Source code for mindspore.ops.function.clip_func

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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


[docs]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
[docs]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
[docs]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: Union(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)
[docs]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")
[docs]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