Didan DengandJiangyun Zhu
ee2b78e826
Signed-off-by: Didan Deng <33117903+wtomin@users.noreply.github.com> Signed-off-by: zjy0516 <riverclouds.zhu@qq.com> Co-authored-by: Jiangyun Zhu <riverclouds.zhu@qq.com>
71 lines
2.5 KiB
Python
71 lines
2.5 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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# Copyright (c) Microsoft Corporation and Jiarui Fang
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# SPDX-License-Identifier: Apache-2.0
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# DeepSpeed Team & Jiarui Fang
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# Adapted from
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# https://github.com/feifeibear/long-context-attention/blob/main/yunchang/attention/layer.py
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import torch
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import torch.nn as nn
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from vllm_omni.diffusion.attention.backends.abstract import AttentionMetadata
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from vllm_omni.diffusion.attention.parallel import build_parallel_attention_strategy
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from vllm_omni.diffusion.attention.selector import get_attn_backend
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class Attention(nn.Module):
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def __init__(
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self,
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num_heads: int,
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head_size: int,
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causal: bool,
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softmax_scale: float,
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num_kv_heads: int | None = None,
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prefix: str = "",
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# ulysses attention
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scatter_idx: int = 2,
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gather_idx: int = 1,
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use_sync: bool = False,
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):
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super().__init__()
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self.attn_backend = get_attn_backend(-1)
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self.attn_impl_cls = self.attn_backend.get_impl_cls()
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self.attention = self.attn_impl_cls(
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num_heads=num_heads,
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head_size=head_size,
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softmax_scale=softmax_scale,
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causal=causal,
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num_kv_heads=num_kv_heads,
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)
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self.softmax_scale = softmax_scale
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self.scatter_idx = scatter_idx
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self.gather_idx = gather_idx
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self.use_sync = use_sync
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# Parallel attention (communication / resharding) is a pluggable strategy.
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# This keeps the attention kernel backend selection orthogonal.
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self.parallel = build_parallel_attention_strategy(
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scatter_idx=scatter_idx,
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gather_idx=gather_idx,
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use_sync=use_sync,
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)
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def forward(
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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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attn_metadata: AttentionMetadata = None,
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) -> torch.Tensor:
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# Parallel strategy may reshard/communicate QKV before the attention kernel.
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query, key, value, attn_metadata, ctx = self.parallel.pre_attention(query, key, value, attn_metadata)
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# shape: (batch_size, seq_len, num_heads, head_size)
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attn_output = self.attention.forward(query, key, value, attn_metadata)
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if isinstance(attn_output, tuple):
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attn_output = attn_output[0]
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# Parallel strategy may need to reverse resharding after the kernel.
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return self.parallel.post_attention(attn_output, ctx)
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