mirror of https://github.com/hpcaitech/ColossalAI
parent
99d9713b02
commit
0f02b8c6e6
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@ -9,6 +9,40 @@ def pipe_split():
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pass
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def avgcompute_split_pass(gm: torch.fx.GraphModule, pp_size: int):
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"""
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In avgcompute_split_pass, we split module by the fwd flops.
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"""
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mod_graph = gm.graph
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# To use avgcompute_split_pass, we need run meta_info_prop interpreter first.
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# If nodes don't have meta info, this pass will fall back to normal balanced split pass.
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check_node = list(mod_graph.nodes)[0]
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if 'tensor_meta' not in check_node.meta:
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return balanced_split_pass(gm, pp_size)
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total_fwd_flop = 0
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for node in mod_graph.nodes:
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total_fwd_flop += node.fwd_flop
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partition_flop = total_fwd_flop // pp_size
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accumulate_fwd_flop = 0
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for node in mod_graph.nodes:
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if pp_size <= 1:
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break
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if 'pipe_split' in node.name:
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continue
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accumulate_fwd_flop += node.fwd_flop
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if accumulate_fwd_flop >= partition_flop:
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total_fwd_flop = total_fwd_flop - accumulate_fwd_flop
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accumulate_fwd_flop = 0
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pp_size -= 1
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partition_flop = total_fwd_flop // pp_size
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with mod_graph.inserting_after(node):
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split_node = mod_graph.create_node('call_function', pipe_split)
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gm.recompile()
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return gm
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def avgnode_split_pass(gm: torch.fx.GraphModule, pp_size: int):
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"""
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In avgnode_split_pass, simpliy split graph by node number.
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@ -104,8 +138,10 @@ def balanced_split_pass_v2(gm: torch.fx.GraphModule, pp_size: int):
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continue
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accumulate_node_size += node.node_size
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if accumulate_node_size >= partition_size:
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total_element_size = total_element_size - accumulate_node_size
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accumulate_node_size = 0
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pp_size -= 1
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partition_size = total_element_size // pp_size
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with mod_graph.inserting_after(node):
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split_node = mod_graph.create_node('call_function', pipe_split)
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gm.recompile()
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@ -112,7 +112,8 @@ class MetaInfoProp(torch.fx.Interpreter):
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n.meta['tensor_meta'] = tensor_meta
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n.meta = {**n.meta, **asdict(meta_info)} # extend MetaInfo to `n.meta`
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# TODO: the attribute node_size should be removed in the future
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setattr(n, 'node_size', activation_size(n.meta.get('fwd_in', 0)) + activation_size(n.meta.get('fwd_tmp', 0)))
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setattr(n, 'node_size', activation_size(n.meta.get('fwd_out', 0)) + activation_size(n.meta.get('fwd_tmp', 0)))
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setattr(n, 'fwd_flop', n.meta.get('fwd_flop', 0))
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n.meta['type'] = type(result)
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# retain the autograd graph
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