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[fsdp, trainer, cfg, doc, tests] feat: batch-invariant actor forward and sampler log-prob formula - #8026
[fsdp, trainer, cfg, doc, tests] feat: batch-invariant actor forward and sampler log-prob formula#8026cr-gao wants to merge 6 commits into
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The training-side switch is the right place to put this, but the install happens too late to change NCCL or cuBLAS, and on SM90+ vLLM 0.29 does not replace matmul at all. The log-prob swap also skips fused kernels, changes the dtype and peak memory of the actor log-probs, and breaks the distillation CPU test that patches logprobs_from_logits. Notes are on the lines.
| if self.engine_config.full_determinism: | ||
| enable_full_determinism(seed=self.engine_config.seed) | ||
| if self.engine_config.batch_invariant: | ||
| enable_batch_invariance() |
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enable_batch_invariance() runs after torch.distributed.get_rank(), so the NCCL and cuBLAS workspace env vars written by vLLM 0.29's init_batch_invariance() cannot affect an already-initialized process group. docs/advance/determinism.md already says those vars have to be exported from main_ppo before ray.init(), which is why full_determinism does it there.
On SM90+ (H20/H100) vLLM 0.29 does not register batch-invariant matmul kernels at all. Matmul invariance is only CUBLAS_WORKSPACE_CONFIG, and that is set too late here. rms_norm_batch_invariant exists but is never registered as an aten op, so a HuggingFace RMSNorm will not pick it up.
The install is process-global and not undone. Actor and ref usually share one worker, so this also changes the ref forward when only the actor flag is set. It also overwrites full_determinism's NCCL_ALGO=Ring and CUBLAS_WORKSPACE_CONFIG=:16:8 (init_batch_invariance sets NCCL_ALGO=allreduce:tree and :4096:8). These two flags are not independent.
| if calculate_entropy: | ||
| inplace_backward = False | ||
| log_probs = logprobs_from_logits( | ||
| log_probs = self._logprobs_from_logits( |
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This swap does not cover every log-prob path, and it breaks an existing CPU test.
use_fused_kernels=True still takes output.log_probs from the fused kernel and never calls _logprobs_from_logits, so the sampler formula is skipped with no error. This branch should reject use_fused_kernels when batch_invariant is on.
Entropy and sum_pi_squared stay on entropy_from_logits / calculate_sum_pi_squared_from_logits. With entropy_coeff > 0 the bonus is not the entropy of the fp32 log-softmax these log-probs come from.
_logprobs_from_logits = staticmethod(logprobs_from_logits) captures the function at import. tests/workers/test_distillation_topk_symmetry_on_cpu.py patches verl.workers.engine.fsdp.transformer_impl.logprobs_from_logits, and its stub is built with object.__new__, so it still calls the original. With flash-attn installed that hits the Triton cross-entropy kernel on CPU. Look the function up by name at the call, or point the stub at the patched function.
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use_fused_kernels=True is rejected now. Entropy and sum_pi_squared should be unrelated to zero-diff so kept them.
| def logprobs_from_logits_sampler(logits: torch.Tensor, labels: torch.Tensor, inplace_backward: bool = True): | ||
| """Log-probabilities computed the way the vLLM sampler reports them: fp32 log_softmax over the | ||
| full vocabulary, then gather. Returns float32 regardless of the logits dtype.""" | ||
| return torch.log_softmax(logits.float(), dim=-1).gather(dim=-1, index=labels.unsqueeze(-1)).squeeze(-1) |
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This materializes a full [tokens, vocab] fp32 log_softmax and ignores inplace_backward. For a ~152k vocab that is the allocation logprobs_from_logits_v2 loops to avoid, and the call site passes inplace_backward=True whenever entropy is off, expecting flash-attn's in-place cross-entropy backward.
Per-row (or chunked) fp32 log_softmax plus gather is the same value as one big log_softmax, and it is what the bf16 branch of logprobs_from_logits_v2 already does. Please chunk it. The fp32 logsumexp path in v2 is a different kernel, so chunked log_softmax is the one that stays bitwise-equal to this formula.
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| full_determinism: bool = False | ||
| # whether to run the forward with vLLM's batch-invariant kernels and the vLLM sampler log-prob formula | ||
| batch_invariant: bool = False |
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batch_invariant is declared on EngineConfig, but only FSDPEngine.__init__ reads it. Megatron, TorchTitan, and AutoModel accept the key and do nothing. full_determinism lives on this base class because every engine implements it; this flag does not.
fsdp.yaml is shared, so the generated config also exposes it on the critic, which never computes log-probs. Put the field on FSDPEngineConfig.
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The follow-up fixes the earlier points. The log-prob path looks up logprobs_from_logits at the call site, so the distillation CPU patch still applies. Fused kernels are rejected. The sampler is chunked. batch_invariant lives on FSDPEngineConfig. Leaving entropy and sum_pi_squared on the usual kernels is fine as documented.
Two things are still off on 5ed5c52.
BATCH_INVARIANT_ENV copies vLLM's inference NCCL profile onto every Ray actor, including FSDP. NCCL_NTHREADS=1, one channel, NCCL_P2P_NET_DISABLE=1, and NCCL_ALGO=allreduce:tree throttle training allreduce. On SM90/SM100 the actor match is the cuBLAS workspace plus the aten softmax / log_softmax / mean / bmm overrides. The rollout process can write the NCCL knobs itself once VLLM_BATCH_INVARIANT=1 is in its env.
export_batch_invariant_env only sees actor.fsdp_config.full_determinism. rollout.full_determinism does not flip NCCL_ALGO to Ring, so the new doc sentence does not match a run that only sets the rollout flag.
| "VLLM_USE_AOT_COMPILE": "0", | ||
| "CUBLAS_WORKSPACE_CONFIG": ":16:8", | ||
| "CUBLASLT_WORKSPACE_SIZE": "1", | ||
| "NCCL_LAUNCH_MODE": "GROUP", |
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Exporting this before ray.init() is the right response to the timing comment. Forwarding the NCCL half of it to every Ray actor is not.
get_ppo_ray_runtime_env sends this whole dict to the FSDP workers as well as the rollout. NCCL_P2P_NET_DISABLE=1, NCCL_MIN/MAX_NCHANNELS=1, NCCL_NTHREADS=1, NCCL_SOCKET_NTHREADS=1, and NCCL_ALGO=allreduce:tree are vLLM's inference allreduce profile. On the training group they pin every collective to one channel and one thread. That is not what matches actor GEMMs to the sampler: on SM90/SM100 vLLM does not replace matmul, so the match is CUBLAS_WORKSPACE_CONFIG=:16:8, CUBLASLT_WORKSPACE_SIZE=1, and the aten softmax / log_softmax / mean / bmm overrides installed in the actor process.
The rollout can apply the NCCL knobs itself. With VLLM_BATCH_INVARIANT=1 already in the process env, vLLM's init_batch_invariance() writes them inside the rollout process before that process initializes NCCL. Keep VLLM_BATCH_INVARIANT, the two cuBLAS keys, and the two VLLM_* compile knobs in the shared runtime env, and leave the NCCL channel / thread / P2P / algo settings off the FSDP workers. A colocated hybrid worker cannot split the two; that case should be called out, rather than slowing every FSDP allreduce by default.
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Both right, thanks. Removed duplicate keys and updated the full_determinism logic.
| os.environ["PYTHONHASHSEED"] = str(rollout_cfg.seed) | ||
| actor_fsdp_cfg = config.actor_rollout_ref.actor.get("fsdp_config") | ||
| if actor_fsdp_cfg is not None and actor_fsdp_cfg.batch_invariant: | ||
| export_batch_invariant_env(full_determinism=actor_fsdp_cfg.full_determinism) |
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full_determinism here is actor.fsdp_config.full_determinism only. rollout.full_determinism, which is the flag the rest of determinism.md and the quick start use, does not reach this call.
The block just above sets VERL_FULL_DETERMINISM, VLLM_BATCH_INVARIANT, and PYTHONHASHSEED when the rollout flag is on. It does not set NCCL_ALGO. This call then writes NCCL_ALGO=allreduce:tree unless the actor FSDP flag is also true. The new doc sentence says that if full_determinism is also on, the entrypoint exports NCCL_ALGO=Ring last. That is true only for actor.fsdp_config.full_determinism. A run with batch_invariant: true and rollout.full_determinism: true, and the actor flag left at its default, still exports allreduce:tree.
enable_full_determinism() still assigns NCCL_ALGO=Ring inside the worker, after NCCL has already read the env, so it does not undo the export. The unit test only calls export_batch_invariant_env(full_determinism=True) directly; it does not cover this wiring. Pass either flag, and say which one in the doc. Ring has to be the value in the process env before the workers start.
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@wuxibin89 Could you pls take a look when you have time? Thanks! |
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Thanks for the follow-ups — early BATCH_INVARIANT_ENV (without the NCCL half), fused-kernel reject, chunked sampler formula, and moving the flag onto FSDPEngineConfig all look right.
Left two residual notes:
enable_batch_invariance()still calls fullinit_batch_invariance(), which runs vLLM'soverride_envs_for_invariance()(NCCL pins +:4096:8) inside the FSDP worker — undoing the entrypoint split for in-process / hybrid cases and disagreeing with the new doc sentence.- The
main_ppoexport gate is actor-only while sharedfsdp.yamlexposes the flag on critic too.
Sampler path + CPU tests LGTM otherwise. Not approving until (1) is addressed or explicitly scoped as hybrid-unsupported with a hard guard.
| from vllm.model_executor.determinism.batch_invariant import init_batch_invariance | ||
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| os.environ["VLLM_BATCH_INVARIANT"] = "1" | ||
| init_batch_invariance() |
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init_batch_invariance() is more than the aten install the docstring describes. Current vLLM main runs override_envs_for_invariance() first, which:
pin_nccl_env(...)— the same inference NCCL profile this PR deliberately stopped forwarding viaBATCH_INVARIANT_ENV(NCCL_P2P_NET_DISABLE=1, single channel/thread,allreduce:tree, plusNCCL_NO_CACHE).- Sets
CUBLAS_WORKSPACE_CONFIG=:4096:8beforeenable_batch_invariant_mode()optionally puts:16:8back on SM90.
That contradicts docs/advance/determinism.md ("batch_invariant does not touch the NCCL settings") and re-introduces the training-side NCCL pollution in-process. On a dedicated FSDP worker the pins are mostly too late for the already-created PG (_init_device_mesh() runs above this call), but they still mutate os.environ, and on a colocated hybrid worker they are exactly the hang/throttle case the entrypoint split was meant to avoid.
For the actor match you only need the aten overrides + TF32 ieee (and the cuBLAS workspace already exported from main_ppo). Prefer calling enable_batch_invariant_mode() (plus the fp32_precision lines) directly, or gate/unpinned_nccl_env around the override path, instead of the full init_batch_invariance() entrypoint.
Also: a missing/old vLLM here surfaces as a raw ImportError on vllm.model_executor.determinism.batch_invariant. Please wrap with a short message that batch_invariant needs a recent vLLM with that module.
| os.environ["PYTHONHASHSEED"] = str(rollout_cfg.seed) | ||
| actor_fsdp_cfg = config.actor_rollout_ref.actor.get("fsdp_config") | ||
| if actor_fsdp_cfg is not None and actor_fsdp_cfg.batch_invariant: | ||
| export_batch_invariant_env() |
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Nit on the gate: export_batch_invariant_env() only runs when actor_rollout_ref.actor.fsdp_config.batch_invariant is set. fsdp.yaml is shared, so the generated trainer config also exposes critic.model.fsdp_config.batch_invariant (and any other FSDP engine that reads the same schema). FSDPEngine.__init__ will still call enable_batch_invariance() for those workers, but without the early cuBLAS workspace export this block is responsible for.
Either strip/ignore the flag outside the actor, or treat "any engine_config.batch_invariant in the resolved config" as the entrypoint trigger. As written, flipping only the critic key is a silent SM90 matmul footgun.
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Priority: ➖ Normal Merge Risk: 🔵 Low · up to
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| Check name | Status | Explanation | Resolution |
|---|---|---|---|
| Docstring Coverage |  |
Docstring coverage is 37.14% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 35 functions across 13 files. (1 skipped:… | Write docstrings for the functions missing them to satisfy the coverage threshold. |
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Check name Status Explanation Title check The title clearly and concisely identifies the main change: adding batch-invariant actor forward behavior and the sampler log-probability formula. Description check The description directly explains the batch_invariant option, implementation, configuration, tests, and reported validation results. 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.
Full details: Docstring Coverage
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Docstring coverage is 37.14% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 35 functions across 13 files. (1 skipped: 1 unsupported.)
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Write docstrings for the functions missing them to satisfy the coverage threshold.
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Review comments at @verl/utils/torch_functional.py:
- Around line 222-223: Update the chunked log-probability calculation around
`chunk_logprobs` and `logprobs_labels` to recompute the per-chunk log-softmax
during backward, using activation checkpointing or an equivalent approach.
Preserve the existing forward values and gathered label log-probabilities while
avoiding retention of each chunk’s full-vocabulary activation.
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docs/advance/determinism.mdtests/trainer/test_constants_ppo_on_cpu.pytests/utils/test_logprobs_from_logits_sampler_on_cpu.pytests/workers/test_engine_batch_invariant_on_cpu.pyverl/trainer/config/_generated_ppo_trainer.yamlverl/trainer/config/engine/fsdp.yamlverl/trainer/constants_ppo.pyverl/trainer/main_ppo.pyverl/utils/torch_functional.pyverl/workers/config/engine.pyverl/workers/engine/fsdp/transformer_impl.pyverl/workers/engine/utils.py
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| chunk_logprobs = F.log_softmax(chunk_logits.float(), dim=-1) | ||
| logprobs_labels.append(chunk_logprobs.gather(dim=-1, index=chunk_labels.unsqueeze(-1)).squeeze(-1)) |
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🩺 Stability & Availability | 🟠 Major | 🏗️ Heavy lift
Bound the sampler’s training-time activation memory.
When logits requires gradients, each chunk’s full-vocabulary log_softmax output remains available for backward. Splitting rows limits a single allocation, but the retained outputs still grow with the total token count. At 8,192 tokens and a 152,000-token vocabulary, those FP32 outputs alone require about 5 GB. This can exhaust GPU memory during an otherwise fitting actor update. Recompute the log-softmax in backward, for example with per-chunk activation checkpointing, while preserving the sampler’s forward values. PyTorch documents the forward-activation retention and checkpointing trade-off. (docs.pytorch.org)
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Review comment at @verl/utils/torch_functional.py around lines 222 - 223:
Update the chunked log-probability calculation around `chunk_logprobs` and
`logprobs_labels` to recompute the per-chunk log-softmax during backward, using
activation checkpointing or an equivalent approach. Preserve the existing
forward values and gathered label log-probabilities while avoiding retention of
each chunk’s full-vocabulary activation.
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review. Visit https://docs.coderabbit.ai/cli?utm_source=ghpr
…er log-prob formula Signed-off-by: cr-gao <gaochenrui@sjtu.edu.cn>
…ariant actor init_batch_invariance() also writes vLLM's NCCL profile (one channel, one thread, Simple protocol) into the environment. The FSDP communicators are created after that call, so the profile throttled every training collective and the weight sync. Revert the env changes outside BATCH_INVARIANT_ENV after installing the kernel overrides. Signed-off-by: cr-gao <gaochenrui@sjtu.edu.cn>
…ny FSDP engine that asks for it The flag sits on the shared FSDP engine schema, so the critic and the ref can set it too; export the cuBLAS env at the entrypoint whenever one of them does, and name the missing vLLM module when the import fails. Signed-off-by: cr-gao <gaochenrui@sjtu.edu.cn>
…diff The count compared the tensors in their native dtypes while the diff metrics cast both to fp32 first; derive it from the same diff so the two cannot disagree when rollout and actor log-probs differ in dtype. Signed-off-by: cr-gao <gaochenrui@sjtu.edu.cn>
…pdate forward and old log-probs Signed-off-by: cr-gao <gaochenrui@sjtu.edu.cn>
…kward instead of keeping it per chunk Signed-off-by: cr-gao <gaochenrui@sjtu.edu.cn>
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Inline comments:
Review comments at @verl/utils/torch_functional.py:
- Around line 223-225: Update logprobs_from_logits_sampler to flatten logits
into token rows and labels into a vector before chunking, so chunk_size limits
token rows for multidimensional inputs. Save the original labels shape and
reshape the concatenated log probabilities back to it before returning.
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| for chunk_logits, chunk_labels in zip(logits.split(chunk_size), labels.split(chunk_size), strict=True): | ||
| logprobs_labels.append(checkpoint(_sampler_chunk_logprobs, chunk_logits, chunk_labels, use_reentrant=False)) | ||
| return torch.cat(logprobs_labels) |
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🩺 Stability & Availability | 🟡 Minor | ⚡ Quick win
🔎 Supported by static analysis
🏁 Script executed:
sed -n '205,236p' verl/utils/torch_functional.py
sed -n '1420,1470p' verl/workers/engine/fsdp/transformer_impl.py
sed -n '1520,1565p' verl/workers/engine/fsdp/transformer_impl.py
sed -n '17,66p' tests/utils/test_logprobs_from_logits_sampler_on_cpu.pyRepository: verl-project/verl
Length of output: 9890
🏁 Script executed:
set -o pipefail
printf '%s\n' '--- all references ---'
rg -n -F --glob '*.py' 'logprobs_from_logits_sampler' . || test "$?" -eq 1
printf '%s\n' '--- call sites with context ---'
python3 - <<'PY'
from pathlib import Path
needle = 'logprobs_from_logits_sampler'
for p in Path('.').rglob('*.py'):
try:
lines = p.read_text().splitlines()
except Exception:
continue
hits = [i for i, line in enumerate(lines) if needle in line]
if not hits:
continue
print(f'### {p}')
for i in hits:
lo=max(0, i-35); hi=min(len(lines), i+35)
print(f'--- lines {lo+1}-{hi} ---')
for n in range(lo, hi):
print(f'{n+1}: {lines[n]}')
PY
printf '%s\n' '--- shape-related FSDP code ---'
rg -n -F --glob 'transformer_impl.py' -e 'logits_rmpad' -e 'input_ids_rmpad_rolled' -e 'prepare_model_inputs' -e 'use_remove_padding' -e 'pad_mode' -e 'nested.nested_tensor_from_jagged' verl/workers/engine/fsdp/transformer_impl.pyRepository: verl-project/verl
Length of output: 36839
Flatten logits before chunking.
The FSDP callers already flatten token rows before calling logprobs_from_logits_sampler, so the reported major FSDP memory failure is not reachable through those paths. However, the repository test passes three-dimensional logits directly, and the helper then splits only the batch dimension. A direct long-sequence call can still allocate an unbounded FP32 sequence chunk.
Preserve the input label shape while chunking flattened token rows:
Suggested fix
--- "a/verl/utils/torch_functional.py"
+++ "b/verl/utils/torch_functional.py"
@@ -219,10 +219,13 @@
recomputes its fp32 log_softmax instead of keeping one full-vocabulary copy per chunk alive.
``inplace_backward`` is accepted for call-site compatibility and unused.
"""
+ output_shape = labels.shape
+ logits = logits.reshape(-1, logits.shape[-1])
+ labels = labels.reshape(-1)
logprobs_labels = []
for chunk_logits, chunk_labels in zip(logits.split(chunk_size), labels.split(chunk_size), strict=True):
logprobs_labels.append(checkpoint(_sampler_chunk_logprobs, chunk_logits, chunk_labels, use_reentrant=False))
- return torch.cat(logprobs_labels)
+ return torch.cat(logprobs_labels).reshape(output_shape)
def _sampler_chunk_logprobs(chunk_logits: torch.Tensor, chunk_labels: torch.Tensor) -> torch.Tensor:🤖 Prompt for AI Agents
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Review comment at @verl/utils/torch_functional.py around lines 223 - 225:
Update logprobs_from_logits_sampler to flatten logits into token rows and labels
into a vector before chunking, so chunk_size limits token rows for
multidimensional inputs. Save the original labels shape and reshape the
concatenated log probabilities back to it before returning.
After applying the fix, consider running `coderabbit review --agent` for local
review. Visit https://docs.coderabbit.ai/cli?utm_source=ghpr
|
@ISEEKYAN @aoshen02 have done great work on train-rollout zero-mismatch on DeepSeek V4: https://vllm.ai/blog/2026-10-14-train-inference-consistency |
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Thanks @cr-gao, this is a clean split between the entrypoint and the worker. I checked the actor side against vLLM 0.29's Sampler and batch_invariant.py and found two things that affect the 0-diff claim outside the T=1 setup in the PR description, plus two small notes.
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Temperature ≠ 1 can't reach 0-diff as written. In vLLM 0.29 the sampler casts the logits to fp32 first and then divides by the fp32 temperature (
Sampler.forward→apply_temperature). The actor divides the bf16 logits bytemperature.to(logits.dtype)in_scale_logits_by_temperatureand only then calls the sampler formula. So both the division and T itself are rounded to bf16 (0.7 → 0.69921875). I mirrored both orders on CPU (2048 rows × 32k vocab, bf16 logits):T bf16(T) PR path vs vLLM order: mismatched tokens mean |Δ| fp32 divide inside the chunk 1.0 1.0 0/2048 0 0/2048 0.9 0.8984375 2048/2048 1.15e-2 0/2048 0.7 0.69921875 2048/2048 1.49e-2 0/2048 0.6 0.6015625 2048/2048 1.93e-2 0/2048 Suggestion: when
batch_invariantis on, skip_scale_logits_by_temperatureand pass the temperature intologprobs_from_logits_sampler, dividing inside_sampler_chunk_logprobsaschunk_logits.float() / temperature.float(). That keeps the chunked memory bound and matches vLLM's order. Separately,processed_logprobsare taken after top-k/top-p masking, so the doc should say the 0-diff target needstop_k=-1,top_p=1(and T=1 until the above lands). -
torch.bmmloses autograd in the training process.enable_batch_invariant_mode()registers a dispatcher-levelaten::bmmoverride (keeps autograd), but it also doestorch.bmm = bmm_batch_invariant. That function allocates the output and launches a Triton kernel, so nograd_fn. In an inference process that's fine. In the FSDP worker, any differentiable call totorch.bmmitself (not@/matmul, which go through the dispatcher) returns a tensor detached from the graph. Two examples are transformers'batched_mmexperts implementation andverl/models/transformers/npu_patch.py. Those weights would then train without gradients from that path, with no error. Restoring the originaltorch.bmmright afterinit_batch_invariance()inenable_batch_invariance()should keep the invariant kernel (via theaten::bmmoverride) and autograd. A GPU test thattorch.bmm(x, y).grad_fn is not Noneafterenable_batch_invariance()would lock it in. -
Minor:
init_batch_invariance()is a no-op unlessvllm.envs.VLLM_BATCH_INVARIANTreads true, and vLLM cachesenvsafter its service starts (enable_envs_cache()). In a process where a vLLM engine was already initialized without the flag, settingos.environfirst won't help and the overrides are skipped silently. An assert after the call (e.g. onbatch_invariant._batch_invariant_MODE) would turn that into an error. -
Question: since the overrides are process-global, the backward and the optimizer step also run on them (on SM80 every GEMM goes through the Triton persistent matmul). Do you have step-time numbers with the flag on vs off? Worth one line in the doc so users know the cost.
The sampler chunking, the fused-kernel reject, the NCCL revert and the mismatch counters look good to me.
| # entropy and the score centering hook reuse the logits in their backward | ||
| inplace_backward = not (calculate_entropy or score_centering) | ||
| log_probs = logprobs_from_logits( | ||
| logprobs_fn = logprobs_from_logits_sampler if self._batch_invariant else logprobs_from_logits |
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With batch_invariant, the logits reaching logprobs_from_logits_sampler were already divided by temperature.to(logits.dtype) in bf16 (L1415 / L1512), while vLLM 0.29 divides the fp32 logits by the fp32 temperature. For any T ≠ 1 every token differs (my CPU mirror: 2048/2048, mean |Δ| ≈ 1.5e-2 at T=0.7; T itself becomes 0.69921875 in bf16). Could the sampler path take the temperature and divide inside the chunk after .float(), skipping _scale_logits_by_temperature?
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| os.environ["VLLM_BATCH_INVARIANT"] = "1" | ||
| before = dict(os.environ) | ||
| init_batch_invariance() |
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init_batch_invariance() also does torch.bmm = bmm_batch_invariant (vLLM 0.29 enable_batch_invariant_mode). That Python-level replacement is a raw Triton launch with no autograd, so a differentiable torch.bmm call in the actor forward loses its grad_fn. The dispatcher-level aten::bmm override registered just before it already covers bmm with autograd intact. Suggest saving torch.bmm before the call and restoring it afterwards.
| - The entrypoint (`main_ppo`) exports, when any FSDP engine config (actor, ref or critic) sets `batch_invariant`, `VLLM_BATCH_INVARIANT=1`, `CUBLAS_WORKSPACE_CONFIG=:16:8`, `CUBLASLT_WORKSPACE_SIZE=1` and vLLM's two compile switches before `ray.init()`, and forwards them to all Ray actors. cuBLAS reads its workspace config when it initializes, so it cannot be set from inside the worker. On SM90/SM100 vLLM registers no matmul override: matmul invariance comes only from this cuBLAS workspace setting and cuBLASLt selection, which is why the env must be exported this early. Because `VLLM_BATCH_INVARIANT=1` reaches every worker, the rollout also runs batch-invariant, and the rollout process applies vLLM's NCCL profile itself; that profile is not forwarded to the FSDP workers, where it would throttle the training collectives. | ||
| - The FSDP engine calls `init_batch_invariance()` in the worker to install the aten overrides (softmax, log_softmax, mean, bmm, and on SM80 matmul) and the TF32/cuBLASLt settings, then reverts the NCCL profile vLLM writes into the environment: the training communicators are created after this point, and that profile would throttle every FSDP collective and the weight sync. The install is process-global: actor and ref share a worker, so the ref forward runs under the same overrides. | ||
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| `batch_invariant` does not touch the NCCL settings; `full_determinism` keeps owning them. `CUBLAS_WORKSPACE_CONFIG=:16:8` is the same under both. |
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On SM80, override_envs_for_invariance() sets CUBLAS_WORKSPACE_CONFIG=:4096:8 and enable_batch_invariant_mode() only resets it to :16:8 on non-SM80. The key is in BATCH_INVARIANT_ENV, so the revert keeps :4096:8 in the worker. It doesn't matter for matmul there (Triton overrides), but "is the same under both" isn't true on SM80. Also worth stating here that the 0-diff target needs T=1, top_k=-1 and top_p=1 (see the top-level comment).
What does this PR do?
Adds an opt-in FSDP engine option
batch_invariant(default off). When set, the training process installs vLLM's batch-invariant kernel overrides (init_batch_invariance(): matmul / softmax / mean / RMSNorm replacements plus the cuBLAS and TF32 settings) and computes log-probs with the vLLM sampler formula, fp32log_softmaxover the full vocabulary then gather. This is the training-side half of exact 0-diff train-rollout log-probs (#7688, verl-project/verl-omni#637): neither of verl's native log-prob paths is bitwise equal to what the sampler reports (measured on 4096×152064 bf16 logits: bf16log_softmaxbranch 4096/4096 tokens differ, fp32gather − logsumexpbranch 559/4096), and the actor's GEMM/softmax reductions have to match the rollout's before any per-model operator alignment can reach zero.Relates to #8025 (raw mismatch metric used as the gate).
Checklist Before Starting
VLLM_BATCH_INVARIANTunderrollout.full_determinism); no training-side option exists on main.[fsdp, cfg, doc, tests] feat: ...Test
GPU, 2×H20, Qwen3-Omni-30B-A3B thinker truncated to 4 layers, full-parameter, vLLM-Omni rollout with
VLLM_BATCH_INVARIANT=1, same weights on both sides, T=1, no truncation: withbatch_invariant=trueand the model-side operators aligned (prototype, tracked in verl-omni#637)training/rollout_logprobs_mismatch_countis 0/8192 at step 1 and at step 2 after a real update and weight sync; with the option off the same run gives 8192/8192 at both steps (mean |Δ| 6.0e-2). A 1-GPU run with micro-batch 8 and 2-GPU runs with micro-batch 4 both give 0 mismatches against the same rollout log-probs, i.e. the actor forward no longer depends on the micro-batch layout. (Gradient norms differ in the 4th decimal between runs; the backward is not bitwise reproducible and is not part of the claim.)API and Usage Example
Design & Code Changes
verl/workers/config/engine.py:EngineConfig.batch_invariant: bool = False(documented on the FSDP config).verl/workers/engine/utils.py:enable_batch_invariance().verl/workers/engine/fsdp/transformer_impl.py: call it inFSDPEngine.__init__and route both log-prob call sites throughself._logprobs_from_logits, which the option switches tologprobs_from_logits_sampler.verl/utils/torch_functional.py:logprobs_from_logits_sampler.verl/trainer/config/engine/fsdp.yaml+ regenerated_generated_ppo_trainer.yaml;docs/advance/determinism.mdsection.Checklist Before Submitting
pre-commit run --files <changed files>: ruff, ruff format, mypy, license, docstring, docs-time, structure checks pass.docs/advance/determinism.md).AI assistance was used for this change. I reviewed every line and ran the tests.