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perf(pt): pass is_inference to compile_fx_inner for compiled eval graphs - #6056
The custom inference compiler entry in sezm_model.py calls compile_fx_inner(fx_gm, fx_inputs) without the is_inference flag. torch.compile's own default path already passes is_inference=True
through its inference compiler (see compile_fx's inference_compiler = functools.partial(fw_compiler_base, is_inference=True)).
This PR makes the custom entry match that behavior for eval graphs;
training graphs are unchanged (is_inference=not self.training).
Where the peak-memory saving actually comes from (corrected during
review — the original text attributed it to pooled memory planning):
with the default configuration (TORCHINDUCTOR_MEMORY_PLANNING unset), memory_plan is never called for either flag value and the generated
wrapper is byte-identical. The flag's only observable effect there is
enabling Inductor's reorder_for_locality pass on the inference graph,
which shortens intermediate live ranges; that reordering is what the
measurements below capture. With explicit memory planning enabled, is_inference=True is additionally what switches memory_plan_reuse()
to memory_plan().
Change
One-line semantic change plus a comment in deepmd/pt/model/model/sezm_model.py::_inductor_inference_compiler.
Accuracy (same input, master vs this PR, compiled eval): energy rel diff
2.2e-10; max force diff 4.5e-6 with max |f| ~ 3.9 — outputs agree to
rounding level; this is a scheduling-only change.
The gain grows when more of the peak is scheduler-visible intermediates:
on a heavier memory-optimized kernel stack the same flag measures −12.5%
peak VRAM (23647→20696 MB), and −13.9% on an H800.
Tests
source/tests/pt/model/test_descriptor_sezm.py on this branch, default
configuration: 61 passed, 122 subtests passed.
test_eval_compile_pins_is_inference_on_the_inference_slot (d52a4ca)
spies on compile_fx_inner with the compile caches disabled and
asserts the eval slot lowers with is_inference=True; a control run
with the flag dropped turns the test red.
ruff check / format clean.
Summary by CodeRabbit
Bug Fixes
Evaluation-mode model compilation now uses inference-specific handling, while training compilation continues to use its existing workflow. Existing compilation options are preserved in both modes. This keeps the behavior for training unchanged and applies the appropriate inference setting during evaluation, improving consistency between the model’s evaluation mode and its compilation behavior.
The custom inference compiler entry calls compile_fx_inner without the
is_inference flag, so Inductor plans memory under training assumptions:
outputs are allocated early and autograd-visible intermediates stay
alive. torch.compile's own default path passes is_inference=True through
its inference compiler; this change matches that for the eval graph
(training keeps False). Scheduling-only change: outputs agree to
rounding level.
Measured at N=4096 (SeZM eval compile, DP_COMPILE_INFER=1):
peak VRAM -12.5% (23647 -> 20696 MB, RTX 5090); independently -13.9% on
an H800 baseline.
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source/tests/pt/model/test_sezm_model.py
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Walkthrough
The SeZM eval compiler callback now calls compile_fx_inner with is_inference=True. A compile-gated regression test checks that the flag reaches the lowering call. Training continues to use a separate torch.compile path.
Changes: SeZM compilation mode
Layer / File(s)
Summary
Set inference compilation flag deepmd/pt/model/model/sezm_model.py, source/tests/pt/model/test_sezm_model.py
The eval callback now sets is_inference=True when it calls compile_fx_inner. The added regression test spies on that call and checks the flag. Comments distinguish the eval callback from the training torch.compile path.
deepmodeling/deepmd-kit#6085: Changes inference-specific Inductor settings used in the same SeZM eval compilation path.
deepmodeling/deepmd-kit#6110: Changes SeZM Inductor options and inference compilation behavior in the same eval pipeline.
Merge Risk: Minimal · up to 6fe39
The eval compiler now selects inference lowering without changing the separate training path. No actionable merge-blocking issue remains; the change is mergeable subject to normal checks.
Pre-merge checks: 5
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Title check
The title states the main change: the SeZM eval compile path now passes is_inference to compile_fx_inner, which matches the raw summary (the sezm_model.py callback now calls compile_fx_inner with is_i…
Docstring Coverage
Docstring coverage is 100.00% which is sufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff with cyclomatic complexity of at least 2. Documentat…
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Full details: Title check
Explanation
The title states the main change: the SeZM eval compile path now passes is_inference to compile_fx_inner, which matches the raw summary (the sezm_model.py callback now calls compile_fx_inner with is_inference=True) and the stated objective (is_inference=not self.training). The title is concise, specific, and follows the conventional commit style. The title is not vague, unrelated, or misleading.
Full details: Docstring Coverage
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Docstring coverage is 100.00% which is sufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff with cyclomatic complexity of at least 2. Documentation-only edits and deletions within surviving functions are included; unchanged diff context and fully deleted functions are excluded. Analyzed 2 functions across 2 files.
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Reviewed the exact one-file diff and its inference/training integration. No new actionable correctness defect found.
The flag is appropriate for this specific graph, rather than being justified by .eval() alone: make_fx has already materialized edge_energy_deriv's energy-to-edge/spin derivatives and force/virial assembly, with create_graph=False. Both aot_module_simplified lowering and compiled eval execution are inside torch.no_grad(). Training uses the separate torch.compile branch with create_graph=True, and the cache distinguishes training/eval. The changed callback therefore does not suppress the first derivatives needed to return forces or alter force-loss double-backward training.
Checked upstream compile_fx_inner and default inference dispatch in PyTorch tags 2.11.0, 2.12.1 and 2.13.0, covering this repository's compile allowlist: all accept is_inference and propagate it to lowering; default inference dispatch supplies True.
Validation remains incomplete. The reported default test_descriptor_sezm.py run does not establish compiled-model coverage. Please run the existing TestSeZMModelCompile eval cache/shape-change and first-frame tests in test_sezm_model.py, plus its force-loss double-backward test, on a supported PyTorch build. The standard CUDA workflow currently pins torch 2.10.0, which skips these compile tests, so a green CUDA badge alone would not establish this coverage.
At 11:00 UTC, the exact-head test/build/CodeQL workflows report action_required and have not provided test results; pre-commit passed and Read the Docs failed. I ran source/AST checks only: PyTorch is not installed in this review environment, and I did not reproduce the GPU parity or memory measurements.
Following the review, ran the requested compile coverage on this branch head (1e3e40c), on a supported build with CUDA available (RTX 5090, torch 2.12.1+cu130):
source/tests/pt/model/test_sezm_model.py::TestSeZMModelCompile: 9 passed (0:09:35) — including test_eval_compile_first_frame_nloc_matches_trace_edge_count, test_compile_cache_slots_and_eval_shape_change, test_eval_compile_policy, and test_forward_backward_double_backward_matches_compile (the force-loss double backward).
❌ Patch coverage is 50.00000% with 1 line in your changes missing coverage. Please review.
✅ Project coverage is 77.88%. Comparing base (1ded5d2) to head (6fe393f). ⚠️ Report is 18 commits behind head on master.
@OutisLi@wanghan-iapcm@njzjz requesting a review, please. Following the 2026-10-13 discussion we validated the combined stack of our six performance PRs (#6056, #6057, #6060, #6070, #6071, #6072) with a multi-scale gate: diamond + bcc-Cu at ~500 / ~2000 / ~4400 atoms, three columns (pure torch.compile with all custom kernels off / current master / our changes on). All accuracy cells sit within 2.2x the pure baseline's own run-to-run band (criterion: 3x), and no tested atom count regresses in time or memory — full numbers in the PR descriptions. Suggested review order for the six: #6056 -> #6057 -> #6060 (small and self-contained) -> #6070 -> #6071 -> #6072.
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Verified at head 1e3e40c (torch 2.11.0+cpu, CPU only):
_inductor_inference_compiler is only reached from the eval branch (aot_module_simplified under torch.no_grad()), so the new kwarg is always is_inference=True; the training graph still goes through torch.compile and is unaffected.
With the flag, Inductor's post-grad reorder_for_locality pass now runs on the eval graph (instrumented: it runs on this head and does not run on the parent commit); config.memory_planning stays off, and reorder_for_peak_memory=False from build_inductor_compile_options(inference=True) is still applied.
compile_fx_inner accepts is_inference as a keyword in torch 2.11 (checked locally; it defaults to False).
Local runs: TestSeZMModelCompile 9 passed; TestSeZMEmbeddingForward::test_compiled_matches_eager and TestSeZMSpinModel::test_compile_matches_eager passed. Compiled eval matches eager for energy, force and virial on the tiny test system.
The CPU CI jobs install torch 2.11.0+cpu, so the compile tests in test_sezm_model.py ran in CI and passed. The VRAM reduction was not reproduced here because no GPU was available.
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One non-blocking clarity suggestion for _inductor_inference_compiler in deepmd/pt/model/model/sezm_model.py:2370-2388: please express this callback's inference-only contract directly with is_inference=True.
The callback is defined only in the evaluation branch, and aot_module_simplified is invoked under torch.no_grad(). Training takes the separate torch.compile branch above. Consequently, not self.training adds a dependency on mutable model state where the graph's inference-only role already determines the value. The added sentence about the training graph keeping False also suggests that training passes through this callback, which it does not.
Please keep the hardware-specific memory measurements and before/after validation results in the PR description rather than in the implementation comment. A short explanation that this callback lowers the forward-only AOTAutograd inference graph is sufficient release-grade documentation.
No functional blocker found in this change. Local validation: 61 tests and 122 subtests passed; actual CPU Inductor inference matched eager energy, force, and virial for 3- and 5-atom inputs; the evaluation cache remained usable after a train/eval switch; training backward produced finite gradients for 82 parameters. GPU memory savings were not independently measured.
@wanghan-iapcm thanks for the verification and approval.
@OutisLi addressed in 31067de: the callback now states its contract directly with is_inference=True, and the implementation comment is reduced to a short description (measurements stay in the PR description only). Both of your points about the misleading training-graph sentence are correct — the callback only exists in the eval branch, so the mutable-state dependency is gone.
Validation on my side (CUDA, torch 2.12.1+cu130): TestSeZMModelCompile passes at the new head. For the record, two tests fail identically on the parent commit in my local environment (test_eval_compile_policy, TestSeZMModelBridging::test_change_out_bias_is_invariant_for_self_labels) — they appear environment-specific (your local run and CI are CPU/torch-2.11) and are untouched by this diff.
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Change is correct and safe at this call site. I re-checked the two things that decide that.
The callback exists only in the eval branch, so is_inference=True cannot reach a training graph from here: training still goes through torch.compile at line 2271, and the cache key includes bool(self.training), so an inference-labelled artifact is never handed to a training slot. The remaining question was whether AOTAutograd could still take the autograd partition and send a forward (and, by the bw_compiler default, a backward) graph through this callback. I instrumented aot_module_simplified with distinguishable compilers over the same call shape the model uses -- make_fx symbolic trace, node.meta["val"] example inputs, torch.no_grad() -- across create_graph true/false and inputs that do and do not require grad. Only the inference compiler was ever invoked: the traced outputs do not require grad, so output_and_mutation_safe downgrades needs_autograd. A control module with a plain differentiable input did select fw, so the machinery works; this call site just never reaches it.
Two non-blocking notes.
Nothing pins the flag. The eval-compile tests assert a cache slot and eager parity to 1e-6, both of which hold with is_inference=True, with False, and with the argument absent -- the flagless call coexisted with the same tests before this PR. A future refactor could remove the argument and reinstate the peak-memory cost with the suite green. Mocking deepmd.pt.model.model.sezm_model.compile_fx_inner and asserting is_inference is True for the eval slot would close it.
One correction to the description's mechanism, worth making so the next reader measures the right thing. is_inference only reaches Inductor's pooled memory planner when config.memory_planning is enabled, and that config is off by default (TORCHINDUCTOR_MEMORY_PLANNING unset). Executing compile_fx_inner on the installed torch with the repo's option set: with memory planning off, memory_plan is not called for either flag value and the generated wrapper code is byte-identical; with it on, is_inference=True is what flips memory_plan_reuse() to memory_plan(). In the default configuration the flag's only observable effect is enabling reorder_for_locality in the post-grad passes, which runs once with the flag and never without. So the measured saving is real but does not come from pooled planning -- likely from that reordering pass shortening live ranges. The repo's option dict deliberately disables reorder_for_peak_memory for this hint-less graph, so enabling a different reordering pass is worth knowing about explicitly.
Minor: the head commit is a merge whose subject is the unedited default merge message.
The cache-slot and eager-parity checks hold with the flag set, cleared,
or absent, so a refactor could drop the argument and reinstate the
peak-memory cost with the suite green. The new test spies on
torch._inductor.compile_fx.compile_fx_inner (the import happens at call
time) with the compile caches disabled, and asserts the eval slot lowers
through is_inference=True. Dropping the flag in a control run turns the
test red.
Thank you for the deep re-verification and the correction — both non-blocking notes are addressed in d52a4ca (rebased on top of the master merge).
Flag pinning.test_eval_compile_pins_is_inference_on_the_inference_slot spies on torch._inductor.compile_fx.compile_fx_inner (the import happens at call time, so the source module is the patch target) with TORCHINDUCTOR_FX_GRAPH_CACHE=0 and TORCHINDUCTOR_AUTOGRAD_CACHE=0 to keep the lowering observable, and asserts the eval slot lowers with is_inference=True. A control run with the flag dropped turns the test red, so the pin is not vacuous.
Mechanism corrected in the description. The pooled-planning attribution is replaced with your finding: with the default configuration memory_plan is never called for either flag value and the wrapper is byte-identical; the saving comes from is_inference enabling the reorder_for_locality pass, which shortens live ranges. The explicit-planning behavior (memory_plan_reuse → memory_plan) is noted as the flag's additional effect only when planning is enabled. The description now also records that this repo deliberately disables reorder_for_peak_memory, so the reader knows the reordering in play is a different pass.
On the merge-commit subject: the branch head is now that merge plus the rebased test commit; I left the history untouched rather than rewriting an approved branch.
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Actionable comments posted: 1
🪄 Fix comments: Fix CodeRabbit comments on this PR
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Inline comments:
Review comments at @source/tests/pt/model/test_sezm_model.py:
- Around line 495-497: In the test class, remove the duplicate skip decorator
from test_eval_compile_pins_is_inference_on_the_inference_slot and restore one
@unittest.skipIf(_SKIP_OFF_COMPILE_TORCH, _SKIP_OFF_COMPILE_TORCH_REASON)
decorator on test_compile_cache_slots_and_eval_shape_change so compile-parity
gating remains aligned with the supported Torch versions.
After applying the fix, consider running `coderabbit review --agent` for local
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source/tests/pt/model/test_sezm_model.py
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The pin-test insertion landed between the existing test's decorator
and its def, so the decorator attached twice to the new test and the
cache-slot test lost its gating against the SUPPORTED_COMPILE_TORCH
allowlist.
Good catch — fixed in 6fe393f. The pin-test insertion had landed between the cache-slot test's decorator and its def, so the decorator attached twice to the new test and the existing test lost its allowlist gating. The duplicate is removed and the decorator restored; the pin test re-ran green after the move.
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Motivation
The custom inference compiler entry in
sezm_model.pycallscompile_fx_inner(fx_gm, fx_inputs)without theis_inferenceflag.torch.compile's own default path already passesis_inference=Truethrough its inference compiler (see
compile_fx'sinference_compiler = functools.partial(fw_compiler_base, is_inference=True)).This PR makes the custom entry match that behavior for eval graphs;
training graphs are unchanged (
is_inference=not self.training).Where the peak-memory saving actually comes from (corrected during
review — the original text attributed it to pooled memory planning):
with the default configuration (
TORCHINDUCTOR_MEMORY_PLANNINGunset),memory_planis never called for either flag value and the generatedwrapper is byte-identical. The flag's only observable effect there is
enabling Inductor's
reorder_for_localitypass on the inference graph,which shortens intermediate live ranges; that reordering is what the
measurements below capture. With explicit memory planning enabled,
is_inference=Trueis additionally what switchesmemory_plan_reuse()to
memory_plan().Change
One-line semantic change plus a comment in
deepmd/pt/model/model/sezm_model.py::_inductor_inference_compiler.Measurements
SeZM model, N=4096 atoms (~646k edges), RTX 5090, torch 2.12.1+cu130,
DP_COMPILE_INFER=1, throughput mode (16 evals, P50):Accuracy (same input, master vs this PR, compiled eval): energy rel diff
2.2e-10; max force diff 4.5e-6 with max |f| ~ 3.9 — outputs agree to
rounding level; this is a scheduling-only change.
The gain grows when more of the peak is scheduler-visible intermediates:
on a heavier memory-optimized kernel stack the same flag measures −12.5%
peak VRAM (23647→20696 MB), and −13.9% on an H800.
Tests
source/tests/pt/model/test_descriptor_sezm.pyon this branch, defaultconfiguration: 61 passed, 122 subtests passed.
test_eval_compile_pins_is_inference_on_the_inference_slot(d52a4ca)spies on
compile_fx_innerwith the compile caches disabled andasserts the eval slot lowers with
is_inference=True; a control runwith the flag dropped turns the test red.
Summary by CodeRabbit