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[FIX] Give symbolic reductions Triton's result dtypes #534

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[FIX] Give symbolic reductions Triton's result dtypes #534

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55 changes: 55 additions & 0 deletions tests/end_to_end/test_sanitizer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1004,6 +1004,61 @@ def test_tl_min_return_indices():
), f"Sanitizer reported {len(reduce_indices_sanitizer.records)} error(s)"


# ======== Reduce Result Dtype Tests ===========


@tilelens.trace(client=SymbolicSanitizer())
@triton.jit
def fp16_softmax_kernel(x_ptr, out_ptr, BLOCK: tl.constexpr):
offs = tl.arange(0, BLOCK)
x = tl.load(x_ptr + offs)
# tl.max promotes fp16 to fp32, so tl.exp gets an fp32 argument.
e = tl.exp(x - tl.max(x, 0))
tl.store(out_ptr + offs, e / tl.sum(e, 0))


def test_reduce_max_promotes_fp16_like_triton():
x = torch.randn((8,), dtype=torch.float16)
out = torch.empty_like(x)
fp16_softmax_kernel[(1,)](x, out, BLOCK=x.numel())


@tilelens.trace(client=SymbolicSanitizer())
@triton.jit
def reduce_dtypes_kernel(i8_ptr, u8_ptr, f16_ptr, bf16_ptr, BLOCK: tl.constexpr):
offs = tl.arange(0, BLOCK)
i8 = tl.load(i8_ptr + offs)
u8 = tl.load(u8_ptr + offs)
f16 = tl.load(f16_ptr + offs)
bf16 = tl.load(bf16_ptr + offs)
tl.static_assert(tl.sum(i8, 0).dtype == tl.int32)
tl.static_assert(tl.sum(u8, 0).dtype == tl.uint32)
tl.static_assert(tl.sum(f16, 0).dtype == tl.float16)
tl.static_assert(tl.sum(f16, 0, dtype=tl.float32).dtype == tl.float32)
tl.static_assert(tl.max(i8, 0).dtype == tl.int32)
tl.static_assert(tl.min(u8, 0).dtype == tl.int32)
tl.static_assert(tl.max(f16, 0).dtype == tl.float32)
tl.static_assert(tl.min(bf16, 0).dtype == tl.float32)
f16_max, f16_idx = tl.max(f16, 0, return_indices=True)
tl.static_assert(f16_max.dtype == tl.float16)
tl.static_assert(f16_idx.dtype == tl.int32)
bf16_min, _ = tl.min(bf16, 0, return_indices=True)
tl.static_assert(bf16_min.dtype == tl.float32)


def test_reduce_result_dtypes_match_triton():
"""tl.sum, tl.max, and tl.min promote small integer and float inputs as
triton.language.standard does."""
n = 8
reduce_dtypes_kernel[(1,)](
torch.ones((n,), dtype=torch.int8),
torch.ones((n,), dtype=torch.uint8),
torch.ones((n,), dtype=torch.float16),
torch.ones((n,), dtype=torch.bfloat16),
BLOCK=n,
)


# ======== Reduce + Broadcast Tests ===========

reduce_broadcast_sanitizer = SymbolicSanitizer()
Expand Down
62 changes: 53 additions & 9 deletions tilelens/clients/symbolic_engine.py
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Original file line number Diff line number Diff line change
Expand Up @@ -87,9 +87,11 @@
TensorPointerStore,
)
from ..core.symbolic_metadata import (
BFLOAT16,
FLOAT32,
INT1,
INT32,
UINT32,
DTYPE_BY_NAME,
SymbolicDType,
SymbolicPointerDType,
Expand Down Expand Up @@ -1613,7 +1615,14 @@ class ReduceSymbolicExpr(SymbolicExpr):
keepdims: SymbolicExpr
axis: SymbolicExpr | None

def __init__(self, op: str, input: Any, axis: Any = None, keepdims: bool = False):
def __init__(
self,
op: str,
input: Any,
axis: Any = None,
keepdims: bool = False,
dtype: Any = None,
):
if op not in self._SUPPORTED_OPS:
raise NotImplementedError(f"Unsupported reduce op: {op}")
super().__init__(op)
Expand Down Expand Up @@ -1650,6 +1659,8 @@ def __init__(self, op: str, input: Any, axis: Any = None, keepdims: bool = False
scalar_ty: SymbolicDType
if op in ("argmax", "argmin"):
scalar_ty = INT32
elif dtype is not None:
scalar_ty = SymbolicExpr._NORMALIZE_VALUE(dtype)
else:
assert self.input.dtype is not None
scalar_ty = self.input.dtype
Expand Down Expand Up @@ -2698,10 +2709,39 @@ def _op_expand_dims_overrider(self, arg, axis):
def _op_broadcast_overrider(self, arg, shape):
return SymbolicExpr.create("broadcast", SymbolicExpr.from_value(arg), shape)

def _op_reduce_sum_overrider(self, input, axis=None, keep_dims=False, **kwargs):
return SymbolicExpr.create(
"sum", SymbolicExpr.from_value(input), axis, keep_dims
)
# tl.sum, tl.max, and tl.min convert their input before reducing
# (triton.language.standard); these give the resulting dtype.
@staticmethod
def _reduce_sum_dtype(dtype: Any) -> Any:
if not isinstance(dtype, SymbolicScalarDType) or dtype.primitive_bitwidth >= 32:
return dtype
if dtype.name.startswith("int") and dtype != INT1:
return INT32
if dtype.name.startswith("uint") or dtype == INT1:
return UINT32
return dtype

@staticmethod
def _reduce_minmax_dtype(dtype: Any, return_indices: bool) -> Any:
if dtype == BFLOAT16:
return FLOAT32
if (
return_indices
or not isinstance(dtype, SymbolicScalarDType)
or dtype.primitive_bitwidth >= 32
):
return dtype
if dtype.name.startswith(("fp", "bf", "float")):
return FLOAT32
return INT32

def _op_reduce_sum_overrider(
self, input, axis=None, keep_dims=False, dtype=None, **kwargs
):
input_sym = SymbolicExpr.from_value(input)
if dtype is None:
dtype = self._reduce_sum_dtype(input_sym.dtype)
return SymbolicExpr.create("sum", input_sym, axis, keep_dims, dtype)

def _op_reduce_xor_overrider(self, input, axis=None, keep_dims=False, **kwargs):
return SymbolicExpr.create(
Expand All @@ -2715,16 +2755,20 @@ def _op_reduce_or_overrider(self, input, axis=None, keep_dims=False, **kwargs):

def _op_reduce_max_overrider(self, input, axis=None, keep_dims=False, **kwargs):
input_sym = SymbolicExpr.from_value(input)
val = SymbolicExpr.create("max", input_sym, axis, keep_dims)
if kwargs.get("return_indices", False):
return_indices = kwargs.get("return_indices", False)
dtype = self._reduce_minmax_dtype(input_sym.dtype, return_indices)
val = SymbolicExpr.create("max", input_sym, axis, keep_dims, dtype)
if return_indices:
idx = SymbolicExpr.create("argmax", input_sym, axis, keep_dims)
return (val, idx)
return val

def _op_reduce_min_overrider(self, input, axis=None, keep_dims=False, **kwargs):
input_sym = SymbolicExpr.from_value(input)
val = SymbolicExpr.create("min", input_sym, axis, keep_dims)
if kwargs.get("return_indices", False):
return_indices = kwargs.get("return_indices", False)
dtype = self._reduce_minmax_dtype(input_sym.dtype, return_indices)
val = SymbolicExpr.create("min", input_sym, axis, keep_dims, dtype)
if return_indices:
idx = SymbolicExpr.create("argmin", input_sym, axis, keep_dims)
return (val, idx)
return val
Expand Down
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