117 lines
3.7 KiB
Python
117 lines
3.7 KiB
Python
# Copyright 2023-2024 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 attention."""
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from __future__ import annotations
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from enum import Enum
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from typing import TYPE_CHECKING, Optional
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from torch import nn
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if TYPE_CHECKING:
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from sglang.srt.layers.quantization.base_config import QuantizationConfig
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from sglang.srt.model_executor.forward_batch_info import ForwardBatch
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class AttentionType(Enum):
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"""
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Attention type.
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Use string to be compatible with `torch.compile`.
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"""
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# Decoder attention between previous layer Q/K/V
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DECODER = "decoder"
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# Encoder attention between previous layer Q/K/V
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ENCODER_ONLY = "encoder_only"
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class RadixAttention(nn.Module):
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"""
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The attention layer implementation.
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"""
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def __init__(
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self,
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num_heads: int,
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head_dim: int,
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scaling: float,
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num_kv_heads: int,
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layer_id: int,
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logit_cap: float = 0.0,
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v_head_dim: int = -1,
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sliding_window_size: int = -1,
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is_cross_attention: bool = False,
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pos_encoding_mode: str = "NONE",
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logit_capping_method: str = "tanh",
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quant_config: Optional[QuantizationConfig] = None,
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attn_type: AttentionType = AttentionType.DECODER,
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use_irope: bool = False,
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prefix: str = "",
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):
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super().__init__()
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self.tp_q_head_num = num_heads
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self.tp_k_head_num = num_kv_heads
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self.tp_v_head_num = num_kv_heads
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self.head_dim = head_dim
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self.qk_head_dim = head_dim
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self.v_head_dim = v_head_dim if v_head_dim != -1 else head_dim
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self.scaling = scaling
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self.layer_id = layer_id
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self.logit_cap = logit_cap
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self.sliding_window_size = sliding_window_size or -1
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self.is_cross_attention = is_cross_attention
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self.use_irope = use_irope
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self.k_scale = None
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self.v_scale = None
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self.k_scale_float = None
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self.v_scale_float = None
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self.quant_method = None
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if quant_config is not None:
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self.quant_method = quant_config.get_quant_method(self, prefix=prefix)
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if self.quant_method is not None:
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self.quant_method.create_weights(self)
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self.attn_type = attn_type
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self.pos_encoding_mode = pos_encoding_mode
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self.logit_capping_method = logit_capping_method
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self.xai_temperature_len = -1
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def forward(
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self,
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q,
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k,
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v,
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forward_batch: ForwardBatch,
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save_kv_cache: bool = True,
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**kwargs,
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):
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if k is not None:
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# For cross-layer sharing, kv can be None
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assert v is not None
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if "k_rope" not in kwargs:
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k = k.view(-1, self.tp_k_head_num, self.qk_head_dim)
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v = v.view(-1, self.tp_v_head_num, self.v_head_dim)
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else:
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k = k.view(-1, self.tp_k_head_num, self.v_head_dim)
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return forward_batch.attn_backend.forward(
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q,
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k,
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v,
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self,
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forward_batch,
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save_kv_cache,
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**kwargs,
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)
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