Merge pull request #1577 from Jackwaterveg/change_init
[ASR] change default initializer to kaiming_uniformpull/1591/head
Jackwaterveg
4 years ago
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GitHub
commit
fcc1762048
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# Copyright (c) 2022 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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import paddle
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from paddle import nn
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from paddlespeech.s2t.modules.initializer import KaimingUniform
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"""
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To align the initializer between paddle and torch,
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the API below are set defalut initializer with priority higger than global initializer.
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"""
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global_init_type = None
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class LayerNorm(nn.LayerNorm):
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def __init__(self,
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normalized_shape,
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epsilon=1e-05,
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weight_attr=None,
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bias_attr=None,
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name=None):
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if weight_attr is None:
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weight_attr = paddle.ParamAttr(
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initializer=nn.initializer.Constant(1.0))
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if bias_attr is None:
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bias_attr = paddle.ParamAttr(
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initializer=nn.initializer.Constant(0.0))
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super(LayerNorm, self).__init__(normalized_shape, epsilon, weight_attr,
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bias_attr, name)
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class BatchNorm1D(nn.BatchNorm1D):
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def __init__(self,
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num_features,
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momentum=0.9,
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epsilon=1e-05,
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weight_attr=None,
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bias_attr=None,
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data_format='NCL',
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name=None):
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if weight_attr is None:
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weight_attr = paddle.ParamAttr(
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initializer=nn.initializer.Constant(1.0))
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if bias_attr is None:
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bias_attr = paddle.ParamAttr(
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initializer=nn.initializer.Constant(0.0))
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super(BatchNorm1D,
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self).__init__(num_features, momentum, epsilon, weight_attr,
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bias_attr, data_format, name)
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class Embedding(nn.Embedding):
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def __init__(self,
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num_embeddings,
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embedding_dim,
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padding_idx=None,
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sparse=False,
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weight_attr=None,
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name=None):
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if weight_attr is None:
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weight_attr = paddle.ParamAttr(initializer=nn.initializer.Normal())
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super(Embedding, self).__init__(num_embeddings, embedding_dim,
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padding_idx, sparse, weight_attr, name)
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class Linear(nn.Linear):
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def __init__(self,
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in_features,
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out_features,
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weight_attr=None,
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bias_attr=None,
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name=None):
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if weight_attr is None:
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if global_init_type == "kaiming_uniform":
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weight_attr = paddle.ParamAttr(initializer=KaimingUniform())
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if bias_attr is None:
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if global_init_type == "kaiming_uniform":
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bias_attr = paddle.ParamAttr(initializer=KaimingUniform())
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super(Linear, self).__init__(in_features, out_features, weight_attr,
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bias_attr, name)
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class Conv1D(nn.Conv1D):
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def __init__(self,
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in_channels,
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out_channels,
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kernel_size,
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stride=1,
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padding=0,
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dilation=1,
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groups=1,
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padding_mode='zeros',
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weight_attr=None,
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bias_attr=None,
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data_format='NCL'):
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if weight_attr is None:
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if global_init_type == "kaiming_uniform":
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print("set kaiming_uniform")
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weight_attr = paddle.ParamAttr(initializer=KaimingUniform())
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if bias_attr is None:
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if global_init_type == "kaiming_uniform":
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bias_attr = paddle.ParamAttr(initializer=KaimingUniform())
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super(Conv1D, self).__init__(
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in_channels, out_channels, kernel_size, stride, padding, dilation,
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groups, padding_mode, weight_attr, bias_attr, data_format)
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class Conv2D(nn.Conv2D):
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def __init__(self,
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in_channels,
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out_channels,
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kernel_size,
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stride=1,
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padding=0,
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dilation=1,
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groups=1,
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padding_mode='zeros',
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weight_attr=None,
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bias_attr=None,
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data_format='NCHW'):
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if weight_attr is None:
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if global_init_type == "kaiming_uniform":
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weight_attr = paddle.ParamAttr(initializer=KaimingUniform())
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if bias_attr is None:
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if global_init_type == "kaiming_uniform":
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bias_attr = paddle.ParamAttr(initializer=KaimingUniform())
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super(Conv2D, self).__init__(
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in_channels, out_channels, kernel_size, stride, padding, dilation,
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groups, padding_mode, weight_attr, bias_attr, data_format)
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@ -0,0 +1,172 @@
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# Copyright (c) 2018 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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import numpy as np
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from paddle.fluid import framework
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from paddle.fluid import unique_name
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from paddle.fluid.core import VarDesc
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from paddle.fluid.initializer import MSRAInitializer
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__all__ = ['KaimingUniform']
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class KaimingUniform(MSRAInitializer):
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r"""Implements the Kaiming Uniform initializer
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This class implements the weight initialization from the paper
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`Delving Deep into Rectifiers: Surpassing Human-Level Performance on
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ImageNet Classification <https://arxiv.org/abs/1502.01852>`_
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by Kaiming He, Xiangyu Zhang, Shaoqing Ren and Jian Sun. This is a
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robust initialization method that particularly considers the rectifier
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nonlinearities.
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In case of Uniform distribution, the range is [-x, x], where
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.. math::
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x = \sqrt{\frac{1.0}{fan\_in}}
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In case of Normal distribution, the mean is 0 and the standard deviation
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is
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.. math::
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\sqrt{\\frac{2.0}{fan\_in}}
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Args:
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fan_in (float32|None): fan_in for Kaiming uniform Initializer. If None, it is\
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inferred from the variable. default is None.
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Note:
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It is recommended to set fan_in to None for most cases.
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Examples:
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.. code-block:: python
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import paddle
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import paddle.nn as nn
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linear = nn.Linear(2,
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4,
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weight_attr=nn.initializer.KaimingUniform())
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data = paddle.rand([30, 10, 2], dtype='float32')
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res = linear(data)
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"""
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def __init__(self, fan_in=None):
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super(KaimingUniform, self).__init__(
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uniform=True, fan_in=fan_in, seed=0)
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def __call__(self, var, block=None):
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"""Initialize the input tensor with MSRA initialization.
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Args:
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var(Tensor): Tensor that needs to be initialized.
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block(Block, optional): The block in which initialization ops
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should be added. Used in static graph only, default None.
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Returns:
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The initialization op
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"""
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block = self._check_block(block)
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assert isinstance(var, framework.Variable)
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assert isinstance(block, framework.Block)
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f_in, f_out = self._compute_fans(var)
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# If fan_in is passed, use it
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fan_in = f_in if self._fan_in is None else self._fan_in
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if self._seed == 0:
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self._seed = block.program.random_seed
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# to be compatible of fp16 initalizers
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if var.dtype == VarDesc.VarType.FP16 or (
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var.dtype == VarDesc.VarType.BF16 and not self._uniform):
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out_dtype = VarDesc.VarType.FP32
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out_var = block.create_var(
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name=unique_name.generate(
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".".join(['masra_init', var.name, 'tmp'])),
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shape=var.shape,
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dtype=out_dtype,
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type=VarDesc.VarType.LOD_TENSOR,
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persistable=False)
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else:
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out_dtype = var.dtype
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out_var = var
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if self._uniform:
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limit = np.sqrt(1.0 / float(fan_in))
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op = block.append_op(
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type="uniform_random",
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inputs={},
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outputs={"Out": out_var},
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attrs={
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"shape": out_var.shape,
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"dtype": int(out_dtype),
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"min": -limit,
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"max": limit,
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"seed": self._seed
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},
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stop_gradient=True)
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else:
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std = np.sqrt(2.0 / float(fan_in))
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op = block.append_op(
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type="gaussian_random",
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outputs={"Out": out_var},
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attrs={
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"shape": out_var.shape,
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"dtype": int(out_dtype),
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"mean": 0.0,
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"std": std,
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"seed": self._seed
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},
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stop_gradient=True)
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if var.dtype == VarDesc.VarType.FP16 or (
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var.dtype == VarDesc.VarType.BF16 and not self._uniform):
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block.append_op(
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type="cast",
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inputs={"X": out_var},
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outputs={"Out": var},
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attrs={"in_dtype": out_var.dtype,
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"out_dtype": var.dtype})
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if not framework.in_dygraph_mode():
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var.op = op
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return op
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class DefaultInitializerContext(object):
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"""
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egs:
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with DefaultInitializerContext("kaiming_uniform"):
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code for setup_model
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"""
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def __init__(self, init_type=None):
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self.init_type = init_type
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def __enter__(self):
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if self.init_type is None:
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return
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else:
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from paddlespeech.s2t.modules import align
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align.global_init_type = self.init_type
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return
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def __exit__(self, exc_type, exc_val, exc_tb):
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from paddlespeech.s2t.modules import align
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align.global_init_type = None
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