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52 lines
1.8 KiB
52 lines
1.8 KiB
#!/usr/bin/env python
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from functools import total_ordering
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import torch
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import torch.distributed as dist
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from torch._utils import _flatten_dense_tensors, _unflatten_dense_tensors
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from colossalai.core import global_context as gpc
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from colossalai.registry import GRADIENT_HANDLER
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from ._base_gradient_handler import BaseGradientHandler
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from ...context.parallel_mode import ParallelMode
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import colossalai
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@GRADIENT_HANDLER.register_module
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class SequenceParallelGradientHandler(BaseGradientHandler):
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"""A helper class to handle all-reduce operations in a data parallel group.
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A all-reduce collective communication will be operated in
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:func:`handle_gradient` among a data parallel group.
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For better performance, it bucketizes the gradients of all parameters that are
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the same type to improve the efficiency of communication.
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"""
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def handle_gradient(self):
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"""A method running a all-reduce operation in a data parallel group.
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"""
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# bucketize and all-reduce
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buckets = {}
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# Pack the buckets.
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for param in self._model.parameters():
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if param.requires_grad and param.grad is not None:
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tp = param.data.type()
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if tp not in buckets:
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buckets[tp] = []
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buckets[tp].append(param)
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# For each bucket, all-reduce and copy all-reduced grads.
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for tp in buckets:
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bucket = buckets[tp]
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grads = [param.grad.data for param in bucket]
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coalesced = _flatten_dense_tensors(grads)
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coalesced /= gpc.get_world_size(ParallelMode.SEQUENCE_DP)
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dist.all_reduce(
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coalesced, group=gpc.get_group(ParallelMode.SEQUENCE_DP))
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for buf, synced in zip(grads, _unflatten_dense_tensors(
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coalesced, grads)):
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buf.copy_(synced)
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