wehub-resource-sync
120 lines
4.1 KiB
Python
120 lines
4.1 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_norm_(
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parameters: Iterable[Tensor] | Tensor,
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max_norm: float,
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norm_type: float = 2.0,
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error_if_nonfinite: bool = False,
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) -> Tensor:
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r"""Clips gradient norm of the iterable parameters.
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Norms are calculated together on all gradients, just as they are
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connected into one vector. 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] or paddle.Tensor): Tensors or a single Tensor
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that will be normalized gradients
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max_norm (float or int): max norm of the gradients
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norm_type (float or int): type of the used p-norm. Can be `inf` for
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infinity norm.
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error_if_nonfinite (bool): if True, throw an error if the total
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norm of the gradients from :attr:`parameters` is `nan`,
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`inf`, or `-inf`.
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Returns:
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Total norm of the parameter gradients (treated as a single vector).
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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=-1.0, max=1.0, dtype='float32')
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>>> max_norm = 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_norm_(linear.parameters(), max_norm)
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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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support_norm_type = [float("inf"), 0, 1, 2]
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if norm_type not in support_norm_type:
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raise ValueError(f'norm_type only support {support_norm_type}')
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grads = [p.grad_ for p in parameters if p.grad_ is not None]
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max_norm = float(max_norm)
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norm_type = float(norm_type)
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if len(grads) == 0:
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return paddle.to_tensor(0.0)
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if norm_type == float("inf"):
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norms = [g.detach().abs().max() for g in grads]
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total_norm = (
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norms[0] if len(norms) == 1 else paddle.max(paddle.stack(norms))
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)
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else:
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total_norm = paddle.linalg.norm(
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paddle.stack(
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[paddle.linalg.norm(g.detach(), norm_type) for g in grads]
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),
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norm_type,
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)
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if error_if_nonfinite and paddle.logical_or(
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total_norm.isnan(), total_norm.isinf()
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):
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raise RuntimeError(
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f'The total norm of {norm_type} order of the gradients from '
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'`parameters` is non-finite, so it cannot be clipped. In any case, '
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'disable this error and scale the gradient by non-finite norm, '
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'set `error_if_nonfinite=False`'
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)
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clip_coef = max_norm / (total_norm + 1e-6)
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# Note: when the coef is clamped to 1, it is redundant to multiply the clamped coef, but this
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# avoids the `if clip_coef < 1:` condition.
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clip_coef_clamped = clip_coef.clip_(max=1.0)
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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_.multiply_(y=clip_coef_clamped)
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return total_norm
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