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fix(cpu/matmul): widen GEMV weight row offsets to long (#429) #431

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fix(cpu/matmul): widen GEMV weight row offsets to long (#429) #431

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5baf20b
fix(cpu/matmul): widen GEMV weight row offsets to long (#429)
jamesburton Aug 7, 2026
0924233
fix(cpu/matmul): widen the sibling 4-row GEMV offsets too (#429)
jamesburton Aug 7, 2026
58cdf4c
docs(cpu/matmul): record why the tiled F16 row offsets stay int (#429)
jamesburton Aug 9, 2026
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46 changes: 26 additions & 20 deletions src/DotLLM.Cpu/Kernels/MatMul.cs
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Original file line number Diff line number Diff line change
Expand Up @@ -58,7 +58,7 @@ public static void GemvF32(float* a, float* x, float* result, int m, int k)

for (int row = 0; row < m; row++)
{
var rowSpan = new ReadOnlySpan<float>(a + row * k, k);
var rowSpan = new ReadOnlySpan<float>(a + (long)row * k, k);
result[row] = TensorPrimitives.Dot(rowSpan, xSpan);
}
}
Expand All @@ -72,7 +72,7 @@ internal static void GemvF32Scalar(float* a, float* x, float* result, int m, int
for (int row = 0; row < m; row++)
{
float sum = 0;
float* rowPtr = a + row * k;
float* rowPtr = a + (long)row * k;
for (int j = 0; j < k; j++)
sum += rowPtr[j] * x[j];
result[row] = sum;
Expand Down Expand Up @@ -138,15 +138,15 @@ internal static void ComputeRows(byte* weightsQ8, byte* xQ8, float* result, int
for (; row + 3 < m; row += 4)
{
VecDotQ8_0Vnni_4Rows(
weightsQ8 + row * rowBytes,
weightsQ8 + (row + 1) * rowBytes,
weightsQ8 + (row + 2) * rowBytes,
weightsQ8 + (row + 3) * rowBytes,
weightsQ8 + (long)row * rowBytes,
weightsQ8 + (long)(row + 1) * rowBytes,
weightsQ8 + (long)(row + 2) * rowBytes,
weightsQ8 + (long)(row + 3) * rowBytes,
xQ8, blockCount, result + row);
}
for (; row < m; row++)
{
result[row] = VecDotQ8_0Avx512(weightsQ8 + row * rowBytes, xQ8, blockCount);
result[row] = VecDotQ8_0Avx512(weightsQ8 + (long)row * rowBytes, xQ8, blockCount);
}
}
else if (Avx512BW.IsSupported)
Expand All @@ -156,15 +156,15 @@ internal static void ComputeRows(byte* weightsQ8, byte* xQ8, float* result, int
for (; row + 3 < m; row += 4)
{
VecDotQ8_0Avx512_4Rows(
weightsQ8 + row * rowBytes,
weightsQ8 + (row + 1) * rowBytes,
weightsQ8 + (row + 2) * rowBytes,
weightsQ8 + (row + 3) * rowBytes,
weightsQ8 + (long)row * rowBytes,
weightsQ8 + (long)(row + 1) * rowBytes,
weightsQ8 + (long)(row + 2) * rowBytes,
weightsQ8 + (long)(row + 3) * rowBytes,
xQ8, blockCount, result + row);
}
for (; row < m; row++)
{
result[row] = VecDotQ8_0Avx512(weightsQ8 + row * rowBytes, xQ8, blockCount);
result[row] = VecDotQ8_0Avx512(weightsQ8 + (long)row * rowBytes, xQ8, blockCount);
}
}
else if (Avx2.IsSupported)
Expand All @@ -174,22 +174,22 @@ internal static void ComputeRows(byte* weightsQ8, byte* xQ8, float* result, int
for (; row + 3 < m; row += 4)
{
VecDotQ8_0Avx2_4Rows(
weightsQ8 + row * rowBytes,
weightsQ8 + (row + 1) * rowBytes,
weightsQ8 + (row + 2) * rowBytes,
weightsQ8 + (row + 3) * rowBytes,
weightsQ8 + (long)row * rowBytes,
weightsQ8 + (long)(row + 1) * rowBytes,
weightsQ8 + (long)(row + 2) * rowBytes,
weightsQ8 + (long)(row + 3) * rowBytes,
xQ8, blockCount, result + row);
}
for (; row < m; row++)
{
result[row] = VecDotQ8_0Avx2(weightsQ8 + row * rowBytes, xQ8, blockCount);
result[row] = VecDotQ8_0Avx2(weightsQ8 + (long)row * rowBytes, xQ8, blockCount);
}
}
else
{
for (int row = 0; row < m; row++)
{
result[row] = VecDotQ8_0Scalar(weightsQ8 + row * rowBytes, xQ8, blockCount);
result[row] = VecDotQ8_0Scalar(weightsQ8 + (long)row * rowBytes, xQ8, blockCount);
}
}
}
Expand Down Expand Up @@ -1564,7 +1564,7 @@ public static void GemvF16(nint weights, float* x, float* y, int m, int k)
float* rowBuf = stackalloc float[k];
for (int row = 0; row < m; row++)
{
var srcRow = new ReadOnlySpan<Half>(weightsHalf + row * k, k);
var srcRow = new ReadOnlySpan<Half>(weightsHalf + (long)row * k, k);
var destRow = new Span<float>(rowBuf, k);
TensorPrimitives.ConvertToSingle(srcRow, destRow);
y[row] = TensorPrimitives.Dot(destRow, new ReadOnlySpan<float>(x, k));
Expand All @@ -1577,7 +1577,7 @@ public static void GemvF16(nint weights, float* x, float* y, int m, int k)
{
for (int row = 0; row < m; row++)
{
var srcRow = new ReadOnlySpan<Half>(weightsHalf + row * k, k);
var srcRow = new ReadOnlySpan<Half>(weightsHalf + (long)row * k, k);
var destRow = rented.AsSpan(0, k);
TensorPrimitives.ConvertToSingle(srcRow, destRow);
y[row] = TensorPrimitives.Dot(destRow, new ReadOnlySpan<float>(x, k));
Expand Down Expand Up @@ -1619,6 +1619,10 @@ public static void GemmF16(nint weights, float* b, float* c, int m, int k, int n
var xSpan = new ReadOnlySpan<float>(xPtr, k);
var destRow = new Span<float>(rowBuf, k);

// `row * k` stays in int on purpose (#429 audit): the whole-tensor offset
// is already carried in 64-bit by `tileWeightsHalf`, and
// row < tileRows <= tileM <= 256 (ComputeTileM clamps), so the residual
// product tops out at 255 * k — 4.2M for a 16384-wide 405B tensor.
for (int row = 0; row < tileRows; row++)
{
var srcRow = new ReadOnlySpan<Half>(tileWeightsHalf + row * k, k);
Expand Down Expand Up @@ -2512,6 +2516,8 @@ private static void GemmTiledF16Worker(nint ctxPtr, int threadIdx, int threadCou
var xSpan = new ReadOnlySpan<float>(xPtr, ctx.K);
for (int row = 0; row < tileRows; row++)
{
// See GemmF16: `row * ctx.K` is deliberately int — the tensor-scale offset
// lives in `tileWeightsHalf` (64-bit) and row < tileRows <= ctx.TileM <= 256.
var srcRow = new ReadOnlySpan<Half>(tileWeightsHalf + row * ctx.K, ctx.K);
TensorPrimitives.ConvertToSingle(srcRow, destRow);
outPtr[row] = TensorPrimitives.Dot(destRow, xSpan);
Expand Down