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https://github.com/ggerganov/llama.cpp.git
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kompute: add mul_mat_q4_k shader (#10097)
This is a more or less direct translation from the Metal implementation to GLSL. Signed-off-by: Sergio Lopez <slp@redhat.com>
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@ -800,6 +800,7 @@ if (GGML_KOMPUTE)
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kompute-shaders/op_mul_mat_q8_0.comp
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kompute-shaders/op_mul_mat_q4_0.comp
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kompute-shaders/op_mul_mat_q4_1.comp
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kompute-shaders/op_mul_mat_q4_k.comp
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kompute-shaders/op_mul_mat_q6_k.comp
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kompute-shaders/op_getrows_f32.comp
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kompute-shaders/op_getrows_f16.comp
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@ -833,6 +834,7 @@ if (GGML_KOMPUTE)
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shaderop_mul_mat_q8_0.h
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shaderop_mul_mat_q4_0.h
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shaderop_mul_mat_q4_1.h
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shaderop_mul_mat_q4_k.h
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shaderop_mul_mat_q6_k.h
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shaderop_getrows_f32.h
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shaderop_getrows_f16.h
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@ -20,6 +20,7 @@
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#include "shaderop_mul_mat_q8_0.h"
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#include "shaderop_mul_mat_q4_0.h"
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#include "shaderop_mul_mat_q4_1.h"
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#include "shaderop_mul_mat_q4_k.h"
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#include "shaderop_mul_mat_q6_k.h"
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#include "shaderop_mul_mat_mat_f32.h"
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#include "shaderop_getrows_f32.h"
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@ -1067,6 +1068,40 @@ static void ggml_vk_mul_mat_q8_0(Args&&... args) {
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ggml_vk_mul_mat_impl(spirv, "q8_0", 1/*We access blocks unaligned*/, std::forward<Args>(args)...);
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}
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static void ggml_vk_mul_mat_q4_k(
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kp::Sequence& seq,
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const std::shared_ptr<kp::Tensor>& inA,
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const std::shared_ptr<kp::Tensor>& inB,
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const std::shared_ptr<kp::Tensor>& out,
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uint32_t inAOff, uint32_t inBOff, uint32_t outOff,
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int32_t ne00, int32_t ne01, int32_t ne02, int32_t ne10,
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int32_t ne11, int32_t ne12, int32_t ne13, int32_t ne0,
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int32_t ne1, int32_t r2, int32_t r3
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) {
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const static auto spirv = getSpirvShader(kp::shader_data::op_mul_mat_q4_k_comp_spv,
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kp::shader_data::op_mul_mat_q4_k_comp_spv_len);
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struct PushConstants {
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uint32_t inAOff, inBOff, outOff;
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int32_t ne00, ne10, ne0, ne1, ne01, ne02, ne12, r2, r3;
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} pushConsts {
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0, 0, 0,
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ne00, ne10, ne0, ne1, ne01, ne02, ne12, r2, r3
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};
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std::shared_ptr<kp::Algorithm> s_algo = nullptr;
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if (!komputeManager()->hasAlgorithm(__func__)) {
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s_algo = komputeManager()->algorithm<uint32_t, PushConstants>(__func__, s_kompute_context->pool.get(), {inA, inB, out}, spirv, {unsigned((ne01 + 3)/4), unsigned(ne11), unsigned(ne12) * unsigned(ne13)}, {}, {pushConsts});
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} else {
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s_algo = komputeManager()->getAlgorithm(__func__);
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s_algo->setTensors({inA, inB, out});
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s_algo->setWorkgroup({unsigned((ne01 + 3)/4), unsigned(ne11), unsigned(ne12) * unsigned(ne13)});
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s_algo->setPushConstants<PushConstants>({pushConsts});
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s_algo->updateDescriptors(s_kompute_context->pool.get());
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}
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seq.record<kp::OpAlgoDispatch>(s_algo);
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}
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static void ggml_vk_mul_mat_q6_k(
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kp::Sequence& seq,
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const std::shared_ptr<kp::Tensor>& inA,
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@ -1384,6 +1419,7 @@ static bool ggml_backend_kompute_device_supports_op(ggml_backend_dev_t dev, cons
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case GGML_TYPE_Q8_0:
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case GGML_TYPE_Q4_0:
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case GGML_TYPE_Q4_1:
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case GGML_TYPE_Q4_K:
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return true;
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default:
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;
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@ -1635,6 +1671,12 @@ static void ggml_vk_graph_compute(struct ggml_kompute_context * ctx, struct ggml
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ne00, ne01, ne02, ne10, ne11, ne12, ne13, ne0, ne1, r2, r3
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);
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break;
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case GGML_TYPE_Q4_K:
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ggml_vk_mul_mat_q4_k(
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seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst,
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ne00, ne01, ne02, ne10, ne11, ne12, ne13, ne0, ne1, ne12/ne02, ne13/ne03
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);
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break;
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case GGML_TYPE_Q6_K:
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ggml_vk_mul_mat_q6_k(
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seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst,
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@ -15,6 +15,7 @@
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#define TWOPI_F 6.283185307179586f
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#define QK_K 256
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#define K_SCALE_SIZE 12
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#define u8BufToU16(buf, idx) (((uint16_t(buf[idx + 1]) << 8)) | buf[idx])
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#define u8BufToFloat16(buf, idx) uint16BitsToHalf u8BufToU16(buf, idx)
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@ -64,6 +65,14 @@ mat4 dequantize_q4_1(const block_q4_1 xb, uint il) {
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return reg;
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}
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#define sizeof_block_q4_k 144
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struct block_q4_k {
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float16_t d;
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float16_t dmin;
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uint8_t scales[K_SCALE_SIZE];
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uint8_t qs[QK_K/2];
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};
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#define sizeof_block_q6_k 210
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struct block_q6_k {
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uint8_t ql[QK_K/2]; // quants, lower 4 bits
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133
ggml/src/kompute-shaders/op_mul_mat_q4_k.comp
Normal file
133
ggml/src/kompute-shaders/op_mul_mat_q4_k.comp
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@ -0,0 +1,133 @@
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#version 450
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#include "common.comp"
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#define N_DST 4
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#define SIZE_OF_BLOCK sizeof_block_q4_k
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layout(local_size_x = 4) in;
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layout(local_size_y = 8) in;
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layout(local_size_z = 1) in;
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layout (binding = 0) readonly buffer tensorInA { block_q4_k inA[]; };
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layout (binding = 1) readonly buffer tensorInB { float inB[]; };
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layout (binding = 2) writeonly buffer tensorOut { float out_[]; };
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layout (push_constant) uniform parameter {
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uint inAOff;
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uint inBOff;
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uint outOff;
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int ne00;
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int ne10;
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int ne0;
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int ne1;
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int ne01;
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int ne02;
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int ne12;
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int r2;
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int r3;
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} pcs;
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void main() {
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const uint16_t kmask1 = uint16_t(0x3f3f);
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const uint16_t kmask2 = uint16_t(0x0f0f);
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const uint16_t kmask3 = uint16_t(0xc0c0);
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const uint ix = gl_SubgroupInvocationID/8; // 0...3
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const uint it = gl_SubgroupInvocationID%8; // 0...7
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const uint iq = it/4; // 0 or 1
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const uint ir = it%4; // 0...3
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const uint nb = pcs.ne00/QK_K;
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const uint r0 = gl_WorkGroupID.x;
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const uint r1 = gl_WorkGroupID.y;
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const uint im = gl_WorkGroupID.z;
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const uint first_row = r0 * N_DST;
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const uint ib_row = first_row * nb;
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const uint i12 = im%pcs.ne12;
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const uint i13 = im/pcs.ne12;
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const uint offset0 = (i12/pcs.r2)*(nb*pcs.ne01) + (i13/pcs.r3)*(nb*pcs.ne01*pcs.ne02);
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const uint xblk = ib_row + offset0 + pcs.inAOff;
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const uint y = r1*pcs.ne10 + im*pcs.ne00*pcs.ne1 + pcs.inBOff;
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float yl[16];
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float yh[16];
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float sumf[N_DST] = {0.f, 0.f, 0.f, 0.f};
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float all_sum = 0.f;
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uint y4 = y + ix * QK_K + 64 * iq + 8 * ir;
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for (uint ib = ix; ib < nb; ib += 4) {
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const uint blk_idx = ib + xblk;
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float sumy[4] = {0.f, 0.f, 0.f, 0.f};
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for (int i = 0; i < 8; ++i) {
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yl[i+0] = inB[y4+i+ 0]; sumy[0] += yl[i+0];
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yl[i+8] = inB[y4+i+ 32]; sumy[1] += yl[i+8];
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yh[i+0] = inB[y4+i+128]; sumy[2] += yh[i+0];
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yh[i+8] = inB[y4+i+160]; sumy[3] += yh[i+8];
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}
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for (int row = 0; row < N_DST; row++) {
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uint row_idx = row * nb;
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uint16_t sc_0 = u8BufToU16(inA[blk_idx + row_idx].scales, iq * 2 + 0);
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uint16_t sc_1 = u8BufToU16(inA[blk_idx + row_idx].scales, iq * 2 + 2);
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uint16_t sc_2 = u8BufToU16(inA[blk_idx + row_idx].scales, iq * 2 + 4);
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uint16_t sc_3 = u8BufToU16(inA[blk_idx + row_idx].scales, iq * 2 + 6);
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uint16_t sc_4 = u8BufToU16(inA[blk_idx + row_idx].scales, iq * 2 + 8);
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uint16_t sc16[4];
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sc16[0] = sc_0 & kmask1;
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sc16[1] = sc_2 & kmask1;
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sc16[2] = ((sc_4 >> 0) & kmask2) | ((sc_0 & kmask3) >> 2);
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sc16[3] = ((sc_4 >> 4) & kmask2) | ((sc_2 & kmask3) >> 2);
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float acc1[4] = {0.f, 0.f, 0.f, 0.f};
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float acc2[4] = {0.f, 0.f, 0.f, 0.f};
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for (int i = 0; i < 8; i += 2) {
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uint16_t q1 = u8BufToU16(inA[blk_idx + row_idx].qs, 32 * iq + 8 * ir + i);
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uint16_t q2 = u8BufToU16(inA[blk_idx + row_idx].qs, 64 + 32 * iq + 8 * ir + i);
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acc1[0] += yl[i+0] * (q1 & 0x000F);
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acc1[1] += yl[i+1] * (q1 & 0x0F00);
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acc1[2] += yl[i+8] * (q1 & 0x00F0);
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acc1[3] += yl[i+9] * (q1 & 0xF000);
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acc2[0] += yh[i+0] * (q2 & 0x000F);
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acc2[1] += yh[i+1] * (q2 & 0x0F00);
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acc2[2] += yh[i+8] * (q2 & 0x00F0);
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acc2[3] += yh[i+9] * (q2 & 0xF000);
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}
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uint8_t sc8_0 = uint8_t(sc16[0] & 0xFF);
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uint8_t sc8_1 = uint8_t(sc16[0] >> 8 );
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uint8_t sc8_2 = uint8_t(sc16[1] & 0xFF);
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uint8_t sc8_3 = uint8_t(sc16[1] >> 8 );
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uint8_t sc8_4 = uint8_t(sc16[2] & 0xFF);
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uint8_t sc8_5 = uint8_t(sc16[2] >> 8 );
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uint8_t sc8_6 = uint8_t(sc16[3] & 0xFF);
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uint8_t sc8_7 = uint8_t(sc16[3] >> 8 );
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float dall = float(inA[blk_idx + row_idx].d);
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float dmin = float(inA[blk_idx + row_idx].dmin);
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sumf[row] += dall * ((acc1[0] + 1.f/256.f * acc1[1]) * sc8_0 +
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(acc1[2] + 1.f/256.f * acc1[3]) * sc8_1 * 1.f/16.f +
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(acc2[0] + 1.f/256.f * acc2[1]) * sc8_4 +
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(acc2[2] + 1.f/256.f * acc2[3]) * sc8_5 * 1.f/16.f) -
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dmin * (sumy[0] * sc8_2 + sumy[1] * sc8_3 + sumy[2] * sc8_6 + sumy[3] * sc8_7);
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}
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y4 += 4 * QK_K;
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}
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for (int row = 0; row < N_DST; ++row) {
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all_sum = subgroupAdd(sumf[row]);
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if (subgroupElect()) {
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out_[r1*pcs.ne0 + im*pcs.ne0*pcs.ne1 + first_row + row + pcs.outOff] = all_sum;
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}
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}
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}
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