[autoparallel] add following node generator (#1673)

* [autoparallel] add following node generator

* polish code

* polish code

* update name of arguments

tests/test_auto_parallel/test_node_handler/test_conv_handler_v2.py

@@ -165,7 +165,7 @@ def test_conv_function_handler():
     assert mapping['output'].data.shape == torch.Size([4, 16, 64, 64])
     assert mapping['output'].type == OperationDataType.OUTPUT
 
-    strategies_vector = handler.register_strategy()
+    handler.register_strategy()
     strategy_name_list = [val.name for val in strategies_vector]
 
     # SS = SR x RS

tests/test_auto_parallel/test_node_handler/test_getitem_handler.py

@@ -0,0 +1,85 @@
+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.getitem_handler import GetItemHandler
+from colossalai.auto_parallel.solver.op_handler.conv_handler_v2 import ConvFunctionHandler
+from colossalai.auto_parallel.solver.sharding_strategy import OperationData, OperationDataType, StrategiesVector
+from colossalai.device.device_mesh import DeviceMesh
+
+
+class GetItemModel(nn.Module):
+
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, input, other):
+        conv_node = nn.functional.conv2d(input, other)
+        x = conv_node[1]
+        return x
+
+
+def test_getitem_function_handler():
+    model = GetItemModel()
+    tracer = ColoTracer()
+    # graph():
+    #     %input_1 : torch.Tensor [#users=1] = placeholder[target=input]
+    #     %other : torch.Tensor [#users=1] = placeholder[target=other]
+    #     %conv2d : [#users=1] = call_function[target=torch.conv2d](args = (%input_1, %other), kwargs = {})
+    #     %getitem : [#users=1] = call_function[target=operator.getitem](args = (%conv2d, 1), kwargs = {})
+    #     return getitem
+    graph = tracer.trace(model,
+                         meta_args={
+                             "input": torch.rand(4, 4, 64, 64).to('meta'),
+                             "other": torch.rand(4, 16, 3, 3).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)
+    conv_mod_node = list(graph.nodes)[2]
+    getitem_mod_node = list(graph.nodes)[3]
+    getitem_strategies_vector = StrategiesVector(getitem_mod_node)
+    conv_strategies_vector = StrategiesVector(conv_mod_node)
+
+    # build handler
+    conv_handler = ConvFunctionHandler(node=conv_mod_node,
+                                       device_mesh=device_mesh,
+                                       strategies_vector=conv_strategies_vector)
+    conv_handler.register_strategy()
+    setattr(conv_mod_node, 'strategies_vector', conv_strategies_vector)
+    getitem_handler = GetItemHandler(node=getitem_mod_node,
+                                     device_mesh=device_mesh,
+                                     strategies_vector=getitem_strategies_vector)
+
+    getitem_handler.register_strategy()
+    # 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 == "conv2d"
+    assert mapping['input'].data.is_meta
+    assert mapping['input'].data.shape == torch.Size([4, 4, 62, 62])
+    assert mapping['input'].type == OperationDataType.ARG
+    assert mapping['input'].logical_shape == torch.Size([4, 4, 62, 62])
+
+    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([4, 62, 62])
+    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(conv_strategies_vector)
+
+
+if __name__ == '__main__':
+    test_getitem_function_handler()