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https://github.com/hpcaitech/ColossalAI.git
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96780e6ee4
* add pipeline shared module wrapper and update load batch * added model parallel process group for amp and clip grad (#86) * added model parallel process group for amp and clip grad * update amp and clip with model parallel process group * remove pipeline_prev/next group (#88) * micro batch offload * optimize pipeline gpu memory usage * pipeline can receive tensor shape (#93) * optimize pipeline gpu memory usage * fix grad accumulation step counter * rename classes and functions Co-authored-by: Frank Lee <somerlee.9@gmail.com>
42 lines
1.9 KiB
Python
42 lines
1.9 KiB
Python
#!/usr/bin/env python
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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 collections import defaultdict
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@GRADIENT_HANDLER.register_module
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class PipelineSharedModuleGradientHandler(BaseGradientHandler):
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"""A helper class to handle all-reduce operations in sub parallel groups.
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A all-reduce collective communication will be operated in
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:func:`handle_gradient` among all sub pipeline parallel groups.
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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 sub pipeline parallel groups.
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"""
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if gpc.pipeline_parallel_size > 1:
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# bucketize and all-reduce
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buckets = defaultdict(lambda: defaultdict(list))
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# Pack the buckets.
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for param in self._model.parameters():
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group = getattr(param, 'pipeline_shared_module_pg', None)
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if param.requires_grad and param.grad is not None and group is not None:
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tp = param.data.type()
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buckets[group][tp].append(param)
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# For each bucket, all-reduce and copy all-reduced grads.
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for group, group_buckets in buckets.items():
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for tp, bucket in group_buckets.items():
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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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dist.all_reduce(coalesced, op=dist.ReduceOp.SUM, group=group)
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for buf, synced in zip(grads, _unflatten_dense_tensors(coalesced, grads)):
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buf.copy_(synced)
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