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/

llama.cpp

	#define _CRT_SECURE_NO_DEPRECATE // Disables "unsafe" warnings on Windows
	#define _USE_MATH_DEFINES // For M_PI on MSVC

	#include "ggml-backend-impl.h"
	#include "ggml-backend.h"
	#include "traits.h"
	#include "ggml-cpu-impl.h"
	#include "ggml-impl.h"
	#include "quants.h"
	#include "ggml-threading.h"
	#include "unary-ops.h"
	#include "binary-ops.h"
	#include "vec.h"
	#include "ops.h"
	#include "ggml.h"
	#include "common.h"

	#if defined(_MSC_VER) \|\| defined(__MINGW32__)
	#include <malloc.h> // using malloc.h with MSC/MINGW
	#elif !defined(__FreeBSD__) && !defined(__NetBSD__) && !defined(__OpenBSD__)
	#include <alloca.h>
	#endif

	#include <assert.h>
	#include <errno.h>
	#include <time.h>
	#include <math.h>
	#include <stdlib.h>
	#include <string.h>
	#include <stdint.h>
	#include <inttypes.h>
	#include <stdio.h>
	#include <float.h>
	#include <limits.h>
	#include <stdarg.h>
	#include <signal.h>
	#if defined(__gnu_linux__)
	#include <syscall.h>
	#endif

	#ifdef GGML_USE_OPENMP
	#include <omp.h>
	#endif

	#if defined(__ARM_FEATURE_SVE) \|\| defined(__ARM_FEATURE_MATMUL_INT8)
	#undef GGML_USE_LLAMAFILE
	#endif

	#ifdef GGML_USE_LLAMAFILE
	#include "llamafile/sgemm.h"
	#endif

	// Note: once we move threading into a separate C++ file
	// will use std::hardware_destructive_interference_size instead of hardcoding it here
	// and we'll use C++ attribute syntax.
	#define GGML_CACHE_LINE 64

	#if defined(__clang__) \|\| defined(__GNUC__)
	#define GGML_CACHE_ALIGN __attribute__((aligned(GGML_CACHE_LINE)))
	#endif

	#if defined(__has_feature)
	#if __has_feature(thread_sanitizer)
	#define GGML_TSAN_ENABLED 1
	#endif
	#else // __has_feature
	#if defined(__SANITIZE_THREAD__)
	#define GGML_TSAN_ENABLED 1
	#endif
	#endif // __has_feature

	#define UNUSED GGML_UNUSED
	#define SWAP(x, y, T) do { T SWAP = x; (x) = y; (y) = SWAP; } while (0)

	// precomputed f32 table for f16 (256 KB) (simd-mappings.h)
	float ggml_table_f32_f16[1 << 16];

	// precomputed f32 table for e8m0 half (1 KB) (simd-mappings.h)
	float ggml_table_f32_e8m0_half[1 << 8];

	#if defined(__ARM_ARCH)
	struct ggml_arm_arch_features_type {
	int sve_cnt;
	} ggml_arm_arch_features = { 0 };
	#endif

	#if defined(__riscv)
	struct ggml_riscv_arch_features_type {
	int rvv_vlen;
	} ggml_riscv_arch_features = { 0 };
	#endif

	#if defined(_WIN32)

	#define WIN32_LEAN_AND_MEAN
	#ifndef NOMINMAX
	#define NOMINMAX
	#endif
	#include <windows.h>

	#if defined(_MSC_VER) && !defined(__clang__)
	#define GGML_CACHE_ALIGN __declspec(align(GGML_CACHE_LINE))

	typedef volatile LONG atomic_int;
	typedef atomic_int atomic_bool;
	typedef atomic_int atomic_flag;

	#define ATOMIC_FLAG_INIT 0

	typedef enum {
	memory_order_relaxed,
	memory_order_consume,
	memory_order_acquire,
	memory_order_release,
	memory_order_acq_rel,
	memory_order_seq_cst
	} memory_order;

	static void atomic_store(atomic_int * ptr, LONG val) {
	InterlockedExchange(ptr, val);
	}
	static void atomic_store_explicit(atomic_int * ptr, LONG val, memory_order mo) {
	// TODO: add support for explicit memory order
	InterlockedExchange(ptr, val);
	}
	static LONG atomic_load(atomic_int * ptr) {
	return InterlockedCompareExchange(ptr, 0, 0);
	}
	static LONG atomic_load_explicit(atomic_int * ptr, memory_order mo) {
	// TODO: add support for explicit memory order
	return InterlockedCompareExchange(ptr, 0, 0);
	}
	static LONG atomic_fetch_add(atomic_int * ptr, LONG inc) {
	return InterlockedExchangeAdd(ptr, inc);
	}
	static LONG atomic_fetch_add_explicit(atomic_int * ptr, LONG inc, memory_order mo) {
	// TODO: add support for explicit memory order
	return InterlockedExchangeAdd(ptr, inc);
	}
	static atomic_bool atomic_flag_test_and_set(atomic_flag * ptr) {
	return InterlockedExchange(ptr, 1);
	}
	static void atomic_flag_clear(atomic_flag * ptr) {
	InterlockedExchange(ptr, 0);
	}
	static void atomic_thread_fence(memory_order mo) {
	MemoryBarrier();
	}
	#else // clang
	#include <stdatomic.h>
	#endif

	typedef HANDLE pthread_t;

	typedef DWORD thread_ret_t;
	static int pthread_create(pthread_t * out, void * unused, thread_ret_t(func)(void ), void * arg) {
	(void) unused;
	HANDLE handle = CreateThread(NULL, 0, (LPTHREAD_START_ROUTINE) func, arg, 0, NULL);
	if (handle == NULL)
	{
	return EAGAIN;
	}

	*out = handle;
	return 0;
	}

	static int pthread_join(pthread_t thread, void * unused) {
	(void) unused;
	int ret = (int) WaitForSingleObject(thread, INFINITE);
	CloseHandle(thread);
	return ret;
	}

	static int sched_yield (void) {
	Sleep (0);
	return 0;
	}
	#else

	#include <pthread.h>
	#include <stdatomic.h>
	#include <sched.h>
	#if defined(__FreeBSD__)
	#include <pthread_np.h>
	#endif

	typedef void * thread_ret_t;

	#include <sys/types.h>
	#include <sys/stat.h>
	#include <unistd.h>

	#endif

	typedef pthread_t ggml_thread_t;

	#define GGML_THREADPOOL_N_THREADS_MASK (0xffffU)
	#define GGML_THREADPOOL_N_THREADS_BITS (16)

	#if defined(__APPLE__)
	#include <unistd.h>
	#include <mach/mach.h>
	#include <TargetConditionals.h>
	#endif

	static const struct ggml_type_traits_cpu type_traits_cpu[GGML_TYPE_COUNT] = {
	[GGML_TYPE_F32] = {
	.from_float = (ggml_from_float_t) ggml_cpu_fp32_to_fp32,
	.vec_dot = (ggml_vec_dot_t) ggml_vec_dot_f32,
	.vec_dot_type = GGML_TYPE_F32,
	.nrows = 1,
	},
	[GGML_TYPE_F16] = {
	.from_float = (ggml_from_float_t) ggml_cpu_fp32_to_fp16,
	.vec_dot = (ggml_vec_dot_t) ggml_vec_dot_f16,
	.vec_dot_type = GGML_TYPE_F16,
	.nrows = 1,
	},
	[GGML_TYPE_Q4_0] = {
	.from_float = quantize_row_q4_0,
	.vec_dot = ggml_vec_dot_q4_0_q8_0,
	.vec_dot_type = GGML_TYPE_Q8_0,
	#if defined (__ARM_FEATURE_MATMUL_INT8)
	.nrows = 2,
	#else
	.nrows = 1,
	#endif
	},
	[GGML_TYPE_Q4_1] = {
	.from_float = quantize_row_q4_1,
	.vec_dot = ggml_vec_dot_q4_1_q8_1,
	.vec_dot_type = GGML_TYPE_Q8_1,
	#if defined (__ARM_FEATURE_MATMUL_INT8)
	.nrows = 2,
	#else
	.nrows = 1,
	#endif
	},
	[GGML_TYPE_Q5_0] = {
	.from_float = quantize_row_q5_0,
	.vec_dot = ggml_vec_dot_q5_0_q8_0,
	.vec_dot_type = GGML_TYPE_Q8_0,
	.nrows = 1,
	},
	[GGML_TYPE_Q5_1] = {
	.from_float = quantize_row_q5_1,
	.vec_dot = ggml_vec_dot_q5_1_q8_1,
	.vec_dot_type = GGML_TYPE_Q8_1,
	.nrows = 1,
	},
	[GGML_TYPE_Q8_0] = {
	.from_float = quantize_row_q8_0,
	.vec_dot = ggml_vec_dot_q8_0_q8_0,
	.vec_dot_type = GGML_TYPE_Q8_0,
	#if defined (__ARM_FEATURE_MATMUL_INT8)
	.nrows = 2,
	#else
	.nrows = 1,
	#endif
	},
	[GGML_TYPE_Q8_1] = {
	.from_float = quantize_row_q8_1,
	.vec_dot_type = GGML_TYPE_Q8_1,
	.nrows = 1,
	},
	[GGML_TYPE_MXFP4] = {
	.from_float = quantize_row_mxfp4,
	.vec_dot = ggml_vec_dot_mxfp4_q8_0,
	.vec_dot_type = GGML_TYPE_Q8_0,
	.nrows = 1,
	},
	[GGML_TYPE_NVFP4] = {
	.from_float = quantize_row_nvfp4,
	.vec_dot = ggml_vec_dot_nvfp4_q8_0,
	.vec_dot_type = GGML_TYPE_Q8_0,
	.nrows = 1,
	},
	[GGML_TYPE_Q2_K] = {
	.from_float = quantize_row_q2_K,
	.vec_dot = ggml_vec_dot_q2_K_q8_K,
	.vec_dot_type = GGML_TYPE_Q8_K,
	.nrows = 1,
	},
	[GGML_TYPE_Q3_K] = {
	.from_float = quantize_row_q3_K,
	.vec_dot = ggml_vec_dot_q3_K_q8_K,
	.vec_dot_type = GGML_TYPE_Q8_K,
	.nrows = 1,
	},
	[GGML_TYPE_Q4_K] = {
	.from_float = quantize_row_q4_K,
	.vec_dot = ggml_vec_dot_q4_K_q8_K,
	.vec_dot_type = GGML_TYPE_Q8_K,
	#if defined (__ARM_FEATURE_MATMUL_INT8)
	.nrows = 2,
	#else
	.nrows = 1,
	#endif
	},
	[GGML_TYPE_Q5_K] = {
	.from_float = quantize_row_q5_K,
	.vec_dot = ggml_vec_dot_q5_K_q8_K,
	.vec_dot_type = GGML_TYPE_Q8_K,
	.nrows = 1,
	},
	[GGML_TYPE_Q6_K] = {
	.from_float = quantize_row_q6_K,
	.vec_dot = ggml_vec_dot_q6_K_q8_K,
	.vec_dot_type = GGML_TYPE_Q8_K,
	#if defined (__ARM_FEATURE_MATMUL_INT8)
	.nrows = 2,
	#else
	.nrows = 1,
	#endif
	},
	[GGML_TYPE_IQ2_XXS] = {
	.from_float = NULL,
	.vec_dot = ggml_vec_dot_iq2_xxs_q8_K,
	.vec_dot_type = GGML_TYPE_Q8_K,
	.nrows = 1,
	},
	[GGML_TYPE_IQ2_XS] = {
	.from_float = NULL,
	.vec_dot = ggml_vec_dot_iq2_xs_q8_K,
	.vec_dot_type = GGML_TYPE_Q8_K,
	.nrows = 1,
	},
	[GGML_TYPE_IQ3_XXS] = {
	// NOTE: from_float for iq3 and iq2_s was removed because these quants require initialization in ggml_quantize_init
	//.from_float = quantize_row_iq3_xxs,
	.vec_dot = ggml_vec_dot_iq3_xxs_q8_K,
	.vec_dot_type = GGML_TYPE_Q8_K,
	.nrows = 1,
	},
	[GGML_TYPE_IQ3_S] = {
	//.from_float = quantize_row_iq3_s,
	.vec_dot = ggml_vec_dot_iq3_s_q8_K,
	.vec_dot_type = GGML_TYPE_Q8_K,
	.nrows = 1,
	},
	[GGML_TYPE_IQ2_S] = {
	//.from_float = quantize_row_iq2_s,
	.vec_dot = ggml_vec_dot_iq2_s_q8_K,
	.vec_dot_type = GGML_TYPE_Q8_K,
	.nrows = 1,
	},
	[GGML_TYPE_IQ1_S] = {
	.from_float = NULL,
	.vec_dot = ggml_vec_dot_iq1_s_q8_K,
	.vec_dot_type = GGML_TYPE_Q8_K,
	.nrows = 1,
	},
	[GGML_TYPE_IQ1_M] = {
	.from_float = NULL,
	.vec_dot = ggml_vec_dot_iq1_m_q8_K,
	.vec_dot_type = GGML_TYPE_Q8_K,
	.nrows = 1,
	},
	[GGML_TYPE_IQ4_NL] = {
	.from_float = quantize_row_iq4_nl,
	.vec_dot = ggml_vec_dot_iq4_nl_q8_0,
	.vec_dot_type = GGML_TYPE_Q8_0,
	.nrows = 1,
	},
	[GGML_TYPE_IQ4_XS] = {
	.from_float = quantize_row_iq4_xs,
	.vec_dot = ggml_vec_dot_iq4_xs_q8_K,
	.vec_dot_type = GGML_TYPE_Q8_K,
	.nrows = 1,
	},
	[GGML_TYPE_Q8_K] = {
	.from_float = quantize_row_q8_K,
	},
	[GGML_TYPE_BF16] = {
	.from_float = (ggml_from_float_t) ggml_cpu_fp32_to_bf16,
	.vec_dot = (ggml_vec_dot_t) ggml_vec_dot_bf16,
	.vec_dot_type = GGML_TYPE_BF16,
	.nrows = 1,
	},
	[GGML_TYPE_TQ1_0] = {
	.from_float = quantize_row_tq1_0,
	.vec_dot = ggml_vec_dot_tq1_0_q8_K,
	.vec_dot_type = GGML_TYPE_Q8_K,
	.nrows = 1,
	},
	[GGML_TYPE_TQ2_0] = {
	.from_float = quantize_row_tq2_0,
	.vec_dot = ggml_vec_dot_tq2_0_q8_K,
	.vec_dot_type = GGML_TYPE_Q8_K,
	.nrows = 1,
	},
	[GGML_TYPE_I32] = {
	.from_float = (ggml_from_float_t) ggml_cpu_fp32_to_i32,
	},
	};

	const struct ggml_type_traits_cpu * ggml_get_type_traits_cpu(enum ggml_type type) {
	return &type_traits_cpu[type];
	}

	//
	// Threading defs
	//

	typedef pthread_t ggml_thread_t;

	#if defined(_WIN32)

	typedef CONDITION_VARIABLE ggml_cond_t;
	typedef SRWLOCK ggml_mutex_t;

	#define ggml_mutex_init(m) InitializeSRWLock(m)
	#define ggml_mutex_destroy(m)
	#define ggml_mutex_lock(m) AcquireSRWLockExclusive(m)
	#define ggml_mutex_unlock(m) ReleaseSRWLockExclusive(m)
	#define ggml_mutex_lock_shared(m) AcquireSRWLockShared(m)
	#define ggml_mutex_unlock_shared(m) ReleaseSRWLockShared(m)

	#define ggml_cond_init(c) InitializeConditionVariable(c)
	#define ggml_cond_destroy(c)
	#define ggml_cond_wait(c, m) SleepConditionVariableSRW(c, m, INFINITE, CONDITION_VARIABLE_LOCKMODE_SHARED)
	#define ggml_cond_broadcast(c) WakeAllConditionVariable(c)

	#define ggml_thread_create pthread_create
	#define ggml_thread_join pthread_join

	#else

	typedef pthread_cond_t ggml_cond_t;
	typedef pthread_mutex_t ggml_mutex_t;

	#define ggml_mutex_init(m) pthread_mutex_init(m, NULL)
	#define ggml_mutex_destroy(m) pthread_mutex_destroy(m)
	#define ggml_mutex_lock(m) pthread_mutex_lock(m)
	#define ggml_mutex_unlock(m) pthread_mutex_unlock(m)
	#define ggml_mutex_lock_shared(m) pthread_mutex_lock(m)
	#define ggml_mutex_unlock_shared(m) pthread_mutex_unlock(m)

	#define ggml_lock_init(x) UNUSED(x)
	#define ggml_lock_destroy(x) UNUSED(x)
	#if defined(__x86_64__) \|\| (defined(_MSC_VER) && defined(_M_AMD64))
	#define ggml_lock_lock(x) _mm_pause()
	#else
	#define ggml_lock_lock(x) UNUSED(x)
	#endif
	#define ggml_lock_unlock(x) UNUSED(x)

	#define GGML_LOCK_INITIALIZER 0
	#define ggml_cond_init(c) pthread_cond_init(c, NULL)
	#define ggml_cond_destroy(c) pthread_cond_destroy(c)
	#define ggml_cond_wait(c, m) pthread_cond_wait(c, m)
	#define ggml_cond_broadcast(c) pthread_cond_broadcast(c)

	#define ggml_thread_create pthread_create
	#define ggml_thread_join pthread_join

	#endif

	// Threadpool def
	struct ggml_threadpool {
	ggml_mutex_t mutex; // mutex for cond.var
	ggml_cond_t cond; // cond.var for waiting for new work

	struct ggml_cgraph * cgraph;
	struct ggml_cplan * cplan;

	// synchronization primitives
	atomic_int n_graph; // updated when there is work to be done (i.e each graph) holds graph and active thread counts.
	atomic_int GGML_CACHE_ALIGN n_barrier;
	atomic_int GGML_CACHE_ALIGN n_barrier_passed;
	atomic_int GGML_CACHE_ALIGN current_chunk; // currently processing chunk during Mat_Mul, shared between all the threads.

	// these are atomic as an annotation for thread-sanitizer
	atomic_bool stop; // Used for stopping the threadpool altogether
	atomic_bool pause; // Used for pausing the threadpool or individual threads
	atomic_int abort; // Used for aborting processing of a graph

	struct ggml_compute_state * workers; // per thread state
	int n_threads; // Number of threads in the pool
	int32_t prio; // Scheduling priority
	uint32_t poll; // Polling level (0 - no polling)

	enum ggml_status ec;
	};

	// Per-thread state
	struct ggml_compute_state {
	#ifndef GGML_USE_OPENMP
	ggml_thread_t thrd;
	int last_graph;
	bool pending;
	#endif
	bool cpumask[GGML_MAX_N_THREADS];
	struct ggml_threadpool * threadpool;
	int ith;
	};

	// Helpers for polling loops
	#if defined(__aarch64__) && ( defined(__clang__) \|\| defined(__GNUC__) )
	static inline void ggml_thread_cpu_relax(void) {
	__asm__ volatile("yield" ::: "memory");
	}
	#elif defined(__x86_64__)
	static inline void ggml_thread_cpu_relax(void) {
	_mm_pause();
	}
	#elif defined(__riscv)
	static inline void ggml_thread_cpu_relax(void) {
	#ifdef __riscv_zihintpause
	__asm__ __volatile__ ("pause");
	#else
	/* Encoding of the pause instruction */
	__asm__ __volatile__ (".4byte 0x100000F");
	#endif
	}
	#else
	static inline void ggml_thread_cpu_relax(void) {;}
	#endif

	//
	// NUMA support
	//

	#define GGML_NUMA_MAX_NODES 8
	#define GGML_NUMA_MAX_CPUS 512

	struct ggml_numa_node {
	uint32_t cpus[GGML_NUMA_MAX_CPUS]; // hardware threads on this node
	uint32_t n_cpus;
	};

	struct ggml_numa_nodes {
	enum ggml_numa_strategy numa_strategy;
	struct ggml_numa_node nodes[GGML_NUMA_MAX_NODES];
	uint32_t n_nodes;
	uint32_t total_cpus; // hardware threads on system
	uint32_t current_node; // node on which main process is execting
	#if defined(__gnu_linux__)
	cpu_set_t cpuset; // cpuset from numactl
	#else
	uint32_t cpuset; // no NUMA support outside of Linux at this time. Use a portable datatype
	#endif
	};

	//
	// ggml state
	//

	struct ggml_state {
	struct ggml_numa_nodes numa;
	};

	static struct ggml_state g_state = {0};

	void ggml_barrier(struct ggml_threadpool * tp) {
	int n_threads = atomic_load_explicit(&tp->n_graph, memory_order_relaxed) & GGML_THREADPOOL_N_THREADS_MASK;
	if (n_threads == 1) {
	return;
	}

	#ifdef GGML_USE_OPENMP
	#pragma omp barrier
	#else
	int n_passed = atomic_load_explicit(&tp->n_barrier_passed, memory_order_relaxed);

	// enter barrier (full seq-cst fence)
	int n_barrier = atomic_fetch_add_explicit(&tp->n_barrier, 1, memory_order_seq_cst);

	if (n_barrier == (n_threads - 1)) {
	// last thread
	atomic_store_explicit(&tp->n_barrier, 0, memory_order_relaxed);

	// exit barrier (full seq-cst fence)
	atomic_fetch_add_explicit(&tp->n_barrier_passed, 1, memory_order_seq_cst);
	return;
	}

	// wait for other threads
	while (atomic_load_explicit(&tp->n_barrier_passed, memory_order_relaxed) == n_passed) {
	ggml_thread_cpu_relax();
	}

	// exit barrier (full seq-cst fence)
	// TSAN doesn't support standalone fence yet, we use a dummy read-modify-write instead
	#ifdef GGML_TSAN_ENABLED
	atomic_fetch_add_explicit(&tp->n_barrier_passed, 0, memory_order_seq_cst);
	#else
	atomic_thread_fence(memory_order_seq_cst);
	#endif
	#endif
	}

	void ggml_threadpool_chunk_set(struct ggml_threadpool * tp, int value) {
	atomic_store_explicit(&tp->current_chunk, value, memory_order_relaxed);
	}

	int ggml_threadpool_chunk_add(struct ggml_threadpool * tp, int value) {
	return atomic_fetch_add_explicit(&tp->current_chunk, value, memory_order_relaxed);
	}

	#if defined(__gnu_linux__)
	static cpu_set_t ggml_get_numa_affinity(void) {
	cpu_set_t cpuset;
	pthread_t thread;
	thread = pthread_self();
	CPU_ZERO(&cpuset);
	pthread_getaffinity_np(thread, sizeof(cpu_set_t), &cpuset);
	return cpuset;
	}
	#else
	static uint32_t ggml_get_numa_affinity(void) {
	return 0; // no NUMA support
	}
	#endif

	void ggml_numa_init(enum ggml_numa_strategy numa_flag) {
	if (g_state.numa.n_nodes > 0) {
	fprintf(stderr, "ggml_numa_init: NUMA already initialized\n");

	return;
	}

	#if defined(__gnu_linux__)
	struct stat st;
	char path[256];
	int rv;

	// set numa scheme
	g_state.numa.numa_strategy = numa_flag;

	GGML_PRINT_DEBUG("numa strategy %u\n",g_state.numa.numa_strategy);

	g_state.numa.cpuset = ggml_get_numa_affinity();

	// enumerate nodes
	while (g_state.numa.n_nodes < GGML_NUMA_MAX_NODES) {
	rv = snprintf(path, sizeof(path), "/sys/devices/system/node/node%u", g_state.numa.n_nodes);
	GGML_ASSERT(rv > 0 && (unsigned)rv < sizeof(path));
	if (stat(path, &st) != 0) { break; }
	++g_state.numa.n_nodes;
	}

	// enumerate CPUs
	while (g_state.numa.total_cpus < GGML_NUMA_MAX_CPUS) {
	rv = snprintf(path, sizeof(path), "/sys/devices/system/cpu/cpu%u", g_state.numa.total_cpus);
	GGML_ASSERT(rv > 0 && (unsigned)rv < sizeof(path));
	if (stat(path, &st) != 0) { break; }
	++g_state.numa.total_cpus;
	}

	GGML_PRINT_DEBUG("found %u numa nodes, %u CPUs\n", g_state.numa.n_nodes, g_state.numa.total_cpus);

	// figure out which node we're on
	uint current_cpu;
	int getcpu_ret = 0;
	#if __GLIBC__ > 2 \|\| (__GLIBC__ == 2 && __GLIBC_MINOR__ > 33) \|\| defined(__COSMOPOLITAN__)
	getcpu_ret = getcpu(&current_cpu, &g_state.numa.current_node);
	#else
	// old glibc doesn't have a wrapper for this call. Fall back on direct syscall
	# if !defined(SYS_getcpu) && defined(SYS_get_cpu)
	# define SYS_getcpu SYS_get_cpu // some older glibc versions use this name
	# endif
	getcpu_ret = syscall(SYS_getcpu, &current_cpu, &g_state.numa.current_node);
	#endif

	if (g_state.numa.n_nodes < 1 \|\| g_state.numa.total_cpus < 1 \|\| getcpu_ret != 0) {
	g_state.numa.n_nodes = 0;
	return;
	}

	GGML_PRINT_DEBUG("found our process on numa node %u, CPU %u\n", g_state.numa.current_node, current_cpu);

	for (uint32_t n = 0; n < g_state.numa.n_nodes; ++n) {
	struct ggml_numa_node * node = &g_state.numa.nodes[n];
	GGML_PRINT_DEBUG("CPUs on node %u:", n);
	node->n_cpus = 0;
	for (uint32_t c = 0; c < g_state.numa.total_cpus; ++c) {
	rv = snprintf(path, sizeof(path), "/sys/devices/system/node/node%u/cpu%u", n, c);
	GGML_ASSERT(rv > 0 && (unsigned)rv < sizeof(path));
	if (stat(path, &st) == 0) {
	node->cpus[node->n_cpus++] = c;
	GGML_PRINT_DEBUG(" %u", c);
	}
	}
	GGML_PRINT_DEBUG("\n");
	}

	if (ggml_is_numa()) {
	FILE *fptr = fopen("/proc/sys/kernel/numa_balancing", "r");
	if (fptr != NULL) {
	char buf[42];
	if (fgets(buf, sizeof(buf), fptr) && strncmp(buf, "0\n", sizeof(buf)) != 0) {
	GGML_LOG_WARN("/proc/sys/kernel/numa_balancing is enabled, this has been observed to impair performance\n");
	}
	fclose(fptr);
	}
	}
	#else
	UNUSED(numa_flag);
	// TODO
	#endif
	}

	bool ggml_is_numa(void) {
	return g_state.numa.n_nodes > 1;
	}

	#if defined(__ARM_ARCH)
	#if defined(__aarch64__) && defined(__ARM_FEATURE_SVE)
	#include <arm_sve.h>
	static void ggml_init_arm_arch_features(void) {
	ggml_arm_arch_features.sve_cnt = svcntb();
	}
	#else
	static void ggml_init_arm_arch_features(void) {}
	#endif
	#endif // __ARM_ARCH

	#if defined(__riscv) && defined(__riscv_v_intrinsic)
	#include <riscv_vector.h>
	static void ggml_init_riscv_arch_features(void) {
	ggml_riscv_arch_features.rvv_vlen = __riscv_vlenb();
	}
	#else
	static void ggml_init_riscv_arch_features(void) {}
	#endif

	struct ggml_tensor * ggml_new_i32(struct ggml_context * ctx, int32_t value) {
	GGML_ASSERT(!ggml_get_no_alloc(ctx));

	struct ggml_tensor * result = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 1);

	ggml_set_i32(result, value);

	return result;
	}

	struct ggml_tensor * ggml_new_f32(struct ggml_context * ctx, float value) {
	GGML_ASSERT(!ggml_get_no_alloc(ctx));

	struct ggml_tensor * result = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 1);

	ggml_set_f32(result, value);

	return result;
	}

	struct ggml_tensor * ggml_set_i32 (struct ggml_tensor * tensor, int32_t value) {
	const int n = ggml_nrows(tensor);
	const int nc = tensor->ne[0];
	const size_t n1 = tensor->nb[1];

	char * const data = tensor->data;

	switch (tensor->type) {
	case GGML_TYPE_I8:
	{
	assert(tensor->nb[0] == sizeof(int8_t));
	for (int i = 0; i < n; i++) {
	ggml_vec_set_i8(nc, (int8_t )(data + in1), value);
	}
	} break;
	case GGML_TYPE_I16:
	{
	assert(tensor->nb[0] == sizeof(int16_t));
	for (int i = 0; i < n; i++) {
	ggml_vec_set_i16(nc, (int16_t )(data + in1), value);
	}
	} break;
	case GGML_TYPE_I32:
	{
	assert(tensor->nb[0] == sizeof(int32_t));
	for (int i = 0; i < n; i++) {
	ggml_vec_set_i32(nc, (int32_t )(data + in1), value);
	}
	} break;
	case GGML_TYPE_F16:
	{
	assert(tensor->nb[0] == sizeof(ggml_fp16_t));
	for (int i = 0; i < n; i++) {
	ggml_vec_set_f16(nc, (ggml_fp16_t )(data + in1), GGML_CPU_FP32_TO_FP16(value));
	}
	} break;
	case GGML_TYPE_BF16:
	{
	assert(tensor->nb[0] == sizeof(ggml_fp16_t));
	for (int i = 0; i < n; i++) {
	ggml_vec_set_bf16(nc, (ggml_bf16_t )(data + in1), GGML_FP32_TO_BF16(value));
	}
	} break;
	case GGML_TYPE_F32:
	{
	assert(tensor->nb[0] == sizeof(float));
	for (int i = 0; i < n; i++) {
	ggml_vec_set_f32(nc, (float )(data + in1), value);
	}
	} break;
	default:
	{
	GGML_ABORT("fatal error");
	}
	}

	return tensor;
	}

	struct ggml_tensor * ggml_set_f32(struct ggml_tensor * tensor, float value) {
	const int n = ggml_nrows(tensor);
	const int nc = tensor->ne[0];
	const size_t n1 = tensor->nb[1];

	char * const data = tensor->data;

	switch (tensor->type) {
	case GGML_TYPE_I8:
	{
	assert(tensor->nb[0] == sizeof(int8_t));
	for (int i = 0; i < n; i++) {
	ggml_vec_set_i8(nc, (int8_t )(data + in1), value);
	}
	} break;
	case GGML_TYPE_I16:
	{
	assert(tensor->nb[0] == sizeof(int16_t));
	for (int i = 0; i < n; i++) {
	ggml_vec_set_i16(nc, (int16_t )(data + in1), value);
	}
	} break;
	case GGML_TYPE_I32:
	{
	assert(tensor->nb[0] == sizeof(int32_t));
	for (int i = 0; i < n; i++) {
	ggml_vec_set_i32(nc, (int32_t )(data + in1), value);
	}
	} break;
	case GGML_TYPE_F16:
	{
	assert(tensor->nb[0] == sizeof(ggml_fp16_t));
	for (int i = 0; i < n; i++) {
	ggml_vec_set_f16(nc, (ggml_fp16_t )(data + in1), GGML_CPU_FP32_TO_FP16(value));
	}
	} break;
	case GGML_TYPE_BF16:
	{
	assert(tensor->nb[0] == sizeof(ggml_bf16_t));
	for (int i = 0; i < n; i++) {
	ggml_vec_set_bf16(nc, (ggml_bf16_t )(data + in1), GGML_FP32_TO_BF16(value));
	}
	} break;
	case GGML_TYPE_F32:
	{
	assert(tensor->nb[0] == sizeof(float));
	for (int i = 0; i < n; i++) {
	ggml_vec_set_f32(nc, (float )(data + in1), value);
	}
	} break;
	default:
	{
	GGML_ABORT("fatal error");
	}
	}

	return tensor;
	}

	int32_t ggml_get_i32_1d(const struct ggml_tensor * tensor, int i) {
	if (!ggml_is_contiguous(tensor)) {
	int64_t id[4] = { 0, 0, 0, 0 };
	ggml_unravel_index(tensor, i, &id[0], &id[1], &id[2], &id[3]);
	return ggml_get_i32_nd(tensor, id[0], id[1], id[2], id[3]);
	}
	switch (tensor->type) {
	case GGML_TYPE_I8:
	{
	GGML_ASSERT(tensor->nb[0] == sizeof(int8_t));
	return ((int8_t *)(tensor->data))[i];
	}
	case GGML_TYPE_I16:
	{
	GGML_ASSERT(tensor->nb[0] == sizeof(int16_t));
	return ((int16_t *)(tensor->data))[i];
	}
	case GGML_TYPE_I32:
	{
	GGML_ASSERT(tensor->nb[0] == sizeof(int32_t));
	return ((int32_t *)(tensor->data))[i];
	}
	case GGML_TYPE_F16:
	{
	GGML_ASSERT(tensor->nb[0] == sizeof(ggml_fp16_t));
	return GGML_CPU_FP16_TO_FP32(((ggml_fp16_t *)(tensor->data))[i]);
	}
	case GGML_TYPE_BF16:
	{
	GGML_ASSERT(tensor->nb[0] == sizeof(ggml_bf16_t));
	return GGML_BF16_TO_FP32(((ggml_bf16_t *)(tensor->data))[i]);
	}
	case GGML_TYPE_F32:
	{
	GGML_ASSERT(tensor->nb[0] == sizeof(float));
	return ((float *)(tensor->data))[i];
	}
	default:
	{
	GGML_ABORT("fatal error");
	}
	}
	}

	void ggml_set_i32_1d(const struct ggml_tensor * tensor, int i, int32_t value) {
	if (!ggml_is_contiguous(tensor)) {
	int64_t id[4] = { 0, 0, 0, 0 };
	ggml_unravel_index(tensor, i, &id[0], &id[1], &id[2], &id[3]);
	ggml_set_i32_nd(tensor, id[0], id[1], id[2], id[3], value);
	return;
	}
	switch (tensor->type) {
	case GGML_TYPE_I8:
	{
	GGML_ASSERT(tensor->nb[0] == sizeof(int8_t));
	((int8_t *)(tensor->data))[i] = value;
	} break;
	case GGML_TYPE_I16:
	{
	GGML_ASSERT(tensor->nb[0] == sizeof(int16_t));
	((int16_t *)(tensor->data))[i] = value;
	} break;
	case GGML_TYPE_I32:
	{
	GGML_ASSERT(tensor->nb[0] == sizeof(int32_t));
	((int32_t *)(tensor->data))[i] = value;
	} break;
	case GGML_TYPE_F16:
	{
	GGML_ASSERT(tensor->nb[0] == sizeof(ggml_fp16_t));
	((ggml_fp16_t *)(tensor->data))[i] = GGML_CPU_FP32_TO_FP16(value);
	} break;
	case GGML_TYPE_BF16:
	{
	GGML_ASSERT(tensor->nb[0] == sizeof(ggml_bf16_t));
	((ggml_bf16_t *)(tensor->data))[i] = GGML_FP32_TO_BF16(value);
	} break;
	case GGML_TYPE_F32:
	{
	GGML_ASSERT(tensor->nb[0] == sizeof(float));
	((float *)(tensor->data))[i] = value;
	} break;
	default:
	{
	GGML_ABORT("fatal error");
	}
	}
	}

	int32_t ggml_get_i32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2, int i3) {
	void * data = (char ) tensor->data + i0tensor->nb[0] + i1tensor->nb[1] + i2tensor->nb[2] + i3*tensor->nb[3];
	switch (tensor->type) {
	case GGML_TYPE_I8:
	return ((int8_t *) data)[0];
	case GGML_TYPE_I16:
	return ((int16_t *) data)[0];
	case GGML_TYPE_I32:
	return ((int32_t *) data)[0];
	case GGML_TYPE_F16:
	return GGML_CPU_FP16_TO_FP32(((ggml_fp16_t *) data)[0]);
	case GGML_TYPE_BF16:
	return GGML_BF16_TO_FP32(((ggml_bf16_t *) data)[0]);
	case GGML_TYPE_F32:
	return ((float *) data)[0];
	default:
	GGML_ABORT("fatal error");
	}
	}

	void ggml_set_i32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2, int i3, int32_t value) {
	void * data = (char ) tensor->data + i0tensor->nb[0] + i1tensor->nb[1] + i2tensor->nb[2] + i3*tensor->nb[3];
	switch (tensor->type) {
	case GGML_TYPE_I8:
	{
	((int8_t *)(data))[0] = value;
	} break;
	case GGML_TYPE_I16:
	{
	((int16_t *)(data))[0] = value;
	} break;
	case GGML_TYPE_I32:
	{
	((int32_t *)(data))[0] = value;
	} break;
	case GGML_TYPE_F16:
	{
	((ggml_fp16_t *)(data))[0] = GGML_CPU_FP32_TO_FP16(value);
	} break;
	case GGML_TYPE_BF16:
	{
	((ggml_bf16_t *)(data))[0] = GGML_FP32_TO_BF16(value);
	} break;
	case GGML_TYPE_F32:
	{
	((float *)(data))[0] = value;
	} break;
	default:
	{
	GGML_ABORT("fatal error");
	}
	}
	}

	float ggml_get_f32_1d(const struct ggml_tensor * tensor, int i) {
	if (!ggml_is_contiguous(tensor)) {
	int64_t id[4] = { 0, 0, 0, 0 };
	ggml_unravel_index(tensor, i, &id[0], &id[1], &id[2], &id[3]);
	return ggml_get_f32_nd(tensor, id[0], id[1], id[2], id[3]);
	}
	switch (tensor->type) {
	case GGML_TYPE_I8:
	{
	return ((int8_t *)(tensor->data))[i];
	}
	case GGML_TYPE_I16:
	{
	return ((int16_t *)(tensor->data))[i];
	}
	case GGML_TYPE_I32:
	{
	return ((int32_t *)(tensor->data))[i];
	}
	case GGML_TYPE_F16:
	{
	return GGML_CPU_FP16_TO_FP32(((ggml_fp16_t *)(tensor->data))[i]);
	}
	case GGML_TYPE_BF16:
	{
	return GGML_BF16_TO_FP32(((ggml_bf16_t *)(tensor->data))[i]);
	}
	case GGML_TYPE_F32:
	{
	return ((float *)(tensor->data))[i];
	}
	default:
	{
	GGML_ABORT("fatal error");
	}
	}
	}

	void ggml_set_f32_1d(const struct ggml_tensor * tensor, int i, float value) {
	if (!ggml_is_contiguous(tensor)) {
	int64_t id[4] = { 0, 0, 0, 0 };
	ggml_unravel_index(tensor, i, &id[0], &id[1], &id[2], &id[3]);
	ggml_set_f32_nd(tensor, id[0], id[1], id[2], id[3], value);
	return;
	}
	switch (tensor->type) {
	case GGML_TYPE_I8:
	{
	((int8_t *)(tensor->data))[i] = value;
	} break;
	case GGML_TYPE_I16:
	{
	((int16_t *)(tensor->data))[i] = value;
	} break;
	case GGML_TYPE_I32:
	{
	((int32_t *)(tensor->data))[i] = value;
	} break;
	case GGML_TYPE_F16:
	{
	((ggml_fp16_t *)(tensor->data))[i] = GGML_CPU_FP32_TO_FP16(value);
	} break;
	case GGML_TYPE_BF16:
	{
	((ggml_bf16_t *)(tensor->data))[i] = GGML_FP32_TO_BF16(value);
	} break;
	case GGML_TYPE_F32:
	{
	((float *)(tensor->data))[i] = value;
	} break;
	default:
	{
	GGML_ABORT("fatal error");
	}
	}
	}

	float ggml_get_f32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2, int i3) {
	void * data = (char ) tensor->data + i0tensor->nb[0] + i1tensor->nb[1] + i2tensor->nb[2] + i3*tensor->nb[3];
	switch (tensor->type) {
	case GGML_TYPE_I8:
	return ((int8_t *) data)[0];
	case GGML_TYPE_I16:
	return ((int16_t *) data)[0];
	case GGML_TYPE_I32:
	return ((int32_t *) data)[0];
	case GGML_TYPE_F16:
	return GGML_CPU_FP16_TO_FP32(((ggml_fp16_t *) data)[0]);
	case GGML_TYPE_BF16:
	return GGML_BF16_TO_FP32(((ggml_bf16_t *) data)[0]);
	case GGML_TYPE_F32:
	return ((float *) data)[0];
	default:
	GGML_ABORT("fatal error");
	}
	}

	void ggml_set_f32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2, int i3, float value) {
	void * data = (char ) tensor->data + i0tensor->nb[0] + i1tensor->nb[1] + i2tensor->nb[2] + i3*tensor->nb[3];
	switch (tensor->type) {
	case GGML_TYPE_I8:
	{
	((int8_t *)(data))[0] = value;
	} break;
	case GGML_TYPE_I16:
	{
	((int16_t *)(data))[0] = value;
	} break;
	case GGML_TYPE_I32:
	{
	((int32_t *)(data))[0] = value;
	} break;
	case GGML_TYPE_F16:
	{
	((ggml_fp16_t *)(data))[0] = GGML_CPU_FP32_TO_FP16(value);
	} break;
	case GGML_TYPE_BF16:
	{
	((ggml_bf16_t *)(data))[0] = GGML_FP32_TO_BF16(value);
	} break;
	case GGML_TYPE_F32:
	{
	((float *)(data))[0] = value;
	} break;
	default:
	{
	GGML_ABORT("fatal error");
	}
	}
	}

	////////////////////////////////////////////////////////////////////////////////

	// ggml_compute_forward_mul_mat

	static void ggml_compute_forward_mul_mat_one_chunk(
	const struct ggml_compute_params * params,
	struct ggml_tensor * dst,
	const enum ggml_type type,
	const int64_t num_rows_per_vec_dot,
	const int64_t ir0_start,
	const int64_t ir0_end,
	const int64_t ir1_start,
	const int64_t ir1_end) {

	const struct ggml_tensor * src0 = dst->src[0];
	const struct ggml_tensor * src1 = dst->src[1];

	GGML_TENSOR_BINARY_OP_LOCALS

	const bool src1_cont = ggml_is_contiguous(src1);

	ggml_vec_dot_t const vec_dot = type_traits_cpu[type].vec_dot;
	enum ggml_type const vec_dot_type = type_traits_cpu[type].vec_dot_type;

	// broadcast factors
	const int64_t r2 = ne12 / ne02;
	const int64_t r3 = ne13 / ne03;

	//printf("ir0_start = %6lld, ir0_end = %6lld, ir1_start = %6lld, ir1_end = %6lld\n", ir0_start, ir0_end, ir1_start, ir1_end);

	// threads with no work simply yield (not sure if it helps)
	if (ir0_start >= ir0_end \|\| ir1_start >= ir1_end) {
	return;
	}

	const void * wdata = (src1->type == vec_dot_type) ? src1->data : params->wdata;
	const size_t row_size = ggml_row_size(vec_dot_type, ne10);

	assert(ne12 % ne02 == 0);
	assert(ne13 % ne03 == 0);

	// block-tiling attempt
	const int64_t blck_0 = 16;
	const int64_t blck_1 = 16;

	const size_t src1_col_stride = src1_cont \|\| src1->type != vec_dot_type ? row_size : nb11;

	// attempt to reduce false-sharing (does not seem to make a difference)
	// 16 * 2, accounting for mmla kernels
	float tmp[32];

	for (int64_t iir1 = ir1_start; iir1 < ir1_end; iir1 += blck_1) {
	for (int64_t iir0 = ir0_start; iir0 < ir0_end; iir0 += blck_0) {
	for (int64_t ir1 = iir1; ir1 < iir1 + blck_1 && ir1 < ir1_end; ir1 += num_rows_per_vec_dot) {
	const int64_t i13 = (ir1 / (ne12 * ne1));
	const int64_t i12 = (ir1 - i13 * ne12 * ne1) / ne1;
	const int64_t i11 = (ir1 - i13 * ne12 * ne1 - i12 * ne1);

	// broadcast src0 into src1
	const int64_t i03 = i13 / r3;
	const int64_t i02 = i12 / r2;

	const int64_t i1 = i11;
	const int64_t i2 = i12;
	const int64_t i3 = i13;

	const char * src0_row = (const char)src0->data + (0 + i02 nb02 + i03 * nb03);

	// desc: when src1 is not a contiguous memory block we have to calculate the offset using the strides
	// if it is, then we have either copied the data to params->wdata and made it contiguous or we are using
	// the original src1 data pointer, so we should index using the indices directly
	// TODO: this is a bit of a hack, we should probably have a better way to handle this
	const char * src1_col = (const char*)wdata +
	(src1_cont \|\| src1->type != vec_dot_type
	? (i11 + i12 * ne11 + i13 * ne12 * ne11) * row_size
	: (i11 * nb11 + i12 * nb12 + i13 * nb13));
	float * dst_col = (float)((char)dst->data + (i1 * nb1 + i2 * nb2 + i3 * nb3));

	//for (int64_t ir0 = iir0; ir0 < iir0 + blck_0 && ir0 < ir0_end; ++ir0) {
	// vec_dot(ne00, &dst_col[ir0], src0_row + ir0*nb01, src1_col);
	//}

	for (int64_t ir0 = iir0; ir0 < iir0 + blck_0 && ir0 < ir0_end; ir0 += num_rows_per_vec_dot) {
	vec_dot(ne00, &tmp[ir0 - iir0], (num_rows_per_vec_dot > 1 ? 16 : 0), src0_row + ir0 * nb01, (num_rows_per_vec_dot > 1 ? nb01 : 0), src1_col, (num_rows_per_vec_dot > 1 ? src1_col_stride : 0), num_rows_per_vec_dot);
	}

	for (int cn = 0; cn < num_rows_per_vec_dot; ++cn) {
	memcpy(&dst_col[iir0 + cn * nb1 / nb0], tmp + (cn * 16), (MIN(iir0 + blck_0, ir0_end) - iir0) * sizeof(float));
	}
	}
	}
	}
	}

	void ggml_compute_forward_mul_mat(
	const struct ggml_compute_params * params,
	struct ggml_tensor * dst) {

	const struct ggml_tensor * src0 = dst->src[0];
	const struct ggml_tensor * src1 = dst->src[1];

	GGML_TENSOR_BINARY_OP_LOCALS

	const int ith = params->ith;
	const int nth = params->nth;

	enum ggml_type const vec_dot_type = type_traits_cpu[src0->type].vec_dot_type;
	ggml_from_float_t const from_float = type_traits_cpu[vec_dot_type].from_float;
	int64_t const vec_dot_num_rows = type_traits_cpu[src0->type].nrows;

	GGML_ASSERT(ne0 == ne01);
	GGML_ASSERT(ne1 == ne11);
	GGML_ASSERT(ne2 == ne12);
	GGML_ASSERT(ne3 == ne13);

	// we don't support permuted src0 or src1
	GGML_ASSERT(nb00 == ggml_type_size(src0->type));
	GGML_ASSERT(nb10 == ggml_type_size(src1->type));

	// dst cannot be transposed or permuted
	GGML_ASSERT(nb0 == sizeof(float));
	GGML_ASSERT(nb0 <= nb1);
	GGML_ASSERT(nb1 <= nb2);
	GGML_ASSERT(nb2 <= nb3);

	// nb01 >= nb00 - src0 is not transposed
	// compute by src0 rows

	// TODO: extract to "extra_op"
	#if GGML_USE_LLAMAFILE
	// broadcast factors
	const int64_t r2 = ne12 / ne02;
	const int64_t r3 = ne13 / ne03;

	const bool src1_cont = ggml_is_contiguous(src1);

	if (src1_cont) {
	for (int64_t i13 = 0; i13 < ne13; i13++)
	for (int64_t i12 = 0; i12 < ne12; i12++)
	if (!llamafile_sgemm(params,
	ne01, ne11, ne00/ggml_blck_size(src0->type),
	(const char )src0->data + i12/r2nb02 + i13/r3*nb03,
	nb01/ggml_type_size(src0->type),
	(const char )src1->data + i12nb12 + i13*nb13,
	nb11/ggml_type_size(src1->type),
	(char )dst->data + i12nb2 + i13*nb3,
	nb1/ggml_type_size(dst->type),
	src0->type,
	src1->type,
	dst->type))
	goto UseGgmlGemm1;
	return;
	}
	UseGgmlGemm1:;
	#endif

	if (src1->type != vec_dot_type) {
	char * wdata = params->wdata;

	const size_t nbw0 = ggml_type_size(vec_dot_type);
	const size_t nbw1 = ggml_row_size(vec_dot_type, ne10);
	const size_t nbw2 = nbw1*ne11;
	const size_t nbw3 = nbw2*ne12;

	assert(params->wsize >= ne13*nbw3);
	GGML_ASSERT(src1->type == GGML_TYPE_F32);

	#if 0
	for (int64_t i13 = 0; i13 < ne13; ++i13) {
	for (int64_t i12 = 0; i12 < ne12; ++i12) {
	for (int64_t i11 = ith; i11 < ne11; i11 += nth) {
	from_float((float )((char ) src1->data + i13nb13 + i12nb12 + i11*nb11),
	(void ) (wdata + i13nbw3 + i12nbw2 + i11nbw1),
	ne10);
	}
	}
	}
	#else
	for (int64_t i13 = 0; i13 < ne13; ++i13) {
	for (int64_t i12 = 0; i12 < ne12; ++i12) {
	for (int64_t i11 = 0; i11 < ne11; ++i11) {
	size_t bs = ggml_blck_size(vec_dot_type);
	int64_t ne10_block_start = (ith * ne10/bs) / nth;
	int64_t ne10_block_end = ((ith + 1) * ne10/bs) / nth;
	from_float((float )((char ) src1->data + i13nb13 + i12nb12 + i11nb11 + ne10_block_startbs*nb10),
	(void ) (wdata + i13nbw3 + i12nbw2 + i11nbw1 + ne10_block_start*nbw0),
	(ne10_block_end - ne10_block_start) * bs);
	}
	}
	}
	#endif
	}

	if (ith == 0) {
	// Every thread starts at ith, so the first unprocessed chunk is nth. This save a bit of coordination right at the start.
	atomic_store_explicit(&params->threadpool->current_chunk, nth, memory_order_relaxed);
	}

	ggml_barrier(params->threadpool);

	#if GGML_USE_LLAMAFILE
	if (src1->type != vec_dot_type) {
	const void* wdata = (src1->type == vec_dot_type) ? src1->data : params->wdata;
	const size_t row_size = ggml_row_size(vec_dot_type, ne10);

	for (int64_t i13 = 0; i13 < ne13; i13++)
	for (int64_t i12 = 0; i12 < ne12; i12++)
	if (!llamafile_sgemm(params,
	ne01, ne11, ne00/ggml_blck_size(src0->type),
	(const char )src0->data + i12/r2nb02 + i13/r3*nb03,
	nb01/ggml_type_size(src0->type),
	(const char )wdata + (i12ne11 + i13ne12ne11)*row_size,
	row_size/ggml_type_size(vec_dot_type),
	(char )dst->data + i12nb2 + i13*nb3,
	nb1/ggml_type_size(dst->type),
	src0->type,
	vec_dot_type,
	dst->type))
	goto UseGgmlGemm2;
	return;
	}
	UseGgmlGemm2:;
	#endif

	// This is the size of the first dimension of the result, so we can iterate that way. (see the ASSERT above, these are the same numbers)
	const int64_t nr0 = ne0;

	// This is the size of the rest of the dimensions of the result
	const int64_t nr1 = ne1 * ne2 * ne3;

	// Now select a reasonable chunk size.
	int chunk_size = 16;

	// We need to step up the size if it's small
	if (nr0 == 1 \|\| nr1 == 1) {
	chunk_size = 64;
	}

	// distribute the work across the inner or outer loop based on which one is larger
	// The number of chunks in the 0/1 dim.
	// CEIL(nr0/chunk_size)
	int64_t nchunk0 = (nr0 + chunk_size - 1) / chunk_size;
	int64_t nchunk1 = (nr1 + chunk_size - 1) / chunk_size;

	// If the chunking is poor for the number of threads on this setup, scrap the whole plan. Re-chunk it by thread.
	// Also, chunking by thread was measured to have perform better on NUMA systems. See https://github.com/ggml-org/llama.cpp/pull/6915
	// In theory, chunking should be just as useful on NUMA and non NUMA systems, but testing disagreed with that.
	if (nchunk0 * nchunk1 < nth * 4 \|\| ggml_is_numa()) {
	// distribute the thread work across the inner or outer loop based on which one is larger
	nchunk0 = nr0 > nr1 ? nth : 1; // parallelize by src0 rows
	nchunk1 = nr0 > nr1 ? 1 : nth; // parallelize by src1 rows
	}

	// The number of elements in each chunk
	const int64_t dr0 = (nr0 + nchunk0 - 1) / nchunk0;
	const int64_t dr1 = (nr1 + nchunk1 - 1) / nchunk1;

	// The first chunk comes from our thread_id, the rest will get auto-assigned.
	int current_chunk = ith;

	while (current_chunk < nchunk0 * nchunk1) {
	const int64_t ith0 = current_chunk % nchunk0;
	const int64_t ith1 = current_chunk / nchunk0;

	const int64_t ir0_start = dr0 * ith0;
	const int64_t ir0_end = MIN(ir0_start + dr0, nr0);

	const int64_t ir1_start = dr1 * ith1;
	const int64_t ir1_end = MIN(ir1_start + dr1, nr1);

	// dot kernels can handle 1 row and col at a time, but mmla kernels can process 2 rows and cols
	int64_t num_rows_per_vec_dot = vec_dot_num_rows;

	// these checks are needed to avoid crossing dim1 boundaries
	// can be optimized, but the logic would become more complicated, so keeping it like this for simplicity
	if ((nr0 % 2 != 0) \|\| (ne11 % 2 != 0) \|\| ((ir0_end - ir0_start) % 2 != 0) \|\| ((ir1_end - ir1_start) % 2 != 0)) {
	num_rows_per_vec_dot = 1;
	}
	ggml_compute_forward_mul_mat_one_chunk(params, dst, src0->type, num_rows_per_vec_dot, ir0_start, ir0_end, ir1_start, ir1_end);

	if (nth >= nchunk0 * nchunk1) {
	break;
	}

	current_chunk = atomic_fetch_add_explicit(&params->threadpool->current_chunk, 1, memory_order_relaxed);
	}
	}

	// ggml_compute_forward_mul_mat_id

	#define MMID_MATRIX_ROW(row_id, i1) matrix_rows[(row_id)ids->ne[0]ids->ne[1] + (i1)]

	struct mmid_row_mapping {
	int32_t i1;
	int32_t i2;
	};

	static void ggml_compute_forward_mul_mat_id_one_chunk(
	struct ggml_tensor * dst,
	const struct ggml_tensor * src0,
	const struct ggml_tensor * src1,
	const struct ggml_tensor * ids,
	const int64_t cur_a,
	const int64_t ir0_start,
	const int64_t ir0_end,
	const int64_t ir1_start,
	const int64_t ir1_end,
	const char * src0_cur,
	const struct mmid_row_mapping * matrix_rows,
	const size_t row_size,
	const bool src1_cont,
	const void * wdata) {

	GGML_TENSOR_BINARY_OP_LOCALS

	const enum ggml_type type = src0->type;

	ggml_vec_dot_t const vec_dot = type_traits_cpu[type].vec_dot;
	enum ggml_type const vec_dot_type = type_traits_cpu[type].vec_dot_type;

	const int64_t blck_0 = 16;
	const int64_t blck_1 = 16;

	float tmp[16];

	for (int64_t iir1 = ir1_start; iir1 < ir1_end; iir1 += blck_1) {
	for (int64_t iir0 = ir0_start; iir0 < ir0_end; iir0 += blck_0) {
	for (int64_t ir1 = iir1; ir1 < iir1 + blck_1 && ir1 < ir1_end; ++ir1) {
	const int64_t _i12 = ir1; // logical row index for this expert

	struct mmid_row_mapping row_mapping = MMID_MATRIX_ROW(cur_a, _i12);
	const int id = row_mapping.i1; // selected expert index

	const int64_t i11 = id % ne11;
	const int64_t i12 = row_mapping.i2; // row index in src1

	const int64_t i1 = id; // selected expert index
	const int64_t i2 = i12; // row

	// desc: when src1 is not a contiguous memory block we have to calculate the offset using the strides
	// if it is, then we have either copied the data to params->wdata and made it contiguous or we are using
	// the original src1 data pointer, so we should index using the indices directly
	// TODO: this is a bit of a hack, we should probably have a better way to handle this
	const char * src1_col = (const char *) wdata +
	(src1_cont \|\| src1->type != vec_dot_type
	? (i11 + i12ne11)row_size
	: (i11nb11 + i12nb12));

	float * dst_col = (float ) ((char ) dst->data + (i1nb1 + i2nb2));

	for (int64_t ir0 = iir0; ir0 < iir0 + blck_0 && ir0 < ir0_end; ++ir0) {
	vec_dot(ne00, &tmp[ir0 - iir0], 0, src0_cur + ir0*nb01, 0, src1_col, 0, 1);
	}

	memcpy(&dst_col[iir0], tmp, (MIN(iir0 + blck_0, ir0_end) - iir0)*sizeof(float));
	}
	}
	}
	}

	static void * incr_ptr_aligned(void ** p, size_t size, size_t align) {

	void * ptr = *p;
	ptr = (void *) GGML_PAD((uintptr_t) ptr, align);
	p = (void ) ((char *) ptr + size);
	return ptr;
	}

	static void ggml_compute_forward_mul_mat_id(
	const struct ggml_compute_params * params,
	struct ggml_tensor * dst) {

	const struct ggml_tensor * src0 = dst->src[0];
	const struct ggml_tensor * src1 = dst->src[1];
	const struct ggml_tensor * ids = dst->src[2];

	GGML_TENSOR_BINARY_OP_LOCALS

	const int ith = params->ith;
	const int nth = params->nth;

	const enum ggml_type type = src0->type;

	const bool src1_cont = ggml_is_contiguous(src1);

	enum ggml_type const vec_dot_type = type_traits_cpu[type].vec_dot_type;
	ggml_from_float_t const from_float = type_traits_cpu[vec_dot_type].from_float;

	// we don't support permuted src0 or src1
	GGML_ASSERT(nb00 == ggml_type_size(type));
	GGML_ASSERT(nb10 == ggml_type_size(src1->type));

	// dst cannot be transposed or permuted
	GGML_ASSERT(nb0 == sizeof(float));
	GGML_ASSERT(nb0 <= nb1);
	GGML_ASSERT(nb1 <= nb2);
	GGML_ASSERT(nb2 <= nb3);

	// row groups
	const int n_ids = ids->ne[0]; // n_expert_used
	const int n_as = ne02; // n_expert

	void * wdata_cur = params->wdata;

	if (src1->type != vec_dot_type) {
	incr_ptr_aligned(&wdata_cur, ggml_row_size(vec_dot_type, ggml_nelements(src1)), sizeof(int64_t));
	}

	int64_t * matrix_row_counts = // [n_as]
	incr_ptr_aligned(&wdata_cur, n_as*sizeof(int64_t), sizeof(int64_t));

	struct mmid_row_mapping * matrix_rows = // [n_as][ids->ne[0]*ids->ne[1]]
	incr_ptr_aligned(&wdata_cur, n_asids->ne[0]ids->ne[1]*sizeof(struct mmid_row_mapping), sizeof(int64_t));

	char (*atomic_current_chunk)[CACHE_LINE_SIZE] = // [n_as]
	incr_ptr_aligned(&wdata_cur, CACHE_LINE_SIZE * n_as, CACHE_LINE_SIZE);

	GGML_ASSERT(params->wsize >= (size_t)((char ) wdata_cur - (char ) params->wdata));

	if (src1->type != vec_dot_type) {
	char * wdata = params->wdata;

	const size_t nbw0 = ggml_type_size(vec_dot_type);
	const size_t nbw1 = ggml_row_size(vec_dot_type, ne10);
	const size_t nbw2 = nbw1*ne11;
	const size_t nbw3 = nbw2*ne12;

	assert(params->wsize >= ne13*nbw3);
	GGML_ASSERT(src1->type == GGML_TYPE_F32);

	#if 0
	for (int64_t i13 = 0; i13 < ne13; ++i13) {
	for (int64_t i12 = ith; i12 < ne12; i12 += nth) {
	for (int64_t i11 = 0; i11 < ne11; ++i11) {
	from_float((float )((char ) src1->data + i13nb13 + i12nb12 + i11*nb11),
	(void ) (wdata + i13nbw3 + i12nbw2 + i11nbw1),
	ne10);
	}
	}
	}
	#else
	for (int64_t i13 = 0; i13 < ne13; ++i13) {
	for (int64_t i12 = 0; i12 < ne12; ++i12) {
	for (int64_t i11 = 0; i11 < ne11; ++i11) {
	size_t bs = ggml_blck_size(vec_dot_type);
	int64_t ne10_block_start = (ith * ne10/bs) / nth;
	int64_t ne10_block_end = ((ith + 1) * ne10/bs) / nth;
	from_float((float )((char ) src1->data + i13nb13 + i12nb12 + i11nb11 + ne10_block_startbs*nb10),
	(void ) (wdata + i13nbw3 + i12nbw2 + i11nbw1 + ne10_block_start*nbw0),
	(ne10_block_end - ne10_block_start) * bs);
	}
	}
	}
	#endif
	}

	if (ith == 0) {
	// initialize matrix_row_counts
	memset(matrix_row_counts, 0, n_as*sizeof(int64_t));

	// group rows by src0 matrix
	for (int64_t iid1 = 0; iid1 < ids->ne[1]; ++iid1) {
	for (int id = 0; id < n_ids; ++id) {
	const int32_t i02 = (const int32_t ) ((const char ) ids->data + iid1ids->nb[1] + id*ids->nb[0]);

	assert(i02 >= 0 && i02 < n_as);

	MMID_MATRIX_ROW(i02, matrix_row_counts[i02]) = (struct mmid_row_mapping) {id, iid1};
	matrix_row_counts[i02] += 1;
	}
	}
	}

	// reset current_chunk
	for (int cur_a = ith; cur_a < n_as; cur_a += nth) {
	atomic_int * current_chunk_ctr = (atomic_int *)(atomic_current_chunk + cur_a);
	*current_chunk_ctr = nth;
	}

	ggml_barrier(params->threadpool);

	for (int cur_a = 0; cur_a < n_as; ++cur_a) {
	const int64_t cne1 = matrix_row_counts[cur_a];

	if (cne1 == 0) {
	continue;
	}

	const char * src0_cur = (const char ) src0->data + cur_a nb02;
	const void * wdata = (src1->type == vec_dot_type) ? src1->data : params->wdata;
	const size_t row_size = ggml_row_size(vec_dot_type, ne10);

	const int64_t nr0 = ne01;
	const int64_t nr1 = cne1;

	int chunk_size = 16;
	if (nr0 == 1 \|\| nr1 == 1) {
	chunk_size = 64;
	}

	// disable for NUMA
	const bool disable_chunking = ggml_is_numa();

	int64_t nchunk0 = (nr0 + chunk_size - 1) / chunk_size;
	int64_t nchunk1 = (nr1 + chunk_size - 1) / chunk_size;

	if (nchunk0 * nchunk1 < nth * 4 \|\| disable_chunking) {
	nchunk0 = nr0 > nr1 ? nth : 1;
	nchunk1 = nr0 > nr1 ? 1 : nth;
	}

	const int64_t dr0 = (nr0 + nchunk0 - 1) / nchunk0;
	const int64_t dr1 = (nr1 + nchunk1 - 1) / nchunk1;

	int current_chunk = ith;

	atomic_int * current_chunk_ctr = (atomic_int *)(atomic_current_chunk + cur_a);

	while (current_chunk < nchunk0 * nchunk1) {
	const int64_t ith0 = current_chunk % nchunk0;
	const int64_t ith1 = current_chunk / nchunk0;

	const int64_t ir0_start = dr0 * ith0;
	const int64_t ir0_end = MIN(ir0_start + dr0, nr0);

	const int64_t ir1_start = dr1 * ith1;
	const int64_t ir1_end = MIN(ir1_start + dr1, nr1);

	ggml_compute_forward_mul_mat_id_one_chunk(
	dst, src0, src1, ids, cur_a,
	ir0_start, ir0_end, ir1_start, ir1_end,
	src0_cur, matrix_rows, row_size, src1_cont, wdata
	);

	if (nth >= nchunk0 * nchunk1) {
	break;
	}

	current_chunk = atomic_fetch_add_explicit(current_chunk_ctr, 1, memory_order_relaxed);
	}
	}
	}

	/////////////////////////////////

	static void ggml_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor) {
	GGML_ASSERT(params);

	if (tensor->op == GGML_OP_NONE \|\| ggml_is_empty(tensor)) {
	return;
	}

	// extra_buffer op?
	if (ggml_cpu_extra_compute_forward(params, tensor)) {
	return;
	}

	switch (tensor->op) {
	case GGML_OP_DUP:
	{
	ggml_compute_forward_dup(params, tensor);
	} break;
	case GGML_OP_ADD:
	{
	ggml_compute_forward_add(params, tensor);
	} break;
	case GGML_OP_ADD_ID:
	{
	ggml_compute_forward_add_id(params, tensor);
	} break;
	case GGML_OP_ADD1:
	{
	ggml_compute_forward_add1(params, tensor);
	} break;
	case GGML_OP_ACC:
	{
	ggml_compute_forward_acc(params, tensor);
	} break;
	case GGML_OP_SUB:
	{
	ggml_compute_forward_sub(params, tensor);
	} break;
	case GGML_OP_MUL:
	{
	ggml_compute_forward_mul(params, tensor);
	} break;
	case GGML_OP_DIV:
	{
	ggml_compute_forward_div(params, tensor);
	} break;
	case GGML_OP_SQR:
	{
	ggml_compute_forward_sqr(params, tensor);
	} break;
	case GGML_OP_SQRT:
	{
	ggml_compute_forward_sqrt(params, tensor);
	} break;
	case GGML_OP_LOG:
	{
	ggml_compute_forward_log(params, tensor);
	} break;
	case GGML_OP_SIN:
	{
	ggml_compute_forward_sin(params, tensor);
	} break;
	case GGML_OP_COS:
	{
	ggml_compute_forward_cos(params, tensor);
	} break;
	case GGML_OP_SUM:
	{
	ggml_compute_forward_sum(params, tensor);
	} break;
	case GGML_OP_SUM_ROWS:
	{
	ggml_compute_forward_sum_rows(params, tensor);
	} break;
	case GGML_OP_CUMSUM:
	{
	ggml_compute_forward_cumsum(params, tensor);
	} break;
	case GGML_OP_MEAN:
	{
	ggml_compute_forward_mean(params, tensor);
	} break;
	case GGML_OP_ARGMAX:
	{
	ggml_compute_forward_argmax(params, tensor);
	} break;
	case GGML_OP_COUNT_EQUAL:
	{
	ggml_compute_forward_count_equal(params, tensor);
	} break;
	case GGML_OP_REPEAT:
	{
	ggml_compute_forward_repeat(params, tensor);
	} break;
	case GGML_OP_REPEAT_BACK:
	{
	ggml_compute_forward_repeat_back(params, tensor);
	} break;
	case GGML_OP_CONCAT:
	{
	ggml_compute_forward_concat(params, tensor);
	} break;
	case GGML_OP_SILU_BACK:
	{
	ggml_compute_forward_silu_back(params, tensor);
	} break;
	case GGML_OP_NORM:
	{
	ggml_compute_forward_norm(params, tensor);
	} break;
	case GGML_OP_RMS_NORM:
	{
	ggml_compute_forward_rms_norm(params, tensor);
	} break;
	case GGML_OP_RMS_NORM_BACK:
	{
	ggml_compute_forward_rms_norm_back(params, tensor);
	} break;
	case GGML_OP_GROUP_NORM:
	{
	ggml_compute_forward_group_norm(params, tensor);
	} break;
	case GGML_OP_L2_NORM:
	{
	ggml_compute_forward_l2_norm(params, tensor);
	} break;
	case GGML_OP_MUL_MAT:
	{
	ggml_compute_forward_mul_mat(params, tensor);
	} break;
	case GGML_OP_MUL_MAT_ID:
	{
	ggml_compute_forward_mul_mat_id(params, tensor);
	} break;
	case GGML_OP_OUT_PROD:
	{
	ggml_compute_forward_out_prod(params, tensor);
	} break;
	case GGML_OP_SCALE:
	{
	ggml_compute_forward_scale(params, tensor);
	} break;
	case GGML_OP_SET:
	{
	ggml_compute_forward_set(params, tensor);
	} break;
	case GGML_OP_CPY:
	{
	ggml_compute_forward_cpy(params, tensor);
	} break;
	case GGML_OP_CONT:
	{
	ggml_compute_forward_cont(params, tensor);
	} break;
	case GGML_OP_GET_ROWS:
	{
	ggml_compute_forward_get_rows(params, tensor);
	} break;
	case GGML_OP_GET_ROWS_BACK:
	{
	ggml_compute_forward_get_rows_back(params, tensor);
	} break;
	case GGML_OP_SET_ROWS:
	{
	ggml_compute_forward_set_rows(params, tensor);
	} break;
	case GGML_OP_DIAG:
	{
	ggml_compute_forward_diag(params, tensor);
	} break;
	case GGML_OP_DIAG_MASK_INF:
	{
	ggml_compute_forward_diag_mask_inf(params, tensor);
	} break;
	case GGML_OP_DIAG_MASK_ZERO:
	{
	ggml_compute_forward_diag_mask_zero(params, tensor);
	} break;
	case GGML_OP_SOFT_MAX:
	{
	ggml_compute_forward_soft_max(params, tensor);
	} break;
	case GGML_OP_SOFT_MAX_BACK:
	{
	ggml_compute_forward_soft_max_ext_back(params, tensor);
	} break;
	case GGML_OP_ROPE:
	{
	ggml_compute_forward_rope(params, tensor);
	} break;
	case GGML_OP_ROPE_BACK:
	{
	ggml_compute_forward_rope_back(params, tensor);
	} break;
	case GGML_OP_CLAMP:
	{
	ggml_compute_forward_clamp(params, tensor);
	} break;
	case GGML_OP_CONV_TRANSPOSE_1D:
	{
	ggml_compute_forward_conv_transpose_1d(params, tensor);
	} break;
	case GGML_OP_IM2COL:
	{
	ggml_compute_forward_im2col(params, tensor);
	} break;
	case GGML_OP_IM2COL_BACK:
	{
	ggml_compute_forward_im2col_back_f32(params, tensor);
	} break;
	case GGML_OP_IM2COL_3D:
	{
	ggml_compute_forward_im2col_3d(params, tensor);
	} break;
	case GGML_OP_CONV_2D:
	{
	ggml_compute_forward_conv_2d(params, tensor);
	} break;
	case GGML_OP_CONV_3D:
	{
	ggml_compute_forward_conv_3d(params, tensor);
	} break;
	case GGML_OP_CONV_2D_DW:
	{
	ggml_compute_forward_conv_2d_dw(params, tensor);
	} break;
	case GGML_OP_CONV_TRANSPOSE_2D:
	{
	ggml_compute_forward_conv_transpose_2d(params, tensor);
	} break;
	case GGML_OP_POOL_1D:
	{
	ggml_compute_forward_pool_1d(params, tensor);
	} break;
	case GGML_OP_POOL_2D:
	{
	ggml_compute_forward_pool_2d(params, tensor);
	} break;
	case GGML_OP_POOL_2D_BACK:
	{
	ggml_compute_forward_pool_2d_back(params, tensor);
	} break;
	case GGML_OP_UPSCALE:
	{
	ggml_compute_forward_upscale(params, tensor);
	} break;
	case GGML_OP_PAD:
	{
	ggml_compute_forward_pad(params, tensor);
	} break;
	case GGML_OP_PAD_REFLECT_1D:
	{
	ggml_compute_forward_pad_reflect_1d(params, tensor);
	} break;
	case GGML_OP_ROLL:
	{
	ggml_compute_forward_roll(params, tensor);
	} break;
	case GGML_OP_ARANGE:
	{
	ggml_compute_forward_arange(params, tensor);
	} break;
	case GGML_OP_TIMESTEP_EMBEDDING:
	{
	ggml_compute_forward_timestep_embedding(params, tensor);
	} break;
	case GGML_OP_ARGSORT:
	{
	ggml_compute_forward_argsort(params, tensor);
	} break;
	case GGML_OP_TOP_K:
	{
	ggml_compute_forward_top_k(params, tensor);
	} break;
	case GGML_OP_LEAKY_RELU:
	{
	ggml_compute_forward_leaky_relu(params, tensor);
	} break;
	case GGML_OP_TRI:
	{
	ggml_compute_forward_tri(params, tensor);
	} break;
	case GGML_OP_FILL:
	{
	ggml_compute_forward_fill(params, tensor);
	} break;
	case GGML_OP_FLASH_ATTN_EXT:
	{
	ggml_compute_forward_flash_attn_ext(params, tensor);
	} break;
	case GGML_OP_FLASH_ATTN_BACK:
	{
	int32_t t = ggml_get_op_params_i32(tensor, 0);
	GGML_ASSERT(t == 0 \|\| t == 1);
	bool masked = t != 0;
	ggml_compute_forward_flash_attn_back(params, masked, tensor);
	} break;
	case GGML_OP_SSM_CONV:
	{
	ggml_compute_forward_ssm_conv(params, tensor);
	} break;
	case GGML_OP_SSM_SCAN:
	{
	ggml_compute_forward_ssm_scan(params, tensor);
	} break;
	case GGML_OP_WIN_PART:
	{
	ggml_compute_forward_win_part(params, tensor);
	} break;
	case GGML_OP_WIN_UNPART:
	{
	ggml_compute_forward_win_unpart(params, tensor);
	} break;
	case GGML_OP_UNARY:
	{
	ggml_compute_forward_unary(params, tensor);
	} break;
	case GGML_OP_GLU:
	{
	ggml_compute_forward_glu(params, tensor);
	} break;
	case GGML_OP_GET_REL_POS:
	{
	ggml_compute_forward_get_rel_pos(params, tensor);
	} break;
	case GGML_OP_ADD_REL_POS:
	{
	ggml_compute_forward_add_rel_pos(params, tensor);
	} break;
	case GGML_OP_RWKV_WKV6:
	{
	ggml_compute_forward_rwkv_wkv6(params, tensor);
	} break;
	case GGML_OP_GATED_LINEAR_ATTN:
	{
	ggml_compute_forward_gla(params, tensor);
	} break;
	case GGML_OP_RWKV_WKV7:
	{
	ggml_compute_forward_rwkv_wkv7(params, tensor);
	} break;
	case GGML_OP_SOLVE_TRI:
	{
	ggml_compute_forward_solve_tri(params, tensor);
	} break;
	case GGML_OP_GATED_DELTA_NET:
	{
	ggml_compute_forward_gated_delta_net(params, tensor);
	} break;
	case GGML_OP_MAP_CUSTOM1:
	{
	ggml_compute_forward_map_custom1(params, tensor);
	}
	break;
	case GGML_OP_MAP_CUSTOM2:
	{
	ggml_compute_forward_map_custom2(params, tensor);
	}
	break;
	case GGML_OP_MAP_CUSTOM3:
	{
	ggml_compute_forward_map_custom3(params, tensor);
	}
	break;
	case GGML_OP_CUSTOM:
	{
	ggml_compute_forward_custom(params, tensor);
	}
	break;
	case GGML_OP_CROSS_ENTROPY_LOSS:
	{
	ggml_compute_forward_cross_entropy_loss(params, tensor);
	}
	break;
	case GGML_OP_CROSS_ENTROPY_LOSS_BACK:
	{
	ggml_compute_forward_cross_entropy_loss_back(params, tensor);
	}
	break;
	case GGML_OP_OPT_STEP_ADAMW:
	{
	ggml_compute_forward_opt_step_adamw(params, tensor);
	}
	break;
	case GGML_OP_OPT_STEP_SGD:
	{
	ggml_compute_forward_opt_step_sgd(params, tensor);
	}
	break;
	case GGML_OP_NONE:
	{
	// nop
	} break;
	case GGML_OP_RESHAPE:
	{
	// nop
	} break;
	case GGML_OP_PERMUTE:
	{
	// nop
	} break;
	case GGML_OP_VIEW:
	{
	// nop
	} break;
	case GGML_OP_TRANSPOSE:
	{
	// nop
	} break;
	case GGML_OP_COUNT:
	{
	GGML_ABORT("fatal error");
	}
	}
	}

	// Android's libc implementation "bionic" does not support setting affinity
	#if defined(__gnu_linux__)
	static void set_numa_thread_affinity(int thread_n) {
	if (!ggml_is_numa()) {
	return;
	}

	int node_num;
	int rv;
	size_t setsize = CPU_ALLOC_SIZE(g_state.numa.total_cpus);

	switch(g_state.numa.numa_strategy) {
	case GGML_NUMA_STRATEGY_DISTRIBUTE:
	// run thread on node_num thread_n / (threads per node)
	node_num = thread_n % g_state.numa.n_nodes;
	break;
	case GGML_NUMA_STRATEGY_ISOLATE:
	// run thread on current_node
	node_num = g_state.numa.current_node;
	break;
	case GGML_NUMA_STRATEGY_NUMACTL:
	// use the cpuset that numactl gave us
	rv = pthread_setaffinity_np(pthread_self(), setsize, &g_state.numa.cpuset);
	if (rv) {
	fprintf(stderr, "warning: pthread_setaffinity_np() failed: %s\n",strerror(rv));
	}
	return;
	default:
	return;
	}

	struct ggml_numa_node * node = &g_state.numa.nodes[node_num];

	cpu_set_t * cpus = CPU_ALLOC(g_state.numa.total_cpus);
	CPU_ZERO_S(setsize, cpus);
	for (size_t i = 0; i < node->n_cpus; ++i) {
	CPU_SET_S(node->cpus[i], setsize, cpus);
	}

	rv = pthread_setaffinity_np(pthread_self(), setsize, cpus);
	if (rv) {
	fprintf(stderr, "warning: pthread_setaffinity_np() failed: %s\n", strerror(rv));
	}

	CPU_FREE(cpus);
	}

	static void clear_numa_thread_affinity(void) {
	if (!ggml_is_numa()) {
	return;
	}

	size_t setsize = CPU_ALLOC_SIZE(g_state.numa.total_cpus);

	cpu_set_t * cpus = CPU_ALLOC(g_state.numa.total_cpus);
	CPU_ZERO_S(setsize, cpus);
	for (unsigned i = 0; i < g_state.numa.total_cpus; ++i) {
	CPU_SET_S(i, setsize, cpus);
	}

	int rv = pthread_setaffinity_np(pthread_self(), setsize, cpus);
	if (rv) {
	fprintf(stderr, "warning: pthread_setaffinity_np() failed: %s\n", strerror(rv));
	}

	CPU_FREE(cpus);
	}
	#else
	// TODO: Windows etc.
	// (the linux implementation may also work on BSD, someone should test)
	static void set_numa_thread_affinity(int thread_n) { UNUSED(thread_n); }
	static void clear_numa_thread_affinity(void) {}
	#endif

	static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
	int n_tasks = 0;

	if (ggml_is_empty(node)) {
	// no need to multi-thread a no-op
	n_tasks = 1;
	return n_tasks;
	}

	switch (node->op) {
	case GGML_OP_CPY:
	case GGML_OP_DUP:
	case GGML_OP_CONT:
	case GGML_OP_ADD:
	case GGML_OP_ADD_ID:
	case GGML_OP_ADD1:
	case GGML_OP_ACC:
	case GGML_OP_CUMSUM:
	case GGML_OP_TRI:
	case GGML_OP_FILL:
	{
	n_tasks = n_threads;
	} break;
	case GGML_OP_SUB:
	case GGML_OP_SQR:
	case GGML_OP_SQRT:
	case GGML_OP_LOG:
	case GGML_OP_SIN:
	case GGML_OP_COS:
	case GGML_OP_SUM:
	case GGML_OP_SUM_ROWS:
	case GGML_OP_MEAN:
	case GGML_OP_ARGMAX:
	{
	n_tasks = 1;
	} break;
	case GGML_OP_COUNT_EQUAL:
	case GGML_OP_SOLVE_TRI:
	case GGML_OP_GATED_DELTA_NET:
	{
	n_tasks = n_threads;
	} break;
	case GGML_OP_REPEAT:
	case GGML_OP_REPEAT_BACK:
	case GGML_OP_LEAKY_RELU:
	{
	n_tasks = 1;
	} break;
	case GGML_OP_UNARY:
	switch (ggml_get_unary_op(node)) {
	case GGML_UNARY_OP_ABS:
	case GGML_UNARY_OP_SGN:
	case GGML_UNARY_OP_NEG:
	case GGML_UNARY_OP_STEP:
	case GGML_UNARY_OP_TANH:
	case GGML_UNARY_OP_ELU:
	case GGML_UNARY_OP_RELU:
	case GGML_UNARY_OP_SIGMOID:
	case GGML_UNARY_OP_HARDSWISH:
	case GGML_UNARY_OP_HARDSIGMOID:
	case GGML_UNARY_OP_EXP:
	case GGML_UNARY_OP_SOFTPLUS:
	case GGML_UNARY_OP_EXPM1:
	case GGML_UNARY_OP_FLOOR:
	case GGML_UNARY_OP_CEIL:
	case GGML_UNARY_OP_ROUND:
	case GGML_UNARY_OP_TRUNC:
	{
	n_tasks = 1;
	} break;

	case GGML_UNARY_OP_GELU:
	case GGML_UNARY_OP_GELU_ERF:
	case GGML_UNARY_OP_GELU_QUICK:
	case GGML_UNARY_OP_SILU:
	case GGML_UNARY_OP_XIELU:
	{
	n_tasks = n_threads;
	} break;
	default:
	GGML_ABORT("fatal error");
	}
	break;
	case GGML_OP_GLU:
	switch (ggml_get_glu_op(node)) {
	case GGML_GLU_OP_REGLU:
	case GGML_GLU_OP_GEGLU:
	case GGML_GLU_OP_SWIGLU:
	case GGML_GLU_OP_SWIGLU_OAI:
	case GGML_GLU_OP_GEGLU_ERF:
	case GGML_GLU_OP_GEGLU_QUICK:
	{
	n_tasks = n_threads;
	} break;
	default:
	GGML_ABORT("fatal error");
	}
	break;
	case GGML_OP_SILU_BACK:
	case GGML_OP_MUL:
	case GGML_OP_DIV:
	case GGML_OP_NORM:
	case GGML_OP_RMS_NORM:
	case GGML_OP_RMS_NORM_BACK:
	case GGML_OP_L2_NORM:
	case GGML_OP_GROUP_NORM:
	case GGML_OP_CONCAT:
	case GGML_OP_MUL_MAT:
	case GGML_OP_MUL_MAT_ID:
	case GGML_OP_OUT_PROD:
	{
	n_tasks = n_threads;
	} break;
	case GGML_OP_GET_ROWS:
	case GGML_OP_SET_ROWS:
	{
	// FIXME: get_rows can use additional threads, but the cost of launching additional threads
	// decreases performance with GPU offloading
	//n_tasks = n_threads;
	n_tasks = 1;
	} break;
	case GGML_OP_SCALE:
	case GGML_OP_SET:
	case GGML_OP_RESHAPE:
	case GGML_OP_VIEW:
	case GGML_OP_PERMUTE:
	case GGML_OP_TRANSPOSE:
	case GGML_OP_GET_ROWS_BACK:
	case GGML_OP_DIAG:
	{
	n_tasks = 1;
	} break;
	case GGML_OP_DIAG_MASK_ZERO:
	case GGML_OP_DIAG_MASK_INF:
	case GGML_OP_SOFT_MAX_BACK:
	case GGML_OP_ROPE:
	case GGML_OP_ROPE_BACK:
	case GGML_OP_ADD_REL_POS:
	{
	n_tasks = n_threads;
	} break;
	case GGML_OP_CLAMP:
	{
	n_tasks = 1; //TODO
	} break;
	case GGML_OP_SOFT_MAX:
	{
	n_tasks = MIN(n_threads, ggml_nrows(node->src[0]));
	} break;
	case GGML_OP_IM2COL:
	case GGML_OP_IM2COL_BACK:
	case GGML_OP_IM2COL_3D:
	case GGML_OP_CONV_2D:
	case GGML_OP_CONV_3D:
	case GGML_OP_CONV_2D_DW:
	case GGML_OP_CONV_TRANSPOSE_1D:
	case GGML_OP_CONV_TRANSPOSE_2D:
	{
	n_tasks = n_threads;
	} break;
	case GGML_OP_POOL_1D:
	case GGML_OP_POOL_2D:
	case GGML_OP_POOL_2D_BACK:
	{
	n_tasks = 1;
	} break;
	case GGML_OP_UPSCALE:
	case GGML_OP_PAD:
	case GGML_OP_PAD_REFLECT_1D:
	case GGML_OP_ROLL:
	case GGML_OP_ARANGE:
	case GGML_OP_TIMESTEP_EMBEDDING:
	case GGML_OP_ARGSORT:
	case GGML_OP_TOP_K:
	case GGML_OP_FLASH_ATTN_EXT:
	case GGML_OP_FLASH_ATTN_BACK:
	case GGML_OP_SSM_CONV:
	case GGML_OP_SSM_SCAN:
	case GGML_OP_RWKV_WKV6:
	case GGML_OP_GATED_LINEAR_ATTN:
	case GGML_OP_RWKV_WKV7:
	{
	n_tasks = n_threads;
	} break;
	case GGML_OP_WIN_PART:
	case GGML_OP_WIN_UNPART:
	case GGML_OP_GET_REL_POS:
	{
	n_tasks = 1;
	} break;
	case GGML_OP_MAP_CUSTOM1:
	{
	struct ggml_map_custom1_op_params p;
	memcpy(&p, node->op_params, sizeof(p));
	if (p.n_tasks == GGML_N_TASKS_MAX) {
	n_tasks = n_threads;
	} else {
	n_tasks = MIN(p.n_tasks, n_threads);
	}
	} break;
	case GGML_OP_MAP_CUSTOM2:
	{
	struct ggml_map_custom2_op_params p;
	memcpy(&p, node->op_params, sizeof(p));
	if (p.n_tasks == GGML_N_TASKS_MAX) {
	n_tasks = n_threads;
	} else {
	n_tasks = MIN(p.n_tasks, n_threads);
	}
	} break;
	case GGML_OP_MAP_CUSTOM3:
	{
	struct ggml_map_custom3_op_params p;
	memcpy(&p, node->op_params, sizeof(p));
	if (p.n_tasks == GGML_N_TASKS_MAX) {
	n_tasks = n_threads;
	} else {
	n_tasks = MIN(p.n_tasks, n_threads);
	}
	} break;
	case GGML_OP_CUSTOM:
	{
	struct ggml_custom_op_params p;
	memcpy(&p, node->op_params, sizeof(p));
	if (p.n_tasks == GGML_N_TASKS_MAX) {
	n_tasks = n_threads;
	} else {
	n_tasks = MIN(p.n_tasks, n_threads);
	}
	} break;
	case GGML_OP_CROSS_ENTROPY_LOSS:
	case GGML_OP_CROSS_ENTROPY_LOSS_BACK:
	case GGML_OP_OPT_STEP_ADAMW:
	case GGML_OP_OPT_STEP_SGD:
	{
	n_tasks = n_threads;
	} break;
	case GGML_OP_NONE:
	{
	n_tasks = 1;
	} break;
	case GGML_OP_COUNT:
	{
	GGML_ABORT("fatal error");
	}
	default:
	{
	fprintf(stderr, "%s: op not implemented: ", __func__);
	if (node->op < GGML_OP_COUNT) {
	fprintf(stderr, "%s\n", ggml_op_name(node->op));
	} else {
	fprintf(stderr, "%d\n", node->op);
	}
	GGML_ABORT("fatal error");
	}
	}

	assert(n_tasks > 0);

	return n_tasks;
	}

	static thread_ret_t ggml_graph_compute_secondary_thread(void* data);

	#if defined(_WIN32)
	#include "windows.h"

	// TODO: support > 64 CPUs
	static bool ggml_thread_apply_affinity(bool * mask) {
	HANDLE h = GetCurrentThread();
	uint64_t bitmask = 0ULL;

	assert(GGML_MAX_N_THREADS >= 64);

	for (int32_t i = 0; i < 8; i++) {
	int32_t idx = i * 8;
	uint8_t val = 0;
	val \|= mask[idx + 0] << 0;
	val \|= mask[idx + 1] << 1;
	val \|= mask[idx + 2] << 2;
	val \|= mask[idx + 3] << 3;
	val \|= mask[idx + 4] << 4;
	val \|= mask[idx + 5] << 5;
	val \|= mask[idx + 6] << 6;
	val \|= mask[idx + 7] << 7;
	bitmask \|= (uint64_t)val << idx;
	}

	for (int32_t i = 64; i < GGML_MAX_N_THREADS; i++) {
	if (mask[i]) {
	fprintf(stderr, "warn: setting thread-affinity for > 64 CPUs isn't supported on windows!\n");
	break;
	}
	}

	DWORD_PTR m = (DWORD_PTR)bitmask;

	m = SetThreadAffinityMask(h, m);

	return m != 0;
	}

	static bool ggml_thread_apply_priority(int32_t prio) {
	// Note that on Windows the Process Priority Class must be updated in order to set Thread priority.
	// This is up to the applications.
	DWORD p = THREAD_PRIORITY_NORMAL;
	switch (prio) {
	case GGML_SCHED_PRIO_LOW: p = THREAD_PRIORITY_BELOW_NORMAL; break;
	case GGML_SCHED_PRIO_NORMAL: p = THREAD_PRIORITY_NORMAL; break;
	case GGML_SCHED_PRIO_MEDIUM: p = THREAD_PRIORITY_ABOVE_NORMAL; break;
	case GGML_SCHED_PRIO_HIGH: p = THREAD_PRIORITY_HIGHEST; break;
	case GGML_SCHED_PRIO_REALTIME: p = THREAD_PRIORITY_TIME_CRITICAL; break;
	}

	if (prio != GGML_SCHED_PRIO_LOW) {
	// Tell Windows that this thread should not be throttled (needs its own CPU core).
	// Newer Windows 11 versions aggressively park (offline) CPU cores and often place
	// all our threads onto the first 4 cores which results in terrible performance with
	// n_threads > 4
	#if _WIN32_WINNT >= 0x0602
	THREAD_POWER_THROTTLING_STATE t;
	ZeroMemory(&t, sizeof(t));
	t.Version = THREAD_POWER_THROTTLING_CURRENT_VERSION;
	t.ControlMask = THREAD_POWER_THROTTLING_EXECUTION_SPEED;
	t.StateMask = 0;

	if (!SetThreadInformation(GetCurrentThread(), ThreadPowerThrottling, &t, sizeof(t))) {
	GGML_LOG_DEBUG("failed to disable thread power throttling %d : (%d)\n", prio, (int) GetLastError());
	return false;
	}
	#endif
	}

	if (prio == GGML_SCHED_PRIO_NORMAL) {
	// Keep inherited policy/priority
	return true;
	}

	if (!SetThreadPriority(GetCurrentThread(), p)) {
	fprintf(stderr, "warn: failed to set thread priority %d : (%d)\n", prio, (int) GetLastError());
	return false;
	}

	return true;
	}

	#elif defined(__APPLE__)
	#include <sys/types.h>
	#include <sys/resource.h>

	static bool ggml_thread_apply_affinity(const bool * mask) {
	// Not supported on Apple platforms
	UNUSED(mask);
	return true;
	}

	static bool ggml_thread_apply_priority(int32_t prio) {
	struct sched_param p;
	int32_t policy = SCHED_OTHER;
	switch (prio) {
	// TODO: there seems to be no way to set lower prio on Apple platforms
	case GGML_SCHED_PRIO_LOW: policy = SCHED_OTHER; p.sched_priority = 0; break;
	case GGML_SCHED_PRIO_NORMAL: policy = SCHED_OTHER; p.sched_priority = 0; break;
	case GGML_SCHED_PRIO_MEDIUM: policy = SCHED_FIFO; p.sched_priority = 40; break;
	case GGML_SCHED_PRIO_HIGH: policy = SCHED_FIFO; p.sched_priority = 80; break;
	case GGML_SCHED_PRIO_REALTIME: policy = SCHED_FIFO; p.sched_priority = 90; break;
	}

	if (prio == GGML_SCHED_PRIO_NORMAL) {
	// Keep inherited policy/priority
	return true;
	}

	int32_t err = pthread_setschedparam(pthread_self(), policy, &p);
	if (err != 0) {
	fprintf(stderr, "warn: failed to set thread priority %d : %s (%d)\n", prio, strerror(err), err);
	return false;
	}

	return true;
	}

	#elif defined(__gnu_linux__)
	// TODO: this may not work on BSD, to be verified

	static bool ggml_thread_apply_affinity(const bool * mask) {
	cpu_set_t cpuset;
	int err;

	CPU_ZERO(&cpuset);

	for (uint32_t i = 0; i < GGML_MAX_N_THREADS; i++) {
	if (mask[i]) {
	GGML_PRINT_DEBUG("Thread %lx: adding %d to cpuset\n", pthread_self(), i);
	CPU_SET(i, &cpuset);
	}
	}

	#ifdef __ANDROID__
	err = sched_setaffinity(0, sizeof(cpuset), &cpuset);
	if (err < 0) {
	err = errno;
	}
	#else
	err = pthread_setaffinity_np(pthread_self(), sizeof(cpuset), &cpuset);
	#endif
	if (err != 0) {
	fprintf(stderr, "warn: failed to set affinity mask 0x%llx : %s (%d)\n", (unsigned long long)mask, strerror(err), err);
	return false;
	}

	return true;
	}

	static bool ggml_thread_apply_priority(int32_t prio) {
	struct sched_param p;
	int32_t policy = SCHED_OTHER;
	switch (prio) {
	case GGML_SCHED_PRIO_LOW: policy = SCHED_BATCH; p.sched_priority = 0; break;
	case GGML_SCHED_PRIO_NORMAL: policy = SCHED_OTHER; p.sched_priority = 0; break;
	case GGML_SCHED_PRIO_MEDIUM: policy = SCHED_FIFO; p.sched_priority = 40; break;
	case GGML_SCHED_PRIO_HIGH: policy = SCHED_FIFO; p.sched_priority = 80; break;
	case GGML_SCHED_PRIO_REALTIME: policy = SCHED_FIFO; p.sched_priority = 90; break;
	}

	if (prio == GGML_SCHED_PRIO_NORMAL) {
	// Keep inherited policy/priority
	return true;
	}

	int32_t err = pthread_setschedparam(pthread_self(), policy, &p);
	if (err != 0) {
	fprintf(stderr, "warn: failed to set thread priority %d : %s (%d)\n", prio, strerror(err), err);
	return false;
	}

	return true;
	}

	#else // unsupported platforms

	static bool ggml_thread_apply_affinity(const bool * mask) {
	UNUSED(mask);
	return true;
	}

	static bool ggml_thread_apply_priority(int32_t prio) {
	UNUSED(prio);
	return true;
	}

	#endif

	static bool ggml_thread_cpumask_is_valid(const bool * mask) {
	for (int i = 0; i < GGML_MAX_N_THREADS; i++) {
	if (mask[i]) { return true; }
	}
	return false;
	}

	static void ggml_thread_cpumask_next(const bool * global_mask, bool * local_mask, bool strict, int32_t* iter) {
	if (!strict) {
	memcpy(local_mask, global_mask, GGML_MAX_N_THREADS);
	return;
	} else {
	memset(local_mask, 0, GGML_MAX_N_THREADS);
	int32_t base_idx = *iter;
	for (int32_t i = 0; i < GGML_MAX_N_THREADS; i++) {
	int32_t idx = base_idx + i;
	if (idx >= GGML_MAX_N_THREADS) {
	// Just a cheaper modulo
	idx -= GGML_MAX_N_THREADS;
	}
	if (global_mask[idx]) {
	local_mask[idx] = 1;
	*iter = idx + 1;
	return;
	}
	}
	}
	}

	void ggml_threadpool_free(struct ggml_threadpool* threadpool) {
	if (!threadpool) return;

	const int n_threads = threadpool->n_threads;

	#ifndef GGML_USE_OPENMP
	struct ggml_compute_state* workers = threadpool->workers;

	ggml_mutex_lock(&threadpool->mutex);

	threadpool->stop = true;
	threadpool->pause = false;

	ggml_cond_broadcast(&threadpool->cond);
	ggml_mutex_unlock(&threadpool->mutex);

	for (int j = 1; j < n_threads; j++) {
	int32_t rc = ggml_thread_join(workers[j].thrd, NULL);
	GGML_ASSERT(rc == GGML_EXIT_SUCCESS \|\| rc == GGML_EXIT_ABORTED);
	UNUSED(rc);
	}

	ggml_mutex_destroy(&threadpool->mutex);
	ggml_cond_destroy(&threadpool->cond);
	#endif // GGML_USE_OPENMP

	const size_t workers_size = sizeof(struct ggml_compute_state) * n_threads;
	ggml_aligned_free(threadpool->workers, workers_size);
	ggml_aligned_free(threadpool, sizeof(struct ggml_threadpool));
	}

	#ifndef GGML_USE_OPENMP
	// pause/resume must be called under mutex
	static void ggml_threadpool_pause_locked(struct ggml_threadpool * threadpool) {
	GGML_PRINT_DEBUG("Pausing threadpool\n");
	threadpool->pause = true;
	ggml_cond_broadcast(&threadpool->cond);
	}

	static void ggml_threadpool_resume_locked(struct ggml_threadpool * threadpool) {
	GGML_PRINT_DEBUG("Resuming threadpool\n");
	threadpool->pause = false;
	ggml_cond_broadcast(&threadpool->cond);
	}
	#endif

	void ggml_threadpool_pause(struct ggml_threadpool * threadpool) {
	#ifndef GGML_USE_OPENMP
	ggml_mutex_lock(&threadpool->mutex);
	if (!threadpool->pause) {
	ggml_threadpool_pause_locked(threadpool);
	}
	ggml_mutex_unlock(&threadpool->mutex);
	#else
	UNUSED(threadpool);
	#endif
	}

	void ggml_threadpool_resume(struct ggml_threadpool * threadpool) {
	#ifndef GGML_USE_OPENMP
	ggml_mutex_lock(&threadpool->mutex);
	if (threadpool->pause) {
	ggml_threadpool_resume_locked(threadpool);
	}
	ggml_mutex_unlock(&threadpool->mutex);
	#else
	UNUSED(threadpool);
	#endif
	}

	struct ggml_cplan ggml_graph_plan(
	const struct ggml_cgraph * cgraph,
	int n_threads,
	struct ggml_threadpool * threadpool) {

	if (threadpool == NULL) {
	//GGML_PRINT_DEBUG("Threadpool is not specified. Will create a disposable threadpool : n_threads %d\n", n_threads);
	}
	if (n_threads <= 0) {
	n_threads = threadpool ? threadpool->n_threads : GGML_DEFAULT_N_THREADS;
	}

	#if defined(__EMSCRIPTEN__) && !defined(__EMSCRIPTEN_PTHREADS__)
	// Emscripten without pthreads support can only use a single thread
	n_threads = 1;
	#endif

	size_t work_size = 0;

	struct ggml_cplan cplan;
	memset(&cplan, 0, sizeof(struct ggml_cplan));

	int max_tasks = 1;

	// thread scheduling for the different operations + work buffer size estimation
	for (int i = 0; i < cgraph->n_nodes; i++) {
	struct ggml_tensor * node = cgraph->nodes[i];

	const int n_tasks = ggml_get_n_tasks(node, n_threads);

	max_tasks = MAX(max_tasks, n_tasks);

	size_t cur = 0;

	if (!ggml_cpu_extra_work_size(n_threads, node, &cur)) {
	switch (node->op) {
	case GGML_OP_CPY:
	case GGML_OP_DUP:
	{
	if (ggml_is_quantized(node->type) \|\|
	// F16 -> BF16 and BF16 -> F16 copies go through intermediate F32
	(node->src[0]->type == GGML_TYPE_F16 && node->src[1] && node->src[1]->type == GGML_TYPE_BF16) \|\|
	(node->src[0]->type == GGML_TYPE_BF16 && node->src[1] && node->src[1]->type == GGML_TYPE_F16) \|\|
	// conversion between F32 and I32
	(node->src[0]->type == GGML_TYPE_F32 && node->src[1] && node->src[1]->type == GGML_TYPE_I32) \|\|
	(node->src[0]->type == GGML_TYPE_I32 && node->src[1] && node->src[1]->type == GGML_TYPE_F32)) {
	cur = ggml_type_size(GGML_TYPE_F32) * node->ne[0] * n_tasks;
	}
	} break;
	case GGML_OP_ADD:
	case GGML_OP_ADD_ID:
	case GGML_OP_ADD1:
	{
	if (ggml_is_quantized(node->src[0]->type)) {
	cur = ggml_type_size(GGML_TYPE_F32) * node->src[0]->ne[0] * n_tasks;
	}
	} break;
	case GGML_OP_ACC:
	{
	if (ggml_is_quantized(node->src[0]->type)) {
	cur = ggml_type_size(GGML_TYPE_F32) * node->src[1]->ne[0] * n_tasks;
	}
	} break;
	case GGML_OP_COUNT_EQUAL:
	{
	cur = ggml_type_size(node->type)*n_tasks;
	} break;
	case GGML_OP_MUL_MAT:
	{
	const enum ggml_type vec_dot_type = type_traits_cpu[node->src[0]->type].vec_dot_type;

	if (node->src[1]->type != vec_dot_type) {
	cur = ggml_row_size(vec_dot_type, ggml_nelements(node->src[1]));
	}
	} break;
	case GGML_OP_MUL_MAT_ID:
	{
	cur = 0;
	const struct ggml_tensor * src0 = node->src[0];
	const struct ggml_tensor * src1 = node->src[1];
	const struct ggml_tensor * ids = node->src[2];
	const enum ggml_type vec_dot_type = type_traits_cpu[src0->type].vec_dot_type;
	const int n_as = src0->ne[2];
	// src1
	if (src1->type != vec_dot_type) {
	cur += ggml_row_size(vec_dot_type, ggml_nelements(src1)) + sizeof(int64_t);
	}
	// matrix_row_counts
	cur += n_as * sizeof(int64_t) + sizeof(int64_t);
	// matrix_rows
	cur += n_asids->ne[0]ids->ne[1]*sizeof(struct mmid_row_mapping) + sizeof(int64_t);
	// atomic_current_chunk
	cur += CACHE_LINE_SIZE*n_as + CACHE_LINE_SIZE;
	} break;
	case GGML_OP_OUT_PROD:
	{
	if (ggml_is_quantized(node->src[0]->type)) {
	cur = ggml_type_size(GGML_TYPE_F32) * node->src[0]->ne[0] * n_tasks;
	}
	} break;
	case GGML_OP_SOFT_MAX:
	case GGML_OP_ROPE:
	case GGML_OP_ROPE_BACK:
	{
	cur = ggml_type_size(GGML_TYPE_F32) * node->ne[0] * n_tasks;
	} break;
	case GGML_OP_CONV_TRANSPOSE_1D:
	{
	GGML_ASSERT(node->src[0]->ne[3] == 1);
	GGML_ASSERT(node->src[1]->ne[2] == 1);
	GGML_ASSERT(node->src[1]->ne[3] == 1);

	const int64_t ne00 = node->src[0]->ne[0]; // K
	const int64_t ne01 = node->src[0]->ne[1]; // Cout
	const int64_t ne02 = node->src[0]->ne[2]; // Cin
	const int64_t ne10 = node->src[1]->ne[0]; // L
	const int64_t ne11 = node->src[1]->ne[1]; // Cin

	if ((node->src[0]->type == GGML_TYPE_F16 \|\|
	node->src[0]->type == GGML_TYPE_BF16) &&
	node->src[1]->type == GGML_TYPE_F32) {
	cur += sizeof(ggml_fp16_t)ne00ne01*ne02;
	cur += sizeof(ggml_fp16_t)ne10ne11;
	} else if (node->src[0]->type == GGML_TYPE_F32 &&
	node->src[1]->type == GGML_TYPE_F32) {
	cur += sizeof(float)ne00ne01*ne02;
	cur += sizeof(float)ne10ne11;
	} else {
	GGML_ABORT("fatal error");
	}
	} break;
	case GGML_OP_CONV_2D:
	case GGML_OP_CONV_3D:
	{
	cur = GGML_IM2COL_WORK_SIZE;
	} break;
	case GGML_OP_CONV_TRANSPOSE_2D:
	{
	const int64_t ne00 = node->src[0]->ne[0]; // W
	const int64_t ne01 = node->src[0]->ne[1]; // H
	const int64_t ne02 = node->src[0]->ne[2]; // Channels Out
	const int64_t ne03 = node->src[0]->ne[3]; // Channels In

	const int64_t ne10 = node->src[1]->ne[0]; // W
	const int64_t ne11 = node->src[1]->ne[1]; // H
	const int64_t ne12 = node->src[1]->ne[2]; // Channels In

	cur += sizeof(ggml_fp16_t)ne00ne01ne02ne03;
	cur += sizeof(ggml_fp16_t)ne10ne11*ne12;
	} break;
	case GGML_OP_TOP_K:
	{
	cur += sizeof(int32_t)node->src[0]->ne[0]n_tasks;
	} break;
	case GGML_OP_FLASH_ATTN_EXT:
	{
	const int64_t neq2 = node->src[0]->ne[2]; // number of query heads
	const int64_t DK = node->src[1]->ne[0];
	const int64_t DV = node->src[2]->ne[0];

	// Tiled flash attention scratch (tile sizes defined in common.h)
	// Per-thread: Q_q + KQ + mask + VKQ32 + V32 + K_f32 + padding
	size_t prefill = sizeof(float)(GGML_FA_TILE_QDK + 2GGML_FA_TILE_QGGML_FA_TILE_KV + GGML_FA_TILE_QDV + GGML_FA_TILE_KVDV + GGML_FA_TILE_KVDK)n_tasks;

	// Decode path: n_kv_chunks = n_tasks (one chunk per thread)
	// Per-thread: VKQ accmulator (DV), partial M, partial S + intra-thread scratch for V, Q and VKQ
	size_t n_chunks = n_tasks;
	size_t decode = sizeof(float)(neq2n_chunks(2+DV) + n_tasks(DK + 2*DV));

	cur += MAX(prefill, decode);
	} break;
	case GGML_OP_FLASH_ATTN_BACK:
	{
	const int64_t D = node->src[0]->ne[0];
	const int64_t ne11 = ggml_up(node->src[1]->ne[1], GGML_SOFT_MAX_UNROLL);
	const int64_t mxDn = MAX(D, ne11) * 2; // *2 because of S and SM in ggml_compute_forward_flash_attn_back
	if (node->src[1]->type == GGML_TYPE_F32) {
	cur = sizeof(float)mxDnn_tasks; // TODO: this can become (n_tasks-1)
	cur += sizeof(float)mxDnn_tasks; // this is overestimated by x2
	} else if (node->src[1]->type == GGML_TYPE_F16) {
	cur = sizeof(float)mxDnn_tasks; // TODO: this can become (n_tasks-1)
	cur += sizeof(float)mxDnn_tasks; // this is overestimated by x2
	} else if (node->src[1]->type == GGML_TYPE_BF16) {
	cur = sizeof(float)mxDnn_tasks; // TODO: this can become (n_tasks-1)
	cur += sizeof(float)mxDnn_tasks; // this is overestimated by x2
	}
	} break;

	case GGML_OP_CROSS_ENTROPY_LOSS:
	{
	cur = ggml_type_size(node->type)(n_tasks + node->src[0]->ne[0]n_tasks);
	} break;
	case GGML_OP_GATED_DELTA_NET:
	{
	const int64_t S_v = node->src[2]->ne[0];
	cur = S_v * sizeof(float) * n_tasks;
	} break;
	case GGML_OP_COUNT:
	{
	GGML_ABORT("fatal error");
	}
	default:
	break;
	}
	}

	work_size = MAX(work_size, cur);
	}

	if (work_size > 0) {
	work_size += CACHE_LINE_SIZE*(n_threads);
	}

	cplan.threadpool = threadpool;
	cplan.n_threads = MIN(max_tasks, n_threads);
	cplan.work_size = work_size;
	cplan.work_data = NULL;

	return cplan;
	}

	static thread_ret_t ggml_graph_compute_thread(void * data) {
	struct ggml_compute_state * state = (struct ggml_compute_state *) data;
	struct ggml_threadpool * tp = state->threadpool;

	const struct ggml_cgraph * cgraph = tp->cgraph;
	const struct ggml_cplan * cplan = tp->cplan;

	set_numa_thread_affinity(state->ith);

	struct ggml_compute_params params = {
	/.ith =/ state->ith,
	/.nth =/ atomic_load_explicit(&tp->n_graph, memory_order_relaxed) & GGML_THREADPOOL_N_THREADS_MASK,
	/.wsize =/ cplan->work_size,
	/.wdata =/ cplan->work_data,
	/.threadpool =/ tp,
	/.use_ref =/ cplan->use_ref,
	};

	#ifdef GGML_USE_OPENMP
	GGML_PRINT_DEBUG("thread #%d compute-start cplan %p\n", state->ith, (const void *)cplan);
	#else
	GGML_PRINT_DEBUG("thread #%d compute-start cplan %p last-graph %d\n", state->ith, (const void *)cplan, state->last_graph);
	#endif

	for (int node_n = 0; node_n < cgraph->n_nodes && atomic_load_explicit(&tp->abort, memory_order_relaxed) != node_n; node_n++) {
	struct ggml_tensor * node = cgraph->nodes[node_n];

	if (ggml_op_is_empty(node->op)) {
	// skip NOPs
	continue;
	}

	if ((node->flags & GGML_TENSOR_FLAG_COMPUTE) == 0) {
	continue;
	}

	ggml_compute_forward(&params, node);

	if (state->ith == 0 && cplan->abort_callback &&
	cplan->abort_callback(cplan->abort_callback_data)) {
	atomic_store_explicit(&tp->abort, node_n + 1, memory_order_relaxed);
	tp->ec = GGML_STATUS_ABORTED;
	}

	if (node_n + 1 < cgraph->n_nodes) {
	ggml_barrier(state->threadpool);
	}
	}

	#ifdef GGML_USE_OPENMP
	GGML_PRINT_DEBUG("thread #%d compute-done cplan %p\n", state->ith, (const void *)cplan);
	#else
	GGML_PRINT_DEBUG("thread #%d compute-done cplan %p last-graph %d\n", state->ith, (const void *)cplan, state->last_graph);
	#endif

	ggml_barrier(state->threadpool);

	return 0;
	}

	#ifndef GGML_USE_OPENMP

	// check if thread is ready to proceed (exit from polling or sleeping)
	// returns true if loops should exit, sets state->pending to indicate new work
	static inline bool ggml_graph_compute_thread_ready(struct ggml_compute_state * state) {
	struct ggml_threadpool * threadpool = state->threadpool;

	if (state->pending \|\| threadpool->stop \|\| threadpool->pause) { return true; }

	// check for new graph/work
	int n_graph = atomic_load_explicit(&threadpool->n_graph, memory_order_relaxed);
	int n_threads = n_graph & GGML_THREADPOOL_N_THREADS_MASK;
	if (n_graph != state->last_graph) {
	state->pending = (state->ith < n_threads);
	state->last_graph = n_graph;
	return true;
	}

	return false;
	}

	// sync thread state after polling
	static inline void ggml_graph_compute_thread_sync(struct ggml_compute_state * state) {
	// TSAN doesn't support standalone fence yet, we use a dummy read-modify-write instead
	#ifdef GGML_TSAN_ENABLED
	atomic_fetch_add_explicit(&state->threadpool->n_graph, 0, memory_order_seq_cst);
	#else
	atomic_thread_fence(memory_order_seq_cst);
	#endif
	UNUSED(state);
	}

	static inline bool ggml_graph_compute_poll_for_work(struct ggml_compute_state * state) {
	struct ggml_threadpool * threadpool = state->threadpool;

	// This seems to make 0 ... 100 a decent range for polling level across modern processors.
	// Perhaps, we can adjust it dynamically based on load and things.
	const uint64_t n_rounds = 1024UL * 128 * threadpool->poll;

	for (uint64_t i=0; !ggml_graph_compute_thread_ready(state) && i < n_rounds; i++) {
	// No new work. Keep polling.
	ggml_thread_cpu_relax();
	}

	return state->pending;
	}

	static inline bool ggml_graph_compute_check_for_work(struct ggml_compute_state * state) {
	struct ggml_threadpool * threadpool = state->threadpool;

	if (ggml_graph_compute_poll_for_work(state)) {
	ggml_graph_compute_thread_sync(state);
	return state->pending;
	}

	ggml_mutex_lock_shared(&threadpool->mutex);
	while (!ggml_graph_compute_thread_ready(state)) {
	// No new work. Wait for the signal.
	GGML_PRINT_DEBUG("thread #%d waiting for work (sleeping)\n", state->ith);
	ggml_cond_wait(&threadpool->cond, &threadpool->mutex);
	}
	ggml_mutex_unlock_shared(&threadpool->mutex);

	return state->pending;
	}

	static thread_ret_t ggml_graph_compute_secondary_thread(void* data) {
	struct ggml_compute_state * state = (struct ggml_compute_state *) data;
	struct ggml_threadpool * threadpool = state->threadpool;

	ggml_thread_apply_priority(threadpool->prio);
	if (ggml_thread_cpumask_is_valid(state->cpumask)) {
	ggml_thread_apply_affinity(state->cpumask);
	}

	while (true) {
	// Check if we need to sleep
	while (threadpool->pause) {
	GGML_PRINT_DEBUG("thread #%d inside pause loop\n", state->ith);
	ggml_mutex_lock_shared(&threadpool->mutex);
	if (threadpool->pause) {
	ggml_cond_wait(&threadpool->cond, &threadpool->mutex);
	}
	GGML_PRINT_DEBUG("thread #%d resuming after wait\n", state->ith);
	ggml_mutex_unlock_shared(&threadpool->mutex);
	}

	// This needs to be checked for after the cond_wait
	if (threadpool->stop) break;

	// Check if there is new work
	// The main thread is the only one that can dispatch new work

	ggml_graph_compute_check_for_work(state);
	if (state->pending) {
	state->pending = false;
	ggml_graph_compute_thread(state);
	}
	}

	return (thread_ret_t) 0;
	}

	// Start processing new graph
	static void ggml_graph_compute_kickoff(struct ggml_threadpool * threadpool, int n_threads)
	{
	// Always take the mutex here because the worker threads are doing hybrid poll/wait

	ggml_mutex_lock(&threadpool->mutex);

	// Update the number of active threads and the graph count
	int n_graph = atomic_load_explicit(&threadpool->n_graph, memory_order_relaxed) >> GGML_THREADPOOL_N_THREADS_BITS;
	n_graph = ((n_graph + 1) << GGML_THREADPOOL_N_THREADS_BITS) \| (n_threads & GGML_THREADPOOL_N_THREADS_MASK);

	GGML_PRINT_DEBUG("compute-kickoff: n_threads %d n_graph %d\n", n_threads, n_graph);

	// Indicate the graph is ready to be processed
	// We need the full seq-cst fence here because of the polling threads (used in thread_sync)
	atomic_store_explicit(&threadpool->n_graph, n_graph, memory_order_seq_cst);

	if (threadpool->pause) {
	// Update main thread prio and affinity to match the threadpool settings
	ggml_thread_apply_priority(threadpool->prio);
	if (ggml_thread_cpumask_is_valid(threadpool->workers[0].cpumask)) {
	ggml_thread_apply_affinity(threadpool->workers[0].cpumask);
	}

	// resume does cond broadcast
	ggml_threadpool_resume_locked(threadpool);
	} else {
	ggml_cond_broadcast(&threadpool->cond);
	}

	ggml_mutex_unlock(&threadpool->mutex);
	}

	#endif // GGML_USE_OPENMP

	static struct ggml_threadpool * ggml_threadpool_new_impl(
	struct ggml_threadpool_params * tpp,
	struct ggml_cgraph * cgraph,
	struct ggml_cplan * cplan) {

	struct ggml_threadpool * threadpool =
	ggml_aligned_malloc(sizeof(struct ggml_threadpool));
	{
	threadpool->cgraph = cgraph;
	threadpool->cplan = cplan;
	threadpool->n_graph = 0;
	threadpool->n_barrier = 0;
	threadpool->n_barrier_passed = 0;
	threadpool->current_chunk = 0;
	threadpool->stop = false;
	threadpool->pause = tpp->paused;
	threadpool->abort = -1;
	threadpool->workers = NULL;
	threadpool->n_threads = tpp->n_threads;
	threadpool->poll = tpp->poll;
	threadpool->prio = tpp->prio;
	threadpool->ec = GGML_STATUS_SUCCESS;
	}

	// Allocate and init workers state
	const size_t workers_size = sizeof(struct ggml_compute_state) * tpp->n_threads;
	struct ggml_compute_state * workers = ggml_aligned_malloc(workers_size);

	memset(workers, 0, workers_size);
	for (int j = 0; j < tpp->n_threads; j++) {
	workers[j].threadpool = threadpool;
	workers[j].ith = j;
	}

	threadpool->workers = workers;

	#ifdef GGML_USE_OPENMP
	int32_t cpumask_iter = 0;

	// Compute CPU masks for each thread
	for (int j = 0; j < tpp->n_threads; j++) {
	ggml_thread_cpumask_next(tpp->cpumask, workers[j].cpumask, tpp->strict_cpu, &cpumask_iter);
	}
	#else // GGML_USE_OPENMP
	ggml_mutex_init(&threadpool->mutex);
	ggml_cond_init(&threadpool->cond);

	// Spin the threads for all workers, and update CPU placements.
	// Place the main thread last (towards the higher numbered CPU cores).

	int32_t cpumask_iter = 0;

	for (int j = 1; j < tpp->n_threads; j++) {
	ggml_thread_cpumask_next(tpp->cpumask, workers[j].cpumask, tpp->strict_cpu, &cpumask_iter);

	int32_t rc = ggml_thread_create(&workers[j].thrd, NULL, ggml_graph_compute_secondary_thread, &workers[j]);
	GGML_ASSERT(rc == 0);
	}

	ggml_thread_cpumask_next(tpp->cpumask, workers[0].cpumask, tpp->strict_cpu, &cpumask_iter);

	if (!threadpool->pause) {
	// Update main thread prio and affinity at the start, otherwise we'll do it in resume
	ggml_thread_apply_priority(threadpool->prio);
	if (ggml_thread_cpumask_is_valid(threadpool->workers[0].cpumask)) {
	ggml_thread_apply_affinity(threadpool->workers[0].cpumask);
	}
	}
	#endif // GGML_USE_OPENMP

	return threadpool;
	}

	struct ggml_threadpool * ggml_threadpool_new(struct ggml_threadpool_params * tpp) {
	return ggml_threadpool_new_impl(tpp, NULL, NULL);
	}

	enum ggml_status ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan) {
	ggml_cpu_init();

	GGML_ASSERT(cplan);
	GGML_ASSERT(cplan->n_threads > 0);
	GGML_ASSERT(cplan->work_size == 0 \|\| cplan->work_data != NULL);

	int n_threads = cplan->n_threads;
	struct ggml_threadpool * threadpool = cplan->threadpool;

	bool disposable_threadpool = false;

	if (threadpool == NULL) {
	//GGML_PRINT_DEBUG("Threadpool is not specified. Will create a disposable threadpool : n_threads %d\n", n_threads);
	disposable_threadpool = true;

	struct ggml_threadpool_params ttp = ggml_threadpool_params_default(n_threads);
	threadpool = ggml_threadpool_new_impl(&ttp, cgraph, cplan);
	} else {
	// Reset some of the parameters that need resetting
	// No worker threads should be accessing the parameters below at this stage
	threadpool->cgraph = cgraph;
	threadpool->cplan = cplan;
	threadpool->current_chunk = 0;
	threadpool->abort = -1;
	threadpool->ec = GGML_STATUS_SUCCESS;
	}

	#ifdef GGML_USE_OPENMP
	if (n_threads > 1) {
	#pragma omp parallel num_threads(n_threads)
	{
	#pragma omp single
	{
	// update the number of threads from the actual number of threads that we got from OpenMP
	n_threads = omp_get_num_threads();
	atomic_store_explicit(&threadpool->n_graph, n_threads, memory_order_relaxed);
	}

	// Apply thread CPU mask and priority
	int ith = omp_get_thread_num();

	ggml_thread_apply_priority(threadpool->prio);
	if (ggml_thread_cpumask_is_valid(threadpool->workers[ith].cpumask)) {
	ggml_thread_apply_affinity(threadpool->workers[ith].cpumask);
	}
	ggml_graph_compute_thread(&threadpool->workers[ith]);
	}
	} else {
	atomic_store_explicit(&threadpool->n_graph, 1, memory_order_relaxed);
	ggml_graph_compute_thread(&threadpool->workers[0]);
	}
	#else
	if (n_threads > threadpool->n_threads) {
	GGML_LOG_WARN("cplan requested more threads (%d) than available (%d)\n", n_threads, threadpool->n_threads);
	n_threads = threadpool->n_threads;
	}

	// Kick all threads to start the new graph
	ggml_graph_compute_kickoff(threadpool, n_threads);

	// This is a work thread too
	ggml_graph_compute_thread(&threadpool->workers[0]);
	#endif

	// don't leave affinity set on the main thread
	clear_numa_thread_affinity();

	enum ggml_status ret = threadpool->ec;

	if (disposable_threadpool) {
	ggml_threadpool_free(threadpool);
	}

	return ret;
	}

	enum ggml_status ggml_graph_compute_with_ctx(struct ggml_context * ctx, struct ggml_cgraph * cgraph, int n_threads) {
	struct ggml_cplan cplan = ggml_graph_plan(cgraph, n_threads, NULL);

	cplan.work_data = (uint8_t *)ggml_new_buffer(ctx, cplan.work_size);

	return ggml_graph_compute(cgraph, &cplan);
	}

	void ggml_cpu_fp32_to_fp32(const float * x, float * y, int64_t n) {
	memcpy(y, x, n * sizeof(float));
	}

	void ggml_cpu_fp32_to_fp16(const float * x, ggml_fp16_t * y, int64_t n) {
	int64_t i = 0;
	#if defined(__F16C__)
	#if defined(__AVX512F__)
	for (; i + 15 < n; i += 16) {
	__m512 x_vec = _mm512_loadu_ps(x + i);
	__m256i y_vec = _mm512_cvtps_ph(x_vec, _MM_FROUND_TO_NEAREST_INT);
	_mm256_storeu_si256((__m256i *)(y + i), y_vec);
	}
	#endif
	for (; i + 7 < n; i += 8) {
	__m256 x_vec = _mm256_loadu_ps(x + i);
	__m128i y_vec = _mm256_cvtps_ph(x_vec, _MM_FROUND_TO_NEAREST_INT);
	_mm_storeu_si128((__m128i *)(y + i), y_vec);
	}
	for (; i + 3 < n; i += 4) {
	__m128 x_vec = _mm_loadu_ps(x + i);
	__m128i y_vec = _mm_cvtps_ph(x_vec, _MM_FROUND_TO_NEAREST_INT);
	_mm_storel_epi64((__m128i *)(y + i), y_vec);
	}
	#elif defined(__riscv_zvfh)
	for (int vl; i < n; i += vl) {
	vl = __riscv_vsetvl_e32m2(n - i);
	vfloat32m2_t vx = __riscv_vle32_v_f32m2(&x[i], vl);
	vfloat16m1_t vy = __riscv_vfncvt_f_f_w_f16m1(vx, vl);
	__riscv_vse16_v_f16m1((_Float16 *)&y[i], vy, vl);
	}
	#endif
	for (; i < n; ++i) {
	y[i] = GGML_CPU_FP32_TO_FP16(x[i]);
	}
	}

	void ggml_cpu_fp16_to_fp32(const ggml_fp16_t * x, float * y, int64_t n) {
	int64_t i = 0;
	#if defined(__F16C__)
	#if defined(__AVX512F__)
	for (; i + 15 < n; i += 16) {
	__m256i x_vec = _mm256_loadu_si256((const __m256i *)(x + i));
	__m512 y_vec = _mm512_cvtph_ps(x_vec);
	_mm512_storeu_ps(y + i, y_vec);
	}
	#endif
	for (; i + 7 < n; i += 8) {
	__m128i x_vec = _mm_loadu_si128((const __m128i *)(x + i));
	__m256 y_vec = _mm256_cvtph_ps(x_vec);
	_mm256_storeu_ps(y + i, y_vec);
	}
	for (; i + 3 < n; i += 4) {
	__m128i x_vec = _mm_loadl_epi64((const __m128i *)(x + i));
	__m128 y_vec = _mm_cvtph_ps(x_vec);
	_mm_storeu_ps(y + i, y_vec);
	}

	#elif defined(__riscv_v_intrinsic) && defined(__riscv_zvfhmin)
	// calculate step size
	const int epr = __riscv_vsetvlmax_e16m2();
	const int step = epr * 2;
	const int np = (n & ~(step - 1));

	// unroll by 2
	for (; i < np; i += step) {
	vfloat16m2_t ax0 = __riscv_vle16_v_f16m2((const _Float16*)x + i, epr);
	vfloat32m4_t ay0 = __riscv_vfwcvt_f_f_v_f32m4(ax0, epr);
	__riscv_vse32_v_f32m4(y + i, ay0, epr);

	vfloat16m2_t ax1 = __riscv_vle16_v_f16m2((const _Float16*)x + i + epr, epr);
	vfloat32m4_t ay1 = __riscv_vfwcvt_f_f_v_f32m4(ax1, epr);
	__riscv_vse32_v_f32m4(y + i + epr, ay1, epr);
	}

	// leftovers
	int vl;
	for (i = np; i < n; i += vl) {
	vl = __riscv_vsetvl_e16m2(n - i);
	vfloat16m2_t ax0 = __riscv_vle16_v_f16m2((const _Float16*)x + i, vl);
	vfloat32m4_t ay0 = __riscv_vfwcvt_f_f_v_f32m4(ax0, vl);
	__riscv_vse32_v_f32m4(y + i, ay0, vl);
	}

	#endif

	for (; i < n; ++i) {
	y[i] = GGML_CPU_FP16_TO_FP32(x[i]);
	}
	}

	void ggml_cpu_fp32_to_bf16(const float * x, ggml_bf16_t * y, int64_t n) {
	int64_t i = 0;
	for (; i < n; ++i) {
	y[i] = GGML_FP32_TO_BF16(x[i]);
	}
	}

	void ggml_cpu_fp32_to_i32(const float * x, int32_t * y, int64_t n) {
	int64_t i = 0;
	for (; i < n; ++i) {
	y[i] = x[i];
	}
	}

	void ggml_cpu_bf16_to_fp32(const ggml_bf16_t * x, float * y, int64_t n) {
	int64_t i = 0;
	#if defined(__AVX2__)
	#if defined(__AVX512F__)
	for (; i + 15 < n; i += 16) {
	_mm512_storeu_ps(y + i,
	_mm512_castsi512_ps(
	_mm512_slli_epi32(
	_mm512_cvtepu16_epi32(
	_mm256_loadu_si256(
	(const __m256i *)(x + i))),
	16)));
	}
	#endif
	for (; i + 7 < n; i += 8) {
	_mm256_storeu_ps(y + i,
	_mm256_castsi256_ps(
	_mm256_slli_epi32(
	_mm256_cvtepu16_epi32(
	_mm_loadu_si128(
	(const __m128i *)(x + i))),
	16)));
	}
	#elif defined(__riscv_v_intrinsic) && defined(__riscv_zvfbfmin)
	// calculate step size
	const int epr = __riscv_vsetvlmax_e16m2();
	const int step = epr * 2;
	const int np = (n & ~(step - 1));

	// unroll by 2
	for (; i < np; i += step) {
	vbfloat16m2_t ax0 = __riscv_vle16_v_bf16m2((const __bf16*)x + i, epr);
	vfloat32m4_t ay0 = __riscv_vfwcvtbf16_f_f_v_f32m4(ax0, epr);
	__riscv_vse32_v_f32m4(y + i, ay0, epr);

	vbfloat16m2_t ax1 = __riscv_vle16_v_bf16m2((const __bf16*)x + i + epr, epr);
	vfloat32m4_t ay1 = __riscv_vfwcvtbf16_f_f_v_f32m4(ax1, epr);
	__riscv_vse32_v_f32m4(y + i + epr, ay1, epr);
	}

	// leftovers
	int vl;
	for (i = np; i < n; i += vl) {
	vl = __riscv_vsetvl_e16m2(n - i);
	vbfloat16m2_t ax0 = __riscv_vle16_v_bf16m2((const __bf16*)x + i, vl);
	vfloat32m4_t ay0 = __riscv_vfwcvtbf16_f_f_v_f32m4(ax0, vl);
	__riscv_vse32_v_f32m4(y + i, ay0, vl);
	}
	#endif
	for (; i < n; i++) {
	y[i] = GGML_BF16_TO_FP32(x[i]);
	}
	}

	int ggml_cpu_has_avx(void) {
	#if defined(__AVX__)
	return 1;
	#else
	return 0;
	#endif
	}

	int ggml_cpu_has_avx_vnni(void) {
	#if defined(__AVXVNNI__)
	return 1;
	#else
	return 0;
	#endif
	}

	int ggml_cpu_has_avx2(void) {
	#if defined(__AVX2__)
	return 1;
	#else
	return 0;
	#endif
	}

	int ggml_cpu_has_avx512(void) {
	#if defined(__AVX512F__)
	return 1;
	#else
	return 0;
	#endif
	}

	int ggml_cpu_has_avx512_vbmi(void) {
	#if defined(__AVX512VBMI__)
	return 1;
	#else
	return 0;
	#endif
	}

	int ggml_cpu_has_avx512_vnni(void) {
	#if defined(__AVX512VNNI__)
	return 1;
	#else
	return 0;
	#endif
	}

	int ggml_cpu_has_avx512_bf16(void) {
	#if defined(__AVX512BF16__)
	return 1;
	#else
	return 0;
	#endif
	}

	int ggml_cpu_has_amx_int8(void) {
	#if defined(__AMX_INT8__)
	return 1;
	#else
	return 0;
	#endif
	}

	int ggml_cpu_has_bmi2(void) {
	#if defined(__BMI2__)
	return 1;
	#else
	return 0;
	#endif
	}

	int ggml_cpu_has_fma(void) {
	#if defined(__FMA__)
	return 1;
	#else
	return 0;
	#endif
	}

	int ggml_cpu_has_arm_fma(void) {
	#if defined(__ARM_FEATURE_FMA)
	return 1;
	#else
	return 0;
	#endif
	}

	int ggml_cpu_has_riscv_v(void) {
	#if defined(__riscv_v_intrinsic)
	return 1;
	#else
	return 0;
	#endif
	}

	int ggml_cpu_get_rvv_vlen(void) {
	#if defined(__riscv) && defined(__riscv_v_intrinsic)
	return ggml_riscv_arch_features.rvv_vlen;
	#else
	return 0;
	#endif
	}

	int ggml_cpu_has_f16c(void) {
	#if defined(__F16C__)
	return 1;
	#else
	return 0;
	#endif
	}

	int ggml_cpu_has_fp16_va(void) {
	#if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC)
	return 1;
	#else
	return 0;
	#endif
	}

	int ggml_cpu_has_wasm_simd(void) {
	#if defined(__wasm_simd128__)
	return 1;
	#else
	return 0;
	#endif
	}

	int ggml_cpu_has_llamafile(void) {
	#if defined(GGML_USE_LLAMAFILE)
	return 1;
	#else
	return 0;
	#endif
	}

	int ggml_cpu_has_sse3(void) {
	#if defined(__SSE3__)
	return 1;
	#else
	return 0;
	#endif
	}

	int ggml_cpu_has_ssse3(void) {
	#if defined(__SSSE3__)
	return 1;
	#else
	return 0;
	#endif
	}

	int ggml_cpu_has_vsx(void) {
	#if defined(__POWER9_VECTOR__)
	return 1;
	#else
	return 0;
	#endif
	}

	int ggml_cpu_has_vxe(void) {
	#if defined(__VXE__) \|\| defined(__VXE2__)
	return 1;
	#else
	return 0;
	#endif
	}

	int ggml_cpu_has_neon(void) {
	#if defined(__ARM_ARCH) && defined(__ARM_NEON)
	return 1;
	#else
	return 0;
	#endif
	}

	int ggml_cpu_has_dotprod(void) {
	#if defined(__ARM_ARCH) && defined(__ARM_FEATURE_DOTPROD)
	return 1;
	#else
	return 0;
	#endif
	}

	int ggml_cpu_has_sve(void) {
	#if defined(__ARM_ARCH) && defined(__ARM_FEATURE_SVE)
	return 1;
	#else
	return 0;
	#endif
	}

	int ggml_cpu_has_matmul_int8(void) {
	#if defined(__ARM_ARCH) && defined(__ARM_FEATURE_MATMUL_INT8)
	return 1;
	#else
	return 0;
	#endif
	}

	int ggml_cpu_get_sve_cnt(void) {
	#if defined(__ARM_ARCH) && defined(__ARM_FEATURE_SVE)
	return ggml_arm_arch_features.sve_cnt;
	#else
	return 0;
	#endif
	}

	int ggml_cpu_has_sme(void) {
	#if defined(__ARM_ARCH) && defined(__ARM_FEATURE_SME)
	return 1;
	#else
	return 0;
	#endif
	}

	void ggml_cpu_init(void) {
	// needed to initialize ggml_time
	{
	struct ggml_init_params params = { 0, NULL, false };
	struct ggml_context * ctx = ggml_init(params);
	ggml_free(ctx);
	}

	ggml_critical_section_start();

	static bool is_first_call = true;

	if (is_first_call) {
	// initialize GELU, Quick GELU, SILU and EXP F32 tables
	{
	const uint64_t t_start = ggml_time_us(); UNUSED(t_start);

	for (int i = 0; i < (1 << 16); ++i) {
	union {
	uint16_t u16;
	ggml_fp16_t fp16;
	} u = {i};
	float f = GGML_COMPUTE_FP16_TO_FP32(u.fp16);
	ggml_table_f32_f16[i] = f;
	ggml_table_gelu_f16[i] = GGML_CPU_FP32_TO_FP16(ggml_gelu_f32(f));
	ggml_table_gelu_quick_f16[i] = GGML_CPU_FP32_TO_FP16(ggml_gelu_quick_f32(f));
	}

	// initialize E8M0 half table (256 entries)
	for (int i = 0; i < (1 << 8); ++i) {
	ggml_table_f32_e8m0_half[i] = GGML_E8M0_TO_FP32_HALF(i);
	}

	const uint64_t t_end = ggml_time_us(); UNUSED(t_end);

	GGML_PRINT_DEBUG("%s: GELU, Quick GELU, SILU and EXP tables initialized in %f ms\n", __func__, (t_end - t_start)/1000.0);

	#ifdef GGML_USE_OPENMP
	//if (!getenv("OMP_WAIT_POLICY")) {
	// // set the wait policy to active, so that OpenMP threads don't sleep
	// setenv("OMP_WAIT_POLICY", "active", 0)
	//}

	if (!getenv("KMP_BLOCKTIME")) {
	// set the time to wait before sleeping a thread
	// this is less aggressive than setting the wait policy to active, but should achieve similar results in most cases
	#ifdef _WIN32
	_putenv_s("KMP_BLOCKTIME", "200"); // 200ms
	#else
	setenv("KMP_BLOCKTIME", "200", 0); // 200ms
	#endif
	}
	#endif
	}

	#if defined(__ARM_ARCH)
	ggml_init_arm_arch_features();
	#endif

	#if defined(__riscv)
	ggml_init_riscv_arch_features();
	#endif

	is_first_call = false;
	}

	ggml_critical_section_end();
	}