[MM][CG] Profile encoder CUDA graph pool memory

[BWAAEEEK]

[MM][CG] Support encoder CUDA graphs for Step3-VL

Summary

This PR adds image-only encoder CUDA Graph support for Step3-VL / StepVL models.

The implementation:

• wires Step3-VL / StepVL into the existing encoder CUDA Graph framework
• adds Step3-specific auto token budgets based on image and patch feature sizes
• limits Step3-VL / StepVL encoder CUDA Graph capture to max_batch_size=1
• falls back to eager execution for unsupported runtime inputs such as precomputed image_embeds
• includes encoder CUDA Graph memory in CUDA graph memory profiling
• adds Step3-VL e2e coverage that verifies actual encoder CUDA Graph hits

Video input is not supported for Step3-VL / StepVL in this PR.

Duplicate / Overlap Check

• Checked #38175. Step3-VL is not covered by an open or merged CUDA Graph PR.
• Searched open PRs for Step3-VL / Step3 / cudagraph / CUDA graph. No open Step3-VL CUDA Graph PR was found.
• #38040 addresses a generic max-batch / budget invariant. This PR does not replace it. Step3-VL / StepVL use a model-specific auto_max_batch_size=1 and reject larger capture batches.
• #41234 is a draft encoder CUDA Graph interface refactor. This PR may need rebase if that lands first.
• #40600 is Kimi-specific and #40830 is Qwen2.5-VL-specific, so they are not Step3-VL duplicates.

Testing

uv tool run ruff check \
  vllm/model_executor/models/step3_vl.py \
  vllm/model_executor/models/step_vl.py \
  vllm/v1/worker/encoder_cudagraph.py \
  vllm/v1/worker/encoder_cudagraph_defs.py \
  vllm/v1/worker/gpu_model_runner.py \
  tests/v1/cudagraph/test_encoder_cudagraph.py \
  tests/v1/worker/test_gpu_model_runner.py \
  tests/models/multimodal/generation/test_vit_cudagraph.py

Result: passed.

git diff --check

Result: passed.

.venv/bin/python -m pytest tests/v1/cudagraph/test_encoder_cudagraph.py -q

Result: 56 passed.

HF_HOME=$PWD/.hf-cache HF_HUB_CACHE=$PWD/.hf-cache/hub HF_XET_CACHE=$PWD/.hf-cache/xet TRITON_CACHE_DIR=$PWD/.triton-cache \
.venv/bin/python -m pytest tests/v1/worker/test_gpu_model_runner.py -q

Result: 28 passed.

HF_HOME=$PWD/.hf-cache HF_HUB_CACHE=$PWD/.hf-cache/hub HF_XET_CACHE=$PWD/.hf-cache/xet TRITON_CACHE_DIR=$PWD/.triton-cache \
.venv/bin/python -m pytest tests/models/multimodal/generation/test_vit_cudagraph.py --optional -q -s -k 'step3_vl and image'

Result: 1 passed.

The Step3-VL optional e2e test also verifies:

• the encoder CUDA Graph manager is initialized
• budget graphs are captured
• graph_hits >= len(images)
• graph_misses == 0

Benchmark

Model: stepfun-ai/Step3-VL-10B

Benchmark: vllm bench mm-processor

Case: random-mm image input, fixed 1512 image tokens, 12 prompts, 2 warmups, max_num_seqs=1, A100.

Eager encoder:

encoder_forward_ms mean/median/std/p99 = 127.58 / 126.99 / 1.22 / 130.33

CUDA Graph encoder:

encoder_forward_ms mean/median/std/p99 = 118.63 / 118.33 / 0.77 / 120.59

CUDA Graph memory:

Estimated CUDA graph memory: 5.16 GiB
Actual CUDA graph pool memory: 4.72 GiB
Difference: 0.44 GiB (9.3%)

Notes

The default Step3-VL CUDA Graph budgets are intentionally bounded. Inputs that exceed the default captured budgets can fall back to eager execution unless users explicitly provide larger encoder_cudagraph_token_budgets.

I used limited AI/tooling assistance while preparing this PR and reviewed the implementation, tests, and benchmark results.

[claude]

Claude Code Review

This pull request is from a fork — automated review is disabled. A repository maintainer can comment @claude review to run a one-time review.

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[mergify]

Documentation preview: https://vllm--41714.org.readthedocs.build/en/41714/

[mergify]

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @BWAAEEEK.

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork

[gemini-code-assist]

Code Review

This pull request implements encoder CUDA graph support for Step3-VL and Step-VL models, introducing architecture-specific auto-budgets and shared memory pools to optimize memory usage. It also integrates encoder graph profiling into the GPU model runner for more accurate memory estimation. A potential ZeroDivisionError was identified in the EncoderCudaGraphManager when calculating the default maximum batch size if the minimum budget is zero.

[BWAAEEEK]

Thanks for running the benchmark and for pointing out the max_batch_size=1 issue mentioned at #42224 .

I reproduced the same direction locally on B200 before the follow-up change:

• 2 images/request: CG ON mean TTFT 329.36 ms, p99 1047.83 ms; CG OFF mean 178.47 ms, p99 321.18 ms
• 1 image/request: CG ON mean TTFT 122.95 ms, p99 247.18 ms; CG OFF mean 96.53 ms, p99 175.91 ms

So I agree the previous version should not be treated as a Step3-VL performance improvement as-is.

For max_batch_size=1: that was intentional/conservative for Step3-VL/StepVL because the current encoder graph budgets are single-item budgets. Opening max_batch_size > 1 is not just removing the guard; capture sizing, patch distribution, and output compaction need additional validation for aggregate multi-item budgets.

I updated the PR to avoid the worst case instead: Step3-VL/StepVL now fall back to the eager encoder path when the grouped runtime item count exceeds the captured encoder graph batch size. This avoids replaying multiple single-item graphs for multi-image batches while keeping the single-image graph path available.

After the change, with the same Step3-VL serve config on B200:

• 2 images/request: mean TTFT 192.48 ms, p99 356.69 ms
• 1 image/request: mean TTFT 117.82 ms, p99 215.73 ms

For reference, the local no-CG baseline for 2 images/request was mean TTFT 178.47 ms, p99 321.18 ms. So this does not make Step3-VL encoder CUDA Graph a general performance win over eager yet, but it removes the large multi-image regression from the current implementation.

Validation run:

• .venv/bin/python -m pytest tests/v1/cudagraph/test_encoder_cudagraph.py -q -> 58 passed
• .venv/bin/python -m pytest tests/models/multimodal/generation/test_vit_cudagraph.py --collect-only -q -> 6 tests collected
• .venv/bin/pre-commit run ruff-check --files ... -> passed

There is still overlap with #42224, and I’m happy to converge on whichever implementation maintainers prefer. My current view is that this PR keeps the broader Step3-VL/StepVL integration and tests, while the follow-up change addresses the benchmark concern without opening the riskier multi-item graph capture path yet.

[shen-shanshan]

Sorry for late, I'm busy with working these days. I'll take a look at this PR today.

[shen-shanshan]

Based on the comments below, the overall workflow of ViT CG LGTM, except some model-specific processing logic, since I'm not so familiar with this model. CC @Isotr0py

[shen-shanshan]

It's better to put this in the else block below if user_budgets: ....

[shen-shanshan]

Maybe we can merge config.auto_token_budgets and model.get_encoder_cudagraph_budget_range into one model-specific method like model.get_encoder_cudagraph_token_budgets, which encapsulates all the calculation logic and directly return the token budget list. Since we should avoid adding too many interfaces for this ViT cuda graph.

[shen-shanshan]

Ditto. Put this in the other if branch below if user_max_vision_items > 0: ....

[shen-shanshan]

Maybe this check is redundant? Since it's has been checked before calling _capture_budget_graph() in capture().

[shen-shanshan]

Why don't call _maybe_init_encoder_cudagraph_manager() here?

Maybe we can merge _create_encoder_cudagraph_manager() into _maybe_init_encoder_cudagraph_manager()?

[shen-shanshan]

If we can assert height = width, why not just pass patch_size once?

[shen-shanshan]

Ditto.

[shen-shanshan]

In fact, I'm not familiar with this model, maybe we can add a comment to explain why we should calculate auto_token_budgets like this.

[shen-shanshan]

Why we have to set auto_max_batch_size=1 for this model? Please add some comments here for better understanding.

[Isotr0py]

+1, I don't think auto_max_batch_size=1 limitation is necessary.

[BWAAEEEK]

Thanks for the review, @shen-shanshan. I pushed an update in 5212e021e addressing the comments.

Changes made:

• Moved the auto budget-range validation into the auto-budget path, so user-provided encoder_cudagraph_token_budgets do not unnecessarily depend on the model auto range.
• Moved auto_max_batch_size validation into the branch that actually uses it, so an explicit user max batch override takes precedence.
• Removed the redundant graph_pool fallback inside _capture_budget_graph(); capture() already initializes the pool before calling it.
• Kept _create_encoder_cudagraph_manager() as a factory because profiling needs a temporary manager/pool, while _maybe_init_encoder_cudagraph_manager() initializes the persistent runtime manager. I added a comment at the profiling call site to make that lifetime difference clearer.
• Updated the Step3-VL / StepVL projected feature-size helper to take one square size instead of passing the same height/width twice.
• Added comments explaining the Step3-VL budget calculation and why the default graph path stays at auto_max_batch_size=1.

For auto_max_batch_size=1: this is intentional for the current Step3-VL / StepVL support. The auto budgets are single-image encoder output budgets computed as image_feature_size + num_patches * patch_feature_size. Multi-item graph capture would need separate validation for aggregate patch-buffer sizing, patch-count distribution, and output compaction. Based on the benchmark feedback, runtime groups larger than the captured graph batch size fall back to eager instead of replaying multiple single-item graphs.

Validation run:

• .venv/bin/pre-commit run ruff-check --files vllm/v1/worker/encoder_cudagraph.py vllm/v1/worker/gpu_model_runner.py vllm/model_executor/models/step3_vl.py vllm/model_executor/models/step_vl.py tests/v1/cudagraph/test_encoder_cudagraph.py -> passed
• .venv/bin/pre-commit run mypy-3.10 --files vllm/v1/worker/encoder_cudagraph.py vllm/v1/worker/gpu_model_runner.py vllm/model_executor/models/step3_vl.py vllm/model_executor/models/step_vl.py tests/v1/cudagraph/test_encoder_cudagraph.py --hook-stage manual -> passed
• .venv/bin/python -m pytest tests/v1/cudagraph/test_encoder_cudagraph.py -q -> 60 passed
• .venv/bin/python -m pytest tests/models/multimodal/generation/test_vit_cudagraph.py --collect-only -q -> 6 tests collected

[Isotr0py]

Seems the try statement is redundant here. Since encoder cuda graph is not enabled by default, there is no need to fall back to eager model when graph capture failed.

[Isotr0py]

IMO, patch_pixel_values shouldn't belong to precomputed buffers if it's coupled with pixel_values.

[Isotr0py]

+1, I don't think auto_max_batch_size=1 limitation is necessary.

[Isotr0py]

Why can't we capture CUDA graph with DP? I think CG manager should be able to capture it.

vllm/vllm/v1/worker/encoder_cudagraph.py

Lines 599 to 619 in 4db300e

	if self.use_dp:
	per_item_out_tokens = self._get_per_item_out_tokens(mm_kwargs)
	(
	local_mm_kwargs,
	image_rank_assignment,
	images_per_rank,
	max_output_tokens_per_rank,
	) = self._dp_shard(mm_kwargs, per_item_out_tokens)
	local_outputs = self._execute_local(local_mm_kwargs)
	result = self._dp_gather(
	local_outputs,
	per_item_out_tokens,
	image_rank_assignment,
	images_per_rank,
	max_output_tokens_per_rank,
	)
	else:
	result = self._execute_local(mm_kwargs)

[mergify]

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @BWAAEEEK.

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork

[Isotr0py]

QQ: do we need to split the stream as well?

[BWAAEEEK]

I don't think we need a separate stream here.

Encoder graph capture is already executed inside the outer graph_capture(device=self.device) context in GPUModelRunner.capture_model(), which switches capture onto a dedicated CUDA stream. The regular model graphs and encoder graphs are captured sequentially in that same capture context, with synchronization between capture phases.

The separate encoder graph pool is only for memory accounting / pool isolation during profiling. I don't think splitting the stream would change the memory estimate, and it may add extra synchronization/lifetime complexity without a clear benefit.

If you prefer, I can make the stream usage explicit by passing the current graph-capture stream through, but my current understanding is that the existing outer context is sufficient.

[BWAAEEEK]

Thanks @shen-shanshan. I updated the branch on top of the merged #42224 direction and reduced this PR to the graph-pool/profiling support. cc @Isotr0py since the previous auto_max_batch_size=1 model-specific path has been removed from this PR.

What changed in this update:

• Removed the Step3-VL / StepVL model-specific changes from the previous revision.
• EncoderCudaGraphManager.capture() can now capture into an explicit graph pool.
• EncoderCudaGraphManager.clear() releases profiling-only encoder graph state after memory profiling.
• profile_cudagraph_memory() creates a temporary encoder CUDA graph manager, captures encoder graphs into a separate profiling pool, measures encoder graph memory, and adds that estimate to the decoder CUDA graph estimate.
• Runtime encoder CUDA graph manager initialization is shared through a helper so profiling can use a temporary manager without keeping profiling graph state alive.

Validation:

• .venv/bin/python -m pytest tests/v1/cudagraph/test_encoder_cudagraph.py -q -> 52 passed
• .venv/bin/python -m pytest tests/models/multimodal/generation/test_vit_cudagraph.py --collect-only -q -> 10 tests collected
• .venv/bin/pre-commit run ruff-format --files vllm/v1/worker/encoder_cudagraph.py vllm/v1/worker/gpu_model_runner.py tests/v1/cudagraph/test_encoder_cudagraph.py -> passed
• .venv/bin/pre-commit run ruff-check --files vllm/v1/worker/encoder_cudagraph.py vllm/v1/worker/gpu_model_runner.py tests/v1/cudagraph/test_encoder_cudagraph.py -> passed
• .venv/bin/pre-commit run mypy-3.10 --files vllm/v1/worker/encoder_cudagraph.py vllm/v1/worker/gpu_model_runner.py tests/v1/cudagraph/test_encoder_cudagraph.py --hook-stage manual -> passed
• .venv/bin/python -m py_compile vllm/v1/worker/encoder_cudagraph.py vllm/v1/worker/gpu_model_runner.py tests/v1/cudagraph/test_encoder_cudagraph.py -> passed
• git diff --check -> passed

While validating the full Step3-VL benchmark path, I also found a separate #42224 follow-up around Step3-VL-specific capture sizing for patched-image inputs. I did not mix that model-specific fix into this scoped graph-pool/profiling PR, and can send it separately if preferred.

[mergify]

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @BWAAEEEK.

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork

[shen-shanshan]

I suppose this is only called in capture_model() in gpu_model_runner, which has initialized encoder_profiling_pool = current_platform.graph_pool_handle(). Maybe we can assume the input graph_pool is not None here.

[BWAAEEEK]

Thanks, updated. The runtime capture path now creates an explicit encoder graph pool and passes it into EncoderCudaGraphManager.capture(), matching the profiling path. capture() now requires a non-None graph_pool and no longer creates a fallback pool internally.

[shen-shanshan]

Overall, LGTM. Also CC @Isotr0py

[Isotr0py]

Overall LGTM, just leave a nit

[Isotr0py]

Seems redundant here, because it's the same as the default implementation.

vllm/vllm/model_executor/models/interfaces.py

Lines 1583 to 1602 in 1fd8bd0

	def postprocess_encoder_output(
	self,
	output: torch.Tensor,
	indices: list[int],
	per_item_out_tokens: list[int],
	dest: dict[int, torch.Tensor] | list[torch.Tensor | None],
	clone: bool = False,
	batch_mm_kwargs: dict[str, Any] | None = None,
	) -> None:
	"""
	Post-process encoder output, directly call scatter_output_slices by default.
	By default, delegates directly to scatter_output_slices.
	Override this for models that require additional processing on the raw
	encoder output prior to scattering, e.g. Step3-VL, which merges features
	according to dynamic patch counts before scattering.
	"""
	from vllm.model_executor.models.utils import scatter_output_slices
	scatter_output_slices(output, indices, per_item_out_tokens, dest, clone)

[BWAAEEEK]

Thanks, removed the redundant mock override. SimpleMockViTModel already inherits SupportsEncoderCudaGraph, so it can use the default postprocess_encoder_output() implementation directly.

[BWAAEEEK]

Thanks @Isotr0py, I addressed the nit and removed the redundant mock postprocess_encoder_output() override. The mock now uses the default implementation from SupportsEncoderCudaGraph directly.

Validation after the update:

• .venv/bin/python -m pytest tests/v1/cudagraph/test_encoder_cudagraph.py -q -> 51 passed
• .venv/bin/python -m pytest tests/models/multimodal/generation/test_vit_cudagraph.py --collect-only -q -> 10 tests collected
• .venv/bin/pre-commit run ruff-format --files tests/v1/cudagraph/test_encoder_cudagraph.py vllm/v1/worker/encoder_cudagraph.py vllm/v1/worker/gpu_model_runner.py -> passed
• .venv/bin/pre-commit run ruff-check --files tests/v1/cudagraph/test_encoder_cudagraph.py vllm/v1/worker/encoder_cudagraph.py vllm/v1/worker/gpu_model_runner.py -> passed
• .venv/bin/pre-commit run mypy-3.10 --files tests/v1/cudagraph/test_encoder_cudagraph.py vllm/v1/worker/encoder_cudagraph.py vllm/v1/worker/gpu_model_runner.py --hook-stage manual -> passed
• .venv/bin/python -m py_compile tests/v1/cudagraph/test_encoder_cudagraph.py vllm/v1/worker/encoder_cudagraph.py vllm/v1/worker/gpu_model_runner.py && git diff --check -> passed

[BWAAEEEK]

@shen-shanshan @Isotr0py CI is green now after the follow-up fix, but auto-merge seems to have been disabled after my fork branch push (push_from_non_writer). Could you re-enable auto-merge or merge when you get a chance? Thanks!