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perf(pt): assemble Wigner-D behind a custom op under compile - #6070
Each eval frame assembles the block-diagonal Wigner-D rotation matrices by
scattering the per-degree blocks into a dense (E, D, D) tensor. Traced
through Inductor, those in-place slice assignments functionalize into a
chain of full-size zeros + index_put + add tensors plus a realized
transpose -- five (E, D, D) surfaces combined by one kernel, ~4 GB of HBM
traffic per frame at the SeZM N=4096 workload (E ~ 646k, D = 16).
(The inverse rotation Dt_full is already a transpose view on master since #6001, with the torch-2.11-only materialization gate; this PR does not
change that behavior -- it shares the same exit between the two assembly
routes and adjusts the comments to describe it.)
Change
Assembly behind a custom op (sezm_nn::wigner_d_assemble). The
operator takes the per-degree blocks (plus the polynomial tail when lmax >= 11) and does the eager scatter: one zeros buffer, in-place block
writes. The traced forward takes the op whenever a fake-tensor mode is
active (is_compiling() is not enough -- this module is traced through
make_fx, where the flag stays False; a fake tensor carries its mode, so the
forward probes for it). The eager path is untouched. An exact autograd
formula returns each block its D-slice.
torch >= 2.1 stays importable. The registration API
(torch.library.custom_op / register_fake / register_autograd)
requires torch >= 2.4 while the package declares torch>=2.1.0, and this
module is imported unconditionally through the descriptor chain. The
registration is gated on the API's presence; on a 2.1-2.3 install the
operator stays unregistered and both the eager and the traced route keep
the autograd-through-scatter implementation (a subprocess test reloads the
module with the API hidden and pins import safety, the eager
forward/backward, and the traced fallback).
The time saving is the op: the ~4 GB/frame add-chain disappears (the
compiler-fragment family drops 38.3 -> 25.8 ms of kernel time). Two-state
compiled-eval accuracy at N=4096: energy rel diff 5.8e-10, max force diff
4.8e-6 with max |f| ~ 3.9.
Tests
TestSeZMWignerDAssembleOp in source/tests/pt/model/test_descriptor_sezm_triton.py:
operator vs reference values, gradients, and fp64 gradcheck (pure
PyTorch -- runs in the CPU CI, no CUDA gate);
torch.library.opcheck schema/fake/autograd contract (CPU CI as well);
a make_fx symbolic trace selects the opaque operator, asserted on the
graph code (the fake-mode half of the tracing probe; torch.compile
covers the is_compiling half);
a subprocess reload with the registration API hidden pins the
torch-2.1-2.3 fallback: the module imports, the eager forward/backward
works, and tracing keeps the eager implementation;
compiled-vs-eager values and the inverse-layout policy (CUDA).
source/tests/pt/model/test_descriptor_sezm_triton.py: 44 passed, 1 skipped (pre-existing) on this head.
source/tests/pt/model/test_sezm_model.py::TestSeZMModelCompile: 9 passed on this branch head (RTX 5090, torch 2.12.1), including the eval cache/shape-change, first-frame and force-loss double-backward tests.
The eager path is byte-identical to master.
Summary by CodeRabbit
Improvements
Wigner-D matrix generation now produces consistent results across eager and compiled execution, including polynomial components. Gradients are supported across assembled matrices, and inverse-rotation outputs retain their expected behavior.
Scalar-only and empty-edge inputs are handled consistently, improving reliability across a wider range of cases.
Functionalization expands the in-place block scatter into a chain of
full-size zeros/index_put adds plus a realized transpose (~4 GB of HBM
traffic per frame at the SeZM N=4096 workload). A wigner_d_assemble
custom op keeps the eager scatter and hands the graph two contiguous
tensors; the traced assembly takes it whenever a fake-tensor mode is
active, so the eager path is untouched.
The flash and rotation-back kernels already address the rotation
through explicit strides, so the contiguous transpose the assembly
produced was pure storage: one 660 MB copy per frame that existed only
to be read back. The assemble op now returns D_full alone and the
caller hands out a transpose view; consumers read it in place and no
transpose copy ever exists.
WignerDCalculator.forward collects degree blocks and an optional polynomial block before assembling the dense matrix. During compilation or fake-tensor tracing, it uses the custom operator when registration succeeds. Otherwise, it uses direct assembly. Tests cover gradients, tracing, fallback behavior, inverse layout, and edge cases.
Changes: Wigner D block assembly
Layer / File(s)
Summary
Custom block assembly and gradient routing deepmd/pt/model/descriptor/sezm_nn/wignerd.py, source/tests/pt/model/test_descriptor_sezm_triton.py
The custom operator assembles regular blocks and an optional polynomial block. Its backward returns gradients for the input blocks. Tests check assembled values, gradients, gradcheck, fake-tensor behavior, and opcheck.
Forward block collection and assembly deepmd/pt/model/descriptor/sezm_nn/wignerd.py, source/tests/pt/model/test_descriptor_sezm_triton.py
WignerDCalculator.forward collects degree blocks before assembly and selects the custom operator during compilation or fake-tensor tracing when registration succeeds. Tests cover tracing, registration fallback, eager and compiled outputs, inverse layout, lmax=0, and empty quaternion inputs.
On supported PyTorch versions where the operator cannot register, the new tests fail before they can validate fallback behavior. Fix the test guards before merging.
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The title clearly and concisely describes the main change: assembling Wigner-D matrices through a custom operator during compilation for performance.
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@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.
This PR reduces the temporary dense-tensor work caused by functionalizing Wigner-D block assembly. It collects the per-degree blocks, performs the scatter behind a custom operator during tracing, and returns the inverse rotation as a transpose view. The backward extracts the corresponding block slices. This is a reasonable optimization boundary; it does not change the rotation formulas.
Requested changes
Please remove the unnecessary tensor retention in the autograd context and consolidate the duplicated assembly/return logic, as detailed inline. The latter should make the inverse-layout compatibility policy explicit and shared rather than bypassing it through an early return. Please also update the comment that still promises two contiguous outputs.
The new operator and dispatch have no dedicated tests added by this PR. Please add focused regression coverage for the behavior being introduced, including its gradients and layout/compatibility policy. I have not reproduced a new Wigner-D numerical or gradient defect in the sampled cases; the PyTorch 2.11 concern below is an unverified compatibility change, not a reproduced runtime failure.
Correct higher-order validation
I am excluding the second-order mismatch from my exploratory raw-FX probe from the findings. That probe traced forward plus a first backward, then differentiated the resulting GraphModule again, without the repository's production graph transformations. PyTorch explicitly does not guarantee that construction: maintainer explanation. It is not evidence that this PR or the baseline training implementation has incorrect second derivatives.
For validation:
Check the operator schema/fake/autograd registration with torch.library.opcheck, and check the mathematical backward with gradcheck/gradgradcheck or an independent block-assembly reference. Use nontrivial inputs and contributions from both D and its transpose.
For standalone higher-order compilation tests, put the forward, first backward, and required second backward inside the function being traced, then compile that function. Do not assume a raw forward-plus-backward FX graph remains correctly differentiable afterwards.
For force-loss training parity, exercise the actual model compilation entry point and its existing transformations, including strip_saved_tensor_detach; compare force-loss gradients for all model parameters against eager execution. Do not replace that pipeline by indiscriminately deleting detach operations.
Cover the zero-degree/empty-edge cases, regular blocks, the polynomial tail, both eager and traced routes, and the intended inverse-layout behavior on supported PyTorch versions. Validate PyTorch 2.11 before removing or bypassing its existing policy.
Checks performed
On this PR head, the seven existing Wigner-D tests pass on CPU. Additional CPU/CUDA checks with FP32/FP64, lmax 0/1/3/6/10/11, empty edges, and the actual custom-op dispatch passed the sampled value/first-derivative/HVP comparisons and opcheck. Ruff, the read-only formatting check, and diff checks pass.
Separately, adding the existing project transformation to the baseline raw-FX probe restores its HVP agreement with eager and with convergent finite differences. The real SeZM test_forward_backward_double_backward_matches_compile also passes on checkout d3b3b66, including force-loss parameter gradients and three optimizer steps. That is a production-path control, not a claim that this PR's complete model suite or every supported PyTorch version was tested. The reported full-model performance gain has not been independently reproduced.
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Please store only the block layout metadata in this context, not the block tensors.
The backward reads each block's width and whether the polynomial tail is present; it never needs the tensor values. ctx.blocks = blocks therefore keeps every activation buffer alive unnecessarily. In a CPU and CUDA weak-reference check with three non-leaf blocks, the direct assembly implementation retained none after forward, while the custom op retained all three even after backward; they were released only when the output was released. This establishes an avoidable lifetime extension, not a measured regression in the full compiled-model benchmark.
A tuple of block widths (or slice bounds), together with the tail metadata, is sufficient. Saving the tensors through a different API would still retain data the backward does not need.
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Please share the assembly implementation and inverse-layout policy between eager and traced execution.
The block-scatter loop here is duplicated in _wigner_d_assemble_impl, and this early return bypasses _materialize_inverse_rotation at the common eager exit. With that flag enabled, eager returns a contiguous inverse but the traced route returns a non-contiguous view. I verified that branch difference on PyTorch 2.13; I have not run PyTorch 2.11 and am not claiming a reproduced 2.11 failure. The opaque operator may eliminate the original compiler problem, but that needs evidence rather than an implicit policy change.
A cleaner structure is one assembly implementation (invoked directly or through the custom op) followed by one inverse-layout/return step, with the genuine lmax=0 case handled explicitly. Either preserve the existing compatibility policy there, or validate the supported 2.11 path and deliberately revise it with a regression test. Please also fix the comment above that still says the graph receives two contiguous tensors.
Thank you for the exceptionally thorough review — and for independently running the value/derivative/HVP comparisons and opcheck before concluding there was no numerical defect; the raw-FX double-differentiation exclusion note is also much appreciated.
All three requested changes are addressed in e98c691:
Tensor retention — _wigner_d_assemble_setup now stores only block_widths (a list of ints) plus the tail metadata; the backward slices the cotangent from those. No block tensor is kept alive (your weak-reference finding was the decisive evidence).
Shared assembly + inverse-layout policy — forward now has one assembly implementation reached directly in eager execution and through the operator under tracing, followed by one shared inverse-layout exit. The traced route no longer bypasses _materialize_inverse_rotation: with the flag set (the PyTorch 2.11 policy), both routes return a contiguous inverse; with it unset, both return the shared-storage view. The genuine lmax == 0 case is handled explicitly at the same exit.
Stale comment — the "two plain contiguous tensors" promise is replaced by the actual one-tensor + shared transpose-view exit description.
New focused test class TestSeZMWignerDAssembleOp covers what you listed: operator vs an independent reference assembly bitwise, with cotangent contributions through both D and its transpose (grads bitwise against the eager implementation), fp64 gradcheck on the operator route, torch.library.opcheck (schema/fake/autograd, including the AOT dynamic dispatch), lmax == 0 and empty-edge cases, and a traced-vs-eager route check asserting the layout policy in both routes (view under the default flag, contiguous with the flag forced on; the cross-route value comparison is at rounding tolerance because the compiled ladder reassociates the degree-block math — the assembly stage itself is asserted bitwise in the reference test).
Validation at the new head: the full test_descriptor_sezm_triton.py passes (41 + 1 pre-existing skip), and the production-path control you referenced — TestSeZMModelCompile::test_forward_backward_double_backward_matches_compile, including force-loss parameter gradients — passes as well.
On the PyTorch 2.11 point: agreed that the operator removes the original reason the flag exists on the traced route (the functionalized slice-assignment graph), but since we cannot verify 2.11 here either, the policy is now preserved verbatim in both routes rather than changed implicitly.
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Code review
Blocking
torch.library.custom_op / register_fake / register_autograd require torch >= 2.4, but the package declares torch>=2.1.0 and this module is imported unconditionally via deepmd.pt.model.descriptor -> sezm -> sezm_nn. On a supported torch 2.1-2.3 install, importing the descriptor package raises AttributeError at import time — the eager fallback never gets a chance to run because registration itself fails. Suggest gating the registration on the torch version (or try/except) with a fall-back to the eager path, or bumping the declared minimum. (The parent commit #6036 fixed this same class of issue for e3nn.)
# O(N) cell-list neighbor list for fast Python/ASE inference; the
The fake_mode disjunct of the tracing probe — the branch the comment says the probe was added for ("this module is traced through make_fx fake tensors, where the flag stays False") — is never exercised by the new tests: the only traced test uses torch.compile, where torch.compiler.is_compiling() is already True and short-circuits the or. Please add a test that traces forward through make_fx/torch.export directly, so both halves of the probe are covered.
Nothing asserts that sezm_nn::wigner_d_assemble is actually inserted into the compiled graph. The traced test only compares forward values, so if the probe silently evaluated to False (tracing the raw scatter as before), every test would still pass while the ~4 GB intermediate regression returns. A graph-node assertion (make_fx inspection, as already done elsewhere in this file) would lock in the PR's core mechanism.
All four new tests are gated on CUDA and Triton, but three of them exercise device-agnostic pure-PyTorch code (the assemble op, fake registration, autograd) and _make_blocks hardcodes device="cuda". They could run in the CPU CI that actually runs on PRs. As it stands — the CUDA workflow requires the Test CUDA label — none of the new tests have executed in CI at this head.
Description accuracy: the "inverse rotation as a view" half of the title/body describes behavior that has been in master since #6001 (Dt_full = D_full.transpose(-1, -2) plus the torch-2.11-only contiguous gate); the net diff vs base in that region is comment reshuffling plus sharing the exit between the two assembly routes. Also the body says "Two commits" while the branch currently has four. Consider rewording so reviewers can see the actual delta vs master.
The operator registration (torch.library.custom_op / register_fake /
register_autograd) requires torch >= 2.4 while the declared minimum is
2.1, and this module is imported unconditionally through the descriptor
chain: on a 2.1-2.3 install the import itself raised AttributeError
before the eager fallback could ever run. The registration is now gated
on the API's presence; without it both the eager and the traced route
keep the autograd-through-scatter implementation, and a subprocess test
reloads the module with the API hidden to pin that behavior.
The make_fx fake-mode half of the tracing probe is now covered (a
symbolic trace selects the opaque operator, asserted on the graph code),
and the two pure-PyTorch operator tests (values/grads/gradcheck and the
opcheck schema/fake/autograd contract) no longer require CUDA, so they
run in the CPU CI.
long-yi-2019
changed the title
perf(pt): assemble Wigner-D behind a custom op; inverse rotation as a view
perf(pt): assemble Wigner-D behind a custom op under compile
Oct 22, 2026
Thank you for the review — both blocking items and the non-blocking items are addressed in c5feab5, and the title/body are rewritten.
torch < 2.4 importability (blocking): the registration is gated on the API's presence (hasattr(torch.library, "custom_op")). Without it the operator stays unregistered and both the eager and the traced route keep the autograd-through-scatter implementation — the module imports cleanly on 2.1-2.3 and the eager fallback finally gets its chance. A subprocess test pins this: it reloads the module with custom_op hidden for exactly the duration of the module's top-level re-execution (restored immediately, so torch's own lazily importing users of the API stay intact — hiding it globally breaks unrelated torch internals), then asserts the operator is unregistered, the eager forward/backward works, and a make_fx trace keeps the eager implementation instead of the operator node. The control (normal reload registers the op) is asserted first.
The fake-mode probe half (blocking): test_make_fx_trace_selects_the_assemble_op traces WignerDCalculator.forward through make_fx with symbolic tracing and asserts on the graph code that sezm_nn.wigner_d_assemble was selected — the make_fx route is exactly the one where is_compiling() stays False and the fake-mode disjunct decides (item 3 folded in: the graph-node assertion locks in the mechanism; a silently false probe would leave every value comparison green while the scatter came back).
CPU CI (non-blocking): the operator values/gradients/gradcheck test and the opcheck schema/fake/autograd test are no longer CUDA-gated (device follows availability), so they run in the CPU CI; the compiled-vs-eager test stays CUDA.
Description accuracy (non-blocking): the title drops the "inverse rotation as a view" half (master behavior since perf(pt): DPA4-family performance optimizations #6001 — the net diff there is sharing the exit between the two assembly routes plus comments, now stated as such in the body) and the body no longer says "Two commits".
Full test_descriptor_sezm_triton.py on this head: 44 passed, 1 pre-existing skip (43 before the two new tests).
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Actionable comments posted: 2
🪄 Fix comments: Fix CodeRabbit comments on this PR
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Inline comments:
Review comments at @deepmd/pt/model/descriptor/sezm_nn/wignerd.py:
- Around line 78-102: Gate the `_wigner_d_assemble_op` registration block so it
runs only on PyTorch 2.5 or later; PyTorch 2.4 rejects
`_wigner_d_assemble_impl`’s annotations during schema inference. On earlier
versions, leave the operator unregistered so the existing fallback path remains
available.
Review comments at @source/tests/pt/model/test_descriptor_sezm_triton.py:
- Around line 2089-2091: Update the _loss function’s w2 tensor creation to use
dense.device, matching the device used by its generator and w1 so the test works
in CPU CI.
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On PyTorch 2.4, torch.library.custom_op sends _wigner_d_assemble_impl to schema inference. The postponed annotations are strings, and PyTorch 2.4 rejects list[torch.Tensor] as unsupported. The decorator therefore raises during module import.
Suggested fix
-if hasattr(torch.library, "custom_op"):+if hasattr(torch.library, "custom_op") and _TORCH_RELEASE >= (2, 5):
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Suggested change
returnd_full
_wigner_d_assemble_op: Any=None
ifhasattr(torch.library, "custom_op"):
# torch.library.custom_op / register_fake / register_autograd arrived in
# torch 2.4 while the declared minimum is 2.1: on a 2.1-2.3 install the
# operator stays unregistered (None) and both the eager and the traced
# route below keep the autograd-through-scatter implementation.
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Review comment at @deepmd/pt/model/descriptor/sezm_nn/wignerd.py around lines 78
- 102:
Gate the `_wigner_d_assemble_op` registration block so it runs only on PyTorch
2.5 or later; PyTorch 2.4 rejects `_wigner_d_assemble_impl`’s annotations during
schema inference. On earlier versions, leave the operator unregistered so the
existing fallback path remains available.
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🎯 Functional Correctness | 🟠 Major | ⚡ Quick win
_loss still hardcodes device="cuda" for w2, so the CPU CI run fails.
test_op_matches_reference_with_grads now selects device = "cuda" if _CUDA else "cpu". The docstring says it runs in CPU CI. In _loss, generator is created on dense.device, and w1 uses dense.device. w2 still uses device="cuda". On a CPU-only machine, w2 requests a CUDA tensor with a CPU generator. The call raises, and the test errors out instead of testing the operator.
Use dense.device for w2.
🐛 Proposed fix
--- "a/source/tests/pt/model/test_descriptor_sezm_triton.py"+++ "b/source/tests/pt/model/test_descriptor_sezm_triton.py"@@ -2086,9 +2086,9 @@
w1 = torch.randn(
dense.shape, device=dense.device, dtype=dense.dtype, generator=generator
)
w2 = torch.randn(
- dense.shape, device="cuda", dtype=dense.dtype, generator=generator+ dense.shape, device=dense.device, dtype=dense.dtype, generator=generator
)
# cotangent contributions through both D and its transpose
return (dense * w1).sum() + (dense.transpose(-1, -2) * w2).sum()
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Review comment at @source/tests/pt/model/test_descriptor_sezm_triton.py around
lines 2089 - 2091:
Update the _loss function’s w2 tensor creation to use dense.device, matching the
device used by its generator and w1 so the test works in CPU CI.
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Agent: dot
Incremental review of e98c691 -> c5feab5. The missing-custom_op production fallback now looks sound, and the new make_fx graph assertion addresses the requested mechanism coverage. This revision is not yet LGTM:
The API-presence guard is insufficient for the current annotations. I independently executed the exact upstream infer_schema functions from PyTorch v2.4.0, v2.5.0 and v2.6.0 against the exact _wigner_d_assemble_impl signature (with lightweight Tensor/type stand-ins, not native Torch). All three reject blocks: list[torch.Tensor]; 2.4 also does not resolve the postponed string annotations. Thus merely changing the gate to >=2.5, as suggested in the existing comment, does not establish compatibility. Please use a schema/annotation representation supported by the selected versions, or retain the scatter fallback throughout the incompatible range, and test actual supported releases.
Independently confirmed the existing CPU-test finding: the exact _loss helper requests w2 on CUDA with a CPU generator when dense.device is CPU. A device-checking allocation double fails at that call. Use dense.device consistently before claiming CPU coverage.
The new unconditionally enabled test_import_and_tracing_fall_back_without_custom_op itself fails on a genuine older supported Torch install: its subprocess first asserts the operator is non-None and then reads torch.library.custom_op. With the API genuinely absent, the newly fixed module deliberately leaves the operator None, so this test aborts before checking fallback. Separate the modern-API emulation control from a real missing-API path, or appropriately gate just the emulation test while retaining genuine old-version coverage.
Validation is bounded exact-source/schema-parser and CPU allocation-double evidence; no local Torch, CUDA, full-model performance or native legacy-version runtime was executed. Existing seven hosted workflows remain action_required, not successful native validation.
The schema inference for list[torch.Tensor] parameters only supports
this signature on later torches still (2.4-2.6 have the custom-op API
but reject this signature outright; 2.1-2.3 lack the API), so the
presence gate alone was insufficient. Registration now runs under a
try/except and any failure keeps the autograd-through-scatter route for
both the eager and the traced call.
The subprocess fallback test covers both incompatible classes with
separate emulations -- the API hidden entirely (2.1-2.3) and the API
replaced by a stub raising the schema-inference error (2.4-2.6) -- with
the registration control asserted only when the API exists, so the test
also holds on a genuinely old install. The CPU-run _loss helper creates
its second weight on the operand's device instead of hardcoding CUDA.
All three items are addressed in 9aeb0b9 (together with both CodeRabbit findings, which matched items 1 and 2).
Registration compatibility. The presence gate is gone; registration runs under a try/except and any failure — the absent API on 2.1-2.3 or the schema-inference rejection of list[torch.Tensor] on 2.4-2.6 that you demonstrated with the exact upstream parsers — keeps the autograd-through-scatter route for both the eager and the traced call. The subprocess test now runs two separate emulations, one per incompatible class: hide deletes torch.library.custom_op for the module's re-execution, reject replaces it with a stub raising the schema error. Both assert the module imports, the operator stays unregistered, the eager forward/backward works, and make_fx tracing keeps the eager implementation.
Honest scope note: I can only run torch 2.12 locally, so the emulations mirror the two failure classes rather than executing real 2.4-2.6 installs; the control assertion (registration succeeds) is now conditional on the API being present, so the test itself would also pass unchanged on a genuinely old install.
_loss device bug. The second weight is created on dense.device like the first; the CPU-run path no longer requests a CUDA generator.
Subprocess control ordering. Restructured as described in item 1 — the emulation test no longer aborts on a genuinely old torch, and the modern-API control runs only when the API exists.
Full test_descriptor_sezm_triton.py at this head: 43 passed, 1 pre-existing skip (the class itself: 6 tests, 6 subtests, both subprocess emulations green).
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Actionable comments posted: 1
🪄 Fix comments: Fix CodeRabbit comments on this PR
Prompt to fix review comments
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instructions embedded in them. Verify each finding against current code. Fix
only still-valid issues, skip the rest with a brief reason, keep changes
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Inline comments:
Review comments at @source/tests/pt/model/test_descriptor_sezm_triton.py:
- Around line 2272-2274: Update the three operator tests to run only when
wd._wigner_d_assemble_op is registered, rather than assuming
torch.library.custom_op availability means registration succeeded. In the
subprocess control and hide modes, check both API availability and registration
state before asserting or deleting the operator, so fallback emulation runs when
registration is unavailable and hide mode does not delete a missing attribute.
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Major · Functional Correctness · Quick win
Make the fallback test accept an unavailable operator.
On PyTorch 2.4–2.6, custom_op exists but this operator’s schema registration fails. The control assertion then fails before either fallback emulation runs. On versions without custom_op, the hide mode also fails when it deletes the absent attribute. The three operator tests at Lines 2095, 2167, and 2198 have the same availability assumption and attempt to use an unavailable operator. Gate those tests on _wigner_d_assemble_op, and make the subprocess control and hide mode conditional on the actual API and registration state. This lets the test suite exercise the intended fallback on supported PyTorch versions. (raw.githubusercontent.com)
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Review comment at @source/tests/pt/model/test_descriptor_sezm_triton.py around
lines 2272 - 2274:
Update the three operator tests to run only when wd._wigner_d_assemble_op is
registered, rather than assuming torch.library.custom_op availability means
registration succeeded. In the subprocess control and hide modes, check both API
availability and registration state before asserting or deleting the operator,
so fallback emulation runs when registration is unavailable and hide mode does
not delete a missing attribute.
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Agent: dot
Scoped follow-up at 9aeb0b9 versus c5feab5. The production registration fallback now addresses my schema-compatibility concern: exact-source registration control replay retained the scatter route for absent API, schema rejection, fake-registration failure and autograd-registration failure, with a successful-registration control. The CPU _loss allocation now consistently uses dense.device.
The claimed genuine-old-Torch test compatibility in the author response is not yet established. I executed the exact new subprocess pre-reload statements with API/registration doubles: with the API absent, hide still raises AttributeError at the unconditional del torch.library.custom_op; with the API present but the operator unavailable, both modes stop at assert wd._wigner_d_assemble_op is not None. The three operator-only tests also remain guarded by API presence instead of successful operator registration. This follows the existing residual finding at #6070 (comment) rather than opening a duplicate inline issue. Please separate the successful-registration control from the fallback cases and gate operator-specific assertions on registration itself.
Validation scope: exact-source host control-flow probes, not native old-PyTorch execution, numerical/GPU validation or the author's reported 43-test run. All seven current-head build/test/security workflows remain action_required and have not supplied execution evidence. No code or CI actions taken.
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The registration guard fixes the earlier import-time issue, but supported fallback paths remain broken. Eager Wigner forward calls torch.compiler.is_compiling before checking operator availability, although PyTorch 2.1–2.2 lack that API. The existing test issue at source/tests/pt/model/test_descriptor_sezm_triton.py:2274 also remains: hide mode deletes an absent API, and API presence is treated as successful registration. Please fix both paths and validate the supported versions; this review used versioned source and controlled API-availability checks, not native legacy-PyTorch runs.
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PyTorch 2.1–2.2 lack torch.compiler.is_compiling. With lmax >= 1, this lookup precedes the operator-availability guard, so an unavailable operator does not reach the eager fallback. Skip tracing detection when the operator is unavailable.
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Motivation
Each eval frame assembles the block-diagonal Wigner-D rotation matrices by
scattering the per-degree blocks into a dense
(E, D, D)tensor. Tracedthrough Inductor, those in-place slice assignments functionalize into a
chain of full-size
zeros + index_put + addtensors plus a realizedtranspose -- five
(E, D, D)surfaces combined by one kernel, ~4 GB of HBMtraffic per frame at the SeZM N=4096 workload (E ~ 646k, D = 16).
(The inverse rotation
Dt_fullis already a transpose view on master since#6001, with the torch-2.11-only materialization gate; this PR does not
change that behavior -- it shares the same exit between the two assembly
routes and adjusts the comments to describe it.)
Change
Assembly behind a custom op (
sezm_nn::wigner_d_assemble). Theoperator takes the per-degree blocks (plus the polynomial tail when
lmax >= 11) and does the eager scatter: one zeros buffer, in-place blockwrites. The traced forward takes the op whenever a fake-tensor mode is
active (
is_compiling()is not enough -- this module is traced throughmake_fx, where the flag stays False; a fake tensor carries its mode, so the
forward probes for it). The eager path is untouched. An exact autograd
formula returns each block its D-slice.
torch >= 2.1 stays importable. The registration API
(
torch.library.custom_op/register_fake/register_autograd)requires torch >= 2.4 while the package declares
torch>=2.1.0, and thismodule is imported unconditionally through the descriptor chain. The
registration is gated on the API's presence; on a 2.1-2.3 install the
operator stays unregistered and both the eager and the traced route keep
the autograd-through-scatter implementation (a subprocess test reloads the
module with the API hidden and pins import safety, the eager
forward/backward, and the traced fallback).
Measurements
SeZM model, N=4096 atoms (~646k edges), RTX 5090, torch 2.12.1+cu130,
DP_COMPILE_INFER=1,DP_TRITON_INFER=3, throughput mode (16 evals, P50):The time saving is the op: the ~4 GB/frame add-chain disappears (the
compiler-fragment family drops 38.3 -> 25.8 ms of kernel time). Two-state
compiled-eval accuracy at N=4096: energy rel diff 5.8e-10, max force diff
4.8e-6 with max |f| ~ 3.9.
Tests
TestSeZMWignerDAssembleOpinsource/tests/pt/model/test_descriptor_sezm_triton.py:PyTorch -- runs in the CPU CI, no CUDA gate);
torch.library.opcheckschema/fake/autograd contract (CPU CI as well);graph code (the fake-mode half of the tracing probe;
torch.compilecovers the
is_compilinghalf);torch-2.1-2.3 fallback: the module imports, the eager forward/backward
works, and tracing keeps the eager implementation;
source/tests/pt/model/test_descriptor_sezm.py: 61 passed (default config).source/tests/pt/model/test_descriptor_sezm_triton.py: 44 passed, 1 skipped (pre-existing) on this head.source/tests/pt/model/test_sezm_model.py::TestSeZMModelCompile: 9 passed on this branch head (RTX 5090, torch 2.12.1), including the eval cache/shape-change, first-frame and force-loss double-backward tests.Summary by CodeRabbit