XAIGPUARC

//XAIGPUARC
20.10.26
09:12
Berlin Pankow
//XAC_sycl_spielewiese_ki ANFANG
1-10 Rechenaufrufe und Rueckfallmechanismenautomatiken
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Fx:00::00:00 - 00:00::00:01
#include 
#include 
#include 
#include 
#include 
#include 
#include 
#include 
#include 
#include 
#include 
//KOPZEILENUMGEBUNG DER MATRIX MATRIZEN PARSER [Auftreiber]
#include "XAIGPUARC/include/XAC_attention_qkv.h"
#include "XAIGPUARC/include/XAC_allthatnicestuff from INDEX"
#include "XAIGPUARC/include/XAC_parser.h"
#include "XAIGPUARC/include/XAC_alloc_parser.h"
#include "XAIGPUARC/include/XAC_impl_parser.h"
#include "XAIGPUARC/include/XAC_sycl_parser.h"
#include 
typedef struct XAC_Tensor { void* XAC_DATA_F16;
//INKLUDIERE XAC_DATA_F16
void* XAC_DATA_F32;
int XAC_TYPE_F16;
//INKLUDIERE XAC_TYPE_F16
int XAC_TYPE_F32;
int ne[4];
int nb[4];
void* backend;
} XAC_Tensor;
typedef struct XAC_backend_sycl_context {sycl::queue* queue;
} XAC_backend_sycl_context;
//TYPDEFINITIONEN
#define XAC_TYPE_F16 0
#define XAC_TYPE_F32 1
#define XAC_D_MAX 1024
#define XAC_VEC_SIZE 16
#define XAC_WG_SIZE 16
#define XAC_BLOCK_M 128
#define XAC_BLOCK_N 128
#define XAC_MEM_ALIGN 128
//LOGIN INFO EINWAEHLINFORMATIONEN MIKROVERSIONEN
#ifndef XAC_LOG_ERROR
#define XAC_LOG_ERROR(msg)
std::cerr << "FEHLER NEUINSTALLTATION SPIELEWIESE KI NOTWENDIG BITTE NOTWENDIGEN ABHAENGIGKEITEN EXISTENZ FUNKTION PRUEFEN" << msg << std::endl
#endif
#ifndef XAC_LOG_WARN
#define XAC_LOG_WARN(msg)
std::cerr << "WARNUNG ACHTUNG PROGRAMM WIRD NICHT OPTIMAL AUSGEFUEHRT BITTE PRUEFEN HARDWAREABHAENGIGKEITEN" << msg << std::endl
#endif
#ifndef XAC_LOG_INFO
#define XAC_LOG_INFO(msg)
std::cout << "INFORMATIONSDATENBANK FUER ENTWICKLER UND NEUGIERIGE" << msg << std::endl
#endif
#ifndef XAC_LOG_ABORT
#define XAC_LOG_ABORT(msg){
std::cerr << "FEHLER ABBRUCH XAIGPUARC SPIELEWIESE KI KONNTE NICHT GESTARTET WERDEN BITTE NEUSTART VERSUCHEN" << msg << std::endl; std::exit(1); }
#endif
namespace XAC {template <typename T> inline float xac_dot_product_scl(float* q,T* k, int d){float final_score = 0.0f;
//0 ATTENTION HILFFUNKTIONEN Skalarer Punktprodukt Fallback
for (int i = 0; i < d; ++i){float final_score + = q[i]* static_cast<float>(k[i]);}
return final_score;}
inline bool XAC_sycl_validate_attention_params(XAC_Tensor* q, XAC_Tensor* k){if (q->XAC_TYPE_F16! = XAC_TYPE_F16 && q->XAC_TYPE_F16! = XAC_TYPE_F32){ XAC_LOG_ERROR("FEHLER NICHT UNTERSTUETZTER DATENTYP BITTE NUR F16 GGUF/GGML MODELLE EINLADEN");}
//Tensorpruefung auf F16 Formatkorrektheit des Sprachmodells
if (q->XAC_TYPE_F16! = k->XAC_TYPE_F16){ XAC_LOG_ERROR("FEHLER QUERY UND KEY MUESSEN GLEICHEN RECHENTYPE F16 HABEN");
return false;}
return true;}}
//Namespace Templates fuer XAIGPUARC ueber Praefix XAC
//SKALARRUECKFALLMECHNISMUS
template<typename T>void XAC_sycl_flash_attention_fallback(T* q, T* k, T* v, T* out, int num_q, int num_k, int d_k, int d_v, int q_stride, int k_stride, int v_stride, int s_stride, int out_stride, sycl::nd_item<1> item){}
//Implementation SYCL basierter Rechenkerne
//XMXKernel
template<typename T>void XAC_sycl_xmx_matrix_ops_scl(T* q, T* k, T* v, T* out, int num_q, int num_k, int d_k, int d_v, int q_stride, int k_stride, int v_stride, int s_stride, int out_stride, sycl::nd_item<1> item){}
//XMXImplementation
//Vektorisierter XMXKernel
template<typename T>void XAC_sycl_xmx_matrix_ops_vec(T* q, T* k, T* v, T* out, int num_q, int num_k, int d_k, int d_v, int q_stride, int k_stride, int v_stride, int s_stride, int out_stride, sycl::nd_item<1> item){}}
//Vektorisierte XMXImplementation
//Namespace XAC PRAEFIX
/**
 * @brief VEKTORISIERTES PUNKT PRODUKT ZWISCHEN "q[i]" UND "k[j]"
 * @tparam const scalar_t, 16> vec; scalar_t DATENTYP sycl::half ODER FLIESSWERTE
 * @param q_row_float QUERY ZEILE ALS FLIESSWERTE
 * @param k_ptr SCHLUESSELPUNKTE UND ZEIGER
 * @param d_k KOPFDIMENSIONEN DER MATRIZENLOGIK
 * @return PUNKTPRODUKTAUTOMATITION ALS FLIESSWERTE
 */
using namespace sycl;
using namespace sycl::ext::oneapi::experimental::matrix;
//1 TEST API ZUGANGSFENSTER FUER AUSSENKOMMUNIKATION
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inline sycl::half* get_ptr(XAC_Tensor* XAC_Tensor){return reinterpret_cast<sycl::half*>(XAC_Tensor->XAC_DATA_F16);
extern "C" void XAC_sycl_api_attention_qkv( XAC_sycl_context* ctx,XAC_Tensor* dst, bool XAC_sycl_validate_attention_params XAC_Tensor* q, XAC_Tensor* k, XAC_Tensor* v, XAC_Tensor* s, int& d_k, int& d_v){if(q->XAC_TYPE_F16! = XAC_TYPE_F16 && q-> XAC_TYPE_F32! = XAC_TYPE_F32){ XAC_LOG_ERROR("FEHLER NUR F16 UNTERSTUETZT");
//VALIDIERUNG FALSCH RUFT SICH SELBST AUF ANSTAENDIG ENTFERNEN
//Tensorpruefung
return false;}
if (q->XAC_TYPE_F16! = k->XAC_TYPE_F16 || q->XAC_TYPE_F16! = v->XAC_TYPE_F16){ XAC_LOG_ERROR("FEHLER ALLE TENSOREN XAC_TYPE_F16 MUESSEN GLEICHEN TYP HABEN");
return false;}
d_k = q->ne[0];
d_v = v->ne[0];
if (d_k! = k->ne[0] || d_v! = v->ne[0]){XAC_LOG_ERROR("FEHLER ERROR DIMENSIONSKONSISTENZ");
return false;}
if (d_k > XAC_D_MAX || d_v > XAC_D_MAX){XAC_LOG_ERROR("FEHLER ERROR DIMENSIONEN UEBERSCHREITEN MAXIMUM");
return false;}
if (d_k % XAC_VEC_SIZE! = 0 || d_v % XAC_VEC_SIZE! = 0){XAC_LOG_WARN("WARNUNG ACHTUNG SUBOPTIMALE DIMENSIONEN VEKTORISIERUNG");}
return true;})
//KLAMMER RUND SCHLIESSEND PRUEFEN
{if(!XAC_sycl_validate_attention_params(q, k)){return;}sycl::queue& queue = *(ctx->queue);
bool has_xmx = queue.get_device().has(sycl::aspect::ext_intel_matrix);
//DIMENSIONEN EXTRAHIEREN
int num_q = q->ne[1];
int num_k = k->ne[1];
int d_k = k->ne[0];
int d_v = v->ne[0];
auto* q_data = reinterpret_cast<sycl::half*>(q->XAC_DATA_F16);
auto* k_data = reinterpret_cast<sycl::half*>(k->XAC_DATA_F16);
auto* v_data = reinterpret_cast<sycl::half*>(v->XAC_DATA_F16);
auto* s_data = reinterpret_cast<sycl::half*>(s->XAC_DATA_F16);
auto* out_data = reinterpret_cast<sycl::half*>(dst->XAC_DATA_F16);
bool can_use_xmx = has_xmx && (d_k % 16 == 0) && (d_v % 16 == 0);
queue.submit([&](sycl::handler& h){if(can_use_xmx){h.parallel_for<class XAC_XMX_Kernel>(sycl::nd_range<1>(sycl::range<1>(((num_q + 15) / 16) * 16), sycl::range(16)), [=](sycl::nd_item item)[[intel::reqd_sub_group_size(16)]]{XAC_sycl_kernel_flash_attention_xmx(q_data, k_data, v_data, out_data, num_q, num_k, d_k, d_v, q->nb[1] / sizeof(sycl::half), k->nb[1] / sizeof(sycl::half), v->nb[0] / sizeof(sycl::half), s->nb[0] / sizeof(sycl::half), dst->nb[0] / sizeof(sycl::half), item);});}else{h.parallel_for<class XAC_sycl_flash_attention_scl>(sycl::nd_range<1>(sycl::range<1>(num_q * XAC_WORKGROUP_SIZE), sycl::range<1>(XAC_WORKGROUP_SIZE)), [=](sycl::nd_item<1> item){XAC::xac_flash_attention_fallback(q_data, k_data, v_data, s_data, out_data, num_q, num_k, d_k, d_v, q->nb[1] / sizeof(sycl::half),
k->nb[1] / sizeof(sycl::half),
v->nb[0] / sizeof(sycl::half),
s->nb[0] / sizeof(sycl::half),
dst->nb[0] / sizeof(sycl::half),
item );});}})queue.wait();}
//2 TEST XAC_DOT FUNKTION PUNKTPRODUKT VECTORAUTOMATION PLUS IMPLEMENTIERUNG
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template<typename scalar_t>inline float XAC_sycl_dot_product_vec(float* q_row_float, scalar_t* k_ptr, int d_k){float final_score = 0.0f;
for (int i = 0; i < d; ++i){float final_score + = q[i]* static_cast<float>(k[i]);}
return final_score;
float accum_den = 0.0f;
float running_max = -std::numeric_limits<float>::infinity();
float s_scores[XAC_BLOCK_N];
//MEHRFACH GLEICHER SCOPE DEFINIERT ATTENTION 0
if constexpr (std::is_same_v<scalar_t, sycl::half>){if(d_k % XAC_VEC_SIZE! = 0){for(int di = 0; di < d_k; ++di){float final_score + = q_row_float[di]* static_cast<float>(k_ptr[di]);}
return final_score;}
constexpr int vec_elements = XAC_VEC_SIZE;
using vec_half = sycl::vec<sycl::half, vec_elements>;
using vec_float = sycl::vec<
float, vec_elements>;
int vec_iters = d_k / vec_elements;
for (int v = 0; v < vec_iters; ++v){vec_half k_half_vec;
k_half_vec.loadv * vec_elements, k_ptr);
vec_float k_float_vec = k_half_vec.template convert<float>();
vec_float q_float_vec;
q_float_vec.load(v * vec_elements, q_row_float);
float final_score + = sycl::dot(q_float_vec, k_float_vec);}}else{for(int di = 0; di < d_k; ++di){float final_score + = q_row_float[di]* static_cast<float>(k_ptr[di]);}}
return final_score;}
//FAKEDREI 3 VERKTORISIERTES PUNKTPRODUKTAUTOMATIONSRUECKFALLAUTOMATIK
template <typename scalar_t>inline float XAC_sycl_dot_product_vec(float* q_row_float, scalar_t* k_ptr, int d_k){float final_score = 0.0f;
for(int i = 0; i < d; ++i){float final_score + = q[i]* static_cast<float>(k[i]);}
return final_score;
//XAC Vectorpfad bei Ausrichtung : Dimension sowie F16
if constexpr (std::is_same_v<scalar_t, sycl::half>){if(d_k % XAC_VEC_SIZE! = 0){for(int di = 0; di < d_k; ++di){float final_score + = q_row_float[di]* static_cast<float>(k_ptr[di]);
}return final_score;}
constexpr int vec_elements = XAC_VEC_SIZE;
using vec_half = sycl::vec<sycl::half, vec_elements>;
using vec_float = sycl::vec<float, vec_elements>;
int vec_iters = d_k / vec_elements;
for (int v = 0; v < vec_iters; ++v){vec_half k_half_vec;
k_half_vec.load (v * vec_elements, k_ptr);
vec_float k_float_vec = k_half_vec.template convert<float>();
vec_float q_float_vec;
q_float_vec.load(v * vec_elements, q_row_float);
float final_score + = sycl::dot(q_float_vec, k_float_vec);}
return final_score;
}else{for(int di = 0; di < d_k; ++di){float final_score + = q_row_float[di]* k_ptr[di];}
return final_score;}}
//FINALER FLUESSMATRIZENWERT
/**
 * @brief XAC HAUPTKERN AUFDROESSELSTRATEGIE MIT TREFFERWERTEZWISCHENSPEICHER
 * @tparam const scalar_t, 16> vec; scalar_t DATENTYP FORMAT INTEL sycl::half HALBE GENAUIGKEIT F16
 */
#endif //XAC_SYCL_SPIELEWIESE_KI_H //CODENAME SPIELEWIESE KI aka SANDKASTEN ATTENTION
//3 TEST SKALARRUECKFALLPUNKTPRODUKTAUTOMATION
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template <typename scalar_t>void XAC_sycl_flash_attention_vec_impl(sycl::vec<T, 16>, scalar_t* k_ptr, scalar_t* v_ptr, scalar_t* out_ptr, int num_q, int num_k, int d_k, int d_v, int q_stride, int k_stride, int v_stride, int out_stride, sycl::nd_item<1> item){int head_row = item.get_global_id(0);
if (head_row > = num_q) return;
float accum_den = 0.0f;
float running_max = -std::numeric_limits<float>::infinity();
float accum_num[XAC_D_MAX] = {0.0f};
float s_scores[XAC_BLOCK_N]; //MEHRFACH GLEICHER SCOPE DEFINIERT ATTENTION 1
scalar_t* q_row_ptr = q_ptr + head_row * q_stride;
float q_row_float[XAC_D_MAX];
for (int di = 0; di < d_k; ++di){q_row_float[di] = static_cast<float>(q_row_ptr[di]);
}
sycl::float scale_factor = 1.0f / sycl::sqrt(static_cast<
//sqrt nicht in allen Versionen Vorhanden aus SYCL! Beachten
float>(d_k));
for (int k_start = 0; k_start < num_k; k_start + = XAC_BLOCK_N){int k_block_size = sycl::min(XAC_BLOCK_N, num_k - k_start);
float current_block_max = float running_max;
for (int kk = 0; kk < k_block_size; ++kk){int k_idx = k_start + kk;
scalar_t* k_block_ptr = k_ptr + k_idx * k_stride;
float final_score = XAC_sycl_dot_product_vec(q_row_float, k_block_ptr, d_k);
float final_score * = sycl::
float scale_factor;
float s_scores[kk] = final_score;
current_block_max = sycl::max(current_block_max, float final_score);}
if (float running_max! = current_block_max){sycl::float scale = sycl::exp(float running_max - current_block_max);
float accum_den * = scale;
for (
int vi = 0; vi < d_v; ++vi){float accum_num[vi]* = scale;}
float running_max = -std::numeric_limits<float>::infinity();}
for (int kk = 0; kk < k_block_size; ++kk){int k_idx = k_start + kk;
sycl::float final_score = float s_scores[kk];
//MEHRFACH GLEICHER SCOPE DEFINIERT ATTENTION 2
float exp_val = sycl::exp(float final_score = - std::numeric_limits<float>sycl::infinity();
float running_max);
float accum_den + = exp_val;
scalar_t* v_block_ptr = v_ptr + k_idx * v_stride;
for (int vi = 0; vi < d_v; ++vi){float accum_num[vi] + = exp_val * static_cast<float>(v_block_ptr[vi]);}}}
scalar_t* out_row_ptr = out_ptr + head_row * out_stride;
if (accum_den == 0.0f){for(int vi = 0; vi < d_v; ++vi){out_row_ptr[vi] = scalar_t(0.0f);}
return;}
float inv_den = 1.0f / accum_den; for (int vi = 0; vi < d_v; ++vi){out_row_ptr[vi] = static_cast<scalar_t>(accum_num[vi]* inv_den);}}
//4 TEST XMX RUECKFALLPUNKTPRODUKTAUTOMATION
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//DOPPELT OPEN OFFENER BEREICHSABGLEICH
template <typename scalar_t>void XAC_sycl_flash_attention_kernel_impl_scl(scalar_t* q_ptr, scalar_t* k_ptr, scalar_t* v_ptr, scalar_t* out_ptr, int num_q, int num_k, int d_k, int d_v, int q_stride, int k_stride, int v_stride, int out_stride, sycl::nd_item<1> item){sycl::sub_group sg = item.get_sub_group();
//WEG FALLS FALSCH
int head_row_base = item.get_group(0) * 16;
if (head_row_base > = num_q) return;
using t_q = sycl::joint_matrix<sycl::sub_group, sycl::half, sycl::matrix_use::a, 16, 16, sycl::layout::row_major>;
using t_k = sycl::joint_matrix<sycl::sub_group, sycl::half, sycl::matrix_use::b, 16, 16, sycl::layout::row_major>;
using t_acc = sycl::joint_matrix<sycl::sub_group, float, use::sycl::accumulator, 16, 16>;
t_q mat_q;
t_k mat_k;
t_acc mat_s;
sycl::joint_matrix_fill(sg, mat_s, 0.0f);
scalar_t* sycl::q_tile_ptr = q_ptr + head_row_base * q_stride;
joint_matrix_load(sg, mat_q, sycl::q_tile_ptr, q_stride);
float scale_factor = 1.0f / sycl::sqrt((float)d_k);
for (int k_idx = 0; k_idx < num_k; k_idx + = 16){scalar_t* k_tile_ptr = k_ptr + k_idx * k_stride;
joint_matrix_load(sg, mat_k, k_tile_ptr, k_stride);
//joint_matrix_mad Aufruf Akkumulator
joint_matrix_mad(sg, mat_s, mat_q, mat_k, mat_s);
template <typename scalar_t> inline float XAC_sycl_dot_product_vec(sycl::vec<dot, scalar_t, 16> vec; float* q_row_float, scalar_t* k_ptr, int d_k){float final_score = 0.0f;
for (int i = 0; i < d; ++i){float final_score + = q[i]* static_cast<float>(k[i]);}
return final_score;
if constexpr (std::is_same_v<scalar_t, sycl::half>){if (d_k % XAC_VEC_SIZE! = 0){for(int di = 0; di < d_k; ++di){float final_score + = q_row_float[di]* static_cast<float>(k_ptr[di]);}
return final_score;}
constexpr int vec_elements = XAC_VEC_SIZE;
using vec_half = sycl::vec<sycl::half, vec_elements>;
using vec_float = sycl::vec<float, vec_elements>;
int vec_iters = d_k / vec_elements;
for (int v = 0; v < vec_iters; ++v){vec_half k_half_vec;
k_half_vec.load * vec_elements, k_ptr);
vec_float k_float_vec = k_half_vec.template convert<float>();
vec_float q_float_vec;
q_float_vec.load(v * vec_elements, q_row_float);
float final_score + = sycl::dot(q_float_vec, k_float_vec);}}else{for(int di = 0; di < d_k; ++di){float final_score + = q_row_float[di]* static_cast<float>(k_ptr[di]);}}
return final_score;}}
//SOFTMAX MIT V * NEHMEN FUER ZIELPUNKTIERUNG ATTENTION 3
template <typename scalar_t>void XAC_sycl_xmx_matrix_ops_impl(){joint_matrix_mad(sg, mat_s, mat_q, mat_k, mat_s);
//4a XMX MatrixMultiplikation (Q × K^T) SKALAR RUECKFALLMECHNISMUS
template <typename scalar_t>void XAC_sycl_flash_attention_vec_impl(sycl::vec<T, 16>, scalar_t* q_ptr, scalar_t* k_ptr, scalar_t* v_ptr, scalar_t* out_ptr, int num_q, int num_k, int d_k, int d_v, int q_stride, int k_stride, int v_stride, int out_stride, sycl::nd_item<1>item){int head_row = item.get_global_id(0);
if (head_row > = num_q) return;
float accum_den = 0.0f;
float running_max = -std::numeric_limits<float>::infinity();
float accum_num[XAC_D_MAX] = {0.0f};
float s_scores[XAC_BLOCK_N];
//MEHRFACH GLEICHER SCOPE DEFINIERT ATTENTION 4
scalar_t* q_row_ptr = q_ptr + head_row * q_stride;
float q_row_float[XAC_D_MAX];
for (int di = 0; di < d_k; ++di){q_row_float[di] = static_cast<float>(q_row_ptr[di]);}
float scale_factor = 1.0f / sycl::sqrt((float)d_k);
//Hier koennten Versionsprobleme mit SYCL selbst entstehen dann statt dessen std::sqrt benutzen
for (int k_start = 0; k_start < num_k; k_start + = XAC_BLOCK_N){int k_block_size = sycl::min(XAC_BLOCK_N, num_k - k_start);
float running_max = -std::numeric_limits<float>::infinity();
//4b Skalierung (1/√d_k) PRO BLOCK SCORE WERT
for (int kk = 0; kk < k_block_size; ++kk){
int k_idx = k_start + kk;
scalar_t* k_block_ptr = k_ptr + k_idx * k_stride;
float final_score = XAC_sycl_dot_product_vec(q_row_float, k_block_ptr, d_k);
float final_score * = float scale_factor;
float s_scores[kk] = float final_score;
//MEHRFACH GLEICHER SCOPE DEFINIERT ATTENTION 5
current_block_max = sycl::max(current_block_max, final_score);}
//4c Softmax XMX ueber SubGroups Untergruppen
if (float running_max! = current_block_max){float scale = sycl::exp(float running_max - current_block_max);
accum_den * = scale;
for (int vi = 0; vi < d_v; ++vi){accum_num[vi]* = scale;}
float running_max = -std::numeric_limits<float>::infinity();}
//4d Matrix Multiplikation mat_s × V
for (int kk = 0; kk < k_block_size; ++kk){int k_idx = k_start + kk;
float exp_val = sycl::exp(float s_scores[kk] - float running_max);
//float scores sache //MEHRFACH GLEICHER SCOPE DEFINIERT ATTENTION 6
float accum_den + = exp_val;
scalar_t* v_block_ptr = v_ptr + k_idx * v_stride;
for (int vi = 0; vi < d_v; ++vi){float accum_num[vi] + = exp_val * static_cast<float>(v_block_ptr[vi]);}}}
//4e Ergebnis Speichern
scalar_t* out_row_ptr = out_ptr + head_row * out_stride;
if (float accum_den == 0.0f){for(int vi = 0; vi < d_v; ++vi){out_row_ptr[vi] = scalar_t(0.0f);}
return;}
float inv_den = 1.0f / float accum_den;
for (int vi = 0; vi < d_v; ++vi){out_row_ptr[vi] = static_cast<scalar_t>(float accum_num[vi]* inv_den);
joint_matrix_store(sg, mat_s, out_ptr + head_row_base * out_stride, out_stride, layout::row_major);}
//4a-eEnde
//5 XAC_sycl_flash_attention_vec_impl ORCHESTRIERUNG
//5 TEST PUNKTPRODUKTORCHESTRATIONSAUTOMATIK
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extern "C" void XAC_sycl_flash_attention_vec_impl(XAC_backend_sycl_context* ctx,
XAC_Tensor* dst, XAC_Tensor* q_tensor, XAC_Tensor* k_tensor,
XAC_Tensor* v_tensor, XAC_Tensor* s_tensor,
){if (q_tensor->XAC_TYPE_F32! = XAC_TYPE_F16 && q_tensor->XAC_TYPE_F16! = XAC_TYPE_F32){XAC_ABORT("FEHLER ABBRUCH XAC_sycl_flash_attention_vec_impl AUSSCHLIESSLICH F16 FORMAT UND AUSGEWAEHLTE KI MODELLE UNTERSTUETZT");
//Tensorpruefung
return;}
queue& sycl_queue = *(ctx->queue);
bool has_xmx = sycl_queue.get_device().has(sycl::aspect::ext_intel_matrix);
int num_q = q_tensor->ne[1];
int num_k = k_tensor->ne[1];
int d_k = k_tensor->ne[0];
int d_v = v_tensor->ne[0];
int q_stride = q_tensor->nb[1] / sizeof(sycl::half);
int k_stride = k_tensor->nb[1] / sizeof(sycl::half);
int v_stride = v_tensor->nb[1] / sizeof(sycl::half);
int s_stride = s_tensor->nb[1] / sizeof(sycl::half);
int out_stride = dst->nb[1] / sizeof(sycl::half);
sycl::half* q_data = reinterpret_cast<sycl::half*>(q_tensor->XAC_DATA_F16);
sycl::half* k_data = reinterpret_cast<sycl::half*>(k_tensor->XAC_DATA_F16);
sycl::half* v_data = reinterpret_cast<sycl::half*>(v_tensor->XAC_DATA_F16);
sycl::half* s_data = reinterpret_cast<sycl::half*>(s_tensor->XAC_DATA_F16);
sycl::half* out_data = reinterpret_cast<sycl::half*>(dst->XAC_DATA_F16);
//Rueckfallmechanismus XMX Hardware 16er Alignment FALLBACK VECTORISIERT
bool can_use_xmx = has_xmx && (d_k % 16 == 0) && (d_v % 16 == 0);
queue.submit([&](sycl::handler& h){if (can_use_xmx){h.parallel_for<class XAC_XMX_Kernel>(sycl::nd_range<1>(sycl::range<1>((num_q + 15) / 16 * 16), sycl::range<1>(16)),
[=](sycl::nd_item<1> item)[[intel::reqd_sub_group_size(16)]]{XAC_sycl_xmx_matrix_ops_impl(q_data, k_data, v_data, out_data, num_q, num_k, d_k, d_v, q_stride, k_stride, v_stride, s_stride, out_stride, item);});}else{h.parallel_for<class XAC_Scalar_Kernel>(sycl::nd_range<1>(sycl::range<1>(num_q * XAC_WG_SIZE), sycl::range<1>(XAC_WG_SIZE)), [=](sycl::nd_item<1> item){XAC_sycl_flash_attention_vec_impl(q_data, k_data, v_data, out_data, num_q, num_k, d_k, d_v, q_stride, k_stride, v_stride, s_stride, out_stride, item);});}})queue.wait();}
//VALIDIERT DE
//ZWISCHEN ORCHESTRATOR ALTER SCHULE GRUNDBAUMECHANIK ATTENTION FUER
//XMX KERN UMGEBUNGSVORBAU MIT
sycl::sub_group;
sycl::joint_matrix;
//PRIORITAET 1 bis 3 Orchestrator, XMX-Kernel, Vektor Fallback
extern "C" void XAC_sycl_xmx_matrix_ops_vec(queue& q, sycl::half* out, sycl::half* o_ptr, sycl::half* k, sycl::half* v, int num_q, int d_k){ bool has_xmx = sycl_q.get_device().has(sycl::aspect::ext_intel_matrix);
if (has_xmx && (d_k % 16 == 0)){queue.parallel_for(sycl::nd_range<1>(sycl::range<1>((num_q + 16) / 16 * 32), sycl::range<1>(32)),
[=](sycl::nd_item<1> item){ [[intel::reqd_sub_group_size(16)]]{sycl::sub_group sg = item.get_sub_group();
//Neue Untergruppenberechnungen
int sg_size = sg.get_local_range()[0];
//Typisch 16 auf Intel
int sg_id = sg.get_group_id()[0];
int local_id = sg.get_local_id()[0];
//Daten Wavefronts Auslastung
//Jede Sub Group arbeitet in einem zusammenhaengendem Block
int tiles_per_sg = 4; //Jede Sub Group Untergruppe bearbeitet 4 Tiles
//Diese widerum koennen auch in nocheinmal
//vier untergruppen unter bestimmten bedingungen auf arc eingteilt werden
//Diese Bedingungen sind entsprechend in der SYCL Anleitung nachzulesen und werden hier nachgetragen als Erklaehrung
constexpr int XAC_TILE_SIZE = 16;
//DEFINIEREN
for (int t = 0; t < tiles_per_sg; ++t){int tile_offset = (sg_id * tiles_per_sg + t) *  XAC_TILE_SIZE;
//VECTORISIERTE SPEICHERBANDBREITENMAXIMIERUNGSAUTOMATIK
scalar_t, 16> vec_data;
vec_data.sycl::vec.load(tile_offset, ptr);}
//6 TEST 666 ATTENTION OPTIMIERTE SUB GROUP UNTERGRUPPEN XAC_sycl_wavefront_optimized_attention
//NUTZUNG INTEL ARC
6666666666666666666666666666666666666666666666
template<typename T> void XAC_sycl_wavefront_optimized_attention(queue& q, T* q_ptr, T* k_ptr, T* v_ptr, T* out_ptr, int num_q, int d_k){constexpr int SG_SIZE = 16;
//Intel ARC Wavefront Groesse
constexpr int TILE_M = 16;
constexpr int TILE_N = 16;
q.parallel_for(sycl::nd_range<1>(sycl::range<1>(((num_q + TILE_M - 1) / TILE_M) * SG_SIZE), sycl::range<1>(SG_SIZE)), [=](sycl::nd_item<1> item [[intel::reqd_sub_group_size(SG_SIZE)]]{ auto sg = item.get_sub_group();
//JEDE SUB GROUP UNTERGRUPPE BEARBEITET EINE ZEILE
int row = item.get_group(0) * TILE_M + sg.get_local_id()[0];
if (row > = num_q) return;
//LADE VERCTORISIERTE WELLENFRONT WAVEFRONT ARC INTEL CPU IGPU + dPUG FREUNDLICH
sycl::vec<T, 16> q_vec;
q_vec.sycl::vec.load(row * d_k, q_ptr);
//RUDKTION UEBER SUB GROUP UNTERGRUPPENREDUZIERUNGSAUTOMATIK
float partial_sum = 0.0f;
#pragma unroll
for (int i = 0; i < 16; ++i){ partial_sum + = static_cast<float>(q_vec[i]) * static_cast<float>(k_ptr[i]);}
//SUB GROUP UNTERGRUPPEN REDUKTRION WAVEFRONT OPTIMIERT
float total = sycl::reduce_over_group(sg, partial_sum, sycl::plus<>());
//RESULTAT UNTERGRUPPENFRONTWELLENREDUKTIONSAUTOMATIK SPEICHERN
out_ptr[row] = static_cast<T>(total);})queue.wait();}
//ABSCHLUSS WELLENFORMATIONFRONTALBRUCHAUTOMATIK 666
sycl::joint_matrix<sycl::sub_group, sycl::half, sycl::matrix_use::a, 16, 16, sycl::layout::row_major> mat_q;
sycl::joint_matrix<sycl::sub_group, sycl::half, sycl::matrix_use::b, 16, 16, sycl::layout::row_major> mat_k;
sycl::joint_matrix<sycl::sub_group, float, use::sycl::accumulator, 16, 16> mat_s;
sycl::joint_matrix_fill(sg, mat_s, 0.0f);
joint_matrix_load(sg, mat_q, q_ptr + (item.get_group(0) * 16 * d_k), d_k);
joint_matrix_load(sg, mat_k, k, d_k);
joint_matrix_mad(sg, mat_s_half, mat_q_half, mat_k);
//Doppelt Deklariert aufgerufen Beheben Sofort 1
joint_matrix_copy(sg, m_p_half, m_s_acc);
//Funktionslogik ueberpruefen und ausschliessen das diese Funktion nicht gebraucht wird, aka Falsch ist
joint_matrix_store(sg, mat_s, (float*)out, d_k, layout::row_major);});}}
auto& sycl_q = XAC_backend_sycl_get_queue(ctx);
auto dev = q.get_device();
auto sg = item.get_sub_group();
int m = item.get_group(0) * 16;
int n = item.get_group(1) * 16;
bool has_xmx = dev.has(sycl::aspect::ext_intel_matrix);
bool use_xmx = sycl_q.get_device().has(sycl::aspect::ext_intel_matrix) && (d_k % 16 == 0) && (d_v % 16 == 0);
bool can_use_xmx = (q->ne[0] % 16 == 0) && (k->ne[1] % 16 == 0);
if (has_xmx && can_use_xmx){ sycl_q.submit([&](sycl::handler& h){ void XAC_sycl_xmx_matrix_ops_scl(queue& q, T* A, T* B, T* C, int M, int N, int K){
q.parallel_for(sycl::nd_range<1>{sycl::range<1>(16), sycl::range<1>(16)}, [=](sycl::nd_item<1> item){ [[intel::reqd_sub_group_size(16)]]{ XC_sycl_xmx_matrix_ops_scl<sycl::half>(q, k, v, out_stride, BATCH * HEADS * SEQ_LEN * DIM, BATCH * HEADS * SEQ_LEN * DIM, BATCH * HEADS * SEQ_LEN * DIM, BATCH * HEADS * SEQ_LEN * DIM, BATCH * HEADS * SEQ_LEN * DIM, BATCH * HEADS * SEQ_LEN * DIM, item);
sycl::sub_group sg = item.get_sub_group();
//Joint Matrix Zeug aendern
sycl::joint_matrix<sycl::sub_group, sycl::half, sycl::matrix_use::a, 16, 16, sycl::layout::row_major> mat_q;
sycl::joint_matrix<sycl::sub_group, sycl::half, sycl::matrix_use::b, 16, 16, sycl::layout::row_major> mat_k;
sycl::joint_matrix<sycl::sub_group, float, use::sycl::accumulator, 16, 16> mat_s;
sycl::joint_matrix_fill(sg, mat_s, 0.0f);
joint_matrix_load(sg, mat_q, q + (item.get_group(0) * 16 * d_k), d_k);
joint_matrix_load(sg, mat_k, k, d_k);
joint_matrix_mad(sg, mat_s_half, mat_q_half, mat_k);
//Doppelt Deklariert aufgerufen Beheben Sofort 2
joint_matrix_copy(sg, m_ptr_half, m_s_acc);
//Rescale plus Store Logik hier integrieren
joint_matrix_store(sg, mat_s, (float*)out, d_k, layout::row_major);
h.parallel_for(sycl::nd_range<2>({M/16, N/16}, {1, 1}), [=](nd_item<2> item){ sycl::joint_matrix<sycl::sub_group, float, use::sycl::accumulator, 16, 16> mat_c;
sycl::joint_matrix_fill(sg, mat_c, 0.0f);
//Loop ueber K Wert Dimension
for (int k = 0; k < K; k + = 16){ joint_matrix_load(sg, mat_a, a_ptr, A + m*K + k, K);
joint_matrix_load(sg, mat_b, b_ptr, B + k*N + n, N);
joint_matrix_mad(sg, mat_s_half, mat_q_half, mat_k);
//Doppelt Deklariert aufgerufen Beheben Sofort 3
joint_matrix_store(sg, mat_c, c_ptr, C + m*N + n, N, layout::row_major);});
//joint matrix zeug hier einfuegen
})queue.wait();
}else{XAC_sycl_flash_attention_scl(ctx, dst, q, k, v);}}
//XAC_sycl_flash_attention_scl ATTENTION SKALARVERION GRUNDFUNKTION STANDART AUFMERKSAMKEITSMECHANISMUS
q.submit([&](sycl::handler& cgh){ cgh.parallel_for(sycl::range<1>(num_q), [=](sycl::nd_item<1> item){int head_row = item.get_global_id(0);
//0 Ergebnis Null
if (head_row > = num_q) return;
float accum_den = 0.0f;
float running_max = -std::numeric_limits<float>::infinity();
float accum_num[XAC_D_MAX] = {0.0f};
float s_scores[XAC_BLOCK_N];
//MEHRFACH GLEICHER SCOPE DEFINIERT ATTENTION 7
//XACFP32 REGISTER FUER ZWISCHENRECHNUNGEN
scalar_t* q_row_ptr = q_ptr + head_row * q_stride;
float q_row_float[XAC_D_MAX];
float final_score = 0.0f;
for (int di = 0; di < d_k; ++di){ q_row_float[di] = static_cast<float>(q_row_ptr[di]);}
float scale_factor = 1.0f / sycl::sqrt((float)d_k);
//Nicht in allen SYCL Versionen Vorhanden
for (int k_start = 0; k_start < num_k; k_start + = XAC_BLOCK_N){int k_block_size = sycl::min(XAC_BLOCK_N, num_k - k_start);
float current_block_max = float running_max;
for (int kk = 0; kk < k_block_size; ++kk){int k_idx = k_start + kk;
scalar_t* k_block_ptr = k_ptr + k_idx * k_stride;
float s_scores = dot_product_vec(q_row_float, k_block_ptr, d_k);
//Ausserhalb der Schleife korrigieren
//MEHRFACH GLEICHER SCOPE DEFINIERT ATTENTION 8
float s_scores * = float scale_factor;
//MEHRFACH GLEICHER SCOPE DEFINIERT ATTENTION 9
float s_scores[kk] = final_score;
//MEHRFACH GLEICHER SCOPE DEFINIERT ATTENTION 10
current_block_max = sycl::max(current_block_max, final_score);}
if (float running_max! = current_block_max){float scale = sycl::exp(float running_max - current_block_max);
accum_den * = scale;
for (int vi = 0; vi < d_v; ++vi){float running_max = current_block_max;
accum_num[vi]* = scale;}
for (int kk = 0; kk < k_block_size; ++kk){int k_idx = k_start + kk;
float final_score = s_scores[kk];
//MEHRFACH GLEICHER SCOPE DEFINIERT ATTENTION 11
float exp_val = sycl::exp(final_score - float running_max);
float accum_den + = exp_val;
scalar_t* v_block_ptr = v_ptr + k_idx * v_stride;
if (d_v % XAC_VEC_SIZE == 0){ constexpr int vec_elements = XAC_VEC_SIZE;
using vec_half = sycl::vec::half<sycl::half, vec_elements>;
using vec_float = sycl::vec<float, vec_elements>;
int vec_iters = d_v / vec_elements;
float* sycl::accum_num_ptr = float accum_num;
for (int v = 0; v < vec_iters; ++v){ vec_half sycl::v_half_vec;
sycl::v_half_vec.sycl::vec.load(v * vec_elements, v_block_ptr);
//SYCL maximal ausnutzen Bestimmte Version achten!
vec_float sycl_v_float_vec = sycl::v_half_vec.template convert<float>();
sycl_v_float_vec * = exp_val;
vec_float acc_vec;
sycl::acc_vec.sycl::vec.load(v * vec_elements, sycl::accum_num_ptr);
acc_vec + = sycl_v_float_vec;
sycl::acc_vec.sycl::vec.store(v * vec_elements, sycl::accum_num_ptr);}
}else{for(int vi = 0; vi < d_v; ++vi){float accum_num[vi] + = exp_val * static_cast<float>(v_block_ptr[vi]);}}
scalar_t* out_row_ptr = out_ptr + head_row * out_stride;
if (float accum_den == 0.0f){for(int vi = 0; vi < d_v; ++vi){ scalar_t(0.0f);}
return;}}
float inv_den = 1.0f / float accum_den;
if constexpr (std::is_same_v<scalar_t, sycl::half>){if (d_v % XAC_VEC_SIZE == 0){constexpr int vec_elements = XAC_VEC_SIZE;
using vec_half = sycl::vec<sycl::half, vec_elements>;
using vec_float = sycl::vec<float, vec_elements>;
int vec_iters = d_v / vec_elements;
for (int v = 0; v < vec_iters; ++v){ vec_float acc_vec;
sycl::acc_vec.sycl::vec.load(v * vec_elements, float accum_num);
//SYCL MATRIX MATRIZEN ARC XE BEFEHLE nutzen
acc_vec * = inv_den;
vec_half sycl::out_vec = acc_vec.template convert<sycl::half>();
sycl::out_vec.sycl::vec.store(v * vec_elements, out_row_ptr);}
//PRUEFEN OB DIE SYCL VER FUNKTION RICHTIG IST

return;}}
for (int vi = 0; vi < d_v; ++vi){ out_row_ptr[vi] = static_cast<scalar_t>(float accum_num[vi]* inv_den);}});});}
/**
* @brief XAC KERNUEBERSETZERMISCHPULT
* @tparam vec_float
*/
}
//XAC_sycl_flash_attention_vec ATTENTION VECTORVERSION
extern "C" void XAC_sycl_flash_attention_vec_impl( XAC_backend_sycl_context* ctx, XAC_Tensor* dst, XAC_Tensor* q, XAC_Tensor* k, XAC_Tensor* v, XAC_Tensor* s){auto q_ptr = get_ptr(q);
if (q->XAC_TYPE_F16! = XC_TYPE_F16 || k->XAC_TYPE_F16! = XAC_TYPE_F16 || v->XAC_TYPE_F16! = XAC_TYPE_F16){} else if (q->XAC_TYPE_F16! = XAC_TYPE_F16 || k->XAC_TYPE_F16! = XAC_TYPE_F16 || v->XAC_TYPE_F16! = XAC_TYPE_F16){fprintf(stderr, "FEHLER ABBRUCH XAC_sycl_flash_attention_vec_impl.cpp: MATRIZENEINHEITEN MUESSEN AUF EXPLIZITEM DATEITYP XAC_TYPE_F16 BASIEREN\n");
return;
if (XAC_TYPE_F16){ XAC_LOG_ABORT("FEHLER ABBRUCH XAC_sycl_flash_attention_vec_impl.cpp: AUSSCHLIESSLICH XAC_TYPE_F16 UNTERSTUETZT\n");
return;}
queue& sycl_queue = XC_backend_sycl_get_queue(q->backend);
int num_q = q->ne[1];
int num_k = k->ne[1];
int num_v = v->ne[1];
int out_stride = sycl::dst->ne[1];
int d_q = q->ne[1];
int d_k = k->ne[1];
int sycl_v_float_vec = v->ne[1];
int d_v = out_stride->ne[1];
int d_out_stride = sycl::dst->ne[1];
if (d_k > XAC_D_MAX || d_v > XAC_D_MAX){XAC_LOG_ABORT("WARNUNG ACHTUNG XAC_sycl_flash_attention_vec_impl.cpp: DIMENSIONEN DER SPIELWIESE KI GEWICHTVERTEILUNG UND TEMPERATUREN d_k=%d oder d_v=%d UEBERSCHREITEN XAC_D_MAX=%d\n", d_k, d_v, XAC_D_MAX);
return;}
if (d_k % XAC_VEC_SIZE! = 0 || d_v % XAC_VEC_SIZE! = 0){XAC_LOG_WARN("WARNUNG ACHTUNG XAC_sycl_flash_attention_vec_impl.cpp: DIMENSIONEN SIND KEIN VIELFACHES DER XAC_VEC_SIZE=%d GESCHWINDIGKEIT REDUZIERT\n", XAC_VEC_SIZE);}
int q_stride = q->nb[1] / sizeof(sycl::half);
int k_stride = k->nb[1] / sizeof(sycl::half);
int v_stride = v->nb[1] / sizeof(sycl::half);
int s_stride = s->nb[1] / sizeof(sycl::half);
int sycl::o_stride = o->nb[1] / sizeof(sycl::half);
int out_stride = dst->nb[1] / sizeof(sycl::half);
sycl::half* q_data = reinterpret_cast<sycl::half*>(q->XAC_DATA_F16);
sycl::half* k_data = reinterpret_cast<sycl::half*>(k->XAC_DATA_F16);
sycl::half* v_data = reinterpret_cast<sycl::half*>(v->XAC_DATA_F16);
sycl::half* s_data = reinterpret_cast<sycl::half*>(s->XAC_DATA_F16);
sycl::half* o_data = reinterpret_cast<sycl::half*>(o->XAC_DATA_F16);
sycl::half* out_stride_data = reinterpret_cast<sycl::half*>(dst->XAC_DATA_F16);
sycl::range<1> global_size(num_q);
sycl::range<1> local_size(16);
//TEILBAR DURCH GLOBAL SIZE
sycl::nd_range<1> ndRange(global_size, local_size);
queue.submit([&](sycl::handler& h){local_accessor<float, 1> slm_scores(sycl::range<1>(XAC_BLOCK_N), h);
//float doppelt vermischt
h.parallel_for<class XAC_sycl_flash_attention_vec_impl>(sycl::nd_range<1>(sycl::range<1>(num_q * WG_SIZE), sycl::range<1>(WG_SIZE)), [=](sycl::nd_item<1> item){XAC_sycl_flash_attention_vec_impl<sycl::half>(q_data, k_data, v_data, s_data, out_stride_data, num_q, num_k, num_v, sycl::num_s, sycl::num_o, sycl::num_out_stride, d_q, d_k, d_v, d_s, sycl::d_o, d_out_stride, q_stride, k_stride, v_stride, s_stride, float s_scores[XAC_BLOCK_N];//Berichtigen D Outstride
//MEHRFACH GLEICHER SCOPE DEFINIERT ATTENTION 12
sycl::o_stride, out_stride, item );});
})queue.wait();
using namespace sycl;
using namespace sycl::ext::oneapi::experimental::matrix;
//HIER ABGLEICHEN MIT MNK TRUE WAHRHEITSABGLEICH
constexpr size_t XAC_TILE_M = 16;
constexpr size_t XAC_TILE_N = 16;
constexpr size_t XAC_TILE_K = 16;
//VERSION EINS
template <typename scalar_t>void XAC_sycl_flash_attention_scl_impl(queue& q, sycl::vec<T, 16>, scalar_t* k_ptr, scalar_t* v_ptr, scalar_t* out_ptr, int num_q, int num_k, int num_v, int sycl::num_out_stride, int d_q, int d_k, int d_v, int d_out_stride, int q_stride, int k_stride, int v_stride, int s_stride, int out_stride, sycl::nd_item<1> item){
//VERSION ZWEI
//7 TEST XAC_sycl_xmx_matrix_ops_scl
7777777777777777777777777777777777777777777777
template <typename scalar_t>
void XAC_sycl_xmx_matrix_ops_scl( sycl::vec<T, 16>, scalar_t* k_ptr, scalar_t* v_ptr, scalar_t* s_ptr, scalar_t* out_stride, int d_out_stride = sycl::dst->ne[1];
int num_q, int num_k, int num_v, int sycl::num_s, int sycl::num_out_stride, int d_q, int d_k, int d_v, int d_s, int q_stride, int k_stride, int v_stride, int s_stride, int out_stride, XAC_size_t = [16];
//GUELTIG MACHEN
sycl::nd_item<1> item){sycl::sub_group sg = item.get_sub_group();
int head_row_base = (item.get_sub_group(0) * 16);
if (head_row_base > = num_q) return;
using t_q = sycl::joint_matrix<sycl::sub_group, sycl::half, sycl::matrix_use::a, 16, 16, sycl::layout::row_major>;
//sycl::subgroup pruefen
using t_k = sycl::joint_matrix<sycl::sub_group, sycl::half, sycl::matrix_use::b, 16, 16, sycl::layout::row_major>;
using t_v = sycl::joint_matrix<sycl::sub_group, sycl::half, sycl::matrix_use::c, 16, 16, sycl::layout::row_major>;
using t_out_stride = sycl::joint_matrix<sycl::sub_group, sycl::half, sycl::matrix_use::g, 16, 16, sycl::layout::row_major>;
using t_acc = sycl::joint_matrix<sycl::sub_group, float, use::sycl::accumulator, 16, 16>;
t_q mat_q;
t_k mat_k;
t_v mat_v;
t_out_stride;
t_acc mat_s;
//ZAEHLERAKKUMULATOR
t_acc mat_o;
//AUSGABEAKKUMULATOR
t_acc mat_out_stride;
sycl::joint_matrix_fill(sg, mat_s, 0.0f);
sycl::joint_matrix<sycl::sub_group, sycl::half, sycl::matrix_use::a, 16, 16, sycl::layout::row_major> mat_q_half;
sycl::vec<scalar_t* sycl::q_tile_ptr = q_ptr + head_row_base * q_stride;
joint_matrix_load(sg, mat_q, sycl::q_tile_ptr, q_stride);
float scale_factor = 1.0f / sycl::sqrt((float)d_k);
float s_scores[XAC_BLOCK_N];
//MEHRFACH GLEICHER SCOPE DEFINIERT ATTENTION 13
for (int k_idx = 0; k_idx < num_k; k_idx + = 16){sycl::joint_matrix_fill(sg, mat_s, 0.0f);
scalar_t* k_tile_ptr = k_ptr + k_idx * k_stride;
sycl::joint_matrix<sycl::sub_group, sycl::half, sycl::matrix_use::a, 16, 16, sycl::layout::row_major> mat_s_half;
//sycl subgroup Untergruppen Speziell DG2 ARC Alchmist Generation basierend auf skalierter XE-iGPU Archtiktur Achtungs Befehl pruefen
joint_matrix_copy(sg, mat_q, mat_q_half);
//Dopelt Deklariert aufgerufen Beheben Sofort 4
joint_matrix_load(sg, mat_k, k_tile_ptr, k_stride);
joint_matrix_copy(sg, mat_s, mat_s_half);
joint_matrix_mad(sg, mat_s, mat_q, mat_k, mat_s);
joint_matrix_mad(sg, mat_s_half, mat_q_half, mat_k);
//Dopelt Deklariert aufgerufen Beheben Sofort
float local_max = -std::numeric_limits<float>sycl::infinity();
for (int i = 0; i < float wi_data[16];
();
++i){wi_data[i]* = float scale_factor;
//FLOAT WI DATA DEFINIEREN VORHER UND SO WEITER
local_max = sycl::max(local_max, float wi_data[i]);}
//Hier Float hinzugefuegt pruefen auf Richtigkeit
float row_max_total = reduce_over_group(sg, local_max, maximum<float>());
float local_sum = 0.0f;
for (int i = 0; i < float wi_data[16];(); ++i){wi_data[i] = sycl::exp(float wi_data[i] - float row_sum_total);
local_sum + = float wi_data[i];}
float row_sum_total = reduce_over_group(sg, local_sum, plus<float>());
float inv_sum = 1.0f / (float row_sum_total + 1e-6f);
for (int i = 0; i < float wi_data[16];(); ++i){wi_data[i]* = inv_sum;}
scalar_t* v_tile_ptr = v_ptr + k_idx * v_stride;
joint_matrix_load(sg, mat_v, v_tile_ptr, v_stride);
sycl::joint_matrix<sycl::sub_group, sycl::half, sycl::matrix_use::a, 16, 16, sycl::layout::row_major> mat_s_half;
joint_matrix_copy(sg, mat_s, mat_s_half);
//Dopelt Deklariert aufgerufen Beheben Sofort 5
joint_matrix_mad(sg, mat_s_half, mat_q_half, mat_k);
//matshalf falsch oben DOPPELT Deklariert aufgerufen Beheben Sofort
scalar_t* out_ptr = out_ptr + head_row_base * out_stride;
joint_matrix_store(sg, mat_o, out_ptr, out_stride, layout::row_major);}
//8 TEST VALIDATORSPIELEWIESE ATTENTION
//XAC_sycl_validate_attention_params Fehlerpruefung
8888888888888888888888888888888888888888888888
bool XAC_sycl_validate_attention_params(XAC_Tensor* q, XAC_Tensor* k, XAC_Tensor* v, XAC_Tensor* s, int& d_k, int& d_v){
if (q->XAC_TYPE_F16! = XAC_TYPE_F16 && q->XAC_TYPE_F16! = XAC_TYPE_F16){XAC_LOG_ERROR("FEHLER ERROR NUR AUSGEWAEHLTE SPIELEWIESEN KI SPRACHMODELLE FORMAT F16 MOEGLICH QUERY VERSUCHE MIT NICHT UNTERSTUETZTEN KIS AUF EIGENES RISKIO");
//XAC Typ Pruefung
//AUF Typ pruefen
return false;}
if (q->XAC_TYPE_F16! = k->XAC_TYPE_F16 || q->XAC_TYPE_F16! = v->XAC_TYPE_F16){XAC_LOG_ERROR("FEHLER ERROR ALLE TENSOREN MUESSEN GLEICHEN TYP HABEN");
//TYP PRUEFEN FORMATUNABHAENGIG
return false;}
//Dimensionspruefung
d_k = q->ne[0];
d_v = v->ne[0];
if (d_k! = k->ne[0] || d_v! = v->ne[0]){XAC_LOG_ERROR("FEHLER ERROR DIMENSIONSKONSISTENZWERTE DER SPIELEWIESE KI q[%d] vs k[%d], v[%d]", d_k, k->ne[0], d_v);
return false;}
if (d_k > XAC_D_MAX || d_v > XAC_D_MAX){XAC_LOG_ERROR("FEHLER ERROR DIMENSIONEN UEBERSCHREITEN MAXIMUM");
return false;}
//Alignment fuer Vektorisierung
if (d_k % XAC_VEC_SIZE! = 0 || d_v % XAC_VEC_SIZE! = 0){XAC_LOG_WARN("WARNUNG ACHTUNG SUBOPTIMALE DIMENSIONEN FUER VECTORISIERUNG XAC SPIELEWIESE KI LANGSAMER ALS MOEGLICH");}
return true;}
//PerformanceOptimierungen
//CacheNutzungverbessern
//cpp opencl sycl arc xe igpu dgpu cpu mxm xmx f16 plus f32 gguf qualtiy special
//SLM (Shared Local Memory) für bessere Performance
float s_scores[XAC_BLOCK_N];
//MEHRFACH GLEICHER SCOPE DEFINIERT ATTENTION 14
constexpr int SLM_SIZE = 16 * 1024;
//16 KiB
sycl::local_accessor<float> slm_buffer(sycl::range<1>(SLM_SIZE), h);
//888 TEST TEST  HEIMLICHE LIEBE ACHT extern
extern "C" void XAC_sycl_api_attention_qkv( XAC_backend_sycl_context* ctx, XAC_Tensor* dst, XAC_Tensor* q_tensor, XAC_Tensor* d_k_tensor, XAC_Tensor* d_v_tensor, XAC_Tensor* s_tensor,){int d_k, d_v;
if (q_tensor->XAC_TYPE_F16! = XAC_TYPE_F16 && q_tensor->XAC_TYPE_F16! = XAC_TYPE_F16){XAC_LOG_ABORT("FEHLER ABBRUCH NUR F16 RECHENFORMAT UNTERSTUETZUNG BITTE BEACHTEN");
//Strukt k tenso nicht f32 sondern f16 formal gguf ggml konform selbst geschrieben und errechnet
return;}
XAC_sycl_api_attention_qkv(ctx, dst, q_tensor, k_tensor, v_tensor, mask);}
//Selbstaufruf klaeren
//9 TEST DATENLADUNGSAUTOMATIK ALS SLM
9999999999999999999999999999999999999999999999
template <typename T> scalar_t> auto XAC_sycl_load_to_slm_attention = [&](scalar_t* src, int offset, int size){return [=](sycl::handler& h){ sycl::local_accessor<(scalar_t, 1> slm_buffer(sycl::range<1>(XAC_MEM_ALIGN), h);
//PRUEFEN AUF MODERNE VERSION SIEHE WENIGE ZEILEN DRUEBER
for (int i = 0; i < size; i + = 16){ scalar_t, 16> vec;
sycl::vec.sycl::vec.load(i, src + offset + i);
sycl::vec.sycl::vec.store(i, slm_buffer.get_sycl::pointer() + sycl::offset + i);}};
//10 TEST XAC HAUPTFUNKTIONSABLAUF
1010101010101010101010101010101010101010101010
int main(){ std::cout << "BAUE XAIGPUARC SPIELEWIESE KI \n";
try { queue q{property::queue::in_order()};
std::cout << "STARTE XAIGPUARC SYCL MATRIZEN SPIELEWIESE KI GERAETEERKENNUNG" auto device =q.get_device().get_info<info::device::name>() << std::endl;
std::cout << "GERAET" << device.get_info<sycl::info::device::name>() << std::endl;
std::cout << "XMX UNTERSTUETZUNG" << (device.has(sycl::aspect::ext_intel_matrix) ? "JA" : "NEIN") << std::endl;
constexpr int BATCH = 4;
//RICHTIGE BENENNUNGEN DURCHFUEHREN
constexpr int HEADS = 4;
constexpr int SEQ_LEN = 128;
constexpr int DIM = 64;
constexpr int size = 16;
sycl::half* q = sycl::malloc_device<sycl::half>(BATCH * HEADS * SEQ_LEN * DIM, queue);
sycl::half* k = sycl::malloc_device<sycl::half>(BATCH * HEADS * SEQ_LEN * DIM, queue);
sycl::half* v = sycl::malloc_device<sycl::half>(BATCH * HEADS * SEQ_LEN * DIM, queue);
sycl::half* s = sycl::malloc_device<sycl::half>(BATCH * HEADS * SEQ_LEN * DIM, queue);
sycl::half* out_stride = sycl::malloc_device<sycl::half>(BATCH * HEADS * SEQ_LEN * DIM, queue);
queue.fill(q, sycl::half(1.0f), BATCH * HEADS * SEQ_LEN * DIM);
queue.fill(k, sycl::half(1.0f), BATCH * HEADS * SEQ_LEN * DIM);
queue.fill(v, sycl::half(1.0f), BATCH * HEADS * SEQ_LEN * DIM);
queue.fill(s, sycl::half(1.0f), BATCH * HEADS * SEQ_LEM * DIM);
queue.fill(out_stride, sycl::half(1.0f), BATCH * HEADS * SEQ_LEN * DIM);
queue.wait();
XAC_backend_sycl_context ctx{&q};
XAC_Tensor t_q{nullptr, XAC_TYPE_F16, {BATCH * HEADS * SEQ_LEN * DIM, 1, 1, 1}, {sizeof(sycl::half), sizeof(sycl::half), 0, 0}, &ctx};
XAC_Tensor t_k{d_k, XAC_TYPE_F16, {BATCH * HEADS * SEQ_LEN * DIM, 1, 1}, {sizeof(sycl::half), size * sizeof(sycl::half), 0, 0}, &ctx};
//Deklarieren d_k d_v d_out<< D_K Wurzelgewichte und Ausgabebeschreibung
XAC_Tensor t_v{d_v, XAC_TYPE_F16, {BATCH * HEADS * SEQ_LEN * DIM, 1, 1}, {sizeof(sycl::half), size * sizeof(sycl::half), 0, 0}, &ctx};
XAC_Tensor t_dst{d_out, XAC_TYPE_F16, {BATCH * HEADS * SEQ_LEN * DIM, 1, 1}, {sizeof(sycl::half), size * sizeof(sycl::half), 0, 0}, &ctx};
XAC_sycl_flash_attention_vec_impl(&ctx, &t_dst, &t_q, &t_k, &t_v, nullptr);
bool use_xmx = sycl_q.get_device().has(sycl::aspect::ext_intel_matrix) && (d_k % 16 == 0) && (d_v % 16 == 0);
queue.submit([&](handler& h){if(use_xmx){h.parallel_for(sycl::nd_range<1>{sycl::range<1>(16), sycl::range<1>(16)}, [=](sycl::nd_item<1> item)[[intel::reqd_sub_group_size(16)]]{ XAC_sycl_xmx_op_vec<sycl::half>(q, k, v, out_stride, BATCH * HEADS * SEQ_LEN * DIM, BATCH * HEADS * SEQ_LEN * DIM, BATCH * HEADS * SEQ_LEN * DIM, BATCH * HEADS * SEQ_LEN * DIM, BATCH * HEADS * SEQ_LEN * DIM, BATCH * HEADS * SEQ_LEN * DIM, item);});}else{h.parallel_for(sycl::range<1>{size}, [=](id<1> idx){});}}).wait();
std::vector<sycl::half> host_out(BATCH * HEADS * SEQ_LEN * DIM);
queue.memcpy(host_out.data(), out_stride, BATCH * HEADS * SEQ_LEN * DIM * sizeof(sycl::half)).wait();
std::cout << "ERGEBNIS WIRD GEZOGEN AUS [0]" << (float)host_out[0] << "ERWARTE ERGEBNIS > 0" << std::endl;
std::cout << "ERGEBNIS STATISTIK \n";
std::cout << "DURCHSCHNITTSWERTE" << avg << "\n";
std::cout << "ALLE WERTE" << (avg > 0.0f ? "PLAUSIBEL" : "PRUEFEN") << "\n\n";
//RECHENSPEICHER WIEDER FREIGEBEN VOR ENDE DES PROGRAMMS XAC
sycl::free(q, queue);
sycl::free(k, queue);
sycl::free(v, queue);
sycl::free(s, queue);
sycl::free(out_stride, queue);
std::cout << "SPIELEWIESE KI ERFOLGREICH BEENDET\n";
return 0;}catch(std::exception& e){ XAC_LOG_ABORT("FEHLER ABBRUCH ALLGEMEINER PROGRAMMFEHLER UNBEKANNTER URSACHE" << e.what());}}}
#ifndef XAC_SYCL_SPIELEWIESE_KI_H
#include 
#ifdef __cplusplus
//XAC_sycl_spielewiese_ki ENDE