80 lines
2.5 KiB
Python
80 lines
2.5 KiB
Python
# Copyright 2025-2026 SGLang Team
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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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# ==============================================================================
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"""Radix linear attention."""
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from __future__ import annotations
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from typing import TYPE_CHECKING, Optional, Tuple, Union
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import torch
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from torch import nn
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if TYPE_CHECKING:
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from sglang.srt.model_executor.forward_batch_info import ForwardBatch
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class RadixLinearAttention(nn.Module):
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"""
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The Linear Attention Layer Implementation.
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"""
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def __init__(
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self,
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layer_id: int,
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num_q_heads: int,
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num_k_heads: int,
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num_v_heads: int,
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head_q_dim: int,
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head_k_dim: int,
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head_v_dim: int,
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# GDN KDA Shared Weights
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conv_weights: Optional[Union[torch.Tensor, Tuple[torch.Tensor, ...]]] = None,
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bias: Optional[Union[torch.Tensor, Tuple[torch.Tensor, ...]]] = None,
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activation: str = "silu",
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A_log: Optional[torch.Tensor] = None,
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dt_bias: Optional[torch.Tensor] = None,
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):
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super().__init__()
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self.layer_id = layer_id
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self.num_q_heads = num_q_heads
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self.num_k_heads = num_k_heads
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self.num_v_heads = num_v_heads
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self.head_q_dim = head_q_dim
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self.head_k_dim = head_k_dim
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self.head_v_dim = head_v_dim
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self.q_dim = num_q_heads * head_q_dim
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self.k_dim = num_k_heads * head_k_dim
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self.v_dim = num_v_heads * head_v_dim
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self.conv_weights = conv_weights
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self.bias = bias
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self.activation = activation
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self.A_log = A_log
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self.dt_bias = dt_bias
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def forward(
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self,
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forward_batch: ForwardBatch,
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mixed_qkv: Union[torch.Tensor, Tuple[torch.Tensor, ...]],
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a: torch.Tensor,
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b: torch.Tensor,
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) -> torch.Tensor:
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return forward_batch.attn_backend.forward(
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layer=self,
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forward_batch=forward_batch,
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mixed_qkv=mixed_qkv,
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a=a,
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b=b,
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)
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