EngineX-Ascend/enginex-ascend-910-llama.cpp
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CUDA: support for mat. mul. with ne03 != ne13 (#11656)

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This commit is contained in:
Johannes Gäßler
2025-02-05 08:58:31 +01:00
committed by GitHub
parent 1ec208083c
commit fa62da9b2d
2 changed files with 81 additions and 60 deletions
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114
ggml/src/ggml-cuda/mmv.cu
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@@ -1,18 +1,21 @@
#include "ggml.h"
#include "common.cuh"
#include "mmv.cuh"
template <typename T, typename type_acc, int block_size>
static __global__ void mul_mat_vec(
const T * __restrict__ x, const float * __restrict__ y, float * __restrict__ dst, const int64_t ncols2, const int64_t stride_row,
const int64_t channel_ratio, const int64_t stride_channel_x, const int64_t stride_channel_y, const int64_t stride_channel_dst) {
const int64_t channel_ratio, const int64_t stride_channel_x, const int64_t stride_channel_y, const int64_t stride_channel_dst,
const int64_t sample_ratio, const int64_t stride_sample_x, const int64_t stride_sample_y, const int64_t stride_sample_dst) {
const int64_t row = blockIdx.x;
const int64_t channel = blockIdx.z;
const int64_t channel = blockIdx.y;
const int64_t sample = blockIdx.z;
const int tid = threadIdx.x;
constexpr int warp_size = ggml_cuda_get_physical_warp_size();
x += (channel/channel_ratio)*stride_channel_x + row*stride_row;
y += channel *stride_channel_y;
dst += channel *stride_channel_dst;
x += (sample/sample_ratio)*stride_sample_x + (channel/channel_ratio)*stride_channel_x + row*stride_row;
y += sample *stride_sample_y + channel *stride_channel_y;
dst += sample *stride_sample_dst + channel *stride_channel_dst;
const float2 * y2 = (const float2 *) y;
@@ -91,12 +94,15 @@ template <typename T, typename type_acc>
static void launch_mul_mat_vec_cuda(
const T * x, const float * y, float * dst,
const int64_t ncols, const int64_t nrows, const int64_t stride_row, const int64_t nchannels_x, const int64_t nchannels_y,
const int64_t stride_channel_x, const int64_t stride_channel_y, const int64_t stride_channel_dst,
const int64_t stride_channel_x, const int64_t stride_channel_y, const int64_t stride_channel_dst, const int64_t nsamples_x,
const int64_t nsamples_y, const int64_t stride_sample_x, const int64_t stride_sample_y, const int64_t stride_sample_dst,
cudaStream_t stream) {
GGML_ASSERT(ncols % 2 == 0);
GGML_ASSERT(stride_row % 2 == 0);
GGML_ASSERT(nchannels_y % nchannels_x == 0);
GGML_ASSERT(nsamples_y % nsamples_x == 0);
const int64_t channel_ratio = nchannels_y / nchannels_x;
const int64_t sample_ratio = nsamples_y / nsamples_x;
int device;
int warp_size;
@@ -118,40 +124,48 @@ static void launch_mul_mat_vec_cuda(
}
const int smem = warp_size*sizeof(float);
const dim3 block_nums(nrows, 1, nchannels_y);
const dim3 block_nums(nrows, nchannels_y, nsamples_y);
const dim3 block_dims(block_size_best, 1, 1);
switch (block_size_best) {
case 32: {
mul_mat_vec<T, type_acc, 32><<<block_nums, block_dims, smem, stream>>>
(x, y, dst, ncols/2, stride_row, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst);
(x, y, dst, ncols/2, stride_row, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst);
} break;
case 64: {
mul_mat_vec<T, type_acc, 64><<<block_nums, block_dims, smem, stream>>>
(x, y, dst, ncols/2, stride_row, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst);
(x, y, dst, ncols/2, stride_row, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst);
} break;
case 96: {
mul_mat_vec<T, type_acc, 96><<<block_nums, block_dims, smem, stream>>>
(x, y, dst, ncols/2, stride_row, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst);
(x, y, dst, ncols/2, stride_row, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst);
} break;
case 128: {
mul_mat_vec<T, type_acc, 128><<<block_nums, block_dims, smem, stream>>>
(x, y, dst, ncols/2, stride_row, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst);
(x, y, dst, ncols/2, stride_row, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst);
} break;
case 160: {
mul_mat_vec<T, type_acc, 160><<<block_nums, block_dims, smem, stream>>>
(x, y, dst, ncols/2, stride_row, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst);
(x, y, dst, ncols/2, stride_row, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst);
} break;
case 192: {
mul_mat_vec<T, type_acc, 192><<<block_nums, block_dims, smem, stream>>>
(x, y, dst, ncols/2, stride_row, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst);
(x, y, dst, ncols/2, stride_row, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst);
} break;
case 224: {
mul_mat_vec<T, type_acc, 224><<<block_nums, block_dims, smem, stream>>>
(x, y, dst, ncols/2, stride_row, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst);
(x, y, dst, ncols/2, stride_row, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst);
} break;
case 256: {
mul_mat_vec<T, type_acc, 256><<<block_nums, block_dims, smem, stream>>>
(x, y, dst, ncols/2, stride_row, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst);
(x, y, dst, ncols/2, stride_row, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst);
} break;
default: {
GGML_ABORT("fatal error");
@@ -163,16 +177,19 @@ template<typename T>
static void mul_mat_vec_cuda(
const T * x, const float * y, float * dst,
const int64_t ncols, const int64_t nrows, const int64_t stride_row, const int64_t nchannels_x, const int64_t nchannels_y,
const int64_t stride_channel_x, const int64_t stride_channel_y, const int64_t stride_channel_dst,
const int64_t stride_channel_x, const int64_t stride_channel_y, const int64_t stride_channel_dst, const int64_t nsamples_x,
const int64_t nsamples_y, const int64_t stride_sample_x, const int64_t stride_sample_y, const int64_t stride_sample_dst,
enum ggml_prec prec, cudaStream_t stream) {
switch (prec) {
case GGML_PREC_DEFAULT: {
launch_mul_mat_vec_cuda<T, half>(x, y, dst, ncols, nrows, stride_row, nchannels_x, nchannels_y,
stride_channel_x, stride_channel_y, stride_channel_dst, stream);
launch_mul_mat_vec_cuda<T, half>
(x, y, dst, ncols, nrows, stride_row, nchannels_x, nchannels_y, stride_channel_x, stride_channel_y, stride_channel_dst,
nsamples_x, nsamples_y, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
} break;
case GGML_PREC_F32: {
launch_mul_mat_vec_cuda<T, float>(x, y, dst, ncols, nrows, stride_row, nchannels_x, nchannels_y,
stride_channel_x, stride_channel_y, stride_channel_dst, stream);
launch_mul_mat_vec_cuda<T, float>
(x, y, dst, ncols, nrows, stride_row, nchannels_x, nchannels_y, stride_channel_x, stride_channel_y, stride_channel_dst,
nsamples_x, nsamples_y, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
} break;
}
}
@@ -181,10 +198,19 @@ void ggml_cuda_mul_mat_vec(ggml_backend_cuda_context & ctx, const ggml_tensor *
GGML_ASSERT(src1->type == GGML_TYPE_F32);
GGML_ASSERT(dst->type == GGML_TYPE_F32);
const int64_t ne00 = src0->ne[0];
const int64_t ne01 = src0->ne[1];
GGML_TENSOR_BINARY_OP_LOCALS;
GGML_ASSERT(src1->ne[1] == 1);
const size_t ts_src0 = ggml_type_size(src0->type);
const size_t ts_src1 = ggml_type_size(src1->type);
const size_t ts_dst = ggml_type_size(dst->type);
GGML_ASSERT(ne11 == 1);
GGML_ASSERT(ne12 == ne2);
GGML_ASSERT(ne13 == ne3);
GGML_ASSERT(nb00 == ts_src0);
GGML_ASSERT(nb10 == ts_src1);
GGML_ASSERT(nb0 == ts_dst);
const int cc = ggml_cuda_info().devices[ggml_cuda_get_device()].cc;
const enum ggml_prec prec = fast_fp16_available(cc) ? ggml_prec(dst->op_params[0]) : GGML_PREC_F32;
@@ -192,29 +218,22 @@ void ggml_cuda_mul_mat_vec(ggml_backend_cuda_context & ctx, const ggml_tensor *
const float * src1_d = (const float *) src1->data;
float * dst_d = (float *) dst->data;
const int64_t ne02 = src0->ne[2];
const int64_t ne12 = src1->ne[2];
GGML_ASSERT(dst->ne[2] == ne12);
GGML_ASSERT(src0->ne[3] == 1);
GGML_ASSERT(src1->ne[3] == 1);
GGML_ASSERT( dst->ne[3] == 1);
const int64_t stride_row = src0->nb[1] / ggml_type_size(src0->type);
const int64_t channel_stride_x = src0->nb[2] / ggml_type_size(src0->type);
const int64_t channel_stride_y = src1->nb[2] / ggml_type_size(src1->type);
const int64_t channel_stride_dst = dst->nb[2] / ggml_type_size( dst->type);
const int64_t s01 = src0->nb[1] / ts_src0;
const int64_t s02 = src0->nb[2] / ts_src0;
const int64_t s12 = src1->nb[2] / ts_src1;
const int64_t s2 = dst->nb[2] / ts_dst;
const int64_t s03 = src0->nb[3] / ts_src0;
const int64_t s13 = src1->nb[3] / ts_src1;
const int64_t s3 = dst->nb[3] / ts_dst;
switch (src0->type) {
case GGML_TYPE_F16: {
const half * src0_d = (const half *) src0->data;
mul_mat_vec_cuda(src0_d, src1_d, dst_d, ne00, ne01, stride_row, ne02, ne12,
channel_stride_x, channel_stride_y, channel_stride_dst, prec, ctx.stream());
mul_mat_vec_cuda(src0_d, src1_d, dst_d, ne00, ne01, s01, ne02, ne12, s02, s12, s2, ne03, ne13, s03, s13, s3, prec, ctx.stream());
} break;
case GGML_TYPE_BF16: {
const nv_bfloat16 * src0_d = (const nv_bfloat16 *) src0->data;
mul_mat_vec_cuda(src0_d, src1_d, dst_d, ne00, ne01, stride_row, ne02, ne12,
channel_stride_x, channel_stride_y, channel_stride_dst, prec, ctx.stream());
mul_mat_vec_cuda(src0_d, src1_d, dst_d, ne00, ne01, s01, ne02, ne12, s02, s12, s2, ne03, ne13, s03, s13, s3, prec, ctx.stream());
} break;
default:
GGML_ABORT("unsupported type: %s", ggml_type_name(src0->type));
@@ -243,20 +262,27 @@ void ggml_cuda_op_mul_mat_vec(
const int64_t stride_row = ne00;
const int64_t nchannels_x = 1;
const int64_t nchannels_y = 1;
const int64_t channel_stride_x = 0;
const int64_t channel_stride_y = 0;
const int64_t channel_stride_dst = 0;
const int64_t stride_channel_x = 0;
const int64_t stride_channel_y = 0;
const int64_t stride_channel_dst = 0;
const int64_t nsamples_x = 1;
const int64_t nsamples_y = 1;
const int64_t stride_sample_x = 0;
const int64_t stride_sample_y = 0;
const int64_t stride_sample_dst = 0;
switch (src0->type) {
case GGML_TYPE_F16: {
const half * src0_d = (const half *) src0_dd_i;
mul_mat_vec_cuda(src0_d, src1_ddf_i, dst_dd_i, ne00, row_diff, stride_row,
nchannels_x, nchannels_y, channel_stride_x, channel_stride_y, channel_stride_dst, prec, stream);
nchannels_x, nchannels_y, stride_channel_x, stride_channel_y, stride_channel_dst,
nsamples_x, nsamples_y, stride_sample_x, stride_sample_y, stride_sample_dst, prec, stream);
} break;
case GGML_TYPE_BF16: {
const nv_bfloat16 * src0_d = (const nv_bfloat16 *) src0_dd_i;
mul_mat_vec_cuda(src0_d, src1_ddf_i, dst_dd_i, ne00, row_diff, stride_row,
nchannels_x, nchannels_y, channel_stride_x, channel_stride_y, channel_stride_dst, prec, stream);
nchannels_x, nchannels_y, stride_channel_x, stride_channel_y, stride_channel_dst,
nsamples_x, nsamples_y, stride_sample_x, stride_sample_y, stride_sample_dst, prec, stream);
} break;
default:
GGML_ABORT("unsupported type: %s", ggml_type_name(src0->type));