names shadowing builtins (#5179)

Co-authored-by: chenyu <chenyu@fastmail.com>

ruff.toml

@@ -26,6 +26,7 @@ lint.select = [
   "C416",   # unnecessary-comprehension
   "RET506", # superfluous-else-raise
   "RET507", # superfluous-else-continue
+  "A",      # builtin-variable-shadowing, builtin-argument-shadowing, builtin-attribute-shadowing
 ]
 
 line-length = 150

test/external/external_test_opt.py

@@ -25,7 +25,7 @@ class CLCache:
     capturing.append(self)
     print("cache: entering")
     return self
-  def __exit__(self, type, value, traceback):
+  def __exit__(self, _type, value, traceback):
     capturing.clear()
     print(f"cache: exiting with size {self.count}", f"allowed {self.allowed}" if self.allowed is not None else "")
     if self.allowed is not None:

test/external/external_test_whisper_librispeech.py

@@ -51,12 +51,12 @@ def run_evaluation(model_name, tinygrad_expected_wer, reference_wer):
 
 class LibriSpeech(torch.utils.data.Dataset):
   def __init__(self):
-    dir = pathlib.Path(__file__).parent.parent.parent / "extra" / "datasets" / "librispeech"
-    if not os.path.exists(dir):
-      os.makedirs(dir)
+    folder = pathlib.Path(__file__).parent.parent.parent / "extra" / "datasets" / "librispeech"
+    if not os.path.exists(folder):
+      os.makedirs(folder)
 
     self.dataset = torchaudio.datasets.LIBRISPEECH(
-      root=dir,
+      root=folder,
       url="test-clean",
       download=True,
     )

test/test_nn.py

@@ -16,14 +16,14 @@ class TestNN(unittest.TestCase):
   @unittest.skipIf(Device.DEFAULT == "WEBGPU", "no int64 on WebGPU")
   def test_sparse_cat_cross_entropy(self):
     # create in tinygrad
-    input = Tensor.randn(5, 5)
+    input_tensor = Tensor.randn(5, 5)
     target = Tensor([0, 0, 0, 1, 2])  # torch doesn't support target=-1
-    torch_input = torch.tensor(input.numpy())
+    torch_input = torch.tensor(input_tensor.numpy())
     torch_target = torch.tensor(target.numpy(), dtype=torch.long)
 
     for smoothing in [0.0, 0.1, 0.5, 1.0]:
       for ignore_index in [-1, 0, 2]:
-        loss = input.sparse_categorical_crossentropy(target, label_smoothing=smoothing, ignore_index=ignore_index)
+        loss = input_tensor.sparse_categorical_crossentropy(target, label_smoothing=smoothing, ignore_index=ignore_index)
         torch_loss = torch.nn.CrossEntropyLoss(reduction='mean', label_smoothing=smoothing, ignore_index=ignore_index)(torch_input, torch_target)
         np.testing.assert_allclose(loss.numpy(), torch_loss.detach().numpy(), atol=1e-5, rtol=1e-6)
 

test/test_uops.py

@@ -12,10 +12,10 @@ from tinygrad.engine.realize import CompiledRunner, lower_schedule_item
 from tinygrad.codegen.uops import UOps, UOp, UOpGraph
 from test.helpers import is_dtype_supported
 
-def _uops_to_prg(uops_list, print=False):
+def _uops_to_prg(uops_list, print_uops=False):
   uops = UOpGraph(uops_list)
   src = Device[Device.DEFAULT].renderer.render("test", uops)
-  if print: uops.print()
+  if print_uops: uops.print()
   has_local = Device[Device.DEFAULT].renderer.has_local
   return CompiledRunner(Program("test", src, Device.DEFAULT, [1,1,1] if has_local else None, [1,1,1] if has_local else None, uops=uops))
 
@@ -59,7 +59,7 @@ def _test_uops_result(output_dtype, uops, res):
   # res = output_fn(uops)
   out = uop(uops, UOps.STORE, None, (buf_store, uop(uops, UOps.CONST, dtypes.int32, (), 0), res))
   buf = Buffer(Device.DEFAULT, 1, output_dtype).allocate()
-  prg = _uops_to_prg([out], print=True)
+  prg = _uops_to_prg([out], print_uops=True)
   prg.exec([buf])
   ret = np.empty(1, _to_np_dtype(output_dtype))
   buf.copyout(ret.data)

test/testextra/test_export_model.py

@@ -31,9 +31,9 @@ class TextModelExport(unittest.TestCase):
 
   def test_multi_output_model_export(self):
     model = MockMultiOutputModel()
-    input = Tensor.rand(2,2)
-    outputs = model(input)
-    prg, _, out_sizes, _ = export_model(model, "", input)
+    input_tensor = Tensor.rand(2,2)
+    outputs = model(input_tensor)
+    prg, _, out_sizes, _ = export_model(model, "", input_tensor)
     prg = json.loads(prg)
 
     assert len(outputs) == len(prg["outputs"]) == len(out_sizes), f"Model and exported outputs don't match: mdl={len(outputs)}, prg={len(prg['outputs'])}, inp_sizes={len(out_sizes)}"  # noqa: E501

test/unit/test_shapetracker.py

@@ -7,10 +7,10 @@ from tinygrad.shape.symbolic import Variable, NumNode
 from itertools import product
 
 def shapetracker_getitem(st, val):
-  locals = {"idx0": val, "valid": 1}
+  _locals = {"idx0": val, "valid": 1}
   idx, valid = st.reshape((st.size,)).expr_idxs()
-  exec(f"valid={valid.render()};idx0={idx.render()}", None, locals)
-  return locals["idx0"] if locals["valid"] else -1
+  exec(f"valid={valid.render()};idx0={idx.render()}", None, _locals)
+  return _locals["idx0"] if _locals["valid"] else -1
 
 class CheckingShapeTracker:
   def __init__(self, shape):