char-fork/llama.cpp
git clone https://git.t4t.associates/char-fork/llama.cpp
f3f65429c
char/aliceai-t5-moe
1#include "diagmask.cuh" 2 3static __global__ void diag_mask_inf_f32(const float * x, float * dst, const int ncols, const int rows_per_channel, const int n_past) { 4 const int col = blockDim.y*blockIdx.y + threadIdx.y; 5 const int row = blockDim.x*blockIdx.x + threadIdx.x; 6 7 if (col >= ncols) { 8 return; 9 } 10 11 const int i = row*ncols + col; 12 //dst[i] = col > (n_past + row % rows_per_channel) ? -INFINITY : x[i]; 13 //dst[i] = x[i] - (col > n_past + row % rows_per_channel) * INT_MAX; // equivalent within rounding error but slightly faster on GPU 14 dst[i] = x[i] - (col > n_past + row % rows_per_channel) * FLT_MAX; 15} 16 17static void diag_mask_inf_f32_cuda(const float * x, float * dst, const int ncols_x, const int nrows_x, const int rows_per_channel, const int n_past, cudaStream_t stream) { 18 const dim3 block_dims(1, CUDA_DIAG_MASK_INF_BLOCK_SIZE, 1); 19 const int block_num_x = (ncols_x + CUDA_DIAG_MASK_INF_BLOCK_SIZE - 1) / CUDA_DIAG_MASK_INF_BLOCK_SIZE; 20 const dim3 block_nums(nrows_x, block_num_x, 1); 21 diag_mask_inf_f32<<<block_nums, block_dims, 0, stream>>>(x, dst, ncols_x, rows_per_channel, n_past); 22} 23 24void ggml_cuda_op_diag_mask_inf(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { 25 const ggml_tensor * src0 = dst->src[0]; 26 const float * src0_d = (const float *)src0->data; 27 float * dst_d = (float *)dst->data; 28 cudaStream_t stream = ctx.stream(); 29 30 GGML_ASSERT(src0->type == GGML_TYPE_F32); 31 GGML_ASSERT( dst->type == GGML_TYPE_F32); 32 33 const int64_t ne00 = src0->ne[0]; 34 const int64_t ne01 = src0->ne[1]; 35 const int nrows0 = ggml_nrows(src0); 36 37 const int n_past = ((int32_t *) dst->op_params)[0]; 38 39 diag_mask_inf_f32_cuda(src0_d, dst_d, ne00, nrows0, ne01, n_past, stream); 40}