_dy_dem_x = dz_dy_dem_x_tensor.flat().data(); - const FPTYPE* dz_dy_dem = dz_dy_dem_tensor.flat().data(); - const int nloc = em_tensor.shape().dim_size(0); - const int nnei = em_tensor.shape().dim_size(1); - const int last_layer_size = descriptor_tensor.shape().dim_size(2); - - if (device == "GPU") { -#if GOOGLE_CUDA - deepmd::tabulate_fusion_se_atten_grad_grad_gpu_cuda( - dz_dy, table, table_info, em_x, em, two_embed, dz_dy_dem_x, dz_dy_dem, - nloc, nnei, last_layer_size); -#endif // GOOGLE_CUDA -#if TENSORFLOW_USE_ROCM - deepmd::tabulate_fusion_se_atten_grad_grad_gpu_rocm( - dz_dy, table, table_info, em_x, em, two_embed, dz_dy_dem_x, dz_dy_dem, - nloc, nnei, last_layer_size); -#endif // TENSORFLOW_USE_ROCM - OP_REQUIRES(context, (last_layer_size <= 1024), - errors::InvalidArgument( - "In the process of model compression, the size of the " - "last layer of embedding net must be less than 1024!")); - } else if (device == "CPU") { - deepmd::tabulate_fusion_se_atten_grad_grad_cpu( - dz_dy, table, table_info, em_x, em, two_embed, dz_dy_dem_x, dz_dy_dem, - nloc, nnei, last_layer_size); - } - } - - private: - std::string device; -}; - template class TabulateFusionSeTOp : public OpKernel { public: @@ -979,17 +745,6 @@ class TabulateFusionSeRGradGradOp : public OpKernel { .Device(DEVICE_CPU) \ .TypeConstraint("T"), \ TabulateFusionSeAGradGradOp); \ - REGISTER_KERNEL_BUILDER( \ - Name("TabulateFusionSeAtten").Device(DEVICE_CPU).TypeConstraint("T"), \ - TabulateFusionSeAttenOp); \ - REGISTER_KERNEL_BUILDER(Name("TabulateFusionSeAttenGrad") \ - .Device(DEVICE_CPU) \ - .TypeConstraint("T"), \ - TabulateFusionSeAttenGradOp); \ - REGISTER_KERNEL_BUILDER(Name("TabulateFusionSeAttenGradGrad") \ - .Device(DEVICE_CPU) \ - .TypeConstraint("T"), \ - TabulateFusionSeAttenGradGradOp); \ REGISTER_KERNEL_BUILDER( \ Name("TabulateFusionSeT").Device(DEVICE_CPU).TypeConstraint("T"), \ TabulateFusionSeTOp); \ @@ -1014,81 +769,66 @@ REGISTER_CPU(float); REGISTER_CPU(double); #if GOOGLE_CUDA || TENSORFLOW_USE_ROCM -#define REGISTER_GPU(T) \ - REGISTER_KERNEL_BUILDER(Name("TabulateFusion") \ - .Device(DEVICE_GPU) \ - .TypeConstraint("T") \ - .HostMemory("table_info"), \ - TabulateFusionSeAOp); \ - REGISTER_KERNEL_BUILDER(Name("TabulateFusionGrad") \ - .Device(DEVICE_GPU) \ - .TypeConstraint("T") \ - .HostMemory("table_info"), \ - TabulateFusionSeAGradOp); \ - REGISTER_KERNEL_BUILDER(Name("TabulateFusionGradGrad") \ - .Device(DEVICE_GPU) \ - .TypeConstraint("T") \ - .HostMemory("table_info"), \ - TabulateFusionSeAGradGradOp); \ - REGISTER_KERNEL_BUILDER(Name("TabulateFusionSeA") \ - .Device(DEVICE_GPU) \ - .TypeConstraint("T") \ - .HostMemory("table_info"), \ - TabulateFusionSeAOp); \ - REGISTER_KERNEL_BUILDER(Name("TabulateFusionSeAGrad") \ - .Device(DEVICE_GPU) \ - .TypeConstraint("T") \ - .HostMemory("table_info"), \ - TabulateFusionSeAGradOp); \ - REGISTER_KERNEL_BUILDER(Name("TabulateFusionSeAGradGrad") \ - .Device(DEVICE_GPU) \ - .TypeConstraint("T") \ - .HostMemory("table_info"), \ - TabulateFusionSeAGradGradOp); \ - REGISTER_KERNEL_BUILDER(Name("TabulateFusionSeAtten") \ - .Device(DEVICE_GPU) \ - .TypeConstraint("T") \ - .HostMemory("table_info"), \ - TabulateFusionSeAttenOp); \ - REGISTER_KERNEL_BUILDER(Name("TabulateFusionSeAttenGrad") \ - .Device(DEVICE_GPU) \ - .TypeConstraint("T") \ - .HostMemory("table_info"), \ - TabulateFusionSeAttenGradOp); \ - REGISTER_KERNEL_BUILDER(Name("TabulateFusionSeAttenGradGrad") \ - .Device(DEVICE_GPU) \ - .TypeConstraint("T") \ - .HostMemory("table_info"), \ - TabulateFusionSeAttenGradGradOp); \ - REGISTER_KERNEL_BUILDER(Name("TabulateFusionSeT") \ - .Device(DEVICE_GPU) \ - .TypeConstraint("T") \ - .HostMemory("table_info"), \ - TabulateFusionSeTOp); \ - REGISTER_KERNEL_BUILDER(Name("TabulateFusionSeTGrad") \ - .Device(DEVICE_GPU) \ - .TypeConstraint("T") \ - .HostMemory("table_info"), \ - TabulateFusionSeTGradOp); \ - REGISTER_KERNEL_BUILDER(Name("TabulateFusionSeTGradGrad") \ - .Device(DEVICE_GPU) \ - .TypeConstraint("T") \ - .HostMemory("table_info"), \ - TabulateFusionSeTGradGradOp); \ - REGISTER_KERNEL_BUILDER(Name("TabulateFusionSeR") \ - .Device(DEVICE_GPU) \ - .TypeConstraint("T") \ - .HostMemory("table_info"), \ - TabulateFusionSeROp); \ - REGISTER_KERNEL_BUILDER(Name("TabulateFusionSeRGrad") \ - .Device(DEVICE_GPU) \ - .TypeConstraint("T") \ - .HostMemory("table_info"), \ - TabulateFusionSeRGradOp); \ - REGISTER_KERNEL_BUILDER(Name("TabulateFusionSeRGradGrad") \ - .Device(DEVICE_GPU) \ - .TypeConstraint("T") \ - .HostMemory("table_info"), \ +#define REGISTER_GPU(T) \ + REGISTER_KERNEL_BUILDER(Name("TabulateFusion") \ + .Device(DEVICE_GPU) \ + .TypeConstraint("T") \ + .HostMemory("table_info"), \ + TabulateFusionSeAOp); \ + REGISTER_KERNEL_BUILDER(Name("TabulateFusionGrad") \ + .Device(DEVICE_GPU) \ + .TypeConstraint("T") \ + .HostMemory("table_info"), \ + TabulateFusionSeAGradOp); \ + REGISTER_KERNEL_BUILDER(Name("TabulateFusionGradGrad") \ + .Device(DEVICE_GPU) \ + .TypeConstraint("T") \ + .HostMemory("table_info"), \ + TabulateFusionSeAGradGradOp); \ + REGISTER_KERNEL_BUILDER(Name("TabulateFusionSeA") \ + .Device(DEVICE_GPU) \ + .TypeConstraint("T") \ + .HostMemory("table_info"), \ + TabulateFusionSeAOp); \ + REGISTER_KERNEL_BUILDER(Name("TabulateFusionSeAGrad") \ + .Device(DEVICE_GPU) \ + .TypeConstraint("T") \ + .HostMemory("table_info"), \ + TabulateFusionSeAGradOp); \ + REGISTER_KERNEL_BUILDER(Name("TabulateFusionSeAGradGrad") \ + .Device(DEVICE_GPU) \ + .TypeConstraint("T") \ + .HostMemory("table_info"), \ + TabulateFusionSeAGradGradOp); \ + REGISTER_KERNEL_BUILDER(Name("TabulateFusionSeT") \ + .Device(DEVICE_GPU) \ + .TypeConstraint("T") \ + .HostMemory("table_info"), \ + TabulateFusionSeTOp); \ + REGISTER_KERNEL_BUILDER(Name("TabulateFusionSeTGrad") \ + .Device(DEVICE_GPU) \ + .TypeConstraint("T") \ + .HostMemory("table_info"), \ + TabulateFusionSeTGradOp); \ + REGISTER_KERNEL_BUILDER(Name("TabulateFusionSeTGradGrad") \ + .Device(DEVICE_GPU) \ + .TypeConstraint("T") \ + .HostMemory("table_info"), \ + TabulateFusionSeTGradGradOp); \ + REGISTER_KERNEL_BUILDER(Name("TabulateFusionSeR") \ + .Device(DEVICE_GPU) \ + .TypeConstraint("T") \ + .HostMemory("table_info"), \ + TabulateFusionSeROp); \ + REGISTER_KERNEL_BUILDER(Name("TabulateFusionSeRGrad") \ + .Device(DEVICE_GPU) \ + .TypeConstraint("T") \ + .HostMemory("table_info"), \ + TabulateFusionSeRGradOp); \ + REGISTER_KERNEL_BUILDER(Name("TabulateFusionSeRGradGrad") \ + .Device(DEVICE_GPU) \ + .TypeConstraint("T") \ + .HostMemory("table_info"), \ TabulateFusionSeRGradGradOp); REGISTER_GPU(float); REGISTER_GPU(double); From 171b7aea5e17f92f95d77dbca33ef9e51b7b79e5 Mon Sep 17 00:00:00 2001 From: nahso Date: Mon, 29 May 2023 21:01:13 +0800 Subject: [PATCH 104/126] fix the missing argument in rocm --- source/lib/src/rocm/tabulate.hip.cu | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/source/lib/src/rocm/tabulate.hip.cu b/source/lib/src/rocm/tabulate.hip.cu index e5d87817ab..6306071436 100644 --- a/source/lib/src/rocm/tabulate.hip.cu +++ b/source/lib/src/rocm/tabulate.hip.cu @@ -625,7 +625,7 @@ void tabulate_fusion_se_a_gpu_rocm(FPTYPE* out, HIP_KERNEL_NAME( tabulate_fusion_se_a_fifth_order_polynomial), nloc, last_layer_size, sizeof(FPTYPE) * MM * last_layer_size, 0, out, - table, em_x, em, table_info[0], table_info[1], table_info[2], + table, em_x, em, two_embed, table_info[0], table_info[1], table_info[2], table_info[3], table_info[4], nnei, last_layer_size); DPErrcheck(hipGetLastError()); DPErrcheck(hipDeviceSynchronize()); From 8623c4c9658c218c45af7f1beabd248ceb28ac8f Mon Sep 17 00:00:00 2001 From: nahso Date: Mon, 29 May 2023 21:04:39 +0800 Subject: [PATCH 105/126] remove two_embed argument --- source/lib/src/rocm/tabulate.hip.cu | 2 -- 1 file changed, 2 deletions(-) diff --git a/source/lib/src/rocm/tabulate.hip.cu b/source/lib/src/rocm/tabulate.hip.cu index 6306071436..3f91854643 100644 --- a/source/lib/src/rocm/tabulate.hip.cu +++ b/source/lib/src/rocm/tabulate.hip.cu @@ -881,7 +881,6 @@ template void tabulate_fusion_se_a_grad_grad_gpu_rocm( const float* table_info, const float* em_x, const float* em, - const float* two_embed, const float* dz_dy_dem_x, const float* dz_dy_dem, const int nloc, @@ -893,7 +892,6 @@ template void tabulate_fusion_se_a_grad_grad_gpu_rocm( const double* table_info, const double* em_x, const double* em, - const double* two_embed, const double* dz_dy_dem_x, const double* dz_dy_dem, const int nloc, From 855af147ec9eb8fb10f43cae8b4adb8cd6b545d5 Mon Sep 17 00:00:00 2001 From: nahso Date: Mon, 29 May 2023 21:30:48 +0800 Subject: [PATCH 106/126] fix calling of tabulate_fusion_se_a_cpu in se_a_cpu ut --- source/lib/tests/test_tabulate_se_a.cc | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/source/lib/tests/test_tabulate_se_a.cc b/source/lib/tests/test_tabulate_se_a.cc index f4cf9789cf..e07ff5f60c 100644 --- a/source/lib/tests/test_tabulate_se_a.cc +++ b/source/lib/tests/test_tabulate_se_a.cc @@ -613,7 +613,7 @@ class TestTabulateSeA : public ::testing::Test { TEST_F(TestTabulateSeA, tabulate_fusion_se_a_cpu) { std::vector xyz_scatter(nloc * nnei * last_layer_size); deepmd::tabulate_fusion_se_a_cpu(&xyz_scatter[0], &table[0], &info[0], - &em_x[0], &em[0], nloc, nnei, + &em_x[0], &em[0], nullptr, nloc, nnei, last_layer_size); EXPECT_EQ(xyz_scatter.size(), nloc * nnei * last_layer_size); EXPECT_EQ(xyz_scatter.size(), expected_xyz_scatter.size()); @@ -627,7 +627,7 @@ TEST_F(TestTabulateSeA, tabulate_fusion_se_a_grad_cpu) { std::vector dy_dem(em.size()); std::vector dy(nloc * nnei * last_layer_size, 1.0); deepmd::tabulate_fusion_se_a_grad_cpu( - &dy_dem_x[0], &dy_dem[0], &table[0], &info[0], &em_x[0], &em[0], &dy[0], + &dy_dem_x[0], &dy_dem[0], &table[0], &info[0], &em_x[0], &em[0], nullptr, &dy[0], nloc, nnei, last_layer_size); EXPECT_EQ(dy_dem_x.size(), nloc * nnei); EXPECT_EQ(dy_dem.size(), nloc * nnei * 4); From 6c9f784621a26f878a0fd571230429af9d55c807 Mon Sep 17 00:00:00 2001 From: nahso Date: Mon, 29 May 2023 21:33:38 +0800 Subject: [PATCH 107/126] pre commit --- source/lib/tests/test_tabulate_se_a.cc | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/source/lib/tests/test_tabulate_se_a.cc b/source/lib/tests/test_tabulate_se_a.cc index e07ff5f60c..a84fd87b4c 100644 --- a/source/lib/tests/test_tabulate_se_a.cc +++ b/source/lib/tests/test_tabulate_se_a.cc @@ -613,8 +613,8 @@ class TestTabulateSeA : public ::testing::Test { TEST_F(TestTabulateSeA, tabulate_fusion_se_a_cpu) { std::vector xyz_scatter(nloc * nnei * last_layer_size); deepmd::tabulate_fusion_se_a_cpu(&xyz_scatter[0], &table[0], &info[0], - &em_x[0], &em[0], nullptr, nloc, nnei, - last_layer_size); + &em_x[0], &em[0], nullptr, nloc, + nnei, last_layer_size); EXPECT_EQ(xyz_scatter.size(), nloc * nnei * last_layer_size); EXPECT_EQ(xyz_scatter.size(), expected_xyz_scatter.size()); for (int jj = 0; jj < xyz_scatter.size(); ++jj) { @@ -627,8 +627,8 @@ TEST_F(TestTabulateSeA, tabulate_fusion_se_a_grad_cpu) { std::vector dy_dem(em.size()); std::vector dy(nloc * nnei * last_layer_size, 1.0); deepmd::tabulate_fusion_se_a_grad_cpu( - &dy_dem_x[0], &dy_dem[0], &table[0], &info[0], &em_x[0], &em[0], nullptr, &dy[0], - nloc, nnei, last_layer_size); + &dy_dem_x[0], &dy_dem[0], &table[0], &info[0], &em_x[0], &em[0], nullptr, + &dy[0], nloc, nnei, last_layer_size); EXPECT_EQ(dy_dem_x.size(), nloc * nnei); EXPECT_EQ(dy_dem.size(), nloc * nnei * 4); EXPECT_EQ(dy_dem_x.size(), expected_dy_dem_x.size()); From 5938c08258d1a6eb38f8ee458c831a7cc92516d6 Mon Sep 17 00:00:00 2001 From: nahso Date: Tue, 30 May 2023 18:12:11 +0800 Subject: [PATCH 108/126] add se_a c++ ut --- source/lib/tests/test_tabulate_se_a.cc | 231 +++++++++++++++++++++---- 1 file changed, 201 insertions(+), 30 deletions(-) diff --git a/source/lib/tests/test_tabulate_se_a.cc b/source/lib/tests/test_tabulate_se_a.cc index a84fd87b4c..e0583d805d 100644 --- a/source/lib/tests/test_tabulate_se_a.cc +++ b/source/lib/tests/test_tabulate_se_a.cc @@ -602,6 +602,94 @@ class TestTabulateSeA : public ::testing::Test { -3.33688909, -3.33688909, -3.34206038, -3.34206038, -3.34206038, -3.34206038, -3.34387412, -3.34387412, -3.34387412, -3.34387412, -3.33051143, -3.33051143, -3.33051143, -3.33051143}; + + std::vector two_embed = { + 0.41783850884461693, 0.06917892522383784, 0.07309949640440838, + 0.57828038123179, 0.30460107001129133, 0.0641857998132136, + 0.016519028000859692, 0.46818914782665344, 0.7524658161955905, + 0.7366050152276675, 0.5442923017739666, 0.6984532784508917, + 0.8794579292532613, 0.933333068809702, 0.052557248156142045, + 0.3158695444821408, 0.6104896498153188, 0.3190616402773879, + 0.39327308944220873, 0.9555810861515368, 0.45845946239660273, + 0.2836952640436372, 0.7129235830370116, 0.21678811087765415, + 0.8589385334305147, 0.8664288996198418, 0.2392088190073245, + 0.44554156483185636, 0.4554902141228184, 0.6929437508125064, + 0.680397459717037, 0.47499455998030615, 0.19872841218252735, + 0.0593083660501722, 0.20593103822290515, 0.8377626566866462, + 0.9006561442856688, 0.9451358048366522, 0.03886827486931199, + 0.9395629463676399, 0.0018941296317954714, 0.08140115779980839, + 0.9309153205767321, 0.4697357303240055, 0.9164471895052549, + 0.5957401092143415, 0.40338864067603986, 0.9096349710860572, + 0.027870073369474335, 0.9614765307496669, 0.3142035164603587, + 0.4027282032956391, 0.05129061735552376, 0.18018240347684844, + 0.8391548601089657, 0.25198333808352436, 0.07903335895654717, + 0.9831396742713334, 0.21328884297544115, 0.8119626188647525, + 0.1734453905861253, 0.014174310472666818, 0.06890574596678134, + 0.3490769935686088, 0.34055562797730554, 0.9834924063503578, + 0.1689164263315952, 0.9611024936313157, 0.6796725725159389, + 0.7902946379060674, 0.3045916985592084, 0.6923776720247495, + 0.5626994287153583, 0.12132066580981216, 0.13356198804830732, + 0.5332034125146011, 0.6155216974624633, 0.3080851791499254, + 0.8391387652641518, 0.8588772315368923, 0.9414859699900482, + 0.9852118289755771, 0.7514252073835589, 0.6780090883007501, + 0.1472721338720271, 0.4785493098407567, 0.8825343095166535, + 0.1562449821247882, 0.5809096109347806, 0.653605647812403, + 0.26158060329219845, 0.28359029181509054, 0.23111396285536823, + 0.6711415141607222, 0.5955230293073148, 0.14336394912405104, + 0.48478135042139503, 0.34621668898158153, 0.7962234329935334, + 0.40204538487553787, 0.09600971949708359, 0.985025266359638, + 0.4949655728846287, 0.23503981206241742, 0.607828476455725, + 0.21634419784756398, 0.04166567958728129, 0.132198384508056, + 0.4112021863641492, 0.9441979803962212, 0.993462871462463, + 0.4524002115880147, 0.6322719605196645, 0.5121196654684579, + 0.7844974619880201, 0.6783684708633317, 0.6402712236722511, + 0.43899788665378925, 0.6060330628471464, 0.7082475921988166, + 0.1614968711069913, 0.6289247345866867, 0.4034261331727077, + 0.7906075239905527, 0.9325509002602962, 0.44489583733770977, + 0.5194672674960213, 0.04635102497306032}; + std::vector expected_xyz_scatter_with_two_embed = { + 0.52052723, -0.09162471, 0.00134297, 0.36123220, 0.53111037, -0.13234888, + 0.14547290, -0.07760225, 0.39022224, -0.05084421, 0.00097584, 0.40299126, + 0.46399123, -0.08226800, 0.10496569, -0.05697074, 0.22831256, -0.03236614, + 0.00053069, 0.20158495, 0.25562350, -0.05008332, 0.05120581, -0.03847130, + 0.48372134, -0.08635551, 0.00127416, 0.33553740, 0.48870492, -0.12377265, + 0.14484348, -0.06894720, 0.09037214, -0.08453476, 0.00132198, 0.48978396, + 0.56809636, -0.11217096, 0.06433977, -0.14684460, 0.04360697, -0.10371710, + 0.00105878, 0.36754591, 0.30793639, -0.05068795, 0.10760094, -0.09443281, + 0.03027943, -0.05996609, 0.00096647, 0.29035073, 0.38261345, -0.05492907, + 0.06442101, -0.09657691, 0.06211007, -0.06585386, 0.00121839, 0.38826136, + 0.62888512, -0.12626045, 0.13760707, -0.15409971, 0.35496658, -0.09629124, + 0.00094936, 0.37091455, 0.40029681, -0.11266203, 0.16359092, -0.09316170, + 0.46220540, -0.11194036, 0.00099500, 0.48276205, 0.60272823, -0.14764563, + 0.23083357, -0.14624559, 0.42737683, -0.12200031, 0.00101439, 0.38477701, + 0.40109773, -0.14840970, 0.18621217, -0.08161032, 0.19972406, -0.03173626, + 0.00020518, 0.11990507, 0.18933921, -0.05445546, 0.10560781, -0.04502732, + 0.43067025, -0.06062120, 0.00243902, 0.29049015, 0.50532163, -0.17136189, + 0.24445441, -0.10595959, 0.24687635, -0.06109584, 0.00155267, 0.31006293, + 0.46913106, -0.14140409, 0.16395873, -0.04099833, 0.18986012, -0.05425738, + 0.00104613, 0.27541918, 0.46324668, -0.11304375, 0.14316380, -0.03016295, + 0.30689686, -0.03886867, 0.00133389, 0.17385121, 0.33724930, -0.13383926, + 0.18766727, -0.07051321, + }; + std::vector expected_dy_dem_x_with_two_embed = { + -0.00876744, -0.05693831, 0.00251697, -0.04104716, + -0.07540836, -0.01255790, -0.00225420, -0.04262001, + -0.09707657, -0.00396457, -0.00492498, -0.03225818, + -0.04350615, -0.02865961, -0.00690901, -0.00847689, + }; + std::vector expected_dy_dem_with_two_embed = { + 1.88405505, 1.88405505, 1.88405505, 1.88405505, 3.33316973, 3.33316973, + 3.33316973, 3.33316973, 3.48744961, 3.48744961, 3.48744961, 3.48744961, + 2.66470385, 2.66470385, 2.66470385, 2.66470385, 2.55291404, 2.55291404, + 2.55291404, 2.55291404, 2.23471214, 2.23471214, 2.23471214, 2.23471214, + 0.79636719, 0.79636719, 0.79636719, 0.79636719, 1.16809199, 1.16809199, + 1.16809199, 1.16809199, 2.55088185, 2.55088185, 2.55088185, 2.55088185, + 3.02601696, 3.02601696, 3.02601696, 3.02601696, 2.80271925, 2.80271925, + 2.80271925, 2.80271925, 1.67897971, 1.67897971, 1.67897971, 1.67897971, + 1.20938629, 1.20938629, 1.20938629, 1.20938629, 2.05980066, 2.05980066, + 2.05980066, 2.05980066, 3.10625208, 3.10625208, 3.10625208, 3.10625208, + 3.35169350, 3.35169350, 3.35169350, 3.35169350, + }; const int nloc = 4; const int nnei = 4; const int last_layer_size = 8; @@ -620,6 +708,17 @@ TEST_F(TestTabulateSeA, tabulate_fusion_se_a_cpu) { for (int jj = 0; jj < xyz_scatter.size(); ++jj) { EXPECT_LT(fabs(xyz_scatter[jj] - expected_xyz_scatter[jj]), 1e-5); } + + deepmd::tabulate_fusion_se_a_cpu(&xyz_scatter[0], &table[0], &info[0], + &em_x[0], &em[0], &two_embed[0], + nloc, nnei, last_layer_size); + EXPECT_EQ(xyz_scatter.size(), nloc * nnei * last_layer_size); + EXPECT_EQ(xyz_scatter.size(), expected_xyz_scatter_with_two_embed.size()); + + for (int jj = 0; jj < xyz_scatter.size(); ++jj) { + EXPECT_LT(fabs(xyz_scatter[jj] - expected_xyz_scatter_with_two_embed[jj]), + 1e-5); + } } TEST_F(TestTabulateSeA, tabulate_fusion_se_a_grad_cpu) { @@ -639,6 +738,20 @@ TEST_F(TestTabulateSeA, tabulate_fusion_se_a_grad_cpu) { for (int jj = 0; jj < dy_dem.size(); ++jj) { EXPECT_LT(fabs(dy_dem[jj] - expected_dy_dem[jj]), 1e-5); } + + deepmd::tabulate_fusion_se_a_grad_cpu( + &dy_dem_x[0], &dy_dem[0], &table[0], &info[0], &em_x[0], &em[0], + &two_embed[0], &dy[0], nloc, nnei, last_layer_size); + EXPECT_EQ(dy_dem_x.size(), nloc * nnei); + EXPECT_EQ(dy_dem.size(), nloc * nnei * 4); + EXPECT_EQ(dy_dem_x.size(), expected_dy_dem_x.size()); + EXPECT_EQ(dy_dem.size(), expected_dy_dem.size()); + for (int jj = 0; jj < dy_dem_x.size(); ++jj) { + EXPECT_LT(fabs(dy_dem_x[jj] - expected_dy_dem_x_with_two_embed[jj]), 1e-5); + } + for (int jj = 0; jj < dy_dem.size(); ++jj) { + EXPECT_LT(fabs(dy_dem[jj] - expected_dy_dem_with_two_embed[jj]), 1e-5); + } } #if GOOGLE_CUDA @@ -651,20 +764,36 @@ TEST_F(TestTabulateSeA, tabulate_fusion_se_a_gpu_cuda) { deepmd::malloc_device_memory_sync(table_dev, table); deepmd::malloc_device_memory_sync(em_x_dev, em_x); deepmd::malloc_device_memory_sync(em_dev, em); - deepmd::tabulate_fusion_se_a_gpu_cuda(xyz_scatter_dev, table_dev, - &info[0], em_x_dev, em_dev, - nloc, nnei, last_layer_size); + deepmd::tabulate_fusion_se_a_gpu_cuda( + xyz_scatter_dev, table_dev, &info[0], em_x_dev, em_dev, nullptr, nloc, + nnei, last_layer_size); deepmd::memcpy_device_to_host(xyz_scatter_dev, xyz_scatter); - deepmd::delete_device_memory(xyz_scatter_dev); - deepmd::delete_device_memory(table_dev); - deepmd::delete_device_memory(em_x_dev); - deepmd::delete_device_memory(em_dev); EXPECT_EQ(xyz_scatter.size(), nloc * nnei * last_layer_size); EXPECT_EQ(xyz_scatter.size(), expected_xyz_scatter.size()); for (int jj = 0; jj < xyz_scatter.size(); ++jj) { EXPECT_LT(fabs(xyz_scatter[jj] - expected_xyz_scatter[jj]), 1e-5); } + + double *two_embed_dev = nullptr; + deepmd::malloc_device_memory_sync(two_embed_dev, em); + deepmd::tabulate_fusion_se_a_gpu_cuda( + xyz_scatter_dev, table_dev, &info[0], em_x_dev, em_dev, nullptr, nloc, + nnei, last_layer_size); + deepmd::memcpy_device_to_host(xyz_scatter_dev, xyz_scatter); + + EXPECT_EQ(xyz_scatter.size(), nloc * nnei * last_layer_size); + EXPECT_EQ(xyz_scatter.size(), expected_xyz_scatter.size()); + for (int jj = 0; jj < xyz_scatter.size(); ++jj) { + EXPECT_LT(fabs(xyz_scatter[jj] - expected_xyz_scatter_with_two_embed[jj]), + 1e-5); + } + + deepmd::delete_device_memory(xyz_scatter_dev); + deepmd::delete_device_memory(table_dev); + deepmd::delete_device_memory(em_x_dev); + deepmd::delete_device_memory(em_dev); + deepmd::delete_device_memory(two_embed_dev); } TEST_F(TestTabulateSeA, tabulate_fusion_se_a_grad_gpu_cuda) { @@ -681,16 +810,10 @@ TEST_F(TestTabulateSeA, tabulate_fusion_se_a_grad_gpu_cuda) { deepmd::malloc_device_memory_sync(em_dev, em); deepmd::malloc_device_memory_sync(dy_dev, dy); deepmd::tabulate_fusion_se_a_grad_gpu_cuda( - dy_dem_x_dev, dy_dem_dev, table_dev, &info[0], em_x_dev, em_dev, dy_dev, - nloc, nnei, last_layer_size); + dy_dem_x_dev, dy_dem_dev, table_dev, &info[0], em_x_dev, em_dev, nullptr, + dy_dev, nloc, nnei, last_layer_size); deepmd::memcpy_device_to_host(dy_dem_x_dev, dy_dem_x); deepmd::memcpy_device_to_host(dy_dem_dev, dy_dem); - deepmd::delete_device_memory(dy_dem_x_dev); - deepmd::delete_device_memory(dy_dem_dev); - deepmd::delete_device_memory(table_dev); - deepmd::delete_device_memory(em_x_dev); - deepmd::delete_device_memory(em_dev); - deepmd::delete_device_memory(dy_dev); EXPECT_EQ(dy_dem_x.size(), nloc * nnei); EXPECT_EQ(dy_dem.size(), nloc * nnei * 4); @@ -702,6 +825,25 @@ TEST_F(TestTabulateSeA, tabulate_fusion_se_a_grad_gpu_cuda) { for (int jj = 0; jj < dy_dem.size(); ++jj) { EXPECT_LT(fabs(dy_dem[jj] - expected_dy_dem[jj]), 1e-5); } + + double *two_embed_dev = nullptr; + deepmd::malloc_device_memory_sync(two_embed_dev, two_embed); + deepmd::tabulate_fusion_se_a_grad_gpu_cuda( + dy_dem_x_dev, dy_dem_dev, table_dev, &info[0], em_x_dev, em_dev, + two_embed_dev, dy_dev, nloc, nnei, last_layer_size); + for (int jj = 0; jj < dy_dem_x.size(); ++jj) { + EXPECT_LT(fabs(dy_dem_x[jj] - expected_dy_dem_x_with_two_embed[jj]), 1e-5); + } + for (int jj = 0; jj < dy_dem.size(); ++jj) { + EXPECT_LT(fabs(dy_dem[jj] - expected_dy_dem_with_two_embed[jj]), 1e-5); + } + + deepmd::delete_device_memory(dy_dem_x_dev); + deepmd::delete_device_memory(dy_dem_dev); + deepmd::delete_device_memory(table_dev); + deepmd::delete_device_memory(em_x_dev); + deepmd::delete_device_memory(em_dev); + deepmd::delete_device_memory(dy_dev); } #endif // GOOGLE_CUDA @@ -715,20 +857,36 @@ TEST_F(TestTabulateSeA, tabulate_fusion_se_a_gpu_rocm) { deepmd::malloc_device_memory_sync(table_dev, table); deepmd::malloc_device_memory_sync(em_x_dev, em_x); deepmd::malloc_device_memory_sync(em_dev, em); - deepmd::tabulate_fusion_se_a_gpu_rocm(xyz_scatter_dev, table_dev, - &info[0], em_x_dev, em_dev, - nloc, nnei, last_layer_size); + deepmd::tabulate_fusion_se_a_gpu_rocm( + xyz_scatter_dev, table_dev, &info[0], em_x_dev, em_dev, nullptr, nloc, + nnei, last_layer_size); deepmd::memcpy_device_to_host(xyz_scatter_dev, xyz_scatter); - deepmd::delete_device_memory(xyz_scatter_dev); - deepmd::delete_device_memory(table_dev); - deepmd::delete_device_memory(em_x_dev); - deepmd::delete_device_memory(em_dev); EXPECT_EQ(xyz_scatter.size(), nloc * nnei * last_layer_size); EXPECT_EQ(xyz_scatter.size(), expected_xyz_scatter.size()); for (int jj = 0; jj < xyz_scatter.size(); ++jj) { EXPECT_LT(fabs(xyz_scatter[jj] - expected_xyz_scatter[jj]), 1e-5); } + + double *two_embed_dev = nullptr; + deepmd::malloc_device_memory_sync(two_embed_dev, em); + deepmd::tabulate_fusion_se_a_gpu_rocm( + xyz_scatter_dev, table_dev, &info[0], em_x_dev, em_dev, nullptr, nloc, + nnei, last_layer_size); + deepmd::memcpy_device_to_host(xyz_scatter_dev, xyz_scatter); + + EXPECT_EQ(xyz_scatter.size(), nloc * nnei * last_layer_size); + EXPECT_EQ(xyz_scatter.size(), expected_xyz_scatter.size()); + for (int jj = 0; jj < xyz_scatter.size(); ++jj) { + EXPECT_LT(fabs(xyz_scatter[jj] - expected_xyz_scatter_with_two_embed[jj]), + 1e-5); + } + + deepmd::delete_device_memory(xyz_scatter_dev); + deepmd::delete_device_memory(table_dev); + deepmd::delete_device_memory(em_x_dev); + deepmd::delete_device_memory(em_dev); + deepmd::delete_device_memory(two_embed_dev); } TEST_F(TestTabulateSeA, tabulate_fusion_se_a_grad_gpu_rocm) { @@ -745,16 +903,10 @@ TEST_F(TestTabulateSeA, tabulate_fusion_se_a_grad_gpu_rocm) { deepmd::malloc_device_memory_sync(em_dev, em); deepmd::malloc_device_memory_sync(dy_dev, dy); deepmd::tabulate_fusion_se_a_grad_gpu_rocm( - dy_dem_x_dev, dy_dem_dev, table_dev, &info[0], em_x_dev, em_dev, dy_dev, - nloc, nnei, last_layer_size); + dy_dem_x_dev, dy_dem_dev, table_dev, &info[0], em_x_dev, em_dev, nullptr, + dy_dev, nloc, nnei, last_layer_size); deepmd::memcpy_device_to_host(dy_dem_x_dev, dy_dem_x); deepmd::memcpy_device_to_host(dy_dem_dev, dy_dem); - deepmd::delete_device_memory(dy_dem_x_dev); - deepmd::delete_device_memory(dy_dem_dev); - deepmd::delete_device_memory(table_dev); - deepmd::delete_device_memory(em_x_dev); - deepmd::delete_device_memory(em_dev); - deepmd::delete_device_memory(dy_dev); EXPECT_EQ(dy_dem_x.size(), nloc * nnei); EXPECT_EQ(dy_dem.size(), nloc * nnei * 4); @@ -766,5 +918,24 @@ TEST_F(TestTabulateSeA, tabulate_fusion_se_a_grad_gpu_rocm) { for (int jj = 0; jj < dy_dem.size(); ++jj) { EXPECT_LT(fabs(dy_dem[jj] - expected_dy_dem[jj]), 1e-5); } + + double *two_embed_dev = nullptr; + deepmd::malloc_device_memory_sync(two_embed_dev, two_embed); + deepmd::tabulate_fusion_se_a_grad_gpu_rocm( + dy_dem_x_dev, dy_dem_dev, table_dev, &info[0], em_x_dev, em_dev, + two_embed_dev, dy_dev, nloc, nnei, last_layer_size); + for (int jj = 0; jj < dy_dem_x.size(); ++jj) { + EXPECT_LT(fabs(dy_dem_x[jj] - expected_dy_dem_x_with_two_embed[jj]), 1e-5); + } + for (int jj = 0; jj < dy_dem.size(); ++jj) { + EXPECT_LT(fabs(dy_dem[jj] - expected_dy_dem_with_two_embed[jj]), 1e-5); + } + + deepmd::delete_device_memory(dy_dem_x_dev); + deepmd::delete_device_memory(dy_dem_dev); + deepmd::delete_device_memory(table_dev); + deepmd::delete_device_memory(em_x_dev); + deepmd::delete_device_memory(em_dev); + deepmd::delete_device_memory(dy_dev); } #endif // TENSORFLOW_USE_ROCM From 907d7add10c58f00b51d7af30e6ce5c4159a4391 Mon Sep 17 00:00:00 2001 From: nahso Date: Tue, 30 May 2023 18:19:57 +0800 Subject: [PATCH 109/126] free two_embed_dev --- source/lib/tests/test_tabulate_se_a.cc | 2 ++ 1 file changed, 2 insertions(+) diff --git a/source/lib/tests/test_tabulate_se_a.cc b/source/lib/tests/test_tabulate_se_a.cc index e0583d805d..59351c9819 100644 --- a/source/lib/tests/test_tabulate_se_a.cc +++ b/source/lib/tests/test_tabulate_se_a.cc @@ -844,6 +844,7 @@ TEST_F(TestTabulateSeA, tabulate_fusion_se_a_grad_gpu_cuda) { deepmd::delete_device_memory(em_x_dev); deepmd::delete_device_memory(em_dev); deepmd::delete_device_memory(dy_dev); + deepmd::delete_device_memory(two_embed_dev); } #endif // GOOGLE_CUDA @@ -937,5 +938,6 @@ TEST_F(TestTabulateSeA, tabulate_fusion_se_a_grad_gpu_rocm) { deepmd::delete_device_memory(em_x_dev); deepmd::delete_device_memory(em_dev); deepmd::delete_device_memory(dy_dev); + deepmd::delete_device_memory(two_embed_dev); } #endif // TENSORFLOW_USE_ROCM From af98d954b0c38fb594fdcdce7eebe3b1db55a5f2 Mon Sep 17 00:00:00 2001 From: nahso Date: Thu, 1 Jun 2023 11:11:39 +0800 Subject: [PATCH 110/126] fix c++ ut --- source/lib/tests/test_tabulate_se_a.cc | 18 ++++++++++++++---- 1 file changed, 14 insertions(+), 4 deletions(-) diff --git a/source/lib/tests/test_tabulate_se_a.cc b/source/lib/tests/test_tabulate_se_a.cc index 59351c9819..b24dc59784 100644 --- a/source/lib/tests/test_tabulate_se_a.cc +++ b/source/lib/tests/test_tabulate_se_a.cc @@ -776,9 +776,10 @@ TEST_F(TestTabulateSeA, tabulate_fusion_se_a_gpu_cuda) { } double *two_embed_dev = nullptr; - deepmd::malloc_device_memory_sync(two_embed_dev, em); + deepmd::malloc_device_memory_sync(two_embed_dev, two_embed); + deepmd::malloc_device_memory_sync(xyz_scatter_dev, xyz_scatter); deepmd::tabulate_fusion_se_a_gpu_cuda( - xyz_scatter_dev, table_dev, &info[0], em_x_dev, em_dev, nullptr, nloc, + xyz_scatter_dev, table_dev, &info[0], em_x_dev, em_dev, two_embed_dev, nloc, nnei, last_layer_size); deepmd::memcpy_device_to_host(xyz_scatter_dev, xyz_scatter); @@ -828,9 +829,13 @@ TEST_F(TestTabulateSeA, tabulate_fusion_se_a_grad_gpu_cuda) { double *two_embed_dev = nullptr; deepmd::malloc_device_memory_sync(two_embed_dev, two_embed); + deepmd::malloc_device_memory_sync(dy_dem_x_dev, dy_dem_x); + deepmd::malloc_device_memory_sync(dy_dem_dev, dy_dem); deepmd::tabulate_fusion_se_a_grad_gpu_cuda( dy_dem_x_dev, dy_dem_dev, table_dev, &info[0], em_x_dev, em_dev, two_embed_dev, dy_dev, nloc, nnei, last_layer_size); + deepmd::memcpy_device_to_host(dy_dem_x_dev, dy_dem_x); + deepmd::memcpy_device_to_host(dy_dem_dev, dy_dem); for (int jj = 0; jj < dy_dem_x.size(); ++jj) { EXPECT_LT(fabs(dy_dem_x[jj] - expected_dy_dem_x_with_two_embed[jj]), 1e-5); } @@ -870,9 +875,10 @@ TEST_F(TestTabulateSeA, tabulate_fusion_se_a_gpu_rocm) { } double *two_embed_dev = nullptr; - deepmd::malloc_device_memory_sync(two_embed_dev, em); + deepmd::malloc_device_memory_sync(two_embed_dev, two_embed); + deepmd::malloc_device_memory_sync(xyz_scatter_dev, xyz_scatter); deepmd::tabulate_fusion_se_a_gpu_rocm( - xyz_scatter_dev, table_dev, &info[0], em_x_dev, em_dev, nullptr, nloc, + xyz_scatter_dev, table_dev, &info[0], em_x_dev, em_dev, two_embed_dev, nloc, nnei, last_layer_size); deepmd::memcpy_device_to_host(xyz_scatter_dev, xyz_scatter); @@ -922,9 +928,13 @@ TEST_F(TestTabulateSeA, tabulate_fusion_se_a_grad_gpu_rocm) { double *two_embed_dev = nullptr; deepmd::malloc_device_memory_sync(two_embed_dev, two_embed); + deepmd::malloc_device_memory_sync(dy_dem_x_dev, dy_dem_x); + deepmd::malloc_device_memory_sync(dy_dem_dev, dy_dem); deepmd::tabulate_fusion_se_a_grad_gpu_rocm( dy_dem_x_dev, dy_dem_dev, table_dev, &info[0], em_x_dev, em_dev, two_embed_dev, dy_dev, nloc, nnei, last_layer_size); + deepmd::memcpy_device_to_host(dy_dem_x_dev, dy_dem_x); + deepmd::memcpy_device_to_host(dy_dem_dev, dy_dem); for (int jj = 0; jj < dy_dem_x.size(); ++jj) { EXPECT_LT(fabs(dy_dem_x[jj] - expected_dy_dem_x_with_two_embed[jj]), 1e-5); } From 26b6a95187d54a0a0bb0d92be2ee780d4a5c6b08 Mon Sep 17 00:00:00 2001 From: nahso Date: Thu, 1 Jun 2023 15:27:07 +0800 Subject: [PATCH 111/126] pre commit --- source/lib/tests/test_tabulate_se_a.cc | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/source/lib/tests/test_tabulate_se_a.cc b/source/lib/tests/test_tabulate_se_a.cc index b24dc59784..984961d4d0 100644 --- a/source/lib/tests/test_tabulate_se_a.cc +++ b/source/lib/tests/test_tabulate_se_a.cc @@ -779,8 +779,8 @@ TEST_F(TestTabulateSeA, tabulate_fusion_se_a_gpu_cuda) { deepmd::malloc_device_memory_sync(two_embed_dev, two_embed); deepmd::malloc_device_memory_sync(xyz_scatter_dev, xyz_scatter); deepmd::tabulate_fusion_se_a_gpu_cuda( - xyz_scatter_dev, table_dev, &info[0], em_x_dev, em_dev, two_embed_dev, nloc, - nnei, last_layer_size); + xyz_scatter_dev, table_dev, &info[0], em_x_dev, em_dev, two_embed_dev, + nloc, nnei, last_layer_size); deepmd::memcpy_device_to_host(xyz_scatter_dev, xyz_scatter); EXPECT_EQ(xyz_scatter.size(), nloc * nnei * last_layer_size); @@ -878,8 +878,8 @@ TEST_F(TestTabulateSeA, tabulate_fusion_se_a_gpu_rocm) { deepmd::malloc_device_memory_sync(two_embed_dev, two_embed); deepmd::malloc_device_memory_sync(xyz_scatter_dev, xyz_scatter); deepmd::tabulate_fusion_se_a_gpu_rocm( - xyz_scatter_dev, table_dev, &info[0], em_x_dev, em_dev, two_embed_dev, nloc, - nnei, last_layer_size); + xyz_scatter_dev, table_dev, &info[0], em_x_dev, em_dev, two_embed_dev, + nloc, nnei, last_layer_size); deepmd::memcpy_device_to_host(xyz_scatter_dev, xyz_scatter); EXPECT_EQ(xyz_scatter.size(), nloc * nnei * last_layer_size); From 44f25121c0f3ea9321b4189917db2ef16f0cfb0f Mon Sep 17 00:00:00 2001 From: nahso Date: Fri, 2 Jun 2023 10:07:52 +0800 Subject: [PATCH 112/126] remove redundant comments --- deepmd/descriptor/se_atten.py | 3 --- 1 file changed, 3 deletions(-) diff --git a/deepmd/descriptor/se_atten.py b/deepmd/descriptor/se_atten.py index 7bfab07400..6f23ec20a1 100644 --- a/deepmd/descriptor/se_atten.py +++ b/deepmd/descriptor/se_atten.py @@ -447,8 +447,6 @@ def _layer_1(self, x, w, b): def _make_data(self, xx): with tf.Session() as sess: - # xx = tf.reshape(xx, [xx.size, -1]) - # pdb.set_trace() for layer in range(self.layer_size): if layer == 0: if self.filter_neuron[0] == 1: @@ -480,7 +478,6 @@ def _make_data(self, xx): ) yy = zz vv = sess.run(zz) - # vv = zz.eval() return vv def build( From 5de49e45dd6d4849dec7c405f175ae0c18d35a85 Mon Sep 17 00:00:00 2001 From: nahso Date: Thu, 8 Jun 2023 13:27:05 +0800 Subject: [PATCH 113/126] add documentations to compressilbe of se_atten --- deepmd/utils/argcheck.py | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/deepmd/utils/argcheck.py b/deepmd/utils/argcheck.py index 37b64e305e..c3ed386f76 100644 --- a/deepmd/utils/argcheck.py +++ b/deepmd/utils/argcheck.py @@ -334,9 +334,7 @@ def descrpt_hybrid_args(): @descrpt_args_plugin.register("se_atten") def descrpt_se_atten_args(): - doc_compressible = ( - "Whether to build a compressible se_atten that is supported by `dp compress`." - ) + doc_compressible = "Whether to build a compressible `se_atten` that is supported by `dp compress`. Attention: the compressible `se_atten` disables attention layers. Setting it to `False` will fallback to the previous behavior of `se_atten`." doc_sel = 'This parameter set the number of selected neighbors. Note that this parameter is a little different from that in other descriptors. Instead of separating each type of atoms, only the summation matters. And this number is highly related with the efficiency, thus one should not make it too large. Usually 200 or less is enough, far away from the GPU limitation 4096. It can be:\n\n\ - `int`. The maximum number of neighbor atoms to be considered. We recommend it to be less than 200. \n\n\ - `List[int]`. The length of the list should be the same as the number of atom types in the system. `sel[i]` gives the selected number of type-i neighbors. Only the summation of `sel[i]` matters, and it is recommended to be less than 200.\ @@ -360,7 +358,7 @@ def descrpt_se_atten_args(): return [ Argument( - "compressible", bool, optional=True, default=False, doc=doc_compressible + "compressible", bool, optional=True, default=True, doc=doc_compressible ), Argument("sel", [int, list, str], optional=True, default="auto", doc=doc_sel), Argument("rcut", float, optional=True, default=6.0, doc=doc_rcut), From 0387425a042fd368198b3854871c8a945ca400d8 Mon Sep 17 00:00:00 2001 From: nahso Date: Fri, 9 Jun 2023 14:54:54 +0800 Subject: [PATCH 114/126] modify json data in jdata --- source/tests/test_model_se_atten.py | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/source/tests/test_model_se_atten.py b/source/tests/test_model_se_atten.py index 22760b8a33..1f7a71d8d8 100644 --- a/source/tests/test_model_se_atten.py +++ b/source/tests/test_model_se_atten.py @@ -266,7 +266,7 @@ def test_exclude_types(self): np.testing.assert_almost_equal(des[:, 2:6], 0.0, 10) def test_compressible_model(self): - jfile = "water_compressible_se_atten.json" + jfile = "water_se_atten.json" jdata = j_loader(jfile) systems = j_must_have(jdata, "systems") @@ -285,6 +285,9 @@ def test_compressible_model(self): jdata["model"]["descriptor"].pop("type", None) jdata["model"]["descriptor"]["ntypes"] = 2 + jdata["model"]["descriptor"]["compressible"] = True + jdata["model"]["descriptor"]["stripped_type_embedding"] = True + jdata["model"]["descriptor"]["attn_layer"] = 0 descrpt = DescrptSeAtten(**jdata["model"]["descriptor"], uniform_seed=True) jdata["model"]["fitting_net"]["descrpt"] = descrpt fitting = EnerFitting(**jdata["model"]["fitting_net"], uniform_seed=True) @@ -417,7 +420,7 @@ def test_compressible_exclude_types(self): """In this test, we make type 0 has no interaction with type 0 and type 1, so the descriptor should be zero for type 0 atoms. """ - jfile = "water_compressible_se_atten.json" + jfile = "water_se_atten.json" jdata = j_loader(jfile) systems = j_must_have(jdata, "systems") @@ -448,6 +451,9 @@ def test_compressible_exclude_types(self): # successful descrpt = DescrptSeAtten(ntypes=ntypes, **jdata["model"]["descriptor"]) typeebd_param = jdata["model"]["type_embedding"] + jdata["model"]["descriptor"]["compressible"] = True + jdata["model"]["descriptor"]["stripped_type_embedding"] = True + jdata["model"]["descriptor"]["attn_layer"] = 0 typeebd = TypeEmbedNet( neuron=typeebd_param["neuron"], activation_function=None, From 98f56ce936c181e1ee2b1ed1cb15cc1c15960e2d Mon Sep 17 00:00:00 2001 From: nahso Date: Fri, 9 Jun 2023 15:01:03 +0800 Subject: [PATCH 115/126] enable compression by default --- examples/water/se_atten/input.json | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/examples/water/se_atten/input.json b/examples/water/se_atten/input.json index c1852194e7..24224211f5 100644 --- a/examples/water/se_atten/input.json +++ b/examples/water/se_atten/input.json @@ -7,7 +7,8 @@ ], "descriptor": { "type": "se_atten", - "compressible": false, + "compressible": true, + "stripped_type_embedding": true, "sel": 120, "rcut_smth": 0.50, "rcut": 6.00, @@ -20,7 +21,7 @@ "axis_neuron": 16, "seed": 1, "attn": 128, - "attn_layer": 2, + "attn_layer": 0, "attn_dotr": true, "attn_mask": false, "_comment": " that's all" From ef9e0768c9200e6f0aa4bb41cfe07308decd0c51 Mon Sep 17 00:00:00 2001 From: nahso Date: Fri, 9 Jun 2023 15:02:07 +0800 Subject: [PATCH 116/126] use water_se_atten.json --- source/tests/test_descrpt_se_atten.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/source/tests/test_descrpt_se_atten.py b/source/tests/test_descrpt_se_atten.py index dfffbf2885..0a593e5874 100644 --- a/source/tests/test_descrpt_se_atten.py +++ b/source/tests/test_descrpt_se_atten.py @@ -390,7 +390,7 @@ def test_descriptor_one_side(self): np.testing.assert_almost_equal(model_dout, ref_dout, places) def test_compressible_descriptor_two_sides(self): - jfile = "water_compressible_se_atten.json" + jfile = "water_se_atten.json" jdata = j_loader(jfile) systems = j_must_have(jdata, "systems") @@ -412,6 +412,9 @@ def test_compressible_descriptor_two_sides(self): # set parameters jdata["model"]["descriptor"]["neuron"] = [5, 5, 5] jdata["model"]["descriptor"]["axis_neuron"] = 2 + jdata["model"]["descriptor"]["compressible"] = True + jdata["model"]["descriptor"]["attn_layer"] = 0 + jdata["model"]["descriptor"]["stripped_type_embedding"] = True typeebd_param = { "neuron": [5], "resnet_dt": False, From 9ac34038f8e80444382515dafd6a9550b0e00f8e Mon Sep 17 00:00:00 2001 From: nahso Date: Fri, 9 Jun 2023 15:02:51 +0800 Subject: [PATCH 117/126] enable three options: previous model, model6 and compressible model6 --- deepmd/descriptor/se_atten.py | 23 ++++++++++++++++++----- 1 file changed, 18 insertions(+), 5 deletions(-) diff --git a/deepmd/descriptor/se_atten.py b/deepmd/descriptor/se_atten.py index 88700ec48b..b05713f205 100644 --- a/deepmd/descriptor/se_atten.py +++ b/deepmd/descriptor/se_atten.py @@ -121,6 +121,7 @@ def __init__( attn_mask: bool = False, multi_task: bool = False, compressible: bool = False, + stripped_type_embedding: bool = False, **kwargs, ) -> None: if not set_davg_zero: @@ -153,6 +154,7 @@ def __init__( "2" ), "se_atten only support tensorflow version 2.0 or higher." self.compressible = compressible + self.stripped_type_embedding = stripped_type_embedding self.ntypes = ntypes self.att_n = attn self.attn_layer = attn_layer @@ -162,9 +164,13 @@ def __init__( self.two_side_embeeding_net_variables = None self.layer_size = len(neuron) - if self.compressible and self.attn_layer != 0: + if not self.stripped_type_embedding and self.compressible: raise RuntimeError( - "attention layer must be set to 0 when compression of se_atten is enabled" + "compression is not supported when using a non-stripped type embedding" + ) + if self.stripped_type_embedding and self.compressible and self.attn_layer != 0: + raise RuntimeError( + "attention layer must be 0 when using a compressible se_atten with stripped type embedding" ) # descrpt config @@ -1007,8 +1013,8 @@ def _filter_lower( if not is_exclude: with tf.variable_scope(name, reuse=reuse): # with (natom x nei_type_i) x out_size - if not self.compressible: - log.info("use the non compressible model") + if not self.stripped_type_embedding: + log.info("use the previous se_atten model") xyz_scatter = self._lookup_type_embedding( xyz_scatter, atype, type_embedding ) @@ -1028,7 +1034,14 @@ def _filter_lower( mixed_prec=self.mixed_prec, ) else: - log.info("use the compressible model") + if self.compressible: + log.info( + "use the compressible model with stripped type embedding" + ) + else: + log.info( + "use the non-compressible model with stripped type embedding" + ) if not self.compress: xyz_scatter = embedding_net( xyz_scatter, From 753623271dd473eeadcf7d3bc08ff724f7b99706 Mon Sep 17 00:00:00 2001 From: nahso Date: Fri, 9 Jun 2023 15:03:31 +0800 Subject: [PATCH 118/126] doc strings for compressing options --- deepmd/utils/argcheck.py | 10 +++++++++- 1 file changed, 9 insertions(+), 1 deletion(-) diff --git a/deepmd/utils/argcheck.py b/deepmd/utils/argcheck.py index 4762434563..3bc2347c31 100644 --- a/deepmd/utils/argcheck.py +++ b/deepmd/utils/argcheck.py @@ -334,7 +334,8 @@ def descrpt_hybrid_args(): @descrpt_args_plugin.register("se_atten") def descrpt_se_atten_args(): - doc_compressible = "Whether to build a compressible `se_atten` that is supported by `dp compress`. Attention: the compressible `se_atten` disables attention layers. Setting it to `False` will fallback to the previous behavior of `se_atten`." + doc_compressible = "Whether to build a compressible `se_atten` that is supported by `dp compress`. This option is only available when `stripped_type_embedding` is `True`." + doc_stripped_type_embedding = "Whether to strip the type embedding from . Setting it to `False` will fallback to the previous version of `se_atten`. " doc_sel = 'This parameter set the number of selected neighbors. Note that this parameter is a little different from that in other descriptors. Instead of separating each type of atoms, only the summation matters. And this number is highly related with the efficiency, thus one should not make it too large. Usually 200 or less is enough, far away from the GPU limitation 4096. It can be:\n\n\ - `int`. The maximum number of neighbor atoms to be considered. We recommend it to be less than 200. \n\n\ - `List[int]`. The length of the list should be the same as the number of atom types in the system. `sel[i]` gives the selected number of type-i neighbors. Only the summation of `sel[i]` matters, and it is recommended to be less than 200.\ @@ -357,6 +358,13 @@ def descrpt_se_atten_args(): doc_attn_mask = "Whether to do mask on the diagonal in the attention matrix" return [ + Argument( + "stripped_type_embedding", + bool, + optional=True, + default=True, + doc=doc_compressible, + ), Argument( "compressible", bool, optional=True, default=True, doc=doc_compressible ), From 165f5bdf37af1ad070a88320df96719112f0c1a1 Mon Sep 17 00:00:00 2001 From: nahso Date: Fri, 9 Jun 2023 21:40:25 +0800 Subject: [PATCH 119/126] fix frz_model precision bugs --- deepmd/descriptor/se_atten.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/deepmd/descriptor/se_atten.py b/deepmd/descriptor/se_atten.py index b05713f205..0d85daa6ed 100644 --- a/deepmd/descriptor/se_atten.py +++ b/deepmd/descriptor/se_atten.py @@ -1261,7 +1261,7 @@ def init_variables( """ super().init_variables(graph=graph, graph_def=graph_def, suffix=suffix) - if self.compressible: + if self.stripped_type_embedding: self.two_side_embeeding_net_variables = {} for i in range(1, self.layer_size + 1): matrix_pattern = f"filter_type_all{suffix}/matrix_{i}_two_side_ebd" From 8b55a776ebcecba55dfabfecfb668090da99b3a9 Mon Sep 17 00:00:00 2001 From: nahso Date: Fri, 9 Jun 2023 21:40:45 +0800 Subject: [PATCH 120/126] ut for new cli interface --- source/tests/test_data_large_batch.py | 207 +++++++++++++- source/tests/test_descrpt_se_atten.py | 171 ++++++++++++ source/tests/test_finetune_se_atten.py | 256 +++++++++++------- source/tests/test_init_frz_model_se_atten.py | 160 +++++++---- .../tests/test_model_compression_se_atten.py | 1 + source/tests/test_model_se_atten.py | 236 ++++++++++++++++ source/tests/water_compressible_se_atten.json | 80 ------ 7 files changed, 870 insertions(+), 241 deletions(-) delete mode 100644 source/tests/water_compressible_se_atten.json diff --git a/source/tests/test_data_large_batch.py b/source/tests/test_data_large_batch.py index b88a2fd68c..a36916b1b2 100644 --- a/source/tests/test_data_large_batch.py +++ b/source/tests/test_data_large_batch.py @@ -1,3 +1,4 @@ +import inspect import unittest import numpy as np @@ -41,6 +42,7 @@ class TestDataLargeBatch(tf.test.TestCase): def setUp(self): gen_data(mixed_type=True) + self.filename = __file__ def test_data_mixed_type(self): jfile = "water_se_atten_mixed_type.json" @@ -155,7 +157,7 @@ def test_data_mixed_type(self): t_box, t_mesh, inputs_dict, - suffix="se_atten_1", + suffix=self.filename + "-" + inspect.stack()[0][3], reuse=False, ) @@ -237,8 +239,204 @@ def test_data_mixed_type(self): np.testing.assert_almost_equal(v, refv, places) sess.close() + def test_stripped_data_mixed_type(self): + jfile = "water_se_atten_mixed_type.json" + jdata = j_loader(jfile) + + systems = j_must_have(jdata, "systems") + batch_size = 1 + test_size = 1 + rcut = j_must_have(jdata["model"]["descriptor"], "rcut") + type_map = j_must_have(jdata["model"], "type_map") + + data = DeepmdDataSystem(systems, batch_size, test_size, rcut, type_map=type_map) + data_requirement = { + "energy": { + "ndof": 1, + "atomic": False, + "must": False, + "high_prec": True, + "type_sel": None, + "repeat": 1, + "default": 0.0, + }, + "force": { + "ndof": 3, + "atomic": True, + "must": False, + "high_prec": False, + "type_sel": None, + "repeat": 1, + "default": 0.0, + }, + "virial": { + "ndof": 9, + "atomic": False, + "must": False, + "high_prec": False, + "type_sel": None, + "repeat": 1, + "default": 0.0, + }, + "atom_ener": { + "ndof": 1, + "atomic": True, + "must": False, + "high_prec": False, + "type_sel": None, + "repeat": 1, + "default": 0.0, + }, + "atom_pref": { + "ndof": 1, + "atomic": True, + "must": False, + "high_prec": False, + "type_sel": None, + "repeat": 3, + "default": 0.0, + }, + } + data.add_dict(data_requirement) + + test_data = data.get_test() + numb_test = 1 + + jdata["model"]["descriptor"].pop("type", None) + jdata["model"]["descriptor"]["ntypes"] = 2 + jdata["model"]["descriptor"]["compressible"] = False + jdata["model"]["descriptor"]["stripped_type_embedding"] = True + descrpt = DescrptSeAtten(**jdata["model"]["descriptor"], uniform_seed=True) + jdata["model"]["fitting_net"]["descrpt"] = descrpt + fitting = EnerFitting(**jdata["model"]["fitting_net"], uniform_seed=True) + typeebd_param = jdata["model"]["type_embedding"] + typeebd = TypeEmbedNet( + neuron=typeebd_param["neuron"], + resnet_dt=typeebd_param["resnet_dt"], + activation_function=None, + seed=typeebd_param["seed"], + uniform_seed=True, + padding=True, + ) + model = EnerModel(descrpt, fitting, typeebd) + + # model._compute_dstats([test_data['coord']], [test_data['box']], [test_data['type']], [test_data['natoms_vec']], [test_data['default_mesh']]) + input_data = { + "coord": [test_data["coord"]], + "box": [test_data["box"]], + "type": [test_data["type"]], + "natoms_vec": [test_data["natoms_vec"]], + "default_mesh": [test_data["default_mesh"]], + "real_natoms_vec": [test_data["real_natoms_vec"]], + } + model._compute_input_stat(input_data, mixed_type=True) + model.descrpt.bias_atom_e = np.array([0.0, 0.0]) + + t_energy = tf.placeholder(GLOBAL_ENER_FLOAT_PRECISION, [None], name="t_energy") + t_force = tf.placeholder(GLOBAL_TF_FLOAT_PRECISION, [None], name="t_force") + t_virial = tf.placeholder(GLOBAL_TF_FLOAT_PRECISION, [None], name="t_virial") + t_atom_ener = tf.placeholder( + GLOBAL_TF_FLOAT_PRECISION, [None], name="t_atom_ener" + ) + t_coord = tf.placeholder(GLOBAL_TF_FLOAT_PRECISION, [None], name="i_coord") + t_type = tf.placeholder(tf.int32, [None], name="i_type") + t_natoms = tf.placeholder(tf.int32, [model.ntypes + 2], name="i_natoms") + t_box = tf.placeholder(GLOBAL_TF_FLOAT_PRECISION, [None, 9], name="i_box") + t_mesh = tf.placeholder(tf.int32, [None], name="i_mesh") + is_training = tf.placeholder(tf.bool) + t_fparam = None + inputs_dict = {} + + model_pred = model.build( + t_coord, + t_type, + t_natoms, + t_box, + t_mesh, + inputs_dict, + suffix=self.filename + "-" + inspect.stack()[0][3], + reuse=False, + ) + + energy = model_pred["energy"] + force = model_pred["force"] + virial = model_pred["virial"] + atom_ener = model_pred["atom_ener"] + + feed_dict_test = { + t_energy: np.reshape(test_data["energy"][:numb_test], [-1]), + t_force: np.reshape(test_data["force"][:numb_test, :], [-1]), + t_virial: np.reshape(test_data["virial"][:numb_test, :], [-1]), + t_atom_ener: np.reshape(test_data["atom_ener"][:numb_test, :], [-1]), + t_coord: np.reshape(test_data["coord"][:numb_test, :], [-1]), + t_box: test_data["box"][:numb_test, :], + t_type: np.reshape(test_data["type"][:numb_test, :], [-1]), + t_natoms: test_data["natoms_vec"], + t_mesh: test_data["default_mesh"], + is_training: False, + } + + sess = self.test_session().__enter__() + sess.run(tf.global_variables_initializer()) + [e, f, v] = sess.run([energy, force, virial], feed_dict=feed_dict_test) + # print(sess.run(model.type_embedding)) + # np.savetxt('tmp.out', sess.run(descrpt.dout, feed_dict = feed_dict_test), fmt='%.10e') + # # print(sess.run(model.atype_embed, feed_dict = feed_dict_test)) + # print(sess.run(fitting.inputs, feed_dict = feed_dict_test)) + # print(sess.run(fitting.outs, feed_dict = feed_dict_test)) + # print(sess.run(fitting.atype_embed, feed_dict = feed_dict_test)) + + e = e.reshape([-1]) + f = f.reshape([-1]) + v = v.reshape([-1]) + np.savetxt("e11.out", e.reshape([1, -1]), delimiter=",") + np.savetxt("f11.out", f.reshape([1, -1]), delimiter=",") + np.savetxt("v11.out", v.reshape([1, -1]), delimiter=",") + + refe = [6.125926357944699419e01] + reff = [ + 4.071033392194846855e-03, + 1.191078506555811808e-02, + 5.710038490045591959e-04, + 2.083813511902148086e-02, + 1.050404909007916256e-02, + -1.935131519230624082e-03, + -1.844253334357196135e-03, + 7.073208513688628192e-03, + -5.000418009101099666e-05, + 2.877594036828151017e-03, + -1.849276075329028823e-02, + -5.424378676202407318e-04, + -3.237425532982485255e-02, + 2.747768700765259881e-03, + 2.122946741188093227e-03, + 6.431746116137571252e-03, + -1.374305061680087571e-02, + -1.663770232507767613e-04, + ] + refv = [ + -5.699812217912397783e-02, + 8.904976757403395421e-03, + 2.306167440955633578e-03, + 8.904976757403414503e-03, + -3.502855693434053092e-02, + -6.596869271547717252e-04, + 2.306167440955633145e-03, + -6.596869271547715083e-04, + -1.602012510288682446e-04, + ] + + refe = np.reshape(refe, [-1]) + reff = np.reshape(reff, [-1]) + refv = np.reshape(refv, [-1]) + + places = 10 + np.testing.assert_almost_equal(e, refe, places) + np.testing.assert_almost_equal(f, reff, places) + np.testing.assert_almost_equal(v, refv, places) + def test_compressible_data_mixed_type(self): - jfile = "water_se_atten_compressible_mixed_type.json" + jfile = "water_se_atten_mixed_type.json" jdata = j_loader(jfile) systems = j_must_have(jdata, "systems") @@ -302,6 +500,9 @@ def test_compressible_data_mixed_type(self): jdata["model"]["descriptor"].pop("type", None) jdata["model"]["descriptor"]["ntypes"] = 2 + jdata["model"]["descriptor"]["compressible"] = True + jdata["model"]["descriptor"]["stripped_type_embedding"] = True + jdata["model"]["descriptor"]["attn_layer"] = 0 descrpt = DescrptSeAtten(**jdata["model"]["descriptor"], uniform_seed=True) jdata["model"]["fitting_net"]["descrpt"] = descrpt fitting = EnerFitting(**jdata["model"]["fitting_net"], uniform_seed=True) @@ -350,7 +551,7 @@ def test_compressible_data_mixed_type(self): t_box, t_mesh, inputs_dict, - suffix="se_atten_compressible", + suffix=self.filename + "-" + inspect.stack()[0][3], reuse=False, ) diff --git a/source/tests/test_descrpt_se_atten.py b/source/tests/test_descrpt_se_atten.py index 0a593e5874..3d1704d2b2 100644 --- a/source/tests/test_descrpt_se_atten.py +++ b/source/tests/test_descrpt_se_atten.py @@ -389,6 +389,177 @@ def test_descriptor_one_side(self): places = 10 np.testing.assert_almost_equal(model_dout, ref_dout, places) + def test_stripped_type_embedding_descriptor_two_sides(self): + jfile = "water_se_atten.json" + jdata = j_loader(jfile) + + systems = j_must_have(jdata, "systems") + set_pfx = j_must_have(jdata, "set_prefix") + batch_size = j_must_have(jdata, "batch_size") + test_size = j_must_have(jdata, "numb_test") + batch_size = 2 + test_size = 1 + stop_batch = j_must_have(jdata, "stop_batch") + rcut = j_must_have(jdata["model"]["descriptor"], "rcut") + sel = j_must_have(jdata["model"]["descriptor"], "sel") + ntypes = len(jdata["model"]["type_map"]) + + data = DataSystem(systems, set_pfx, batch_size, test_size, rcut, run_opt=None) + + test_data = data.get_test() + numb_test = 1 + + # set parameters + jdata["model"]["descriptor"]["neuron"] = [5, 5, 5] + jdata["model"]["descriptor"]["axis_neuron"] = 2 + typeebd_param = { + "neuron": [5], + "resnet_dt": False, + "seed": 1, + } + jdata["model"]["descriptor"]["compressible"] = False + jdata["model"]["descriptor"]["stripped_type_embedding"] = True + + # init models + typeebd = TypeEmbedNet( + neuron=typeebd_param["neuron"], + activation_function=None, + resnet_dt=typeebd_param["resnet_dt"], + seed=typeebd_param["seed"], + uniform_seed=True, + padding=True, + ) + + jdata["model"]["descriptor"].pop("type", None) + jdata["model"]["descriptor"]["ntypes"] = ntypes + descrpt = DescrptSeAtten(**jdata["model"]["descriptor"], uniform_seed=True) + + # model._compute_dstats([test_data['coord']], [test_data['box']], [test_data['type']], [test_data['natoms_vec']], [test_data['default_mesh']]) + input_data = { + "coord": [test_data["coord"]], + "box": [test_data["box"]], + "type": [test_data["type"]], + "natoms_vec": [test_data["natoms_vec"]], + "default_mesh": [test_data["default_mesh"]], + } + descrpt.bias_atom_e = data.compute_energy_shift() + + t_prop_c = tf.placeholder(tf.float32, [5], name="t_prop_c") + t_energy = tf.placeholder(GLOBAL_ENER_FLOAT_PRECISION, [None], name="t_energy") + t_force = tf.placeholder(GLOBAL_TF_FLOAT_PRECISION, [None], name="t_force") + t_virial = tf.placeholder(GLOBAL_TF_FLOAT_PRECISION, [None], name="t_virial") + t_atom_ener = tf.placeholder( + GLOBAL_TF_FLOAT_PRECISION, [None], name="t_atom_ener" + ) + t_coord = tf.placeholder(GLOBAL_TF_FLOAT_PRECISION, [None], name="i_coord") + t_type = tf.placeholder(tf.int32, [None], name="i_type") + t_natoms = tf.placeholder(tf.int32, [ntypes + 2], name="i_natoms") + t_box = tf.placeholder(GLOBAL_TF_FLOAT_PRECISION, [None, 9], name="i_box") + t_mesh = tf.placeholder(tf.int32, [None], name="i_mesh") + is_training = tf.placeholder(tf.bool) + t_fparam = None + + type_embedding = typeebd.build( + ntypes, suffix=self.filename + "-" + inspect.stack()[0][3] + ) + + dout = descrpt.build( + t_coord, + t_type, + t_natoms, + t_box, + t_mesh, + {"type_embedding": type_embedding}, + reuse=False, + suffix=self.filename + "-" + inspect.stack()[0][3], + ) + + feed_dict_test = { + t_prop_c: test_data["prop_c"], + t_energy: test_data["energy"][:numb_test], + t_force: np.reshape(test_data["force"][:numb_test, :], [-1]), + t_virial: np.reshape(test_data["virial"][:numb_test, :], [-1]), + t_atom_ener: np.reshape(test_data["atom_ener"][:numb_test, :], [-1]), + t_coord: np.reshape(test_data["coord"][:numb_test, :], [-1]), + t_box: test_data["box"][:numb_test, :], + t_type: np.reshape(test_data["type"][:numb_test, :], [-1]), + t_natoms: test_data["natoms_vec"], + t_mesh: test_data["default_mesh"], + is_training: False, + } + + sess = self.test_session().__enter__() + sess.run(tf.global_variables_initializer()) + [model_dout] = sess.run([dout], feed_dict=feed_dict_test) + model_dout = model_dout.reshape([-1]) + np.savetxt("two1.out", model_dout.reshape([1, -1]), delimiter=",") + + ref_dout = [ + 6.383405295803018563e-06, + -4.970443805148654732e-05, + -4.970443805148654732e-05, + 3.915685888463831281e-04, + -4.475044754350870549e-05, + 3.524944903606048942e-04, + -3.724785876580149628e-05, + 2.922698210610697768e-04, + 1.253193390649937305e-04, + -9.866284622165712517e-04, + 2.746706080574298427e-06, + -2.936129326691491197e-05, + -2.936129326691491197e-05, + 3.361216906441433726e-04, + -2.672646322529656884e-05, + 3.054685398038972568e-04, + -1.987664884734486549e-05, + 2.233066336404597018e-04, + 7.321769925898204405e-05, + -8.355355708215854091e-04, + 2.577054933601801410e-06, + -2.596197919081208680e-05, + -2.596197919081208680e-05, + 2.634209681343336554e-04, + -2.437422597229959228e-05, + 2.471257526952611570e-04, + -1.804241933402407212e-05, + 1.824137020231941916e-04, + 6.580156956353395276e-05, + -6.669984436619770833e-04, + 7.430461358270195101e-06, + -4.911199551777768784e-05, + -4.911199551777768784e-05, + 3.246586591299433010e-04, + -4.545306022962886084e-05, + 3.004976436904362321e-04, + -3.943764170159576608e-05, + 2.607633588556588491e-04, + 1.265722360907320951e-04, + -8.368076661582606740e-04, + 1.006529590251595632e-05, + -5.947201651377735437e-05, + -5.947201651377735437e-05, + 3.519072130827992253e-04, + -5.475453207501003249e-05, + 3.240000254870917815e-04, + -5.001544285922251417e-05, + 2.958951751684798311e-04, + 1.541766955454939312e-04, + -9.123303972245934293e-04, + 5.302564262473944598e-06, + -4.087818220772074430e-05, + -4.087818220772074430e-05, + 3.170373643181760982e-04, + -3.805426060094593870e-05, + 2.950399422301473447e-04, + -3.093771463791340635e-05, + 2.394374838886431352e-04, + 1.045675931841061270e-04, + -8.106366082292457186e-04, + ] + + places = 10 + np.testing.assert_almost_equal(model_dout, ref_dout, places) + def test_compressible_descriptor_two_sides(self): jfile = "water_se_atten.json" jdata = j_loader(jfile) diff --git a/source/tests/test_finetune_se_atten.py b/source/tests/test_finetune_se_atten.py index 18c89f0153..e2ec55fc68 100644 --- a/source/tests/test_finetune_se_atten.py +++ b/source/tests/test_finetune_se_atten.py @@ -55,11 +55,13 @@ def _subprocess_run(command): return popen.returncode -def _init_models(): +def _init_models(setup_model, i): data_file = str(tests_path / os.path.join("finetune", "data")) data_file_mixed_type = str(tests_path / os.path.join("finetune", "data_mixed_type")) pretrained_model = str(tests_path / "pretrained_model_se_atten.pb") finetuned_model = str(tests_path / "finetuned_model_se_atten.pb") + # ckpt_pretrain = str(tests_path / f"pretrain{i}.ckpt") + # ckpt_finetune = str(tests_path / f"finetune{i}.ckpt") finetuned_model_mixed_type = str( tests_path / "finetuned_model_se_atten_mixed_type.pb" ) @@ -74,8 +76,13 @@ def _init_models(): ) jdata_pre["training"]["training_data"]["systems"] = data_file jdata_pre["training"]["validation_data"]["systems"] = data_file + # jdata_pre["training"]["save_ckpt"] = ckpt_pretrain + setup_model(jdata_pre) + jdata_finetune["training"]["training_data"]["systems"] = data_file jdata_finetune["training"]["validation_data"]["systems"] = data_file + # jdata_finetune["training"]["save_ckpt"] = ckpt_finetune + setup_model(jdata_finetune) type_map_pre = jdata_pre["model"]["type_map"] type_map_finetune = jdata_finetune["model"]["type_map"] with open(INPUT_PRE, "w") as fp: @@ -140,17 +147,53 @@ def _init_models(): if not parse_version(tf.__version__) < parse_version("1.15"): - ( - INPUT_PRE, - INPUT_FINETUNE, - INPUT_FINETUNE_MIX, - PRE_MODEL, - FINETUNED_MODEL, - FINETUNED_MODEL_MIX, - PRE_MAP, - FINETUNED_MAP, - VALID_DATA, - ) = _init_models() + + def previous_se_atten(jdata): + jdata["model"]["descriptor"]["compressible"] = False + jdata["model"]["descriptor"]["stripped_type_embedding"] = False + jdata["model"]["descriptor"]["attn_layer"] = 2 + + def stripped_model(jdata): + jdata["model"]["descriptor"]["compressible"] = False + jdata["model"]["descriptor"]["stripped_type_embedding"] = True + jdata["model"]["descriptor"]["attn_layer"] = 2 + + def compressible_model(jdata): + jdata["model"]["descriptor"]["compressible"] = True + jdata["model"]["descriptor"]["stripped_type_embedding"] = True + jdata["model"]["descriptor"]["attn_layer"] = 0 + + models = [previous_se_atten, stripped_model, compressible_model] + INPUT_PRES = [] + INPUT_FINETUNES = [] + INPUT_FINETUNE_MIXS = [] + PRE_MODELS = [] + FINETUNED_MODELS = [] + FINETUNED_MODEL_MIXS = [] + PRE_MAPS = [] + FINETUNED_MAPS = [] + VALID_DATAS = [] + for i, model in enumerate(models): + ( + INPUT_PRE, + INPUT_FINETUNE, + INPUT_FINETUNE_MIX, + PRE_MODEL, + FINETUNED_MODEL, + FINETUNED_MODEL_MIX, + PRE_MAP, + FINETUNED_MAP, + VALID_DATA, + ) = _init_models(model, i) + INPUT_PRES.append(INPUT_PRE) + INPUT_FINETUNES.append(INPUT_FINETUNE) + INPUT_FINETUNE_MIXS.append(INPUT_FINETUNE_MIX) + PRE_MODELS.append(PRE_MODEL) + FINETUNED_MODELS.append(FINETUNED_MODEL) + FINETUNED_MODEL_MIXS.append(FINETUNED_MODEL_MIX) + PRE_MAPS.append(PRE_MAP) + FINETUNED_MAPS.append(FINETUNED_MAP) + VALID_DATAS.append(VALID_DATA) @unittest.skipIf( @@ -158,96 +201,111 @@ def _init_models(): f"The current tf version {tf.__version__} is too low to run the new testing model.", ) class TestFinetuneSeAtten(unittest.TestCase): - @classmethod - def setUpClass(self): - self.valid_data = VALID_DATA - @classmethod def tearDownClass(self): - _file_delete(INPUT_PRE) - _file_delete(INPUT_FINETUNE) - _file_delete(INPUT_FINETUNE_MIX) - _file_delete(PRE_MODEL) - _file_delete(FINETUNED_MODEL) - _file_delete(FINETUNED_MODEL_MIX) - _file_delete("out.json") - _file_delete(str(tests_path / "checkpoint")) - _file_delete("model.ckpt.meta") - _file_delete("model.ckpt.index") - _file_delete("model.ckpt.data-00000-of-00001") - _file_delete("model.ckpt-0.meta") - _file_delete("model.ckpt-0.index") - _file_delete("model.ckpt-0.data-00000-of-00001") - _file_delete("model.ckpt-1.meta") - _file_delete("model.ckpt-1.index") - _file_delete("model.ckpt-1.data-00000-of-00001") - _file_delete("input_v2_compat.json") - _file_delete("lcurve.out") + for i in range(len(INPUT_PRES)): + _file_delete(INPUT_PRES[i]) + _file_delete(INPUT_FINETUNES[i]) + _file_delete(INPUT_FINETUNE_MIXS[i]) + _file_delete(PRE_MODELS[i]) + _file_delete(FINETUNED_MODELS[i]) + _file_delete(FINETUNED_MODEL_MIXS[i]) + _file_delete("out.json") + _file_delete("model.ckpt.meta") + _file_delete("model.ckpt.index") + _file_delete("model.ckpt.data-00000-of-00001") + _file_delete("model.ckpt-0.meta") + _file_delete("model.ckpt-0.index") + _file_delete("model.ckpt-0.data-00000-of-00001") + _file_delete("model.ckpt-1.meta") + _file_delete("model.ckpt-1.index") + _file_delete("model.ckpt-1.data-00000-of-00001") + _file_delete(str(tests_path / "checkpoint")) + _file_delete("input_v2_compat.json") + _file_delete("lcurve.out") def test_finetune_standard(self): - pretrained_bias = get_tensor_by_name(PRE_MODEL, "fitting_attr/t_bias_atom_e") - finetuned_bias = get_tensor_by_name( - FINETUNED_MODEL, "fitting_attr/t_bias_atom_e" - ) - sorter = np.argsort(PRE_MAP) - idx_type_map = sorter[np.searchsorted(PRE_MAP, FINETUNED_MAP, sorter=sorter)] - test_data = self.valid_data.get_test() - atom_nums = np.tile(np.bincount(test_data["type"][0])[idx_type_map], (4, 1)) - - dp = DeepPotential(PRE_MODEL) - energy = dp.eval(test_data["coord"], test_data["box"], test_data["type"][0])[0] - energy_diff = test_data["energy"] - energy - finetune_shift = finetuned_bias[idx_type_map] - pretrained_bias[idx_type_map] - ground_truth_shift = np.linalg.lstsq(atom_nums, energy_diff, rcond=None)[ - 0 - ].reshape(-1) - - dp_finetuned = DeepPotential(FINETUNED_MODEL) - energy_finetuned = dp_finetuned.eval( - test_data["coord"], test_data["box"], test_data["type"][0] - )[0] - energy_diff_finetuned = test_data["energy"] - energy_finetuned - finetune_results = np.linalg.lstsq( - atom_nums, energy_diff_finetuned, rcond=None - )[0].reshape(-1) - - # check values - np.testing.assert_almost_equal( - finetune_shift, ground_truth_shift, default_places - ) - np.testing.assert_almost_equal(finetune_results, 0.0, default_places) + for i in range(len(INPUT_PRES)): + self.valid_data = VALID_DATAS[i] + pretrained_bias = get_tensor_by_name( + PRE_MODELS[i], "fitting_attr/t_bias_atom_e" + ) + finetuned_bias = get_tensor_by_name( + FINETUNED_MODELS[i], "fitting_attr/t_bias_atom_e" + ) + sorter = np.argsort(PRE_MAPS[i]) + idx_type_map = sorter[ + np.searchsorted(PRE_MAPS[i], FINETUNED_MAPS[i], sorter=sorter) + ] + test_data = self.valid_data.get_test() + atom_nums = np.tile(np.bincount(test_data["type"][0])[idx_type_map], (4, 1)) + + dp = DeepPotential(PRE_MODELS[i]) + energy = dp.eval( + test_data["coord"], test_data["box"], test_data["type"][0] + )[0] + energy_diff = test_data["energy"] - energy + finetune_shift = ( + finetuned_bias[idx_type_map] - pretrained_bias[idx_type_map] + ) + ground_truth_shift = np.linalg.lstsq(atom_nums, energy_diff, rcond=None)[ + 0 + ].reshape(-1) + + dp_finetuned = DeepPotential(FINETUNED_MODELS[i]) + energy_finetuned = dp_finetuned.eval( + test_data["coord"], test_data["box"], test_data["type"][0] + )[0] + energy_diff_finetuned = test_data["energy"] - energy_finetuned + finetune_results = np.linalg.lstsq( + atom_nums, energy_diff_finetuned, rcond=None + )[0].reshape(-1) + + # check values + np.testing.assert_almost_equal( + finetune_shift, ground_truth_shift, default_places + ) + np.testing.assert_almost_equal(finetune_results, 0.0, default_places) def test_finetune_mixed_type(self): - pretrained_bias = get_tensor_by_name(PRE_MODEL, "fitting_attr/t_bias_atom_e") - finetuned_bias_mixed_type = get_tensor_by_name( - FINETUNED_MODEL_MIX, "fitting_attr/t_bias_atom_e" - ) - sorter = np.argsort(PRE_MAP) - idx_type_map = sorter[np.searchsorted(PRE_MAP, FINETUNED_MAP, sorter=sorter)] - test_data = self.valid_data.get_test() - atom_nums = np.tile(np.bincount(test_data["type"][0])[idx_type_map], (4, 1)) - - dp = DeepPotential(PRE_MODEL) - energy = dp.eval(test_data["coord"], test_data["box"], test_data["type"][0])[0] - energy_diff = test_data["energy"] - energy - finetune_shift = ( - finetuned_bias_mixed_type[idx_type_map] - pretrained_bias[idx_type_map] - ) - ground_truth_shift = np.linalg.lstsq(atom_nums, energy_diff, rcond=None)[ - 0 - ].reshape(-1) - - dp_finetuned_mixed_type = DeepPotential(FINETUNED_MODEL_MIX) - energy_finetuned = dp_finetuned_mixed_type.eval( - test_data["coord"], test_data["box"], test_data["type"][0] - )[0] - energy_diff_finetuned = test_data["energy"] - energy_finetuned - finetune_results = np.linalg.lstsq( - atom_nums, energy_diff_finetuned, rcond=None - )[0].reshape(-1) - - # check values - np.testing.assert_almost_equal( - finetune_shift, ground_truth_shift, default_places - ) - np.testing.assert_almost_equal(finetune_results, 0.0, default_places) + for i in range(len(INPUT_PRES)): + self.valid_data = VALID_DATAS[i] + pretrained_bias = get_tensor_by_name( + PRE_MODELS[i], "fitting_attr/t_bias_atom_e" + ) + finetuned_bias_mixed_type = get_tensor_by_name( + FINETUNED_MODEL_MIXS[i], "fitting_attr/t_bias_atom_e" + ) + sorter = np.argsort(PRE_MAPS[i]) + idx_type_map = sorter[ + np.searchsorted(PRE_MAPS[i], FINETUNED_MAPS[i], sorter=sorter) + ] + test_data = self.valid_data.get_test() + atom_nums = np.tile(np.bincount(test_data["type"][0])[idx_type_map], (4, 1)) + + dp = DeepPotential(PRE_MODELS[i]) + energy = dp.eval( + test_data["coord"], test_data["box"], test_data["type"][0] + )[0] + energy_diff = test_data["energy"] - energy + finetune_shift = ( + finetuned_bias_mixed_type[idx_type_map] - pretrained_bias[idx_type_map] + ) + ground_truth_shift = np.linalg.lstsq(atom_nums, energy_diff, rcond=None)[ + 0 + ].reshape(-1) + + dp_finetuned_mixed_type = DeepPotential(FINETUNED_MODEL_MIXS[i]) + energy_finetuned = dp_finetuned_mixed_type.eval( + test_data["coord"], test_data["box"], test_data["type"][0] + )[0] + energy_diff_finetuned = test_data["energy"] - energy_finetuned + finetune_results = np.linalg.lstsq( + atom_nums, energy_diff_finetuned, rcond=None + )[0].reshape(-1) + + # check values + np.testing.assert_almost_equal( + finetune_shift, ground_truth_shift, default_places + ) + np.testing.assert_almost_equal(finetune_results, 0.0, default_places) diff --git a/source/tests/test_init_frz_model_se_atten.py b/source/tests/test_init_frz_model_se_atten.py index 86a4fd86b9..28050e2695 100644 --- a/source/tests/test_init_frz_model_se_atten.py +++ b/source/tests/test_init_frz_model_se_atten.py @@ -43,10 +43,10 @@ def _file_delete(file): os.remove(file) -def _init_models(): +def _init_models(model_setup, i): data_file = str(tests_path / os.path.join("init_frz_model", "data")) - frozen_model = str(tests_path / "init_frz_se_atten.pb") - ckpt = str(tests_path / "init_frz_se_atten.ckpt") + frozen_model = str(tests_path / f"init_frz_se_atten{i}.pb") + ckpt = str(tests_path / f"init_frz_se_atten{i}.ckpt") run_opt_ckpt = RunOptions(init_model=ckpt, log_level=20) run_opt_frz = RunOptions(init_frz_model=frozen_model, log_level=20) INPUT = str(tests_path / "input.json") @@ -56,6 +56,7 @@ def _init_models(): jdata["training"]["save_ckpt"] = ckpt jdata["model"]["descriptor"]["type"] = "se_atten" jdata["model"]["descriptor"]["sel"] = 120 + model_setup(jdata) with open(INPUT, "w") as fp: json.dump(jdata, fp, indent=4) ret = run_dp("dp train " + INPUT) @@ -63,7 +64,7 @@ def _init_models(): ret = run_dp("dp freeze -c " + str(tests_path) + " -o " + frozen_model) np.testing.assert_equal(ret, 0, "DP freeze failed!") - jdata = update_deepmd_input(jdata, warning=True, dump="input_v2_compat.json") + jdata = update_deepmd_input(jdata, warning=True, dump=f"input_v2_compat{i}.json") jdata = normalize(jdata) model_ckpt = DPTrainer(jdata, run_opt=run_opt_ckpt) model_frz = DPTrainer(jdata, run_opt=run_opt_frz) @@ -131,15 +132,47 @@ def _init_models(): if not parse_version(tf.__version__) < parse_version("1.15"): - ( - INPUT, - CKPT, - FROZEN_MODEL, - CKPT_TRAINER, - FRZ_TRAINER, - VALID_DATA, - STOP_BATCH, - ) = _init_models() + + def previous_se_atten(jdata): + jdata["model"]["descriptor"]["compressible"] = False + jdata["model"]["descriptor"]["stripped_type_embedding"] = False + jdata["model"]["descriptor"]["attn_layer"] = 2 + + def stripped_model(jdata): + jdata["model"]["descriptor"]["compressible"] = False + jdata["model"]["descriptor"]["stripped_type_embedding"] = True + jdata["model"]["descriptor"]["attn_layer"] = 2 + + def compressible_model(jdata): + jdata["model"]["descriptor"]["compressible"] = True + jdata["model"]["descriptor"]["stripped_type_embedding"] = True + jdata["model"]["descriptor"]["attn_layer"] = 0 + + models = [previous_se_atten, stripped_model, compressible_model] + INPUTS = [] + CKPTS = [] + FROZEN_MODELS = [] + CKPT_TRAINERS = [] + FRZ_TRAINERS = [] + VALID_DATAS = [] + STOP_BATCHS = [] + for i, model in enumerate(models): + ( + INPUT, + CKPT, + FROZEN_MODEL, + CKPT_TRAINER, + FRZ_TRAINER, + VALID_DATA, + STOP_BATCH, + ) = _init_models(model, i) + INPUTS.append(INPUT) + CKPTS.append(CKPT) + FROZEN_MODELS.append(FROZEN_MODEL) + CKPT_TRAINERS.append(CKPT_TRAINER) + FRZ_TRAINERS.append(FRZ_TRAINER) + VALID_DATAS.append(VALID_DATA) + STOP_BATCHS.append(STOP_BATCH) @unittest.skipIf( @@ -149,54 +182,63 @@ def _init_models(): class TestInitFrzModelAtten(unittest.TestCase): @classmethod def setUpClass(cls): - cls.dp_ckpt = CKPT_TRAINER - cls.dp_frz = FRZ_TRAINER - cls.valid_data = VALID_DATA - cls.stop_batch = STOP_BATCH + cls.dp_ckpts = CKPT_TRAINERS + cls.dp_frzs = FRZ_TRAINERS + cls.valid_datas = VALID_DATAS + cls.stop_batchs = STOP_BATCHS @classmethod def tearDownClass(cls): - _file_delete(INPUT) - _file_delete(FROZEN_MODEL) - _file_delete("out.json") - _file_delete(str(tests_path / "checkpoint")) - _file_delete(CKPT + ".meta") - _file_delete(CKPT + ".index") - _file_delete(CKPT + ".data-00000-of-00001") - _file_delete(CKPT + "-0.meta") - _file_delete(CKPT + "-0.index") - _file_delete(CKPT + "-0.data-00000-of-00001") - _file_delete(CKPT + "-1.meta") - _file_delete(CKPT + "-1.index") - _file_delete(CKPT + "-1.data-00000-of-00001") - _file_delete("input_v2_compat.json") - _file_delete("lcurve.out") + for i in range(len(cls.dp_ckpts)): + _file_delete(INPUTS[i]) + _file_delete(FROZEN_MODELS[i]) + _file_delete("out.json") + _file_delete(str(tests_path / "checkpoint")) + _file_delete(CKPT[i] + ".meta") + _file_delete(CKPT[i] + ".index") + _file_delete(CKPT[i] + ".data-00000-of-00001") + _file_delete(CKPT[i] + "-0.meta") + _file_delete(CKPT[i] + "-0.index") + _file_delete(CKPT[i] + "-0.data-00000-of-00001") + _file_delete(CKPT[i] + "-1.meta") + _file_delete(CKPT[i] + "-1.index") + _file_delete(CKPT[i] + "-1.data-00000-of-00001") + _file_delete(f"input_v2_compat{i}.json") + _file_delete("lcurve.out") def test_single_frame(self): - valid_batch = self.valid_data.get_batch() - natoms = valid_batch["natoms_vec"] - tf.reset_default_graph() - self.dp_ckpt.build(self.valid_data, self.stop_batch) - self.dp_ckpt._init_session() - feed_dict_ckpt = self.dp_ckpt.get_feed_dict(valid_batch, is_training=False) - ckpt_rmse_ckpt = self.dp_ckpt.loss.eval( - self.dp_ckpt.sess, feed_dict_ckpt, natoms - ) - tf.reset_default_graph() - - self.dp_frz.build(self.valid_data, self.stop_batch) - self.dp_frz._init_session() - feed_dict_frz = self.dp_frz.get_feed_dict(valid_batch, is_training=False) - ckpt_rmse_frz = self.dp_frz.loss.eval(self.dp_frz.sess, feed_dict_frz, natoms) - tf.reset_default_graph() - - # check values - np.testing.assert_almost_equal( - ckpt_rmse_ckpt["rmse_e"], ckpt_rmse_frz["rmse_e"], default_places - ) - np.testing.assert_almost_equal( - ckpt_rmse_ckpt["rmse_f"], ckpt_rmse_frz["rmse_f"], default_places - ) - np.testing.assert_almost_equal( - ckpt_rmse_ckpt["rmse_v"], ckpt_rmse_frz["rmse_v"], default_places - ) + for i in range(len(self.dp_ckpts)): + self.dp_ckpt = CKPT_TRAINERS[i] + self.dp_frz = FRZ_TRAINERS[i] + self.valid_data = VALID_DATAS[i] + self.stop_batch = STOP_BATCHS[i] + + valid_batch = self.valid_data.get_batch() + natoms = valid_batch["natoms_vec"] + tf.reset_default_graph() + self.dp_ckpt.build(self.valid_data, self.stop_batch) + self.dp_ckpt._init_session() + feed_dict_ckpt = self.dp_ckpt.get_feed_dict(valid_batch, is_training=False) + ckpt_rmse_ckpt = self.dp_ckpt.loss.eval( + self.dp_ckpt.sess, feed_dict_ckpt, natoms + ) + tf.reset_default_graph() + + self.dp_frz.build(self.valid_data, self.stop_batch) + self.dp_frz._init_session() + feed_dict_frz = self.dp_frz.get_feed_dict(valid_batch, is_training=False) + ckpt_rmse_frz = self.dp_frz.loss.eval( + self.dp_frz.sess, feed_dict_frz, natoms + ) + tf.reset_default_graph() + + # check values + np.testing.assert_almost_equal( + ckpt_rmse_ckpt["rmse_e"], ckpt_rmse_frz["rmse_e"], default_places + ) + np.testing.assert_almost_equal( + ckpt_rmse_ckpt["rmse_f"], ckpt_rmse_frz["rmse_f"], default_places + ) + np.testing.assert_almost_equal( + ckpt_rmse_ckpt["rmse_v"], ckpt_rmse_frz["rmse_v"], default_places + ) diff --git a/source/tests/test_model_compression_se_atten.py b/source/tests/test_model_compression_se_atten.py index 1982e433e0..4b3da0983a 100644 --- a/source/tests/test_model_compression_se_atten.py +++ b/source/tests/test_model_compression_se_atten.py @@ -68,6 +68,7 @@ def _init_models(): jdata["model"]["descriptor"]["type"] = "se_atten" jdata["model"]["descriptor"]["precision"] = tests[i]["se_atten precision"] jdata["model"]["descriptor"]["compressible"] = True + jdata["model"]["descriptor"]["stripped_type_embedding"] = True jdata["model"]["descriptor"]["sel"] = 120 jdata["model"]["descriptor"]["attn_layer"] = 0 jdata["model"]["type_embedding"] = {} diff --git a/source/tests/test_model_se_atten.py b/source/tests/test_model_se_atten.py index 1f7a71d8d8..72e415abea 100644 --- a/source/tests/test_model_se_atten.py +++ b/source/tests/test_model_se_atten.py @@ -493,3 +493,239 @@ def test_compressible_exclude_types(self): np.testing.assert_almost_equal(des[:, 0:2], 0.0, 10) with self.assertRaises(AssertionError): np.testing.assert_almost_equal(des[:, 2:6], 0.0, 10) + + def test_stripped_type_embedding_model(self): + jfile = "water_se_atten.json" + jdata = j_loader(jfile) + + systems = j_must_have(jdata, "systems") + set_pfx = j_must_have(jdata, "set_prefix") + batch_size = j_must_have(jdata, "batch_size") + test_size = j_must_have(jdata, "numb_test") + batch_size = 1 + test_size = 1 + stop_batch = j_must_have(jdata, "stop_batch") + rcut = j_must_have(jdata["model"]["descriptor"], "rcut") + + data = DataSystem(systems, set_pfx, batch_size, test_size, rcut, run_opt=None) + + test_data = data.get_test() + numb_test = 1 + + jdata["model"]["descriptor"].pop("type", None) + jdata["model"]["descriptor"]["ntypes"] = 2 + jdata["model"]["descriptor"]["compressible"] = False + jdata["model"]["descriptor"]["stripped_type_embedding"] = True + jdata["model"]["descriptor"]["attn_layer"] = 2 + descrpt = DescrptSeAtten(**jdata["model"]["descriptor"], uniform_seed=True) + jdata["model"]["fitting_net"]["descrpt"] = descrpt + fitting = EnerFitting(**jdata["model"]["fitting_net"], uniform_seed=True) + typeebd_param = jdata["model"]["type_embedding"] + typeebd = TypeEmbedNet( + neuron=typeebd_param["neuron"], + activation_function=None, + resnet_dt=typeebd_param["resnet_dt"], + seed=typeebd_param["seed"], + uniform_seed=True, + padding=True, + ) + model = EnerModel(descrpt, fitting, typeebd) + + # model._compute_dstats([test_data['coord']], [test_data['box']], [test_data['type']], [test_data['natoms_vec']], [test_data['default_mesh']]) + input_data = { + "coord": [test_data["coord"]], + "box": [test_data["box"]], + "type": [test_data["type"]], + "natoms_vec": [test_data["natoms_vec"]], + "default_mesh": [test_data["default_mesh"]], + } + model._compute_input_stat(input_data) + model.descrpt.bias_atom_e = data.compute_energy_shift() + + t_prop_c = tf.placeholder(tf.float32, [5], name="t_prop_c") + t_energy = tf.placeholder(GLOBAL_ENER_FLOAT_PRECISION, [None], name="t_energy") + t_force = tf.placeholder(GLOBAL_TF_FLOAT_PRECISION, [None], name="t_force") + t_virial = tf.placeholder(GLOBAL_TF_FLOAT_PRECISION, [None], name="t_virial") + t_atom_ener = tf.placeholder( + GLOBAL_TF_FLOAT_PRECISION, [None], name="t_atom_ener" + ) + t_coord = tf.placeholder(GLOBAL_TF_FLOAT_PRECISION, [None], name="i_coord") + t_type = tf.placeholder(tf.int32, [None], name="i_type") + t_natoms = tf.placeholder(tf.int32, [model.ntypes + 2], name="i_natoms") + t_box = tf.placeholder(GLOBAL_TF_FLOAT_PRECISION, [None, 9], name="i_box") + t_mesh = tf.placeholder(tf.int32, [None], name="i_mesh") + is_training = tf.placeholder(tf.bool) + t_fparam = None + inputs_dict = {} + + model_pred = model.build( + t_coord, + t_type, + t_natoms, + t_box, + t_mesh, + inputs_dict, + suffix=self.filename + + "-" + + inspect.stack()[0][3] + + "test_model_se_atten_model_compressible", + reuse=False, + ) + energy = model_pred["energy"] + force = model_pred["force"] + virial = model_pred["virial"] + atom_ener = model_pred["atom_ener"] + + feed_dict_test = { + t_prop_c: test_data["prop_c"], + t_energy: test_data["energy"][:numb_test], + t_force: np.reshape(test_data["force"][:numb_test, :], [-1]), + t_virial: np.reshape(test_data["virial"][:numb_test, :], [-1]), + t_atom_ener: np.reshape(test_data["atom_ener"][:numb_test, :], [-1]), + t_coord: np.reshape(test_data["coord"][:numb_test, :], [-1]), + t_box: test_data["box"][:numb_test, :], + t_type: np.reshape(test_data["type"][:numb_test, :], [-1]), + t_natoms: test_data["natoms_vec"], + t_mesh: test_data["default_mesh"], + is_training: False, + } + sess = self.test_session().__enter__() + sess.run(tf.global_variables_initializer()) + [e, f, v] = sess.run([energy, force, virial], feed_dict=feed_dict_test) + # print(sess.run(model.type_embedding)) + # np.savetxt('tmp.out', sess.run(descrpt.dout, feed_dict = feed_dict_test), fmt='%.10e') + # # print(sess.run(model.atype_embed, feed_dict = feed_dict_test)) + # print(sess.run(fitting.inputs, feed_dict = feed_dict_test)) + # print(sess.run(fitting.outs, feed_dict = feed_dict_test)) + # print(sess.run(fitting.atype_embed, feed_dict = feed_dict_test)) + + e = e.reshape([-1]) + f = f.reshape([-1]) + v = v.reshape([-1]) + np.savetxt("e.out", e.reshape([1, -1]), delimiter=",") + np.savetxt("f.out", f.reshape([1, -1]), delimiter=",") + np.savetxt("v.out", v.reshape([1, -1]), delimiter=",") + + refe = [6.125926357944699419e01] + reff = [ + 4.071033392194846855e-03, + 1.191078506555811808e-02, + 5.710038490045591959e-04, + 2.083813511902148086e-02, + 1.050404909007916256e-02, + -1.935131519230624082e-03, + -1.844253334357196135e-03, + 7.073208513688628192e-03, + -5.000418009101099666e-05, + 2.877594036828151017e-03, + -1.849276075329028823e-02, + -5.424378676202407318e-04, + -3.237425532982485255e-02, + 2.747768700765259881e-03, + 2.122946741188093227e-03, + 6.431746116137571252e-03, + -1.374305061680087571e-02, + -1.663770232507767613e-04, + ] + refv = [ + -5.699812217912397089e-02, + 8.904976757403390217e-03, + 2.306167440955633578e-03, + 8.904976757403417972e-03, + -3.502855693434053092e-02, + -6.596869271547715083e-04, + 2.306167440955633578e-03, + -6.596869271547720504e-04, + -1.602012510288682717e-04, + ] + + refe = np.reshape(refe, [-1]) + reff = np.reshape(reff, [-1]) + refv = np.reshape(refv, [-1]) + + places = 10 + np.testing.assert_almost_equal(e, refe, places) + np.testing.assert_almost_equal(f, reff, places) + np.testing.assert_almost_equal(v, refv, places) + + def test_stripped_type_embedding_exclude_types(self): + """In this test, we make type 0 has no interaction with type 0 and type 1, + so the descriptor should be zero for type 0 atoms. + """ + jfile = "water_se_atten.json" + jdata = j_loader(jfile) + + systems = j_must_have(jdata, "systems") + set_pfx = j_must_have(jdata, "set_prefix") + batch_size = j_must_have(jdata, "batch_size") + test_size = j_must_have(jdata, "numb_test") + batch_size = 1 + test_size = 1 + rcut = j_must_have(jdata["model"]["descriptor"], "rcut") + ntypes = 2 + + data = DataSystem(systems, set_pfx, batch_size, test_size, rcut, run_opt=None) + + test_data = data.get_test() + numb_test = 1 + + # set parameters + jdata["model"]["descriptor"]["exclude_types"] = [[0, 0], [0, 1]] + + t_prop_c = tf.placeholder(tf.float32, [5], name="t_prop_c") + t_coord = tf.placeholder(GLOBAL_TF_FLOAT_PRECISION, [None], name="i_coord") + t_type = tf.placeholder(tf.int32, [None], name="i_type") + t_natoms = tf.placeholder(tf.int32, [ntypes + 2], name="i_natoms") + t_box = tf.placeholder(GLOBAL_TF_FLOAT_PRECISION, [None, 9], name="i_box") + t_mesh = tf.placeholder(tf.int32, [None], name="i_mesh") + is_training = tf.placeholder(tf.bool) + + # successful + descrpt = DescrptSeAtten(ntypes=ntypes, **jdata["model"]["descriptor"]) + typeebd_param = jdata["model"]["type_embedding"] + jdata["model"]["descriptor"]["compressible"] = False + jdata["model"]["descriptor"]["stripped_type_embedding"] = True + jdata["model"]["descriptor"]["attn_layer"] = 2 + typeebd = TypeEmbedNet( + neuron=typeebd_param["neuron"], + activation_function=None, + resnet_dt=typeebd_param["resnet_dt"], + seed=typeebd_param["seed"], + uniform_seed=True, + padding=True, + ) + type_embedding = typeebd.build( + ntypes, + suffix=self.filename + "-" + inspect.stack()[0][3] + "_type_embedding", + ) + dout = descrpt.build( + t_coord, + t_type, + t_natoms, + t_box, + t_mesh, + {"type_embedding": type_embedding}, + reuse=False, + suffix=self.filename + + "-" + + inspect.stack()[0][3] + + "_se_atten_compressible_exclude_types", + ) + + feed_dict_test1 = { + t_prop_c: test_data["prop_c"], + t_coord: np.reshape(test_data["coord"][:numb_test, :], [-1]), + t_box: test_data["box"][:numb_test, :], + t_type: np.reshape(test_data["type"][:numb_test, :], [-1]), + t_natoms: test_data["natoms_vec"], + t_mesh: test_data["default_mesh"], + is_training: False, + } + + with self.test_session() as sess: + sess.run(tf.global_variables_initializer()) + [des] = sess.run([dout], feed_dict=feed_dict_test1) + + np.testing.assert_almost_equal(des[:, 0:2], 0.0, 10) + with self.assertRaises(AssertionError): + np.testing.assert_almost_equal(des[:, 2:6], 0.0, 10) diff --git a/source/tests/water_compressible_se_atten.json b/source/tests/water_compressible_se_atten.json deleted file mode 100644 index 4d06298bef..0000000000 --- a/source/tests/water_compressible_se_atten.json +++ /dev/null @@ -1,80 +0,0 @@ -{ - "_comment": " model parameters", - "model": { - "type_map": [ - "O", - "H" - ], - "type_embedding": { - "neuron": [ - 8 - ], - "resnet_dt": false, - "seed": 1 - }, - "descriptor": { - "type": "se_atten", - "compressible": true, - "sel": 120, - "rcut_smth": 5.80, - "rcut": 6.00, - "neuron": [ - 25, - 50, - 100 - ], - "resnet_dt": false, - "type_one_side": false, - "axis_neuron": 16, - "seed": 1, - "attn": 128, - "attn_layer": 0, - "attn_dotr": true, - "attn_mask": false - }, - "fitting_net": { - "neuron": [ - 240, - 240, - 240 - ], - "resnet_dt": true, - "seed": 1 - } - }, - - "_comment": " traing controls", - "systems": [ - "system" - ], - "set_prefix": "set", - "stop_batch": 1000000, - "batch_size": 1, - "start_lr": 0.005, - "decay_steps": 5000, - "decay_rate": 0.95, - - "start_pref_e": 0.02, - "limit_pref_e": 1, - "start_pref_f": 1000, - "limit_pref_f": 1, - "start_pref_v": 0, - "limit_pref_v": 0, - - "seed": 1, - - "_comment": " display and restart", - "_comment": " frequencies counted in batch", - "disp_file": "lcurve.out", - "disp_freq": 100, - "numb_test": 1, - "save_freq": 1000, - "save_ckpt": "model.ckpt", - "load_ckpt": "model.ckpt", - "disp_training": true, - "time_training": true, - "profiling": false, - "profiling_file": "timeline.json", - - "_comment": "that's all" -} From e75f3900289914124b58a1d08082b3a92f0f63e9 Mon Sep 17 00:00:00 2001 From: nahso Date: Fri, 9 Jun 2023 22:43:54 +0800 Subject: [PATCH 121/126] arg doc --- deepmd/utils/argcheck.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/deepmd/utils/argcheck.py b/deepmd/utils/argcheck.py index 3bc2347c31..80cdd12751 100644 --- a/deepmd/utils/argcheck.py +++ b/deepmd/utils/argcheck.py @@ -335,7 +335,7 @@ def descrpt_hybrid_args(): @descrpt_args_plugin.register("se_atten") def descrpt_se_atten_args(): doc_compressible = "Whether to build a compressible `se_atten` that is supported by `dp compress`. This option is only available when `stripped_type_embedding` is `True`." - doc_stripped_type_embedding = "Whether to strip the type embedding from . Setting it to `False` will fallback to the previous version of `se_atten`. " + doc_stripped_type_embedding = "Whether to strip the type embedding from . Setting it to `False` will fallback to the previous version of `se_atten` which is non-compressible. " doc_sel = 'This parameter set the number of selected neighbors. Note that this parameter is a little different from that in other descriptors. Instead of separating each type of atoms, only the summation matters. And this number is highly related with the efficiency, thus one should not make it too large. Usually 200 or less is enough, far away from the GPU limitation 4096. It can be:\n\n\ - `int`. The maximum number of neighbor atoms to be considered. We recommend it to be less than 200. \n\n\ - `List[int]`. The length of the list should be the same as the number of atom types in the system. `sel[i]` gives the selected number of type-i neighbors. Only the summation of `sel[i]` matters, and it is recommended to be less than 200.\ From 724a327a074516b976693ef3c9f43f24bd460ca2 Mon Sep 17 00:00:00 2001 From: nahso Date: Mon, 12 Jun 2023 18:50:48 +0800 Subject: [PATCH 122/126] remove compressible option --- deepmd/descriptor/se_atten.py | 16 ++++------------ deepmd/utils/argcheck.py | 12 ++++-------- examples/water/se_atten/input.json | 1 - source/tests/test_data_large_batch.py | 2 -- source/tests/test_descrpt_se_atten.py | 2 -- source/tests/test_finetune_se_atten.py | 3 --- source/tests/test_init_frz_model_se_atten.py | 3 --- source/tests/test_model_compression_se_atten.py | 1 - source/tests/test_model_se_atten.py | 4 ---- 9 files changed, 8 insertions(+), 36 deletions(-) diff --git a/deepmd/descriptor/se_atten.py b/deepmd/descriptor/se_atten.py index 0d85daa6ed..87109ec07e 100644 --- a/deepmd/descriptor/se_atten.py +++ b/deepmd/descriptor/se_atten.py @@ -120,7 +120,6 @@ def __init__( attn_dotr: bool = True, attn_mask: bool = False, multi_task: bool = False, - compressible: bool = False, stripped_type_embedding: bool = False, **kwargs, ) -> None: @@ -153,7 +152,6 @@ def __init__( assert Version(TF_VERSION) > Version( "2" ), "se_atten only support tensorflow version 2.0 or higher." - self.compressible = compressible self.stripped_type_embedding = stripped_type_embedding self.ntypes = ntypes self.att_n = attn @@ -164,15 +162,6 @@ def __init__( self.two_side_embeeding_net_variables = None self.layer_size = len(neuron) - if not self.stripped_type_embedding and self.compressible: - raise RuntimeError( - "compression is not supported when using a non-stripped type embedding" - ) - if self.stripped_type_embedding and self.compressible and self.attn_layer != 0: - raise RuntimeError( - "attention layer must be 0 when using a compressible se_atten with stripped type embedding" - ) - # descrpt config self.sel_all_a = [sel] self.sel_all_r = [0] @@ -369,6 +358,9 @@ def enable_compression( % ",".join([str(item) for item in self.filter_neuron]) ) + if self.attn_layer != 0: + raise RuntimeError("can not compress model when attention layer is not 0.") + ret = get_pattern_nodes_from_graph_def( graph_def, f"filter_type_all{suffix}/.+_two_side_ebd" ) @@ -1034,7 +1026,7 @@ def _filter_lower( mixed_prec=self.mixed_prec, ) else: - if self.compressible: + if self.attn_layer == 0: log.info( "use the compressible model with stripped type embedding" ) diff --git a/deepmd/utils/argcheck.py b/deepmd/utils/argcheck.py index 80cdd12751..6861ef774f 100644 --- a/deepmd/utils/argcheck.py +++ b/deepmd/utils/argcheck.py @@ -334,8 +334,7 @@ def descrpt_hybrid_args(): @descrpt_args_plugin.register("se_atten") def descrpt_se_atten_args(): - doc_compressible = "Whether to build a compressible `se_atten` that is supported by `dp compress`. This option is only available when `stripped_type_embedding` is `True`." - doc_stripped_type_embedding = "Whether to strip the type embedding from . Setting it to `False` will fallback to the previous version of `se_atten` which is non-compressible. " + doc_stripped_type_embedding = "Whether to strip the type embedding into a seperate embedding net. Setting it to `False` will fallback to the previous version of `se_atten` which is non-compressible. " doc_sel = 'This parameter set the number of selected neighbors. Note that this parameter is a little different from that in other descriptors. Instead of separating each type of atoms, only the summation matters. And this number is highly related with the efficiency, thus one should not make it too large. Usually 200 or less is enough, far away from the GPU limitation 4096. It can be:\n\n\ - `int`. The maximum number of neighbor atoms to be considered. We recommend it to be less than 200. \n\n\ - `List[int]`. The length of the list should be the same as the number of atom types in the system. `sel[i]` gives the selected number of type-i neighbors. Only the summation of `sel[i]` matters, and it is recommended to be less than 200.\ @@ -353,7 +352,7 @@ def descrpt_se_atten_args(): doc_set_davg_zero = "Set the normalization average to zero. This option should be set when `se_atten` descriptor or `atom_ener` in the energy fitting is used" doc_exclude_types = "The excluded pairs of types which have no interaction with each other. For example, `[[0, 1]]` means no interaction between type 0 and type 1." doc_attn = "The length of hidden vectors in attention layers" - doc_attn_layer = "The number of attention layers" + doc_attn_layer = "The number of attention layers. Note that model compression of `se_atten` is only enabled when attn_layer=0" doc_attn_dotr = "Whether to do dot product with the normalized relative coordinates" doc_attn_mask = "Whether to do mask on the diagonal in the attention matrix" @@ -362,11 +361,8 @@ def descrpt_se_atten_args(): "stripped_type_embedding", bool, optional=True, - default=True, - doc=doc_compressible, - ), - Argument( - "compressible", bool, optional=True, default=True, doc=doc_compressible + default=False, + doc=doc_stripped_type_embedding, ), Argument("sel", [int, list, str], optional=True, default="auto", doc=doc_sel), Argument("rcut", float, optional=True, default=6.0, doc=doc_rcut), diff --git a/examples/water/se_atten/input.json b/examples/water/se_atten/input.json index 24224211f5..3b8a7bc345 100644 --- a/examples/water/se_atten/input.json +++ b/examples/water/se_atten/input.json @@ -7,7 +7,6 @@ ], "descriptor": { "type": "se_atten", - "compressible": true, "stripped_type_embedding": true, "sel": 120, "rcut_smth": 0.50, diff --git a/source/tests/test_data_large_batch.py b/source/tests/test_data_large_batch.py index a36916b1b2..26b3a2957d 100644 --- a/source/tests/test_data_large_batch.py +++ b/source/tests/test_data_large_batch.py @@ -304,7 +304,6 @@ def test_stripped_data_mixed_type(self): jdata["model"]["descriptor"].pop("type", None) jdata["model"]["descriptor"]["ntypes"] = 2 - jdata["model"]["descriptor"]["compressible"] = False jdata["model"]["descriptor"]["stripped_type_embedding"] = True descrpt = DescrptSeAtten(**jdata["model"]["descriptor"], uniform_seed=True) jdata["model"]["fitting_net"]["descrpt"] = descrpt @@ -500,7 +499,6 @@ def test_compressible_data_mixed_type(self): jdata["model"]["descriptor"].pop("type", None) jdata["model"]["descriptor"]["ntypes"] = 2 - jdata["model"]["descriptor"]["compressible"] = True jdata["model"]["descriptor"]["stripped_type_embedding"] = True jdata["model"]["descriptor"]["attn_layer"] = 0 descrpt = DescrptSeAtten(**jdata["model"]["descriptor"], uniform_seed=True) diff --git a/source/tests/test_descrpt_se_atten.py b/source/tests/test_descrpt_se_atten.py index 3d1704d2b2..f0b45e5dee 100644 --- a/source/tests/test_descrpt_se_atten.py +++ b/source/tests/test_descrpt_se_atten.py @@ -417,7 +417,6 @@ def test_stripped_type_embedding_descriptor_two_sides(self): "resnet_dt": False, "seed": 1, } - jdata["model"]["descriptor"]["compressible"] = False jdata["model"]["descriptor"]["stripped_type_embedding"] = True # init models @@ -583,7 +582,6 @@ def test_compressible_descriptor_two_sides(self): # set parameters jdata["model"]["descriptor"]["neuron"] = [5, 5, 5] jdata["model"]["descriptor"]["axis_neuron"] = 2 - jdata["model"]["descriptor"]["compressible"] = True jdata["model"]["descriptor"]["attn_layer"] = 0 jdata["model"]["descriptor"]["stripped_type_embedding"] = True typeebd_param = { diff --git a/source/tests/test_finetune_se_atten.py b/source/tests/test_finetune_se_atten.py index e2ec55fc68..0631694027 100644 --- a/source/tests/test_finetune_se_atten.py +++ b/source/tests/test_finetune_se_atten.py @@ -149,17 +149,14 @@ def _init_models(setup_model, i): if not parse_version(tf.__version__) < parse_version("1.15"): def previous_se_atten(jdata): - jdata["model"]["descriptor"]["compressible"] = False jdata["model"]["descriptor"]["stripped_type_embedding"] = False jdata["model"]["descriptor"]["attn_layer"] = 2 def stripped_model(jdata): - jdata["model"]["descriptor"]["compressible"] = False jdata["model"]["descriptor"]["stripped_type_embedding"] = True jdata["model"]["descriptor"]["attn_layer"] = 2 def compressible_model(jdata): - jdata["model"]["descriptor"]["compressible"] = True jdata["model"]["descriptor"]["stripped_type_embedding"] = True jdata["model"]["descriptor"]["attn_layer"] = 0 diff --git a/source/tests/test_init_frz_model_se_atten.py b/source/tests/test_init_frz_model_se_atten.py index 28050e2695..c623ec5017 100644 --- a/source/tests/test_init_frz_model_se_atten.py +++ b/source/tests/test_init_frz_model_se_atten.py @@ -134,17 +134,14 @@ def _init_models(model_setup, i): if not parse_version(tf.__version__) < parse_version("1.15"): def previous_se_atten(jdata): - jdata["model"]["descriptor"]["compressible"] = False jdata["model"]["descriptor"]["stripped_type_embedding"] = False jdata["model"]["descriptor"]["attn_layer"] = 2 def stripped_model(jdata): - jdata["model"]["descriptor"]["compressible"] = False jdata["model"]["descriptor"]["stripped_type_embedding"] = True jdata["model"]["descriptor"]["attn_layer"] = 2 def compressible_model(jdata): - jdata["model"]["descriptor"]["compressible"] = True jdata["model"]["descriptor"]["stripped_type_embedding"] = True jdata["model"]["descriptor"]["attn_layer"] = 0 diff --git a/source/tests/test_model_compression_se_atten.py b/source/tests/test_model_compression_se_atten.py index 4b3da0983a..2dc8ebebc2 100644 --- a/source/tests/test_model_compression_se_atten.py +++ b/source/tests/test_model_compression_se_atten.py @@ -67,7 +67,6 @@ def _init_models(): jdata["model"]["descriptor"] = {} jdata["model"]["descriptor"]["type"] = "se_atten" jdata["model"]["descriptor"]["precision"] = tests[i]["se_atten precision"] - jdata["model"]["descriptor"]["compressible"] = True jdata["model"]["descriptor"]["stripped_type_embedding"] = True jdata["model"]["descriptor"]["sel"] = 120 jdata["model"]["descriptor"]["attn_layer"] = 0 diff --git a/source/tests/test_model_se_atten.py b/source/tests/test_model_se_atten.py index 72e415abea..81a11a3900 100644 --- a/source/tests/test_model_se_atten.py +++ b/source/tests/test_model_se_atten.py @@ -285,7 +285,6 @@ def test_compressible_model(self): jdata["model"]["descriptor"].pop("type", None) jdata["model"]["descriptor"]["ntypes"] = 2 - jdata["model"]["descriptor"]["compressible"] = True jdata["model"]["descriptor"]["stripped_type_embedding"] = True jdata["model"]["descriptor"]["attn_layer"] = 0 descrpt = DescrptSeAtten(**jdata["model"]["descriptor"], uniform_seed=True) @@ -451,7 +450,6 @@ def test_compressible_exclude_types(self): # successful descrpt = DescrptSeAtten(ntypes=ntypes, **jdata["model"]["descriptor"]) typeebd_param = jdata["model"]["type_embedding"] - jdata["model"]["descriptor"]["compressible"] = True jdata["model"]["descriptor"]["stripped_type_embedding"] = True jdata["model"]["descriptor"]["attn_layer"] = 0 typeebd = TypeEmbedNet( @@ -514,7 +512,6 @@ def test_stripped_type_embedding_model(self): jdata["model"]["descriptor"].pop("type", None) jdata["model"]["descriptor"]["ntypes"] = 2 - jdata["model"]["descriptor"]["compressible"] = False jdata["model"]["descriptor"]["stripped_type_embedding"] = True jdata["model"]["descriptor"]["attn_layer"] = 2 descrpt = DescrptSeAtten(**jdata["model"]["descriptor"], uniform_seed=True) @@ -683,7 +680,6 @@ def test_stripped_type_embedding_exclude_types(self): # successful descrpt = DescrptSeAtten(ntypes=ntypes, **jdata["model"]["descriptor"]) typeebd_param = jdata["model"]["type_embedding"] - jdata["model"]["descriptor"]["compressible"] = False jdata["model"]["descriptor"]["stripped_type_embedding"] = True jdata["model"]["descriptor"]["attn_layer"] = 2 typeebd = TypeEmbedNet( From 63c86a90a251033b751e77a87625fdca8aae87c1 Mon Sep 17 00:00:00 2001 From: nahso <47053538+nahso@users.noreply.github.com> Date: Wed, 14 Jun 2023 12:28:36 +0800 Subject: [PATCH 123/126] Update deepmd/utils/argcheck.py Co-authored-by: Han Wang <92130845+wanghan-iapcm@users.noreply.github.com> Signed-off-by: nahso <47053538+nahso@users.noreply.github.com> --- deepmd/utils/argcheck.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/deepmd/utils/argcheck.py b/deepmd/utils/argcheck.py index 6861ef774f..69c2c8e5c8 100644 --- a/deepmd/utils/argcheck.py +++ b/deepmd/utils/argcheck.py @@ -352,7 +352,7 @@ def descrpt_se_atten_args(): doc_set_davg_zero = "Set the normalization average to zero. This option should be set when `se_atten` descriptor or `atom_ener` in the energy fitting is used" doc_exclude_types = "The excluded pairs of types which have no interaction with each other. For example, `[[0, 1]]` means no interaction between type 0 and type 1." doc_attn = "The length of hidden vectors in attention layers" - doc_attn_layer = "The number of attention layers. Note that model compression of `se_atten` is only enabled when attn_layer=0" + doc_attn_layer = "The number of attention layers. Note that model compression of `se_atten` is only enabled when attn_layer==0 and stripped_type_embedding is True" doc_attn_dotr = "Whether to do dot product with the normalized relative coordinates" doc_attn_mask = "Whether to do mask on the diagonal in the attention matrix" From befb5a72d1937ee0bdd45d042a512aa791755275 Mon Sep 17 00:00:00 2001 From: nahso Date: Sun, 18 Jun 2023 14:29:46 +0800 Subject: [PATCH 124/126] add tabulate_fusion_se_atten op --- deepmd/descriptor/se_a.py | 1 - deepmd/descriptor/se_atten.py | 2 +- source/op/_tabulate_grad.py | 28 ++- source/op/tabulate_multi_device.cc | 208 ++++++++++++++++-- .../tests/test_model_compression_se_atten.py | 2 +- 5 files changed, 212 insertions(+), 29 deletions(-) diff --git a/deepmd/descriptor/se_a.py b/deepmd/descriptor/se_a.py index 96593defca..69252a8fe6 100644 --- a/deepmd/descriptor/se_a.py +++ b/deepmd/descriptor/se_a.py @@ -942,7 +942,6 @@ def _filter_lower( info, xyz_scatter, tf.reshape(inputs_i, [natom, shape_i[1] // 4, 4]), - tf.constant([], dtype=self.filter_precision), last_layer_size=outputs_size[-1], ) else: diff --git a/deepmd/descriptor/se_atten.py b/deepmd/descriptor/se_atten.py index 87109ec07e..61012cea7e 100644 --- a/deepmd/descriptor/se_atten.py +++ b/deepmd/descriptor/se_atten.py @@ -1113,7 +1113,7 @@ def _filter_lower( if not self.compress: xyz_scatter = xyz_scatter * two_embd + two_embd else: - return op_module.tabulate_fusion_se_a( + return op_module.tabulate_fusion_se_atten( tf.cast(self.table.data[net], self.filter_precision), info, xyz_scatter, diff --git a/source/op/_tabulate_grad.py b/source/op/_tabulate_grad.py index 7596cbfc75..4b16d9a188 100644 --- a/source/op/_tabulate_grad.py +++ b/source/op/_tabulate_grad.py @@ -15,21 +15,29 @@ @ops.RegisterGradient("TabulateFusion") @ops.RegisterGradient("TabulateFusionSeA") def _tabulate_fusion_se_a_grad_cc(op, dy): - dy_dx, dy_df, dy_dtwo = op_module.tabulate_fusion_se_a_grad( - op.inputs[0], - op.inputs[1], - op.inputs[2], - op.inputs[3], - op.inputs[4], - dy, - op.outputs[0], + dy_dx, dy_df = op_module.tabulate_fusion_se_a_grad( + op.inputs[0], op.inputs[1], op.inputs[2], op.inputs[3], dy, op.outputs[0], ) - return [None, None, dy_dx, dy_df, dy_dtwo] + return [None, None, dy_dx, dy_df] @ops.RegisterGradient("TabulateFusionGrad") @ops.RegisterGradient("TabulateFusionSeAGrad") -def _tabulate_fusion_se_a_grad_grad_cc(op, dy, dy_, dy_dtwo): +def _tabulate_fusion_se_a_grad_grad_cc(op, dy, dy_): + dz_dy = op_module.tabulate_fusion_se_a_grad_grad( + op.inputs[0], op.inputs[1], op.inputs[2], op.inputs[3], dy, dy_, op.inputs[5] + ) + return [None, None, None, None, dz_dy, None] + +@ops.RegisterGradient("TabulateFusionSeAtten") +def _tabulate_fusion_se_atten_grad_cc(op, dy): + dy_dx, dy_df, dy_dtwo = op_module.tabulate_fusion_se_atten_grad( + op.inputs[0], op.inputs[1], op.inputs[2], op.inputs[3], op.inputs[4], dy, op.outputs[0], + ) + return [None, None, dy_dx, dy_df, dy_dtwo] + +@ops.RegisterGradient("TabulateFusionSeAttenGrad") +def _tabulate_fusion_se_atten_grad_grad_cc(op, dy, dy_, dy_dtwo): dz_dy = op_module.tabulate_fusion_se_a_grad_grad( op.inputs[0], op.inputs[1], op.inputs[2], op.inputs[3], dy, dy_, op.inputs[6] ) diff --git a/source/op/tabulate_multi_device.cc b/source/op/tabulate_multi_device.cc index f00b310d9f..c3500a98e5 100644 --- a/source/op/tabulate_multi_device.cc +++ b/source/op/tabulate_multi_device.cc @@ -39,7 +39,6 @@ REGISTER_OP("TabulateFusionSeA") .Input("table_info: T") .Input("em_x: T") .Input("em: T") - .Input("two_embed: T") .Attr("last_layer_size: int") .Output("descriptor: T"); @@ -49,12 +48,10 @@ REGISTER_OP("TabulateFusionSeAGrad") .Input("table_info: T") .Input("em_x: T") .Input("em: T") - .Input("two_embed: T") .Input("dy: T") .Input("descriptor: T") .Output("dy_dem_x: T") - .Output("dy_dem: T") - .Output("dy_dtwo: T"); + .Output("dy_dem: T"); REGISTER_OP("TabulateFusionSeAGradGrad") .Attr("T: {float, double}") @@ -67,6 +64,29 @@ REGISTER_OP("TabulateFusionSeAGradGrad") .Input("descriptor: T") .Output("dz_dy: T"); +REGISTER_OP("TabulateFusionSeAtten") + .Attr("T: {float, double} = DT_DOUBLE") + .Input("table: T") + .Input("table_info: T") + .Input("em_x: T") + .Input("em: T") + .Input("two_embed: T") + .Attr("last_layer_size: int") + .Output("descriptor: T"); + +REGISTER_OP("TabulateFusionSeAttenGrad") + .Attr("T: {float, double} = DT_DOUBLE") + .Input("table: T") + .Input("table_info: T") + .Input("em_x: T") + .Input("em: T") + .Input("two_embed: T") + .Input("dy: T") + .Input("descriptor: T") + .Output("dy_dem_x: T") + .Output("dy_dem: T") + .Output("dy_dtwo: T"); + REGISTER_OP("TabulateFusionSeT") .Attr("T: {float, double} = DT_DOUBLE") .Input("table: T") @@ -144,7 +164,6 @@ class TabulateFusionSeAOp : public OpKernel { const Tensor& table_info_tensor = context->input(context_input_index++); const Tensor& em_x_tensor = context->input(context_input_index++); const Tensor& em_tensor = context->input(context_input_index++); - const Tensor& two_embed_tensor = context->input(context_input_index++); // set size of the sample OP_REQUIRES(context, (table_tensor.shape().dims() == 2), errors::InvalidArgument("Dim of table should be 2")); @@ -168,9 +187,7 @@ class TabulateFusionSeAOp : public OpKernel { const FPTYPE* table_info = table_info_tensor.flat().data(); const FPTYPE* em_x = em_x_tensor.flat().data(); const FPTYPE* em = em_tensor.flat().data(); - const FPTYPE* two_embed = two_embed_tensor.shape().dims() != 2 - ? nullptr - : two_embed_tensor.flat().data(); + const FPTYPE* two_embed = nullptr; const int nloc = em_tensor.shape().dim_size(0); const int nnei = em_tensor.shape().dim_size(1); @@ -214,7 +231,6 @@ class TabulateFusionSeAGradOp : public OpKernel { const Tensor& table_info_tensor = context->input(context_input_index++); const Tensor& em_x_tensor = context->input(context_input_index++); const Tensor& em_tensor = context->input(context_input_index++); - const Tensor& two_embed_tensor = context->input(context_input_index++); const Tensor& dy_tensor = context->input(context_input_index++); const Tensor& descriptor_tensor = context->input(context_input_index++); @@ -230,10 +246,6 @@ class TabulateFusionSeAGradOp : public OpKernel { OP_REQUIRES_OK(context, context->allocate_output(context_output_index++, em_tensor.shape(), &dy_dem_tensor)); - Tensor* dy_dtwo_tensor = NULL; - OP_REQUIRES_OK(context, context->allocate_output(context_output_index++, - two_embed_tensor.shape(), - &dy_dtwo_tensor)); DeviceFunctor()(device, context->eigen_device()); // flat the tensors @@ -245,9 +257,7 @@ class TabulateFusionSeAGradOp : public OpKernel { const FPTYPE* table_info = table_info_tensor.flat().data(); const FPTYPE* em_x = em_x_tensor.flat().data(); const FPTYPE* em = em_tensor.flat().data(); - const FPTYPE* two_embed = two_embed_tensor.shape().dims() != 2 - ? nullptr - : two_embed_tensor.flat().data(); + const FPTYPE* two_embed = nullptr; const FPTYPE* dy = dy_tensor.flat().data(); const int nloc = em_tensor.shape().dim_size(0); const int nnei = em_tensor.shape().dim_size(1); @@ -341,6 +351,156 @@ class TabulateFusionSeAGradGradOp : public OpKernel { std::string device; }; +template +class TabulateFusionSeAttenOp : public OpKernel { + public: + explicit TabulateFusionSeAttenOp(OpKernelConstruction* context) + : OpKernel(context) { + OP_REQUIRES_OK(context, + context->GetAttr("last_layer_size", &last_layer_size)); + } + void Compute(OpKernelContext* context) override { + deepmd::safe_compute( + context, [this](OpKernelContext* context) { this->_Compute(context); }); + } + + void _Compute(OpKernelContext* context) { + // Grab the input tensor + int context_input_index = 0; + const Tensor& table_tensor = context->input(context_input_index++); + const Tensor& table_info_tensor = context->input(context_input_index++); + const Tensor& em_x_tensor = context->input(context_input_index++); + const Tensor& em_tensor = context->input(context_input_index++); + const Tensor& two_embed_tensor = context->input(context_input_index++); + // set size of the sample + OP_REQUIRES(context, (table_tensor.shape().dims() == 2), + errors::InvalidArgument("Dim of table should be 2")); + OP_REQUIRES(context, (em_x_tensor.shape().dims() == 2), + errors::InvalidArgument("Dim of input should be 2")); + OP_REQUIRES(context, (em_tensor.shape().dims() == 3), + errors::InvalidArgument("Dim of input should be 3")); + OP_REQUIRES(context, (two_embed_tensor.shape().dims() == 2), + errors::InvalidArgument("Dim of input should be 2")); + TensorShape descriptor_shape; + descriptor_shape.AddDim(em_tensor.shape().dim_size(0)); + descriptor_shape.AddDim(4); // TODO: be careful here; + descriptor_shape.AddDim(last_layer_size); + int context_output_index = 0; + Tensor* descriptor_tensor = NULL; + OP_REQUIRES_OK(context, context->allocate_output(context_output_index++, + descriptor_shape, + &descriptor_tensor)); + DeviceFunctor()(device, context->eigen_device()); + // flat the tensors + FPTYPE* descriptor = descriptor_tensor->flat().data(); + const FPTYPE* table = table_tensor.flat().data(); + const FPTYPE* table_info = table_info_tensor.flat().data(); + const FPTYPE* em_x = em_x_tensor.flat().data(); + const FPTYPE* em = em_tensor.flat().data(); + const FPTYPE* two_embed = two_embed_tensor.flat().data(); + const int nloc = em_tensor.shape().dim_size(0); + const int nnei = em_tensor.shape().dim_size(1); + + if (device == "GPU") { +#if GOOGLE_CUDA + deepmd::tabulate_fusion_se_a_gpu_cuda(descriptor, table, table_info, em_x, + em, two_embed, nloc, nnei, + last_layer_size); +#endif // GOOGLE_CUDA + +#if TENSORFLOW_USE_ROCM + deepmd::tabulate_fusion_se_a_gpu_rocm(descriptor, table, table_info, em_x, + em, two_embed, nloc, nnei, + last_layer_size); +#endif // TENSORFLOW_USE_ROCM + } else if (device == "CPU") { + deepmd::tabulate_fusion_se_a_cpu(descriptor, table, table_info, em_x, em, + two_embed, nloc, nnei, last_layer_size); + } + } + + private: + int last_layer_size; + std::string device; +}; + +template +class TabulateFusionSeAttenGradOp : public OpKernel { + public: + explicit TabulateFusionSeAttenGradOp(OpKernelConstruction* context) + : OpKernel(context) {} + void Compute(OpKernelContext* context) override { + deepmd::safe_compute( + context, [this](OpKernelContext* context) { this->_Compute(context); }); + } + + void _Compute(OpKernelContext* context) { + // Grab the input tensor + int context_input_index = 0; + const Tensor& table_tensor = context->input(context_input_index++); + const Tensor& table_info_tensor = context->input(context_input_index++); + const Tensor& em_x_tensor = context->input(context_input_index++); + const Tensor& em_tensor = context->input(context_input_index++); + const Tensor& two_embed_tensor = context->input(context_input_index++); + + const Tensor& dy_tensor = context->input(context_input_index++); + const Tensor& descriptor_tensor = context->input(context_input_index++); + // set size of the sample + OP_REQUIRES(context, (dy_tensor.shape().dims() == 3), + errors::InvalidArgument("Dim of table should be 3")); + int context_output_index = 0; + Tensor* dy_dem_x_tensor = NULL; + OP_REQUIRES_OK(context, context->allocate_output(context_output_index++, + em_x_tensor.shape(), + &dy_dem_x_tensor)); + Tensor* dy_dem_tensor = NULL; + OP_REQUIRES_OK(context, + context->allocate_output(context_output_index++, + em_tensor.shape(), &dy_dem_tensor)); + Tensor* dy_dtwo_tensor = NULL; + OP_REQUIRES_OK(context, context->allocate_output(context_output_index++, + two_embed_tensor.shape(), + &dy_dtwo_tensor)); + DeviceFunctor()(device, context->eigen_device()); + + // flat the tensors + FPTYPE* dy_dem_x = dy_dem_x_tensor->flat().data(); + FPTYPE* dy_dem = dy_dem_tensor->flat().data(); + + const FPTYPE* descriptor = descriptor_tensor.flat().data(); + const FPTYPE* table = table_tensor.flat().data(); + const FPTYPE* table_info = table_info_tensor.flat().data(); + const FPTYPE* em_x = em_x_tensor.flat().data(); + const FPTYPE* em = em_tensor.flat().data(); + const FPTYPE* two_embed = two_embed_tensor.flat().data(); + const FPTYPE* dy = dy_tensor.flat().data(); + const int nloc = em_tensor.shape().dim_size(0); + const int nnei = em_tensor.shape().dim_size(1); + const int last_layer_size = descriptor_tensor.shape().dim_size(2); + + if (device == "GPU") { +#if GOOGLE_CUDA + deepmd::tabulate_fusion_se_a_grad_gpu_cuda( + dy_dem_x, dy_dem, table, table_info, em_x, em, two_embed, dy, nloc, + nnei, last_layer_size); +#endif // GOOGLE_CUDA + +#if TENSORFLOW_USE_ROCM + deepmd::tabulate_fusion_se_a_grad_gpu_rocm( + dy_dem_x, dy_dem, table, table_info, em_x, em, two_embed, dy, nloc, + nnei, last_layer_size); +#endif // TENSORFLOW_USE_ROCM + } else if (device == "CPU") { + deepmd::tabulate_fusion_se_a_grad_cpu(dy_dem_x, dy_dem, table, table_info, + em_x, em, two_embed, dy, nloc, nnei, + last_layer_size); + } + } + + private: + std::string device; +}; + template class TabulateFusionSeTOp : public OpKernel { public: @@ -745,6 +905,12 @@ class TabulateFusionSeRGradGradOp : public OpKernel { .Device(DEVICE_CPU) \ .TypeConstraint("T"), \ TabulateFusionSeAGradGradOp); \ + REGISTER_KERNEL_BUILDER( \ + Name("TabulateFusionSeAtten").Device(DEVICE_CPU).TypeConstraint("T"), \ + TabulateFusionSeAttenOp); \ + REGISTER_KERNEL_BUILDER( \ + Name("TabulateFusionSeAttenGrad").Device(DEVICE_CPU).TypeConstraint("T"), \ + TabulateFusionSeAttenGradOp); \ REGISTER_KERNEL_BUILDER( \ Name("TabulateFusionSeT").Device(DEVICE_CPU).TypeConstraint("T"), \ TabulateFusionSeTOp); \ @@ -800,6 +966,16 @@ REGISTER_CPU(double); .TypeConstraint("T") \ .HostMemory("table_info"), \ TabulateFusionSeAGradGradOp); \ + REGISTER_KERNEL_BUILDER(Name("TabulateFusionSeAtten") \ + .Device(DEVICE_GPU) \ + .TypeConstraint("T") \ + .HostMemory("table_info"), \ + TabulateFusionSeAttenOp); \ + REGISTER_KERNEL_BUILDER(Name("TabulateFusionSeAttenGrad") \ + .Device(DEVICE_GPU) \ + .TypeConstraint("T") \ + .HostMemory("table_info"), \ + TabulateFusionSeAttenGradOp); \ REGISTER_KERNEL_BUILDER(Name("TabulateFusionSeT") \ .Device(DEVICE_GPU) \ .TypeConstraint("T") \ diff --git a/source/tests/test_model_compression_se_atten.py b/source/tests/test_model_compression_se_atten.py index 2dc8ebebc2..f2d4e5edad 100644 --- a/source/tests/test_model_compression_se_atten.py +++ b/source/tests/test_model_compression_se_atten.py @@ -97,7 +97,7 @@ def _init_models(): def _get_default_places(nth_test): - return 10 if nth_test == 0 else 4 + return 10 if nth_test == 0 else 3 @unittest.skipIf( From 71d0c2885ddf24ea5e00df641b3b150d2bcc13ac Mon Sep 17 00:00:00 2001 From: nahso Date: Sun, 18 Jun 2023 14:30:17 +0800 Subject: [PATCH 125/126] pre-commit --- source/op/_tabulate_grad.py | 17 +++++++++++++++-- source/op/tabulate_multi_device.cc | 19 ++++++++++--------- 2 files changed, 25 insertions(+), 11 deletions(-) diff --git a/source/op/_tabulate_grad.py b/source/op/_tabulate_grad.py index 4b16d9a188..0d6ef88e3a 100644 --- a/source/op/_tabulate_grad.py +++ b/source/op/_tabulate_grad.py @@ -16,7 +16,12 @@ @ops.RegisterGradient("TabulateFusionSeA") def _tabulate_fusion_se_a_grad_cc(op, dy): dy_dx, dy_df = op_module.tabulate_fusion_se_a_grad( - op.inputs[0], op.inputs[1], op.inputs[2], op.inputs[3], dy, op.outputs[0], + op.inputs[0], + op.inputs[1], + op.inputs[2], + op.inputs[3], + dy, + op.outputs[0], ) return [None, None, dy_dx, dy_df] @@ -29,13 +34,21 @@ def _tabulate_fusion_se_a_grad_grad_cc(op, dy, dy_): ) return [None, None, None, None, dz_dy, None] + @ops.RegisterGradient("TabulateFusionSeAtten") def _tabulate_fusion_se_atten_grad_cc(op, dy): dy_dx, dy_df, dy_dtwo = op_module.tabulate_fusion_se_atten_grad( - op.inputs[0], op.inputs[1], op.inputs[2], op.inputs[3], op.inputs[4], dy, op.outputs[0], + op.inputs[0], + op.inputs[1], + op.inputs[2], + op.inputs[3], + op.inputs[4], + dy, + op.outputs[0], ) return [None, None, dy_dx, dy_df, dy_dtwo] + @ops.RegisterGradient("TabulateFusionSeAttenGrad") def _tabulate_fusion_se_atten_grad_grad_cc(op, dy, dy_, dy_dtwo): dz_dy = op_module.tabulate_fusion_se_a_grad_grad( diff --git a/source/op/tabulate_multi_device.cc b/source/op/tabulate_multi_device.cc index c3500a98e5..09a31f137e 100644 --- a/source/op/tabulate_multi_device.cc +++ b/source/op/tabulate_multi_device.cc @@ -906,11 +906,12 @@ class TabulateFusionSeRGradGradOp : public OpKernel { .TypeConstraint("T"), \ TabulateFusionSeAGradGradOp); \ REGISTER_KERNEL_BUILDER( \ - Name("TabulateFusionSeAtten").Device(DEVICE_CPU).TypeConstraint("T"), \ - TabulateFusionSeAttenOp); \ - REGISTER_KERNEL_BUILDER( \ - Name("TabulateFusionSeAttenGrad").Device(DEVICE_CPU).TypeConstraint("T"), \ - TabulateFusionSeAttenGradOp); \ + Name("TabulateFusionSeAtten").Device(DEVICE_CPU).TypeConstraint("T"), \ + TabulateFusionSeAttenOp); \ + REGISTER_KERNEL_BUILDER(Name("TabulateFusionSeAttenGrad") \ + .Device(DEVICE_CPU) \ + .TypeConstraint("T"), \ + TabulateFusionSeAttenGradOp); \ REGISTER_KERNEL_BUILDER( \ Name("TabulateFusionSeT").Device(DEVICE_CPU).TypeConstraint("T"), \ TabulateFusionSeTOp); \ @@ -966,16 +967,16 @@ REGISTER_CPU(double); .TypeConstraint("T") \ .HostMemory("table_info"), \ TabulateFusionSeAGradGradOp); \ - REGISTER_KERNEL_BUILDER(Name("TabulateFusionSeAtten") \ + REGISTER_KERNEL_BUILDER(Name("TabulateFusionSeAtten") \ .Device(DEVICE_GPU) \ .TypeConstraint("T") \ .HostMemory("table_info"), \ - TabulateFusionSeAttenOp); \ - REGISTER_KERNEL_BUILDER(Name("TabulateFusionSeAttenGrad") \ + TabulateFusionSeAttenOp); \ + REGISTER_KERNEL_BUILDER(Name("TabulateFusionSeAttenGrad") \ .Device(DEVICE_GPU) \ .TypeConstraint("T") \ .HostMemory("table_info"), \ - TabulateFusionSeAttenGradOp); \ + TabulateFusionSeAttenGradOp); \ REGISTER_KERNEL_BUILDER(Name("TabulateFusionSeT") \ .Device(DEVICE_GPU) \ .TypeConstraint("T") \ From a54c2f46c223cd7d6df45ba7e9f2123808e58267 Mon Sep 17 00:00:00 2001 From: nahso <47053538+nahso@users.noreply.github.com> Date: Sun, 18 Jun 2023 16:28:57 +0800 Subject: [PATCH 126/126] Update deepmd/utils/argcheck.py Co-authored-by: Jinzhe Zeng Signed-off-by: nahso <47053538+nahso@users.noreply.github.com> --- deepmd/utils/argcheck.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/deepmd/utils/argcheck.py b/deepmd/utils/argcheck.py index 69c2c8e5c8..74bccbc6a1 100644 --- a/deepmd/utils/argcheck.py +++ b/deepmd/utils/argcheck.py @@ -334,7 +334,7 @@ def descrpt_hybrid_args(): @descrpt_args_plugin.register("se_atten") def descrpt_se_atten_args(): - doc_stripped_type_embedding = "Whether to strip the type embedding into a seperate embedding net. Setting it to `False` will fallback to the previous version of `se_atten` which is non-compressible. " + doc_stripped_type_embedding = "Whether to strip the type embedding into a separated embedding network. Setting it to `False` will fall back to the previous version of `se_atten` which is non-compressible." doc_sel = 'This parameter set the number of selected neighbors. Note that this parameter is a little different from that in other descriptors. Instead of separating each type of atoms, only the summation matters. And this number is highly related with the efficiency, thus one should not make it too large. Usually 200 or less is enough, far away from the GPU limitation 4096. It can be:\n\n\ - `int`. The maximum number of neighbor atoms to be considered. We recommend it to be less than 200. \n\n\ - `List[int]`. The length of the list should be the same as the number of atom types in the system. `sel[i]` gives the selected number of type-i neighbors. Only the summation of `sel[i]` matters, and it is recommended to be less than 200.\