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opencl: add iq4_nl support (#22272)

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* opencl: add general support for iq4_nl

* opencl: add iq4_nl gemm/gemv for adreno

* opencl: pack 2 lut entries into a uint
This commit is contained in:
lhez
2026-04-25 21:21:58 -07:00
committed by GitHub
parent b760272f1a
commit f454bd7eb8
8 changed files with 1695 additions and 0 deletions
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ggml/src/ggml-opencl/CMakeLists.txt
+5
View File
@@ -96,6 +96,8 @@ set(GGML_OPENCL_KERNELS
mul_mv_q6_k_f32_flat
mul_mv_q8_0_f32
mul_mv_q8_0_f32_flat
mul_mv_iq4_nl_f32
mul_mv_iq4_nl_f32_flat
mul_mv_mxfp4_f32
mul_mv_mxfp4_f32_flat
mul_mv_id_q4_0_f32_8x_flat
@@ -110,12 +112,15 @@ 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_iq4_nl_f32_l4_lm
mul_mm_q4_k_f32_l4_lm
mul_mm_q5_k_f32_l4_lm
mul_mm_q6_k_f32_l4_lm
mul_mm_q8_0_f32_8x4
gemv_noshuffle_q4_1_f32
gemm_noshuffle_q4_1_f32
gemv_noshuffle_iq4_nl_f32
gemm_noshuffle_iq4_nl_f32
gemv_noshuffle_general_q8_0_f32
gemv_noshuffle_q4_k_f32
gemm_noshuffle_q4_k_f32
ggml/src/ggml-opencl/ggml-opencl.cpp
+594
View File
@@ -545,6 +545,9 @@ struct ggml_backend_opencl_context {
cl_kernel kernel_convert_block_q5_K_noshuffle;
cl_kernel kernel_restore_block_q5_K_noshuffle;
cl_kernel kernel_convert_block_q6_K, kernel_restore_block_q6_K;
cl_kernel kernel_convert_block_iq4_nl, kernel_restore_block_iq4_nl;
cl_kernel kernel_convert_block_iq4_nl_noshuffle;
cl_kernel kernel_restore_block_iq4_nl_noshuffle;
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;
@@ -556,6 +559,8 @@ struct ggml_backend_opencl_context {
cl_kernel kernel_mul_mv_q6_K_f32_flat;
cl_kernel kernel_mul_mv_mxfp4_f32, kernel_mul_mv_mxfp4_f32_flat;
cl_kernel kernel_mul_mv_q8_0_f32, kernel_mul_mv_q8_0_f32_flat;
cl_kernel kernel_mul_mv_iq4_nl_f32;
cl_kernel kernel_mul_mv_iq4_nl_f32_flat;
cl_kernel kernel_solve_tri_f32;
cl_kernel kernel_im2col_f32, kernel_im2col_f16;
cl_kernel kernel_argsort_f32_i32;
@@ -594,6 +599,7 @@ struct ggml_backend_opencl_context {
cl_kernel kernel_mul_mm_q4_k_f32_l4_lm;
cl_kernel kernel_mul_mm_q5_k_f32_l4_lm;
cl_kernel kernel_mul_mm_q6_k_f32_l4_lm;
cl_kernel kernel_mul_mm_iq4_nl_f32_l4_lm;
std::vector<ProfilingInfo> profiling_info;
@@ -734,6 +740,8 @@ struct ggml_backend_opencl_context {
cl_kernel kernel_gemm_noshuffle_q6_K_f32;
cl_kernel kernel_gemv_noshuffle_q5_k_f32;
cl_kernel kernel_gemm_noshuffle_q5_k_f32;
cl_kernel kernel_gemv_noshuffle_iq4_nl_f32;
cl_kernel kernel_gemm_noshuffle_iq4_nl_f32;
#endif // GGML_OPENCL_USE_ADRENO_KERNELS
void free() {
@@ -954,6 +962,10 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve
CL_CHECK((backend_ctx->kernel_restore_block_q6_K = clCreateKernel(backend_ctx->program_cvt, "kernel_restore_block_q6_K", &err), err));
CL_CHECK((backend_ctx->kernel_convert_block_q6_K_noshuffle = clCreateKernel(backend_ctx->program_cvt, "kernel_convert_block_q6_K_noshuffle", &err), err));
CL_CHECK((backend_ctx->kernel_restore_block_q6_K_noshuffle = clCreateKernel(backend_ctx->program_cvt, "kernel_restore_block_q6_K_noshuffle", &err), err));
CL_CHECK((backend_ctx->kernel_convert_block_iq4_nl = clCreateKernel(backend_ctx->program_cvt, "kernel_convert_block_iq4_nl", &err), err));
CL_CHECK((backend_ctx->kernel_restore_block_iq4_nl = clCreateKernel(backend_ctx->program_cvt, "kernel_restore_block_iq4_nl", &err), err));
CL_CHECK((backend_ctx->kernel_convert_block_iq4_nl_noshuffle = clCreateKernel(backend_ctx->program_cvt, "kernel_convert_block_iq4_nl_noshuffle", &err), err));
CL_CHECK((backend_ctx->kernel_restore_block_iq4_nl_noshuffle = clCreateKernel(backend_ctx->program_cvt, "kernel_restore_block_iq4_nl_noshuffle", &err), err));
GGML_LOG_CONT(".");
}
@@ -1359,6 +1371,40 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve
GGML_LOG_CONT(".");
}
// mul_mv_iq4_nl_f32
{
#ifdef GGML_OPENCL_EMBED_KERNELS
const std::string kernel_src {
#include "mul_mv_iq4_nl_f32.cl.h"
};
#else
const std::string kernel_src = read_file("mul_mv_iq4_nl_f32.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_iq4_nl_f32 = clCreateKernel(prog, "kernel_mul_mv_iq4_nl_f32", &err), err));
CL_CHECK(clReleaseProgram(prog));
GGML_LOG_CONT(".");
}
// mul_mv_iq4_nl_f32_flat
{
#ifdef GGML_OPENCL_EMBED_KERNELS
const std::string kernel_src {
#include "mul_mv_iq4_nl_f32_flat.cl.h"
};
#else
const std::string kernel_src = read_file("mul_mv_iq4_nl_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_iq4_nl_f32_flat = clCreateKernel(prog, "kernel_mul_mv_iq4_nl_f32_flat", &err), err));
CL_CHECK(clReleaseProgram(prog));
GGML_LOG_CONT(".");
}
// mul_mv_mxfp4_f32
{
#ifdef GGML_OPENCL_EMBED_KERNELS
@@ -1567,6 +1613,23 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve
GGML_LOG_CONT(".");
}
// mul_mm_iq4_nl_f32_l4_lm
{
#ifdef GGML_OPENCL_EMBED_KERNELS
const std::string kernel_src {
#include "mul_mm_iq4_nl_f32_l4_lm.cl.h"
};
#else
const std::string kernel_src = read_file("mul_mm_iq4_nl_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_iq4_nl_f32_l4_lm = clCreateKernel(prog, "kernel_mul_mm_iq4_nl_f32_l4_lm", &err), err));
CL_CHECK(clReleaseProgram(prog));
GGML_LOG_CONT(".");
}
// mul_mm_q4_k_f32_l4_lm
{
#ifdef GGML_OPENCL_EMBED_KERNELS
@@ -2647,6 +2710,45 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve
GGML_LOG_CONT(".");
}
// gemm_noshuffle_iq4_nl_f32
{
#ifdef GGML_OPENCL_EMBED_KERNELS
const std::string kernel_src {
#include "gemm_noshuffle_iq4_nl_f32.cl.h"
};
#else
const std::string kernel_src = read_file("gemm_noshuffle_iq4_nl_f32.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_gemm_noshuffle_iq4_nl_f32 = clCreateKernel(prog, "kernel_gemm_noshuffle_iq4_nl_f32", &err), err));
CL_CHECK(clReleaseProgram(prog));
GGML_LOG_CONT(".");
}
// gemv_noshuffle_iq4_nl_f32
{
std::string CL_gemv_compile_opts = std::string("-cl-std=") + opencl_c_std +
" -cl-mad-enable ";
if (backend_ctx->has_vector_subgroup_broadcast) {
CL_gemv_compile_opts += " -DVECTOR_SUB_GROUP_BROADCAST ";
}
#ifdef GGML_OPENCL_EMBED_KERNELS
const std::string kernel_src {
#include "gemv_noshuffle_iq4_nl_f32.cl.h"
};
#else
const std::string kernel_src = read_file("gemv_noshuffle_iq4_nl_f32.cl");
#endif
cl_program prog = build_program_from_source(
backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_gemv_compile_opts);
CL_CHECK((backend_ctx->kernel_gemv_noshuffle_iq4_nl_f32 = clCreateKernel(prog, "kernel_gemv_noshuffle_iq4_nl_f32", &err), err));
CL_CHECK(clReleaseProgram(prog));
GGML_LOG_CONT(".");
}
// mul_mm_q8_0_f32_8x4
{
#ifdef GGML_OPENCL_EMBED_KERNELS
@@ -3597,6 +3699,30 @@ struct ggml_tensor_extra_cl_q8_0 {
}
};
struct ggml_tensor_extra_cl_iq4_nl {
cl_mem q = nullptr;
cl_mem q_img = nullptr;
cl_mem d = nullptr;
cl_mem d_img = nullptr;
size_t size_q = 0;
size_t size_d = 0;
~ggml_tensor_extra_cl_iq4_nl() {
reset();
}
void reset() {
if (q != nullptr) { CL_CHECK(clReleaseMemObject(q)); q = nullptr; }
if (d != nullptr) { CL_CHECK(clReleaseMemObject(d)); d = nullptr; }
q_img = nullptr;
d_img = nullptr;
size_q = 0;
size_d = 0;
}
};
struct ggml_tensor_extra_cl_q4_K {
// Quantized values
cl_mem q = nullptr;
@@ -4097,6 +4223,7 @@ static bool ggml_opencl_supports_op(ggml_backend_dev_t dev, const struct ggml_te
return op->src[1]->type == GGML_TYPE_F32;
} else if (op->src[0]->type == GGML_TYPE_Q4_0 || op->src[0]->type == GGML_TYPE_Q4_1 ||
op->src[0]->type == GGML_TYPE_MXFP4 ||
op->src[0]->type == GGML_TYPE_IQ4_NL ||
op->src[0]->type == GGML_TYPE_Q4_K ||
op->src[0]->type == GGML_TYPE_Q5_K ||
op->src[0]->type == GGML_TYPE_Q6_K) {
@@ -4295,6 +4422,12 @@ 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_iq4_nl * e : temp_tensor_extras_iq4_nl) {
delete e;
}
for (ggml_tensor_extra_cl_iq4_nl * e : temp_tensor_extras_iq4_nl_in_use) {
delete e;
}
for (ggml_tensor_extra_cl_q4_K * e : temp_tensor_extras_q4_K) {
delete e;
}
@@ -4390,6 +4523,21 @@ struct ggml_backend_opencl_buffer_context {
return extra;
}
ggml_tensor_extra_cl_iq4_nl * ggml_opencl_alloc_temp_tensor_extra_iq4_nl() {
ggml_tensor_extra_cl_iq4_nl * extra;
if (temp_tensor_extras_iq4_nl.empty()) {
extra = new ggml_tensor_extra_cl_iq4_nl();
} else {
extra = temp_tensor_extras_iq4_nl.back();
temp_tensor_extras_iq4_nl.pop_back();
}
temp_tensor_extras_iq4_nl_in_use.push_back(extra);
extra->reset();
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()) {
@@ -4461,6 +4609,11 @@ struct ggml_backend_opencl_buffer_context {
}
temp_tensor_extras_q8_0_in_use.clear();
for (ggml_tensor_extra_cl_iq4_nl * e : temp_tensor_extras_iq4_nl_in_use) {
temp_tensor_extras_iq4_nl.push_back(e);
}
temp_tensor_extras_iq4_nl_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);
}
@@ -4492,6 +4645,8 @@ 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_iq4_nl *> temp_tensor_extras_iq4_nl;
std::vector<ggml_tensor_extra_cl_iq4_nl *> temp_tensor_extras_iq4_nl_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_q5_K *> temp_tensor_extras_q5_K;
@@ -5123,6 +5278,87 @@ static void ggml_backend_opencl_buffer_set_tensor(ggml_backend_buffer_t buffer,
return;
}
if (tensor->type == GGML_TYPE_IQ4_NL) {
ggml_tensor_extra_cl * extra_orig = (ggml_tensor_extra_cl *)tensor->extra;
GGML_ASSERT(extra_orig && "Tensors in OpenCL backend should have been allocated and initialized");
ggml_backend_opencl_buffer_context * ctx = (ggml_backend_opencl_buffer_context *) buffer->context;
ggml_tensor_extra_cl_iq4_nl * extra = ctx->ggml_opencl_alloc_temp_tensor_extra_iq4_nl();
size_t size_d = ggml_nelements(tensor)/ggml_blck_size(tensor->type)*sizeof(ggml_fp16_t);
size_t size_q = ggml_nelements(tensor)/ggml_blck_size(tensor->type)*(ggml_blck_size(tensor->type)/2);
GGML_ASSERT(size_d + 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 scales.
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 quants.
region.origin = align_to(previous_origin + size_d, 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);
#ifdef GGML_OPENCL_USE_ADRENO_KERNELS
cl_kernel kernel = backend_ctx->kernel_convert_block_iq4_nl;
if (use_adreno_kernels(backend_ctx, tensor)) {
kernel = backend_ctx->kernel_convert_block_iq4_nl_noshuffle;
}
#else
cl_kernel kernel = backend_ctx->kernel_convert_block_iq4_nl;
#endif
cl_ulong n_blk = ggml_nelements(tensor)/ggml_blck_size(tensor->type);
cl_uchar mask_0F = 0x0F;
cl_uchar mask_F0 = 0xF0;
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->d));
CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_uchar), &mask_0F));
CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_uchar), &mask_F0));
CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_ulong), &n_blk));
size_t global_work_size[] = {(size_t)CEIL_DIV(n_blk, 64)*64, 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;
#ifdef GGML_OPENCL_USE_ADRENO_KERNELS
if (use_adreno_kernels(backend_ctx, tensor)) {
int M = tensor->ne[1];
int K = tensor->ne[0];
GGML_ASSERT(K % 32 == 0);
// Transpose q as ushort
transpose_2d_as_16b(backend_ctx, extra->q, extra->q, size_q, K/4, M);
// Transpose d as ushort
transpose_2d_as_16b(backend_ctx, extra->d, extra->d, size_d, K/32, M);
}
#endif
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");
@@ -5775,6 +6011,78 @@ static void ggml_backend_opencl_buffer_get_tensor(ggml_backend_buffer_t buffer,
CL_CHECK(clReleaseMemObject(data_device));
return;
}
if (tensor->type == GGML_TYPE_IQ4_NL) {
ggml_tensor_extra_cl_iq4_nl * extra = (ggml_tensor_extra_cl_iq4_nl *)tensor->extra;
cl_int err;
cl_mem data_device = clCreateBuffer(context, CL_MEM_READ_WRITE,
ggml_nbytes(tensor), NULL, &err);
CL_CHECK(err);
#ifdef GGML_OPENCL_USE_ADRENO_KERNELS
if (use_adreno_kernels(backend_ctx, tensor)) {
static ggml_cl_buffer buf_trans_q;
static ggml_cl_buffer buf_trans_d;
static ggml_cl_buffer buf_unpacked;
cl_int M = tensor->ne[1];
cl_int K = tensor->ne[0];
GGML_ASSERT(K % 32 == 0);
size_t size_q = (ggml_nelements(tensor)/ggml_blck_size(tensor->type))*(ggml_blck_size(tensor->type)/2);
size_t size_d = (ggml_nelements(tensor)/ggml_blck_size(tensor->type))*sizeof(ggml_fp16_t);
GGML_ASSERT(size_d + size_q == ggml_nbytes(tensor) && "Incorrect tensor size");
buf_trans_q.allocate(backend_ctx->context, size_q);
buf_trans_d.allocate(backend_ctx->context, size_d);
buf_unpacked.allocate(backend_ctx->context, ggml_nbytes(tensor));
// transpose q, d back
transpose_2d_as_16b(backend_ctx, extra->q, buf_trans_q.buffer, size_q, M, K/4);
transpose_2d_as_16b(backend_ctx, extra->d, buf_trans_d.buffer, size_d, M, K/32);
cl_uchar mask_0F = 0x0F;
cl_uchar mask_F0 = 0xF0;
cl_kernel kernel = backend_ctx->kernel_restore_block_iq4_nl_noshuffle;
cl_ulong n_blk = ggml_nelements(tensor)/ggml_blck_size(tensor->type);
CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &buf_trans_q.buffer));
CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &buf_trans_d.buffer));
CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &buf_unpacked.buffer));
CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_uchar), &mask_0F));
CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_uchar), &mask_F0));
CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_ulong), &n_blk));
size_t global_work_size[] = {(size_t)n_blk, 1, 1};
size_t local_work_size[] = {1, 1, 1};
CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, NULL));
CL_CHECK(clEnqueueReadBuffer(queue, buf_unpacked.buffer, CL_TRUE, offset, size, data, 0, NULL, NULL));
return;
}
#endif
cl_kernel kernel = backend_ctx->kernel_restore_block_iq4_nl;
cl_ulong n_blk = ggml_nelements(tensor)/ggml_blck_size(tensor->type);
CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra->q));
CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &extra->d));
CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &data_device));
CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_ulong), &n_blk));
size_t global_work_size[] = {(size_t)n_blk, 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_Q4_K) {
ggml_tensor_extra_cl_q4_K * extra = (ggml_tensor_extra_cl_q4_K *)tensor->extra;
@@ -9840,6 +10148,178 @@ static void ggml_cl_mul_mat_q4_1_f32_adreno(ggml_backend_t backend, const ggml_t
#endif
}
static void ggml_cl_mul_mat_iq4_nl_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);
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 * extra1 = (ggml_tensor_extra_cl *)src1->extra;
ggml_tensor_extra_cl * extrad = (ggml_tensor_extra_cl *)dst->extra;
ggml_tensor_extra_cl_iq4_nl * extra0_iq4_nl = (ggml_tensor_extra_cl_iq4_nl *)src0->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 % 32 == 0);
cl_context context = backend_ctx->context;
cl_kernel kernel;
cl_int err;
cl_image_format img_fmt;
cl_image_desc img_desc;
cl_buffer_region region;
int M = ne01;
int N = ne1;
int K = ne00;
if (ne1 == 1) {
cl_mem q_img = nullptr;
cl_mem b_sub_buf = nullptr;
cl_mem b_img = nullptr;
// image for q
img_fmt = { CL_R, CL_UNSIGNED_INT32};
memset(&img_desc, 0, sizeof(img_desc));
img_desc.image_type = CL_MEM_OBJECT_IMAGE1D_BUFFER;
img_desc.image_width = M * K / 2 / 4;
img_desc.buffer = extra0_iq4_nl->q;
CL_CHECK((q_img = clCreateImage(context, CL_MEM_READ_ONLY, &img_fmt, &img_desc, NULL, &err), err));
// subbuffer for activations
region.origin = offset1;
region.size = K * N * sizeof(float);
CL_CHECK((b_sub_buf = clCreateSubBuffer(extra1->data_device, 0, CL_BUFFER_CREATE_TYPE_REGION, &region, &err), err));
// image for activations
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 = 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_iq4_nl_f32;
CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &q_img));
CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &extra0_iq4_nl->d));
CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &b_img));
CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_mem), &extrad->data_device));
CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_ulong), &offsetd));
CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_int), &ne00));
CL_CHECK(clSetKernelArg(kernel, 6, sizeof(cl_int), &ne01));
size_t local_work_size[3] = {64, 4, 1};
size_t global_work_size[3] = {(size_t)CEIL_DIV(ne01/2, 64)*64, 4, 1};
backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst);
CL_CHECK(clReleaseMemObject(q_img));
CL_CHECK(clReleaseMemObject(b_sub_buf));
CL_CHECK(clReleaseMemObject(b_img));
} else {
cl_mem b_sub_buf = nullptr;
cl_mem b_sub_buf_trans = nullptr;
cl_mem b_img = nullptr;
cl_mem b_img_trans = nullptr;
// subbuffer for activations
region.origin = offset1;
region.size = K * N * sizeof(float);
CL_CHECK((b_sub_buf = clCreateSubBuffer(extra1->data_device, 0, CL_BUFFER_CREATE_TYPE_REGION, &region, &err), err));
// image for activations
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 = 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));
// pad N to multiple of 8
int extra_elements = N % 8;
int padding = 0;
if (extra_elements > 0){
padding = 8 - extra_elements;
}
// subbuffer for transposed activations
region.origin = 0;
region.size = K * (N + padding) * sizeof(float)/2;
backend_ctx->prealloc_act_trans.allocate(context, region.size);
CL_CHECK((b_sub_buf_trans = clCreateSubBuffer(backend_ctx->prealloc_act_trans.buffer, 0, CL_BUFFER_CREATE_TYPE_REGION, &region, &err), err));
// image for transposed activations
img_fmt = {CL_RGBA, CL_HALF_FLOAT};
memset(&img_desc, 0, sizeof(img_desc));
img_desc.image_type = CL_MEM_OBJECT_IMAGE1D_BUFFER;
img_desc.image_width = K * (N + padding) / 4;
img_desc.buffer = b_sub_buf_trans;
CL_CHECK((b_img_trans = clCreateImage(context, 0, &img_fmt, &img_desc, NULL, &err), err));
// transpose activations
int height_B = N/4;
if (height_B == 0) {
height_B = 1;
}
int width_B = K/4;
int padded_height_B = (N + padding)/4;
kernel = backend_ctx->kernel_transpose_32_16;
CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &b_img));
CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &b_img_trans));
CL_CHECK(clSetKernelArg(kernel, 2, sizeof(int), &height_B));
CL_CHECK(clSetKernelArg(kernel, 3, sizeof(int), &width_B));
CL_CHECK(clSetKernelArg(kernel, 4, sizeof(int), &padded_height_B));
size_t local_work_size_t[2] = { 1, 16 };
size_t global_work_size_t[2] = { (size_t)width_B, (size_t)padded_height_B };
backend_ctx->enqueue_ndrange_kernel(kernel, 2, global_work_size_t, local_work_size_t, dst);
// gemm
kernel = backend_ctx->kernel_gemm_noshuffle_iq4_nl_f32;
int padded_N = N + padding;
CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra0_iq4_nl->q));
CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &extra0_iq4_nl->d));
CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &b_img_trans));
CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_mem), &extrad->data_device));
CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_ulong), &offsetd));
CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_int), &ne01));
CL_CHECK(clSetKernelArg(kernel, 6, sizeof(cl_int), &padded_N));
CL_CHECK(clSetKernelArg(kernel, 7, sizeof(cl_int), &ne00));
CL_CHECK(clSetKernelArg(kernel, 8, sizeof(cl_int), &ne1));
size_t global_work_size[3] = {(size_t)CEIL_DIV(ne1, 8), (size_t)CEIL_DIV(ne01, 4), 1};
size_t local_work_size[3] = {1, 128, 1};
backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst);
CL_CHECK(clReleaseMemObject(b_sub_buf));
CL_CHECK(clReleaseMemObject(b_sub_buf_trans));
CL_CHECK(clReleaseMemObject(b_img));
CL_CHECK(clReleaseMemObject(b_img_trans));
}
#else
GGML_UNUSED(backend);
GGML_UNUSED(src0);
GGML_UNUSED(src1);
GGML_UNUSED(dst);
#endif
}
static void ggml_cl_mul_mat_q8_0_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);
@@ -10634,6 +11114,7 @@ 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_iq4_nl * extra0_iq4_nl = (ggml_tensor_extra_cl_iq4_nl *)src0->extra;
ggml_tensor_extra_cl_q4_K * extra0_q4_K = (ggml_tensor_extra_cl_q4_K *)src0->extra;
ggml_tensor_extra_cl_q5_K * extra0_q5_K = (ggml_tensor_extra_cl_q5_K *)src0->extra;
ggml_tensor_extra_cl_q6_K * extra0_q6_K = (ggml_tensor_extra_cl_q6_K *)src0->extra;
@@ -10738,6 +11219,12 @@ static void ggml_cl_mul_mat(ggml_backend_t backend, const ggml_tensor * src0, co
return;
}
// iq4_nl x fp32
if (src0t == GGML_TYPE_IQ4_NL && src1t == GGML_TYPE_F32) {
ggml_cl_mul_mat_iq4_nl_f32_adreno(backend, src0, src1, dst);
return;
}
// q8_0 x fp32
if (src0t == GGML_TYPE_Q8_0 && src1t == GGML_TYPE_F32 &&
enable_adreno_trans_weight(backend_ctx, src0)) {
@@ -11302,6 +11789,48 @@ 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_IQ4_NL: {
if (ne11 < 32) {
break;
}
if (!ggml_is_contiguous(src0) || !ggml_is_contiguous(src1)) {
break;
}
kernel = backend_ctx->kernel_mul_mm_iq4_nl_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_iq4_nl->q));
CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &extra0_iq4_nl->d));
CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extra1->data_device));
CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_ulong), &offset1));
CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_mem), &extrad->data_device));
CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_ulong), &offsetd));
CL_CHECK(clSetKernelArg(kernel, 6, sizeof(int), &ne00));
CL_CHECK(clSetKernelArg(kernel, 7, sizeof(int), &ne01));
CL_CHECK(clSetKernelArg(kernel, 8, sizeof(int), &ne02));
CL_CHECK(clSetKernelArg(kernel, 9, sizeof(int), &ne11));
CL_CHECK(clSetKernelArg(kernel, 10, sizeof(int), &ne12));
CL_CHECK(clSetKernelArg(kernel, 11, sizeof(int), &ne10)); // stride_a
CL_CHECK(clSetKernelArg(kernel, 12, sizeof(int), &ne10)); // stride_b
CL_CHECK(clSetKernelArg(kernel, 13, sizeof(int), &ne01)); // stride_d
CL_CHECK(clSetKernelArg(kernel, 14, sizeof(int), &batch_stride_a));
CL_CHECK(clSetKernelArg(kernel, 15, sizeof(int), &batch_stride_b));
CL_CHECK(clSetKernelArg(kernel, 16, sizeof(int), &batch_stride_d));
CL_CHECK(clSetKernelArg(kernel, 17, sizeof(int), &r2));
CL_CHECK(clSetKernelArg(kernel, 18, 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_Q4_K: {
if (ne11 < 32) {
break;
@@ -11829,6 +12358,70 @@ 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_IQ4_NL: {
#ifdef GGML_OPENCL_SOA_Q
kernel = backend_ctx->kernel_mul_mv_iq4_nl_f32_flat;
if (backend_ctx->gpu_family == INTEL) {
nth0 = 16;
nth1 = 1;
ndst = 8;
} else if (backend_ctx->gpu_family == ADRENO) {
nth0 = 64;
nth1 = 1;
ndst = 8;
} else {
GGML_ASSERT(false && "TODO: Unknown GPU");
}
CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra0_iq4_nl->q));
CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &extra0_iq4_nl->d));
CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extra1->data_device));
CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_ulong), &offset1));
CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_mem), &extrad->data_device));
CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_ulong), &offsetd));
CL_CHECK(clSetKernelArg(kernel, 6, sizeof(int), &ne00));
CL_CHECK(clSetKernelArg(kernel, 7, sizeof(int), &ne01));
CL_CHECK(clSetKernelArg(kernel, 8, sizeof(int), &ne02));
CL_CHECK(clSetKernelArg(kernel, 9, sizeof(int), &ne10));
CL_CHECK(clSetKernelArg(kernel, 10, sizeof(int), &ne12));
CL_CHECK(clSetKernelArg(kernel, 11, sizeof(int), &ne0));
CL_CHECK(clSetKernelArg(kernel, 12, sizeof(int), &ne1));
CL_CHECK(clSetKernelArg(kernel, 13, sizeof(int), &r2));
CL_CHECK(clSetKernelArg(kernel, 14, sizeof(int), &r3));
#else
kernel = backend_ctx->kernel_mul_mv_iq4_nl_f32;
if (backend_ctx->gpu_family == INTEL) {
nth0 = 16;
nth1 = 1;
ndst = 4;
} else if (backend_ctx->gpu_family == ADRENO) {
nth0 = 64;
nth1 = 1;
ndst = 4;
} else {
GGML_ASSERT(false && "TODO: Unknown GPU");
}
CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra0->data_device));
CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_ulong), &offset0));
CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extra1->data_device));
CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_ulong), &offset1));
CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_mem), &extrad->data_device));
CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_ulong), &offsetd));
CL_CHECK(clSetKernelArg(kernel, 6, sizeof(int), &ne00));
CL_CHECK(clSetKernelArg(kernel, 7, sizeof(int), &ne01));
CL_CHECK(clSetKernelArg(kernel, 8, sizeof(int), &ne02));
CL_CHECK(clSetKernelArg(kernel, 9, sizeof(int), &ne10));
CL_CHECK(clSetKernelArg(kernel, 10, sizeof(int), &ne12));
CL_CHECK(clSetKernelArg(kernel, 11, sizeof(int), &ne0));
CL_CHECK(clSetKernelArg(kernel, 12, sizeof(int), &ne1));
CL_CHECK(clSetKernelArg(kernel, 13, sizeof(int), &r2));
CL_CHECK(clSetKernelArg(kernel, 14, sizeof(int), &r3));
#endif // GGML_OPENCL_SOA_Q
break;
}
@@ -12131,6 +12724,7 @@ static void ggml_cl_mul_mat(ggml_backend_t backend, const ggml_tensor * src0, co
if (src0t == GGML_TYPE_Q4_0 || src0t == GGML_TYPE_MXFP4 ||
src0t == GGML_TYPE_Q4_1 ||
src0t == GGML_TYPE_Q8_0 ||
src0t == GGML_TYPE_IQ4_NL ||
src0t == GGML_TYPE_Q2_K) {
// Each SIMD group produces N_DST values in the result. Assuming each
// workgroup has N_SIMDGROUP SIMD groups, then each workgroup will
ggml/src/ggml-opencl/kernels/cvt.cl
+107
View File
@@ -87,6 +87,17 @@ struct block_q6_K {
half d; // super-block scale
};
//------------------------------------------------------------------------------
// block_iq4_nl
//------------------------------------------------------------------------------
#define QK4_NL 32
struct block_iq4_nl
{
half d;
uint8_t qs[QK4_NL / 2];
};
//------------------------------------------------------------------------------
// kernel_convert_block_q4_0
// Convert the block_q4_0 format to 2 separate arrays (AOS -> SOA).
@@ -895,3 +906,99 @@ kernel void kernel_restore_block_q6_K_noshuffle(
b->scales[i] = s[i];
}
}
//------------------------------------------------------------------------------
// kernel_convert_block_iq4_nl
// Convert the block_iq4_nl format to 2 separate arrays (AOS -> SOA).
//------------------------------------------------------------------------------
kernel void kernel_convert_block_iq4_nl(
global struct block_iq4_nl * src0,
global uchar * dst_q,
global half * dst_d,
uchar mask_0F,
uchar mask_F0,
ulong n_blk
) {
if (get_global_id(0) >= n_blk) {
return;
}
global struct block_iq4_nl * b = (global struct block_iq4_nl *) src0 + get_global_id(0);
global uchar * q = (global uchar *) dst_q + QK4_NL/2*get_global_id(0);
global half * d = (global half *) dst_d + get_global_id(0);
*d = b->d;
for (int i = 0; i < QK4_NL/2; ++i) {
q[i] = b->qs[i];
}
}
kernel void kernel_restore_block_iq4_nl(
global uchar * src_q,
global half * src_d,
global struct block_iq4_nl * dst,
ulong n_blk
) {
if (get_global_id(0) >= n_blk) {
return;
}
global struct block_iq4_nl * b = (global struct block_iq4_nl *) dst + get_global_id(0);
global uchar * q = (global uchar *) src_q + QK4_NL/2*get_global_id(0);
global half * d = (global half *) src_d + get_global_id(0);
b->d = *d;
for (int i = 0; i < QK4_NL/2; ++i) {
b->qs[i] = q[i];
}
}
kernel void kernel_convert_block_iq4_nl_noshuffle(
global struct block_iq4_nl * src0,
global uchar * dst_q,
global half * dst_d,
uchar mask_0F,
uchar mask_F0,
ulong n_blk
) {
if (get_global_id(0) >= n_blk) {
return;
}
global struct block_iq4_nl * b = (global struct block_iq4_nl *) src0 + get_global_id(0);
global uchar * q = (global uchar *) dst_q + QK4_NL/2*get_global_id(0);
global half * d = (global half *) dst_d + get_global_id(0);
*d = b->d;
for (int i = 0; i < QK4_NL/4; ++i) {
uchar x0 = b->qs[2*i + 0];
uchar x1 = b->qs[2*i + 1];
q[i + 0 ] = convert_uchar(x0 & mask_0F) | convert_uchar((x1 & mask_0F) << 4);
q[i + QK4_NL/4] = convert_uchar((x0 & mask_F0) >> 4) | convert_uchar(x1 & mask_F0);
}
}
kernel void kernel_restore_block_iq4_nl_noshuffle(
global uchar * src_q,
global half * src_d,
global struct block_iq4_nl * dst,
uchar mask_0F,
uchar mask_F0,
ulong n_blk
) {
if (get_global_id(0) >= n_blk) {
return;
}
global struct block_iq4_nl * b = (global struct block_iq4_nl *) dst + get_global_id(0);
global uchar * q = (global uchar *) src_q + QK4_NL/2*get_global_id(0);
global half * d = (global half *) src_d + get_global_id(0);
b->d = *d;
for (int i = 0; i < QK4_NL/4; ++i) {
uchar x0 = q[i + 0 ];
uchar x1 = q[i + QK4_NL/4];
b->qs[2*i + 0] = convert_uchar((x0 & mask_0F) | ((x1 & mask_0F) << 4));
b->qs[2*i + 1] = convert_uchar(((x0 & mask_F0) >> 4) | (x1 & mask_F0));
}
}
ggml/src/ggml-opencl/kernels/gemm_noshuffle_iq4_nl_f32.cl
+150
View File
@@ -0,0 +1,150 @@
#pragma OPENCL EXTENSION cl_khr_fp16 : enable
#pragma OPENCL EXTENSION cl_qcom_reqd_sub_group_size : enable
#ifdef cl_qcom_reqd_sub_group_size
#define ADRENO_GPU 1
#define REQD_SUBGROUP_SIZE_128 __attribute__((qcom_reqd_sub_group_size("full")))
#endif
constant half kvalues_iq4nl[16] = {
(half)-127.f, (half)-104.f, (half)-83.f, (half)-65.f,
(half) -49.f, (half) -35.f, (half)-22.f, (half)-10.f,
(half) 1.f, (half) 13.f, (half) 25.f, (half) 38.f,
(half) 53.f, (half) 69.f, (half) 89.f, (half)113.f
};
// Packed LUT: 2 FP16 values per uint, 8 unique constant loads instead of 16
constant uint iq4nl_packed[8] = {
0xD680D7F0u, // idx 0,1: -127, -104
0xD410D530u, // idx 2,3: -83, -65
0xD060D220u, // idx 4,5: -49, -35
0xC900CD80u, // idx 6,7: -22, -10
0x4A803C00u, // idx 8,9: 1, 13
0x50C04E40u, // idx 10,11: 25, 38
0x545052A0u, // idx 12,13: 53, 69
0x57105590u // idx 14,15: 89, 113
};
// Packed dequant: 1 uint constant load (8-way divergence) + shift + as_half
#define IQ4_NL_DEQUANT(nibble) as_half((ushort)(iq4nl_packed[(nibble) >> 1] >> (((nibble) & 1u) << 4)))
#ifdef ADRENO_GPU
REQD_SUBGROUP_SIZE_128
#endif
kernel void kernel_gemm_noshuffle_iq4_nl_f32(
global const ushort * src0_q,
global const half * src0_d,
read_only image1d_buffer_t src1,
global float * dst,
ulong offsetd,
int m,
int n,
int k,
int n_no_padding
) {
dst = (global float *)((global char *)dst + offsetd);
int m_4 = m >> 2;
int n_4 = n >> 2;
int gy = get_global_id(0);
int gx = get_global_id(1);
int gx_2 = gx << 2;
half8 c0 = 0, c1 = 0, c2 = 0, c3 = 0;
half8 B;
half4 dequantized_weights;
global const ushort * weight_ptr = src0_q + gx_2;
global const half * scale_ptr = src0_d + gx_2;
for (int i = 0; i < k; i += 4) {
B.s0123 = read_imageh(src1, gy*2 + (i)*(n_4));
B.s4567 = read_imageh(src1, gy*2 + (i)*(n_4)+1);
ushort4 bits4 = vload4(0, weight_ptr + (i/4)*(m));
half4 scale = vload4(0, scale_ptr + (i/32)*(m));
// j=0
dequantized_weights.s0 = IQ4_NL_DEQUANT(bits4.s0 & 0x000Fu) * scale.s0;
dequantized_weights.s1 = IQ4_NL_DEQUANT(bits4.s1 & 0x000Fu) * scale.s1;
dequantized_weights.s2 = IQ4_NL_DEQUANT(bits4.s2 & 0x000Fu) * scale.s2;
dequantized_weights.s3 = IQ4_NL_DEQUANT(bits4.s3 & 0x000Fu) * scale.s3;
c0 += B * dequantized_weights.s0;
c1 += B * dequantized_weights.s1;
c2 += B * dequantized_weights.s2;
c3 += B * dequantized_weights.s3;
// j=1
B.s0123 = read_imageh(src1, gy*2 + (i+1)*(n_4));
B.s4567 = read_imageh(src1, gy*2 + (i+1)*(n_4)+1);
dequantized_weights.s0 = IQ4_NL_DEQUANT((bits4.s0 >> 4) & 0x000Fu) * scale.s0;
dequantized_weights.s1 = IQ4_NL_DEQUANT((bits4.s1 >> 4) & 0x000Fu) * scale.s1;
dequantized_weights.s2 = IQ4_NL_DEQUANT((bits4.s2 >> 4) & 0x000Fu) * scale.s2;
dequantized_weights.s3 = IQ4_NL_DEQUANT((bits4.s3 >> 4) & 0x000Fu) * scale.s3;
c0 += B * dequantized_weights.s0;
c1 += B * dequantized_weights.s1;
c2 += B * dequantized_weights.s2;
c3 += B * dequantized_weights.s3;
// j=2
B.s0123 = read_imageh(src1, gy*2 + (i+2)*(n_4));
B.s4567 = read_imageh(src1, gy*2 + (i+2)*(n_4)+1);
dequantized_weights.s0 = IQ4_NL_DEQUANT((bits4.s0 >> 8) & 0x000Fu) * scale.s0;
dequantized_weights.s1 = IQ4_NL_DEQUANT((bits4.s1 >> 8) & 0x000Fu) * scale.s1;
dequantized_weights.s2 = IQ4_NL_DEQUANT((bits4.s2 >> 8) & 0x000Fu) * scale.s2;
dequantized_weights.s3 = IQ4_NL_DEQUANT((bits4.s3 >> 8) & 0x000Fu) * scale.s3;
c0 += B * dequantized_weights.s0;
c1 += B * dequantized_weights.s1;
c2 += B * dequantized_weights.s2;
c3 += B * dequantized_weights.s3;
// j=3
B.s0123 = read_imageh(src1, gy*2 + (i+3)*(n_4));
B.s4567 = read_imageh(src1, gy*2 + (i+3)*(n_4)+1);
dequantized_weights.s0 = IQ4_NL_DEQUANT((bits4.s0 >> 12) & 0x000Fu) * scale.s0;
dequantized_weights.s1 = IQ4_NL_DEQUANT((bits4.s1 >> 12) & 0x000Fu) * scale.s1;
dequantized_weights.s2 = IQ4_NL_DEQUANT((bits4.s2 >> 12) & 0x000Fu) * scale.s2;
dequantized_weights.s3 = IQ4_NL_DEQUANT((bits4.s3 >> 12) & 0x000Fu) * scale.s3;
c0 += B * dequantized_weights.s0;
c1 += B * dequantized_weights.s1;
c2 += B * dequantized_weights.s2;
c3 += B * dequantized_weights.s3;
}
int idx = (gy<<3)*m + (gx<<2);
if(idx+3 < m*n_no_padding){
vstore4((float4)(c0.s0, c1.s0, c2.s0, c3.s0), 0, dst + idx);
idx += m;
}
if(idx+3 < m*n_no_padding){
vstore4((float4)(c0.s1, c1.s1, c2.s1, c3.s1), 0, dst + idx);
idx += m;
}
if(idx+3 < m*n_no_padding){
vstore4((float4)(c0.s2, c1.s2, c2.s2, c3.s2), 0, dst + idx);
idx += m;
}
if(idx+3 < m*n_no_padding){
vstore4((float4)(c0.s3, c1.s3, c2.s3, c3.s3), 0, dst + idx);
idx += m;
}
if(idx+3 < m*n_no_padding){
vstore4((float4)(c0.s4, c1.s4, c2.s4, c3.s4), 0, dst + idx);
idx += m;
}
if(idx+3 < m*n_no_padding){
vstore4((float4)(c0.s5, c1.s5, c2.s5, c3.s5), 0, dst + idx);
idx += m;
}
if(idx+3 < m*n_no_padding){
vstore4((float4)(c0.s6, c1.s6, c2.s6, c3.s6), 0, dst + idx);
idx += m;
}
if(idx+3 < m*n_no_padding){
vstore4((float4)(c0.s7, c1.s7, c2.s7, c3.s7), 0, dst + idx);
}
}
ggml/src/ggml-opencl/kernels/gemv_noshuffle_iq4_nl_f32.cl
+302
View File
@@ -0,0 +1,302 @@
#pragma OPENCL EXTENSION cl_khr_fp16 : enable
#pragma OPENCL EXTENSION cl_khr_subgroups : enable
#ifdef 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")))
#endif
#define QK4_NL 32
#define NSUBGROUPS 4
#define SUBGROUP_SIZE 64
constant half kvalues_iq4nl[16] = {
(half)-127.f, (half)-104.f, (half)-83.f, (half)-65.f,
(half) -49.f, (half) -35.f, (half)-22.f, (half)-10.f,
(half) 1.f, (half) 13.f, (half) 25.f, (half) 38.f,
(half) 53.f, (half) 69.f, (half) 89.f, (half)113.f
};
// Packed LUT: 2 FP16 values per uint, 8 unique constant loads instead of 16
constant uint iq4nl_packed[8] = {
0xD680D7F0u, // idx 0,1: -127, -104
0xD410D530u, // idx 2,3: -83, -65
0xD060D220u, // idx 4,5: -49, -35
0xC900CD80u, // idx 6,7: -22, -10
0x4A803C00u, // idx 8,9: 1, 13
0x50C04E40u, // idx 10,11: 25, 38
0x545052A0u, // idx 12,13: 53, 69
0x57105590u // idx 14,15: 89, 113
};
// Packed dequant: 1 uint constant load (8-way divergence) + shift + as_half
#define IQ4_NL_DEQUANT(nibble) as_half((ushort)(iq4nl_packed[(nibble) >> 1] >> (((nibble) & 1u) << 4)))
#define dequantizeBlockAccum_ns_sgbroadcast_1_hi(total_sums, bits4, scale, y) \
float shared_y; \
shared_y = sub_group_broadcast(y.s0, 0); \
total_sums.s0 += IQ4_NL_DEQUANT((bits4.s0 & 0x000F)) * scale.s0 * shared_y; \
total_sums.s1 += IQ4_NL_DEQUANT((bits4.s1 & 0x000F)) * scale.s1 * shared_y; \
shared_y = sub_group_broadcast(y.s1, 0); \
total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s0 & 0x00F0) >> 4)) * scale.s0 * shared_y; \
total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s1 & 0x00F0) >> 4)) * scale.s1 * shared_y; \
shared_y = sub_group_broadcast(y.s2, 0); \
total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s0 & 0x0F00) >> 8)) * scale.s0 * shared_y; \
total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s1 & 0x0F00) >> 8)) * scale.s1 * shared_y; \
shared_y = sub_group_broadcast(y.s3, 0); \
total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s0 & 0xF000) >> 12)) * scale.s0 * shared_y; \
total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s1 & 0xF000) >> 12)) * scale.s1 * shared_y; \
shared_y = sub_group_broadcast(y.s4, 0); \
total_sums.s0 += IQ4_NL_DEQUANT((bits4.s2 & 0x000F)) * scale.s0 * shared_y; \
total_sums.s1 += IQ4_NL_DEQUANT((bits4.s3 & 0x000F)) * scale.s1 * shared_y; \
shared_y = sub_group_broadcast(y.s5, 0); \
total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s2 & 0x00F0) >> 4)) * scale.s0 * shared_y; \
total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s3 & 0x00F0) >> 4)) * scale.s1 * shared_y; \
shared_y = sub_group_broadcast(y.s6, 0); \
total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s2 & 0x0F00) >> 8)) * scale.s0 * shared_y; \
total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s3 & 0x0F00) >> 8)) * scale.s1 * shared_y; \
shared_y = sub_group_broadcast(y.s7, 0); \
total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s2 & 0xF000) >> 12)) * scale.s0 * shared_y; \
total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s3 & 0xF000) >> 12)) * scale.s1 * shared_y; \
shared_y = sub_group_broadcast(y.s0, 1); \
total_sums.s0 += IQ4_NL_DEQUANT((bits4.s4 & 0x000F)) * scale.s0 * shared_y; \
total_sums.s1 += IQ4_NL_DEQUANT((bits4.s5 & 0x000F)) * scale.s1 * shared_y; \
shared_y = sub_group_broadcast(y.s1, 1); \
total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s4 & 0x00F0) >> 4)) * scale.s0 * shared_y; \
total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s5 & 0x00F0) >> 4)) * scale.s1 * shared_y; \
shared_y = sub_group_broadcast(y.s2, 1); \
total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s4 & 0x0F00) >> 8)) * scale.s0 * shared_y; \
total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s5 & 0x0F00) >> 8)) * scale.s1 * shared_y; \
shared_y = sub_group_broadcast(y.s3, 1); \
total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s4 & 0xF000) >> 12)) * scale.s0 * shared_y; \
total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s5 & 0xF000) >> 12)) * scale.s1 * shared_y; \
shared_y = sub_group_broadcast(y.s4, 1); \
total_sums.s0 += IQ4_NL_DEQUANT((bits4.s6 & 0x000F)) * scale.s0 * shared_y; \
total_sums.s1 += IQ4_NL_DEQUANT((bits4.s7 & 0x000F)) * scale.s1 * shared_y; \
shared_y = sub_group_broadcast(y.s5, 1); \
total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s6 & 0x00F0) >> 4)) * scale.s0 * shared_y; \
total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s7 & 0x00F0) >> 4)) * scale.s1 * shared_y; \
shared_y = sub_group_broadcast(y.s6, 1); \
total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s6 & 0x0F00) >> 8)) * scale.s0 * shared_y; \
total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s7 & 0x0F00) >> 8)) * scale.s1 * shared_y; \
shared_y = sub_group_broadcast(y.s7, 1); \
total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s6 & 0xF000) >> 12)) * scale.s0 * shared_y; \
total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s7 & 0xF000) >> 12)) * scale.s1 * shared_y; \
#define dequantizeBlockAccum_ns_sgbroadcast_1_lo(total_sums, bits4, scale, y) \
shared_y = sub_group_broadcast(y.s0, 2); \
total_sums.s0 += IQ4_NL_DEQUANT((bits4.s0 & 0x000F)) * scale.s0 * shared_y; \
total_sums.s1 += IQ4_NL_DEQUANT((bits4.s1 & 0x000F)) * scale.s1 * shared_y; \
shared_y = sub_group_broadcast(y.s1, 2); \
total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s0 & 0x00F0) >> 4)) * scale.s0 * shared_y; \
total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s1 & 0x00F0) >> 4)) * scale.s1 * shared_y; \
shared_y = sub_group_broadcast(y.s2, 2); \
total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s0 & 0x0F00) >> 8)) * scale.s0 * shared_y; \
total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s1 & 0x0F00) >> 8)) * scale.s1 * shared_y; \
shared_y = sub_group_broadcast(y.s3, 2); \
total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s0 & 0xF000) >> 12)) * scale.s0 * shared_y; \
total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s1 & 0xF000) >> 12)) * scale.s1 * shared_y; \
shared_y = sub_group_broadcast(y.s4, 2); \
total_sums.s0 += IQ4_NL_DEQUANT((bits4.s2 & 0x000F)) * scale.s0 * shared_y; \
total_sums.s1 += IQ4_NL_DEQUANT((bits4.s3 & 0x000F)) * scale.s1 * shared_y; \
shared_y = sub_group_broadcast(y.s5, 2); \
total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s2 & 0x00F0) >> 4)) * scale.s0 * shared_y; \
total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s3 & 0x00F0) >> 4)) * scale.s1 * shared_y; \
shared_y = sub_group_broadcast(y.s6, 2); \
total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s2 & 0x0F00) >> 8)) * scale.s0 * shared_y; \
total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s3 & 0x0F00) >> 8)) * scale.s1 * shared_y; \
shared_y = sub_group_broadcast(y.s7, 2); \
total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s2 & 0xF000) >> 12)) * scale.s0 * shared_y; \
total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s3 & 0xF000) >> 12)) * scale.s1 * shared_y; \
shared_y = sub_group_broadcast(y.s0, 3); \
total_sums.s0 += IQ4_NL_DEQUANT((bits4.s4 & 0x000F)) * scale.s0 * shared_y; \
total_sums.s1 += IQ4_NL_DEQUANT((bits4.s5 & 0x000F)) * scale.s1 * shared_y; \
shared_y = sub_group_broadcast(y.s1, 3); \
total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s4 & 0x00F0) >> 4)) * scale.s0 * shared_y; \
total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s5 & 0x00F0) >> 4)) * scale.s1 * shared_y; \
shared_y = sub_group_broadcast(y.s2, 3); \
total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s4 & 0x0F00) >> 8)) * scale.s0 * shared_y; \
total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s5 & 0x0F00) >> 8)) * scale.s1 * shared_y; \
shared_y = sub_group_broadcast(y.s3, 3); \
total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s4 & 0xF000) >> 12)) * scale.s0 * shared_y; \
total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s5 & 0xF000) >> 12)) * scale.s1 * shared_y; \
shared_y = sub_group_broadcast(y.s4, 3); \
total_sums.s0 += IQ4_NL_DEQUANT((bits4.s6 & 0x000F)) * scale.s0 * shared_y; \
total_sums.s1 += IQ4_NL_DEQUANT((bits4.s7 & 0x000F)) * scale.s1 * shared_y; \
shared_y = sub_group_broadcast(y.s5, 3); \
total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s6 & 0x00F0) >> 4)) * scale.s0 * shared_y; \
total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s7 & 0x00F0) >> 4)) * scale.s1 * shared_y; \
shared_y = sub_group_broadcast(y.s6, 3); \
total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s6 & 0x0F00) >> 8)) * scale.s0 * shared_y; \
total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s7 & 0x0F00) >> 8)) * scale.s1 * shared_y; \
shared_y = sub_group_broadcast(y.s7, 3); \
total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s6 & 0xF000) >> 12)) * scale.s0 * shared_y; \
total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s7 & 0xF000) >> 12)) * scale.s1 * shared_y; \
#define dequantizeBlockAccum_ns_sgbroadcast_8_hi(total_sums, bits4, scale, y) \
float8 shared_y; \
shared_y = sub_group_broadcast(y, 0); \
total_sums.s0 += IQ4_NL_DEQUANT((bits4.s0 & 0x000F)) * scale.s0 * shared_y.s0; \
total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s0 & 0x00F0) >> 4)) * scale.s0 * shared_y.s1; \
total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s0 & 0x0F00) >> 8)) * scale.s0 * shared_y.s2; \
total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s0 & 0xF000) >> 12)) * scale.s0 * shared_y.s3; \
total_sums.s0 += IQ4_NL_DEQUANT((bits4.s2 & 0x000F)) * scale.s0 * shared_y.s4; \
total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s2 & 0x00F0) >> 4)) * scale.s0 * shared_y.s5; \
total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s2 & 0x0F00) >> 8)) * scale.s0 * shared_y.s6; \
total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s2 & 0xF000) >> 12)) * scale.s0 * shared_y.s7; \
total_sums.s1 += IQ4_NL_DEQUANT((bits4.s1 & 0x000F)) * scale.s1 * shared_y.s0; \
total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s1 & 0x00F0) >> 4)) * scale.s1 * shared_y.s1; \
total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s1 & 0x0F00) >> 8)) * scale.s1 * shared_y.s2; \
total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s1 & 0xF000) >> 12)) * scale.s1 * shared_y.s3; \
total_sums.s1 += IQ4_NL_DEQUANT((bits4.s3 & 0x000F)) * scale.s1 * shared_y.s4; \
total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s3 & 0x00F0) >> 4)) * scale.s1 * shared_y.s5; \
total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s3 & 0x0F00) >> 8)) * scale.s1 * shared_y.s6; \
total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s3 & 0xF000) >> 12)) * scale.s1 * shared_y.s7; \
shared_y = sub_group_broadcast(y, 1); \
total_sums.s0 += IQ4_NL_DEQUANT((bits4.s4 & 0x000F)) * scale.s0 * shared_y.s0; \
total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s4 & 0x00F0) >> 4)) * scale.s0 * shared_y.s1; \
total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s4 & 0x0F00) >> 8)) * scale.s0 * shared_y.s2; \
total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s4 & 0xF000) >> 12)) * scale.s0 * shared_y.s3; \
total_sums.s0 += IQ4_NL_DEQUANT((bits4.s6 & 0x000F)) * scale.s0 * shared_y.s4; \
total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s6 & 0x00F0) >> 4)) * scale.s0 * shared_y.s5; \
total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s6 & 0x0F00) >> 8)) * scale.s0 * shared_y.s6; \
total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s6 & 0xF000) >> 12)) * scale.s0 * shared_y.s7; \
total_sums.s1 += IQ4_NL_DEQUANT((bits4.s5 & 0x000F)) * scale.s1 * shared_y.s0; \
total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s5 & 0x00F0) >> 4)) * scale.s1 * shared_y.s1; \
total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s5 & 0x0F00) >> 8)) * scale.s1 * shared_y.s2; \
total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s5 & 0xF000) >> 12)) * scale.s1 * shared_y.s3; \
total_sums.s1 += IQ4_NL_DEQUANT((bits4.s7 & 0x000F)) * scale.s1 * shared_y.s4; \
total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s7 & 0x00F0) >> 4)) * scale.s1 * shared_y.s5; \
total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s7 & 0x0F00) >> 8)) * scale.s1 * shared_y.s6; \
total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s7 & 0xF000) >> 12)) * scale.s1 * shared_y.s7; \
#define dequantizeBlockAccum_ns_sgbroadcast_8_lo(total_sums, bits4, scale, y) \
shared_y = sub_group_broadcast(y, 2); \
total_sums.s0 += IQ4_NL_DEQUANT((bits4.s0 & 0x000F)) * scale.s0 * shared_y.s0; \
total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s0 & 0x00F0) >> 4)) * scale.s0 * shared_y.s1; \
total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s0 & 0x0F00) >> 8)) * scale.s0 * shared_y.s2; \
total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s0 & 0xF000) >> 12)) * scale.s0 * shared_y.s3; \
total_sums.s0 += IQ4_NL_DEQUANT((bits4.s2 & 0x000F)) * scale.s0 * shared_y.s4; \
total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s2 & 0x00F0) >> 4)) * scale.s0 * shared_y.s5; \
total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s2 & 0x0F00) >> 8)) * scale.s0 * shared_y.s6; \
total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s2 & 0xF000) >> 12)) * scale.s0 * shared_y.s7; \
total_sums.s1 += IQ4_NL_DEQUANT((bits4.s1 & 0x000F)) * scale.s1 * shared_y.s0; \
total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s1 & 0x00F0) >> 4)) * scale.s1 * shared_y.s1; \
total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s1 & 0x0F00) >> 8)) * scale.s1 * shared_y.s2; \
total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s1 & 0xF000) >> 12)) * scale.s1 * shared_y.s3; \
total_sums.s1 += IQ4_NL_DEQUANT((bits4.s3 & 0x000F)) * scale.s1 * shared_y.s4; \
total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s3 & 0x00F0) >> 4)) * scale.s1 * shared_y.s5; \
total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s3 & 0x0F00) >> 8)) * scale.s1 * shared_y.s6; \
total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s3 & 0xF000) >> 12)) * scale.s1 * shared_y.s7; \
shared_y = sub_group_broadcast(y, 3); \
total_sums.s0 += IQ4_NL_DEQUANT((bits4.s4 & 0x000F)) * scale.s0 * shared_y.s0; \
total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s4 & 0x00F0) >> 4)) * scale.s0 * shared_y.s1; \
total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s4 & 0x0F00) >> 8)) * scale.s0 * shared_y.s2; \
total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s4 & 0xF000) >> 12)) * scale.s0 * shared_y.s3; \
total_sums.s0 += IQ4_NL_DEQUANT((bits4.s6 & 0x000F)) * scale.s0 * shared_y.s4; \
total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s6 & 0x00F0) >> 4)) * scale.s0 * shared_y.s5; \
total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s6 & 0x0F00) >> 8)) * scale.s0 * shared_y.s6; \
total_sums.s0 += IQ4_NL_DEQUANT(((bits4.s6 & 0xF000) >> 12)) * scale.s0 * shared_y.s7; \
total_sums.s1 += IQ4_NL_DEQUANT((bits4.s5 & 0x000F)) * scale.s1 * shared_y.s0; \
total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s5 & 0x00F0) >> 4)) * scale.s1 * shared_y.s1; \
total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s5 & 0x0F00) >> 8)) * scale.s1 * shared_y.s2; \
total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s5 & 0xF000) >> 12)) * scale.s1 * shared_y.s3; \
total_sums.s1 += IQ4_NL_DEQUANT((bits4.s7 & 0x000F)) * scale.s1 * shared_y.s4; \
total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s7 & 0x00F0) >> 4)) * scale.s1 * shared_y.s5; \
total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s7 & 0x0F00) >> 8)) * scale.s1 * shared_y.s6; \
total_sums.s1 += IQ4_NL_DEQUANT(((bits4.s7 & 0xF000) >> 12)) * scale.s1 * shared_y.s7; \
#ifdef ADRENO_GPU
REQD_SUBGROUP_SIZE_64
#endif
kernel void kernel_gemv_noshuffle_iq4_nl_f32(
read_only image1d_buffer_t src0_q,
global half2 * src0_d,
read_only image1d_buffer_t src1,
global float * dst,
ulong offsetd,
int ne00,
int ne01)
{
uint groupId = get_local_id(1);
uint gid = get_global_id(0);
ushort slid = get_sub_group_local_id();
uint K = ne00;
uint M = ne01;
uint LINE_STRIDE_A = M / 2;
uint BLOCK_STRIDE_A = NSUBGROUPS * M;
private uint4 regA;
private half2 regS;
private float8 regB;
private float2 totalSum = (float2)(0.0f);
// loop along K in block granularity, skip 4 blocks every iter
for (uint k = groupId; k < (K / QK4_NL); k += NSUBGROUPS) {
regS = src0_d[gid + k * LINE_STRIDE_A]; // each fiber loads scale of two rows
// first 4 fibers in each wave load 8 B values to its private scope
if (slid < 4) {
regB.s0123 = read_imagef(src1, (slid * 2 + k * 8));
regB.s4567 = read_imagef(src1, (1 + slid * 2 + k * 8));
}
// load half weights for two blocks in consecutive rows
regA.s0 = read_imageui(src0_q, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 0)).x;
regA.s1 = read_imageui(src0_q, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 1)).x;
regA.s2 = read_imageui(src0_q, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 2)).x;
regA.s3 = read_imageui(src0_q, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 3)).x;
#ifdef VECTOR_SUB_GROUP_BROADCAST
dequantizeBlockAccum_ns_sgbroadcast_8_hi(totalSum, as_ushort8(regA), regS, regB);
#else
dequantizeBlockAccum_ns_sgbroadcast_1_hi(totalSum, as_ushort8(regA), regS, regB);
#endif // VECTOR_SUB_GROUP_BROADCAST
regA.s0 = read_imageui(src0_q, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 4)).x;
regA.s1 = read_imageui(src0_q, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 5)).x;
regA.s2 = read_imageui(src0_q, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 6)).x;
regA.s3 = read_imageui(src0_q, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 7)).x;
#ifdef VECTOR_SUB_GROUP_BROADCAST
dequantizeBlockAccum_ns_sgbroadcast_8_lo(totalSum, as_ushort8(regA), regS, regB);
#else
dequantizeBlockAccum_ns_sgbroadcast_1_lo(totalSum, as_ushort8(regA), regS, regB);
#endif // VECTOR_SUB_GROUP_BROADCAST
}
// reduction in local memory, assumes #wave=4
local float2 reduceLM[SUBGROUP_SIZE * 3];
if (groupId == 1) {
reduceLM[SUBGROUP_SIZE * 0 + slid] = totalSum;
}
if (groupId == 2) {
reduceLM[SUBGROUP_SIZE * 1 + slid] = totalSum;
}
if (groupId == 3) {
reduceLM[SUBGROUP_SIZE * 2 + slid] = totalSum;
}
barrier(CLK_LOCAL_MEM_FENCE);
if (groupId == 0) {
totalSum += reduceLM[SUBGROUP_SIZE * 0 + slid];
}
if (groupId == 0) {
totalSum += reduceLM[SUBGROUP_SIZE * 1 + slid];
}
if (groupId == 0) {
totalSum += reduceLM[SUBGROUP_SIZE * 2 + slid];
}
// 2 outputs per fiber in wave 0
if (groupId == 0) {
dst = (global float*)((global char*)dst + offsetd);
vstore2(totalSum, 0, &(dst[gid * 2]));
}
}
ggml/src/ggml-opencl/kernels/mul_mm_iq4_nl_f32_l4_lm.cl
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#pragma OPENCL EXTENSION cl_khr_fp16 : enable
#define LOAD_VEC_A 8
#define LOAD_VEC_B 4
#define BM 64
#define BN 64
#define BK 32
#define TM 4
#define TN 8
constant float kvalues_iq4nl[16] = {
-127.f, -104.f, -83.f, -65.f, -49.f, -35.f, -22.f, -10.f,
1.f, 13.f, 25.f, 38.f, 53.f, 69.f, 89.f, 113.f
};
kernel void kernel_mul_mm_iq4_nl_f32_l4_lm(
global uchar4 * src0_q,
global half * src0_d,
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 / 4;
int iqs = idx % 4;
float d = (float)src0_d[ib];
global uchar4 * qs = src0_q + ib*4 + iqs;
uchar4 q = *qs;
// IQ4_NL: use lookup table instead of linear (nibble - 8)
float4 v1 = (float4)(kvalues_iq4nl[(q.s0 )&0x0F], kvalues_iq4nl[(q.s1 )&0x0F],
kvalues_iq4nl[(q.s2 )&0x0F], kvalues_iq4nl[(q.s3 )&0x0F])*d;
float4 v2 = (float4)(kvalues_iq4nl[(q.s0>>4)&0x0F], kvalues_iq4nl[(q.s1>>4)&0x0F],
kvalues_iq4nl[(q.s2>>4)&0x0F], kvalues_iq4nl[(q.s3>>4)&0x0F])*d;
buf_a[(loadr_a * 4 + 0) * BM + loadc_a + l] = v1.s0;
buf_a[(loadr_a * 4 + 1) * BM + loadc_a + l] = v1.s1;
buf_a[(loadr_a * 4 + 2) * BM + loadc_a + l] = v1.s2;
buf_a[(loadr_a * 4 + 3) * BM + loadc_a + l] = v1.s3;
buf_a[(loadr_a * 4 + 16) * BM + loadc_a + l] = v2.s0;
buf_a[(loadr_a * 4 + 17) * BM + loadc_a + l] = v2.s1;
buf_a[(loadr_a * 4 + 18) * BM + loadc_a + l] = v2.s2;
buf_a[(loadr_a * 4 + 19) * BM + loadc_a + l] = v2.s3;
} else {
buf_a[(loadr_a * 4 + 0) * BM + loadc_a + l] = 0.0f;
buf_a[(loadr_a * 4 + 1) * BM + loadc_a + l] = 0.0f;
buf_a[(loadr_a * 4 + 2) * BM + loadc_a + l] = 0.0f;
buf_a[(loadr_a * 4 + 3) * BM + loadc_a + l] = 0.0f;
buf_a[(loadr_a * 4 + 16) * BM + loadc_a + l] = 0.0f;
buf_a[(loadr_a * 4 + 17) * BM + loadc_a + l] = 0.0f;
buf_a[(loadr_a * 4 + 18) * BM + loadc_a + l] = 0.0f;
buf_a[(loadr_a * 4 + 19) * 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];
}
}
}
}
ggml/src/ggml-opencl/kernels/mul_mv_iq4_nl_f32.cl
+164
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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
#define QK4_NL 32
typedef char int8_t;
typedef uchar uint8_t;
typedef short int16_t;
typedef ushort uint16_t;
typedef int int32_t;
typedef uint uint32_t;
constant float kvalues_iq4nl[16] = {
-127.f, -104.f, -83.f, -65.f, -49.f, -35.f, -22.f, -10.f,
1.f, 13.f, 25.f, 38.f, 53.f, 69.f, 89.f, 113.f
};
//------------------------------------------------------------------------------
// block_iq4_nl
//------------------------------------------------------------------------------
struct block_iq4_nl
{
half d;
uint8_t qs[QK4_NL / 2];
};
//------------------------------------------------------------------------------
// mul_vec_q_n_f32
//------------------------------------------------------------------------------
// Compute inner product between half a block of iq4_nl and 16 floats (yl).
// il indicates where the quants begin (0 or 8).
inline float block_iq4_nl_dot_y(
global struct block_iq4_nl * qb_curr,
private float * yl,
int il
) {
float d = qb_curr->d;
float acc = 0.f;
global uchar * qs = qb_curr->qs + il;
for (int i = 0; i < 8; ++i) {
acc += yl[i] * kvalues_iq4nl[qs[i] & 0x0F];
acc += yl[i+8] * kvalues_iq4nl[qs[i] >> 4];
}
return d * acc;
}
#ifdef INTEL_GPU
#define N_DST 4 // each subgroup group works on 4 rows
#define N_SUBGROUP 1 // number of subgroups in a thread group
#define N_SUBGROUP_SIZE 16 // assuming subgroup size is 16
#elif defined (ADRENO_GPU)
#define N_DST 4
#define N_SUBGROUP 1
#define N_SUBGROUP_SIZE 64
#endif
inline void mul_vec_q_n_f32(
global void * src0,
global float * src1,
global float * dst,
int ne00,
int ne01,
int ne02,
int ne10,
int ne12,
int ne0,
int ne1,
int r2,
int r3
) {
const ulong nb = ne00/QK4_NL;
int r0 = get_group_id(0);
int r1 = get_group_id(1);
int im = get_group_id(2);
int first_row = (r0 * N_SUBGROUP + get_sub_group_id()) * N_DST;
int i12 = im%ne12;
int i13 = im/ne12;
ulong offset0 = first_row * nb + (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02);
global struct block_iq4_nl * x = (global struct block_iq4_nl *) src0 + offset0;
global float * y = (global float *) src1 + r1*ne10 + im*ne00*ne1;
float yl[16]; // src1 vector cache
float sumf[N_DST]={0.f};
int ix = get_sub_group_local_id()/2;
int il = 8*(get_sub_group_local_id()%2);
global float * yb = y + ix * QK4_NL + il;
// each thread in a SIMD group deals with half a block.
for (int ib = ix; ib < nb; ib += N_SUBGROUP_SIZE/2) {
for (int i = 0; i < 8; ++i) {
yl[i] = yb[i];
yl[i+8] = yb[i+16];
}
for (int row = 0; row < N_DST; row++) {
sumf[row] += block_iq4_nl_dot_y(x+ib+row*nb, yl, il);
}
yb += QK4_NL * (N_SUBGROUP_SIZE/2);
}
float tot[N_DST] = {
sub_group_reduce_add(sumf[0]), sub_group_reduce_add(sumf[1]),
sub_group_reduce_add(sumf[2]), sub_group_reduce_add(sumf[3])};
for (int row = 0; row < N_DST; ++row) {
if (get_sub_group_local_id() == 0 && first_row + row < ne01) {
dst[r1*ne0 + im*ne0*ne1 + first_row + row] = tot[row];
}
}
}
#ifdef INTEL_GPU
REQD_SUBGROUP_SIZE_16
#elif defined (ADRENO_GPU)
REQD_SUBGROUP_SIZE_64
#endif
kernel void kernel_mul_mv_iq4_nl_f32(
global void * src0,
ulong offset0,
global float * src1,
ulong offset1,
global float * dst,
ulong offsetd,
int ne00,
int ne01,
int ne02,
int ne10,
int ne12,
int ne0,
int ne1,
int r2,
int r3
) {
src0 = (global void*)((global char*)src0 + offset0);
src1 = (global float*)((global char*)src1 + offset1);
dst = (global float*)((global char*)dst + offsetd);
mul_vec_q_n_f32(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3);
}
ggml/src/ggml-opencl/kernels/mul_mv_iq4_nl_f32_flat.cl
+202
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@@ -0,0 +1,202 @@
#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
#define QK4_NL 32
typedef char int8_t;
typedef uchar uint8_t;
typedef short int16_t;
typedef ushort uint16_t;
typedef int int32_t;
typedef uint uint32_t;
constant float kvalues_iq4nl[16] = {
-127.f, -104.f, -83.f, -65.f, -49.f, -35.f, -22.f, -10.f,
1.f, 13.f, 25.f, 38.f, 53.f, 69.f, 89.f, 113.f
};
//------------------------------------------------------------------------------
// block_iq4_nl
//------------------------------------------------------------------------------
struct block_iq4_nl
{
half d;
uint8_t qs[QK4_NL / 2];
};
// Compute dot product between half a block of iq4_nl quants and activations.
// x points to the quant bytes, dh points to the scale.
// yl has 16 activation values: [0..7] for low nibbles, [8..15] for high nibbles.
// il indicates offset into the quant bytes (0 or 8).
inline float block_iq4_nl_dot_y_flat(
global uchar * x,
global half * dh,
private float * yl,
int il
) {
float d = *dh;
global uchar * qs = x + il;
float acc = 0.f;
for (int i = 0; i < 8; ++i) {
acc += yl[i] * kvalues_iq4nl[qs[i] & 0x0F];
acc += yl[i+8] * kvalues_iq4nl[qs[i] >> 4];
}
return d * acc;
}
#undef N_DST
#undef N_SIMDGROUP
#undef N_SIMDWIDTH
#ifdef INTEL_GPU
#define N_DST 8 // each subgroup works on 8 rows
#define N_SUBGROUP 1 // number of subgroups in a thread group
#define N_SUBGROUP_SIZE 16 // assuming subgroup size is 16
#elif defined (ADRENO_GPU)
#define N_DST 8
#define N_SUBGROUP 1
#define N_SUBGROUP_SIZE 64
#endif
inline void mul_vec_q_n_f32_8x_flat(
global uchar * src0_q,
global half * src0_d,
global float * src1,
global float * dst,
int ne00,
int ne01,
int ne02,
int ne10,
int ne12,
int ne0,
int ne1,
int r2,
int r3
) {
const ulong nb = ne00/QK4_NL;
int r0 = get_group_id(0);
int r1 = get_group_id(1);
int im = get_group_id(2);
int first_row = (r0 * N_SUBGROUP + get_sub_group_id()) * N_DST;
int i12 = im%ne12;
int i13 = im/ne12;
// The number of scales is the same as the number of blocks.
ulong offset0_d = first_row * nb + (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02);
// Each block contains QK4_NL/2 uchars, hence offset for qs is as follows.
ulong offset0_q = (first_row * nb + (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02)) * QK4_NL/2;
global uchar * x = (global uchar *) src0_q + offset0_q;
global half * d = (global half *) src0_d + offset0_d;
global float * y = (global float *) src1 + r1*ne10 + im*ne00*ne1;
float yl[16];
float8 sumf = 0.f;
int ix = get_sub_group_local_id()/2;
int il = 8*(get_sub_group_local_id()%2);
global float * yb = y + ix*QK4_NL + il;
for (int ib = ix; ib < nb; ib += N_SUBGROUP_SIZE/2) {
for (int i = 0; i < 8; ++i) {
yl[i] = yb[i];
yl[i+8] = yb[i+16];
}
sumf.s0 += block_iq4_nl_dot_y_flat(x + ib*QK4_NL/2 + 0*nb*QK4_NL/2, d + ib + 0*nb, yl, il);
sumf.s1 += block_iq4_nl_dot_y_flat(x + ib*QK4_NL/2 + 1*nb*QK4_NL/2, d + ib + 1*nb, yl, il);
sumf.s2 += block_iq4_nl_dot_y_flat(x + ib*QK4_NL/2 + 2*nb*QK4_NL/2, d + ib + 2*nb, yl, il);
sumf.s3 += block_iq4_nl_dot_y_flat(x + ib*QK4_NL/2 + 3*nb*QK4_NL/2, d + ib + 3*nb, yl, il);
sumf.s4 += block_iq4_nl_dot_y_flat(x + ib*QK4_NL/2 + 4*nb*QK4_NL/2, d + ib + 4*nb, yl, il);
sumf.s5 += block_iq4_nl_dot_y_flat(x + ib*QK4_NL/2 + 5*nb*QK4_NL/2, d + ib + 5*nb, yl, il);
sumf.s6 += block_iq4_nl_dot_y_flat(x + ib*QK4_NL/2 + 6*nb*QK4_NL/2, d + ib + 6*nb, yl, il);
sumf.s7 += block_iq4_nl_dot_y_flat(x + ib*QK4_NL/2 + 7*nb*QK4_NL/2, d + ib + 7*nb, yl, il);
yb += QK4_NL * (N_SUBGROUP_SIZE/2);
}
float8 tot = (float8)(
sub_group_reduce_add(sumf.s0), sub_group_reduce_add(sumf.s1),
sub_group_reduce_add(sumf.s2), sub_group_reduce_add(sumf.s3),
sub_group_reduce_add(sumf.s4), sub_group_reduce_add(sumf.s5),
sub_group_reduce_add(sumf.s6), sub_group_reduce_add(sumf.s7)
);
if (get_sub_group_local_id() == 0) {
if (first_row + 0 < ne01) {
dst[r1*ne0 + im*ne0*ne1 + first_row + 0] = tot.s0;
}
if (first_row + 1 < ne01) {
dst[r1*ne0 + im*ne0*ne1 + first_row + 1] = tot.s1;
}
if (first_row + 2 < ne01) {
dst[r1*ne0 + im*ne0*ne1 + first_row + 2] = tot.s2;
}
if (first_row + 3 < ne01) {
dst[r1*ne0 + im*ne0*ne1 + first_row + 3] = tot.s3;
}
if (first_row + 4 < ne01) {
dst[r1*ne0 + im*ne0*ne1 + first_row + 4] = tot.s4;
}
if (first_row + 5 < ne01) {
dst[r1*ne0 + im*ne0*ne1 + first_row + 5] = tot.s5;
}
if (first_row + 6 < ne01) {
dst[r1*ne0 + im*ne0*ne1 + first_row + 6] = tot.s6;
}
if (first_row + 7 < ne01) {
dst[r1*ne0 + im*ne0*ne1 + first_row + 7] = tot.s7;
}
}
}
#ifdef INTEL_GPU
REQD_SUBGROUP_SIZE_16
#elif defined (ADRENO_GPU)
REQD_SUBGROUP_SIZE_64
#endif
kernel void kernel_mul_mv_iq4_nl_f32_flat(
global uchar * src0_q,
global half * src0_d,
global float * src1,
ulong offset1,
global float * dst,
ulong offsetd,
int ne00,
int ne01,
int ne02,
int ne10,
int ne12,
int ne0,
int ne1,
int r2,
int r3
) {
src1 = (global float*)((global char*)src1 + offset1);
dst = (global float*)((global char*)dst + offsetd);
mul_vec_q_n_f32_8x_flat(src0_q, src0_d, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3);
}