02c26dcfc7
### What this PR does / why we need it?
[Feat] Supports Aclgraph for bge-m3
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
```
pytest -s tests/e2e/singlecard/test_embedding.py
pytest -s tests/e2e/singlecard/test_embedding_aclgraph.py
```
to start an online server with bs 10, each batch's seq length=8192, we
set --max-num-batched-tokens=8192*10 to ensure encoder is not chunked:
```
vllm serve /home/data/bge-m3 --max_model_len 1024 --served-model-name "bge-m3" --task embed --host 0.0.0.0 --port 9095 --max-num-batched-tokens 81920 --compilation-config '{"cudagraph_capture_sizes":[8192, 10240, 20480, 40960, 81920]}'
```
For bs10, each batch's seq length=8192, QPS is improved from 85 to 104,
which is a 22% improvement, lots of host bound is reduced.
- vLLM version: v0.11.0rc3
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0
---------
Signed-off-by: xuyexiong <xuyexiong@huawei.com>
Co-authored-by: wangyongjun <1104133197@qq.com>
118 lines
4.8 KiB
Python
118 lines
4.8 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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device: torch.device = None,
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):
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# NOTE: The device argument specifies the target NPU
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# to be used for the newly added FIA operator.
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# Only pass this parameter when using the new FIA operator.
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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.device = device
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self.pooling_mask = None
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if torch.version.cann.startswith("8.3"):
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assigned_mask_dim = 2048
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self.chunked_prefill_attn_mask = torch.triu(
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torch.ones(assigned_mask_dim, assigned_mask_dim),
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diagonal=1).to(torch.int8).to(device)
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@staticmethod
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def get_mask_scale_factor(dtype: torch.dtype = torch.float16):
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if dtype == torch.float16:
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mask_scale_factor = 1
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elif dtype == torch.bfloat16:
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mask_scale_factor = -10000
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else:
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raise ValueError(
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"The current operation now only supports data types: torch.float16 and "
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"torch.bfloat16. Please ensure the input is of one of these types."
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)
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return mask_scale_factor
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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)
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return self.attn_mask_cache[:max_seq_len, :max_seq_len].contiguous(
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).to(device, non_blocking=True)
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def get_pooling_mask(self, device):
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if self.pooling_mask is None:
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# the compressed attention mask for npu_fusion_attention sparse mode 4
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self.pooling_mask = torch.triu(torch.ones(
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2048, 2048), diagonal=1).to(torch.bool).to(device,
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non_blocking=True)
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return self.pooling_mask
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def get_splitfuse_attn_mask(
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self,
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seq_lens: torch.Tensor = None,
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position: torch.Tensor = None,
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dtype: torch.dtype = None,
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device: torch.device = None,
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) -> torch.Tensor:
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if torch.version.cann.startswith("8.3"):
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return self.chunked_prefill_attn_mask
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else:
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if dtype not in [torch.float16, torch.bfloat16]:
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raise ValueError(
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"splitfuse_attn_mask now only supports bf16 and fp16")
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max_seq_len = max(seq_lens, default=0)
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self._update_attn_cache(max_seq_len, dtype)
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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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mask_scale_factor = AttentionMaskBuilder.get_mask_scale_factor(
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dtype)
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attn_mask = torch.index_select(self.attn_mask_cache,
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dim=0,
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index=position)[:, :max_seq_len]
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attn_mask *= mask_scale_factor
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return attn_mask.contiguous().to(device, non_blocking=True)
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def _update_attn_cache(self, seqlen: int, dtype: torch.dtype):
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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.dtype != dtype:
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self.attn_mask_cache = self.attn_mask_cache.to(dtype)
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