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[model, ops, perf] feat: add npu cross entropy fast path to accelerate chunk logp computation in RL scenarios - #841
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This pull request modernizes type hints across several files by replacing Optional[T] with T | None and removing unused imports. It also introduces NPU fused cross-entropy support in veomni/ops/kernels/cross_entropy/chunk_logprobs.py using torch_npu.npu_cross_entropy_loss when running on NPU devices, along with a manual test script to verify its correctness and performance. The reviewer noted potential issues in the NPU implementation, specifically a potential device mismatch if labels and logits are on different devices, and a fragile 4-tuple unpacking of the npu_cross_entropy_loss output, suggesting a more robust approach.
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| labels_1d = labels.reshape(-1).to(torch.int64) | ||
| loss, _, _, _ = _torch_npu.npu_cross_entropy_loss( | ||
| logits_2d, | ||
| labels_1d, | ||
| reduction="none", | ||
| ignore_index=ignore_index, | ||
| ) |
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There are two potential issues here that can lead to runtime failures:
- Device Mismatch: If
labelsis on the CPU whilelogitsis on the NPU,_torch_npu.npu_cross_entropy_losswill fail with a device mismatch error. We should explicitly movelabels_1dtologits.device. - API Compatibility: Hardcoding a 4-tuple unpack (
loss, _, _, _ = ...) is fragile because the number of returned elements fromnpu_cross_entropy_losscan vary across differenttorch_npu/ CANN versions, or it might return a single tensor in future releases. Safely checking if the output is a tuple and extracting the first element is much more robust.
| labels_1d = labels.reshape(-1).to(torch.int64) | |
| loss, _, _, _ = _torch_npu.npu_cross_entropy_loss( | |
| logits_2d, | |
| labels_1d, | |
| reduction="none", | |
| ignore_index=ignore_index, | |
| ) | |
| labels_1d = labels.reshape(-1).to(device=logits.device, dtype=torch.int64) | |
| outputs = _torch_npu.npu_cross_entropy_loss( | |
| logits_2d, | |
| labels_1d, | |
| reduction="none", | |
| ignore_index=ignore_index, | |
| ) | |
| loss = outputs[0] if isinstance(outputs, tuple) else outputs |
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@Luosuu @FoolPlayer Could you help review this PR, thanks. |
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Review summary
Baseline: main@a092340b2713 — ahead by 2, behind by 73. GitHub reports mergeable: true (mergeable_state: blocked), which is consistent with what I found: git apply --check fails on all four patchgen-pkg/ files, but only because main has since made the identical Optional[X] → X | None edit in those same files (ff89f190, #873), so a three-way merge absorbs them. The two files that carry the actual feature apply cleanly.
1. Does it solve a real problem? Yes in principle — _per_token_log_probs_from_logits is on the PPO/DPO logprob-recompute hot path and the fallback it replaces on NPU is log_softmax over [chunk, vocab] followed by a gather, so a fused kernel is a sensible target. But the stated mechanism does not hold up. Per the Ascend op-plugin documentation, npu_cross_entropy_loss returns four tensors and the second, log_prob, has shape [N, C] — the same [chunk, vocab] tensor the docstring claims to avoid. It is produced for the kernel's own backward and this code discards it as _, so the memory argument in the new docstring is incorrect; any win is fusion/launch-count, not allocation. Please re-measure and restate the claim (the manual script only times, it never reports memory).
2. Comments / docs / tests complete? No, and there is a correctness question I could not resolve from the code alone. The Ascend docs specify ignore_index as "必须小于C，当小于0时视为无忽略标签" — must be less than C, and when negative it is treated as "no ignore label". VeOmni passes ignore_index=-100 and passes labels that literally contain -100, so on this reading the ignored rows are handed to the kernel as out-of-range class indices rather than being masked. Both existing paths handle this explicitly and the contract is documented (chunk_logprobs_function docstring: "positions equal to ignore_index produce 0.0 in both outputs (no gradient flows through them either)"), so this needs to be verified on hardware and pinned by a test. The new file cannot do that: it contains no assert at all.
The repository CI angle matters here too — gpu_unit_tests.yml:171 runs pytest -s -x tests/ops as a whole directory, so tests/ops/test_npu_chunk_logprobs.py will be collected by GPU CI and contribute zero tests, because nothing in it is named test_*.
3. Clean, conventional, no conflict/duplication with main? Mixed. Positives worth stating: ruff check and ruff format --check both pass with the feature hunks applied (All checks passed! / 613 files already formatted), the branch is correctly placed before the _FA_CE_AVAILABLE check (flash_attn is not importable on NPU, so no path is shadowed), forward has already upcast logits to fp32 by the time this helper is called so the dtype is stable, and the docstring's note that the custom autograd Function still owns backward is accurate and important — the Ascend docs confirm only loss supports gradients.
Against that: the 20-line patchgen-pkg/ typing churn is unrelated to the PR's subject, already done on main, and incomplete as a cleanup anyway (patch_spec.py and codegen.py still have 21 and 9 Optional uses). The feature also bypasses the existing configuration seam — see the inline comment on the dispatch line. And the title [ops, perf]: Add npu cross entropy fast path… fails check_pr_title.yml:43, which requires a type keyword between the bracket and the colon: [ops, perf] feat: add npu cross entropy fast path….
Verdict: needs changes. The blocking items are (a) confirm and test the ignore_index=-100 behaviour, (b) drop the patchgen-pkg/ hunks, (c) turn the manual script into a real pytest case plus a scripts/ benchmark, and (d) route the fast path through an explicit implementation choice rather than a device sniff. Correct the memory claim and fix the title while you are there.
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| path: Path, | ||
| *, | ||
| extra_ignore: Optional[tuple[str, ...]] = None, | ||
| extra_ignore: tuple[str, ...] | None = None, |
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These four patchgen-pkg/ files (20 additions / 20 deletions) are a pure Optional[X] → X | None modernization with no connection to the NPU cross-entropy fast path, and main has already made the identical change in all four via ff89f190 (#873) — _normalize.py, check_patchgen.py, cli.py and run_codegen.py now contain zero Optional references. That is why git apply --check rejects every one of them while GitHub still reports the PR as mergeable.
As a cleanup it is also incomplete: patch_spec.py (21 uses) and codegen.py (9 uses) are untouched. Please drop these four files from this PR so the diff is just the feature; if the remaining two files are worth converting, a separate [misc] refactor: PR is the right home.
| ) -> torch.Tensor: | ||
| """Per-token log-probs through Ascend's fused cross-entropy kernel. | ||
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| ``npu_cross_entropy_loss`` fuses log_softmax and NLL loss on NPU, avoiding |
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This claim is not correct. The Ascend op-plugin documentation for npu_cross_entropy_loss lists four return values, and the second one, log_prob, is specified as "shape为[N, C]，与input一致" — it is the kernel's saved-for-backward output and is allocated unconditionally. Line 109 discards it as _, so the full [chunk, vocab] tensor this docstring says is avoided is still materialized, just inside the operator instead of in Python.
The fused path may still be faster (one kernel launch instead of log_softmax + gather + where), which is a fine justification on its own. Please restate the rationale in throughput terms and, if a memory improvement is being claimed, back it with a measurement — the manual script added in this PR reports only ms/iter.
| logits_2d, | ||
| labels_1d, | ||
| reduction="none", | ||
| ignore_index=ignore_index, |
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This is the blocking correctness question. The Ascend documentation specifies this parameter as "ignore_index（int）: … 数值必须小于C，当小于0时表示不指定忽略标签" — the value must be less than C, and a negative value means no ignore label is specified. VeOmni's IGNORE_INDEX is -100, and line 108 forwards labels unmodified, so rows whose label is -100 are passed to the kernel as class indices while the masking request is (on this reading) ignored.
Contrast the two paths this replaces, both of which are explicit about it:
mask = labels != ignore_index
safe_labels = labels.clamp(min=0).unsqueeze(-1) # never index with -100
log_probs = logits.log_softmax(dim=-1).gather(-1, safe_labels).squeeze(-1)
return torch.where(mask, log_probs, torch.zeros_like(log_probs))and the fa_ce path, whose behaviour the existing docstring pins down: "fa_ce sets per-token loss to 0 at IGN positions internally, the negation is then -0.0 which compares equal to 0.0". The public contract in chunk_logprobs_function promises the same: "positions equal to ignore_index produce 0.0 in both outputs (no gradient flows through them either)".
Please confirm on hardware what the kernel returns at ignored rows and add a test that asserts exact zeros there. If the kernel does not honour a negative ignore_index, the mask has to be applied here — clamp(min=0) before the call and torch.where after — which would also change the performance picture and is worth measuring.
One further documented constraint to guard or note: input N is restricted to (0, 200000]. chunk_size defaults to 1024 (cross_entropy/__init__.py:126,393) so the default is far inside the limit, but the parameter is caller-supplied, so a clear error beats an operator-level failure.
| reduction="none", | ||
| ignore_index=ignore_index, | ||
| ) | ||
| return -loss.view(*batch_shape) |
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logits.shape[:-1] is empty for a 1-D [V] input, and Tensor.view() with no arguments raises. Every current caller passes 2-D chunks so this is unreachable today, but loss.view(batch_shape) (passing the torch.Size as a single argument) handles the rank-1 case for free and reads the same.
Also worth a brief note: labels.reshape(-1).to(torch.int64) on line 108 is a silent no-op for the callers here (labels are already int64), but it is the only path that performs the cast — if it is defensive, say so, otherwise drop it so a caller passing an unexpected dtype fails loudly like it does on the other two paths.
| @@ -100,6 +131,9 @@ def _per_token_log_probs_from_logits( | |||
| positions internally, the negation is then ``-0.0`` which compares | |||
| equal to ``0.0``). | |||
| """ | |||
| if _NPU_CE_AVAILABLE and logits.device.type == "npu": | |||
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Two concerns with selecting the path by device type here rather than through the existing configuration seam.
No opt-out, and it contradicts a documented design decision. cross_entropy/__init__.py:496-503 states that cross_entropy_loss_implementation='npu' is a back-compat alias for chunk_loss — "The kernel is hardware-agnostic (pure F.linear + eager CE) and works on both CUDA and NPU. "npu" is kept as a back-compat alias for the same kernel — both names install the identical mapping." After this change both names still install the same mapping but the numbers differ by device, and no setting can restore the reference log_softmax + gather behaviour on NPU. That matters specifically for this module, whose docstring makes bitwise reproducibility a design goal ("what makes per-token log-probs bitwise identical to verl's FusedLinearForPPOFunction"): the first thing anyone bisecting an NPU-only logprob discrepancy will want is to turn the fast path off. Please wire it through an explicit implementation value or an OpsConfigSlot — note veomni/ops/config/singleton.py exists precisely so behaviour is selected without environment variables — and update that stale comment either way.
Wider blast radius than the PR describes. This helper is also called by chunk_topk_distill.py:157, so the top-k distillation kernel's forward changes on NPU too; the PR title and description only mention RL logprob recompute. And tests/ops/test_chunk_logprobs.py:145,395,514 plus tests/models/test_return_log_probs_e2e.py:110 call this helper directly to compute their expected values, so on an NPU host those tests would compare the new path against itself and could no longer detect a divergence from the reference formulation. Please mention both in the description, and consider having the tests pin the reference path explicitly.
| # See the License for the specific language governing permissions and | ||
| # limitations under the License. | ||
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| """Manual check for the NPU fused CE fast path. |
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This file is named test_*.py and lives in tests/ops/, but it is a benchmark script, not a test: there is no assert anywhere in it and no function named test_*. Two consequences:
gpu_unit_tests.yml:171runspytest -s -x tests/opsover the whole directory, so CI will import this module and collect zero tests from it. The PR checklist item "Added tests to CI workflow" is not actually satisfied, and theignore_indexquestion raised above stays unguarded.- A module-level
argparsedriver inside a pytest-collected package is fragile — anyone who later adds atest_*wrapper will findparse_args()consuming pytest's own argv.
Suggested split: a real tests/ops/test_npu_chunk_logprobs.py with pytestmark = pytest.mark.skipif(not IS_NPU_AVAILABLE, reason=...) that asserts (a) closeness to log_softmax + gather at a fixed seed and (b) exact zeros at labels == ignore_index — that second assertion is the one that would settle the ignore_index semantics — and move the timing driver to scripts/, where the argparse interface fits. tests/ops/ files are covered automatically by the whole-directory run, so a real test there needs no workflow change; the benchmark under scripts/ needs none either.
| import torch | ||
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| _REPO_ROOT = Path(__file__).resolve().parents[2] |
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sys.path.insert(0, _REPO_ROOT) is not needed for a file under tests/ — pytest already puts the rootdir on sys.path, and running it directly works with python -m. Prepending to sys.path[0] gives the repository root precedence over the standard library for the whole process, and it is the only reason the # noqa: E402 on the import below is required. Dropping both lets the import sit with the others at the top of the file.
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| def synchronize() -> None: | ||
| if device.type == "npu": | ||
| torch.npu.synchronize() |
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torch.npu.synchronize() hardcodes the backend. veomni/utils/device.py exposes synchronize() (along with get_device_type, get_torch_device, IS_NPU_AVAILABLE) specifically so call sites stay device-agnostic, and the surrounding code already branches on device.type. Using the helper also removes the need for the hasattr(torch, "npu") probe at line 67.
| if device.type == "npu": | ||
| new_out = new_npu_fused_ce(logits, labels) | ||
| else: | ||
| print("[WARN] CPU has no torch_npu fast path; comparing the fallback against itself.") |
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The CPU branch compares old_log_softmax_gather against itself, so the precision block below prints max_abs_diff=0 and the [SPEEDUP] line is skipped — a clean run on CPU therefore looks like a pass while having verified nothing. Combined with the absence of assertions, someone running this on a CPU box would reasonably conclude the fast path is validated.
If a CPU mode is useful at all, make it compare against a genuinely independent reference (e.g. F.cross_entropy(..., reduction="none"), which also exercises the same ignore_index contract on a backend where its semantics are documented and testable). Otherwise exit early with a clear message.
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Heads-up on ordering: #1142 ( What changes in #1142:
How to register and call an op: For this PR specifically:
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📝 Walkthrough
Merge Risk: 🟡 Moderate · up to
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| Check name | Status | Explanation |
|---|---|---|
| Description Check |  |
Check skipped - CodeRabbit’s high-level summary is enabled. |
| Title check | |
The title clearly identifies the NPU cross-entropy fast path and its purpose of accelerating chunk log-probability computation in RL scenarios. |
| Docstring Coverage | |
Docstring coverage is 90.00% which is sufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 10 functions across 2 files. |
| 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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Review comments at @veomni/models/loss_utils/chunk_logprobs.py:
- Around line 86-92: Register the per-token logits-to-log-probabilities NPU
kernel as a dedicated VeomniOp row using NpuKernelRequirement(), rather than
reusing cross_entropy_loss/standard/chunk_loss. Expose and invoke it through a
handle owned by the consuming model, and guard the torch_npu import with
IS_NPU_AVAILABLE or keep it within the NPU kernel module.
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| try: | ||
| import torch_npu as _torch_npu | ||
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| _NPU_CE_AVAILABLE = hasattr(_torch_npu, "npu_cross_entropy_loss") | ||
| except ImportError: | ||
| _torch_npu = None | ||
| _NPU_CE_AVAILABLE = False |
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📐 Maintainability & Code Quality | 🟠 Major | 🏗️ Heavy lift
🔎 Supported by static analysis
🏁 Script executed:
git rev-parse HEAD; git diff --name-status d3be8d76b33204323765facbcd5f54f92bfe8c7b 28d840ef029e39f3861303b4748a2ba535340784 -- veomni/models/loss_utils/chunk_logprobs.py
sed -n '75,145p' veomni/models/loss_utils/chunk_logprobs.py
rg -n 'NpuKernelRequirement|VeomniOp|register_op|is_torch_npu_available|IS_NPU_AVAILABLE' veomni/ops veomni/models/loss_utils/chunk_logprobs.py veomni/utils/import_utils.py .agents/knowledge/constraints.md | head -100Repository: ByteDance-Seed/VeOmni
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set -o pipefail
printf '%s\n' '--- constraints ---'
nl -ba .agents/knowledge/constraints.md | sed -n '215,250p'
printf '%s\n' '--- import/device guards ---'
nl -ba veomni/utils/import_utils.py | sed -n '100,135p'
nl -ba veomni/utils/device.py | sed -n '1,75p'
printf '%s\n' '--- registry guidance ---'
nl -ba veomni/ops/README.md | sed -n '110,215p'
printf '%s\n' '--- current loss registrations ---'
nl -ba veomni/ops/kernels/loss/__init__.py | sed -n '1,125p'
printf '%s\n' '--- registry requirement semantics ---'
nl -ba veomni/ops/platform/requirement.py | sed -n '1,105p'
nl -ba veomni/ops/registry.py | sed -n '300,455p'
printf '%s\n' '--- helper callers and imports ---'
rg -n -F --glob '*.py' -e '_per_token_log_probs_from_logits' -e 'loss_utils.chunk_logprobs' -e 'veomni.ops.kernels.loss' veomni tests || test "$?" -eq 1
printf '%s\n' '--- base-to-head diff for relevant file and registry tree ---'
git diff --stat d3be8d76b33204323765facbcd5f54f92bfe8c7b 28d840ef029e39f3861303b4748a2ba535340784 -- veomni/ops veomni/models/loss_utils/chunk_logprobs.pyRepository: ByteDance-Seed/VeOmni
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rg --files veomni/ops/kernels/loss/cross_entropy_loss
nl -ba veomni/ops/kernels/loss/cross_entropy_loss/standard/chunk_loss.py | sed -n '1,240p'
printf '%s\n' '--- registered loss callers and op handles ---'
rg -n -F --glob '*.py' -e 'VeomniOp(' -e 'resolve_op_impl' -e 'cross_entropy_loss' veomni/models veomni/ops | head -160
printf '%s\n' '--- registry provenance at base and head ---'
git diff --name-status d3be8d76b33204323765facbcd5f54f92bfe8c7b 28d840ef029e39f3861303b4748a2ba535340784 -- veomni/ops
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git ls-tree -r --name-only d3be8d76b33204323765facbcd5f54f92bfe8c7b -- veomni/ops | sed -n '1,80p'
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git log --oneline --all -- veomni/ops | sed -n '1,20p'
printf '%s\n' '--- exact helper state at base and head ---'
git show d3be8d76b33204323765facbcd5f54f92bfe8c7b:veomni/models/loss_utils/chunk_logprobs.py 2>/dev/null | nl -ba | sed -n '70,155p'
nl -ba veomni/models/loss_utils/chunk_logprobs.py | sed -n '80,145p'Repository: ByteDance-Seed/VeOmni
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Register the NPU log-probability kernel through VeomniOp.
The VeomniOp refactor is already present in the PR base, so this is not conditional on a future rebase. Register this per-token logits-to-log-probabilities contract as a separate row with NpuKernelRequirement(). The existing cross_entropy_loss/standard/chunk_loss row is not compatible because it requires hidden, labels, and weight, and returns a scalar loss.
Call the new row through a handle owned by the consuming model. Guard the vendor-only import with IS_NPU_AVAILABLE or keep it inside the NPU kernel module.
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Review comment at @veomni/models/loss_utils/chunk_logprobs.py around lines 86 -
92:
Register the per-token logits-to-log-probabilities NPU kernel as a dedicated
VeomniOp row using NpuKernelRequirement(), rather than reusing
cross_entropy_loss/standard/chunk_loss. Expose and invoke it through a handle
owned by the consuming model, and guard the torch_npu import with
IS_NPU_AVAILABLE or keep it within the NPU kernel module.
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review. Visit https://docs.coderabbit.ai/cli?utm_source=ghpr
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Review comments at @tests/models/base/test_npu_chunk_logprobs.py:
- Line 100: Update the NPU-specific tests at the referenced device
initializations to use torch.device("npu") instead of get_device_type(),
ensuring they cannot select CUDA when both devices are available. In the
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Review comments at @tests/models/base/test_npu_chunk_logprobs.py:
- Around line 45-46: Update the reference gather in the code around
`safe_labels` so ignored labels are replaced with an in-range index before
gathering, including when `ignore_index` exceeds the vocabulary. Preserve the
existing `torch.where` behavior that zeros ignored results.
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| safe_labels = labels.clamp(min=0).unsqueeze(-1) | ||
| log_probs = logits.log_softmax(dim=-1).gather(-1, safe_labels).squeeze(-1) |
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🎯 Functional Correctness | 🟡 Minor | ⚡ Quick win
Replace ignored labels before the reference gather.
If --ignore-index is positive and outside the vocabulary, clamp(min=0) leaves that value unchanged. For example, --ignore-index 151936 --ignore-ratio 1 makes gather fail before the benchmark can compare outputs. Replace ignored labels with an in-range index before gathering; the existing torch.where can still zero their results. PyTorch permits an ignore_index outside the class range. (docs.pytorch.org)
Proposed fix
--- "a/tests/models/base/test_npu_chunk_logprobs.py"
+++ "b/tests/models/base/test_npu_chunk_logprobs.py"
@@ -42,7 +42,7 @@
) -> torch.Tensor:
"""Reference decomposed ``clamp + log_softmax + gather + where`` implementation."""
mask = labels != ignore_index
- safe_labels = labels.clamp(min=0).unsqueeze(-1)
+ safe_labels = labels.masked_fill(~mask, 0).unsqueeze(-1)
log_probs = logits.log_softmax(dim=-1).gather(-1, safe_labels).squeeze(-1)
return torch.where(mask, log_probs, torch.zeros_like(log_probs))
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| safe_labels = labels.clamp(min=0).unsqueeze(-1) | |
| log_probs = logits.log_softmax(dim=-1).gather(-1, safe_labels).squeeze(-1) | |
| safe_labels = labels.masked_fill(~mask, 0).unsqueeze(-1) | |
| log_probs = logits.log_softmax(dim=-1).gather(-1, safe_labels).squeeze(-1) |
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Review comment at @tests/models/base/test_npu_chunk_logprobs.py around lines 45
- 46:
Update the reference gather in the code around `safe_labels` so ignored labels
are replaced with an in-range index before gathering, including when
`ignore_index` exceeds the vocabulary. Preserve the existing `torch.where`
behavior that zeros ignored results.
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4 participants
What does this PR do?
As title.
Checklist Before Starting
[{modules}] {type}: {description}format (enforced by check_pr_title.yml)agent,ci,ckpt,config,data,dist,docker,docs,logging,lora,misc,model,omni,optim,ops,parallel,perf,release,task,trainerfeat,fix,refactor,chore,test[BREAKING]— e.g.[BREAKING][parallel, model] feat: dynamic batchingTest
Run
python tests/ops/test_npu_chunk_logprobs.py --device npu --dtype float32Results in deferent chunksize. I manually adjusted the chunksize to investigate performance under various scenarios. The left graph corresponds to 3072, while the right graph uses the default value of 1024:
In the left figure, the red line represents the original method and the purple line stands for the method with NPU operators replaced;
In the right figure, the yellow line represents the original method and the red line stands for the method with NPU operators replaced.
API and Usage Example
It will automatically detect whether the device is NPU and then perform replacement.
Design & Code Changes
Checklist Before Submitting
tasks/training scripts were moved or renamed: updateddocs/examples and verifiedpython3 scripts/ci/check_doc_task_paths.pypasses (also enforced by the Check doc task paths CI workflow)Summary by CodeRabbit
New Features
Tests