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
# distributed under the License is distributed on an "AS IS" BASIS,
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"""debug_ops"""
from types import FunctionType, MethodType

from mindspore import context
from ..._checkparam import Validator as validator
from ..._checkparam import Rel
from ...common import dtype as mstype
from ..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'{class_name} 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 'name' the value should by valid string in {class_name}, but got an empty string.")

    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}


[docs]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.""" self.add_prim_attr("side_effect_io", True)
[docs]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.""" 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 'value' the dim should be {image_dim} in {self.__class__.__name__}," f" but got {len(v_shape)}.") return SUMMARY_RETURN_VALUE
[docs]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.""" self.add_prim_attr("side_effect_io", True)
[docs]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.""" 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 'value' the type should be tensor in {self.__class__.__name__}, " f"shape should not be [].") return SUMMARY_RETURN_VALUE
[docs]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: >>> def clip_gradient(dx): ... ret = dx ... if ret > 1.0: ... ret = 1.0 ... ... if ret < 0.2: ... ret = 0.2 ... ... 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(1.1, 0.1)) ... print("clip_gradient:", fd(1.1, 0.1)) ... """ @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
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) -> Tensor or None`, where grad is the gradient passed to the primitive and gradient may be modified and passed to next primitive. The difference between a hook function and callback of InsertGradientOf is that a 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. Inputs: - **inputs** (Tensor) - The variable to hook. Raises: TypeError: If `inputs` are not a Tensor. TypeError: If `hook_fn` is not a function of python. Examples: >>> def hook_fn(grad_out): ... print(grad_out) ... >>> grad_all = GradOperation(get_all=True) >>> hook = ops.HookBackward(hook_fn) >>> def hook_test(x, y): ... z = x * y ... z = hook(z) ... z = z * y ... return z ... >>> def backward(x, y): ... return grad_all(hook_test)(x, y) ... >>> output = backward(1, 2) >>> print(output) """ def __init__(self, hook_fn, cell_id=""): """Initialize HookBackward.""" super(HookBackward, self).__init__(self.__class__.__name__) self.add_prim_attr("cell_id", cell_id) self.init_attrs["cell_id"] = cell_id if not isinstance(hook_fn, (FunctionType, MethodType)): raise TypeError("Hook function should be python function type.") self.register_hook(hook_fn) self.cell_id = cell_id def infer_shape(self, *inputs_shape): if len(inputs_shape) == 1: return inputs_shape[0] return inputs_shape def infer_dtype(self, *inputs_type): if len(inputs_type) == 1: return inputs_type[0] return inputs_type
[docs]class Print(PrimitiveWithInfer): """ Outputs the tensor or string to stdout. Note: In pynative mode, please use python print function. In graph mode, the bool, int and float would be converted into Tensor to print, str remains unchanged. Inputs: - **input_x** (Union[Tensor, bool, int, float, str]) - The graph node to attach to. Supports multiple inputs which are separated by ','. Outputs: Tensor, has the same data type and shape as original `input_x`. Raises: TypeError: If `input_x` is not one of the following: Tensor, bool, int, float, str. Supported Platforms: ``Ascend`` ``GPU`` 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.""" self.add_prim_attr("side_effect_io", True) def __call__(self, *args): for arg in args: print(arg) def infer_shape(self, *inputs): return [1] def infer_dtype(self, *inputs): # check argument types except the last one (io state). for ele in inputs[:-1]: validator.check_subclass("input", ele, [mstype.tensor, mstype.int_, mstype.float_, mstype.bool_, mstype.string], self.name) return mstype.int32
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""" 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