forked from mindspore-Ecosystem/mindspore
665 lines
24 KiB
Python
665 lines
24 KiB
Python
# Copyright 2022 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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"""test the feature of mutable in graph"""
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import numpy as np
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import pytest
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import mindspore.nn as nn
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from mindspore import Tensor
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from mindspore.ops.composite import GradOperation
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from mindspore.ops import operations as P
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from mindspore.common import dtype as mstype
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from mindspore.common import mutable
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from mindspore import context
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context.set_context(mode=context.GRAPH_MODE)
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def compare(a, b):
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if isinstance(a, (list, tuple)):
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if not a and b:
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return False
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for aa, bb in zip(a, b):
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if not compare(aa, bb):
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return False
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return True
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return np.allclose(a.asnumpy(), b)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_cal_mutable_tensor():
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"""
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Feature: Support mutable in graph.
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Description: Get the matmul result for one tensor defined in graph which is set mutable.
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Expectation: Get the correct result.
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"""
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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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def construct(self, x):
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y = mutable(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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out = self.matmul(x, y)
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return out
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class Net1(nn.Cell):
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def __init__(self):
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super(Net1, self).__init__()
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self.matmul = P.MatMul()
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self.y = mutable(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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def construct(self, x):
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out = self.matmul(x, self.y)
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return out
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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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net = Net()
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output = net(x)
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p = Tensor([[0.5, 0.6, 0.4], [1.2, 1.3, 1.1]], dtype=mstype.float32)
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q = 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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expect_output = P.MatMul()(p, q)
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assert np.allclose(output.asnumpy(), expect_output.asnumpy())
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net = Net1()
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output = net(x)
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assert np.allclose(output.asnumpy(), expect_output.asnumpy())
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_grad_const_tensor_to_mutable():
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"""
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Feature: Support mutable in graph.
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Description: Get gradient with respect to tensor input defined in graph which is set mutable.
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Expectation: Get the correct gradients.
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"""
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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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def construct(self, x, y):
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out = self.matmul(x, y)
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return out
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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):
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x = mutable(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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gradient_function = self.grad_op(self.net)
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return gradient_function(x, y)
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class GradNetWrtX1(nn.Cell):
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def __init__(self, net):
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super(GradNetWrtX1, self).__init__()
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self.net = net
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self.grad_op = GradOperation()
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self.x = mutable(Tensor([[0.5, 0.6, 0.4], [1.2, 1.3, 1.1]], dtype=mstype.float32))
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def construct(self):
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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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gradient_function = self.grad_op(self.net)
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return gradient_function(self.x, y)
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grad_net = GradNetWrtX(Net())
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output = grad_net()
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expect_output = np.array([[1.4100001, 1.5999999, 6.6],
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[1.4100001, 1.5999999, 6.6]]).astype(np.float32)
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assert np.allclose(output.asnumpy(), expect_output)
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grad_net = GradNetWrtX1(Net())
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output = grad_net()
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assert np.allclose(output.asnumpy(), expect_output)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_grad_const_tensor_arg_to_mutable():
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"""
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Feature: Support mutable in graph.
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Description: Get gradient with respect to const tensor input defined outside the graph which is set mutable.
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Expectation: Get the correct gradients.
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"""
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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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def construct(self, x, y):
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out = self.matmul(x, y)
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return out
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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):
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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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gradient_function = self.grad_op(self.net)
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return gradient_function(mutable(x), y)
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x = Tensor([[0.5, 0.6, 0.4], [1.2, 1.3, 1.1]], dtype=mstype.float32, const_arg=True)
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grad_net = GradNetWrtX(Net())
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output = grad_net(x)
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expect_output = np.array([[1.4100001, 1.5999999, 6.6],
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[1.4100001, 1.5999999, 6.6]]).astype(np.float32)
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assert np.allclose(output.asnumpy(), expect_output)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_grad_const_tuple_tensor_to_mutable():
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"""
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Feature: Support mutable in graph.
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Description: Get gradient with respect to tuple tensor input defined in graph which is set mutable.
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Expectation: Get the correct gradients.
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"""
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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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def construct(self, z):
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x = z[0]
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y = z[1]
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out = self.matmul(x, y)
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return out
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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):
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x = mutable((Tensor([[0.5, 0.6, 0.4], [1.2, 1.3, 1.1]], dtype=mstype.float32),
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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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gradient_function = self.grad_op(self.net)
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return gradient_function(x)
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class GradNetWrtX1(nn.Cell):
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def __init__(self, net):
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super(GradNetWrtX1, self).__init__()
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self.net = net
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self.grad_op = GradOperation()
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self.x = mutable((Tensor([[0.5, 0.6, 0.4], [1.2, 1.3, 1.1]], dtype=mstype.float32),
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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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def construct(self):
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gradient_function = self.grad_op(self.net)
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return gradient_function(self.x)
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grad_net = GradNetWrtX(Net())
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output = grad_net()
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assert isinstance(output, tuple)
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expect = [np.array([[1.4100001, 1.5999999, 6.6],
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[1.4100001, 1.5999999, 6.6]]).astype(np.float32),
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np.array([[1.7, 1.7, 1.7],
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[1.9, 1.9, 1.9],
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[1.5, 1.5, 1.5]]).astype(np.float32)]
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assert compare(output, expect)
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grad_net = GradNetWrtX1(Net())
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output = grad_net()
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assert isinstance(output, tuple)
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assert compare(output, expect)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_grad_const_list_tensor_to_mutable():
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"""
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Feature: Support mutable in graph.
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Description: Get gradient with respect to list tensor input defined in graph which is set mutable.
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Expectation: Get the correct gradients.
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"""
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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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def construct(self, z):
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x = z[0]
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y = z[1]
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out = self.matmul(x, y)
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return out
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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):
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x = mutable([Tensor([[0.5, 0.6, 0.4], [1.2, 1.3, 1.1]], dtype=mstype.float32),
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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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gradient_function = self.grad_op(self.net)
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return gradient_function(x)
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class GradNetWrtX1(nn.Cell):
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def __init__(self, net):
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super(GradNetWrtX1, self).__init__()
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self.net = net
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self.grad_op = GradOperation()
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self.x = mutable([Tensor([[0.5, 0.6, 0.4], [1.2, 1.3, 1.1]], dtype=mstype.float32),
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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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def construct(self):
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gradient_function = self.grad_op(self.net)
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return gradient_function(self.x)
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grad_net = GradNetWrtX(Net())
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output = grad_net()
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assert isinstance(output, tuple)
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expect = [np.array([[1.4100001, 1.5999999, 6.6],
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[1.4100001, 1.5999999, 6.6]]).astype(np.float32),
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np.array([[1.7, 1.7, 1.7],
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[1.9, 1.9, 1.9],
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[1.5, 1.5, 1.5]]).astype(np.float32)]
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assert compare(output, expect)
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grad_net = GradNetWrtX1(Net())
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output = grad_net()
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assert isinstance(output, tuple)
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assert compare(output, expect)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_grad_const_tuple_or_list_tensor_arg_to_mutable():
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"""
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Feature: Support mutable in graph.
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Description: Get gradient with respect to const tuple or list tensor input defined outside graph which is
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set mutable.
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Expectation: Get the correct gradients.
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"""
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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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def construct(self, z):
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x = z[0]
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y = z[1]
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out = self.matmul(x, y)
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return out
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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):
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gradient_function = self.grad_op(self.net)
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return gradient_function(mutable(x))
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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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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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grad_net = GradNetWrtX(Net())
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output = grad_net(x)
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assert isinstance(output, tuple)
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expect = [np.array([[1.4100001, 1.5999999, 6.6],
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[1.4100001, 1.5999999, 6.6]]).astype(np.float32),
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np.array([[1.7, 1.7, 1.7],
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[1.9, 1.9, 1.9],
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[1.5, 1.5, 1.5]]).astype(np.float32)]
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assert compare(output, expect)
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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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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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output = grad_net(x)
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assert isinstance(output, tuple)
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expect = [np.array([[1.4100001, 1.5999999, 6.6],
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[1.4100001, 1.5999999, 6.6]]).astype(np.float32),
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np.array([[1.7, 1.7, 1.7],
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[1.9, 1.9, 1.9],
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[1.5, 1.5, 1.5]]).astype(np.float32)]
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assert compare(output, expect)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_grad_const_list_and_tuple_tensor_to_mutable():
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"""
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Feature: Support mutable in graph.
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Description: Get gradient with respect to list and tuple nested tensor input defined in graph which is
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set mutable.
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Expectation: Get the correct gradients.
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"""
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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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def construct(self, z):
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x = z[0][0]
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y = z[1]
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out = self.matmul(x, y)
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return out
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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):
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x = mutable([(Tensor([[0.5, 0.6, 0.4], [1.2, 1.3, 1.1]], dtype=mstype.float32),
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Tensor([[0.5, 0.6, 4.0], [1.2, 1.3, 1.1]], dtype=mstype.float32)),
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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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gradient_function = self.grad_op(self.net)
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return gradient_function(x)
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class GradNetWrtX1(nn.Cell):
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def __init__(self, net):
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super(GradNetWrtX1, self).__init__()
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self.net = net
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self.grad_op = GradOperation()
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self.x = mutable([(Tensor([[0.5, 0.6, 0.4], [1.2, 1.3, 1.1]], dtype=mstype.float32),
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Tensor([[0.5, 0.6, 4.0], [1.2, 1.3, 1.1]], dtype=mstype.float32)),
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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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def construct(self):
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gradient_function = self.grad_op(self.net)
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return gradient_function(self.x)
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grad_net = GradNetWrtX(Net())
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output = grad_net()
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assert isinstance(output, tuple)
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expect = [(np.array([[1.4100001, 1.5999999, 6.6],
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[1.4100001, 1.5999999, 6.6]]).astype(np.float32),
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np.array([[0.00000000e+00, 0.00000000e+00, 0.00000000e+00],
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[0.00000000e+00, 0.00000000e+00, 0.00000000e+00]]).astype(np.float32)),
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np.array([[1.7, 1.7, 1.7],
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[1.9, 1.9, 1.9],
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[1.5, 1.5, 1.5]]).astype(np.float32)]
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assert compare(output, expect)
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grad_net = GradNetWrtX1(Net())
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output = grad_net()
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assert isinstance(output, tuple)
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assert compare(output, expect)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_grad_const_dict_tensor_to_mutable():
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"""
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Feature: Support mutable in graph.
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Description: Get gradient with respect to dict tensor input defined in graph which is set mutable.
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Expectation: Get the correct gradients.
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"""
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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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def construct(self, z):
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x = z['a']
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y = z['b']
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out = self.matmul(x, y)
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return out
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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()
|
|
|
|
def construct(self):
|
|
x = mutable({'a': Tensor([[0.5, 0.6, 0.4], [1.2, 1.3, 1.1]], dtype=mstype.float32),
|
|
'b': Tensor([[0.01, 0.3, 1.1], [0.1, 0.2, 1.3], [2.1, 1.2, 3.3]], dtype=mstype.float32)})
|
|
gradient_function = self.grad_op(self.net)
|
|
return gradient_function(x)
|
|
|
|
class GradNetWrtX1(nn.Cell):
|
|
def __init__(self, net):
|
|
super(GradNetWrtX1, self).__init__()
|
|
self.net = net
|
|
self.grad_op = GradOperation()
|
|
self.x = mutable({'a': Tensor([[0.5, 0.6, 0.4], [1.2, 1.3, 1.1]], dtype=mstype.float32),
|
|
'b': Tensor([[0.01, 0.3, 1.1], [0.1, 0.2, 1.3], [2.1, 1.2, 3.3]], dtype=mstype.float32)})
|
|
|
|
def construct(self):
|
|
gradient_function = self.grad_op(self.net)
|
|
return gradient_function(self.x)
|
|
|
|
grad_net = GradNetWrtX(Net())
|
|
output = grad_net()
|
|
assert isinstance(output, tuple)
|
|
expect = [np.array([[1.4100001, 1.5999999, 6.6],
|
|
[1.4100001, 1.5999999, 6.6]]).astype(np.float32),
|
|
np.array([[1.7, 1.7, 1.7],
|
|
[1.9, 1.9, 1.9],
|
|
[1.5, 1.5, 1.5]]).astype(np.float32)]
|
|
assert compare(output, expect)
|
|
grad_net = GradNetWrtX1(Net())
|
|
output = grad_net()
|
|
assert isinstance(output, tuple)
|
|
assert compare(output, expect)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_grad_const_dict_tensor_arg_to_mutable():
|
|
"""
|
|
Feature: Support mutable in graph.
|
|
Description: Get gradient with respect to const dict tensor input defined outside graph which is set mutable.
|
|
Expectation: Get the correct gradients.
|
|
"""
|
|
|
|
class Net(nn.Cell):
|
|
def __init__(self):
|
|
super(Net, self).__init__()
|
|
self.matmul = P.MatMul()
|
|
|
|
def construct(self, z):
|
|
x = z['a']
|
|
y = z['b']
|
|
out = self.matmul(x, y)
|
|
return out
|
|
|
|
class GradNetWrtX(nn.Cell):
|
|
def __init__(self, net):
|
|
super(GradNetWrtX, self).__init__()
|
|
self.net = net
|
|
self.grad_op = GradOperation()
|
|
|
|
def construct(self, x):
|
|
gradient_function = self.grad_op(self.net)
|
|
return gradient_function(mutable(x))
|
|
|
|
x = {'a': Tensor([[0.5, 0.6, 0.4], [1.2, 1.3, 1.1]], dtype=mstype.float32),
|
|
'b': Tensor([[0.01, 0.3, 1.1], [0.1, 0.2, 1.3], [2.1, 1.2, 3.3]], dtype=mstype.float32)}
|
|
grad_net = GradNetWrtX(Net())
|
|
output = grad_net(x)
|
|
assert isinstance(output, tuple)
|
|
expect = [np.array([[1.4100001, 1.5999999, 6.6],
|
|
[1.4100001, 1.5999999, 6.6]]).astype(np.float32),
|
|
np.array([[1.7, 1.7, 1.7],
|
|
[1.9, 1.9, 1.9],
|
|
[1.5, 1.5, 1.5]]).astype(np.float32)]
|
|
assert compare(output, expect)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_grad_const_dict_and_tuple_tensor_to_mutable():
|
|
"""
|
|
Feature: Support mutable in graph.
|
|
Description: Get gradient with respect to const dict tuple nested tensor input defined in graph which is
|
|
set mutable.
|
|
Expectation: Get the correct gradients.
|
|
"""
|
|
|
|
class Net(nn.Cell):
|
|
def __init__(self):
|
|
super(Net, self).__init__()
|
|
self.matmul = P.MatMul()
|
|
|
|
def construct(self, z):
|
|
x = z['a'][0]
|
|
y = z['b']
|
|
out = self.matmul(x, y)
|
|
return out
|
|
|
|
class GradNetWrtX(nn.Cell):
|
|
def __init__(self, net):
|
|
super(GradNetWrtX, self).__init__()
|
|
self.net = net
|
|
self.grad_op = GradOperation()
|
|
|
|
def construct(self):
|
|
x = mutable({'a': (Tensor([[0.5, 0.6, 0.4], [1.2, 1.3, 1.1]], dtype=mstype.float32),
|
|
Tensor([[0.5, 0.6, 4.0], [1.2, 1.3, 1.1]], dtype=mstype.float32)),
|
|
'b': Tensor([[0.01, 0.3, 1.1], [0.1, 0.2, 1.3], [2.1, 1.2, 3.3]], dtype=mstype.float32)})
|
|
gradient_function = self.grad_op(self.net)
|
|
return gradient_function(x)
|
|
|
|
class GradNetWrtX1(nn.Cell):
|
|
def __init__(self, net):
|
|
super(GradNetWrtX1, self).__init__()
|
|
self.net = net
|
|
self.grad_op = GradOperation()
|
|
self.x = mutable({'a': (Tensor([[0.5, 0.6, 0.4], [1.2, 1.3, 1.1]], dtype=mstype.float32),
|
|
Tensor([[0.5, 0.6, 4.0], [1.2, 1.3, 1.1]], dtype=mstype.float32)),
|
|
'b': Tensor([[0.01, 0.3, 1.1], [0.1, 0.2, 1.3], [2.1, 1.2, 3.3]], dtype=mstype.float32)})
|
|
|
|
def construct(self):
|
|
gradient_function = self.grad_op(self.net)
|
|
return gradient_function(self.x)
|
|
|
|
grad_net = GradNetWrtX(Net())
|
|
output = grad_net()
|
|
assert isinstance(output, tuple)
|
|
expect = [(np.array([[1.4100001, 1.5999999, 6.6],
|
|
[1.4100001, 1.5999999, 6.6]]).astype(np.float32),
|
|
np.array([[0.00000000e+00, 0.00000000e+00, 0.00000000e+00],
|
|
[0.00000000e+00, 0.00000000e+00, 0.00000000e+00]]).astype(np.float32)),
|
|
np.array([[1.7, 1.7, 1.7],
|
|
[1.9, 1.9, 1.9],
|
|
[1.5, 1.5, 1.5]]).astype(np.float32)]
|
|
assert compare(output, expect)
|
|
grad_net = GradNetWrtX1(Net())
|
|
output = grad_net()
|
|
assert isinstance(output, tuple)
|
|
assert compare(output, expect)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_grad_const_dict_and_tuple_tensor_arg_to_mutable():
|
|
"""
|
|
Feature: Support mutable in graph.
|
|
Description: Get gradient with respect to const dict tuple nested tensor input defined outside graph which is
|
|
set mutable.
|
|
Expectation: Get the correct gradients.
|
|
"""
|
|
|
|
class Net(nn.Cell):
|
|
def __init__(self):
|
|
super(Net, self).__init__()
|
|
self.matmul = P.MatMul()
|
|
|
|
def construct(self, z):
|
|
x = z['a'][0]
|
|
y = z['b']
|
|
out = self.matmul(x, y)
|
|
return out
|
|
|
|
class GradNetWrtX(nn.Cell):
|
|
def __init__(self, net):
|
|
super(GradNetWrtX, self).__init__()
|
|
self.net = net
|
|
self.grad_op = GradOperation()
|
|
|
|
def construct(self, x):
|
|
gradient_function = self.grad_op(self.net)
|
|
return gradient_function(mutable(x))
|
|
|
|
x = {'a': (Tensor([[0.5, 0.6, 0.4], [1.2, 1.3, 1.1]], dtype=mstype.float32),
|
|
Tensor([[0.5, 0.6, 4.0], [1.2, 1.3, 1.1]], dtype=mstype.float32)),
|
|
'b': Tensor([[0.01, 0.3, 1.1], [0.1, 0.2, 1.3], [2.1, 1.2, 3.3]], dtype=mstype.float32)}
|
|
grad_net = GradNetWrtX(Net())
|
|
output = grad_net(x)
|
|
assert isinstance(output, tuple)
|
|
expect = [(np.array([[1.4100001, 1.5999999, 6.6],
|
|
[1.4100001, 1.5999999, 6.6]]).astype(np.float32),
|
|
np.array([[0.00000000e+00, 0.00000000e+00, 0.00000000e+00],
|
|
[0.00000000e+00, 0.00000000e+00, 0.00000000e+00]]).astype(np.float32)),
|
|
np.array([[1.7, 1.7, 1.7],
|
|
[1.9, 1.9, 1.9],
|
|
[1.5, 1.5, 1.5]]).astype(np.float32)]
|
|
assert compare(output, expect)
|
|
|
|
|
|
def test_grad_mutable_in_primal():
|
|
"""
|
|
Feature: Support mutable in graph.
|
|
Description: Get gradient with respect to const tensor input defined outside the graph which is set mutable
|
|
and uses mutable in primal graph.
|
|
Expectation: Get the correct gradients.
|
|
"""
|
|
|
|
class Net(nn.Cell):
|
|
def __init__(self):
|
|
super(Net, self).__init__()
|
|
self.matmul = P.MatMul()
|
|
|
|
def construct(self, x, y):
|
|
out = self.matmul(mutable(x), y)
|
|
return out
|
|
|
|
class GradNetWrtX(nn.Cell):
|
|
def __init__(self, net):
|
|
super(GradNetWrtX, self).__init__()
|
|
self.net = net
|
|
self.grad_op = GradOperation()
|
|
|
|
def construct(self, x):
|
|
y = Tensor([[0.01, 0.3, 1.1], [0.1, 0.2, 1.3], [2.1, 1.2, 3.3]], dtype=mstype.float32)
|
|
gradient_function = self.grad_op(self.net)
|
|
return gradient_function(mutable(x), y)
|
|
|
|
x = Tensor([[0.5, 0.6, 0.4], [1.2, 1.3, 1.1]], dtype=mstype.float32, const_arg=True)
|
|
grad_net = GradNetWrtX(Net())
|
|
output = grad_net(x)
|
|
expect_output = np.array([[1.4100001, 1.5999999, 6.6],
|
|
[1.4100001, 1.5999999, 6.6]]).astype(np.float32)
|
|
assert np.allclose(output.asnumpy(), expect_output)
|