[autoparallel] add tensor constructor handler (#2082)

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()