[autoparallel] where_handler_v2 (#1688)

* where generator

* [autoparallel] where_handler_v2

tests/test_auto_parallel/test_node_handler/test_where_handler_v2.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.where_handler_v2 import WhereHandler
+from colossalai.auto_parallel.solver.sharding_strategy import OperationData, OperationDataType, StrategiesVector
+from colossalai.device.device_mesh import DeviceMesh
+
+
+class ConvModel(nn.Module):
+
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, condition, x, y):
+        output = torch.where(condition, x, y)
+        return output
+
+
+def test_where_handler():
+    model = ConvModel()
+    tracer = ColoTracer()
+    # graph():
+    #     %condition : torch.Tensor [#users=1] = placeholder[target=condition]
+    #     %x : torch.Tensor [#users=1] = placeholder[target=x]
+    #     %y : torch.Tensor [#users=1] = placeholder[target=y]
+    #     %where : [#users=1] = call_function[target=torch.where](args = (%condition, %x, %y), kwargs = {})
+    #     return where
+    graph = tracer.trace(model,
+                         meta_args={
+                             "condition": torch.rand(4, 4, 64, 64).to('meta'),
+                             "x": torch.rand(4, 1, 64, 64).to('meta'),
+                             "y": torch.rand(1, 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)
+    where_node = list(graph.nodes)[3]
+    strategies_vector = StrategiesVector(where_node)
+
+    # build handler
+    handler = WhereHandler(node=where_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['condition'].name == "condition"
+    assert mapping['condition'].data.is_meta
+    assert mapping['condition'].data.shape == torch.Size([4, 4, 64, 64])
+    assert mapping['condition'].type == OperationDataType.ARG
+    assert mapping['condition'].logical_shape == torch.Size([4, 4, 64, 64])
+
+    assert mapping['x'].name == "x"
+    assert mapping['x'].data.is_meta
+    assert mapping['x'].data.shape == torch.Size([4, 1, 64, 64])
+    assert mapping['x'].type == OperationDataType.ARG
+    assert mapping['x'].logical_shape == torch.Size([4, 4, 64, 64])
+
+    assert mapping['y'].name == "y"
+    assert mapping['y'].data.is_meta
+    assert mapping['y'].data.shape == torch.Size([1, 4, 64, 64])
+    assert mapping['y'].type == OperationDataType.ARG
+    assert mapping['y'].logical_shape == torch.Size([4, 4, 64, 64])
+
+    assert mapping['output'].name == "where"
+    assert mapping['output'].data.is_meta
+    assert mapping['output'].data.shape == torch.Size([4, 4, 64, 64])
+    assert mapping['output'].type == OperationDataType.OUTPUT
+
+    handler.register_strategy()
+    strategy_name_list = [val.name for val in strategies_vector]
+    # 4*3 + 4*3/2*2 + 1
+    assert len(strategy_name_list) == 25
+
+
+if __name__ == '__main__':
+    test_where_handler()