[autoparallel] implement softmax handler (#2132)

tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_softmax_handler.py

@@ -0,0 +1,186 @@
+from functools import partial
+
+import pytest
+import torch
+import torch.multiprocessing as mp
+import torch.nn as nn
+import torch.nn.functional as F
+
+from colossalai.auto_parallel.tensor_shard.node_handler.linear_handler import LinearFunctionHandler
+from colossalai.auto_parallel.tensor_shard.node_handler.softmax_handler import SoftmaxHandler
+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 LinearSplitModel(nn.Module):
+
+    def __init__(self, softmax_dim):
+        super().__init__()
+        self.softmax_dim = softmax_dim
+
+    def forward(self, input, other):
+        linear_node = F.linear(input, other, bias=None)
+        softmax_node = F.softmax(linear_node, self.softmax_dim)
+        return softmax_node
+
+
+def check_split_handler(rank, softmax_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(softmax_dim=softmax_dim).cuda()
+
+    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()
+
+    # 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})
+    #     %softmax : [#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
+    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)
+
+    softmax_handler = SoftmaxHandler(node=split_node,
+                                     device_mesh=device_mesh,
+                                     strategies_vector=split_strategies_vector)
+
+    softmax_handler.register_strategy(compute_resharding_cost=False)
+
+    # check operation data mapping
+    mapping = softmax_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
+
+    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['softmax_dim'].name == "softmax_dim"
+    assert mapping['softmax_dim'].data == softmax_dim
+    assert mapping['softmax_dim'].type == OperationDataType.ARG
+
+    assert mapping['output'].name == "softmax"
+    assert mapping['output'].data.shape == torch.Size([8, 16, 64, 64])
+    assert mapping['output'].logical_shape == torch.Size([8, 16, 64, 64])
+    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]
+
+    if softmax_dim == 0:
+        assert '[R, R, R, S1] -> [R, R, R, S1]_0' in strategy_name_list
+        assert '[R, S0, R, S1] -> [R, S0, R, S1]_1' in strategy_name_list
+        assert '[R, R, S0, S1] -> [R, R, S0, S1]_2' in strategy_name_list
+        assert '[R, R, R, S0] -> [R, R, R, S0]_3' in strategy_name_list
+        assert '[R, S1, R, S0] -> [R, S1, R, S0]_4' in strategy_name_list
+        assert '[R, R, S1, S0] -> [R, R, S1, S0]_5' in strategy_name_list
+        assert '[R, R, R, R] -> [R, R, R, R]_6' in strategy_name_list
+        assert '[R, S0, R, R] -> [R, S0, R, R]_7' in strategy_name_list
+        assert '[R, R, S0, R] -> [R, R, S0, R]_8' in strategy_name_list
+        assert '[R, R, R, R] -> [R, R, R, R]_9' in strategy_name_list
+        assert '[R, S1, R, R] -> [R, S1, R, R]_10' in strategy_name_list
+        assert '[R, R, S1, R] -> [R, R, S1, R]_11' in strategy_name_list
+        assert '[R, R, R, S1] -> [R, R, R, S1]_12' in strategy_name_list
+        assert '[R, R, R, S0] -> [R, R, R, S0]_13' in strategy_name_list
+        assert '[R, R, R, R] -> [R, R, R, R]_14' in strategy_name_list
+        assert '[R, R, R, R] -> [R, R, R, R]_15' in strategy_name_list
+        assert '[R, R, R, S0] -> [R, R, R, S0]_16' in strategy_name_list
+        assert '[R, R, R, S1] -> [R, R, R, S1]_17' in strategy_name_list
+        assert '[R, R, R, R] -> [R, R, R, R]_18' in strategy_name_list
+        assert '[R, S01, R, R] -> [R, S01, R, R]_19' in strategy_name_list
+        assert '[R, R, S01, R] -> [R, R, S01, R]_20' in strategy_name_list
+        assert '[R, R, R, R] -> [R, R, R, R]_21' in strategy_name_list
+        assert '[R, R, R, S01] -> [R, R, R, S01]_22' in strategy_name_list
+
+    if softmax_dim == 1:
+        assert '[S0, R, R, S1] -> [S0, R, R, S1]_0' in strategy_name_list
+        assert '[R, R, R, S1] -> [R, R, R, S1]_1' in strategy_name_list
+        assert '[R, R, S0, S1] -> [R, R, S0, S1]_2' in strategy_name_list
+        assert '[S1, R, R, S0] -> [S1, R, R, S0]_3' in strategy_name_list
+        assert '[R, R, R, S0] -> [R, R, R, S0]_4' in strategy_name_list
+        assert '[R, R, S1, S0] -> [R, R, S1, S0]_5' in strategy_name_list
+        assert '[S0, R, R, R] -> [S0, R, R, R]_6' in strategy_name_list
+        assert '[R, R, R, R] -> [R, R, R, R]_7' in strategy_name_list
+        assert '[R, R, S0, R] -> [R, R, S0, R]_8' in strategy_name_list
+        assert '[S1, R, R, R] -> [S1, R, R, R]_9' in strategy_name_list
+        assert '[R, R, R, R] -> [R, R, R, R]_10' in strategy_name_list
+        assert '[R, R, S1, R] -> [R, R, S1, R]_11' in strategy_name_list
+        assert '[R, R, R, S1] -> [R, R, R, S1]_12' in strategy_name_list
+        assert '[R, R, R, S0] -> [R, R, R, S0]_13' in strategy_name_list
+        assert '[R, R, R, R] -> [R, R, R, R]_14' in strategy_name_list
+        assert '[R, R, R, R] -> [R, R, R, R]_15' in strategy_name_list
+        assert '[R, R, R, S0] -> [R, R, R, S0]_16' in strategy_name_list
+        assert '[R, R, R, S1] -> [R, R, R, S1]_17' in strategy_name_list
+        assert '[S01, R, R, R] -> [S01, R, R, R]_18' in strategy_name_list
+        assert '[R, R, R, R] -> [R, R, R, R]_19' in strategy_name_list
+        assert '[R, R, S01, R] -> [R, R, S01, R]_20' in strategy_name_list
+        assert '[R, R, R, R] -> [R, R, R, R]_21' in strategy_name_list
+        assert '[R, R, R, S01] -> [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('softmax_dim', [0, 1, 2, 3])
+@parameterize('model_cls', [LinearSplitModel])
+def test_split_handler(softmax_dim, model_cls):
+    world_size = 4
+    run_func = partial(check_split_handler,
+                       softmax_dim=softmax_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()