[autoparallel] added new linear module handler (#1616)

colossalai/auto_parallel/solver/op_handler/dot_handler_v2.py

@@ -0,0 +1,139 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from .node_handler import ModuleHandler, NodeHandler
+from ..sharding_strategy import ShardingStrategy_V2, StrategyGenerator_V2, OperationDataType, OperationData
+from typing import List, Dict
+from .registry import operator_registry
+
+__all__ = ['LinearModuleHandler']
+
+
+class DotProductStrategyGenerator(StrategyGenerator_V2):
+    """TODO: to be implemented"""
+    pass
+
+
+class MatVecStrategyGenerator(StrategyGenerator_V2):
+    """TODO: to be implemented"""
+    pass
+
+
+class LinearProjectionStrategyGenerator(StrategyGenerator_V2):
+
+    def update_compute_cost(self, strategy: ShardingStrategy_V2) -> ShardingStrategy_V2:
+        """TODO: to be implemented"""
+        pass
+
+    def update_memory_cost(self, strategy: ShardingStrategy_V2) -> ShardingStrategy_V2:
+        """TODO: to be implemented"""
+        pass
+
+    def generate(self, operand_mapping: Dict[str, OperationData]) -> List[ShardingStrategy_V2]:
+        """TODO: to be implemented"""
+        pass
+
+    def validate(self, *args, **kwargs) -> bool:
+        """TODO: to be implemented"""
+        pass
+
+
+class BatchedMatMulStrategyGenerator(StrategyGenerator_V2):
+    """TODO: to be implemented"""
+    pass
+
+
+@operator_registry.register(torch.nn.Linear)
+class LinearModuleHandler(ModuleHandler):
+    """
+    A LinearModuleHandler which deals with the sharding strategies for nn.Linear module.
+    """
+
+    def register_strategy_generator(self) -> List[StrategyGenerator_V2]:
+        generators = []
+        generators.append(LinearProjectionStrategyGenerator(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[::-1])
+        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
+
+    def post_process(self, strategy: ShardingStrategy_V2):
+        """
+        Convert the sharding spec of the weight parameter back to its original shape.
+        """
+        for op_data, sharding_spec in strategy.input_sharding_specs.items():
+            if op_data.name == "weight":
+                assert op_data.logical_shape != op_data.data.shape
+                dim_partition_dict = sharding_spec.dim_partition_dict
+                # switch first and last dim of the linear module weight
+                dim_partition_dict[0], dim_partition_dict[-1] = dim_partition_dict[-1], dim_partition_dict[0]
+
+                # re-init the sharding spec
+                sharding_spec.__init__(sharding_spec.device_mesh, sharding_spec.entire_shape, dim_partition_dict)
+        return strategy
+
+
+@operator_registry.register(F.linear)
+class LinearFunctionHandler(NodeHandler):
+    """
+    A LinearModuleHandler which deals with the sharding strategies for nn.Linear module.
+    """
+
+    def register_strategy_generator(self) -> List[StrategyGenerator_V2]:
+        generators = []
+        generators.append(LinearProjectionStrategyGenerator(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=str(self.node.args[1]),
+                                               type=OperationDataType.ARG,
+                                               data=self.node.args[1]._meta_data,
+                                               logical_shape=self.node.args[1]._meta_data.shape[::-1])
+        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.node.args[2] is not None:
+            physical_bias_operand = OperationData(name=str(self.node.args[2]),
+                                                  type=OperationDataType.ARG,
+                                                  data=self.node.args[2]._meta_data)
+            mapping['bias'] = physical_bias_operand
+        return mapping
+
+    def post_process(self, strategy: ShardingStrategy_V2):
+        """
+        Convert the sharding spec of the weight parameter back to its original shape.
+        """
+        for op_data, sharding_spec in strategy.input_sharding_specs.items():
+            if op_data.name == str(self.node.args[1]):
+                assert op_data.logical_shape != op_data.data.shape
+                dim_partition_dict = sharding_spec.dim_partition_dict
+                # switch first and last dim of the linear module weight
+                dim_partition_dict[0], dim_partition_dict[-1] = dim_partition_dict[-1], dim_partition_dict[0]
+
+                # re-init the sharding spec
+                sharding_spec.__init__(sharding_spec.device_mesh, sharding_spec.entire_shape, dim_partition_dict)
+        return strategy

colossalai/auto_parallel/solver/op_handler/node_handler.py

@@ -2,7 +2,7 @@ from abc import ABC, abstractmethod
 from torch.fx.node import Node
 from colossalai.device.device_mesh import DeviceMesh
 from typing import Dict, List
-from ..sharding_strategy import StrategiesVector, Operand, StrategyGenerator_V2
+from ..sharding_strategy import ShardingStrategy, ShardingStrategy_V2, StrategiesVector, OperationData, StrategyGenerator_V2
 
 
 class NodeHandler(ABC):
@@ -36,8 +36,15 @@ class NodeHandler(ABC):
         for generator in self.strategy_generator:
             strategies = generator.generate(operand_mapping)
             self.strategies_vector.extend(strategies)
+
+        self.strategies_vector = map(self.post_process, self.strategies_vector)
         return self.strategies_vector
 
+    def post_process(self, strategy: ShardingStrategy_V2):
+        # tranform the strategy generated
+        # e.g. to process the sharding strategy for the transposed weights
+        return strategy
+
     @abstractmethod
     def register_strategy_generator(self) -> List[StrategyGenerator_V2]:
         """
@@ -46,21 +53,40 @@ class NodeHandler(ABC):
         pass
 
     @abstractmethod
-    def get_operand_mapping(self) -> Dict[str, Operand]:
+    def get_operation_data_mapping(self) -> Dict[str, OperationData]:
         """
-        Returns the mapping between the logical operand name to its physical operands.
-        A logical operand is defined by the strategy generator, for example, a matrix multiplication 
-        operation has two operands "input" and "other". For a nn.Linear module, the physical operand for "input" is
-        the module input and the physical operand for "other" is the module weight.
+        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=OperandType.ARG),
-                "other": Operand(name="weight", type=OperandType.PARAM),
-                "bias": Operand(name="bias", type=OperandType.PARAM)
+                "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 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))
+        # convert named parameters from list to dict
+        named_parameters = {k: v for k, v in named_parameters}
+        self.module = module
+        self.named_parameters = named_parameters

colossalai/auto_parallel/solver/sharding_strategy.py

@@ -1,6 +1,10 @@
 from dataclasses import dataclass
 from abc import ABC, abstractmethod
 from enum import Enum
+import operator
+import torch
+from functools import reduce
+
 from colossalai.device.device_mesh import DeviceMesh
 from colossalai.tensor.sharding_spec import ShardingSpec
 from typing import Dict, List, Union, Tuple, Any
@@ -40,18 +44,35 @@ class ShardingStrategy:
     input_shardings: List[ShardingSpec] = None
 
 
-class OperandType(Enum):
+class OperationDataType(Enum):
     """
-    An operand can come from the argument list of an operator or the parameter list of a module.
+    An operation can come from the argument list of an operator or the parameter list of a module.
     """
     ARG = 0
     PARAM = 1
+    OUTPUT = 2
 
 
 @dataclass
-class Operand:
+class OperationData:
+    """
+    OperationData is the data related to an operator, the data can be the operand or the output.
+
+    Args:
+        name (str): the name of the operation-related data
+        type (OperationDataType): the type of the operation data
+        data (torch.Tensor): the value for this data, usually it is a meta tensor.
+        logical_shape (Tuple[int]): the logical shape of the data, it can be different from the its actual shape in memory.
+    """
     name: str
-    type: OperandType
+    type: OperationDataType
+    data: torch.Tensor
+    logical_shape: Tuple[int] = None
+
+    def __post_init__(self):
+        # if no logical shape is specified, use the data shape as the logical shape
+        if self.logical_shape is None:
+            self.logical_shape = self.data.shape
 
 
 @dataclass
@@ -69,6 +90,20 @@ class TrainCycleItem:
     total: Any
 
 
+class CommunicationType(Enum):
+    FWD_ALL_REDUCE = 0
+    BWD_ALL_REDUCE = 1
+
+
+@dataclass
+class CommunicationAction:
+    """
+    The actions 
+    """
+    type: CommunicationType
+    mesh_dim: int
+
+
 @dataclass
 class ShardingStrategy_V2:
     """
@@ -86,12 +121,35 @@ class ShardingStrategy_V2:
                                                   strategy.(default to None)
     """
     name: str
-    output_sharding_spec: ShardingSpec
+    sharding_specs: Dict[OperationData, ShardingSpec] = None
     compute_cost: TrainCycleItem = None
     communication_cost: TrainCycleItem = None
     memory_cost: TrainCycleItem = None
-    input_sharding_specs: Dict[Operand, ShardingSpec] = None
-    input_resharding_costs: Dict[Operand, List[float]] = None
+    input_resharding_costs: Dict[OperationData, List[float]] = None
+    communication_actions: Dict[OperationData, List[CommunicationAction]] = None
+
+    @property
+    def input_sharding_specs(self) -> Dict[OperationData, ShardingSpec]:
+        specs = {}
+        specs.update(self._get_sharding_spec(OperationDataType.ARG))
+        specs.update(self._get_sharding_spec(OperationDataType.PARAM))
+        return specs
+
+    @property
+    def argument_sharding_specs(self) -> Dict[OperationData, ShardingSpec]:
+        return self._get_sharding_spec(OperationDataType.ARG)
+
+    @property
+    def param_sharding_specs(self) -> Dict[OperationData, ShardingSpec]:
+        return self._get_sharding_spec(OperationDataType.PARAM)
+
+    @property
+    def output_sharding_specs(self) -> Dict[OperationData, ShardingSpec]:
+        return self._get_sharding_spec(OperationDataType.OUTPUT)
+
+    def _get_sharding_spec(self, operation_data_type: OperationDataType):
+        specs = {k: v for k, v in self.sharding_specs.items() if k.type == operation_data_type}
+        return specs
 
 
 class StrategyGenerator_V2(ABC):
@@ -104,9 +162,57 @@ class StrategyGenerator_V2(ABC):
     def __init__(self, device_mesh: DeviceMesh):
         self.device_mesh = device_mesh
 
-    @abstractmethod
-    def generate(self, operand_mapping: Dict[str, Operand]) -> List[ShardingStrategy_V2]:
+    def update_communication_cost(self, strategy: ShardingStrategy_V2) -> ShardingStrategy_V2:
         """
+        Compute the communication cost involved in the forward and backward iteration.
+        """
+
+        comm_cost = TrainCycleItem(fwd=0, bwd=0)
+
+        def _compute_and_add(data: OperationData, action: CommunicationAction):
+            sharded_shape = strategy.sharding_specs[data].get_sharded_shape_per_device()
+            dtype = operand.data.dtype
+            size_per_elem_bytes = torch.tensor([], dtype=dtype).element_size()
+            num_bytes = size_per_elem_bytes * reduce(operator.mul, sharded_shape)
+            cost = self.device_mesh.all_reduce_cost(num_bytes=num_bytes, mesh_dim=action.mesh_dim)
+
+            # compute the fwd
+            if action.type == CommunicationType.FWD_ALL_REDUCE:
+                comm_cost.fwd += cost
+            elif action.type == CommunicationType.BWD_ALL_REDUCE:
+                comm_cost.fwd += cost
+            else:
+                raise ValueError(f"Found unknown CommunicationType {action.type}")
+
+        # check if communication action exists
+        # if so, loop over each action and compute the cost of each action
+        if strategy.communication_actions is not None:
+            for operand, actions in strategy.communication_actions:
+                for action in actions:
+                    _compute_and_add(operand, action)
+
+        # update the communication cost attribute in-place
+        strategy.communication_cost = comm_cost
+        return strategy
+
+    @abstractmethod
+    def update_compute_cost(self, strategy: ShardingStrategy_V2) -> ShardingStrategy_V2:
+        """
+        Customize this method to compute the computation flops.
+        """
+        pass
+
+    @abstractmethod
+    def update_memory_cost(self, strategy: ShardingStrategy_V2) -> ShardingStrategy_V2:
+        """
+        Customize this method to compute the memory cost in bytes.
+        """
+        pass
+
+    @abstractmethod
+    def generate(self, operand_mapping: Dict[str, OperationData]) -> List[ShardingStrategy_V2]:
+        """
+        Generate all possible sharding strategies for this operation.
         """
         pass
 

tests/test_auto_parallel/test_linear_handler_v2.py

@@ -0,0 +1,104 @@
+from colossalai.fx.tracer.meta_patch.patched_module import linear
+import torch
+import torch.nn as nn
+from colossalai.fx import ColoTracer, ColoGraphModule
+from colossalai.auto_parallel.solver.op_handler.dot_handler_v2 import LinearModuleHandler, LinearFunctionHandler
+from colossalai.auto_parallel.solver.sharding_strategy import OperationData, OperationDataType, StrategiesVector
+from colossalai.device.device_mesh import DeviceMesh
+
+
+def test_linear_module_handler():
+    model = nn.Sequential(nn.Linear(10, 20).to('meta'))
+    tracer = ColoTracer()
+    graph = tracer.trace(model, meta_args={"input": torch.rand(4, 10).to('meta')})
+    gm = ColoGraphModule(model, graph)
+    physical_mesh_id = torch.arange(0, 4)
+
+    print(graph)
+    mesh_shape = (2, 2)
+    device_mesh = DeviceMesh(physical_mesh_id, mesh_shape)
+    linear_mod_node = list(graph.nodes)[1]
+    strategies_vector = StrategiesVector(linear_mod_node)
+
+    # build handler
+    handler = LinearModuleHandler(node=linear_mod_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, 10])
+    assert mapping['input'].type == OperationDataType.ARG
+    assert mapping['input'].logical_shape == torch.Size([4, 10])
+
+    assert mapping['other'].name == "weight"
+    assert mapping['other'].data.is_meta
+    assert mapping['other'].data.shape == torch.Size([20, 10])
+    assert mapping['other'].type == OperationDataType.PARAM
+    assert mapping['other'].logical_shape == torch.Size([10, 20])
+
+    assert mapping['bias'].name == "bias"
+    assert mapping['bias'].data.is_meta
+    assert mapping['bias'].data.shape == torch.Size([20])
+    assert mapping['bias'].type == OperationDataType.PARAM
+    assert mapping['other'].logical_shape == torch.Size([10, 20])
+
+    assert mapping['output'].name == "_0"
+    assert mapping['output'].data.is_meta
+    assert mapping['output'].data.shape == torch.Size([4, 20])
+    assert mapping['output'].type == OperationDataType.OUTPUT
+
+
+def test_linear_function_handler():
+    model = nn.Linear(10, 20).to('meta')
+    tracer = ColoTracer()
+    graph = tracer.trace(model, meta_args={"input": torch.rand(4, 10).to('meta')})
+    gm = ColoGraphModule(model, graph)
+    physical_mesh_id = torch.arange(0, 4)
+
+    print(graph)
+    mesh_shape = (2, 2)
+    device_mesh = DeviceMesh(physical_mesh_id, mesh_shape)
+    linear_func_node = list(graph.nodes)[3]
+    strategies_vector = StrategiesVector(linear_func_node)
+
+    # build handler
+    handler = LinearFunctionHandler(node=linear_func_node, device_mesh=device_mesh, strategies_vector=strategies_vector)
+
+    # # check operation data mapping
+    mapping = handler.get_operation_data_mapping()
+
+    assert mapping['input'].name == "input_1"
+    assert mapping['input'].data.is_meta
+    assert mapping['input'].data.shape == torch.Size([4, 10])
+    assert mapping['input'].type == OperationDataType.ARG
+    assert mapping['input'].logical_shape == torch.Size([4, 10])
+
+    assert mapping['other'].name == "weight"
+    assert mapping['other'].data.is_meta
+    assert mapping['other'].data.shape == torch.Size([20, 10])
+    assert mapping['other'].type == OperationDataType.ARG
+    assert mapping['other'].logical_shape == torch.Size([10, 20])
+
+    assert mapping['bias'].name == "bias"
+    assert mapping['bias'].data.is_meta
+    assert mapping['bias'].data.shape == torch.Size([20])
+    assert mapping['bias'].type == OperationDataType.ARG
+    assert mapping['other'].logical_shape == torch.Size([10, 20])
+
+    assert mapping['output'].name == "linear"
+    assert mapping['output'].data.is_meta
+    assert mapping['output'].data.shape == torch.Size([4, 20])
+    assert mapping['output'].type == OperationDataType.OUTPUT
+
+
+if __name__ == '__main__':
+    # test_linear_module_handler()
+    test_linear_function_handler()