[autoparallel] adapt handlers with attention block (#1990)

* [autoparallel] adapt handlers with attention block

* polish

colossalai/auto_parallel/tensor_shard/node_handler/reshape_handler.py

@@ -12,6 +12,7 @@ __all__ = ['ReshapeHandler']
 
 @operator_registry.register(torch.reshape)
 @operator_registry.register(torch.Tensor.split)
+@operator_registry.register(torch.split)
 @operator_registry.register(torch.flatten)
 @operator_registry.register(torch.Tensor.transpose)
 @operator_registry.register(torch.Tensor.permute)

colossalai/auto_parallel/tensor_shard/node_handler/strategy/batch_norm_generator.py

@@ -220,7 +220,9 @@ class BatchNormStrategyGenerator(StrategyGenerator):
             logical_process_axis=mesh_dim_0,
             comm_type=CommType.IMPLICIT)
 
-        communication_action_mapping = {"output": output_comm_action}
+        # TODO: Temporary solution has no communication cost,
+        # above action should be added after the SyncBN replace pass completed.
+        communication_action_mapping = {}
 
         return self.get_sharding_strategy(name=name,
                                           sharding_spec_mapping=sharding_spec_mapping,
@@ -256,7 +258,9 @@ class BatchNormStrategyGenerator(StrategyGenerator):
             logical_process_axis=[mesh_dim_0, mesh_dim_1],
             comm_type=CommType.IMPLICIT)
 
-        communication_action_mapping = {"output": output_comm_action}
+        # TODO: Temporary solution has no communication cost,
+        # above action should be added after the SyncBN replace pass completed.
+        communication_action_mapping = {}
 
         return self.get_sharding_strategy(name=name,
                                           sharding_spec_mapping=sharding_spec_mapping,
@@ -302,7 +306,9 @@ class BatchNormStrategyGenerator(StrategyGenerator):
             logical_process_axis=[mesh_dim_0],
             comm_type=CommType.IMPLICIT)
 
-        communication_action_mapping = {"output": output_comm_action}
+        # TODO: Temporary solution has no communication cost,
+        # above action should be added after the SyncBN replace pass completed.
+        communication_action_mapping = {}
 
         return self.get_sharding_strategy(name=name,
                                           sharding_spec_mapping=sharding_spec_mapping,

colossalai/auto_parallel/tensor_shard/node_handler/strategy/getitem_generator.py

@@ -69,7 +69,7 @@ class TensorStrategyGenerator(GetItemStrategyGenerator):
 
     def collate_strategies(self) -> List[ShardingStrategy]:
         strategy_list = []
-        for strategy in self.predecessor_node.strategies_vector:
+        for index, strategy in enumerate(self.predecessor_node.strategies_vector):
             dim_partition_dict_mapping = {}
             communication_action_mapping = {}
             dim_partition_dict_for_input = strategy.output_sharding_specs[self.op_data["input"]].dim_partition_dict
@@ -96,7 +96,7 @@ class TensorStrategyGenerator(GetItemStrategyGenerator):
                     arg_index=0)
                 communication_action_mapping["input"] = input_communication_action
 
-            name = f'{sharding_spec_mapping["output"].sharding_sequence} = {sharding_spec_mapping["input"].sharding_sequence}'
+            name = f'{sharding_spec_mapping["output"].sharding_sequence} = {sharding_spec_mapping["input"].sharding_sequence}_{index}'
 
             strategy = self.get_sharding_strategy(name=name,
                                                   sharding_spec_mapping=sharding_spec_mapping,
@@ -121,7 +121,7 @@ class TensorTupleStrategyGenerator(GetItemStrategyGenerator):
         strategy_list = []
         index = self.op_data["index"].data
 
-        for strategy in self.predecessor_node.strategies_vector:
+        for strategy_index, strategy in enumerate(self.predecessor_node.strategies_vector):
             # the sharding spec for input in this case is a tuple of ShardingSpec.
             sharding_spec_for_input = strategy.output_sharding_specs[self.op_data["input"]]
             dim_partition_dict_for_output = sharding_spec_for_input[index].dim_partition_dict
@@ -132,8 +132,11 @@ class TensorTupleStrategyGenerator(GetItemStrategyGenerator):
             }
             sharding_spec_mapping = self.to_sharding_spec_mapping(dim_partition_dict_mapping)
             sharding_spec_mapping["input"] = sharding_spec_for_input
-
-            name = f'{sharding_spec_mapping["output"].sharding_sequence} = {sharding_spec_mapping["input"].sharding_sequence}'
+            input_sharding_info = f"get the {index} element from ("
+            for sharding_spec in sharding_spec_for_input:
+                input_sharding_info += f'{sharding_spec.sharding_sequence}, '
+            input_sharding_info += ")"
+            name = f'{sharding_spec_mapping["output"].sharding_sequence} = {input_sharding_info}_{strategy_index}'
 
             strategy = self.get_sharding_strategy(name=name,
                                                   sharding_spec_mapping=sharding_spec_mapping,

colossalai/auto_parallel/tensor_shard/node_handler/strategy/where_generator.py

@@ -1,9 +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.utils import (enumerate_all_possible_1d_sharding,
-                                                         enumerate_all_possible_2d_sharding)
+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,
+    ignore_sharding_exception,
+)
 
 from .strategy_generator import StrategyGenerator
 
@@ -50,6 +53,7 @@ class WhereGenerator(StrategyGenerator):
         memory_cost = TrainCycleItem(fwd=fwd_mem_cost, bwd=bwd_mem_cost, total=total_mem_cost)
         strategy.memory_cost = memory_cost
 
+    @ignore_sharding_exception
     def _generate_strategy_with_dim_partition(self, dim_partition):
         dim_partition_dict_mapping = {
             "condition": dim_partition,

colossalai/auto_parallel/tensor_shard/node_handler/unary_elementwise_handler.py

@@ -14,6 +14,11 @@ __all__ = ['UnaryElementwiseHandler']
 @operator_registry.register(torch.Tensor.type)
 @operator_registry.register(torch.abs)
 @operator_registry.register(torch.nn.ReLU)
+# TODO: softmax need to be relocated
+@operator_registry.register(torch.nn.functional.softmax)
+@operator_registry.register(torch.nn.modules.dropout.Dropout)
+@operator_registry.register(torch.Tensor.contiguous)
+@operator_registry.register(torch.nn.functional.dropout)
 class UnaryElementwiseHandler(NodeHandler):
     """
     A UnaryElementwiseHandler which deals with the sharding strategies for UnaryElementwise Op.

colossalai/auto_parallel/tensor_shard/node_handler/where_handler.py

@@ -57,24 +57,6 @@ class WhereHandler(NodeHandler):
         logical_operand.logical_shape = target_shape
         return logical_operand
 
-    def register_strategy(self, compute_resharding_cost: bool = False) -> StrategiesVector:
-        """
-        Register different sharding strategies for the current node.
-        """
-        strategy_generators = self.get_strategy_generator()
-
-        for generator in strategy_generators:
-            strategies = generator.generate()
-            strategies_vector = map(self.post_process, strategies)
-            # compute the resharding costs based on the previous node
-            # strategies if specified
-            if compute_resharding_cost:
-                strategies = list(map(self.update_resharding_cost, strategies))
-            self.strategies_vector.extend(strategies)
-
-        self.strategies_vector = list(strategies_vector)
-        return self.strategies_vector
-
     def post_process(self, strategy: ShardingStrategy):
         logical_op_data_mapping, physical_op_data_mapping = self.get_operation_data_mapping()
         for key in logical_op_data_mapping.keys():