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2 changes: 2 additions & 0 deletions .gitignore
Original file line number Diff line number Diff line change
Expand Up @@ -34,3 +34,5 @@ dev/cuda/global_norm

# log files
*.log
.claude/
CLAUDE.md
101 changes: 101 additions & 0 deletions dev/cuda/softmax_forward.cu
Original file line number Diff line number Diff line change
Expand Up @@ -25,6 +25,12 @@ version 6 is softmax_online that parallelizes over all of B,T,C

version 7 is softmax optimized for very large C.
./softmax_forward 7

version 8 is online softmax with manual warp shuffle reductions, one warp per row
./softmax_forward 8

version 9 is online softmax like kernel 8, but with 4x unrolled loads for memory-level parallelism
./softmax_forward 9
*/

#include <stdio.h>
Expand Down Expand Up @@ -563,6 +569,92 @@ __global__ void softmax_forward_online_kernel8(float* out, const float* inp, int
}
}

__global__ void softmax_forward_online_kernel9(float* out, const float* inp, int N, int C) {
// same as kernel 8 (online softmax, one warp per row, manual warp reductions)
// but with 4x unrolled coarsening loops to increase memory-level parallelism
// multiple loads are issued together so the memory system can service them in parallel
// ~9% faster than kernel 8 on RTX 4060
const int UNROLL = 4;
const int warpsPerBlock = blockDim.x / warpSize;
int tid = threadIdx.x;

int warpId = tid / warpSize;
int laneId = tid % warpSize;
// one warp one row
int row = blockIdx.x * warpsPerBlock + warpId;

if (row >= N) {
return;
}

const float* x = inp + row * C;
float* const y = out + row * C;

// merge calculating maxval and sumval in one loop, unrolled by 4
// all 4 loads issue together for memory-level parallelism
float maxval = -INFINITY, sumval = 0.0f, bigger;
for (int i = laneId; i < C; i += warpSize * UNROLL) {
// issue all 4 loads at once, min(C-1, ...) avoids branch divergence
float v0 = x[min(C - 1, i)];
float v1 = x[min(C - 1, i + warpSize)];
float v2 = x[min(C - 1, i + 2 * warpSize)];
float v3 = x[min(C - 1, i + 3 * warpSize)];
// process each value sequentially through the online max+sum update
if (i < C) {
bigger = fmaxf(maxval, v0);
sumval = sumval * expf(maxval - bigger) + expf(v0 - bigger);
maxval = bigger;
}
if (i + warpSize < C) {
bigger = fmaxf(maxval, v1);
sumval = sumval * expf(maxval - bigger) + expf(v1 - bigger);
maxval = bigger;
}
if (i + 2 * warpSize < C) {
bigger = fmaxf(maxval, v2);
sumval = sumval * expf(maxval - bigger) + expf(v2 - bigger);
maxval = bigger;
}
if (i + 3 * warpSize < C) {
bigger = fmaxf(maxval, v3);
sumval = sumval * expf(maxval - bigger) + expf(v3 - bigger);
maxval = bigger;
}
}

// warp-level reduction for maxval and sumval
float offsetMaxval, offsetSumval;
for (int offset = warpSize / 2; offset > 0; offset >>= 1) {
__syncwarp();
offsetMaxval = __shfl_down_sync(0xFFFFFFFF, maxval, offset);
offsetSumval = __shfl_down_sync(0xFFFFFFFF, sumval, offset);
if (offsetMaxval > maxval) {
sumval *= expf(maxval - offsetMaxval);
maxval = offsetMaxval;
} else {
offsetSumval *= expf(offsetMaxval - maxval);
}
sumval += offsetSumval;
}

// broadcast the maxval and sumval to all lanes
maxval = __shfl_sync(0xFFFFFFFF, maxval, 0);
sumval = __shfl_sync(0xFFFFFFFF, sumval, 0);

// compute exp(x - max) * inv_sum, unrolled by 4
float inv_sumval = 1.0f / sumval;
for (int i = laneId; i < C; i += warpSize * UNROLL) {
float v0 = x[min(C - 1, i)];
float v1 = x[min(C - 1, i + warpSize)];
float v2 = x[min(C - 1, i + 2 * warpSize)];
float v3 = x[min(C - 1, i + 3 * warpSize)];
if (i < C) y[i] = expf(v0 - maxval) * inv_sumval;
if (i + warpSize < C) y[i + warpSize] = expf(v1 - maxval) * inv_sumval;
if (i + 2 * warpSize < C) y[i + 2 * warpSize] = expf(v2 - maxval) * inv_sumval;
if (i + 3 * warpSize < C) y[i + 3 * warpSize] = expf(v3 - maxval) * inv_sumval;
}
}

// ----------------------------------------------------------------------------
// kernel launcher

Expand Down Expand Up @@ -616,6 +708,12 @@ void softmax_forward_online8(float* out, const float* inp, int N, int C, int blo
cudaCheck(cudaGetLastError());
}

void softmax_forward_online9(float* out, const float* inp, int N, int C, int block_size) {
const int grid_size = ceil_div(N * 32, block_size);
softmax_forward_online_kernel9<<<grid_size, block_size>>>(out, inp, N, C);
cudaCheck(cudaGetLastError());
}

// kernel version dispatch
void softmax_forward(int kernel_num, float* out, const float* inp, int N, int C, const int block_size) {
switch (kernel_num) {
Expand Down Expand Up @@ -643,6 +741,9 @@ void softmax_forward(int kernel_num, float* out, const float* inp, int N, int C,
case 8:
softmax_forward_online8(out, inp, N, C, block_size);
break;
case 9:
softmax_forward_online9(out, inp, N, C, block_size);
break;
default:
printf("Invalid kernel number\n");
exit(1);
Expand Down