wehub-resource-sync
240 lines
8.5 KiB
Python
240 lines
8.5 KiB
Python
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
from __future__ import annotations
|
|
|
|
import paddle
|
|
from paddle import _C_ops, pir
|
|
|
|
from ...base import core, framework, unique_name
|
|
from ...base.data_feeder import check_variable_and_dtype
|
|
from ...base.framework import (
|
|
_current_expected_place,
|
|
in_dygraph_mode,
|
|
in_pir_mode,
|
|
)
|
|
from .initializer import Initializer
|
|
|
|
__all__ = []
|
|
|
|
|
|
class UniformInitializer(Initializer):
|
|
"""Implements the random uniform distribution initializer
|
|
|
|
Args:
|
|
low (float, optional): Lower boundary of the uniform distribution. Default is :math:`-1.0`.
|
|
high (float, optional): Upper boundary of the uniform distribution. Default is :math:`1.0`.
|
|
seed (int, optional): Random seed. Default is 0.
|
|
diag_num (int, optional): the number of diagonal elements to initialize.
|
|
If set to 0, diagonal initialization will be not performed. Default is 0.
|
|
diag_step (int, optional): Step size between two diagonal elements,
|
|
which is generally the width of the square matrix. Default is 0.
|
|
diag_val (float, optional): the value of the diagonal element to be initialized,
|
|
default 1.0. It takes effect only if the diag_num is greater than 0. Default is :math:`1.0`.
|
|
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
low: float = -1.0,
|
|
high: float = 1.0,
|
|
seed: int = 0,
|
|
diag_num: int = 0,
|
|
diag_step: int = 0,
|
|
diag_val: float = 1.0,
|
|
) -> None:
|
|
assert low is not None
|
|
assert high is not None
|
|
assert high >= low
|
|
assert seed is not None
|
|
assert diag_num is not None
|
|
assert diag_step is not None
|
|
assert diag_val is not None
|
|
if diag_num > 0 or diag_step > 0:
|
|
assert diag_num > 0 and diag_step > 0
|
|
super().__init__()
|
|
self._low = low
|
|
self._high = high
|
|
self._seed = seed
|
|
self._diag_num = diag_num
|
|
self._diag_step = diag_step
|
|
self._diag_val = diag_val
|
|
|
|
def forward(
|
|
self, var: paddle.Tensor, block: pir.Block | None = None
|
|
) -> paddle.Tensor | None:
|
|
"""Initialize the input tensor with Uniform distribution.
|
|
|
|
Args:
|
|
var(Tensor): Tensor that needs to be initialized.
|
|
block(Block|None, optional): The block in which initialization ops
|
|
should be added. Used in static graph only, default None.
|
|
|
|
Returns:
|
|
The initialization op
|
|
"""
|
|
assert not (
|
|
isinstance(var, framework.EagerParamBase) and var.is_dist()
|
|
), (
|
|
"Currently, uniform initializer not support lazy init for dist param."
|
|
)
|
|
block = self._check_block(block)
|
|
|
|
assert isinstance(block, (framework.Block, pir.Block))
|
|
if not in_dygraph_mode():
|
|
check_variable_and_dtype(
|
|
var,
|
|
"Out",
|
|
["uint16", "float16", "float32", "float64"],
|
|
"uniform_random",
|
|
)
|
|
|
|
if self._seed == 0:
|
|
self._seed = block.program.random_seed
|
|
|
|
# to be compatible of fp16 initializers
|
|
if var.dtype == core.VarDesc.VarType.FP16:
|
|
out_dtype = core.VarDesc.VarType.FP32
|
|
out_var = block.create_var(
|
|
name=unique_name.generate(
|
|
".".join(['uniform_random', var.name, 'tmp'])
|
|
),
|
|
shape=var.shape,
|
|
dtype=out_dtype,
|
|
type=core.VarDesc.VarType.DENSE_TENSOR,
|
|
persistable=False,
|
|
)
|
|
else:
|
|
out_dtype = var.dtype
|
|
out_var = var
|
|
|
|
if in_dygraph_mode():
|
|
out_var = _C_ops.uniform(
|
|
var.shape,
|
|
out_dtype,
|
|
self._low,
|
|
self._high,
|
|
self._seed,
|
|
var.place if var.place._type() else _current_expected_place(),
|
|
)
|
|
if var.dtype == core.VarDesc.VarType.FP16:
|
|
var_tmp = _C_ops.cast(out_var, var.dtype)
|
|
var_tmp._share_underline_tensor_to(var)
|
|
else:
|
|
out_var._share_underline_tensor_to(var)
|
|
return None
|
|
elif in_pir_mode():
|
|
if var.dtype == core.DataType.FLOAT16:
|
|
out_dtype = core.DataType.FLOAT32
|
|
else:
|
|
out_dtype = var.dtype
|
|
out_var = _C_ops.uniform(
|
|
var.shape,
|
|
out_dtype,
|
|
self._low,
|
|
self._high,
|
|
self._seed,
|
|
_current_expected_place(),
|
|
)
|
|
if (
|
|
var.dtype == core.DataType.FLOAT16
|
|
and out_var.dtype != core.DataType.FLOAT16
|
|
):
|
|
return _C_ops.cast(out_var, var.dtype)
|
|
return out_var
|
|
else:
|
|
op = block.append_op(
|
|
type="uniform_random",
|
|
inputs={},
|
|
outputs={"Out": out_var},
|
|
attrs={
|
|
"shape": var.shape,
|
|
"dtype": out_dtype,
|
|
"min": self._low,
|
|
"max": self._high,
|
|
"seed": self._seed,
|
|
"diag_num": self._diag_num,
|
|
"diag_step": self._diag_step,
|
|
"diag_val": self._diag_val,
|
|
},
|
|
stop_gradient=True,
|
|
)
|
|
|
|
if var.dtype == core.VarDesc.VarType.FP16:
|
|
block.append_op(
|
|
type="cast",
|
|
inputs={"X": out_var},
|
|
outputs={"Out": var},
|
|
attrs={"in_dtype": out_var.dtype, "out_dtype": var.dtype},
|
|
)
|
|
|
|
var.op = op
|
|
return op
|
|
|
|
|
|
class Uniform(UniformInitializer):
|
|
"""The uniform distribution initializer.
|
|
|
|
Args:
|
|
low (float, optional): Lower boundary of the uniform distribution. Default is :math:`-1.0`.
|
|
high (float, optional): Upper boundary of the uniform distribution. Default is :math:`1.0`.
|
|
name (str|None, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None.
|
|
|
|
Returns:
|
|
A parameter initialized by uniform distribution.
|
|
|
|
Examples:
|
|
.. code-block:: pycon
|
|
|
|
>>> import paddle
|
|
>>> paddle.seed(1)
|
|
>>> data = paddle.ones(shape=[3, 1, 2], dtype='float32')
|
|
>>> weight_attr = paddle.framework.ParamAttr(
|
|
... name="linear_weight",
|
|
... initializer=paddle.nn.initializer.Uniform(low=-0.5, high=0.5),
|
|
... )
|
|
>>> bias_attr = paddle.framework.ParamAttr(
|
|
... name="linear_bias",
|
|
... initializer=paddle.nn.initializer.Uniform(low=-0.5, high=0.5),
|
|
... )
|
|
>>> linear = paddle.nn.Linear(2, 2, weight_attr=weight_attr, bias_attr=bias_attr)
|
|
>>> print(linear.weight)
|
|
Parameter containing:
|
|
Tensor(shape=[2, 2], dtype=float32, place=Place(cpu), stop_gradient=False,
|
|
[[-0.48212373, 0.26492310],
|
|
[ 0.17605734, -0.45379421]])
|
|
|
|
>>> print(linear.bias)
|
|
Parameter containing:
|
|
Tensor(shape=[2], dtype=float32, place=Place(cpu), stop_gradient=False,
|
|
[-0.11236754, 0.46462214])
|
|
|
|
>>> res = linear(data)
|
|
>>> print(res)
|
|
Tensor(shape=[3, 1, 2], dtype=float32, place=Place(cpu), stop_gradient=False,
|
|
[[[-0.41843393, 0.27575102]],
|
|
[[-0.41843393, 0.27575102]],
|
|
[[-0.41843393, 0.27575102]]])
|
|
"""
|
|
|
|
def __init__(
|
|
self, low: float = -1.0, high: float = 1.0, name: str | None = None
|
|
) -> None:
|
|
assert low is not None, 'low should not be None'
|
|
assert high is not None, 'high should not be None'
|
|
assert high >= low, 'high should greater or equal than low'
|
|
super().__init__(
|
|
low=low, high=high, seed=0, diag_num=0, diag_step=0, diag_val=1.0
|
|
)
|