test_py_func_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.

import paddle.fluid as fluid
import paddle
import unittest
import six
import numpy as np


def tanh(x):
    return np.tanh(x)


def tanh_grad(y, dy):
    return np.array(dy) * (1 - np.square(np.array(y)))


def cross_entropy(logits, labels):
    logits = np.array(logits)
    labels = np.array(labels)
    M = logits.shape[0]
    N = logits.shape[1]
    ret = np.ndarray([M, 1]).astype(logits.dtype)
    for idx in six.moves.range(M):
        ret[idx][0] = -np.log(logits[idx][labels[idx][0]])
    return ret


def cross_entropy_grad(logits, labels, bwd_dout):
    logits = np.array(logits)
    labels = np.array(labels)
    bwd_dout = np.array(bwd_dout)
    M = logits.shape[0]
    N = logits.shape[1]
    dlogits = np.zeros([M, N]).astype(logits.dtype)
    for idx in six.moves.range(M):
        dlogits[idx][labels[idx][0]] = -bwd_dout[idx] / logits[idx][labels[idx][
            0]]
    return dlogits, None


def simple_fc_net(img, label, use_py_func_op):
    hidden = img
    for idx in range(4):
        hidden = fluid.layers.fc(
            hidden,
            size=200,
            bias_attr=fluid.ParamAttr(
                initializer=fluid.initializer.Constant(value=1.0)))
        if not use_py_func_op:
            hidden = fluid.layers.tanh(hidden)
        else:
            new_hidden = fluid.default_main_program().current_block(
            ).create_var(
                name='hidden_{}'.format(idx),
                dtype='float32',
                shape=hidden.shape)
            hidden = fluid.layers.py_func(
                func=tanh,
                x=hidden,
                out=new_hidden,
                backward_func=tanh_grad,
                skip_vars_in_backward_input=hidden)

    prediction = fluid.layers.fc(hidden, size=10, act='softmax')
    if not use_py_func_op:
        loss = fluid.layers.cross_entropy(input=prediction, label=label)
    else:
        loss = fluid.default_main_program().current_block().create_var(
            name='loss', dtype='float32', shape=[-1, 1])
        fluid.layers.py_func(
            func=cross_entropy,
            x=[prediction, label],
            out=loss,
            backward_func=cross_entropy_grad,
            skip_vars_in_backward_input=loss)
    loss = fluid.layers.mean(loss)
    return loss


def reader():
    for _ in six.moves.range(100):
        yield np.random.random([784]), np.random.random_integers(
            size=[1], low=0, high=9)


def test_main(use_cuda, use_py_func_op):
    if use_cuda and not fluid.core.is_compiled_with_cuda():
        return None

    with fluid.program_guard(fluid.Program(), fluid.Program()):
        with fluid.scope_guard(fluid.core.Scope()):
            fluid.default_main_program().random_seed = 1
            fluid.default_startup_program().random_seed = 1
            np.random.seed(1)

            img = fluid.layers.data(name='image', shape=[784], dtype='float32')
            label = fluid.layers.data(name='label', shape=[1], dtype='int64')
            loss = simple_fc_net(img, label, use_py_func_op)
            optimizer = fluid.optimizer.SGD(learning_rate=1e-3)
            optimizer.minimize(loss)

            place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
            feeder = fluid.DataFeeder(feed_list=[img, label], place=place)
            r = paddle.batch(reader, batch_size=10)

            exe = fluid.Executor(place)
            exe.run(fluid.default_startup_program())
            ret = []
            for epoch_id in six.moves.range(2):
                for d in r():
                    L, = exe.run(feed=feeder.feed(d), fetch_list=[loss])
                    ret.append(L[0])

            return np.array(ret)


class TestPyFuncOp(unittest.TestCase):
    def test_loss_diff(self):
        losses = []
        for use_cuda in [True, False]:
            for use_py_func_op in [True, False]:
                L = test_main(use_cuda, use_py_func_op)
                if L is not None:
                    losses.append(L)

        for idx in six.moves.range(len(losses) - 1):
            max_diff = np.max(np.abs(losses[idx] - losses[0]))
            self.assertAlmostEqual(max_diff, 0, delta=1e-3)


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