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

@@ -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/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