mirror of https://github.com/hpcaitech/ColossalAI
154 lines
6.6 KiB
Python
154 lines
6.6 KiB
Python
# coding=utf-8
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# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Dataloaders."""
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import torch
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import random
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from colossalai.core import global_context as gpc
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from colossalai.context import ParallelMode
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def build_pretraining_data_loader(dataset, consumed_samples, micro_batch_size, dataloader_type='single', num_workers=0):
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"""Build dataloader given an input dataset."""
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if dataset is None:
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return None
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# Megatron sampler
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if dataloader_type == 'single':
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batch_sampler = MegatronPretrainingSampler(total_samples=len(dataset),
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consumed_samples=consumed_samples,
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micro_batch_size=micro_batch_size,
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data_parallel_rank=gpc.get_local_rank(ParallelMode.DATA),
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data_parallel_size=gpc.get_world_size(ParallelMode.DATA))
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elif dataloader_type == 'cyclic':
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batch_sampler = MegatronPretrainingRandomSampler(total_samples=len(dataset),
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consumed_samples=consumed_samples,
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micro_batch_size=micro_batch_size,
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data_parallel_rank=gpc.get_local_rank(ParallelMode.DATA),
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data_parallel_size=gpc.get_world_size(ParallelMode.DATA))
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else:
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raise Exception('{} dataloader type is not supported.'.format(dataloader_type))
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# Torch dataloader.
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return torch.utils.data.DataLoader(dataset, batch_sampler=batch_sampler, num_workers=num_workers, pin_memory=True)
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class MegatronPretrainingSampler:
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def __init__(self,
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total_samples,
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consumed_samples,
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micro_batch_size,
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data_parallel_rank,
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data_parallel_size,
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drop_last=True):
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# Keep a copy of input params for later use.
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self.total_samples = total_samples
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self.consumed_samples = consumed_samples
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self.micro_batch_size = micro_batch_size
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self.data_parallel_rank = data_parallel_rank
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self.micro_batch_times_data_parallel_size = \
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self.micro_batch_size * data_parallel_size
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self.drop_last = drop_last
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# Sanity checks.
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assert self.total_samples > 0, \
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'no sample to consume: {}'.format(self.total_samples)
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assert self.consumed_samples < self.total_samples, \
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'no samples left to consume: {}, {}'.format(self.consumed_samples,
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self.total_samples)
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assert self.micro_batch_size > 0
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assert data_parallel_size > 0
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assert self.data_parallel_rank < data_parallel_size, \
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'data_parallel_rank should be smaller than data size: {}, ' \
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'{}'.format(self.data_parallel_rank, data_parallel_size)
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def __len__(self):
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return self.total_samples
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def get_start_end_idx(self):
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start_idx = self.data_parallel_rank * self.micro_batch_size
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end_idx = start_idx + self.micro_batch_size
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return start_idx, end_idx
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def __iter__(self):
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batch = []
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# Last batch will be dropped if drop_last is not set False
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for idx in range(self.consumed_samples, self.total_samples):
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batch.append(idx)
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if len(batch) == self.micro_batch_times_data_parallel_size:
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start_idx, end_idx = self.get_start_end_idx()
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yield batch[start_idx:end_idx]
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batch = []
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# Check the last partial batch and see drop_last is set
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if len(batch) > 0 and not self.drop_last:
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start_idx, end_idx = self.get_start_end_idx()
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yield batch[start_idx:end_idx]
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class MegatronPretrainingRandomSampler:
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def __init__(self, total_samples, consumed_samples, micro_batch_size, data_parallel_rank, data_parallel_size):
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# Keep a copy of input params for later use.
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self.total_samples = total_samples
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self.consumed_samples = consumed_samples
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self.micro_batch_size = micro_batch_size
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self.data_parallel_rank = data_parallel_rank
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self.data_parallel_size = data_parallel_size
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self.micro_batch_times_data_parallel_size = \
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self.micro_batch_size * data_parallel_size
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self.last_batch_size = \
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self.total_samples % self.micro_batch_times_data_parallel_size
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# Sanity checks.
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assert self.total_samples > 0, \
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'no sample to consume: {}'.format(self.total_samples)
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assert self.micro_batch_size > 0
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assert data_parallel_size > 0
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assert self.data_parallel_rank < data_parallel_size, \
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'data_parallel_rank should be smaller than data size: {}, ' \
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'{}'.format(self.data_parallel_rank, data_parallel_size)
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def __len__(self):
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return self.total_samples
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def __iter__(self):
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active_total_samples = self.total_samples - self.last_batch_size
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self.epoch = self.consumed_samples // active_total_samples
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current_epoch_samples = self.consumed_samples % active_total_samples
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assert current_epoch_samples % self.micro_batch_times_data_parallel_size == 0
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# data sharding and random sampling
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bucket_size = (self.total_samples // self.micro_batch_times_data_parallel_size) \
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* self.micro_batch_size
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bucket_offset = current_epoch_samples // self.data_parallel_size
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start_idx = self.data_parallel_rank * bucket_size
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g = torch.Generator()
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g.manual_seed(self.epoch)
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random_idx = torch.randperm(bucket_size, generator=g).tolist()
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idx_range = [start_idx + x for x in random_idx[bucket_offset:]]
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batch = []
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# Last batch if not complete will be dropped.
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for idx in idx_range:
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batch.append(idx)
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if len(batch) == self.micro_batch_size:
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self.consumed_samples += self.micro_batch_times_data_parallel_size
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yield batch
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batch = []
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