# Copyright (c) 2020 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. from __future__ import print_function import unittest import numpy as np from op_test import OpTest import paddle import paddle.fluid as fluid import paddle.fluid.dygraph as dg class TestKronOp(OpTest): def setUp(self): self.op_type = "kron" self.dtype = self._init_dtype() x = np.random.uniform(size=(10, 10)).astype(self.dtype) y = np.random.uniform(size=(10, 10)).astype(self.dtype) out_ref = np.kron(x, y) self.inputs = {'X': x, 'Y': y} self.outputs = {'Out': out_ref} def _init_dtype(self): return "float64" def test_check_output(self): self.check_output() def test_check_grad(self): self.check_grad(['X', 'Y'], 'Out') def test_check_grad_ignore_x(self): self.check_grad(['Y'], 'Out', no_grad_set=set('X')) def test_check_grad_ignore_y(self): self.check_grad(['X'], 'Out', no_grad_set=set('Y')) class TestKronOp2(TestKronOp): def setUp(self): self.op_type = "kron" self.dtype = self._init_dtype() x = np.random.uniform(size=(5, 5, 4)).astype(self.dtype) y = np.random.uniform(size=(10, 10)).astype(self.dtype) out_ref = np.kron(x, y) self.inputs = {'X': x, 'Y': y} self.outputs = {'Out': out_ref} class TestKronOp3(TestKronOp): def setUp(self): self.op_type = "kron" self.dtype = self._init_dtype() x = np.random.uniform(size=(10, 10)).astype(self.dtype) y = np.random.uniform(size=(5, 5, 4)).astype(self.dtype) out_ref = np.kron(x, y) self.inputs = {'X': x, 'Y': y} self.outputs = {'Out': out_ref} class TestKronLayer(unittest.TestCase): def test_case(self): a = np.random.randn(10, 10).astype(np.float64) b = np.random.randn(10, 10).astype(np.float64) place = fluid.CPUPlace() with dg.guard(place): a_var = dg.to_variable(a) b_var = dg.to_variable(b) c_var = paddle.kron(a_var, b_var) np.testing.assert_allclose(c_var.numpy(), np.kron(a, b)) def test_case_with_output(self): a = np.random.randn(10, 10).astype(np.float64) b = np.random.randn(10, 10).astype(np.float64) main = fluid.Program() start = fluid.Program() with fluid.unique_name.guard(): with fluid.program_guard(main, start): a_var = fluid.data("a", [-1, -1], dtype="float64") b_var = fluid.data("b", [-1, -1], dtype="float64") out_var = paddle.kron(a_var, b_var) place = fluid.CPUPlace() exe = fluid.Executor(place) exe.run(start) c, = exe.run(main, feed={'a': a, 'b': b}, fetch_list=[out_var]) np.testing.assert_allclose(c, np.kron(a, b)) class TestComplexKronOp(OpTest): def setUp(self): self.op_type = "kron" self.x_shape = np.array([10, 10]) self.y_shape = np.array([3, 35]) self.out_shape = self.x_shape * self.y_shape self.init_base_dtype() self.init_input_output() self.init_grad_input_output() self.inputs = { 'X': OpTest.np_dtype_to_fluid_dtype(self.x), 'Y': OpTest.np_dtype_to_fluid_dtype(self.y) } self.attrs = {'axis': -1, 'use_mkldnn': False} self.outputs = {'Out': self.out} def init_base_dtype(self): self.dtype = np.float64 def init_input_output(self): self.x = np.random.random(self.x_shape).astype( self.dtype) + 1J * np.random.random(self.x_shape).astype(self.dtype) self.y = np.random.random(self.y_shape).astype( self.dtype) + 1J * np.random.random(self.y_shape).astype(self.dtype) self.out = np.kron(self.x, self.y) def init_grad_input_output(self): self.grad_out = np.ones(self.out_shape, self.dtype) + 1J * np.ones( self.out_shape, self.dtype) self.grad_x = self.get_grad_x_by_numpy() self.grad_y = self.get_grad_y_by_numpy() def get_grad_x_by_numpy(self): grad_x = np.zeros(self.x_shape, np.complex) for x_i in range(self.x_shape[0]): for x_j in range(self.x_shape[1]): for i in range(self.y_shape[0]): for j in range(self.y_shape[1]): idx_i = x_i * self.y_shape[0] + i idx_j = x_j * self.y_shape[1] + j grad_x[x_i][x_j] += self.grad_out[idx_i][ idx_j] * np.conj(self.y[i][j]) return grad_x def get_grad_y_by_numpy(self): grad_y = np.zeros(self.y_shape, np.complex) for y_i in range(self.y_shape[0]): for y_j in range(self.y_shape[1]): for x_i in range(self.x_shape[0]): for x_j in range(self.x_shape[1]): idx_i = x_i * self.y_shape[0] + y_i idx_j = x_j * self.y_shape[1] + y_j grad_y[y_i][y_j] += self.grad_out[idx_i][ idx_j] * np.conj(self.x[x_i][x_j]) return grad_y def test_check_output(self): self.check_output() def test_check_grad_normal(self): self.check_grad( ['X', 'Y'], 'Out', user_defined_grads=[self.grad_x, self.grad_y], user_defined_grad_outputs=[self.grad_out]) def test_check_grad_ingore_x(self): self.check_grad( ['Y'], 'Out', no_grad_set=set("X"), user_defined_grads=[self.grad_y], user_defined_grad_outputs=[self.grad_out]) def test_check_grad_ingore_y(self): self.check_grad( ['X'], 'Out', no_grad_set=set('Y'), user_defined_grads=[self.grad_x], user_defined_grad_outputs=[self.grad_out]) class TestKronOpTypePromotion(TestComplexKronOp): def init_input_output(self): self.x = np.random.random(self.x_shape).astype(self.dtype) self.y = np.random.random(self.y_shape).astype( self.dtype) + 1J * np.random.random(self.y_shape).astype(self.dtype) self.out = np.kron(self.x, self.y) def init_grad_input_output(self): self.grad_out = np.ones(self.out_shape, self.dtype) + 1J * np.ones( self.out_shape, self.dtype) self.grad_x = self.get_grad_x_by_numpy().real self.grad_y = self.get_grad_y_by_numpy() if __name__ == '__main__': paddle.enable_static() unittest.main()