# coding=utf-8
# Copyright 2021 The OneFlow Authors. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
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import oneflow as flow
from oneflow import nn
from libai.layers import Linear, build_activation
[docs]class MLP(nn.Module):
"""MLP
MLP will take the input with h hidden state, project it to intermediate
hidden dimension, perform gelu transformation, and project the
state back into h hidden dimension.
Arguments:
hidden_size: size of each input and output sample.
ffn_hidden_size: size of each intermediate sample.
output_dropout_prob: Output dropout probability. Defaults to 0.0.
init_method: method to initialize the first linear weight.
Defaults to :func:`nn.init.xavier_normal_`.
output_layer_init_method: method to initialize the second linear weight. If set to None,
it will use ``init_method`` instead. Defaults to None.
bias_gelu_fusion: If set to ``True``, it will fuse bias adding and elementwise
gelu activation. Defaults to ``False``.
bias_dropout_fusion: If set to ``True``, it will fuse bias adding and dropout.
Defaults to ``False``.
layer_idx: A layer_idx sign which determines the placement. It will be used in
pipeline parallelism. Defaults to 0.
"""
def __init__(
self,
hidden_size,
ffn_hidden_size,
output_dropout_prob=0.0,
init_method=nn.init.xavier_normal_,
output_layer_init_method=None,
bias_gelu_fusion=False,
bias_dropout_fusion=False,
*,
layer_idx=0,
):
super().__init__()
self.output_dropout_prob = output_dropout_prob
self.bias_gelu_fusion = bias_gelu_fusion
self.bias_dropout_fusion = bias_dropout_fusion
if output_layer_init_method is None:
output_layer_init_method = init_method
self.dense_h_to_4h = Linear(
hidden_size,
ffn_hidden_size,
bias=True,
parallel="col",
skip_bias_add=bias_gelu_fusion,
init_method=init_method,
layer_idx=layer_idx,
)
if not bias_gelu_fusion:
self.activation_func = build_activation("gelu")
self.dense_4h_to_h = Linear(
ffn_hidden_size,
hidden_size,
bias=True,
parallel="row",
skip_bias_add=bias_dropout_fusion,
init_method=output_layer_init_method,
layer_idx=layer_idx,
)
if not bias_dropout_fusion:
self.dropout = nn.Dropout(self.output_dropout_prob)
def forward(self, hidden_states):
intermediate = self.dense_h_to_4h(hidden_states)
if self.bias_gelu_fusion:
intermediate, bias = intermediate
intermediate = flow._C.fused_bias_add_gelu(
intermediate, bias, axis=intermediate.ndim - 1
)
else:
intermediate = self.activation_func(intermediate)
output = self.dense_4h_to_h(intermediate)
if self.bias_dropout_fusion:
output, bias = output
output = flow._C.fused_bias_add_dropout(
output, bias, p=self.output_dropout_prob, axis=output.ndim - 1
)
else:
output = self.dropout(output)
return output