ColossalAI/colossalai/auto_parallel/passes/runtime_apply_pass.py

179 lines
7.8 KiB
Python

from copy import deepcopy
from typing import Dict, List
import torch
from torch.fx.node import Node
from colossalai.auto_parallel.tensor_shard.sharding_strategy import (
CommAction,
CommType,
OperationData,
OperationDataType,
)
from colossalai.device.device_mesh import DeviceMesh
from colossalai.tensor.comm_spec import CommSpec
from colossalai.tensor.shape_consistency import ShapeConsistencyManager
shape_consistency_manager = ShapeConsistencyManager()
def runtime_apply(node: Node, origin_dict: Dict, input_dict: Dict, node_index: int, user_node_index: int):
"""
This method will be invoked during runtime to do the shape consistency, which make sure the activations is converted into
the user node expected form.
"""
origin_sharding_spec = origin_dict[node_index]
target_sharding_spec = input_dict[node_index][user_node_index]
return shape_consistency_manager.apply_for_autoparallel_runtime(node, origin_sharding_spec, target_sharding_spec)
def runtime_comm_spec_apply(tensor: torch.Tensor, comm_actions_dict: Dict, node_index: int, op_data_name: str):
"""
This method will be invoked during runtime to apply the comm action following the instruction of comm spec.
"""
comm_action = comm_actions_dict[node_index][op_data_name]
if isinstance(comm_action.comm_spec, CommSpec):
rst = comm_action.comm_spec.covert_spec_to_action(tensor)
else:
origin_sharding_spec = comm_action.comm_spec['src_spec']
tgt_sharding_spec = comm_action.comm_spec['tgt_spec']
rst = shape_consistency_manager.apply_for_autoparallel_runtime(tensor, origin_sharding_spec, tgt_sharding_spec)
return rst
def _preprocess_graph(nodes: List[Node]):
"""
This method is used to extract all the placeholders with sharding information,
and mapping the nodes into the index of the origin graph.
"""
# mapping the node into the origin graph index
node_to_index_dict = {}
index = 0
for node in nodes:
if node.target == 'sharding_spec_convert_dict':
input_dict_node = node
continue
if node.target == 'origin_node_sharding_spec_dict':
origin_dict_node = node
continue
if node.target == 'comm_actions_dict':
comm_actions_dict_node = node
continue
if not hasattr(node, 'best_strategy'):
continue
node_to_index_dict[node] = index
index += 1
return input_dict_node, origin_dict_node, comm_actions_dict_node, node_to_index_dict
def _shape_consistency_apply(gm: torch.fx.GraphModule):
"""
This pass is used to add the shape consistency node to the origin graph.
"""
mod_graph = gm.graph
nodes = tuple(mod_graph.nodes)
input_dict_node, origin_dict_node, _, node_to_index_dict = _preprocess_graph(nodes)
for node in nodes:
if not hasattr(node, 'best_strategy') or node.op == 'output':
continue
for user_node_index, user_node in enumerate(node.strategies_vector.successor_nodes):
with mod_graph.inserting_before(user_node):
shape_consistency_node = mod_graph.create_node('call_function',
runtime_apply,
args=(node, origin_dict_node, input_dict_node,
node_to_index_dict[node], user_node_index))
new_args = list(user_node.args)
new_kwargs = dict(user_node.kwargs)
# the origin node may be a positional argument or key word argument of user node
if node in new_args:
# substitute the origin node with shape_consistency_node
origin_index_args = new_args.index(node)
new_args[origin_index_args] = shape_consistency_node
user_node.args = tuple(new_args)
elif str(node) in new_kwargs:
# substitute the origin node with shape_consistency_node
new_kwargs[str(node)] = shape_consistency_node
user_node.kwargs = new_kwargs
return gm
def _comm_spec_apply(gm: torch.fx.GraphModule):
"""
This pass is used to add the comm spec apply node to the origin graph.
"""
mod_graph = gm.graph
nodes = tuple(mod_graph.nodes)
_, _, comm_actions_dict_node, node_to_index_dict = _preprocess_graph(nodes)
for node in nodes:
if not hasattr(node, 'best_strategy') or node.op == 'output':
continue
comm_actions = node.best_strategy.communication_actions
for op_data, comm_action in comm_actions.items():
if comm_action.comm_type == CommType.HOOK:
continue
if comm_action.comm_type == CommType.BEFORE:
if op_data.type == OperationDataType.OUTPUT:
comm_object = node
elif comm_action.key_for_kwarg is not None:
comm_object = node.kwargs[comm_action.key_for_kwarg]
else:
comm_object = node.args[comm_action.arg_index]
with mod_graph.inserting_before(node):
comm_spec_apply_node = mod_graph.create_node('call_function',
runtime_comm_spec_apply,
args=(comm_object, comm_actions_dict_node,
node_to_index_dict[node], op_data.name))
# the origin node may be a positional argument or key word argument of user node
if comm_action.key_for_kwarg is not None:
# substitute the origin node with comm_spec_apply_node
new_kwargs = dict(node.kwargs)
new_kwargs[comm_action.key_for_kwarg] = comm_spec_apply_node
node.kwargs = new_kwargs
else:
# substitute the origin node with comm_spec_apply_node
new_args = list(node.args)
new_args[comm_action.arg_index] = comm_spec_apply_node
node.args = tuple(new_args)
elif comm_action.comm_type == CommType.AFTER:
with mod_graph.inserting_after(node):
comm_spec_apply_node = mod_graph.create_node('call_function',
runtime_comm_spec_apply,
args=(node, comm_actions_dict_node,
node_to_index_dict[node], op_data.name))
user_list = list(node.users.keys())
for user in user_list:
if user == comm_spec_apply_node:
continue
new_args = list(user.args)
new_kwargs = dict(user.kwargs)
# the origin node may be a positional argument or key word argument of user node
if node in new_args:
# substitute the origin node with comm_spec_apply_node
new_args[new_args.index(node)] = comm_spec_apply_node
user.args = tuple(new_args)
elif str(node) in new_kwargs:
# substitute the origin node with comm_spec_apply_node
new_kwargs[str(node)] = comm_spec_apply_node
user.kwargs = new_kwargs
return gm
def runtime_apply_pass(gm: torch.fx.GraphModule):
"""
The method manages all the passes acting on the distributed training runtime.
"""
gm = _shape_consistency_apply(gm)
gm = _comm_spec_apply(gm)
return gm