[autoparallel]add embedding handler (#2089)

* [autoparallel] add embedding handler

* fix bugs

colossalai/auto_parallel/tensor_shard/node_handler/__init__.py

@@ -3,6 +3,7 @@ from .batch_norm_handler import BatchNormModuleHandler
 from .binary_elementwise_handler import BinaryElementwiseHandler
 from .bmm_handler import AddBMMFunctionHandler, BMMFunctionHandler
 from .conv_handler import ConvFunctionHandler, ConvModuleHandler
+from .embedding_handler import EmbeddingFunctionHandler, EmbeddingModuleHandler
 from .experimental import PermuteHandler, ViewHandler
 from .getatrr_handler import GetattrHandler
 from .getitem_handler import GetItemHandler
@@ -23,5 +24,6 @@ __all__ = [
     'LayerNormModuleHandler', 'BatchNormModuleHandler', 'ConvModuleHandler', 'ConvFunctionHandler',
     'UnaryElementwiseHandler', 'ReshapeHandler', 'PlacehodlerHandler', 'OuputHandler', 'WhereHandler',
     'NormPoolingHandler', 'BinaryElementwiseHandler', 'MatMulHandler', 'operator_registry', 'ADDMMFunctionHandler',
-    'GetItemHandler', 'GetattrHandler', 'ViewHandler', 'PermuteHandler', 'TensorConstructorHandler'
+    'GetItemHandler', 'GetattrHandler', 'ViewHandler', 'PermuteHandler', 'TensorConstructorHandler',
+    'EmbeddingModuleHandler', 'EmbeddingFunctionHandler'
 ]

colossalai/auto_parallel/tensor_shard/node_handler/embedding_handler.py

@@ -0,0 +1,230 @@
+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/strategy/__init__.py

@@ -1,6 +1,7 @@
 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
@@ -25,5 +26,5 @@ __all__ = [
     'BatchNormStrategyGenerator', 'GetItemStrategyGenerator', 'TensorStrategyGenerator', 'TensorTupleStrategyGenerator',
     'LayerNormGenerator', 'ReshapeGenerator', 'PlaceholderGenerator', 'OutputGenerator', 'WhereGenerator',
     'ReshapeGenerator', 'NormalPoolStrategyGenerator', 'BinaryElementwiseStrategyGenerator', 'GetattrGenerator',
-    'TensorConstructorGenerator'
+    'TensorConstructorGenerator', 'EmbeddingStrategyGenerator'
 ]

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

@@ -0,0 +1,310 @@
+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

tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_embedding_handler.py

@@ -0,0 +1,286 @@
+from functools import partial
+
+import pytest
+import torch
+import torch.multiprocessing as mp
+import torch.nn as nn
+
+from colossalai.auto_parallel.tensor_shard.node_handler.embedding_handler import (
+    EmbeddingFunctionHandler,
+    EmbeddingModuleHandler,
+)
+from colossalai.auto_parallel.tensor_shard.sharding_strategy import OperationData, OperationDataType, StrategiesVector
+from colossalai.device.device_mesh import DeviceMesh
+from colossalai.fx import ColoGraphModule, ColoTracer
+from colossalai.initialize import launch
+from colossalai.logging import disable_existing_loggers
+from colossalai.testing import assert_close, parameterize, rerun_if_address_is_in_use
+from colossalai.testing.pytest_wrapper import run_on_environment_flag
+from colossalai.utils import free_port
+from tests.test_auto_parallel.test_tensor_shard.test_node_handler.utils import numerical_test_for_node_strategy
+
+NUM_EMBEDDINGS = 16
+EMBEDDING_DIMS = 32
+
+
+class EmbeddingModule(nn.Module):
+
+    def __init__(self, num_embeddings, embedding_dims):
+        super().__init__()
+        self.embedding = nn.Embedding(num_embeddings, embedding_dims)
+
+    def forward(self, input):
+        x = self.embedding(input)
+        return x
+
+
+def check_embedding_module_handler(rank, world_size, port):
+    disable_existing_loggers()
+    launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
+    model = EmbeddingModule(num_embeddings=NUM_EMBEDDINGS, embedding_dims=EMBEDDING_DIMS).cuda()
+    # graph():
+    #     %input_1 : torch.Tensor [#users=1] = placeholder[target=input]
+    #     %embedding : [#users=1] = call_module[target=embedding](args = (%input_1,), kwargs = {})
+    #     return embedding
+    input = torch.rand(4, 16, 16) * NUM_EMBEDDINGS
+    input = input.to(torch.int64).cuda()
+
+    physical_mesh_id = torch.arange(0, 4)
+    mesh_shape = (2, 2)
+    device_mesh = DeviceMesh(physical_mesh_id, mesh_shape, init_process_group=True)
+
+    # index of embedding node in computation graph
+    node_index = 1
+    # total number of embedding strategies
+    strategy_number = 19
+    numerical_test_for_node_strategy(model=model,
+                                     device_mesh=device_mesh,
+                                     node_index=node_index,
+                                     strategy_number=strategy_number,
+                                     input_args=[input],
+                                     meta_arg_names=['input'])
+
+    tracer = ColoTracer()
+    graph = tracer.trace(model, meta_args={"input": torch.rand(4, 16, 16).to('meta')})
+    gm = ColoGraphModule(model, graph)
+    embedding_node = list(graph.nodes)[1]
+    strategies_vector = StrategiesVector(embedding_node)
+
+    # build handler
+    handler = EmbeddingModuleHandler(node=embedding_node, device_mesh=device_mesh, strategies_vector=strategies_vector)
+
+    # check operation data mapping
+    mapping = handler.get_operation_data_mapping()
+
+    for name, op_data in mapping.items():
+        op_data: OperationData
+        # make sure they have valid values
+        assert op_data.logical_shape is not None
+        assert op_data.data is not None
+
+    assert mapping['input'].name == "input_1"
+    # assert mapping['input'].data.is_meta
+    assert mapping['input'].data.shape == torch.Size([4, 16, 16])
+    assert mapping['input'].type == OperationDataType.ARG
+    assert mapping['input'].logical_shape == torch.Size([1024])
+
+    assert mapping['other'].name == "weight"
+    assert mapping['other'].data.shape == torch.Size([NUM_EMBEDDINGS, EMBEDDING_DIMS])
+    assert mapping['other'].type == OperationDataType.PARAM
+    assert mapping['other'].logical_shape == torch.Size([NUM_EMBEDDINGS, EMBEDDING_DIMS])
+
+    assert mapping['output'].name == "embedding"
+    assert mapping['output'].data.shape == torch.Size([4, 16, 16, EMBEDDING_DIMS])
+    assert mapping['output'].type == OperationDataType.OUTPUT
+    assert mapping['output'].logical_shape == torch.Size([1024, EMBEDDING_DIMS])
+
+    strategies_vector = handler.register_strategy(compute_resharding_cost=False)
+    strategy_name_list = [val.name for val in strategies_vector]
+
+    # RR = RR x RR
+    assert 'RR = R x RR' in strategy_name_list
+
+    # SR = SR x RR
+    assert 'S0R = S0 x RR_0' in strategy_name_list
+    assert 'S0R = S0 x RR_1' in strategy_name_list
+    assert 'S0R = S0 x RR_2' in strategy_name_list
+    assert 'S1R = S1 x RR_0' in strategy_name_list
+    assert 'S1R = S1 x RR_1' in strategy_name_list
+    assert 'S1R = S1 x RR_2' in strategy_name_list
+
+    # SS = SR x RS
+    assert 'S0S1 = S0 x RS1_0' in strategy_name_list
+    assert 'S0S1 = S0 x RS1_1' in strategy_name_list
+    assert 'S0S1 = S0 x RS1_2' in strategy_name_list
+    assert 'S1S0 = S1 x RS0_0' in strategy_name_list
+    assert 'S1S0 = S1 x RS0_1' in strategy_name_list
+    assert 'S1S0 = S1 x RS0_2' in strategy_name_list
+
+    # RS= RR x RS
+    assert 'RS0 = R x RS0' in strategy_name_list
+    assert 'RS1 = R x RS1' in strategy_name_list
+
+    # S01R = S01R x RR
+    assert 'S01R = S01 x RR_0' in strategy_name_list
+    assert 'S01R = S01 x RR_1' in strategy_name_list
+    assert 'S01R = S01 x RR_2' in strategy_name_list
+
+    # RS01 = RR x RS01
+    assert 'RS01 = R x RS01' in strategy_name_list
+
+    for strategy in strategies_vector:
+        input_sharding_spec = strategy.get_sharding_spec_by_name('input_1')
+        weight_sharding_spec = strategy.get_sharding_spec_by_name('weight')
+        output_sharding_spec = strategy.get_sharding_spec_by_name('embedding')
+
+        # make sure the sharding matches across different operation data
+        assert output_sharding_spec.sharding_sequence[-1] == weight_sharding_spec.sharding_sequence[-1]
+        assert input_sharding_spec.sharding_sequence == output_sharding_spec.sharding_sequence[:-1]
+
+
+class EmbeddingFunction(nn.Module):
+
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, input, others):
+        x = nn.functional.embedding(input, others)
+        return x
+
+
+def check_embedding_function_handler(rank, world_size, port):
+    disable_existing_loggers()
+    launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
+    model = EmbeddingFunction().cuda()
+    physical_mesh_id = torch.arange(0, 4)
+    mesh_shape = (2, 2)
+    device_mesh = DeviceMesh(physical_mesh_id, mesh_shape, init_process_group=True)
+    input = torch.rand(4, 16, 16) * NUM_EMBEDDINGS
+    input = input.to(torch.int64).cuda()
+    others = torch.rand(NUM_EMBEDDINGS, EMBEDDING_DIMS).cuda()
+    input_args = [input, others]
+    meta_arg_names = ['input', 'others']
+    input_kwargs = {}
+    # total number of embedding strategies
+    strategy_number = 19
+    node_index = 2
+    numerical_test_for_node_strategy(model=model,
+                                     device_mesh=device_mesh,
+                                     node_index=node_index,
+                                     strategy_number=strategy_number,
+                                     input_args=input_args,
+                                     meta_arg_names=meta_arg_names,
+                                     input_kwargs=input_kwargs)
+    tracer = ColoTracer()
+    # graph():
+    #     %input_1 : torch.Tensor [#users=1] = placeholder[target=input]
+    #     %others : torch.Tensor [#users=1] = placeholder[target=others]
+    #     %embedding : [#users=1] = call_function[target=torch.nn.functional.embedding](args = (%input_1, %others), kwargs = {padding_idx: None, max_norm: None, norm_type: 2.0, scale_grad_by_freq: False, sparse: False})
+    #     return embedding
+    meta_args = {
+        "input": torch.rand(4, 16, 16).to('meta'),
+        "others": torch.rand(NUM_EMBEDDINGS, EMBEDDING_DIMS).to('meta')
+    }
+    graph = tracer.trace(model, meta_args=meta_args)
+    gm = ColoGraphModule(model, graph)
+
+    embedding_node = list(graph.nodes)[2]
+    strategies_vector = StrategiesVector(embedding_node)
+
+    # build handler
+    handler = EmbeddingFunctionHandler(node=embedding_node,
+                                       device_mesh=device_mesh,
+                                       strategies_vector=strategies_vector)
+
+    # check operation data mapping
+    mapping = handler.get_operation_data_mapping()
+
+    for name, op_data in mapping.items():
+        op_data: OperationData
+        # make sure they have valid values
+        assert op_data.logical_shape is not None
+        assert op_data.data is not None
+
+    assert mapping['input'].name == "input_1"
+    assert mapping['input'].data.is_meta
+    assert mapping['input'].data.shape == torch.Size([4, 16, 16])
+    assert mapping['input'].type == OperationDataType.ARG
+    assert mapping['input'].logical_shape == torch.Size([1024])
+
+    assert mapping['other'].name == "others"
+    assert mapping['other'].data.is_meta
+    assert mapping['other'].data.shape == torch.Size([NUM_EMBEDDINGS, EMBEDDING_DIMS])
+    assert mapping['other'].type == OperationDataType.ARG
+    assert mapping['other'].logical_shape == torch.Size([NUM_EMBEDDINGS, EMBEDDING_DIMS])
+
+    assert mapping['output'].name == "embedding"
+    assert mapping['output'].data.is_meta
+    assert mapping['output'].data.shape == torch.Size([4, 16, 16, EMBEDDING_DIMS])
+    assert mapping['output'].type == OperationDataType.OUTPUT
+    assert mapping['output'].logical_shape == torch.Size([1024, EMBEDDING_DIMS])
+
+    handler.register_strategy(compute_resharding_cost=False)
+    strategy_name_list = [val.name for val in strategies_vector]
+
+    # RR = RR x RR
+    assert 'RR = R x RR' in strategy_name_list
+
+    # SR = SR x RR
+    assert 'S0R = S0 x RR_0' in strategy_name_list
+    assert 'S0R = S0 x RR_1' in strategy_name_list
+    assert 'S0R = S0 x RR_2' in strategy_name_list
+    assert 'S1R = S1 x RR_0' in strategy_name_list
+    assert 'S1R = S1 x RR_1' in strategy_name_list
+    assert 'S1R = S1 x RR_2' in strategy_name_list
+
+    # SS = SR x RS
+    assert 'S0S1 = S0 x RS1_0' in strategy_name_list
+    assert 'S0S1 = S0 x RS1_1' in strategy_name_list
+    assert 'S0S1 = S0 x RS1_2' in strategy_name_list
+    assert 'S1S0 = S1 x RS0_0' in strategy_name_list
+    assert 'S1S0 = S1 x RS0_1' in strategy_name_list
+    assert 'S1S0 = S1 x RS0_2' in strategy_name_list
+
+    # RS= RR x RS
+    assert 'RS0 = R x RS0' in strategy_name_list
+    assert 'RS1 = R x RS1' in strategy_name_list
+
+    # S01R = S01R x RR
+    assert 'S01R = S01 x RR_0' in strategy_name_list
+    assert 'S01R = S01 x RR_1' in strategy_name_list
+    assert 'S01R = S01 x RR_2' in strategy_name_list
+
+    # RS01 = RR x RS01
+    assert 'RS01 = R x RS01' in strategy_name_list
+
+    for strategy in strategies_vector:
+        input_sharding_spec = strategy.get_sharding_spec_by_name('input_1')
+        weight_sharding_spec = strategy.get_sharding_spec_by_name('others')
+        output_sharding_spec = strategy.get_sharding_spec_by_name('embedding')
+
+        # make sure the sharding matches across different operation data
+        assert output_sharding_spec.sharding_sequence[-1] == weight_sharding_spec.sharding_sequence[-1]
+        assert input_sharding_spec.sharding_sequence == output_sharding_spec.sharding_sequence[:-1]
+
+
+@run_on_environment_flag(name='AUTO_PARALLEL')
+@pytest.mark.dist
+@rerun_if_address_is_in_use()
+def test_embedding_module_handler():
+    world_size = 4
+    run_func = partial(check_embedding_module_handler, world_size=world_size, port=free_port())
+    mp.spawn(run_func, nprocs=world_size)
+
+
+@run_on_environment_flag(name='AUTO_PARALLEL')
+@pytest.mark.dist
+@rerun_if_address_is_in_use()
+def test_embedding_function_handler():
+    world_size = 4
+    run_func = partial(check_embedding_function_handler, world_size=world_size, port=free_port())
+    mp.spawn(run_func, nprocs=world_size)
+
+
+if __name__ == '__main__':
+    test_embedding_module_handler()
+    test_embedding_function_handler()

tests/test_auto_parallel/test_tensor_shard/test_node_handler/utils.py

@@ -13,7 +13,7 @@ from colossalai.auto_parallel.tensor_shard.solver.solver import Solver
 from colossalai.device.device_mesh import DeviceMesh
 from colossalai.fx.tracer.tracer import ColoTracer
 from colossalai.tensor.shape_consistency import to_global
-from colossalai.testing.comparison import assert_close, assert_close_loose
+from colossalai.testing.comparison import assert_close
 
 
 def _build_model_to_compare(model: torch.nn.Module, input_args: List[torch.Tensor],
@@ -32,8 +32,12 @@ def _build_model_to_compare(model: torch.nn.Module, input_args: List[torch.Tenso
             param.register_hook(hook_fn)
 
         arg_to_compare = copy.deepcopy(input_tensor)
+
+        # only Tensors of floating point and complex dtype can require gradients
+        if arg_to_compare.dtype != torch.int64:
             arg_to_compare.requires_grad = True
             wrapper(arg_to_compare, arg_index)
+
         args_to_compare.append(arg_to_compare)
 
     for name, input_kwarg in input_kwargs.items():
@@ -46,8 +50,12 @@ def _build_model_to_compare(model: torch.nn.Module, input_args: List[torch.Tenso
             param.register_hook(hook_fn)
 
         kwarg_to_compare = copy.deepcopy(input_kwarg)
+
+        # only Tensors of floating point and complex dtype can require gradients
+        if kwarg_to_compare.dtype != torch.int64:
             kwarg_to_compare.requires_grad = True
             wrapper(kwarg_to_compare, name)
+
         kwargs_to_compare[name] = kwarg_to_compare
 
     return model_to_compare, args_to_compare, kwargs_to_compare
@@ -160,7 +168,6 @@ def assert_close_helper(first: torch.Tensor,
     """
     This method is used to check whether the average difference between two tensors is as close as expected.
     """
-    # average_diff_tensor = ((first - second)/(second+0.1)).sum()/second.numel()
     try:
         if isinstance(first, (tuple, list)):
             for first_element, second_element in zip(first, second):