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- # 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.
- # ============================================================================
- """test jvp in graph mode"""
- import numpy as np
- import pytest
- import mindspore.nn as nn
- import mindspore.context as context
- from mindspore import Tensor
- from mindspore.nn.grad import Vjp
-
- context.set_context(mode=context.GRAPH_MODE)
-
-
- class SingleInputNet(nn.Cell):
- def construct(self, x):
- return x**3
-
-
- class MultipleInputsOutputNet(nn.Cell):
- def construct(self, x, y):
- return 2*x, y**3
-
-
- @pytest.mark.level0
- @pytest.mark.platform_x86_cpu
- @pytest.mark.env_onecard
- def test_vjp_single_input_graph():
- x = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
- v = Tensor(np.array([[1, 1], [1, 1]]).astype(np.float32))
- net = SingleInputNet()
- expect_primal = Tensor(np.array([[1, 8], [27, 64]]).astype(np.float32))
- expect_grad = Tensor(np.array([[3, 12], [27, 48]]).astype(np.float32))
- primal, grad = Vjp(net)(x, v)
- assert np.allclose(primal.asnumpy(), expect_primal.asnumpy())
- assert np.allclose(grad.asnumpy(), expect_grad.asnumpy())
-
-
-
- @pytest.mark.level0
- @pytest.mark.platform_x86_cpu
- @pytest.mark.env_onecard
- def test_vjp_multiple_inputs_default_v_graph():
- 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))
- net = MultipleInputsOutputNet()
- expect_primal_0 = Tensor(np.array([[2, 4], [6, 8]]).astype(np.float32))
- expect_primal_1 = Tensor(np.array([[1, 8], [27, 64]]).astype(np.float32))
- expect_grad_0 = Tensor(np.array([[2, 2], [2, 2]]).astype(np.float32))
- expect_grad_1 = Tensor(np.array([[3, 12], [27, 48]]).astype(np.float32))
- primal, grad = Vjp(net)(x, y, (v, v))
- assert isinstance(primal, tuple)
- assert len(primal) == 2
- assert np.allclose(primal[0].asnumpy(), expect_primal_0.asnumpy())
- assert np.allclose(primal[1].asnumpy(), expect_primal_1.asnumpy())
- assert isinstance(grad, tuple)
- assert len(grad) == 2
- assert np.allclose(grad[0].asnumpy(), expect_grad_0.asnumpy())
- assert np.allclose(grad[1].asnumpy(), expect_grad_1.asnumpy())
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