wehub-resource-sync
1013 lines
36 KiB
Python
1013 lines
36 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.w
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import sys
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import unittest
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import numpy as np
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import paddle
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sys.path.append("..")
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from op_test import OpTest, get_device_place, get_places
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from paddle import base
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# 2D normal case
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class TestSolveOp(OpTest):
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def config(self):
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self.python_api = paddle.linalg.solve
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self.input_x_matrix_shape = [15, 15]
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self.input_y_matrix_shape = [15, 10]
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self.dtype = "float64"
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def setUp(self):
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paddle.enable_static()
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self.config()
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self.op_type = "solve"
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np.random.seed(2021)
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self.inputs = {
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'X': np.random.random(self.input_x_matrix_shape).astype(self.dtype),
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'Y': np.random.random(self.input_y_matrix_shape).astype(self.dtype),
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}
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self.outputs = {
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'Out': np.linalg.solve(self.inputs['X'], self.inputs['Y'])
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}
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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_check_grad_normal(self):
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self.check_grad(['X', 'Y'], 'Out', check_pir=True)
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# x broadcast + 3D batch case
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class TestSolveOpBatched_case0(OpTest):
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def setUp(self):
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self.python_api = paddle.linalg.solve
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self.op_type = "solve"
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self.dtype = "float64"
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np.random.seed(2021)
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self.inputs = {
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'X': np.random.random((11, 11)).astype(self.dtype),
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'Y': np.random.random((2, 11, 7)).astype(self.dtype),
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}
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result = np.linalg.solve(self.inputs['X'], self.inputs['Y'])
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self.outputs = {'Out': result}
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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_check_grad_normal(self):
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self.check_grad(
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['X', 'Y'], 'Out', max_relative_error=1e-1, check_pir=True
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)
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# 3D batch + y vector case
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class TestSolveOpBatched_case1(OpTest):
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def setUp(self):
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self.python_api = paddle.linalg.solve
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self.op_type = "solve"
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self.dtype = "float64"
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np.random.seed(2021)
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self.inputs = {
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'X': np.random.random((20, 6, 6)).astype(self.dtype),
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'Y': np.random.random((20, 6)).astype(self.dtype),
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}
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result = np.empty_like(self.inputs['Y'])
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for i in range(self.inputs['X'].shape[0]):
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result[i] = np.linalg.solve(
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self.inputs['X'][i], self.inputs['Y'][i]
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)
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self.outputs = {'Out': result}
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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_check_grad_normal(self):
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self.check_grad(
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['X', 'Y'], 'Out', max_relative_error=0.04, check_pir=True
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)
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# 3D batch + y broadcast case
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class TestSolveOpBatched_case2(OpTest):
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def setUp(self):
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self.python_api = paddle.linalg.solve
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self.op_type = "solve"
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self.dtype = "float64"
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np.random.seed(2021)
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self.inputs = {
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'X': np.random.random((2, 10, 10)).astype(self.dtype),
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'Y': np.random.random((1, 10, 10)).astype(self.dtype),
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}
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result = np.linalg.solve(self.inputs['X'], self.inputs['Y'])
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self.outputs = {'Out': result}
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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_check_grad_normal(self):
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self.check_grad(
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['X', 'Y'], 'Out', max_relative_error=0.02, check_pir=True
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)
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# x broadcast + 3D batch case
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class TestSolveOpBatched_case3(OpTest):
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def setUp(self):
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self.python_api = paddle.linalg.solve
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self.op_type = "solve"
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self.dtype = "float64"
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np.random.seed(2021)
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self.inputs = {
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'X': np.random.random((1, 10, 10)).astype(self.dtype),
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'Y': np.random.random((2, 10, 10)).astype(self.dtype),
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}
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result = np.linalg.solve(self.inputs['X'], self.inputs['Y'])
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self.outputs = {'Out': result}
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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_check_grad_normal(self):
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self.check_grad(
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['X', 'Y'], 'Out', max_relative_error=0.02, check_pir=True
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)
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# 3D normal batch case
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class TestSolveOpBatched_case4(OpTest):
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def setUp(self):
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self.python_api = paddle.linalg.solve
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self.op_type = "solve"
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self.dtype = "float64"
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np.random.seed(2021)
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self.inputs = {
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'X': np.random.random((3, 6, 6)).astype(self.dtype),
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'Y': np.random.random((3, 6, 7)).astype(self.dtype),
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}
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result = np.linalg.solve(self.inputs['X'], self.inputs['Y'])
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self.outputs = {'Out': result}
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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_check_grad_normal(self):
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self.check_grad(['X', 'Y'], 'Out', check_pir=True)
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# 4D normal batch case
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class TestSolveOpBatched_case5(OpTest):
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def setUp(self):
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self.python_api = paddle.linalg.solve
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self.op_type = "solve"
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self.dtype = "float64"
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np.random.seed(2021)
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self.inputs = {
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'X': np.random.random((2, 2, 6, 6)).astype(self.dtype),
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'Y': np.random.random((2, 2, 6, 6)).astype(self.dtype),
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}
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result = np.linalg.solve(self.inputs['X'], self.inputs['Y'])
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self.outputs = {'Out': result}
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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_check_grad_normal(self):
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self.check_grad(['X', 'Y'], 'Out', check_pir=True)
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# 4D batch + y broadcast case
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class TestSolveOpBatched_case6(OpTest):
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def setUp(self):
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self.python_api = paddle.linalg.solve
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self.op_type = "solve"
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self.dtype = "float64"
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np.random.seed(2021)
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self.inputs = {
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'X': np.random.random((2, 2, 6, 6)).astype(self.dtype),
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'Y': np.random.random((1, 2, 6, 9)).astype(self.dtype),
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}
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result = np.linalg.solve(self.inputs['X'], self.inputs['Y'])
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self.outputs = {'Out': result}
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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_check_grad_normal(self):
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self.check_grad(['X', 'Y'], 'Out', check_pir=True)
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# 5D normal batch case
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class TestSolveOpBatched_case7(OpTest):
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def setUp(self):
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self.python_api = paddle.linalg.solve
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self.op_type = "solve"
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self.dtype = "float64"
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np.random.seed(2021)
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self.inputs = {
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'X': np.random.random((2, 2, 2, 4, 4)).astype(self.dtype),
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'Y': np.random.random((2, 2, 2, 4, 4)).astype(self.dtype),
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}
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result = np.linalg.solve(self.inputs['X'], self.inputs['Y'])
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self.outputs = {'Out': result}
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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_check_grad_normal(self):
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self.check_grad(
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['X', 'Y'], 'Out', max_relative_error=0.04, check_pir=True
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)
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# 5D batch + y broadcast case
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class TestSolveOpBatched_case8(OpTest):
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def setUp(self):
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self.python_api = paddle.linalg.solve
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self.op_type = "solve"
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self.dtype = "float64"
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np.random.seed(2021)
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self.inputs = {
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'X': np.random.random((2, 2, 2, 4, 4)).astype(self.dtype),
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'Y': np.random.random((1, 2, 2, 4, 7)).astype(self.dtype),
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}
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result = np.linalg.solve(self.inputs['X'], self.inputs['Y'])
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self.outputs = {'Out': result}
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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_check_grad_normal(self):
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self.check_grad(
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['X', 'Y'], 'Out', max_relative_error=0.04, check_pir=True
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)
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class TestSolveOpError(unittest.TestCase):
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def test_errors(self):
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with paddle.static.program_guard(
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paddle.static.Program(), paddle.static.Program()
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):
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# The input type of solve_op must be Variable.
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x1 = base.create_lod_tensor(
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np.array([[-1]]), [[1]], base.CPUPlace()
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)
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y1 = base.create_lod_tensor(
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np.array([[-1]]), [[1]], base.CPUPlace()
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)
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self.assertRaises(TypeError, paddle.linalg.solve, x1, y1)
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# The data type of input must be float32 or float64.
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x2 = paddle.static.data(name="x2", shape=[30, 30], dtype="bool")
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y2 = paddle.static.data(name="y2", shape=[30, 10], dtype="bool")
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self.assertRaises(TypeError, paddle.linalg.solve, x2, y2)
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x3 = paddle.static.data(name="x3", shape=[30, 30], dtype="int32")
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y3 = paddle.static.data(name="y3", shape=[30, 10], dtype="int32")
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self.assertRaises(TypeError, paddle.linalg.solve, x3, y3)
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x4 = paddle.static.data(name="x4", shape=[30, 30], dtype="int64")
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y4 = paddle.static.data(name="y4", shape=[30, 10], dtype="int64")
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self.assertRaises(TypeError, paddle.linalg.solve, x4, y4)
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x5 = paddle.static.data(name="x5", shape=[30, 30], dtype="float16")
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y5 = paddle.static.data(name="y5", shape=[30, 10], dtype="float16")
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self.assertRaises(TypeError, paddle.linalg.solve, x5, y5)
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# The number of dimensions of input'X must be >= 2.
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x6 = paddle.static.data(name="x6", shape=[30], dtype="float64")
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y6 = paddle.static.data(name="y6", shape=[30], dtype="float64")
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self.assertRaises(ValueError, paddle.linalg.solve, x6, y6)
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# The inner-most 2 dimensions of input'X should be equal to each other
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x7 = paddle.static.data(name="x7", shape=[2, 3, 4], dtype="float64")
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y7 = paddle.static.data(name="y7", shape=[2, 4, 3], dtype="float64")
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self.assertRaises(ValueError, paddle.linalg.solve, x7, y7)
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# The shape of y should not be 1 when left = False. (if y is vector it should be a row vector)
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x8 = paddle.static.data(name="x8", shape=[3, 3], dtype="float64")
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y8 = paddle.static.data(name="y8", shape=[3], dtype="float64")
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self.assertRaises(ValueError, paddle.linalg.solve, x8, y8, False)
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# The height of x should equal the width of y when left = False.
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x9 = paddle.static.data(name="x9", shape=[2, 5, 5], dtype="float64")
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y9 = paddle.static.data(name="y9", shape=[5, 3], dtype="float64")
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self.assertRaises(ValueError, paddle.linalg.solve, x9, y9, False)
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# 2D + vector case, FP64
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class TestSolveOpAPI_1(unittest.TestCase):
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def setUp(self):
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np.random.seed(2021)
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self.place = get_places()
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self.dtype = "float64"
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def check_static_result(self, place):
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paddle.enable_static()
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with base.program_guard(base.Program(), base.Program()):
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paddle_input_x = paddle.static.data(
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name="input_x", shape=[3, 3], dtype=self.dtype
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)
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paddle_input_y = paddle.static.data(
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name="input_y", shape=[3], dtype=self.dtype
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)
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paddle_result = paddle.linalg.solve(paddle_input_x, paddle_input_y)
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np_input_x = np.random.random([3, 3]).astype(self.dtype)
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np_input_y = np.random.random([3]).astype(self.dtype)
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np_result = np.linalg.solve(np_input_x, np_input_y)
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exe = base.Executor(place)
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fetches = exe.run(
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base.default_main_program(),
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feed={"input_x": np_input_x, "input_y": np_input_y},
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fetch_list=[paddle_result],
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)
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np.testing.assert_allclose(fetches[0], np_result, rtol=1e-05)
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def test_static(self):
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for place in self.place:
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self.check_static_result(place=place)
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def test_dygraph(self):
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def run(place):
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paddle.disable_static(place)
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np.random.seed(2021)
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input_x_np = np.random.random([3, 3]).astype(self.dtype)
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input_y_np = np.random.random([3]).astype(self.dtype)
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tensor_input_x = paddle.to_tensor(input_x_np)
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tensor_input_y = paddle.to_tensor(input_y_np)
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numpy_output = np.linalg.solve(input_x_np, input_y_np)
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paddle_output = paddle.linalg.solve(tensor_input_x, tensor_input_y)
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np.testing.assert_allclose(
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numpy_output, paddle_output.numpy(), rtol=1e-05
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)
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self.assertEqual(numpy_output.shape, paddle_output.numpy().shape)
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paddle.enable_static()
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for place in self.place:
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run(place)
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# 2D normal case, FP64
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class TestSolveOpAPI_2(unittest.TestCase):
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def setUp(self):
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np.random.seed(2021)
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self.place = get_places()
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self.dtype = "float64"
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def check_static_result(self, place):
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paddle.enable_static()
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with base.program_guard(base.Program(), base.Program()):
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paddle_input_x = paddle.static.data(
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name="input_x", shape=[10, 10], dtype=self.dtype
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)
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paddle_input_y = paddle.static.data(
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name="input_y", shape=[10, 4], dtype=self.dtype
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)
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paddle_result = paddle.linalg.solve(paddle_input_x, paddle_input_y)
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np_input_x = np.random.random([10, 10]).astype(self.dtype)
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np_input_y = np.random.random([10, 4]).astype(self.dtype)
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np_result = np.linalg.solve(np_input_x, np_input_y)
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exe = base.Executor(place)
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fetches = exe.run(
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base.default_main_program(),
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feed={"input_x": np_input_x, "input_y": np_input_y},
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fetch_list=[paddle_result],
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)
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np.testing.assert_allclose(fetches[0], np_result, rtol=1e-05)
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def test_static(self):
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for place in self.place:
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self.check_static_result(place=place)
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def test_dygraph(self):
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def run(place):
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paddle.disable_static(place)
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np.random.seed(2021)
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input_x_np = np.random.random([10, 10]).astype(self.dtype)
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input_y_np = np.random.random([10, 4]).astype(self.dtype)
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tensor_input_x = paddle.to_tensor(input_x_np)
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tensor_input_y = paddle.to_tensor(input_y_np)
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numpy_output = np.linalg.solve(input_x_np, input_y_np)
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paddle_output = paddle.linalg.solve(tensor_input_x, tensor_input_y)
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np.testing.assert_allclose(
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numpy_output, paddle_output.numpy(), rtol=1e-05
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)
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self.assertEqual(numpy_output.shape, paddle_output.numpy().shape)
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paddle.enable_static()
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for place in self.place:
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run(place)
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# 2D normal case, FP32
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class TestSolveOpAPI_3(unittest.TestCase):
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def setUp(self):
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np.random.seed(2021)
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self.place = get_places()
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self.dtype = "float32"
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def check_static_result(self, place):
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paddle.enable_static()
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with base.program_guard(base.Program(), base.Program()):
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paddle_input_x = paddle.static.data(
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name="input_x", shape=[10, 10], dtype=self.dtype
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)
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paddle_input_y = paddle.static.data(
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name="input_y", shape=[10, 4], dtype=self.dtype
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)
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paddle_result = paddle.linalg.solve(paddle_input_x, paddle_input_y)
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np_input_x = np.random.random([10, 10]).astype(self.dtype)
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np_input_y = np.random.random([10, 4]).astype(self.dtype)
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np_result = np.linalg.solve(np_input_x, np_input_y)
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exe = base.Executor(place)
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fetches = exe.run(
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base.default_main_program(),
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feed={"input_x": np_input_x, "input_y": np_input_y},
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fetch_list=[paddle_result],
|
||
)
|
||
np.testing.assert_allclose(fetches[0], np_result, rtol=0.0001)
|
||
|
||
def test_static(self):
|
||
for place in self.place:
|
||
self.check_static_result(place=place)
|
||
|
||
def test_dygraph(self):
|
||
def run(place):
|
||
paddle.disable_static(place)
|
||
np.random.seed(2021)
|
||
input_x_np = np.random.random([10, 10]).astype(self.dtype)
|
||
input_y_np = np.random.random([10, 4]).astype(self.dtype)
|
||
|
||
tensor_input_x = paddle.to_tensor(input_x_np)
|
||
tensor_input_y = paddle.to_tensor(input_y_np)
|
||
|
||
numpy_output = np.linalg.solve(input_x_np, input_y_np)
|
||
paddle_output = paddle.linalg.solve(tensor_input_x, tensor_input_y)
|
||
np.testing.assert_allclose(
|
||
numpy_output, paddle_output.numpy(), rtol=0.0001
|
||
)
|
||
self.assertEqual(numpy_output.shape, paddle_output.numpy().shape)
|
||
paddle.enable_static()
|
||
|
||
for place in self.place:
|
||
run(place)
|
||
|
||
|
||
# 3D + y broadcast case, FP64
|
||
class TestSolveOpAPI_4(unittest.TestCase):
|
||
def setUp(self):
|
||
np.random.seed(2021)
|
||
self.place = get_places()
|
||
self.dtype = "float64"
|
||
|
||
def check_static_result(self, place):
|
||
with base.program_guard(base.Program(), base.Program()):
|
||
paddle_input_x = paddle.static.data(
|
||
name="input_x", shape=[2, 3, 3], dtype=self.dtype
|
||
)
|
||
paddle_input_y = paddle.static.data(
|
||
name="input_y", shape=[1, 3, 3], dtype=self.dtype
|
||
)
|
||
paddle_result = paddle.linalg.solve(paddle_input_x, paddle_input_y)
|
||
|
||
np_input_x = np.random.random([2, 3, 3]).astype(self.dtype)
|
||
np_input_y = np.random.random([1, 3, 3]).astype(self.dtype)
|
||
|
||
np_result = np.linalg.solve(np_input_x, np_input_y)
|
||
|
||
exe = base.Executor(place)
|
||
fetches = exe.run(
|
||
base.default_main_program(),
|
||
feed={"input_x": np_input_x, "input_y": np_input_y},
|
||
fetch_list=[paddle_result],
|
||
)
|
||
np.testing.assert_allclose(fetches[0], np_result, rtol=1e-05)
|
||
|
||
def test_static(self):
|
||
for place in self.place:
|
||
self.check_static_result(place=place)
|
||
|
||
def test_dygraph(self):
|
||
def run(place):
|
||
paddle.disable_static(place)
|
||
np.random.seed(2021)
|
||
input_x_np = np.random.random([2, 3, 3]).astype(self.dtype)
|
||
input_y_np = np.random.random([1, 3, 3]).astype(self.dtype)
|
||
|
||
tensor_input_x = paddle.to_tensor(input_x_np)
|
||
tensor_input_y = paddle.to_tensor(input_y_np)
|
||
|
||
numpy_output = np.linalg.solve(input_x_np, input_y_np)
|
||
paddle_output = paddle.linalg.solve(tensor_input_x, tensor_input_y)
|
||
np.testing.assert_allclose(
|
||
numpy_output, paddle_output.numpy(), rtol=1e-05
|
||
)
|
||
self.assertEqual(numpy_output.shape, paddle_output.numpy().shape)
|
||
paddle.enable_static()
|
||
|
||
for place in self.place:
|
||
run(place)
|
||
|
||
|
||
def np_transpose_last_2dim(x):
|
||
x_new_dims = list(range(len(x.shape)))
|
||
x_new_dims[-1], x_new_dims[-2] = x_new_dims[-2], x_new_dims[-1]
|
||
x = np.transpose(x, x_new_dims)
|
||
return x
|
||
|
||
|
||
def np_solve_right(x, y):
|
||
x = np_transpose_last_2dim(x)
|
||
y = np_transpose_last_2dim(y)
|
||
out = np.linalg.solve(x, y)
|
||
out = np_transpose_last_2dim(out)
|
||
return out
|
||
|
||
|
||
# 2D + vector right case, FP64
|
||
class TestSolveOpAPIRight_1(unittest.TestCase):
|
||
def setUp(self):
|
||
np.random.seed(2021)
|
||
self.place = get_places()
|
||
self.dtype = "float64"
|
||
|
||
def check_static_result(self, place):
|
||
with base.program_guard(base.Program(), base.Program()):
|
||
paddle_input_x = paddle.static.data(
|
||
name="input_x", shape=[3, 3], dtype=self.dtype
|
||
)
|
||
paddle_input_y = paddle.static.data(
|
||
name="input_y", shape=[1, 3], dtype=self.dtype
|
||
)
|
||
paddle_result = paddle.linalg.solve(
|
||
paddle_input_x, paddle_input_y, left=False
|
||
)
|
||
|
||
np_input_x = np.random.random([3, 3]).astype(self.dtype)
|
||
np_input_y = np.random.random([1, 3]).astype(self.dtype)
|
||
|
||
np_result = np_solve_right(np_input_x, np_input_y)
|
||
|
||
exe = base.Executor(place)
|
||
fetches = exe.run(
|
||
base.default_main_program(),
|
||
feed={"input_x": np_input_x, "input_y": np_input_y},
|
||
fetch_list=[paddle_result],
|
||
)
|
||
np.testing.assert_allclose(fetches[0], np_result, rtol=1e-05)
|
||
|
||
def test_static(self):
|
||
for place in self.place:
|
||
self.check_static_result(place=place)
|
||
|
||
def test_dygraph(self):
|
||
def run(place):
|
||
paddle.disable_static(place)
|
||
np.random.seed(2021)
|
||
input_x_np = np.random.random([3, 3]).astype(self.dtype)
|
||
input_y_np = np.random.random([1, 3]).astype(self.dtype)
|
||
|
||
tensor_input_x = paddle.to_tensor(input_x_np)
|
||
tensor_input_y = paddle.to_tensor(input_y_np)
|
||
|
||
numpy_output = np_solve_right(input_x_np, input_y_np)
|
||
paddle_output = paddle.linalg.solve(
|
||
tensor_input_x, tensor_input_y, left=False
|
||
)
|
||
np.testing.assert_allclose(
|
||
numpy_output, paddle_output.numpy(), rtol=1e-05
|
||
)
|
||
self.assertEqual(numpy_output.shape, paddle_output.numpy().shape)
|
||
paddle.enable_static()
|
||
|
||
for place in self.place:
|
||
run(place)
|
||
|
||
|
||
# 2D normal right case, FP64
|
||
class TestSolveOpAPIRight_2(unittest.TestCase):
|
||
def setUp(self):
|
||
np.random.seed(2021)
|
||
self.place = get_places()
|
||
self.dtype = "float64"
|
||
|
||
def check_static_result(self, place):
|
||
paddle.enable_static()
|
||
with base.program_guard(base.Program(), base.Program()):
|
||
paddle_input_x = paddle.static.data(
|
||
name="input_x", shape=[10, 10], dtype=self.dtype
|
||
)
|
||
paddle_input_y = paddle.static.data(
|
||
name="input_y", shape=[4, 10], dtype=self.dtype
|
||
)
|
||
paddle_result = paddle.linalg.solve(
|
||
paddle_input_x, paddle_input_y, left=False
|
||
)
|
||
|
||
np_input_x = np.random.random([10, 10]).astype(self.dtype)
|
||
np_input_y = np.random.random([4, 10]).astype(self.dtype)
|
||
|
||
np_result = np_solve_right(np_input_x, np_input_y)
|
||
|
||
exe = base.Executor(place)
|
||
fetches = exe.run(
|
||
base.default_main_program(),
|
||
feed={"input_x": np_input_x, "input_y": np_input_y},
|
||
fetch_list=[paddle_result],
|
||
)
|
||
np.testing.assert_allclose(fetches[0], np_result, rtol=1e-05)
|
||
|
||
def test_static(self):
|
||
for place in self.place:
|
||
self.check_static_result(place=place)
|
||
|
||
def test_dygraph(self):
|
||
def run(place):
|
||
paddle.disable_static(place)
|
||
np.random.seed(2021)
|
||
input_x_np = np.random.random([10, 10]).astype(self.dtype)
|
||
input_y_np = np.random.random([4, 10]).astype(self.dtype)
|
||
tensor_input_x = paddle.to_tensor(input_x_np)
|
||
tensor_input_y = paddle.to_tensor(input_y_np)
|
||
|
||
numpy_output = np_solve_right(input_x_np, input_y_np)
|
||
paddle_output = paddle.linalg.solve(
|
||
tensor_input_x, tensor_input_y, left=False
|
||
)
|
||
np.testing.assert_allclose(
|
||
numpy_output, paddle_output.numpy(), rtol=1e-05
|
||
)
|
||
self.assertEqual(numpy_output.shape, paddle_output.numpy().shape)
|
||
paddle.enable_static()
|
||
|
||
for place in self.place:
|
||
run(place)
|
||
|
||
|
||
# 2D normal right case, FP32
|
||
class TestSolveOpAPIRight_3(unittest.TestCase):
|
||
def setUp(self):
|
||
np.random.seed(2021)
|
||
self.place = get_places()
|
||
self.dtype = "float32"
|
||
|
||
def check_static_result(self, place):
|
||
paddle.enable_static()
|
||
with base.program_guard(base.Program(), base.Program()):
|
||
paddle_input_x = paddle.static.data(
|
||
name="input_x", shape=[10, 10], dtype=self.dtype
|
||
)
|
||
paddle_input_y = paddle.static.data(
|
||
name="input_y", shape=[6, 10], dtype=self.dtype
|
||
)
|
||
paddle_result = paddle.linalg.solve(
|
||
paddle_input_x, paddle_input_y, left=False
|
||
)
|
||
|
||
np_input_x = np.random.random([10, 10]).astype(self.dtype)
|
||
np_input_y = np.random.random([6, 10]).astype(self.dtype)
|
||
|
||
np_result = np_solve_right(np_input_x, np_input_y)
|
||
|
||
exe = base.Executor(place)
|
||
fetches = exe.run(
|
||
base.default_main_program(),
|
||
feed={"input_x": np_input_x, "input_y": np_input_y},
|
||
fetch_list=[paddle_result],
|
||
)
|
||
np.testing.assert_allclose(fetches[0], np_result, rtol=0.0001)
|
||
|
||
def test_static(self):
|
||
for place in self.place:
|
||
self.check_static_result(place=place)
|
||
|
||
def test_dygraph(self):
|
||
def run(place):
|
||
paddle.disable_static(place)
|
||
np.random.seed(2021)
|
||
input_x_np = np.random.random([10, 10]).astype(self.dtype)
|
||
input_y_np = np.random.random([6, 10]).astype(self.dtype)
|
||
|
||
tensor_input_x = paddle.to_tensor(input_x_np)
|
||
tensor_input_y = paddle.to_tensor(input_y_np)
|
||
|
||
numpy_output = np_solve_right(input_x_np, input_y_np)
|
||
paddle_output = paddle.linalg.solve(
|
||
tensor_input_x, tensor_input_y, left=False
|
||
)
|
||
np.testing.assert_allclose(
|
||
numpy_output, paddle_output.numpy(), rtol=0.0001
|
||
)
|
||
self.assertEqual(numpy_output.shape, paddle_output.numpy().shape)
|
||
paddle.enable_static()
|
||
|
||
for place in self.place:
|
||
run(place)
|
||
|
||
|
||
# 3D + y broadcast right case, FP64
|
||
class TestSolveOpAPIRight_4(unittest.TestCase):
|
||
def setUp(self):
|
||
np.random.seed(2021)
|
||
self.place = get_places()
|
||
self.dtype = "float64"
|
||
|
||
def check_static_result(self, place):
|
||
with base.program_guard(base.Program(), base.Program()):
|
||
paddle_input_x = paddle.static.data(
|
||
name="input_x", shape=[2, 3, 3], dtype=self.dtype
|
||
)
|
||
paddle_input_y = paddle.static.data(
|
||
name="input_y", shape=[1, 3, 3], dtype=self.dtype
|
||
)
|
||
paddle_result = paddle.linalg.solve(
|
||
paddle_input_x, paddle_input_y, left=False
|
||
)
|
||
|
||
np_input_x = np.random.random([2, 3, 3]).astype(self.dtype)
|
||
np_input_y = np.random.random([1, 3, 3]).astype(self.dtype)
|
||
|
||
np_result = np_solve_right(np_input_x, np_input_y)
|
||
|
||
exe = base.Executor(place)
|
||
fetches = exe.run(
|
||
base.default_main_program(),
|
||
feed={"input_x": np_input_x, "input_y": np_input_y},
|
||
fetch_list=[paddle_result],
|
||
)
|
||
np.testing.assert_allclose(fetches[0], np_result, rtol=1e-05)
|
||
|
||
def test_static(self):
|
||
for place in self.place:
|
||
self.check_static_result(place=place)
|
||
|
||
def test_dygraph(self):
|
||
def run(place):
|
||
paddle.disable_static(place)
|
||
np.random.seed(2021)
|
||
input_x_np = np.random.random([2, 3, 3]).astype(self.dtype)
|
||
input_y_np = np.random.random([1, 3, 3]).astype(self.dtype)
|
||
|
||
tensor_input_x = paddle.to_tensor(input_x_np)
|
||
tensor_input_y = paddle.to_tensor(input_y_np)
|
||
|
||
numpy_output = np_solve_right(input_x_np, input_y_np)
|
||
paddle_output = paddle.linalg.solve(
|
||
tensor_input_x, tensor_input_y, left=False
|
||
)
|
||
np.testing.assert_allclose(
|
||
numpy_output, paddle_output.numpy(), rtol=1e-05
|
||
)
|
||
self.assertEqual(numpy_output.shape, paddle_output.numpy().shape)
|
||
paddle.enable_static()
|
||
|
||
for place in self.place:
|
||
run(place)
|
||
|
||
|
||
class TestSolveOpSingularAPI(unittest.TestCase):
|
||
# Singular matrix is not invertible
|
||
def setUp(self):
|
||
self.places = get_places()
|
||
self.dtype = "float64"
|
||
|
||
def check_static_result(self, place):
|
||
with base.program_guard(base.Program(), base.Program()):
|
||
x = paddle.static.data(name="x", shape=[4, 4], dtype=self.dtype)
|
||
y = paddle.static.data(name="y", shape=[4, 4], dtype=self.dtype)
|
||
|
||
result = paddle.linalg.solve(x, y)
|
||
|
||
input_x_np = np.ones([4, 4]).astype(self.dtype)
|
||
input_y_np = np.ones([4, 4]).astype(self.dtype)
|
||
|
||
exe = base.Executor(place)
|
||
try:
|
||
exe.run(
|
||
base.default_main_program(),
|
||
feed={"x": input_x_np, "y": input_y_np},
|
||
fetch_list=[result],
|
||
)
|
||
except RuntimeError:
|
||
print("The mat is singular")
|
||
except ValueError:
|
||
print("The mat is singular")
|
||
|
||
def test_static(self):
|
||
for place in self.places:
|
||
paddle.enable_static()
|
||
self.check_static_result(place=place)
|
||
|
||
def test_dygraph(self):
|
||
for place in self.places:
|
||
with base.dygraph.guard(place):
|
||
input_x_np = np.ones([4, 4]).astype(self.dtype)
|
||
input_y_np = np.ones([4, 4]).astype(self.dtype)
|
||
input_x = paddle.to_tensor(input_x_np)
|
||
input_y = paddle.to_tensor(input_y_np)
|
||
try:
|
||
paddle.linalg.solve(input_x, input_y)
|
||
except RuntimeError:
|
||
print("The mat is singular")
|
||
except ValueError:
|
||
print("The mat is singular")
|
||
|
||
|
||
class TestSolveOpAPIZeroDimCase(unittest.TestCase):
|
||
def setUp(self):
|
||
np.random.seed(2021)
|
||
self.place = get_places()
|
||
self.dtype = "float32"
|
||
|
||
def check_static_result(self, place, x_shape, y_shape, np_y_shape):
|
||
paddle.enable_static()
|
||
with base.program_guard(base.Program(), base.Program()):
|
||
paddle_input_x = paddle.static.data(
|
||
name="input_x", shape=x_shape, dtype=self.dtype
|
||
)
|
||
paddle_input_y = paddle.static.data(
|
||
name="input_y", shape=y_shape, dtype=self.dtype
|
||
)
|
||
paddle_result = paddle.linalg.solve(
|
||
paddle_input_x, paddle_input_y, left=False
|
||
)
|
||
|
||
np_input_x = np.random.random(x_shape).astype(self.dtype)
|
||
np_input_y = np.random.random(np_y_shape).astype(self.dtype)
|
||
|
||
np_result = np.linalg.solve(np_input_x, np_input_y)
|
||
|
||
exe = base.Executor(place)
|
||
fetches = exe.run(
|
||
base.default_main_program(),
|
||
feed={"input_x": np_input_x, "input_y": np_input_y},
|
||
fetch_list=[paddle_result],
|
||
)
|
||
np.testing.assert_allclose(fetches[0], np_result, rtol=0.0001)
|
||
|
||
def test_static(self):
|
||
for place in self.place:
|
||
self.check_static_result(
|
||
place=place,
|
||
x_shape=[10, 0, 0],
|
||
y_shape=[10, 0, 0],
|
||
np_y_shape=[10, 0, 0],
|
||
)
|
||
with self.assertRaises(ValueError):
|
||
self.check_static_result(
|
||
place=place,
|
||
x_shape=[10, 0, 0],
|
||
y_shape=[10],
|
||
np_y_shape=[10],
|
||
)
|
||
|
||
def test_dygraph(self):
|
||
def run(place, x_shape, y_shape):
|
||
with base.dygraph.guard(place):
|
||
input_x_np = np.random.random(x_shape).astype(self.dtype)
|
||
input_y_np = np.random.random(y_shape).astype(self.dtype)
|
||
|
||
tensor_input_x = paddle.to_tensor(
|
||
input_x_np, stop_gradient=False
|
||
)
|
||
tensor_input_y = paddle.to_tensor(
|
||
input_y_np, stop_gradient=False
|
||
)
|
||
|
||
numpy_output = np.linalg.solve(input_x_np, input_y_np)
|
||
paddle_output = paddle.linalg.solve(
|
||
tensor_input_x, tensor_input_y, left=True
|
||
)
|
||
np.testing.assert_allclose(
|
||
numpy_output, paddle_output.numpy(), rtol=0.00011
|
||
)
|
||
self.assertEqual(
|
||
numpy_output.shape, paddle_output.numpy().shape
|
||
)
|
||
loss = paddle.sum(paddle_output)
|
||
loss.backward()
|
||
np.testing.assert_allclose(
|
||
tensor_input_x.grad.shape, tensor_input_x.shape
|
||
)
|
||
np.testing.assert_allclose(
|
||
tensor_input_y.grad.shape, tensor_input_y.shape
|
||
)
|
||
|
||
for place in self.place:
|
||
run(place, x_shape=[1, 10, 10], y_shape=[1, 10, 10])
|
||
run(place, x_shape=[0, 10, 10], y_shape=[0, 10, 10])
|
||
run(place, x_shape=[0, 10, 10], y_shape=[1, 10, 10])
|
||
run(place, x_shape=[10, 0, 0], y_shape=[10, 0, 0])
|
||
run(place, x_shape=[10, 1, 1], y_shape=[10, 1, 0])
|
||
|
||
with self.assertRaises(ValueError):
|
||
run(place, x_shape=[10, 0, 0], y_shape=[10])
|
||
|
||
|
||
class TestSolveAPI_Compatibility(unittest.TestCase):
|
||
def setUp(self):
|
||
np.random.seed(2025)
|
||
self.places = ['cpu', get_device_place()]
|
||
self.n = 4
|
||
self.shape_A = [self.n, self.n]
|
||
self.shape_B = [self.n, 1]
|
||
self.dtype = "float64"
|
||
self.init_data()
|
||
|
||
def init_data(self):
|
||
A = np.random.rand(*self.shape_A).astype(self.dtype)
|
||
self.np_A = np.dot(A, A.T) + np.eye(self.n)
|
||
self.np_B = np.random.rand(*self.shape_B).astype(self.dtype)
|
||
|
||
def test_dygraph_Compatibility(self):
|
||
paddle.disable_static()
|
||
A = paddle.to_tensor(self.np_A)
|
||
B = paddle.to_tensor(self.np_B)
|
||
paddle_dygraph_out = []
|
||
# Position args (args)
|
||
out1 = paddle.linalg.solve(A, B)
|
||
paddle_dygraph_out.append(out1)
|
||
# Key words args (kwargs) for paddle
|
||
out2 = paddle.linalg.solve(x=A, y=B)
|
||
paddle_dygraph_out.append(out2)
|
||
# Key words args for torch compatibility
|
||
out3 = paddle.linalg.solve(A=A, B=B)
|
||
paddle_dygraph_out.append(out3)
|
||
# Key words args for out
|
||
out4 = paddle.zeros_like(B)
|
||
paddle.linalg.solve(A, B, out=out4)
|
||
paddle_dygraph_out.append(out4)
|
||
# Numpy reference output
|
||
ref_out = np.linalg.solve(self.np_A, self.np_B)
|
||
|
||
for out in paddle_dygraph_out:
|
||
np.testing.assert_allclose(
|
||
ref_out, out.numpy(), rtol=1e-05, atol=1e-08
|
||
)
|
||
paddle.enable_static()
|
||
|
||
def test_static_Compatibility(self):
|
||
paddle.enable_static()
|
||
main = paddle.static.Program()
|
||
startup = paddle.static.Program()
|
||
with paddle.base.program_guard(main, startup):
|
||
A = paddle.static.data(
|
||
name="A", shape=self.shape_A, dtype=self.dtype
|
||
)
|
||
B = paddle.static.data(
|
||
name="B", shape=self.shape_B, dtype=self.dtype
|
||
)
|
||
# Position args (args)
|
||
out1 = paddle.linalg.solve(A, B)
|
||
# Key words args (kwargs) for paddle
|
||
out2 = paddle.linalg.solve(x=A, y=B)
|
||
# Key words args for torch compatibility
|
||
out3 = paddle.linalg.solve(A=A, B=B)
|
||
# Numpy reference output
|
||
ref_out = np.linalg.solve(self.np_A, self.np_B)
|
||
|
||
fetch_list = [out1, out2, out3]
|
||
for place in self.places:
|
||
exe = paddle.base.Executor(place)
|
||
fetches = exe.run(
|
||
main,
|
||
feed={"A": self.np_A, "B": self.np_B},
|
||
fetch_list=fetch_list,
|
||
)
|
||
for out in fetches:
|
||
np.testing.assert_allclose(
|
||
out, ref_out, rtol=1e-05, atol=1e-08
|
||
)
|
||
|
||
|
||
if __name__ == "__main__":
|
||
unittest.main()
|