643e6f5486
### What this PR does / why we need it?
If a small batch of short requests is sent first, forming a chunk with a
length <128, it will corrupt the `attn_mask_cache`, causing subsequent
requests that do not form a chunk to have accuracy issues.
The root cause of this problem is the use of in-place multiplication.
Modifying it to use out-of-place multiplication will resolve the
accuracy problem.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Yes.
- vLLM version: v0.9.2
- vLLM main:
ad6c2e1a0b
---------
Signed-off-by: ApsarasX <apsarax@outlook.com>
105 lines
4.1 KiB
Python
105 lines
4.1 KiB
Python
#
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# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import torch
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def _generate_attn_mask(max_seq_len, dtype):
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# Construct lower triangle matrix.
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mask_flag = torch.tril(
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torch.ones((max_seq_len, max_seq_len),
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dtype=torch.bool)).view(max_seq_len, max_seq_len)
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# Create upper triangle matrix used to mark mask positions.
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mask_flag = ~mask_flag
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# Currently for fp16 dtype, the mask value should be set to -inf.
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# TODO: Eliminate this part in the future.
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if dtype == torch.float16:
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mask_value = torch.finfo(torch.float32).min
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else:
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mask_value = 1
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attn_mask = torch.masked_fill(torch.zeros(size=(max_seq_len, max_seq_len)),
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mask_flag, mask_value).to(dtype)
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return attn_mask
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class AttentionMaskBuilder:
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def __init__(
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self,
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max_seq_len: int,
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dtype: torch.dtype,
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):
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attn_mask = _generate_attn_mask(max_seq_len, dtype)
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self._seq_len_cached = attn_mask.shape[0]
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self.attn_mask_cache = attn_mask
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self.splitfuse_mask_value = -10000
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def get_attn_mask(self, max_seq_len: int, dtype: torch.dtype,
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device: torch.device):
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self._update_attn_cache(max_seq_len, dtype, device)
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return self.attn_mask_cache[:max_seq_len, :max_seq_len].contiguous()
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def get_splitfuse_attn_mask(
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self,
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seq_lens,
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query_lens,
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position,
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dtype,
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device,
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) -> torch.Tensor:
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max_seq_len = max(seq_lens, default=0)
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if max_seq_len <= self._seq_len_cached:
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self._update_attn_cache(max_seq_len, dtype, device)
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# FIXME: Currently the mask value of chunked-prefill situation and Prefill-Only situation
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# is not the same. Fix this in the future when kernel is ready.
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if self.attn_mask_cache.numel(
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) > 1 and self.attn_mask_cache[0][1] > 0:
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attn_mask = self.get_attn_mask( # type: ignore
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max_seq_len, dtype, device)
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# Do not use in-place multiplication to avoid modifying `self.attn_mask_cache`!
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attn_mask = attn_mask * -10000
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else:
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attn_mask = self.attn_mask_cache
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return torch.index_select(attn_mask, dim=0,
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index=position)[:, :max_seq_len]
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total_q_len = sum(query_lens)
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attn_mask = torch.zeros((total_q_len, max_seq_len),
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dtype=dtype,
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device="cpu")
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current_row = 0
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for i in range(len(query_lens)):
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seq_len = seq_lens[i]
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q_len = query_lens[i]
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context_len = seq_len - q_len
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assert context_len >= 0
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attn_mask[current_row:current_row + q_len,
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context_len:] = self.splitfuse_mask_value
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right_tensor = attn_mask[current_row:current_row + q_len,
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context_len:seq_len]
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right_tensor.masked_fill_(
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right_tensor.tril() == self.splitfuse_mask_value, 0)
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current_row += q_len
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return attn_mask.to(device, non_blocking=True)
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def _update_attn_cache(self, seqlen: int, dtype: torch.dtype,
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device: torch.device):
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if seqlen > self._seq_len_cached:
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self._seq_len_cached = seqlen
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self.attn_mask_cache = _generate_attn_mask(seqlen, dtype)
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if self.attn_mask_cache.device != device:
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self.attn_mask_cache = self.attn_mask_cache.to(device)
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