[autoparallel] adapt handlers with attention block (#1990)

* [autoparallel] adapt handlers with attention block

* polish

tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_getitem_handler.py

@@ -3,6 +3,8 @@ import torch.nn as nn
 
 from colossalai.auto_parallel.tensor_shard.node_handler.conv_handler import ConvFunctionHandler
 from colossalai.auto_parallel.tensor_shard.node_handler.getitem_handler import GetItemHandler
+from colossalai.auto_parallel.tensor_shard.node_handler.placeholder_handler import PlacehodlerHandler
+from colossalai.auto_parallel.tensor_shard.node_handler.reshape_handler import ReshapeHandler
 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
@@ -10,7 +12,7 @@ from colossalai.fx.tracer.meta_patch.patched_module import linear
 from colossalai.testing.pytest_wrapper import run_on_environment_flag
 
 
-class GetItemModel(nn.Module):
+class GetItemFromTensorModel(nn.Module):
 
     def __init__(self):
         super().__init__()
@@ -21,8 +23,8 @@ class GetItemModel(nn.Module):
         return x
 
 
-def test_getitem_function_handler():
-    model = GetItemModel()
+def test_getitem_from_tensor_handler():
+    model = GetItemFromTensorModel()
     tracer = ColoTracer()
     # graph():
     #     %input_1 : torch.Tensor [#users=1] = placeholder[target=input]
@@ -83,5 +85,83 @@ def test_getitem_function_handler():
     assert len(getitem_strategies_vector) == len(conv_strategies_vector)
 
 
+class GetItemFromTupleModel(nn.Module):
+
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, input):
+        split_node = torch.split(input, 2, 0)
+        x = split_node[1]
+        return x
+
+
+def test_getitem_from_tuple_handler():
+    model = GetItemFromTupleModel()
+    tracer = ColoTracer()
+    # graph():
+    #     %input_1 : torch.Tensor [#users=1] = placeholder[target=input]
+    #     %split : [#users=1] = call_function[target=torch.functional.split](args = (%conv2d, 2), kwargs = {dim: 0})
+    #     %getitem : [#users=1] = call_function[target=operator.getitem](args = (%split, 1), kwargs = {})
+    #     return getitem
+    graph = tracer.trace(model, meta_args={
+        "input": torch.rand(4, 4, 64, 64).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)
+    input_node = list(graph.nodes)[0]
+    split_node = list(graph.nodes)[1]
+    getitem_node = list(graph.nodes)[2]
+    input_strategies_vector = StrategiesVector(input_node)
+    getitem_strategies_vector = StrategiesVector(getitem_node)
+    split_strategies_vector = StrategiesVector(split_node)
+
+    # build handler
+    input_handler = PlacehodlerHandler(
+        node=input_node,
+        device_mesh=device_mesh,
+        strategies_vector=input_strategies_vector,
+        placeholder_option='replicated',
+    )
+    input_handler.register_strategy(compute_resharding_cost=False)
+    setattr(input_node, 'strategies_vector', input_strategies_vector)
+    split_handler = ReshapeHandler(node=split_node, device_mesh=device_mesh, strategies_vector=split_strategies_vector)
+    split_handler.register_strategy(compute_resharding_cost=False)
+    setattr(split_node, 'strategies_vector', split_strategies_vector)
+    getitem_handler = GetItemHandler(node=getitem_node,
+                                     device_mesh=device_mesh,
+                                     strategies_vector=getitem_strategies_vector)
+    getitem_handler.register_strategy(compute_resharding_cost=False)
+    setattr(getitem_node, 'strategies_vector', getitem_strategies_vector)
+
+    # check operation data mapping
+    mapping = getitem_handler.get_operation_data_mapping()
+
+    for name, op_data in mapping.items():
+        op_data: OperationData
+        # make sure they have valid values
+        assert op_data.data is not None
+
+    assert mapping['input'].name == "split"
+    assert mapping['input'].type == OperationDataType.ARG
+    assert mapping['input'].logical_shape == (torch.Size([2, 4, 64, 64]), torch.Size([2, 4, 64, 64]))
+
+    assert mapping['index'].name == "index"
+    assert isinstance(mapping['index'].data, int)
+    assert mapping['index'].type == OperationDataType.ARG
+
+    assert mapping['output'].name == "getitem"
+    assert mapping['output'].data.is_meta
+    assert mapping['output'].data.shape == torch.Size([2, 4, 64, 64])
+    assert mapping['output'].type == OperationDataType.OUTPUT
+
+    # getitem is a following strategy handler, so the number of strategies is equal to the predecessor node.
+    assert len(getitem_strategies_vector) == len(split_strategies_vector)
+
+
 if __name__ == '__main__':
-    test_getitem_function_handler()
+    test_getitem_from_tensor_handler()
+    test_getitem_from_tuple_handler()