flake8 (#1323)

* flake8: Ignore frequent violations, correct infrequent ones

* Ignore some rules in test

* Reorder test ignores

* Lint test + main

* EOF indent

* Include all E71,E72 errors

* Test the failing case in CI

* Revert "Test the failing case in CI"

This reverts commit 110add0a70.

* Push to test!
This reverts commit f317532779.

* ok back to passing
This reverts commit ba5052685f.

* Prove that CI fails when formatting is incorrect.

* Fix formatting

* Remove duplicitous E117 rule

* Use flake8 config for precommit

---------

Co-authored-by: waifairer <waifairer@gmail.com>

.flake8

@@ -0,0 +1,8 @@
+[flake8] # https://flake8.pycqa.org/en/6.0.0/user/options.html#cmdoption-flake8-select
+filename =
+    */tinygrad/*.py,
+    */test/*.py
+select=F,W6,E71,E72,E112,E113,E124,E203,E272,E303,E304,E502,E702,E703,E731,W191
+indent-size=2
+per-file-ignores =
+    test/*: F401, F403, F405, F541, E722, E731, F811, F821, F841

.github/workflows/test.yml

@@ -30,7 +30,7 @@ jobs:
     - name: Lint with pylint
       run: python -m pylint --disable=all -e W0311 -e C0303 --jobs=0 --indent-string='  ' **/*.py
     - name: Lint with flake8
-      run: flake8 tinygrad/ --indent-size=2 --select=F,E112,E113,E203,E304,E502,E702,E703,E71,E72,E731,W191,W6 --statistics -j4
+      run: flake8 --statistics -j4
     - name: Lint tinygrad with pylint
       run: pylint tinygrad/
     - name: Run mypy

.pre-commit-config.yaml

@@ -9,7 +9,7 @@ repos:
         pass_filenames: false
       - id: flake8
         name: flake8
-        entry: flake8 tinygrad/ --indent-size=2 --select=F,E112,E113,E203,E304,E502,E702,E703,E71,E72,E731,W191,W6 --statistics -j4
+        entry: flake8 --statistics -j4
         language: system
         always_run: true
         pass_filenames: false

test/external/external_test_opt.py

@@ -79,7 +79,7 @@ class TestInferenceMinKernels(unittest.TestCase):
     img = Tensor.randn(1, 3, 224, 224)
     with CLCache(223): # NOTE: this is way too high
       out = model.forward(img)
-      assert len(GlobalCounters.cache) == 0, f"ViT prerealized?"
+      assert len(GlobalCounters.cache) == 0, "ViT prerealized?"
       out.realize()
 
   def test_llama(self):

test/test_custom_function.py

@@ -34,7 +34,7 @@ def atan2_cpu(ret:LazyBuffer, a:LazyBuffer, b:LazyBuffer):
 # NOTE: The derivative of atan2 doesn't need a custom op! https://www.liquisearch.com/atan2/derivative
 # In general, it is also optional to write a backward function, just your backward pass won't work without it
 
-from tinygrad.ops import ASTRunner, LazyOp, LoadOps, BinaryOps, UnaryOps
+from tinygrad.ops import LazyOp, LoadOps, BinaryOps, UnaryOps
 from tinygrad.lazy import LazyBuffer
 from tinygrad.tensor import Function
 
@@ -51,7 +51,7 @@ class ATan2(Function):
 
 # *** third, we use our lovely new mlop in some tests ***
 
-from tinygrad.tensor import Tensor, Device
+from tinygrad.tensor import Tensor
 
 @unittest.skipUnless(Device.DEFAULT in ["CPU", "GPU"], "atan2 is only implemented for CPU and GPU")
 class TestCustomFunction(unittest.TestCase):

test/test_ops.py

@@ -233,7 +233,7 @@ class TestOps(unittest.TestCase):
   def test_mul_number(self):
     helper_test_op([(), ()], lambda x,y: x*y, Tensor.mul)
   def test_mul_const(self):
-    helper_test_op([(45,65)], lambda x: x*2,  lambda x: x*2)
+    helper_test_op([(45,65)], lambda x: x*2, lambda x: x*2)
     helper_test_op([(45,65)], lambda x: x*-1, lambda x: x*-1)
     helper_test_op([(45,65)], lambda x: 255*x, lambda x: 255*x)
   def test_div(self):
@@ -249,17 +249,17 @@ class TestOps(unittest.TestCase):
     helper_test_op([()], lambda x: 2/x, lambda x: 2/x)
   @unittest.skipIf(Device.DEFAULT in ["METAL", "WEBGPU"], "WEBGPU does not have support for inf/nan, METAL has issues with -inf")
   def test_mul_const_naninf(self):
-    helper_test_op([(45,65)], lambda x: x*float("inf"),  lambda x: x*float("inf"))
+    helper_test_op([(45,65)], lambda x: x*float("inf"), lambda x: x*float("inf"))
     helper_test_op([(45,65)], lambda x: x*-float("inf"), lambda x: x*-float("inf"))
-    helper_test_op([(45,65)], lambda x: x*float("nan"),  lambda x: x*float("nan"))
+    helper_test_op([(45,65)], lambda x: x*float("nan"), lambda x: x*float("nan"))
   @unittest.skipIf(Device.DEFAULT in ["METAL", "WEBGPU"], "WEBGPU does not have support for inf/nan, METAL has issues with -inf")
   def test_div_const_naninf(self):
-    helper_test_op([(45,65)], lambda x: x/float("inf"),  lambda x: x/float("inf"))
+    helper_test_op([(45,65)], lambda x: x/float("inf"), lambda x: x/float("inf"))
     helper_test_op([(45,65)], lambda x: x/-float("inf"), lambda x: x/-float("inf"))
-    helper_test_op([(45,65)], lambda x: x/float("nan"),  lambda x: x/float("nan"))
+    helper_test_op([(45,65)], lambda x: x/float("nan"), lambda x: x/float("nan"))
-    helper_test_op([(45,65)], lambda x: float("inf")/x,    lambda x: float("inf")/x)
+    helper_test_op([(45,65)], lambda x: float("inf")/x, lambda x: float("inf")/x)
     helper_test_op([(45,65)], lambda x: (-float("inf"))/x, lambda x: (-float("inf"))/x)
-    helper_test_op([(45,65)], lambda x: float("nan")/x,    lambda x: float("nan")/x)
+    helper_test_op([(45,65)], lambda x: float("nan")/x, lambda x: float("nan")/x)
   def test_pow(self):
     # TODO: why is a=0 for these tests?
     helper_test_op([(45,65)], lambda x: x**2, lambda x: Tensor.pow(x,2), a=0)
@@ -608,7 +608,6 @@ class TestOps(unittest.TestCase):
     helper_test_op([(3,4,5,6)], lambda x: x.movedim((3,2,1,0),(0,1,2,3)), lambda x: x.permute(order=(3,2,1,0)))
     helper_test_op([()], lambda x: x.permute(()), lambda x: x.permute(()))
 
-
   def test_reshape(self):
     helper_test_op([(4,3,6,6)], lambda x: torch.reshape(x, (-1,3,6,6)), lambda x: x.reshape(shape=(-1,3,6,6)))
     helper_test_op([(4,3,6,6)], lambda x: torch.reshape(x, (-1,1,6,6)), lambda x: x.reshape(shape=(-1,1,6,6)))
@@ -978,8 +977,7 @@ class TestOps(unittest.TestCase):
   def test_padding_add(self):
     helper_test_op([(64,64), (60,60)],
       lambda x,w: x+torch.nn.functional.pad(w, (2,2,2,2)),
-      lambda x,w: x+w.pad2d((2,2,2,2)),
+      lambda x,w: x+w.pad2d((2,2,2,2)))
-    )
 
   def test_dilated_conv2d(self):
     bs = 4

tinygrad/codegen/linearizer.py

@@ -100,7 +100,7 @@ def get_grouped_maybe_float4(*values:List[Token], grouping_allowed=True):
 
 # TODO: generic visitor pattern?
 def expand_node(idx:Node) -> List[Node]:
-  if isinstance(idx, Variable):  return [idx] if idx.expr is not None else [Variable.num(j) for j in range(idx.min, idx.max+1)]
+  if isinstance(idx, Variable): return [idx] if idx.expr is not None else [Variable.num(j) for j in range(idx.min, idx.max+1)]
   if isinstance(idx, NumNode): return [idx]
   if isinstance(idx, MulNode): return [x*idx.b for x in expand_node(idx.a)]
   if isinstance(idx, SumNode): return [Variable.sum(list(it)) for it in itertools.product(*[expand_node(x) for x in idx.nodes])]

tinygrad/tensor.py

@@ -170,7 +170,6 @@ class Tensor:
   def eye(dim:int, **kwargs):
     return Tensor([1], **kwargs).pad(((0,dim),)).reshape(1, dim+1).expand(dim, dim+1).reshape(dim*(dim+1)).shrink(((0,dim*dim),)).reshape(dim, dim)
 
-
   # ***** rng hlops *****
 
   @staticmethod
@@ -282,7 +281,7 @@ class Tensor:
     valid_slices = list(filterfalse(lambda x: x is None, orig_slices))
     valid_slices = [v if isinstance(v, slice) else slice(y := normalize_int(v, i, dim_sz), y+1) for i, (v, dim_sz) in enumerate(zip(valid_slices, self.shape))]
     start, stop, strides = zip(*y) if (y := [s.indices(dim_sz) for s, dim_sz in zip(valid_slices, self.shape)]) else ((), (), ())
-    new_slice = tuple((s, e)  if st > 0 else (e+1, s+1) for s, e, st in zip(start, stop, strides))
+    new_slice = tuple((s, e) if st > 0 else (e+1, s+1) for s, e, st in zip(start, stop, strides))
     new_shape = tuple(e - s for s, e in new_slice)
     # Shrink
     sliced_tensor = self.shrink(new_slice)