mirror of https://github.com/hpcaitech/ColossalAI
56 lines
2.0 KiB
Python
56 lines
2.0 KiB
Python
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import torch
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def ensure_divisibility(numerator, denominator):
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"""Ensure that numerator is divisible by the denominator."""
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assert numerator % denominator == 0, '{} is not divisible by {}'.format(
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numerator, denominator)
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def divide(numerator, denominator):
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"""Ensure that numerator is divisible by the denominator and return
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the division value."""
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ensure_divisibility(numerator, denominator)
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return numerator // denominator
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def split_tensor_along_last_dim(tensor, num_partitions,
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contiguous_split_chunks=False):
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"""Split a tensor along its last dimension.
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Arguments:
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tensor: input tensor.
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num_partitions: number of partitions to split the tensor
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contiguous_split_chunks: If True, make each chunk contiguous
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in memory.
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"""
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# Get the size and dimension.
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last_dim = tensor.dim() - 1
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last_dim_size = divide(tensor.size()[last_dim], num_partitions)
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# Split.
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tensor_list = torch.split(tensor, last_dim_size, dim=last_dim)
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# Note: torch.split does not create contiguous tensors by default.
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if contiguous_split_chunks:
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return tuple(chunk.contiguous() for chunk in tensor_list)
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return tensor_list
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class VocabUtility:
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"""Split the vocabulary into `world_size` chunks amd return the
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first and last index of the vocabulary belonging to the `rank`
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partition: Note that indices in [fist, last)"""
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@staticmethod
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def vocab_range_from_per_partition_vocab_size(per_partition_vocab_size,
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rank, world_size):
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index_f = rank * per_partition_vocab_size
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index_l = index_f + per_partition_vocab_size
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return index_f, index_l
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@staticmethod
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def vocab_range_from_global_vocab_size(global_vocab_size, rank, world_size):
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per_partition_vocab_size = divide(global_vocab_size, world_size)
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return VocabUtility.vocab_range_from_per_partition_vocab_size(
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per_partition_vocab_size, rank, world_size)
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