From f711d3070a6d2c59ebfba7c23982af1226a256e9 Mon Sep 17 00:00:00 2001 From: yunfan Date: Sun, 6 Dec 2020 03:48:08 +0000 Subject: [PATCH] dist_trainer for fp16 --- fastNLP/core/dist_trainer.py | 76 +++++++++++++++++---------------- tests/core/test_dist_trainer.py | 10 +++-- 2 files changed, 47 insertions(+), 39 deletions(-) diff --git a/fastNLP/core/dist_trainer.py b/fastNLP/core/dist_trainer.py index 2f6dffbb..35970581 100644 --- a/fastNLP/core/dist_trainer.py +++ b/fastNLP/core/dist_trainer.py @@ -29,14 +29,10 @@ from .dataset import DataSet from .losses import _prepare_losser from .optimizer import Optimizer from .utils import _build_args +from .utils import _build_fp16_env from .utils import _get_func_signature from .utils import _move_dict_value_to_device -try: - from apex import amp -except: - amp = None - __all__ = [ 'get_local_rank', 'DistTrainer', @@ -72,7 +68,7 @@ class DistTrainer(): dev_data=None, metrics=None, metric_key=None, update_every=1, print_every=10, validate_every=-1, save_path=None, device='auto', - fp16='', use_tqdm=True, **kwargs): + fp16=False, use_tqdm=True, **kwargs): r""" :param train_data: 训练集， :class:`~fastNLP.DataSet` 类型。 @@ -103,12 +99,15 @@ class DistTrainer(): :param str,None save_path: 将模型保存路径，如果路径不存在，将自动创建文件夹。如果为None，则不保存模型。如果dev_data为None，则保存 最后一次迭代的模型。保存的时候不仅保存了参数，还保存了模型结构。即便使用DataParallel，这里也只保存模型。 :param str device: 指定 device，可以是 gpu，cpu 或 auto - :param str fp16: 指定半精度训练的优化等级，可为 O1，O2 或 O3，若为空字符串则不使用半精度。 + :param bool fp16: 指定是否使用半精度训练。 :param bool use_tqdm: 是否使用tqdm来显示训练进度; 如果为False，则将loss打印在终端中。 :param kwargs: 支持配置可选参数 bool test_use_tqdm: 在dev上验证的时候是否开启tqdm Sampler test_sampler: 在evaluate的时候使用的sampler int dev_batch_size: 在evaluate时，使用的evaluate的batch大小 + bool test_use_fp16: test时使用fp16 + bool set_grad_to_none: zero_grad时将grad设为None而不是0 + GradScaler gradscaler: 自定义的梯度 scaler """ assert device in ['auto', 'cuda', 'cpu'], "Please set correct device in [auto', 'cuda', 'cpu']" if device == 'auto': @@ -147,14 +146,19 @@ class DistTrainer(): self.use_tqdm = use_tqdm model.to(self.device) - optimizer = self._get_optimizer(optimizer) # init fp16, must before DataParallel init - if len(self.fp16): - assert isinstance(self.fp16, str), "Please set Apex AMP optimization level selected in ['O0', 'O1', 'O2', 'O3']" - _check_fp16() - assert device == 'cuda', "Amp requires cuda device" - model, optimizer = amp.initialize(model, optimizer, opt_level=self.fp16) + autocast, GradScaler = _build_fp16_env(dummy=not self.fp16) + self.auto_cast = autocast + user_grad_scaler = getattr(kwargs, 'gradscaler', None) + if user_grad_scaler is not None: + assert self.fp16, "must set fp16=True to enable gradscaler" + grad_scaler = user_grad_scaler + else: + grad_scaler = GradScaler() + self.grad_scaler = grad_scaler + + self.set_grad_to_none = getattr(kwargs, 'set_grad_to_none', True) # init DataParallel if parse_version(torch.__version__)>=parse_version('1.1'): @@ -165,6 +169,7 @@ class DistTrainer(): output_device=self.local_rank) self.model = self.ddp_model.module + optimizer = self._get_optimizer(optimizer) self.optimizer = optimizer if isinstance(self.train_data, DataSet): self.sampler = DistributedSampler(self.train_data) @@ -197,11 +202,9 @@ class DistTrainer(): self.logger = logger self.logger.info("Setup Distributed Trainer") self.logger.warning("Process pid: {}, rank: {}, local rank: {}, device: {}, fp16: {}".format( - os.getpid(), self.rank, self.local_rank, self.device, self.fp16 if self.fp16 else False)) + os.getpid(), self.rank, self.local_rank, self.device, self.fp16)) self.logger.info("Num of processes: {}".format(self.world_size)) self.logger.info("Use device: {}".format(device)) - self.logger.info("Training with fp16: {}, optimization level: {}".format( - len(self.fp16) > 0, self.fp16 if self.fp16 else None)) def _maybe_no_sync(self): """ @@ -343,28 +346,20 @@ class DistTrainer(): indices = data_iterator.get_batch_indices() # negative sampling; replace unknown; re-weight batch_y self.callback_manager.on_batch_begin(batch_x, batch_y, indices) - prediction = self._data_forward(self.ddp_model, batch_x) + with self.auto_cast(): + prediction = self._data_forward(self.ddp_model, batch_x) + # edit prediction + self.callback_manager.on_loss_begin(batch_y, prediction) + loss = self._compute_loss(prediction, batch_y) - # edit prediction - self.callback_manager.on_loss_begin(batch_y, prediction) - loss = self._compute_loss(prediction, batch_y) - if self.update_every > 1: - loss = loss / self.update_every - avg_loss += loss.item() + avg_loss += loss.detach() # Is loss NaN or inf? requires_grad = False self.callback_manager.on_backward_begin(loss) - - # with self._maybe_no_sync(): - if self.fp16: - with amp.scale_loss(loss, self.optimizer) as scale_loss: - scale_loss.backward() - else: - loss.backward() - + self.grad_scaler.scale(loss).backward() self.callback_manager.on_backward_end() - - self._update() + if self.step % self.update_every == 0: + self._update() self.callback_manager.on_step_end() if self.step % self.print_every == 0: @@ -390,13 +385,22 @@ class DistTrainer(): self.pbar = None # ============ tqdm end ============== # + def _clear_grad_opt(self, optimizer): + if self.set_grad_to_none: + for group in optimizer.param_groups: + for p in group['params']: + if p.grad is not None: + p.grad = None + else: + optimizer.zero_grad() + def _update(self): r"""Perform weight update on a model. """ - if self.step % self.update_every == 0: - self.optimizer.step() - self.ddp_model.zero_grad() + self.grad_scaler.step(self.optimizer) + self.grad_scaler.update() + self._clear_grad_opt(self.optimizer) def _data_forward(self, network, x): x = _build_args(self._forward_func, **x) diff --git a/tests/core/test_dist_trainer.py b/tests/core/test_dist_trainer.py index d2a11a76..4311277a 100644 --- a/tests/core/test_dist_trainer.py +++ b/tests/core/test_dist_trainer.py @@ -6,13 +6,14 @@ from argparse import ArgumentParser import numpy as np import torch.cuda +import torch.distributed as dist from fastNLP import AccuracyMetric from fastNLP import CrossEntropyLoss, BCELoss from fastNLP import DataSet from fastNLP import Instance from fastNLP import SGD -from fastNLP.core.callback import EchoCallback +from fastNLP.core.callback import EchoCallback, GradientClipCallback from fastNLP.core.dist_trainer import DistTrainer, get_local_rank from fastNLP.models.base_model import NaiveClassifier @@ -103,7 +104,7 @@ class TestDistTrainer(unittest.TestCase): model=model, train_data=data_set, optimizer=SGD(lr=0.1), loss=CrossEntropyLoss(pred="predict", target="y"), batch_size_per_gpu=8, n_epochs=3, print_every=50, save_path=self.save_path, - fp16='O1' + fp16=True ) trainer.train() """ @@ -113,18 +114,20 @@ class TestDistTrainer(unittest.TestCase): shutil.rmtree(self.save_path) def run3(self): + # test callbacks, especially clip-norm set_rng_seed(100) data_set, model = prepare_env() trainer = DistTrainer( data_set, model, optimizer=None, loss=BCELoss(pred="predict", target="y"), n_epochs=3, print_every=50, - callbacks_all=[EchoCallback('callbacks_all')], + callbacks_all=[GradientClipCallback()], callbacks_master=[EchoCallback('callbacks_master')] ) trainer.train() def run4(self): + # test metrics, save, and others set_rng_seed(100) data_set, model = prepare_env() @@ -173,4 +176,5 @@ if __name__ == '__main__': parser.add_argument('--test', type=int) args, _ = parser.parse_known_args() if args.test and hasattr(runner, 'run%s' % args.test): + dist.init_process_group("nccl") getattr(runner, 'run%s' % args.test)()