test_dataset.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.
"""
TestCases for Dataset,
including create, config, run, etc.
"""

import os
import tempfile
import unittest

import paddle
import paddle.fluid as fluid
import paddle.fluid.core as core


class TestDataset(unittest.TestCase):
    """TestCases for Dataset."""

    def setUp(self):
        self.use_data_loader = False
        self.epoch_num = 10
        self.drop_last = False

    def test_dataset_create(self):
        """Testcase for dataset create."""
        try:
            dataset = paddle.distributed.InMemoryDataset()
        except:
            self.assertTrue(False)

        try:
            dataset = paddle.distributed.QueueDataset()
        except:
            self.assertTrue(False)

        try:
            dataset = paddle.distributed.fleet.dataset.FileInstantDataset()
        except:
            self.assertTrue(False)

        try:
            dataset = paddle.distributed.fleet.dataset.MyOwnDataset()
            self.assertTrue(False)
        except:
            self.assertTrue(True)

    def test_config(self):
        """
        Testcase for python config.
        """
        dataset = fluid.InMemoryDataset()
        dataset.set_parse_ins_id(True)
        dataset.set_parse_content(True)
        dataset._set_trainer_num(1)
        self.assertTrue(dataset.parse_ins_id)
        self.assertTrue(dataset.parse_content)
        self.assertEqual(dataset.trainer_num, 1)

    def test_shuffle_by_uid(self):
        """
        Testcase for shuffle_by_uid.
        """
        dataset = paddle.distributed.InMemoryDataset()
        dataset._set_uid_slot('6048')
        dataset._set_shuffle_by_uid(True)

    def test_run_with_dump(self):
        """
        Testcase for InMemoryDataset from create to run.
        """

        temp_dir = tempfile.TemporaryDirectory()
        dump_a_path = os.path.join(temp_dir.name, 'test_run_with_dump_a.txt')
        dump_b_path = os.path.join(temp_dir.name, 'test_run_with_dump_b.txt')

        with open(dump_a_path, "w") as f:
            data = "1 a 1 a 1 1 2 3 3 4 5 5 5 5 1 1\n"
            data += "1 b 1 b 1 2 2 3 4 4 6 6 6 6 1 2\n"
            data += "1 c 1 c 1 3 2 3 5 4 7 7 7 7 1 3\n"
            f.write(data)
        with open(dump_b_path, "w") as f:
            data = "1 d 1 d 1 4 2 3 3 4 5 5 5 5 1 4\n"
            data += "1 e 1 e 1 5 2 3 4 4 6 6 6 6 1 5\n"
            data += "1 f 1 f 1 6 2 3 5 4 7 7 7 7 1 6\n"
            data += "1 g 1 g 1 7 2 3 6 4 8 8 8 8 1 7\n"
            f.write(data)

        slots = ["slot1", "slot2", "slot3", "slot4"]
        slots_vars = []
        for slot in slots:
            var = paddle.static.data(
                name=slot, shape=[-1, 1], dtype="int64", lod_level=1
            )
            slots_vars.append(var)

        dataset = paddle.distributed.InMemoryDataset()
        dataset.init(
            batch_size=32, thread_num=3, pipe_command="cat", use_var=slots_vars
        )
        dataset.update_settings(pipe_command="cat1")
        dataset._init_distributed_settings(
            parse_ins_id=True,
            parse_content=True,
            fea_eval=True,
            candidate_size=10000,
        )
        dataset.set_filelist([dump_a_path, dump_b_path])
        dataset.load_into_memory()
        dataset.local_shuffle()

        paddle.enable_static()

        exe = paddle.static.Executor(paddle.CPUPlace())
        startup_program = paddle.static.Program()
        main_program = paddle.static.Program()
        exe.run(startup_program)
        for i in range(2):
            try:
                exe.train_from_dataset(main_program, dataset)
            except ImportError as e:
                pass
            except Exception as e:
                self.assertTrue(False)

        temp_dir.cleanup()

    def test_dataset_config(self):
        """Testcase for dataset configuration."""
        dataset = fluid.core.Dataset("MultiSlotDataset")
        dataset.set_thread_num(12)
        dataset.set_filelist(["a.txt", "b.txt", "c.txt"])
        dataset.set_trainer_num(4)
        dataset.set_hdfs_config("my_fs_name", "my_fs_ugi")
        dataset.set_download_cmd("./read_from_afs my_fs_name my_fs_ugi")
        dataset.set_enable_pv_merge(False)

        thread_num = dataset.get_thread_num()
        self.assertEqual(thread_num, 12)

        filelist = dataset.get_filelist()
        self.assertEqual(len(filelist), 3)
        self.assertEqual(filelist[0], "a.txt")
        self.assertEqual(filelist[1], "b.txt")
        self.assertEqual(filelist[2], "c.txt")

        trainer_num = dataset.get_trainer_num()
        self.assertEqual(trainer_num, 4)

        name, ugi = dataset.get_hdfs_config()
        self.assertEqual(name, "my_fs_name")
        self.assertEqual(ugi, "my_fs_ugi")

        download_cmd = dataset.get_download_cmd()
        self.assertEqual(download_cmd, "./read_from_afs my_fs_name my_fs_ugi")

    def test_set_download_cmd(self):
        """
        Testcase for InMemoryDataset from create to run.
        """
        temp_dir = tempfile.TemporaryDirectory()
        filename1 = os.path.join(
            temp_dir.name, "afs:test_in_memory_dataset_run_a.txt"
        )
        filename2 = os.path.join(
            temp_dir.name, "afs:test_in_memory_dataset_run_b.txt"
        )

        with open(filename1, "w") as f:
            data = "1 1 2 3 3 4 5 5 5 5 1 1\n"
            data += "1 2 2 3 4 4 6 6 6 6 1 2\n"
            data += "1 3 2 3 5 4 7 7 7 7 1 3\n"
            f.write(data)
        with open(filename2, "w") as f:
            data = "1 4 2 3 3 4 5 5 5 5 1 4\n"
            data += "1 5 2 3 4 4 6 6 6 6 1 5\n"
            data += "1 6 2 3 5 4 7 7 7 7 1 6\n"
            data += "1 7 2 3 6 4 8 8 8 8 1 7\n"
            f.write(data)

        slots = ["slot1", "slot2", "slot3", "slot4"]
        slots_vars = []
        for slot in slots:
            var = paddle.static.data(
                name=slot, shape=[-1, 1], dtype="int64", lod_level=1
            )
            slots_vars.append(var)

        dataset = paddle.distributed.InMemoryDataset()
        dataset.init(
            batch_size=32,
            thread_num=3,
            pipe_command="cat",
            download_cmd="cat",
            use_var=slots_vars,
        )
        dataset.set_filelist([filename1, filename2])
        dataset.load_into_memory()
        paddle.enable_static()

        exe = paddle.static.Executor(paddle.CPUPlace())
        startup_program = paddle.static.Program()
        main_program = paddle.static.Program()
        exe = fluid.Executor(fluid.CPUPlace())
        exe.run(startup_program)
        if self.use_data_loader:
            data_loader = fluid.io.DataLoader.from_dataset(
                dataset, fluid.cpu_places(), self.drop_last
            )
            for i in range(self.epoch_num):
                for data in data_loader():
                    exe.run(main_program, feed=data)
        else:
            for i in range(self.epoch_num):
                try:
                    exe.train_from_dataset(main_program, dataset)
                except Exception as e:
                    self.assertTrue(False)

        temp_dir.cleanup()

    def test_in_memory_dataset_run(self):
        """
        Testcase for InMemoryDataset from create to run.
        """
        temp_dir = tempfile.TemporaryDirectory()
        filename1 = os.path.join(
            temp_dir.name, "test_in_memory_dataset_run_a.txt"
        )
        filename2 = os.path.join(
            temp_dir.name, "test_in_memory_dataset_run_b.txt"
        )

        with open(filename1, "w") as f:
            data = "1 1 2 3 3 4 5 5 5 5 1 1\n"
            data += "1 2 2 3 4 4 6 6 6 6 1 2\n"
            data += "1 3 2 3 5 4 7 7 7 7 1 3\n"
            f.write(data)
        with open(filename2, "w") as f:
            data = "1 4 2 3 3 4 5 5 5 5 1 4\n"
            data += "1 5 2 3 4 4 6 6 6 6 1 5\n"
            data += "1 6 2 3 5 4 7 7 7 7 1 6\n"
            data += "1 7 2 3 6 4 8 8 8 8 1 7\n"
            f.write(data)

        slots = ["slot1", "slot2", "slot3", "slot4"]
        slots_vars = []
        for slot in slots:
            var = paddle.static.data(
                name=slot, shape=[-1, 1], dtype="int64", lod_level=1
            )
            slots_vars.append(var)

        dataset = paddle.distributed.InMemoryDataset()
        dataset.init(
            batch_size=32, thread_num=3, pipe_command="cat", use_var=slots_vars
        )
        dataset._init_distributed_settings(fea_eval=True, candidate_size=1)
        dataset.set_filelist([filename1, filename2])
        dataset.load_into_memory()
        dataset.slots_shuffle(["slot1"])
        dataset.local_shuffle()
        dataset._set_generate_unique_feasigns(True, 15)
        dataset._generate_local_tables_unlock(0, 11, 1, 25, 15)
        exe = fluid.Executor(fluid.CPUPlace())
        exe.run(fluid.default_startup_program())
        if self.use_data_loader:
            data_loader = fluid.io.DataLoader.from_dataset(
                dataset, fluid.cpu_places(), self.drop_last
            )
            for i in range(self.epoch_num):
                for data in data_loader():
                    exe.run(fluid.default_main_program(), feed=data)
        else:
            for i in range(self.epoch_num):
                try:
                    exe.train_from_dataset(
                        fluid.default_main_program(), dataset
                    )
                except Exception as e:
                    self.assertTrue(False)

        temp_dir.cleanup()

    def test_in_memory_dataset_gpugraph_mode(self):
        """
        Testcase for InMemoryDataset in gpugraph mode.
        """
        dataset = fluid.DatasetFactory().create_dataset("InMemoryDataset")
        dataset.set_feed_type("SlotRecordInMemoryDataFeed")
        graph_config = {
            "walk_len": 24,
            "walk_degree": 10,
            "once_sample_startid_len": 80000,
            "sample_times_one_chunk": 5,
            "window": 3,
            "debug_mode": 0,
            "batch_size": 800,
            "meta_path": "cuid2clk-clk2cuid;cuid2conv-conv2cuid;clk2cuid-cuid2clk;clk2cuid-cuid2conv",
            "gpu_graph_training": 1,
        }
        dataset.set_graph_config(graph_config)
        dataset.set_pass_id(0)
        dataset.get_pass_id()
        dataset.get_epoch_finish()

    def test_in_memory_dataset_masterpatch(self):
        """
        Testcase for InMemoryDataset from create to run.
        """
        temp_dir = tempfile.TemporaryDirectory()
        filename1 = os.path.join(
            temp_dir.name, "test_in_memory_dataset_masterpatch_a.txt"
        )
        filename2 = os.path.join(
            temp_dir.name, "test_in_memory_dataset_masterpatch_b.txt"
        )

        with open(filename1, "w") as f:
            data = "1 id1 1 1 2 3 3 4 5 5 5 5 1 1\n"
            data += "1 id1 1 2 2 3 4 4 6 6 6 6 1 2\n"
            data += "1 id2 1 1 1 1 1 0 1 0\n"
            data += "1 id3 1 0 1 0 1 1 1 1\n"
            data += "1 id3 1 1 1 1 1 0 1 0\n"
            data += "1 id4 1 0 1 0 1 1 1 1\n"
            data += "1 id4 1 0 1 0 1 1 1 1\n"
            data += "1 id5 1 1 1 1 1 0 1 0\n"
            data += "1 id5 1 1 1 1 1 0 1 0\n"
            f.write(data)
        with open(filename2, "w") as f:
            data = "1 id6 1 4 2 3 3 4 5 5 5 5 1 4\n"
            data += "1 id6 1 1 2 3 4 4 6 6 6 6 1 5\n"
            data += "1 id6 1 6 2 3 5 4 7 7 7 7 1 6\n"
            data += "1 id6 1 7 2 3 6 4 8 8 8 8 1 7\n"
            f.write(data)

        slots = ["slot1", "slot2", "slot3", "slot4"]
        slots_vars = []
        train_program = fluid.Program()
        startup_program = fluid.Program()
        with fluid.program_guard(train_program, startup_program):
            for slot in slots[:2]:
                var = paddle.static.data(
                    name=slot, shape=[-1, 1], dtype="int64", lod_level=1
                )
                slots_vars.append(var)
            for slot in slots[2:]:
                var = paddle.static.data(
                    name=slot, shape=[-1, 1], dtype="float32", lod_level=1
                )
                slots_vars.append(var)

        dataset = paddle.distributed.InMemoryDataset()
        dataset.init(
            batch_size=32, thread_num=1, pipe_command="cat", use_var=slots_vars
        )
        dataset._init_distributed_settings(parse_ins_id=True)
        dataset.set_filelist(
            [
                "test_in_memory_dataset_masterpatch_a.txt",
                "test_in_memory_dataset_masterpatch_b.txt",
            ]
        )
        dataset.load_into_memory()
        dataset.local_shuffle()

        exe = fluid.Executor(fluid.CPUPlace())
        exe.run(startup_program)

        for i in range(2):
            try:
                exe.train_from_dataset(train_program, dataset)
            except ImportError as e:
                pass
            except Exception as e:
                self.assertTrue(False)

        # dataset._set_merge_by_lineid(2)
        dataset.update_settings(merge_size=2)
        dataset.dataset.merge_by_lineid()

        temp_dir.cleanup()

    def test_in_memory_dataset_masterpatch1(self):
        """
        Testcase for InMemoryDataset from create to run.
        """
        temp_dir = tempfile.TemporaryDirectory()
        filename1 = os.path.join(
            temp_dir.name, "test_in_memory_dataset_masterpatch1_a.txt"
        )
        filename2 = os.path.join(
            temp_dir.name, "test_in_memory_dataset_masterpatch1_b.txt"
        )

        with open(filename1, "w") as f:
            data = "1 id1 1 1 2 3 3 4 5 5 5 5 1 1\n"
            data += "1 id1 1 2 2 3 4 4 6 6 6 6 1 2\n"
            data += "1 id2 1 1 1 1 1 0 1 0\n"
            data += "1 id3 1 0 1 0 1 1 1 1\n"
            data += "1 id3 1 1 1 1 1 0 1 0\n"
            data += "1 id4 1 0 1 0 1 1 1 1\n"
            data += "1 id4 1 0 1 0 1 1 1 1\n"
            data += "1 id5 1 1 1 1 1 0 1 0\n"
            data += "1 id5 1 1 1 1 1 0 1 0\n"
            f.write(data)
        with open(filename2, "w") as f:
            data = "1 id6 1 4 2 3 3 4 5 5 5 5 1 4\n"
            data += "1 id6 1 1 2 3 4 4 6 6 6 6 1 5\n"
            data += "1 id6 1 6 2 3 5 4 7 7 7 7 1 6\n"
            data += "1 id6 1 7 2 3 6 4 8 8 8 8 1 7\n"
            f.write(data)

        slots_vars = []
        train_program = fluid.Program()
        startup_program = fluid.Program()
        with fluid.program_guard(train_program, startup_program):
            var1 = paddle.static.data(
                name="slot1", shape=[-1, 1], dtype="int64", lod_level=0
            )
            var2 = paddle.static.data(
                name="slot2", shape=[-1, 1], dtype="int64", lod_level=0
            )
            var3 = paddle.static.data(
                name="slot3", shape=[-1, 1], dtype="float32", lod_level=0
            )
            var4 = paddle.static.data(
                name="slot4", shape=[-1, 1], dtype="float32", lod_level=0
            )
            slots_vars = [var1, var2, var3, var4]

        dataset = paddle.distributed.InMemoryDataset()
        dataset.init(
            batch_size=32, thread_num=1, pipe_command="cat", use_var=slots_vars
        )
        dataset._init_distributed_settings(parse_ins_id=True)
        dataset.set_filelist(
            [
                "test_in_memory_dataset_masterpatch1_a.txt",
                "test_in_memory_dataset_masterpatch1_b.txt",
            ]
        )
        dataset.load_into_memory()
        dataset.local_shuffle()

        exe = fluid.Executor(fluid.CPUPlace())
        exe.run(startup_program)

        for i in range(2):
            try:
                exe.train_from_dataset(train_program, dataset)
            except ImportError as e:
                pass
            except Exception as e:
                self.assertTrue(False)

        dataset._set_merge_by_lineid(2)
        dataset.dataset.merge_by_lineid()

        temp_dir.cleanup()

    def test_in_memory_dataset_run_2(self):
        """
        Testcase for InMemoryDataset from create to run.
        Use CUDAPlace
        Use float type id
        """
        temp_dir = tempfile.TemporaryDirectory()
        filename1 = os.path.join(
            temp_dir.name, "test_in_memory_dataset_run_a.txt"
        )
        filename2 = os.path.join(
            temp_dir.name, "test_in_memory_dataset_run_b.txt"
        )

        with open(filename1, "w") as f:
            data = "1 1 2 3 3 4 5 5 5 5 1 1\n"
            data += "1 2 2 3 4 4 6 6 6 6 1 2\n"
            data += "1 3 2 3 5 4 7 7 7 7 1 3\n"
            f.write(data)
        with open(filename2, "w") as f:
            data = "1 4 2 3 3 4 5 5 5 5 1 4\n"
            data += "1 5 2 3 4 4 6 6 6 6 1 5\n"
            data += "1 6 2 3 5 4 7 7 7 7 1 6\n"
            data += "1 7 2 3 6 4 8 8 8 8 1 7\n"
            f.write(data)

        slots = ["slot1_f", "slot2_f", "slot3_f", "slot4_f"]
        slots_vars = []
        for slot in slots:
            var = paddle.static.data(
                name=slot, shape=[-1, 1], dtype="float32", lod_level=1
            )
            slots_vars.append(var)

        dataset = paddle.distributed.InMemoryDataset()
        dataset.init(
            batch_size=32, thread_num=3, pipe_command="cat", use_var=slots_vars
        )
        dataset.set_filelist([filename1, filename2])
        dataset.load_into_memory()
        dataset.local_shuffle()

        exe = fluid.Executor(
            fluid.CPUPlace()
            if not core.is_compiled_with_cuda()
            else fluid.CUDAPlace(0)
        )
        exe.run(fluid.default_startup_program())

        for i in range(2):
            try:
                exe.train_from_dataset(fluid.default_main_program(), dataset)
                exe.train_from_dataset(
                    fluid.default_main_program(), dataset, thread=1
                )
                exe.train_from_dataset(
                    fluid.default_main_program(), dataset, thread=2
                )
                exe.train_from_dataset(
                    fluid.default_main_program(), dataset, thread=2
                )
                exe.train_from_dataset(
                    fluid.default_main_program(), dataset, thread=3
                )
                exe.train_from_dataset(
                    fluid.default_main_program(), dataset, thread=4
                )
            except ImportError as e:
                pass
            except Exception as e:
                self.assertTrue(False)

        if self.use_data_loader:
            data_loader = fluid.io.DataLoader.from_dataset(
                dataset, fluid.cpu_places(), self.drop_last
            )
            for i in range(self.epoch_num):
                for data in data_loader():
                    exe.run(fluid.default_main_program(), feed=data)
        else:
            for i in range(self.epoch_num):
                try:
                    exe.train_from_dataset(
                        fluid.default_main_program(), dataset
                    )
                except Exception as e:
                    self.assertTrue(False)

        dataset._set_merge_by_lineid(2)
        dataset._set_parse_ins_id(False)
        dataset._set_fleet_send_sleep_seconds(2)
        dataset.preload_into_memory()
        dataset.wait_preload_done()
        dataset.preload_into_memory(1)
        dataset.wait_preload_done()
        dataset.dataset.merge_by_lineid()
        dataset._set_merge_by_lineid(30)
        dataset._set_parse_ins_id(False)
        dataset.load_into_memory()
        dataset.dataset.merge_by_lineid()
        dataset.update_settings(
            batch_size=1,
            thread_num=2,
            input_type=1,
            pipe_command="cat",
            use_var=[],
            fs_name="",
            fs_ugi="",
            download_cmd="cat",
            merge_size=-1,
            parse_ins_id=False,
            parse_content=False,
            fleet_send_batch_size=2,
            fleet_send_sleep_seconds=2,
            fea_eval=True,
        )
        fleet_ptr = fluid.core.Fleet()
        fleet_ptr.set_client2client_config(1, 1, 1)
        fleet_ptr.get_cache_threshold(0)

        temp_dir.cleanup()

    def test_queue_dataset_run(self):
        """
        Testcase for QueueDataset from create to run.
        """
        temp_dir = tempfile.TemporaryDirectory()
        filename1 = os.path.join(temp_dir.name, "test_queue_dataset_run_a.txt")
        filename2 = os.path.join(temp_dir.name, "test_queue_dataset_run_b.txt")

        with open(filename1, "w") as f:
            data = "1 1 2 3 3 4 5 5 5 5 1 1\n"
            data += "1 2 2 3 4 4 6 6 6 6 1 2\n"
            data += "1 3 2 3 5 4 7 7 7 7 1 3\n"
            f.write(data)
        with open(filename2, "w") as f:
            data = "1 4 2 3 3 4 5 5 5 5 1 4\n"
            data += "1 5 2 3 4 4 6 6 6 6 1 5\n"
            data += "1 6 2 3 5 4 7 7 7 7 1 6\n"
            data += "1 7 2 3 6 4 8 8 8 8 1 7\n"
            f.write(data)

        slots = ["slot1", "slot2", "slot3", "slot4"]
        slots_vars = []
        for slot in slots:
            var = paddle.static.data(
                name=slot, shape=[-1, 1], dtype="int64", lod_level=1
            )
            slots_vars.append(var)

        dataset = paddle.distributed.QueueDataset()
        dataset.init(
            batch_size=32, thread_num=3, pipe_command="cat", use_var=slots_vars
        )
        dataset.set_filelist([filename1, filename2])

        exe = fluid.Executor(fluid.CPUPlace())
        exe.run(fluid.default_startup_program())
        if self.use_data_loader:
            data_loader = fluid.io.DataLoader.from_dataset(
                dataset, fluid.cpu_places(), self.drop_last
            )
            for i in range(self.epoch_num):
                for data in data_loader():
                    exe.run(fluid.default_main_program(), feed=data)
        else:
            for i in range(self.epoch_num):
                try:
                    exe.train_from_dataset(
                        fluid.default_main_program(), dataset
                    )
                except Exception as e:
                    self.assertTrue(False)

        dataset2 = paddle.distributed.QueueDataset()
        dataset2.init(
            batch_size=32, thread_num=3, pipe_command="cat", use_var=slots_vars
        )
        dataset.set_filelist([])
        try:
            exe.train_from_dataset(fluid.default_main_program(), dataset2)
        except ImportError as e:
            print("warning: we skip trainer_desc_pb2 import problem in windows")
        except Exception as e:
            self.assertTrue(False)

        temp_dir.cleanup()

    def test_queue_dataset_run_2(self):
        """
        Testcase for QueueDataset from create to run.
        Use CUDAPlace
        Use float type id
        """
        temp_dir = tempfile.TemporaryDirectory()
        filename1 = os.path.join(temp_dir.name, "test_queue_dataset_run_a.txt")
        filename2 = os.path.join(temp_dir.name, "test_queue_dataset_run_b.txt")

        with open(filename1, "w") as f:
            data = "1 1 2 3 3 4 5 5 5 5 1 1\n"
            data += "1 2 2 3 4 4 6 6 6 6 1 2\n"
            data += "1 3 2 3 5 4 7 7 7 7 1 3\n"
            f.write(data)
        with open(filename2, "w") as f:
            data = "1 4 2 3 3 4 5 5 5 5 1 4\n"
            data += "1 5 2 3 4 4 6 6 6 6 1 5\n"
            data += "1 6 2 3 5 4 7 7 7 7 1 6\n"
            data += "1 7 2 3 6 4 8 8 8 8 1 7\n"
            f.write(data)

        slots = ["slot1_f", "slot2_f", "slot3_f", "slot4_f"]
        slots_vars = []
        for slot in slots:
            var = paddle.static.data(
                name=slot, shape=[-1, 1], dtype="float32", lod_level=1
            )
            slots_vars.append(var)

        dataset = paddle.distributed.QueueDataset()
        dataset.init(
            batch_size=32, thread_num=3, pipe_command="cat", use_var=slots_vars
        )
        dataset.set_filelist([filename1, filename2])

        exe = fluid.Executor(
            fluid.CPUPlace()
            if not core.is_compiled_with_cuda()
            else fluid.CUDAPlace(0)
        )
        exe.run(fluid.default_startup_program())
        if self.use_data_loader:
            data_loader = fluid.io.DataLoader.from_dataset(
                dataset, fluid.cpu_places(), self.drop_last
            )
            for i in range(self.epoch_num):
                for data in data_loader():
                    exe.run(fluid.default_main_program(), feed=data)
        else:
            for i in range(self.epoch_num):
                try:
                    exe.train_from_dataset(
                        fluid.default_main_program(), dataset
                    )
                except Exception as e:
                    self.assertTrue(False)

        temp_dir.cleanup()

    def test_queue_dataset_run_3(self):
        """
        Testcase for QueueDataset from create to run.
        Use CUDAPlace
        Use float type id
        """
        temp_dir = tempfile.TemporaryDirectory()
        filename1 = os.path.join(temp_dir.name, "test_queue_dataset_run_a.txt")
        filename2 = os.path.join(temp_dir.name, "test_queue_dataset_run_b.txt")

        with open(filename1, "w") as f:
            data = "2 1 2 2 5 4 2 2 7 2 1 3\n"
            data += "2 6 2 2 1 4 2 2 4 2 2 3\n"
            data += "2 5 2 2 9 9 2 2 7 2 1 3\n"
            data += "2 7 2 2 1 9 2 3 7 2 5 3\n"
            f.write(data)
        with open(filename2, "w") as f:
            data = "2 1 2 2 5 4 2 2 7 2 1 3\n"
            data += "2 6 2 2 1 4 2 2 4 2 2 3\n"
            data += "2 5 2 2 9 9 2 2 7 2 1 3\n"
            data += "2 7 2 2 1 9 2 3 7 2 5 3\n"
            f.write(data)

        slots = ["slot1", "slot2", "slot3", "slot4"]
        slots_vars = []
        for slot in slots:
            var = fluid.data(
                name=slot, shape=[None, 1], dtype="int64", lod_level=1
            )
            slots_vars.append(var)

        dataset = paddle.distributed.InMemoryDataset()
        dataset.init(
            batch_size=1,
            thread_num=2,
            input_type=1,
            pipe_command="cat",
            use_var=slots_vars,
        )
        dataset.set_filelist([filename1, filename2])
        dataset.load_into_memory()

        exe = fluid.Executor(
            fluid.CPUPlace()
            if not core.is_compiled_with_cuda()
            else fluid.CUDAPlace(0)
        )
        exe.run(fluid.default_startup_program())
        if self.use_data_loader:
            data_loader = fluid.io.DataLoader.from_dataset(
                dataset, fluid.cpu_places(), self.drop_last
            )
            for i in range(self.epoch_num):
                for data in data_loader():
                    exe.run(fluid.default_main_program(), feed=data)
        else:
            for i in range(self.epoch_num):
                try:
                    exe.train_from_dataset(
                        fluid.default_main_program(), dataset
                    )
                except Exception as e:
                    self.assertTrue(False)

        temp_dir.cleanup()

    def test_run_with_inmemory_dataset_train_debug_mode(self):
        """
        Testcase for InMemoryDataset from create to run.
        """

        temp_dir = tempfile.TemporaryDirectory()
        dump_a_path = os.path.join(temp_dir.name, 'test_run_with_dump_a.txt')
        dump_b_path = os.path.join(temp_dir.name, 'test_run_with_dump_b.txt')

        with open(dump_a_path, "w") as f:
            data = "1 a 1 a 1 1 2 3 3 4 5 5 5 5 1 1\n"
            data += "1 b 1 b 1 2 2 3 4 4 6 6 6 6 1 2\n"
            data += "1 c 1 c 1 3 2 3 5 4 7 7 7 7 1 3\n"
            f.write(data)
        with open(dump_b_path, "w") as f:
            data = "1 d 1 d 1 4 2 3 3 4 5 5 5 5 1 4\n"
            data += "1 e 1 e 1 5 2 3 4 4 6 6 6 6 1 5\n"
            data += "1 f 1 f 1 6 2 3 5 4 7 7 7 7 1 6\n"
            data += "1 g 1 g 1 7 2 3 6 4 8 8 8 8 1 7\n"
            f.write(data)

        slots = ["slot1", "slot2", "slot3", "slot4"]
        slots_vars = []
        for slot in slots:
            var = paddle.static.data(
                name=slot, shape=[-1, 1], dtype="int64", lod_level=1
            )
            slots_vars.append(var)

        dataset = paddle.distributed.InMemoryDataset()
        dataset.init(
            batch_size=32,
            thread_num=1,
            pipe_command="cat",
            data_feed_type="SlotRecordInMemoryDataFeed",
            use_var=slots_vars,
        )
        dataset._init_distributed_settings(
            parse_ins_id=True,
            parse_content=True,
            fea_eval=True,
            candidate_size=10000,
        )
        dataset.set_filelist([dump_a_path, dump_b_path])
        dataset.load_into_memory()

        paddle.enable_static()

        exe = paddle.static.Executor(paddle.CPUPlace())
        startup_program = paddle.static.Program()
        main_program = paddle.static.Program()
        exe.run(startup_program)
        for i in range(2):
            try:
                exe.train_from_dataset(main_program, dataset, debug=True)
            except ImportError as e:
                pass
            except Exception as e:
                self.assertTrue(False)

        temp_dir.cleanup()

    def test_cuda_in_memory_dataset_run(self):
        """
        Testcase for cuda inmemory dataset hogwild_worker train to run(barrier).
        """
        temp_dir = tempfile.TemporaryDirectory()
        filename1 = os.path.join(
            temp_dir.name, "test_in_memory_dataset_run_a.txt"
        )
        filename2 = os.path.join(
            temp_dir.name, "test_in_memory_dataset_run_b.txt"
        )

        with open(filename1, "w") as f:
            data = "1 1 2 3 3 4 5 5 5 5 1 1\n"
            data += "1 2 2 3 4 4 6 6 6 6 1 2\n"
            data += "1 3 2 3 5 4 7 7 7 7 1 3\n"
            f.write(data)
        with open(filename2, "w") as f:
            data = "1 4 2 3 3 4 5 5 5 5 1 4\n"
            data += "1 5 2 3 4 4 6 6 6 6 1 5\n"
            data += "1 6 2 3 5 4 7 7 7 7 1 6\n"
            data += "1 7 2 3 6 4 8 8 8 8 1 7\n"
            f.write(data)

        slots = ["slot1", "slot2", "slot3", "slot4"]
        slots_vars = []
        for slot in slots:
            var = paddle.static.data(
                name=slot, shape=[-1, 1], dtype="int64", lod_level=1
            )
            slots_vars.append(var)

        dataset = fluid.DatasetFactory().create_dataset("InMemoryDataset")
        dataset.set_feed_type("SlotRecordInMemoryDataFeed")
        dataset.set_batch_size(1)
        dataset.set_pipe_command("cat")
        dataset.set_use_var(slots_vars)
        dataset.set_filelist([filename1, filename2])

        graph_config = {
            "walk_len": 24,
            "walk_degree": 10,
            "once_sample_startid_len": 80000,
            "sample_times_one_chunk": 5,
            "window": 3,
            "debug_mode": 0,
            "batch_size": 800,
            "meta_path": "cuid2clk-clk2cuid;cuid2conv-conv2cuid;clk2cuid-cuid2clk;clk2cuid-cuid2conv",
            "gpu_graph_training": 1,
        }
        dataset.set_graph_config(graph_config)
        dataset.set_pass_id(2)
        pass_id = dataset.get_pass_id()

        dataset.load_into_memory()

        dataset.get_memory_data_size()

        exe = fluid.Executor(
            fluid.CPUPlace()
            if not core.is_compiled_with_cuda()
            else fluid.CUDAPlace(0)
        )
        exe.run(fluid.default_startup_program())
        for i in range(self.epoch_num):
            try:
                exe.train_from_dataset(fluid.default_main_program(), dataset)
            except Exception as e:
                self.assertTrue(False)
        temp_dir.cleanup()


class TestDatasetWithDataLoader(TestDataset):
    """
    Test Dataset With Data Loader class. TestCases.
    """

    def setUp(self):
        """
        Test Dataset With Data Loader, setUp.
        """
        self.use_data_loader = True
        self.epoch_num = 10
        self.drop_last = False


class TestDatasetWithFetchHandler(unittest.TestCase):
    """
    Test Dataset With Fetch Handler. TestCases.
    """

    def net(self):
        """
        Test Dataset With Fetch Handler. TestCases.
        """
        slots = ["slot1", "slot2", "slot3", "slot4"]
        slots_vars = []
        poolings = []
        for slot in slots:
            data = paddle.static.data(
                name=slot, shape=[-1, 1], dtype="int64", lod_level=1
            )
            var = fluid.layers.cast(x=data, dtype='float32')
            pool = paddle.static.nn.sequence_lod.sequence_pool(
                input=var, pool_type='AVERAGE'
            )

            slots_vars.append(data)
            poolings.append(pool)

        concated = fluid.layers.concat(poolings, axis=1)
        fc = paddle.static.nn.fc(x=concated, activation='tanh', size=32)
        return slots_vars, fc

    def get_dataset(self, inputs, files):
        """
        Test Dataset With Fetch Handler. TestCases.

        Args:
            inputs(list): inputs of get_dataset
            files(list): files of  get_dataset
        """
        dataset = paddle.distributed.QueueDataset()
        dataset.init(
            batch_size=32, thread_num=3, pipe_command="cat", use_var=inputs
        )
        dataset.set_filelist(files)
        return dataset

    def setUp(self):
        """
        Test Dataset With Fetch Handler. TestCases.
        """
        self.temp_dir = tempfile.TemporaryDirectory()
        self.filename1 = os.path.join(
            self.temp_dir.name, "test_queue_dataset_run_a.txt"
        )
        self.filename2 = os.path.join(
            self.temp_dir.name, "test_queue_dataset_run_b.txt"
        )

        with open(self.filename1, "w") as f:
            data = "1 1 2 3 3 4 5 5 5 5 1 1\n"
            data += "1 2 2 3 4 4 6 6 6 6 1 2\n"
            data += "1 3 2 3 5 4 7 7 7 7 1 3\n"
            f.write(data)
        with open(self.filename2, "w") as f:
            data = "1 4 2 3 3 4 5 5 5 5 1 4\n"
            data += "1 5 2 3 4 4 6 6 6 6 1 5\n"
            data += "1 6 2 3 5 4 7 7 7 7 1 6\n"
            data += "1 7 2 3 6 4 8 8 8 8 1 7\n"
            f.write(data)

    def tearDown(self):
        """
        Test Dataset With Fetch Handler. TestCases.
        """
        self.temp_dir.cleanup()

    def test_dataset_none(self):
        """
        Test Dataset With Fetch Handler. TestCases.
        """
        slots_vars, out = self.net()
        files = [self.filename1, self.filename2]
        dataset = self.get_dataset(slots_vars, files)

        exe = fluid.Executor(fluid.CPUPlace())
        exe.run(fluid.default_startup_program())

        # test dataset->None
        try:
            exe.train_from_dataset(fluid.default_main_program(), None)
        except ImportError as e:
            print("warning: we skip trainer_desc_pb2 import problem in windows")
        except RuntimeError as e:
            error_msg = "dataset is need and should be initialized"
            self.assertEqual(error_msg, str(e))
        except Exception as e:
            self.assertTrue(False)

    def test_infer_from_dataset(self):
        """
        Test Dataset With Fetch Handler. TestCases.
        """
        slots_vars, out = self.net()
        files = [self.filename1, self.filename2]
        dataset = self.get_dataset(slots_vars, files)

        exe = fluid.Executor(fluid.CPUPlace())
        exe.run(fluid.default_startup_program())

        try:
            exe.infer_from_dataset(fluid.default_main_program(), dataset)
        except ImportError as e:
            print("warning: we skip trainer_desc_pb2 import problem in windows")
        except Exception as e:
            self.assertTrue(False)

    def test_fetch_handler(self):
        """
        Test Dataset With Fetch Handler. TestCases.
        """
        slots_vars, out = self.net()
        files = [self.filename1, self.filename2]
        dataset = self.get_dataset(slots_vars, files)

        exe = fluid.Executor(fluid.CPUPlace())
        exe.run(fluid.default_startup_program())

        fh = fluid.executor.FetchHandler(out.name)
        fh.help()

        try:
            exe.train_from_dataset(
                program=fluid.default_main_program(),
                dataset=dataset,
                fetch_handler=fh,
            )
        except ImportError as e:
            print("warning: we skip trainer_desc_pb2 import problem in windows")
        except RuntimeError as e:
            error_msg = "dataset is need and should be initialized"
            self.assertEqual(error_msg, str(e))
        except Exception as e:
            self.assertTrue(False)


class TestDataset2(unittest.TestCase):
    """TestCases for Dataset."""

    def setUp(self):
        """TestCases for Dataset."""
        self.use_data_loader = False
        self.epoch_num = 10
        self.drop_last = False

    def test_dataset_fleet(self):
        """
        Testcase for InMemoryDataset from create to run.
        """
        temp_dir = tempfile.TemporaryDirectory()
        filename1 = os.path.join(
            temp_dir.name, "test_in_memory_dataset2_run_a.txt"
        )
        filename2 = os.path.join(
            temp_dir.name, "test_in_memory_dataset2_run_b.txt"
        )

        self.skipTest("parameter server will add pslib UT later")

        with open(filename1, "w") as f:
            data = "1 1 2 3 3 4 5 5 5 5 1 1\n"
            data += "1 2 2 3 4 4 6 6 6 6 1 2\n"
            data += "1 3 2 3 5 4 7 7 7 7 1 3\n"
            f.write(data)
        with open(filename2, "w") as f:
            data = "1 4 2 3 3 4 5 5 5 5 1 4\n"
            data += "1 5 2 3 4 4 6 6 6 6 1 5\n"
            data += "1 6 2 3 5 4 7 7 7 7 1 6\n"
            data += "1 7 2 3 6 4 8 8 8 8 1 7\n"
            f.write(data)

        train_program = fluid.Program()
        startup_program = fluid.Program()
        scope = fluid.Scope()
        from paddle.fluid.incubate.fleet.parameter_server.distribute_transpiler import (
            fleet,
        )

        with fluid.program_guard(train_program, startup_program):
            slots = ["slot1_ff", "slot2_ff", "slot3_ff", "slot4_ff"]
            slots_vars = []
            for slot in slots:
                var = paddle.static.data(
                    name=slot, shape=[-1, 1], dtype="float32", lod_level=1
                )
                slots_vars.append(var)
            fake_cost = paddle.subtract(slots_vars[0], slots_vars[-1])
            fake_cost = paddle.mean(fake_cost)
        with fluid.scope_guard(scope):
            place = fluid.CPUPlace()
            exe = fluid.Executor(place)
            try:
                fleet.init()
            except ImportError as e:
                print("warning: no mpi4py")
            adam = fluid.optimizer.Adam(learning_rate=0.000005)
            try:
                adam = fleet.distributed_optimizer(adam)
                adam.minimize([fake_cost], [scope])
            except AttributeError as e:
                print("warning: no mpi")
            except ImportError as e:
                print("warning: no mpi4py")
            exe.run(startup_program)
            dataset = paddle.distributed.InMemoryDataset()

            dataset.init(
                batch_size=32,
                thread_num=3,
                pipe_command="cat",
                use_var=slots_vars,
            )
            dataset.set_filelist([filename1, filename2])
            dataset.load_into_memory()
            fleet._opt_info = None
            fleet._fleet_ptr = None

        temp_dir.cleanup()

    def test_dataset_fleet2(self):
        """
        Testcase for InMemoryDataset from create to run.
        """
        temp_dir = tempfile.TemporaryDirectory()
        filename1 = os.path.join(
            temp_dir.name, "test_in_memory_dataset2_run2_a.txt"
        )
        filename2 = os.path.join(
            temp_dir.name, "test_in_memory_dataset2_run2_b.txt"
        )

        with open(filename1, "w") as f:
            data = "1 1 2 3 3 4 5 5 5 5 1 1\n"
            data += "1 2 2 3 4 4 6 6 6 6 1 2\n"
            data += "1 3 2 3 5 4 7 7 7 7 1 3\n"
            f.write(data)
        with open(filename2, "w") as f:
            data = "1 4 2 3 3 4 5 5 5 5 1 4\n"
            data += "1 5 2 3 4 4 6 6 6 6 1 5\n"
            data += "1 6 2 3 5 4 7 7 7 7 1 6\n"
            data += "1 7 2 3 6 4 8 8 8 8 1 7\n"
            f.write(data)

        train_program = fluid.Program()
        startup_program = fluid.Program()
        scope = fluid.Scope()
        from paddle.fluid.incubate.fleet.parameter_server.pslib import fleet

        with fluid.program_guard(train_program, startup_program):
            slots = ["slot1_ff", "slot2_ff", "slot3_ff", "slot4_ff"]
            slots_vars = []
            for slot in slots:
                var = paddle.static.data(
                    name=slot, shape=[-1, 1], dtype="float32", lod_level=1
                )
                slots_vars.append(var)
            fake_cost = paddle.subtract(slots_vars[0], slots_vars[-1])
            fake_cost = paddle.mean(fake_cost)
        with fluid.scope_guard(scope):
            place = fluid.CPUPlace()
            exe = fluid.Executor(place)
            try:
                fleet.init()
            except ImportError as e:
                print("warning: no mpi4py")
            adam = fluid.optimizer.Adam(learning_rate=0.000005)
            try:
                adam = fleet.distributed_optimizer(
                    adam,
                    strategy={
                        "fs_uri": "fs_uri_xxx",
                        "fs_user": "fs_user_xxx",
                        "fs_passwd": "fs_passwd_xxx",
                        "fs_hadoop_bin": "fs_hadoop_bin_xxx",
                    },
                )
                adam.minimize([fake_cost], [scope])
            except AttributeError as e:
                print("warning: no mpi")
            except ImportError as e:
                print("warning: no mpi4py")
            exe.run(startup_program)
            dataset = paddle.distributed.InMemoryDataset()
            dataset.init(
                batch_size=32,
                thread_num=3,
                pipe_command="cat",
                use_var=slots_vars,
            )
            dataset.set_filelist([filename1, filename2])
            dataset.load_into_memory()
            try:
                dataset.global_shuffle(fleet)
            except:
                print("warning: catch expected error")
            fleet._opt_info = None
            fleet._fleet_ptr = None
            dataset = paddle.distributed.InMemoryDataset()
            dataset.init(fs_name="", fs_ugi="")
            d = paddle.distributed.fleet.DatasetBase()
            try:
                dataset._set_feed_type("MultiSlotInMemoryDataFeed")
            except:
                print("warning: catch expected error")
            dataset.thread_num = 0
            try:
                dataset._prepare_to_run()
            except:
                print("warning: catch expected error")
            try:
                dataset.preprocess_instance()
            except:
                print("warning: catch expected error")
            try:
                dataset.set_current_phase(1)
            except:
                print("warning: catch expected error")
            try:
                dataset.postprocess_instance()
            except:
                print("warning: catch expected error")
            dataset._set_fleet_send_batch_size(1024)
            try:
                dataset.global_shuffle()
            except:
                print("warning: catch expected error")
            # dataset.get_pv_data_size()
            dataset.get_memory_data_size()
            dataset.get_shuffle_data_size()
            dataset = paddle.distributed.QueueDataset()
            try:
                dataset.local_shuffle()
            except:
                print("warning: catch expected error")
            try:
                dataset.global_shuffle()
            except:
                print("warning: catch expected error")
            dataset = paddle.distributed.fleet.FileInstantDataset()
            try:
                dataset.local_shuffle()
            except:
                print("warning: catch expected error")
            try:
                dataset.global_shuffle()
            except:
                print("warning: catch expected error")

        temp_dir.cleanup()

    def test_bosps_dataset_fleet2(self):
        """
        Testcase for InMemoryDataset from create to run.
        """
        temp_dir = tempfile.TemporaryDirectory()
        filename1 = os.path.join(
            temp_dir.name, "test_in_memory_dataset2_run2_a.txt"
        )
        filename2 = os.path.join(
            temp_dir.name, "test_in_memory_dataset2_run2_b.txt"
        )

        with open(filename1, "w") as f:
            data = "1 1 2 3 3 4 5 5 5 5 1 1\n"
            data += "1 2 2 3 4 4 6 6 6 6 1 2\n"
            data += "1 3 2 3 5 4 7 7 7 7 1 3\n"
            f.write(data)
        with open(filename2, "w") as f:
            data = "1 4 2 3 3 4 5 5 5 5 1 4\n"
            data += "1 5 2 3 4 4 6 6 6 6 1 5\n"
            data += "1 6 2 3 5 4 7 7 7 7 1 6\n"
            data += "1 7 2 3 6 4 8 8 8 8 1 7\n"
            f.write(data)

        train_program = fluid.Program()
        startup_program = fluid.Program()
        scope = fluid.Scope()
        from paddle.fluid.incubate.fleet.parameter_server.pslib import fleet

        with fluid.program_guard(train_program, startup_program):
            slots = ["slot1_ff", "slot2_ff", "slot3_ff", "slot4_ff"]
            slots_vars = []
            for slot in slots:
                var = paddle.static.data(
                    name=slot, shape=[-1, 1], dtype="float32", lod_level=1
                )
                slots_vars.append(var)
            fake_cost = paddle.subtract(slots_vars[0], slots_vars[-1])
            fake_cost = paddle.mean(fake_cost)
        with fluid.scope_guard(scope):
            place = fluid.CPUPlace()
            exe = fluid.Executor(place)
            try:
                fleet.init()
            except ImportError as e:
                print("warning: no mpi4py")
            adam = fluid.optimizer.Adam(learning_rate=0.000005)
            try:
                adam = fleet.distributed_optimizer(
                    adam,
                    strategy={
                        "fs_uri": "fs_uri_xxx",
                        "fs_user": "fs_user_xxx",
                        "fs_passwd": "fs_passwd_xxx",
                        "fs_hadoop_bin": "fs_hadoop_bin_xxx",
                    },
                )
                adam.minimize([fake_cost], [scope])
            except AttributeError as e:
                print("warning: no mpi")
            except ImportError as e:
                print("warning: no mpi4py")
            exe.run(startup_program)
            dataset = paddle.distributed.fleet.BoxPSDataset()
            dataset.init(
                batch_size=32,
                thread_num=3,
                pipe_command="cat",
                use_var=slots_vars,
            )
            dataset.set_filelist([filename1, filename2])
            dataset.load_into_memory()
            try:
                dataset.global_shuffle(fleet)
            except:
                print("warning: catch expected error")
            fleet._opt_info = None
            fleet._fleet_ptr = None
            dataset = paddle.distributed.fleet.BoxPSDataset()
            dataset.init(
                rank_offset="",
                pv_batch_size=1,
                fs_name="",
                fs_ugi="",
                data_feed_type="MultiSlotInMemoryDataFeed",
                parse_logkey=True,
                merge_by_sid=True,
                enable_pv_merge=True,
            )
            d = paddle.distributed.fleet.DatasetBase()
            try:
                dataset._set_feed_type("MultiSlotInMemoryDataFeed")
            except:
                print("warning: catch expected error")
            dataset.thread_num = 0
            try:
                dataset._prepare_to_run()
            except:
                print("warning: catch expected error")
            dataset._set_parse_logkey(True)
            dataset._set_merge_by_sid(True)
            dataset._set_enable_pv_merge(True)
            try:
                dataset.preprocess_instance()
            except:
                print("warning: catch expected error")
            try:
                dataset.set_current_phase(1)
            except:
                print("warning: catch expected error")
            try:
                dataset.postprocess_instance()
            except:
                print("warning: catch expected error")
            dataset._set_fleet_send_batch_size(1024)
            try:
                dataset.global_shuffle()
            except:
                print("warning: catch expected error")
            # dataset.get_pv_data_size()
            dataset.get_memory_data_size()
            dataset.get_shuffle_data_size()
        temp_dir.cleanup()


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