wehub-resource-sync
145 lines
4.6 KiB
Python
145 lines
4.6 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 paddle
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from paddle import _C_ops
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from ...base import core, framework
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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_dynamic_or_pir_mode,
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)
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# TODO: define the initializers of Constant in neural network
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from .initializer import Initializer
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__all__ = []
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class ConstantInitializer(Initializer):
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"""Implements the constant initializer
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Args:
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value (float32, optional): constant value to initialize the variable. Default: 0.0.
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"""
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def __init__(self, value: float = 0.0, force_cpu: bool = False) -> None:
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assert value is not None
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super().__init__()
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self._value = value
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self._force_cpu = force_cpu
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def forward(
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self,
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var: paddle.Tensor,
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block: paddle.pir.Block | None = None,
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) -> paddle.Tensor | None:
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"""Initialize the input tensor with constant.
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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(
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var,
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(
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framework.Variable,
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framework.EagerParamBase,
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paddle.pir.Value,
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paddle.pir.core.ParameterMeta,
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),
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)
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assert isinstance(block, (framework.Block, paddle.pir.Block))
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if in_dynamic_or_pir_mode():
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place = _current_expected_place()
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if self._force_cpu:
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place = core.CPUPlace()
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if in_dygraph_mode():
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if isinstance(var, framework.EagerParamBase) and var.is_dist():
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out_var = _C_ops.full(
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var._local_shape, float(self._value), var.dtype, place
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)
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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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else:
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_C_ops.full_(
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var, var.shape, float(self._value), var.dtype, place
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)
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return None
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else:
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return _C_ops.full(
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var.shape, float(self._value), var.dtype, place
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)
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else:
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op = block.append_op(
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type="fill_constant",
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outputs={"Out": var},
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attrs={
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"shape": var.shape,
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"dtype": int(var.dtype),
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"value": float(self._value),
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'str_value': str(float(self._value)),
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'force_cpu': self._force_cpu,
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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 Constant(ConstantInitializer):
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"""Implement the constant initializer.
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Args:
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value (float32|float64, optional): constant value to initialize the parameter. Default: 0.0.
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Examples:
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.. code-block:: pycon
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>>> import paddle
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>>> import paddle.nn as nn
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>>> paddle.seed(2023)
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>>> data = paddle.rand([30, 10, 2], dtype='float32')
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>>> linear = nn.Linear(2, 4, weight_attr=nn.initializer.Constant(value=2.0))
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>>> res = linear(data)
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>>> print(linear.weight)
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Parameter containing:
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Tensor(shape=[2, 4], dtype=float32, place=Place(cpu), stop_gradient=False,
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[[2., 2., 2., 2.],
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[2., 2., 2., 2.]])
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"""
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def __init__(self, value: float = 0.0) -> None:
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if value is None:
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raise ValueError("value must not be none.")
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super().__init__(value=value, force_cpu=False)
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