# 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 import paddle import paddle.fluid as fluid import paddle.fluid.layers as layers import paddle.fluid.core as core from op_test import OpTest, skip_check_grad_ci from gradient_checker import grad_check from decorator_helper import prog_scope @skip_check_grad_ci( reason="The input of cholesky_op should always be symmetric positive-definite. " "However, OpTest calculates the numeric gradient of each element in input " "via small finite difference, which makes the input no longer symmetric " "positive-definite thus can not compute the Cholesky decomposition. " "While we can use the gradient_checker.grad_check to perform gradient " "check of cholesky_op, since it supports check gradient with a program " "and we can construct symmetric positive-definite matrices in the program") class TestCholeskyOp(OpTest): def setUp(self): self.op_type = "cholesky" self._input_shape = (2, 32, 32) self._upper = True self.init_config() self.trans_dims = list(range(len(self._input_shape) - 2)) + [ len(self._input_shape) - 1, len(self._input_shape) - 2 ] self.root_data = np.random.random(self._input_shape).astype("float64") # construct symmetric positive-definite matrice input_data = np.matmul( self.root_data, self.root_data.transpose(self.trans_dims)) + 1e-05 output_data = np.linalg.cholesky(input_data).astype("float64") if self._upper: output_data = output_data.transpose(self.trans_dims) self.inputs = {"X": input_data} self.attrs = {"upper": self._upper} self.outputs = {"Out": output_data} def test_check_output(self): self.check_output() def test_check_grad(self): places = [fluid.CPUPlace()] if core.is_compiled_with_cuda(): places.append(fluid.CUDAPlace(0)) for p in places: self.func(p) @prog_scope() def func(self, place): # use small size since Jacobian gradients is time consuming root_data = self.root_data[..., :3, :3] prog = fluid.Program() with fluid.program_guard(prog): root = layers.create_parameter( dtype=root_data.dtype, shape=root_data.shape) root_t = layers.transpose(root, self.trans_dims) x = layers.matmul(x=root, y=root_t) + 1e-05 out = paddle.cholesky(x, upper=self.attrs["upper"]) grad_check(root, out, x_init=root_data, place=place) def init_config(self): self._upper = True class TestCholeskyOpLower(TestCholeskyOp): def init_config(self): self._upper = False class TestCholeskyOp2D(TestCholeskyOp): def init_config(self): self._input_shape = (64, 64) class TestDygraph(unittest.TestCase): def test_dygraph(self): paddle.disable_static() a = np.random.rand(3, 3) a_t = np.transpose(a, [1, 0]) x_data = np.matmul(a, a_t) + 1e-03 x = paddle.to_variable(x_data) out = paddle.cholesky(x, upper=False) if __name__ == "__main__": unittest.main()