mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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142 lines
3.8 KiB
Python
142 lines
3.8 KiB
Python
"""
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# Copyright (c) 2025 PaddlePaddle Authors. 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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# Adapt from
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# https://github.com/sgl-project/sglang/blob/main/python/sglang/srt/layers/attention/base_attn_backend.py
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"""
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from __future__ import annotations
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from abc import ABC, abstractmethod
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from dataclasses import dataclass
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from typing import TYPE_CHECKING
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import paddle
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if TYPE_CHECKING:
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from fastdeploy.model_executor.forward_meta import ForwardMeta
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@dataclass
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class AttentionMetadata(ABC):
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pass
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class AttentionBackend(ABC):
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"""The base class of attention backends"""
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@abstractmethod
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def init_attention_metadata(self, forward_meta: ForwardMeta):
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"""Initialize the forward metadata."""
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raise NotImplementedError
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def forward(
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self,
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q: paddle.Tensor,
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k: paddle.Tensor,
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v: paddle.Tensor,
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qkv: paddle.Tensor,
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compressed_kv: paddle.Tensor,
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k_pe: paddle.Tensor,
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layer: paddle.nn.Layer,
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forward_meta: ForwardMeta,
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) -> paddle.Tensor:
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"""
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Run a forward.
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args:
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q: The query tensor.
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k: The key tensor.
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v: The value tensor.
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layer: The layer that will be used for the forward.
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compressed_kv: optional compressed key-value cache (for MLA)
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k_pe: optional key positional encoding (for MLA)
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forward_meta: The forward metadata.
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"""
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if forward_meta.forward_mode.is_mixed():
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return self.forward_mixed(
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q,
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k,
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v,
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qkv,
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compressed_kv,
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k_pe,
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layer,
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forward_meta,
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)
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elif forward_meta.forward_mode.is_decode():
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return self.forward_decode(
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q,
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k,
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v,
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qkv,
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compressed_kv,
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k_pe,
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layer,
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forward_meta,
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)
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else:
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return self.forward_extend(
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q,
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k,
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v,
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qkv,
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compressed_kv,
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k_pe,
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layer,
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forward_meta,
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)
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def forward_mixed(
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self,
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q: paddle.Tensor,
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k: paddle.Tensor,
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v: paddle.Tensor,
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qkv: paddle.Tensor,
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compressed_kv: paddle.Tensor,
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k_pe: paddle.Tensor,
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layer: paddle.nn.Layer,
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forward_meta: ForwardMeta,
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) -> paddle.Tensor:
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"""Run a forward for mix."""
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raise NotImplementedError
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def forward_decode(
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self,
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q: paddle.Tensor,
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k: paddle.Tensor,
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v: paddle.Tensor,
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qkv: paddle.Tensor,
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compressed_kv: paddle.Tensor,
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k_pe: paddle.Tensor,
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layer: paddle.nn.Layer,
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forward_meta: ForwardMeta,
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) -> paddle.Tensor:
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"""Run a forward for decode."""
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raise NotImplementedError
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def forward_extend(
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self,
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q: paddle.Tensor,
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k: paddle.Tensor,
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v: paddle.Tensor,
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qkv: paddle.Tensor,
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compressed_kv: paddle.Tensor,
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k_pe: paddle.Tensor,
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layer: paddle.nn.Layer,
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forward_meta: ForwardMeta,
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) -> paddle.Tensor:
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"""Run a forward for extend."""
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raise NotImplementedError
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