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add E275 missing-whitespace-after-keyword linting rule (#6149)

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requires space after keywords like `assert`, `not`, `return`, `else`
This commit is contained in:
chenyu
2024-08-17 16:44:34 -04:00
committed by GitHub
parent da4fa77e92
commit f7950fc2b6
7 changed files with 21 additions and 20 deletions
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1
ruff.toml
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@@ -12,6 +12,7 @@ lint.select = [
# "E124",
"E203", # whitespace-before-punctuation
"E272", # multiple-spaces-before-keyword
"E275", # missing-whitespace-after-keyword
"E303", # too-many-blank-lines
"E304", # blank-line-after-decorator
"E501", # line-too-long

14
test/test_gc.py
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@@ -13,25 +13,25 @@ class TestGC(unittest.TestCase):
a = Tensor.rand(4, 4, requires_grad=True)
b = Tensor.zeros(4, 4, requires_grad=True)
(a*b).mean().backward()
assert(tensors_allocated() > 0)
assert (tensors_allocated() > 0)
del a,b
assert(tensors_allocated() == 1) # one for Tensor._rng_counter
assert (tensors_allocated() == 1) # one for Tensor._rng_counter
def test_gc_complex(self):
a = Tensor(np.zeros((4, 4), dtype=np.float32), requires_grad=True)
b = Tensor.rand(4, 4, requires_grad=True)
assert(tensors_allocated() == 3)
assert (tensors_allocated() == 3)
(a*b).mean().backward()
assert(tensors_allocated() == 5)
assert (tensors_allocated() == 5)
del b
assert(tensors_allocated() == 3)
assert (tensors_allocated() == 3)
b = Tensor(np.zeros((4, 4), dtype=np.float32), requires_grad=True)
print(tensors_allocated())
(a*b).mean().backward()
print(tensors_allocated())
assert(tensors_allocated() == 5)
assert (tensors_allocated() == 5)
del b
assert(tensors_allocated() == 3)
assert (tensors_allocated() == 3)
if __name__ == '__main__':
unittest.main()

2
test/unit/test_helpers.py
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@@ -148,7 +148,7 @@ class TestFetch(unittest.TestCase):
self.assertRaises(Exception, fetch, 'http://www.google.com/404', allow_caching=False)
def test_fetch_small(self):
assert(len(fetch('https://google.com', allow_caching=False).read_bytes())>0)
assert (len(fetch('https://google.com', allow_caching=False).read_bytes())>0)
def test_fetch_img(self):
img = fetch("https://avatars.githubusercontent.com/u/132956020", allow_caching=False)

12
test/unit/test_shapetracker.py
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@@ -367,38 +367,38 @@ class TestSimplifyingShapeTracker(unittest.TestCase):
self.st = self.st.expand((10, 10))
self.st = self.st.reshape((100,))
print(self.st.views)
assert(len(self.st.views) == 2)
assert (len(self.st.views) == 2)
self.st = self.st.reshape((10, 10))
print(self.st.views)
self.st = self.st.simplify()
print(self.st.views)
assert(len(self.st.views) == 1)
assert (len(self.st.views) == 1)
# multiview simplify
def test_expand_contract_different_shape(self):
self.st.expand((10, 10))
self.st.reshape((100,))
print(self.st.views)
assert(len(self.st.views) == 2)
assert (len(self.st.views) == 2)
self.st.reshape((2, 5, 2, 5))
print(self.st.views)
self.st = self.st.simplify()
print(self.st.views)
assert(len(self.st.views) == 1)
assert (len(self.st.views) == 1)
# multiview simplify
def test_expand_contract_still_complex(self):
self.st.expand((10, 10))
self.st.reshape((100,))
print(self.st.views)
assert(len(self.st.views) == 2)
assert (len(self.st.views) == 2)
self.st.reshape((5, 20))
self.st = self.st.simplify()
print(self.st.views)
assert(len(self.st.views) == 2)
assert (len(self.st.views) == 2)
# Tensor.zeros(2, 4).permute(1,0).reshape(2, 4)
# (d1*4 + d0%4), d1=x//4, d0=x%4 = ((x//4)*4) + (x%4)%4

8
tinygrad/codegen/kernel.py
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@@ -278,7 +278,7 @@ class Kernel:
def _create_tc_opts(self, reduceop:UOp, tc:TensorCore, axis:int, opt_level:int) -> Optional[TensorCoreOptions]:
has_cast = tc.dtype_in != tc.dtype_out
if has_cast and not(reduceop.src[0].op is UOps.CAST and reduceop.src[0].dtype == tc.dtype_out): return None
if has_cast and not (reduceop.src[0].op is UOps.CAST and reduceop.src[0].dtype == tc.dtype_out): return None
mul_op = reduceop.src[0].src[0] if has_cast else reduceop.src[0]
if mul_op.arg is not BinaryOps.MUL: return None
@@ -295,10 +295,10 @@ class Kernel:
buf0_strides, buf1_strides = self.sts[buf0].real_strides(), self.sts[buf1].real_strides()
axis_buf0 = [(i,self.full_shape[i],buf1_strides[i]) for i,s in enumerate(buf0_strides[:self.first_reduce]) if s == 0]
axis_buf1 = [(i,self.full_shape[i],buf0_strides[i]) for i,s in enumerate(buf1_strides[:self.first_reduce]) if s == 0]
if not(axis_buf0 and axis_buf1 and ((self.shape_len-self.first_reduce) == 1 or (opt_level >= 1))): return None
if not (axis_buf0 and axis_buf1 and ((self.shape_len-self.first_reduce) == 1 or (opt_level >= 1))): return None
axis_choices = list(itertools.product(axis_buf0, axis_buf1, range(self.first_reduce, self.shape_len)))
if not(axis < len(axis_choices)): return None
if not (axis < len(axis_choices)): return None
s0, s1, s2 = axis_choices[-(axis+1)][0][0], axis_choices[-(axis+1)][1][0], axis_choices[-(axis+1)][2] # s0 is n, s1 is m, s2 is k
axis_pads = tuple((x, tc.dims[i]) for i, x in enumerate([s0, s1, s2]) if self.full_shape[x]%tc.dims[i] != 0)
@@ -437,7 +437,7 @@ class Kernel:
self.upcast()
elif opt.op is OptOps.UPCAST: # yellow
check(axis < self.first_reduce, "upcast is for non-reduce")
check(not(self.tensor_core and self.global_dims <= axis < self.global_dims+len(self.tensor_core.threads)), "can't upcast TC locals")
check(not (self.tensor_core and self.global_dims <= axis < self.global_dims+len(self.tensor_core.threads)), "can't upcast TC locals")
check(amt <= 16, "don't upcast more than 16")
self.shift_to(axis, amt, insert_before=None)
self.upcast()

2
tinygrad/helpers.py
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@@ -347,5 +347,5 @@ def pretty_print(x:Any, rep:Callable, srcfn=lambda x: x.src, cache=None, d=0)->s
if cache[s][1] == 1: dfs(s, cache)
if cache is None: dfs(x, cache:={})
if (cx:=cache.setdefault(x, [0,0,False]))[2]: return f"{' '*d} x{cx[0]}"
cx[2], srcs = True, ('None' if srcfn(x) is None else''.join(f'\n{pretty_print(s, rep, srcfn, cache, d+2)},' for s in srcfn(x)))
cx[2], srcs = True, ('None' if srcfn(x) is None else ''.join(f'\n{pretty_print(s, rep, srcfn, cache, d+2)},' for s in srcfn(x)))
return f"{' '*d}{f'x{cx[0]}:=' * (cx[1]>1)}{rep(x)}" % srcs

2
tinygrad/renderer/assembly.py
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@@ -131,7 +131,7 @@ class PTXRenderer(Renderer):
def render_cast(self, d:str, a:str, dtype:DType, atype:DType, bitcast=False, pred=False) -> List[str]:
if bitcast: return [f"mov.b{self.types[dtype][1:]} {d}, {a};"]
if atype == dtypes.bool: return[f"selp.b{self.types[dtype][1:]} {d}, {render_val(1, dtype)}, {render_val(0, dtype)}, {a};"]
if atype == dtypes.bool: return [f"selp.b{self.types[dtype][1:]} {d}, {render_val(1, dtype)}, {render_val(0, dtype)}, {a};"]
if dtype == dtypes.bool: return [f"setp.ne.b{self.types[atype][1:]} {d}, {a}, {self.render_const(0, atype)};"]
rnd = ('.rzi' if dtypes.is_int(dtype) and dtypes.is_float(atype) else
'.rn' if dtypes.is_float(dtype) and (dtype.itemsize < atype.itemsize or dtypes.is_int(atype) or atype == dtypes.bool) else '')