[autoparallel] add sum handler (#2101)

colossalai/auto_parallel/tensor_shard/node_handler/__init__.py

@@ -15,6 +15,7 @@ from .output_handler import OuputHandler
 from .placeholder_handler import PlacehodlerHandler
 from .registry import operator_registry
 from .reshape_handler import ReshapeHandler
+from .sum_handler import SumHandler
 from .tensor_constructor_handler import TensorConstructorHandler
 from .unary_elementwise_handler import UnaryElementwiseHandler
 from .where_handler import WhereHandler
@@ -25,5 +26,5 @@ __all__ = [
     'UnaryElementwiseHandler', 'ReshapeHandler', 'PlacehodlerHandler', 'OuputHandler', 'WhereHandler',
     'NormPoolingHandler', 'BinaryElementwiseHandler', 'MatMulHandler', 'operator_registry', 'ADDMMFunctionHandler',
     'GetItemHandler', 'GetattrHandler', 'ViewHandler', 'PermuteHandler', 'TensorConstructorHandler',
-    'EmbeddingModuleHandler', 'EmbeddingFunctionHandler'
+    'EmbeddingModuleHandler', 'EmbeddingFunctionHandler', 'SumHandler'
 ]

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

@@ -16,6 +16,7 @@ from .output_generator import OutputGenerator
 from .placeholder_generator import PlaceholderGenerator
 from .reshape_generator import ReshapeGenerator
 from .strategy_generator import StrategyGenerator
+from .sum_generator import SumGenerator
 from .tensor_constructor_generator import TensorConstructorGenerator
 from .unary_elementwise_generator import UnaryElementwiseGenerator
 from .where_generator import WhereGenerator
@@ -26,5 +27,5 @@ __all__ = [
     'BatchNormStrategyGenerator', 'GetItemStrategyGenerator', 'TensorStrategyGenerator', 'TensorTupleStrategyGenerator',
     'LayerNormGenerator', 'ReshapeGenerator', 'PlaceholderGenerator', 'OutputGenerator', 'WhereGenerator',
     'ReshapeGenerator', 'NormalPoolStrategyGenerator', 'BinaryElementwiseStrategyGenerator', 'GetattrGenerator',
-    'TensorConstructorGenerator', 'EmbeddingStrategyGenerator'
+    'TensorConstructorGenerator', 'EmbeddingStrategyGenerator', 'SumGenerator'
 ]

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

@@ -0,0 +1,113 @@
+import copy
+import operator
+from functools import reduce
+from typing import List
+
+from colossalai.auto_parallel.tensor_shard.node_handler.strategy.strategy_generator import FollowingStrategyGenerator
+from colossalai.auto_parallel.tensor_shard.sharding_strategy import (
+    CommAction,
+    CommType,
+    MemoryCost,
+    ShardingStrategy,
+    TrainCycleItem,
+)
+from colossalai.auto_parallel.tensor_shard.utils import (
+    check_keep_sharding_status,
+    detect_reshape_mapping,
+    infer_output_dim_partition_dict,
+)
+from colossalai.tensor.shape_consistency import CollectiveCommPattern
+from colossalai.tensor.sharding_spec import ShardingSpec
+
+__all__ = ['SumGenerator']
+
+
+class SumGenerator(FollowingStrategyGenerator):
+    """
+    SumGenerator deals with the sharding strategies of torch.sum op.
+    """
+
+    def validate(self) -> bool:
+        return super().validate()
+
+    def update_compute_cost(self, strategy: ShardingStrategy):
+        sharded_input_shape = strategy.sharding_specs[self.op_data['input']].get_sharded_shape_per_device()
+        sharded_output_shape = strategy.sharding_specs[self.op_data['output']].get_sharded_shape_per_device()
+        input_size_product = reduce(operator.mul, sharded_input_shape)
+        output_size_product = reduce(operator.mul, sharded_output_shape)
+
+        compute_cost = TrainCycleItem(fwd=input_size_product,
+                                      bwd=output_size_product,
+                                      total=input_size_product + output_size_product)
+
+        strategy.compute_cost = compute_cost
+
+    def update_memory_cost(self, strategy: ShardingStrategy):
+        '''
+        Compute the memory cost per device with this specific strategy.
+        '''
+        forward_size_mapping = {
+            'input': self._compute_size_in_bytes(strategy, "input"),
+            'output': self._compute_size_in_bytes(strategy, "output")
+        }
+
+        backward_size_mapping = copy.deepcopy(forward_size_mapping)
+        backward_size_mapping.pop("output")
+        # compute fwd cost incurred
+        # fwd_cost = input + output
+        fwd_activation_cost = sum([v for k, v in forward_size_mapping.items() if not self.is_param(k)])
+        fwd_parameter_cost = sum([v for k, v in forward_size_mapping.items() if self.is_param(k)])
+        fwd_mem_cost = MemoryCost(activation=fwd_activation_cost, parameter=fwd_parameter_cost)
+
+        # compute bwd cost incurred
+        # bwd_cost = input_grad
+        bwd_activation_cost = sum([v for k, v in backward_size_mapping.items() if not self.is_param(k)])
+        bwd_parameter_cost = sum([v for k, v in backward_size_mapping.items() if self.is_param(k)])
+        bwd_mem_cost = MemoryCost(activation=bwd_activation_cost, parameter=bwd_parameter_cost)
+
+        # compute total cost
+        total_mem_cost = MemoryCost(activation=fwd_activation_cost + bwd_activation_cost,
+                                    parameter=fwd_parameter_cost + bwd_parameter_cost)
+        memory_cost = TrainCycleItem(fwd=fwd_mem_cost, bwd=bwd_mem_cost, total=total_mem_cost)
+        strategy.memory_cost = memory_cost
+
+    def collate_strategies(self) -> List[ShardingStrategy]:
+        strategy_list = []
+        for index, strategy in enumerate(self.predecessor_node.strategies_vector):
+            dim_partition_dict_mapping = {}
+            communication_action_mapping = {}
+            input_sharding_spec = strategy.output_sharding_specs[self.op_data["input"]]
+            dim_partition_dict_for_input = copy.deepcopy(input_sharding_spec.dim_partition_dict)
+            sum_dims, sum_mapping_dict = self.op_data['sum_info'].data
+
+            # TODO: a better way to handle the distributed sum is sum all the data on chip and then do all reduce
+            # among all the shard groups
+            recover_dims = []
+            dim_partition_dict_for_output = {}
+            for dim in dim_partition_dict_for_input:
+                if dim in sum_dims:
+                    recover_dims.append(dim)
+                elif dim in sum_mapping_dict:
+                    dim_partition_dict_for_output[sum_mapping_dict[dim]] = dim_partition_dict_for_input[dim]
+                else:
+                    raise RuntimeError(f'dim {dim} is not in sum_mapping_dict or sum_dims')
+
+            for dim in recover_dims:
+                dim_partition_dict_for_input.pop(dim)
+
+            dim_partition_dict_mapping = {
+                "input": dim_partition_dict_for_input,
+                "output": dim_partition_dict_for_output,
+            }
+            sharding_spec_mapping = self.to_sharding_spec_mapping(dim_partition_dict_mapping)
+            # add index into name to pass the duplicated check
+            # we keep same strategies with different name for node merging, and it will not increase the searching space,
+            # because in solver, this node will be merged into other nodes, and solver will not create a new variable for this node.
+            name = f'{sharding_spec_mapping["input"].sharding_sequence} -> {sharding_spec_mapping["output"].sharding_sequence}_{index}'
+
+            strategy = self.get_sharding_strategy(name=name,
+                                                  sharding_spec_mapping=sharding_spec_mapping,
+                                                  communication_action_mapping=communication_action_mapping)
+            strategy_list.append(strategy)
+
+        return strategy_list

colossalai/auto_parallel/tensor_shard/node_handler/sum_handler.py

@@ -0,0 +1,81 @@
+from typing import Dict, List
+
+import torch
+
+from ..sharding_strategy import OperationData, OperationDataType
+from .node_handler import NodeHandler
+from .registry import operator_registry
+from .strategy import StrategyGenerator, SumGenerator
+
+__all__ = ['SumHandler']
+
+
+@operator_registry.register(torch.Tensor.sum)
+@operator_registry.register(torch.sum)
+class SumHandler(NodeHandler):
+    """
+    A SumHandler which deals with the sharding strategies for torch.sum or torch.Tensor.sum.
+    """
+
+    def get_strategy_generator(self) -> List[StrategyGenerator]:
+        op_data_mapping = self.get_operation_data_mapping()
+        generators = []
+        generators.append(SumGenerator(op_data_mapping, self.device_mesh, self.node.args[0]))
+        return generators
+
+    def get_operation_data_mapping(self) -> Dict[str, OperationData]:
+        # check if the input operand is a parameter
+        if isinstance(self.node.args[0]._meta_data, torch.nn.parameter.Parameter):
+            data_type = OperationDataType.PARAM
+        else:
+            data_type = OperationDataType.ARG
+
+        input_data = self.node.args[0]._meta_data
+        physical_input_operand = OperationData(name=str(self.node.args[0]), type=data_type, data=input_data)
+
+        if len(self.node.args) > 1:
+            sum_dims = self.node.args[1]
+        else:
+            sum_dims = tuple(range(self.node.args[0]._meta_data.dim()))
+
+        if isinstance(sum_dims, int):
+            sum_dims = (sum_dims,)
+
+        # recover negative value to positive
+        num_dims = self.node.args[0]._meta_data.dim()
+        for i in range(len(sum_dims)):
+            if sum_dims[i] < 0:
+                sum_dims[i] += num_dims
+
+        # mapping the input dims to output dims
+        # For examples:
+        #   input: torch.rand(2, 3, 4, 5)
+        #   output: torch.sum(input, (0, 2))
+        #   sum_mapping_dict = {1: 0, 3: 1}
+        #   sum_mapping_dict[1] = 0 means the 0th dim of output is the 1st dim of input
+        #   sum_mapping_dict[3] = 1 means the 1st dim of output is the 3rd dim of input
+        sum_mapping_dict = {}
+        if 'keepdim' in self.node.kwargs and self.node.kwargs['keepdim']:
+            for i in range(num_dims):
+                sum_mapping_dict.update({i: i})
+        else:
+            output_index = 0
+            for i in range(num_dims):
+                if i not in sum_dims:
+                    sum_mapping_dict.update({i: output_index})
+                    output_index += 1
+            assert output_index == self.node._meta_data.dim()
+
+        sum_info = (sum_dims, sum_mapping_dict)
+        physical_shape_operand = OperationData(name='sum_info', type=OperationDataType.ARG, data=sum_info)
+
+        output_data = self.node._meta_data
+        physical_output_operand = OperationData(name=str(self.node), type=OperationDataType.OUTPUT, data=output_data)
+
+        mapping = {
+            "input": physical_input_operand,
+            "sum_info": physical_shape_operand,
+            "output": physical_output_operand
+        }
+
+        return mapping

tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_sum_handler.py

@@ -0,0 +1,235 @@
+from functools import partial
+
+import pytest
+import torch
+import torch.multiprocessing as mp
+import torch.nn as nn
+
+from colossalai.auto_parallel.tensor_shard.node_handler.conv_handler import ConvFunctionHandler
+from colossalai.auto_parallel.tensor_shard.node_handler.linear_handler import LinearFunctionHandler
+from colossalai.auto_parallel.tensor_shard.node_handler.sum_handler import SumHandler
+from colossalai.auto_parallel.tensor_shard.sharding_strategy import OperationData, OperationDataType, StrategiesVector
+from colossalai.device.device_mesh import DeviceMesh
+from colossalai.fx import ColoGraphModule, ColoTracer
+from colossalai.initialize import launch
+from colossalai.logging import disable_existing_loggers
+from colossalai.testing import assert_close, parameterize, rerun_if_address_is_in_use
+from colossalai.testing.pytest_wrapper import run_on_environment_flag
+from colossalai.utils import free_port
+from tests.test_auto_parallel.test_tensor_shard.test_node_handler.utils import numerical_test_for_node_strategy
+
+
+class LinearSumModel(nn.Module):
+
+    def __init__(self, sum_dims, keepdim):
+        super().__init__()
+        self.sum_dims = sum_dims
+        self.keepdim = keepdim
+
+    def forward(self, input, other):
+        linear_node = nn.functional.linear(input, other, bias=None)
+        if self.sum_dims is not None:
+            sum_node = torch.sum(linear_node, self.sum_dims, keepdim=self.keepdim)
+        else:
+            sum_node = torch.sum(linear_node, keepdim=self.keepdim)
+        return sum_node
+
+
+def check_sum_handler(rank, sum_dims, keepdim, world_size, port):
+    disable_existing_loggers()
+    launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
+    model = LinearSumModel(sum_dims=sum_dims, keepdim=keepdim).cuda()
+    physical_mesh_id = torch.arange(0, 4)
+    mesh_shape = (2, 2)
+    device_mesh = DeviceMesh(physical_mesh_id, mesh_shape, init_process_group=True)
+
+    input = torch.rand(8, 16, 64, 32).to('cuda')
+    other = torch.rand(64, 32).to('cuda')
+    # index of linear node in computation graph
+    node_index = 2
+    # total number of linear strategies
+    strategy_number = 24
+
+    numerical_test_for_node_strategy(model=model,
+                                     device_mesh=device_mesh,
+                                     node_index=node_index,
+                                     strategy_number=strategy_number,
+                                     input_args=[input, other],
+                                     meta_arg_names=['input', 'other'],
+                                     node_type='following')
+
+    tracer = ColoTracer()
+
+    # graph():
+    #     %input_1 : torch.Tensor [#users=1] = placeholder[target=input]
+    #     %other : torch.Tensor [#users=1] = placeholder[target=other]
+    #     %linear : [#users=1] = call_function[target=torch._C._nn.linear](args = (%input_1, %other), kwargs = {bias: None})
+    #     %sum_1 : [#users=1] = call_function[target=torch.sum](args = (%linear,), kwargs = {})
+    #     return sum_1
+    graph = tracer.trace(model,
+                         meta_args={
+                             "input": torch.rand(8, 16, 64, 32).to('meta'),
+                             "other": torch.rand(64, 32).to('meta'),
+                         })
+    gm = ColoGraphModule(model, graph)
+
+    previous_mod_node = list(graph.nodes)[2]
+    sum_node = list(graph.nodes)[3]
+    sum_strategies_vector = StrategiesVector(sum_node)
+    previous_strategies_vector = StrategiesVector(previous_mod_node)
+
+    # build handler
+
+    assert len(previous_strategies_vector) == 0
+    linear_handler = LinearFunctionHandler(node=previous_mod_node,
+                                           device_mesh=device_mesh,
+                                           strategies_vector=previous_strategies_vector)
+    linear_handler.register_strategy(compute_resharding_cost=False)
+    setattr(previous_mod_node, 'strategies_vector', previous_strategies_vector)
+
+    sum_handler = SumHandler(node=sum_node, device_mesh=device_mesh, strategies_vector=sum_strategies_vector)
+
+    sum_handler.register_strategy(compute_resharding_cost=False)
+
+    # sum handler is a following strategy handler, so the number of strategies is equal to the predecessor node.
+    assert len(sum_strategies_vector) == len(previous_strategies_vector)
+    strategy_name_list = [strategy.name for strategy in sum_strategies_vector]
+
+    # check operation data mapping
+    mapping = sum_handler.get_operation_data_mapping()
+
+    for name, op_data in mapping.items():
+        op_data: OperationData
+        # make sure they have valid values
+        assert op_data.data is not None
+
+    assert mapping['input'].name == "linear"
+    assert mapping['input'].data.is_meta
+    assert mapping['input'].data.shape == torch.Size([8, 16, 64, 64])
+    assert mapping['input'].type == OperationDataType.ARG
+    assert mapping['input'].logical_shape == torch.Size([8, 16, 64, 64])
+
+    assert mapping['output'].name == "sum_1"
+    sum_node_shape = torch.empty([8, 16, 64, 64]).sum(sum_dims, keepdim=keepdim).shape
+    assert mapping['output'].logical_shape == sum_node_shape
+    assert mapping['output'].type == OperationDataType.OUTPUT
+
+    # check strategy name
+    if sum_dims == (0, 2) and keepdim == False:
+        assert '[R, R, R, S1] -> [R, S1]_0' in strategy_name_list
+        assert '[R, S0, R, S1] -> [S0, S1]_1' in strategy_name_list
+        assert '[R, R, R, S1] -> [R, S1]_2' in strategy_name_list
+        assert '[R, R, R, S0] -> [R, S0]_3' in strategy_name_list
+        assert '[R, S1, R, S0] -> [S1, S0]_4' in strategy_name_list
+        assert '[R, R, R, S0] -> [R, S0]_5' in strategy_name_list
+        assert '[R, R, R, R] -> [R, R]_6' in strategy_name_list
+        assert '[R, S0, R, R] -> [S0, R]_7' in strategy_name_list
+        assert '[R, R, R, R] -> [R, R]_8' in strategy_name_list
+        assert '[R, R, R, R] -> [R, R]_9' in strategy_name_list
+        assert '[R, S1, R, R] -> [S1, R]_10' in strategy_name_list
+        assert '[R, R, R, R] -> [R, R]_11' in strategy_name_list
+        assert '[R, R, R, S1] -> [R, S1]_12' in strategy_name_list
+        assert '[R, R, R, S0] -> [R, S0]_13' in strategy_name_list
+        assert '[R, R, R, R] -> [R, R]_14' in strategy_name_list
+        assert '[R, R, R, R] -> [R, R]_15' in strategy_name_list
+        assert '[R, R, R, S0] -> [R, S0]_16' in strategy_name_list
+        assert '[R, R, R, S1] -> [R, S1]_17' in strategy_name_list
+        assert '[R, R, R, R] -> [R, R]_18' in strategy_name_list
+        assert '[R, S01, R, R] -> [S01, R]_19' in strategy_name_list
+        assert '[R, R, R, R] -> [R, R]_20' in strategy_name_list
+        assert '[R, R, R, R] -> [R, R]_21' in strategy_name_list
+        assert '[R, R, R, S01] -> [R, S01]_22' in strategy_name_list
+        assert '[R, R, R, R] -> [R, R]_23' in strategy_name_list
+
+    if sum_dims == (0, 2) and keepdim == True:
+        assert '[R, R, R, S1] -> [R, R, R, S1]_0' in strategy_name_list
+        assert '[R, S0, R, S1] -> [R, S0, R, S1]_1' in strategy_name_list
+        assert '[R, R, R, S1] -> [R, R, R, S1]_2' in strategy_name_list
+        assert '[R, R, R, S0] -> [R, R, R, S0]_3' in strategy_name_list
+        assert '[R, S1, R, S0] -> [R, S1, R, S0]_4' in strategy_name_list
+        assert '[R, R, R, S0] -> [R, R, R, S0]_5' in strategy_name_list
+        assert '[R, R, R, R] -> [R, R, R, R]_6' in strategy_name_list
+        assert '[R, S0, R, R] -> [R, S0, R, R]_7' in strategy_name_list
+        assert '[R, R, R, R] -> [R, R, R, R]_8' in strategy_name_list
+        assert '[R, R, R, R] -> [R, R, R, R]_9' in strategy_name_list
+        assert '[R, S1, R, R] -> [R, S1, R, R]_10' in strategy_name_list
+        assert '[R, R, R, R] -> [R, R, R, R]_11' in strategy_name_list
+        assert '[R, R, R, S1] -> [R, R, R, S1]_12' in strategy_name_list
+        assert '[R, R, R, S0] -> [R, R, R, S0]_13' in strategy_name_list
+        assert '[R, R, R, R] -> [R, R, R, R]_14' in strategy_name_list
+        assert '[R, R, R, R] -> [R, R, R, R]_15' in strategy_name_list
+        assert '[R, R, R, S0] -> [R, R, R, S0]_16' in strategy_name_list
+        assert '[R, R, R, S1] -> [R, R, R, S1]_17' in strategy_name_list
+        assert '[R, R, R, R] -> [R, R, R, R]_18' in strategy_name_list
+        assert '[R, S01, R, R] -> [R, S01, R, R]_19' in strategy_name_list
+        assert '[R, R, R, R] -> [R, R, R, R]_20' in strategy_name_list
+        assert '[R, R, R, R] -> [R, R, R, R]_21' in strategy_name_list
+        assert '[R, R, R, S01] -> [R, R, R, S01]_22' in strategy_name_list
+        assert '[R, R, R, R] -> [R, R, R, R]_23' in strategy_name_list
+
+    if sum_dims == 1 and keepdim == False:
+        assert '[S0, R, R, S1] -> [S0, R, S1]_0' in strategy_name_list
+        assert '[R, R, R, S1] -> [R, R, S1]_1' in strategy_name_list
+        assert '[R, R, S0, S1] -> [R, S0, S1]_2' in strategy_name_list
+        assert '[S1, R, R, S0] -> [S1, R, S0]_3' in strategy_name_list
+        assert '[R, R, R, S0] -> [R, R, S0]_4' in strategy_name_list
+        assert '[R, R, S1, S0] -> [R, S1, S0]_5' in strategy_name_list
+        assert '[S0, R, R, R] -> [S0, R, R]_6' in strategy_name_list
+        assert '[R, R, R, R] -> [R, R, R]_7' in strategy_name_list
+        assert '[R, R, S0, R] -> [R, S0, R]_8' in strategy_name_list
+        assert '[S1, R, R, R] -> [S1, R, R]_9' in strategy_name_list
+        assert '[R, R, R, R] -> [R, R, R]_10' in strategy_name_list
+        assert '[R, R, S1, R] -> [R, S1, R]_11' in strategy_name_list
+        assert '[R, R, R, S1] -> [R, R, S1]_12' in strategy_name_list
+        assert '[R, R, R, S0] -> [R, R, S0]_13' in strategy_name_list
+        assert '[R, R, R, R] -> [R, R, R]_14' in strategy_name_list
+        assert '[R, R, R, R] -> [R, R, R]_15' in strategy_name_list
+        assert '[R, R, R, S0] -> [R, R, S0]_16' in strategy_name_list
+        assert '[R, R, R, S1] -> [R, R, S1]_17' in strategy_name_list
+        assert '[S01, R, R, R] -> [S01, R, R]_18' in strategy_name_list
+        assert '[R, R, R, R] -> [R, R, R]_19' in strategy_name_list
+        assert '[R, R, S01, R] -> [R, S01, R]_20' in strategy_name_list
+        assert '[R, R, R, R] -> [R, R, R]_21' in strategy_name_list
+        assert '[R, R, R, S01] -> [R, R, S01]_22' in strategy_name_list
+        assert '[R, R, R, R] -> [R, R, R]_23' in strategy_name_list
+
+    if sum_dims == 1 and keepdim == True:
+        assert '[S0, R, R, S1] -> [S0, R, R, S1]_0' in strategy_name_list
+        assert '[R, R, R, S1] -> [R, R, R, S1]_1' in strategy_name_list
+        assert '[R, R, S0, S1] -> [R, R, S0, S1]_2' in strategy_name_list
+        assert '[S1, R, R, S0] -> [S1, R, R, S0]_3' in strategy_name_list
+        assert '[R, R, R, S0] -> [R, R, R, S0]_4' in strategy_name_list
+        assert '[R, R, S1, S0] -> [R, R, S1, S0]_5' in strategy_name_list
+        assert '[S0, R, R, R] -> [S0, R, R, R]_6' in strategy_name_list
+        assert '[R, R, R, R] -> [R, R, R, R]_7' in strategy_name_list
+        assert '[R, R, S0, R] -> [R, R, S0, R]_8' in strategy_name_list
+        assert '[S1, R, R, R] -> [S1, R, R, R]_9' in strategy_name_list
+        assert '[R, R, R, R] -> [R, R, R, R]_10' in strategy_name_list
+        assert '[R, R, S1, R] -> [R, R, S1, R]_11' in strategy_name_list
+        assert '[R, R, R, S1] -> [R, R, R, S1]_12' in strategy_name_list
+        assert '[R, R, R, S0] -> [R, R, R, S0]_13' in strategy_name_list
+        assert '[R, R, R, R] -> [R, R, R, R]_14' in strategy_name_list
+        assert '[R, R, R, R] -> [R, R, R, R]_15' in strategy_name_list
+        assert '[R, R, R, S0] -> [R, R, R, S0]_16' in strategy_name_list
+        assert '[R, R, R, S1] -> [R, R, R, S1]_17' in strategy_name_list
+        assert '[S01, R, R, R] -> [S01, R, R, R]_18' in strategy_name_list
+        assert '[R, R, R, R] -> [R, R, R, R]_19' in strategy_name_list
+        assert '[R, R, S01, R] -> [R, R, S01, R]_20' in strategy_name_list
+        assert '[R, R, R, R] -> [R, R, R, R]_21' in strategy_name_list
+        assert '[R, R, R, S01] -> [R, R, R, S01]_22' in strategy_name_list
+        assert '[R, R, R, R] -> [R, R, R, R]_23' in strategy_name_list
+
+
+@run_on_environment_flag(name='AUTO_PARALLEL')
+@pytest.mark.dist
+@rerun_if_address_is_in_use()
+@parameterize('sum_dims', [(0, 2), 1])
+@parameterize('keepdim', [False, True])
+def test_sum_handler(sum_dims, keepdim):
+    world_size = 4
+    run_func = partial(check_sum_handler, sum_dims=sum_dims, keepdim=keepdim, world_size=world_size, port=free_port())
+    mp.spawn(run_func, nprocs=world_size)
+
+
+if __name__ == '__main__':
+    test_sum_handler()