ruff cleanup (#4594)

* check editor config

* no editorconfig, it doesn't work

* ruff cleanups

.editorconfig

@@ -1,4 +0,0 @@
-# 2 space indentation
-[*.py]
-indent_style = space
-indent_size = 2

.github/workflows/test.yml

@@ -84,7 +84,7 @@ jobs:
     - name: Lint with ruff
       run: |
         pip3 install --upgrade --force-reinstall ruff
-        python3 -m ruff check . --preview
+        python3 -m ruff check .
     - name: Lint tinygrad with pylint
       run: python -m pylint tinygrad/
     - name: Run mypy

.pre-commit-config.yaml

@@ -9,7 +9,7 @@ repos:
         pass_filenames: false
       - id: ruff
         name: ruff
-        entry: ruff check . --preview
+        entry: ruff check .
         language: system
         always_run: true
         pass_filenames: false

ruff.toml

@@ -1,7 +1,9 @@
 indent-width = 2
+preview = true
+target-version = "py38"
 
 lint.select = [
-  "F",
+  "F",  # Pyflakes
   "W6",
   "E71",
   "E72",
@@ -10,8 +12,8 @@ lint.select = [
   # "E124",
   "E203",   # whitespace-before-punctuation
   "E272",   # multiple-spaces-before-keyword
-  # "E303",
-  # "E304",
+  "E303",
+  "E304",
   "E501",   # line-too-long
   # "E502",
   "E702",   # multiple-statements-on-one-line-semicolon

test/imported/test_indexing.py

@@ -378,7 +378,6 @@ class TestIndexing(unittest.TestCase):
     numpy_testing_assert_equal_helper(strided[rows, columns],
                       np.array([[1, 3], [11, 13]]))
 
-
     # setting values
 
     # strided is [[10, 11],

test/test_fuzz_shape_ops.py

@@ -25,7 +25,6 @@ def st_shape(draw) -> tuple[int, ...]:
   assume(prod([d for d in s if d]) <= 1024 ** 4)
   return s
 
-
 def tensors_for_shape(s:tuple[int, ...]) -> tuple[torch.tensor, Tensor]:
   x = np.arange(prod(s)).reshape(s)
   return torch.from_numpy(x), Tensor(x)
@@ -51,7 +50,6 @@ class TestShapeOps(unittest.TestCase):
     assert len(tor) == len(ten)
     assert all([np.array_equal(tor.numpy(), ten.numpy()) for (tor, ten) in zip(tor, ten)])
 
-
   @settings.get_profile(__file__)
   @given(st_shape(), st_int32, st_int32)
   def test_chunk(self, s:tuple[int, ...], dim:int, num:int):

test/test_nn.py

@@ -182,7 +182,6 @@ class TestNN(unittest.TestCase):
     np.testing.assert_allclose(gb.numpy(), torch_layer.bias.grad.numpy(), atol=5e-4, rtol=1e-5)
     np.testing.assert_allclose(gx.numpy(), torch_x.grad.numpy(), atol=5e-4, rtol=1e-5)
 
-
   @unittest.skipIf(CI and Device.DEFAULT == "WEBGPU", "runs out of memory in CI")
   def test_conv_transpose1d(self):
     BS, C1, W = 4, 16, 224//4

tinygrad/codegen/kernel.py

@@ -388,7 +388,6 @@ class Kernel:
         return True
     return False
 
-
   def apply_tensor_cores(self, use_tensor_cores=1, extra_opts:Optional[List[Opt]]=None, axis:int=0, tc_opt:int=getenv("TC_OPT")) -> bool:
     """ Attempts to apply a tensor core optimization to the kernel.  If one exists and applies properly, return true, otherwise return false.
     Tensor cores are optimized instructions that matrix multiply-accumulate across a wave of threads: D(M, N) = A(M, K) * B(K, N) + C(M, N).