dygraph_sharding_stage3.py

# -*- coding: UTF-8 -*-

# Copyright (c) 2021 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 numpy as np
import argparse
import ast
import time
import paddle
import paddle.fluid as fluid
from paddle.fluid.dygraph.nn import Linear
from paddle.distributed import fleet
from paddle.fluid.dygraph import nn

from paddle.distributed.fleet.meta_optimizers.dygraph_optimizer.sharding_optimizer_stage2 import ShardingOptimizerStage2
from paddle.distributed.fleet.meta_parallel.sharding.sharding_stage2 import ShardingStage2
from paddle.distributed.fleet.meta_parallel.sharding.sharding_stage3 import ShardingStage3
from paddle.distributed.fleet.meta_parallel.sharding.sharding_utils import ShardingScaler

epoch = 10
batch_size = 32
paddle.seed(2021)
np.random.seed(2021)
base_lr = 0.1
momentum_rate = 0.9
l2_decay = 1e-4
fleet.init(is_collective=True)


class MLP(fluid.Layer):
    def __init__(self, linear_size=1000, param_attr=None, bias_attr=None):
        super(MLP, self).__init__()

        self._linear1 = Linear(linear_size, linear_size)
        self._linear2 = Linear(linear_size, linear_size)
        self._linear3 = Linear(linear_size, 10)

    def forward(self, inputs):
        y = self._linear1(inputs)
        y = self._linear2(y)
        y = self._linear3(y)
        return y


def reader_decorator(linear_size=1000):
    def __reader__():
        for _ in range(100):
            img = np.random.rand(linear_size).astype('float32')
            label = np.ones(1).astype('int64')
            yield img, label

    return __reader__


def optimizer_setting(model, use_pure_fp16, opt_group=False):
    clip = paddle.nn.ClipGradByGlobalNorm(clip_norm=1.0)
    optimizer = paddle.optimizer.AdamW(
        parameters=[{
            "params": model.parameters()
        }] if opt_group else model.parameters(),
        learning_rate=0.001,
        weight_decay=0.00001,
        grad_clip=clip,
        multi_precision=use_pure_fp16)

    return optimizer


def train_mlp(model,
              sharding_stage,
              use_pure_fp16=False,
              accumulate_grad=False,
              opt_group=False,
              recompute=False):
    group = paddle.distributed.new_group([0, 1])
    if opt_group:
        optimizer = optimizer_setting(
            model=model, use_pure_fp16=use_pure_fp16, opt_group=opt_group)
    else:
        optimizer = optimizer_setting(model=model, use_pure_fp16=use_pure_fp16)

    if use_pure_fp16:
        model = paddle.amp.decorate(
            models=model, level='O2', save_dtype='float32')
        scaler = paddle.amp.GradScaler(init_loss_scaling=32768)
        scaler = ShardingScaler(scaler)
    if sharding_stage == 2:
        optimizer = ShardingOptimizerStage2(
            params=model.parameters(), optim=optimizer, group=group)
        model = ShardingStage2(
            model,
            optimizer,
            group=group,
            buffer_max_size=2**21,
            accumulate_grads=accumulate_grad)
    elif sharding_stage == 3:
        model = ShardingStage3(
            model, optimizer=optimizer, group=group, sync_comm=recompute)

    train_reader = paddle.batch(
        reader_decorator(), batch_size=batch_size, drop_last=True)

    train_loader = paddle.io.DataLoader.from_generator(
        capacity=32,
        use_double_buffer=True,
        iterable=True,
        return_list=True,
        use_multiprocess=True)
    train_loader.set_sample_list_generator(train_reader)

    for eop in range(epoch):
        model.train()
        for batch_id, data in enumerate(train_loader()):
            img, label = data
            label.stop_gradient = True
            img.stop_gradient = True
            with paddle.amp.auto_cast(True, level='O2'):
                out = model(img)
                loss = paddle.nn.functional.cross_entropy(
                    input=out, label=label)
            avg_loss = paddle.mean(x=loss.cast(dtype=paddle.float32))
            if not accumulate_grad:
                if not use_pure_fp16:
                    avg_loss.backward()
                    optimizer.step()
                else:
                    scaler.scale(avg_loss).backward()
                    scaler.step(optimizer)
                    scaler.update()
                optimizer.clear_grad()
        if accumulate_grad:
            if not use_pure_fp16:
                avg_loss.backward()
                optimizer.step()
            else:
                scaler.scale(avg_loss).backward()
                scaler.step(optimizer)
                scaler.update()
            optimizer.clear_grad()
    if sharding_stage == 3:
        model.get_all_parameters()
    return model.parameters()


def test_stage2_stage3():
    mlp, mlp1, mlp2, mlp3, mlp4, mlp5, mlp6, mlp7, mlp8 = MLP(), MLP(), MLP(
    ), MLP(), MLP(), MLP(), MLP(), MLP(), MLP()
    state_dict = mlp.state_dict()
    mlp1.set_state_dict(state_dict)
    mlp2.set_state_dict(state_dict)
    mlp3.set_state_dict(state_dict)
    mlp4.set_state_dict(state_dict)
    mlp5.set_state_dict(state_dict)
    mlp6.set_state_dict(state_dict)
    mlp7.set_state_dict(state_dict)
    mlp8.set_state_dict(state_dict)
    # fp32 
    stage2_params = train_mlp(
        mlp1, sharding_stage=2, use_pure_fp16=False, opt_group=True)
    stage3_params = train_mlp(
        mlp2, sharding_stage=3, use_pure_fp16=False, opt_group=True)
    for i in range(len(stage2_params)):
        for j in range(len(stage3_params)):
            if stage2_params[i].name == stage3_params[j].name:
                np.testing.assert_allclose(
                    stage2_params[i].numpy(),
                    stage3_params[j].numpy(),
                    rtol=1e-6)
    # fp32 accumulate grad
    stage2_params = train_mlp(
        mlp3,
        sharding_stage=2,
        use_pure_fp16=False,
        accumulate_grad=True,
        opt_group=True)
    stage3_params = train_mlp(
        mlp4,
        sharding_stage=3,
        use_pure_fp16=False,
        accumulate_grad=True,
        opt_group=True)
    for i in range(len(stage2_params)):
        for j in range(len(stage3_params)):
            if stage2_params[i].name == stage3_params[j].name:
                np.testing.assert_allclose(
                    stage2_params[i].numpy(),
                    stage3_params[j].numpy(),
                    rtol=1e-6)
    # fp16
    stage2_params = train_mlp(
        mlp5, sharding_stage=2, use_pure_fp16=True, opt_group=False)
    stage3_params = train_mlp(
        mlp6, sharding_stage=3, use_pure_fp16=True, opt_group=False)
    for i in range(len(stage2_params)):
        for j in range(len(stage3_params)):
            if stage2_params[i].name == stage3_params[j].name:
                np.testing.assert_allclose(
                    stage2_params[i].numpy(),
                    stage3_params[j].numpy(),
                    rtol=1e-6)
    # fp16 recompute
    stage3_params = train_mlp(
        mlp7, sharding_stage=3, use_pure_fp16=True, opt_group=False)
    stage3_params_re = train_mlp(
        mlp8,
        sharding_stage=3,
        use_pure_fp16=True,
        opt_group=False,
        recompute=True)
    for i in range(len(stage3_params)):
        for j in range(len(stage3_params_re)):
            if stage3_params[i].name == stage3_params_re[j].name:
                np.testing.assert_allclose(
                    stage3_params[i].numpy(),
                    stage3_params_re[j].numpy(),
                    rtol=1e-6)
    return


if __name__ == '__main__':
    test_stage2_stage3()