[FIX] Support constexpr .to() in interpreter mode#310
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[FIX] Support constexpr .to() in interpreter mode#310
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mark14wu
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In compiled Triton, constexpr.to(dtype) works via the compiler pipeline, but in interpreter mode constexpr has no .to() method, and raw Python scalars passed as constexprs are not wrapped. Three coupled fixes: - Monkey-patch tl.constexpr.to() for interpreter mode - Wrap scalar constexprs in tl.constexpr in _grid_executor_call - Unwrap tl.constexpr in SymbolicExpr.from_value to prevent type errors
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# Conflicts: # tests/end_to_end/test_sanitizer.py
Jokeren
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Mar 9, 2026
triton_viz/core/patch.py
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| call_args[name] = arg | ||
| ret = arg | ||
| call_args[name] = ( | ||
| tl.constexpr(arg) if isinstance(arg, (int, float, bool)) else arg |
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Why should you make it constexpr only for int, float, and bool?
…g, universal normalization, and bool support - Replace no-op _implicit_cvt shim with numpy-based cast that handles truncation (float→int) and bitcast (reinterpret bits) - Move constexpr monkey-patch from module-level to scoped _patch_constexpr() via _LangPatchScope, with hasattr guards for upstream compatibility - Add __getattr__ proxy on constexpr for attribute access (e.g. DTYPE.is_int()) - Universally wrap all constexpr args via _normalize_constexpr_arg(), not just int/float/bool, enabling dtype/tuple/custom-type meta-params - Add bool to symbolic engine's builtin_scala_types with int1 dtype inference - Add E2E tests for real cast, bitcast, dtype meta-param, tuple meta-param, grid lambda, and bool cast - Add unit tests for bool symbolic inference and constexpr patch lifecycle
Triton's semantic.cast() raises ValueError when source and destination primitive bitwidths differ for bitcast. Our interpreter-mode _constexpr_to was silently truncating via a bytes slice instead. Add an explicit size check and remove the unnecessary slice.
Unit tests assert that _constexpr_to returns a tensor with the exact destination dtype (uint8, int16, uint32, float64). E2E test verifies unsigned integer division with a constexpr denominator.
Replace _implicit_cvt return path with _typed_scalar_tensor helper that wraps the cast result using the exact target dtype. This fixes dtype demotion for uint8, int16, uint32, float64, and bool/int1 casts.
… values to grid lambda - Reject invalid fp_downcast_rounding values (e.g. "RTZ", "foo") with ValueError instead of silently falling back to rtne. - Raise OverflowError for integer literals outside [-2**63, 2**64) in _src_np_dtype, matching upstream Triton's representable range. - Pass raw host values (not tl.constexpr wrappers) to grid lambdas so isinstance checks in grid/heuristic functions behave as in real Triton.
…dth validation - Replace dst_np.type(value) with np.array(src).astype(dst) to get proper wrap/truncate semantics matching upstream Triton's cast_impl (e.g. 300->uint8=44, -1->uint32=4294967295). - Validate bitcast using Triton primitive_bitwidth instead of numpy storage itemsize, so bool(int1)->int8 bitcast correctly rejects (1 bit != 8 bits) instead of passing on storage size equality. - Extract _src_triton_dtype() mirroring upstream to_tensor type rules; _src_np_dtype() now delegates to it.
Jokeren
requested changes
Mar 21, 2026
| def _patch_constexpr(scope: _LangPatchScope) -> None: | ||
| """Patch tl.constexpr with .to() and __getattr__ for interpreter mode.""" | ||
| if not hasattr(tl.constexpr, "to"): | ||
| scope.set_attr(tl.constexpr, "to", _constexpr_to) |
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There isn't a constexpr.to method. This is tensor.to
@builtin
def to(self, dtype: dtype, fp_downcast_rounding: Optional[str] = None, bitcast: bool = False, _semantic=None):
"""
Alias for :py:func:`tensor.cast`.
"""
return cast(self, dtype, fp_downcast_rounding, bitcast, _semantic=_semantic)
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Summary
constexpr.to(dtype)works via the compiler pipeline, but in interpreter modeconstexprhas no.to()methodconstexprsare not wrapped intl.constexpr.to(), wrap scalars intl.constexpr, unwrap inSymbolicExpr.from_valueTest plan
test_float_no_attr_to—eps.to(dtype)where eps is a constexpr float