mirror of
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117 lines
4.3 KiB
Python
117 lines
4.3 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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"""
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from typing import Optional
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import paddle
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from paddle import nn
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from fastdeploy.worker.model_runner import ForwardMeta
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class Attention(nn.Layer):
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"""
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The AttentionLayer.
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"""
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def __init__(
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self,
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llm_config,
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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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rope_type: str = "",
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qkv_bias: Optional[paddle.Tensor] = None,
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qkv_scale: Optional[paddle.Tensor] = None,
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prefix: str = "",
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out_scale: float = -1.,
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linear_shift=None,
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linear_smooth=None,
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use_neox_rotary_style=False,
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) -> None:
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"""
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Initializes `LMLayer` with the given parameters.
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Args:
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llm_config (dict): The config of LM model.
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layer_id (int): The id of current layer.
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logit_cap (float, optional): The cap for logits. Defaults to 0.0.
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v_head_dim (int, optional): The head dim of value. Defaults to -1.
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rope_type (str, optional): The type of RoPE. Defaults to "".
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qkv_bias (Optional[paddle.Tensor], optional): The bias of QKV. Defaults to None.
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qkv_scale (Optional[paddle.Tensor], optional): The scale of QKV. Defaults to None.
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prefix (str, optional): The name of current layer. Defaults to "".
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linear_shift (Optional[paddle.Tensor], optional): The shift of linear. Defaults to None.
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linear_smooth (Optional[paddle.Tensor], optional): The smooth of linear. Defaults to None.
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Raises:
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ValueError: If the `v_head_dim` is less than 0.
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"""
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super().__init__()
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self.num_heads = llm_config.model_config.num_attention_heads // llm_config.parallel_config.mp_size
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self.head_dim = llm_config.model_config.hidden_size // llm_config.model_config.num_attention_heads
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self.kv_num_heads = llm_config.model_config.num_key_value_heads // llm_config.parallel_config.mp_size
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self.layer_id = layer_id
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self.logit_cap = logit_cap
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self.v_head_dim = v_head_dim if v_head_dim > 0 else self.head_dim
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self.rope_type = rope_type
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self.qk_head_dim = self.head_dim
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# not use
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self.tp_q_head_num = self.num_heads
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self.tp_k_head_num = self.num_heads
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self.tp_v_head_num = self.num_heads
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# not use
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self.scaling = 1.0 / (self.head_dim**0.5)
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self.linear_shift = linear_shift
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self.linear_smooth = linear_smooth
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self.qkv_bias = qkv_bias
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self.qkv_scale = qkv_scale
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self._dtype = self._helper.get_default_dtype()
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self.out_scale = out_scale
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self.use_neox_rotary_style = use_neox_rotary_style
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if llm_config.kvcache_config is not None:
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self.kvcache_quant_method = llm_config.kvcache_config.kvcache_quant_config.get_quant_method(
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self)
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self.kvcache_quant_method.create_weights(self)
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if llm_config.quant_config is not None:
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self.quant_max_bound = llm_config.quant_config.quant_max_bound
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self.quant_min_bound = llm_config.quant_config.quant_min_bound
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def forward(
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self,
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q: paddle.Tensor = None,
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k: paddle.Tensor = None,
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v: paddle.Tensor = None,
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qkv: paddle.Tensor = None,
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forward_meta: ForwardMeta = None,
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):
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"""
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The forward function of attention layer.
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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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forward_meta: the forward meta data
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"""
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return forward_meta.attn_backend.forward(
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q,
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k,
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v,
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qkv,
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self,
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forward_meta,
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)
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