Source code for mindspore.ops.operations.debug_ops

# Copyright 2020-2021 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
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# Unless required by applicable law or agreed to in writing, software
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# ============================================================================
"""debug_ops"""
from __future__ import absolute_import
from types import FunctionType, MethodType

from mindspore import context
from mindspore import log as logger
from mindspore._c_expression import security
from mindspore._c_expression import Tensor as Tensor_
from mindspore._checkparam import Validator as validator
from mindspore._checkparam import Rel
from mindspore.common import dtype as mstype
from mindspore.common.parameter import Parameter
from mindspore.ops.primitive import prim_attr_register, Primitive, PrimitiveWithInfer


def _check_mode(class_name):
    """Check for PyNative mode."""
    mode = context.get_context('mode')
    if mode == context.PYNATIVE_MODE:
        raise RuntimeError(f"For '{class_name}', the operator does not support PyNative mode.")


def _check_summary_param(name, value, class_name):
    """Checks the name and value is valid for summary."""
    _check_mode(class_name)
    n_type = name['dtype']
    n_value = name['value']
    validator.check_value_type('name', n_type, [type(mstype.string)], class_name)
    if not n_value:
        raise ValueError(f"For '{class_name}', the name must be valid string, but got '{n_value}'.")

    v_type = value['dtype']
    validator.check_value_type('value', v_type, [type(mstype.tensor)], class_name)


# Note: The return value of the summary operator is not used,
# so there's nothing special about the return `dtype` or `shape`, any value is ok.
# The `value` should be set to None, else summary operators may be optimized at compile graph phase,
# it cause summary operators can not record data in constant folding scene.
SUMMARY_RETURN_VALUE = {'dtype': mstype.int32, 'shape': [1], 'value': None}


[文档]class ScalarSummary(Primitive): """ Outputs a scalar to a protocol buffer through a scalar summary operator. Inputs: - **name** (str) - The name of the input variable, it must not be an empty string. - **value** (Tensor) - The value of scalar, and the shape of value must be [] or [1]. Raises: TypeError: If `name` is not a str. TypeError: If `value` is not a Tensor. Supported Platforms: ``Ascend`` ``GPU`` ``CPU`` Examples: >>> import mindspore.nn as nn >>> import mindspore.ops as ops >>> >>> >>> class SummaryDemo(nn.Cell): ... def __init__(self,): ... super(SummaryDemo, self).__init__() ... self.summary = ops.ScalarSummary() ... self.add = ops.Add() ... ... def construct(self, x, y): ... name = "x" ... self.summary(name, x) ... x = self.add(x, y) ... return x ... """ @prim_attr_register def __init__(self): """Initialize ScalarSummary.""" if security.enable_security(): raise ValueError('The Summary is not supported, please without `-s on` and recompile source.') self.add_prim_attr("side_effect_io", True) def __call__(self, *args): logger.warning("PyNative not support Operator {}".format(self.__class__.__name__))
[文档]class ImageSummary(PrimitiveWithInfer): """ Outputs the image tensor to protocol buffer through image summary operator. Inputs: - **name** (str) - The name of the input variable, it must not be an empty string. - **value** (Tensor) - The value of image, the rank of tensor must be 4. Raises: TypeError: If `name` is not a str. TypeError: If `value` is not a Tensor. Supported Platforms: ``Ascend`` ``GPU`` ``CPU`` Examples: >>> import mindspore.nn as nn >>> import mindspore.ops as ops >>> >>> >>> class Net(nn.Cell): ... def __init__(self): ... super(Net, self).__init__() ... self.summary = ops.ImageSummary() ... ... def construct(self, x): ... name = "image" ... out = self.summary(name, x) ... return out ... """ @prim_attr_register def __init__(self): """Initialize ImageSummary.""" if security.enable_security(): raise ValueError('The Summary is not supported, please without `-s on` and recompile source.') self.add_prim_attr("side_effect_io", True) def __infer__(self, name, value): _check_summary_param(name, value, self.__class__.__name__) # The shape dim of image should be 4. v_shape = value['shape'] image_dim = 4 if len(v_shape) != image_dim: raise ValueError(f"For '{self.name}', the dimension of 'value' must be {image_dim}," f" but got {len(v_shape)}.") return SUMMARY_RETURN_VALUE def __call__(self, *args): logger.warning("PyNative not support Operator {}".format(self.__class__.__name__))
[文档]class TensorSummary(Primitive): """ Outputs a tensor to a protocol buffer through a tensor summary operator. Inputs: - **name** (str) - The name of the input variable. - **value** (Tensor) - The value of tensor, and the rank of tensor must be greater than 0. Raises: TypeError: If `name` is not a str. TypeError: If `value` is not a Tensor. Supported Platforms: ``Ascend`` ``GPU`` ``CPU`` Examples: >>> import mindspore.nn as nn >>> import mindspore.ops as ops >>> >>> >>> class SummaryDemo(nn.Cell): ... def __init__(self,): ... super(SummaryDemo, self).__init__() ... self.summary = ops.TensorSummary() ... self.add = ops.Add() ... ... def construct(self, x, y): ... x = self.add(x, y) ... name = "x" ... self.summary(name, x) ... return x ... """ @prim_attr_register def __init__(self): """Initialize TensorSummary.""" if security.enable_security(): raise ValueError('The Summary is not supported, please without `-s on` and recompile source.') self.add_prim_attr("side_effect_io", True) def __call__(self, *args): logger.warning("PyNative not support Operator {}".format(self.__class__.__name__))
[文档]class HistogramSummary(PrimitiveWithInfer): """ Outputs the tensor to protocol buffer through histogram summary operator. Inputs: - **name** (str) - The name of the input variable. - **value** (Tensor) - The value of tensor, and the rank of tensor must be greater than 0. Raises: TypeError: If `name` is not a str. TypeError: If `value` is not a Tensor. Supported Platforms: ``Ascend`` ``GPU`` ``CPU`` Examples: >>> import mindspore.nn as nn >>> import mindspore.ops as ops >>> >>> >>> class SummaryDemo(nn.Cell): ... def __init__(self,): ... super(SummaryDemo, self).__init__() ... self.summary = ops.HistogramSummary() ... self.add = ops.Add() ... ... def construct(self, x, y): ... x = self.add(x, y) ... name = "x" ... self.summary(name, x) ... return x ... """ @prim_attr_register def __init__(self): """Initialize HistogramSummary.""" if security.enable_security(): raise ValueError('The Summary is not supported, please without `-s on` and recompile source.') self.add_prim_attr("side_effect_io", True) def __infer__(self, name, value): _check_summary_param(name, value, self.__class__.__name__) v_shape = value['shape'] # In the summary, the histogram value should be a tensor whose shape is not []. if not v_shape: raise ValueError(f"For '{self.name}', the type of 'value' must be tensor, " f"its shape should not be [], but got {v_shape}.") return SUMMARY_RETURN_VALUE def __call__(self, *args): logger.warning("PyNative not support Operator {}".format(self.__class__.__name__))
[文档]class InsertGradientOf(PrimitiveWithInfer): """ Attaches callback to the graph node that will be invoked on the node's gradient. Args: f (Function): MindSpore's Function. Callback function. Inputs: - **input_x** (Any) - The graph node to attach to. Outputs: Tensor, returns `input_x` directly. `InsertGradientOf` does not affect the forward result. Raises: TypeError: If `f` is not a function of MindSpore. Supported Platforms: ``Ascend`` ``GPU`` ``CPU`` Examples: >>> import numpy as np >>> from mindspore import Tensor, ops, ms_function >>> a = Tensor(np.array([1.0]).astype(np.float32)) >>> b = Tensor(np.array([0.2]).astype(np.float32)) >>> def clip_gradient(dx): ... ret = dx ... if ret > a: ... ret = a ... ... if ret < b: ... ret = b ... ... return ret ... >>> clip = ops.InsertGradientOf(clip_gradient) >>> grad_all = ops.GradOperation(get_all=True) >>> def InsertGradientOfClipDemo(): ... def clip_test(x, y): ... x = clip(x) ... y = clip(y) ... c = x * y ... return c ... ... @ms_function ... def f(x, y): ... return clip_test(x, y) ... ... def fd(x, y): ... return grad_all(clip_test)(x, y) ... ... print("forward: ", f(Tensor(np.array([1.1]).astype(np.float32)), ... Tensor(np.array([0.1]).astype(np.float32)))) ... print("clip_gradient:", fd(Tensor(np.array([1.1]).astype(np.float32)), ... Tensor(np.array([0.1]).astype(np.float32)))) >>> InsertGradientOfClipDemo() forward: [0.11000001] clip_gradient: (Tensor(shape=[1], dtype=Float32, value= [ 2.00000003e-01]), Tensor(shape=[1], dtype=Float32, value= [ 1.00000000e+00])) """ @prim_attr_register def __init__(self, f): """Initialize InsertGradientOf.""" self.add_prim_attr('side_effect_backprop', True) self.f = f def infer_shape(self, x_shape): return x_shape def infer_dtype(self, x_type): return x_type
[文档]class HookBackward(PrimitiveWithInfer): """ This operation is used as a tag to hook gradient in intermediate variables. Note that this function is only supported in pynative mode. Note: The hook function must be defined like `hook_fn(grad) -> new gradient or None`, where the 'grad' is the gradient passed to the primitive. The 'grad' may be modified by returning a new gradient and passed to next primitive. The difference between a hook function and callback of InsertGradientOf is that the hook function is executed in the python environment while callback will be parsed and added to the graph. Args: hook_fn (Function): Python function. hook function. cell_id (str): Used to identify whether the function registered by the hook is actually registered on the specified cell object. For example, 'nn.Conv2d' is a cell object. The default value of cell_id is empty string(""), in this case, the system will automatically register a value of cell_id. The value of cell_id currently does not support custom values. Inputs: - **input** (Tensor) - The variable to hook. Outputs: - **output** (Tensor) - Returns `input` directly. `HookBackward` does not affect the forward result. Raises: TypeError: If `input` is not a tensor. TypeError: If `hook_fn` is not a function of python. Supported Platforms: ``Ascend`` ``GPU`` ``CPU`` Examples: >>> import mindspore as ms >>> from mindspore import ops >>> from mindspore import Tensor >>> from mindspore.ops import GradOperation >>> ms.set_context(mode=ms.PYNATIVE_MODE) >>> def hook_fn(grad): ... print(grad) ... >>> hook = ops.HookBackward(hook_fn) >>> def hook_test(x, y): ... z = x * y ... z = hook(z) ... z = z * y ... return z ... >>> grad_all = GradOperation(get_all=True) >>> def backward(x, y): ... return grad_all(hook_test)(x, y) ... >>> output = backward(Tensor(1, ms.float32), Tensor(2, ms.float32)) (Tensor(shape=[], dtype=Float32, value= 2),) >>> print(output) (Tensor(shape=[], dtype=Float32, value= 4), Tensor(shape=[], dtype=Float32, value= 4)) """ def __init__(self, hook_fn, cell_id=""): """Initialize HookBackward.""" super(HookBackward, self).__init__(self.__class__.__name__) if not isinstance(hook_fn, (FunctionType, MethodType)): raise TypeError(f"For '{self.name}', the type of 'hook_fn' must be python function, " f"but got {type(hook_fn)}.") if cell_id != "": logger.warning(f"The args 'cell_id' of HookBackward will be removed in a future version. If the value of " f"'cell_id' is set, the hook function will not work.") self.add_prim_attr("cell_id", cell_id) self.init_attrs["cell_id"] = cell_id self.cell_id = cell_id self.add_backward_hook_fn(hook_fn) def infer_shape(self, *inputs_shape): if len(inputs_shape) == 1: return inputs_shape[0] return inputs_shape def infer_dtype(self, *inputs_type): for dtype in inputs_type: validator.check_subclass("input", dtype, [mstype.tensor], self.name) if len(inputs_type) == 1: return inputs_type[0] return inputs_type
[文档]class Print(Primitive): """ Outputs the inputs to stdout. Refer to :func:`mindspore.ops.print` for more detail. Supported Platforms: ``Ascend`` ``GPU`` ``CPU`` Examples: >>> class PrintDemo(nn.Cell): ... def __init__(self): ... super(PrintDemo, self).__init__() ... self.print = ops.Print() ... ... def construct(self, x, y): ... self.print('Print Tensor x and Tensor y:', x, y) ... return x ... >>> x = Tensor(np.ones([2, 1]).astype(np.int32)) >>> y = Tensor(np.ones([2, 2]).astype(np.int32)) >>> net = PrintDemo() >>> result = net(x, y) Print Tensor x and Tensor y: Tensor(shape=[2, 1], dtype=Int32, value= [[1] [1]]) Tensor(shape=[2, 2], dtype=Int32, value= [[1 1] [1 1]]) """ @prim_attr_register def __init__(self): """Initialize Print.""" if security.enable_security(): raise ValueError( 'The Print is not supported, please without `-s on` and recompile source.') self.add_prim_attr("side_effect_io", True) def __call__(self, *args): for arg in args: if isinstance(arg, Parameter): print(Tensor_.__repr__(arg)) elif isinstance(arg, Tensor_): print(arg.__repr__()) else: print(arg)
class Assert(PrimitiveWithInfer): """ Asserts that the given condition is True. If input condition evaluates to false, print the list of tensor in data. Args: summarize (int): Print this many entries of each tensor. Inputs: - **condition** [Union[Tensor[bool], bool]] - The condition to evaluate. - **input_data** (Union(tuple[Tensor], list[Tensor])) - The tensors to print out when condition is false. Raises: TypeError: If `summarize` is not an int. TypeError: If `condition` is neither a Tensor nor a bool. TypeError: If `input_data` is neither a tuple nor a list. Examples: >>> class AssertDemo(nn.Cell): ... def __init__(self): ... super(AssertDemo, self).__init__() ... self.assert1 = ops.Assert(summarize=10) ... self.add = ops.Add() ... ... def construct(self, x, y): ... data = self.add(x, y) ... self.assert1(True, [data]) ... return data ... """ @prim_attr_register def __init__(self, summarize=3): """Initialize Assert""" if security.enable_security(): raise ValueError( 'The Assert is not supported, please without `-s on` and recompile source.') self.add_prim_attr("side_effect_io", True) self.summarize = validator.check_value_type("summarize", summarize, [int], self.name) def infer_shape(self, condition, inputs): condition_len = len(condition) validator.check_int(condition_len, 1, Rel.LE, "condition's rank", self.name) if condition_len == 1: validator.check_equal_int(condition[0], 1, "condition[0]", self.name) return [1] def infer_dtype(self, condition, inputs): validator.check_scalar_or_tensor_types_same({"condition": condition}, [mstype.bool_], self.name) for dtype in inputs: validator.check_subclass("input", dtype, [mstype.tensor], self.name) return mstype.int32