[autoparallel] add split handler (#2032)

* [autoparallel] add split handler

* add numerical test and runtime passes

tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_split_handler.py

@@ -0,0 +1,270 @@
+from functools import partial
+
+import pytest
+import torch
+import torch.multiprocessing as mp
+import torch.nn as nn
+
+from colossalai.auto_parallel.tensor_shard.node_handler.conv_handler import ConvFunctionHandler
+from colossalai.auto_parallel.tensor_shard.node_handler.experimental import SplitHandler
+from colossalai.auto_parallel.tensor_shard.node_handler.linear_handler import LinearFunctionHandler
+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.initialize import launch
+from colossalai.logging import disable_existing_loggers
+from colossalai.testing import assert_close, parameterize, rerun_if_address_is_in_use
+from colossalai.testing.pytest_wrapper import run_on_environment_flag
+from colossalai.utils import free_port
+from tests.test_auto_parallel.test_tensor_shard.test_node_handler.utils import numerical_test_for_node_strategy
+
+
+class ConvSplitModel(nn.Module):
+
+    def __init__(self, split_size, split_dim):
+        super().__init__()
+        self.split_size = split_size
+        self.split_dim = split_dim
+
+    def forward(self, input, other):
+        conv_node = nn.functional.conv2d(input, other, bias=None)
+        split_node = conv_node.split(self.split_size, dim=self.split_dim)
+        return split_node
+
+
+class LinearSplitModel(nn.Module):
+
+    def __init__(self, split_size, split_dim):
+        super().__init__()
+        self.split_size = split_size
+        self.split_dim = split_dim
+
+    def forward(self, input, other):
+        linear_node = nn.functional.linear(input, other, bias=None)
+        split_node = linear_node.split(self.split_size, dim=self.split_dim)
+        return split_node
+
+
+def check_split_handler(rank, split_size, split_dim, model_cls, world_size, port):
+    disable_existing_loggers()
+    launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
+    model = model_cls(split_size=split_size, split_dim=split_dim).cuda()
+
+    if model_cls.__name__ == 'ConvSplitModel':
+        input = torch.rand(8, 8, 66, 66).to('cuda')
+        other = torch.rand(16, 8, 3, 3).to('cuda')
+        # index of conv node in computation graph
+        node_index = 2
+        # total number of conv strategies
+        strategy_number = 16
+    if model_cls.__name__ == 'LinearSplitModel':
+        input = torch.rand(8, 16, 64, 32).to('cuda')
+        other = torch.rand(64, 32).to('cuda')
+        # index of linear node in computation graph
+        node_index = 2
+        # total number of linear strategies
+        strategy_number = 23
+
+    physical_mesh_id = torch.arange(0, 4)
+    mesh_shape = (2, 2)
+    device_mesh = DeviceMesh(physical_mesh_id, mesh_shape, init_process_group=True)
+
+    numerical_test_for_node_strategy(model=model,
+                                     device_mesh=device_mesh,
+                                     node_index=node_index,
+                                     strategy_number=strategy_number,
+                                     input_args=[input, other],
+                                     meta_arg_names=['input', 'other'],
+                                     node_type='following')
+    tracer = ColoTracer()
+    if model_cls.__name__ == 'ConvSplitModel':
+        # graph():
+        #     %input_1 : torch.Tensor [#users=1] = placeholder[target=input]
+        #     %other : torch.Tensor [#users=1] = placeholder[target=other]
+        #     %conv2d : [#users=1] = call_function[target=torch.conv2d](args = (%input_1, %other), kwargs = {})
+        #     %split : [#users=1] = call_method[target=split](args = (%conv2d,), kwargs = {})
+        #     return split
+        graph = tracer.trace(model,
+                             meta_args={
+                                 "input": torch.rand(8, 8, 66, 66).to('meta'),
+                                 "other": torch.rand(16, 8, 3, 3).to('meta'),
+                             })
+
+    if model_cls.__name__ == 'LinearSplitModel':
+        # graph():
+        #     %input_1 : torch.Tensor [#users=1] = placeholder[target=input]
+        #     %other : torch.Tensor [#users=1] = placeholder[target=other]
+        #     %linear : [#users=1] = call_function[target=torch._C._nn.linear](args = (%input_1, %other), kwargs = {bias: None})
+        #     %split : [#users=1] = call_method[target=split](args = (%linear,), kwargs = {})
+        #     return split
+        graph = tracer.trace(model,
+                             meta_args={
+                                 "input": torch.rand(8, 16, 64, 32).to('meta'),
+                                 "other": torch.rand(64, 32).to('meta'),
+                             })
+
+    gm = ColoGraphModule(model, graph)
+
+    previous_mod_node = list(graph.nodes)[2]
+    split_node = list(graph.nodes)[3]
+    split_strategies_vector = StrategiesVector(split_node)
+    previous_strategies_vector = StrategiesVector(previous_mod_node)
+
+    # build handler
+    if model_cls.__name__ == 'ConvSplitModel':
+
+        conv_handler = ConvFunctionHandler(node=previous_mod_node,
+                                           device_mesh=device_mesh,
+                                           strategies_vector=previous_strategies_vector)
+        conv_handler.register_strategy(compute_resharding_cost=False)
+        setattr(previous_mod_node, 'strategies_vector', previous_strategies_vector)
+
+    if model_cls.__name__ == 'LinearSplitModel':
+        assert len(previous_strategies_vector) == 0
+        linear_handler = LinearFunctionHandler(node=previous_mod_node,
+                                               device_mesh=device_mesh,
+                                               strategies_vector=previous_strategies_vector)
+        linear_handler.register_strategy(compute_resharding_cost=False)
+        setattr(previous_mod_node, 'strategies_vector', previous_strategies_vector)
+
+    split_handler = SplitHandler(node=split_node, device_mesh=device_mesh, strategies_vector=split_strategies_vector)
+
+    split_handler.register_strategy(compute_resharding_cost=False)
+
+    # check operation data mapping
+    mapping = split_handler.get_operation_data_mapping()
+
+    for name, op_data in mapping.items():
+        op_data: OperationData
+        # make sure they have valid values
+        assert op_data.data is not None
+
+    if model_cls.__name__ == 'ConvSplitModel':
+        assert mapping['input'].name == "conv2d"
+    else:
+        assert mapping['input'].name == "linear"
+    assert mapping['input'].data.is_meta
+    assert mapping['input'].data.shape == torch.Size([8, 16, 64, 64])
+    assert mapping['input'].type == OperationDataType.ARG
+    assert mapping['input'].logical_shape == torch.Size([8, 16, 64, 64])
+
+    assert mapping['output'].name == "split"
+    split_items = torch.empty([8, 16, 64, 64]).split(split_size, split_dim)
+    assert mapping['output'].logical_shape == tuple([item.shape for item in split_items])
+    assert mapping['output'].type == OperationDataType.OUTPUT
+
+    # reshape handler is a following strategy handler, so the number of strategies is equal to the predecessor node.
+    assert len(split_strategies_vector) == len(previous_strategies_vector)
+    strategy_name_list = [strategy.name for strategy in split_strategies_vector]
+    for name in strategy_name_list:
+        print(name)
+    if model_cls.__name__ == 'ConvSplitModel':
+
+        if split_dim == 0:
+            assert '[R, S1, R, R]_0' in strategy_name_list
+            assert '[R, S0, R, R]_1' in strategy_name_list
+            assert '[R, R, R, R]_2' in strategy_name_list
+            assert '[R, R, R, R]_3' in strategy_name_list
+            assert '[R, R, R, R]_4' in strategy_name_list
+            assert '[R, R, R, R]_5' in strategy_name_list
+            assert '[R, S1, R, R]_6' in strategy_name_list
+            assert '[R, S0, R, R]_7' in strategy_name_list
+            assert '[R, R, R, R]_8' in strategy_name_list
+            assert '[R, R, R, R]_9' in strategy_name_list
+            assert '[R, S0, R, R]_10' in strategy_name_list
+            assert '[R, S1, R, R]_11' in strategy_name_list
+            assert '[R, R, R, R]_12' in strategy_name_list
+            assert '[R, R, R, R]_13' in strategy_name_list
+            assert '[R, R, R, R]_14' in strategy_name_list
+            assert '[R, S01, R, R]_15' in strategy_name_list
+
+        if split_dim == 1:
+            assert '[S0, R, R, R]_0' in strategy_name_list
+            assert '[S1, R, R, R]_1' in strategy_name_list
+            assert '[S0, R, R, R]_2' in strategy_name_list
+            assert '[S1, R, R, R]_3' in strategy_name_list
+            assert '[S0, R, R, R]_4' in strategy_name_list
+            assert '[S1, R, R, R]_5' in strategy_name_list
+            assert '[R, R, R, R]_6' in strategy_name_list
+            assert '[R, R, R, R]_7' in strategy_name_list
+            assert '[R, R, R, R]_8' in strategy_name_list
+            assert '[R, R, R, R]_9' in strategy_name_list
+            assert '[R, R, R, R]_10' in strategy_name_list
+            assert '[R, R, R, R]_11' in strategy_name_list
+            assert '[R, R, R, R]_12' in strategy_name_list
+            assert '[S01, R, R, R]_13' in strategy_name_list
+            assert '[R, R, R, R]_14' in strategy_name_list
+            assert '[R, R, R, R]_15' in strategy_name_list
+
+    if model_cls.__name__ == 'LinearSplitModel':
+
+        if split_dim == 0:
+            assert '[R, R, R, S1]_0' in strategy_name_list
+            assert '[R, S0, R, S1]_1' in strategy_name_list
+            assert '[R, R, S0, S1]_2' in strategy_name_list
+            assert '[R, R, R, S0]_3' in strategy_name_list
+            assert '[R, S1, R, S0]_4' in strategy_name_list
+            assert '[R, R, S1, S0]_5' in strategy_name_list
+            assert '[R, R, R, R]_6' in strategy_name_list
+            assert '[R, S0, R, R]_7' in strategy_name_list
+            assert '[R, R, S0, R]_8' in strategy_name_list
+            assert '[R, R, R, R]_9' in strategy_name_list
+            assert '[R, S1, R, R]_10' in strategy_name_list
+            assert '[R, R, S1, R]_11' in strategy_name_list
+            assert '[R, R, R, S1]_12' in strategy_name_list
+            assert '[R, R, R, S0]_13' in strategy_name_list
+            assert '[R, R, R, R]_14' in strategy_name_list
+            assert '[R, R, R, R]_15' in strategy_name_list
+            assert '[R, R, R, S0]_16' in strategy_name_list
+            assert '[R, R, R, S1]_17' in strategy_name_list
+            assert '[R, R, R, R]_18' in strategy_name_list
+            assert '[R, S01, R, R]_19' in strategy_name_list
+            assert '[R, R, S01, R]_20' in strategy_name_list
+            assert '[R, R, R, R]_21' in strategy_name_list
+            assert '[R, R, R, S01]_22' in strategy_name_list
+
+        if split_dim == 1:
+            assert '[S0, R, R, S1]_0' in strategy_name_list
+            assert '[R, R, R, S1]_1' in strategy_name_list
+            assert '[R, R, S0, S1]_2' in strategy_name_list
+            assert '[S1, R, R, S0]_3' in strategy_name_list
+            assert '[R, R, R, S0]_4' in strategy_name_list
+            assert '[R, R, S1, S0]_5' in strategy_name_list
+            assert '[S0, R, R, R]_6' in strategy_name_list
+            assert '[R, R, R, R]_7' in strategy_name_list
+            assert '[R, R, S0, R]_8' in strategy_name_list
+            assert '[S1, R, R, R]_9' in strategy_name_list
+            assert '[R, R, R, R]_10' in strategy_name_list
+            assert '[R, R, S1, R]_11' in strategy_name_list
+            assert '[R, R, R, S1]_12' in strategy_name_list
+            assert '[R, R, R, S0]_13' in strategy_name_list
+            assert '[R, R, R, R]_14' in strategy_name_list
+            assert '[R, R, R, R]_15' in strategy_name_list
+            assert '[R, R, R, S0]_16' in strategy_name_list
+            assert '[R, R, R, S1]_17' in strategy_name_list
+            assert '[S01, R, R, R]_18' in strategy_name_list
+            assert '[R, R, R, R]_19' in strategy_name_list
+            assert '[R, R, S01, R]_20' in strategy_name_list
+            assert '[R, R, R, R]_21' in strategy_name_list
+            assert '[R, R, R, S01]_22' in strategy_name_list
+
+
+@run_on_environment_flag(name='AUTO_PARALLEL')
+@pytest.mark.dist
+@rerun_if_address_is_in_use()
+@parameterize('split_size', [2])
+@parameterize('split_dim', [0, 1, 2])
+@parameterize('model_cls', [ConvSplitModel, LinearSplitModel])
+def test_split_handler(split_size, split_dim, model_cls):
+    world_size = 4
+    run_func = partial(check_split_handler,
+                       split_size=split_size,
+                       split_dim=split_dim,
+                       model_cls=model_cls,
+                       world_size=world_size,
+                       port=free_port())
+    mp.spawn(run_func, nprocs=world_size)
+
+
+if __name__ == '__main__':
+    test_split_handler()

tests/test_auto_parallel/test_tensor_shard/test_node_handler/utils.py

@@ -118,10 +118,15 @@ def numerical_test_for_node_strategy(model: torch.nn.Module,
         assert_close_helper(output, output_to_compare, strategy_index=strategy_index, type='forward output')
 
         # backward result compare
-        loss = output.sum()
-        loss_to_compare = output_to_compare.sum()
-        loss.backward()
+        if isinstance(output, (tuple, list)):
+            loss = output[0].sum()
+            loss_to_compare = output_to_compare[0].sum()
+        else:
+            loss = output.sum()
+            loss_to_compare = output_to_compare.sum()
+
         loss_to_compare.backward()
+        loss.backward()
         for key in grad_to_shard_dict.keys():
             grad_to_shard = grad_to_shard_dict[key]
             grad_to_compare = grad_to_compare_dict[key]
@@ -157,6 +162,10 @@ def assert_close_helper(first: torch.Tensor,
     """
     # average_diff_tensor = ((first - second)/(second+0.1)).sum()/second.numel()
     try:
-        assert_close(first, second, rtol=rtol, atol=atol)
+        if isinstance(first, (tuple, list)):
+            for first_element, second_element in zip(first, second):
+                assert_close(first_element, second_element, rtol=rtol, atol=atol)
+        else:
+            assert_close(first, second, rtol=rtol, atol=atol)
     except:
         print(f'strategy index {strategy_index} encounter assert_close error on {type}')