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minor: zero workspace buffer init for flashinfer trtllm-gen attn #22603

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Merged
merged 5 commits into from
Aug 15, 2025
Merged

minor: zero workspace buffer init for flashinfer trtllm-gen attn #22603

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379f9a6
init
yyihuang Aug 10, 2025
3e3e279
upd test
yyihuang Aug 10, 2025
1a3a34e
upd
yyihuang Aug 11, 2025
bda441a
Merge branch 'main' into init_zero_workspace
mgoin Aug 15, 2025
9db5edc
Merge branch 'main' into init_zero_workspace
mgoin Aug 15, 2025
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4 changes: 2 additions & 2 deletions tests/kernels/attention/test_flashinfer_trtllm_attention.py
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Original file line number Diff line number Diff line change
Expand Up @@ -113,7 +113,7 @@ def test_flashinfer_trtllm_decode_with_baseline(
kv_indices = torch.tensor(kv_indices, dtype=torch.int32)
kv_last_page_lens = torch.tensor(kv_last_page_lens, dtype=torch.int32)

workspace_buffer = torch.empty(128 * 1024 * 1024, dtype=torch.int8)
workspace_buffer = torch.zeros(128 * 1024 * 1024, dtype=torch.int8)
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high

The workspace buffer is created with torch.int8 dtype, while the main implementation in vllm/v1/attention/backends/flashinfer.py uses torch.uint8. While this might not cause issues with a zero-initialized buffer, using an inconsistent data type can lead to subtle bugs if the underlying kernel has specific expectations about the data being signed or unsigned. For consistency and to prevent potential correctness issues, it's recommended to use torch.uint8 here.

Suggested change
workspace_buffer = torch.zeros(128 * 1024 * 1024, dtype=torch.int8)
workspace_buffer = torch.zeros(128 * 1024 * 1024, dtype=torch.uint8)

wrapper = flashinfer.BatchDecodeWithPagedKVCacheWrapper(
workspace_buffer,
kv_layout,
Expand Down Expand Up @@ -247,7 +247,7 @@ def test_flashinfer_trtllm_prefill_with_baseline(
kv_indices = torch.tensor(kv_indices, dtype=torch.int32)
kv_last_page_lens = torch.tensor(kv_last_page_lens, dtype=torch.int32)

workspace_buffer = torch.empty(128 * 1024 * 1024, dtype=torch.int8)
workspace_buffer = torch.zeros(128 * 1024 * 1024, dtype=torch.int8)
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high

The workspace buffer here is created with torch.int8, which is inconsistent with the torch.uint8 used in the main implementation. To ensure consistency across the codebase and avoid potential issues related to signed versus unsigned byte interpretation by the FlashInfer kernel, it is advisable to use torch.uint8 for this buffer as well.

Suggested change
workspace_buffer = torch.zeros(128 * 1024 * 1024, dtype=torch.int8)
workspace_buffer = torch.zeros(128 * 1024 * 1024, dtype=torch.uint8)

wrapper = flashinfer.BatchPrefillWithPagedKVCacheWrapper(
workspace_buffer, kv_layout)
wrapper.plan(q_indptr,
Expand Down
2 changes: 1 addition & 1 deletion vllm/attention/backends/flashinfer.py
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Expand Up @@ -203,7 +203,7 @@ def __init__(self, runner):

def _get_workspace_buffer(self):
if self._workspace_buffer is None:
self._workspace_buffer = torch.empty(
self._workspace_buffer = torch.zeros(
FLASHINFER_WORKSPACE_BUFFER_SIZE,
dtype=torch.uint8,
device=self.runner.device)
Expand Down
2 changes: 1 addition & 1 deletion vllm/v1/attention/backends/flashinfer.py
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Expand Up @@ -252,7 +252,7 @@ def __init__(self, kv_cache_spec: AttentionSpec, layer_names: list[str],

def _get_workspace_buffer(self):
if self._workspace_buffer is None:
self._workspace_buffer = torch.empty(
self._workspace_buffer = torch.zeros(
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Also need to update in vllm/attention/backends/flashinfer.py

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Updated. Thanks for your review!

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Sorry for accidentally pushing to another PR. It's added now.

FLASHINFER_WORKSPACE_BUFFER_SIZE,
dtype=torch.uint8,
device=self.device)
Expand Down