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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__all__ = []
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class UniformInitializer(Initializer):
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"""Implements the random uniform distribution initializer
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Args:
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low (float, optional): Lower boundary of the uniform distribution. Default is :math:`-1.0`.
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high (float, optional): Upper boundary of the uniform distribution. Default is :math:`1.0`.
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seed (int, optional): Random seed. Default is 0.
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diag_num (int, optional): the number of diagonal elements to initialize.
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If set to 0, diagonal initialization will be not performed. Default is 0.
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diag_step (int, optional): Step size between two diagonal elements,
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which is generally the width of the square matrix. Default is 0.
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diag_val (float, optional): the value of the diagonal element to be initialized,
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default 1.0. It takes effect only if the diag_num is greater than 0. Default is :math:`1.0`.
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"""
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def __init__(
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self,
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low: float = -1.0,
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high: float = 1.0,
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seed: int = 0,
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diag_num: int = 0,
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diag_step: int = 0,
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diag_val: float = 1.0,
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) -> None:
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assert low is not None
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assert high is not None
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assert high >= low
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assert seed is not None
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assert diag_num is not None
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assert diag_step is not None
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assert diag_val is not None
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if diag_num > 0 or diag_step > 0:
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assert diag_num > 0 and diag_step > 0
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super().__init__()
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self._low = low
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self._high = high
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self._seed = seed
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self._diag_num = diag_num
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self._diag_step = diag_step
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self._diag_val = diag_val
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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 Uniform 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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), (
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"Currently, uniform initializer not support lazy init for dist param."
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)
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block = self._check_block(block)
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assert isinstance(block, (framework.Block, pir.Block))
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if not in_dygraph_mode():
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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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"uniform_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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# to be compatible of fp16 initializers
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if var.dtype == core.VarDesc.VarType.FP16:
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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(['uniform_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.uniform(
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var.shape,
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out_dtype,
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self._low,
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self._high,
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self._seed,
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var.place if var.place._type() else _current_expected_place(),
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)
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if var.dtype == core.VarDesc.VarType.FP16:
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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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if var.dtype == core.DataType.FLOAT16:
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out_dtype = core.DataType.FLOAT32
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else:
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out_dtype = var.dtype
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out_var = _C_ops.uniform(
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var.shape,
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out_dtype,
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self._low,
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self._high,
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self._seed,
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_current_expected_place(),
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)
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if (
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var.dtype == core.DataType.FLOAT16
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and out_var.dtype != core.DataType.FLOAT16
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):
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return _C_ops.cast(out_var, var.dtype)
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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="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": var.shape,
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"dtype": out_dtype,
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"min": self._low,
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"max": self._high,
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"seed": self._seed,
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"diag_num": self._diag_num,
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"diag_step": self._diag_step,
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"diag_val": self._diag_val,
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},
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stop_gradient=True,
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)
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if var.dtype == core.VarDesc.VarType.FP16:
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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 Uniform(UniformInitializer):
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"""The uniform distribution initializer.
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Args:
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low (float, optional): Lower boundary of the uniform distribution. Default is :math:`-1.0`.
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high (float, optional): Upper boundary of the uniform distribution. Default is :math:`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 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.Uniform(low=-0.5, high=0.5),
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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.Uniform(low=-0.5, high=0.5),
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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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[[-0.48212373, 0.26492310],
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[ 0.17605734, -0.45379421]])
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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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[-0.11236754, 0.46462214])
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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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[[[-0.41843393, 0.27575102]],
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[[-0.41843393, 0.27575102]],
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[[-0.41843393, 0.27575102]]])
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"""
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def __init__(
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self, low: float = -1.0, high: float = 1.0, name: str | None = None
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) -> None:
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assert low is not None, 'low should not be None'
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assert high is not None, 'high should not be None'
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assert high >= low, 'high should greater or equal than low'
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super().__init__(
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low=low, high=high, seed=0, diag_num=0, diag_step=0, diag_val=1.0
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)
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