engine_api.py

# Copyright (c) 2022 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 tempfile
import os
import numpy as np
import paddle
import paddle.static as static
import paddle.utils as utils
import paddle.nn as nn
import paddle.nn.functional as F
from paddle.io import Dataset

from paddle.distributed.fleet import auto

paddle.enable_static()

global_process_mesh = auto.ProcessMesh(mesh=[0, 1])
PP_MESH_0 = auto.ProcessMesh([0])
PP_MESH_1 = auto.ProcessMesh([1])
epoch_num = 1
batch_size = 2
batch_num = 10
hidden_size = 1024
sequence_len = 512
image_size = hidden_size
class_num = 10

paddle.seed(44)

is_fetch = True
is_feed = True
my_feed_vars = []


class MyDataset(Dataset):
    def __init__(self, num_samples):
        super(MyDataset, self).__init__()
        self.num_samples = num_samples

    def __getitem__(self, index):
        input = np.random.uniform(size=image_size).astype("float32")
        label = np.random.randint(0, class_num - 1, dtype="int64")
        return input, label

    def __len__(self):
        return self.num_samples


def get_random_inputs_and_labels(image_shape, label_shape):
    input = np.random.random(size=image_shape).astype('float32')
    label = np.random.random(size=label_shape).astype('int64')
    return input, label


def batch_generator_creator():
    def __reader__():
        for _ in range(batch_num):
            batch_input, batch_label = get_random_inputs_and_labels(
                [batch_size, image_size], [batch_size, 1]
            )
            yield batch_input, batch_label

    return __reader__


class MLPLayer(nn.Layer):
    def __init__(
        self,
        hidden_size=1024,
        intermediate_size=4 * 1024,
        dropout_ratio=0.1,
        initializer_range=0.02,
    ):
        super(MLPLayer, self).__init__()
        d_model = hidden_size
        dim_feedforward = intermediate_size
        weight_attr = paddle.ParamAttr(
            initializer=nn.initializer.Normal(mean=0.0, std=initializer_range)
        )
        bias_attr = None

        self.linear0 = nn.Linear(
            d_model, dim_feedforward, weight_attr, bias_attr=bias_attr
        )
        self.linear1 = nn.Linear(
            dim_feedforward, d_model, weight_attr, bias_attr=bias_attr
        )
        self.linear2 = nn.Linear(d_model, 1, weight_attr, bias_attr=bias_attr)
        self.norm = nn.LayerNorm(d_model, epsilon=1e-5)
        self.dropout = nn.Dropout(dropout_ratio, mode="upscale_in_train")

    def forward(self, input):
        out = auto.shard_op(self.norm, PP_MESH_0)(input)
        out = self.linear0(out)
        if is_feed:
            my_feed_vars.append((out, out.shape))
        out = F.gelu(out, approximate=True)
        out = auto.shard_op(self.linear1, PP_MESH_1)(out)
        out = self.dropout(out)
        out = self.linear2(out)
        if is_feed:
            my_feed_vars.append((out, out.shape))
        if is_fetch:
            auto.fetch(out, "my_fetch", logging=True)
        return out


def train_high_level(fetch):
    global is_fetch
    is_fetch = fetch
    mlp = MLPLayer(
        hidden_size=hidden_size,
        intermediate_size=4 * hidden_size,
        dropout_ratio=0.1,
        initializer_range=0.02,
    )
    loss = paddle.nn.CrossEntropyLoss()
    optimizer = paddle.optimizer.Adam(
        learning_rate=0.00001,
        beta1=0.9,
        beta2=0.999,
        epsilon=1e-08,
        grad_clip=None,
    )
    metric = paddle.metric.Accuracy()

    strategy = auto.Strategy()
    strategy.auto_mode = "semi"

    engine = auto.Engine(mlp, loss, optimizer, metric, strategy=strategy)

    # train
    train_dataset = MyDataset(batch_num * batch_size)
    eval_dataset1 = MyDataset(5 * batch_size)

    history = engine.fit(
        train_data=train_dataset,
        epochs=2,
        batch_size=batch_size,
        valid_data=eval_dataset1,
        log_freq=1,
    )

    # eval
    eval_dataset2 = MyDataset(batch_size)
    engine.evaluate(eval_dataset2, batch_size=batch_size)

    # predict
    test_dataset = MyDataset(batch_size)
    outputs = engine.predict(test_dataset, batch_size=batch_size)

    # save
    temp_dir = tempfile.TemporaryDirectory()
    model_filename = os.path.join(temp_dir.name, 'mlp')
    engine.save(model_filename, training=True)
    engine.load(model_filename)
    temp_dir.cleanup()


def train_low_level():
    mlp = MLPLayer(
        hidden_size=hidden_size,
        intermediate_size=4 * hidden_size,
        dropout_ratio=0.1,
        initializer_range=0.02,
    )
    loss = paddle.nn.CrossEntropyLoss()
    optimizer = paddle.optimizer.Adam(
        learning_rate=0.00001,
        beta1=0.9,
        beta2=0.999,
        epsilon=1e-08,
        grad_clip=None,
    )
    metric = paddle.metric.Accuracy()

    strategy = auto.Strategy()
    strategy.auto_mode = "semi"

    engine = auto.Engine(mlp, loss, optimizer, metrics=None, strategy=strategy)

    feed_dict = {}
    for feed_var, shape in my_feed_vars:
        feed_dict[feed_var.name] = np.zeros(shape, dtype="float32")

    # Build normal normal dataloader
    # train
    train_dataset = MyDataset(batch_num * batch_size)
    train_dataloader = engine.dataloader(
        train_dataset, batch_size=batch_size, mode="train"
    )
    engine.prepare(mode="train")
    for data in train_dataloader:
        outs = engine.run(data, feed=feed_dict, mode="train")

    # eval
    eval_dataset2 = MyDataset(batch_size)
    eval_dataloader = engine.dataloader(
        eval_dataset2, batch_size=batch_size, mode="eval"
    )
    engine.prepare(mode="eval")
    for data in eval_dataloader:
        outs = engine.run(data, feed=feed_dict, mode="eval")

    # predict
    engine.to_mode("predict")
    test_dataset = MyDataset(batch_size)
    predict_dataloader = engine.dataloader(test_dataset, batch_size=batch_size)
    engine.prepare()
    for data in predict_dataloader:
        outs = engine.run(data, feed=feed_dict)

    # save
    temp_dir = tempfile.TemporaryDirectory()
    model_filename = os.path.join(temp_dir.name, 'mlp')
    engine.save(model_filename, training=True)
    engine.load(model_filename)
    temp_dir.cleanup()

    # Build dataloader from generator
    # train
    train_dataset = MyDataset(batch_num * batch_size)
    train_dataloader = engine.dataloader_from_generator(
        train_dataset, batch_size=batch_size, mode="train"
    )
    engine.prepare(mode="train")
    for data in train_dataloader:
        outs = engine.run(data, feed=feed_dict, mode="train")

    # eval
    engine.to_mode("eval")
    eval_dataset2 = MyDataset(batch_size)
    eval_dataloader = engine.dataloader_from_generator(
        eval_dataset2, batch_size=batch_size
    )
    engine.prepare()
    for data in eval_dataloader:
        outs = engine.run(data, feed=feed_dict)

    # predict
    test_dataset = MyDataset(batch_size)
    predict_dataloader = engine.dataloader_from_generator(
        test_dataset, batch_size=batch_size, mode="predict"
    )
    engine.prepare(mode="predict")
    for data in predict_dataloader:
        outs = engine.run(data, feed=feed_dict, mode="predict")

    # save
    temp_dir = tempfile.TemporaryDirectory()
    model_filename = os.path.join(temp_dir.name, 'mlp')
    engine.save(model_filename, training=True)
    engine.load(model_filename)
    temp_dir.cleanup()


def train_builtin_data_vars():
    mlp = MLPLayer(
        hidden_size=hidden_size,
        intermediate_size=4 * hidden_size,
        dropout_ratio=0.1,
        initializer_range=0.02,
    )
    loss = paddle.nn.CrossEntropyLoss()
    optimizer = paddle.optimizer.Adam(
        learning_rate=0.00001,
        beta1=0.9,
        beta2=0.999,
        epsilon=1e-08,
        grad_clip=None,
    )
    metric = paddle.metric.Accuracy()

    strategy = auto.Strategy()
    strategy.auto_mode = "semi"

    engine = auto.Engine(mlp, loss, optimizer, metric, strategy=strategy)

    # train
    engine.to_mode("train")

    input_spec = static.InputSpec([batch_size, image_size], 'float32', 'input')
    label_spec = static.InputSpec([batch_size, 1], 'int64', 'label')
    engine.prepare(inputs_spec=[input_spec], labels_spec=[label_spec])

    with static.program_guard(engine.main_program, engine.startup_program):
        feed_list = engine.inputs + engine.labels
        print(feed_list)
        loader = paddle.io.DataLoader.from_generator(
            feed_list=feed_list, capacity=4 * batch_size, iterable=False
        )

        places = static.cuda_places()
        loader.set_batch_generator(batch_generator_creator(), places=places)

    for _ in range(epoch_num):
        loader.start()  # call DataLoader.start() before each epoch starts
        try:
            while True:
                engine.run()
        except paddle.fluid.core.EOFException:
            loader.reset()  # call DataLoader.reset() after catching EOFException


def train_non_builtin_data_vars():
    main_program = static.Program()
    startup_program = static.Program()
    with static.program_guard(
        main_program, startup_program
    ), utils.unique_name.guard():
        input = static.data(
            name="input", shape=[batch_size, image_size], dtype='float32'
        )
        label = static.data(name="label", shape=[batch_size, 1], dtype='int64')

        loader = paddle.io.DataLoader.from_generator(
            feed_list=[input, label], capacity=4 * batch_size, iterable=False
        )
        places = static.cuda_places()
        loader.set_batch_generator(batch_generator_creator(), places=places)

        mlp = MLPLayer(
            hidden_size=hidden_size,
            intermediate_size=4 * hidden_size,
            dropout_ratio=0.1,
            initializer_range=0.02,
        )
        loss = paddle.nn.CrossEntropyLoss()
        optimizer = paddle.optimizer.Adam(
            learning_rate=0.00001,
            beta1=0.9,
            beta2=0.999,
            epsilon=1e-08,
            grad_clip=None,
        )
        metric = paddle.metric.Accuracy()
        predict = mlp(input)
        loss_var = loss(predict, label)

    strategy = auto.Strategy()
    strategy.auto_mode = "semi"

    engine = auto.Engine(
        loss=loss_var, optimizer=optimizer, metrics=metric, strategy=strategy
    )

    # train
    engine.to_mode("train")
    engine.prepare(
        inputs=[input],
        labels=[label],
        main_program=main_program,
        startup_program=startup_program,
    )
    for _ in range(epoch_num):
        loader.start()  # call DataLoader.start() before each epoch starts
        try:
            while True:
                engine.run()
        except paddle.fluid.core.EOFException:
            loader.reset()  # call DataLoader.reset() after catching EOFException


def get_cost():
    main_program = static.default_main_program()
    startup_program = static.default_startup_program()
    with static.program_guard(
        main_program, startup_program
    ), utils.unique_name.guard():
        input = static.data(
            name="input", shape=[batch_size, image_size], dtype='float32'
        )
        label = static.data(name="label", shape=[batch_size, 1], dtype='int64')

        loader = paddle.io.DataLoader.from_generator(
            feed_list=[input, label], capacity=4 * batch_size, iterable=False
        )
        places = static.cuda_places()
        loader.set_batch_generator(batch_generator_creator(), places=places)

        mlp = MLPLayer(
            hidden_size=hidden_size,
            intermediate_size=4 * hidden_size,
            dropout_ratio=0.1,
            initializer_range=0.02,
        )
        loss = paddle.nn.CrossEntropyLoss()
        optimizer = paddle.optimizer.Adam(
            learning_rate=0.00001,
            beta1=0.9,
            beta2=0.999,
            epsilon=1e-08,
            grad_clip=None,
        )
        metric = paddle.metric.Accuracy()
        predict = mlp(input)
        loss_var = loss(predict, label)

    strategy = auto.Strategy()
    strategy.auto_mode = "semi"

    engine = auto.Engine(
        loss=loss_var, optimizer=optimizer, metrics=metric, strategy=strategy
    )
    engine.cost()


def get_cost_by_spec():
    mlp = MLPLayer(
        hidden_size=hidden_size,
        intermediate_size=4 * hidden_size,
        dropout_ratio=0.1,
        initializer_range=0.02,
    )
    loss = paddle.nn.CrossEntropyLoss()
    optimizer = paddle.optimizer.Adam(
        learning_rate=0.00001,
        beta1=0.9,
        beta2=0.999,
        epsilon=1e-08,
        grad_clip=None,
    )
    metric = paddle.metric.Accuracy()

    strategy = auto.Strategy()
    strategy.auto_mode = "semi"

    engine = auto.Engine(mlp, loss, optimizer, metric, strategy=strategy)

    input_spec = static.InputSpec([batch_size, image_size], 'float32', 'input')
    label_spec = static.InputSpec([batch_size, 1], 'int64', 'label')
    engine.cost(mode="eval", inputs_spec=[input_spec], labels_spec=[label_spec])


if __name__ == "__main__":
    train_high_level(fetch=True)
    train_high_level(fetch=False)
    train_low_level()
    train_builtin_data_vars()
    train_non_builtin_data_vars()
    get_cost()
    get_cost_by_spec()