[autoparallel] add tensor constructor handler (#2082)

colossalai/auto_parallel/tensor_shard/node_handler/__init__.py

@@ -14,6 +14,7 @@ from .output_handler import OuputHandler
 from .placeholder_handler import PlacehodlerHandler
 from .registry import operator_registry
 from .reshape_handler import ReshapeHandler
+from .tensor_constructor_handler import TensorConstructorHandler
 from .unary_elementwise_handler import UnaryElementwiseHandler
 from .where_handler import WhereHandler
 
@@ -22,5 +23,5 @@ __all__ = [
     'LayerNormModuleHandler', 'BatchNormModuleHandler', 'ConvModuleHandler', 'ConvFunctionHandler',
     'UnaryElementwiseHandler', 'ReshapeHandler', 'PlacehodlerHandler', 'OuputHandler', 'WhereHandler',
     'NormPoolingHandler', 'BinaryElementwiseHandler', 'MatMulHandler', 'operator_registry', 'ADDMMFunctionHandler',
-    'GetItemHandler', 'GetattrHandler', 'ViewHandler', 'PermuteHandler'
+    'GetItemHandler', 'GetattrHandler', 'ViewHandler', 'PermuteHandler', 'TensorConstructorHandler'
 ]

colossalai/auto_parallel/tensor_shard/node_handler/reshape_handler.py

@@ -11,6 +11,7 @@ __all__ = ['ReshapeHandler']
 
 
 @operator_registry.register(torch.flatten)
+@operator_registry.register(torch.Tensor.unsqueeze)
 @operator_registry.register(torch.nn.AdaptiveAvgPool2d)
 class ReshapeHandler(NodeHandler):
     """

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

@@ -15,6 +15,7 @@ from .output_generator import OutputGenerator
 from .placeholder_generator import PlaceholderGenerator
 from .reshape_generator import ReshapeGenerator
 from .strategy_generator import StrategyGenerator
+from .tensor_constructor_generator import TensorConstructorGenerator
 from .unary_elementwise_generator import UnaryElementwiseGenerator
 from .where_generator import WhereGenerator
 
@@ -23,5 +24,6 @@ __all__ = [
     'BatchedMatMulStrategyGenerator', 'ConvStrategyGenerator', 'UnaryElementwiseGenerator',
     'BatchNormStrategyGenerator', 'GetItemStrategyGenerator', 'TensorStrategyGenerator', 'TensorTupleStrategyGenerator',
     'LayerNormGenerator', 'ReshapeGenerator', 'PlaceholderGenerator', 'OutputGenerator', 'WhereGenerator',
-    'ReshapeGenerator', 'NormalPoolStrategyGenerator', 'BinaryElementwiseStrategyGenerator', 'GetattrGenerator'
+    'ReshapeGenerator', 'NormalPoolStrategyGenerator', 'BinaryElementwiseStrategyGenerator', 'GetattrGenerator',
+    'TensorConstructorGenerator'
 ]

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

@@ -0,0 +1,67 @@
+import copy
+from typing import List
+
+from colossalai.auto_parallel.tensor_shard.sharding_strategy import (
+    CommAction,
+    CommType,
+    MemoryCost,
+    ShardingStrategy,
+    TrainCycleItem,
+)
+from colossalai.tensor.shape_consistency import CollectiveCommPattern
+from colossalai.tensor.sharding_spec import ShardingSpec
+
+from .strategy_generator import StrategyGenerator
+
+__all__ = ['TensorConstructorGenerator']
+
+
+class TensorConstructorGenerator(StrategyGenerator):
+    """
+    TensorConstructorGenerator which deals with
+    the sharding strategies for tensor constructor operation, such as torch.arange.
+    """
+
+    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 = input + output
+        fwd_activation_cost = sum([v for k, v in forward_size_mapping.items() if not self.is_param(k)])
+        fwd_parameter_cost = sum([v for k, v in forward_size_mapping.items() if self.is_param(k)])
+        fwd_mem_cost = MemoryCost(activation=fwd_activation_cost, parameter=fwd_parameter_cost)
+
+        # compute bwd cost incurred
+        bwd_mem_cost = MemoryCost(activation=0, parameter=0)
+
+        # compute total cost
+        total_mem_cost = MemoryCost(activation=fwd_activation_cost, parameter=fwd_parameter_cost)
+        memory_cost = TrainCycleItem(fwd=fwd_mem_cost, bwd=bwd_mem_cost, total=total_mem_cost)
+        strategy.memory_cost = memory_cost
+
+    def collate_strategies(self) -> List[ShardingStrategy]:
+        strategy_list = []
+        dim_partition_dict_mapping = {
+            "output": {},
+        }
+        communication_action_mapping = {}
+        sharding_spec_mapping = self.to_sharding_spec_mapping(dim_partition_dict_mapping)
+
+        name = 'Replica Tensor Constructor'
+
+        strategy = self.get_sharding_strategy(name=name,
+                                              sharding_spec_mapping=sharding_spec_mapping,
+                                              communication_action_mapping=communication_action_mapping)
+        strategy_list.append(strategy)
+
+        return strategy_list

colossalai/auto_parallel/tensor_shard/node_handler/tensor_constructor_handler.py

@@ -0,0 +1,32 @@
+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
+from .strategy.tensor_constructor_generator import TensorConstructorGenerator
+
+__all__ = ['TensorConstructorHandler']
+
+
+@operator_registry.register(torch.arange)
+class TensorConstructorHandler(NodeHandler):
+    """
+    A TensorConstructorHandler which deals with the sharding strategies for tensor constructor operations, such as torch.arange.
+    """
+
+    def get_strategy_generator(self) -> List[StrategyGenerator]:
+        op_data_mapping = self.get_operation_data_mapping()
+        generators = []
+        generators.append(TensorConstructorGenerator(op_data_mapping, self.device_mesh))
+        return generators
+
+    def get_operation_data_mapping(self) -> Dict[str, OperationData]:
+        output_data = self.node._meta_data
+        physical_output_operand = OperationData(name=str(self.node), type=OperationDataType.OUTPUT, data=output_data)
+
+        mapping = {"output": physical_output_operand}
+
+        return mapping

tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_tensor_constructor.py

@@ -0,0 +1,66 @@
+import torch
+import torch.nn as nn
+
+from colossalai.auto_parallel.tensor_shard.node_handler.tensor_constructor_handler import TensorConstructorHandler
+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
+
+
+class TensorConstructorModel(nn.Module):
+
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, x):
+        arange_node = torch.arange(x.size()[0])
+        x = x + arange_node
+        return x
+
+
+def test_where_handler():
+    model = TensorConstructorModel()
+    tracer = ColoTracer()
+    # graph():
+    #     %x : torch.Tensor [#users=2] = placeholder[target=x]
+    #     %size : [#users=1] = call_method[target=size](args = (%x,), kwargs = {})
+    #     %getitem : [#users=1] = call_function[target=operator.getitem](args = (%size, 0), kwargs = {})
+    #     %arange : [#users=1] = call_function[target=torch.arange](args = (%getitem,), kwargs = {})
+    #     %add : [#users=1] = call_function[target=operator.add](args = (%x, %arange), kwargs = {})
+    #     return add
+    graph = tracer.trace(model, meta_args={
+        "x": torch.rand(10).to('meta'),
+    })
+    gm = ColoGraphModule(model, graph)
+    physical_mesh_id = torch.arange(0, 4)
+
+    mesh_shape = (2, 2)
+    device_mesh = DeviceMesh(physical_mesh_id, mesh_shape)
+    arange_node = list(graph.nodes)[3]
+    strategies_vector = StrategiesVector(arange_node)
+
+    # build handler
+    handler = TensorConstructorHandler(node=arange_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['output'].name == "arange"
+    assert mapping['output'].data.is_meta
+    assert mapping['output'].data.shape == torch.Size([10])
+    assert mapping['output'].type == OperationDataType.OUTPUT
+
+    handler.register_strategy(compute_resharding_cost=False)
+    strategy_name_list = [val.name for val in strategies_vector]
+
+    assert 'Replica Tensor Constructor' in strategy_name_list
+
+
+if __name__ == '__main__':
+    test_where_handler()