test_cholesky_op.py

#   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()