Source code for mindspore.nn.grad.cell_grad

# Copyright 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
#
# 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
# limitations under the License.
# ============================================================================
"""cell grad"""
from ..cell import Cell
from ...ops import composite as C
from ...ops import operations as P
from ...ops.primitive import Primitive
from ...common import dtype as mstype
from ...common.api import ms_function


class _FirstGrad(Cell):
    def __init__(self, fn):
        super(_FirstGrad, self).__init__()
        self.first_grad_op = C.GradOperation(sens_param=True, get_all=True)
        self.fn = fn

    def construct(self, u, first_grad_input):
        return self.first_grad_op(self.fn)(*first_grad_input, u)


class _JvpFirstGrad(Cell):
    def __init__(self):
        super(_JvpFirstGrad, self).__init__()
        self.first_grad_op = C.GradOperation(sens_param=True, get_all=True)

    def construct(self, u, fn, first_grad_input):
        return self.first_grad_op(fn)(*first_grad_input, u)


class _FirstGradSingleValue(Cell):
    def __init__(self, fn):
        super(_FirstGradSingleValue, self).__init__()
        self.first_grad_single_value_op = C.GradOperation(sens_param=True)
        self.fn = fn

    def construct(self, u, first_grad_single_value_input):
        return self.first_grad_single_value_op(self.fn)(*first_grad_single_value_input, u)


class _JvpFirstGradSingleValue(Cell):
    def __init__(self):
        super(_JvpFirstGradSingleValue, self).__init__()
        self.first_grad_single_value_op = C.GradOperation(sens_param=True)

    def construct(self, u, fn, first_grad_single_value_input):
        return self.first_grad_single_value_op(fn)(*first_grad_single_value_input, u)



[文档]class Jvp(Cell): """ Compute the jacobian-vector-product of the given fn. Jvp is equivalent to forward mode autodiff. Args: fn (Cell): The fn that takes Tensor inputs and returns a tuple of Tensors or a Tensor. Inputs: - **inputs** (Tensors) - The inputs to `fn`. - **v** (Tensors or Tuple of Tensors) - The vector for which the Jacobian vector product is computed. Must have the same size as the input of `fn`. Outputs: A tuple with 2 Tensors or Tuple of Tensors: - **net_output** (Tensors or Tuple of Tensors) - The output of `fn(inputs)`. - **jvp** (Tensors or Tuple of Tensors) - The result of the jacobian vector product. Supported Platforms: ``Ascend`` ``GPU`` ``CPU`` Examples: >>> from mindspore.nn import Jvp >>> class Net(nn.Cell): ... def construct(self, x, y): ... return x**3 + y >>> x = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32)) >>> y = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32)) >>> v = Tensor(np.array([[1, 1], [1, 1]]).astype(np.float32)) >>> output = Jvp(Net())(x, y, (v, v)) >>> print(output[0]) [[ 2. 10.] [30. 68.]] >>> print(output[1]) [[ 4. 13.] [28. 49.]] """ def __init__(self, fn): super(Jvp, self).__init__() self.fn = fn self.oneslike = P.OnesLike() self.first_grad = _FirstGrad(fn) self.first_grad.add_flags(enable_tuple_grad=True) self.first_grad_single_value = _FirstGradSingleValue(fn) self.first_grad_single_value.add_flags(enable_tuple_grad=True) self.second_grad_op = C.GradOperation(sens_param=True) self.issubclass_ = P.IsSubClass() self.typeof = Primitive('typeof') self.make_tuple = Primitive('MakeTuple') self.tuple_len = Primitive("tuple_len") @ms_function def construct(self, *args): jvp_input = args[0:-1] v = args[-1] output = self.fn(*jvp_input) if self.issubclass_(self.typeof(output), mstype.tuple_): u = self.make_tuple() for i in range(self.tuple_len(output)): u = u + self.make_tuple(self.oneslike(output[i])) else: u = self.oneslike(output) if self.tuple_len(jvp_input) == 1: second_gradient_net = self.second_grad_op(self.first_grad_single_value) gradient_output = second_gradient_net(u, jvp_input, v) else: second_gradient_net = self.second_grad_op(self.first_grad) gradient_output = second_gradient_net(u, jvp_input, v) return output, gradient_output
class _JvpInner(Cell): """ Compute the jacobian-vector-product of the given network. Jvp is equivalent to forward mode autodiff. This class implements the inner process of function jvp. """ def __init__(self): super(_JvpInner, self).__init__() self.oneslike = P.OnesLike() self.first_grad = _JvpFirstGrad() self.first_grad.add_flags(enable_tuple_grad=True) self.first_grad_single_value = _JvpFirstGradSingleValue() self.first_grad_single_value.add_flags(enable_tuple_grad=True) self.second_grad_op = C.GradOperation(sens_param=True) self.issubclass_ = P.IsSubClass() self.typeof = Primitive('typeof') self.make_tuple = Primitive('MakeTuple') self.tuple_len = Primitive("tuple_len") def construct(self, *args): fn = args[0] v = args[1] jvp_input = args[2:] output = fn(*jvp_input) if self.issubclass_(self.typeof(output), mstype.tuple_): u = self.make_tuple() for i in range(self.tuple_len(output)): u = u + self.make_tuple(self.oneslike(output[i])) else: u = self.oneslike(output) if self.tuple_len(jvp_input) == 1: second_gradient_net = self.second_grad_op(self.first_grad_single_value) gradient_output = second_gradient_net(u, fn, jvp_input, v) else: second_gradient_net = self.second_grad_op(self.first_grad) gradient_output = second_gradient_net(u, fn, jvp_input, v) return output, gradient_output
[文档]class Vjp(Cell): """ Computes the dot product between a vector `v` and the Jacobian of the given fn at the point given by the inputs. Args: fn (Cell): The fn that takes Tensor inputs and returns a tuple of Tensors or a Tensor. Inputs: - **inputs** (Tensors) - The inputs to `fn`. Must be a tuple or a list. - **v** (Tensors or Tuple of Tensors) - The vector for which the vector Jacobian product is computed. Must have the same size as the output of `fn`. Outputs: A tuple with 2 Tensors or Tuple of Tensors: - **net_output** (Tensors or Tuple of Tensors) - The output of `fn(inputs)`. - **vjp** (Tensors or Tuple of Tensors) - The result of the dot product. Supported Platforms: ``Ascend`` ``GPU`` ``CPU`` Examples: >>> from mindspore.nn import Vjp >>> class Net(nn.Cell): ... def construct(self, x, y): ... return x**3 + y >>> x = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32)) >>> y = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32)) >>> v = Tensor(np.array([[1, 1], [1, 1]]).astype(np.float32)) >>> output = Vjp(Net())(x, y, v) >>> print(output[0]) [[ 2. 10.] [30. 68.]] >>> print(output[1][0]) [[ 3. 12.] [27. 48.]] >>> print(output[1][1]) [[1. 1.] [1. 1.]] """ def __init__(self, fn): super(Vjp, self).__init__() self.fn = fn self.grad = C.GradOperation(get_all=True, sens_param=True) self.grad_single_value = C.GradOperation(sens_param=True) self.issubclass_ = P.IsSubClass() self.typeof = Primitive('typeof') self.tuple_len = Primitive("tuple_len") @ms_function def construct(self, *args): front_input = args[0:-1] output = self.fn(*front_input) if self.tuple_len(front_input) == 1: gradient_output = self.grad_single_value(self.fn)(*args) else: gradient_output = self.grad(self.fn)(*args) return output, gradient_output
class _VjpInner(Cell): """ Computes the dot product between a vector `v` and the Jacobian of the given network at the point given by the inputs. This class implements the inner process of function vjp. """ def __init__(self): super(_VjpInner, self).__init__() self.grad = C.GradOperation(get_all=True, sens_param=True) self.grad_single_value = C.GradOperation(sens_param=True) self.tuple_len = Primitive("tuple_len") def construct(self, *args): fn = args[0] front_input = args[1:-1] input_with_v = args[1:] output = fn(*front_input) if self.tuple_len(front_input) == 1: gradient_output = self.grad_single_value(fn)(*input_with_v) else: gradient_output = self.grad(fn)(*input_with_v) return output, gradient_output