#include "common.cuh" #include "argmax.cuh" #include "sum.cuh" #include static __global__ void argmax_f32( const float * x, int32_t * dst, const int64_t ncols, const int64_t nrows) { int argmax_thread = 0; const int64_t row0 = (int64_t)blockIdx.x*WARP_SIZE; #pragma unroll for (int64_t row1 = 0; row1 < WARP_SIZE; ++row1) { const int64_t row = row0 + row1; if (row >= nrows) { break; } float maxval = -FLT_MAX; int argmax = -1; for (int32_t col = threadIdx.x; col < ncols; col += WARP_SIZE) { const float val = x[row*ncols + col]; const int bigger = val > maxval; const int not_bigger = bigger ^ 0x00000001; maxval = maxval*not_bigger + val*bigger; argmax = argmax*not_bigger + col*bigger; } #pragma unroll for (int mask = 16; mask > 0; mask >>= 1) { const float val = __shfl_xor_sync(0xFFFFFFFF, maxval, mask, WARP_SIZE); const int col = __shfl_xor_sync(0xFFFFFFFF, argmax, mask, WARP_SIZE); const int bigger = val > maxval; const int not_bigger = bigger ^ 0x00000001; maxval = maxval*not_bigger + val*bigger; argmax = argmax*not_bigger + col*bigger; } const int store = row1 == threadIdx.x; argmax_thread += store*argmax; } const int row = row0 + threadIdx.x; if (row >= nrows) { return; } dst[row] = argmax_thread; } void ggml_cuda_argmax(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { const ggml_tensor * src0 = dst->src[0]; GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_I32); GGML_ASSERT(ggml_is_contiguous(src0)); const int64_t ne00 = src0->ne[0]; const int64_t nrows = ggml_nrows(src0); const float * src0_d = (const float *) src0->data; int32_t * dst_d = (int32_t *) dst->data; cudaStream_t stream = ctx.stream(); const int64_t num_blocks = (nrows + WARP_SIZE - 1) / WARP_SIZE; const dim3 blocks_dim(WARP_SIZE, 1, 1); const dim3 blocks_num(num_blocks, 1, 1); argmax_f32<<>>(src0_d, dst_d, ne00, nrows); }