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[autockpt] make it work. (#2257)

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Super Daniel 2 years ago committed by GitHub
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  1. 14
      colossalai/auto_parallel/passes/comm_metainfo_pass.py
  2. 2
      colossalai/auto_parallel/tensor_shard/node_handler/binary_elementwise_handler.py
  3. 4
      colossalai/auto_parallel/tensor_shard/node_handler/reshape_handler.py
  4. 4
      colossalai/auto_parallel/tensor_shard/node_handler/unary_elementwise_handler.py

14
colossalai/auto_parallel/passes/comm_metainfo_pass.py

@ -54,7 +54,7 @@ def _construct_meta_info(node: Node, origin_sharding_spec: ShardingSpec,
return meta_info
def _runtime_apply_meta_info(node: Node, original_sharding_spec_dict, sharding_spec_dict) -> MetaInfo:
def _runtime_apply_meta_info(node: Node, origin_spec_dict, sharding_spec_dict) -> MetaInfo:
"""
This method is used to construct `MetaInto` for shape consistency node
"""
@ -62,8 +62,8 @@ def _runtime_apply_meta_info(node: Node, original_sharding_spec_dict, sharding_s
# extract node index and user node index
args = node.args
node_index, user_node_index = args[3], args[4]
origin_sharding_spec, target_sharding_spec = original_sharding_spec_dict[node_index], sharding_spec_dict[
node_index][user_node_index]
origin_sharding_spec, target_sharding_spec = origin_spec_dict[node_index], sharding_spec_dict[node_index][
user_node_index]
return _construct_meta_info(node, origin_sharding_spec, target_sharding_spec)
@ -98,16 +98,16 @@ def _runtime_comm_spec_apply_meta_info(node: Node, comm_actions_dict: Dict) -> M
return meta_info
def comm_metainfo_pass(gm: GraphModule, sharding_spec_dict: Dict, original_sharding_spec_dict: Dict,
comm_actions_dict: Dict):
def comm_metainfo_pass(gm: GraphModule, sharding_spec_dict: Dict, origin_spec_dict: Dict,
comm_actions_dict: Dict) -> GraphModule:
"""
The method manages all the metainfo of the communication node (run_time_apply, runtime_comm_spec_apply) in the graph.
"""
for node in gm.graph.nodes:
if node.target == runtime_apply:
setattr(node, 'best_metainfo',
_runtime_apply_meta_info(node, original_sharding_spec_dict, sharding_spec_dict))
setattr(node, 'best_metainfo', _runtime_apply_meta_info(node, origin_spec_dict, sharding_spec_dict))
elif node.target == runtime_comm_spec_apply:
setattr(node, 'best_metainfo', _runtime_comm_spec_apply_meta_info(node, comm_actions_dict))
else:
pass
return gm

2
colossalai/auto_parallel/tensor_shard/node_handler/binary_elementwise_handler.py

@ -16,7 +16,7 @@ __all__ = ['BinaryElementwiseHandler']
@operator_registry.register(BCAST_FUNC_OP)
class BinaryElementwiseHandler(NodeHandler):
class BinaryElementwiseHandler(MetaInfoNodeHandler):
"""
An BinaryBcastOpHandler is a node handler which deals with operations which have two
operands and broadcasting occurs such as torch.add.

4
colossalai/auto_parallel/tensor_shard/node_handler/reshape_handler.py

@ -3,7 +3,7 @@ from typing import Dict, List
import torch
from ..sharding_strategy import OperationData, OperationDataType
from .node_handler import NodeHandler
from .node_handler import MetaInfoNodeHandler, NodeHandler
from .registry import operator_registry
from .strategy import ReshapeGenerator, StrategyGenerator
@ -13,7 +13,7 @@ __all__ = ['ReshapeHandler']
@operator_registry.register(torch.flatten)
@operator_registry.register(torch.Tensor.unsqueeze)
@operator_registry.register(torch.nn.AdaptiveAvgPool2d)
class ReshapeHandler(NodeHandler):
class ReshapeHandler(MetaInfoNodeHandler):
"""
A ReshapeHandler which deals with the sharding strategies for Reshape Op, such as torch.reshape.
"""

4
colossalai/auto_parallel/tensor_shard/node_handler/unary_elementwise_handler.py

@ -3,7 +3,7 @@ from typing import Dict, List
import torch
from ..sharding_strategy import OperationData, OperationDataType
from .node_handler import NodeHandler
from .node_handler import MetaInfoNodeHandler, NodeHandler
from .registry import operator_registry
from .strategy import StrategyGenerator, UnaryElementwiseGenerator
@ -19,7 +19,7 @@ __all__ = ['UnaryElementwiseHandler']
@operator_registry.register(torch.nn.modules.dropout.Dropout)
@operator_registry.register(torch.Tensor.contiguous)
@operator_registry.register(torch.nn.functional.dropout)
class UnaryElementwiseHandler(NodeHandler):
class UnaryElementwiseHandler(MetaInfoNodeHandler):
"""
A UnaryElementwiseHandler which deals with the sharding strategies for UnaryElementwise Op.
"""

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