[misc] update pre-commit and run all files (#4752)

* [misc] update pre-commit

* [misc] run pre-commit

* [misc] remove useless configuration files

* [misc] ignore cuda for clang-format

tests/test_fx/test_pipeline/test_hf_model/hf_utils.py

@@ -1,11 +1,12 @@
-import torch
-from torch.fx import symbolic_trace
-from torch.fx import GraphModule
-from colossalai.fx.passes.adding_split_node_pass import split_with_split_nodes_pass, balanced_split_pass
-from colossalai.fx import ColoTracer
 import inspect
 import random
+
 import numpy as np
+import torch
+from torch.fx import GraphModule
+
+from colossalai.fx import ColoTracer
+from colossalai.fx.passes.adding_split_node_pass import balanced_split_pass, split_with_split_nodes_pass
 
 MANUAL_SEED = 0
 random.seed(MANUAL_SEED)
@@ -26,7 +27,7 @@ def split_model_and_compare_output(model, data_gen):
     # tracing model
     tracer = ColoTracer()
     try:
-        meta_args = {k: v.to('meta') for k, v in kwargs.items()}
+        meta_args = {k: v.to("meta") for k, v in kwargs.items()}
         graph = tracer.trace(root=model, meta_args=meta_args)
     except Exception as e:
         raise RuntimeError(f"Failed to trace {model.__class__.__name__}, error: {e}")
@@ -49,16 +50,16 @@ def split_model_and_compare_output(model, data_gen):
         output_part1 = model_part1(output_part0)
     else:
         if len(output_part0) > len(sig.parameters):
-            output_part0 = output_part0[:len(sig.parameters)]
+            output_part0 = output_part0[: len(sig.parameters)]
         output_part1 = model_part1(*output_part0)
 
     # get output tensor from HFOutput datastructure
-    if 'logits' in output:
-        output_to_compare = output['logits']
-    elif 'prediction_logits' in output:
-        output_to_compare = output['prediction_logits']
+    if "logits" in output:
+        output_to_compare = output["logits"]
+    elif "prediction_logits" in output:
+        output_to_compare = output["prediction_logits"]
     else:
-        output_to_compare = output['last_hidden_state']
+        output_to_compare = output["last_hidden_state"]
 
     # compare output
     if isinstance(output_part1, torch.Tensor):

tests/test_fx/test_pipeline/test_hf_model/test_albert.py

@@ -7,7 +7,7 @@ BATCH_SIZE = 2
 SEQ_LENGHT = 16
 
 
-@pytest.mark.skip('balance split v2 is not ready')
+@pytest.mark.skip("balance split v2 is not ready")
 def test_single_sentence_albert():
     MODEL_LIST = [
         transformers.AlbertModel,
@@ -17,12 +17,14 @@ def test_single_sentence_albert():
         transformers.AlbertForTokenClassification,
     ]
 
-    config = transformers.AlbertConfig(vocab_size=100,
-                                       embedding_size=128,
-                                       hidden_size=128,
-                                       num_hidden_layers=2,
-                                       num_attention_heads=4,
-                                       intermediate_size=256)
+    config = transformers.AlbertConfig(
+        vocab_size=100,
+        embedding_size=128,
+        hidden_size=128,
+        num_hidden_layers=2,
+        num_attention_heads=4,
+        intermediate_size=256,
+    )
 
     def data_gen():
         input_ids = torch.zeros((BATCH_SIZE, SEQ_LENGHT), dtype=torch.int64)
@@ -36,5 +38,5 @@ def test_single_sentence_albert():
         split_model_and_compare_output(model, data_gen)
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     test_single_sentence_albert()

tests/test_fx/test_pipeline/test_hf_model/test_bert.py

@@ -7,7 +7,7 @@ BATCH_SIZE = 2
 SEQ_LENGHT = 16
 
 
-@pytest.mark.skip('balance split v2 is not ready')
+@pytest.mark.skip("balance split v2 is not ready")
 def test_single_sentence_bert():
     MODEL_LIST = [
         transformers.BertModel,
@@ -18,11 +18,9 @@ def test_single_sentence_bert():
         transformers.BertForTokenClassification,
     ]
 
-    config = transformers.BertConfig(vocab_size=100,
-                                     hidden_size=128,
-                                     num_hidden_layers=4,
-                                     num_attention_heads=4,
-                                     intermediate_size=256)
+    config = transformers.BertConfig(
+        vocab_size=100, hidden_size=128, num_hidden_layers=4, num_attention_heads=4, intermediate_size=256
+    )
 
     def data_gen():
         input_ids = torch.zeros((BATCH_SIZE, SEQ_LENGHT), dtype=torch.int64)
@@ -36,5 +34,5 @@ def test_single_sentence_bert():
         split_model_and_compare_output(model, data_gen)
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     test_single_sentence_bert()

tests/test_fx/test_pipeline/test_hf_model/test_gpt.py

@@ -9,14 +9,14 @@ NUM_EPOCHS = 2
 NUM_CHUNKS = 1
 
 
-@pytest.mark.skip('balance split v2 is not ready')
+@pytest.mark.skip("balance split v2 is not ready")
 def test_gpt():
     MODEL_LIST = [
         transformers.GPT2Model,
         transformers.GPT2LMHeadModel,
         transformers.GPT2DoubleHeadsModel,
         transformers.GPT2ForTokenClassification,
-    # transformers.GPT2ForSequenceClassification, # not supported yet
+        # transformers.GPT2ForSequenceClassification, # not supported yet
     ]
     config = transformers.GPT2Config(n_position=64, n_layer=4, n_head=8)
 
@@ -32,5 +32,5 @@ def test_gpt():
         split_model_and_compare_output(model, data_gen)
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     test_gpt()

tests/test_fx/test_pipeline/test_hf_model/test_opt.py

@@ -7,7 +7,7 @@ BATCH_SIZE = 1
 SEQ_LENGHT = 16
 
 
-@pytest.mark.skip('balance split v2 is not ready')
+@pytest.mark.skip("balance split v2 is not ready")
 def test_opt():
     MODEL_LIST = [
         transformers.OPTModel,
@@ -27,5 +27,5 @@ def test_opt():
         split_model_and_compare_output(model, data_gen)
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     test_opt()

tests/test_fx/test_pipeline/test_hf_model/test_t5.py

@@ -7,7 +7,7 @@ BATCH_SIZE = 1
 SEQ_LENGHT = 16
 
 
-@pytest.mark.skip('balance split v2 is not ready')
+@pytest.mark.skip("balance split v2 is not ready")
 def test_t5():
     MODEL_LIST = [
         transformers.T5Model,
@@ -39,5 +39,5 @@ def test_t5():
         split_model_and_compare_output(model, data_gen_func)
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     test_t5()

tests/test_fx/test_pipeline/test_timm_model/test_timm.py

@@ -4,9 +4,8 @@ import torch
 from timm_utils import split_model_and_compare_output
 
 
-@pytest.mark.skip('balance split v2 is not ready')
+@pytest.mark.skip("balance split v2 is not ready")
 def test_timm_models_without_control_flow():
-
     MODEL_LIST = [
         tm.resnest.resnest50d,
         tm.beit.beit_base_patch16_224,
@@ -25,24 +24,28 @@ def test_timm_models_without_control_flow():
         split_model_and_compare_output(model, data)
 
 
-@pytest.mark.skip('balance split v2 is not ready')
+@pytest.mark.skip("balance split v2 is not ready")
 def test_timm_models_with_control_flow():
     torch.backends.cudnn.deterministic = True
 
     MODEL_LIST_WITH_CONTROL_FLOW = [
-        tm.convnext.convnext_base, tm.vgg.vgg11, tm.dpn.dpn68, tm.densenet.densenet121, tm.rexnet.rexnet_100,
-        tm.swin_transformer.swin_base_patch4_window7_224
+        tm.convnext.convnext_base,
+        tm.vgg.vgg11,
+        tm.dpn.dpn68,
+        tm.densenet.densenet121,
+        tm.rexnet.rexnet_100,
+        tm.swin_transformer.swin_base_patch4_window7_224,
     ]
 
     data = torch.rand(2, 3, 224, 224)
 
-    meta_args = {'x': data.to('meta')}
+    meta_args = {"x": data.to("meta")}
 
     for model_cls in MODEL_LIST_WITH_CONTROL_FLOW:
         model = model_cls()
         split_model_and_compare_output(model, data, meta_args)
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     test_timm_models_without_control_flow()
     test_timm_models_with_control_flow()

tests/test_fx/test_pipeline/test_timm_model/timm_utils.py

@@ -1,11 +1,12 @@
-import torch
-from torch.fx import symbolic_trace
-from torch.fx import GraphModule
-from colossalai.fx.passes.adding_split_node_pass import split_with_split_nodes_pass, balanced_split_pass
-from colossalai.fx import ColoTracer
 import inspect
 import random
+
 import numpy as np
+import torch
+from torch.fx import GraphModule
+
+from colossalai.fx import ColoTracer
+from colossalai.fx.passes.adding_split_node_pass import balanced_split_pass, split_with_split_nodes_pass
 
 MANUAL_SEED = 0
 random.seed(MANUAL_SEED)
@@ -46,6 +47,6 @@ def split_model_and_compare_output(model, data, meta_args=None):
         output_part1 = model_part1(output_part0)
     else:
         if len(output_part0) > len(sig.parameters):
-            output_part0 = output_part0[:len(sig.parameters)]
+            output_part0 = output_part0[: len(sig.parameters)]
         output_part1 = model_part1(*output_part0)
     assert output.equal(output_part1)

tests/test_fx/test_pipeline/test_topo/test_topo.py

@@ -7,7 +7,7 @@ BATCH_SIZE = 1
 SEQ_LENGHT = 16
 
 
-@pytest.mark.skip('ShapeProp is not compatible with PyTorch 1.11.0')
+@pytest.mark.skip("ShapeProp is not compatible with PyTorch 1.11.0")
 def test_opt():
     MODEL_LIST = [
         MLP,
@@ -15,10 +15,7 @@ def test_opt():
     ]
 
     CONFIGS = [
-        {
-            'dim': 10,
-            'layers': 12
-        },
+        {"dim": 10, "layers": 12},
         transformers.OPTConfig(vocab_size=100, hidden_size=128, num_hidden_layers=4, num_attention_heads=4),
     ]
 
@@ -45,5 +42,5 @@ def test_opt():
         check_topo(top_mod, topo)
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     test_opt()

tests/test_fx/test_pipeline/test_topo/topo_utils.py

@@ -6,7 +6,7 @@ from torch.fx import GraphModule
 
 from colossalai.fx import ColoTracer
 from colossalai.fx.passes.adding_split_node_pass import balanced_split_pass, split_with_split_nodes_pass
-from colossalai.legacy.pipeline.middleware import Partition, PartitionInputVal, PartitionOutputVal, Topo
+from colossalai.legacy.pipeline.middleware import Partition, Topo
 from colossalai.legacy.pipeline.middleware.adaptor import get_fx_topology
 
 MANUAL_SEED = 0
@@ -16,11 +16,10 @@ torch.manual_seed(MANUAL_SEED)
 
 
 class MLP(torch.nn.Module):
-
     def __init__(self, config={}):
         super().__init__()
-        dim = config['dim']
-        layers = config['layers']
+        dim = config["dim"]
+        layers = config["layers"]
         self.layers = torch.nn.ModuleList()
 
         for _ in range(layers):
@@ -41,7 +40,7 @@ def split_model_and_get_DAG(model, data_gen):
     # tracing model
     tracer = ColoTracer()
     try:
-        meta_args = {k: v.to('meta') for k, v in kwargs.items()}
+        meta_args = {k: v.to("meta") for k, v in kwargs.items()}
         graph = tracer.trace(root=model, meta_args=meta_args)
     except Exception as e:
         raise RuntimeError(f"Failed to trace {model.__class__.__name__}, error: {e}")
@@ -55,7 +54,7 @@ def split_model_and_get_DAG(model, data_gen):
     topo = get_fx_topology(top_module)
     for submodule in split_submodules:
         if isinstance(submodule, torch.fx.GraphModule):
-            setattr(submodule, '_topo', topo)
+            setattr(submodule, "_topo", topo)
 
     return top_module, split_submodules[0]._topo
 
@@ -64,7 +63,7 @@ def check_input(top_module, input_partition: Partition):
     partition_output = input_partition.get_output_vals()
     arg_pos = 0
     for node in top_module.graph.nodes:
-        if node.op == 'placeholder':
+        if node.op == "placeholder":
             cur_checkee = partition_output[arg_pos]
             to_partition_and_offset = cur_checkee.get()
             assert len(to_partition_and_offset) == len(node.users.keys())
@@ -80,7 +79,7 @@ def check_submod(top_module, part_id, mid_partition: Partition):
     cnt = 1
     cur_node = None
     for node in top_module.graph.nodes:
-        if node.name.startswith('submod'):
+        if node.name.startswith("submod"):
             cnt += 1
         if cnt == part_id:
             cur_node = node

tests/test_fx/test_pipeline/test_torchvision/test_torchvision.py

@@ -19,14 +19,21 @@ torch.manual_seed(MANUAL_SEED)
 torch.backends.cudnn.deterministic = True
 
 
-@pytest.mark.skip('balance split v2 is not ready')
+@pytest.mark.skip("balance split v2 is not ready")
 def test_torchvision_models():
     MODEL_LIST = [
-        tm.vgg11, tm.resnet18, tm.densenet121, tm.mobilenet_v3_small, tm.resnext50_32x4d, tm.wide_resnet50_2,
-        tm.regnet_x_16gf, tm.efficientnet_b0, tm.mnasnet0_5
+        tm.vgg11,
+        tm.resnet18,
+        tm.densenet121,
+        tm.mobilenet_v3_small,
+        tm.resnext50_32x4d,
+        tm.wide_resnet50_2,
+        tm.regnet_x_16gf,
+        tm.efficientnet_b0,
+        tm.mnasnet0_5,
     ]
 
-    if version.parse(torchvision.__version__) >= version.parse('0.12.0'):
+    if version.parse(torchvision.__version__) >= version.parse("0.12.0"):
         MODEL_LIST.extend([tm.vit_b_16, tm.convnext_small])
 
     tracer = ColoTracer()
@@ -57,10 +64,10 @@ def test_torchvision_models():
             output_part1 = model_part1(output_part0)
         else:
             if len(output_part0) > len(sig.parameters):
-                output_part0 = output_part0[:len(sig.parameters)]
+                output_part0 = output_part0[: len(sig.parameters)]
             output_part1 = model_part1(*output_part0)
         assert output.equal(output_part1)
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     test_torchvision_models()