wehub-resource-sync
71 lines
2.3 KiB
Python
71 lines
2.3 KiB
Python
# Copyright (c) 2023 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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from typing import TYPE_CHECKING
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import paddle
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if TYPE_CHECKING:
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from collections.abc import Iterable
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from paddle import Tensor
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__all__ = []
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@paddle.autograd.no_grad()
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def clip_grad_value_(
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parameters: Iterable[Tensor] | Tensor,
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clip_value: float,
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) -> None:
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r"""
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Clips gradient of an iterable of parameters at specified value.
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The gradient will be modified in place.
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This API can only run in dynamic graph mode, not static graph mode.
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Args:
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parameters (Iterable[paddle.Tensor]|paddle.Tensor): Tensors or a single Tensor
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that will be normalized gradients
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clip_value (float|int): maximum allowed value of the gradients.
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The gradients are clipped in the range
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:math:`\left[\text{-clip\_value}, \text{clip\_value}\right]`
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Example:
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.. code-block:: pycon
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>>> import paddle
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>>> x = paddle.uniform([10, 10], min=-10.0, max=10.0, dtype='float32')
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>>> clip_value = float(5.0)
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>>> linear = paddle.nn.Linear(in_features=10, out_features=10)
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>>> out = linear(x)
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>>> loss = paddle.mean(out)
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>>> loss.backward()
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>>> paddle.nn.utils.clip_grad_value_(linear.parameters(), clip_value)
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>>> sdg = paddle.optimizer.SGD(learning_rate=0.1, parameters=linear.parameters())
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>>> sdg.step()
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"""
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if not paddle.in_dynamic_mode():
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raise RuntimeError('this API can only run in dynamic mode.')
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if isinstance(parameters, paddle.Tensor):
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parameters = [parameters]
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clip_value = float(clip_value)
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for _, p in enumerate(parameters):
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if p.grad is not None:
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p.grad.clip_(min=-clip_value, max=clip_value)
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