chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
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# 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 paddle
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from paddle import _C_ops, pir
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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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from .lazy_init import lazy_init_helper
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__all__ = []
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class NormalInitializer(Initializer):
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"""Implements the Random Normal(Gaussian) distribution initializer
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Args:
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loc (float|complex, optional): mean of the normal distribution. Default is 0.0.
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scale (float, optional): standard deviation of the normal distribution. Default is 1.0.
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seed (int, optional): random seed. Default is 0.
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"""
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def __init__(
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self, loc: float = 0.0, scale: float = 1.0, seed: int = 0
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) -> None:
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assert loc is not None
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assert scale is not None
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assert seed is not None
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super().__init__()
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self._mean = loc
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self._std_dev = scale
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self._seed = seed
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if isinstance(self._mean, complex):
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if self._mean.real != self._mean.imag:
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raise ValueError(
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"if mean is a complex number, its real part should equal imag part, "
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f"but got real part: {self._mean.real} != imag part: {self._mean.imag}"
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)
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self._mean = self._mean.real
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def forward(
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self, var: paddle.Tensor, block: pir.Block | None = None
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) -> paddle.Tensor | None:
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"""Initialize the input tensor with Normal distribution.
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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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assert not (
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isinstance(var, framework.EagerParamBase) and var.is_dist()
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), "Currently, normal initializer not support lazy init for dist param."
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block = self._check_block(block)
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assert isinstance(block, (framework.Block, pir.Block))
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check_variable_and_dtype(
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var,
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"Out",
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[
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"uint16",
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"float16",
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"float32",
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"float64",
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"complex64",
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"complex128",
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],
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"gaussian_random",
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)
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if self._seed == 0:
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self._seed = block.program.random_seed
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if in_dygraph_mode():
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place = _current_expected_place()
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out_var = _C_ops.gaussian(
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var.shape,
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self._mean,
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self._std_dev,
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self._seed,
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var.dtype,
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place,
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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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place = _current_expected_place()
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out_var = _C_ops.gaussian(
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var.shape,
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self._mean,
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self._std_dev,
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self._seed,
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var.dtype,
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place,
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)
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return out_var
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else:
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op = block.append_op(
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type="gaussian_random",
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outputs={"Out": var},
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attrs={
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"shape": var.shape,
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"dtype": var.dtype,
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"mean": self._mean,
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"std": self._std_dev,
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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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var.op = op
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return op
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class Normal(NormalInitializer):
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"""The Random Normal (Gaussian) distribution initializer.
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Args:
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mean (float|complex, optional): mean of the normal distribution. Default is 0.0.
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std (float, optional): standard deviation of the normal distribution. Default is 1.0.
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name(str|None, optional): The default value is None. Normally there is no need for user to set this
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property. For more information, please refer to :ref:`api_guide_Name`. Default: None.
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Returns:
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A parameter initialized by Random Normal (Gaussian) distribution.
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Examples:
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.. code-block:: pycon
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>>> import paddle
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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.Normal(mean=0.0, std=2.0),
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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.Normal(mean=0.0, std=2.0),
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... )
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>>> # doctest: +SKIP('name has been used')
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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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[[ 2.1973135 -2.2697184],
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[-1.9104223 -1.0541488]])
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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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[ 0.7885926 -0.74719954])
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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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[[[ 1.0754838 -4.071067 ]],
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[[ 1.0754838 -4.071067 ]],
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[[ 1.0754838 -4.071067 ]]])
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"""
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def __init__(
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self, mean: float = 0.0, std: float = 1.0, name: str | None = None
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) -> None:
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assert mean is not None, 'mean should not be None'
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assert std is not None, 'std should not be None'
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super().__init__(loc=mean, scale=std, seed=0)
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class TruncatedNormalInitializer(Initializer):
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"""Implements the Random TruncatedNormal(Gaussian) distribution initializer
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Note:
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It is better to set `a <= mean <= b`.
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If `mean < a - 2*std` or `mean > b + 2*std`, the distribution of values may be incorrect.
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Args:
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loc (float, optional): Mean of the normal distribution. Default is :math:`0.0`.
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scale (float, optional): Standard deviation of the normal distribution. Default is :math:`1.0`.
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seed (int, optional): random seed. Default is 0.
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a (float, optional): The minimum cutoff value. Default is -2.0.
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b (float, optional): The maximum cutoff value. Default is 2.0.
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"""
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def __init__(
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self,
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loc: float = 0.0,
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scale: float = 1.0,
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seed: int = 0,
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a: float = -2.0,
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b: float = 2.0,
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) -> None:
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assert loc is not None
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assert scale is not None
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assert seed is not None
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assert a is not None
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assert b is not None
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super().__init__()
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self._mean = loc
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self._std_dev = scale
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self._seed = seed
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self._a = a
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self._b = b
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def forward(
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self, var: paddle.Tensor, block: pir.Block | None = None
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) -> paddle.Tensor | None:
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"""Initialize the input tensor with TruncatedNormal distribution.
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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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if lazy_init_helper().state:
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expected = (
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framework.Variable,
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paddle.pir.core.ParameterMeta,
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core.eager.Tensor,
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)
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else:
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expected = (
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framework.Variable,
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paddle.pir.Value,
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paddle.pir.core.ParameterMeta,
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)
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assert isinstance(var, expected)
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assert isinstance(block, (framework.Block, pir.Block))
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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 initializers
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if var.dtype in [core.VarDesc.VarType.FP16, core.VarDesc.VarType.BF16]:
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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(['truncated_gaussian_random', var.name, 'tmp'])
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),
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shape=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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else:
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out_dtype = var.dtype
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out_var = var
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if in_dygraph_mode():
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out_var = _C_ops.truncated_gaussian_random(
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var.shape,
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self._mean,
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self._std_dev,
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self._seed,
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self._a,
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self._b,
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out_dtype,
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_current_expected_place(),
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)
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if var.dtype in [
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core.VarDesc.VarType.FP16,
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core.VarDesc.VarType.BF16,
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]:
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var_tmp = _C_ops.cast(out_var, var.dtype)
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var_tmp._share_underline_tensor_to(var)
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else:
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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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out_var = _C_ops.truncated_gaussian_random(
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var.shape,
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self._mean,
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self._std_dev,
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self._seed,
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self._a,
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self._b,
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out_dtype,
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_current_expected_place(),
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)
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if var.dtype in [
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core.VarDesc.VarType.FP16,
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core.VarDesc.VarType.BF16,
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]:
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var_tmp = _C_ops.cast(out_var, var.dtype)
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var_tmp._share_underline_tensor_to(var)
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return out_var
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else:
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op = block.append_op(
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type="truncated_gaussian_random",
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outputs={"Out": out_var},
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attrs={
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"shape": var.shape,
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"dtype": out_dtype,
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"mean": self._mean,
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"std": self._std_dev,
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"seed": self._seed,
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"a": self._a,
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"b": self._b,
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},
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stop_gradient=True,
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)
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if var.dtype in [
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core.VarDesc.VarType.FP16,
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core.VarDesc.VarType.BF16,
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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={"in_dtype": out_var.dtype, "out_dtype": var.dtype},
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)
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var.op = op
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return op
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class TruncatedNormal(TruncatedNormalInitializer):
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"""The truncated normal distribution (Gaussian distribution) initializer.
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Note:
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It is better to set `a <= mean <= b`.
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If `mean < a - 2*std` or `mean > b + 2*std`, the distribution of values may be incorrect.
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Args:
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mean (float, optional): Mean of the normal distribution. Default is :math:`0.0`.
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std (float, optional): Standard deviation of the normal distribution. Default is :math:`1.0`.
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a (float, optional): The minimum cutoff value. Default is -2.0.
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b (float, optional): The maximum cutoff value. Default is 2.0.
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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 truncated normal distribution (Gaussian distribution).
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Examples:
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.. code-block:: pycon
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>>> import paddle
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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.TruncatedNormal(mean=0.0, std=2.0),
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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.TruncatedNormal(mean=0.0, std=2.0),
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... )
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>>> # doctest: +SKIP('name has been used')
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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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[[-1.0981836 1.4140984],
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[ 3.1390522 -2.8266568]])
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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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[ -2.1546738 -1.6570673])
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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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[[[-0.11380529 -3.0696259 ]],
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[[-0.11380529 -3.0696259 ]],
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[[-0.11380529 -3.0696259 ]]])
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"""
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def __init__(
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self,
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mean: float = 0.0,
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std: float = 1.0,
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a: float = -2.0,
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b: float = 2.0,
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name: str | None = None,
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) -> None:
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assert mean is not None, 'mean should not be None'
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assert std is not None, 'std should not be None'
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assert a is not None, 'a should not be None'
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assert b is not None, 'b should not be None'
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super().__init__(loc=mean, scale=std, seed=0, a=a, b=b)
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