test_cond.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 numpy as np
import os
import unittest
import paddle
import paddle.fluid as fluid
import paddle.fluid.core as core
import paddle.fluid.layers as layers
import paddle.fluid.framework as framework
from paddle.fluid.backward import append_backward
from paddle.fluid.framework import Program, program_guard
from simple_nets import simple_fc_net_with_inputs, batchnorm_fc_with_inputs

np.random.seed(123)


class TestCondInputOutput(unittest.TestCase):

    def test_return_single_var(self):
        """
        pseudocode:

        if 0.23 < 0.1:
            return 2
        else:
            return -1
        """

        def true_func():
            return layers.fill_constant(shape=[2, 3], dtype='int32', value=2)

        def false_func():
            return layers.fill_constant(shape=[3, 2], dtype='int32', value=-1)

        main_program = Program()
        startup_program = Program()
        with program_guard(main_program, startup_program):
            x = layers.fill_constant(shape=[1], dtype='float32', value=0.1)
            y = layers.fill_constant(shape=[1], dtype='float32', value=0.23)
            pred = layers.less_than(y, x)
            out = layers.cond(pred, true_func, false_func)
            # out is one tensor

        place = fluid.CUDAPlace(
            0) if core.is_compiled_with_cuda() else fluid.CPUPlace()
        exe = fluid.Executor(place)
        ret = exe.run(main_program, fetch_list=[out.name])
        self.assertTrue(
            np.allclose(np.asarray(ret), np.full((3, 2), -1, np.int32)))

    def test_return_var_tuple(self):
        """
        pseudocode:

        if True:
            return 1, True
        else:
            return 3, 2
        """

        def true_func():
            return layers.fill_constant(shape=[1, 2], dtype='int32',
                                        value=1), layers.fill_constant(
                                            shape=[2, 3],
                                            dtype='bool',
                                            value=True)

        def false_func():
            return layers.fill_constant(shape=[3, 4], dtype='float32',
                                        value=3), layers.fill_constant(
                                            shape=[4, 5],
                                            dtype='int64',
                                            value=2)

        main_program = Program()
        startup_program = Program()
        with program_guard(main_program, startup_program):
            pred = layers.fill_constant(shape=[1], dtype='bool', value=True)
            out = layers.cond(pred, true_func, false_func)
            # out is a tuple containing 2 tensors

        place = fluid.CUDAPlace(
            0) if core.is_compiled_with_cuda() else fluid.CPUPlace()
        exe = fluid.Executor(place)
        ret = exe.run(main_program, fetch_list=out)
        self.assertTrue(
            np.allclose(np.asarray(ret[0]), np.full((1, 2), 1, np.int32)))
        self.assertTrue(
            np.allclose(np.asarray(ret[1]), np.full((2, 3), True, bool)))

    def test_pass_and_modify_var(self):
        """
        pseudocode:
        for i in range(5):
            a = 7
            if i % 2 == 0:
                a = a * (i + 1)
            else:
                a = a - (i - 1)
        """

        def true_func(a, i):
            a = a * (i + 1)
            return a

        def false_func(a, i):
            a = a - (i - 1)
            return a

        main_program = Program()
        startup_program = Program()
        with program_guard(main_program, startup_program):
            a = layers.fill_constant(shape=[3, 2, 1], dtype='int32', value=7)
            i = fluid.data(name="i", shape=[1], dtype='int32')
            pred = ((i % 2) == 0)
            a = layers.cond(pred, lambda: true_func(a, i),
                            lambda: false_func(a, i))
        place = fluid.CUDAPlace(
            0) if core.is_compiled_with_cuda() else fluid.CPUPlace()
        exe = fluid.Executor(place)
        for feed_i in range(5):
            expected_a = 7 * (feed_i + 1) if feed_i % 2 == 0 else 8 - feed_i
            ret = exe.run(main_program,
                          feed={'i': np.full((1), feed_i, np.int32)},
                          fetch_list=[a])
            self.assertTrue(
                np.allclose(np.asarray(ret),
                            np.full((3, 2, 1), expected_a, np.int32)))

    def test_return_none(self):
        """
        pseudocode: test doing nothing in branches
        for i in range(5):
            if i % 2 == 0:
                pass
            else:
                pass
        """

        def true_func():
            pass

        def false_func():
            return None

        main_program = Program()
        startup_program = Program()
        with program_guard(main_program, startup_program):
            i = fluid.data(name="i", shape=[1], dtype='int32')
            pred = ((i % 2) == 0)
            out1 = layers.cond(pred, true_func, false_func)
            out2 = layers.cond(pred, None, false_func)
            out3 = layers.cond(pred, true_func, None)
        place = fluid.CUDAPlace(
            0) if core.is_compiled_with_cuda() else fluid.CPUPlace()
        exe = fluid.Executor(place)
        for feed_i in range(5):
            # Test that output is None is runnable
            exe.run(main_program, feed={'i': np.full((1), feed_i, np.int32)})
            self.assertIsNone(out1)
            self.assertIsNone(out2)
            self.assertIsNone(out3)

    def test_wrong_structure_exception(self):
        """
        test returning different number of tensors cannot merge into output
        """

        def func_return_none():
            return None

        def func_return_one_tensor():
            return layers.fill_constant(shape=[2, 7], dtype='int32', value=3)

        def func_return_two_tensors():
            return layers.fill_constant(shape=[3, 1], dtype='int32',
                                        value=7), layers.fill_constant(
                                            shape=[3, 1],
                                            dtype='int32',
                                            value=8)

        main_program = Program()
        startup_program = Program()
        with program_guard(main_program, startup_program):
            i = fluid.data(name="i", shape=[1], dtype='int32')
            pred = ((i % 2) == 0)
            with self.assertRaises(TypeError):
                out = layers.cond(pred, i, func_return_one_tensor)

            with self.assertRaises(TypeError):
                out = layers.cond(pred, func_return_one_tensor, np.asarray([3]))

            with self.assertRaises(Exception) as e:
                out = layers.cond(pred, func_return_none,
                                  func_return_one_tensor)
            self.assertTrue(
                "Incompatible return values of true_fn and false_fn in cond" in
                str(e.exception))

            with self.assertRaises(Exception) as e:
                out = layers.cond(pred, func_return_two_tensors,
                                  func_return_none)
            self.assertTrue(
                "Incompatible return values of true_fn and false_fn in cond" in
                str(e.exception))

            with self.assertRaises(Exception) as e:
                out = layers.cond(pred, func_return_one_tensor,
                                  func_return_two_tensors)
            self.assertTrue(
                "Incompatible return values of true_fn and false_fn in cond" in
                str(e.exception))

    def test_extremely_simple_net_with_op_in_condition(self):
        main_program = fluid.Program()
        startup_program = fluid.Program()
        with fluid.program_guard(main_program, startup_program):
            a = fluid.layers.fill_constant(shape=[1],
                                           dtype='float32',
                                           value=1.23)
            a.stop_gradient = False
            b = fluid.layers.fill_constant(shape=[1],
                                           dtype='float32',
                                           value=1.25)
            b.stop_gradient = False
            out = layers.cond(a - b < -1.0, lambda: a, lambda: b)
        append_backward(out)

        place = fluid.CUDAPlace(
            0) if core.is_compiled_with_cuda() else fluid.CPUPlace()
        exe = fluid.Executor(place)
        ret = exe.run(main_program,
                      fetch_list=[out, b, a.grad_name, b.grad_name])
        # Note: fill_constant has loss of precision, you have to assertEqual
        # with values doens't lose precision in float-point number.
        self.assertEqual(ret[0][0], ret[1][0])
        self.assertEqual(ret[2][0], 0.0)
        self.assertEqual(ret[3][0], 1.0)


class TestCondNestedControlFlow(unittest.TestCase):

    def test_cond_inside_cond(self):
        """
        pseudocode:
        for i in range(1, 10):
            a = 2 * i
            if i < 5:
                if i >= 3:
                    return a + a 
                else:
                    return a - a
            else:
                if i < 8:
                    return a * a
                else:
                    return a / a
        """

        def less_than_branch(i, a):
            return layers.cond(i >= 3.0, lambda: layers.elementwise_add(a, a),
                               lambda: layers.elementwise_sub(a, a))

        def greater_equal_branch(i, a):
            return layers.cond(i < 8.0, lambda: layers.elementwise_mul(a, a),
                               lambda: layers.elementwise_div(a, a))

        main_program = Program()
        startup_program = Program()
        with program_guard(main_program, startup_program):
            i = fluid.data(name="i", shape=[1], dtype='float32')
            a = 2.0 * i
            out = layers.cond(i < 5.0, lambda: less_than_branch(i, a),
                              lambda: greater_equal_branch(i, a))
            mean = paddle.mean(out)
            append_backward(mean)

        place = fluid.CUDAPlace(
            0) if core.is_compiled_with_cuda() else fluid.CPUPlace()
        exe = fluid.Executor(place)
        for feed_i in range(0, 10):
            expected_a = 2.0 * feed_i
            if feed_i < 5:
                expected_ret = expected_a + expected_a if feed_i >= 3 else 0.0
                expected_a_grad = 2.0 if feed_i >= 3 else 0.0
            else:
                expected_ret = expected_a * expected_a if feed_i < 8 else 1.0
                expected_a_grad = 2.0 * expected_a if feed_i < 8 else 0.0
            ret = exe.run(main_program,
                          feed={'i': np.full((1), feed_i, np.float32)},
                          fetch_list=[out.name, a.grad_name])
            self.assertEqual(ret[0][0], expected_ret)
            self.assertEqual(ret[1][0], expected_a_grad)

    def test_cond_op_in_condition(self):
        main_program = fluid.Program()
        startup_program = fluid.Program()

        with fluid.program_guard(main_program, startup_program):
            a = fluid.layers.fill_constant(shape=[1],
                                           dtype='float32',
                                           value=1.23)
            a.stop_gradient = False
            b = fluid.layers.fill_constant(shape=[1],
                                           dtype='float32',
                                           value=1.24)
            b.stop_gradient = False
            out = fluid.layers.cond(
                a < b, lambda: fluid.layers.cond(
                    a - b < -1.0, lambda: fluid.layers.elementwise_add(a, b),
                    lambda: fluid.layers.elementwise_mul(a, b)), lambda:
                fluid.layers.cond(a == b, lambda: fluid.layers.elementwise_sub(
                    a, b), lambda: fluid.layers.elementwise_pow(a, b)))
            append_backward(out)

        place = fluid.CUDAPlace(
            0) if core.is_compiled_with_cuda() else fluid.CPUPlace()
        exe = fluid.Executor(place)
        ret = exe.run(main_program, fetch_list=[out, a.grad_name, b.grad_name])
        # Note: fill_constant has loss of precision, so we assertAlmostEqual.
        self.assertAlmostEqual(ret[0][0], 1.5252)
        self.assertAlmostEqual(ret[1][0], 1.24)
        self.assertAlmostEqual(ret[2][0], 1.23)


class TestCondBackward(unittest.TestCase):

    def backward_value_helper(self, cond_func, use_cuda, use_parallel_exe):
        """
        Helper function that compares calculated backward value is close to dy/dx
        """
        main_program = Program()
        main_program.random_seed = 123
        startup_program = Program()
        startup_program.random_seed = 123
        with program_guard(main_program, startup_program):
            img = fluid.data(name='image', shape=[-1, 9], dtype='float32')
            img.stop_gradient = False
            label = fluid.data(name='label', shape=[-1, 1], dtype='int64')
            i = fluid.data(name="i", shape=[1], dtype='int32')
            loss = cond_func(i, img, label)
            append_backward(loss)
        place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
        exe = fluid.Executor(place)
        exe.run(startup_program)

        num_devices = 1
        if use_parallel_exe:
            os.environ['CPU_NUM'] = str(2)
            exe = fluid.ParallelExecutor(use_cuda=use_cuda,
                                         main_program=main_program,
                                         loss_name=loss.name)
            num_devices = exe.device_count

        delta = 0.005
        for feed_i in range(0, 10):
            feed_img = np.random.random(size=[1, 9]).astype(np.float32)
            feed_label = np.random.randint(low=0,
                                           high=10,
                                           size=[1, 1],
                                           dtype=np.int64)
            if use_parallel_exe:
                img_grad, loss_value = exe.run(
                    feed={
                        'i': np.full((num_devices), feed_i, np.int32),
                        'image': np.repeat(feed_img, num_devices, axis=0),
                        'label': np.repeat(feed_label, num_devices, axis=0)
                    },
                    fetch_list=[img.grad_name, loss.name])
            else:
                img_grad, loss_value = exe.run(
                    main_program,
                    feed={
                        'i': np.full((1), feed_i, np.int32),
                        'image': feed_img,
                        'label': feed_label
                    },
                    fetch_list=[img.grad_name, loss.name])

            numerical_grad = np.zeros(shape=[num_devices, 9], dtype=np.float32)
            feed_img_delta = np.copy(feed_img)
            for j in range(9):
                feed_img_delta[0][j] = feed_img[0][j] + delta
                if use_parallel_exe:
                    loss_delta = exe.run(feed={
                        'i':
                        np.full((num_devices), feed_i, np.int32),
                        'image':
                        np.repeat(feed_img_delta, num_devices, axis=0),
                        'label':
                        np.repeat(feed_label, num_devices, axis=0)
                    },
                                         fetch_list=[loss.name])
                    multi_device_grad = (loss_delta[0] -
                                         loss_value[0]) / delta / num_devices
                    for d in range(num_devices):
                        numerical_grad[d][j] = multi_device_grad[d]
                else:
                    loss_delta = exe.run(main_program,
                                         feed={
                                             'i': np.full((1), feed_i,
                                                          np.int32),
                                             'image': feed_img_delta,
                                             'label': feed_label
                                         },
                                         fetch_list=[loss.name])
                    numerical_grad[0][j] = (loss_delta[0] -
                                            loss_value[0]) / delta
                feed_img_delta[0][j] = feed_img[0][j]
            self.assertTrue(
                np.isclose(img_grad, numerical_grad, atol=0.05,
                           rtol=0.05).all())

    def add_optimizer_helper(self, cond_func, use_cuda, use_parallel_exe):
        """
        Test that program is runnable when add optimizer
        """
        main_program = Program()
        startup_program = Program()
        with program_guard(main_program, startup_program):
            img = fluid.data(name='image', shape=[-1, 784], dtype='float32')
            label = fluid.data(name='label', shape=[-1, 1], dtype='int64')
            i = fluid.data(name="i", shape=[1], dtype='int32')
            loss = cond_func(i, img, label)
            optimizer = fluid.optimizer.SGD(learning_rate=0.1)
            optimizer.minimize(loss)

        place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
        exe = fluid.Executor(place)
        exe.run(startup_program)
        if use_parallel_exe:
            os.environ['CPU_NUM'] = str(2)
            exe = fluid.ParallelExecutor(use_cuda=use_cuda,
                                         main_program=main_program,
                                         loss_name=loss.name)
            num_devices = exe.device_count

        for feed_i in range(0, 10):
            feed_img = np.random.random(size=[16, 784]).astype(np.float32)
            feed_label = np.random.randint(low=0,
                                           high=10,
                                           size=[16, 1],
                                           dtype=np.int64)
            if use_parallel_exe:
                exe.run(feed={
                    'i': np.full((num_devices), feed_i, np.int32),
                    'image': np.repeat(feed_img, num_devices, axis=0),
                    'label': np.repeat(feed_label, num_devices, axis=0)
                },
                        fetch_list=[loss.name])
            else:
                exe.run(main_program,
                        feed={
                            'i': np.full((1), feed_i, np.int32),
                            'image': feed_img,
                            'label': feed_label
                        },
                        fetch_list=[loss])

    def test_cond_backward(self):

        def cond_func(i, img, label):
            predicate = ((i % 2) == 0)
            return layers.cond(
                predicate,
                lambda: simple_fc_net_with_inputs(img, label, class_num=10),
                lambda: batchnorm_fc_with_inputs(img, label, class_num=10))

        for use_parallel_exe in [False, True]:
            if use_parallel_exe and os.name == "nt":
                print(
                    "Skip use_parallel_exe=True in Windows because of flaky test when using PE under old Windows machine"
                )
                continue

            self.backward_value_helper(cond_func, core.is_compiled_with_cuda(),
                                       use_parallel_exe)
            self.add_optimizer_helper(cond_func, core.is_compiled_with_cuda(),
                                      use_parallel_exe)

    def test_half_nested_cond_backward(self):

        def branch(i, img, label):
            return layers.cond(
                (i % 2) == 0,
                lambda: simple_fc_net_with_inputs(img, label, class_num=10),
                lambda: batchnorm_fc_with_inputs(img, label, class_num=10))

        def cond_func_simple_net_at_true(i, img, label):
            return layers.cond(i < 5, lambda: branch(i, img, label),
                               lambda: paddle.mean(img))

        def cond_func_simple_net_at_false(i, img, label):
            return layers.cond(i < 5, lambda: paddle.mean(img),
                               lambda: branch(i, img, label))

        for use_parallel_exe in [False, True]:
            if use_parallel_exe and os.name == "nt":
                print(
                    "Skip use_parallel_exe=True in Windows because of flaky test when using PE under old Windows machine"
                )
                continue

            self.backward_value_helper(cond_func_simple_net_at_true,
                                       core.is_compiled_with_cuda(),
                                       use_parallel_exe)
            self.add_optimizer_helper(cond_func_simple_net_at_true,
                                      core.is_compiled_with_cuda(),
                                      use_parallel_exe)
            self.backward_value_helper(cond_func_simple_net_at_false,
                                       core.is_compiled_with_cuda(),
                                       use_parallel_exe)
            self.add_optimizer_helper(cond_func_simple_net_at_false,
                                      core.is_compiled_with_cuda(),
                                      use_parallel_exe)

    def test_nested_cond_backward(self):

        def branch(i, img, label, mod_two):
            if mod_two:
                predicate = ((i % 2) == 0)
            else:
                predicate = ((i % 2) != 0)
            return layers.cond(
                predicate,
                lambda: simple_fc_net_with_inputs(img, label, class_num=10),
                lambda: batchnorm_fc_with_inputs(img, label, class_num=10))

        def cond_func(i, img, label):
            return layers.cond(i < 5, lambda: branch(i, img, label, True),
                               lambda: branch(i, img, label, False))

        for use_parallel_exe in [False, True]:
            if use_parallel_exe and os.name == "nt":
                print(
                    "Skip use_parallel_exe=True in Windows because of flaky test when using PE under old Windows machine"
                )
                continue
            self.backward_value_helper(cond_func, core.is_compiled_with_cuda(),
                                       use_parallel_exe)
            self.add_optimizer_helper(cond_func, core.is_compiled_with_cuda(),
                                      use_parallel_exe)


class TestCondWithError(unittest.TestCase):

    def test_input_type_error(self):
        main_program = framework.Program()
        startup_program = framework.Program()
        with framework.program_guard(main_program, startup_program):
            pred = fluid.data(name='y', shape=[1], dtype='bool')

            def func():
                return pred

            with self.assertRaises(TypeError):
                layers.cond(None, func, func)

            with self.assertRaises(TypeError):
                layers.cond(pred, func, set())

            with self.assertRaises(TypeError):
                layers.cond(pred, set(), func)

            with self.assertRaises(TypeError):
                layers.cond(pred, func, func, set())


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