[autoparallel] add conv handler v2 (#1663)

tests/test_auto_parallel/test_node_handler/test_conv_handler_v2.py

@@ -0,0 +1,210 @@
+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.conv_handler_v2 import ConvModuleHandler, ConvFunctionHandler
+from colossalai.auto_parallel.solver.sharding_strategy import OperationData, OperationDataType, StrategiesVector
+from colossalai.device.device_mesh import DeviceMesh
+
+
+def test_conv_module_handler():
+    model = nn.Sequential(nn.Conv2d(4, 16, 3, padding=1).to('meta'))
+    tracer = ColoTracer()
+    # graph():
+    #     %input_1 : torch.Tensor [#users=1] = placeholder[target=input]
+    #     %_0 : [#users=1] = call_module[target=0](args = (%input_1,), kwargs = {})
+    #     return _0
+    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)
+    conv_mod_node = list(graph.nodes)[1]
+    strategies_vector = StrategiesVector(conv_mod_node)
+
+    # build handler
+    handler = ConvModuleHandler(node=conv_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, 4, 64, 64])
+    assert mapping['input'].type == OperationDataType.ARG
+    assert mapping['input'].logical_shape == torch.Size([4, 4, 64, 64])
+
+    assert mapping['other'].name == "weight"
+    assert mapping['other'].data.is_meta
+    assert mapping['other'].data.shape == torch.Size([16, 4, 3, 3])
+    assert mapping['other'].type == OperationDataType.PARAM
+    assert mapping['other'].logical_shape == torch.Size([4, 16, 3, 3])
+
+    assert mapping['bias'].name == "bias"
+    assert mapping['bias'].data.is_meta
+    assert mapping['bias'].data.shape == torch.Size([16])
+    assert mapping['bias'].type == OperationDataType.PARAM
+    assert mapping['bias'].logical_shape == torch.Size([16])
+
+    assert mapping['output'].name == "_0"
+    assert mapping['output'].data.is_meta
+    assert mapping['output'].data.shape == torch.Size([4, 16, 64, 64])
+    assert mapping['output'].type == OperationDataType.OUTPUT
+
+    strategies_vector = handler.register_strategy()
+    strategy_name_list = [val.name for val in strategies_vector]
+
+    # SS = SR x RS
+    assert 'S0S1 = S0R x RS1' in strategy_name_list
+    assert 'S1S0 = S1R x RS0' in strategy_name_list
+
+    # SR = SR x RR
+    assert 'S0R = S0R x RR' in strategy_name_list
+    assert 'S1R = S1R x RR' in strategy_name_list
+
+    # SR = SS x SR
+    assert 'S0R = S0S1 x S1R' in strategy_name_list
+    assert 'S1R = S1S0 x S0R' in strategy_name_list
+
+    # RS = RS x SS
+    assert 'RS0 = RS1 x S1S0' in strategy_name_list
+    assert 'RS1 = RS0 x S0S1' in strategy_name_list
+
+    # RR = RS x SR
+    assert 'RR = RS0 x S0R' in strategy_name_list
+    assert 'RR = RS1 x S1R' in strategy_name_list
+
+    # RS= RR x RS
+    assert 'RS0 = RR x RS0' in strategy_name_list
+    assert 'RS1 = RR x RS1' in strategy_name_list
+
+    # RR = RR x RR
+    assert 'RR = RR x RR' in strategy_name_list
+
+    # S01R = S01R x RR
+    assert 'S01R = S01R x RR' in strategy_name_list
+
+    # RR = RS01 x S01R
+    assert 'RR = RS01 x S01R' in strategy_name_list
+
+    # RS01 = RR x RS01
+    assert 'RS01 = RR x RS01' in strategy_name_list
+
+
+class ConvModel(nn.Module):
+
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, input, others, bias=None):
+        x = nn.functional.conv2d(input, others, bias=bias, padding=1)
+        return x
+
+
+def test_conv_function_handler():
+    model = ConvModel()
+    tracer = ColoTracer()
+    # graph():
+    #     %input_1 : torch.Tensor [#users=1] = placeholder[target=input]
+    #     %others : torch.Tensor [#users=1] = placeholder[target=others]
+    #     %conv2d : [#users=1] = call_function[target=torch.conv2d](args = (%input_1, %others), kwargs = {})
+    #     return conv2d
+    graph = tracer.trace(model,
+                         meta_args={
+                             "input": torch.rand(4, 4, 64, 64).to('meta'),
+                             "others": torch.rand(16, 4, 3, 3).to('meta'),
+                             "bias": torch.rand(16).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)[3]
+    strategies_vector = StrategiesVector(conv_mod_node)
+
+    # build handler
+    handler = ConvFunctionHandler(node=conv_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, 4, 64, 64])
+    assert mapping['input'].type == OperationDataType.ARG
+    assert mapping['input'].logical_shape == torch.Size([4, 4, 64, 64])
+
+    assert mapping['other'].name == "others"
+    assert mapping['other'].data.is_meta
+    assert mapping['other'].data.shape == torch.Size([16, 4, 3, 3])
+    assert mapping['other'].type == OperationDataType.ARG
+    assert mapping['other'].logical_shape == torch.Size([4, 16, 3, 3])
+
+    assert mapping['bias'].name == "bias"
+    assert mapping['bias'].data.is_meta
+    assert mapping['bias'].data.shape == torch.Size([16])
+    assert mapping['bias'].type == OperationDataType.ARG
+    assert mapping['bias'].logical_shape == torch.Size([16])
+
+    assert mapping['output'].name == "conv2d"
+    assert mapping['output'].data.is_meta
+    assert mapping['output'].data.shape == torch.Size([4, 16, 64, 64])
+    assert mapping['output'].type == OperationDataType.OUTPUT
+
+    strategies_vector = handler.register_strategy()
+    strategy_name_list = [val.name for val in strategies_vector]
+
+    # SS = SR x RS
+    assert 'S0S1 = S0R x RS1' in strategy_name_list
+    assert 'S1S0 = S1R x RS0' in strategy_name_list
+
+    # SR = SR x RR
+    assert 'S0R = S0R x RR' in strategy_name_list
+    assert 'S1R = S1R x RR' in strategy_name_list
+
+    # SR = SS x SR
+    assert 'S0R = S0S1 x S1R' in strategy_name_list
+    assert 'S1R = S1S0 x S0R' in strategy_name_list
+
+    # RS = RS x SS
+    assert 'RS0 = RS1 x S1S0' in strategy_name_list
+    assert 'RS1 = RS0 x S0S1' in strategy_name_list
+
+    # RR = RS x SR
+    assert 'RR = RS0 x S0R' in strategy_name_list
+    assert 'RR = RS1 x S1R' in strategy_name_list
+
+    # RS= RR x RS
+    assert 'RS0 = RR x RS0' in strategy_name_list
+    assert 'RS1 = RR x RS1' in strategy_name_list
+
+    # RR = RR x RR
+    assert 'RR = RR x RR' in strategy_name_list
+
+    # S01R = S01R x RR
+    assert 'S01R = S01R x RR' in strategy_name_list
+
+    # RR = RS01 x S01R
+    assert 'RR = RS01 x S01R' in strategy_name_list
+
+    # RS01 = RR x RS01
+    assert 'RS01 = RR x RS01' in strategy_name_list
+
+
+if __name__ == '__main__':
+    test_conv_module_handler()
+    test_conv_function_handler()

tests/test_auto_parallel/test_node_handler/test_linear_handler_v2.py

@@ -48,7 +48,7 @@ def test_linear_module_handler():
     assert mapping['bias'].data.is_meta
     assert mapping['bias'].data.shape == torch.Size([32])
     assert mapping['bias'].type == OperationDataType.PARAM
-    assert mapping['other'].logical_shape == torch.Size([16, 32])
+    assert mapping['bias'].logical_shape == torch.Size([32])
 
     assert mapping['output'].name == "_0"
     assert mapping['output'].data.is_meta