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opencl: add bin kernel kernel_gemm_noshuffle_q6_k_f32_32b_trans_ila_a8_bin #28678

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Merged
max-krasnyansky merged 2 commits into from
Sep 18, 2026
Merged

opencl: add bin kernel kernel_gemm_noshuffle_q6_k_f32_32b_trans_ila_a8_bin #28678

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4bf5008
opencl: add A8 Q6_K non-MoE binary kernel
shaofeiqi Sep 1, 2026
d828312
opencl: fix layout compatibility
shaofeiqi Sep 16, 2026
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1 change: 1 addition & 0 deletions ggml/src/ggml-opencl/CMakeLists.txt
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Original file line number Diff line number Diff line change
Expand Up @@ -191,6 +191,7 @@ set(GGML_OPENCL_KERNELS
gemv_noshuffle_q6_k_f32_tiled
gemm_noshuffle_q6_k_f32
gemm_noshuffle_q6_k_f32_tiled
gemv_noshuffle_q6_k_f32_32b_trans
gemv_noshuffle_q5_k_f32
gemm_noshuffle_q5_k_f32
mul
Expand Down
275 changes: 259 additions & 16 deletions ggml/src/ggml-opencl/ggml-opencl.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -1246,6 +1246,8 @@ struct ggml_backend_opencl_context {
cl_kernel kernel_gemv_noshuffle_q6_K_f32_mc3; // multi-column (N=3) verify GEMV
cl_kernel kernel_gemm_noshuffle_q6_K_f32;
cl_kernel kernel_gemm_noshuffle_q6_K_f32_cok;
cl_kernel kernel_gemm_noshuffle_q6_k_f32_32b_trans_ila_a8_bin;
cl_kernel kernel_gemv_noshuffle_q6_k_f32_32b_trans;
cl_kernel kernel_gemv_noshuffle_q5_k_f32;
cl_kernel kernel_gemv_noshuffle_q5_k_f32_mc3; // multi-column (N=3) verify GEMV (spec/MTP)
cl_kernel kernel_gemm_noshuffle_q5_k_f32;
Expand Down Expand Up @@ -4366,6 +4368,43 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) {
}
}

backend_ctx->kernel_gemv_noshuffle_q6_k_f32_32b_trans = nullptr;
backend_ctx->kernel_gemm_noshuffle_q6_k_f32_32b_trans_ila_a8_bin = nullptr;
if (backend_ctx->adreno_gen == ADRENO_GPU_GEN::X2E) {
{
std::string opts = std::string("-cl-std=") + opencl_c_std +
" -cl-mad-enable "
" -DSIMDGROUP_WIDTH=" +
std::to_string(backend_ctx->adreno_wave_size);
#ifdef GGML_OPENCL_EMBED_KERNELS
const std::string kernel_src {
#include "gemv_noshuffle_q6_k_f32_32b_trans.cl.h"
};
#else
const std::string kernel_src = read_file("gemv_noshuffle_q6_k_f32_32b_trans.cl");
#endif
cl_program prog = build_program_from_source(backend_ctx, kernel_src.c_str(), opts);
CL_CHECK((backend_ctx->kernel_gemv_noshuffle_q6_k_f32_32b_trans =
clCreateKernel(prog, "kernel_gemv_noshuffle_q6_k_f32_32b_trans", &err), err));
CL_CHECK(clReleaseProgram(prog));
GGML_LOG_CONT(".");
}

if (use_adreno_bin_kernels(backend_ctx)) {
size_t bin_size = 0;
const char * kernel_bin = (const char *)backend_ctx->get_adreno_bin_kernel("gemm_noshuffle_q6_k_f32_32b_trans_ila_a8", &bin_size);
if (kernel_bin && bin_size > 0) {
cl_program bin_prog =
build_program_from_binary(backend_ctx->context, backend_ctx->device, kernel_bin, "", bin_size);

CL_CHECK((backend_ctx->kernel_gemm_noshuffle_q6_k_f32_32b_trans_ila_a8_bin =
clCreateKernel(bin_prog, "kernel_gemm_noshuffle_q6_k_f32_32b_trans_ila_a8", &err), err));
CL_CHECK(clReleaseProgram(bin_prog));
GGML_LOG_CONT(".");
}
}
}

std::string CL_moe_compile_opts = std::string("-cl-std=") + opencl_c_std +
" -cl-mad-enable "
" -cl-fast-relaxed-math";
Expand Down Expand Up @@ -7291,6 +7330,8 @@ struct ggml_tensor_extra_cl_q6_K {
cl_mem ql_img = nullptr;
// Upper 2 bits of quantized weights.
cl_mem qh = nullptr;
// Upper 2 bits as image1d_buffer_t
cl_mem qh_img = nullptr;
// Scales for each block.
cl_mem s = nullptr;
// Scales for each super block.
Expand Down Expand Up @@ -7326,6 +7367,10 @@ struct ggml_tensor_extra_cl_q6_K {
CL_CHECK(clReleaseMemObject(ql_img));
ql_img = nullptr;
}
if (qh_img != nullptr) {
CL_CHECK(clReleaseMemObject(qh_img));
qh_img = nullptr;
}

size_ql = 0;
size_qh = 0;
Expand Down Expand Up @@ -8564,6 +8609,21 @@ static inline bool use_flat_gemv_for_large_m_q6_K(const ggml_backend_opencl_cont
&& tensor->ne[2] == 1 && tensor->ne[3] == 1;
}

inline bool use_q6_k_bin_kernels(const ggml_backend_opencl_context *backend_ctx, const ggml_tensor *tensor) {
#ifdef GGML_OPENCL_USE_ADRENO_KERNELS
if (!backend_ctx->kernel_gemv_noshuffle_q6_k_f32_32b_trans ||
!backend_ctx->kernel_gemm_noshuffle_q6_k_f32_32b_trans_ila_a8_bin) {
return false;
}
return (tensor->ne[0] % 256 == 0) && (tensor->ne[1] % 64 == 0) &&
!use_q6k_tiled(backend_ctx, tensor) && !use_flat_gemv_for_large_m_q6_K(backend_ctx, tensor);
#else
GGML_UNUSED(backend_ctx);
GGML_UNUSED(tensor);
return false;
#endif
}

inline bool use_q4_k_bin_kernels(const ggml_backend_opencl_context *backend_ctx, const ggml_tensor *tensor) {
#ifdef GGML_OPENCL_USE_ADRENO_KERNELS
if (!backend_ctx->kernel_gemv_noshuffle_q4_k_f32_32b_trans ||
Expand Down Expand Up @@ -11179,18 +11239,39 @@ static void ggml_backend_opencl_buffer_set_tensor(ggml_backend_buffer_t buffer,
cl_int M = tensor->ne[1]; // ne01
cl_int K = tensor->ne[0]; // ne00

// Transpose ql as ushort
transpose_2d_as_16b(backend_ctx,
extra->ql, extra->ql, size_ql, K/4, M);
if (use_q6_k_bin_kernels(backend_ctx, tensor)) {
GGML_ASSERT(K % 256 == 0);
GGML_ASSERT(M % 64 == 0);

// Transpose qh as uchar
transpose_2d_as_8b(backend_ctx,
extra->qh, extra->qh, size_qh, K/4, M);
transpose_2d_as_32b(backend_ctx, extra->ql, extra->ql, size_ql, K/8, M);
transpose_2d_as_32b(backend_ctx, extra->qh, extra->qh, size_qh, K/16, M);

// Transpose s as ushort
transpose_2d_as_16b(backend_ctx,
extra->s, extra->s, size_s, K/16/2, M);
cl_image_format wimg_fmt = { CL_R, CL_UNSIGNED_INT32 };
cl_image_desc wimg_desc;
memset(&wimg_desc, 0, sizeof(wimg_desc));
wimg_desc.image_type = CL_MEM_OBJECT_IMAGE1D_BUFFER;
wimg_desc.image_width = static_cast<size_t>(ggml_nelements(tensor) / 8);
wimg_desc.buffer = extra->ql;
CL_CHECK((extra->ql_img = clCreateImage(context, CL_MEM_READ_ONLY, &wimg_fmt, &wimg_desc, NULL, &err), err));

memset(&wimg_desc, 0, sizeof(wimg_desc));
wimg_desc.image_type = CL_MEM_OBJECT_IMAGE1D_BUFFER;
wimg_desc.image_width = static_cast<size_t>(ggml_nelements(tensor) / 16);
wimg_desc.buffer = extra->qh;
CL_CHECK((extra->qh_img = clCreateImage(context, CL_MEM_READ_ONLY, &wimg_fmt, &wimg_desc, NULL, &err), err));
} else {
// Transpose ql as ushort
transpose_2d_as_16b(backend_ctx,
extra->ql, extra->ql, size_ql, K/4, M);

// Transpose qh as uchar
transpose_2d_as_8b(backend_ctx,
extra->qh, extra->qh, size_qh, K/4, M);

// Transpose s as ushort
transpose_2d_as_16b(backend_ctx,
extra->s, extra->s, size_s, K/16/2, M);
}
// Transpose d as ushort
transpose_2d_as_16b(backend_ctx,
extra->d, extra->d, size_d, K/256, M);
Expand Down Expand Up @@ -12318,23 +12399,32 @@ static void ggml_backend_opencl_buffer_get_tensor(ggml_backend_buffer_t buffer,

buf_trans_ql.allocate(backend_ctx->context, size_ql);
buf_trans_qh.allocate(backend_ctx->context, size_qh);
buf_trans_s.allocate(backend_ctx->context, size_s);
buf_trans_d.allocate(backend_ctx->context, size_d);
buf_unpacked.allocate(backend_ctx->context, ggml_nbytes(tensor));

// transpose ql, qh, s and d back
transpose_2d_as_16b(backend_ctx, extra->ql, buf_trans_ql.buffer, size_ql, M, K/4);
transpose_2d_as_8b(backend_ctx, extra->qh, buf_trans_qh.buffer, size_qh, M, K/4);
transpose_2d_as_16b(backend_ctx, extra->s, buf_trans_s.buffer, size_s, M, K/16/2);
transpose_2d_as_16b(backend_ctx, extra->d, buf_trans_d.buffer, size_d, M, K/256);
cl_mem s_buffer;
if (use_q6_k_bin_kernels(backend_ctx, tensor)) {
transpose_2d_as_32b(backend_ctx, extra->ql, buf_trans_ql.buffer, size_ql, M, K/8);
transpose_2d_as_32b(backend_ctx, extra->qh, buf_trans_qh.buffer, size_qh, M, K/16);
// s is left row-major, untransposed, for the binary layout.
s_buffer = extra->s;
} else {
// transpose ql, qh, s and d back
buf_trans_s.allocate(backend_ctx->context, size_s);
transpose_2d_as_16b(backend_ctx, extra->ql, buf_trans_ql.buffer, size_ql, M, K/4);
transpose_2d_as_8b(backend_ctx, extra->qh, buf_trans_qh.buffer, size_qh, M, K/4);
transpose_2d_as_16b(backend_ctx, extra->s, buf_trans_s.buffer, size_s, M, K/16/2);
s_buffer = buf_trans_s.buffer;
}
transpose_2d_as_16b(backend_ctx, extra->d, buf_trans_d.buffer, size_d, M, K/256);

// unpack
cl_uchar mask = 0xFF;
cl_ulong n_blk = ggml_nelements(tensor)/ggml_blck_size(tensor->type);
cl_kernel kernel = backend_ctx->kernel_restore_block_q6_K_noshuffle;
CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &buf_trans_ql.buffer));
CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &buf_trans_qh.buffer));
CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &buf_trans_s.buffer));
CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &s_buffer));
CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_mem), &buf_trans_d.buffer));
CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_mem), &buf_unpacked.buffer));
CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_uchar), &mask));
Expand Down Expand Up @@ -21112,6 +21202,145 @@ static void ggml_cl_mul_mat_q4_k_f32_adreno(ggml_backend_t backend, const ggml_t
#endif
}

#ifdef GGML_OPENCL_USE_ADRENO_KERNELS
static void ggml_cl_mul_mat_q6_K_f32_adreno_ila(ggml_backend_t backend, const ggml_tensor * src0,
const ggml_tensor * src1, ggml_tensor * dst) {
GGML_ASSERT(src0);
GGML_ASSERT(src0->extra);
GGML_ASSERT(src1);
GGML_ASSERT(src1->extra);
GGML_ASSERT(dst);
GGML_ASSERT(dst->extra);

ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context;

ggml_tensor_extra_cl_q6_K * extra0_q6_K = (ggml_tensor_extra_cl_q6_K *)src0->extra;
ggml_tensor_extra_cl * extra1 = (ggml_tensor_extra_cl *)src1->extra;
ggml_tensor_extra_cl * extrad = (ggml_tensor_extra_cl *)dst->extra;

cl_ulong offset1 = extra1->offset + src1->view_offs;
cl_ulong offsetd = extrad->offset + dst->view_offs;

const int ne00 = src0->ne[0];
const int ne01 = src0->ne[1];

const int ne1 = dst->ne[1];

GGML_ASSERT(ne00 % ggml_blck_size(src0->type) == 0);

cl_context context = backend_ctx->context;
cl_kernel kernel;

cl_int err;
cl_buffer_region region;
cl_image_format img_fmt;
cl_image_desc img_desc;

const int M = ne01;
const int N = ne1;
const int K = ne00;

if (ne1 == 1) {
cl_mem b_sub_buf = nullptr;
cl_mem b_img = nullptr;

region.origin = offset1;
region.size = (size_t)K * N * sizeof(float);
CL_CHECK((b_sub_buf = clCreateSubBuffer(extra1->data_device, 0, CL_BUFFER_CREATE_TYPE_REGION, &region, &err), err));

img_fmt = { CL_RGBA, CL_FLOAT };
memset(&img_desc, 0, sizeof(img_desc));
img_desc.image_type = CL_MEM_OBJECT_IMAGE1D_BUFFER;
img_desc.image_width = (size_t)K * N / 4;
img_desc.buffer = b_sub_buf;
CL_CHECK((b_img = clCreateImage(context, CL_MEM_READ_ONLY, &img_fmt, &img_desc, NULL, &err), err));

kernel = backend_ctx->kernel_gemv_noshuffle_q6_k_f32_32b_trans;
CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra0_q6_K->ql_img));
CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &extra0_q6_K->qh_img));
CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extra0_q6_K->s));
CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_mem), &extra0_q6_K->d));
CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_mem), &b_img));
CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_mem), &extrad->data_device));
CL_CHECK(clSetKernelArg(kernel, 6, sizeof(cl_ulong), &offsetd));
CL_CHECK(clSetKernelArg(kernel, 7, sizeof(cl_int), &ne00));
CL_CHECK(clSetKernelArg(kernel, 8, sizeof(cl_int), &ne01));

size_t local_work_size[3] = { 64, 8, 1 };
size_t global_work_size[3] = { (size_t)ne01, 8, 1 };
backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst);

CL_CHECK(clReleaseMemObject(b_img));
CL_CHECK(clReleaseMemObject(b_sub_buf));
} else {
const int gemm_tile_n = 64;
int N_pad = CEIL_DIV(N, gemm_tile_n) * gemm_tile_n;

cl_mem b_sub_buf = nullptr;
cl_mem b_padded = nullptr;
cl_mem b_buf = nullptr;
if (N_pad == N) {
region.origin = offset1;
region.size = (size_t)K * N * sizeof(float);
CL_CHECK((b_sub_buf = clCreateSubBuffer(extra1->data_device, 0, CL_BUFFER_CREATE_TYPE_REGION, &region, &err), err));
b_buf = b_sub_buf;
} else {
CL_CHECK((b_padded = clCreateBuffer(context, CL_MEM_READ_WRITE, (size_t)K * N_pad * sizeof(float), NULL, &err), err));
const float zero = 0.0f;
CL_CHECK(clEnqueueFillBuffer(backend_ctx->queue, b_padded, &zero, sizeof(zero), 0, (size_t)K * N_pad * sizeof(float), 0, NULL, NULL));
CL_CHECK(clEnqueueCopyBuffer(backend_ctx->queue, extra1->data_device, b_padded, offset1, 0, (size_t)K * N * sizeof(float), 0, NULL, NULL));
b_buf = b_padded;
}

img_fmt = { CL_R, CL_FLOAT };
memset(&img_desc, 0, sizeof(img_desc));
img_desc.image_type = CL_MEM_OBJECT_IMAGE1D_BUFFER;
img_desc.image_width = (size_t)K * N_pad;
img_desc.buffer = b_buf;
cl_mem b_img;
CL_CHECK((b_img = clCreateImage(context, CL_MEM_READ_ONLY, &img_fmt, &img_desc, NULL, &err), err));

region.origin = offsetd;
region.size = (size_t)M * N * sizeof(float);
cl_mem d_sub_buf;
CL_CHECK((d_sub_buf = clCreateSubBuffer(extrad->data_device, 0, CL_BUFFER_CREATE_TYPE_REGION, &region, &err), err));
img_fmt = { CL_R, CL_FLOAT };
memset(&img_desc, 0, sizeof(img_desc));
img_desc.image_type = CL_MEM_OBJECT_IMAGE1D_BUFFER;
img_desc.image_width = (size_t)M * N;
img_desc.buffer = d_sub_buf;
cl_mem d_img;
CL_CHECK((d_img = clCreateImage(context, CL_MEM_WRITE_ONLY, &img_fmt, &img_desc, NULL, &err), err));

kernel = backend_ctx->kernel_gemm_noshuffle_q6_k_f32_32b_trans_ila_a8_bin;
CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra0_q6_K->ql_img));
CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &extra0_q6_K->qh));
CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extra0_q6_K->s));
CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_mem), &extra0_q6_K->d));
CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_mem), &b_img));
CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_mem), &d_img));
CL_CHECK(clSetKernelArg(kernel, 6, sizeof(cl_uint), &ne00));
CL_CHECK(clSetKernelArg(kernel, 7, sizeof(cl_uint), &ne01));
CL_CHECK(clSetKernelArg(kernel, 8, sizeof(int), &N));

size_t local_work_size[3] = { 64, 2, 2 };
size_t m_tiles = (size_t)CEIL_DIV(M, 64);
size_t global_work_size[3] = { 64, m_tiles, (size_t)CEIL_DIV(N_pad, gemm_tile_n) };
backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst);

CL_CHECK(clReleaseMemObject(b_img));
if (b_sub_buf) {
CL_CHECK(clReleaseMemObject(b_sub_buf));
}
if (b_padded) {
CL_CHECK(clReleaseMemObject(b_padded));
}
CL_CHECK(clReleaseMemObject(d_img));
CL_CHECK(clReleaseMemObject(d_sub_buf));
}
}
#endif // GGML_OPENCL_USE_ADRENO_KERNELS

static void ggml_cl_mul_mat_q6_K_f32_adreno(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
#ifdef GGML_OPENCL_USE_ADRENO_KERNELS
GGML_ASSERT(src0);
Expand Down Expand Up @@ -21160,6 +21389,20 @@ static void ggml_cl_mul_mat_q6_K_f32_adreno(ggml_backend_t backend, const ggml_t
// (the #1 MTP bottleneck; mc3 above can't, it reads the noshuffle layout).
const bool use_q6k_tiled_mc = q6k_mc3 && (ne1 == 3) && (ne01 >= 32768) && use_q6k_tiled(backend_ctx, src0);

const bool use_bin = use_q6_k_bin_kernels(backend_ctx, src0);

if (use_bin) {
if (use_q6k_mc3 || use_q6k_tiled_mc) {
static bool warned = false;
if (!warned) {
GGML_LOG_WARN("ggml_opencl: GGML_OPENCL_Q6K_MC3 is bypassed by Q6_K binary kernels\n");
warned = true;
}
}
ggml_cl_mul_mat_q6_K_f32_adreno_ila(backend, src0, src1, dst);
return;
}

if (ne1 == 1 || use_q6k_mc3 || use_q6k_tiled_mc) {
cl_mem ql_img = nullptr;
cl_mem qh_img = nullptr;
Expand Down
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