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

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()