[fusheng-ji]

Evidence location: the measurement outputs of this PR (JSON, figures, audit files) are not part of its diff. They are on the fork's pr_assets branch under pr495/ (links in this description are fixed to that commit). H100 at the current head a320e65a: pending/h100-rerun-20261028/pr495/ (commit 1dd3e8fc). H100 at the earlier head 61d30a0c: pr495/hopper-head-61d30a0c. Paths such as reports/experiments/... below name where a runner writes its output. H100 performance rerun at head b1906159 (peak memory of Liger, FLA and flash-attn): pr495/hopper/ (commit 3ec9b5a2).

Status

• 9f0da481 (review): moved the three LogP evidence scripts (fused_logp_backward_evidence.py, fused_logp_provider_evidence.py, plot_fused_logp_backward_evidence.py) to benchmarks/operators/logprob/ per refactor: organize models, backends, contracts and train and rollout engines #497. REPO_ROOT, imports, usage commands and docs/operators/fused-logp.md are updated, and the kernel is unchanged. tests/benchmarks/test_fused_logp_*_evidence.py: 18 passed, 2 skipped.

• H100-94GB at head b1906159 (performance check only): benchmarks/operators/logprob/fused_logp_backward_evidence.py --checks performance, single GPU, extension rebuilt with TORCH_CUDA_ARCH_LIST=9.0, torch 2.13.0+cu130, triton 3.7.1, liger-kernel 0.8.4, fla-core 0.5.2; the flash-attn column comes from a second run with --flash-attn-src (flash-attention 94e22c9, cross_entropy.py sha256 cb9ea79a…). Another job was running on a different GPU of the same node. Forward+backward / backward only / peak at 32768×151936, BF16: fused kernel 38.2 / 21.9 ms / 9.27 GiB; Liger 25.7 / 13.4 ms / 9.27 GiB; FLA 15.2 / 10.2 ms / 9.27 GiB; flash-attn 14.3 / 10.1 ms / 9.27 GiB. Peak memory is measured above the logits the candidate reads or, for Liger, overwrites. Liger writes its gradient into its input, and this harness gives it a private copy of the logits only because one logits tensor is reused across candidates. A caller that cannot donate its logits pays one more logits copy: with that copy counted, Liger peaks at 18.55 GiB at 32768 rows (9.27 GiB more, measured on the same H100). Raw data: pr495/hopper/, JSON h100-perf-b1906159.json and h100-perf-b1906159-flashattn.json.

• H100-94GB at head a320e65a: two GPUs, pytest 394 passed / 0 failed / 16 skipped (15 Ascend kernels not compiled, 1 CUDA-dispatch guard). A separate KERNEL_ALIGN_FORCE_SM90=1 rebuild: the 18 SM90 tests passed. Idle GPU. At 32768×151936, BF16, the fused kernel is 38.1 ms forward+backward, 21.8 ms backward, peak 9.27 GiB; flash-attn cross entropy on the same shape is 14.2 / 10.1 ms. Fused gradient vs the shared FP64 reference: max abs 3.87e-4, correctly rounded 99.9991%. Batch-size sweep: 0/0 of 2232 for every candidate. These timings are this H100 measurement, not the B200 table. Since 61d30a0c the fused kernel code is unchanged; the differences are comments and docstrings, tests, benchmark scripts and the merge of main. Raw data: pending/h100-rerun-20261028/pr495/ (commit 1dd3e8fc); run description in its README.md.

• Earlier H100-94GB run at head 61d30a0c: default TORCH_CUDA_ARCH_LIST=9.0 build, two GPUs, pytest 371 passed / 0 failed / 34 skipped (18 SM90 kernels absent from that build, 15 Ascend, 1 dispatch). A separate KERNEL_ALIGN_FORCE_SM90=1 rebuild exports batch_invariant_logp_sm90; those 18 SM90 tests passed. Idle GPU. At 32768×151936, BF16, the fused kernel is 38.0 ms forward+backward, 21.8 ms backward, peak 9.27 GiB (the large-N case ran). flash-attn cross entropy on the same shape is 14.2 / 10.1 ms. Fused gradient vs the shared FP64 reference: max abs 3.87e-4, correctly rounded 99.9991%. Batch-size sweep: 0/0 of 2232 for every candidate, including FLA. These timings are this H100 measurement, not the B200 table. 61d30a0c changes tests and the evidence runner; the fused kernel is the same as at f30fc21e, and this run reproduces that H100 measurement. Raw data: pr495/hopper-head-61d30a0c.

• Earlier H100-94GB measurement at f30fc21e: the same suite, 371 passed / 34 skipped, and the same 18 SM90 tests passed after KERNEL_ALIGN_FORCE_SM90=1. Reported fused timing 38.1 ms / 21.8 ms, peak 9.27 GiB. Raw data: pr_assets pr495/hopper/.

• B200, torch 2.13, re-measured at head 61d30a0c (an earlier run at e612412c gave the same numbers): the fused backward is slower than the tables below. After the device guard and the 64-bit loop counters, the fused backward takes 28.4 ms (backward only) and 33.2 ms (forward + backward) at 32768 rows, against 12.5 / 27.2 ms in the tables below, which were measured before those changes. Memory is unchanged at 9.27 GiB. The other rows reproduce the earlier numbers (chunked fallback 63.6 / 58.9 ms, torch 44.3 / 19.3 ms, Liger 16.2 / 11.6 ms, FLA 7.1 / 3.7 ms, flash-attn 6.2 / 3.7 ms), so the run was not disturbed; only the fused kernel changed. flash-attn's cross entropy is now 5.4x faster end to end and 7.7x on the backward alone, not 4.4x / 3.4x, which strengthens the open question below. I have not isolated the cause; the 64-bit loop counters added for the INT_MAX fix are the likely one. Tests: 371 passed / 34 skipped on two GPUs (369 / 36 on one), including the multi-GPU device-guard and INT_MAX regressions; shared-gradient accuracy is unchanged. Since e612412c the fused logp kernel, its binding and the autograd wrapper are unchanged; the other differences come from the merge of main and from the review fixes to tests and benchmark scripts. Raw data: pr_assets pr495/b200-gaps/. Until the tables are regenerated, the fused-kernel rows and the "4.4x / 3.4x" statement below are superseded by this entry.

• Open question for @Flink-ddd and the reviewers: keep this kernel, or reuse an existing one? flash-attn's Triton cross entropy, the kernel verl's logprobs_from_logits calls, is batch-invariant, uses the same memory as this PR's kernel, and is faster: 6.2 vs 27.2 ms forward + backward at 32768 rows, and 3.7 vs 12.5 ms for the backward alone. This kernel's remaining differences are a smaller dlogits error (max 1.1e-3 vs 2.6e-3 of the largest gradient; 99.9986% vs 99.990% of elements correctly rounded) and that it needs no Triton or flash-attn dependency. Under the reuse rule from RFC [RFC][MiniMax-H3][CUDA/ROCm] WS1/WS2 kernel roadmap, ablation matrix and integration plan #420 that would point to reusing flash-attn's kernel, or vendoring it (BSD-3). That adds a dependency, so it's your call. Details are under Prior art.

• Added: existing implementations in the evidence (9cec6d0 runner, de63477 evidence): Liger, FLA and flash-attn cross entropy, plus a dense batch-size sweep. These replace the earlier torch-only comparison (f338e29, 4b81603).

Summary

Addresses #174 (long-sequence logp memory safety and workspace sizing) for the generic CUDA fused logp op (FusedLogpGenericOp / _FusedLogpAutograd in rl_engine/kernels/ops/cuda/loss/logp.py).

The previous backward materialized FP32 softmax and an FP32 gradient over the full [N, V] logits, about 10 bytes of transient memory per logit. At 32k response tokens × 152k vocab, forward + backward peaked at 46 GiB. This is where 8k–32k CoT training runs out of memory.

The PR has two commits:

1. Bounded row-chunked FP32 backward (fused_logp_backward_chunked). The VJP is computed over row chunks of at most FUSED_LOGP_BWD_CHUNK_ELEMS = 2^27 FP32 elements, so the transient workspace is ~1 GiB regardless of N. The output is bitwise identical to the previous implementation for any chunk size. This path is kept as the fallback for _C builds that do not have the new kernel.
2. Fused CUDA backward kernel (_C.fused_logp_backward). It computes dlogits = grad * (one_hot - softmax) with one block per row and writes it directly in the logits dtype, so the backward allocates nothing beyond the returned gradient.
   • Its max / sum-exp passes visit each thread's elements in the same order as fused_logp_forward_kernel, with the same 256-thread block and the same blockReduceMax / blockReduceSum. The softmax used in the backward is therefore bitwise consistent with the forward logp.
   • Loads are batched 8 at a time (kFusedLogpBackwardUnroll) for memory-level parallelism. Accumulation order does not change: the unrolled kernel is bitwise identical to the non-unrolled version on all checked cases.
   • It is deterministic and batch-invariant, with one block per row and a fixed reduction tree.
   • _FusedLogpAutograd.backward uses the kernel when the backend provides it and otherwise uses the chunked path.

Both paths also fix target handling at the boundary; see the behavior changes below.

Not touched: the forward kernels, the SM90 fused_logp_sm90 path, linear_logp, and the PyTorch / Triton / Ascend logp ops.

Prior art & reuse decision

Each implementation computes the selected-token logp and its gradient from BF16 logits; for the cross-entropy kernels, logp = -loss of a per-token cross entropy. Batch invariance was checked bitwise: eight rows computed alone vs at the front, middle and back of batches of every size 1–9 and 2^k−1 / 2^k / 2^k+1 up to 2048, over three seeds, for both logp and dlogits.

Implementation	Batch-invariant (logp / dlogits)	Peak memory, 32k rows	Forward + backward, 32k rows	Notes
torch log_softmax + gather	yes / yes	55.6 GiB	44.0 ms	builds the FP32 [N, V] softmax, which is what #174 is about
vLLM batch-invariant log_softmax	yes / no backward	n/a	n/a	inference only
Liger cross_entropy (reduction="none")	yes / yes	9.3 GiB	15.8 ms	writes the gradient into the logits; 74.3% of dlogits correctly rounded; the 9.3 GiB assumes the caller donates its logits, otherwise one more logits copy (18.5 GiB at 32k rows on H100)
FLA fused_cross_entropy	no (92 / 92 of 2232 comparisons differ)	9.3 GiB	7.1 ms	a few percent of rows change by one FP32 ulp with the batch size
flash-attn Triton cross_entropy_loss (verl's path)	yes / yes	9.3 GiB	6.2 ms	99.990% of dlogits correctly rounded; returns FP32 logp
this PR's fused kernel	yes / yes	9.3 GiB	27.2 ms	99.9986% of dlogits correctly rounded; no new dependency

Decision: open; see the question under Status. This PR's kernel is not the only batch-invariant backward that fits long sequences: flash-attn's and Liger's do too, and flash-attn's is 4.4× faster end to end and 3.4× faster on the backward alone. The PR keeps its own kernel until the reviewers decide. If the decision is to reuse, the chunked fallback and the boundary-target fixes below still apply.

Comparison charts

B200, 32768 rows, V = 151936, BF16	torch log_softmax + gather	chunked fallback	fused kernel	Liger CE	FLA CE	flash-attn CE (verl)
forward + backward	44.0 ms	59.4 ms	27.2 ms	15.8 ms	7.1 ms	6.2 ms
backward only	19.3 ms	44.8 ms	12.5 ms	11.3 ms	3.7 ms	3.7 ms
peak memory (incl. returned grad)	55.6 GiB	10.3 GiB	9.3 GiB	9.3 GiB	9.3 GiB	9.3 GiB
dlogits max error / max, vs FP64	2.6e-3	1.1e-3	1.1e-3	1.1e-3	2.6e-3	2.6e-3
dlogits correctly rounded	99.994%	99.9995%	99.9986%	74.3%	99.990%	99.990%
batch-size sweep, logp / dlogits differing (of 2232)	0 / 0	0 / 0	0 / 0	0 / 0	92 / 92	0 / 0

The FP64 reference is fed the same upstream gradient each path receives. docs/usage/evidence/fused-logp-backward-b200/report.json was written by benchmarks/fused_logp_backward_evidence.py --flash-attn-src <checkout> (now benchmarks/operators/logprob/fused_logp_backward_evidence.py) from a clean tree at 574d3ef on an otherwise idle B200 (torch 2.13.0+cu130, triton 3.7.1, liger-kernel 0.8.4, fla-core 0.5.2, flash-attention 94e22c9). The previous VJP (57.1 ms, 46.4 GiB) is still in the report and the figure.

Behavior changes

• Out-of-range targets. The forward kernel already returns a constant 0 for targets outside [0, V), such as the ignore index -100. The old backward indexed probs[rows, labels] directly. Python wrapped -100 into a real vocab column, giving a nonzero gradient inconsistent with the forward, and targets >= V raised an indexing error. Those rows now get an exactly zero gradient.
• Gradient numerics for valid targets. With the fused kernel, valid-target gradients are no longer bit-for-bit equal to the old torch.softmax-based VJP, because the softmax now comes from the forward kernel's own reductions.
  • Measured max abs difference against the chunked/old path: ~1e-8 for BF16, ~8e-6 for FP16, ~2e-6 for FP32.
  • The FP32 difference comes from the extension being built with --use_fast_math, so expf is the fast intrinsic. The forward kernel has the same property.
  • Against an FP64 reference, BF16 and FP16 error is identical to the old path.
  • On builds without the kernel, the chunked fallback is bitwise identical to the old path.

Results

Single B200, V = 151936, BF16 logits, median of 5. All variants call the same _C.fused_logp forward through _FusedLogpAutograd; only the backward differs. Peak memory is measured from before the step and includes the returned BF16 gradient (2.3 / 4.6 / 9.3 GiB).

Forward + backward

N rows	Before	Chunked fallback	Fused kernel
8192	11.59 GiB / 14.5 ms	3.32 GiB / 15.0 ms	2.32 GiB / 7.0 ms
16384	23.18 GiB / 28.7 ms	5.64 GiB / 29.8 ms	4.64 GiB / 13.7 ms
32768	46.37 GiB / 57.1 ms	10.27 GiB / 59.3 ms	9.27 GiB / 27.2 ms

Backward only

N rows	Before	Chunked fallback	Fused kernel
8192	10.6 ms	11.2 ms	3.2 ms
16384	21.2 ms	22.3 ms	6.2 ms
32768	42.2 ms	44.4 ms	12.2 ms

The fused backward reads each row 3 times and writes it once. At 32k rows it runs about 2.4× above the HBM floor (~5 ms at ~8 TB/s). A single-pass online max/sum would remove one read, but it would break the bitwise consistency with the forward's two-pass reduction, so this PR does not do it.

The chunk size of the fallback was chosen from this sweep at N = 32768:

chunk_elems	rows/chunk	transient	time
2^22	27	0.03 GiB	114.8 ms
2^24	110	0.13 GiB	54.8 ms
2^26	441	0.50 GiB	48.8 ms
2^27	883	1.00 GiB	44.4 ms
2^28	1766	2.00 GiB	43.3 ms

Tests

New tests/test_fused_logp_chunked_backward.py (46 tests).

Chunked fallback:

• bitwise equal to the previous VJP for FP32/BF16 at chunk sizes of 1/3/7/64 rows (uneven tails included), on CPU and CUDA
• agreement with the autograd log_softmax + gather gradient
• zero rows for out-of-range targets (-100, V, V+5) while other rows stay bitwise unchanged

Fused kernel:

• agreement with an FP64 reference for FP32/BF16/FP16 at V = 1, 255, 1031 and 151936
• bitwise run-to-run determinism, and batch invariance across row subsets
• out-of-range targets and an -inf-masked vocab tail: zero gradient, all finite
• input validation (grad dtype, target dtype, length mismatch) and empty input

Dispatch:

• FusedLogpGenericOp autograd uses the fused kernel
• a backend without fused_logp_backward falls back to the chunked path

Workspace at 8k / 16k / 32k rows × 151936 vocab:

• fused: zero bytes beyond the output
• chunked: within the bound
• skipped when free device memory is insufficient

pytest -q tests/test_fused_logp_chunked_backward.py
# 46 passed

pytest -q tests/test_fused_logp_chunked_backward.py tests/test_logp.py \
  tests/test_deterministic_logp.py tests/test_batch_invariant_logp.py \
  tests/test_op_accuracy.py tests/test_rl_kernel_loss_step.py tests/test_logprob_comparison.py \
  tests/test_gradient_invariance.py tests/test_ws1_gtest_gpu.py \
  tests/test_ws1_chain_integration.py tests/test_tolerance_contract.py
# 327 passed, 34 skipped

All 34 skips need hardware not present (SM90-only kernels, Hopper-only comparisons, Ascend), plus one CUDA-dispatch guard in test_logp.py.

Environment: 1× NVIDIA B200, CUDA 13.0, PyTorch 2.13.0+cu130, Python 3.12. The extension was built with TORCH_CUDA_ARCH_LIST=10.0 python setup.py build_ext --inplace. Formatting was checked with black 24.4.2 and isort 5.13.2 (--line-length=100), as pinned in .pre-commit-config.yaml.

Not verified:

• ROCm: not compiled and not run. fused_logp_backward is registered next to fused_logp and is also built for ROCm; it only uses the existing HIP-compatible helpers (fused_logp_shfl_down_32, blockReduce*).
• Hopper (SM90): measured at head 61d30a0c; see Status. Ampere: not run. The kernel uses no architecture-specific features.

Benchmark script (forward + backward)

import torch
import rl_engine.kernels.ops.cuda.loss.logp as m
from rl_engine.kernels.ops.base import _C

def original_vjp(logits, labels, g, dt):
    probs = torch.softmax(logits.float(), dim=-1)
    rows = torch.arange(logits.size(0), device=logits.device)
    probs[rows, labels] -= 1.0
    return (-g.reshape(-1, 1).float() * probs).to(dt)

class ForwardOnly:  # a _C build without fused_logp_backward
    fused_logp = staticmethod(_C.fused_logp)

chunked = m.fused_logp_backward_chunked
V = 151936
for n in (8192, 16384, 32768):
    x0 = torch.randn(n, V, device="cuda", dtype=torch.bfloat16)
    y = torch.randint(0, V, (n,), device="cuda")
    for name, backend, vjp in (("before", ForwardOnly(), original_vjp),
                               ("chunked", ForwardOnly(), chunked),
                               ("fused", _C, chunked)):
        m.fused_logp_backward_chunked = vjp
        def step():
            x = x0.detach().requires_grad_(True)
            m._FusedLogpAutograd.apply(x, y, backend).float().sum().backward()
            return x.grad
        step()  # warm-up
        torch.cuda.synchronize(); torch.cuda.reset_peak_memory_stats()
        base = torch.cuda.memory_allocated()
        s, e = torch.cuda.Event(True), torch.cuda.Event(True); ts = []
        for _ in range(5):
            s.record(); g = step(); e.record(); torch.cuda.synchronize()
            ts.append(s.elapsed_time(e)); del g
        peak = (torch.cuda.max_memory_allocated() - base) / 2**30
        print(n, name, f"peak={peak:.2f}GiB", f"t={sorted(ts)[2]:.1f}ms")
    m.fused_logp_backward_chunked = chunked
    del x0; torch.cuda.empty_cache()

Relation to other work

#234 also edits the backward in rl_engine/kernels/ops/cuda/loss/logp.py and csrc/fused_logp_kernel.cu. It currently conflicts with main; whichever lands second will need a rebase.

Summary by CodeRabbit

• New Features
  • Added a fused CUDA backward path for log-probability calculations, with input validation and support for the active device and stream. Invalid target IDs produce zero gradients.
• Performance and Memory
  • Added chunked backward processing for fallback paths to limit temporary memory use on long sequences.
• Compatibility
  • ROCm continues to use the generic forward path and a bounded-memory backward fallback; the CUDA-only fused backward path is unavailable there.
• Documentation
  • Expanded operator guidance with backend support and performance, accuracy, and validation details.

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Walkthrough

Adds a CUDA fused log-probability backward operation and a bounded row-chunked fallback. Adds tests for dispatch, accuracy, workspace, device and stream behavior, and provider comparisons. Adds benchmark, reporting, plotting, and operator documentation.

Changes: Fused log-probability backward

Layer / File(s)	Summary
CUDA backward kernel and API rl_engine/_C.pyi, csrc/bindings/ops.cpp, csrc/cuda/logprob/fused_logp_kernel.cu, tests/backends/cuda/test_fused_logp_backward_indexing.py	Adds a CUDA-only backward operation that computes the scaled one-hot-minus-softmax gradient. The binding validates inputs and launches on the logits device’s current stream. Vocabulary loops use 64-bit indices.
Python dispatch and chunked fallback rl_engine/backends/cuda/logprob/logp.py, tests/backends/cuda/test_fused_logp_chunked_backward.py, tests/backends/rocm/test_rocm_logprob_backend.py, docs/operators/fused-logp.md	Uses the fused kernel on supported non-ROCm builds and a bounded row-chunked implementation otherwise. Tests cover fallback dispatch, invalid targets, memory bounds, device and stream handling, and ROCm exclusion.
Backward benchmark and evidence tools benchmarks/operators/logprob/fused_logp_backward_evidence.py, benchmarks/operators/logprob/plot_fused_logp_backward_evidence.py, tests/benchmarks/test_fused_logp_backward_evidence.py, docs/operators/fused-logp.md	Adds candidate comparisons for accuracy, rollout agreement, row and batch invariance, timing, and memory. Adds JSON reporting, plotting, related tests, and benchmark documentation.

Log-probability provider evidence

Layer / File(s)	Summary
Provider execution and comparisons benchmarks/operators/logprob/fused_logp_provider_evidence.py, tests/benchmarks/test_fused_logp_provider_evidence.py	Adds shared-input comparisons for Vime and vLLM, with optional flash-attn scoring. The runner records tensor comparisons, provenance, and layout checks. Tests cover comparison metrics, provider readbacks, environment handling, and vocabulary layouts.
Provider verdicts and report collection benchmarks/operators/logprob/fused_logp_provider_evidence.py, tests/benchmarks/test_fused_logp_provider_evidence.py	Adds aggregate comparison verdicts and single- or multi-rank report collection. Tests cover non-finite values and drift that is diagnostic or affects the replacement verdict.

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

Change: Feature

Sequence Diagram(s)

sequenceDiagram
  participant FusedLogpAutograd
  participant FusedLogpBackwardBinding
  participant FusedLogpBackwardKernel
  FusedLogpAutograd->>FusedLogpBackwardBinding: call fused_logp_backward
  FusedLogpBackwardBinding->>FusedLogpBackwardKernel: launch on logits device current stream
  FusedLogpBackwardKernel-->>FusedLogpBackwardBinding: write logits gradients
  FusedLogpBackwardBinding-->>FusedLogpAutograd: return logits gradients

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Possibly related issues

• NPU + verl + RL-Kernel + Qwen3.6 Series MoE #563: The PR adds fused and chunked CUDA backward paths for the full FP32 target-logprob backward workspace objective. It does not add the issue’s Ascend integration.

Merge Risk: Minimal · previous review 7d5e6

No merge-blocking issue is established by the available evidence.

Security architecture risk: Low · up to efcac

The new backward path admits exceptionally large vocabulary sizes that can overflow its loop indices, potentially causing invalid GPU memory access or stalled GPU work. Ordinary vocabulary sizes do not trigger this case. Reaching it requires control of tensor dimensions and substantial GPU memory; remote or cross-tenant exposure has not been established.

Retained concerns

• Medium · security · inferred: The new public backward API admits dimensions outside the safe domain of its signed loop arithmetic. With the default launch configuration and a valid target, V=2147482624 passes validation, but base += tile exceeds INT_MAX. Overflow can invalidate the loop termination and memory bounds, potentially faulting or stalling the caller's CUDA workload. This is newly exposed backward behavior, not merely the unchanged forward limit. It requires an exceptionally large tensor; one BF16 row plus its returned gradient needs approximately 8 GiB, and attacker-controlled production reachability is unproven.

Security review details

Security Blast Radius

• inferred — The demonstrated exposure is callers able to submit tensors to the native method or existing generic autograd path. The index-overflow concern could affect GPU work sharing the caller's CUDA context. Supplied evidence does not establish remote shape control, cross-tenant access, privilege escalation, or exposure of other services or data stores.

Security Findings and Attack Paths

• inferred — The source-derived failure path is caller-controlled vocabulary dimension, acceptance by the INT_MAX admission check, then signed overflow in the unrolled valid-target loop. This can undermine bounds and termination despite valid tensor ranks, lengths, devices, and target IDs. The path requires substantial memory and was not executed; it is an inferred concern, not a verified exploit.

Trust Boundaries and Controls

• observed — Host validation checks tensor compatibility, and invalid targets are handled before target indexing. Device equality does not itself select the active CUDA device: the new wrapper launches on the current stream without an explicit device guard. The existing forward uses the same convention, so this observation alone does not establish newly expanded multi-device exposure.

Resilience and Maintainability Implications

• observed — Per-invocation saved tensors and fresh output allocation avoid shared writable gradient buffers. The native wrapper checks launch errors. Tests specify repeated execution, batch invariance, malformed inputs, and empty batches; they do not establish overlapping-call isolation or containment of asynchronous execution failures.

Hardening Proposals

• proposed — Align the admitted dimension range with the kernel arithmetic by using wider loop indices or a bound that includes unrolled increments and offsets. Validate this boundary independently of ordinary numerical-comparison tests.

Pre-merge checks: 4

Check name	Status	Explanation
Title check		The title "perf(logp): fused zero-workspace backward for the generic CUDA fused logp (#174)" matches the main change. The changeset adds a fused CUDA backward kernel and entry point (fused_logp_backwa…
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.
Description Check		Check skipped - CodeRabbit’s high-level summary is enabled.

Full details: Title check

Explanation

The title "perf(logp): fused zero-workspace backward for the generic CUDA fused logp (#174)" matches the main change. The changeset adds a fused CUDA backward kernel and entry point (fused_logp_backward), a chunked FP32 backward fallback, and dispatch logic in logp.py. The PR objectives describe the same goal: a fused backward that allocates no workspace beyond the returned gradient, with a chunked fallback for other builds. The title is concise and specific, and it uses the conventional commit prefix. The title does not mention the ROCm restriction, the evidence scripts, or the tests, but those details are secondary to the main change.

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• benchmarks/operators/logprob/fused_logp_backward_evidence.py
• tests/benchmarks/test_fused_logp_backward_evidence.py

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Review comments at @rl_engine/kernels/ops/cuda/loss/logp.py:
- Line 50: After scaling `probs` in the loss backward path, explicitly zero rows
marked invalid by `valid` before copying into `grad_logits`; this ensures
invalid-label rows with all `-inf` logits produce zero gradients rather than
NaNs. Add a test covering that case.

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• rl_engine/kernels/ops/cuda/loss/logp.py

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[Flink-ddd]

Please add a device guard and a multi-GPU regression test for fused_logp_backward, and fix the int32 loop overflow near INT_MAX.

Please also rerun the accuracy comparison with identical effective upstream gradients, the current BF16 and FP32 paths use different reference gradients. I'd favor reusing flash-attn if it meets our train and rollout bitwise contract.

Since this backward is also enabled on ROCm, please add build/runtime validation or keep that path disabled there until validated.

[fusheng-ji]

Please add a device guard and a multi-GPU regression test for fused_logp_backward, and fix the int32 loop overflow near INT_MAX.

Please also rerun the accuracy comparison with identical effective upstream gradients, the current BF16 and FP32 paths use different reference gradients. I'd favor reusing flash-attn if it meets our train and rollout bitwise contract.

Since this backward is also enabled on ROCm, please add build/runtime validation or keep that path disabled there until validated.

Hi @Flink-ddd. Thanks for your time and the review. The fixes and additional experiments are pushed through 7b095615.

• Device guard: Added CUDAGuard before copies, allocations, and launch, using the logits device’s current stream. The two-GPU regression fails against the old extension and passes with the fix.
• Integer overflow: Changed vocabulary loop counters and unrolled offsets to int64_t, including forward loops. The boundary regression reproduces the old overflow near INT_MAX under UBSan.
• ROCm: Disabled the fused backward kernel and binding on HIP. Python dispatch also bypasses stale ROCm extensions exposing the symbol and uses the bounded chunked fallback.
• Accuracy: Reran all implementations with identical effective upstream gradients and one common FP64 reference.

For accuracy, the upstream gradient is quantized to BF16 once and promoted losslessly to FP32. The runner asserts that each output boundary preserves those values. Inputs are BF16 logits, with 257 rows and vocabulary size 151936.

Implementation	Maximum absolute gradient error vs FP64	Correctly rounded BF16 gradients
PyTorch log_softmax + gather	0.00321335	99.992161%
Previous full FP32 VJP	0.00321335	99.999498%
Chunked fallback	0.00321335	99.999498%
Fused CUDA kernel	0.00321335	99.998632%
Liger CE	0.00321335	74.264804%
FLA CE	0.00321335	99.991864%
flash-attn CE	0.00321335	99.989418%

The earlier maximum-error advantage claim is withdrawn. It resulted from comparing different effective upstream gradients.

I also completed the previously missing actual Vime/vLLM WS2 provider experiment. It calls the genuine vLLM Sampler.forward and both integration providers, observing their actual logp/LSE outputs without replacing numerical kernels. Each topology covers 48 configurations: three seeds, BF16/FP32, temperatures 1.0/0.7, padding/support masks, permutations, chunking, and active-row compaction. TP=2 uses two physical B200s with NCCL.

For the coordinated CE experiment, the same pinned flash-attn implementation scores training shards and the full rollout vocabulary using the actual sampler-selected IDs. Differences between CE and the existing implementation are recorded as diagnostics; the replacement gate compares CE training against CE rollout.

Same-batch training → rollout scoring	Differing logp rows	Differing LSE rows	Maximum absolute difference
Existing WS2: TP1 → TP1	0/1200	0/1200	0
Existing WS2: TP2 → TP1	0/1200	0/1200	0
Coordinated CE: TP1 → TP1	0/1200	0/1200	0
Coordinated CE: TP2 → TP1	282/1200	282/1200	1.907e-6

The rollout adapter scores the full gathered vocabulary at TP=1. Both TP=2 ranks reproduce the reported counts; they are replicated checks of the same configurations.

CE passes the tested same-topology training/no_grad, permutation, chunking, and compaction checks. However, the tested distributed CE composition does not satisfy the cross-TP bitwise contract, so I retained the existing forward implementation.

The experiment also exposed an existing WS2 reference-provider limitation: both providers agree within the same layout, but changing the layout produces logp/LSE differences:

• Single-row chunks: 22/1200 rows per output.
• Seven-row chunks: 27/1200.
• Active-row compaction: 6/456.

An independent PyTorch-only reproduction isolated the local tile’s torch.sum: its accumulation order changes with the row count. These baseline failures are explicitly recorded; this experiment does not claim that the existing WS2 provider passes the complete layout contract.

Validation includes 12 passing new provider regression tests. The earlier checks had 279 passes and 34 skips; one additional Triton failure was reproduced on the unchanged baseline. Vime WS2 top-p replay is explicitly rejected and remains unvalidated.

The [provider report, complete raw data, and reproduction commands] document these results. This is a provider-level experiment on frozen logits, without an end-to-end model run.

[Flink-ddd]

Please move the three LogP benchmark scripts to benchmarks/operators/logprob/ and update their imports and commands to follow #497. also cc @KJLdefeated PTAL

[fusheng-ji]

@Flink-ddd Thanks for the approval. The three LogP scripts are now in benchmarks/operators/logprob/ (9f0da481). REPO_ROOT points at the repository root again, and the test imports, usage commands and docs/operators/fused-logp.md use the new paths. The historical "written by" line keeps the old path, with a note of where the script lives now. tests/benchmarks/test_fused_logp_*_evidence.py: 18 passed, 2 skipped.

[coderabbitai]

• Fix comments: Fix CodeRabbit comments on this PR

[github-actions]

In distributed mode a rank that raises inside run_case (e.g. RuntimeError from _vllm_forward/_vime_forward on missing locals, or the "silently changed WS2 backend" check) exits the loop before reaching this collective, so the remaining ranks block indefinitely in all_gather_object (a hang, not a failure). Additionally, the computed _summary verdict is only written to JSON — the process never exits non-zero when passes is False, so a failed comparison is silently "successful" for CI. Please wrap the run/collective so a rank error is propagated (e.g. gather an error flag / use a timeout) and exit non-zero when the summary fails.

[fusheng-ji]

Not changed.

• Hang: the multi-rank mode runs under torchrun, as the docstring shows. If one rank raises, it exits non-zero, and torchrun's agent stops the other workers and reports a ChildFailedError. They do not wait in all_gather_object. Without torchrun, the NCCL watchdog timeout still ends the run.
• Exit status: the script records evidence and is not a CI gate; it needs GPUs and a real vLLM Sampler, so no CI job runs it. Its summary is expected to contain failing verdicts. The flash-attn candidate's coordinated_replacement_passes_sampled_contract is the measured negative result reported in the PR's Status section. A non-zero exit would report that finding as a runner failure. A provider error still raises, so a broken run does exit non-zero.

[fusheng-ji]

Replies to the three OpenCodeReview summary items:

1. fused_logp_backward_evidence.py L290, self-comparison rows: documented in d4ce8fb8. The three candidates built on _FusedLogpAutograd run the same _C.fused_logp forward as the BF16 rollout reference, so for them same_provider_no_grad and project_rollout_bf16 only show that the autograd wrapper leaves the forward unchanged. The train_rollout_invariance docstring now says so. It also names the independent comparisons: the FP32 rollout entrypoints (fused_logp_forward_fp32, fused_logp_forward_out) and the other candidates. The metric is unchanged, so the recorded evidence stays valid.
2. fused_logp_provider_evidence.py L811, rank failure: the same point as the inline finding; answered there.
3. test_fused_logp_chunked_backward.py L237, masked-tail target: fixed in d4ce8fb8. The test now applies labels %= 1000 before setting the two out-of-range rows, so no valid target can fall in the -inf tail. With seed 6 every label is already below 1000 ([478, 188, 288, 982, 344, 275, 499, 731]), so this run is unchanged; the test just no longer depends on the seed.

[coderabbitai]

• Fix comments: Fix CodeRabbit comments on this PR

[fusheng-ji]

test_fused_logp_backward_indexing.py L64, undefined identifiers in the harness: not changed. The regex extracts two declarations, const int64_t stride = blockDim.x; and const int64_t tile = stride * kUnroll; (csrc/cuda/logprob/fused_logp_kernel.cu:197-198). The harness defines blockDim and kUnroll (test L64-65), and the test passes at this head. The two names in the finding cannot occur in that kernel: it is template <typename scalar_t, int kUnroll> (L184) with no BlockSize parameter, and kFusedLogpBackwardUnroll is declared at L353, after the kernel body, so using it inside the kernel would not compile in the .cu either. If the pasted text ever refers to a new identifier, the build assertion reports the compiler's "not declared in this scope" at the pasted line, which names what went out of sync. Predefining speculative constants would let the harness keep compiling against kernel text it no longer mirrors.

[fusheng-ji]

Replies to the six OpenCodeReview summary items:

1. plot_fused_logp_backward_evidence.py L102-L103, all-zero errors on a log axis: fixed in 2a107594. The y-limit uses the largest positive value and falls back to 1.0.
2. fused_logp_backward_evidence.py L231-L232, dtype of the compared gradient: assertion not changed; the report now says which gradient dtype it compared (2a107594). _step calls out.backward(grad.to(out.dtype)), so the cast that has to be lossless is the one to out.dtype. g.dtype is the leaf's dtype and the upstream gradient is never cast to it, so grad.to(g.dtype) would check a cast that is never applied. Each accuracy record now has a grad_dtype field next to output_dtype.
3. fused_logp_kernel.cu L353, constant outside the CUDA guard: fixed in 2a107594. kFusedLogpBackwardUnroll is declared inside the same !__HIPCC__ guard as its only user, the backward launch. The file compiles for sm_90 with CUDA 13; I have not built it with hipcc.
4. test_fused_logp_backward_indexing.py L25-L27, missing markers raise IndexError: fixed in 2a107594. The harness asserts that both markers exist and fails with the same "out of sync, not an overflow regression" message as the other pattern checks.
5. test_fused_logp_chunked_backward.py L184-L185, which path the test takes: not changed. _FusedLogpAutograd.backward uses the fused kernel only when hasattr(ctx.backend, "fused_logp_backward") (rl_engine/backends/cuda/logprob/logp.py:98), and the test's _ForwardOnlyBackend has no such attribute (test L122-125), so only the chunked branch is reachable whatever the global _C exposes. The branch selection itself is pinned by test_autograd_backward_dispatch_uses_chunked_on_hip (L130), which records the calls.
6. L338-L339, transient == 0: not changed. That equality is the documented contract: the fused kernel allocates nothing beyond its output, and a binding-side temporary is exactly the regression it should catch. The output sizes at 8192, 16384 and 32768 rows are multiples of 512 bytes, so allocator rounding adds nothing.

[fusheng-ji]

Replies to the four OpenCodeReview summary items:

1. fused_logp_backward_evidence.py L149-L151, a bad --flash-attn-src aborts the run: changed to a clear error in 7c23f3d2, not to a skip. The flag is an explicit opt-in, and skipping a candidate the caller asked for would give an evidence report without it and no sign of a typo. The runner now exits with --flash-attn-src has no flash_attn/ops/triton/cross_entropy.py: <path> before importing anything.
2. plot_fused_logp_backward_evidence.py L39-L41, KeyError on a subset report: fixed in 7c23f3d2. The plot checks for accuracy and row_invariance first and exits with the names of the missing sections and the hint to rerun with those checks. The docstring already said both are required.
3. test_fused_logp_backward_indexing.py L41, row offset not covered: the kernel is not changed, the test is. The row offset is int64_t row = blockIdx.x; logits + row * vocab_size (fused_logp_kernel.cu:192-194), so the product is 64-bit; the harness replays the loops whose variables are 32-bit in other kernels. The long-sequence test only checked finiteness, so 7c23f3d2 makes it compare rows 0, n/2 and n-1 with the reference (atol=1e-5, rtol=1.6e-2, as the other tests there). At 16384 rows the last row starts at element 2.49e9, past INT_MAX. Both implementations pass at 8192 and 16384 rows on a Blackwell GPU with the extension built from this tree. The 32768-row case skipped for lack of free memory on the shared GPU, so it is not verified.
4. test_fused_logp_chunked_backward.py L338-L339, transient == 0 and the caching allocator: not changed; same item as the earlier round. fused_logp_backward (fused_logp_kernel.cu:713) allocates only torch::empty_like(logits_contig) for its output, and the file has no cudaMalloc, so an allocation outside the torch allocator is not possible there; a temporary on the binding side would show up in max_memory_allocated(), which is the regression the equality guards.

[hongleng]

Hi @fusheng-ji, I'm preparing #642 Task 19 against main, extracting the shared selected-logprob wrapper and autograd forward/VJP while keeping CUDA and MUSA backward dispatch backend-specific.

I've reviewed #495 and plan to preserve the CUDA-local fused_logp_backward_chunked patch surface used by your tests and evidence scripts, as well as the existing native/fallback behavior.

I'll proceed independently and adapt to #495 if it merges first. Please let me know if you see any compatibility concerns with this boundary.

[fusheng-ji]

Thanks for checking before starting. The boundary you describe works with #495. The parts of the patch surface that its tests and evidence runner rely on:

• rl_engine.backends.cuda.logprob.logp.fused_logp_backward_chunked stays a module-level name that _FusedLogpAutograd.backward looks up at call time. tests/backends/cuda/test_fused_logp_chunked_backward.py replaces it with monkeypatch.setattr(logp_module, "fused_logp_backward_chunked", ...) to record which branch ran, so a shared VJP that binds the function earlier (an import into another module, a default argument) would no longer see the patch.
• _FusedLogpAutograd and fused_logp_backward_rows_per_chunk stay importable from that module: the tests import both, and benchmarks/operators/logprob/fused_logp_backward_evidence.py calls logp_mod._FusedLogpAutograd.apply(...).
• The dispatch in backward is by capability: the fused kernel only when hasattr(ctx.backend, "fused_logp_backward") and the build is not HIP, the MUSA branch first, the chunked path otherwise. The tests use a forward-only backend object without that attribute to reach the chunked branch, and a backend that has it to reach the fused one, so keeping the attribute check where it is keeps them valid.
• The chunked path receives grad_output in its original dtype and ctx.input_dtype; the fused path gets a flattened float32 gradient (logp.py, backward).

I have no other concerns. If #495 merges first, the rebase on your side is these call sites only.

[github-actions]

train["logp"]/train["lse"] come from the Vime provider on the local vocab shard, while rollout[...] come from vLLM on the full vocab. _comparison hard-raises ValueError whenever shapes or dtypes differ. The two independent providers can legitimately emit different dtypes (e.g. BF16 shard output vs FP32 selected logprob) or, when tp_world_size > 1, different shapes, so a single divergence aborts the entire evidence run instead of recording a mismatch. This also means the "bitwise equal" claim is only valid when the shard output has actually been TP-reduced to full-vocab values. Validate the dtype/shape boundary explicitly and record it, rather than letting _comparison crash the run.

[fusheng-ji]

Replies to the five OpenCodeReview summary items:

1. fused_logp_backward_evidence.py L456-L459, peak_gib and IN_PLACE candidates: not changed after measuring. I first moved the baseline before setup() (e5d368c1) and reverted that in b1906159. peak_gib is what a candidate allocates above the logits it reads; for Liger those logits are overwritten. The private copy exists because the harness reuses one logits tensor for every candidate; a caller that gives its logits to Liger needs none. On an H100 at 32768 x 151936 BF16, Liger allocates 9.27 GiB inside the step above its input (three runs, identical), the same as the fused kernel's 9.27 GiB output; counting the harness's copy would give 18.55 GiB, which charges Liger for something the fused kernel's input also is. The runner now says this next to the baseline, and the PR table notes that a caller who must keep its logits pays one more copy (18.6 GiB). The H100 performance check at b1906159 (32768 rows: fused, Liger, FLA and flash-attn all 9.27 GiB) is in the PR description, raw data under pr495/hopper/.
2. test_fused_logp_backward_indexing.py L35, harness replays only extracted lines: not changed. It is meant to replay exactly the INT_MAX-sensitive loop control (stride, tile, the four loop headers and unrolled indices) and fails when the source no longer matches, so a moved pattern is a failure, not a pass. The row offset is int64_t row = blockIdx.x (fused_logp_kernel.cu:192-194), and the long-sequence tests run the real kernel at 8192 and 16384 rows, where the last row starts past INT_MAX, and compare rows 0, n/2 and n-1 with the reference. I did not extend the regex harness to execute kernel bodies.
3. test_fused_logp_chunked_backward.py L332, two chunk_elems defaults: fixed in e5d368c1. The bound and the measured call now both take FUSED_LOGP_BWD_CHUNK_ELEMS explicitly.
4. same file L254, validation only on the fused binding: fixed in part, in e5d368c1. fused_logp_backward_chunked now raises ValueError for a grad_output or labels whose length differs from the row count; before, a length-1 grad broadcast silently. The test runs on CPU. The dtype checks are not copied on purpose: the fallback accepts a BF16 upstream gradient and int32 labels and upcasts them, which the autograd wrapper relies on, and the test pins that the result equals the FP32/int64 one. Through autograd the lengths always match, so this guards direct callers.
5. test_fused_logp_backward_indexing.py L105, UBSan not available: fixed in e5d368c1. The test first builds an empty program with -fsanitize=undefined and skips, with the compiler's message, if that fails. I ran it with a compiler wrapper that refuses the flag: skipped; with the real compiler it passes. The file and test_fused_logp_chunked_backward.py pass on a B200: 76 passed.

[hongleng]

Thanks for the detailed compatibility notes! I'll preserve the CUDA-local symbol lookups, patchable entry points, dispatch conditions, and gradient dtype behavior in Task 19.

I'll proceed against main and adapt the relevant call sites if #495 merges first.