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llama.cpp
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opencl: add flattened Q4_K mv and general Q4_K mm

This commit is contained in:
shaoqi 2026-03-19 12:35:07 -07:00
parent a0bbcdd9b6
commit 7794488b32
5 changed files with 733 additions and 0 deletions
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2
ggml/src/ggml-opencl/CMakeLists.txt
View File

@ -89,6 +89,7 @@ set(GGML_OPENCL_KERNELS
mul_mv_q4_1_f32
mul_mv_q4_1_f32_flat
mul_mv_q4_k_f32
mul_mv_q4_k_f32_flat
mul_mv_q6_k_f32
mul_mv_q6_k_f32_flat
mul_mv_q8_0_f32
@ -107,6 +108,7 @@ set(GGML_OPENCL_KERNELS
mul_mm_q4_0_f32_l4_lm
mul_mm_q4_1_f32_l4_lm
mul_mm_q8_0_f32_l4_lm
mul_mm_q4_k_f32_l4_lm
mul_mm_q6_k_f32_l4_lm
mul_mm_q8_0_f32_8x4
gemv_noshuffle_q4_1_f32

289
ggml/src/ggml-opencl/ggml-opencl.cpp
View File

@ -534,11 +534,13 @@ struct ggml_backend_opencl_context {
cl_kernel kernel_restore_block_q4_0_noshuffle;
cl_kernel kernel_convert_block_q4_1_noshuffle;
cl_kernel kernel_restore_block_q4_1_noshuffle;
cl_kernel kernel_convert_block_q4_K, kernel_restore_block_q4_K;
cl_kernel kernel_convert_block_q6_K, kernel_restore_block_q6_K;
cl_kernel kernel_mul_mat_q4_0_f32_1d_8x_flat, kernel_mul_mat_q4_0_f32_1d_16x_flat;
cl_kernel kernel_mul_mv_q4_1_f32;
cl_kernel kernel_mul_mv_q4_1_f32_flat;
cl_kernel kernel_mul_mv_q4_K_f32;
cl_kernel kernel_mul_mv_q4_K_f32_flat;
cl_kernel kernel_mul_mv_q6_K_f32;
cl_kernel kernel_mul_mv_q6_K_f32_flat;
cl_kernel kernel_mul_mv_mxfp4_f32, kernel_mul_mv_mxfp4_f32_flat;
@ -578,6 +580,7 @@ struct ggml_backend_opencl_context {
cl_kernel kernel_mul_mm_q4_0_f32_l4_lm;
cl_kernel kernel_mul_mm_q4_1_f32_l4_lm;
cl_kernel kernel_mul_mm_q8_0_f32_l4_lm;
cl_kernel kernel_mul_mm_q4_k_f32_l4_lm;
cl_kernel kernel_mul_mm_q6_k_f32_l4_lm;
std::vector<ProfilingInfo> profiling_info;
@ -917,6 +920,8 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve
CL_CHECK((backend_ctx->kernel_convert_block_q8_0 = clCreateKernel(backend_ctx->program_cvt, "kernel_convert_block_q8_0", &err), err));
CL_CHECK((backend_ctx->kernel_restore_block_q8_0 = clCreateKernel(backend_ctx->program_cvt, "kernel_restore_block_q8_0", &err), err));
CL_CHECK((backend_ctx->kernel_restore_block_q8_0_trans = clCreateKernel(backend_ctx->program_cvt, "kernel_restore_block_q8_0_trans", &err), err));
CL_CHECK((backend_ctx->kernel_convert_block_q4_K = clCreateKernel(backend_ctx->program_cvt, "kernel_convert_block_q4_K", &err), err));
CL_CHECK((backend_ctx->kernel_restore_block_q4_K = clCreateKernel(backend_ctx->program_cvt, "kernel_restore_block_q4_K", &err), err));
CL_CHECK((backend_ctx->kernel_convert_block_q6_K = clCreateKernel(backend_ctx->program_cvt, "kernel_convert_block_q6_K", &err), err));
CL_CHECK((backend_ctx->kernel_restore_block_q6_K = clCreateKernel(backend_ctx->program_cvt, "kernel_restore_block_q6_K", &err), err));
GGML_LOG_CONT(".");
@ -1209,6 +1214,23 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve
GGML_LOG_CONT(".");
}
// mul_mv_q4_k_f32_flat
{
#ifdef GGML_OPENCL_EMBED_KERNELS
const std::string kernel_src {
#include "mul_mv_q4_k_f32_flat.cl.h"
};
#else
const std::string kernel_src = read_file("mul_mv_q4_k_f32_flat.cl");
#endif
cl_program prog =
build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts);
CL_CHECK((backend_ctx->kernel_mul_mv_q4_K_f32_flat = clCreateKernel(prog, "kernel_mul_mv_q4_K_f32_flat", &err), err));
CL_CHECK(clReleaseProgram(prog));
GGML_LOG_CONT(".");
}
// mul_mv_q6_k_f32
{
#ifdef GGML_OPENCL_EMBED_KERNELS
@ -1482,6 +1504,23 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve
GGML_LOG_CONT(".");
}
// mul_mm_q4_k_f32_l4_lm
{
#ifdef GGML_OPENCL_EMBED_KERNELS
const std::string kernel_src {
#include "mul_mm_q4_k_f32_l4_lm.cl.h"
};
#else
const std::string kernel_src = read_file("mul_mm_q4_k_f32_l4_lm.cl");
#endif
cl_program prog =
build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts);
CL_CHECK((backend_ctx->kernel_mul_mm_q4_k_f32_l4_lm = clCreateKernel(prog, "kernel_mul_mm_q4_k_f32_l4_lm", &err), err));
CL_CHECK(clReleaseProgram(prog));
GGML_LOG_CONT(".");
}
// mul_mm_q6_k_f32_l4_lm
{
#ifdef GGML_OPENCL_EMBED_KERNELS
@ -3347,6 +3386,40 @@ struct ggml_tensor_extra_cl_q8_0 {
}
};
struct ggml_tensor_extra_cl_q4_K {
// Quantized values
cl_mem q = nullptr;
// Scales for each super block.
cl_mem s = nullptr;
// Scales
cl_mem d = nullptr;
// Min
cl_mem dm = nullptr;
~ggml_tensor_extra_cl_q4_K() {
reset();
}
void reset() {
if (q != nullptr) {
CL_CHECK(clReleaseMemObject(q));
q = nullptr;
}
if (s != nullptr) {
CL_CHECK(clReleaseMemObject(s));
s = nullptr;
}
if (d != nullptr) {
CL_CHECK(clReleaseMemObject(d));
d = nullptr;
}
if (dm != nullptr) {
CL_CHECK(clReleaseMemObject(dm));
dm = nullptr;
}
}
};
struct ggml_tensor_extra_cl_q6_K {
// Lower 4 bits of quantized weights.
cl_mem ql = nullptr;
@ -3956,6 +4029,12 @@ struct ggml_backend_opencl_buffer_context {
for (ggml_tensor_extra_cl_q8_0 * e : temp_tensor_extras_q8_0_in_use) {
delete e;
}
for (ggml_tensor_extra_cl_q4_K * e : temp_tensor_extras_q4_K) {
delete e;
}
for (ggml_tensor_extra_cl_q4_K * e : temp_tensor_extras_q4_K_in_use) {
delete e;
}
for (ggml_tensor_extra_cl_q6_K * e : temp_tensor_extras_q6_K) {
delete e;
}
@ -4039,6 +4118,21 @@ struct ggml_backend_opencl_buffer_context {
return extra;
}
ggml_tensor_extra_cl_q4_K * ggml_opencl_alloc_temp_tensor_extra_q4_K() {
ggml_tensor_extra_cl_q4_K * extra;
if (temp_tensor_extras_q4_K.empty()) {
extra = new ggml_tensor_extra_cl_q4_K();
} else {
extra = temp_tensor_extras_q4_K.back();
temp_tensor_extras_q4_K.pop_back();
}
temp_tensor_extras_q4_K_in_use.push_back(extra);
extra->reset();
return extra;
}
ggml_tensor_extra_cl_q6_K * ggml_opencl_alloc_temp_tensor_extra_q6_K() {
ggml_tensor_extra_cl_q6_K * extra;
if (temp_tensor_extras_q6_K.empty()) {
@ -4080,6 +4174,11 @@ struct ggml_backend_opencl_buffer_context {
}
temp_tensor_extras_q8_0_in_use.clear();
for (ggml_tensor_extra_cl_q4_K * e : temp_tensor_extras_q4_K_in_use) {
temp_tensor_extras_q4_K.push_back(e);
}
temp_tensor_extras_q4_K_in_use.clear();
for (ggml_tensor_extra_cl_q6_K * e : temp_tensor_extras_q6_K_in_use) {
temp_tensor_extras_q6_K.push_back(e);
}
@ -4101,6 +4200,8 @@ struct ggml_backend_opencl_buffer_context {
std::vector<ggml_tensor_extra_cl_mxfp4 *> temp_tensor_extras_mxfp4_in_use;
std::vector<ggml_tensor_extra_cl_q8_0 *> temp_tensor_extras_q8_0;
std::vector<ggml_tensor_extra_cl_q8_0 *> temp_tensor_extras_q8_0_in_use;
std::vector<ggml_tensor_extra_cl_q4_K *> temp_tensor_extras_q4_K;
std::vector<ggml_tensor_extra_cl_q4_K *> temp_tensor_extras_q4_K_in_use;
std::vector<ggml_tensor_extra_cl_q6_K *> temp_tensor_extras_q6_K;
std::vector<ggml_tensor_extra_cl_q6_K *> temp_tensor_extras_q6_K_in_use;
@ -4835,6 +4936,83 @@ static void ggml_backend_opencl_buffer_set_tensor(ggml_backend_buffer_t buffer,
return;
}
if (tensor->type == GGML_TYPE_Q4_K) {
ggml_tensor_extra_cl * extra_orig = (ggml_tensor_extra_cl *)tensor->extra;
GGML_ASSERT(extra_orig && "Tesnors in OpenCL backend should have been allocated and initialized");
// Allocate the new extra and create aliases from the original.
ggml_backend_opencl_buffer_context * ctx = (ggml_backend_opencl_buffer_context *) buffer->context;
ggml_tensor_extra_cl_q4_K * extra = ctx->ggml_opencl_alloc_temp_tensor_extra_q4_K();
size_t size_d = ggml_nelements(tensor)/ggml_blck_size(tensor->type)*sizeof(ggml_fp16_t);
size_t size_dm = ggml_nelements(tensor)/ggml_blck_size(tensor->type)*sizeof(ggml_fp16_t);
size_t size_s = ggml_nelements(tensor)/ggml_blck_size(tensor->type)*(3 * ggml_blck_size(tensor->type) / 64);
size_t size_q = ggml_nelements(tensor)/ggml_blck_size(tensor->type)*ggml_blck_size(tensor->type)/2;
GGML_ASSERT(size_d + size_dm + size_s + size_q == ggml_nbytes(tensor) && "Incorrect tensor size");
cl_int err;
cl_mem data_device = clCreateBuffer(context, CL_MEM_READ_WRITE,
ggml_nbytes(tensor), NULL, &err);
CL_CHECK(err);
CL_CHECK(clEnqueueWriteBuffer(
queue, data_device, CL_TRUE, 0,
ggml_nbytes(tensor), data, 0, NULL, NULL));
cl_buffer_region region;
// Create subbuffer for d.
region.origin = align_to(extra_orig->offset + tensor->view_offs + offset, backend_ctx->alignment);
region.size = size_d;
extra->d = clCreateSubBuffer(
extra_orig->data_device, CL_MEM_READ_WRITE,
CL_BUFFER_CREATE_TYPE_REGION, &region, &err);
CL_CHECK(err);
auto previous_origin = region.origin;
// Create subbuffer for mins.
region.origin = align_to(previous_origin + size_d, backend_ctx->alignment);
region.size = size_dm;
extra->dm = clCreateSubBuffer(
extra_orig->data_device, CL_MEM_READ_WRITE,
CL_BUFFER_CREATE_TYPE_REGION, &region, &err);
CL_CHECK(err);
previous_origin = region.origin;
// Create subbuffer for s.
region.origin = align_to(previous_origin + size_dm, backend_ctx->alignment);
region.size = size_s;
extra->s = clCreateSubBuffer(
extra_orig->data_device, CL_MEM_READ_WRITE,
CL_BUFFER_CREATE_TYPE_REGION, &region, &err);
CL_CHECK(err);
previous_origin = region.origin;
// Create subbuffer for quants.
region.origin = align_to(previous_origin + size_s, backend_ctx->alignment);
region.size = size_q;
extra->q = clCreateSubBuffer(
extra_orig->data_device, CL_MEM_READ_WRITE,
CL_BUFFER_CREATE_TYPE_REGION, &region, &err);
CL_CHECK(err);
cl_kernel kernel = backend_ctx->kernel_convert_block_q4_K;
CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &data_device));
CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &extra->q));
CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extra->s));
CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_mem), &extra->d));
CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_mem), &extra->dm));
size_t global_work_size[] = {(size_t)ggml_nelements(tensor)/ggml_blck_size(tensor->type), 1, 1};
size_t local_work_size[] = {64, 1, 1};
cl_event evt;
CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, &evt));
CL_CHECK(clWaitForEvents(1, &evt));
CL_CHECK(clReleaseMemObject(data_device));
tensor->extra = extra;
return;
}
if (tensor->type == GGML_TYPE_Q6_K) {
ggml_tensor_extra_cl * extra_orig = (ggml_tensor_extra_cl *)tensor->extra;
GGML_ASSERT(extra_orig && "Tesnors in OpenCL backend should have been allocated and initialized");
@ -5245,6 +5423,34 @@ static void ggml_backend_opencl_buffer_get_tensor(ggml_backend_buffer_t buffer,
CL_CHECK(clReleaseMemObject(data_device));
return;
}
if (tensor->type == GGML_TYPE_Q4_K) {
ggml_tensor_extra_cl_q4_K * extra = (ggml_tensor_extra_cl_q4_K *)tensor->extra;
cl_int err;
cl_mem data_device = clCreateBuffer(context, CL_MEM_READ_WRITE,
ggml_nbytes(tensor), NULL, &err);
CL_CHECK(err);
cl_kernel kernel = backend_ctx->kernel_restore_block_q4_K;
CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra->q));
CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &extra->s));
CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extra->d));
CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_mem), &extra->dm));
CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_mem), &data_device));
size_t global_work_size[] = {(size_t)ggml_nelements(tensor)/ggml_blck_size(tensor->type), 1, 1};
size_t local_work_size[] = {1, 1, 1};
cl_event evt;
CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL,
global_work_size, local_work_size, 0, NULL, &evt));
CL_CHECK(clWaitForEvents(1, &evt));
CL_CHECK(clEnqueueReadBuffer(
queue, data_device, CL_TRUE, offset,
size, data, 0, NULL, NULL));
CL_CHECK(clReleaseMemObject(data_device));
return;
}
if (tensor->type == GGML_TYPE_Q6_K) {
ggml_tensor_extra_cl_q6_K * extra = (ggml_tensor_extra_cl_q6_K *)tensor->extra;
@ -9357,6 +9563,7 @@ static void ggml_cl_mul_mat(ggml_backend_t backend, const ggml_tensor * src0, co
ggml_tensor_extra_cl_q4_1 * extra0_q4_1 = (ggml_tensor_extra_cl_q4_1 *)src0->extra;
ggml_tensor_extra_cl_mxfp4 * extra0_mxfp4 = (ggml_tensor_extra_cl_mxfp4 *)src0->extra;
ggml_tensor_extra_cl_q8_0 * extra0_q8_0 = (ggml_tensor_extra_cl_q8_0 *)src0->extra;
ggml_tensor_extra_cl_q4_K * extra0_q4_K = (ggml_tensor_extra_cl_q4_K *)src0->extra;
ggml_tensor_extra_cl_q6_K * extra0_q6_K = (ggml_tensor_extra_cl_q6_K *)src0->extra;
#endif
@ -10005,6 +10212,50 @@ static void ggml_cl_mul_mat(ggml_backend_t backend, const ggml_tensor * src0, co
backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst);
return;
}
case GGML_TYPE_Q4_K: {
if (ne11 < 32) {
break;
}
if (!ggml_is_contiguous(src0) || !ggml_is_contiguous(src1)) {
break;
}
kernel = backend_ctx->kernel_mul_mm_q4_k_f32_l4_lm;
nth0 = 128; // calculated as (BM*BN)/(TM*TN)
int batch_stride_a = ne00*ne01;
int batch_stride_b = ne10*ne11;
int batch_stride_d = ne0*ne1;
CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra0_q4_K->q));
CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &extra0_q4_K->s));
CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extra0_q4_K->d));
CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_mem), &extra0_q4_K->dm));
CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_mem), &extra1->data_device));
CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_ulong), &offset1));
CL_CHECK(clSetKernelArg(kernel, 6, sizeof(cl_mem), &extrad->data_device));
CL_CHECK(clSetKernelArg(kernel, 7, sizeof(cl_ulong), &offsetd));
CL_CHECK(clSetKernelArg(kernel, 8, sizeof(int), &ne00));
CL_CHECK(clSetKernelArg(kernel, 9, sizeof(int), &ne01));
CL_CHECK(clSetKernelArg(kernel, 10, sizeof(int), &ne02));
CL_CHECK(clSetKernelArg(kernel, 11, sizeof(int), &ne11));
CL_CHECK(clSetKernelArg(kernel, 12, sizeof(int), &ne12));
CL_CHECK(clSetKernelArg(kernel, 13, sizeof(int), &ne10)); // stride_a
CL_CHECK(clSetKernelArg(kernel, 14, sizeof(int), &ne10)); // stride_b
CL_CHECK(clSetKernelArg(kernel, 15, sizeof(int), &ne01)); // stride_d
CL_CHECK(clSetKernelArg(kernel, 16, sizeof(int), &batch_stride_a));
CL_CHECK(clSetKernelArg(kernel, 17, sizeof(int), &batch_stride_b));
CL_CHECK(clSetKernelArg(kernel, 18, sizeof(int), &batch_stride_d));
CL_CHECK(clSetKernelArg(kernel, 19, sizeof(int), &r2));
CL_CHECK(clSetKernelArg(kernel, 20, sizeof(int), &r3));
// 64 is block tile size BM and BN - change here when BM and BN in the kernel are changed.
size_t global_work_size[] = {(size_t)(CEIL_DIV(ne01, 64)*nth0), (size_t)(CEIL_DIV(ne11, 64)), (size_t)ne12*ne13};
size_t local_work_size[] = {(size_t)nth0, 1, 1};
backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst);
return;
}
case GGML_TYPE_Q6_K: {
if (ne11 < 32) {
break;
@ -10449,6 +10700,43 @@ static void ggml_cl_mul_mat(ggml_backend_t backend, const ggml_tensor * src0, co
case GGML_TYPE_Q2_K:
case GGML_TYPE_Q3_K:
case GGML_TYPE_Q4_K: {
#ifdef GGML_OPENCL_SOA_Q
kernel = backend_ctx->kernel_mul_mv_q4_K_f32_flat;
if (backend_ctx->gpu_family == INTEL) {
nth0 = 16;
nth1 = 1;
ndst = 4;
} else if (backend_ctx->gpu_family == ADRENO) {
nth0 = 64;
nth1 = 2;
ndst = 16;
} else {
GGML_ASSERT(false && "TODO: Unknown GPU");
}
CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra0_q4_K->q));
CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &extra0_q4_K->s));
CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extra0_q4_K->d));
CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_mem), &extra0_q4_K->dm));
CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_mem), &extra1->data_device));
CL_CHECK(clSetKernelArg(kernel, 5, sizeof(int), &offset1));
CL_CHECK(clSetKernelArg(kernel, 6, sizeof(cl_mem), &extrad->data_device));
CL_CHECK(clSetKernelArg(kernel, 7, sizeof(int), &offsetd));
CL_CHECK(clSetKernelArg(kernel, 8, sizeof(int), &ne00));
CL_CHECK(clSetKernelArg(kernel, 9, sizeof(int), &ne01));
CL_CHECK(clSetKernelArg(kernel, 10, sizeof(cl_ulong), &nb01));
CL_CHECK(clSetKernelArg(kernel, 11, sizeof(cl_ulong), &nb02));
CL_CHECK(clSetKernelArg(kernel, 12, sizeof(cl_ulong), &nb03));
CL_CHECK(clSetKernelArg(kernel, 13, sizeof(int), &ne12));
CL_CHECK(clSetKernelArg(kernel, 14, sizeof(cl_ulong), &nb11));
CL_CHECK(clSetKernelArg(kernel, 15, sizeof(cl_ulong), &nb12));
CL_CHECK(clSetKernelArg(kernel, 16, sizeof(cl_ulong), &nb13));
CL_CHECK(clSetKernelArg(kernel, 17, sizeof(int), &ne0));
CL_CHECK(clSetKernelArg(kernel, 18, sizeof(int), &ne1));
CL_CHECK(clSetKernelArg(kernel, 19, sizeof(int), &r2));
CL_CHECK(clSetKernelArg(kernel, 20, sizeof(int), &r3));
#else
kernel = backend_ctx->kernel_mul_mv_q4_K_f32;
if (backend_ctx->gpu_family == INTEL) {
@ -10482,6 +10770,7 @@ static void ggml_cl_mul_mat(ggml_backend_t backend, const ggml_tensor * src0, co
CL_CHECK(clSetKernelArg(kernel, 16, sizeof(int), &ne1));
CL_CHECK(clSetKernelArg(kernel, 17, sizeof(int), &r2));
CL_CHECK(clSetKernelArg(kernel, 18, sizeof(int), &r3));
#endif // GGML_OPENCL_SOA_Q
break;
}
case GGML_TYPE_Q5_K:

67
ggml/src/ggml-opencl/kernels/cvt.cl
View File

@ -28,6 +28,7 @@
#define QK8_0 32
#define QR8_0 1
#define QK_K 256
#define K_SCALE_SIZE (3 * QK_K / 64)
#define K_QUANTS_PER_ITERATION 2
typedef char int8_t;
@ -55,6 +56,16 @@ struct block_q4_1 {
uchar qs[QK4_1 / 2]; // nibbles / quants
};
//------------------------------------------------------------------------------
// block_q4_k
//------------------------------------------------------------------------------
struct block_q4_K {
half d; // delta
half dm; // min
uchar s[K_SCALE_SIZE];
uchar q[QK_K / 2]; // nibbles / quants
};
//------------------------------------------------------------------------------
// block_q6_K
//------------------------------------------------------------------------------
@ -408,6 +419,62 @@ kernel void kernel_restore_block_q8_0_trans(
}
}
//------------------------------------------------------------------------------
// kernel_convert_block_q4_K
// Convert the block_q4_K format to 4 separate arrays (AOS -> SOA).
// This kernel does not deshuffle the bits.
// Each thread processes a super block.
//------------------------------------------------------------------------------
kernel void kernel_convert_block_q4_K(
global struct block_q4_K * src0,
global uchar * dst_q,
global uchar * dst_s,
global half * dst_d,
global half * dst_dm
) {
global struct block_q4_K * b = (global struct block_q4_K *) src0 + get_global_id(0);
global uchar * q = (global uchar *) dst_q + QK_K/2*get_global_id(0);
global uchar * s = (global uchar *) dst_s + K_SCALE_SIZE*get_global_id(0);
global half * d = (global half *) dst_d + get_global_id(0);
global half * dm = (global half *) dst_dm + get_global_id(0);
*d = b->d;
*dm = b->dm;
for (int i = 0; i < QK_K/2; ++i) {
q[i] = b->q[i];
}
for (int i = 0; i < K_SCALE_SIZE; ++i) {
s[i] = b->s[i];
}
}
// Restore block_q4_K from flattened arrays.
// Each thread processes a super block.
kernel void kernel_restore_block_q4_K(
global uchar * src_q,
global uchar * src_s,
global half * src_d,
global half * src_dm,
global struct block_q4_K * dst
) {
global struct block_q4_K * b = (global struct block_q4_K *) dst + get_global_id(0);
global uchar * q = (global uchar *) src_q + QK_K/2*get_global_id(0);
global uchar * s = (global uchar *) src_s + K_SCALE_SIZE*get_global_id(0);
global half * d = (global half *) src_d + get_global_id(0);
global half * dm = (global half *) src_dm + get_global_id(0);
b->d = *d;
b->dm = *dm;
for (int i = 0; i < QK_K/2; ++i) {
b->q[i] = q[i];
}
for (int i = 0; i < K_SCALE_SIZE; ++i) {
b->s[i] = s[i];
}
}
//------------------------------------------------------------------------------
// kernel_convert_block_q6_K
// Convert the block_q6_K format to 3 separate arrays (AOS -> SOA).

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#pragma OPENCL EXTENSION cl_khr_fp16 : enable
#define LOAD_VEC_A 4
#define LOAD_VEC_B 4
#define BM 64
#define BN 64
#define BK 32
#define TM 4
#define TN 8
kernel void kernel_mul_mm_q4_k_f32_l4_lm(
global uchar4 * src0_q,
global uchar * src0_s,
global half * src0_d,
global half * src0_dm,
global float4 * src1,
ulong offset1,
global float * dst,
ulong offsetd,
int ne00,
int ne01,
int ne02,
int ne11,
int ne12,
int stride_a,
int stride_b,
int stride_d,
int batch_stride_a,
int batch_stride_b,
int batch_stride_d,
int r2,
int r3
) {
src1 = (global float4*)((global char*)src1 + offset1);
dst = (global float *)((global char*)dst + offsetd);
local float buf_a[BM * BK];
local float buf_b[BN * BK];
const int batch_idx = get_global_id(2);
const int i13 = batch_idx / ne12;
const int i12 = batch_idx % ne12;
const int i03 = i13 / r3;
const int i02 = i12 / r2;
const int batch_idx_a = i03 * ne02 + i02;
const int ir = get_group_id(0);
const int ic = get_group_id(1);
const int tid = get_local_id(0);
const int th_r = tid % (BM / TM);
const int th_c = tid / (BM / TM);
const int loadr_a = get_local_id(0) % (BK / LOAD_VEC_A);
const int loadc_a = get_local_id(0) / (BK / LOAD_VEC_A);
const int loadr_b = get_local_id(0) % (BK / LOAD_VEC_B);
const int loadc_b = get_local_id(0) / (BK / LOAD_VEC_B);
const int loadstride_a = get_local_size(0) * LOAD_VEC_A / BK;
const int loadstride_b = get_local_size(0) * LOAD_VEC_B / BK;
int pos_a = (batch_idx_a * batch_stride_a + ir * BM * stride_a) / LOAD_VEC_A;
int pos_b = (batch_idx * batch_stride_b + ic * BN * stride_b) / LOAD_VEC_B;
float sums[TM * TN];
float cache_a[TM];
float cache_b[TN];
for (int i = 0; i < TM * TN; i++) {
sums[i] = 0.0f;
}
for (int block = 0; block < ne00; block += BK) {
for (int l = 0; l < BM; l += loadstride_a) {
if (ir*BM + loadc_a + l < ne01) {
int idx = pos_a + (loadc_a + l) * stride_a / LOAD_VEC_A + loadr_a;
int ib = idx / 64;
int iqs = (idx % 64) * 2;
int n = iqs / 32;
int b = (iqs % 32) / 16;
int is = 2 * n + b;
int qsi = n * 32 + (iqs % 16) * 2;
char * scales = src0_s + ib * 12;
int scidx0 = (is < 4) ? is : (is + 4);
int scidx1 = (is < 4) ? is : (is - 4);
int scidxmask1 = (is < 4) ? 0x30 : 0xC0;
int scidxshift1 = (is < 4) ? 0 : 2;
int mbidx0 = is + 4;
int mbidx1 = (is < 4) ? is + 4 : is;
int mbidxmask0 = (is < 4) ? 0xF : 0xF0;
int mbidxshift0 = (is < 4) ? 0 : 4;
int mbidxmask1 = (is < 4) ? 0x30 : 0xC0;
int mbidxshift1 = (is < 4) ? 0 : 2;
uchar sc = (scales[scidx0] & 0xF) | ((scales[scidx1] & scidxmask1) >> scidxshift1);
uchar mbyte = ((scales[mbidx0] & mbidxmask0) >> mbidxshift0) | ((scales[mbidx1] & mbidxmask1) >> mbidxshift1);
float d = (float)src0_d[ib] * (float)sc;
float m = -(float)src0_dm[ib] * (float)mbyte;
global uchar4 * qs = src0_q + ib*32 + (qsi >> 2);
uchar4 q = *qs;
float4 v1 = (convert_float4((uchar4)((q.s0 >> (b * 4))&0x0F, (q.s1 >> (b * 4))&0x0F, (q.s2 >> (b * 4))&0x0F, (q.s3 >> (b * 4))&0x0F)))*d + m;
buf_a[(loadr_a * LOAD_VEC_A + 0) * BM + loadc_a + l] = v1.s0;
buf_a[(loadr_a * LOAD_VEC_A + 1) * BM + loadc_a + l] = v1.s1;
buf_a[(loadr_a * LOAD_VEC_A + 2) * BM + loadc_a + l] = v1.s2;
buf_a[(loadr_a * LOAD_VEC_A + 3) * BM + loadc_a + l] = v1.s3;
} else {
buf_a[(loadr_a * LOAD_VEC_A + 0) * BM + loadc_a + l] = 0.0f;
buf_a[(loadr_a * LOAD_VEC_A + 1) * BM + loadc_a + l] = 0.0f;
buf_a[(loadr_a * LOAD_VEC_A + 2) * BM + loadc_a + l] = 0.0f;
buf_a[(loadr_a * LOAD_VEC_A + 3) * BM + loadc_a + l] = 0.0f;
}
}
for (int l = 0; l < BN; l += loadstride_b) {
if (ic*BN + loadc_b + l < ne11) {
int idx = pos_b + (loadc_b + l) * stride_b / LOAD_VEC_B + loadr_b;
buf_b[(loadr_b * LOAD_VEC_B + 0) * BN + loadc_b + l] = src1[idx].s0;
buf_b[(loadr_b * LOAD_VEC_B + 1) * BN + loadc_b + l] = src1[idx].s1;
buf_b[(loadr_b * LOAD_VEC_B + 2) * BN + loadc_b + l] = src1[idx].s2;
buf_b[(loadr_b * LOAD_VEC_B + 3) * BN + loadc_b + l] = src1[idx].s3;
} else {
buf_b[(loadr_b * LOAD_VEC_B + 0) * BN + loadc_b + l] = 0.0f;
buf_b[(loadr_b * LOAD_VEC_B + 1) * BN + loadc_b + l] = 0.0f;
buf_b[(loadr_b * LOAD_VEC_B + 2) * BN + loadc_b + l] = 0.0f;
buf_b[(loadr_b * LOAD_VEC_B + 3) * BN + loadc_b + l] = 0.0f;
}
}
barrier(CLK_LOCAL_MEM_FENCE);
pos_a += BK / LOAD_VEC_A;
pos_b += BK / LOAD_VEC_B;
for (int i = 0; i < BK; i++) {
for (int j = 0; j < TM; j++) {
cache_a[j] = buf_a[(i) * BM + th_r * TM + j];
}
for (int j = 0; j < TN; j++) {
cache_b[j] = buf_b[(i) * BN + th_c * TN + j];
}
for (int cc = 0; cc < TN; cc++) {
for (int cr = 0; cr < TM; cr++) {
const int sums_idx = cc*TM + cr;
sums[sums_idx] = mad(cache_a[cr], cache_b[cc], sums[sums_idx]);
}
}
}
barrier(CLK_LOCAL_MEM_FENCE);
}
const int dr = ir * BM + th_r * TM;
const int dc = ic * BN + th_c * TN;
const int offsets = batch_idx * batch_stride_d;
for (int cc = 0; cc < TN; cc++) {
for (int cr = 0; cr < TM; cr++) {
if (dr + cr < ne01 && dc + cc < ne11) {
dst[offsets + (dc + cc) * stride_d + dr + cr] = sums[cc * TM + cr];
}
}
}
}

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#pragma OPENCL EXTENSION cl_khr_fp16 : enable
#ifdef cl_intel_subgroups
#pragma OPENCL EXTENSION cl_intel_subgroups : enable
#else
#pragma OPENCL EXTENSION cl_khr_subgroups : enable
#endif
#ifdef cl_intel_required_subgroup_size
#pragma OPENCL EXTENSION cl_intel_required_subgroup_size : enable
#define INTEL_GPU 1
#define REQD_SUBGROUP_SIZE_16 __attribute__((intel_reqd_sub_group_size(16)))
#define REQD_SUBGROUP_SIZE_32 __attribute__((intel_reqd_sub_group_size(32)))
#elif defined(cl_qcom_reqd_sub_group_size)
#pragma OPENCL EXTENSION cl_qcom_reqd_sub_group_size : enable
#define ADRENO_GPU 1
#define REQD_SUBGROUP_SIZE_64 __attribute__((qcom_reqd_sub_group_size("half")))
#define REQD_SUBGROUP_SIZE_128 __attribute__((qcom_reqd_sub_group_size("full")))
#endif
//------------------------------------------------------------------------------
// block_q4_K
//------------------------------------------------------------------------------
#define QK_K 256
#define BLOCK_Q4K_SIZE 144
#define K_SCALE_SIZE 12
// 8 blocks of 32 elements each
// weight is represented as x = a * q + b
typedef struct {
half d; // super-block scale for quantized scales
half dmin; // super-block scale for quantized mins
uchar scales[K_SCALE_SIZE]; // scales and mins, quantized with 6 bits
uchar qs[QK_K/2]; // 4-bit quants
} block_q4_K;
#undef N_DST
#undef N_SIMDGROUP
#undef N_SIMDWIDTH
#ifdef INTEL_GPU
#define N_DST 4 // number of rows each SIMD group works on
#define N_SIMDGROUP 1 // number of SIMD groups in a thread group
#define N_SIMDWIDTH 16 // SIMD group size
#elif defined (ADRENO_GPU)
#define N_DST 16
#define N_SIMDGROUP 2
#define N_SIMDWIDTH 64
#endif
#undef BLOCK_STRIDE
// number of (super) blocks each subgroup processes
// each thread in a subgroup processes a block (32 weights)
#define BLOCK_STRIDE (N_SIMDWIDTH/8)
#ifdef INTEL_GPU
REQD_SUBGROUP_SIZE_16
#elif defined (ADRENO_GPU)
REQD_SUBGROUP_SIZE_64
#endif
kernel void kernel_mul_mv_q4_K_f32_flat(
global uchar * src0_q,
global uchar * src0_s,
global half * src0_d,
global half * src0_dm,
global char * src1,
int offset1,
global char * dst,
int offsetd,
int ne00,
int ne01,
ulong nb01,
ulong nb02,
ulong nb03,
int ne12,
ulong nb11,
ulong nb12,
ulong nb13,
int ne0,
int ne1,
int r2,
int r3
) {
src1 = src1 + offset1;
dst = dst + offsetd;
ushort kmask1 = 0x3f3f;
ushort kmask2 = 0x0f0f;
ushort kmask3 = 0xc0c0;
int ix = get_sub_group_local_id()/8;
int it = get_sub_group_local_id()%8;
int iq = it/4;
int ir = it%4;
int nb = ne00/QK_K;
int r0 = get_group_id(0);
int r1 = get_group_id(1);
int im = get_group_id(2);
int first_row = (r0 * N_SIMDGROUP + get_sub_group_id()) * N_DST;
int i12 = im%ne12;
int i13 = im/ne12;
int offset_src0 = (first_row*nb01 + (i12/r2)*nb02 + (i13/r3)*nb03)/BLOCK_Q4K_SIZE;
uint blk = nb01 / BLOCK_Q4K_SIZE;
global uchar * blk_q = (global uchar *)src0_q + offset_src0*(QK_K/2);
global uchar * blk_s = (global uchar *)src0_s + offset_src0*K_SCALE_SIZE;
global half * blk_d = (global half *)src0_d + offset_src0;
global half * blk_dm = (global half *)src0_dm + offset_src0;
int offset_src1 = r1*nb11 + (i12)*nb12 + (i13)*nb13;
global float * y = (global float *)(src1 + offset_src1);
float yl[16];
float yh[16];
float sumf[N_DST] = {0.f};
float all_sum;
global float * y4 = y + ix * QK_K + 64 * iq + 8 * ir;
ushort sc16[4];
uchar * sc8 = (uchar *)sc16;
for (int ib = ix; ib < nb; ib += BLOCK_STRIDE) {
float4 sumy = {0.f, 0.f, 0.f, 0.f};
for (int i = 0; i < 8; ++i) {
yl[i+0] = y4[i+0];
sumy.s0 += yl[i+0];
yl[i+8] = y4[i+32];
sumy.s1 += yl[i+8];
yh[i+0] = y4[i+128];
sumy.s2 += yh[i+0];
yh[i+8] = y4[i+160];
sumy.s3 += yh[i+8];
}
global ushort * q1 = (global ushort *)(blk_q + ib * (QK_K/2)) + (16 * iq + 4 * ir);
global ushort * sc = (global ushort *)(blk_s + ib * K_SCALE_SIZE) + iq;
global half * d = blk_d + ib;
global half * dm = blk_dm + ib;
for (int row = 0; row < N_DST; row++) {
sc16[0] = sc[0] & kmask1;
sc16[1] = sc[2] & kmask1;
sc16[2] = ((sc[4] >> 0) & kmask2) | ((sc[0] & kmask3) >> 2);
sc16[3] = ((sc[4] >> 4) & kmask2) | ((sc[2] & kmask3) >> 2);
global ushort * q2 = q1 + 32;
float4 acc1 = {0.f, 0.f, 0.f, 0.f};
float4 acc2 = {0.f, 0.f, 0.f, 0.f};
for (int i = 0; i < 8; i += 2) {
acc1.s0 += yl[i+0] * (q1[i/2] & 0x000F);
acc1.s1 += yl[i+1] * (q1[i/2] & 0x0F00);
acc1.s2 += yl[i+8] * (q1[i/2] & 0x00F0);
acc1.s3 += yl[i+9] * (q1[i/2] & 0xF000);
acc2.s0 += yh[i+0] * (q2[i/2] & 0x000F);
acc2.s1 += yh[i+1] * (q2[i/2] & 0x0F00);
acc2.s2 += yh[i+8] * (q2[i/2] & 0x00F0);
acc2.s3 += yh[i+9] * (q2[i/2] & 0xF000);
}
float dall = *d;
float dmin = *dm;
sumf[row] += dall * ((acc1.s0 + 1.f/256.f * acc1.s1) * sc8[0] +
(acc1.s2 + 1.f/256.f * acc1.s3) * sc8[1] * 1.f/16.f +
(acc2.s0 + 1.f/256.f * acc2.s1) * sc8[4] +
(acc2.s2 + 1.f/256.f * acc2.s3) * sc8[5] * 1.f/16.f) -
dmin * (sumy.s0 * sc8[2] + sumy.s1 * sc8[3] + sumy.s2 * sc8[6] + sumy.s3 * sc8[7]);
q1 += blk*64;
sc += blk*6;
d += blk;
dm += blk;
}
y4 += BLOCK_STRIDE * QK_K;
}
global float * dst_f32 = (global float *) dst + im*ne0*ne1 + r1*ne0;
for (int row = 0; row < N_DST; ++row) {
all_sum = sub_group_reduce_add(sumf[row]);
if (first_row + row < ne01) {
if (get_sub_group_local_id() == 0) {
dst_f32[first_row + row] = all_sum;
}
}
}
}