wehub-resource-sync
156 lines
6.7 KiB
Python
156 lines
6.7 KiB
Python
# Copyright (c) ModelScope Contributors. All rights reserved.
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import torch
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from swift.utils import get_logger
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logger = get_logger()
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# Code borrowed from megatron-lm
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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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# Code borrowed from megatron-lm
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class MegatronPretrainingRandomSampler:
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def __init__(
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self,
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dataset,
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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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data_sharding,
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shuffle: bool = True,
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group_by_length: bool = False,
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):
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# Keep a copy of input params for later use.
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self.dataset = dataset
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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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if group_by_length:
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if data_sharding:
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data_sharding = False
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logger.warning('`group_by_length=True` is incompatible with `data_sharding=True`. '
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'Setting `data_sharding=False` to enable length grouping.')
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if not shuffle:
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raise ValueError('shuffle must be True when group_by_length is True')
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self.data_sharding = data_sharding
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self.shuffle = shuffle
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self.group_by_length = group_by_length
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self.lengths = self.dataset['lengths'] if group_by_length else None
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if self.lengths is not None:
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self.lengths = [max(length) if isinstance(length, list) else length for length in self.lengths]
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self.micro_batch_times_data_parallel_size = self.micro_batch_size * data_parallel_size
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self.last_batch_size = 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, '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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if self.shuffle:
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# data sharding and random sampling
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if self.data_sharding:
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bucket_size = (self.total_samples // self.micro_batch_times_data_parallel_size) * 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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else:
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full_bucket_size = (self.total_samples // self.micro_batch_size) * self.micro_batch_size
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full_bucket_offset = current_epoch_samples
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g = torch.Generator()
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g.manual_seed(self.epoch)
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if self.group_by_length:
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from transformers.trainer_pt_utils import get_length_grouped_indices
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idx_range_total = get_length_grouped_indices(
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self.lengths, self.micro_batch_times_data_parallel_size, generator=g)
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else:
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idx_range_total = torch.randperm(full_bucket_size, generator=g).tolist()
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idx_range_active = idx_range_total[full_bucket_offset:]
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idx_range = idx_range_active[self.data_parallel_rank::self.data_parallel_size]
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else:
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full_bucket_size = (self.total_samples // self.micro_batch_size) * self.micro_batch_size
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full_bucket_offset = current_epoch_samples
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idx_range = range(full_bucket_offset + self.data_parallel_rank, full_bucket_size, self.data_parallel_size)
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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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