[autoparallel] Add conv handler to generate strategies and costs info for conv (#1467)

colossalai/auto_parallel/solver/conv_handler.py

+import operator
+from functools import reduce
+import torch
+from colossalai.tensor.sharding_spec import ShardingSpec
+from colossalai.auto_parallel.solver.sharding_strategy import ShardingStrategy, StrategiesVector
+
+
+class ConvHandler:
+    '''
+    The ConvHandler is used to generate every possible strategies for a Conv node.
+
+    Argument:
+        input_node(Node): the input node in conv node argument list.
+        input_index(int): the index of input node in the conv node argument list.
+        weight(torch.Tensor): Weight of the conv node.
+        output_node(Node): Output_node is the output of the conv node.
+        device_mesh(DeviceMesh): A logical view of a physical mesh.
+        strategies_vector(StrategiesVector): all the strategies generated in this handler will be recorded into the strategies_vector.
+        shape_consistency_manager(ShapeConsistencyManager): ShapeConsistencyManager will give the resharding costs of the different sharding specs. 
+    '''
+
+    def __init__(self, input_node, input_index, weight, output_node, device_mesh, strategies_vector,
+                 shape_consistency_manager):
+        self.input_node = input_node
+        self.input_data = self.input_node._meta_data
+        self.weight = weight
+        self.input_index = input_index
+        self.output_node = output_node
+        self.output = self.output_node._meta_data
+        self.device_mesh = device_mesh
+        self.strategies_vector = strategies_vector
+        self.shape_consistency_manager = shape_consistency_manager
+        self._sanity_check()
+
+    def _sanity_check(self):
+        '''
+        In sanity check, we need make sure the input data having correct dimension size.
+        For Conv1d, the dim of input data should be 3([N, C, L]).
+        For Conv2d, the dim of input data should be 4([N, C, H, W]).
+        For Conv3d, the dim of input data should be 5([N, C, H, W, D]).
+        '''
+        assert self.input_data.dim() in (3, 4,
+                                         5), f'We suppose the dim of input fed into conv op should in range of [3, 5].'
+
+    def _generate_sharding_spec_for_input(self, dim_partition_dict_for_input):
+        '''
+        Generate sharding spec for the input node.
+        '''
+        entire_shape_for_input = self.input_data.shape
+        sharding_spec_for_input = ShardingSpec(device_mesh=self.device_mesh,
+                                               entire_shape=entire_shape_for_input,
+                                               dim_partition_dict=dim_partition_dict_for_input)
+        return sharding_spec_for_input
+
+    def _generate_sharding_spec_for_weight(self, dim_partition_dict_for_weight):
+        '''
+        Generate sharding spec for the weight.
+        '''
+        entire_shape_for_weight = self.weight.shape
+        sharding_spec_for_weight = ShardingSpec(device_mesh=self.device_mesh,
+                                                entire_shape=entire_shape_for_weight,
+                                                dim_partition_dict=dim_partition_dict_for_weight)
+        return sharding_spec_for_weight
+
+    def _generate_sharding_spec_for_output(self, dim_partition_dict_for_output):
+        '''
+        Generate sharding spec for the output node.
+        '''
+        entire_shape_for_output = self.output.shape
+        sharding_spec_for_output = ShardingSpec(device_mesh=self.device_mesh,
+                                                entire_shape=entire_shape_for_output,
+                                                dim_partition_dict=dim_partition_dict_for_output)
+        return sharding_spec_for_output
+
+    def _generate_resharding_costs(self, resharding_costs, sharding_spec_for_input):
+        '''
+        Compute the resharding costs with this specific strategy.
+
+        Note: The resharding_cost of weight is NOT counted.
+
+        Argument:
+            resharding_costs(Dict[int, List[float]]): The resharding cost generated in this method will be appended into this dictionary. 
+                                                      Resharding_cost[i][j] means the cost of i-th argument in the output node argument list
+                                                      with j-th strategy in its strategies_vector transforms to sharding spec wanted in this
+                                                      strategy.
+            sharding_spec_for_input(ShardingSpec): ShardingSpec of the input node.
+        '''
+        # The resharding_cost of weight is counted due to sharing weight cases.
+        resharding_costs[self.input_index] = []
+        for stategy in self.input_node.strategies_vector.strategies:
+            _, _, resharding_cost = self.shape_consistency_manager.shape_consistency(stategy, sharding_spec_for_input)
+            resharding_costs[self.input_index].append(resharding_cost)
+
+    def _generate_compute_cost(self, bs, channel_in, channel_out):
+        '''
+        Compute the computation cost per device with this specific strategy.
+
+        Note: compute_cost need to be devided by TFLOPS, now it just shows the computation size.
+
+        Argument:
+            bs(int): Batch size of the input data.
+            channel_in(int): The channel dimension of input data.
+            channel_out(int): The out channel of the conv weight.
+
+        Return:
+            compute_cost(float): Computation cost per device with this specific strategy
+        '''
+        # TODO: compute_cost need to be devided by TFLOPS, now it just shows the computation size.
+        # 1D: (L) * N * Cout * Cin * kernel
+        # 2D: (H * W) * N * Cout * Cin * kernel
+        # 3D: (H * W  * D) * N * Cout * Cin * kernel
+        output_size = self.output.shape[2:]
+        output_size_product = reduce(operator.mul, output_size, 1)
+        kernel_size = self.weight.shape[2:]
+        kernel_size_product = reduce(operator.mul, kernel_size, 1)
+        compute_cost = output_size_product * bs * channel_in * channel_out * kernel_size_product
+        return compute_cost
+
+    def split_input_batch_weight_out_channel(self, mesh_dim_0, mesh_dim_1):
+        name = f'S{mesh_dim_0}S{mesh_dim_1} = S{mesh_dim_0}R x RS{mesh_dim_1}'
+
+        dim_partition_dict_for_input = {0: [mesh_dim_0]}
+        sharding_spec_for_input = self._generate_sharding_spec_for_input(dim_partition_dict_for_input)
+
+        dim_partition_dict_for_weight = {1: [mesh_dim_1]}
+        sharding_spec_for_weight = self._generate_sharding_spec_for_weight(dim_partition_dict_for_weight)
+
+        dim_partition_dict_for_output = {0: [mesh_dim_0], 1: [mesh_dim_1]}
+        sharding_spec_for_ouput = self._generate_sharding_spec_for_output(dim_partition_dict_for_output)
+
+        # generate resharding cost for this strategy
+        resharding_costs = {}
+        self._generate_resharding_costs(resharding_costs, sharding_spec_for_input)
+
+        # compute the computation cost of this strategy
+        bs = self.input_data.shape[0] // self.device_mesh.shape[mesh_dim_0]
+        channel_in = self.input_data.shape[1]
+        channel_out = self.weight.shape[1] // self.device_mesh.shape[mesh_dim_1]
+        compute_cost = self._generate_compute_cost(bs, channel_in, channel_out)
+
+        # compute the memory cost of this strategy
+        dtype = self.input_data.dtype
+        numel = self.output.numel()
+        size_per_elem_bytes = torch.tensor([], dtype=dtype).element_size()
+        sharding_size = self.device_mesh.shape[mesh_dim_0] * self.device_mesh.shape[mesh_dim_1]
+        memory_cost = numel * size_per_elem_bytes / sharding_size
+
+        # This strategy do not need to do all_reduce operation
+        communication_cost = 0
+        sharding_strategies = ShardingStrategy(name,
+                                               output_sharding_spec=sharding_spec_for_ouput,
+                                               compute_cost=compute_cost,
+                                               communication_cost=communication_cost,
+                                               memory_cost=memory_cost,
+                                               resharding_costs=resharding_costs,
+                                               input_shardings=(sharding_spec_for_input, sharding_spec_for_weight))
+        self.strategies_vector.strategies.append(sharding_strategies)
+
+    def split_input_both_dim_weight_in_channel(self, mesh_dim_0, mesh_dim_1):
+        name = f'S{mesh_dim_0}R = S{mesh_dim_0}S{mesh_dim_1} x S{mesh_dim_1}R'
+
+        dim_partition_dict_for_input = {0: [mesh_dim_0], 1: [mesh_dim_1]}
+        sharding_spec_for_input = self._generate_sharding_spec_for_input(dim_partition_dict_for_input)
+
+        dim_partition_dict_for_weight = {0: [mesh_dim_0]}
+        sharding_spec_for_weight = self._generate_sharding_spec_for_weight(dim_partition_dict_for_weight)
+
+        dim_partition_dict_for_output = {0: [mesh_dim_0]}
+        sharding_spec_for_ouput = self._generate_sharding_spec_for_output(dim_partition_dict_for_output)
+
+        # generate resharding cost for this strategy
+        resharding_costs = {}
+        self._generate_resharding_costs(resharding_costs, sharding_spec_for_input)
+
+        # compute the computation cost of this strategy
+        bs = self.input_data.shape[0] // self.device_mesh.shape[mesh_dim_0]
+        channel_in = self.input_data.shape[1] // self.device_mesh.shape[mesh_dim_1]
+        channel_out = self.weight.shape[1]
+        compute_cost = self._generate_compute_cost(bs, channel_in, channel_out)
+
+        # compute the memory cost of this strategy
+        dtype = self.input_data.dtype
+        numel = self.output.numel()
+        size_per_elem_bytes = torch.tensor([], dtype=dtype).element_size()
+        sharding_size = self.device_mesh.shape[mesh_dim_0]
+        memory_cost = numel * size_per_elem_bytes / sharding_size
+
+        # compute the communication cost of this strategy
+        communication_cost = self.device_mesh.all_reduce_cost(memory_cost, mesh_dim_1)
+        sharding_strategies = ShardingStrategy(name,
+                                               output_sharding_spec=sharding_spec_for_ouput,
+                                               compute_cost=compute_cost,
+                                               communication_cost=communication_cost,
+                                               memory_cost=memory_cost,
+                                               resharding_costs=resharding_costs,
+                                               input_shardings=(sharding_spec_for_input, sharding_spec_for_weight))
+        self.strategies_vector.strategies.append(sharding_strategies)
+
+    def split_input_in_channel_weight_both_channel(self, mesh_dim_0, mesh_dim_1):
+        name = f'RS{mesh_dim_1} = RS{mesh_dim_0} x S{mesh_dim_0}S{mesh_dim_1}'
+
+        dim_partition_dict_for_input = {1: [mesh_dim_0]}
+        sharding_spec_for_input = self._generate_sharding_spec_for_input(dim_partition_dict_for_input)
+
+        dim_partition_dict_for_weight = {0: [mesh_dim_0], 1: [mesh_dim_1]}
+        sharding_spec_for_weight = self._generate_sharding_spec_for_weight(dim_partition_dict_for_weight)
+
+        dim_partition_dict_for_output = {1: [mesh_dim_1]}
+        sharding_spec_for_ouput = self._generate_sharding_spec_for_output(dim_partition_dict_for_output)
+
+        # generate resharding cost for this strategy
+        resharding_costs = {}
+        self._generate_resharding_costs(resharding_costs, sharding_spec_for_input)
+
+        # compute the computation cost of this strategy
+        bs = self.input_data.shape[0]
+        channel_in = self.input_data.shape[1] // self.device_mesh.shape[mesh_dim_0]
+        channel_out = self.weight.shape[1] // self.device_mesh.shape[mesh_dim_1]
+        compute_cost = self._generate_compute_cost(bs, channel_in, channel_out)
+
+        # compute the memory cost of this strategy
+        dtype = self.input_data.dtype
+        numel = self.output.numel()
+        size_per_elem_bytes = torch.tensor([], dtype=dtype).element_size()
+        sharding_size = self.device_mesh.shape[mesh_dim_0]
+        memory_cost = numel * size_per_elem_bytes / sharding_size
+
+        # compute the communication cost of this strategy
+        communication_cost = self.device_mesh.all_reduce_cost(memory_cost, mesh_dim_1)
+        sharding_strategies = ShardingStrategy(name,
+                                               output_sharding_spec=sharding_spec_for_ouput,
+                                               compute_cost=compute_cost,
+                                               communication_cost=communication_cost,
+                                               memory_cost=memory_cost,
+                                               resharding_costs=resharding_costs,
+                                               input_shardings=(sharding_spec_for_input, sharding_spec_for_weight))
+        self.strategies_vector.strategies.append(sharding_strategies)
+
+    def split_weight_out_channel(self, mesh_dim_0):
+        name = f'RS{mesh_dim_0} = RR x RS{mesh_dim_0}'
+
+        dim_partition_dict_for_input = {}
+        sharding_spec_for_input = self._generate_sharding_spec_for_input(dim_partition_dict_for_input)
+
+        dim_partition_dict_for_weight = {1: [mesh_dim_0]}
+        sharding_spec_for_weight = self._generate_sharding_spec_for_weight(dim_partition_dict_for_weight)
+
+        dim_partition_dict_for_output = {1: [mesh_dim_0]}
+        sharding_spec_for_ouput = self._generate_sharding_spec_for_output(dim_partition_dict_for_output)
+
+        # generate resharding cost for this strategy
+        resharding_costs = {}
+        self._generate_resharding_costs(resharding_costs, sharding_spec_for_input)
+
+        # compute the computation cost of this strategy
+        bs = self.input_data.shape[0]
+        channel_in = self.input_data.shape[1]
+        channel_out = self.weight.shape[1] // self.device_mesh.shape[mesh_dim_0]
+        compute_cost = self._generate_compute_cost(bs, channel_in, channel_out)
+
+        # compute the memory cost of this strategy
+        dtype = self.input_data.dtype
+        numel = self.output.numel()
+        size_per_elem_bytes = torch.tensor([], dtype=dtype).element_size()
+        sharding_size = self.device_mesh.shape[mesh_dim_0]
+        memory_cost = numel * size_per_elem_bytes / sharding_size
+
+        # This strategy do not need to do all_reduce operation
+        communication_cost = 0
+
+        sharding_strategies = ShardingStrategy(name,
+                                               output_sharding_spec=sharding_spec_for_ouput,
+                                               compute_cost=compute_cost,
+                                               communication_cost=communication_cost,
+                                               memory_cost=memory_cost,
+                                               resharding_costs=resharding_costs,
+                                               input_shardings=(sharding_spec_for_input, sharding_spec_for_weight))
+        self.strategies_vector.strategies.append(sharding_strategies)
+
+    def non_split(self):
+        name = f'RR = RR x RR'
+
+        dim_partition_dict_for_input = {}
+        sharding_spec_for_input = self._generate_sharding_spec_for_input(dim_partition_dict_for_input)
+
+        dim_partition_dict_for_weight = {}
+        sharding_spec_for_weight = self._generate_sharding_spec_for_weight(dim_partition_dict_for_weight)
+
+        dim_partition_dict_for_output = {}
+        sharding_spec_for_ouput = self._generate_sharding_spec_for_output(dim_partition_dict_for_output)
+
+        # generate resharding cost for this strategy
+        resharding_costs = {}
+        self._generate_resharding_costs(resharding_costs, sharding_spec_for_input)
+
+        # compute the computation cost of this strategy
+        bs = self.input_data.shape[0]
+        channel_in = self.input_data.shape[1]
+        channel_out = self.weight.shape[1]
+        compute_cost = self._generate_compute_cost(bs, channel_in, channel_out)
+
+        # compute the memory cost of this strategy
+        dtype = self.input_data.dtype
+        numel = self.output.numel()
+        size_per_elem_bytes = torch.tensor([], dtype=dtype).element_size()
+        memory_cost = numel * size_per_elem_bytes
+
+        # This strategy do not need to do all_reduce operation
+        communication_cost = 0
+
+        sharding_strategies = ShardingStrategy(name,
+                                               output_sharding_spec=sharding_spec_for_ouput,
+                                               compute_cost=compute_cost,
+                                               communication_cost=communication_cost,
+                                               memory_cost=memory_cost,
+                                               resharding_costs=resharding_costs,
+                                               input_shardings=(sharding_spec_for_input, sharding_spec_for_weight))
+        self.strategies_vector.strategies.append(sharding_strategies)
+
+    def register_strategy_into_strategies_vector(self):
+        '''
+        Generate every possible strategies for a Conv node, and record all strategies into the strategies_vector.
+
+        Example:
+            physical_mesh_id = torch.arange(0, 4)
+            mesh_shape = (2, 2)
+            # [[0, 1]
+            #  [2, 3]]
+            device_mesh = DeviceMesh(physical_mesh_id, mesh_shape)
+            shape_consistency_manager = ShapeConsistencyManager()
+
+            model = ConvModel(16, 32)
+            input_sample = {'x': torch.rand(4, 16, 64, 64).to('meta')}
+            # graph():
+            #     %x : torch.Tensor [#users=1] = placeholder[target=x]
+            #     %mul : [#users=1] = call_function[target=operator.mul](args = (%x, 2), kwargs = {})
+            #     %conv : [#users=1] = call_module[target=conv](args = (%mul,), kwargs = {})
+            #     return conv
+            graph = tracer.trace(root=model, meta_args=input_sample)
+            gm = GraphModule(model, graph, model.__class__.__name__)
+            gm.recompile()
+            # [x, mul, conv, output]
+            nodes = [node for node in gm.graph.nodes]
+
+            # strategies_for_input = [[R, R, R, R], [R, S0, R, R], [R, S1, R, R], [S0, R, R, R], [S0, S1, R, R], [S1, R, R, R], [S1, S0, R, R]]
+            strategies_vector_for_input = StrategiesVector(node=nodes[0], in_nodes=[nodes[1], 2], strategies=strategies_for_input)
+            setattr(nodes[1], 'strategies_vector', strategies_vector_for_input)
+            
+            strategies_vector = StrategiesVector(node=nodes[2], in_nodes=[nodes[1], ])
+            conv_handler = ConvHandler(input_node=nodes[1], input_index=0, weight=dict(gm.named_modules())[nodes[2].name].weight, output_node=nodes[2],
+                                    device_mesh=device_mesh, strategies_vector=strategies_vector, shape_consistency_manager=shape_consistency_manager)
+            conv_handler.register_strategy_into_strategies_vector()
+            for strategy in conv_handler.strategies_vector.strategies:
+                print(f'{strategy.name}: compute_cost is {strategy.compute_cost}, communication_cost is {strategy.communication_cost}, memory_cost is {strategy.memory_cost}, resharding_costs is {strategy.resharding_costs}')
+        
+        Output:
+            S0S1 = S0R x RS1: compute_cost is 8856576, communication_cost is 0, memory_cost is 492032.0, resharding_costs is {0: [0, 32769.001, 131074.2, 0, 32769.1, 131074.2, 98307.201]}
+            S1S0 = S1R x RS0: compute_cost is 8856576, communication_cost is 0, memory_cost is 492032.0, resharding_costs is {0: [0, 131074.2, 32769.001, 131074.2, 98307.201, 0, 32769.1]}
+            S0R = S0S1 x S1R: compute_cost is 8856576, communication_cost is 984065.01, memory_cost is 984064.0, resharding_costs is {0: [0, 65538.002, 0, 0, 0, 65538.002, 196614.402]}
+            S1R = S1S0 x S0R: compute_cost is 8856576, communication_cost is 984065.01, memory_cost is 984064.0, resharding_costs is {0: [0, 0, 65538.002, 65538.002, 196614.402, 0, 0]}
+            RS1 = RS0 x S0S1: compute_cost is 8856576, communication_cost is 984065.01, memory_cost is 984064.0, resharding_costs is {0: [0, 0, 131074.2, 32769.001, 98307.201, 131074.2, 32769.1]}
+            RS0 = RS1 x S1S0: compute_cost is 8856576, communication_cost is 984065.01, memory_cost is 984064.0, resharding_costs is {0: [0, 131074.2, 0, 131074.2, 32769.1, 32769.001, 98307.201]}
+            RS0 = RR x RS0: compute_cost is 17713152, communication_cost is 0, memory_cost is 984064.0, resharding_costs is {0: [0, 65537.1, 65537.1, 65537.1, 131075.30000000002, 65537.1, 131075.30000000002]}
+            RS1 = RR x RS1: compute_cost is 17713152, communication_cost is 0, memory_cost is 984064.0, resharding_costs is {0: [0, 65537.1, 65537.1, 65537.1, 131075.30000000002, 65537.1, 131075.30000000002]}
+            RR = RR x RR: compute_cost is 35426304, communication_cost is 0, memory_cost is 1968128, resharding_costs is {0: [0, 65537.1, 65537.1, 65537.1, 131075.30000000002, 65537.1, 131075.30000000002]}
+        '''
+        # SS = SR x RS
+        self.split_input_batch_weight_out_channel(0, 1)
+        self.split_input_batch_weight_out_channel(1, 0)
+
+        # SR = SS x SR
+        self.split_input_both_dim_weight_in_channel(0, 1)
+        self.split_input_both_dim_weight_in_channel(1, 0)
+
+        # RS = RS x SS
+        self.split_input_in_channel_weight_both_channel(0, 1)
+        self.split_input_in_channel_weight_both_channel(1, 0)
+
+        # RS = RR x RS
+        self.split_weight_out_channel(0)
+        self.split_weight_out_channel(1)
+
+        # RR= RR x RR
+        self.non_split()

colossalai/auto_parallel/solver/sharding_strategy.py

+class ShardingStrategy:
+    '''
+    ShardingStrategy is a structure containing sharding strategies of inputs and output of this node
+    and costs information using in solver.
+
+    Argument:
+        name(str): express the sharding strategies in string, such as 'S0S1 = S0R x RS1'.
+        output_sharding_spec(ShardingSpec): ShardingSpec of the output node.
+        compute_cost(float): Computation cost to complete this strategy.(default to 0)
+        communication_cost(float): Communication cost to complete this strategy.(default to 0)
+        memory_cost(float): Memory cost of the output node using this strategy.(default to 0)
+        resharding_costs(Dict[int, List[float]]): resharding_cost[i][j] means the cost of i-th argument in the output node argument list
+                                                  with j-th strategy in its strategies_vector transforms to sharding spec wanted in this
+                                                  strategy.(default to None)
+        input_shardings(List(ShardingSpec)): The ShardingSpecs of the input nodes.
+    '''
+
+    def __init__(self,
+                 name,
+                 output_sharding_spec,
+                 compute_cost=0,
+                 communication_cost=0,
+                 memory_cost=0,
+                 resharding_costs=None,
+                 input_shardings=None):
+        self.name = name
+        self.output_sharding_spec = output_sharding_spec
+        self.compute_cost = compute_cost
+        self.communication_cost = communication_cost
+        self.memory_cost = memory_cost
+        self.resharding_costs = resharding_costs
+        self.input_shardings = input_shardings
+
+
+class StrategiesVector:
+    '''
+    Each node in fx graph will have a corresponding StrategiesVector, to store all the possible
+    strategies of the node.
+
+    Argument:
+        node(Node): node to build corresponding strategies_vector.
+        in_nodes(List[Node]): input nodes in the argument list of the node.
+        following_nodes(List[Node]): the nodes take the target node as their argument.
+        strategies(List[ShardingStrategy]): enumerate all the possible sharding strategies of the node.
+    '''
+
+    def __init__(self, node, in_nodes, following_nodes=None, strategies=[]):
+        self.node = node
+        self.in_nodes = in_nodes
+        self.following_nodes = following_nodes
+        self.strategies = strategies
+
+    def check_merge(self):
+        pass

colossalai/tensor/sharding_spec.py

@@ -199,7 +199,7 @@ class ShardingSpec:
             if not dim_spec.is_replica:
                 if index not in new_dim_partition_dict:
                     new_dim_partition_dict[index] = []
-                new_dim_partition_dict[index].append(dim_spec.shard_list)
+                new_dim_partition_dict[index].extend(dim_spec.shard_list)
         self.dim_partition_dict = new_dim_partition_dict
 
     def sharding_sequence_difference(self, other):

tests/test_auto_parallel/test_conv_handler.py

+import torch
+from torch.fx import GraphModule
+import torch.nn as nn
+import pytest
+
+from colossalai.fx.proxy import ColoProxy
+from colossalai.fx.tracer.tracer import ColoTracer
+from colossalai.tensor.sharding_spec import ShardingSpec, _DimSpec
+from colossalai.auto_parallel.solver.conv_handler import ConvHandler
+from colossalai.auto_parallel.solver.sharding_strategy import ShardingStrategy, StrategiesVector
+from colossalai.tensor.shape_consistency import ShapeConsistencyManager
+from colossalai.device.device_mesh import DeviceMesh
+
+
+class ConvModel(nn.Module):
+
+    def __init__(self, c_in, c_out):
+        super().__init__()
+        self.conv = nn.Conv2d(c_in, c_out, kernel_size=3)
+
+    def forward(self, x):
+        x = x * 2
+        x = self.conv(x)
+        return x
+
+
+def test_conv_handler():
+    physical_mesh_id = torch.arange(0, 4)
+    mesh_shape = (2, 2)
+    # [[0, 1]
+    #  [2, 3]]
+    device_mesh = DeviceMesh(physical_mesh_id, mesh_shape)
+    entire_shape = torch.Size((4, 16, 64, 64))
+    shape_consistency_manager = ShapeConsistencyManager()
+
+    tracer = ColoTracer()
+    model = ConvModel(16, 32)
+    input_sample = {'x': torch.rand(4, 16, 64, 64).to('meta')}
+    # graph():
+    #     %x : torch.Tensor [#users=1] = placeholder[target=x]
+    #     %mul : [#users=1] = call_function[target=operator.mul](args = (%x, 2), kwargs = {})
+    #     %conv : [#users=1] = call_module[target=conv](args = (%mul,), kwargs = {})
+    #     return conv
+    graph = tracer.trace(root=model, meta_args=input_sample)
+    gm = GraphModule(model, graph, model.__class__.__name__)
+    gm.recompile()
+    # [x, mul, conv, output]
+    nodes = [node for node in gm.graph.nodes]
+
+    strategies_for_input = []
+    sharding_option = (None, 0, 1)
+    for first_sharding_index in sharding_option:
+        for second_sharding_index in sharding_option:
+            if first_sharding_index is not None and second_sharding_index == first_sharding_index:
+                continue
+            if first_sharding_index is None:
+                first_dim_spec = _DimSpec([])
+            else:
+                first_dim_spec = _DimSpec([first_sharding_index])
+
+            if second_sharding_index is None:
+                second_dim_spec = _DimSpec([])
+            else:
+                second_dim_spec = _DimSpec([second_sharding_index])
+
+            replica_dim_spec = _DimSpec([])
+            sharding_sequence = [first_dim_spec, second_dim_spec, replica_dim_spec, replica_dim_spec]
+            sharding_spec = ShardingSpec(device_mesh=device_mesh,
+                                         entire_shape=entire_shape,
+                                         sharding_sequence=sharding_sequence)
+            strategies_for_input.append(sharding_spec)
+
+    # strategies_for_input = [[R, R, R, R], [R, S0, R, R], [R, S1, R, R], [S0, R, R, R], [S0, S1, R, R], [S1, R, R, R], [S1, S0, R, R]]
+    strategies_vector_for_input = StrategiesVector(node=nodes[0],
+                                                   in_nodes=[nodes[1], 2],
+                                                   strategies=strategies_for_input)
+    setattr(nodes[1], 'strategies_vector', strategies_vector_for_input)
+
+    strategies_vector = StrategiesVector(node=nodes[2], in_nodes=[
+        nodes[1],
+    ])
+    conv_handler = ConvHandler(input_node=nodes[1],
+                               input_index=0,
+                               weight=dict(gm.named_modules())[nodes[2].name].weight,
+                               output_node=nodes[2],
+                               device_mesh=device_mesh,
+                               strategies_vector=strategies_vector,
+                               shape_consistency_manager=shape_consistency_manager)
+    conv_handler.register_strategy_into_strategies_vector()
+
+    # ['S0S1 = S0R x RS1', 'S1S0 = S1R x RS0', 'S0R = S0S1 x S1R', 'S1R = S1S0 x S0R', 'RS1 = RS0 x S0S1', 'RS0 = RS1 x S1S0', 'RS0 = RR x RS0', 'RS1 = RR x RS1', 'RR = RR x RR']
+    strategy_name_list = [strategy.name for strategy in conv_handler.strategies_vector.strategies]
+
+    # SS = SR x RS
+    assert 'S0S1 = S0R x RS1' in strategy_name_list
+    assert 'S1S0 = S1R x RS0' in strategy_name_list
+
+    # SR = SS x SR
+    assert 'S0R = S0S1 x S1R' in strategy_name_list
+    assert 'S1R = S1S0 x S0R' in strategy_name_list
+
+    # RS = RS x SS
+    assert 'RS0 = RS1 x S1S0' in strategy_name_list
+    assert 'RS1 = RS0 x S0S1' in strategy_name_list
+
+    # RS = RR x RS
+    assert 'RS0 = RR x RS0' in strategy_name_list
+    assert 'RS1 = RR x RS1' in strategy_name_list
+
+    # RR= RR x RR
+    assert 'RR = RR x RR' in strategy_name_list
+
+
+if __name__ == '__main__':
+    test_conv_handler()