mirror of
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138 lines
5.0 KiB
Python
138 lines
5.0 KiB
Python
"""
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# Copyright (c) 2024 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 Dict, Optional
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import numpy as np
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import paddle
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from paddle import nn
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from paddle.distributed import fleet
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from fastdeploy.config import FDConfig
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from .utils import get_tensor
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class ParallelLMHead(nn.Layer):
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"""
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"Parallelized LM head.
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"""
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def __init__(
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self,
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fd_config: FDConfig,
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num_embeddings: int,
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embedding_dim: int,
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prefix: str = "",
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with_bias: bool = False,
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) -> None:
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"""
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Parallelized LMhead.
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Args:
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fd_config (FDConfig): Arguments related to inference, containing
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attributes such as weight_dtype, act_dtype, mp_size, hidden_size, head_dim,
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num_attention_heads, and ffn_hidden_size.
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num_embeddings (int): vocabulary size.
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embedding_dim (int): size of hidden state.
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prefix (str): The name of current layer. Defaults to "".
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with_bias (bool): whether to have bias. Default: False.
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"""
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super(ParallelLMHead, self).__init__()
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self.weight_key: str = prefix + ".weight"
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if with_bias:
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self.bias_key: Optional[str] = prefix + ".bias"
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else:
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self.bias_key: Optional[str] = None
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self.use_ep: bool = fd_config.parallel_config.use_ep
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self.column_cut = True
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ColumnParallelLinear = fleet.meta_parallel.ColumnParallelLinear
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RowParallelLinear = fleet.meta_parallel.RowParallelLinear
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self.tie_word_embeddings: bool = fd_config.model_config.tie_word_embeddings
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if self.use_ep:
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self.weight = self.create_parameter(
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shape=[embedding_dim, num_embeddings],
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dtype=paddle.get_default_dtype(),
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is_bias=False,
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)
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else:
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if self.column_cut:
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need_gather = True
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self.linear = ColumnParallelLinear(
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embedding_dim,
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num_embeddings,
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mp_group=fleet.get_hybrid_communicate_group().get_model_parallel_group(),
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weight_attr=None,
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has_bias=True if self.bias_key is not None else False,
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gather_output=need_gather,
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fuse_matmul_bias=False, # False diff更小
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)
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else:
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self.linear = RowParallelLinear(
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embedding_dim,
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num_embeddings,
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mp_group=fleet.get_hybrid_communicate_group().get_model_parallel_group(),
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weight_attr=None,
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has_bias=True if self.bias_key is not None else False,
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input_is_parallel=False,
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fuse_matmul_bias=False, # False diff更小
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)
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def load_state_dict(self, state_dict: Dict[str, paddle.Tensor | np.ndarray]):
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"""
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Load the checkpoint state dictionary into the layer.
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Args:
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state_dict (dict): A dictionary containing the checkpoint weights and biases.
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"""
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if self.use_ep:
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self.weight.set_value(get_tensor(state_dict.pop(self.weight_key)).astype(paddle.get_default_dtype()))
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else:
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if self.tie_word_embeddings:
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self.linear.weight.set_value(
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get_tensor(state_dict.pop(self.weight_key)).astype(paddle.get_default_dtype()).transpose([1, 0])
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)
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else:
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weight_tensor = get_tensor(state_dict.pop(self.weight_key)).astype(paddle.get_default_dtype())
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if self.linear.weight.shape != weight_tensor.shape:
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weight_tensor = weight_tensor.transpose([1, 0])
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self.linear.weight.set_value(weight_tensor)
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if self.bias_key is not None:
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bias = get_tensor(state_dict.pop(self.bias_key)).astype(paddle.get_default_dtype())
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self.linear.bias.set_value(bias)
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def forward(self, input: paddle.Tensor) -> paddle.Tensor:
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"""
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Defines the forward computation of the layer.
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Args:
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input (Tensor): The input tensor to the layer.
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Returns:
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Tensor: The output tensor after processing through the layer.
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"""
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logits = input
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if self.use_ep:
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logits = paddle.matmul(logits, self.weight)
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else:
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logits = self.linear(logits)
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return logits
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