[autoparallel] Add metainfo support for F.linear (#1987)

* [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

colossalai/auto_parallel/meta_profiler/meta_registry/linear.py

@@ -19,10 +19,13 @@ from ..registry import meta_register
 __all__ = ['linear_meta_info']
 
 
+@meta_register.register(torch.nn.functional.linear)
 @meta_register.register(torch.nn.Linear)
 def linear_meta_info(*args, **kwargs) -> Tuple[TrainCycleItem, TrainCycleItem, List[torch.Tensor]]:
-    """torch.nn.Linear meta info generator
-    The atens graph of torch.nn.Linear with bias is
+    """torch.nn.Linear & torch.nn.functional.linear meta info generator
+    NOTE: currently we separate the bias part from the biased linear ops, we will consider the memory consumption in add metainfo generator,
+    but we will hold the bias mechanism in the linear metainfo generator for future use.
+
     graph():
     %input_2 : [#users=2] = placeholder[target=placeholder](default=)
     %addmm_default : [#users=1] = call_function[target=torch.ops.aten.addmm.default](args = (None, %input_2, None), kwargs = {})
@@ -65,7 +68,7 @@ def linear_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]
 
     # process the dimension of input and output
     if len(input_tensor.shape) > 2:
@@ -76,9 +79,14 @@ def linear_meta_info(*args, **kwargs) -> Tuple[TrainCycleItem, TrainCycleItem, L
         output_tensor: torch.Tensor
         output_tensor = output_tensor.view(-1, output_tensor.shape[-1])
 
-    if len(args) == 4:
-        bias_tensor = next(filter(lambda x: x.name == 'bias', args)).data
+    if len(weight_tensors) > 1:
         has_bias = True
+        if len(weight_tensors[0].shape) == 2:
+            weight_tensor, bias_tensor = weight_tensors
+        else:
+            bias_tensor, weight_tensor = weight_tensors
+    else:
+        weight_tensor = weight_tensors[0]
 
     if has_bias:
         # calculate cost with bias

colossalai/auto_parallel/meta_profiler/metainfo.py

@@ -92,8 +92,12 @@ class MetaInfo:
         Compute meta info based on sharding strategy and the given target function.
         """
 
-        assert meta_register.has(self._target.__class__), f'{self._target.__class__} not found in the meta registry'
-        meta_func = meta_register.get(self._target.__class__)
+        try:
+            # module
+            meta_func = meta_register.get(self._target.__class__)
+        except:
+            # function
+            meta_func = meta_register.get(self._target)
 
         # construct args for meta_func
         args = [self.compute_sharded_tensor(k, v) for k, v in self._strategy.sharding_specs.items()]

tests/test_auto_parallel/test_tensor_shard/test_metainfo/test_linear_metainfo.py

@@ -20,7 +20,17 @@ if torch.__version__ >= '1.12.0':
     from colossalai.auto_parallel.meta_profiler import MetaInfo, meta_register
 
 
-def _linear_module_mem_test(rank, bias, world_size, port):
+class MyModule(nn.Module):
+
+    def __init__(self, in_features=64, out_features=128):
+        super().__init__()
+        self.fc_weight = nn.Parameter(torch.randn(out_features, in_features))
+
+    def forward(self, input):
+        return nn.functional.linear(input, self.fc_weight)
+
+
+def _linear_module_mem_test(rank, world_size, port):
     """This function is for linear memory test
     Test and print real memory cost and estimated, this test will not be executed except with the tag AUTO_PARALLEL
 
@@ -32,7 +42,7 @@ def _linear_module_mem_test(rank, bias, world_size, port):
     """
     disable_existing_loggers()
     launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
-    model = nn.Sequential(nn.Linear(64, 128, bias=bias)).cuda()
+    model = nn.Sequential(nn.Linear(64, 128, bias=False)).cuda()
     input = torch.rand(8, 8, 16, 64).cuda()
     input.requires_grad = True
     physical_mesh_id = torch.arange(0, 4)
@@ -52,11 +62,50 @@ def _linear_module_mem_test(rank, bias, world_size, port):
 @run_on_environment_flag(name='AUTO_PARALLEL')
 @pytest.mark.dist
 @rerun_if_address_is_in_use()
-def test_linear_meta_concrete_info_match(bias=False):
+def test_linear_module_meta_concrete_info_match():
     world_size = 4
-    run_func_module = partial(_linear_module_mem_test, bias=bias, world_size=world_size, port=free_port())
+    run_func_module = partial(_linear_module_mem_test, world_size=world_size, port=free_port())
+    mp.spawn(run_func_module, nprocs=world_size)
+
+
+def _linear_function_mem_test(rank, world_size, port):
+    """This function is for linear 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 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')
+    model = MyModule().cuda()
+    input = torch.rand(8, 8, 16, 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)
+
+    # memory test
+    mem_test_for_node_strategy(rank=rank,
+                               model=model,
+                               device_mesh=device_mesh,
+                               node_index=2,
+                               strategy_number=13,
+                               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_linear_function_meta_concrete_info_match():
+    world_size = 4
+    run_func_module = partial(_linear_function_mem_test, world_size=world_size, port=free_port())
     mp.spawn(run_func_module, nprocs=world_size)
 
 
 if __name__ == '__main__':
-    test_linear_meta_concrete_info_match()
+    # test_linear_module_meta_concrete_info_match()
+    test_linear_function_meta_concrete_info_match()

tests/test_auto_parallel/test_tensor_shard/test_metainfo/utils.py

@@ -7,6 +7,7 @@ from torch.fx import GraphModule
 
 from colossalai.auto_parallel.passes.runtime_apply_pass import runtime_apply_pass
 from colossalai.auto_parallel.passes.runtime_preparation_pass import runtime_preparation_pass
+from colossalai.auto_parallel.tensor_shard.sharding_strategy import OperationDataType
 from colossalai.auto_parallel.tensor_shard.solver import SolverOptions, StrategiesConstructor
 from colossalai.device.device_mesh import DeviceMesh
 from colossalai.fx.tracer.tracer import ColoTracer
@@ -49,8 +50,9 @@ def mem_test_for_node_strategy(rank: int,
 
         # construct the strategy for the output node
         placeholder_strategy = list(graph.nodes)[-1].strategies_vector[0]
+
         output_key = next(key for key in target_node.strategies_vector[strategy_index].sharding_specs.keys()
-                          if key in placeholder_strategy.sharding_specs)
+                          if key.type == OperationDataType.OUTPUT)
         placeholder_strategy.sharding_specs[output_key] = target_node.strategies_vector[strategy_index].sharding_specs[
             output_key]
 
@@ -104,8 +106,12 @@ def mem_test_for_node_strategy(rank: int,
             )
 
             # estimated memory
-            metainfo = MetaInfo(target_node.strategies_vector[strategy_index],
-                                target_node.graph.owning_module.get_submodule(target_node.target))
+            if target_node.op == "call_module":
+                metainfo = MetaInfo(target_node.strategies_vector[strategy_index],
+                                    target_node.graph.owning_module.get_submodule(target_node.target))
+            else:
+                metainfo = MetaInfo(target_node.strategies_vector[strategy_index], target_node.target)
+
             print("estimated memory:")
             print(
                 f"forward activation: {metainfo.memory_cost.fwd.activation / 1024} kb, forward param: {metainfo.memory_cost.fwd.parameter / 1024} kb"