1ffca8673f
### What this PR does / why we need it?
Add basic 310p support. Only dense models work with eager mode now.
- vLLM version: v0.13.0
- vLLM main:
2f4e6548ef
---------
Signed-off-by: Tflowers-0129 <2906339855@qq.com>
Signed-off-by: Shaoxu Cheng <2906339855@qq.com>
105 lines
3.5 KiB
Python
105 lines
3.5 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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# This file is a part of the vllm-ascend project.
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#
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import einops
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import torch
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import torch.nn.functional as F
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import torch_npu
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import vllm_ascend.envs as envs_ascend
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from vllm_ascend.ops.mm_encoder_attention import MAX_PAD_SIZE, MIN_PAD_SIZE
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from vllm_ascend.ops.mm_encoder_attention import AscendMMEncoderAttention as _Base
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class AscendMMEncoderAttention310(_Base):
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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def forward_oot(
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self,
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query: torch.Tensor,
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key: torch.Tensor,
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value: torch.Tensor,
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cu_seqlens: torch.Tensor | None = None,
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max_seqlen: int | None = None,
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**kwargs,
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):
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bsz, q_len = query.size()[:2]
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kv_len = key.size(1)
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q, k, v = self.reshape_qkv_to_3d(query, key, value, bsz, q_len, kv_len)
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enable_pad = envs_ascend.USE_OPTIMIZED_MODEL and self.head_size > MIN_PAD_SIZE and self.head_size < MAX_PAD_SIZE
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origin_shape = q.shape[-1]
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if enable_pad:
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pad_len = MAX_PAD_SIZE - origin_shape
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q = F.pad(q, (0, pad_len), mode="constant", value=0)
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k = F.pad(k, (0, pad_len), mode="constant", value=0)
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v = F.pad(v, (0, pad_len), mode="constant", value=0)
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origin_dim = origin_shape
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cur_dim = q.shape[-1]
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pad16 = (16 - cur_dim % 16) % 16
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if pad16:
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q = F.pad(q, (0, pad16), mode="constant", value=0)
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k = F.pad(k, (0, pad16), mode="constant", value=0)
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v = F.pad(v, (0, pad16), mode="constant", value=0)
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if cu_seqlens is None:
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cu_seqlens = torch.arange(
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0,
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(bsz + 1) * q_len,
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step=q_len,
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dtype=torch.int32,
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device=query.device,
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)
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total_q_tokens = bsz * q_len
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context_flat = q.new_empty((total_q_tokens, self.num_heads, q.shape[-1]))
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st = 0
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seg_lens = torch.diff(cu_seqlens).to("cpu", dtype=torch.int64).tolist()
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for seg_len in seg_lens:
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seg_len = int(seg_len)
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ed = st + seg_len
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q_i = q[st:ed].unsqueeze(0) # [1, S, H, D]
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k_i = k[st:ed].unsqueeze(0)
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v_i = v[st:ed].unsqueeze(0)
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qs = int(q_i.shape[1])
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kvs = int(k_i.shape[1])
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out_i = torch_npu.npu_prompt_flash_attention(
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q_i,
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k_i,
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v_i,
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input_layout="BSND",
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num_heads=self.num_heads,
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num_key_value_heads=self.num_kv_heads,
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scale_value=self.head_size**-0.5,
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pre_tokens=qs,
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next_tokens=kvs,
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)
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context_flat[st:ed] = out_i[0]
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st = ed
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context_flat = context_flat[..., :origin_dim]
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context_layer = einops.rearrange(context_flat, "(b s) h d -> b s h d", b=bsz).contiguous()
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return context_layer
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