188 lines
6.1 KiB
Python
188 lines
6.1 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 pytest
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import mindspore
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from mindspore import context, ops, nn, Tensor
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from mindspore.ops.primitive import constexpr
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from mindspore.ops.operations import linalg_ops, array_ops
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context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
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RTOL = 1.e-5
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ATOL = 1.e-6
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k_0 = Tensor(0, mindspore.int32)
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matmul = ops.MatMul()
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batch_matmul = ops.BatchMatMul()
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transpose = ops.Transpose()
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@constexpr
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def make_zero_matrix(shape, dtype):
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return Tensor(np.zeros(shape), dtype)
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def matrix_diag(diagonal, shape):
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assist_matrix = make_zero_matrix(shape, ops.DType()(diagonal))
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return array_ops.MatrixSetDiagV3()(assist_matrix, diagonal, k_0)
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class SvdNet(nn.Cell):
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def __init__(self, full_matrices=False, compute_uv=True):
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super(SvdNet, self).__init__()
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self.svd = linalg_ops.Svd(full_matrices=full_matrices, compute_uv=compute_uv)
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def construct(self, a):
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return self.svd(a)
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_svd_net1():
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"""
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Feature: Svd
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Description: test cases for svd: m >= n and full_matrices=False, compute_uv=False
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Expectation: the result match to numpy
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"""
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a = np.random.rand(3, 2)
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tensor_a = Tensor(a, dtype=mindspore.float32)
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mscp_svd_net = SvdNet(False, False)
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s, _, _ = mscp_svd_net(tensor_a)
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n_s = np.linalg.svd(a, full_matrices=False, compute_uv=False)
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assert np.allclose(n_s, s.asnumpy(), rtol=RTOL, atol=ATOL)
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_svd_net2():
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"""
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Feature: Svd
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Description: test cases for svd: m >= n and full_matrices=True, compute_uv=True
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Expectation: the result match to numpy
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"""
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a = np.random.rand(3, 2)
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tensor_a = Tensor(a, dtype=mindspore.float64)
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mscp_svd_net = SvdNet(True, True)
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s, u, v = mscp_svd_net(tensor_a)
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output = matmul(u, matmul(matrix_diag(s, (3, 2)), transpose(v, (1, 0))))
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assert np.allclose(a, output.asnumpy(), rtol=RTOL, atol=ATOL)
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_svd_net3():
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"""
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Feature: Svd
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Description: test cases for svd: m >= n and full_matrices=False, compute_uv=True
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Expectation: the result match to numpy
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"""
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a = np.random.rand(3, 2)
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tensor_a = Tensor(a, dtype=mindspore.float32)
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s, u, v = ops.svd(tensor_a, False, True)
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output = matmul(u, matmul(matrix_diag(s, (2, 2)), transpose(v, (1, 0))))
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assert np.allclose(a, output.asnumpy(), rtol=RTOL, atol=ATOL)
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_svd_net4():
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"""
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Feature: Svd
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Description: test cases for svd: m < n and full_matrices=True, compute_uv=True
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Expectation: the result match to numpy
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"""
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a = np.random.rand(2, 3)
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tensor_a = Tensor(a, dtype=mindspore.float64)
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s, u, v = ops.svd(tensor_a, True, True)
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output = matmul(u, matmul(matrix_diag(s, (2, 3)), transpose(v, (1, 0))))
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assert np.allclose(a, output.asnumpy(), rtol=RTOL, atol=ATOL)
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_svd_net5():
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"""
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Feature: Svd
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Description: test cases for svd: inputs shape is (a, b, m, n), m > n
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Expectation: the result match to numpy
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"""
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a = np.random.rand(5, 5, 3, 2)
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tensor_a = Tensor(a, dtype=mindspore.float32)
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s, u, v = ops.svd(tensor_a, True, True)
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output = batch_matmul(u, batch_matmul(matrix_diag(s, (5, 5, 3, 2)), transpose(v, (0, 1, 3, 2))))
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assert np.allclose(a, output.asnumpy(), rtol=RTOL, atol=ATOL)
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_svd_net6():
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"""
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Feature: Svd
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Description: test cases for svd: specific input 3*2
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Expectation: the result match to numpy
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"""
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a = np.array([[1, 2], [-4, -5], [2, 1]])
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tensor_a = Tensor(a, dtype=mindspore.float32)
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s, u, v = linalg_ops.Svd(full_matrices=True, compute_uv=True)(tensor_a)
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output = matmul(u, matmul(matrix_diag(s, (3, 2)), transpose(v, (1, 0))))
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assert np.allclose(a, output.asnumpy(), rtol=RTOL, atol=ATOL)
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_svd_vmap1():
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"""
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Feature: Svd
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Description: test cases for svd: vmap
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Expectation: the result match to numpy
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"""
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a = np.random.rand(5, 3, 3)
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tensor_a = Tensor(a, dtype=mindspore.float32)
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net = SvdNet(True, True)
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svd_vmap = ops.vmap(net, (0,), 0)
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outs = svd_vmap(tensor_a)
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outs_expect = tensor_a.svd(full_matrices=True, compute_uv=True)
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assert np.allclose(outs_expect[0].asnumpy(), outs[0].asnumpy(), rtol=RTOL, atol=ATOL)
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assert np.allclose(outs_expect[1].asnumpy(), outs[1].asnumpy(), rtol=RTOL, atol=ATOL)
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assert np.allclose(outs_expect[2].asnumpy(), outs[2].asnumpy(), rtol=RTOL, atol=ATOL)
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_svd_vmap2():
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"""
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Feature: Svd
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Description: test cases for svd: vmap
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Expectation: the result match to numpy
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"""
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a = np.random.rand(5, 3, 3)
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tensor_a = Tensor(a, dtype=mindspore.float32)
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net = SvdNet(True, False)
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svd_vmap = ops.vmap(net, (0,), 0)
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s, _, _ = svd_vmap(tensor_a)
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s_expect = tensor_a.svd(full_matrices=True, compute_uv=False)
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assert np.allclose(s_expect.asnumpy(), s.asnumpy(), rtol=RTOL, atol=ATOL)
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