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Use LoadN for short BF16 dot product inputs instead of scalar conversion #2703

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Use LoadN for short BF16 dot product inputs instead of scalar conversion #2703

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0478c13
Use `LoadN` for short BF16 dot product inputs instead of scalar conve…
vekterli Sep 9, 2025
df82cf0
Only use `LoadN` fallback when compiling with GCC
vekterli Sep 26, 2025
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34 changes: 10 additions & 24 deletions hwy/contrib/dot/dot-inl.h
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Original file line number Diff line number Diff line change
Expand Up @@ -174,21 +174,12 @@ struct Dot {
(kAssumptions & kMultipleOfVector) != 0;
constexpr bool kIsPaddedToVector = (kAssumptions & kPaddedToVector) != 0;

// Won't be able to do a full vector load without padding => scalar loop.
// Won't be able to do a full vector load without padding => partial load.
if (!kIsAtLeastOneVector && !kIsMultipleOfVector && !kIsPaddedToVector &&
HWY_UNLIKELY(num_elements < NF)) {
// Only 2x unroll to avoid excessive code size.
float sum0 = 0.0f;
float sum1 = 0.0f;
size_t i = 0;
for (; i + 2 <= num_elements; i += 2) {
sum0 += pa[i + 0] * ConvertScalarTo<float>(pb[i + 0]);
sum1 += pa[i + 1] * ConvertScalarTo<float>(pb[i + 1]);
}
for (; i < num_elements; ++i) {
sum1 += pa[i] * ConvertScalarTo<float>(pb[i]);
}
return sum0 + sum1;
const VF a = LoadN(df, pa, num_elements);
const VF b = PromoteTo(df, LoadN(dbfh, pb, num_elements));
return ReduceSum(df, Mul(a, b));
}

// Compiler doesn't make independent sum* accumulators, so unroll manually.
Expand Down Expand Up @@ -279,19 +270,14 @@ struct Dot {
(kAssumptions & kMultipleOfVector) != 0;
constexpr bool kIsPaddedToVector = (kAssumptions & kPaddedToVector) != 0;

// Won't be able to do a full vector load without padding => scalar loop.
// Won't be able to do a full vector load without padding => partial load.
if (!kIsAtLeastOneVector && !kIsMultipleOfVector && !kIsPaddedToVector &&
HWY_UNLIKELY(num_elements < N)) {
float sum0 = 0.0f; // Only 2x unroll to avoid excessive code size for..
float sum1 = 0.0f; // this unlikely(?) case.
for (; i + 2 <= num_elements; i += 2) {
sum0 += F32FromBF16(pa[i + 0]) * F32FromBF16(pb[i + 0]);
sum1 += F32FromBF16(pa[i + 1]) * F32FromBF16(pb[i + 1]);
}
if (i < num_elements) {
sum1 += F32FromBF16(pa[i]) * F32FromBF16(pb[i]);
}
return sum0 + sum1;
const auto a = LoadN(d, pa, num_elements);
const auto b = LoadN(d, pb, num_elements);
V sum1 = Zero(df32);
V sum0 = ReorderWidenMulAccumulate(df32, a, b, Zero(df32), sum1);
return ReduceSum(df32, Add(sum0, sum1));
}

// See comment in the other Compute() overload. Unroll 2x, but we need
Expand Down
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