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[autoparallel] remove redundancy comm node (#1893)

pull/1940/head
YuliangLiu0306 2022-11-15 10:53:41 +08:00 committed by GitHub
parent 9183e0dec5
commit 36c0f3ea5b
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5 changed files with 23 additions and 20 deletions
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2
colossalai/auto_parallel/passes/runtime_apply_pass.py
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@ -81,6 +81,8 @@ def _shape_consistency_apply(gm: torch.fx.GraphModule):
continue
for user_node_index, user_node in enumerate(node.strategies_vector.successor_nodes):
if node.sharding_spec.sharding_sequence_difference(node.target_sharding_specs[user_node_index]) == 0:
continue
with mod_graph.inserting_before(user_node):
shape_consistency_node = mod_graph.create_node('call_function',
runtime_apply,

8
colossalai/auto_parallel/passes/runtime_preparation_pass.py
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@ -47,6 +47,7 @@ def _solution_annotatation(gm: torch.fx.GraphModule, solution: List[int]):
target_sharding_spec = user_node.best_strategy.get_sharding_spec_by_name(str(node.name))
target_sharding_specs.append(target_sharding_spec)
sharding_spec_convert_dict[index] = target_sharding_specs
setattr(node, 'target_sharding_specs', target_sharding_specs)
# the get_attr node strategy is kind of pending strategy, which means we will change it
# to the same strategy of the user node.
if node.op == 'get_attr':
@ -95,7 +96,8 @@ def _module_params_sharding(gm: torch.fx.GraphModule, device_mesh):
"""
mod_graph = gm.graph
nodes = tuple(mod_graph.nodes)
# This stream is created for overlaping the communication and computation.
reduction_stream = torch.cuda.Stream()
for node in nodes:
if node.op == 'call_module':
target_module = node.graph.owning_module.get_submodule(node.target)
@ -122,7 +124,7 @@ def _module_params_sharding(gm: torch.fx.GraphModule, device_mesh):
def wrapper(param, comm_spec):
def hook_fn(grad):
_all_reduce(grad, comm_spec)
_all_reduce(grad, comm_spec, async_op=False)
param.register_hook(hook_fn)
@ -172,7 +174,7 @@ def _module_params_sharding(gm: torch.fx.GraphModule, device_mesh):
def wrapper(param, comm_spec):
def hook_fn(grad):
_all_reduce(grad, comm_spec)
_all_reduce(grad, comm_spec, async_op=False)
param.register_hook(hook_fn)

8
colossalai/auto_parallel/tensor_shard/node_handler/node_handler.py
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@ -74,11 +74,13 @@ class NodeHandler(ABC):
if op_data.type == OperationDataType.PARAM:
resharding_cost = TrainCycleItem(fwd=0, bwd=0, total=0)
else:
dtype = op_data.data.dtype
size_per_elem_bytes = torch.tensor([], dtype=dtype).element_size()
_, _, resharding_cost = shape_consistency_manager.shape_consistency(
prev_sharding_spec, current_sharding_spec)
resharding_cost = TrainCycleItem(fwd=resharding_cost["forward"],
bwd=resharding_cost["backward"],
total=resharding_cost["total"])
resharding_cost = TrainCycleItem(fwd=resharding_cost["forward"] * size_per_elem_bytes,
bwd=resharding_cost["backward"] * size_per_elem_bytes,
total=resharding_cost["total"] * size_per_elem_bytes)
resharding_costs[node].append(resharding_cost)
strategy.resharding_costs = resharding_costs
return strategy

16
colossalai/auto_parallel/tensor_shard/node_handler/strategy/batch_norm_generator.py
View File

@ -218,7 +218,7 @@ class BatchNormStrategyGenerator(StrategyGenerator):
sharding_spec=sharding_spec_mapping["output"],
communication_pattern=CollectiveCommPattern.ALLREDUCE_FWD_IDENTITY_BWD,
logical_process_axis=mesh_dim_0,
comm_type=CommType.AFTER)
comm_type=CommType.IMPLICIT)
communication_action_mapping = {"output": output_comm_action}
@ -254,7 +254,7 @@ class BatchNormStrategyGenerator(StrategyGenerator):
sharding_spec=sharding_spec_mapping["output"],
communication_pattern=CollectiveCommPattern.ALLREDUCE_FWD_IDENTITY_BWD,
logical_process_axis=[mesh_dim_0, mesh_dim_1],
comm_type=CommType.AFTER)
comm_type=CommType.IMPLICIT)
communication_action_mapping = {"output": output_comm_action}
@ -300,7 +300,7 @@ class BatchNormStrategyGenerator(StrategyGenerator):
sharding_spec=sharding_spec_mapping["output"],
communication_pattern=CollectiveCommPattern.ALLREDUCE_FWD_IDENTITY_BWD,
logical_process_axis=[mesh_dim_0],
comm_type=CommType.AFTER)
comm_type=CommType.IMPLICIT)
communication_action_mapping = {"output": output_comm_action}
@ -331,14 +331,14 @@ class BatchNormStrategyGenerator(StrategyGenerator):
# TODO: The strategies below should be uncommented after runtime
# passes ready.
# SR = SR x R WITH SYNC_BN
# strategy_list.append(self.split_input_batch(0))
# strategy_list.append(self.split_input_batch(1))
strategy_list.append(self.split_input_batch(0))
strategy_list.append(self.split_input_batch(1))
# SS = SS x S WITH SYNC_BN
# strategy_list.append(self.split_input_both_dim(0, 1))
# strategy_list.append(self.split_input_both_dim(1, 0))
strategy_list.append(self.split_input_both_dim(0, 1))
strategy_list.append(self.split_input_both_dim(1, 0))
# S01R = S01R x R WITH SYNC_BN
# strategy_list.append(self.split_input_batch_1d(0, 1))
strategy_list.append(self.split_input_batch_1d(0, 1))
return strategy_list

9
colossalai/tensor/comm_spec.py
View File

@ -23,9 +23,7 @@ def _all_gather(tensor, comm_spec):
torch.zeros(tensor.shape, dtype=tensor.dtype, device=tensor.device)
for _ in range(comm_spec.device_mesh.mesh_shape[comm_spec.logical_process_axis])
]
tensor = tensor
group = process_group
dist.all_gather(tensor_list, tensor, group=group)
dist.all_gather(tensor_list, tensor, group=process_group)
output = torch.cat(tuple(tensor_list), comm_spec.gather_dim).contiguous()
return output
@ -37,7 +35,6 @@ def _split(tensor, comm_spec):
process_groups_list = comm_spec.device_mesh.process_groups_dict[comm_spec.logical_process_axis]
for rank_list, _ in process_groups_list:
if dist.get_rank() in rank_list:
tensor = tensor
dim = comm_spec.shard_dim
length = tensor.shape[comm_spec.shard_dim] // len(rank_list)
start = length * rank_list.index(dist.get_rank())
@ -69,7 +66,7 @@ def _all_to_all(tensor, comm_spec):
return output
def _all_reduce(tensor, comm_spec):
def _all_reduce(tensor, comm_spec, async_op=False):
'''
Implement all reduce operation on device mesh based on information provided by comm_spec.
'''
@ -78,7 +75,7 @@ def _all_reduce(tensor, comm_spec):
if dist.get_rank() in rank_list:
if not tensor.is_contiguous():
tensor = tensor.contiguous()
dist.all_reduce(tensor, op=ReduceOp.SUM, group=process_group)
dist.all_reduce(tensor, op=ReduceOp.SUM, group=process_group, async_op=async_op)
return tensor