# Copyright 2022 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
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# ============================================================================
"""Defines clip operators with functional form."""
from __future__ import absolute_import
from mindspore.ops import composite as C
from mindspore.ops import operations as P
from mindspore.ops import functional as F
from mindspore.nn.cell import Cell
from mindspore.common.tensor import Tensor
from mindspore.common import dtype as mstype
from mindspore.ops.primitive import _primexpr
from mindspore.ops._primitive_cache import _get_cache_prim
from mindspore import _checkparam as Validator
from mindspore.ops.auto_generate import clamp_tensor, clamp_scalar
__all__ = [
'clip_by_value',
'clip_by_norm',
'clamp',
'clip',
'clip_by_global_norm',
]
apply_global_norm = C.MultitypeFuncGraph("apply_global_norm")
cast_op = P.Cast()
expand_dims = P.ExpandDims().add_prim_attr("grad_scale", True)
get_square_sum = C.MultitypeFuncGraph("get_square_sum")
greater_equal_ = P.GreaterEqual()
hyper_map = C.HyperMap()
max_op = P.Maximum()
min_op = P.Minimum()
partial_op = P.Partial()
scalar2tensor_op = P.ScalarToTensor()
def _old_norm(norm_type, x):
"""Add norm function"""
out = F.pow((F.reduce_sum(F.pow(x, norm_type))), 1. / norm_type).astype(x.dtype)
return out
def _cal_total_norm(x, norm_type):
if norm_type == float('inf'):
func = lambda data: data.abs().max()
total_norm = max(hyper_map(func, x))
else:
total_norm = _old_norm(norm_type, F.stack(hyper_map(partial_op(_old_norm, norm_type), x)))
return total_norm
[文档]def clip_by_norm(x, max_norm, norm_type=2.0, error_if_nonfinite=False):
r"""
Clip norm of a set of input Tensors. This norm is the result of calculating the norm of all elements in the input
separately, connecting them into a vector, and then calculating the norm.
Note:
The interface is suitable for gradient clipping scenarios, and only supports input of type float.
Args:
x (Union(Tensor, list[Tensor], tuple[Tensor])): Input that wishes to be clipped.
max_norm (Union(float, int)): The upper limit of the norm for this group of network parameters.
norm_type (Union(float, int)): Norm type. Default: ``2.0``.
error_if_nonfinite (bool): If it is ``True``, an exception is thrown if the total norm from the input
is nan, inf or -inf. If it is ``False``, no exception will be thrown.Default: ``False`` .
Returns:
Tensors, a list or tuple of Tensors, representing clipped Tensors.
Raises:
RuntimeError: If the total norm from the `x` is nan, inf or -inf.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> from mindspore import Tensor, ops
>>> x = Tensor([[0.8748, 0.1425, 0.0076], [0.7721, 0.4084, 0.0552], [4.6376, 0.2914, 2.1120]])
>>> out = ops.clip_by_norm(x, max_norm=1)
>>> print(out)
[[0.16650201 0.02712224 0.00144652]
[0.14695495 0.07773139 0.0105063 ]
[0.8826814 0.0554626 0.40198016]]
"""
is_tensor = False
if isinstance(x, Tensor):
x = [x]
is_tensor = True
total_norm = _cal_total_norm(x, norm_type)
if error_if_nonfinite and F.logical_or(total_norm.isnan(), total_norm.isinf()):
raise RuntimeError(f"For clip_by_norm, the total norm of order {norm_type} from input is non-finite.")
clip_coef = max_norm / (total_norm + 1e-6)
if clip_coef < 1:
ret = hyper_map(partial_op(F.mul, clip_coef), x)
else:
ret = x
if is_tensor:
return ret[0]
return ret
[文档]def clip_by_value(x, clip_value_min=None, clip_value_max=None):
r"""
Clips tensor values to a specified min and max.
Limits the value of :math:`x` to a range, whose lower limit is `clip_value_min`
and upper limit is `clip_value_max` .
.. math::
out_i= \left\{
\begin{array}{align}
clip\_value\_max & \text{ if } x_i\ge clip\_value\_max \\
x_i & \text{ if } clip\_value\_min \lt x_i \lt clip\_value\_max \\
clip\_value\_min & \text{ if } x_i \le clip\_value\_min \\
\end{array}\right.
Note:
- `clip_value_min` and `clip_value_max` cannot be None at the same time;
- When `clip_value_min` is None and `clip_value_max` is not None, the elements in Tensor
larger than `clip_value_max` will become `clip_value_max`;
- When `clip_value_min` is not None and `clip_value_max` is None, the elements in Tensor
smaller than `clip_value_min` will become `clip_value_min`;
- If `clip_value_min` is greater than `clip_value_max`, the value of all elements in Tensor
will be set to `clip_value_max`;
- The data type of `x`, `clip_value_min` and `clip_value_max` should support implicit type
conversion and cannot be bool type.
Args:
x (Union(Tensor, list[Tensor], tuple[Tensor])): Input data, which type is Tensor or a list or tuple of Tensor.
Tensors of arbitrary dimensions are supported.
clip_value_min (Union(Tensor, float, int)): The minimum value. Default: ``None`` .
clip_value_max (Union(Tensor, float, int)): The maximum value. Default: ``None`` .
Returns:
(Union(Tensor, tuple[Tensor], list[Tensor])), a clipped Tensor or a tuple or a list of clipped Tensor.
The data type and shape are the same as x.
Raises:
ValueError: If both `clip_value_min` and `clip_value_max` are None.
TypeError: If the type of `x` is not in Tensor or list[Tensor] or tuple[Tensor].
TypeError: If the type of `clip_value_min` is not in None, Tensor, float or int.
TypeError: If the type of `clip_value_max` is not in None, Tensor, float or int.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> # case 1: the data type of x is Tensor
>>> import mindspore
>>> from mindspore import Tensor, ops
>>> import numpy as np
>>> min_value = Tensor(5, mindspore.float32)
>>> max_value = Tensor(20, mindspore.float32)
>>> x = Tensor(np.array([[1., 25., 5., 7.], [4., 11., 6., 21.]]), mindspore.float32)
>>> output = ops.clip_by_value(x, min_value, max_value)
>>> print(output)
[[ 5. 20. 5. 7.]
[ 5. 11. 6. 20.]]
>>> # case 2: the data type of x is list[Tensor]
>>> min_value = 5
>>> max_value = 20
>>> x = Tensor(np.array([[1., 25., 5., 7.], [4., 11., 6., 21.]]), mindspore.float32)
>>> y = Tensor(np.array([[1., 25., 5., 7.], [4., 11., 6., 21.]]), mindspore.float32)
>>> output = ops.clip_by_value([x,y], min_value, max_value)
>>> for out in output:
... print(out)
[[ 5. 20. 5. 7.]
[ 5. 11. 6. 20.]]
[[ 5. 20. 5. 7.]
[ 5. 11. 6. 20.]]
"""
def _clip_by_value(clip_min, clip_max, x):
if not isinstance(x, Tensor):
raise TypeError("For 'clip_by_value', the type of argument 'x' must be "
"Tensor or tuple[Tensor] or list[Tensor]")
result = x
if clip_min is not None:
result = max_op(result, cast_op(clip_min, x.dtype))
if clip_max is not None:
result = min_op(result, cast_op(clip_max, x.dtype))
return result
if clip_value_min is None and clip_value_max is None:
raise ValueError("For 'clip_by_value', at least one of "
"'clip_value_min' or 'clip_value_max' must not be None")
if not isinstance(x, (Tensor, tuple, list)):
raise TypeError("For 'clip_by_value', the type of argument 'x' must be "
"Tensor or tuple[Tensor] or list[Tensor]")
if not isinstance(clip_value_min, (type(None), Tensor, float, int)):
raise TypeError("For 'clip_by_value', the type of argument 'clip_value_min' must be "
"not one of None, Tensor, float, int")
if not isinstance(clip_value_max, (type(None), Tensor, float, int)):
raise TypeError("For 'clip_by_value', the type of argument 'clip_value_max' must be "
"not one of None, Tensor, float, int")
if isinstance(clip_value_min, (float, int)):
clip_value_min = scalar2tensor_op(clip_value_min)
if isinstance(clip_value_max, (float, int)):
clip_value_max = scalar2tensor_op(clip_value_max)
if isinstance(x, Tensor):
return _clip_by_value(clip_value_min, clip_value_max, x)
results = hyper_map(partial_op(_clip_by_value, clip_value_min, clip_value_max), x)
if isinstance(x, tuple):
results = tuple(results)
return results
[文档]def clamp(input, min=None, max=None):
r"""
Clamps tensor values between the specified minimum value and maximum value.
Limits the value of :math:`input` to a range, whose lower limit is `min` and upper limit is `max` .
.. math::
out_i= \left\{
\begin{array}{align}
max & \text{ if } input_i\ge max \\
input_i & \text{ if } min \lt input_i \lt max \\
min & \text{ if } input_i \le min \\
\end{array}\right.
Note:
- `min` and `max` cannot be None at the same time;
- When `min` is None and `max` is not None, the elements in Tensor larger than `max` will become `max`;
- When `min` is not None and `max` is None, the elements in Tensor smaller than `min` will become `min`;
- If `min` is greater than `max`, the value of all elements in Tensor will be set to `max`;
- The data type of `input`, `min` and `max` should support implicit type conversion and cannot be bool type.
Args:
input (Tensor): Input data, which type is Tensor. Tensors of arbitrary dimensions are supported.
min (Union(Tensor, float, int), optional): The minimum value. Default: ``None`` .
max (Union(Tensor, float, int), optional): The maximum value. Default: ``None`` .
Returns:
Tensor, a clipped Tensor.
The data type and shape are the same as input.
Raises:
ValueError: If both `min` and `max` are None.
TypeError: If the type of `input` is not in Tensor.
TypeError: If the type of `min` is not in None, Tensor, float or int.
TypeError: If the type of `max` is not in None, Tensor, float or int.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> # case 1: the data type of input is Tensor
>>> import mindspore
>>> from mindspore import Tensor, ops
>>> import numpy as np
>>> min_value = Tensor(5, mindspore.float32)
>>> max_value = Tensor(20, mindspore.float32)
>>> input = Tensor(np.array([[1., 25., 5., 7.], [4., 11., 6., 21.]]), mindspore.float32)
>>> output = ops.clamp(input, min_value, max_value)
>>> print(output)
[[ 5. 20. 5. 7.]
[ 5. 11. 6. 20.]]
"""
if isinstance(min, Tensor) or isinstance(max, Tensor):
return clamp_tensor(input, min, max)
return clamp_scalar(input, min, max)
[文档]def clip(input, min=None, max=None):
r"""
Alias for :func:`mindspore.ops.clamp` .
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
"""
return clamp(input, min, max)
@get_square_sum.register("Tensor")
def _get_square_sum(x):
norm = _get_cache_prim(P.ReduceSum)(False)(F.square(x), ())
norm = expand_dims(F.cast(norm, mstype.float32), 0)
return 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, Validator.GT, self.cls_name)
self.clip_norm = Tensor([clip_norm], mstype.float32)
self.hyper_map = C.HyperMap()
def construct(self, x):
square_sum = self.hyper_map(get_square_sum, x)
global_norm = F.sqrt(F.addn(square_sum))
cond = 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
@_primexpr
def _check_value(clip_norm):
Validator.check_number("clip_norm", clip_norm, 0.0, Validator.GT, "clip_by_global_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.
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]]))
"""
_check_value(clip_norm)
clip_val = _ClipByGlobalNorm(clip_norm, use_norm)(x)
return clip_val