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Reject opt_level=None when the kernel is defined #115

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Reject opt_level=None when the kernel is defined #115

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2 changes: 2 additions & 0 deletions changelog.d/opt-level-none.md
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Original file line number Diff line number Diff line change
@@ -0,0 +1,2 @@
- `opt_level=None` (including inside `ByTarget`) is now rejected with a `ValueError`
when the kernel is defined, instead of failing with an `AssertionError` at compile time.
7 changes: 3 additions & 4 deletions src/cuda/tile/_compiler_options.py
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Original file line number Diff line number Diff line change
Expand Up @@ -72,10 +72,9 @@ def _validate_occupancy(occupancy: None | int):
raise ValueError(f'occupancy should be [1, 32], got {occupancy}')


def _validate_opt_level(opt_level: None | int):
if opt_level is not None:
if opt_level < 0 or opt_level > 3:
raise ValueError(f'opt_level should be [0, 3], got {opt_level}')
def _validate_opt_level(opt_level: int):
if not isinstance(opt_level, int) or opt_level < 0 or opt_level > 3:
raise ValueError(f'opt_level should be an integer in [0, 3], got {opt_level!r}')


def _validate_num_worker_warps(num_worker_warps: None | int):
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2 changes: 1 addition & 1 deletion src/cuda/tile/_execution.py
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Expand Up @@ -115,7 +115,7 @@ def __init__(self,
/, *,
num_ctas: None | int | ByTarget[int] = None,
occupancy: None | int | ByTarget[int] = None,
opt_level: None | int | ByTarget[int] = 3,
opt_level: int | ByTarget[int] = 3,
num_worker_warps: None | int | ByTarget[int] = None):
if not isinstance(function, FunctionType):
raise TypeError("`kernel` decorator must be applied to a Python function")
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7 changes: 7 additions & 0 deletions test/test_compiler_options.py
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Expand Up @@ -85,6 +85,13 @@ def test_opt_level_for_target_default():
assert CompilerOptions().opt_level_for_target("sm_100") == 3


@pytest.mark.parametrize("opt_level", [None, -1, 4, ct.ByTarget(sm_100=None),
ct.ByTarget(default=None)])
def test_opt_level_rejects_invalid(opt_level):
with pytest.raises(ValueError, match=r"opt_level should be an integer in \[0, 3\]"):
CompilerOptions(opt_level=opt_level)


def _tensor():
return torch.zeros(64, dtype=torch.float32, device='cuda:0')

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