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[Misc] Add attention mask (#1673)

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Move attention mark from V0 to common place.
- vLLM version: v0.9.2
- vLLM main:
b942c094e3

Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
This commit is contained in:
wangxiyuan
2025-07-09 09:12:03 +08:00
committed by GitHub
parent cc1588be50
commit 392fd7239b
5 changed files with 216 additions and 107 deletions
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105
vllm_ascend/attention/attention.py
View File

@@ -35,6 +35,7 @@ from vllm.attention.backends.utils import (PAD_SLOT_ID, CommonAttentionState,
from vllm.utils import async_tensor_h2d, make_tensor_with_pad
from vllm_ascend.ascend_config import get_ascend_config
from vllm_ascend.attention.attention_mask import AttentionMaskBuilder
from vllm_ascend.ops.cache import concat_and_cache_mla
from vllm_ascend.utils import (ACL_FORMAT_FRACTAL_NZ, aligned_16,
enable_custom_op, is_310p, nd_to_nz_2d)
@@ -44,108 +45,6 @@ from vllm_ascend.worker.model_runner import (
_ALLOWED_NUM_QUERIES_PER_KV = [32, 64, 128]
def generate_attn_mask(max_seq_len: int, dtype=torch.float16, mask_value=None):
# Construct lower triangle matrix.
mask_flag = torch.tril(
torch.ones((max_seq_len, max_seq_len),
dtype=torch.bool)).view(max_seq_len, max_seq_len)
# Create upper triangle matrix used to mark mask positions.
mask_flag = ~mask_flag
# Currently for fp16 dtype, the mask value should be set to -inf.
# TODO: Eliminate this part in the future.
if mask_value is None:
if dtype == torch.float16:
mask_value = torch.finfo(torch.float32).min
else:
mask_value = 1
attn_mask = torch.masked_fill(torch.zeros(size=(max_seq_len, max_seq_len)),
mask_flag, mask_value).to(dtype)
return attn_mask
class AttentionMaskBuilder:
def __init__(self, attn_mask: torch.Tensor):
self._seq_len_cached = attn_mask.shape[0]
self.attn_mask_cache = attn_mask
self.splitfuse_mask_value = -10000
@classmethod
def initialize_from_len(cls,
max_seq_len: int,
dtype: torch.dtype = torch.float16,
mask_value: Optional[int] = None):
return cls(generate_attn_mask(max_seq_len, dtype, mask_value))
def update_attn_cache(self, seqlen: int, dtype: torch.dtype,
device: torch.device):
if seqlen > self._seq_len_cached or self.attn_mask_cache.dtype != dtype:
self._seq_len_cached = seqlen
self.attn_mask_cache = generate_attn_mask(seqlen, dtype)
if self.attn_mask_cache.device != device:
self.attn_mask_cache = self.attn_mask_cache.to(device)
def get_attn_mask(self, max_seq_len: int, dtype: torch.dtype,
device: torch.device):
self.update_attn_cache(max_seq_len, dtype, device)
return self.attn_mask_cache[:max_seq_len, :max_seq_len].contiguous()
def get_decode_attn_mask(
self,
input_lengths: torch.tensor,
max_s: int,
dtype: torch.dtype,
device: torch.device,
):
self.update_attn_cache(max_s, dtype, device)
return (self.attn_mask_cache.index_select(
0, input_lengths)[:, :max_s].view(-1, 1, max_s).contiguous())
def get_splitfuse_attn_mask(
self,
seq_lens,
query_lens,
position,
dtype,
device,
) -> torch.Tensor:
max_seq_len = max(seq_lens, default=0)
if max_seq_len <= self._seq_len_cached:
self.update_attn_cache(max_seq_len, dtype, device)
# FIXME: Currently the mask value of chunked-prefill situation and Prefill-Only situation
# is not the same. Fix this in the future when kernel is ready.
if self.attn_mask_cache.numel(
) > 1 and self.attn_mask_cache[0][1] > 0:
attn_mask = self.get_attn_mask( # type: ignore
max_seq_len, dtype, device)
attn_mask *= -10000
else:
attn_mask = self.attn_mask_cache
return torch.index_select(attn_mask, dim=0,
index=position)[:, :max_seq_len]
total_q_len = sum(query_lens)
attn_mask = torch.zeros((total_q_len, max_seq_len),
dtype=dtype,
device="cpu")
current_row = 0
for i in range(len(query_lens)):
seq_len = seq_lens[i]
q_len = query_lens[i]
context_len = seq_len - q_len
assert context_len >= 0
attn_mask[current_row:current_row + q_len,
context_len:] = self.splitfuse_mask_value
right_tensor = attn_mask[current_row:current_row + q_len,
context_len:seq_len]
right_tensor.masked_fill_(
right_tensor.tril() == self.splitfuse_mask_value, 0)
current_row += q_len
return attn_mask.to(device, non_blocking=True)
class AscendAttentionBackend(AttentionBackend):
@staticmethod
@@ -524,7 +423,7 @@ class AscendMetadataBuilder(CommonMetadataBuilder[AscendMetadata]):
self.compress_mask = None
self.chunk_mask = None
if AscendMetadataBuilder._attn_mask_builder is None:
AscendMetadataBuilder._attn_mask_builder = AttentionMaskBuilder.initialize_from_len(
AscendMetadataBuilder._attn_mask_builder = AttentionMaskBuilder(
128, self.input_builder.runner.model_config.dtype)
def _add_seq_group(

103
vllm_ascend/attention/attention_mask.py Normal file
View File

@@ -0,0 +1,103 @@
#
# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
def _generate_attn_mask(max_seq_len, dtype):
# Construct lower triangle matrix.
mask_flag = torch.tril(
torch.ones((max_seq_len, max_seq_len),
dtype=torch.bool)).view(max_seq_len, max_seq_len)
# Create upper triangle matrix used to mark mask positions.
mask_flag = ~mask_flag
# Currently for fp16 dtype, the mask value should be set to -inf.
# TODO: Eliminate this part in the future.
if dtype == torch.float16:
mask_value = torch.finfo(torch.float32).min
else:
mask_value = 1
attn_mask = torch.masked_fill(torch.zeros(size=(max_seq_len, max_seq_len)),
mask_flag, mask_value).to(dtype)
return attn_mask
class AttentionMaskBuilder:
def __init__(
self,
max_seq_len: int,
dtype: torch.dtype,
):
attn_mask = _generate_attn_mask(max_seq_len, dtype)
self._seq_len_cached = attn_mask.shape[0]
self.attn_mask_cache = attn_mask
self.splitfuse_mask_value = -10000
def get_attn_mask(self, max_seq_len: int, dtype: torch.dtype,
device: torch.device):
self._update_attn_cache(max_seq_len, dtype, device)
return self.attn_mask_cache[:max_seq_len, :max_seq_len].contiguous()
def get_splitfuse_attn_mask(
self,
seq_lens,
query_lens,
position,
dtype,
device,
) -> torch.Tensor:
max_seq_len = max(seq_lens, default=0)
if max_seq_len <= self._seq_len_cached:
self._update_attn_cache(max_seq_len, dtype, device)
# FIXME: Currently the mask value of chunked-prefill situation and Prefill-Only situation
# is not the same. Fix this in the future when kernel is ready.
if self.attn_mask_cache.numel(
) > 1 and self.attn_mask_cache[0][1] > 0:
attn_mask = self.get_attn_mask( # type: ignore
max_seq_len, dtype, device)
attn_mask *= -10000
else:
attn_mask = self.attn_mask_cache
return torch.index_select(attn_mask, dim=0,
index=position)[:, :max_seq_len]
total_q_len = sum(query_lens)
attn_mask = torch.zeros((total_q_len, max_seq_len),
dtype=dtype,
device="cpu")
current_row = 0
for i in range(len(query_lens)):
seq_len = seq_lens[i]
q_len = query_lens[i]
context_len = seq_len - q_len
assert context_len >= 0
attn_mask[current_row:current_row + q_len,
context_len:] = self.splitfuse_mask_value
right_tensor = attn_mask[current_row:current_row + q_len,
context_len:seq_len]
right_tensor.masked_fill_(
right_tensor.tril() == self.splitfuse_mask_value, 0)
current_row += q_len
return attn_mask.to(device, non_blocking=True)
def _update_attn_cache(self, seqlen: int, dtype: torch.dtype,
device: torch.device):
if seqlen > self._seq_len_cached:
self._seq_len_cached = seqlen
self.attn_mask_cache = _generate_attn_mask(seqlen, dtype)
if self.attn_mask_cache.device != device:
self.attn_mask_cache = self.attn_mask_cache.to(device)