[Bugfix] fix flashinfer cudagraph capture for PP

[SolitaryThinker]

The previous cudagraph capture assumes only a single pass through the batch sizes and clobbers the pre-allocated max_batch_size indptr and last_page_len tensors. However with PP, we need to capture the graphs for each VE, thus the clobbered tensors results in a size mismatch in the flashinfer wrapper.

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[comaniac]

Thanks for the fix. Will the engine crash without this PR when enabling CUDA graph + PP + FlashInfer? If so we could try to add a test to cover it. Also cc @LiuXiaoxuanPKU

[SolitaryThinker]

Yes, currently the cudagraph capture fails as flashinfer wrapper asserts that the batch_size in begin_foward() matches the buffer size it was initialized with.

[rank0]:   File "/home/ray/anaconda3/lib/python3.10/runpy.py", line 196, in _run_module_as_main
[rank0]:     return _run_code(code, main_globals, None,
[rank0]:   File "/home/ray/anaconda3/lib/python3.10/runpy.py", line 86, in _run_code
[rank0]:     exec(code, run_globals)
[rank0]:   File "/mnt/user_storage/vllm-nsight/vllm/entrypoints/openai/api_server.py", line 317, in <module>
[rank0]:     run_server(args)
[rank0]:   File "/mnt/user_storage/vllm-nsight/vllm/entrypoints/openai/api_server.py", line 231, in run_server
[rank0]:     if llm_engine is not None else AsyncLLMEngine.from_engine_args(
[rank0]:   File "/mnt/user_storage/vllm-nsight/vllm/engine/async_llm_engine.py", line 466, in from_engine_args
[rank0]:     engine = cls(
[rank0]:   File "/mnt/user_storage/vllm-nsight/vllm/engine/async_llm_engine.py", line 380, in __init__
[rank0]:     self.engine = self._init_engine(*args, **kwargs)
[rank0]:   File "/mnt/user_storage/vllm-nsight/vllm/engine/async_llm_engine.py", line 547, in _init_engine
[rank0]:     return engine_class(*args, **kwargs)
[rank0]:   File "/mnt/user_storage/vllm-nsight/vllm/engine/llm_engine.py", line 265, in __init__
[rank0]:     self._initialize_kv_caches()
[rank0]:   File "/mnt/user_storage/vllm-nsight/vllm/engine/llm_engine.py", line 377, in _initialize_kv_caches
[rank0]:     self.model_executor.initialize_cache(num_gpu_blocks, num_cpu_blocks)
[rank0]:   File "/mnt/user_storage/vllm-nsight/vllm/executor/distributed_gpu_executor.py", line 62, in initialize_cache
[rank0]:     self._run_workers("initialize_cache",
[rank0]:   File "/mnt/user_storage/vllm-nsight/vllm/executor/ray_gpu_executor.py", line 350, in _run_workers
[rank0]:     self.driver_worker.execute_method(method, *driver_args,
[rank0]:   File "/mnt/user_storage/vllm-nsight/vllm/worker/worker_base.py", line 383, in execute_method
[rank0]:     raise e
[rank0]:   File "/mnt/user_storage/vllm-nsight/vllm/worker/worker_base.py", line 374, in execute_method
[rank0]:     return executor(*args, **kwargs)
[rank0]:   File "/mnt/user_storage/vllm-nsight/vllm/worker/worker.py", line 220, in initialize_cache
[rank0]:     self._warm_up_model()
[rank0]:   File "/mnt/user_storage/vllm-nsight/vllm/worker/worker.py", line 236, in _warm_up_model
[rank0]:     self.model_runner.capture_model(self.gpu_cache)
[rank0]:   File "/home/ray/anaconda3/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
[rank0]:     return func(*args, **kwargs)
[rank0]:   File "/mnt/user_storage/vllm-nsight/vllm/worker/model_runner.py", line 1096, in capture_model
[rank0]:     attn_metadata.begin_forward()
[rank0]:   File "/mnt/user_storage/vllm-nsight/vllm/attention/backends/flashinfer.py", line 163, in begin_forward
[rank0]:     self.decode_wrapper.begin_forward(
[rank0]:   File "/home/ray/anaconda3/lib/python3.10/site-packages/flashinfer/decode.py", line 450, in begin_forward
[rank0]:     raise ValueError(
[rank0]: ValueError: The batch size should be fixed in cudagraph mode, the runtime batch size 256  mismatches the batch size set during initialization 1

[comaniac]

Do you think it's possible to add a unit test to tests/distributed/test_pipeline_parallel.py if it's straightforward?

[SolitaryThinker]

@comaniac Let me know if this makes sense, not sure if there are other attn backends I should add?

[comaniac]

LGTM! We could merge it if test passed.

[SolitaryThinker]

/ready