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109 lines
4.6 KiB
109 lines
4.6 KiB
import builtins
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import operator
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from typing import List
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import torch
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from colossalai.tensor.shape_consistency import ShapeConsistencyManager
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from colossalai.tensor.sharding_spec import ShardingSpec
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def apply(*args, **kwargs):
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shape_consistency_manager = ShapeConsistencyManager()
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return shape_consistency_manager.apply(*args, **kwargs)
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def solution_annotation_pass(gm: torch.fx.GraphModule, solution: List[int], device_mesh):
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mod_graph = gm.graph
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nodes = tuple(mod_graph.nodes)
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# the dict to get origin sharding spec of node
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origin_node_sharding_spec_dict = {}
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for node_index, (node, strategy_index) in enumerate(zip(nodes, solution)):
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strategies_vector = node.strategies_vector
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setattr(node, "best_strategy", strategies_vector[strategy_index])
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setattr(node, "sharding_spec", strategies_vector[strategy_index].output_sharding_spec)
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origin_node_sharding_spec_dict[node_index] = strategies_vector[strategy_index].output_sharding_spec
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# apply the sharding spec of parameters
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for node in nodes:
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if node.op == "call_module":
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target_module = node.graph.owning_module.get_submodule(node.target)
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origin_sharding_spec = ShardingSpec(device_mesh, target_module.weight.shape, {})
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setattr(target_module.weight, "sharding_spec", origin_sharding_spec)
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target_weight_sharding_spec = node.best_strategy.input_shardings[1]
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target_module.weight.data = target_module.weight.data.permute((1, 0, 2, 3))
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apply(target_module.weight, target_weight_sharding_spec)
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target_module.weight.data = target_module.weight.data.permute((1, 0, 2, 3))
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# the dict to get input sharding specs of user node
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sharding_spec_convert_dict = {}
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for index, node in enumerate(nodes):
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target_sharding_specs = []
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for user_node in node.strategies_vector.successor_nodes:
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node_index = user_node.strategies_vector.predecessor_nodes.index(node)
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target_sharding_spec = user_node.best_strategy.input_shardings[node_index]
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target_sharding_specs.append(target_sharding_spec)
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sharding_spec_convert_dict[index] = target_sharding_specs
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# add above dicts into graph
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for node in nodes:
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if node.op != "placeholder":
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with mod_graph.inserting_before(node):
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input_specs_node = mod_graph.create_node("placeholder", target="sharding_spec_convert_dict")
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origin_specs_node = mod_graph.create_node("placeholder", target="origin_node_sharding_spec_dict")
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break
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return sharding_spec_convert_dict, origin_node_sharding_spec_dict
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def shape_consistency_pass(gm: torch.fx.GraphModule):
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mod_graph = gm.graph
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nodes = tuple(mod_graph.nodes)
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input_dict_node = None
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origin_dict_node = None
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# mapping the node into the origin graph index
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node_to_index_dict = {}
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index = 0
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for node in nodes:
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if node.target == "sharding_spec_convert_dict":
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input_dict_node = node
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continue
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if node.target == "origin_node_sharding_spec_dict":
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origin_dict_node = node
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continue
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if not hasattr(node, "best_strategy"):
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continue
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node_to_index_dict[node] = index
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index += 1
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assert input_dict_node is not None
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# add shape consistency apply function into graph
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for node in nodes:
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if not hasattr(node, "best_strategy"):
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continue
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with mod_graph.inserting_after(node):
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origin_spec_node = mod_graph.create_node(
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"call_function", operator.getitem, args=(origin_dict_node, node_to_index_dict[node])
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)
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with mod_graph.inserting_after(origin_spec_node):
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set_sharding_spec_node = mod_graph.create_node(
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"call_function", builtins.setattr, args=(node, "sharding_spec", origin_spec_node)
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)
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for user_node in node.strategies_vector.successor_nodes:
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node_index = user_node.strategies_vector.predecessor_nodes.index(node)
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with mod_graph.inserting_before(user_node):
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input_specs_node = mod_graph.create_node(
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"call_function", operator.getitem, args=(input_dict_node, node_to_index_dict[node])
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)
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with mod_graph.inserting_before(user_node):
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sharding_spec_node = mod_graph.create_node(
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"call_function", operator.getitem, args=(input_specs_node, node_index)
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)
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with mod_graph.inserting_before(user_node):
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shape_consistency_node = mod_graph.create_node("call_function", apply, args=(node, sharding_spec_node))
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return gm
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