test_cast_op.py

#   Copyright (c) 2018 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.core as core
import paddle.fluid as fluid
from paddle.fluid import compiler, Program, program_guard
from op_test import OpTest, convert_uint16_to_float, convert_float_to_uint16
from paddle.fluid.framework import _test_eager_guard
import gradient_checker
from decorator_helper import prog_scope
import paddle.fluid.layers as layers


class TestCastOpFp32ToFp64(OpTest):

    def setUp(self):
        ipt = np.random.random(size=[10, 10])
        self.inputs = {'X': ipt.astype('float32')}
        self.outputs = {'Out': ipt.astype('float64')}
        self.attrs = {
            'in_dtype': int(core.VarDesc.VarType.FP32),
            'out_dtype': int(core.VarDesc.VarType.FP64)
        }
        self.op_type = 'cast'

    def test_check_output(self):
        self.check_output()

    def test_grad(self):
        self.check_grad(['X'], ['Out'])


class TestCastOpFp16ToFp32(OpTest):

    def setUp(self):
        ipt = np.random.random(size=[10, 10])
        self.inputs = {'X': ipt.astype('float16')}
        self.outputs = {'Out': ipt.astype('float32')}
        self.attrs = {
            'in_dtype': int(core.VarDesc.VarType.FP16),
            'out_dtype': int(core.VarDesc.VarType.FP32)
        }
        self.op_type = 'cast'
        self.__class__.no_need_check_grad = True

    def test_check_output(self):
        self.check_output(atol=1e-3)


class TestCastOpFp32ToFp16(OpTest):

    def setUp(self):
        ipt = np.random.random(size=[10, 10])
        self.inputs = {'X': ipt.astype('float32')}
        self.outputs = {'Out': ipt.astype('float16')}
        self.attrs = {
            'in_dtype': int(core.VarDesc.VarType.FP32),
            'out_dtype': int(core.VarDesc.VarType.FP16)
        }
        self.op_type = 'cast'
        self.__class__.no_need_check_grad = True

    def test_check_output(self):
        self.check_output(atol=1e-3)


class TestCastOpBf16ToFp32(OpTest):

    def setUp(self):
        ipt = np.array(np.random.randint(10, size=[10, 10])).astype('uint16')
        self.inputs = {'X': ipt}
        self.outputs = {'Out': convert_uint16_to_float(ipt)}
        self.attrs = {
            'in_dtype': int(core.VarDesc.VarType.BF16),
            'out_dtype': int(core.VarDesc.VarType.FP32)
        }
        self.op_type = 'cast'
        self.__class__.no_need_check_grad = True

    def test_check_output(self):
        self.check_output()


class TestCastOpFp32ToBf16(OpTest):

    def setUp(self):
        ipt = np.random.random(size=[10, 10]).astype('float32')
        self.inputs = {'X': ipt}
        self.outputs = {'Out': convert_float_to_uint16(ipt)}
        self.attrs = {
            'in_dtype': int(core.VarDesc.VarType.FP32),
            'out_dtype': int(core.VarDesc.VarType.BF16)
        }
        self.op_type = 'cast'
        self.__class__.no_need_check_grad = True

    def test_check_output(self):
        self.check_output()


class TestCastOpError(unittest.TestCase):

    def test_errors(self):
        with program_guard(Program(), Program()):
            # The input type of cast_op must be Variable.
            x1 = fluid.create_lod_tensor(np.array([[-1]]), [[1]],
                                         fluid.CPUPlace())
            self.assertRaises(TypeError, fluid.layers.cast, x1, 'int32')


class TestCastOpEager(unittest.TestCase):

    def test_eager(self):
        with paddle.fluid.dygraph.base.guard():
            with _test_eager_guard():
                x = paddle.ones([2, 2], dtype="float16")
                x.stop_gradient = False
                out = paddle.cast(x, "float32")
                np.testing.assert_array_equal(out,
                                              np.ones([2, 2]).astype('float32'))
                out.backward()
                np.testing.assert_array_equal(x.gradient(), x.numpy())
                self.assertTrue(x.gradient().dtype == np.float16)


class TestCastDoubleGradCheck(unittest.TestCase):

    def cast_wrapper(self, x):
        return paddle.cast(x[0], 'float64')

    @prog_scope()
    def func(self, place):
        # the shape of input variable should be clearly specified, not inlcude -1.
        eps = 0.005
        dtype = np.float32

        data = layers.data('data', [2, 3, 4], False, dtype)
        data.persistable = True
        out = paddle.cast(data, 'float64')
        data_arr = np.random.uniform(-1, 1, data.shape).astype(dtype)

        gradient_checker.double_grad_check([data],
                                           out,
                                           x_init=[data_arr],
                                           place=place,
                                           eps=eps)
        fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": True})
        gradient_checker.double_grad_check_for_dygraph(self.cast_wrapper,
                                                       [data],
                                                       out,
                                                       x_init=[data_arr],
                                                       place=place)

    def test_grad(self):
        paddle.enable_static()
        places = [fluid.CPUPlace()]
        if core.is_compiled_with_cuda():
            places.append(fluid.CUDAPlace(0))
        for p in places:
            self.func(p)


class TestCastTripleGradCheck(unittest.TestCase):

    def cast_wrapper(self, x):
        return paddle.cast(x[0], 'float64')

    @prog_scope()
    def func(self, place):
        # the shape of input variable should be clearly specified, not inlcude -1.
        eps = 0.005
        dtype = np.float32

        data = layers.data('data', [2, 3, 4], False, dtype)
        data.persistable = True
        out = paddle.cast(data, 'float64')
        data_arr = np.random.uniform(-1, 1, data.shape).astype(dtype)

        gradient_checker.triple_grad_check([data],
                                           out,
                                           x_init=[data_arr],
                                           place=place,
                                           eps=eps)
        fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": True})
        gradient_checker.triple_grad_check_for_dygraph(self.cast_wrapper,
                                                       [data],
                                                       out,
                                                       x_init=[data_arr],
                                                       place=place)

    def test_grad(self):
        paddle.enable_static()
        places = [fluid.CPUPlace()]
        if core.is_compiled_with_cuda():
            places.append(fluid.CUDAPlace(0))
        for p in places:
            self.func(p)


if __name__ == '__main__':
    paddle.enable_static()
    unittest.main()