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- /**
- * llama.cpp - commit 46e3556e01b824e52395fb050b29804b6cff2a7c - do not edit this file
- *
- * MIT License
- *
- * Copyright (c) 2023-2024 The ggml authors
- *
- * Permission is hereby granted, free of charge, to any person obtaining a copy
- * of this software and associated documentation files (the "Software"), to deal
- * in the Software without restriction, including without limitation the rights
- * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
- * copies of the Software, and to permit persons to whom the Software is
- * furnished to do so, subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be included in all
- * copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
- * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
- * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
- * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
- * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
- * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
- * SOFTWARE.
- */
- #include <algorithm>
- #include <cstdint>
- #include "argmax.cuh"
- #include "common.cuh"
- #include "sum.cuh"
- static __global__ void argmax_f32(const float * __restrict__ x, int32_t * __restrict__ dst, const int64_t ncols) {
- const int64_t row = blockIdx.x;
- float maxval = -FLT_MAX;
- int argmax = -1;
- const float * rowx = x + row * ncols;
- for (int32_t col = threadIdx.x; col < ncols; col += blockDim.x) {
- const float val = rowx[col];
- if (val > maxval) {
- maxval = val;
- argmax = col;
- }
- }
- #pragma unroll
- for (int offset = 16; offset > 0; offset >>= 1) {
- const float val = __shfl_xor_sync(0xFFFFFFFF, maxval, offset, WARP_SIZE);
- const int col = __shfl_xor_sync(0xFFFFFFFF, argmax, offset, WARP_SIZE);
- if (val > maxval) {
- maxval = val;
- argmax = col;
- }
- }
- const int n_warps = blockDim.x / WARP_SIZE;
- const int lane_id = threadIdx.x % WARP_SIZE;
- const int warp_id = threadIdx.x / WARP_SIZE;
- if (n_warps > 1) {
- constexpr int max_warps = 1024 / WARP_SIZE;
- __shared__ float shared_maxval[max_warps];
- __shared__ int shared_argmax[max_warps];
- if (lane_id == 0) {
- shared_maxval[warp_id] = maxval;
- shared_argmax[warp_id] = argmax;
- }
- __syncthreads();
- if (warp_id == 0) {
- if (lane_id < n_warps) {
- maxval = shared_maxval[lane_id];
- argmax = shared_argmax[lane_id];
- }
- #pragma unroll
- for (int offset = 16; offset > 0; offset >>= 1) {
- const float val = __shfl_xor_sync(0xFFFFFFFF, maxval, offset, WARP_SIZE);
- const int col = __shfl_xor_sync(0xFFFFFFFF, argmax, offset, WARP_SIZE);
- if (val > maxval) {
- maxval = val;
- argmax = col;
- }
- }
- }
- }
- if (warp_id == 0 && lane_id == 0) {
- dst[row] = argmax;
- }
- }
- 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;
- const int64_t num_threads = std::min<int64_t>(1024, (ne00 + WARP_SIZE - 1) / WARP_SIZE * WARP_SIZE);
- const dim3 blocks_dim(num_threads, 1, 1);
- const dim3 blocks_num(num_blocks, 1, 1);
- argmax_f32<<<blocks_num, blocks_dim, 0, stream>>>(src0_d, dst_d, ne00);
- }
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