# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import unittest import numpy as np from op_test import OpTest, skip_check_grad_ci from numpy.linalg import multi_dot from op_test import OpTest import paddle from paddle.fluid.framework import _test_eager_guard paddle.enable_static() #the unittest of multi_dot #compare the result of paddle multi_dot and numpy multi_dot class TestMultiDotOp(OpTest): def setUp(self): self.op_type = "multi_dot" self.python_api = paddle.linalg.multi_dot self.dtype = self.get_dtype() self.get_inputs_and_outputs() def get_dtype(self): return "float64" def get_inputs_and_outputs(self): self.A = np.random.random((2, 8)).astype(self.dtype) self.B = np.random.random((8, 4)).astype(self.dtype) self.inputs = {'X': [('x0', self.A), ('x1', self.B)]} self.outputs = {'Out': multi_dot([self.A, self.B])} def test_check_output(self): self.check_output(check_eager=True) def test_check_grad(self): self.check_grad(['x0'], 'Out', check_eager=True) self.check_grad(['x1'], 'Out', check_eager=True) #(A*B)*C class TestMultiDotOp3Mat(TestMultiDotOp): def get_inputs_and_outputs(self): self.A = np.random.random((2, 10)).astype(self.dtype) self.B = np.random.random((10, 4)).astype(self.dtype) self.C = np.random.random((4, 3)).astype(self.dtype) self.inputs = {'X': [('x0', self.A), ('x1', self.B), ('x2', self.C)]} self.outputs = {'Out': multi_dot([self.A, self.B, self.C])} def test_check_grad(self): self.check_grad(['x0'], 'Out', check_eager=True) self.check_grad(['x1'], 'Out', check_eager=True) self.check_grad(['x2'], 'Out', check_eager=True) #A*(B*C) class TestMultiDotOp3Mat2(TestMultiDotOp): def get_inputs_and_outputs(self): self.A = np.random.random((3, 4)).astype(self.dtype) self.B = np.random.random((4, 8)).astype(self.dtype) self.C = np.random.random((8, 2)).astype(self.dtype) self.inputs = {'X': [('x0', self.A), ('x1', self.B), ('x2', self.C)]} self.outputs = {'Out': multi_dot([self.A, self.B, self.C])} def test_check_grad(self): self.check_grad(['x0'], 'Out', check_eager=True) self.check_grad(['x1'], 'Out', check_eager=True) self.check_grad(['x2'], 'Out', check_eager=True) class TestMultiDotOp4Mat(TestMultiDotOp): def get_inputs_and_outputs(self): self.A = np.random.random((8, 6)).astype(self.dtype) self.B = np.random.random((6, 3)).astype(self.dtype) self.C = np.random.random((3, 4)).astype(self.dtype) self.D = np.random.random((4, 5)).astype(self.dtype) self.inputs = { 'X': [('x0', self.A), ('x1', self.B), ('x2', self.C), ('x3', self.D)] } self.outputs = {'Out': multi_dot([self.A, self.B, self.C, self.D])} def test_check_grad(self): self.check_grad(['x0'], 'Out', check_eager=True) self.check_grad(['x1'], 'Out', check_eager=True) self.check_grad(['x2'], 'Out', check_eager=True) self.check_grad(['x3'], 'Out', check_eager=True) class TestMultiDotOpFirst1D(TestMultiDotOp): def get_inputs_and_outputs(self): self.A = np.random.random((4)).astype(self.dtype) self.B = np.random.random((4, 3)).astype(self.dtype) self.inputs = {'X': [('x0', self.A), ('x1', self.B)]} self.outputs = {'Out': multi_dot([self.A, self.B])} class TestMultiDotOp3MatFirst1D(TestMultiDotOp3Mat): def get_inputs_and_outputs(self): self.A = np.random.random((4)).astype(self.dtype) self.B = np.random.random((4, 3)).astype(self.dtype) self.C = np.random.random((3, 3)).astype(self.dtype) self.inputs = {'X': [('x0', self.A), ('x1', self.B), ('x2', self.C)]} self.outputs = {'Out': multi_dot([self.A, self.B, self.C])} class TestMultiDotOp4MatFirst1D(TestMultiDotOp4Mat): def get_inputs_and_outputs(self): self.A = np.random.random((4)).astype(self.dtype) self.B = np.random.random((4, 3)).astype(self.dtype) self.C = np.random.random((3, 4)).astype(self.dtype) self.D = np.random.random((4, 5)).astype(self.dtype) self.inputs = { 'X': [('x0', self.A), ('x1', self.B), ('x2', self.C), ('x3', self.D)] } self.outputs = {'Out': multi_dot([self.A, self.B, self.C, self.D])} class TestMultiDotOpLast1D(TestMultiDotOp): def get_inputs_and_outputs(self): self.A = np.random.random((3, 6)).astype(self.dtype) self.B = np.random.random((6)).astype(self.dtype) self.inputs = {'X': [('x0', self.A), ('x1', self.B)]} self.outputs = {'Out': multi_dot([self.A, self.B])} class TestMultiDotOp3MatLast1D(TestMultiDotOp3Mat): def get_inputs_and_outputs(self): self.A = np.random.random((2, 4)).astype(self.dtype) self.B = np.random.random((4, 3)).astype(self.dtype) self.C = np.random.random((3)).astype(self.dtype) self.inputs = {'X': [('x0', self.A), ('x1', self.B), ('x2', self.C)]} self.outputs = {'Out': multi_dot([self.A, self.B, self.C])} def test_check_grad(self): self.check_grad(['x0'], 'Out', check_eager=True) self.check_grad(['x1'], 'Out', check_eager=True) self.check_grad(['x2'], 'Out', check_eager=True) class TestMultiDotOp4MatLast1D(TestMultiDotOp4Mat): def get_inputs_and_outputs(self): self.A = np.random.random((2, 3)).astype(self.dtype) self.B = np.random.random((3, 2)).astype(self.dtype) self.C = np.random.random((2, 3)).astype(self.dtype) self.D = np.random.random((3)).astype(self.dtype) self.inputs = { 'X': [('x0', self.A), ('x1', self.B), ('x2', self.C), ('x3', self.D)] } self.outputs = {'Out': multi_dot([self.A, self.B, self.C, self.D])} class TestMultiDotOpFirstAndLast1D(TestMultiDotOp): def get_inputs_and_outputs(self): self.A = np.random.random((4, )).astype(self.dtype) self.B = np.random.random((4)).astype(self.dtype) self.inputs = {'X': [('x0', self.A), ('x1', self.B)]} self.outputs = {'Out': multi_dot([self.A, self.B])} class TestMultiDotOp3MatFirstAndLast1D(TestMultiDotOp3Mat): def get_inputs_and_outputs(self): self.A = np.random.random((6, )).astype(self.dtype) self.B = np.random.random((6, 4)).astype(self.dtype) self.C = np.random.random((4)).astype(self.dtype) self.inputs = {'X': [('x0', self.A), ('x1', self.B), ('x2', self.C)]} self.outputs = {'Out': multi_dot([self.A, self.B, self.C])} class TestMultiDotOp4MatFirstAndLast1D(TestMultiDotOp4Mat): def get_inputs_and_outputs(self): self.A = np.random.random((3, )).astype(self.dtype) self.B = np.random.random((3, 4)).astype(self.dtype) self.C = np.random.random((4, 2)).astype(self.dtype) self.D = np.random.random((2)).astype(self.dtype) self.inputs = { 'X': [('x0', self.A), ('x1', self.B), ('x2', self.C), ('x3', self.D)] } self.outputs = {'Out': multi_dot([self.A, self.B, self.C, self.D])} #####python API test####### class TestMultiDotOpError(unittest.TestCase): def test_errors(self): with paddle.static.program_guard(paddle.static.Program(), paddle.static.Program()): # The inputs type of multi_dot must be list matrix. input1 = 12 self.assertRaises(TypeError, paddle.linalg.multi_dot, [input1, input1]) # The inputs dtype of multi_dot must be float64, float64 or float16. input2 = paddle.static.data(name='input2', shape=[10, 10], dtype="int32") self.assertRaises(TypeError, paddle.linalg.multi_dot, [input2, input2]) # the number of tensor must be larger than 1 x0 = paddle.static.data(name='x0', shape=[3, 2], dtype="float64") self.assertRaises(ValueError, paddle.linalg.multi_dot, [x0]) #the first tensor must be 1D or 2D x1 = paddle.static.data(name='x1', shape=[3, 2, 3], dtype="float64") x2 = paddle.static.data(name='x2', shape=[3, 2], dtype="float64") self.assertRaises(ValueError, paddle.linalg.multi_dot, [x1, x2]) #the last tensor must be 1D or 2D x3 = paddle.static.data(name='x3', shape=[3, 2], dtype="float64") x4 = paddle.static.data(name='x4', shape=[3, 2, 2], dtype="float64") self.assertRaises(ValueError, paddle.linalg.multi_dot, [x3, x4]) #the tensor must be 2D, except first and last tensor x5 = paddle.static.data(name='x5', shape=[3, 2], dtype="float64") x6 = paddle.static.data(name='x6', shape=[2], dtype="float64") x7 = paddle.static.data(name='x7', shape=[2, 2], dtype="float64") self.assertRaises(ValueError, paddle.linalg.multi_dot, [x5, x6, x7]) class APITestMultiDot(unittest.TestCase): def test_out(self): paddle.enable_static() with paddle.static.program_guard(paddle.static.Program()): x0 = paddle.static.data(name='x0', shape=[3, 2], dtype="float64") x1 = paddle.static.data(name='x1', shape=[2, 3], dtype='float64') result = paddle.linalg.multi_dot([x0, x1]) exe = paddle.static.Executor(paddle.CPUPlace()) data1 = np.random.rand(3, 2).astype("float64") data2 = np.random.rand(2, 3).astype("float64") np_res, = exe.run(feed={ 'x0': data1, 'x1': data2 }, fetch_list=[result]) expected_result = np.linalg.multi_dot([data1, data2]) np.testing.assert_allclose( np_res, expected_result, rtol=1e-05, atol=1e-05, err_msg='two value is {}\n{}, check diff!'.format( np_res, expected_result)) def test_dygraph_without_out(self): paddle.disable_static() device = paddle.CPUPlace() input_array1 = np.random.rand(3, 4).astype("float64") input_array2 = np.random.rand(4, 3).astype("float64") data1 = paddle.to_tensor(input_array1) data2 = paddle.to_tensor(input_array2) out = paddle.linalg.multi_dot([data1, data2]) expected_result = np.linalg.multi_dot([input_array1, input_array2]) np.testing.assert_allclose(expected_result, out.numpy(), rtol=1e-05) def test_dygraph_final_state_api(self): with _test_eager_guard(): self.test_dygraph_without_out() if __name__ == "__main__": unittest.main()