[Bugfix] Fix workspace buffer None issue for Flashinfer TRTLLM Backend

[elvischenv]

Essential Elements of an Effective PR Description Checklist

• The purpose of the PR, such as "Fix some issue (link existing issues this PR will resolve)".
• The test plan, such as providing test command.
• The test results, such as pasting the results comparison before and after, or e2e results
• (Optional) The necessary documentation update, such as updating supported_models.md and examples for a new model.

Purpose

There is an always valid workspace_buffer initialized in attn_metadata.decode_wrapper._float_workspace_buffer, so no need to store the extra one attn_metadata.workspace_buffer. They should be the same.

Original PR(#19825) initialize attn_metadata via the following way, self._workspace_buffer will be None at the beginning, and will be allocated after ONE round of self._plan(). So it will only get the correct assignment at the 2nd round of build().

attn_metadata = FlashInferMetadata(
    ....
    workspace_buffer=self._workspace_buffer,
)

self._plan(...)

There is one PR(#21137) likely fixed the issue a while ago, but I think it is better to just remove this duplicated attribute.

attn_metadata = FlashInferMetadata(
    ....
    workspace_buffer=self._get_workspace_buffer(),
)

Also fixed unit test for the breakage API for #21485.

Test Plan

tests/kernels/attention/test_flashinfer_trtllm_decode_attention.py
lm_eavl

Test Result

===== 96 passed, 1 warning in 121.20s (0:02:01) =====

vllm (pretrained=nvidia/Llama-4-Scout-17B-16E-Instruct-FP8,quantization=modelopt,tensor_parallel_size=1,max_model_len=2048,kv_cache_dtype=auto,trust_remote_code=True), gen_kwargs: (temperature=0.0), limit: 500.0, num_fewshot: 5, batch_size: 200
|Tasks|Version|     Filter     |n-shot|  Metric   |   |Value|   |Stderr|
|-----|------:|----------------|-----:|-----------|---|----:|---|-----:|
|gsm8k|      3|flexible-extract|     5|exact_match|↑  |0.932|±  |0.0113|
|     |       |strict-match    |     5|exact_match|↑  |0.914|±  |0.0126|

(Optional) Documentation Update

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[gemini-code-assist]

Code Review

This pull request fixes a potential None pointer issue with the workspace buffer in the FlashInfer TRTLLM backend by removing a redundant workspace_buffer attribute from FlashInferMetadata and instead sourcing the buffer directly from the decode_wrapper. The change is logical and improves robustness. However, I've identified a critical potential issue where the code now uses _float_workspace_buffer while the older, more established backend implementation uses _int_workspace_buffer for the same function call. This discrepancy needs to be addressed to prevent potential runtime errors or incorrect computations.

[elvischenv]

CC @mgoin for review. Thanks.

[mgoin]

LGTM and verified locally, thank you!

[mgoin]

Failures in CI look related

[2025-07-28T19:57:25Z] x = tensor([[[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
--
  | [2025-07-28T19:57:25Z]           0., 0....., 0.,
  | [2025-07-28T19:57:25Z]           0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]]],
  | [2025-07-28T19:57:25Z]        dtype=torch.float16)
  | [2025-07-28T19:57:25Z] expected_shape = torch.Size([1, 12, 64]), expected_dtype = torch.float16
  | [2025-07-28T19:57:25Z] expected_device = device(type='cuda', index=0), name = 'out'
  | [2025-07-28T19:57:25Z]
  | [2025-07-28T19:57:25Z]     def _check_shape_dtype_device(
  | [2025-07-28T19:57:25Z]         x: torch.Tensor,
  | [2025-07-28T19:57:25Z]         expected_shape: Sequence[int],
  | [2025-07-28T19:57:25Z]         expected_dtype: torch.dtype,
  | [2025-07-28T19:57:25Z]         expected_device: torch.device,
  | [2025-07-28T19:57:25Z]         name: str,
  | [2025-07-28T19:57:25Z]     ) -> None:
  | [2025-07-28T19:57:25Z]         if x.shape != torch.Size(expected_shape):
  | [2025-07-28T19:57:25Z]             raise ValueError(
  | [2025-07-28T19:57:25Z]                 f"Invalid shape of {name}: expected {expected_shape}, got {x.shape}"
  | [2025-07-28T19:57:25Z]             )
  | [2025-07-28T19:57:25Z]         if x.dtype != expected_dtype:
  | [2025-07-28T19:57:25Z]             raise ValueError(
  | [2025-07-28T19:57:25Z]                 f"Invalid dtype of {name}: expected {expected_dtype}, got {x.dtype}"
  | [2025-07-28T19:57:25Z]             )
  | [2025-07-28T19:57:25Z]         if x.device != expected_device:
  | [2025-07-28T19:57:25Z] >           raise ValueError(
  | [2025-07-28T19:57:25Z]                 f"Invalid device of {name}: expected {expected_device}, got {x.device}"
  | [2025-07-28T19:57:25Z]             )
  | [2025-07-28T19:57:25Z] E           ValueError: Invalid device of out: expected cuda:0, got cpu

https://buildkite.com/vllm/ci/builds/25201/steps/canvas?jid=01985287-7b06-437d-bd29-9098067f382f#01985287-7b06-437d-bd29-9098067f382f/212-1920

[elvischenv]

Thanks @mgoin, fixed that in the latest commit. There is still one failure but seems to be not related?
https://buildkite.com/vllm/ci/builds/25234/steps/canvas?sid=0198541a-8a77-4f8c-929a-4b4aca2a39cb#0198541a-8b58-4920-ae6c-8dc672a23598/211-1103

[2025-07-29T03:51:09Z] >           assert end - start < 60, (
[2025-07-29T03:51:09Z]                 "Expected vLLM to gracefully shutdown in <60s "
[2025-07-29T03:51:09Z]                 "if there is an error in the startup.")
[2025-07-29T03:51:09Z] E           AssertionError: Expected vLLM to gracefully shutdown in <60s if there is an error in the startup.
[2025-07-29T03:51:09Z] E           assert (758.660869583 - 697.165792236) < 60

[mgoin]

Retrying that test now