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[autoparallel] update CommSpec to CommActions #1768

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Oct 28, 2022
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[autoparallel] update CommSpec to CommActions #1768

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d09955f
[autoparallel] update CommSpec to CommActions
YuliangLiu0306 Oct 27, 2022
d083b8f
polish code
YuliangLiu0306 Oct 27, 2022
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9 changes: 5 additions & 4 deletions colossalai/auto_parallel/tensor_shard/node_handler/linear_handler.py
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Original file line number Diff line number Diff line change
Expand Up @@ -202,16 +202,17 @@ def get_operation_data_mapping(self) -> Dict[str, OperationData]:

mapping = {"input": physical_input_operand, "other": physical_other_operand, "output": physical_output}

if self.node.args[2] is not None:
if 'bias' in self.node.kwargs and self.node.kwargs['bias'] is not None:
# check if the other operand is a parameter
if isinstance(self.node.args[2]._meta_data, torch.nn.parameter.Parameter):
if isinstance(self.node.kwargs["bias"]._meta_data, torch.nn.parameter.Parameter):
data_type = OperationDataType.PARAM
else:
data_type = OperationDataType.ARG
physical_bias_operand = OperationData(name=str(self.node.args[2]),
physical_bias_operand = OperationData(name=str(self.node.kwargs["bias"]),
type=data_type,
data=self.node.args[2]._meta_data)
data=self.node.kwargs["bias"]._meta_data)
mapping['bias'] = physical_bias_operand

return mapping

def post_process(self, strategy: ShardingStrategy):
Expand Down
28 changes: 18 additions & 10 deletions colossalai/auto_parallel/tensor_shard/node_handler/strategy/batch_norm_generator.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,7 +3,12 @@
from functools import reduce
from typing import List

from colossalai.auto_parallel.tensor_shard.sharding_strategy import MemoryCost, ShardingStrategy, TrainCycleItem
from colossalai.auto_parallel.tensor_shard.sharding_strategy import (
CommType,
MemoryCost,
ShardingStrategy,
TrainCycleItem,
)
from colossalai.tensor.shape_consistency import CollectiveCommPattern

from .strategy_generator import StrategyGenerator
Expand Down Expand Up @@ -204,12 +209,13 @@ def split_input_batch(self, mesh_dim_0):
# For SyncBN case, we don't need to do communication for weight and bias.
# TODO: the communication happens interally at SyncBN operation. We need to replace the BN operation
# to SyncBN operation instead of inserting a communication node.
output_comm_spec = self.get_communication_spec(
output_comm_action = self.get_communication_action(
sharding_spec=sharding_spec_mapping["output"],
communication_pattern=CollectiveCommPattern.ALLREDUCE_FWD_IDENTITY_BWD,
logical_process_axis=mesh_dim_0)
logical_process_axis=mesh_dim_0,
comm_type=CommType.AFTER)

communication_action_mapping = {"output": output_comm_spec}
communication_action_mapping = {"output": output_comm_action}

return self.get_sharding_strategy(name=name,
sharding_spec_mapping=sharding_spec_mapping,
Expand Down Expand Up @@ -238,12 +244,13 @@ def split_input_batch_1d(self, mesh_dim_0, mesh_dim_1):
# For SyncBN case, we don't need to do communication for gradients of weight and bias.
# TODO: the communication happens interally at SyncBN operation. We need to replace the BN operation
# to SyncBN operation instead of inserting a communication node.
output_comm_spec = self.get_communication_spec(
output_comm_action = self.get_communication_action(
sharding_spec=sharding_spec_mapping["output"],
communication_pattern=CollectiveCommPattern.ALLREDUCE_FWD_IDENTITY_BWD,
logical_process_axis=[mesh_dim_0, mesh_dim_1])
logical_process_axis=[mesh_dim_0, mesh_dim_1],
comm_type=CommType.AFTER)

communication_action_mapping = {"output": output_comm_spec}
communication_action_mapping = {"output": output_comm_action}

return self.get_sharding_strategy(name=name,
sharding_spec_mapping=sharding_spec_mapping,
Expand Down Expand Up @@ -282,12 +289,13 @@ def split_input_both_dim(self, mesh_dim_0, mesh_dim_1):
# For SyncBN case, we don't need to do communication for gradients of weight and bias.
# TODO: the communication happens interally at SyncBN operation. We need to replace the BN operation
# to SyncBN operation instead of inserting a communication node.
output_comm_spec = self.get_communication_spec(
output_comm_action = self.get_communication_action(
sharding_spec=sharding_spec_mapping["output"],
communication_pattern=CollectiveCommPattern.ALLREDUCE_FWD_IDENTITY_BWD,
logical_process_axis=[mesh_dim_0])
logical_process_axis=[mesh_dim_0],
comm_type=CommType.AFTER)

communication_action_mapping = {"output": output_comm_spec}
communication_action_mapping = {"output": output_comm_action}

return self.get_sharding_strategy(name=name,
sharding_spec_mapping=sharding_spec_mapping,
Expand Down
15 changes: 11 additions & 4 deletions colossalai/auto_parallel/tensor_shard/node_handler/strategy/getitem_generator.py
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Original file line number Diff line number Diff line change
@@ -1,7 +1,12 @@
import copy
from typing import List

from colossalai.auto_parallel.tensor_shard.sharding_strategy import (MemoryCost, ShardingStrategy, TrainCycleItem)
from colossalai.auto_parallel.tensor_shard.sharding_strategy import (
CommType,
MemoryCost,
ShardingStrategy,
TrainCycleItem,
)
from colossalai.tensor.shape_consistency import CollectiveCommPattern

from .strategy_generator import FollowingStrategyGenerator
Expand Down Expand Up @@ -83,11 +88,13 @@ def collate_strategies(self) -> List[ShardingStrategy]:
}
sharding_spec_mapping = self.to_sharding_spec_mapping(dim_partition_dict_mapping)
if gather_input:
input_communication_spec = self.get_communication_spec(
input_communication_action = self.get_communication_action(
sharding_spec_mapping["input"],
communication_pattern=CollectiveCommPattern.GATHER_FWD_SPLIT_BWD,
logical_process_axis=logical_process_axis)
communication_action_mapping["input"] = input_communication_spec
logical_process_axis=logical_process_axis,
comm_type=CommType.BEFORE,
arg_index=0)
communication_action_mapping["input"] = input_communication_action

name = f'{sharding_spec_mapping["output"].sharding_sequence} = {sharding_spec_mapping["input"].sharding_sequence}'

Expand Down
27 changes: 18 additions & 9 deletions colossalai/auto_parallel/tensor_shard/node_handler/strategy/layer_norm_generator.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,9 +3,16 @@
from functools import reduce
from typing import List

from colossalai.auto_parallel.tensor_shard.sharding_strategy import (MemoryCost, ShardingStrategy, TrainCycleItem)
from colossalai.auto_parallel.tensor_shard.utils import (enumerate_all_possible_1d_sharding,
enumerate_all_possible_2d_sharding)
from colossalai.auto_parallel.tensor_shard.sharding_strategy import (
CommType,
MemoryCost,
ShardingStrategy,
TrainCycleItem,
)
from colossalai.auto_parallel.tensor_shard.utils import (
enumerate_all_possible_1d_sharding,
enumerate_all_possible_2d_sharding,
)
from colossalai.tensor.shape_consistency import CollectiveCommPattern

from .strategy_generator import StrategyGenerator
Expand Down Expand Up @@ -107,18 +114,20 @@ def _generate_strategy_with_dim_partition(self, dim_partition):
total_mesh_dim_list = total_mesh_dim_list[0]
communication_action_mapping = {}

other_comm_spec = self.get_communication_spec(
other_comm_action = self.get_communication_action(
sharding_spec=sharding_spec_mapping["other"],
communication_pattern=CollectiveCommPattern.IDENTITY_FWD_ALLREDUCE_BWD,
logical_process_axis=total_mesh_dim_list)
communication_action_mapping["other"] = other_comm_spec
logical_process_axis=total_mesh_dim_list,
comm_type=CommType.HOOK)
communication_action_mapping["other"] = other_comm_action

if self.has_bias:
bias_comm_spec = self.get_communication_spec(
bias_comm_action = self.get_communication_action(
sharding_spec=sharding_spec_mapping["bias"],
communication_pattern=CollectiveCommPattern.IDENTITY_FWD_ALLREDUCE_BWD,
logical_process_axis=total_mesh_dim_list)
communication_action_mapping["bias"] = bias_comm_spec
logical_process_axis=total_mesh_dim_list,
comm_type=CommType.HOOK)
communication_action_mapping["bias"] = bias_comm_action

strategy = self.get_sharding_strategy(name=name,
sharding_spec_mapping=sharding_spec_mapping,
Expand Down
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