# Copyright 2020 Huawei Technologies Co., Ltd
#
# 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.
# ============================================================================
"""Operations for clipping tensors to min/max values."""
from __future__ import absolute_import
from mindspore.nn.cell import Cell
from mindspore.ops import composite as C
from mindspore.ops import functional as F
from mindspore.ops import operations as P
from mindspore.common.tensor import Tensor
from mindspore.common import dtype as mstype
from mindspore._checkparam import Rel
from mindspore._checkparam import Validator as validator
from mindspore.ops.primitive import constexpr
# The attribute grad_scale is needed for enabling the parallel mode
# If this is removed, c.clip_by_global_norm will have precision error in semi/auto parallel mode.
expand_dims = P.ExpandDims().add_prim_attr("grad_scale", True)
get_square_sum = C.MultitypeFuncGraph("get_square_sum")
@get_square_sum.register("Tensor")
def _get_square_sum(x):
norm = P.ReduceSum(False)(F.square(x), ())
norm = expand_dims(F.cast(norm, mstype.float32), 0)
return norm
apply_global_norm = C.MultitypeFuncGraph("apply_global_norm")
@apply_global_norm.register("Tensor", "Tensor", "Tensor")
def _apply_global_norm(clip_norm, global_norm, x):
x_dtype = F.dtype(x)
x = x * clip_norm / global_norm
x = F.cast(x, x_dtype)
return x
class _ClipByGlobalNorm(Cell):
r"""
Clips tensor values by the ratio of the sum of their norms.
Args:
clip_norm (Union(float, int)): The clipping ratio. Default: 1.0
use_norm (Union(float, None)): The global norm. Default: None
Inputs:
- **x** (Union(tuple[Tensor], list[Tensor])) - Input data to clip.
Outputs:
Tensor, a clipped Tensor.
"""
def __init__(self, clip_norm=1.0, use_norm=None):
"""Initialize _ClipByGlobalNorm."""
super(_ClipByGlobalNorm, self).__init__()
# Add interface. This parameter is not used at present
if use_norm is not None:
raise ValueError(f"For '{self.cls_name}', input 'use_norm' only supports None currently, "
f"but got 'use_norm': {use_norm}")
validator.check_number("clip_norm", clip_norm, 0.0, Rel.GT, self.cls_name)
self.clip_norm = Tensor([clip_norm], mstype.float32)
self.hyper_map = C.HyperMap()
self.greater_equal = P.GreaterEqual()
def construct(self, x):
square_sum = self.hyper_map(get_square_sum, x)
global_norm = F.sqrt(F.addn(square_sum))
cond = self.greater_equal(global_norm, self.clip_norm)
global_norm = F.select(cond, global_norm, self.clip_norm)
clip_x = self.hyper_map(F.partial(apply_global_norm, self.clip_norm, global_norm), x)
return clip_x
@constexpr
def _check_value(clip_norm):
validator.check_number("clip_norm", clip_norm, 0.0, Rel.GT, "clip_by_global_norm")
return clip_norm
[文档]def clip_by_global_norm(x, clip_norm=1.0, use_norm=None):
r"""
Clips tensor values by the ratio of the sum of their norms.
Note:
- Input `x` should be a tuple or list of tensors. Otherwise, it will raise an error.
- On the SEMI_AUTO_PARALLEL mode or AUTO_PARALLEL mode, if the input `x` is the gradient,
the gradient norm values on all devices will be automatically aggregated by allreduce inserted
after the local square sum of the gradients.
Args:
x (Union(tuple[Tensor], list[Tensor])): Input data to clip.
The shape of each Tensor in tuple is :math:`(N,*)` where :math:`*` means,
any number of additional dimensions.
clip_norm (Union(float, int)): The clipping ratio, it should be greater than 0. Default: 1.0
use_norm (None): The global norm. Default: None. Currently only none is supported.
Returns:
tuple[Tensor], a clipped Tensor. It has the same data type as `x` and each Tensor in the output tuple is the
same as the original input shape.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> from mindspore import Tensor, ops
>>> import numpy as np
>>> x1 = np.array([[2., 3.], [1., 2.]]).astype(np.float32)
>>> x2 = np.array([[1., 4.], [3., 1.]]).astype(np.float32)
>>> input_x = (Tensor(x1), Tensor(x2))
>>> out = ops.clip_by_global_norm(input_x, 1.0)
>>> print(out)
(Tensor(shape=[2, 2], dtype=Float32, value=
[[ 2.98142403e-01, 4.47213590e-01],
[ 1.49071202e-01, 2.98142403e-01]]), Tensor(shape=[2, 2], dtype=Float32, value=
[[ 1.49071202e-01, 5.96284807e-01],
[ 4.47213590e-01, 1.49071202e-01]]))
"""
clip_norm = _check_value(clip_norm)
clip_val = _ClipByGlobalNorm(clip_norm, use_norm)(x)
return clip_val