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#include "common.cuh"
#include "argmax.cuh"
#include "sum.cuh"
#include <cstdint>
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<<<blocks_num, blocks_dim, 0, stream>>>(src0_d, dst_d, ne00, nrows);
}
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