[autoparallel] added binary elementwise node handler (#1758)

* [autoparallel] added binary elementwise node handler

* polish code

tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_binary_elementwise_handler.py

@@ -0,0 +1,173 @@
+import torch
+import torch.nn as nn
+
+from colossalai.auto_parallel.tensor_shard.node_handler import BinaryElementwiseHandler
+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
+from colossalai.testing import parameterize
+
+
+@parameterize('op', [torch.add])
+@parameterize('other_dim', [1, 2])
+def test_binary_elementwise_handler_with_tensor(op, other_dim):
+
+    class BinaryElementwiseOpModel(nn.Module):
+
+        def __init__(self, op):
+            super().__init__()
+            self.op = op
+
+        def forward(self, x1, x2):
+            out = self.op(x1, x2)
+            return out
+
+    model = BinaryElementwiseOpModel(op)
+    tracer = ColoTracer()
+
+    meta_args = {'x1': torch.rand(4, 4).to('meta'), 'x2': torch.rand([4] * other_dim).to('meta')}
+    graph = tracer.trace(model, meta_args=meta_args)
+    print(graph)
+    gm = ColoGraphModule(model, graph)
+    physical_mesh_id = torch.arange(0, 4)
+    mesh_shape = (2, 2)
+    device_mesh = DeviceMesh(physical_mesh_id, mesh_shape)
+    op_node = list(graph.nodes)[2]
+    strategies_vector = StrategiesVector(op_node)
+
+    # build handler
+    handler = BinaryElementwiseHandler(node=op_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 == "x1"
+    assert mapping['input'].data.is_meta
+    assert mapping['input'].data.shape == torch.Size([4, 4])
+    assert mapping['input'].type == OperationDataType.ARG
+    assert mapping['input'].logical_shape == torch.Size([4, 4])
+
+    assert mapping['other'].name == "x2"
+    assert mapping['other'].data.is_meta
+    assert mapping['other'].data.shape == torch.Size([4] * other_dim)
+    assert mapping['other'].type == OperationDataType.ARG
+    assert mapping['other'].logical_shape == torch.Size([4, 4])
+
+    assert mapping['output'].name == str(op_node)
+    assert mapping['output'].data.is_meta
+    assert mapping['output'].data.shape == torch.Size([4, 4])
+    assert mapping['output'].type == OperationDataType.OUTPUT
+    assert mapping['output'].logical_shape == torch.Size([4, 4])
+
+    strategies_vector = handler.register_strategy(compute_resharding_cost=False)
+    strategy_name_list = [val.name for val in strategies_vector]
+
+    # one strategy will be converted to different physical sharding spec
+    assert len(strategy_name_list) == 9
+
+    # check if the sharding strategy is correct
+    assert '[S0, S1] = [S0, S1] <binary-elementwise-op> [S0, S1]' in strategy_name_list
+    assert '[S1, S0] = [S1, S0] <binary-elementwise-op> [S1, S0]' in strategy_name_list
+    assert '[S01, R] = [S01, R] <binary-elementwise-op> [S01, R]' in strategy_name_list
+    assert '[R, S01] = [R, S01] <binary-elementwise-op> [R, S01]' in strategy_name_list
+    assert '[S0, R] = [S0, R] <binary-elementwise-op> [S0, R]' in strategy_name_list
+    assert '[R, S0] = [R, S0] <binary-elementwise-op> [R, S0]' in strategy_name_list
+    assert '[S1, R] = [S1, R] <binary-elementwise-op> [S1, R]' in strategy_name_list
+    assert '[R, S1] = [R, S1] <binary-elementwise-op> [R, S1]' in strategy_name_list
+    assert '[R, R] = [R, R] <binary-elementwise-op> [R, R]' in strategy_name_list
+
+    for strategy in strategies_vector:
+        input_sharding_spec = strategy.get_sharding_spec_by_name('x1')
+        other_sharding_spec = strategy.get_sharding_spec_by_name('x2')
+        output_sharding_spec = strategy.get_sharding_spec_by_name(str(op_node))
+
+        # make sure the sharding spec is the same for input and output
+        assert input_sharding_spec.sharding_sequence == output_sharding_spec.sharding_sequence
+
+        # since the dim of the other can change, we make sure at least its last dim sharding is the same
+        if len(other_sharding_spec.sharding_sequence) == 2:
+            assert input_sharding_spec.sharding_sequence == other_sharding_spec.sharding_sequence
+        elif len(other_sharding_spec.sharding_sequence) == 1:
+            assert input_sharding_spec.sharding_sequence[-1] == other_sharding_spec.sharding_sequence[-1]
+
+
+@parameterize('op', [torch.add])
+@parameterize('other', [1, 2])
+def test_binary_elementwise_handler_with_int(op, other):
+
+    class BinaryElementwiseOpModel(nn.Module):
+
+        def __init__(self, op, const):
+            super().__init__()
+            self.op = op
+            self.const = const
+
+        def forward(self, x1):
+            out = self.op(x1, self.const)
+            return out
+
+    model = BinaryElementwiseOpModel(op, other)
+    tracer = ColoTracer()
+
+    meta_args = {'x1': torch.rand(4, 4).to('meta')}
+    graph = tracer.trace(model, meta_args=meta_args)
+    print(graph)
+    gm = ColoGraphModule(model, graph)
+    physical_mesh_id = torch.arange(0, 4)
+    mesh_shape = (2, 2)
+    device_mesh = DeviceMesh(physical_mesh_id, mesh_shape)
+    op_node = list(graph.nodes)[1]
+    strategies_vector = StrategiesVector(op_node)
+
+    # build handler
+    handler = BinaryElementwiseHandler(node=op_node, device_mesh=device_mesh, strategies_vector=strategies_vector)
+
+    # check operation data mapping
+    mapping = handler.get_operation_data_mapping()
+
+    assert mapping['input'].name == "x1"
+    assert mapping['input'].data.is_meta
+    assert mapping['input'].data.shape == torch.Size([4, 4])
+    assert mapping['input'].type == OperationDataType.ARG
+    assert mapping['input'].logical_shape == torch.Size([4, 4])
+
+    assert mapping['output'].name == str(op_node)
+    assert mapping['output'].data.is_meta
+    assert mapping['output'].data.shape == torch.Size([4, 4])
+    assert mapping['output'].type == OperationDataType.OUTPUT
+    assert mapping['output'].logical_shape == torch.Size([4, 4])
+
+    strategies_vector = handler.register_strategy(compute_resharding_cost=False)
+    strategy_name_list = [val.name for val in strategies_vector]
+
+    # one strategy will be converted to different physical sharding spec
+    assert len(strategy_name_list) == 9
+
+    # check if the sharding strategy is correct
+    assert '[S0, S1] = [S0, S1] <binary-elementwise-op> [S0, S1]' in strategy_name_list
+    assert '[S1, S0] = [S1, S0] <binary-elementwise-op> [S1, S0]' in strategy_name_list
+    assert '[S01, R] = [S01, R] <binary-elementwise-op> [S01, R]' in strategy_name_list
+    assert '[R, S01] = [R, S01] <binary-elementwise-op> [R, S01]' in strategy_name_list
+    assert '[S0, R] = [S0, R] <binary-elementwise-op> [S0, R]' in strategy_name_list
+    assert '[R, S0] = [R, S0] <binary-elementwise-op> [R, S0]' in strategy_name_list
+    assert '[S1, R] = [S1, R] <binary-elementwise-op> [S1, R]' in strategy_name_list
+    assert '[R, S1] = [R, S1] <binary-elementwise-op> [R, S1]' in strategy_name_list
+    assert '[R, R] = [R, R] <binary-elementwise-op> [R, R]' in strategy_name_list
+
+    for strategy in strategies_vector:
+        input_sharding_spec = strategy.get_sharding_spec_by_name('x1')
+        output_sharding_spec = strategy.get_sharding_spec_by_name(str(op_node))
+
+        # make sure the sharding spec is the same for input and output
+        assert input_sharding_spec.sharding_sequence == output_sharding_spec.sharding_sequence
+
+
+if __name__ == '__main__':
+    test_binary_elementwise_handler_with_tensor()
+    test_binary_elementwise_handler_with_int()