forked from huawei/mindspore2022
527 lines
21 KiB
Python
527 lines
21 KiB
Python
# This is the Python adaptation and derivative work of Myia (https://github.com/mila-iqia/myia/).
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#
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# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""Basic composite operations."""
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from functools import partial
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from types import FunctionType
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from mindspore import context
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from ..._c_expression import EnvInstance_, GradOperation_, HyperMap_, Map_, MultitypeFuncGraph_, Tail_, \
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TupleAdd_, TupleSlice_, UnpackCall_, ZipOperation_, ListAppend_, TupleGetItemTensor_
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from ...common import dtype as mstype
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from ...common.api import ms_function, _pynative_exec, _wrap_func
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from .. import functional as F
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from ...common.tensor import Tensor
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from .. import signature as sig
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__all__ = [EnvInstance_, TupleAdd_, TupleSlice_, UnpackCall_, TupleGetItemTensor_]
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def add_flags(fn=None, **flags):
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"""
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An decorator to add flag for a function.
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Note:
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Only supports bool value.
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Args:
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fn (Function): Function or cell to add flag. Default: None.
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flags (dict): Flags use kwargs. Default: None.
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Returns:
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Function, the fn added flags.
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Examples:
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>>> add_flags(net, predit=True)
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"""
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def deco(fn):
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# need set the attr and access on c++
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if not hasattr(fn, "_mindspore_flags"):
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fn._mindspore_flags = {}
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fn._mindspore_flags.update({**flags})
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return fn
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ret = deco
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if fn is not None:
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ret = deco(fn)
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return ret
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def core(fn=None, **flags):
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"""
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A decorator to add flag to a function.
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By default, the function is marked core=True using this decorator to
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set flag to a graph.
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Args:
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fn (Function): Function to add flag. Default: None.
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flags (dict): The following flags can be set core, which indicates that this is a core function or
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other flag. Default: None.
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"""
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# need set the attr and access on c++
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def deco(fn):
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fn._mindspore_flags = {
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'core': True,
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**flags,
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}
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return fn
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if fn is not None:
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ret = deco(fn)
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else:
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ret = deco
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return ret
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class GradOperation(GradOperation_):
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"""
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An higher-order function which is used to generate the gradient function for the input function.
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The gradient function generated by `GradOperation` higher-order function can be customized by construction args.
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Given an input function `net = Net()` that take `x` and `y` as inputs, and has a parameter `z`,
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see `Net` in Examples.
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To generate a gradient function that returns gradients with respect to the first input
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(see `GradNetWrtX` in Examples).
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1. Construct a `GradOperation` higher-order function with default arguments:
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`grad_op = GradOperation()`.
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2. Call it with input function as argument to get the gradient function: `gradient_function = grad_op(net)`.
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3. Call the gradient function with input function's inputs to get the gradients with respect to the first input:
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`grad_op(net)(x, y)`.
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To generate a gradient function that returns gradients with respect to all inputs (see `GradNetWrtXY` in Examples).
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1. Construct a `GradOperation` higher-order function with `get_all=True` which
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indicates getting gradients with respect to all inputs, they are `x` and `y` in example function `Net()`:
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`grad_op = GradOperation(get_all=True)`.
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2. Call it with input function as argument to get the gradient function: `gradient_function = grad_op(net)`.
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3. Call the gradient function with input function's inputs to get the gradients with respect to all inputs:
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`gradient_function(x, y)`.
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To generate a gradient function that returns gradients with respect to given parameters
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(see `GradNetWithWrtParams` in Examples).
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1. Construct a `GradOperation` higher-order function with `get_by_list=True`:
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`grad_op = GradOperation(get_by_list=True)`.
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2. Construct a `ParameterTuple` that will be passed along input function when constructing
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`GradOperation` higher-order function, it will be used as a parameter filter that determine
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which gradient to return: `params = ParameterTuple(net.trainable_params())`.
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3. Call it with input function and `params` as arguments to get the gradient function:
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`gradient_function = grad_op(net, params)`.
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4. Call the gradient function with input function's inputs to get the gradients with
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respect to given parameters: `gradient_function(x, y)`.
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To generate a gradient function that returns gradients with respect to all inputs and given parameters
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in the format of ((dx, dy), (dz))(see `GradNetWrtInputsAndParams` in Examples).
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1. Construct a `GradOperation` higher-order function with `get_all=True` and `get_by_list=True`:
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`grad_op = GradOperation(get_all=True, get_by_list=True)`.
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2. Construct a `ParameterTuple` that will be passed along input function when constructing
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`GradOperation` higher-order function: `params = ParameterTuple(net.trainable_params())`.
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3. Call it with input function and `params` as arguments to get the gradient function:
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`gradient_function = grad_op(net, params)`.
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4. Call the gradient function with input function's inputs
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to get the gradients with respect to all inputs and given parameters: `gradient_function(x, y)`.
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We can configure the sensitiviy(gradient with respect to output) by setting `sens_param=True` and
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passing in an extra sensitiviy input to the gradient function, the sensitiviy input should be
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with same shape and type with input function's output(see `GradNetWrtXYWithSensParam` in Examples).
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1. Construct a `GradOperation` higher-order function with `get_all=True` and `sens_param=True`:
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`grad_op = GradOperation(get_all=True, sens_param=True)`.
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2. Define grad_wrt_output as sens_param which works as the gradient with respect to output:
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`grad_wrt_output = Tensor(np.ones([2, 2]).astype(np.float32))`.
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3. Call it with input function as argument to get the gradient function:
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`gradient_function = grad_op(net)`.
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4. Call the gradient function with input function's inputs and sens_param to
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get the gradients with respect to all inputs:
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`gradient_function(x, y, grad_wrt_output)`.
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Args:
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get_all (bool): If True, get all the gradients with respect to inputs. Default: False.
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get_by_list (bool): If True, get all the gradients with respect to Parameter variables.
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If get_all and get_by_list are both False, get the gradient with respect to first input.
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If get_all and get_by_list are both True, get the gradients with respect to inputs and Parameter variables
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at the same time in the form of ((gradients with respect to inputs),
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(gradients with respect to parameters)). Default: False.
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sens_param (bool): Whether append sensitivity(gradient with respect to output) as input. If sens_param is False,
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a 'ones_like(outputs)' sensitivity will be attached automatically. Default: False.
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Returns:
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The higher-order function which takes a function as argument and returns gradient function for it.
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Examples:
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>>> class Net(nn.Cell):
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>>> def __init__(self):
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>>> super(Net, self).__init__()
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>>> self.matmul = P.MatMul()
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>>> self.z = Parameter(Tensor(np.array([1.0], np.float32)), name='z')
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>>> def construct(self, x, y):
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>>> x = x * self.z
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>>> out = self.matmul(x, y)
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>>> return out
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>>>
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>>> class GradNetWrtX(nn.Cell):
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>>> def __init__(self, net):
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>>> super(GradNetWrtX, self).__init__()
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>>> self.net = net
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>>> self.grad_op = GradOperation()
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>>> def construct(self, x, y):
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>>> gradient_function = self.grad_op(self.net)
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>>> return gradient_function(x, y)
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>>>
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>>> x = Tensor([[0.5, 0.6, 0.4], [1.2, 1.3, 1.1]], dtype=mstype.float32)
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>>> y = Tensor([[0.01, 0.3, 1.1], [0.1, 0.2, 1.3], [2.1, 1.2, 3.3]], dtype=mstype.float32)
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>>> GradNetWrtX(Net())(x, y)
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Tensor(shape=[2, 3], dtype=Float32,
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[[1.4100001 1.5999999 6.6 ]
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[1.4100001 1.5999999 6.6 ]])
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>>>
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>>> class GradNetWrtXY(nn.Cell):
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>>> def __init__(self, net):
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>>> super(GradNetWrtXY, self).__init__()
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>>> self.net = net
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>>> self.grad_op = GradOperation(get_all=True)
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>>> def construct(self, x, y):
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>>> gradient_function = self.grad_op(self.net)
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>>> return gradient_function(x, y)
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>>>
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>>> x = Tensor([[0.8, 0.6, 0.2], [1.8, 1.3, 1.1]], dtype=mstype.float32)
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>>> y = Tensor([[0.11, 3.3, 1.1], [1.1, 0.2, 1.4], [1.1, 2.2, 0.3]], dtype=mstype.float32)
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>>> GradNetWrtXY(Net())(x, y)
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(Tensor(shape=[2, 3], dtype=Float32,
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[[4.5099998 2.7 3.6000001]
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[4.5099998 2.7 3.6000001]]), Tensor(shape=[3, 3], dtype=Float32,
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[[2.6 2.6 2.6 ]
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[1.9 1.9 1.9 ]
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[1.3000001 1.3000001 1.3000001]]))
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>>>
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>>> class GradNetWrtXYWithSensParam(nn.Cell):
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>>> def __init__(self, net):
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>>> super(GradNetWrtXYWithSensParam, self).__init__()
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>>> self.net = net
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>>> self.grad_op = GradOperation(get_all=True, sens_param=True)
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>>> self.grad_wrt_output = Tensor([[0.1, 0.6, 0.2], [0.8, 1.3, 1.1]], dtype=mstype.float32)
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>>> def construct(self, x, y):
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>>> gradient_function = self.grad_op(self.net)
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>>> return gradient_function(x, y, self.grad_wrt_output)
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>>>
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>>> x = Tensor([[0.8, 0.6, 0.2], [1.8, 1.3, 1.1]], dtype=mstype.float32)
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>>> y = Tensor([[0.11, 3.3, 1.1], [1.1, 0.2, 1.4], [1.1, 2.2, 0.3]], dtype=mstype.float32)
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>>> GradNetWrtXYWithSensParam(Net())(x, y)
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(Tensor(shape=[2, 3], dtype=Float32,
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[[2.211 0.51 1.4900001]
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[5.588 2.68 4.07 ]]), Tensor(shape=[3, 3], dtype=Float32,
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[[1.52 2.82 2.14 ]
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[1.1 2.05 1.55 ]
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[0.90000004 1.55 1.25 ]]))
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>>>
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>>> class GradNetWithWrtParams(nn.Cell):
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>>> def __init__(self, net):
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>>> super(GradNetWithWrtParams, self).__init__()
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>>> self.net = net
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>>> self.params = ParameterTuple(net.trainable_params())
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>>> self.grad_op = GradOperation(get_by_list=True)
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>>> def construct(self, x, y):
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>>> gradient_function = self.grad_op(self.net, self.params)
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>>> return gradient_function(x, y)
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>>>
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>>> x = Tensor([[0.8, 0.6, 0.2], [1.8, 1.3, 1.1]], dtype=mstype.float32)
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>>> y = Tensor([[0.11, 3.3, 1.1], [1.1, 0.2, 1.4], [1.1, 2.2, 0.3]], dtype=mstype.float32)
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>>> GradNetWithWrtParams(Net())(x, y)
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(Tensor(shape=[1], dtype=Float32, [21.536]),)
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>>>
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>>> class GradNetWrtInputsAndParams(nn.Cell):
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>>> def __init__(self, net):
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>>> super(GradNetWrtInputsAndParams, self).__init__()
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>>> self.net = net
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>>> self.params = ParameterTuple(net.trainable_params())
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>>> self.grad_op = GradOperation(get_all=True, get_by_list=True)
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>>> def construct(self, x, y):
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>>> gradient_function = self.grad_op(self.net, self.params)
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>>> return gradient_function(x, y)
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>>>
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>>> x = Tensor([[0.1, 0.6, 1.2], [0.5, 1.3, 0.1]], dtype=mstype.float32)
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>>> y = Tensor([[0.12, 2.3, 1.1], [1.3, 0.2, 2.4], [0.1, 2.2, 0.3]], dtype=mstype.float32)
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>>> GradNetWrtInputsAndParams(Net())(x, y)
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((Tensor(shape=[2, 3], dtype=Float32,
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[[3.52 3.9 2.6 ]
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[3.52 3.9 2.6 ]]), Tensor(shape=[3, 3], dtype=Float32,
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[[0.6 0.6 0.6 ]
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[1.9 1.9 1.9 ]
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[1.3000001 1.3000001 1.3000001]])), (Tensor(shape=[1], dtype=Float32, [12.902]),))
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"""
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def __init__(self, get_all=False, get_by_list=False, sens_param=False):
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self.get_all = get_all
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self.get_by_list = get_by_list
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self.sens_param = sens_param
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GradOperation_.__init__(self, 'grad', get_all, get_by_list, sens_param)
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self.grad_fn = None
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self.fn = None
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self.need_forward = False
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def _pynative_forward_run(self, args, kwargs, fn):
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""" Pynative forward run to build grad graph. """
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if self.sens_param:
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args = args[:-1]
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for arg in args:
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if not isinstance(arg, Tensor):
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raise TypeError("grad inputs should be tensor in pynative mode")
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if isinstance(fn, FunctionType):
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_pynative_exec.set_grad_flag(True)
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_pynative_exec.new_graph(fn, *args, **kwargs)
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output = fn(*args, **kwargs)
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_pynative_exec.end_graph(fn, output, *args, **kwargs)
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else:
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if fn.already_run and not fn.requires_grad:
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raise ValueError("obj must set_grad.")
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if not fn.already_run:
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self.need_forward = True
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if self.need_forward:
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fn.set_grad()
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fn(*args, **kwargs)
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fn.already_run = False
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def __call__(self, fn, weights=None):
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grad_ = GradOperation(self.get_all, self.get_by_list, self.sens_param)
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if self.grad_fn is None or self.fn != fn:
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if context.get_context("mode") == context.GRAPH_MODE:
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if self.get_by_list:
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@ms_function(obj=fn)
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def after_grad(*args):
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return grad_(fn, weights)(*args)
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else:
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@ms_function(obj=fn)
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def after_grad(*args):
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return grad_(fn)(*args)
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else:
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@_wrap_func
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def after_grad(*args, **kwargs):
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self._pynative_forward_run(args, kwargs, fn)
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_pynative_exec.grad(grad_, fn, weights, *args, **kwargs)
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out = _pynative_exec(*args, **kwargs)
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_pynative_exec.clear()
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return out
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self.grad_fn = after_grad
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self.fn = fn
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return self.grad_fn
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class MultitypeFuncGraph(MultitypeFuncGraph_):
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"""
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Generate multiply graph.
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MultitypeFuncGraph is a class used to generate graphs for function with different type as input.
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Args:
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name (str): Operator name.
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read_value (bool): If the registered function not need to set value on Parameter,
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and all inputs will pass by value. Set `read_value` to True. Default: False.
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Raises:
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ValueError: Cannot find matching fn for the given args.
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Examples:
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>>> # `add` is a metagraph object which will add two objects according to
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>>> # input type using ".register" decorator.
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>>> add = MultitypeFuncGraph('add')
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"""
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def __init__(self, name, read_value=False):
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MultitypeFuncGraph_.__init__(self, name)
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self.entries = list()
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if read_value:
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self.set_signatures((
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sig.make_sig('args', sig.sig_rw.RW_READ, sig.sig_kind.KIND_VAR_POSITIONAL),))
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def __call__(self, *args):
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types = tuple(map(mstype.get_py_obj_dtype, args))
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for sigs, fn in self.entries:
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if len(sigs) != len(types):
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continue
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if any(not mstype.issubclass_(type_, sig) for sig, type_ in zip(sigs, types)):
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continue
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output = fn(*args)
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return output
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raise ValueError("Cannot find fn match given args.")
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def register(self, *type_names):
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"""Register a function for the given type string."""
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def deco(fn):
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types = tuple(map(mstype.typing.str_to_type, type_names))
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self.register_fn(type_names, fn)
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self.entries.append((types, fn))
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return fn
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return deco
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class HyperMap(HyperMap_):
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"""
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Hypermap will apply the set operation on input sequences.
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Which will apply the operations of every elements of the sequence.
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Args:
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ops (Union[MultitypeFuncGraph, None]): `ops` is the operation to apply. If `ops` is `None`,
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the operations should be putted in the first input of the instance.
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Inputs:
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- **args** (Tuple[sequence]) - If `ops` is not `None`, all the inputs should be the same length sequences,
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and each row of the sequences. e.g. If args length is 2, and for `i` in length of each sequence
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`(args[0][i], args[1][i])` will be the input of the operation.
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If `ops` is not `None`, the first input is the operation, and the other is inputs.
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Outputs:
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sequence, the output will be same type and same length of sequence from input and the value of each element
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is the result of operation apply each row of element. e.g. `operation(args[0][i], args[1][i])`.
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"""
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def __init__(self, ops=None):
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self.ops = ops
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if ops:
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HyperMap_.__init__(self, ops)
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else:
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HyperMap_.__init__(self)
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def __call__(self, *args):
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func = self.ops
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args_list = args
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hypermap = self
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if self.ops is None:
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func = args[0]
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args_list = args[1:]
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hypermap = partial(self, func)
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# is leaf
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if not isinstance(args_list[0], (tuple, list)):
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return func(*args_list)
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return tuple(map(hypermap, *args_list))
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class Map(Map_):
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"""
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Map will apply the set operation on input sequences.
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Which will apply the operations of every elements of the sequence.
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Args:
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ops (Union[MultitypeFuncGraph, None]): `ops` is the operation to apply. If `ops` is `None`,
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the operations should be putted in the first input of the instance.
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Inputs:
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- **args** (Tuple[sequence]) - If `ops` is not `None`, all the inputs should be the same length sequences,
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and each row of the sequences. e.g. If args length is 2, and for `i` in length of each sequence
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`(args[0][i], args[1][i])` will be the input of the operation.
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If `ops` is not `None`, the first input is the operation, and the other is inputs.
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Outputs:
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sequence, the output will be same type and same length of sequence from input and the value of each element
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is the result of operation apply each row of element. e.g. `operation(args[0][i], args[1][i])`.
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"""
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def __init__(self, ops=None):
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self.ops = ops
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if ops:
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Map_.__init__(self, ops)
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else:
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|
Map_.__init__(self)
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def __call__(self, *args):
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func = self.ops
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args_list = args
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if self.ops is None:
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|
func = args[0]
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args_list = args[1:]
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return tuple(map(func, *args_list))
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|
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|
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class _ListAppend(ListAppend_):
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|
"""
|
|
A metafuncgraph class that append one element to list.
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|
|
|
Args:
|
|
name (str): The name of the metafuncgraph object.
|
|
"""
|
|
def __init__(self, name):
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|
ListAppend_.__init__(self, name)
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|
|
|
def __call__(self, *args):
|
|
pass
|
|
|
|
|
|
_append = _ListAppend("append")
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|
|
|
|
|
class _Tail(Tail_):
|
|
"""
|
|
A metafuncgraph class that generates tail elements of the tuple.
|
|
|
|
Args:
|
|
name (str): The name of the metafuncgraph object.
|
|
"""
|
|
|
|
def __init__(self, name):
|
|
Tail_.__init__(self, name)
|
|
|
|
def __call__(self, *args):
|
|
pass
|
|
|
|
|
|
tail = _Tail('tail')
|
|
|
|
|
|
class _ZipOperation(ZipOperation_):
|
|
"""Generates a tuple of zip iterations for inputs."""
|
|
|
|
def __init__(self, name):
|
|
ZipOperation_.__init__(self, name)
|
|
|
|
def __call__(self, *args):
|
|
pass
|
|
|
|
|
|
zip_operation = _ZipOperation('zip_operation')
|
|
"""`zip_operation` will generate a tuple of zip iterations of inputs."""
|
|
|
|
|
|
env_get = MultitypeFuncGraph("env_get")
|
|
|
|
|
|
@env_get.register("EnvType", "Tensor")
|
|
def _tensor_env_get(env, parameter):
|
|
"""Used to get env."""
|
|
return F.env_getitem(env, F.ref_to_embed(parameter), F.zeros_like(parameter))
|