perf(pt): fused Triton zonal scatter for the GIE head (DP_GIE_SCATTER_INFER)

[long-yi-2019]

What

One Triton operator replaces the reference composition of the geometric initial embedding's message path ("broadcast the per-edge zonal coupling over each degree's radial rows, then index_add-scatter it onto the destination nodes"):

• Forward: a node-parallel kernel walks the stable destination CSR and accumulates the message in registers, writing the already-padded (N, D, C) node layout with a zero scalar row. The per-edge message surface — (E, D-1, C), 1.2 GB at the production shape — is never materialized, and it also stops being held until the backward has re-read it, which is where most of the peak-memory win comes from.
• Backward: one edge-parallel kernel emits both operand gradients ((E, D-1) coupling and (E, lmax, C) radial) in a single pass; no gather of a materialized message gradient.
• The degree normalization stays in the traced graph so its own cotangent (it depends on the cutoff envelope, i.e. on geometry) keeps flowing through the compiler unchanged.
• The reduction follows the stable-sort CSR order, which matches the eager index_add_ addition order, so outputs sit at the usual scatter-order noise floor rather than drifting.

Boundary and layout contract

Same operator boundary as the existing CUDA zonal_scatter (DP_CUDA_INFER >= 1), serving the Triton inference stack instead. Inputs are flattened to contiguous in the traced graph before entering the op (the contract every other sezm_triton operator already uses). The kernel specializes on the packed contiguous row layout of the lmax = 3 ladder (row groups 3/5/7); any other configuration, dtype or training keeps the reference composition, so the flag is strictly opt-in and the reference path is untouched.

The flag is DP_GIE_SCATTER_INFER, default off, read at module construction.

Numbers

DPA4-Neo checkpoint, N=4096 diamond frame, RTX 5090, torch.compile inference, same tree two-state (flag off vs on):

	flag off	flag on
per-eval P50	153.06 ms	151.32 ms
peak VRAM (alloc)	23935 MB	22693 MB
reserved	26512 MB	24205 MB

The -1.2 GB peak drop is the removed (E, 15, 32) message surface. At the kernel level the message build+scatter composites and the backward composite that re-read the saved message (together ~3.4 ms at this edge count) are replaced by a 0.2 ms forward and a 0.5 ms backward kernel.

Correctness against the flag-off state: energy rel 7.7e-10, forces rel 1.1e-6 (the established scatter-order noise floor for this benchmark).

Tests

Operator vs reference composition (forward plus shared-cotangent gradients, contiguous and sliced-view radial inputs), CPU reference vs hand-written sequential accumulation, compiled-vs-eager (forward and gradients — this guards the custom-op launch-grid trap), and a module-level two-state check of GeometricInitialEmbedding with gradients.

Merge-order note

Touches embedding.py and kernels/utils.py, which #6071 also touches (banded Wigner storage); the hunks are disjoint and both directions rebase trivially.

Summary by CodeRabbit

• Performance
  • Added an optional GPU-accelerated path for geometric embedding calculations during inference in supported configurations. When acceleration is unavailable or the configuration is unsupported, the existing processing path remains in use.
  • The accelerated path applies only to eligible float32 inference configurations; training behavior is unchanged.

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Reviewing files that changed from the base of the PR and between 4f42e82 and e26dbcc.

📒 Files selected for processing: 2

• deepmd/pt/model/descriptor/sezm_nn/embedding.py
• deepmd/pt_expt/kernels/triton/sezm/gie_scatter.py

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📝 Walkthrough

Adds eager and Triton implementations of zonal scatter with autograd support. The embedding uses the fused operation when its selector, dtype, layout, and forward conditions are met. Tests compare outputs and gradients with reference implementations.

Changes: GIE zonal scatter

Layer / File(s)	Summary
Zonal scatter forward deepmd/pt_expt/kernels/triton/sezm/gie_scatter.py, source/tests/pt/model/test_descriptor_sezm_triton.py	Adds slot layout handling, an eager fallback, and Triton forward dispatch for eligible inputs. Tests compare results with reference accumulation.
Zonal scatter gradients deepmd/pt_expt/kernels/triton/sezm/gie_scatter.py, source/tests/pt/model/test_descriptor_sezm_triton.py	Adds Triton and eager backward implementations, autograd registration, and the public gie_zonal_scatter operation. Tests compare gradients and eager with compiled execution.
Embedding inference path deepmd/pt_expt/kernels/utils.py, deepmd/pt/model/descriptor/sezm_nn/embedding.py, source/tests/pt/model/test_descriptor_sezm_triton.py	Adds the environment selector and an embedding path that calls the fused operation when its runtime and layout conditions are satisfied. Tests compare fused dispatch with reference composition and check training behavior.

Priority: ➖ Normal

Estimated code review effort: 4 (Complex) | ~60 minutes

Change: Refactor

Sequence Diagram(s)

sequenceDiagram
  participant GeometricInitialEmbedding
  participant use_gie_scatter_infer
  participant gie_zonal_scatter
  participant TritonForwardKernel
  GeometricInitialEmbedding->>use_gie_scatter_infer: Read DP_GIE_SCATTER_INFER
  GeometricInitialEmbedding->>gie_zonal_scatter: Pass zonal features, radial features, and destination CSR data
  gie_zonal_scatter->>TritonForwardKernel: Dispatch eligible CUDA float32 lmax 3 inputs
  TritonForwardKernel-->>GeometricInitialEmbedding: Return scattered output
  GeometricInitialEmbedding->>GeometricInitialEmbedding: Apply inverse-square-root degree normalization

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Merge Risk: 🟡 Moderate · up to e26db

With Triton enabled, gradients for supported non-power-of-two channel counts can read beyond the radial tensor, potentially affecting force results or causing a CUDA error. This material risk should be fixed before merge.

Pre-merge checks: 4 · 1

Failed checks: 1 warning

Check name	Status	Explanation	Resolution
Docstring Coverage		Docstring coverage is 48.00% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 25 functions across 4 files.	Write docstrings for the functions missing them to satisfy the coverage threshold.

Passed checks: 4 passed

Check name	Status	Explanation
Description Check		Check skipped - CodeRabbit’s high-level summary is enabled.
Title check		The title clearly and concisely identifies the main change: an opt-in fused Triton zonal scatter path for the GIE head controlled by DP_GIE_SCATTER_INFER.
Linked Issues check		Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check		Check skipped because no linked issues were found for this pull request.

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Actionable comments posted: 2

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• 🪄 Fix CodeRabbit comments on this PR

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Inline comments:
Review comments at @deepmd/pt_expt/kernels/triton/sezm/gie_scatter.py:
- Line 107: Update _forward_impl and _backward_impl to route non-power-of-two
channel dimensions to the reference implementation before Triton dispatch, and
apply the same guard when assigning _triton_scatter in
GeometricInitialEmbedding; ensure the public gie_zonal_scatter entrypoint is
protected independently of the embedding guard.

Review comments at @deepmd/pt/model/descriptor/sezm_nn/embedding.py:
- Around line 329-334: Update the fused Triton dispatch condition in
embedding.py, lines 329–334, to require evaluation mode and the supported
CUDA/force-fused-scatter target; keep training on the existing non-fused path.
The missing autograd formula at gie_scatter.py, lines 412–414, requires no
direct change for this fix.

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📥 Commits

Reviewing files that changed from the base of the PR and between 64d3266 and 288e49c.

📒 Files selected for processing (4)

• deepmd/pt/model/descriptor/sezm_nn/embedding.py
• deepmd/pt_expt/kernels/triton/sezm/gie_scatter.py
• deepmd/pt_expt/kernels/utils.py
• source/tests/pt/model/test_descriptor_sezm_triton.py

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[long-yi-2019]

@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.

[iProzd]

I reviewed the exact head. The existing findings at gie_scatter.py:107 and embedding.py:329 remain correctness blockers: arbitrary channel counts can fail Triton compilation, and the fused backward can be selected during training without the required higher-order autograd support. Please address those threads, then rerun the failing pre-commit and documentation checks.

[iProzd]

I reviewed the exact head. The existing findings at gie_scatter.py:107 and embedding.py:329 remain correctness blockers: arbitrary channel counts can fail Triton compilation, and the fused backward can be selected during training without the required higher-order autograd support. Please address those threads, then rerun the failing pre-commit and documentation checks.

[njzjz-bot]

Reviewed only 288e49c → 4f42e82 and the existing review threads. The training gate is now present. The padded channel extent fixes the earlier Triton range constraint, but introduces the out-of-bounds backward reads described inline; the prior resolved channel thread did not identify this omission in the new fix.

Validation: exact-source inspection plus arithmetic bounds and NumPy reduction checks, not CUDA/Triton execution. For the added test shape (777, 3, 40), the final radial load accesses 24 floats beyond the contiguous allocation. Current Actions still require approval, so they provide no execution evidence for this head.

Agent: dot

[njzjz-bot]

[P2] Mask the padded channels in backward's radial loads

Expanding offs_c to BC makes channels C..BC-1 real load lanes, but the three rd0/rd1/rd2 loads at lines 245–252 still use only mask_e[:, None]. For the newly added case C=40, BC=64 with contiguous radial.shape == (777, 3, 40), the last edge's slot-2 load reads float offsets 93,240–93,263 from an allocation containing only 93,240 floats. The forward loads and gradient stores are masked, but those masks do not protect these backward reads.

The padded values also enter tl.sum(g*rd, 2) at lines 254–256; zeroing padded g cannot prevent NaN/Inf data from contaminating valid zonal gradients. Please apply mask_e[:, None] & cmask[None, :] with other=0.0 to all three radial loads, and validate the non-power-of-two backward path with bounds/sanitizer coverage. This is an address-arithmetic/source finding, not a claimed CUDA crash reproduction.

Agent: dot

[long-yi-2019]

Thank you for the review — both findings are real and addressed in 4f42e82:

• Arbitrary channel counts (gie_scatter.py): channels now ride a padded power-of-two block (BC = next_power_of_2(C)) with an explicit cmask = offs_c < C on every channel-typed load, store and accumulator in both the forward and the backward kernel, so any channel count compiles and matches the reference. A (777, 33, channels=40) case is added to the operator test (forward + shared-cotangent gradients, contiguous and sliced-view radial).
• Training / higher-order safety (embedding.py): the fused dispatch now additionally requires not self.training — training keeps the reference composition, whose autograd is exact to all orders, while the fused backward registers no next-derivative formula (force-loss training differentiates the backward itself). A training-mode subtest asserts the module takes the reference path (outputs and both gradients equal) even with the operator bound.

Validation at the new head: the full test_descriptor_sezm_triton.py passes (41 + 1 pre-existing skip). On the checks: pre-commit is green on the branch; the RTD failure is repo-wide (same red check on other open PRs, e.g. #6058) and this diff touches no docs inputs.