wehub-resource-sync
624 lines
18 KiB
Python
624 lines
18 KiB
Python
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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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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import unittest
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import numpy as np
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from op_test import OpTest, get_places
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from utils import dygraph_guard, static_guard
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import paddle
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from paddle import base, static
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from paddle.base import core
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paddle.enable_static()
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class TestMatrixPowerOp(OpTest):
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def config(self):
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self.matrix_shape = [10, 10]
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self.dtype = "float64"
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self.n = 0
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def setUp(self):
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self.op_type = "matrix_power"
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self.python_api = paddle.tensor.matrix_power
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self.config()
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np.random.seed(123)
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mat = np.random.random(self.matrix_shape).astype(self.dtype)
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powered_mat = np.linalg.matrix_power(mat, self.n)
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self.inputs = {"X": mat}
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self.outputs = {"Out": powered_mat}
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self.attrs = {"n": self.n}
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def test_check_output(self):
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self.check_output(check_pir=True)
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def test_grad(self):
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self.check_grad(
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["X"],
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"Out",
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numeric_grad_delta=1e-5,
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max_relative_error=1e-7,
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check_pir=True,
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)
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class TestMatrixPowerOpN1(TestMatrixPowerOp):
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def config(self):
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self.matrix_shape = [10, 10]
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self.dtype = "float64"
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self.n = 1
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class TestMatrixPowerOpN2(TestMatrixPowerOp):
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def config(self):
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self.matrix_shape = [10, 10]
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self.dtype = "float64"
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self.n = 2
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class TestMatrixPowerOpN3(TestMatrixPowerOp):
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def config(self):
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self.matrix_shape = [10, 10]
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self.dtype = "float64"
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self.n = 3
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class TestMatrixPowerOpN4(TestMatrixPowerOp):
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def config(self):
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self.matrix_shape = [10, 10]
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self.dtype = "float64"
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self.n = 4
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class TestMatrixPowerOpN5(TestMatrixPowerOp):
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def config(self):
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self.matrix_shape = [10, 10]
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self.dtype = "float64"
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self.n = 5
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class TestMatrixPowerOpN6(TestMatrixPowerOp):
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def config(self):
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self.matrix_shape = [10, 10]
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self.dtype = "float64"
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self.n = 6
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class TestMatrixPowerOpN10(TestMatrixPowerOp):
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def config(self):
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self.matrix_shape = [10, 10]
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self.dtype = "float64"
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self.n = 10
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class TestMatrixPowerOpNMinus(TestMatrixPowerOp):
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def config(self):
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self.matrix_shape = [10, 10]
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self.dtype = "float64"
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self.n = -1
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def test_grad(self):
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self.check_grad(
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["X"],
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"Out",
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numeric_grad_delta=1e-5,
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max_relative_error=1e-6,
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check_pir=True,
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)
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class TestMatrixPowerOpNMinus2(TestMatrixPowerOpNMinus):
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def config(self):
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self.matrix_shape = [10, 10]
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self.dtype = "float64"
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self.n = -2
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class TestMatrixPowerOpNMinus3(TestMatrixPowerOpNMinus):
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def config(self):
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self.matrix_shape = [10, 10]
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self.dtype = "float64"
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self.n = -3
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class TestMatrixPowerOpNMinus4(TestMatrixPowerOpNMinus):
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def config(self):
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self.matrix_shape = [10, 10]
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self.dtype = "float64"
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self.n = -4
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class TestMatrixPowerOpNMinus5(TestMatrixPowerOpNMinus):
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def config(self):
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self.matrix_shape = [10, 10]
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self.dtype = "float64"
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self.n = -5
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class TestMatrixPowerOpNMinus6(TestMatrixPowerOpNMinus):
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def config(self):
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self.matrix_shape = [10, 10]
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self.dtype = "float64"
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self.n = -6
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class TestMatrixPowerOpNMinus10(TestMatrixPowerOp):
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def config(self):
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self.matrix_shape = [10, 10]
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self.dtype = "float64"
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self.n = -10
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def test_grad(self):
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self.check_grad(
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["X"],
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"Out",
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numeric_grad_delta=1e-5,
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max_relative_error=1e-6,
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check_pir=True,
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)
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class TestMatrixPowerOpBatched1(TestMatrixPowerOp):
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def config(self):
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self.matrix_shape = [8, 4, 4]
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self.dtype = "float64"
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self.n = 5
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class TestMatrixPowerOpBatched2(TestMatrixPowerOp):
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def config(self):
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self.matrix_shape = [2, 6, 4, 4]
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self.dtype = "float64"
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self.n = 4
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class TestMatrixPowerOpBatched3(TestMatrixPowerOp):
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def config(self):
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self.matrix_shape = [2, 6, 4, 4]
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self.dtype = "float64"
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self.n = 0
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class TestMatrixPowerOpBatchedLong(TestMatrixPowerOp):
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def config(self):
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self.matrix_shape = [1, 2, 3, 4, 4, 3, 3]
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self.dtype = "float64"
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self.n = 3
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class TestMatrixPowerOpLarge1(TestMatrixPowerOp):
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def config(self):
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self.matrix_shape = [32, 32]
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self.dtype = "float64"
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self.n = 3
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class TestMatrixPowerOpLarge2(TestMatrixPowerOp):
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def config(self):
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self.matrix_shape = [10, 10]
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self.dtype = "float64"
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self.n = 32
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class TestMatrixPowerOpZeroSize(TestMatrixPowerOp):
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def config(self):
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self.matrix_shape = [0, 0]
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self.dtype = "float32"
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self.n = 32
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class TestMatrixPowerOpZeroSize1(TestMatrixPowerOp):
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def config(self):
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self.matrix_shape = [0, 0]
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self.dtype = "float32"
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self.n = 0
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class TestMatrixPowerOpZeroSize2(TestMatrixPowerOp):
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def config(self):
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self.matrix_shape = [0, 0]
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self.dtype = "float32"
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self.n = -1
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class TestMatrixPowerOpBatchedZeroSize1(TestMatrixPowerOp):
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def config(self):
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self.matrix_shape = [2, 0, 4, 4]
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self.dtype = "float32"
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self.n = 4
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class TestMatrixPowerOpBatchedZeroSize2(TestMatrixPowerOp):
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def config(self):
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self.matrix_shape = [2, 0, 4, 4]
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self.dtype = "float32"
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self.n = 0
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class TestMatrixPowerOpBatchedZeroSize3(TestMatrixPowerOp):
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def config(self):
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self.matrix_shape = [2, 0, 4, 4]
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self.dtype = "float32"
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self.n = -1
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class TestMatrixPowerOpBatchedZeroSize4(TestMatrixPowerOp):
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def config(self):
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self.matrix_shape = [2, 6, 0, 0]
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self.dtype = "float32"
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self.n = 1
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class TestMatrixPowerOpBatchedZeroSize5(TestMatrixPowerOp):
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def config(self):
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self.matrix_shape = [2, 6, 0, 0]
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self.dtype = "float32"
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self.n = 0
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class TestMatrixPowerOpBatchedZeroSize6(TestMatrixPowerOp):
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def config(self):
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self.matrix_shape = [2, 6, 0, 0]
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self.dtype = "float32"
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self.n = -1
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@unittest.skipIf(
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core.is_compiled_with_xpu(),
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"Skip complex due to lack of mean support",
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)
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class TestMatrixPowerOpComplex64(TestMatrixPowerOp):
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def config(self):
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self.matrix_shape = [10, 10]
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self.dtype = "complex64"
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self.n = 2
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def test_grad(self):
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self.check_grad(["X"], "Out", max_relative_error=1e-2, check_pir=True)
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@unittest.skipIf(
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core.is_compiled_with_xpu(),
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"Skip complex due to lack of mean support",
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)
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class TestMatrixPowerOpBatchedComplex64(TestMatrixPowerOpComplex64):
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def config(self):
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self.matrix_shape = [2, 8, 4, 4]
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self.dtype = "complex64"
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self.n = 2
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@unittest.skipIf(
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core.is_compiled_with_xpu(),
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"Skip complex due to lack of mean support",
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)
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class TestMatrixPowerOpLarge1Complex64(TestMatrixPowerOpComplex64):
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def config(self):
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self.matrix_shape = [32, 32]
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self.dtype = "complex64"
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self.n = 2
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@unittest.skipIf(
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core.is_compiled_with_xpu(),
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"Skip complex due to lack of mean support",
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)
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class TestMatrixPowerOpLarge2Complex64(TestMatrixPowerOpComplex64):
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def config(self):
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self.matrix_shape = [10, 10]
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self.dtype = "complex64"
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self.n = 32
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@unittest.skipIf(
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core.is_compiled_with_xpu(),
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"Skip complex due to lack of mean support",
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)
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class TestMatrixPowerOpComplex64Minus(TestMatrixPowerOpComplex64):
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def config(self):
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self.matrix_shape = [10, 10]
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self.dtype = "complex64"
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self.n = -1
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@unittest.skipIf(
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core.is_compiled_with_xpu(),
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"Skip complex due to lack of mean support",
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)
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class TestMatrixPowerOpComplex128(TestMatrixPowerOp):
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def config(self):
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self.matrix_shape = [10, 10]
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self.dtype = "complex128"
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self.n = 2
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def test_grad(self):
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self.check_grad(["X"], "Out", max_relative_error=1e-2, check_pir=True)
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@unittest.skipIf(
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core.is_compiled_with_xpu(),
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"Skip complex due to lack of mean support",
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)
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class TestMatrixPowerOpBatchedComplex128(TestMatrixPowerOpComplex128):
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def config(self):
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self.matrix_shape = [2, 8, 4, 4]
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self.dtype = "complex128"
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self.n = 2
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@unittest.skipIf(
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core.is_compiled_with_xpu(),
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"Skip complex due to lack of mean support",
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)
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class TestMatrixPowerOpLarge1Complex128(TestMatrixPowerOpComplex128):
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def config(self):
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self.matrix_shape = [32, 32]
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self.dtype = "complex128"
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self.n = 2
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@unittest.skipIf(
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core.is_compiled_with_xpu(),
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"Skip complex due to lack of mean support",
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)
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class TestMatrixPowerOpLarge2Complex128(TestMatrixPowerOpComplex128):
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def config(self):
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self.matrix_shape = [10, 10]
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self.dtype = "complex128"
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self.n = 32
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@unittest.skipIf(
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core.is_compiled_with_xpu(),
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"Skip complex due to lack of mean support",
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)
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class TestMatrixPowerOpComplex128Minus(TestMatrixPowerOpComplex128):
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def config(self):
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self.matrix_shape = [10, 10]
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self.dtype = "complex128"
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self.n = -1
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class TestMatrixPowerOpFP32(TestMatrixPowerOp):
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def config(self):
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self.matrix_shape = [10, 10]
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self.dtype = "float32"
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self.n = 2
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def test_grad(self):
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self.check_grad(["X"], "Out", max_relative_error=1e-2, check_pir=True)
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class TestMatrixPowerOpBatchedFP32(TestMatrixPowerOpFP32):
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def config(self):
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self.matrix_shape = [2, 8, 4, 4]
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self.dtype = "float32"
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self.n = 2
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class TestMatrixPowerOpLarge1FP32(TestMatrixPowerOpFP32):
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def config(self):
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self.matrix_shape = [32, 32]
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self.dtype = "float32"
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self.n = 2
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class TestMatrixPowerOpLarge2FP32(TestMatrixPowerOpFP32):
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def config(self):
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self.matrix_shape = [10, 10]
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self.dtype = "float32"
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self.n = 32
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class TestMatrixPowerOpFP32Minus(TestMatrixPowerOpFP32):
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def config(self):
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self.matrix_shape = [10, 10]
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self.dtype = "float32"
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self.n = -1
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class TestMatrixPowerAPI(unittest.TestCase):
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def setUp(self):
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np.random.seed(123)
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self.places = get_places()
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def check_static_result(self, place):
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with static.program_guard(static.Program(), static.Program()):
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input_x = paddle.static.data(
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name="input_x", shape=[4, 4], dtype="float64"
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)
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result = paddle.linalg.matrix_power(x=input_x, n=-2)
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input_np = np.random.random([4, 4]).astype("float64")
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result_np = np.linalg.matrix_power(input_np, -2)
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exe = base.Executor(place)
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fetches = exe.run(
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feed={"input_x": input_np},
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fetch_list=[result],
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)
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np.testing.assert_allclose(
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fetches[0], np.linalg.matrix_power(input_np, -2), rtol=1e-05
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)
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def test_static(self):
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for place in self.places:
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self.check_static_result(place=place)
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def test_dygraph(self):
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for place in self.places:
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with base.dygraph.guard(place):
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input_np = np.random.random([4, 4]).astype("float64")
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input = paddle.to_tensor(input_np)
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result = paddle.linalg.matrix_power(input, -2)
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np.testing.assert_allclose(
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result.numpy(),
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np.linalg.matrix_power(input_np, -2),
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rtol=1e-05,
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)
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class TestMatrixPowerAPIError(unittest.TestCase):
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def test_errors(self):
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input_np = np.random.random([4, 4]).astype("float64")
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# input must be Variable.
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self.assertRaises(TypeError, paddle.linalg.matrix_power, input_np)
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# n must be int
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for n in [2.0, '2', -2.0]:
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input = paddle.static.data(
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name="input_float32", shape=[4, 4], dtype='float32'
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)
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self.assertRaises(TypeError, paddle.linalg.matrix_power, input, n)
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# The data type of input must be float32 or float64.
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for dtype in ["bool", "int32", "int64", "float16"]:
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input = paddle.static.data(
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name="input_" + dtype, shape=[4, 4], dtype=dtype
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)
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self.assertRaises(TypeError, paddle.linalg.matrix_power, input, 2)
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# The number of dimensions of input must be >= 2.
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input = paddle.static.data(name="input_2", shape=[4], dtype="float32")
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self.assertRaises(ValueError, paddle.linalg.matrix_power, input, 2)
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# The inner-most 2 dimensions of input should be equal to each other
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input = paddle.static.data(
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name="input_3", shape=[4, 5], dtype="float32"
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)
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self.assertRaises(ValueError, paddle.linalg.matrix_power, input, 2)
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def test_old_ir_errors(self):
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if paddle.framework.use_pir_api():
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return
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# When out is set, the data type must be the same as input.
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input = paddle.static.data(
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name="input_1", shape=[4, 4], dtype="float32"
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)
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out = paddle.static.data(name="output", shape=[4, 4], dtype="float64")
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self.assertRaises(TypeError, paddle.linalg.matrix_power, input, 2, out)
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class TestMatrixPowerSingularAPI(unittest.TestCase):
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def setUp(self):
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self.places = get_places()
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def check_static_result(self, place):
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with static.program_guard(static.Program(), static.Program()):
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input = paddle.static.data(
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name="input", shape=[4, 4], dtype="float64"
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)
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result = paddle.linalg.matrix_power(x=input, n=-2)
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input_np = np.zeros([4, 4]).astype("float64")
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exe = base.Executor(place)
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try:
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fetches = exe.run(
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feed={"input": input_np},
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fetch_list=[result],
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)
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except RuntimeError as ex:
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print("The mat is singular")
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except ValueError as ex:
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print("The mat is singular")
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def test_static(self):
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paddle.enable_static()
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for place in self.places:
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self.check_static_result(place=place)
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paddle.disable_static()
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def test_dygraph(self):
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for place in self.places:
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with base.dygraph.guard(place):
|
|
input_np = np.ones([4, 4]).astype("float64")
|
|
input = paddle.to_tensor(input_np)
|
|
try:
|
|
result = paddle.linalg.matrix_power(input, -2)
|
|
except RuntimeError as ex:
|
|
print("The mat is singular")
|
|
except ValueError as ex:
|
|
print("The mat is singular")
|
|
|
|
|
|
class TestMatrixPowerEmptyTensor(unittest.TestCase):
|
|
def _get_places(self):
|
|
return get_places()
|
|
|
|
def _test_matrix_power_empty_static(self, place):
|
|
with (
|
|
static_guard(),
|
|
paddle.static.program_guard(
|
|
paddle.static.Program(), paddle.static.Program()
|
|
),
|
|
):
|
|
x2 = paddle.static.data(name='x2', shape=[0, 6], dtype='float32')
|
|
x3 = paddle.static.data(name='x3', shape=[6, 0], dtype='float32')
|
|
x4 = paddle.static.data(
|
|
name='x4', shape=[0, 0, 2, 3], dtype='float32'
|
|
)
|
|
self.assertRaises(TypeError, paddle.linalg.matrix_power, x2)
|
|
self.assertRaises(TypeError, paddle.linalg.matrix_power, x3)
|
|
self.assertRaises(TypeError, paddle.linalg.matrix_power, x4)
|
|
|
|
x = paddle.static.data(name='x', shape=[0, 0], dtype='float32')
|
|
y = paddle.linalg.matrix_power(x, 2)
|
|
x5 = paddle.static.data(
|
|
name='x5', shape=[2, 3, 0, 0], dtype='float32'
|
|
)
|
|
y5 = paddle.linalg.matrix_power(x5, 2)
|
|
exe = paddle.static.Executor(place)
|
|
res = exe.run(
|
|
feed={
|
|
'x2': np.zeros((0, 6), dtype='float32'),
|
|
'x3': np.zeros((6, 0), dtype='float32'),
|
|
'x4': np.zeros((0, 0, 2, 3), dtype='float32'),
|
|
'x': np.zeros((0, 0), dtype='float32'),
|
|
'x5': np.zeros((2, 3, 0, 0), dtype='float32'),
|
|
},
|
|
fetch_list=[y, y5],
|
|
)
|
|
self.assertEqual(res[0].shape, (0, 0))
|
|
self.assertEqual(res[1].shape, (2, 3, 0, 0))
|
|
|
|
def _test_matrix_power_empty_dynamic(self):
|
|
with dygraph_guard():
|
|
x2 = paddle.full((0, 6), 1.0, dtype='float32')
|
|
x3 = paddle.full((6, 0), 1.0, dtype='float32')
|
|
x4 = paddle.full((2, 3, 0, 0), 1.0, dtype='float32')
|
|
x5 = paddle.full((0, 0, 2, 3), 1.0, dtype='float32')
|
|
self.assertRaises(TypeError, paddle.linalg.matrix_power, x2)
|
|
self.assertRaises(TypeError, paddle.linalg.matrix_power, x3)
|
|
self.assertRaises(TypeError, paddle.linalg.matrix_power, x5)
|
|
x = paddle.full((0, 0), 1.0, dtype='float32')
|
|
y = paddle.linalg.matrix_power(x, 2)
|
|
y4 = paddle.linalg.matrix_power(x4, 2)
|
|
self.assertEqual(y4.shape, [2, 3, 0, 0])
|
|
self.assertEqual(y.shape, [0, 0])
|
|
|
|
def test_matrix_power_empty_tensor(self):
|
|
for place in self._get_places():
|
|
self._test_matrix_power_empty_static(place)
|
|
self._test_matrix_power_empty_dynamic()
|
|
|
|
|
|
if __name__ == "__main__":
|
|
paddle.enable_static()
|
|
unittest.main()
|