Merge branch 'main' of https://github.com/hpcaitech/ColossalAI

colossalai/auto_parallel/tensor_shard/node_handler/embedding_handler.py

+from typing import Dict, List, Union
+
+import torch
+import torch.nn.functional as F
+
+from colossalai.auto_parallel.tensor_shard.utils import update_partition_dim
+from colossalai.logging import get_dist_logger
+from colossalai.tensor.sharding_spec import ShardingNotDivisibleError
+
+from ..sharding_strategy import OperationData, OperationDataType, ShardingStrategy
+from .node_handler import ModuleHandler, NodeHandler
+from .registry import operator_registry
+from .strategy import EmbeddingStrategyGenerator, StrategyGenerator
+
+__all__ = ['EmbeddingModuleHandler', 'EmbeddingFunctionHandler']
+
+
+def _convert_logical_sharding_to_physical_sharding_spec_for_embedding(strategy: ShardingStrategy, input_name: str,
+                                                                      output_name: str) -> List[ShardingStrategy]:
+    """
+    This function converts the logical sharding spec to the physical sharding spec for both the input and output
+    of the embedding operation.
+
+    Args:
+        strategy (ShardingStrategy): the logical strategy generated by the strategy generator.
+        input_name (str): the name of the OperationData object for the input.
+        output_name (str): the name of the OperationData object for the output.
+    """
+    # the result will be a list of strategies
+    sharding_strategies = []
+
+    # get operation data
+    input_op_data = strategy.get_op_data_by_name(input_name)
+    output_op_data = strategy.get_op_data_by_name(output_name)
+    input_sharding_spec = strategy.get_sharding_spec_by_name(input_op_data.name)
+    output_sharding_spec = strategy.get_sharding_spec_by_name(output_op_data.name)
+
+    # recover the last logical dimension to physical dimension
+    last_logical_output_dims = len(output_op_data.logical_shape) - 1
+    last_physical_output_dims = output_op_data.data.dim() - 1
+
+    # get logger for debug message
+    logger = get_dist_logger()
+
+    # For the input of the embedding operation, it can be multi-dimensional. The sharding spec is only generated for
+    # logical 1D non-matrix dimension, the logical non-matrix dimension can belong to the 0th to Nth dimension of the
+    # physical input shape. Thus, we enumerate to get all possible cases.
+    if input_sharding_spec.dim_partition_dict:
+        # if bool(input_sharding_spec.dim_partition_dict), it means that the
+        # the generated sharding strategy does shard the non-matrix dimension,
+        # in this case, we need to do enumeration
+        num_input_dims = input_op_data.data.dim()
+        for i in range(num_input_dims):
+            strategy_copy = strategy.clone()
+            input_sharding_spec = strategy_copy.get_sharding_spec_by_name(input_op_data.name)
+            output_sharding_spec = strategy_copy.get_sharding_spec_by_name(output_op_data.name)
+            try:
+                # replace the 0th dimension in the logical sharding with ith dimension in the physical sharding
+                update_partition_dim(sharding_spec=input_sharding_spec,
+                                     dim_mapping={0: i},
+                                     physical_shape=input_op_data.data.shape,
+                                     inplace=True)
+
+                if last_logical_output_dims in output_sharding_spec.dim_partition_dict:
+                    dim_mapping = {0: i, last_logical_output_dims: last_physical_output_dims}
+                else:
+                    dim_mapping = {0: i}
+
+                update_partition_dim(sharding_spec=output_sharding_spec,
+                                     dim_mapping=dim_mapping,
+                                     physical_shape=output_op_data.data.shape,
+                                     inplace=True)
+
+                strategy_copy.name = f'{strategy.name}_{i}'
+                sharding_strategies.append(strategy_copy)
+
+            except ShardingNotDivisibleError as e:
+                logger.debug(
+                    f'Errored occurred when converting the logical sharding spec to the physical one. Error details: {e}'
+                )
+    else:
+        # the generated sharding strategy does not shard the non-matrix dimension,
+        # in this case, we don't need to do enumeration
+        # but instead, we still need to convert the logical shape to physical shape
+        strategy_copy = strategy.clone()
+        input_sharding_spec = strategy_copy.get_sharding_spec_by_name(input_op_data.name)
+        output_sharding_spec = strategy_copy.get_sharding_spec_by_name(output_op_data.name)
+
+        # after updating, the logical shape will be replaced by the physical shape
+        update_partition_dim(sharding_spec=input_sharding_spec,
+                             dim_mapping={},
+                             physical_shape=input_op_data.data.shape,
+                             inplace=True)
+
+        if last_logical_output_dims in output_sharding_spec.dim_partition_dict:
+            dim_mapping = {last_logical_output_dims: last_physical_output_dims}
+        else:
+            dim_mapping = {}
+
+        update_partition_dim(sharding_spec=output_sharding_spec,
+                             dim_mapping=dim_mapping,
+                             physical_shape=output_op_data.data.shape,
+                             inplace=True)
+        sharding_strategies.append(strategy_copy)
+
+    return sharding_strategies
+
+
+@operator_registry.register(torch.nn.Embedding)
+class EmbeddingModuleHandler(ModuleHandler):
+    """
+    A EmbeddingModuleHandler which deals with the sharding strategies for nn.Embedding module.
+    """
+
+    def get_strategy_generator(self) -> List[StrategyGenerator]:
+        op_data_mapping = self.get_operation_data_mapping()
+        generators = []
+        generators.append(EmbeddingStrategyGenerator(op_data_mapping, self.device_mesh))
+        return generators
+
+    def get_operation_data_mapping(self) -> Dict[str, OperationData]:
+        # In nn.Embedding operation, all the dimensions of input will be treated as the batch dimension,
+        # and then the sharding spec will be generated based on the logical 1D tensor.
+        # After that, the logical sharding info will be enumerated among all the physical dimensions.
+        # Finally, the input will be transformed back to its original shape in self.post_process
+        input_meta_data = self.node.args[0]._meta_data
+        input_logical_shape = input_meta_data.view(-1).shape
+        physical_input_operand = OperationData(name=str(self.node.args[0]),
+                                               type=OperationDataType.ARG,
+                                               data=input_meta_data,
+                                               logical_shape=input_logical_shape)
+
+        physical_other_operand = OperationData(name="weight",
+                                               type=OperationDataType.PARAM,
+                                               data=self.named_parameters['weight'])
+
+        # Same as input, in nn.Embedding operation, all the dimensions of output will be treated as
+        # (batch dimension, embedding dimension), and then the sharding spec will be generated based
+        # on the logical 2D tensor.
+        # After that, the logical sharding info of batch dimension will be enumerated among all the physical dimensions.
+        # Finally, the output will be transformed back to its original shape in self.post_process
+        output_meta_data = self.node._meta_data
+        output_logical_shape = output_meta_data.view(-1, output_meta_data.shape[-1]).shape
+        physical_output = OperationData(name=str(self.node),
+                                        type=OperationDataType.OUTPUT,
+                                        data=output_meta_data,
+                                        logical_shape=output_logical_shape)
+
+        mapping = {"input": physical_input_operand, "other": physical_other_operand, "output": physical_output}
+
+        return mapping
+
+    def post_process(self, strategy: ShardingStrategy) -> Union[ShardingStrategy, List[ShardingStrategy]]:
+        """
+        Convert the sharding spec from the logical shape to the physical shape.
+        """
+        # create multiple sharding strategies for the inputs
+        # as input can be multi-dimensinal and the partition dim is only 2D,
+        # we need to map the partition at logical dim 0 to one of the first few dimensions of the input and output
+        strategies = _convert_logical_sharding_to_physical_sharding_spec_for_embedding(strategy=strategy,
+                                                                                       input_name=str(
+                                                                                           self.node.args[0]),
+                                                                                       output_name=str(self.node))
+        return strategies
+
+
+@operator_registry.register(F.embedding)
+class EmbeddingFunctionHandler(NodeHandler):
+    """
+    A EmbeddingFunctionHandler which deals with the sharding strategies for F.embedding.
+    """
+
+    def get_strategy_generator(self) -> List[StrategyGenerator]:
+        op_data_mapping = self.get_operation_data_mapping()
+        generators = []
+        generators.append(EmbeddingStrategyGenerator(op_data_mapping, self.device_mesh))
+        return generators
+
+    def get_operation_data_mapping(self) -> Dict[str, OperationData]:
+        # In F.embedding operation, all the dimensions of input will be treated as the batch dimension,
+        # and then the sharding spec will be generated based on the logical 1D tensor.
+        # After that, the logical sharding info will be enumerated among all the physical dimensions.
+        # Finally, the input will be transformed back to its original shape in self.post_process
+        input_meta_data = self.node.args[0]._meta_data
+        input_logical_shape = input_meta_data.view(-1).shape
+        physical_input_operand = OperationData(name=str(self.node.args[0]),
+                                               type=OperationDataType.ARG,
+                                               data=self.node.args[0]._meta_data,
+                                               logical_shape=input_logical_shape)
+
+        # check if the other operand is a parameter
+        if isinstance(self.node.args[1]._meta_data, torch.nn.parameter.Parameter):
+            data_type = OperationDataType.PARAM
+        else:
+            data_type = OperationDataType.ARG
+
+        physical_other_operand = OperationData(name=str(self.node.args[1]),
+                                               type=data_type,
+                                               data=self.node.args[1]._meta_data)
+
+        # Same as input, in F.embedding operation, all the dimensions of output will be treated as
+        # (batch dimension, embedding dimension), and then the sharding spec will be generated based
+        # on the logical 2D tensor.
+        # After that, the logical sharding info of batch dimension will be enumerated among all the physical dimensions.
+        # Finally, the output will be transformed back to its original shape in self.post_process
+        output_meta_data = self.node._meta_data
+        output_logical_shape = output_meta_data.view(-1, output_meta_data.shape[-1]).shape
+        physical_output = OperationData(
+            name=str(self.node),
+            type=OperationDataType.OUTPUT,
+            data=self.node._meta_data,
+            logical_shape=output_logical_shape,
+        )
+
+        mapping = {"input": physical_input_operand, "other": physical_other_operand, "output": physical_output}
+
+        return mapping
+
+    def post_process(self, strategy: ShardingStrategy):
+        """
+        Convert the sharding spec from the logical shape to the physical shape.
+        """
+        # create multiple sharding strategies for the inputs
+        # as input can be multi-dimensinal and the partition dim is only 2D,
+        # we need to map the partition at logical dim 0 to one of the first few dimensions of the input and output
+        strategies = _convert_logical_sharding_to_physical_sharding_spec_for_embedding(strategy=strategy,
+                                                                                       input_name=str(
+                                                                                           self.node.args[0]),
+                                                                                       output_name=str(self.node))
+        return strategies

colossalai/auto_parallel/tensor_shard/node_handler/getattr_handler.py

+from typing import Dict, List
+
+from ..sharding_strategy import OperationData, OperationDataType
+from .node_handler import NodeHandler
+from .strategy import GetattrGenerator, StrategyGenerator
+
+__all__ = ['GetattrHandler']
+
+
+class GetattrHandler(NodeHandler):
+    """
+    A GetattrHandler which deals with the sharding strategies for Getattr Node.
+    """
+
+    def get_strategy_generator(self) -> List[StrategyGenerator]:
+        op_data_mapping = self.get_operation_data_mapping()
+        generators = []
+        generators.append(GetattrGenerator(op_data_mapping, self.device_mesh))
+        return generators
+
+    def get_operation_data_mapping(self) -> Dict[str, OperationData]:
+        # use transposed shape for strategies
+        # the strategies will be transformed back to its original shape in self.post_process
+
+        # There are only two possible types for get_attr node:
+        # 1. torch.Tensor(torch.nn.Parameters or torch.nn.Buffers)
+        # 2. torch.nn.Module
+        # temporarily, we just support first case in Tracer, so we don't have to worry about
+        # issue related to the node._meta_data type.
+        physical_output = OperationData(name=str(self.node), type=OperationDataType.OUTPUT, data=self.node._meta_data)
+
+        mapping = {"output": physical_output}
+
+        return mapping

colossalai/auto_parallel/tensor_shard/node_handler/getitem_handler.py

+import operator
+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, TensorStrategyGenerator, TensorTupleStrategyGenerator
+
+__all__ = ['GetItemHandler']
+
+
+@operator_registry.register(operator.getitem)
+class GetItemHandler(NodeHandler):
+    """
+    A GetItemHandler which deals with the sharding strategies for operator.getitem.
+    """
+
+    def get_strategy_generator(self) -> List[StrategyGenerator]:
+        op_data_mapping = self.get_operation_data_mapping()
+        generators = []
+        if isinstance(op_data_mapping["input"].data, torch.Tensor):
+            generators.append(TensorStrategyGenerator(op_data_mapping, self.device_mesh, self.node.args[0]))
+        else:
+            generators.append(TensorTupleStrategyGenerator(op_data_mapping, self.device_mesh, self.node.args[0]))
+
+        return generators
+
+    def get_operation_data_mapping(self) -> Dict[str, OperationData]:
+        # use transposed shape for strategies
+        # the strategies will be transformed back to its original shape in self.post_process
+        physical_input_operand = OperationData(name=str(self.node.args[0]),
+                                               type=OperationDataType.ARG,
+                                               data=self.node.args[0]._meta_data)
+        physical_other_operand = OperationData(name="index", type=OperationDataType.ARG, data=self.node.args[1])
+        physical_output = OperationData(name=str(self.node), type=OperationDataType.OUTPUT, data=self.node._meta_data)
+
+        mapping = {"input": physical_input_operand, "index": physical_other_operand, "output": physical_output}
+
+        return mapping

colossalai/auto_parallel/tensor_shard/node_handler/layer_norm_handler.py

+from typing import Dict, List
+
+import torch
+
+from ..sharding_strategy import OperationData, OperationDataType
+from .node_handler import MetaInfoModuleHandler, ModuleHandler
+from .registry import operator_registry
+from .strategy import LayerNormGenerator, StrategyGenerator
+
+__all__ = ['LayerNormModuleHandler']
+
+
+@operator_registry.register(torch.nn.LayerNorm)
+class LayerNormModuleHandler(MetaInfoModuleHandler):
+    """
+    A LayerNormModuleHandler which deals with the sharding strategies for nn.LayerNorm module.
+    """
+
+    def get_strategy_generator(self) -> List[StrategyGenerator]:
+        op_data_mapping = self.get_operation_data_mapping()
+        generators = []
+        generators.append(LayerNormGenerator(op_data_mapping, self.device_mesh))
+        return generators
+
+    def get_operation_data_mapping(self) -> Dict[str, OperationData]:
+        # use transposed shape for strategies
+        # the strategies will be transformed back to its original shape in self.post_process
+        physical_input_operand = OperationData(name=str(self.node.args[0]),
+                                               type=OperationDataType.ARG,
+                                               data=self.node.args[0]._meta_data)
+        physical_other_operand = OperationData(name="weight",
+                                               type=OperationDataType.PARAM,
+                                               data=self.named_parameters['weight'],
+                                               logical_shape=self.named_parameters['weight'].shape)
+        physical_output = OperationData(name=str(self.node), type=OperationDataType.OUTPUT, data=self.node._meta_data)
+
+        mapping = {"input": physical_input_operand, "other": physical_other_operand, "output": physical_output}
+
+        if self.named_parameters['bias'] is not None:
+            physical_bias_operand = OperationData(name="bias",
+                                                  type=OperationDataType.PARAM,
+                                                  data=self.named_parameters['bias'])
+            mapping['bias'] = physical_bias_operand
+        return mapping

colossalai/auto_parallel/tensor_shard/node_handler/linear_handler.py

+from typing import Dict, List, Union
+
+import torch
+import torch.nn.functional as F
+
+from colossalai.auto_parallel.tensor_shard.utils import (
+    check_sharding_spec_validity,
+    transpose_partition_dim,
+    update_partition_dim,
+)
+from colossalai.logging import get_dist_logger
+from colossalai.tensor.sharding_spec import ShardingNotDivisibleError
+
+from ..sharding_strategy import OperationData, OperationDataType, ShardingStrategy, StrategiesVector
+from .node_handler import MetaInfoModuleHandler, MetaInfoNodeHandler, ModuleHandler, NodeHandler
+from .registry import operator_registry
+from .strategy import LinearProjectionStrategyGenerator, StrategyGenerator
+
+__all__ = ['LinearModuleHandler', 'LinearFunctionHandler']
+
+
+def _update_sharding_spec_for_transposed_weight_for_linear(strategy: ShardingStrategy,
+                                                           weight_name: str) -> ShardingStrategy:
+    """
+    This function is a helper function used by both module node handler and function node handler. This function will
+    convert the sharding spec for the transposed weight to the correct partititon spec.
+
+    Args:
+        strategy (ShardingStrategy): the strategy generated by the strategy generator.
+        weight_name (str): the name of the OperationData object for the weight.
+    """
+    # switch the dimensions of the transposed weight
+    sharding_spec = strategy.get_sharding_spec_by_name(weight_name)
+    op_data = strategy.get_op_data_by_name(weight_name)
+    assert op_data.logical_shape[0] == op_data.data.shape[1] and \
+           op_data.logical_shape[1] == op_data.data.shape[0], \
+           "Expected the logical shape  of the linear operator's weight is equal to transposed physical shape"
+    dim_size = len(op_data.logical_shape)
+    transpose_partition_dim(sharding_spec, 0, dim_size - 1)
+    return strategy
+
+
+def _convert_logical_sharding_to_physical_sharding_spec_for_linear(strategy: ShardingStrategy, input_name: str,
+                                                                   output_name: str) -> List[ShardingStrategy]:
+    """
+    This function converts the logical sharding spec to the physical sharding spec for both the input and output of the linear operation. The input and output
+    should have the same sharding spec.
+
+    Args:
+        strategy (ShardingStrategy): the logical strategy generated by the strategy generator.
+        input_name (str): the name of the OperationData object for the input.
+        output_name (str): the name of the OperationData object for the output.
+
+
+    """
+    # the result will be a list of strategies
+    sharding_strategies = []
+
+    # get operation data
+    input_op_data = strategy.get_op_data_by_name(input_name)
+    output_op_data = strategy.get_op_data_by_name(output_name)
+    input_sharding_spec = strategy.get_sharding_spec_by_name(input_op_data.name)
+    output_sharding_spec = strategy.get_sharding_spec_by_name(output_op_data.name)
+
+    # recover the last logical dimension to physical dimension
+    last_logical_input_dims = len(input_op_data.logical_shape) - 1
+    last_logical_output_dims = len(output_op_data.logical_shape) - 1
+    last_physical_input_dims = input_op_data.data.dim() - 1
+    last_physical_output_dims = output_op_data.data.dim() - 1
+
+    if last_logical_input_dims in input_sharding_spec.dim_partition_dict:
+        input_last_dim_mapping = {last_logical_input_dims: last_physical_input_dims}
+    else:
+        input_last_dim_mapping = {}
+
+    if last_logical_output_dims in output_sharding_spec.dim_partition_dict:
+        output_last_dim_mapping = {last_logical_output_dims: last_physical_output_dims}
+    else:
+        output_last_dim_mapping = {}
+
+    # get logger for debug message
+    logger = get_dist_logger()
+
+    # for the input of the linear operation, it can be multi-dimensional. The sharding spec generated is only
+    # 2D, where the first dimension is non-matrix dimension and the last dimension is the matrix dimension.
+    # the logical non-matrix dimension can belong to the 0th to (N-1)th dimension of the physical input shape.
+    # Thus, we enumerate to get all possible cases.
+    if 0 in input_sharding_spec.dim_partition_dict:
+        # if 0 is in the dim_partition_dict, it means that the
+        # the generated sharding strategy does shard the non-matrix dimension,
+        # in this case, we need to do enumeration
+        num_input_dims = input_op_data.data.dim()
+        for i in range(num_input_dims - 1):
+            strategy_copy = strategy.clone()
+            input_sharding_spec = strategy_copy.get_sharding_spec_by_name(input_op_data.name)
+            output_sharding_spec = strategy_copy.get_sharding_spec_by_name(output_op_data.name)
+            try:
+                # replace the 0th dimension in the logical sharding with ith dimension in the physical sharding
+                input_dim_mapping = {0: i}
+                input_dim_mapping.update(input_last_dim_mapping)
+
+                update_partition_dim(sharding_spec=input_sharding_spec,
+                                     dim_mapping=input_dim_mapping,
+                                     physical_shape=input_op_data.data.shape,
+                                     inplace=True)
+                output_dim_mapping = {0: i}
+                output_dim_mapping.update(output_last_dim_mapping)
+
+                update_partition_dim(sharding_spec=output_sharding_spec,
+                                     dim_mapping=output_dim_mapping,
+                                     physical_shape=output_op_data.data.shape,
+                                     inplace=True)
+                strategy_copy.name = f'{strategy.name}_{i}'
+                sharding_strategies.append(strategy_copy)
+            except ShardingNotDivisibleError as e:
+                logger.debug(
+                    f'Errored occurred when converting the logical sharding spec to the physical one. Error details: {e}'
+                )
+    else:
+        # the generated sharding strategy does not shard the non-matrix dimension,
+        # in this case, we don't need to do enumeration
+        # but instead, we still need to convert the logical shape to physical shape
+        strategy_copy = strategy.clone()
+        input_sharding_spec = strategy_copy.get_sharding_spec_by_name(input_op_data.name)
+        output_sharding_spec = strategy_copy.get_sharding_spec_by_name(output_op_data.name)
+
+        # after updating, the logical shape will be replaced by the physical shape
+        input_dim_mapping = {}
+        input_dim_mapping.update(input_last_dim_mapping)
+        update_partition_dim(sharding_spec=input_sharding_spec,
+                             dim_mapping=input_dim_mapping,
+                             physical_shape=input_op_data.data.shape,
+                             inplace=True)
+
+        output_dim_mapping = {}
+        output_dim_mapping.update(output_last_dim_mapping)
+        update_partition_dim(sharding_spec=output_sharding_spec,
+                             dim_mapping=output_dim_mapping,
+                             physical_shape=output_op_data.data.shape,
+                             inplace=True)
+        sharding_strategies.append(strategy_copy)
+    return sharding_strategies
+
+
+@operator_registry.register(torch.nn.Linear)
+class LinearModuleHandler(MetaInfoModuleHandler):
+    """
+    A LinearModuleHandler which deals with the sharding strategies for nn.Linear module.
+    """
+
+    def get_strategy_generator(self) -> List[StrategyGenerator]:
+        op_data_mapping = self.get_operation_data_mapping()
+        generators = []
+        generators.append(
+            LinearProjectionStrategyGenerator(op_data_mapping,
+                                              self.device_mesh,
+                                              linear_projection_type='linear',
+                                              solver_perference=self.solver_perference))
+        return generators
+
+    def get_operation_data_mapping(self) -> Dict[str, OperationData]:
+        # use transposed shape for strategies
+        # the strategies will be transformed back to its original shape in self.post_process
+        input_meta_data = self.node.args[0]._meta_data
+        input_logical_shape = input_meta_data.view(-1, input_meta_data.shape[-1]).shape
+        physical_input_operand = OperationData(name=str(self.node.args[0]),
+                                               type=OperationDataType.ARG,
+                                               data=input_meta_data,
+                                               logical_shape=input_logical_shape)
+        physical_other_operand = OperationData(name="weight",
+                                               type=OperationDataType.PARAM,
+                                               data=self.named_parameters['weight'],
+                                               logical_shape=self.named_parameters['weight'].shape[::-1])
+        output_meta_data = self.node._meta_data
+        output_logical_shape = output_meta_data.view(-1, output_meta_data.shape[-1]).shape
+        physical_output = OperationData(name=str(self.node),
+                                        type=OperationDataType.OUTPUT,
+                                        data=output_meta_data,
+                                        logical_shape=output_logical_shape)
+
+        mapping = {"input": physical_input_operand, "other": physical_other_operand, "output": physical_output}
+
+        if 'bias' in self.named_parameters is not None:
+            physical_bias_operand = OperationData(name="bias",
+                                                  type=OperationDataType.PARAM,
+                                                  data=self.named_parameters['bias'])
+            mapping['bias'] = physical_bias_operand
+        return mapping
+
+    def post_process(self, strategy: ShardingStrategy) -> Union[ShardingStrategy, List[ShardingStrategy]]:
+        """
+        Convert the sharding spec from the logical shape to the physical shape. In this function, two tasks are completed:
+        1. the sharding spec is updated for the transposed weight
+        2. the input and output sharding specs are updated to physical shape.
+        """
+        # switch the dimensions of the transposed weight
+        strategy = _update_sharding_spec_for_transposed_weight_for_linear(strategy=strategy, weight_name='weight')
+
+        # create multiple sharding strategies for the inputs
+        # as input can be multi-dimensinal and the partition dim is only 2D,
+        # we need to map the partition at dim 0 to one of the first few dimensions of the input
+        strategies = _convert_logical_sharding_to_physical_sharding_spec_for_linear(strategy=strategy,
+                                                                                    input_name=str(self.node.args[0]),
+                                                                                    output_name=str(self.node))
+        return strategies
+
+
+@operator_registry.register(F.linear)
+class LinearFunctionHandler(MetaInfoNodeHandler):
+    """
+    A LinearFunctionHandler which deals with the sharding strategies for F.Linear.
+    """
+
+    def get_strategy_generator(self) -> List[StrategyGenerator]:
+        op_data_mapping = self.get_operation_data_mapping()
+        generators = []
+        generators.append(
+            LinearProjectionStrategyGenerator(op_data_mapping, self.device_mesh, linear_projection_type='linear'))
+        return generators
+
+    def get_operation_data_mapping(self) -> Dict[str, OperationData]:
+        # use transposed shape for strategies
+        # the strategies will be transformed back to its original shape in self.post_process
+        input_meta_data = self.node.args[0]._meta_data
+        input_logical_shape = input_meta_data.view(-1, input_meta_data.shape[-1]).shape
+        physical_input_operand = OperationData(name=str(self.node.args[0]),
+                                               type=OperationDataType.ARG,
+                                               data=self.node.args[0]._meta_data,
+                                               logical_shape=input_logical_shape)
+
+        # check if the other operand is a parameter
+        if isinstance(self.node.args[1]._meta_data, torch.nn.parameter.Parameter):
+            data_type = OperationDataType.PARAM
+        else:
+            data_type = OperationDataType.ARG
+
+        physical_other_operand = OperationData(name=str(self.node.args[1]),
+                                               type=data_type,
+                                               data=self.node.args[1]._meta_data,
+                                               logical_shape=self.node.args[1]._meta_data.shape[::-1])
+        output_meta_data = self.node._meta_data
+        output_logical_shape = output_meta_data.view(-1, output_meta_data.shape[-1]).shape
+        physical_output = OperationData(
+            name=str(self.node),
+            type=OperationDataType.OUTPUT,
+            data=self.node._meta_data,
+            logical_shape=output_logical_shape,
+        )
+
+        mapping = {"input": physical_input_operand, "other": physical_other_operand, "output": physical_output}
+
+        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.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.kwargs["bias"]),
+                                                  type=data_type,
+                                                  data=self.node.kwargs["bias"]._meta_data)
+            mapping['bias'] = physical_bias_operand
+
+        return mapping
+
+    def post_process(self, strategy: ShardingStrategy):
+        # switch the dimensions of the transposed weight
+        strategy = _update_sharding_spec_for_transposed_weight_for_linear(strategy=strategy,
+                                                                          weight_name=str(self.node.args[1]))
+        # create multiple sharding strategies for the inputs
+        # as input can be multi-dimensinal and the partition dim is only 2D,
+        # we need to map the partition at dim 0 to one of the first few dimensions of the input
+        strategies = _convert_logical_sharding_to_physical_sharding_spec_for_linear(strategy=strategy,
+                                                                                    input_name=str(self.node.args[0]),
+                                                                                    output_name=str(self.node))
+        return strategies

colossalai/auto_parallel/tensor_shard/node_handler/matmul_handler.py

+import operator
+from abc import ABC, abstractmethod
+from copy import deepcopy
+from enum import Enum
+from functools import reduce
+from typing import Dict, List, Union
+
+import torch
+
+from colossalai.auto_parallel.tensor_shard.utils.broadcast import (
+    BroadcastType,
+    get_broadcast_dim_info,
+    get_broadcast_shape,
+)
+from colossalai.tensor.sharding_spec import ShardingSpecException
+
+from ..sharding_strategy import OperationData, OperationDataType, ShardingStrategy
+from ..utils import recover_sharding_spec_for_broadcast_shape
+from .node_handler import MetaInfoNodeHandler, NodeHandler
+from .registry import operator_registry
+from .strategy import (
+    BatchedMatMulStrategyGenerator,
+    DotProductStrategyGenerator,
+    LinearProjectionStrategyGenerator,
+    MatVecStrategyGenerator,
+    StrategyGenerator,
+)
+
+
+class MatMulType(Enum):
+    """
+    The MatMulType is categorized into 4 types based on the reference of torch.matmul
+    in https://pytorch.org/docs/stable/generated/torch.matmul.html.
+
+    DOT: dot product, both tensors are 1D, these two tensors need to have the same number of elements
+    MM: matrix-matrix product, both tensors are 2D or the 1st tensor is 1D and the 2nd tensor is 2D
+    MV: matrix-vector product: the 1st tensor is 2D and the 2nd tensor is 1D
+    BMM: batched matrix-matrix multiplication, one tensor is at least 1D and the other is at least 3D
+    """
+    DOT = 0
+    MM = 1
+    MV = 2
+    BMM = 3
+
+
+def get_matmul_type(input_dim: int, other_dim: int):
+    """
+    Determine which type of matmul operation should be executed for the given tensor dimensions.
+
+    Args:
+        input_dim (int): the number of dimensions for the input tenosr
+        other_dim (int): the number of dimensions for the other tenosr
+    """
+    if input_dim == 1 and other_dim == 1:
+        matmul_type = MatMulType.DOT
+    elif input_dim in [1, 2] and other_dim == 2:
+        matmul_type = MatMulType.MM
+    elif input_dim == 2 and other_dim == 1:
+        matmul_type = MatMulType.MV
+    elif input_dim >= 1 and other_dim >= 1 and (input_dim > 2 or other_dim > 2):
+        matmul_type = MatMulType.BMM
+    else:
+        raise ValueError(
+            f"The input and other tensors are of {input_dim} and {other_dim} which cannot used to execute matmul operation"
+        )
+    return matmul_type
+
+
+class BmmTransform(ABC):
+    """
+    BmmTransform is an abstraction of the shape conversion between logical and physical operation data
+    during the strategy generation.
+    """
+
+    @abstractmethod
+    def apply(self, shape_mapping: Dict[str, List[int]]):
+        pass
+
+    @abstractmethod
+    def recover(self, op_data_mapping: Dict[str, OperationData], strategy: ShardingStrategy):
+        pass
+
+
+class Padder(BmmTransform):
+    """
+    Add padding to the matrix dimensions for batched matrix multiplication.
+    """
+
+    def __init__(self) -> None:
+        # keep the padding dim, op_name -> padded_dim
+        self.padded_dim_mapping = {}
+
+    def apply(self, shape_mapping: Dict[str, List[int]]):
+        mapping_copy = deepcopy(shape_mapping)
+        input_shape = mapping_copy['input']
+        other_shape = mapping_copy['other']
+
+        if len(input_shape) == 1:
+            # if the input is a 1D tensor, 1 is prepended to its shape
+            # and it will be removed afterwards
+            input_shape.insert(0, 1)
+            self.padded_dim_mapping['input'] = -2
+            self.padded_dim_mapping['output'] = -2
+        elif len(other_shape) == 1:
+            # if the other is a 1D tensor, 1 is appended to its shape
+            # and it will be removed afterwards
+            other_shape = other_shape.append(1)
+            self.padded_dim_mapping['other'] = -1
+            self.padded_dim_mapping['output'] = -1
+        return mapping_copy
+
+    def recover(self, op_data_mapping: Dict[str, OperationData], strategy: ShardingStrategy):
+        input_op_data = op_data_mapping['input']
+        other_op_data = op_data_mapping['other']
+
+        def _remove_padded_dim(key, strategy):
+            op_data = op_data_mapping[key]
+            sharding_spec = strategy.get_sharding_spec_by_name(op_data.name)
+            tensor_shape = list(sharding_spec.entire_shape)
+            dim_partition_list = [None] * len(tensor_shape)
+
+            # padded dim is a negative number as the padded dim must be a matrix dim
+            padded_dim = self.padded_dim_mapping[key]
+
+            # compute the new dim partition
+            for tensor_dim, mesh_dims in sharding_spec.dim_partition_dict.items():
+                dim_partition_list[tensor_dim] = mesh_dims
+            dim_partition_list.pop(padded_dim)
+            unpadded_dim_partition_list = {k: v for k, v in enumerate(dim_partition_list) if v is not None}
+
+            # compute unpadded tensor shape
+            tensor_shape.pop(padded_dim)
+
+            assert tensor_shape == list(op_data.data.shape), f'{tensor_shape} vs {list(op_data.data.shape)}'
+
+            # update sharding spec
+            sharding_spec.__init__(sharding_spec.device_mesh, tensor_shape, unpadded_dim_partition_list)
+
+        # enumerate all sharding strategies
+        strategies = []
+        try:
+            strategy_copy = strategy.clone()
+
+            # only one of input and other will be padded
+            if 'input' in self.padded_dim_mapping:
+                _remove_padded_dim('input', strategy_copy)
+                _remove_padded_dim('output', strategy_copy)
+            elif 'other' in self.padded_dim_mapping:
+                _remove_padded_dim('other', strategy_copy)
+                _remove_padded_dim('output', strategy_copy)
+
+            strategies.append(strategy_copy)
+        except ShardingSpecException as e:
+            pass
+        return strategies
+
+
+class Broadcaster(BmmTransform):
+    """
+    Broadcast the non-matrix dimensions for batched matrix multiplication.
+    """
+
+    def __init__(self) -> None:
+        self.broadcast_dim_info = {}
+
+    def apply(self, shape_mapping: Dict[str, List[int]]):
+        mapping_copy = shape_mapping.copy()
+
+        # get shapes
+        input_shape = mapping_copy['input']
+        other_shape = mapping_copy['other']
+
+        # sanity check
+        assert len(input_shape) > 1 and len(other_shape) > 1
+
+        # broadcast the batch dim and record
+        bcast_non_matrix_dims = get_broadcast_shape(input_shape[:-2], other_shape[:-2])
+
+        # store the broadcast dim info
+        input_broadcast_dim_info = get_broadcast_dim_info(bcast_non_matrix_dims, input_shape[:-2])
+        other_broadcast_dim_info = get_broadcast_dim_info(bcast_non_matrix_dims, other_shape[:-2])
+        self.broadcast_dim_info['input'] = input_broadcast_dim_info
+        self.broadcast_dim_info['other'] = other_broadcast_dim_info
+
+        # create the full logical shape
+        input_shape = bcast_non_matrix_dims + input_shape[-2:]
+        other_shape = bcast_non_matrix_dims + other_shape[-2:]
+        assert len(input_shape) == len(other_shape)
+
+        mapping_copy['input'] = input_shape
+        mapping_copy['other'] = other_shape
+
+        return mapping_copy
+
+    def recover(self, op_data_mapping: Dict[str, OperationData], strategy: ShardingStrategy):
+        # remove sharding on the broadcast dim
+        def _remove_sharding_on_broadcast_dim(key, strategy):
+            op_data = op_data_mapping[key]
+            sharding_spec = strategy.get_sharding_spec_by_name(op_data.name)
+            tensor_shape = list(sharding_spec.entire_shape)
+
+            for dim_idx, broadcast_type in self.broadcast_dim_info[key].items():
+                if broadcast_type == BroadcastType.MULTIPLE:
+                    # if the dim is originally 1 and multiplied during broadcast
+                    # we set its sharding to R
+                    # e.g. [1, 2, 4] x [4, 4, 8] -> [4, 2, 8]
+                    # the dim 0 of [1, 2, 4] is multiplied to 4
+                    tensor_shape[dim_idx] = 1
+                elif broadcast_type == BroadcastType.PADDDING:
+                    # if the dim is padded
+                    # we remove its sharding
+                    tensor_shape[dim_idx] = None
+
+            tensor_shape_before_broadcast = [dim for dim in tensor_shape if dim is not None]
+
+            physical_sharding_spec, removed_dims = recover_sharding_spec_for_broadcast_shape(
+                logical_sharding_spec=sharding_spec,
+                logical_shape=sharding_spec.entire_shape,
+                physical_shape=tensor_shape_before_broadcast)
+            strategy.sharding_specs[op_data] = physical_sharding_spec
+
+        # enumerate all sharding strategies
+        strategies = []
+        try:
+            strategy_copy = strategy.clone()
+            _remove_sharding_on_broadcast_dim('input', strategy_copy)
+            _remove_sharding_on_broadcast_dim('other', strategy_copy)
+            strategies.append(strategy_copy)
+        except ShardingSpecException as e:
+            pass
+        return strategies
+
+
+class Viewer(BmmTransform):
+    """
+    Change the shape of the tensor from N-D to 3D
+    """
+
+    def __init__(self) -> None:
+        self.batch_dims_before_view = None
+
+    def apply(self, shape_mapping: Dict[str, List[int]]):
+        mapping_copy = shape_mapping.copy()
+        self.batch_dims_before_view = list(mapping_copy['input'][:-2])
+
+        # get shapes
+        input_shape = shape_mapping['input']
+        other_shape = shape_mapping['other']
+
+        # view to 3d tensor
+        assert len(input_shape) >= 3 and len(other_shape) >= 3
+        input_shape = [reduce(operator.mul, input_shape[:-2])] + input_shape[-2:]
+        other_shape = [reduce(operator.mul, other_shape[:-2])] + other_shape[-2:]
+        output_shape = input_shape[:2] + other_shape[2:]
+        mapping_copy['input'] = input_shape
+        mapping_copy['other'] = other_shape
+        mapping_copy['output'] = output_shape
+        return mapping_copy
+
+    def recover(self, op_data_mapping: Dict[str, OperationData], strategy: ShardingStrategy):
+        # get operation data
+        def _update_sharding_spec(key, strategy, physical_batch_dim):
+            """
+            Map the logical batch dim to the physical batch dim
+            """
+            op_data = op_data_mapping[key]
+            sharding_spec = strategy.get_sharding_spec_by_name(op_data.name)
+            dim_partition_dict = sharding_spec.dim_partition_dict
+            entire_shape = sharding_spec.entire_shape
+
+            # upddate the dimension index for the matrix dimensions
+            if 2 in dim_partition_dict:
+                dim_partition_dict[len(self.batch_dims_before_view) + 1] = dim_partition_dict.pop(2)
+            if 1 in dim_partition_dict:
+                dim_partition_dict[len(self.batch_dims_before_view)] = dim_partition_dict.pop(1)
+
+            # map the logical batch dim to phyiscal batch dim
+            if 0 in dim_partition_dict:
+                batch_dim_shard = dim_partition_dict.pop(0)
+                dim_partition_dict[physical_batch_dim] = batch_dim_shard
+
+            # the new shape will be the batch dims + the last 2 matrix dims
+            shape_before_view = self.batch_dims_before_view + list(entire_shape[-2:])
+            sharding_spec.__init__(sharding_spec.device_mesh, shape_before_view, dim_partition_dict)
+
+        num_batch_dim_before_view = len(self.batch_dims_before_view)
+
+        # enumerate all sharding strategies
+        strategies = []
+        for i in range(num_batch_dim_before_view):
+            # create a new strategy
+            strategy_copy = strategy.clone()
+            try:
+                _update_sharding_spec('input', strategy_copy, i)
+                _update_sharding_spec('other', strategy_copy, i)
+                _update_sharding_spec('output', strategy_copy, i)
+                strategies.append(strategy_copy)
+            except ShardingSpecException as e:
+                continue
+        return strategies
+
+
+def _get_bmm_logical_shape(input_shape, other_shape, transforms):
+    """
+    Compute the logical shapes for BMM operation. BMM has a general representation
+    [b, i, k] = [b, i, j] x [b, j, k]
+
+    The dimension b is called non-matrix (batch) dimension and the remaining dimensions are called matrix dimensions
+    The logical shape for the bmm operands will undergo three stages
+        1. append/prepend the 1 to the 1D tensor if there is any
+        2. broadcast the non-matrix dimensions
+        3. reshape to 3 dimensions
+
+    """
+    shape_mapping = {'input': input_shape, 'other': other_shape}
+
+    for transform in transforms:
+        shape_mapping = transform.apply(shape_mapping)
+
+    input_shape = shape_mapping.get('input', None)
+    other_shape = shape_mapping.get('other', None)
+    output_shape = shape_mapping.get('output', None)
+
+    return input_shape, other_shape, output_shape
+
+
+@operator_registry.register(torch.matmul)
+@operator_registry.register(torch.Tensor.matmul)
+class MatMulHandler(MetaInfoNodeHandler):
+    """
+    The MatMulHandler is a node handler which handles the sharding strategy generation for the matmul operation.
+    According to https://pytorch.org/docs/stable/generated/torch.matmul.html, the operations will vary depending on
+    the operands.
+    """
+
+    def __init__(self, *args, **kwargs) -> None:
+        super().__init__(*args, **kwargs)
+
+        # check which type of operation this matmul will call
+        self.input_meta_data = self.node.args[0]._meta_data
+        self.other_meta_data = self.node.args[1]._meta_data
+        self.output_meta_data = self.node._meta_data
+
+        input_dim = self.input_meta_data.dim()
+        other_dim = self.other_meta_data.dim()
+        self.matmul_type = get_matmul_type(input_dim, other_dim)
+
+        if self.matmul_type == MatMulType.BMM:
+            # bmm operation can possibly involve padding, broadcasting and view
+            # these transforms will be used to create logical shape and
+            # recover physical sharding spec
+            self.transforms = [Padder(), Broadcaster(), Viewer()]
+        else:
+            self.transforms = None
+
+    def get_strategy_generator(self) -> List[StrategyGenerator]:
+        generators = []
+        op_data_mapping = self.get_operation_data_mapping()
+        if self.matmul_type == MatMulType.BMM:
+            generators.append(BatchedMatMulStrategyGenerator(op_data_mapping, self.device_mesh))
+        elif self.matmul_type == MatMulType.DOT:
+            generators.append(DotProductStrategyGenerator(op_data_mapping, self.device_mesh))
+        elif self.matmul_type == MatMulType.MV:
+            generators.append(MatVecStrategyGenerator(op_data_mapping, self.device_mesh))
+        elif self.matmul_type == MatMulType.MM:
+            generators.append(
+                LinearProjectionStrategyGenerator(op_data_mapping, self.device_mesh, linear_projection_type='linear'))
+        return generators
+
+    def get_operation_data_mapping(self) -> Dict[str, OperationData]:
+        logical_shape_func = {
+            MatMulType.DOT: self._get_logical_shape_for_dot,
+            MatMulType.MM: self._get_logical_shape_for_mm,
+            MatMulType.MV: self._get_logical_shape_for_mv,
+            MatMulType.BMM: self._get_logical_shape_for_bmm
+        }
+        logical_shapes = logical_shape_func[self.matmul_type]()
+        op_data_mapping = self._get_op_data_mapping(*logical_shapes)
+        return op_data_mapping
+
+    def _get_op_data_mapping(self, input_logical_shape, other_logical_shape, output_logical_shape):
+        # convert list to torch.Size
+        if input_logical_shape:
+            input_logical_shape = torch.Size(input_logical_shape)
+
+        if other_logical_shape:
+            other_logical_shape = torch.Size(other_logical_shape)
+
+        if output_logical_shape:
+            output_logical_shape = torch.Size(output_logical_shape)
+
+        # create op data
+        input_op_data = OperationData(name=str(self.node.args[0]),
+                                      type=OperationDataType.ARG,
+                                      data=self.input_meta_data,
+                                      logical_shape=input_logical_shape)
+        other_op_data = OperationData(name=str(self.node.args[1]),
+                                      type=OperationDataType.ARG,
+                                      data=self.other_meta_data,
+                                      logical_shape=other_logical_shape)
+        output_op_data = OperationData(name=str(self.node),
+                                       type=OperationDataType.OUTPUT,
+                                       data=self.output_meta_data,
+                                       logical_shape=output_logical_shape)
+
+        mapping = {'input': input_op_data, 'other': other_op_data, 'output': output_op_data}
+        return mapping
+
+    def _get_logical_shape_for_dot(self):
+        """
+        The operands for the dot operation have the same logical shape as the physical shape
+        """
+        return None, None, None
+
+    def _get_logical_shape_for_mm(self):
+        """
+        We need to handle the input tensor for a matrix-matrix multiplcation as the input
+        tensor can be a 1D or 2D tensor. If it is a 1D tensor, 1 will be prepended to its shape
+        (e.g. [4] -> [1, 4]).
+        """
+        if self.input_meta_data.dim() == 1:
+            input_logical_shape = [1] + list(self.input_meta_data.shape)
+            input_logical_shape = torch.Size(input_logical_shape)
+        else:
+            input_logical_shape = None
+        return input_logical_shape, None, None
+
+    def _get_logical_shape_for_mv(self):
+        """
+        No broadcasting or dim insertion occurs for matrix-vector operation.
+        """
+        return None, None, None
+
+    def _get_logical_shape_for_bmm(self):
+        input_physical_shape = list(self.input_meta_data.shape)
+        other_physical_shape = list(self.other_meta_data.shape)
+        return _get_bmm_logical_shape(input_physical_shape, other_physical_shape, self.transforms)
+
+    def post_process(self, strategy: ShardingStrategy) -> Union[ShardingStrategy, List[ShardingStrategy]]:
+        if self.matmul_type in [MatMulType.DOT, MatMulType.MV]:
+            return strategy
+        elif self.matmul_type == MatMulType.MM:
+            if self.input_meta_data.dim() == 1:
+                # if a 1 is prepended to the input shape (this occurs when input is a 1D tensor)
+                # we need to remove that dim
+                input_sharding_spec = strategy.get_sharding_spec_by_name(str(self.node.args[0]))
+                input_physical_shape = self.node.args[0]._meta_data.shape
+                dim_partition_dict = input_sharding_spec.dim_partition_dict
+
+                # remove the partitioning in the dim 0
+                if 0 in dim_partition_dict:
+                    dim_partition_dict.pop(0, None)
+
+                # move the partitioning in dim 1 to dim 0
+                if -1 in dim_partition_dict:
+                    shard = dim_partition_dict.pop(-1)
+                    dim_partition_dict[0] = shard
+                if 1 in dim_partition_dict:
+                    shard = dim_partition_dict.pop(1)
+                    dim_partition_dict[0] = shard
+
+                # re-init the sharding spec
+                input_sharding_spec.__init__(input_sharding_spec.device_mesh,
+                                             entire_shape=input_physical_shape,
+                                             dim_partition_dict=dim_partition_dict)
+                return strategy
+            else:
+                return strategy
+        elif self.matmul_type == MatMulType.BMM:
+            op_data_mapping = self.get_operation_data_mapping()
+
+            strategies = [strategy]
+            # recover the physical sharding spec
+            for transform in self.transforms[::-1]:
+                recovered_stragies = []
+                for strategy_ in strategies:
+                    output = transform.recover(op_data_mapping, strategy_)
+                    if isinstance(output, ShardingStrategy):
+                        recovered_stragies.append(output)
+                    elif isinstance(output, (list, tuple)):
+                        recovered_stragies.extend(output)
+                    else:
+                        raise TypeError(
+                            f"Found unexpected output type {type(output)} from the recover method of BmmTransform")
+                strategies = recovered_stragies
+            for index, strategies in enumerate(strategies):
+                strategies.name = f"{strategies.name}_{index}"
+            return strategies

colossalai/auto_parallel/tensor_shard/node_handler/node_handler.py

+from abc import ABC, abstractmethod
+from typing import Dict, List, Tuple, Union
+
+import torch
+from torch.fx.node import Node
+
+from colossalai.auto_parallel.meta_profiler.shard_metainfo import ShardMetaInfo, meta_register
+from colossalai.auto_parallel.tensor_shard.options import ShardOption, SolverPerference
+from colossalai.auto_parallel.tensor_shard.sharding_strategy import (
+    OperationData,
+    OperationDataType,
+    ShardingSpec,
+    ShardingStrategy,
+    StrategiesVector,
+    TrainCycleItem,
+)
+from colossalai.auto_parallel.tensor_shard.utils import check_sharding_spec_validity
+from colossalai.device.device_mesh import DeviceMesh
+from colossalai.logging import get_dist_logger
+from colossalai.tensor.shape_consistency import ShapeConsistencyManager
+
+from .strategy import StrategyGenerator
+
+
+class NodeHandler(ABC):
+    '''
+    The NodeHandler is an abstract class used to generate every possible strategies for an operator node.
+
+    Args:
+        node (Node): the input node in node argument list.
+        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.
+    '''
+
+    def __init__(self,
+                 node: Node,
+                 device_mesh: DeviceMesh,
+                 strategies_vector: StrategiesVector,
+                 shard_option: ShardOption = ShardOption.STANDARD,
+                 solver_perference: SolverPerference = SolverPerference.STANDARD) -> None:
+        self.node = node
+        self.predecessor_node = list(node._input_nodes.keys())
+        self.successor_node = list(node.users.keys())
+        self.device_mesh = device_mesh
+        self.strategies_vector = strategies_vector
+        self.shard_option = shard_option
+        self.solver_perference = solver_perference
+
+    def update_resharding_cost(self, strategy: ShardingStrategy) -> None:
+        """
+        Compute the resharding costs and save the costs in the ShardingStrategy object.
+        """
+        # TODO: test this function when other handlers are ready
+        resharding_costs = {}
+        shape_consistency_manager = ShapeConsistencyManager()
+
+        for node in self.predecessor_node:
+            node_name = str(node)
+            # get the current sharding spec generated by this node handler
+
+            # we will not compute the resharding costs for the node not counted in the strategy.
+            # And the node with tuple or list output need to be handled below.
+            node_in_strategy = [op_data.name for op_data in strategy.sharding_specs.keys()]
+            if str(node) not in node_in_strategy:
+                continue
+
+            op_data = strategy.get_op_data_by_name(node_name)
+            current_sharding_spec = strategy.sharding_specs[op_data]
+            # get the sharding specs for this node generated
+            # in its own node handler
+            assert hasattr(node, 'strategies_vector'), \
+                f'The predecessor node {node_name} has no strategy vector to compute the resharding cost.'
+            prev_strategy_vector = node.strategies_vector
+            prev_sharding_specs = [
+                prev_strategy.get_sharding_spec_by_name(node_name) for prev_strategy in prev_strategy_vector
+            ]
+
+            # create data structrure to store costs
+            if node not in resharding_costs:
+                resharding_costs[node] = []
+
+            def _compute_resharding_cost(
+                    prev_sharding_spec: Union[ShardingSpec,
+                                              List[ShardingSpec]], current_sharding_spec: Union[ShardingSpec,
+                                                                                                List[ShardingSpec]],
+                    data: Union[torch.Tensor, List[torch.Tensor], Tuple[torch.Tensor]]) -> TrainCycleItem:
+                """
+                This is a helper function to compute the resharding cost for a specific strategy of a node.
+                """
+                if prev_sharding_spec is None:
+                    return TrainCycleItem(fwd=0, bwd=0, total=0)
+                elif isinstance(prev_sharding_spec, ShardingSpec):
+                    if isinstance(data, torch.Tensor):
+                        dtype = data.dtype
+                        size_per_elem_bytes = torch.tensor([], dtype=dtype).element_size()
+                        _, _, consistency_cost = shape_consistency_manager.shape_consistency(
+                            prev_sharding_spec, current_sharding_spec)
+
+                        resharding_cost = TrainCycleItem(fwd=consistency_cost["forward"] * size_per_elem_bytes,
+                                                         bwd=consistency_cost["backward"] * size_per_elem_bytes,
+                                                         total=consistency_cost["total"] * size_per_elem_bytes)
+                        return resharding_cost
+                    else:
+                        # This raise is used to check if we have missed any type of data.
+                        # It could be merged into Parameter branch, which means we won't handle
+                        # non-tensor arguments.
+                        raise ValueError(f'Unsupported data type {type(data)}')
+                else:
+                    assert isinstance(prev_sharding_spec, (tuple, list)), \
+                        f'prev_sharding_spec should be in type of ShardingSpec, List[ShardingSpec], \
+                            or Tuple[ShardingSpec], but got {type(prev_sharding_spec)}'
+
+                    fwd_cost = 0
+                    bwd_cost = 0
+                    total_cost = 0
+                    for index, (prev_sharding_spec_item,
+                                current_sharding_spec_item) in enumerate(zip(prev_sharding_spec,
+                                                                             current_sharding_spec)):
+                        item_cost = _compute_resharding_cost(prev_sharding_spec_item, current_sharding_spec_item,
+                                                             data[index])
+                        fwd_cost += item_cost.fwd
+                        bwd_cost += item_cost.bwd
+                        total_cost += item_cost.total
+                    resharding_cost = TrainCycleItem(fwd=fwd_cost, bwd=bwd_cost, total=total_cost)
+                    return resharding_cost
+
+            # for each sharding spec generated by the predecessor's node handler
+            # compute the resharding cost to switch to the sharding spec generated
+            # by the current node handler
+            for prev_sharding_spec in prev_sharding_specs:
+                resharding_cost = _compute_resharding_cost(prev_sharding_spec, current_sharding_spec, op_data.data)
+                resharding_costs[node].append(resharding_cost)
+        strategy.resharding_costs = resharding_costs
+        return strategy
+
+    def get_target_function(self) -> callable:
+        """
+        This function is used to get the target function for the node handler.
+        The target function is used to analyze the costs of strategies.
+        """
+        if self.node.op in ('placeholder', 'get_attr', 'output'):
+            return None
+
+        if self.node.op == 'call_module':
+            target = self.node.graph.owning_module.get_submodule(self.node.target)
+        elif self.node.op == 'call_function':
+            target = self.node.target
+        elif self.node.op == 'call_method':
+            target = getattr(self.node.args[0]._meta_data.__class__, self.node.target)
+        else:
+            raise ValueError(f'Unsupported node type: {self.node.op}')
+
+        return target
+
+    def register_strategy(self, compute_resharding_cost: bool = True) -> StrategiesVector:
+        """
+        Register different sharding strategies for the current node.
+        """
+        strategy_generators = self.get_strategy_generator()
+        for generator in strategy_generators:
+            strategies = generator.generate()
+
+            # postprocess a strategy
+            # postprocess can produce one strategy or multiple strategies
+            post_processed_strategies_map = map(self.post_process, strategies)
+            post_processed_strategies = []
+
+            for strategy in post_processed_strategies_map:
+                if isinstance(strategy, (list, tuple)):
+                    post_processed_strategies.extend(strategy)
+                else:
+                    post_processed_strategies.append(strategy)
+
+            # compute the resharding costs based on the previous node
+            # strategies if specified
+            if compute_resharding_cost:
+                updated_strategies = map(self.update_resharding_cost, post_processed_strategies)
+                post_processed_strategies = list(updated_strategies)
+
+            self.strategies_vector.extend(post_processed_strategies)
+
+        # validating the correctness of the sharding strategy
+        for strategy in self.strategies_vector:
+            for op_data, sharding_spec in strategy.sharding_specs.items():
+                if op_data.data is not None and isinstance(op_data.data, torch.Tensor):
+                    check_sharding_spec_validity(sharding_spec, op_data.data)
+
+        remove_strategy_list = []
+        for strategy in self.strategies_vector:
+            shard_axis_list = []
+            last_axis = len(self.device_mesh.mesh_shape) - 1
+            for op_data, sharding_spec in strategy.sharding_specs.items():
+                if op_data.data is not None and isinstance(op_data.data, torch.Tensor):
+                    for dim, shard_axes in sharding_spec.dim_partition_dict.items():
+                        for shard_axis in shard_axes:
+                            if shard_axis not in shard_axis_list:
+                                shard_axis_list.append(shard_axis)
+
+            shard_level = len(shard_axis_list)
+            using_last_axis = last_axis in shard_axis_list or -1 in shard_axis_list
+            if self.shard_option == ShardOption.SHARD and shard_level == 0:
+                remove_strategy_list.append(strategy)
+            if self.shard_option == ShardOption.FULL_SHARD and shard_level <= 1:
+                remove_strategy_list.append(strategy)
+            if self.shard_option == ShardOption.SHARD_LAST_AXIS:
+                if shard_level != 1 or using_last_axis == False:
+                    remove_strategy_list.append(strategy)
+
+        for strategy in remove_strategy_list:
+            self.strategies_vector.remove(strategy)
+
+        return self.strategies_vector
+
+    def post_process(self, strategy: ShardingStrategy) -> Union[ShardingStrategy, List[ShardingStrategy]]:
+        # tranform the strategy generated
+        # e.g. to process the sharding strategy for the transposed weights
+        return strategy
+
+    @abstractmethod
+    def get_strategy_generator(self) -> List[StrategyGenerator]:
+        """
+        Define which generators should be used by this NodeHandler object.
+        """
+        pass
+
+    @abstractmethod
+    def get_operation_data_mapping(self) -> Dict[str, OperationData]:
+        """
+        Returns the mapping between the logical operation data to its physical data.
+        A logical operation data is a data associated with an operation, which can be input and output. It is
+        defined by the strategy generator, for example, a matrix multiplication operation has two operands "input"
+        and "other" and one result "output". For a nn.Linear module, the physical operand for "input" is
+        the module input, the physical operand for "other" is the module weight, and the physical result for "output"
+        is the module output.
+        Note that the operand name is specified by the StrategyGenerator object.
+
+        For example:
+
+            # for a linear layer
+            mapping = {
+                "input": Operand(name=str(self.node.args[0]), type=OperationDataType.ARG, data=self.node.args[0]._meta_data),
+                "other": Operand(name="weight", type=OperationDataType.PARAM, data=self.named_parameters['weight']),
+                "bias": Operand(name="bias", type=OperationDataType.PARAM, data=self.named_parameters['bias']),
+                "output": Operand(name=str(self.node), type=OperationDataType.OUTPUT, data=self.node._meta_data),
+            }
+        """
+        pass
+
+
+class MetaInfoNodeHandler(NodeHandler):
+    """
+    This is a base class to handle the nodes patched in the meta profiler.
+
+    Note: this class will be integrated into the NodeHandler class in the future, after
+    all the functions are patched.
+    """
+
+    def register_strategy(self, compute_resharding_cost: bool = True) -> StrategiesVector:
+        """
+        This method is inherited from NodeHandler. It will register the strategies first,
+        and rewrite the memory_cost and compute_cost of the strategy using the ShardMetaInfo class.
+        """
+        super().register_strategy(compute_resharding_cost=compute_resharding_cost)
+        target = self.get_target_function()
+        # Currently we haven't patched all the torch functions and modules, so if the target
+        # is not patched, we will use the default cost model to compute the cost.
+        # TODO: patch all torch functions and modules to make it clean
+        if meta_register.has(target.__class__) or meta_register.has(target):
+            strategies_info = []
+            for strategy in self.strategies_vector:
+                metainfo = ShardMetaInfo(strategy, target)
+                strategy.compute_cost = metainfo.compute_cost
+                strategy.memory_cost = metainfo.memory_cost
+                strategies_info.append(metainfo)
+
+            # attach metainfos to the handler
+            setattr(self, "strategies_info", strategies_info)
+
+        else:
+            logger = get_dist_logger()
+            logger.warning(f'The target function {target} is not patched yet, ')
+
+        return self.strategies_vector
+
+
+class ModuleHandler(NodeHandler):
+
+    def __init__(self, *args, **kwargs) -> None:
+        super().__init__(*args, **kwargs)
+
+        # set attributes to access module parameters for convenience
+        assert self.node.graph.owning_module is not None, \
+            f'The graph is not associated with a module, please make sure it can be used to instantiate a GraphModule object.'
+        module = self.node.graph.owning_module.get_submodule(self.node.target)
+        named_parameters = list(module.named_parameters(recurse=False))
+        named_buffers = list(module.named_buffers(recurse=False))
+        # convert named parameters from list to dict
+        named_parameters = {k: v for k, v in named_parameters}
+        named_buffers = {k: v for k, v in named_buffers}
+        self.module = module
+        self.named_parameters = named_parameters
+        self.named_buffers = named_buffers
+
+
+class MetaInfoModuleHandler(ModuleHandler):
+    """
+    This is a base class to handle the module patched in the meta profiler.
+
+    Note: this class will be integrated into the ModuleHandler class in the future, after
+    all the modules are patched.
+    """
+
+    def register_strategy(self, compute_resharding_cost: bool = True) -> StrategiesVector:
+        """
+        This method is inherited from NodeHandler. It will register the strategies first,
+        and rewrite the memory_cost and compute_cost of the strategy using the ShardMetaInfo class.
+        """
+        super().register_strategy(compute_resharding_cost=compute_resharding_cost)
+        target = self.get_target_function()
+        # Currently we haven't patched all the torch functions and modules, so if the target
+        # is not patched, we will use the default cost model to compute the cost.
+        # TODO: patch all torch functions and modules to make it clean
+        if meta_register.has(target.__class__) or meta_register.has(target):
+            strategies_info = []
+            for strategy in self.strategies_vector:
+                metainfo = ShardMetaInfo(strategy, target)
+                strategy.compute_cost = metainfo.compute_cost
+                strategy.memory_cost = metainfo.memory_cost
+                strategies_info.append(metainfo)
+
+            # attach metainfos to the handler
+            setattr(self, "strategies_info", strategies_info)
+
+        else:
+            logger = get_dist_logger()
+            logger.warning(f'The target function {target} is not patched yet')
+
+        return self.strategies_vector

colossalai/auto_parallel/tensor_shard/node_handler/normal_pooling_handler.py

+from typing import Dict, List
+
+import torch
+
+from ..sharding_strategy import OperationData, OperationDataType
+from .node_handler import MetaInfoModuleHandler, ModuleHandler
+from .registry import operator_registry
+from .strategy import NormalPoolStrategyGenerator, StrategyGenerator
+
+__all__ = ['NormPoolingHandler']
+
+
+@operator_registry.register(torch.nn.MaxPool1d)
+@operator_registry.register(torch.nn.MaxPool2d)
+@operator_registry.register(torch.nn.MaxPool1d)
+@operator_registry.register(torch.nn.AvgPool1d)
+@operator_registry.register(torch.nn.AvgPool2d)
+@operator_registry.register(torch.nn.AvgPool3d)
+class NormPoolingHandler(MetaInfoModuleHandler):
+    """
+    A NormPoolingHandler which deals with the sharding strategies for nn.MaxPoolxd module.
+    """
+
+    def get_strategy_generator(self) -> List[StrategyGenerator]:
+        op_data_mapping = self.get_operation_data_mapping()
+        generators = []
+        generators.append(NormalPoolStrategyGenerator(op_data_mapping, self.device_mesh))
+        return generators
+
+    def get_operation_data_mapping(self) -> Dict[str, OperationData]:
+        # use transposed shape for strategies
+        # the strategies will be transformed back to its original shape in self.post_process
+        physical_input_operand = OperationData(name=str(self.node.args[0]),
+                                               type=OperationDataType.ARG,
+                                               data=self.node.args[0]._meta_data)
+        physical_weight_operand = OperationData(name="kernel", type=OperationDataType.ARG, data=self.module.kernel_size)
+        physical_output = OperationData(name=str(self.node), type=OperationDataType.OUTPUT, data=self.node._meta_data)
+
+        mapping = {"input": physical_input_operand, "other": physical_weight_operand, "output": physical_output}
+
+        return mapping

colossalai/auto_parallel/tensor_shard/node_handler/output_handler.py

+from typing import Dict, List
+
+import torch
+
+from colossalai.device.device_mesh import DeviceMesh
+
+from ..sharding_strategy import OperationData, OperationDataType, StrategiesVector
+from .node_handler import NodeHandler
+from .strategy import OutputGenerator, StrategyGenerator
+
+__all__ = ['OutputHandler']
+
+
+class OutputHandler(NodeHandler):
+    """
+    A OutputHandler which deals with the sharding strategies for Output Node.
+    """
+
+    def __init__(self, node: torch.fx.node.Node, device_mesh: DeviceMesh, strategies_vector: StrategiesVector,
+                 output_option: str) -> None:
+        super().__init__(node, device_mesh, strategies_vector)
+        self.output_option = output_option
+
+    def get_strategy_generator(self) -> List[StrategyGenerator]:
+        op_data_mapping = self.get_operation_data_mapping()
+        generators = []
+        generators.append(OutputGenerator(op_data_mapping, self.device_mesh, self.predecessor_node, self.output_option))
+        return generators
+
+    def get_operation_data_mapping(self) -> Dict[str, OperationData]:
+        # use transposed shape for strategies
+        # the strategies will be transformed back to its original shape in self.post_process
+        mapping = {}
+        output_meta_data = []
+        for index, input_node in enumerate(self.predecessor_node):
+            input_meta_data = input_node._meta_data
+            physical_inputs = OperationData(name=str(input_node), type=OperationDataType.ARG, data=input_meta_data)
+            name_key = f'input_{index}'
+            mapping[name_key] = physical_inputs
+            output_meta_data.append(input_meta_data)
+
+        assert len(output_meta_data) > 0, f'Output node {self.node} has no input node.'
+        if len(output_meta_data) == 1:
+            output_meta_data = output_meta_data[0]
+        else:
+            output_meta_data = tuple(output_meta_data)
+
+        self.node._meta_data = output_meta_data
+        physical_output = OperationData(name=str(self.node), type=OperationDataType.OUTPUT, data=self.node._meta_data)
+
+        mapping["output"] = physical_output
+        return mapping

colossalai/auto_parallel/tensor_shard/node_handler/permute_handler.py

+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 PermuteGenerator, StrategyGenerator
+
+__all__ = ['PermuteHandler']
+
+
+@operator_registry.register(torch.Tensor.permute)
+@operator_registry.register(torch.permute)
+class PermuteHandler(NodeHandler):
+    """
+    A PermuteHandler which deals with the sharding strategies for torch.permute or torch.transpose.
+    """
+
+    def get_strategy_generator(self) -> List[StrategyGenerator]:
+        op_data_mapping = self.get_operation_data_mapping()
+        generators = []
+        generators.append(PermuteGenerator(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)
+
+        permute_dims = []
+        if self.node.op == 'call_method':
+            # torch.Tensor.permute (input, *dims)
+            for arg in self.node.args:
+                if isinstance(arg, torch.fx.Node):
+                    if isinstance(arg._meta_data, int):
+                        permute_dims.append(arg._meta_data)
+                else:
+                    assert isinstance(arg, int), 'The argument in permute node should be either type of Node or int.'
+                    permute_dims.append(arg)
+        else:
+            # torch.permute (input, dims)
+            for arg in self.node.args:
+                if isinstance(arg, torch.fx.Node):
+                    if isinstance(arg._meta_data, (tuple, list)):
+                        permute_dims.extend(arg._meta_data)
+                else:
+                    assert isinstance(
+                        arg,
+                        (tuple, list)), 'The argument in permute node should be type of Node, Tuple[int] or List[int].'
+                    permute_dims.extend(arg)
+
+        num_dims = self.node._meta_data.dim()
+        for i in range(num_dims):
+            # recover negative value to positive
+            if permute_dims[i] < 0:
+                permute_dims[i] += num_dims
+
+        physical_shape_operand = OperationData(name='permute_dims', type=OperationDataType.ARG, data=list(permute_dims))
+
+        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,
+            "permute_dims": physical_shape_operand,
+            "output": physical_output_operand
+        }
+
+        return mapping

colossalai/auto_parallel/tensor_shard/node_handler/placeholder_handler.py

+from typing import Dict, List
+
+from torch.fx.node import Node
+
+from colossalai.device.device_mesh import DeviceMesh
+
+from ..sharding_strategy import OperationData, OperationDataType, StrategiesVector
+from .node_handler import NodeHandler
+from .strategy import PlaceholderGenerator, StrategyGenerator
+
+__all__ = ['PlaceholderHandler']
+
+
+class PlaceholderHandler(NodeHandler):
+    """
+    A PlaceholderHandler which deals with the sharding strategies for Placeholder Node.
+    """
+
+    def __init__(self, node: Node, device_mesh: DeviceMesh, strategies_vector: StrategiesVector,
+                 placeholder_option: str) -> None:
+        super().__init__(node, device_mesh, strategies_vector)
+        self.placeholder_option = placeholder_option
+
+    def get_strategy_generator(self) -> List[StrategyGenerator]:
+        op_data_mapping = self.get_operation_data_mapping()
+        generators = []
+        generators.append(
+            PlaceholderGenerator(op_data_mapping, self.device_mesh, placeholder_option=self.placeholder_option))
+        return generators
+
+    def get_operation_data_mapping(self) -> Dict[str, OperationData]:
+        # use transposed shape for strategies
+        # the strategies will be transformed back to its original shape in self.post_process
+        physical_output = OperationData(name=str(self.node), type=OperationDataType.OUTPUT, data=self.node._meta_data)
+
+        mapping = {"output": physical_output}
+
+        return mapping

colossalai/auto_parallel/tensor_shard/node_handler/registry.py

+class Registry:
+    # TODO: refactor the registry classes used in colossalai.registry, colossalai.fx and here
+
+    def __init__(self, name):
+        self.name = name
+        self.store = {}
+
+    def register(self, source):
+
+        def wrapper(func):
+            if isinstance(source, (list, tuple)):
+                # support register a list of items for this func
+                for element in source:
+                    self.store[element] = func
+            else:
+                self.store[source] = func
+            return func
+
+        return wrapper
+
+    def get(self, source):
+        assert source in self.store, f'{source} not found in the {self.name} registry'
+        target = self.store[source]
+        return target
+
+    def has(self, source):
+        return source in self.store
+
+
+operator_registry = Registry('operator')

colossalai/auto_parallel/tensor_shard/node_handler/softmax_handler.py

+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 SoftmaxGenerator, StrategyGenerator
+
+__all__ = ['SoftmaxHandler']
+
+
+@operator_registry.register(torch.nn.Softmax)
+@operator_registry.register(torch.nn.functional.softmax)
+class SoftmaxHandler(NodeHandler):
+    """
+    A SoftmaxHandler which deals with the sharding strategies for
+    torch.nn.Softmax or torch.nn.functional.softmax.
+    """
+
+    def get_strategy_generator(self) -> List[StrategyGenerator]:
+        op_data_mapping = self.get_operation_data_mapping()
+        generators = []
+        generators.append(SoftmaxGenerator(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)
+
+        softmax_dim = self.node.kwargs['dim']
+
+        num_dims = self.node.args[0]._meta_data.dim()
+        # recover negative value to positive
+        if softmax_dim < 0:
+            softmax_dim += num_dims
+
+        physical_dim_operand = OperationData(name='softmax_dim', type=OperationDataType.ARG, data=softmax_dim)
+
+        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,
+            "softmax_dim": physical_dim_operand,
+            "output": physical_output_operand
+        }
+
+        return mapping

colossalai/auto_parallel/tensor_shard/node_handler/split_handler.py

+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 SplitGenerator, StrategyGenerator
+
+__all__ = ['SplitHandler']
+
+
+@operator_registry.register(torch.Tensor.split)
+@operator_registry.register(torch.split)
+class SplitHandler(NodeHandler):
+    """
+    A SplitHandler which deals with the sharding strategies for torch.permute or torch.split.
+    """
+
+    def get_strategy_generator(self) -> List[StrategyGenerator]:
+        op_data_mapping = self.get_operation_data_mapping()
+        generators = []
+        generators.append(SplitGenerator(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)
+        split_size = self.node.args[1]
+        if len(self.node.args) == 3:
+            # (input, split_size, split_dim)
+            split_dim = self.node.args[2]
+        else:
+            if self.node.kwargs:
+                split_dim = self.node.kwargs['dim']
+            else:
+                split_dim = 0
+
+        num_dims = self.node.args[0]._meta_data.dim()
+        # recover negative value to positive
+        if split_dim < 0:
+            split_dim += num_dims
+
+        split_info = (split_size, split_dim)
+        physical_shape_operand = OperationData(name='split_info', type=OperationDataType.ARG, data=split_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,
+            "split_info": physical_shape_operand,
+            "output": physical_output_operand
+        }
+
+        return mapping

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

+from .batch_norm_generator import BatchNormStrategyGenerator
+from .binary_elementwise_generator import BinaryElementwiseStrategyGenerator
+from .conv_strategy_generator import ConvStrategyGenerator
+from .embedding_generator import EmbeddingStrategyGenerator
+from .getattr_generator import GetattrGenerator
+from .getitem_generator import GetItemStrategyGenerator, TensorStrategyGenerator, TensorTupleStrategyGenerator
+from .layer_norm_generator import LayerNormGenerator
+from .matmul_strategy_generator import (
+    BatchedMatMulStrategyGenerator,
+    DotProductStrategyGenerator,
+    LinearProjectionStrategyGenerator,
+    MatVecStrategyGenerator,
+)
+from .normal_pooling_generator import NormalPoolStrategyGenerator
+from .output_generator import OutputGenerator
+from .placeholder_generator import PlaceholderGenerator
+from .reshape_generator import (
+    DefaultReshapeGenerator,
+    PermuteGenerator,
+    SplitGenerator,
+    TransposeGenerator,
+    ViewGenerator,
+)
+from .softmax_generator import SoftmaxGenerator
+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
+
+__all__ = [
+    'StrategyGenerator', 'DotProductStrategyGenerator', 'MatVecStrategyGenerator', 'LinearProjectionStrategyGenerator',
+    'BatchedMatMulStrategyGenerator', 'ConvStrategyGenerator', 'UnaryElementwiseGenerator',
+    'BatchNormStrategyGenerator', 'GetItemStrategyGenerator', 'TensorStrategyGenerator', 'TensorTupleStrategyGenerator',
+    'LayerNormGenerator', 'PlaceholderGenerator', 'OutputGenerator', 'WhereGenerator', 'NormalPoolStrategyGenerator',
+    'BinaryElementwiseStrategyGenerator', 'GetattrGenerator', 'TensorConstructorGenerator',
+    'EmbeddingStrategyGenerator', 'SumGenerator', 'SoftmaxGenerator', 'ViewGenerator', 'PermuteGenerator',
+    'TransposeGenerator', 'SplitGenerator', 'DefaultReshapeGenerator'
+]

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

+import copy
+import operator
+from functools import reduce
+from typing import List
+
+from colossalai.auto_parallel.tensor_shard.sharding_strategy import (
+    CommType,
+    MemoryCost,
+    ShardingStrategy,
+    TrainCycleItem,
+)
+from colossalai.auto_parallel.tensor_shard.utils import ignore_sharding_exception
+from colossalai.tensor.shape_consistency import CollectiveCommPattern
+
+from .strategy_generator import StrategyGenerator
+
+__all__ = ['BatchNormStrategyGenerator']
+
+
+class BatchNormStrategyGenerator(StrategyGenerator):
+    """
+    A StrategyGenerator which deals with the sharding strategies of batch normalization.
+
+    To keep the math consistency, there are two way to do BatchNorm if the input
+    shards on batch dimension:
+    1. We gather the input partitions through batch dimension, then do the normal BatchNorm.
+    2. We do the SyncBatchNorm on the each input partition seperately, the SyncBN op will help
+       us to keep the computing correctness.
+    In this generator, both methods will be considered.
+    """
+
+    def validate(self) -> bool:
+        '''
+        In sanity check, we need make sure the input data having correct dimension size.
+        For BatchNorm1d, the dim of input data should be 3([N, C, L]).
+        For BatchNorm2d, the dim of input data should be 4([N, C, H, W]).
+        For BatchNorm3d, the dim of input data should be 5([N, C, H, W, D]).
+        '''
+        input_op_data = self.op_data['input']
+        assert input_op_data.data.dim() in (
+            3, 4, 5), f'We suppose the dim of input fed into conv op should in range of [3, 5].'
+
+    def update_compute_cost(self, strategy: ShardingStrategy):
+        '''
+        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.
+        '''
+        # TODO: a constant coefficient need to be added.
+        # 1D: (L) * N * Cin
+        # 2D: (H * W) * N  * Cin
+        # 3D: (H * W  * D) * N  * Cin
+        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()
+        if self.has_bias:
+            # bias add is an element wise operation, so the cost is equal to product of output shape.
+            bias_compute_cost = reduce(operator.mul, sharded_output_shape)
+        input_product = reduce(operator.mul, sharded_input_shape, 1)
+        forward_compute_cost = input_product
+        backward_activation_compute_cost = input_product
+        backward_weight_compute_cost = input_product
+        backward_compute_cost = backward_weight_compute_cost + backward_activation_compute_cost
+        if self.has_bias:
+            forward_compute_cost += bias_compute_cost
+            backward_compute_cost += bias_compute_cost
+        total_compute_cost = forward_compute_cost + backward_compute_cost
+        compute_cost = TrainCycleItem(fwd=forward_compute_cost, bwd=backward_compute_cost, total=total_compute_cost)
+        strategy.compute_cost = compute_cost
+
+    def update_memory_cost(self, strategy: ShardingStrategy):
+        forward_size_mapping = {
+            'input': self._compute_size_in_bytes(strategy, "input"),
+            'other': self._compute_size_in_bytes(strategy, "other"),
+            'output': self._compute_size_in_bytes(strategy, "output"),
+            'running_mean': self._compute_size_in_bytes(strategy, "running_mean"),
+            'running_var': self._compute_size_in_bytes(strategy, "running_var"),
+        }
+
+        if self.has_bias:
+            bias_size = self._compute_size_in_bytes(strategy, "bias")
+            forward_size_mapping['bias'] = bias_size
+
+        backward_size_mapping = copy.deepcopy(forward_size_mapping)
+        backward_size_mapping.pop("output")
+        # compute fwd cost incurred
+        # fwd_cost = input + other + bias + output
+        fwd_activation_cost = sum(
+            [v for k, v in forward_size_mapping.items() if not self.is_param(k) and not self.is_buffer(k)])
+        fwd_parameter_cost = sum([v for k, v in forward_size_mapping.items() if self.is_param(k)])
+        fwd_buffer_cost = sum([v for k, v in forward_size_mapping.items() if self.is_buffer(k)])
+        fwd_mem_cost = MemoryCost(activation=fwd_activation_cost, parameter=fwd_parameter_cost, buffer=fwd_buffer_cost)
+
+        # compute bwd cost incurred
+        # bwd_cost = input_grad + other_grad + bias_grad
+        bwd_activation_cost = sum(
+            [v for k, v in backward_size_mapping.items() if not self.is_param(k) and not self.is_buffer(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,
+                                    buffer=fwd_buffer_cost)
+        memory_cost = TrainCycleItem(fwd=fwd_mem_cost, bwd=bwd_mem_cost, total=total_mem_cost)
+        strategy.memory_cost = memory_cost
+
+    @ignore_sharding_exception
+    def split_input_channel(self, mesh_dim_0):
+        name = f'RS{mesh_dim_0} = RS{mesh_dim_0} x S{mesh_dim_0}'
+        dim_partition_dict_mapping = {
+            "input": {
+                1: [mesh_dim_0]
+            },
+            "other": {
+                0: [mesh_dim_0]
+            },
+            "output": {
+                1: [mesh_dim_0]
+            },
+            "running_mean": {
+                0: [mesh_dim_0]
+            },
+            "running_var": {
+                0: [mesh_dim_0]
+            },
+            "num_batches_tracked": {},
+        }
+        if self.has_bias:
+            dim_partition_dict_mapping["bias"] = {0: [mesh_dim_0]}
+
+        sharding_spec_mapping = self.to_sharding_spec_mapping(dim_partition_dict_mapping)
+
+        communication_action_mapping = {}
+
+        return self.get_sharding_strategy(name=name,
+                                          sharding_spec_mapping=sharding_spec_mapping,
+                                          communication_action_mapping=communication_action_mapping)
+
+    @ignore_sharding_exception
+    def split_input_channel_1d(self, mesh_dim_0, mesh_dim_1):
+        name = f'RS{mesh_dim_0}{mesh_dim_1} = RS{mesh_dim_0}{mesh_dim_1} x S{mesh_dim_0}{mesh_dim_1}'
+        dim_partition_dict_mapping = {
+            "input": {
+                1: [mesh_dim_0, mesh_dim_1]
+            },
+            "other": {
+                0: [mesh_dim_0, mesh_dim_1]
+            },
+            "output": {
+                1: [mesh_dim_0, mesh_dim_1]
+            },
+            "running_mean": {
+                0: [mesh_dim_0, mesh_dim_1]
+            },
+            "running_var": {
+                0: [mesh_dim_0, mesh_dim_1]
+            },
+            "num_batches_tracked": {},
+        }
+        if self.has_bias:
+            dim_partition_dict_mapping["bias"] = {0: [mesh_dim_0, mesh_dim_1]}
+
+        sharding_spec_mapping = self.to_sharding_spec_mapping(dim_partition_dict_mapping)
+
+        communication_action_mapping = {}
+
+        return self.get_sharding_strategy(name=name,
+                                          sharding_spec_mapping=sharding_spec_mapping,
+                                          communication_action_mapping=communication_action_mapping)
+
+    @ignore_sharding_exception
+    def non_split(self):
+        name = f'RR = RR x R'
+        dim_partition_dict_mapping = {
+            "input": {},
+            "other": {},
+            "output": {},
+            "running_mean": {},
+            "running_var": {},
+            "num_batches_tracked": {},
+        }
+        if self.has_bias:
+            dim_partition_dict_mapping["bias"] = {}
+
+        sharding_spec_mapping = self.to_sharding_spec_mapping(dim_partition_dict_mapping)
+
+        communication_action_mapping = {}
+
+        return self.get_sharding_strategy(name=name,
+                                          sharding_spec_mapping=sharding_spec_mapping,
+                                          communication_action_mapping=communication_action_mapping)
+
+    @ignore_sharding_exception
+    def split_input_batch(self, mesh_dim_0):
+        name = f'S{mesh_dim_0}R = S{mesh_dim_0}R x R WITH SYNC_BN'
+        dim_partition_dict_mapping = {
+            "input": {
+                0: [mesh_dim_0]
+            },
+            "other": {},
+            "output": {
+                0: [mesh_dim_0]
+            },
+            "running_mean": {},
+            "running_var": {},
+            "num_batches_tracked": {},
+        }
+        if self.has_bias:
+            dim_partition_dict_mapping["bias"] = {}
+
+        sharding_spec_mapping = self.to_sharding_spec_mapping(dim_partition_dict_mapping)
+
+        # set communication action
+        # 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_action = self.get_communication_action(
+            sharding_spec=sharding_spec_mapping["output"],
+            communication_pattern=CollectiveCommPattern.ALLREDUCE_FWD_IDENTITY_BWD,
+            logical_process_axis=mesh_dim_0,
+            comm_type=CommType.IMPLICIT)
+
+        # 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,
+                                          communication_action_mapping=communication_action_mapping)
+
+    @ignore_sharding_exception
+    def split_input_batch_1d(self, mesh_dim_0, mesh_dim_1):
+        name = f'S{mesh_dim_0}{mesh_dim_1}R = S{mesh_dim_0}{mesh_dim_1}R x R WITH SYNC_BN'
+        dim_partition_dict_mapping = {
+            "input": {
+                0: [mesh_dim_0, mesh_dim_1]
+            },
+            "other": {},
+            "output": {
+                0: [mesh_dim_0, mesh_dim_1]
+            },
+            "running_mean": {},
+            "running_var": {},
+            "num_batches_tracked": {},
+        }
+        if self.has_bias:
+            dim_partition_dict_mapping["bias"] = {}
+
+        sharding_spec_mapping = self.to_sharding_spec_mapping(dim_partition_dict_mapping)
+
+        # set communication action
+        # 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_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],
+            comm_type=CommType.IMPLICIT)
+
+        # 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,
+                                          communication_action_mapping=communication_action_mapping)
+
+    @ignore_sharding_exception
+    def split_input_both_dim(self, mesh_dim_0, mesh_dim_1):
+        name = f'S{mesh_dim_0}S{mesh_dim_1} = S{mesh_dim_0}S{mesh_dim_1} x S{mesh_dim_1} WITH SYNC_BN'
+        dim_partition_dict_mapping = {
+            "input": {
+                0: [mesh_dim_0],
+                1: [mesh_dim_1],
+            },
+            "other": {
+                0: [mesh_dim_1],
+            },
+            "output": {
+                0: [mesh_dim_0],
+                1: [mesh_dim_1],
+            },
+            "running_mean": {
+                0: [mesh_dim_1],
+            },
+            "running_var": {
+                0: [mesh_dim_1],
+            },
+            "num_batches_tracked": {},
+        }
+        if self.has_bias:
+            dim_partition_dict_mapping["bias"] = {
+                0: [mesh_dim_1],
+            }
+
+        sharding_spec_mapping = self.to_sharding_spec_mapping(dim_partition_dict_mapping)
+
+        # set communication action
+        # 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_action = self.get_communication_action(
+            sharding_spec=sharding_spec_mapping["output"],
+            communication_pattern=CollectiveCommPattern.ALLREDUCE_FWD_IDENTITY_BWD,
+            logical_process_axis=[mesh_dim_0],
+            comm_type=CommType.IMPLICIT)
+
+        # 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,
+                                          communication_action_mapping=communication_action_mapping)
+
+    def collate_strategies(self) -> List[ShardingStrategy]:
+        '''
+        Generate every possible strategies for a BatchNorm node, and record all strategies into the strategies_vector.
+        '''
+
+        strategy_list = []
+        # RS = RS x S
+        strategy_list.append(self.split_input_channel(0))
+        strategy_list.append(self.split_input_channel(1))
+
+        # RR = RR x R
+        strategy_list.append(self.non_split())
+
+        # RS01 = RS01 x S01
+        strategy_list.append(self.split_input_channel_1d(0, 1))
+
+        # The strategies with SYNC_BN are temporarily commented,
+        # because it requires some additional passes to keep runtime
+        # computation correctness.
+
+        # 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))
+
+        # 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))
+
+        # S01R = S01R x R WITH SYNC_BN
+        strategy_list.append(self.split_input_batch_1d(0, 1))
+
+        return strategy_list

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

+import operator
+from functools import reduce
+from typing import List
+
+import torch
+
+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 colossalai.tensor.sharding_spec import ShardingSpecException
+
+from .strategy_generator import StrategyGenerator
+
+__all__ = ['BinaryElementwiseStrategyGenerator']
+
+
+class BinaryElementwiseStrategyGenerator(StrategyGenerator):
+    """
+    An BinaryElementwiseStrategyGenerator is a node handler which deals with elementwise operations
+    which have two operands and broadcasting occurs such as torch.add.
+
+    The logical shape for this operation will be `input <op> other`.
+    """
+
+    def validate(self) -> bool:
+        assert len(self.op_data) == 3, \
+            f'BinaryElementwiseStrategyGenerator only accepts three operation data (input, other and output), but got {len(self.op_data)}'
+        for name, op_data in self.op_data.items():
+            if not isinstance(op_data.data, (torch.Tensor, int, float)):
+                raise TypeError(f'The operation data {name} is not a torch.Tensor/int/float.')
+
+    def update_compute_cost(self, strategy: ShardingStrategy) -> ShardingStrategy:
+        shape = strategy.sharding_specs[self.op_data['input']].get_sharded_shape_per_device()
+
+        # since elementwise ops are not compute-intensive,
+        # we approximate the backward compute cost
+        # to be twice the fwd compute cost
+        fwd_compute_cost = reduce(operator.mul, shape)
+        bwd_compute_cost = fwd_compute_cost * 2
+        compute_cost = TrainCycleItem(fwd=fwd_compute_cost,
+                                      bwd=bwd_compute_cost,
+                                      total=fwd_compute_cost + bwd_compute_cost)
+        strategy.compute_cost = compute_cost
+
+    def update_memory_cost(self, strategy: ShardingStrategy) -> ShardingStrategy:
+        # all input, output and outputs have the same shape
+        shape = strategy.sharding_specs[self.op_data['input']].get_sharded_shape_per_device()
+
+        # compute fwd memory cost in bytes
+        # as the elementwise ops are not memory-intensive
+        # we approximate the fwd memroy cost to be the output
+        # and the backward memory cost to be grad of input and other
+        input_bytes = self._compute_size_in_bytes(strategy, 'input')
+        other_bytes = self._compute_size_in_bytes(strategy, 'other')
+        output_bytes = self._compute_size_in_bytes(strategy, 'output')
+        fwd_memory_cost = MemoryCost(activation=output_bytes)
+        bwd_memory_cost = MemoryCost(activation=input_bytes + other_bytes)
+        total_memory_cost = MemoryCost(activation=input_bytes + other_bytes + output_bytes)
+        memory_cost = TrainCycleItem(fwd=fwd_memory_cost, bwd=bwd_memory_cost, total=total_memory_cost)
+        strategy.memory_cost = memory_cost
+
+    @ignore_sharding_exception
+    def enumerate_all_possible_output(self, mesh_dim_0, mesh_dim_1):
+        # we check for the output logical shape to get the number of dimensions
+        dim_partition_list = []
+        dim_size = len(self.op_data['output'].logical_shape)
+
+        # enumerate all the 2D sharding cases
+        sharding_list_2d = enumerate_all_possible_2d_sharding(mesh_dim_0, mesh_dim_1, dim_size)
+        dim_partition_list.extend(sharding_list_2d)
+
+        # enumerate all the 1D sharding cases
+        sharding_list_1d_on_dim_0 = enumerate_all_possible_1d_sharding(mesh_dim_0, dim_size)
+        dim_partition_list.extend(sharding_list_1d_on_dim_0)
+        sharding_list_1d_on_dim_1 = enumerate_all_possible_1d_sharding(mesh_dim_1, dim_size)
+        dim_partition_list.extend(sharding_list_1d_on_dim_1)
+
+        # add empty dict for fully replicated case
+        dim_partition_list.append({})
+
+        # sharding strategy bookkeeping
+        strategy_list = []
+
+        # convert these dim partition dict to sharding strategy
+        for dim_partition_dict in dim_partition_list:
+            dim_partition_dict_mapping = dict(input=dim_partition_dict,
+                                              other=dim_partition_dict,
+                                              output=dim_partition_dict)
+
+            try:
+                sharding_spec_mapping = self.to_sharding_spec_mapping(dim_partition_dict_mapping)
+                communication_action_mapping = {}
+
+                # get name
+                sharding_seq = sharding_spec_mapping['input'].sharding_sequence
+                name = f'{sharding_seq} = {sharding_seq} <binary-elementwise-op> {sharding_seq}'
+                sharding_strategy = self.get_sharding_strategy(
+                    name=name,
+                    sharding_spec_mapping=sharding_spec_mapping,
+                    communication_action_mapping=communication_action_mapping)
+                strategy_list.append(sharding_strategy)
+            except ShardingSpecException:
+                continue
+        return strategy_list
+
+    def collate_strategies(self) -> List[ShardingStrategy]:
+        strategy_list = self.enumerate_all_possible_output(0, 1)
+        return strategy_list

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

+import copy
+import operator
+import warnings
+from functools import reduce
+from typing import List
+
+from colossalai.auto_parallel.tensor_shard.sharding_strategy import (
+    CommAction,
+    CommType,
+    MemoryCost,
+    ShardingStrategy,
+    TrainCycleItem,
+)
+from colossalai.auto_parallel.tensor_shard.utils import ignore_sharding_exception
+from colossalai.tensor.shape_consistency import CollectiveCommPattern
+
+from .strategy_generator import StrategyGenerator
+
+
+class ConvStrategyGenerator(StrategyGenerator):
+    """
+    ConvStrategyGenerator is a generic class to generate strategies.
+    The operation data is defined as `output = input x other + bias`.
+    """
+
+    def validate(self) -> bool:
+        '''
+        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]).
+        '''
+        input_op_data = self.op_data['input']
+        assert input_op_data.data.dim() in (
+            3, 4, 5), f'We suppose the dim of input fed into conv op should in range of [3, 5].'
+
+    def update_compute_cost(self, strategy: ShardingStrategy):
+        '''
+        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.
+        '''
+        # 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
+        sharded_input_shape = strategy.sharding_specs[self.op_data['input']].get_sharded_shape_per_device()
+        sharded_other_shape = strategy.sharding_specs[self.op_data['other']].get_sharded_shape_per_device()
+        sharded_output_shape = strategy.sharding_specs[self.op_data['output']].get_sharded_shape_per_device()
+        if self.has_bias:
+            # bias add is an element wise operation, so the cost is equal to product of output shape.
+            bias_compute_cost = reduce(operator.mul, sharded_output_shape)
+
+        output_size = sharded_output_shape[2:]
+        output_size_product = reduce(operator.mul, output_size)
+        input_size = sharded_input_shape[2:]
+        input_size_product = reduce(operator.mul, input_size, 1)
+        kernel_size = sharded_other_shape[2:]
+        kernel_size_product = reduce(operator.mul, kernel_size, 1)
+        batch_size = sharded_input_shape[0]
+        channel_in = sharded_input_shape[1]
+        channel_out = sharded_other_shape[1]
+
+        forward_compute_cost = output_size_product * batch_size * channel_in * channel_out * kernel_size_product
+
+        backward_activation_cost = input_size_product * batch_size * channel_in * channel_out * kernel_size_product
+        backward_weight_cost = output_size_product * batch_size * channel_in * channel_out * kernel_size_product
+        backward_compute_cost = backward_weight_cost + backward_activation_cost
+        if self.has_bias:
+            forward_compute_cost += bias_compute_cost
+            backward_compute_cost += bias_compute_cost
+        total_compute_cost = forward_compute_cost + backward_compute_cost
+
+        compute_cost = TrainCycleItem(fwd=forward_compute_cost, bwd=backward_compute_cost, total=total_compute_cost)
+        strategy.compute_cost = compute_cost
+
+    def update_memory_cost(self, strategy: ShardingStrategy):
+        forward_size_mapping = {
+            'input': self._compute_size_in_bytes(strategy, "input"),
+            'other': self._compute_size_in_bytes(strategy, "other"),
+            'output': self._compute_size_in_bytes(strategy, "output")
+        }
+
+        if self.has_bias:
+            bias_size = self._compute_size_in_bytes(strategy, "bias")
+            forward_size_mapping['bias'] = bias_size
+
+        backward_size_mapping = copy.deepcopy(forward_size_mapping)
+        backward_size_mapping.pop("output")
+        # compute fwd cost incurred
+        # fwd_cost = input + other + bias + 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 + other_grad + bias_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
+
+    @ignore_sharding_exception
+    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_mapping = {
+            "input": {
+                0: [mesh_dim_0]
+            },
+            "other": {
+                1: [mesh_dim_1]
+            },
+            "output": {
+                0: [mesh_dim_0],
+                1: [mesh_dim_1]
+            },
+        }
+        if self.has_bias:
+            dim_partition_dict_mapping["bias"] = {0: [mesh_dim_1]}
+
+        sharding_spec_mapping = self.to_sharding_spec_mapping(dim_partition_dict_mapping)
+
+        # set communication action
+        input_comm_action = self.get_communication_action(
+            sharding_spec=sharding_spec_mapping["input"],
+            communication_pattern=CollectiveCommPattern.IDENTITY_FWD_ALLREDUCE_BWD,
+            logical_process_axis=mesh_dim_1,
+            comm_type=CommType.BEFORE,
+            arg_index=0)
+        communication_action_mapping = {"input": input_comm_action}
+
+        if self.is_param("other"):
+            other_comm_action = self.get_communication_action(
+                sharding_spec_mapping["other"],
+                communication_pattern=CollectiveCommPattern.IDENTITY_FWD_ALLREDUCE_BWD,
+                logical_process_axis=mesh_dim_0,
+                comm_type=CommType.HOOK)
+
+        else:
+            other_comm_action = self.get_communication_action(
+                sharding_spec_mapping["other"],
+                communication_pattern=CollectiveCommPattern.IDENTITY_FWD_ALLREDUCE_BWD,
+                logical_process_axis=mesh_dim_0,
+                comm_type=CommType.BEFORE,
+                arg_index=1)
+
+        communication_action_mapping["other"] = other_comm_action
+
+        if self.has_bias:
+            if self.is_param('bias'):
+                bias_comm_action = self.get_communication_action(
+                    sharding_spec_mapping["bias"],
+                    communication_pattern=CollectiveCommPattern.IDENTITY_FWD_ALLREDUCE_BWD,
+                    logical_process_axis=mesh_dim_0,
+                    comm_type=CommType.HOOK)
+            else:
+                bias_comm_action = self.get_communication_action(
+                    sharding_spec_mapping["bias"],
+                    communication_pattern=CollectiveCommPattern.IDENTITY_FWD_ALLREDUCE_BWD,
+                    logical_process_axis=mesh_dim_0,
+                    comm_type=CommType.BEFORE,
+                    key_for_kwarg='bias')
+            communication_action_mapping["bias"] = bias_comm_action
+
+        return self.get_sharding_strategy(name=name,
+                                          sharding_spec_mapping=sharding_spec_mapping,
+                                          communication_action_mapping=communication_action_mapping)
+
+    @ignore_sharding_exception
+    def split_input_batch(self, mesh_dim_0):
+        name = f'S{mesh_dim_0}R = S{mesh_dim_0}R x RR'
+
+        dim_partition_dict_mapping = {
+            "input": {
+                0: [mesh_dim_0]
+            },
+            "other": {},
+            "output": {
+                0: [mesh_dim_0],
+            },
+        }
+        if self.has_bias:
+            dim_partition_dict_mapping["bias"] = {}
+
+        sharding_spec_mapping = self.to_sharding_spec_mapping(dim_partition_dict_mapping)
+
+        communication_action_mapping = {}
+        if self.is_param("other"):
+            other_comm_action = self.get_communication_action(
+                sharding_spec_mapping["other"],
+                communication_pattern=CollectiveCommPattern.IDENTITY_FWD_ALLREDUCE_BWD,
+                logical_process_axis=mesh_dim_0,
+                comm_type=CommType.HOOK)
+
+        else:
+            other_comm_action = self.get_communication_action(
+                sharding_spec_mapping["other"],
+                communication_pattern=CollectiveCommPattern.IDENTITY_FWD_ALLREDUCE_BWD,
+                logical_process_axis=mesh_dim_0,
+                comm_type=CommType.BEFORE,
+                arg_index=1)
+
+        communication_action_mapping["other"] = other_comm_action
+
+        if self.has_bias:
+            if self.is_param('bias'):
+                bias_comm_action = self.get_communication_action(
+                    sharding_spec_mapping["bias"],
+                    communication_pattern=CollectiveCommPattern.IDENTITY_FWD_ALLREDUCE_BWD,
+                    logical_process_axis=mesh_dim_0,
+                    comm_type=CommType.HOOK)
+            else:
+                bias_comm_action = self.get_communication_action(
+                    sharding_spec_mapping["bias"],
+                    communication_pattern=CollectiveCommPattern.IDENTITY_FWD_ALLREDUCE_BWD,
+                    logical_process_axis=mesh_dim_0,
+                    comm_type=CommType.BEFORE,
+                    key_for_kwarg='bias')
+            communication_action_mapping["bias"] = bias_comm_action
+
+        return self.get_sharding_strategy(name=name,
+                                          sharding_spec_mapping=sharding_spec_mapping,
+                                          communication_action_mapping=communication_action_mapping)
+
+    @ignore_sharding_exception
+    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_mapping = {
+            "input": {
+                0: [mesh_dim_0],
+                1: [mesh_dim_1],
+            },
+            "other": {
+                0: [mesh_dim_1]
+            },
+            "output": {
+                0: [mesh_dim_0],
+            },
+        }
+        if self.has_bias:
+            dim_partition_dict_mapping["bias"] = {}
+
+        sharding_spec_mapping = self.to_sharding_spec_mapping(dim_partition_dict_mapping)
+
+        # set communication action
+        output_comm_action = self.get_communication_action(
+            sharding_spec_mapping["output"],
+            communication_pattern=CollectiveCommPattern.ALLREDUCE_FWD_IDENTITY_BWD,
+            logical_process_axis=mesh_dim_1,
+            comm_type=CommType.AFTER)
+
+        communication_action_mapping = {"output": output_comm_action}
+
+        if self.is_param("other"):
+            other_comm_action = self.get_communication_action(
+                sharding_spec_mapping["other"],
+                communication_pattern=CollectiveCommPattern.IDENTITY_FWD_ALLREDUCE_BWD,
+                logical_process_axis=mesh_dim_0,
+                comm_type=CommType.HOOK)
+
+        else:
+            other_comm_action = self.get_communication_action(
+                sharding_spec_mapping["other"],
+                communication_pattern=CollectiveCommPattern.IDENTITY_FWD_ALLREDUCE_BWD,
+                logical_process_axis=mesh_dim_0,
+                comm_type=CommType.BEFORE,
+                arg_index=1)
+        communication_action_mapping["other"] = other_comm_action
+        if self.has_bias:
+            if self.is_param("bias"):
+                bias_comm_action = self.get_communication_action(
+                    sharding_spec_mapping["bias"],
+                    communication_pattern=CollectiveCommPattern.IDENTITY_FWD_ALLREDUCE_BWD,
+                    logical_process_axis=mesh_dim_0,
+                    comm_type=CommType.HOOK)
+            else:
+                bias_comm_action = self.get_communication_action(
+                    sharding_spec_mapping["bias"],
+                    communication_pattern=CollectiveCommPattern.IDENTITY_FWD_ALLREDUCE_BWD,
+                    logical_process_axis=mesh_dim_0,
+                    comm_type=CommType.BEFORE,
+                    key_for_kwarg='bias')
+            communication_action_mapping["bias"] = bias_comm_action
+
+        return self.get_sharding_strategy(name=name,
+                                          sharding_spec_mapping=sharding_spec_mapping,
+                                          communication_action_mapping=communication_action_mapping)
+
+    @ignore_sharding_exception
+    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_mapping = {
+            "input": {
+                1: [mesh_dim_0],
+            },
+            "other": {
+                0: [mesh_dim_0],
+                1: [mesh_dim_1],
+            },
+            "output": {
+                1: [mesh_dim_1],
+            },
+        }
+
+        if self.has_bias:
+            dim_partition_dict_mapping["bias"] = {
+                0: [mesh_dim_1],
+            }
+
+        sharding_spec_mapping = self.to_sharding_spec_mapping(dim_partition_dict_mapping)
+
+        # set communication action
+        output_comm_action = self.get_communication_action(
+            sharding_spec_mapping["output"],
+            communication_pattern=CollectiveCommPattern.ALLREDUCE_FWD_IDENTITY_BWD,
+            logical_process_axis=mesh_dim_0,
+            comm_type=CommType.AFTER)
+        input_comm_action = self.get_communication_action(
+            sharding_spec_mapping["input"],
+            communication_pattern=CollectiveCommPattern.IDENTITY_FWD_ALLREDUCE_BWD,
+            logical_process_axis=mesh_dim_1,
+            comm_type=CommType.BEFORE,
+            arg_index=0)
+
+        communication_action_mapping = {"output": output_comm_action, "input": input_comm_action}
+
+        return self.get_sharding_strategy(name=name,
+                                          sharding_spec_mapping=sharding_spec_mapping,
+                                          communication_action_mapping=communication_action_mapping)
+
+    @ignore_sharding_exception
+    def split_input_in_channel_weight_in_channel(self, mesh_dim_0):
+        name = f'RR = RS{mesh_dim_0} x S{mesh_dim_0}R'
+
+        dim_partition_dict_mapping = {
+            "input": {
+                1: [mesh_dim_0],
+            },
+            "other": {
+                0: [mesh_dim_0],
+            },
+            "output": {},
+        }
+
+        if self.has_bias:
+            dim_partition_dict_mapping["bias"] = {}
+
+        sharding_spec_mapping = self.to_sharding_spec_mapping(dim_partition_dict_mapping)
+
+        # set communication action
+        output_comm_action = self.get_communication_action(
+            sharding_spec_mapping["output"],
+            communication_pattern=CollectiveCommPattern.ALLREDUCE_FWD_IDENTITY_BWD,
+            logical_process_axis=mesh_dim_0,
+            comm_type=CommType.AFTER)
+
+        communication_action_mapping = {"output": output_comm_action}
+
+        return self.get_sharding_strategy(name=name,
+                                          sharding_spec_mapping=sharding_spec_mapping,
+                                          communication_action_mapping=communication_action_mapping)
+
+    @ignore_sharding_exception
+    def split_weight_out_channel(self, mesh_dim_0):
+        name = f'RS{mesh_dim_0} = RR x RS{mesh_dim_0}'
+
+        dim_partition_dict_mapping = {
+            "input": {},
+            "other": {
+                1: [mesh_dim_0],
+            },
+            "output": {
+                1: [mesh_dim_0],
+            },
+        }
+
+        if self.has_bias:
+            dim_partition_dict_mapping["bias"] = {
+                0: [mesh_dim_0],
+            }
+
+        sharding_spec_mapping = self.to_sharding_spec_mapping(dim_partition_dict_mapping)
+
+        # set communication action
+        input_comm_action = self.get_communication_action(
+            sharding_spec_mapping["input"],
+            communication_pattern=CollectiveCommPattern.IDENTITY_FWD_ALLREDUCE_BWD,
+            logical_process_axis=mesh_dim_0,
+            comm_type=CommType.BEFORE,
+            arg_index=0)
+
+        communication_action_mapping = {"input": input_comm_action}
+
+        return self.get_sharding_strategy(name=name,
+                                          sharding_spec_mapping=sharding_spec_mapping,
+                                          communication_action_mapping=communication_action_mapping)
+
+    @ignore_sharding_exception
+    def non_split(self):
+        name = f'RR = RR x RR'
+
+        dim_partition_dict_mapping = {
+            "input": {},
+            "other": {},
+            "output": {},
+        }
+
+        if self.has_bias:
+            dim_partition_dict_mapping["bias"] = {}
+
+        sharding_spec_mapping = self.to_sharding_spec_mapping(dim_partition_dict_mapping)
+
+        return self.get_sharding_strategy(name=name,
+                                          sharding_spec_mapping=sharding_spec_mapping,
+                                          communication_action_mapping={})
+
+    @ignore_sharding_exception
+    def split_1d_parallel_on_input_batch(self, mesh_dim_0, mesh_dim_1):
+        name = f'S{mesh_dim_0}{mesh_dim_1}R = S{mesh_dim_0}{mesh_dim_1}R x RR'
+
+        dim_partition_dict_mapping = {
+            "input": {
+                0: [mesh_dim_0, mesh_dim_1],
+            },
+            "other": {},
+            "output": {
+                0: [mesh_dim_0, mesh_dim_1],
+            },
+        }
+
+        if self.has_bias:
+            dim_partition_dict_mapping["bias"] = {}
+
+        sharding_spec_mapping = self.to_sharding_spec_mapping(dim_partition_dict_mapping)
+
+        communication_action_mapping = {}
+        if self.is_param("other"):
+            other_comm_action = self.get_communication_action(
+                sharding_spec_mapping["other"],
+                communication_pattern=CollectiveCommPattern.IDENTITY_FWD_ALLREDUCE_BWD,
+                logical_process_axis=[mesh_dim_0, mesh_dim_1],
+                comm_type=CommType.HOOK)
+        else:
+            other_comm_action = self.get_communication_action(
+                sharding_spec_mapping["other"],
+                communication_pattern=CollectiveCommPattern.IDENTITY_FWD_ALLREDUCE_BWD,
+                logical_process_axis=[mesh_dim_0, mesh_dim_1],
+                comm_type=CommType.BEFORE,
+                arg_index=1)
+
+        communication_action_mapping["other"] = other_comm_action
+
+        if self.has_bias:
+            if self.is_param("bias"):
+                bias_comm_action = self.get_communication_action(
+                    sharding_spec_mapping["bias"],
+                    communication_pattern=CollectiveCommPattern.IDENTITY_FWD_ALLREDUCE_BWD,
+                    logical_process_axis=[mesh_dim_0, mesh_dim_1],
+                    comm_type=CommType.HOOK)
+            else:
+                bias_comm_action = self.get_communication_action(
+                    sharding_spec_mapping["bias"],
+                    communication_pattern=CollectiveCommPattern.IDENTITY_FWD_ALLREDUCE_BWD,
+                    logical_process_axis=[mesh_dim_0, mesh_dim_1],
+                    comm_type=CommType.BEFORE,
+                    key_for_kwarg='bias')
+            communication_action_mapping["bias"] = bias_comm_action
+
+        return self.get_sharding_strategy(name=name,
+                                          sharding_spec_mapping=sharding_spec_mapping,
+                                          communication_action_mapping=communication_action_mapping)
+
+    @ignore_sharding_exception
+    def split_1d_parallel_on_in_channel(self, mesh_dim_0, mesh_dim_1):
+        name = f'RR = RS{mesh_dim_0}{mesh_dim_1} x S{mesh_dim_0}{mesh_dim_1}R'
+        dim_partition_dict_mapping = {
+            "input": {
+                1: [mesh_dim_0, mesh_dim_1],
+            },
+            "other": {
+                0: [mesh_dim_0, mesh_dim_1],
+            },
+            "output": {},
+        }
+
+        if self.has_bias:
+            dim_partition_dict_mapping["bias"] = {}
+
+        sharding_spec_mapping = self.to_sharding_spec_mapping(dim_partition_dict_mapping)
+
+        # set communication action
+        output_comm_action = self.get_communication_action(
+            sharding_spec_mapping["output"],
+            communication_pattern=CollectiveCommPattern.ALLREDUCE_FWD_IDENTITY_BWD,
+            logical_process_axis=[mesh_dim_0, mesh_dim_1],
+            comm_type=CommType.AFTER)
+
+        communication_action_mapping = {"output": output_comm_action}
+
+        return self.get_sharding_strategy(name=name,
+                                          sharding_spec_mapping=sharding_spec_mapping,
+                                          communication_action_mapping=communication_action_mapping)
+
+    @ignore_sharding_exception
+    def split_1d_parallel_on_out_channel(self, mesh_dim_0, mesh_dim_1):
+        name = f'RS{mesh_dim_0}{mesh_dim_1} = RR x RS{mesh_dim_0}{mesh_dim_1}'
+        dim_partition_dict_mapping = {
+            "input": {},
+            "other": {
+                1: [mesh_dim_0, mesh_dim_1],
+            },
+            "output": {
+                1: [mesh_dim_0, mesh_dim_1],
+            },
+        }
+
+        if self.has_bias:
+            dim_partition_dict_mapping["bias"] = {
+                0: [mesh_dim_0, mesh_dim_1],
+            }
+
+        sharding_spec_mapping = self.to_sharding_spec_mapping(dim_partition_dict_mapping)
+
+        # set communication action
+        input_comm_action = self.get_communication_action(
+            sharding_spec_mapping["input"],
+            communication_pattern=CollectiveCommPattern.IDENTITY_FWD_ALLREDUCE_BWD,
+            logical_process_axis=[mesh_dim_0, mesh_dim_1],
+            comm_type=CommType.BEFORE,
+            arg_index=0)
+
+        communication_action_mapping = {"input": input_comm_action}
+
+        return self.get_sharding_strategy(name=name,
+                                          sharding_spec_mapping=sharding_spec_mapping,
+                                          communication_action_mapping=communication_action_mapping)
+
+    def collate_strategies(self) -> List[ShardingStrategy]:
+        strategies = []
+        # SS = SR x RS
+        strategies.append(self.split_input_batch_weight_out_channel(0, 1))
+        strategies.append(self.split_input_batch_weight_out_channel(1, 0))
+
+        # SR = SR x RR
+        strategies.append(self.split_input_batch(0))
+        strategies.append(self.split_input_batch(1))
+
+        # SR = SS x SR
+        strategies.append(self.split_input_both_dim_weight_in_channel(0, 1))
+        strategies.append(self.split_input_both_dim_weight_in_channel(1, 0))
+
+        # RS = RS x SS
+        strategies.append(self.split_input_in_channel_weight_both_channel(0, 1))
+        strategies.append(self.split_input_in_channel_weight_both_channel(1, 0))
+
+        # RR = RS x SR
+        strategies.append(self.split_input_in_channel_weight_in_channel(0))
+        strategies.append(self.split_input_in_channel_weight_in_channel(1))
+
+        # RS = RR x RS
+        strategies.append(self.split_weight_out_channel(0))
+        strategies.append(self.split_weight_out_channel(1))
+
+        # RR= RR x RR
+        strategies.append(self.non_split())
+
+        # S01R = S01R x RR
+        strategies.append(self.split_1d_parallel_on_input_batch(0, 1))
+
+        # RR = RS01 x S01R
+        strategies.append(self.split_1d_parallel_on_in_channel(0, 1))
+
+        # RS01 = RR x RS01
+        strategies.append(self.split_1d_parallel_on_out_channel(0, 1))
+
+        return strategies

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

+import copy
+import operator
+import warnings
+from functools import reduce
+from typing import List
+
+from colossalai.auto_parallel.tensor_shard.sharding_strategy import (
+    CommAction,
+    CommType,
+    MemoryCost,
+    ShardingStrategy,
+    TrainCycleItem,
+)
+from colossalai.auto_parallel.tensor_shard.utils import ignore_sharding_exception
+from colossalai.tensor.shape_consistency import CollectiveCommPattern
+
+from .strategy_generator import StrategyGenerator
+
+
+class EmbeddingStrategyGenerator(StrategyGenerator):
+    """
+    EmbeddingStrategyGenerator is a generic class to generate strategies for nn.Embedding or F.embedding.
+    The operation data is defined as `output = input x other`.
+    """
+
+    def validate(self) -> bool:
+        return super().validate()
+
+    def update_compute_cost(self, strategy: ShardingStrategy):
+        '''
+        Compute the computation cost per device with this specific strategy.
+
+        Note: The computation cost for the embedding handler is estimated as dense computing now.
+              It may not be accurate.
+        '''
+        # TODO: estimate the embedding computation cost as sparse operation
+        sharded_input_shape = strategy.sharding_specs[self.op_data['input']].get_sharded_shape_per_device()
+        sharded_other_shape = strategy.sharding_specs[self.op_data['other']].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)
+        other_size_product = reduce(operator.mul, sharded_other_shape)
+        output_size_product = reduce(operator.mul, sharded_output_shape)
+
+        forward_compute_cost = input_size_product * other_size_product
+
+        backward_activation_cost = other_size_product * output_size_product / sharded_output_shape[-1]
+        backward_weight_cost = input_size_product * other_size_product
+        backward_compute_cost = backward_weight_cost + backward_activation_cost
+
+        total_compute_cost = forward_compute_cost + backward_compute_cost
+
+        compute_cost = TrainCycleItem(fwd=forward_compute_cost, bwd=backward_compute_cost, total=total_compute_cost)
+        strategy.compute_cost = compute_cost
+
+    def update_memory_cost(self, strategy: ShardingStrategy):
+        forward_size_mapping = {
+            'input': self._compute_size_in_bytes(strategy, "input"),
+            'other': self._compute_size_in_bytes(strategy, "other"),
+            '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 + other + 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 + other_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
+
+    @ignore_sharding_exception
+    def non_split(self):
+        name = f'RR = R x RR'
+
+        dim_partition_dict_mapping = {
+            "input": {},
+            "other": {},
+            "output": {},
+        }
+
+        sharding_spec_mapping = self.to_sharding_spec_mapping(dim_partition_dict_mapping)
+
+        return self.get_sharding_strategy(name=name,
+                                          sharding_spec_mapping=sharding_spec_mapping,
+                                          communication_action_mapping={})
+
+    @ignore_sharding_exception
+    def split_input(self, mesh_dim_0):
+        name = f'S{mesh_dim_0}R = S{mesh_dim_0} x RR'
+
+        dim_partition_dict_mapping = {
+            "input": {
+                0: [mesh_dim_0]
+            },
+            "other": {},
+            "output": {
+                0: [mesh_dim_0],
+            },
+        }
+
+        sharding_spec_mapping = self.to_sharding_spec_mapping(dim_partition_dict_mapping)
+
+        communication_action_mapping = {}
+        if self.is_param("other"):
+            other_comm_action = self.get_communication_action(
+                sharding_spec_mapping["other"],
+                communication_pattern=CollectiveCommPattern.IDENTITY_FWD_ALLREDUCE_BWD,
+                logical_process_axis=mesh_dim_0,
+                comm_type=CommType.HOOK)
+
+        else:
+            other_comm_action = self.get_communication_action(
+                sharding_spec_mapping["other"],
+                communication_pattern=CollectiveCommPattern.IDENTITY_FWD_ALLREDUCE_BWD,
+                logical_process_axis=mesh_dim_0,
+                comm_type=CommType.BEFORE,
+                arg_index=1)
+
+        communication_action_mapping["other"] = other_comm_action
+
+        return self.get_sharding_strategy(name=name,
+                                          sharding_spec_mapping=sharding_spec_mapping,
+                                          communication_action_mapping=communication_action_mapping)
+
+    @ignore_sharding_exception
+    def split_input_and_embedding_dim(self, mesh_dim_0, mesh_dim_1):
+        name = f'S{mesh_dim_0}S{mesh_dim_1} = S{mesh_dim_0} x RS{mesh_dim_1}'
+
+        dim_partition_dict_mapping = {
+            "input": {
+                0: [mesh_dim_0],
+            },
+            "other": {
+                1: [mesh_dim_1],
+            },
+            "output": {
+                0: [mesh_dim_0],
+                1: [mesh_dim_1],
+            },
+        }
+
+        sharding_spec_mapping = self.to_sharding_spec_mapping(dim_partition_dict_mapping)
+
+        # set communication action
+        input_comm_action = self.get_communication_action(
+            sharding_spec_mapping["input"],
+            communication_pattern=CollectiveCommPattern.IDENTITY_FWD_ALLREDUCE_BWD,
+            logical_process_axis=mesh_dim_1,
+            comm_type=CommType.BEFORE,
+            arg_index=0)
+        communication_action_mapping = {"input": input_comm_action}
+
+        if self.is_param("other"):
+            other_comm_action = self.get_communication_action(
+                sharding_spec_mapping["other"],
+                communication_pattern=CollectiveCommPattern.IDENTITY_FWD_ALLREDUCE_BWD,
+                logical_process_axis=mesh_dim_0,
+                comm_type=CommType.HOOK)
+
+        else:
+            other_comm_action = self.get_communication_action(
+                sharding_spec_mapping["other"],
+                communication_pattern=CollectiveCommPattern.IDENTITY_FWD_ALLREDUCE_BWD,
+                logical_process_axis=mesh_dim_0,
+                comm_type=CommType.BEFORE,
+                arg_index=1)
+
+        communication_action_mapping["other"] = other_comm_action
+
+        return self.get_sharding_strategy(name=name,
+                                          sharding_spec_mapping=sharding_spec_mapping,
+                                          communication_action_mapping=communication_action_mapping)
+
+    @ignore_sharding_exception
+    def split_1d_parallel_on_input(self, mesh_dim_0, mesh_dim_1):
+        name = f'S{mesh_dim_0}{mesh_dim_1}R = S{mesh_dim_0}{mesh_dim_1} x RR'
+
+        dim_partition_dict_mapping = {
+            "input": {
+                0: [mesh_dim_0, mesh_dim_1]
+            },
+            "other": {},
+            "output": {
+                0: [mesh_dim_0, mesh_dim_1],
+            },
+        }
+
+        sharding_spec_mapping = self.to_sharding_spec_mapping(dim_partition_dict_mapping)
+
+        # set communication action
+        communication_action_mapping = {}
+
+        if self.is_param("other"):
+            other_comm_action = self.get_communication_action(
+                sharding_spec_mapping["other"],
+                communication_pattern=CollectiveCommPattern.IDENTITY_FWD_ALLREDUCE_BWD,
+                logical_process_axis=[mesh_dim_0, mesh_dim_1],
+                comm_type=CommType.HOOK)
+
+        else:
+            other_comm_action = self.get_communication_action(
+                sharding_spec_mapping["other"],
+                communication_pattern=CollectiveCommPattern.IDENTITY_FWD_ALLREDUCE_BWD,
+                logical_process_axis=[mesh_dim_0, mesh_dim_1],
+                comm_type=CommType.BEFORE,
+                arg_index=1)
+
+        communication_action_mapping["other"] = other_comm_action
+
+        return self.get_sharding_strategy(name=name,
+                                          sharding_spec_mapping=sharding_spec_mapping,
+                                          communication_action_mapping=communication_action_mapping)
+
+    @ignore_sharding_exception
+    def split_embedding_dim(self, mesh_dim_0):
+        name = f'RS{mesh_dim_0} = R x RS{mesh_dim_0}'
+
+        dim_partition_dict_mapping = {
+            "input": {},
+            "other": {
+                1: [mesh_dim_0],
+            },
+            "output": {
+                1: [mesh_dim_0],
+            },
+        }
+
+        sharding_spec_mapping = self.to_sharding_spec_mapping(dim_partition_dict_mapping)
+
+        # set communication action
+        input_comm_action = self.get_communication_action(
+            sharding_spec_mapping["input"],
+            communication_pattern=CollectiveCommPattern.IDENTITY_FWD_ALLREDUCE_BWD,
+            logical_process_axis=mesh_dim_0,
+            comm_type=CommType.BEFORE,
+            arg_index=0)
+
+        communication_action_mapping = {"input": input_comm_action}
+
+        return self.get_sharding_strategy(name=name,
+                                          sharding_spec_mapping=sharding_spec_mapping,
+                                          communication_action_mapping=communication_action_mapping)
+
+    @ignore_sharding_exception
+    def split_1d_parallel_on_embedding_dim(self, mesh_dim_0, mesh_dim_1):
+        name = f'RS{mesh_dim_0}{mesh_dim_1} = R x RS{mesh_dim_0}{mesh_dim_1}'
+
+        dim_partition_dict_mapping = {
+            "input": {},
+            "other": {
+                1: [mesh_dim_0, mesh_dim_1],
+            },
+            "output": {
+                1: [mesh_dim_0, mesh_dim_1],
+            },
+        }
+
+        sharding_spec_mapping = self.to_sharding_spec_mapping(dim_partition_dict_mapping)
+
+        # set communication action
+        input_comm_action = self.get_communication_action(
+            sharding_spec_mapping["input"],
+            communication_pattern=CollectiveCommPattern.IDENTITY_FWD_ALLREDUCE_BWD,
+            logical_process_axis=[mesh_dim_0, mesh_dim_1],
+            comm_type=CommType.BEFORE,
+            arg_index=0)
+
+        communication_action_mapping = {"input": input_comm_action}
+
+        return self.get_sharding_strategy(name=name,
+                                          sharding_spec_mapping=sharding_spec_mapping,
+                                          communication_action_mapping=communication_action_mapping)
+
+    def collate_strategies(self) -> List[ShardingStrategy]:
+        strategies = []
+
+        # RR= R x RR
+        strategies.append(self.non_split())
+
+        # SR = S x RR
+        strategies.append(self.split_input(0))
+        strategies.append(self.split_input(1))
+
+        # SS = S x RS
+        strategies.append(self.split_input_and_embedding_dim(0, 1))
+        strategies.append(self.split_input_and_embedding_dim(1, 0))
+
+        # S01R = S01 x RR
+        strategies.append(self.split_1d_parallel_on_input(0, 1))
+
+        # RS = R x RS
+        strategies.append(self.split_embedding_dim(0))
+        strategies.append(self.split_embedding_dim(1))
+
+        # RS01 = R x RS01
+        strategies.append(self.split_1d_parallel_on_embedding_dim(0, 1))
+
+        return strategies

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

+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,
+    ignore_sharding_exception,
+)
+from colossalai.tensor.sharding_spec import ShardingSpecException
+
+from .strategy_generator import StrategyGenerator
+
+__all__ = ['GetattrGenerator']
+
+
+class GetattrGenerator(StrategyGenerator):
+    """
+    PlaceholderGenerator is a generic class to generate strategies for placeholder node.
+    """
+
+    def validate(self) -> bool:
+        return super().validate()
+
+    def update_compute_cost(self, strategy: ShardingStrategy):
+        compute_cost = TrainCycleItem(fwd=10, bwd=10, total=20)
+        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 = {'output': self._compute_size_in_bytes(strategy, "output")}
+
+        # compute fwd cost incurred
+        # fwd_cost = output
+        fwd_activation_cost = sum([v for k, v in forward_size_mapping.items()])
+        fwd_mem_cost = MemoryCost(activation=fwd_activation_cost, parameter=0)
+
+        bwd_mem_cost = MemoryCost(activation=0, parameter=0)
+
+        # compute total cost
+        total_mem_cost = MemoryCost(activation=fwd_activation_cost, parameter=0)
+        memory_cost = TrainCycleItem(fwd=fwd_mem_cost, bwd=bwd_mem_cost, total=total_mem_cost)
+        strategy.memory_cost = memory_cost
+
+    @ignore_sharding_exception
+    def enumerate_all_possible_output(self, mesh_dim_0, mesh_dim_1):
+        # we check for the output logical shape to get the number of dimensions
+        dim_partition_list = []
+        dim_size = len(self.op_data['output'].logical_shape)
+
+        # enumerate all the 2D sharding cases
+        sharding_list_2d = enumerate_all_possible_2d_sharding(mesh_dim_0, mesh_dim_1, dim_size)
+        dim_partition_list.extend(sharding_list_2d)
+
+        # enumerate all the 1D sharding cases
+        sharding_list_1d_on_dim_0 = enumerate_all_possible_1d_sharding(mesh_dim_0, dim_size)
+        dim_partition_list.extend(sharding_list_1d_on_dim_0)
+        sharding_list_1d_on_dim_1 = enumerate_all_possible_1d_sharding(mesh_dim_1, dim_size)
+        dim_partition_list.extend(sharding_list_1d_on_dim_1)
+
+        # add empty dict for fully replicated case
+        dim_partition_list.append({})
+
+        # sharding strategy bookkeeping
+        strategy_list = []
+
+        # convert these dim partition dict to sharding strategy
+        for dim_partition_dict in dim_partition_list:
+            dim_partition_dict_mapping = dict(output=dim_partition_dict)
+
+            try:
+                sharding_spec_mapping = self.to_sharding_spec_mapping(dim_partition_dict_mapping)
+                communication_action_mapping = {}
+
+                # get name
+                name = f"get_attr {sharding_spec_mapping['output'].sharding_sequence}"
+                sharding_strategy = self.get_sharding_strategy(
+                    name=name,
+                    sharding_spec_mapping=sharding_spec_mapping,
+                    communication_action_mapping=communication_action_mapping)
+                strategy_list.append(sharding_strategy)
+            except ShardingSpecException:
+                continue
+
+        return strategy_list
+
+    def collate_strategies(self) -> List[ShardingStrategy]:
+        return self.enumerate_all_possible_output(0, 1)