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[FRONTEND] Replaced range with enumerate calls (#1110)

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Using range(len(...)) is not pythonic. 
Python does not have not index-based loops. Instead, it uses collection
iterators. Python has a built-in method enumerate which adds a counter
to an iterable. Using this, you can access the counter and the value
from the iterable at the same time. It is therefore recommended to
replace range(len(...)) with enumerate(...).

for ex 


5bcf60a5c0/python/triton/language/extern.py (L68)



f62d556fff/python/triton/language/extern.py (L68)

Signed-off-by: GitHub <noreply@github.com>
Co-authored-by: Keren Zhou <kerenzhou@openai.com>
This commit is contained in:
Nishant Sikarwar
2023-01-31 04:52:11 +05:30
committed by GitHub
parent e5dbe35cc1
commit 653c8dc124
2 changed files with 6 additions and 7 deletions
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4
python/triton/language/extern.py
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@@ -65,9 +65,9 @@ def elementwise(lib_name: str, lib_path: str, args: list, arg_type_symbol_dict:
if not all_scalar:
broadcast_arg = dispatch_args[0]
# Get the broadcast shape over all the arguments
for i in range(len(dispatch_args)):
for i, item in enumerate(dispatch_args):
_, broadcast_arg = semantic.binary_op_type_checking_impl(
dispatch_args[i], broadcast_arg, _builder)
item, broadcast_arg, _builder)
# Change the shape of each argument based on the broadcast shape
for i in range(len(dispatch_args)):
dispatch_args[i], _ = semantic.binary_op_type_checking_impl(

9
python/triton/language/semantic.py
View File

@@ -533,10 +533,10 @@ def broadcast_impl_shape(input: tl.tensor,
raise ValueError(f"Cannot broadcast, rank mismatch: {src_shape}, {shape}")
if shape == src_shape:
return input
for i in range(len(src_shape)):
if shape[i] != src_shape[i] and src_shape[i] != 1:
for i, item in enumerate(src_shape):
if shape[i] != item and item != 1:
raise ValueError(f"Cannot broadcast, the expanded size of the tensor ({shape[i]})"
f" must match the existing size ({src_shape[i]}) at non-singleton dimension"
f" must match the existing size ({item}) at non-singleton dimension"
f" {i}: {src_shape}, {shape}")
ret_ty = tl.block_type(input.type.scalar, shape)
return tl.tensor(builder.create_broadcast(input.handle, shape), ret_ty)
@@ -576,8 +576,7 @@ def broadcast_impl_value(lhs: tl.tensor,
assert len(rhs_shape) == len(lhs_shape)
ret_shape = []
for i in range(len(lhs_shape)):
left = lhs_shape[i]
for i, left in enumerate(lhs_shape):
right = rhs_shape[i]
if left == 1:
ret_shape.append(right)