wehub-resource-sync
433 lines
15 KiB
Python
433 lines
15 KiB
Python
# Copyright (c) 2020 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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from __future__ import annotations
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import math
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import paddle
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from paddle import _C_ops
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from ...base import core, framework, unique_name
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from ...base.data_feeder import check_variable_and_dtype
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from ...base.framework import (
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_current_expected_place,
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in_dygraph_mode,
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in_pir_mode,
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)
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from .initializer import Initializer
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__all__ = []
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class XavierInitializer(Initializer):
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r"""
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This class implements the Xavier weight initializer from the paper
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`Understanding the difficulty of training deep feedforward neural
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networks <http://proceedings.mlr.press/v9/glorot10a/glorot10a.pdf>`_
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by Xavier Glorot and Yoshua Bengio.
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This initializer is designed to keep the scale of the gradients
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approximately same in all the layers. In case of Uniform distribution,
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the range is [-x, x], where
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.. math::
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x = gain \times \sqrt{\\frac{6.0}{fan\_in + fan\_out}}
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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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gain \times \sqrt{\\frac{2.0}{fan\_in + fan\_out}}
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Args:
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uniform (bool, optional): whether to use uniform ,if False use normal distribution. Default is True.
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fan_in (float|None, optional): fan_in for Xavier initialization. If None, it is
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inferred from the variable. Default is None.
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fan_out (float|None, optional): fan_out for Xavier initialization. If None, it is
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inferred from the variable. Default is None.
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seed (int, optional): Random seed. Default is 0.
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gain (float, optional): Scaling Tensor. Default is 1.0.
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Note:
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It is recommended to set fan_in and fan_out to None for most cases.
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"""
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def __init__(
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self,
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uniform: bool = True,
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fan_in: float | None = None,
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fan_out: float | None = None,
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seed: int = 0,
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gain: float = 1.0,
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) -> None:
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assert uniform is not None
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assert seed is not None
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super().__init__()
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self._uniform = uniform
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self._fan_in = fan_in
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self._fan_out = fan_out
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self._seed = seed
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self._gain = gain
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def forward(
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self, var: paddle.Tensor, block: paddle.pir.Block | None = None
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) -> paddle.Tensor | None:
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"""Initialize the input tensor with Xavier initialization.
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Args:
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var(Tensor): Tensor that needs to be initialized.
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block(Block|None, 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(block, (framework.Block, paddle.pir.Block))
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if not isinstance(var, paddle.pir.core.ParameterMeta):
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check_variable_and_dtype(
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var,
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"Out",
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["uint16", "float16", "float32", "float64"],
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"xavier_init",
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)
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f_in, f_out = self._compute_fans(var)
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# If fan_in and fan_out are passed, use them
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fan_in = f_in if self._fan_in is None else self._fan_in
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fan_out = f_out if self._fan_out is None else self._fan_out
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if self._seed == 0:
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self._seed = block.program.random_seed
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out_var_shape = (
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var._local_shape
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if (isinstance(var, framework.EagerParamBase) and var.is_dist())
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else var.shape
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)
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# to be compatible of fp16 initializers
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origin_dtype = var.dtype
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if origin_dtype == core.VarDesc.VarType.FP16 or (
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origin_dtype == core.VarDesc.VarType.BF16 and not self._uniform
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):
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out_dtype = core.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(['xavier_init', var.name, 'tmp'])
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),
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shape=out_var_shape,
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dtype=out_dtype,
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type=core.VarDesc.VarType.DENSE_TENSOR,
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persistable=False,
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)
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elif (
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origin_dtype in (core.DataType.FLOAT16, core.DataType.BFLOAT16)
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and not self._uniform
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):
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out_dtype = core.DataType.FLOAT32
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out_var = var
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else:
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out_dtype = origin_dtype
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out_var = var
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if in_dygraph_mode():
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if self._uniform:
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if 0 in [fan_in, fan_out]:
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limit = 0.0
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else:
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limit = self._gain * math.sqrt(
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6.0 / float(fan_in + fan_out)
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)
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out_var = _C_ops.uniform(
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out_var_shape,
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out_dtype,
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-limit,
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limit,
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self._seed,
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_current_expected_place(),
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)
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else:
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if 0 in [fan_in, fan_out]:
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std = 0.0
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else:
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std = self._gain * math.sqrt(2.0 / float(fan_in + fan_out))
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place = _current_expected_place()
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out_var = _C_ops.gaussian(
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out_var_shape,
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0.0,
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std,
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self._seed,
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out_dtype,
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place,
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)
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if origin_dtype == core.VarDesc.VarType.FP16 or (
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origin_dtype
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in [
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core.VarDesc.VarType.BF16,
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core.DataType.FLOAT16,
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core.DataType.BFLOAT16,
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]
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and not self._uniform
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):
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out_var = _C_ops.cast(out_var, origin_dtype)
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if isinstance(var, framework.EagerParamBase) and var.is_dist():
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# lazy init for dist tensor
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out_var = (
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paddle.distributed.auto_parallel.api.dtensor_from_local(
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out_var, var.process_mesh, var.placements
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)
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)
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out_var._share_underline_tensor_to(var)
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return None
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elif in_pir_mode():
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if self._uniform:
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if 0 in [fan_in, fan_out]:
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limit = 0.0
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else:
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limit = self._gain * math.sqrt(
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6.0 / float(fan_in + fan_out)
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)
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out_var = paddle._pir_ops.uniform(
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out_var.shape,
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out_dtype,
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-limit,
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limit,
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self._seed,
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_current_expected_place(),
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)
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else:
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if 0 in [fan_in, fan_out]:
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std = 0.0
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else:
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std = self._gain * math.sqrt(2.0 / float(fan_in + fan_out))
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out_var = _C_ops.gaussian(
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out_var.shape,
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0.0,
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std,
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self._seed,
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out_dtype,
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_current_expected_place(),
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)
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if (
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origin_dtype in (core.DataType.FLOAT16, core.DataType.BFLOAT16)
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and not self._uniform
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):
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return _C_ops.cast(out_var, origin_dtype)
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return out_var
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else:
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if self._uniform:
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if 0 in [fan_in, fan_out]:
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limit = 0.0
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else:
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limit = self._gain * math.sqrt(
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6.0 / float(fan_in + fan_out)
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)
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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": 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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)
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else:
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if 0 in [fan_in, fan_out]:
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std = 0.0
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else:
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std = self._gain * math.sqrt(2.0 / float(fan_in + fan_out))
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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": out_var.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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)
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if origin_dtype == core.VarDesc.VarType.FP16 or (
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origin_dtype == core.VarDesc.VarType.BF16 and not self._uniform
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):
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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={
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"in_dtype": out_var.dtype,
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"out_dtype": origin_dtype,
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},
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)
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var.op = op
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return op
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class XavierNormal(XavierInitializer):
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r"""
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This class implements the Xavier weight initializer from the paper
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`Understanding the difficulty of training deep feedforward neural
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networks <http://proceedings.mlr.press/v9/glorot10a/glorot10a.pdf>`_
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by Xavier Glorot and Yoshua Bengio, using a normal distribution whose mean is :math:`0` and standard deviation is
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.. math::
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gain \times \sqrt{\frac{2.0}{fan\_in + fan\_out}}.
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Args:
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fan_in (float|None, optional): fan_in for Xavier initialization, which is
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inferred from the Tensor. Default is None.
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fan_out (float|None, optional): fan_out for Xavier initialization, which is
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inferred from the Tensor. Default is None.
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gain (float, optional): Scaling Tensor. Default is 1.0.
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name (str|None, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None.
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Returns:
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A parameter initialized by Xavier weight, using a normal distribution.
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Examples:
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.. code-block:: pycon
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>>> import paddle
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>>> paddle.seed(1)
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>>> data = paddle.ones(shape=[3, 1, 2], dtype='float32')
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>>> weight_attr = paddle.framework.ParamAttr(
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... name="linear_weight",
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... initializer=paddle.nn.initializer.XavierNormal(),
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... )
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>>> bias_attr = paddle.framework.ParamAttr(
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... name="linear_bias",
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... initializer=paddle.nn.initializer.XavierNormal(),
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... )
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>>> linear = paddle.nn.Linear(2, 2, weight_attr=weight_attr, bias_attr=bias_attr)
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>>> print(linear.weight)
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Parameter containing:
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Tensor(shape=[2, 2], dtype=float32, place=Place(cpu), stop_gradient=False,
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[[-0.21607460, 0.08382989],
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[ 0.29147008, -0.07049121]])
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>>> print(linear.bias)
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Parameter containing:
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Tensor(shape=[2], dtype=float32, place=Place(cpu), stop_gradient=False,
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[1.06076419, 0.87684733])
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>>> res = linear(data)
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>>> print(res)
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Tensor(shape=[3, 1, 2], dtype=float32, place=Place(cpu), stop_gradient=False,
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[[[1.13615966, 0.89018601]],
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[[1.13615966, 0.89018601]],
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[[1.13615966, 0.89018601]]])
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"""
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def __init__(
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self,
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fan_in: float | None = None,
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fan_out: float | None = None,
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gain: float = 1.0,
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name: str | None = None,
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) -> None:
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super().__init__(
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uniform=False, fan_in=fan_in, fan_out=fan_out, seed=0, gain=gain
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)
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class XavierUniform(XavierInitializer):
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r"""
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This class implements the Xavier weight initializer from the paper
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`Understanding the difficulty of training deep feedforward neural
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networks <http://proceedings.mlr.press/v9/glorot10a/glorot10a.pdf>`_
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by Xavier Glorot and Yoshua Bengio.
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This initializer is designed to keep the scale of the gradients
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approximately same in all the layers. In case of Uniform distribution,
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the range is :math:`[-x,x]`, where
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.. math::
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x = gain \times \sqrt{\frac{6.0}{fan\_in + fan\_out}}.
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Args:
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fan_in (float|None, optional): fan_in for Xavier initialization, which is
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inferred from the Tensor. Default is None.
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fan_out (float|None, optional): fan_out for Xavier initialization, which is
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inferred from the Tensor. Default is None.
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gain (float, optional): Scaling Tensor. Default is 1.0.
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name (str|None, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None.
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Returns:
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A parameter initialized by Xavier weight, using a uniform distribution.
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Examples:
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.. code-block:: pycon
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>>> import paddle
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>>> paddle.seed(1)
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>>> data = paddle.ones(shape=[3, 1, 2], dtype='float32')
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>>> weight_attr = paddle.framework.ParamAttr(
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... name="linear_weight",
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... initializer=paddle.nn.initializer.XavierUniform(),
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... )
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>>> bias_attr = paddle.framework.ParamAttr(
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... name="linear_bias",
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... initializer=paddle.nn.initializer.XavierUniform(),
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... )
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>>> linear = paddle.nn.Linear(2, 2, weight_attr=weight_attr, bias_attr=bias_attr)
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>>> print(linear.weight)
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Parameter containing:
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Tensor(shape=[2, 2], dtype=float32, place=Place(cpu), stop_gradient=False,
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[[-1.18095720, 0.64892638],
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[ 0.43125069, -1.11156428]])
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>>> print(linear.bias)
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Parameter containing:
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Tensor(shape=[2], dtype=float32, place=Place(cpu), stop_gradient=False,
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[-0.27524316, 1.13808715])
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>>> res = linear(data)
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>>> print(res)
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Tensor(shape=[3, 1, 2], dtype=float32, place=Place(cpu), stop_gradient=False,
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[[[-1.02494967, 0.67544925]],
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[[-1.02494967, 0.67544925]],
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[[-1.02494967, 0.67544925]]])
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"""
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def __init__(
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self,
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fan_in: float | None = None,
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fan_out: float | None = None,
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gain: float = 1.0,
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name: str | None = None,
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) -> None:
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super().__init__(
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uniform=True, fan_in=fan_in, fan_out=fan_out, seed=0, gain=gain
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)
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