[autoparallel] support addmm in tracer and solver (#1961)

* [fx] patch addmm

* [autoparallel] support addmm in tracer and solver

tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_addmm_handler.py

@@ -0,0 +1,156 @@
+from faulthandler import disable
+from functools import partial
+from xml.dom import WrongDocumentErr
+
+import pytest
+import torch
+import torch.multiprocessing as mp
+import torch.nn as nn
+from typing_extensions import Self
+
+from colossalai.auto_parallel.tensor_shard.node_handler import ADDMMFunctionHandler
+from colossalai.auto_parallel.tensor_shard.sharding_strategy import (
+    OperationData,
+    OperationDataType,
+    ShardingStrategy,
+    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 AddmmModel(nn.Module):
+
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, input, m1, m2):
+        x = torch.addmm(input, m1, m2)
+        return x
+
+
+def check_linear_function_handler(rank, input_shape, world_size, port):
+    disable_existing_loggers()
+    launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
+    model = AddmmModel().cuda()
+    physical_mesh_id = torch.arange(0, 4)
+    mesh_shape = (2, 2)
+    device_mesh = DeviceMesh(physical_mesh_id, mesh_shape, init_process_group=True)
+
+    input = torch.rand(input_shape).cuda()
+    m1 = torch.rand(4, 8).cuda()
+    m2 = torch.rand(8, 16).cuda()
+    # the index of addmm node in computation graph
+    node_index = 3
+    # strategy number of linear node
+    strategy_number = 10
+    # construct input args
+    input_args = [input, m1, m2]
+    # construct meta arg names
+    meta_arg_names = ['input', 'm1', 'm2']
+    numerical_test_for_node_strategy(model=model,
+                                     device_mesh=device_mesh,
+                                     node_index=node_index,
+                                     strategy_number=strategy_number,
+                                     input_args=input_args,
+                                     meta_arg_names=meta_arg_names)
+
+    tracer = ColoTracer()
+    graph = tracer.trace(model,
+                         meta_args={
+                             "input": torch.rand(input_shape).to('meta'),
+                             'm1': torch.rand(4, 8).to('meta'),
+                             'm2': torch.rand(8, 16).to('meta'),
+                         })
+    gm = ColoGraphModule(model, graph)
+    # [input_1, m1, m2, addmm, output]
+    node_list = list(graph.nodes)
+    addmm_node = node_list[3]
+    strategies_vector = StrategiesVector(addmm_node)
+
+    # build handler
+    handler = ADDMMFunctionHandler(node=addmm_node, device_mesh=device_mesh, strategies_vector=strategies_vector)
+
+    handler.register_strategy(compute_resharding_cost=False)
+    strategy_name_list = [val.name for val in strategies_vector]
+
+    # check operation data mapping
+    mapping = handler.get_operation_data_mapping()
+
+    assert mapping['input'].name == "m1"
+    assert mapping['input'].data.shape == torch.Size([4, 8])
+    assert mapping['input'].type == OperationDataType.ARG
+    assert mapping['input'].logical_shape == torch.Size([4, 8])
+
+    assert mapping['other'].name == "m2"
+    assert mapping['other'].data.shape == torch.Size([8, 16])
+    assert mapping['other'].type == OperationDataType.ARG
+    assert mapping['other'].logical_shape == torch.Size([8, 16])
+
+    assert mapping['bias'].name == "input_1"
+    assert mapping['bias'].data.shape == torch.Size(input_shape)
+    assert mapping['bias'].type == OperationDataType.ARG
+    assert mapping['bias'].logical_shape == torch.Size([4, 16])
+
+    assert mapping['output'].name == "addmm"
+    assert mapping['output'].data.shape == torch.Size([4, 16])
+    assert mapping['output'].type == OperationDataType.OUTPUT
+
+    # one strategy will be converted to different physical sharding spec
+    assert len(strategy_name_list) > 8
+
+    # SS = SR x RS
+    assert 'S0S1 = S0R x RS1' in strategy_name_list
+    assert 'S1S0 = S1R x RS0' 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
+
+    for strategy in strategies_vector:
+        strategy: ShardingStrategy
+        input_sharding_spec = strategy.get_sharding_spec_by_name('m1')
+        weight_sharding_spec = strategy.get_sharding_spec_by_name('m2')
+        output_sharding_spec = strategy.get_sharding_spec_by_name('addmm')
+        bias_sharding_spec = strategy.get_sharding_spec_by_name('input_1')
+
+        # make sure the sharding matches across different operation data
+        assert input_sharding_spec.sharding_sequence[:-1] == output_sharding_spec.sharding_sequence[:-1]
+        assert weight_sharding_spec.sharding_sequence[0] == input_sharding_spec.sharding_sequence[1]
+        assert weight_sharding_spec.sharding_sequence[1] == output_sharding_spec.sharding_sequence[1]
+        assert bias_sharding_spec.sharding_sequence[-1] == output_sharding_spec.sharding_sequence[-1]
+
+
+@parameterize('input_shape', [(16,), (4, 16)])
+@run_on_environment_flag(name='AUTO_PARALLEL')
+@pytest.mark.dist
+@rerun_if_address_is_in_use()
+def test_addmm_handler(input_shape):
+    world_size = 4
+    run_func_function = partial(check_linear_function_handler,
+                                input_shape=input_shape,
+                                world_size=world_size,
+                                port=free_port())
+    mp.spawn(run_func_function, nprocs=world_size)
+
+
+if __name__ == '__main__':
+    test_addmm_handler()