vbatts/llama.cpp
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k_quants: hopefully much faster Q4_K on older GPUs

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On the GTX-1660 that I have available to represent
"old GPUs", token prediction drops from 65.5 ms/tok
to 41.5 ms/tok!
This commit is contained in:
Iwan Kawrakow 2023-06-18 16:37:25 +03:00
parent 8596af4277
commit 1677059ba1
1 changed files with 16 additions and 10 deletions
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26
ggml-cuda.cu
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@ -618,22 +618,25 @@ static __global__ void dequantize_mul_mat_vec_q3_k(const void * vx, const float
} }
} }
static __global__ void dequantize_mul_mat_vec_q4_k(const void * vx, const float * yy, float * dst, const int ncols) { static __global__ void dequantize_mul_mat_vec_q4_k(const void * vx, const float * yy, float * dst, const int ncols, int nrows) {
const uint16_t kmask1 = 0x3f3f; const uint16_t kmask1 = 0x3f3f;
const uint16_t kmask2 = 0x0f0f; const uint16_t kmask2 = 0x0f0f;
const uint16_t kmask3 = 0xc0c0; const uint16_t kmask3 = 0xc0c0;
const int row = blockIdx.x; const int row = blockIdx.y*blockDim.y + threadIdx.y;
if (row > nrows) return;
const int num_blocks_per_row = ncols / QK_K; const int num_blocks_per_row = ncols / QK_K;
const int ib0 = row*num_blocks_per_row; const int ib0 = row*num_blocks_per_row;
const int tid = threadIdx.x/2; // 0...15 const int tid = threadIdx.x/K_QUANTS_PER_ITERATION; // 0...31 or 0...16
const int ix = threadIdx.x%2; const int ix = threadIdx.x%K_QUANTS_PER_ITERATION; // 0 or 0,1
const int il = tid/4; // 0...3 const int step = 8/K_QUANTS_PER_ITERATION; // 8 or 4
const int ir = tid - 4*il;// 0...3
const int n = 4; const int il = tid/step; // 0...3
const int ir = tid - step*il; // 0...7 or 0...3
const int n = 2 * K_QUANTS_PER_ITERATION; // 2 or 4
const int im = il/2; // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224 const int im = il/2; // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224
const int in = il%2; const int in = il%2;
@ -649,7 +652,7 @@ static __global__ void dequantize_mul_mat_vec_q4_k(const void * vx, const float
float tmp = 0; // partial sum for thread in warp float tmp = 0; // partial sum for thread in warp
for (int i = ix; i < num_blocks_per_row; i += 2) { for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
const uint8_t * q1 = x[i].qs + q_offset; const uint8_t * q1 = x[i].qs + q_offset;
const uint8_t * q2 = q1 + 64; const uint8_t * q2 = q1 + 64;
@ -1275,8 +1278,11 @@ static void dequantize_mul_mat_vec_q3_K_cuda(const void * vx, const float * y, f
static void dequantize_mul_mat_vec_q4_K_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) { static void dequantize_mul_mat_vec_q4_K_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
GGML_ASSERT(ncols % QK_K == 0); GGML_ASSERT(ncols % QK_K == 0);
const dim3 block_dims(32, 1, 1); const int ny = 2 / K_QUANTS_PER_ITERATION;
dequantize_mul_mat_vec_q4_k<<<nrows, block_dims, 0, stream>>>(vx, y, dst, ncols); const int block_num_y = (nrows + ny - 1) / ny;
const dim3 block_nums(1, block_num_y, 1);
const dim3 block_dims(32, ny, 1);
dequantize_mul_mat_vec_q4_k<<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
} }
static void dequantize_mul_mat_vec_q5_K_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) { static void dequantize_mul_mat_vec_q5_K_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {