fix typo for usort config in pyproject.toml

Summary:
The `usort` config in `pyproject.toml` has no effect due to a typo. Fixing the typo make `usort` do more and generate the changes in the PR. Except `pyproject.toml`, all changes are generated by `lintrunner -a --take UFMT --all-files`.

X-link: pytorch/pytorch#127126
Approved by: https://github.com/kit1980

Reviewed By: PaliC

Differential Revision: D57894793

fbshipit-source-id: 6af4fcb5eae8fd114024359beb3fae7f3928de2e

userbenchmark/dynamo/dynamobench/common.py

@@ -36,20 +36,21 @@
  Type,
  TYPE_CHECKING,
 )
-
 from typing_extensions import Self
 from unittest.mock import MagicMock
 
 import numpy as np
 import pandas as pd
 import psutil
+from scipy.stats import gmean, ttest_ind
+from tqdm.auto import tqdm, trange
+
 import torch
 import torch._dynamo
 import torch._dynamo.utils
 import torch._export
 import torch.distributed
 import torch.multiprocessing as mp
-from scipy.stats import gmean, ttest_ind
 from torch._C import _has_cuda as HAS_CUDA, _has_xpu as HAS_XPU
 from torch._dynamo.profiler import fx_insert_profiling, Profiler
 from torch._dynamo.testing import (
@@ -59,8 +60,6 @@
  same,
 )
 
-from tqdm.auto import tqdm, trange
-
 try:
  from torch._dynamo.utils import (
  clone_inputs,
@@ -74,15 +73,14 @@
  graph_break_reasons,
  maybe_enable_compiled_autograd,
  )
+
 import torch._functorch.config
 from torch._functorch.aot_autograd import set_model_name
 from torch._inductor import config as inductor_config, metrics
 from torch._subclasses.fake_tensor import FakeTensorMode
-
 from torch.utils import _pytree as pytree
 from torch.utils._pytree import tree_map, tree_map_only
 
-
 try:
  import torch_xla
  import torch_xla.core.xla_model as xm
@@ -2343,17 +2341,17 @@ def get_benchmark_indices(self, length):
 
  def get_fsdp_auto_wrap_policy(self, model_name: str):
  from diffusers.models.transformer_2d import Transformer2DModel
-
- from torch.distributed.fsdp.wrap import (
- ModuleWrapPolicy,
- size_based_auto_wrap_policy,
- )
  from torchbenchmark.models.nanogpt.model import Block
  from transformers.models.llama.modeling_llama import LlamaDecoderLayer
 
  from transformers.models.t5.modeling_t5 import T5Block
  from transformers.models.whisper.modeling_whisper import WhisperEncoderLayer
 
+ from torch.distributed.fsdp.wrap import (
+ ModuleWrapPolicy,
+ size_based_auto_wrap_policy,
+ )
+
  # handcrafted wrap policy
  MODEL_FSDP_WRAP = {
  "stable_diffusion_unet": (Transformer2DModel,),

userbenchmark/dynamo/dynamobench/torchbench.py

@@ -10,9 +10,10 @@
 from collections import namedtuple
 from os.path import abspath, exists
 
-import torch
 import yaml
 
+import torch
+
 try:
  from .common import BenchmarkRunner, main
 except ImportError: