forked from mindspore-Ecosystem/mindspore
233 lines
9.2 KiB
Python
233 lines
9.2 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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import numpy as np
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import scipy as osp
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import pytest
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import mindspore.nn as nn
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from mindspore import Tensor, context
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from mindspore.ops import operations as P
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from mindspore.common import dtype as ms_type
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from mindspore.ops.functional import vmap
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class MatrixDeterminantNet(nn.Cell):
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def __init__(self):
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super(MatrixDeterminantNet, self).__init__()
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self.matrix_determinant = P.MatrixDeterminant()
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def construct(self, input_x):
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output = self.matrix_determinant(input_x)
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return output
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def matrix_determinant_scipy_benchmark(input_x):
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"""
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Feature: generate a matrix determinant numpy benchmark.
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Description: The input shape [..., M, M] need to match output [...].
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Expectation: match to scipy mindspore MatrixDeterminant.
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"""
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result = osp.linalg.det(input_x)
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return result
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class LogMatrixDeterminantNet(nn.Cell):
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def __init__(self):
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super(LogMatrixDeterminantNet, self).__init__()
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self.log_matrix_determinant = P.LogMatrixDeterminant()
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def construct(self, input_x):
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output = self.log_matrix_determinant(input_x)
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return output
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def log_matrix_determinant_np_benchmark(input_x):
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"""
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Feature: generate a log matrix determinant numpy benchmark.
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Description: The input shape [..., M, M] need to match output [...].
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Expectation: match to np mindspore LogMatrixDeterminant.
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"""
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result = np.linalg.slogdet(input_x)
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return result
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class DeterminantVMapNet(nn.Cell):
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def __init__(self, forward_net, in_axes, out_axes):
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super(DeterminantVMapNet, self).__init__()
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self.net = forward_net
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self.in_axes = in_axes
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self.out_axes = out_axes
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def construct(self, input_x):
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return vmap(self.net, self.in_axes, self.out_axes)(input_x)
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@pytest.mark.level0
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@pytest.mark.env_onecard
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.parametrize("data_shape", [(4, 4), (5, 5)])
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@pytest.mark.parametrize("data_type", [np.float32, np.float64, np.complex64, np.complex128])
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def test_matrix_determinant(data_shape, data_type):
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"""
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Feature: Test MatrixDeterminant.
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Description: The input shape [..., M, M] need to match output [...].
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Expectation: match to scipy benchmark.
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"""
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context.set_context(mode=context.GRAPH_MODE)
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input_x = np.random.random(data_shape).astype(data_type)
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error = 1e-6
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if data_type == np.float32:
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error = 1e-3
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benchmark_output = matrix_determinant_scipy_benchmark(input_x)
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matrix_determinant = MatrixDeterminantNet()
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output = matrix_determinant(Tensor(input_x))
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np.testing.assert_allclose(output.asnumpy(), benchmark_output, rtol=error, atol=error)
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context.set_context(mode=context.PYNATIVE_MODE)
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output = matrix_determinant(Tensor(input_x))
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np.testing.assert_allclose(output.asnumpy(), benchmark_output, rtol=error, atol=error)
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@pytest.mark.level0
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@pytest.mark.env_onecard
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.parametrize("data_shape", [(4, 4), (5, 5)])
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@pytest.mark.parametrize("data_type", [np.float32, np.float64, np.complex64, np.complex128])
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def test_log_matrix_determinant(data_shape, data_type):
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"""
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Feature: Test LogMatrixDeterminant.
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Description: The input shape [..., M, M] need to match output [...].
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Expectation: match to np benchmark.
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"""
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context.set_context(mode=context.GRAPH_MODE)
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input_x = np.random.random(data_shape).astype(data_type)
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error = 1e-6
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if data_type == np.float32:
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error = 1e-3
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benchmark_output = log_matrix_determinant_np_benchmark(input_x)
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log_matrix_determinant = LogMatrixDeterminantNet()
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output = log_matrix_determinant(Tensor(input_x))
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np.testing.assert_allclose(output[0].asnumpy(), benchmark_output[0], rtol=error, atol=error)
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np.testing.assert_allclose(output[1].asnumpy(), benchmark_output[1], rtol=error, atol=error)
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context.set_context(mode=context.PYNATIVE_MODE)
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output = log_matrix_determinant(Tensor(input_x))
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np.testing.assert_allclose(output[0].asnumpy(), benchmark_output[0], rtol=error, atol=error)
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np.testing.assert_allclose(output[1].asnumpy(), benchmark_output[1], rtol=error, atol=error)
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@pytest.mark.level0
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@pytest.mark.env_onecard
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@pytest.mark.platform_x86_gpu_training
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def test_matrix_determinant_dy_shape():
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"""
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Feature: Test MatrixDeterMinant DynamicShape.
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Description: The input data type only check float32 is ok.
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Expectation: match to np benchmark.
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"""
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context.set_context(mode=context.GRAPH_MODE)
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matrix_determinant_net = MatrixDeterminantNet()
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loss = 1e-6
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input_shape = (4, 4)
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data_type = np.float32
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ms_data_type = ms_type.float32
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input_x_np = np.random.random(input_shape).astype(data_type)
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benchmark_output = matrix_determinant_scipy_benchmark(input_x_np)
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input_dyn = Tensor(shape=[4, None], dtype=ms_data_type)
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matrix_determinant_net.set_inputs(input_dyn)
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ms_result = matrix_determinant_net(Tensor(input_x_np))
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np.testing.assert_allclose(benchmark_output, ms_result.asnumpy(), rtol=loss, atol=loss)
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context.set_context(mode=context.PYNATIVE_MODE)
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ms_result = matrix_determinant_net(Tensor(input_x_np))
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np.testing.assert_allclose(benchmark_output, ms_result.asnumpy(), rtol=loss, atol=loss)
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@pytest.mark.level0
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@pytest.mark.env_onecard
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@pytest.mark.platform_x86_gpu_training
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def test_log_matrix_determinant_dy_shape():
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"""
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Feature: Test LogMatrixDeterMinant DynamicShape.
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Description: The input data type only check float32 is ok.
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Expectation: match to np benchmark.
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"""
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context.set_context(mode=context.GRAPH_MODE)
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context.set_context(mode=context.GRAPH_MODE)
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log_matrix_determinant_net = LogMatrixDeterminantNet()
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loss = 1e-6
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input_shape = (4, 4)
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data_type = np.float32
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ms_data_type = ms_type.float32
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input_x_np = np.random.random(input_shape).astype(data_type)
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benchmark_output = log_matrix_determinant_np_benchmark(input_x_np)
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input_dyn = Tensor(shape=[4, None], dtype=ms_data_type)
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log_matrix_determinant_net.set_inputs(input_dyn)
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ms_result = log_matrix_determinant_net(Tensor(input_x_np))
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np.testing.assert_allclose(ms_result[0].asnumpy(), benchmark_output[0], rtol=loss, atol=loss)
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np.testing.assert_allclose(ms_result[1].asnumpy(), benchmark_output[1], rtol=loss, atol=loss)
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context.set_context(mode=context.PYNATIVE_MODE)
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ms_result = log_matrix_determinant_net(Tensor(input_x_np))
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np.testing.assert_allclose(ms_result[0].asnumpy(), benchmark_output[0], rtol=loss, atol=loss)
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np.testing.assert_allclose(ms_result[1].asnumpy(), benchmark_output[1], rtol=loss, atol=loss)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_matrix_determinant_vmap():
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"""
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Feature: test MatrixDeterMinant vmap on GPU.
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Description: inputs(input_x) with batch.
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Expectation: the result match with expect
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"""
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context.set_context(mode=context.GRAPH_MODE)
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loss = 1e-6
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data_type = np.float32
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# Case : in_axes input_x batch remains 0
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input_x = Tensor(np.array([[[[-4.5, -1.5], [7.0, 6.0]], [[2.5, 0.5], [3.0, 9.0]]],
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[[[-4.5, -1.5], [7.0, 6.0]], [[2.5, 0.5], [3.0, 9.0]]]]).astype(data_type))
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in_axes = 0
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out_axes = 0
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benchmark_output = np.array([[-16.5, 21.], [-16.5, 21.]]).astype(data_type)
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matrix_determinant = MatrixDeterminantNet()
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ms_result = DeterminantVMapNet(matrix_determinant, in_axes, out_axes)(Tensor(input_x))
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assert np.allclose(ms_result.asnumpy(), benchmark_output, rtol=loss, atol=loss)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_log_matrix_determinant_vmap():
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"""
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Feature: test logMatrixDeterMinant vmap on GPU.
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Description: inputs(input_x) with batch.
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Expectation: the result match with expect
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"""
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context.set_context(mode=context.GRAPH_MODE)
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loss = 1e-6
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data_type = np.float32
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# Case : in_axes input_x batch remains 0
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input_x = Tensor(np.array([[[[-4.5, -1.5], [7.0, 6.0]], [[2.5, 0.5], [3.0, 9.0]]],
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[[[-4.5, -1.5], [7.0, 6.0]], [[2.5, 0.5], [3.0, 9.0]]]]).astype(data_type))
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in_axes = 0
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out_axes = 0
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benchmark_output_sign = np.array([[-1., 1.], [-1., 1.]]).astype(data_type)
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benchmark_output_determinant = np.array([[2.80336046, 3.04452229], [2.80336046, 3.04452229]]).astype(data_type)
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matrix_determinant = LogMatrixDeterminantNet()
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ms_result = DeterminantVMapNet(matrix_determinant, in_axes, out_axes)(Tensor(input_x))
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assert np.allclose(ms_result[0].asnumpy(), benchmark_output_sign, rtol=loss, atol=loss)
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assert np.allclose(ms_result[1].asnumpy(), benchmark_output_determinant, rtol=loss, atol=loss)
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