| from .torch_core import * |
| from .basic_train import Learner,LearnerCallback |
| from torch.nn.parallel import DistributedDataParallel, DataParallel |
| from torch.utils.data.distributed import DistributedSampler |
|
|
| from fastai.text import TextLMDataBunch |
|
|
| __all__ = ['DistributedRecorder', 'DistributedTrainer', 'read_metrics', 'setup_distrib'] |
|
|
| def rnn_reset(self): |
| if hasattr(self.module, 'reset'): self.module.reset() |
| DistributedDataParallel.reset = rnn_reset |
|
|
| class ParallelTrainer(LearnerCallback): |
| _order = -20 |
| def on_train_begin(self, **kwargs): self.learn.model = DataParallel(self.learn.model) |
| def on_train_end (self, **kwargs): self.learn.model = self.learn.model.module |
|
|
| class DistributedTrainer(LearnerCallback): |
| _order = -20 |
| def __init__(self, learn:Learner, cuda_id:int=0): |
| super().__init__(learn) |
| self.cuda_id,self.train_sampler = cuda_id,None |
|
|
| def _change_dl(self, dl, shuffle): |
| old_dl = dl |
| sampler = OurDistributedSampler(dl.dataset, shuffle=shuffle) |
| new_dl = dl.new(shuffle=False, sampler=sampler) |
| return old_dl,new_dl,sampler |
|
|
| def on_train_begin(self, **kwargs): |
| self.learn.model = DistributedDataParallel(self.model, device_ids=[self.cuda_id], output_device=self.cuda_id) |
| shuffle = self.data.train_dl.init_kwargs['shuffle'] if hasattr(self.data.train_dl, 'init_kwargs') else True |
| self.old_train_dl,self.data.train_dl,self.train_sampler = self._change_dl(self.data.train_dl, shuffle) |
| if hasattr(self.data, 'valid_dl') and self.data.valid_dl is not None: |
| self.old_valid_dl,self.data.valid_dl,self.valid_sampler = self._change_dl(self.data.valid_dl, shuffle) |
| self.rank = rank_distrib() |
| self.recorder.silent = (self.rank != 0) |
|
|
| def on_epoch_begin(self, epoch, **kwargs): self.train_sampler.set_epoch(epoch) |
|
|
| def on_train_end(self, **kwargs): |
| self.learn.model = self.learn.model.module |
| self.learn.data.train_dl = self.old_train_dl |
| if hasattr(self.learn.data, 'valid_dl') and self.learn.data.valid_dl is not None: |
| self.learn.data.valid_dl = self.old_valid_dl |
|
|
| class DistributedRecorder(LearnerCallback): |
| def __init__(self, learn:Learner, cuda_id:int=0, cache_dir:PathOrStr='tmp'): |
| super().__init__(learn) |
| self.cuda_id,self.cache_dir = cuda_id,cache_dir |
|
|
| def on_train_begin(self, **kwargs): |
| os.makedirs(self.learn.path/self.cache_dir, exist_ok=True) |
|
|
| def on_epoch_end(self, **kwargs): self.save_stats() |
| def on_train_end(self, **kwargs): self.save_stats() |
|
|
| def save_stats(self): |
| cache_path,recorder = self.learn.path/self.cache_dir,self.learn.recorder |
| np.save(cache_path/f'losses_{self.cuda_id}', np.array(recorder.losses)) |
| stats = np.array([[v] + m for v,m in zip(recorder.val_losses,recorder.metrics)]) |
| np.save(cache_path/f'metrics_{self.cuda_id}', stats) |
|
|
| def _learner_parallel(learn:Learner): |
| "Use nn.DataParallel when training and remove when done" |
| if not torch.cuda.is_available(): warnings.warn('CUDA is not available, check your drivers - training will continue on CPU', ResourceWarning) |
| learn.callbacks.append(ParallelTrainer(learn)) |
| return learn |
|
|
| def _learner_distributed(learn:Learner, cuda_id:int, cache_dir:PathOrStr='tmp'): |
| "Put `learn` on distributed training with `cuda_id`." |
| learn.callbacks.append(DistributedTrainer(learn, cuda_id)) |
| learn.callbacks.append(DistributedRecorder(learn, cuda_id, cache_dir)) |
| return learn |
|
|
| Learner.to_distributed = _learner_distributed |
| Learner.to_parallel = _learner_parallel |
|
|
| def read_metrics(cache_path:PathOrStr, n_gpus:int, reduce:bool=True): |
| losses,metrics = [],[] |
| for i in range(n_gpus): |
| losses.append(np.load(cache_path/f'losses_{i}.npy')[None]) |
| metrics.append(np.load(cache_path/f'metrics_{i}.npy')[None]) |
| if reduce: |
| losses,metrics = np.concatenate(losses,0),np.concatenate(metrics,0) |
| return losses.mean(0),metrics.mean(0) |
| return losses,metrics |
|
|
| def setup_distrib(gpu:Any=None): |
| if gpu is None: return gpu |
| gpu = int(gpu) |
| torch.cuda.set_device(int(gpu)) |
| if num_distrib() > 1: |
| torch.distributed.init_process_group(backend='nccl', init_method='env://') |
| return gpu |
|
|
| class OurDistributedSampler(DistributedSampler): |
| "A sampler for language models with the option to not shuffle." |
| def __init__(self, dataset, num_replicas=None, rank=None, shuffle=True): |
| super().__init__(dataset, num_replicas=num_replicas, rank=rank) |
| self.shuffle = shuffle |
| |
| def __iter__(self): |
| if self.shuffle: |
| g = torch.Generator() |
| g.manual_seed(self.epoch) |
| indices = torch.randperm(len(self.dataset), generator=g).tolist() |
| else: indices = torch.arange(len(self.dataset)).tolist() |
|
|
| |
| indices += indices[:(self.total_size - len(indices))] |
| assert len(indices) == self.total_size |
|
|
| |
| indices = indices[self.rank:self.total_size:self.num_replicas] |
| assert len(indices) == self.num_samples |
|
|
| return iter(indices) |
|
|
