[autoparallel] Add F.conv metainfo (#2069)

* [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

* [autoparallel] add batchnorm metainfo class

* [autoparallel] fix batchnorm unit test function declaration

* [fx] restore profiler

* [fx] add relu metainfo class

* [fx] restore profiler

* [autoparallel] modify metainfo input

* [autoparallel] add pooling metainfo

* [autoparallel] add F.linear metainfo generator

* [autoparallel] add binary elementwise metainfo

* [fx] recover profiler

* [autoparallel] fix forward memory calculation

* [autoparallel] modify constants.py

* [autoparallel] remove redundant print

* [autoparallel] add F.conv metainfo

* [autoparallel] linear fix

colossalai/auto_parallel/meta_profiler/meta_registry/conv.py

@@ -22,6 +22,9 @@ __all__ = ['convnd_meta_info']
 @meta_register.register(torch.nn.Conv1d)
 @meta_register.register(torch.nn.Conv2d)
 @meta_register.register(torch.nn.Conv3d)
+@meta_register.register(torch.nn.functional.conv1d)
+@meta_register.register(torch.nn.functional.conv2d)
+@meta_register.register(torch.nn.functional.conv3d)
 def convnd_meta_info(*args, **kwargs) -> Tuple[TrainCycleItem, TrainCycleItem, List[torch.Tensor]]:
     """torch.nn.Conv1d, torch.nn.Conv2d, torch.nn.Conv3d meta info generator
     The atens graph of torch.nn.Convnd with bias is
@@ -57,12 +60,19 @@ def convnd_meta_info(*args, **kwargs) -> Tuple[TrainCycleItem, TrainCycleItem, L
     has_bias: bool = False
     input_tensor = next(filter(lambda x: x.type == OperationDataType.ARG, args)).data
     output_tensor = next(filter(lambda x: x.type == OperationDataType.OUTPUT, args)).data
-    weight_tensor = next(filter(lambda x: x.name == 'weight', args)).data
+    weight_tensors = [x.data for x in args if x.type == OperationDataType.PARAM]
 
     # check if conv has bias
-    if len(args) == 4:
-        bias_tensor = next(filter(lambda x: x.name == 'bias', args)).data
+    if len(weight_tensors) > 1:
         has_bias = True
+        # bias tensor's shape only has one dimension
+        if len(weight_tensors[0].shape) == 1:
+            bias_tensor, weight_tensor = weight_tensors
+        else:
+            weight_tensor, bias_tensor = weight_tensors
+
+    else:
+        weight_tensor = weight_tensors[0]
 
     # construct input args for forward
     fwd_args = [None] * 9

colossalai/auto_parallel/meta_profiler/meta_registry/linear.py

@@ -143,7 +143,7 @@ def linear_meta_info(*args, **kwargs) -> Tuple[TrainCycleItem, TrainCycleItem, L
         # NOTE: Linear don't have buffer and temp in forward and backward phase
         # the forward activation cost is the size of output_tensor, parameter cost is the size of weight_tensor
         # NOTE: currently in SPMD solver we always believe that there will be a new tensor created in forward
-        fwd_memory_cost = MemoryCost(activation=activation_size(output_tensor),
+        fwd_memory_cost = MemoryCost(activation=activation_size([input_tensor, output_tensor]),
                                      parameter=activation_size(weight_tensor),
                                      temp=0,
                                      buffer=0)

tests/test_auto_parallel/test_tensor_shard/test_metainfo/test_conv_metainfo.py

@@ -15,6 +15,16 @@ from colossalai.utils import free_port
 from tests.test_auto_parallel.test_tensor_shard.test_metainfo.utils import mem_test_for_node_strategy
 
 
+class ConvFunctionModule(nn.Module):
+
+    def __init__(self, in_channels=4, out_channels=64, kernel_size=3):
+        super().__init__()
+        self.conv_weight = nn.Parameter(torch.randn(out_channels, in_channels, kernel_size, kernel_size))
+
+    def forward(self, input):
+        return nn.functional.conv2d(input, self.conv_weight)
+
+
 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
@@ -57,5 +67,47 @@ def test_conv_meta_concrete_info_match(bias=False):
     mp.spawn(run_func_module, nprocs=world_size)
 
 
+def _conv_function_mem_test(rank, world_size, port):
+    """This function is for conv function memory test
+    Test and print real memory cost and estimated, this test will not be executed except with the tag AUTO_PARALLEL
+
+    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 = ConvFunctionModule().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 target node in computation graph
+    node_index = 2
+    # total number of target node 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_function_concrete_info_match():
+    world_size = 4
+    run_func_module = partial(_conv_function_mem_test, world_size=world_size, port=free_port())
+    mp.spawn(run_func_module, nprocs=world_size)
+
+
 if __name__ == '__main__':
-    test_conv_meta_concrete_info_match()
+    # test_conv_meta_concrete_info_match()
+    test_conv_function_concrete_info_match()

tests/test_auto_parallel/test_tensor_shard/test_metainfo/test_linear_metainfo.py

@@ -92,7 +92,7 @@ def _linear_function_mem_test(rank, world_size, port):
                                model=model,
                                device_mesh=device_mesh,
                                node_index=2,
-                               strategy_number=13,
+                               strategy_number=23,
                                input_args=[input],
                                meta_arg_names=["input"])