# -*- 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 os import shutil import tempfile 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.fluid.framework import _test_eager_guard 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 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.Momentum( parameters=[{ "params": list(model.parameters()) }] if opt_group else list(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, batch_size=100, opt_group=False, sync_comm=False, test_minimize=False, save_model=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) elif sharding_stage == 3: model = ShardingStage3( model, optimizer=optimizer, group=group, sync_comm=sync_comm) # check optimizer.minimize() error if test_minimize: try: optimizer.minimize() except: print( "====== Find sharding_stage3_optimizer.minimize() error ======") return 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 batch_size == 20: avg_loss = avg_loss / 5 if not use_pure_fp16: avg_loss.backward() else: scaler.scale(avg_loss).backward() if not accumulate_grad: if not use_pure_fp16: optimizer.step() else: scaler.step(optimizer) scaler.update() optimizer.clear_grad() if accumulate_grad: if not use_pure_fp16: optimizer.step() else: scaler.step(optimizer) scaler.update() optimizer.clear_grad() if sharding_stage == 3: model.get_all_parameters() if save_model: return model, optimizer return model.parameters() def test_stage2_stage3(): mlp, mlp1, mlp2, mlp3, mlp4, mlp5, mlp6, mlp7, mlp8, mlp9, mlp10 = MLP( ), MLP(), 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) mlp9.set_state_dict(state_dict) mlp10.set_state_dict(state_dict) # fp32 stage2_params = train_mlp( mlp1, sharding_stage=2, use_pure_fp16=False, opt_group=False) stage3_params = train_mlp( mlp2, sharding_stage=3, use_pure_fp16=False, opt_group=False) for i in range(len(stage2_params)): np.testing.assert_allclose( stage2_params[i].numpy(), stage3_params[i].numpy(), rtol=1e-6, atol=1e-6) # fp32 accumulate grad stage3_params = train_mlp( mlp3, sharding_stage=3, use_pure_fp16=False, accumulate_grad=True, opt_group=True) stage3_params_add = train_mlp( mlp4, sharding_stage=3, use_pure_fp16=False, accumulate_grad=True, batch_size=20, opt_group=True) for i in range(len(stage3_params)): np.testing.assert_allclose( stage3_params[i].numpy(), stage3_params_add[i].numpy(), rtol=1e-6, atol=1e-4) # 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)): np.testing.assert_allclose( stage2_params[i].numpy(), stage3_params[i].numpy(), rtol=1e-4, atol=1e-3) # fp16 sync_comm 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, sync_comm=True) for i in range(len(stage3_params)): np.testing.assert_allclose( stage3_params[i].numpy(), stage3_params_re[i].numpy(), rtol=1e-6) # save/load model output_dir = tempfile.mkdtemp() model_file = os.path.join(output_dir, "model.pdmodel") optimizer_file = os.path.join(output_dir, "model.pdopt") model_stage3, optimizer_stage3 = train_mlp( mlp9, sharding_stage=3, use_pure_fp16=False, opt_group=False, save_model=True) paddle.save(model_stage3.state_dict(), model_file) paddle.save(optimizer_stage3.state_dict(), optimizer_file) m_state_dict = paddle.load(model_file) opt_state_dict = paddle.load(optimizer_file) model_stage3.set_state_dict(m_state_dict) optimizer_stage3.set_state_dict(opt_state_dict) shutil.rmtree(output_dir) # check optimizer.minimize() error train_mlp( mlp10, sharding_stage=3, use_pure_fp16=False, opt_group=False, test_minimize=True) if __name__ == '__main__': with _test_eager_guard(): pass test_stage2_stage3()