[autoparallel] add conv metainfo class for auto parallel (#1796)

* [fx] metainfo class for auto parallel

* [fx] add unit test for linear metainfo

* [fx] fix bwd param for linear

* [fx] modify unit test

* [fx] modify unit test

* [fx] modify import

* [fx] modify import

* [fx] modify import

* [fx] move meta profiler to auto parallel

* [fx] add conv metainfo class

* [fx] restore profiler

* [fx] restore meta profiler

* [autoparallel] modify unit test

* [fx] modify unit test

tests/test_auto_parallel/test_tensor_shard/test_metainfo/test_conv_metainfo.py

@@ -0,0 +1,61 @@
+from functools import partial
+
+import pytest
+import torch
+import torch.multiprocessing as mp
+import torch.nn as nn
+
+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.pytest_wrapper import run_on_environment_flag
+from colossalai.testing.utils import parameterize, rerun_if_address_is_in_use
+from colossalai.utils import free_port
+from tests.test_auto_parallel.test_tensor_shard.test_metainfo.utils import mem_test_for_node_strategy
+
+
+def _conv_module_mem_test(rank, bias, world_size, port):
+    """This function is for conv memory test
+    Test and print real memory cost and estimated, this test will not be executed except with the tag AUTO_PARALLEL
+
+    Args:
+    Args:
+        rank: device rank
+        bias: indicate whether conv module need bias
+        world_size: number of devices
+        port: port for initializing process group
+    """
+    disable_existing_loggers()
+    launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
+    model = nn.Sequential(nn.Conv2d(4, 64, 3, padding=1, bias=bias)).cuda()
+    input = torch.rand(4, 4, 64, 64).cuda()
+    input.requires_grad = True
+    physical_mesh_id = torch.arange(0, 4)
+    mesh_shape = (2, 2)
+    device_mesh = DeviceMesh(physical_mesh_id, mesh_shape, init_process_group=True)
+
+    # index of conv node in computation graph
+    node_index = 1
+    # total number of conv strategies
+    strategy_number = 16
+    mem_test_for_node_strategy(rank=rank,
+                               model=model,
+                               device_mesh=device_mesh,
+                               node_index=node_index,
+                               strategy_number=strategy_number,
+                               input_args=[input],
+                               meta_arg_names=['input'])
+
+
+@run_on_environment_flag(name='AUTO_PARALLEL')
+@pytest.mark.dist
+@rerun_if_address_is_in_use()
+def test_conv_meta_concrete_info_match(bias=False):
+    world_size = 4
+    run_func_module = partial(_conv_module_mem_test, bias=bias, world_size=world_size, port=free_port())
+    mp.spawn(run_func_module, nprocs=world_size)
+
+
+if __name__ == '__main__':
+    test_conv_meta_concrete_info_match()

tests/test_auto_parallel/test_tensor_shard/test_metainfo/test_linear_metainfo.py

@@ -20,48 +20,15 @@ if torch.__version__ >= '1.12.0':
     from colossalai.auto_parallel.meta_profiler import MetaInfo, meta_register
 
 
-@pytest.mark.skipif(torch.__version__ < '1.12.0', reason='PyTorch version is too low')
-@parameterize('bias', [True, False])
-def test_linear_metainfo(bias):
-    model = nn.Sequential(nn.Linear(16, 32, bias=bias).to('meta'))
-
-    tracer = ColoTracer()
-    graph = tracer.trace(model, meta_args={"input": torch.rand(2, 2, 4, 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)
-    linear_mod_node = list(graph.nodes)[1]
-    strategies_vector = StrategiesVector(linear_mod_node)
-
-    # build handler
-    handler = LinearModuleHandler(node=linear_mod_node, device_mesh=device_mesh, strategies_vector=strategies_vector)
-
-    # build strategy
-    strategies_vector = handler.register_strategy(compute_resharding_cost=False)
-
-    # assert module is registered
-    assert meta_register.has(linear_mod_node.graph.owning_module.get_submodule(linear_mod_node.target).__class__)
-
-    # check metainfo
-    for strategy in strategies_vector:
-        strategy: ShardingStrategy
-        try:
-            metainfo = MetaInfo(strategy,
-                                linear_mod_node.graph.owning_module.get_submodule(linear_mod_node.target).__class__)
-
-        except:
-            raise RuntimeError(f"Failed to compute metainfo for {strategy}")
-
-
-def _linear_mem_test(rank, bias, world_size, port):
+def _linear_module_mem_test(rank, bias, world_size, port):
     """This function is for linear memory test
-    Test and print real memory cost and estimated, this test will not be executed
-    in unit test.
+    Test and print real memory cost and estimated, this test will not be executed except with the tag AUTO_PARALLEL
 
     Args:
-        bias (bool, optional): Indicate whether we need bias for Linear. Defaults to True.
+        rank: device rank
+        bias: indicate whether linear module need bias
+        world_size: number of devices
+        port: port for initializing process group
     """
     disable_existing_loggers()
     launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
@@ -87,11 +54,9 @@ def _linear_mem_test(rank, bias, world_size, port):
 @rerun_if_address_is_in_use()
 def test_linear_meta_concrete_info_match(bias=False):
     world_size = 4
-    run_func_module = partial(_linear_mem_test, bias=bias, world_size=world_size, port=free_port())
+    run_func_module = partial(_linear_module_mem_test, bias=bias, world_size=world_size, port=free_port())
     mp.spawn(run_func_module, nprocs=world_size)
 
 
 if __name__ == '__main__':
-    # test_linear_metainfo()
-    # _linear_mem_test(bias=True)
     test_linear_meta_concrete_info_match()