cl_kernel kernel_mul_mat_q4_0_f32_8x_flat;
cl_kernel kernel_convert_block_q4_0_noshuffle;
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_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_transpose_32;
cl_kernel kernel_transpose_32_16;
cl_kernel kernel_transpose_16;
+ cl_kernel kernel_transpose_8_buf;
cl_kernel kernel_transpose_16_buf;
+ cl_kernel kernel_transpose_32_buf;
cl_kernel kernel_transpose_16_4x1;
// Gemm and Gemv related programs, kernels, etc
cl_kernel CL_mul_mat_vec_q4_0_f32_1d_4x_flat_4096_1_4096;
cl_kernel CL_mul_mat_vec_q4_0_f32_1d_4x_flat_11008_1_4096;
cl_kernel CL_mul_mat_vec_q4_0_f32_1d_4x_flat_32000_1_4096;
+ cl_kernel kernel_gemv_noshuffle_q4_1_f32;
+ cl_kernel kernel_gemm_noshuffle_q4_1_f32;
cl_kernel kernel_mul_mm_q8_0_f32_8x4;
cl_kernel CL_mul_mat_vec_q8_0_f32;
#endif // GGML_OPENCL_USE_ADRENO_KERNELS
CL_CHECK((backend_ctx->kernel_restore_block_q4_0_noshuffle = clCreateKernel(backend_ctx->program_cvt, "kernel_restore_block_q4_0_noshuffle", &err), err));
CL_CHECK((backend_ctx->kernel_convert_block_q4_0 = clCreateKernel(backend_ctx->program_cvt, "kernel_convert_block_q4_0", &err), err));
CL_CHECK((backend_ctx->kernel_restore_block_q4_0 = clCreateKernel(backend_ctx->program_cvt, "kernel_restore_block_q4_0", &err), err));
+ CL_CHECK((backend_ctx->kernel_convert_block_q4_1_noshuffle = clCreateKernel(backend_ctx->program_cvt, "kernel_convert_block_q4_1_noshuffle", &err), err));
+ CL_CHECK((backend_ctx->kernel_restore_block_q4_1_noshuffle = clCreateKernel(backend_ctx->program_cvt, "kernel_restore_block_q4_1_noshuffle", &err), err));
CL_CHECK((backend_ctx->kernel_convert_block_q4_1 = clCreateKernel(backend_ctx->program_cvt, "kernel_convert_block_q4_1", &err), err));
CL_CHECK((backend_ctx->kernel_restore_block_q4_1 = clCreateKernel(backend_ctx->program_cvt, "kernel_restore_block_q4_1", &err), err));
CL_CHECK((backend_ctx->kernel_convert_block_mxfp4 = clCreateKernel(backend_ctx->program_cvt, "kernel_convert_block_mxfp4", &err), err));
CL_CHECK((backend_ctx->kernel_transpose_32_16 = clCreateKernel(backend_ctx->program_transpose, "kernel_transpose_32_16", &err), err));
CL_CHECK((backend_ctx->kernel_transpose_32 = clCreateKernel(backend_ctx->program_transpose, "kernel_transpose_32", &err), err));
CL_CHECK((backend_ctx->kernel_transpose_16 = clCreateKernel(backend_ctx->program_transpose, "kernel_transpose_16", &err), err));
+ CL_CHECK((backend_ctx->kernel_transpose_8_buf = clCreateKernel(backend_ctx->program_transpose, "kernel_transpose_8_buf", &err), err));
CL_CHECK((backend_ctx->kernel_transpose_16_buf = clCreateKernel(backend_ctx->program_transpose, "kernel_transpose_16_buf", &err), err));
+ CL_CHECK((backend_ctx->kernel_transpose_32_buf = clCreateKernel(backend_ctx->program_transpose, "kernel_transpose_32_buf", &err), err));
CL_CHECK((backend_ctx->kernel_transpose_16_4x1 = clCreateKernel(backend_ctx->program_transpose, "kernel_transpose_16_4x1", &err), err));
GGML_LOG_CONT(".");
}
GGML_LOG_CONT(".");
}
+ // gemm_noshuffle_q4_1_f32
+ {
+#ifdef GGML_OPENCL_EMBED_KERNELS
+ const std::string kernel_src {
+ #include "gemm_noshuffle_q4_1_f32.cl.h"
+ };
+#else
+ const std::string kernel_src = read_file("gemm_noshuffle_q4_1_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_q4_1_f32 = clCreateKernel(prog, "kernel_gemm_noshuffle_q4_1_f32", &err), err));
+ CL_CHECK(clReleaseProgram(prog));
+ GGML_LOG_CONT(".");
+ }
+
+ // gemv_noshuffle_q4_1_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_BROADCAT ";
+ }
+
+#ifdef GGML_OPENCL_EMBED_KERNELS
+ const std::string kernel_src {
+ #include "gemv_noshuffle_q4_1_f32.cl.h"
+ };
+#else
+ const std::string kernel_src = read_file("gemv_noshuffle_q4_1_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_q4_1_f32 = clCreateKernel(prog, "kernel_gemv_noshuffle_q4_1_f32", &err), err));
+ CL_CHECK(clReleaseProgram(prog));
+ GGML_LOG_CONT(".");
+ }
+
// mul_mm_q8_0_f32_8x4
{
#ifdef GGML_OPENCL_EMBED_KERNELS
cl_program prog = build_program_from_source(
backend_ctx->context, backend_ctx->device, kernel_src_CL_gemv_general.c_str(), CL_gemv_compile_opts);
- CL_CHECK((backend_ctx->CL_mul_mat_vec_q8_0_f32 = clCreateKernel(prog, "kernel_gemv_noshuffle", &err), err));
+ CL_CHECK((backend_ctx->CL_mul_mat_vec_q8_0_f32 = clCreateKernel(prog, "kernel_gemv_noshuffle_q8_0_f32", &err), err));
CL_CHECK(clReleaseProgram(prog));
GGML_LOG_CONT(".");
}
}
}
+#ifdef GGML_OPENCL_USE_ADRENO_KERNELS
+static void transpose_2d(
+ ggml_backend_opencl_context * backend_ctx,
+ cl_kernel kernel,
+ cl_mem src, cl_mem dst, size_t size,
+ cl_int stride, cl_int rows,
+ bool blocking = true
+) {
+ static ggml_cl_buffer buf;
+
+ cl_event evt;
+ cl_int err;
+
+ buf.allocate(backend_ctx->context, size);
+
+ cl_mem trans;
+ cl_buffer_region region;
+
+ region.origin = 0;
+ region.size = size;
+ CL_CHECK((trans = clCreateSubBuffer(
+ buf.buffer, CL_MEM_READ_WRITE,
+ CL_BUFFER_CREATE_TYPE_REGION, ®ion, &err), err));
+
+ CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &src));
+ CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &trans));
+ CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_int), &stride));
+ CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_int), &rows));
+
+ size_t local_size[3] = {64, 1, 1};
+ size_t global_size[3] = {(size_t)stride, (size_t)rows, 1};;
+ CL_CHECK(clEnqueueNDRangeKernel(backend_ctx->queue, kernel, 3, NULL,
+ global_size, local_size, 0, NULL, NULL));
+
+ if (blocking) {
+ CL_CHECK(clEnqueueCopyBuffer(backend_ctx->queue, trans, dst, 0, 0, size, 0, NULL, &evt));
+ CL_CHECK(clWaitForEvents(1, &evt));
+ CL_CHECK(clReleaseEvent(evt));
+ } else {
+ CL_CHECK(clEnqueueCopyBuffer(backend_ctx->queue, trans, dst, 0, 0, size, 0, NULL, NULL));
+ }
+
+ CL_CHECK(clReleaseMemObject(trans));
+}
+
+static void transpose_2d_as_8b(
+ ggml_backend_opencl_context * backend_ctx,
+ cl_mem src, cl_mem dst, size_t size,
+ cl_int stride, cl_int rows,
+ bool blocking = true
+) {
+ transpose_2d(backend_ctx, backend_ctx->kernel_transpose_8_buf,
+ src, dst, size, stride, rows, blocking);
+}
+
+static void transpose_2d_as_16b(
+ ggml_backend_opencl_context * backend_ctx,
+ cl_mem src, cl_mem dst, size_t size,
+ cl_int stride, cl_int rows,
+ bool blocking = true
+) {
+ transpose_2d(backend_ctx, backend_ctx->kernel_transpose_16_buf,
+ src, dst, size, stride, rows, blocking);
+}
+
+static void transpose_2d_as_32b(
+ ggml_backend_opencl_context * backend_ctx,
+ cl_mem src, cl_mem dst, size_t size,
+ cl_int stride, cl_int rows,
+ bool blocking = true
+) {
+ transpose_2d(backend_ctx, backend_ctx->kernel_transpose_32_buf,
+ src, dst, size, stride, rows, blocking);
+}
+#endif // GGML_OPENCL_USE_ADRENO_KERNELS
+
//------------------------------------------------------------------------------
// Tensor extra management
//------------------------------------------------------------------------------
CL_BUFFER_CREATE_TYPE_REGION, ®ion, &err);
CL_CHECK(err);
+ #ifdef GGML_OPENCL_USE_ADRENO_KERNELS
+ cl_kernel kernel = backend_ctx->kernel_convert_block_q4_1;
+
+ if (use_adreno_kernels(backend_ctx, tensor)) {
+ kernel = backend_ctx->kernel_convert_block_q4_1_noshuffle;
+ }
+ #else
cl_kernel kernel = backend_ctx->kernel_convert_block_q4_1;
+ #endif // GGML_OPENCL_USE_ADRENO_KERNELS
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));
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);
+ // Transpose m as ushort
+ transpose_2d_as_16b(backend_ctx, extra->m, extra->m, size_m, K/32, M);
+ }
+#endif // GGML_OPENCL_USE_ADRENO_KERNELS
return;
}
if (tensor->type == GGML_TYPE_MXFP4) {
if (tensor->type == GGML_TYPE_Q4_1) {
ggml_tensor_extra_cl_q4_1 * extra = (ggml_tensor_extra_cl_q4_1 *)tensor->extra;
+#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_m;
+ 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 % ggml_blck_size(tensor->type) == 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);
+ size_t size_m = (ggml_nelements(tensor)/ggml_blck_size(tensor->type))*sizeof(ggml_fp16_t);
+ GGML_ASSERT(size_d + size_q + size_m == ggml_nbytes(tensor) && "Incorrect tensor size");
+
+ buf_trans_q.allocate(backend_ctx->context, size_q);
+ buf_trans_m.allocate(backend_ctx->context, size_m);
+ buf_trans_d.allocate(backend_ctx->context, size_d);
+ buf_unpacked.allocate(backend_ctx->context, ggml_nbytes(tensor));
+
+ // transpose q, d, m 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);
+ transpose_2d_as_16b(backend_ctx, extra->m, buf_trans_m.buffer, size_m, M, K/32);
+
+ cl_uchar mask_0F = 0x0F;
+ cl_uchar mask_F0 = 0xF0;
+
+ 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_kernel kernel = backend_ctx->kernel_restore_block_q4_1_noshuffle;
+ 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_trans_m.buffer));
+ CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_mem), &buf_unpacked.buffer));
+ CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_uchar), &mask_0F));
+ CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_uchar), &mask_F0));
+
+ 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_int err;
cl_mem data_device = clCreateBuffer(context, CL_MEM_READ_WRITE,
ggml_nbytes(tensor), NULL, &err);
int ne00 = tensor->ne[0];
int ne01 = tensor->ne[1];
- GGML_ASSERT(tensor->ne[2] == 1); // ???
- GGML_ASSERT(tensor->ne[3] == 1); // ???
+ GGML_ASSERT(tensor->ne[2] == 1);
+ GGML_ASSERT(tensor->ne[3] == 1);
CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra->q));
CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &extra->d));
CL_CHECK(clReleaseMemObject(D_sub_buffer));
}
+static void ggml_cl_mul_mat_q4_1_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_q4_1 * extra0_q4_1 = (ggml_tensor_extra_cl_q4_1 *)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 % ggml_blck_size(src0->type) == 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_q4_1->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, ®ion, &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_q4_1_f32;
+
+ CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &q_img));
+ CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &extra0_q4_1->d));
+ CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extra0_q4_1->m));
+ CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_mem), &b_img));
+ 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(cl_int), &ne00));
+ CL_CHECK(clSetKernelArg(kernel, 7, 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, ®ion, &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, ®ion, &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_q4_1_f32;
+ int padded_N = N + padding;
+
+ CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra0_q4_1->q));
+ CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &extra0_q4_1->d));
+ CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extra0_q4_1->m));
+ CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_mem), &b_img_trans));
+ 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(cl_int), &ne01));
+ CL_CHECK(clSetKernelArg(kernel, 7, sizeof(cl_int), &padded_N));
+ CL_CHECK(clSetKernelArg(kernel, 8, sizeof(cl_int), &ne00));
+ CL_CHECK(clSetKernelArg(kernel, 9, 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);
int padding;
// <--------------------------------------------> //
+ // NOTE: Kernels using image1d_buffer_t (e.g., src0_q) would normally require
+ // a limit check, but q4_0 / q4_1 tensors are very unlikely to exceed that
+ // limit, so the check is omitted.
+
+ // q4_1 x fp32
+ if (src0t == GGML_TYPE_Q4_1 && src1t == GGML_TYPE_F32) {
+ ggml_cl_mul_mat_q4_1_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)) {
--- /dev/null
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+#pragma OPENCL EXTENSION cl_qcom_reqd_sub_group_size : 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_128 __attribute__((qcom_reqd_sub_group_size("full")))
+#endif
+
+#ifdef ADRENO_GPU
+REQD_SUBGROUP_SIZE_128
+#endif
+
+kernel void kernel_gemm_noshuffle_q4_1_f32(
+ global const ushort * src0_q,
+ global const half * src0_d,
+ global const half * src0_m,
+ 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;
+ global const half* min_ptr = src0_m + 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));
+ half4 minv = vload4(0, min_ptr + (i/32)*(m));
+
+ // j=0
+ dequantized_weights.s0 = (bits4.s0 & (0x000F)) * scale.s0 + minv.s0;
+ dequantized_weights.s1 = (bits4.s1 & (0x000F)) * scale.s1 + minv.s1;
+ dequantized_weights.s2 = (bits4.s2 & (0x000F)) * scale.s2 + minv.s2;
+ dequantized_weights.s3 = (bits4.s3 & (0x000F)) * scale.s3 + minv.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 = ((bits4.s0 & (0x00F0)) >> 4) * scale.s0 + minv.s0;
+ dequantized_weights.s1 = ((bits4.s1 & (0x00F0)) >> 4) * scale.s1 + minv.s1;
+ dequantized_weights.s2 = ((bits4.s2 & (0x00F0)) >> 4) * scale.s2 + minv.s2;
+ dequantized_weights.s3 = ((bits4.s3 & (0x00F0)) >> 4) * scale.s3 + minv.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 = ((bits4.s0 & (0x0F00)) >> 8) * scale.s0 + minv.s0;
+ dequantized_weights.s1 = ((bits4.s1 & (0x0F00)) >> 8) * scale.s1 + minv.s1;
+ dequantized_weights.s2 = ((bits4.s2 & (0x0F00)) >> 8) * scale.s2 + minv.s2;
+ dequantized_weights.s3 = ((bits4.s3 & (0x0F00)) >> 8) * scale.s3 + minv.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 = ((bits4.s0 & (0xF000)) >> 12) * scale.s0 + minv.s0;
+ dequantized_weights.s1 = ((bits4.s1 & (0xF000)) >> 12) * scale.s1 + minv.s1;
+ dequantized_weights.s2 = ((bits4.s2 & (0xF000)) >> 12) * scale.s2 + minv.s2;
+ dequantized_weights.s3 = ((bits4.s3 & (0xF000)) >> 12) * scale.s3 + minv.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);
+ }
+}
--- /dev/null
+#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_0 32
+#define NSUBGROUPS 4
+#define SUBGROUP_SIZE 64
+
+#define dequantizeBlockAccum_ns_sgbroadcast_1_hi(total_sums, bits4, scale, minv, y) \
+ float shared_y; \
+ shared_y = sub_group_broadcast(y.s0, 0); \
+ total_sums.s0 += ((bits4.s0 & 0x000F) * scale.s0 + minv.s0) * shared_y; \
+ total_sums.s1 += ((bits4.s1 & 0x000F) * scale.s1 + minv.s1) * shared_y; \
+ shared_y = sub_group_broadcast(y.s1, 0); \
+ total_sums.s0 += (((bits4.s0 & 0x00F0) >> 4) * scale.s0 + minv.s0) * shared_y; \
+ total_sums.s1 += (((bits4.s1 & 0x00F0) >> 4) * scale.s1 + minv.s1) * shared_y; \
+ shared_y = sub_group_broadcast(y.s2, 0); \
+ total_sums.s0 += (((bits4.s0 & 0x0F00) >> 8) * scale.s0 + minv.s0) * shared_y; \
+ total_sums.s1 += (((bits4.s1 & 0x0F00) >> 8) * scale.s1 + minv.s1) * shared_y; \
+ shared_y = sub_group_broadcast(y.s3, 0); \
+ total_sums.s0 += (((bits4.s0 & 0xF000) >> 12) * scale.s0 + minv.s0) * shared_y; \
+ total_sums.s1 += (((bits4.s1 & 0xF000) >> 12) * scale.s1 + minv.s1) * shared_y; \
+ shared_y = sub_group_broadcast(y.s4, 0); \
+ total_sums.s0 += ((bits4.s2 & 0x000F) * scale.s0 + minv.s0) * shared_y; \
+ total_sums.s1 += ((bits4.s3 & 0x000F) * scale.s1 + minv.s1) * shared_y; \
+ shared_y = sub_group_broadcast(y.s5, 0); \
+ total_sums.s0 += (((bits4.s2 & 0x00F0) >> 4) * scale.s0 + minv.s0) * shared_y; \
+ total_sums.s1 += (((bits4.s3 & 0x00F0) >> 4) * scale.s1 + minv.s1) * shared_y; \
+ shared_y = sub_group_broadcast(y.s6, 0); \
+ total_sums.s0 += (((bits4.s2 & 0x0F00) >> 8) * scale.s0 + minv.s0) * shared_y; \
+ total_sums.s1 += (((bits4.s3 & 0x0F00) >> 8) * scale.s1 + minv.s1) * shared_y; \
+ shared_y = sub_group_broadcast(y.s7, 0); \
+ total_sums.s0 += (((bits4.s2 & 0xF000) >> 12) * scale.s0 + minv.s0) * shared_y; \
+ total_sums.s1 += (((bits4.s3 & 0xF000) >> 12) * scale.s1 + minv.s1) * shared_y; \
+ shared_y = sub_group_broadcast(y.s0, 1); \
+ total_sums.s0 += ((bits4.s4 & 0x000F) * scale.s0 + minv.s0) * shared_y; \
+ total_sums.s1 += ((bits4.s5 & 0x000F) * scale.s1 + minv.s1) * shared_y; \
+ shared_y = sub_group_broadcast(y.s1, 1); \
+ total_sums.s0 += (((bits4.s4 & 0x00F0) >> 4) * scale.s0 + minv.s0) * shared_y; \
+ total_sums.s1 += (((bits4.s5 & 0x00F0) >> 4) * scale.s1 + minv.s1) * shared_y; \
+ shared_y = sub_group_broadcast(y.s2, 1); \
+ total_sums.s0 += (((bits4.s4 & 0x0F00) >> 8) * scale.s0 + minv.s0) * shared_y; \
+ total_sums.s1 += (((bits4.s5 & 0x0F00) >> 8) * scale.s1 + minv.s1) * shared_y; \
+ shared_y = sub_group_broadcast(y.s3, 1); \
+ total_sums.s0 += (((bits4.s4 & 0xF000) >> 12) * scale.s0 + minv.s0) * shared_y; \
+ total_sums.s1 += (((bits4.s5 & 0xF000) >> 12) * scale.s1 + minv.s1) * shared_y; \
+ shared_y = sub_group_broadcast(y.s4, 1); \
+ total_sums.s0 += ((bits4.s6 & 0x000F) * scale.s0 + minv.s0) * shared_y; \
+ total_sums.s1 += ((bits4.s7 & 0x000F) * scale.s1 + minv.s1) * shared_y; \
+ shared_y = sub_group_broadcast(y.s5, 1); \
+ total_sums.s0 += (((bits4.s6 & 0x00F0) >> 4) * scale.s0 + minv.s0) * shared_y; \
+ total_sums.s1 += (((bits4.s7 & 0x00F0) >> 4) * scale.s1 + minv.s1) * shared_y; \
+ shared_y = sub_group_broadcast(y.s6, 1); \
+ total_sums.s0 += (((bits4.s6 & 0x0F00) >> 8) * scale.s0 + minv.s0) * shared_y; \
+ total_sums.s1 += (((bits4.s7 & 0x0F00) >> 8) * scale.s1 + minv.s1) * shared_y; \
+ shared_y = sub_group_broadcast(y.s7, 1); \
+ total_sums.s0 += (((bits4.s6 & 0xF000) >> 12) * scale.s0 + minv.s0) * shared_y; \
+ total_sums.s1 += (((bits4.s7 & 0xF000) >> 12) * scale.s1 + minv.s1) * shared_y; \
+
+
+#define dequantizeBlockAccum_ns_sgbroadcast_1_lo(total_sums, bits4, scale, minv, y) \
+ shared_y = sub_group_broadcast(y.s0, 2); \
+ total_sums.s0 += ((bits4.s0 & 0x000F) * scale.s0 + minv.s0) * shared_y; \
+ total_sums.s1 += ((bits4.s1 & 0x000F) * scale.s1 + minv.s1) * shared_y; \
+ shared_y = sub_group_broadcast(y.s1, 2); \
+ total_sums.s0 += (((bits4.s0 & 0x00F0) >> 4) * scale.s0 + minv.s0) * shared_y; \
+ total_sums.s1 += (((bits4.s1 & 0x00F0) >> 4) * scale.s1 + minv.s1) * shared_y; \
+ shared_y = sub_group_broadcast(y.s2, 2); \
+ total_sums.s0 += (((bits4.s0 & 0x0F00) >> 8) * scale.s0 + minv.s0) * shared_y; \
+ total_sums.s1 += (((bits4.s1 & 0x0F00) >> 8) * scale.s1 + minv.s1) * shared_y; \
+ shared_y = sub_group_broadcast(y.s3, 2); \
+ total_sums.s0 += (((bits4.s0 & 0xF000) >> 12) * scale.s0 + minv.s0) * shared_y; \
+ total_sums.s1 += (((bits4.s1 & 0xF000) >> 12) * scale.s1 + minv.s1) * shared_y; \
+ shared_y = sub_group_broadcast(y.s4, 2); \
+ total_sums.s0 += ((bits4.s2 & 0x000F) * scale.s0 + minv.s0) * shared_y; \
+ total_sums.s1 += ((bits4.s3 & 0x000F) * scale.s1 + minv.s1) * shared_y; \
+ shared_y = sub_group_broadcast(y.s5, 2); \
+ total_sums.s0 += (((bits4.s2 & 0x00F0) >> 4) * scale.s0 + minv.s0) * shared_y; \
+ total_sums.s1 += (((bits4.s3 & 0x00F0) >> 4) * scale.s1 + minv.s1) * shared_y; \
+ shared_y = sub_group_broadcast(y.s6, 2); \
+ total_sums.s0 += (((bits4.s2 & 0x0F00) >> 8) * scale.s0 + minv.s0) * shared_y; \
+ total_sums.s1 += (((bits4.s3 & 0x0F00) >> 8) * scale.s1 + minv.s1) * shared_y; \
+ shared_y = sub_group_broadcast(y.s7, 2); \
+ total_sums.s0 += (((bits4.s2 & 0xF000) >> 12) * scale.s0 + minv.s0) * shared_y; \
+ total_sums.s1 += (((bits4.s3 & 0xF000) >> 12) * scale.s1 + minv.s1) * shared_y; \
+ shared_y = sub_group_broadcast(y.s0, 3); \
+ total_sums.s0 += ((bits4.s4 & 0x000F) * scale.s0 + minv.s0) * shared_y; \
+ total_sums.s1 += ((bits4.s5 & 0x000F) * scale.s1 + minv.s1) * shared_y; \
+ shared_y = sub_group_broadcast(y.s1, 3); \
+ total_sums.s0 += (((bits4.s4 & 0x00F0) >> 4) * scale.s0 + minv.s0) * shared_y; \
+ total_sums.s1 += (((bits4.s5 & 0x00F0) >> 4) * scale.s1 + minv.s1) * shared_y; \
+ shared_y = sub_group_broadcast(y.s2, 3); \
+ total_sums.s0 += (((bits4.s4 & 0x0F00) >> 8) * scale.s0 + minv.s0) * shared_y; \
+ total_sums.s1 += (((bits4.s5 & 0x0F00) >> 8) * scale.s1 + minv.s1) * shared_y; \
+ shared_y = sub_group_broadcast(y.s3, 3); \
+ total_sums.s0 += (((bits4.s4 & 0xF000) >> 12) * scale.s0 + minv.s0) * shared_y; \
+ total_sums.s1 += (((bits4.s5 & 0xF000) >> 12) * scale.s1 + minv.s1) * shared_y; \
+ shared_y = sub_group_broadcast(y.s4, 3); \
+ total_sums.s0 += ((bits4.s6 & 0x000F) * scale.s0 + minv.s0) * shared_y; \
+ total_sums.s1 += ((bits4.s7 & 0x000F) * scale.s1 + minv.s1) * shared_y; \
+ shared_y = sub_group_broadcast(y.s5, 3); \
+ total_sums.s0 += (((bits4.s6 & 0x00F0) >> 4) * scale.s0 + minv.s0) * shared_y; \
+ total_sums.s1 += (((bits4.s7 & 0x00F0) >> 4) * scale.s1 + minv.s1) * shared_y; \
+ shared_y = sub_group_broadcast(y.s6, 3); \
+ total_sums.s0 += (((bits4.s6 & 0x0F00) >> 8) * scale.s0 + minv.s0) * shared_y; \
+ total_sums.s1 += (((bits4.s7 & 0x0F00) >> 8) * scale.s1 + minv.s1) * shared_y; \
+ shared_y = sub_group_broadcast(y.s7, 3); \
+ total_sums.s0 += (((bits4.s6 & 0xF000) >> 12) * scale.s0 + minv.s0) * shared_y; \
+ total_sums.s1 += (((bits4.s7 & 0xF000) >> 12) * scale.s1 + minv.s1) * shared_y; \
+
+
+#define dequantizeBlockAccum_ns_sgbroadcast_8_hi(total_sums, bits4, scale, minv, y) \
+ float8 shared_y; \
+ shared_y = sub_group_broadcast(y, 0); \
+ total_sums.s0 += ((bits4.s0 & 0x000F) * scale.s0 + minv.s0) * shared_y.s0; \
+ total_sums.s0 += (((bits4.s0 & 0x00F0) >> 4) * scale.s0 + minv.s0) * shared_y.s1; \
+ total_sums.s0 += (((bits4.s0 & 0x0F00) >> 8) * scale.s0 + minv.s0) * shared_y.s2; \
+ total_sums.s0 += (((bits4.s0 & 0xF000) >> 12) * scale.s0 + minv.s0) * shared_y.s3; \
+ total_sums.s0 += ((bits4.s2 & 0x000F) * scale.s0 + minv.s0) * shared_y.s4; \
+ total_sums.s0 += (((bits4.s2 & 0x00F0) >> 4) * scale.s0 + minv.s0) * shared_y.s5; \
+ total_sums.s0 += (((bits4.s2 & 0x0F00) >> 8) * scale.s0 + minv.s0) * shared_y.s6; \
+ total_sums.s0 += (((bits4.s2 & 0xF000) >> 12) * scale.s0 + minv.s0) * shared_y.s7; \
+ total_sums.s1 += ((bits4.s1 & 0x000F) * scale.s1 + minv.s1) * shared_y.s0; \
+ total_sums.s1 += (((bits4.s1 & 0x00F0) >> 4) * scale.s1 + minv.s1) * shared_y.s1; \
+ total_sums.s1 += (((bits4.s1 & 0x0F00) >> 8) * scale.s1 + minv.s1) * shared_y.s2; \
+ total_sums.s1 += (((bits4.s1 & 0xF000) >> 12) * scale.s1 + minv.s1) * shared_y.s3; \
+ total_sums.s1 += ((bits4.s3 & 0x000F) * scale.s1 + minv.s1) * shared_y.s4; \
+ total_sums.s1 += (((bits4.s3 & 0x00F0) >> 4) * scale.s1 + minv.s1) * shared_y.s5; \
+ total_sums.s1 += (((bits4.s3 & 0x0F00) >> 8) * scale.s1 + minv.s1) * shared_y.s6; \
+ total_sums.s1 += (((bits4.s3 & 0xF000) >> 12) * scale.s1 + minv.s1) * shared_y.s7; \
+ shared_y = sub_group_broadcast(y, 1); \
+ total_sums.s0 += ((bits4.s4 & 0x000F) * scale.s0 + minv.s0) * shared_y.s0; \
+ total_sums.s0 += (((bits4.s4 & 0x00F0) >> 4) * scale.s0 + minv.s0) * shared_y.s1; \
+ total_sums.s0 += (((bits4.s4 & 0x0F00) >> 8) * scale.s0 + minv.s0) * shared_y.s2; \
+ total_sums.s0 += (((bits4.s4 & 0xF000) >> 12) * scale.s0 + minv.s0) * shared_y.s3; \
+ total_sums.s0 += ((bits4.s6 & 0x000F) * scale.s0 + minv.s0) * shared_y.s4; \
+ total_sums.s0 += (((bits4.s6 & 0x00F0) >> 4) * scale.s0 + minv.s0) * shared_y.s5; \
+ total_sums.s0 += (((bits4.s6 & 0x0F00) >> 8) * scale.s0 + minv.s0) * shared_y.s6; \
+ total_sums.s0 += (((bits4.s6 & 0xF000) >> 12) * scale.s0 + minv.s0) * shared_y.s7; \
+ total_sums.s1 += ((bits4.s5 & 0x000F) * scale.s1 + minv.s1) * shared_y.s0; \
+ total_sums.s1 += (((bits4.s5 & 0x00F0) >> 4) * scale.s1 + minv.s1) * shared_y.s1; \
+ total_sums.s1 += (((bits4.s5 & 0x0F00) >> 8) * scale.s1 + minv.s1) * shared_y.s2; \
+ total_sums.s1 += (((bits4.s5 & 0xF000) >> 12) * scale.s1 + minv.s1) * shared_y.s3; \
+ total_sums.s1 += ((bits4.s7 & 0x000F) * scale.s1 + minv.s1) * shared_y.s4; \
+ total_sums.s1 += (((bits4.s7 & 0x00F0) >> 4) * scale.s1 + minv.s1) * shared_y.s5; \
+ total_sums.s1 += (((bits4.s7 & 0x0F00) >> 8) * scale.s1 + minv.s1) * shared_y.s6; \
+ total_sums.s1 += (((bits4.s7 & 0xF000) >> 12) * scale.s1 + minv.s1) * shared_y.s7; \
+
+
+#define dequantizeBlockAccum_ns_sgbroadcast_8_lo(total_sums, bits4, scale, minv, y) \
+ shared_y = sub_group_broadcast(y, 2); \
+ total_sums.s0 += ((bits4.s0 & 0x000F) * scale.s0 + minv.s0) * shared_y.s0; \
+ total_sums.s0 += (((bits4.s0 & 0x00F0) >> 4) * scale.s0 + minv.s0) * shared_y.s1; \
+ total_sums.s0 += (((bits4.s0 & 0x0F00) >> 8) * scale.s0 + minv.s0) * shared_y.s2; \
+ total_sums.s0 += (((bits4.s0 & 0xF000) >> 12) * scale.s0 + minv.s0) * shared_y.s3; \
+ total_sums.s0 += ((bits4.s2 & 0x000F) * scale.s0 + minv.s0) * shared_y.s4; \
+ total_sums.s0 += (((bits4.s2 & 0x00F0) >> 4) * scale.s0 + minv.s0) * shared_y.s5; \
+ total_sums.s0 += (((bits4.s2 & 0x0F00) >> 8) * scale.s0 + minv.s0) * shared_y.s6; \
+ total_sums.s0 += (((bits4.s2 & 0xF000) >> 12) * scale.s0 + minv.s0) * shared_y.s7; \
+ total_sums.s1 += ((bits4.s1 & 0x000F) * scale.s1 + minv.s1) * shared_y.s0; \
+ total_sums.s1 += (((bits4.s1 & 0x00F0) >> 4) * scale.s1 + minv.s1) * shared_y.s1; \
+ total_sums.s1 += (((bits4.s1 & 0x0F00) >> 8) * scale.s1 + minv.s1) * shared_y.s2; \
+ total_sums.s1 += (((bits4.s1 & 0xF000) >> 12) * scale.s1 + minv.s1) * shared_y.s3; \
+ total_sums.s1 += ((bits4.s3 & 0x000F) * scale.s1 + minv.s1) * shared_y.s4; \
+ total_sums.s1 += (((bits4.s3 & 0x00F0) >> 4) * scale.s1 + minv.s1) * shared_y.s5; \
+ total_sums.s1 += (((bits4.s3 & 0x0F00) >> 8) * scale.s1 + minv.s1) * shared_y.s6; \
+ total_sums.s1 += (((bits4.s3 & 0xF000) >> 12) * scale.s1 + minv.s1) * shared_y.s7; \
+ shared_y = sub_group_broadcast(y, 3); \
+ total_sums.s0 += ((bits4.s4 & 0x000F) * scale.s0 + minv.s0) * shared_y.s0; \
+ total_sums.s0 += (((bits4.s4 & 0x00F0) >> 4) * scale.s0 + minv.s0) * shared_y.s1; \
+ total_sums.s0 += (((bits4.s4 & 0x0F00) >> 8) * scale.s0 + minv.s0) * shared_y.s2; \
+ total_sums.s0 += (((bits4.s4 & 0xF000) >> 12) * scale.s0 + minv.s0) * shared_y.s3; \
+ total_sums.s0 += ((bits4.s6 & 0x000F) * scale.s0 + minv.s0) * shared_y.s4; \
+ total_sums.s0 += (((bits4.s6 & 0x00F0) >> 4) * scale.s0 + minv.s0) * shared_y.s5; \
+ total_sums.s0 += (((bits4.s6 & 0x0F00) >> 8) * scale.s0 + minv.s0) * shared_y.s6; \
+ total_sums.s0 += (((bits4.s6 & 0xF000) >> 12) * scale.s0 + minv.s0) * shared_y.s7; \
+ total_sums.s1 += ((bits4.s5 & 0x000F) * scale.s1 + minv.s1) * shared_y.s0; \
+ total_sums.s1 += (((bits4.s5 & 0x00F0) >> 4) * scale.s1 + minv.s1) * shared_y.s1; \
+ total_sums.s1 += (((bits4.s5 & 0x0F00) >> 8) * scale.s1 + minv.s1) * shared_y.s2; \
+ total_sums.s1 += (((bits4.s5 & 0xF000) >> 12) * scale.s1 + minv.s1) * shared_y.s3; \
+ total_sums.s1 += ((bits4.s7 & 0x000F) * scale.s1 + minv.s1) * shared_y.s4; \
+ total_sums.s1 += (((bits4.s7 & 0x00F0) >> 4) * scale.s1 + minv.s1) * shared_y.s5; \
+ total_sums.s1 += (((bits4.s7 & 0x0F00) >> 8) * scale.s1 + minv.s1) * shared_y.s6; \
+ total_sums.s1 += (((bits4.s7 & 0xF000) >> 12) * scale.s1 + minv.s1) * shared_y.s7; \
+
+#ifdef ADRENO_GPU
+REQD_SUBGROUP_SIZE_64
+#endif
+kernel void kernel_gemv_noshuffle_q4_1_f32(
+ read_only image1d_buffer_t src0_q,
+ global half2 * src0_d,
+ global half2 * src0_m,
+ 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 half2 regM;
+ 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_0); k += NSUBGROUPS) {
+ regS = src0_d[gid + k * LINE_STRIDE_A]; // each fiber loads scale of two rows
+ regM = src0_m[gid + k * LINE_STRIDE_A]; // each fiber loads min 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_BROADCAT
+ dequantizeBlockAccum_ns_sgbroadcast_8_hi(totalSum, as_ushort8(regA), regS, regM, regB);
+#else
+ dequantizeBlockAccum_ns_sgbroadcast_1_hi(totalSum, as_ushort8(regA), regS, regM, regB);
+#endif // VECTOR_SUB_GROUP_BROADCAT
+
+ 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_BROADCAT
+ dequantizeBlockAccum_ns_sgbroadcast_8_lo(totalSum, as_ushort8(regA), regS, regM, regB);
+#else
+ dequantizeBlockAccum_ns_sgbroadcast_1_lo(totalSum, as_ushort8(regA), regS, regM, regB);
+#endif // VECTOR_SUB_GROUP_BROADCAT
+ }
+
+ // 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]));
+ }
+
+}
overview / pkg / ggml / sources / llama.cpp / commitdiff