[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_autochunk/test_autochunk_transformer/benchmark_autochunk_transformer.py

@@ -1,5 +1,5 @@
 import time
-from typing import Any, Dict, List
+from typing import Any
 
 import torch
 import torch.fx
@@ -64,8 +64,10 @@ def _benchmark_autochunk_gpt_gm(
     para_mem = float(parameter_size(model)) / 1024**2 * 6
     act_mem = _benchmark_memory(gm, inputs)
     speed = _benchmark_speed(gm, inputs)
-    print("gpt autochunk, time: %.4fs, act mem: %.2fMB, para mem: %.2fMB, all mem: %.2fMB" %
-          (speed, act_mem, para_mem, act_mem + para_mem))
+    print(
+        "gpt autochunk, time: %.4fs, act mem: %.2fMB, para mem: %.2fMB, all mem: %.2fMB"
+        % (speed, act_mem, para_mem, act_mem + para_mem)
+    )
 
 
 def _benchmark_autochunk_gpt_origin(
@@ -86,8 +88,10 @@ def _benchmark_autochunk_gpt_origin(
     para_mem = float(parameter_size(model)) / 1024**2 * 6
     act_mem = _benchmark_memory(model, inputs)
     speed = _benchmark_speed(model, inputs)
-    print("gpt origin, time: %.4fs, act mem: %.2fMB, para mem: %.2fMB, all mem: %.2fMB" %
-          (speed, act_mem, para_mem, act_mem + para_mem))
+    print(
+        "gpt origin, time: %.4fs, act mem: %.2fMB, para mem: %.2fMB, all mem: %.2fMB"
+        % (speed, act_mem, para_mem, act_mem + para_mem)
+    )
     return act_mem
 
 
@@ -115,6 +119,7 @@ def _benchmark_speed(model, inputs, loop=5):
 
 def benchmark_autochunk_gpt(batch=1, seq=512, n_embd=768, n_head=12):
     from test_autochunk_gpt import GPT2Config, GPT2Model, get_data
+
     model = GPT2Model
     config = GPT2Config(n_embd=n_embd, n_positions=seq, n_layer=2, n_head=n_head)
     model = model(config=config)
@@ -125,7 +130,7 @@ def benchmark_autochunk_gpt(batch=1, seq=512, n_embd=768, n_head=12):
         try:
             _benchmark_autochunk_gpt_gm(model, get_data(shape), max_mem * ratio)
         except RuntimeError as e:
-            if e.args[0] == 'Search failed. Try a larger memory threshold.':
+            if e.args[0] == "Search failed. Try a larger memory threshold.":
                 break
         except Exception as e:
             raise e

tests/test_autochunk/test_autochunk_transformer/test_autochunk_gpt.py

@@ -5,6 +5,7 @@ import torch
 
 try:
     from transformers import GPT2Config, GPT2Model
+
     MODELS = [GPT2Model]
     HAS_REPO = True
 except:
@@ -52,13 +53,15 @@ def test_autochunk_gpt(model, shape, max_memory):
 
 
 if __name__ == "__main__":
-    run_test(rank=0,
-             data=get_data((BATCH_SIZE, SEQ_LENGTH)),
-             max_memory=None,
-             model=GPT2Model,
-             config=GPT2Config(n_embd=96, n_position=SEQ_LENGTH, n_layer=2, n_head=4),
-             print_code=False,
-             print_est_mem=False,
-             print_mem=False,
-             print_progress=False,
-             eval_mem=False)
+    run_test(
+        rank=0,
+        data=get_data((BATCH_SIZE, SEQ_LENGTH)),
+        max_memory=None,
+        model=GPT2Model,
+        config=GPT2Config(n_embd=96, n_position=SEQ_LENGTH, n_layer=2, n_head=4),
+        print_code=False,
+        print_est_mem=False,
+        print_mem=False,
+        print_progress=False,
+        eval_mem=False,
+    )

tests/test_autochunk/test_autochunk_transformer/test_autochunk_transformer_utils.py

@@ -38,11 +38,9 @@ def assert_codegen_run(
     meta_tensors = [meta_args[i] if i in meta_args else concrete_args[i] for i in sequence]
     meta_tensors = [MetaTensor(i, fake_device="cuda:0") if isinstance(i, torch.Tensor) else i for i in meta_tensors]
     interp.propagate(*meta_tensors)
-    codegen = AutoChunkCodeGen(meta_graph,
-                               max_memory=max_memory,
-                               print_mem=print_est_mem,
-                               print_progress=print_progress,
-                               eval_mem=eval_mem)
+    codegen = AutoChunkCodeGen(
+        meta_graph, max_memory=max_memory, print_mem=print_est_mem, print_progress=print_progress, eval_mem=eval_mem
+    )
     chunks = codegen.chunk_infos
 
     # trace and recompile
@@ -85,9 +83,9 @@ def assert_allclose(out_model: Any, out_gm: Any) -> None:
     assert allclose for out
     """
     if isinstance(out_model, torch.Tensor):
-        assert torch.allclose(out_model, out_gm,
-                              atol=1e-4), "fx_out doesn't comply with original output, diff is %.2e" % torch.mean(
-                                  torch.abs(out_model - out_gm))
+        assert torch.allclose(
+            out_model, out_gm, atol=1e-4
+        ), "fx_out doesn't comply with original output, diff is %.2e" % torch.mean(torch.abs(out_model - out_gm))
     elif isinstance(out_model, dict):
         for k in out_model.keys():
             assert_allclose(out_model[k], out_gm[k])
@@ -123,19 +121,21 @@ def run_test(
     )
 
     # build model and input
-    chunks = assert_codegen_run(model,
-                                data=data,
-                                max_memory=max_memory,
-                                print_code=print_code,
-                                print_est_mem=print_est_mem,
-                                print_mem=print_mem,
-                                print_progress=print_progress,
-                                eval_mem=eval_mem)
+    chunks = assert_codegen_run(
+        model,
+        data=data,
+        max_memory=max_memory,
+        print_code=print_code,
+        print_est_mem=print_est_mem,
+        print_mem=print_mem,
+        print_progress=print_progress,
+        eval_mem=eval_mem,
+    )
 
     if get_chunk_target is not None:
         chunk_found = [i["region"] for i in chunks]
         chunk_target = get_chunk_target()[max_memory]
-        assert (chunk_found == chunk_target), "found regions %s doesn't equal target regions %s" % (
+        assert chunk_found == chunk_target, "found regions %s doesn't equal target regions %s" % (
             str(chunk_found),
             str(chunk_target),
         )