cliprs 0.2.1

#define _CRT_SECURE_NO_DEPRECATE // Disables ridiculous "unsafe" warnigns on Windows

#include "../include/ggml/ggml.h"

#ifdef GGML_USE_K_QUANTS
#include "k_quants.h"
#endif

#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 <float.h>
#include <inttypes.h>
#include <limits.h>
#include <math.h>
#include <signal.h>
#include <stdarg.h>
#include <stdint.h>
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <time.h>

#ifdef GGML_USE_METAL
#include <unistd.h>
#endif

// static_assert should be a #define, but if it's not,
// fall back to the _Static_assert C11 keyword.
// if C99 - static_assert is noop
// ref: https://stackoverflow.com/a/53923785/4039976
#ifndef static_assert
#if defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 201100L)
#define static_assert(cond, msg) _Static_assert(cond, msg)
#else
#define static_assert(cond, msg) struct global_scope_noop_trick
#endif
#endif

#if defined(_MSC_VER)
// disable "possible loss of data" to avoid hundreds of casts
// we should just be careful :)
#pragma warning(disable : 4244 4267)

// disable POSIX deprecation warnigns
// these functions are never going away, anyway
#pragma warning(disable : 4996)
#endif

#if defined(_WIN32)

#include <windows.h>

typedef volatile LONG atomic_int;
typedef atomic_int atomic_bool;

static void atomic_store(atomic_int * ptr, LONG val) { InterlockedExchange(ptr, val); }
static LONG atomic_load(atomic_int * ptr) { return InterlockedCompareExchange(ptr, 0, 0); }
static LONG atomic_fetch_add(atomic_int * ptr, LONG inc) { return InterlockedExchangeAdd(ptr, inc); }
static LONG atomic_fetch_sub(atomic_int * ptr, LONG dec) { return atomic_fetch_add(ptr, -(dec)); }

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;
    return (int)WaitForSingleObject(thread, INFINITE);
}

static int sched_yield(void) {
    Sleep(0);
    return 0;
}
#else
#include <pthread.h>
#include <stdatomic.h>

typedef void * thread_ret_t;

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

#endif
#ifdef GGML_USE_CPU_HBM
#include <hbwmalloc.h>
#endif

// __FMA__ and __F16C__ are not defined in MSVC, however they are implied with AVX2/AVX512
#if defined(_MSC_VER) && (defined(__AVX2__) || defined(__AVX512F__))
#ifndef __FMA__
#define __FMA__
#endif
#ifndef __F16C__
#define __F16C__
#endif
#ifndef __SSE3__
#define __SSE3__
#endif
#endif

/*#define GGML_PERF*/
#define GGML_DEBUG 0
#define GGML_GELU_FP16
#define GGML_GELU_QUICK_FP16
#define GGML_SILU_FP16
// #define GGML_CROSS_ENTROPY_EXP_FP16
// #define GGML_FLASH_ATTN_EXP_FP16

#define GGML_SOFT_MAX_UNROLL 4
#define GGML_VEC_DOT_UNROLL 2

//
// logging
//

#if (GGML_DEBUG >= 1)
#define GGML_PRINT_DEBUG(...) printf(__VA_ARGS__)
#else
#define GGML_PRINT_DEBUG(...)
#endif

#if (GGML_DEBUG >= 5)
#define GGML_PRINT_DEBUG_5(...) printf(__VA_ARGS__)
#else
#define GGML_PRINT_DEBUG_5(...)
#endif

#if (GGML_DEBUG >= 10)
#define GGML_PRINT_DEBUG_10(...) printf(__VA_ARGS__)
#else
#define GGML_PRINT_DEBUG_10(...)
#endif

#define GGML_PRINT(...) printf(__VA_ARGS__)

#ifdef GGML_USE_ACCELERATE
// uncomment to use vDSP for soft max computation
// note: not sure if it is actually faster
// #define GGML_SOFT_MAX_ACCELERATE
#endif

//
// logging
//

#if (GGML_DEBUG >= 1)
#define GGML_PRINT_DEBUG(...) printf(__VA_ARGS__)
#else
#define GGML_PRINT_DEBUG(...)
#endif

#if (GGML_DEBUG >= 5)
#define GGML_PRINT_DEBUG_5(...) printf(__VA_ARGS__)
#else
#define GGML_PRINT_DEBUG_5(...)
#endif

#if (GGML_DEBUG >= 10)
#define GGML_PRINT_DEBUG_10(...) printf(__VA_ARGS__)
#else
#define GGML_PRINT_DEBUG_10(...)
#endif

#define GGML_PRINT(...) printf(__VA_ARGS__)

//
// end of logging block
//

#if defined(_MSC_VER) || defined(__MINGW32__)
#define GGML_ALIGNED_MALLOC(size) _aligned_malloc(size, GGML_MEM_ALIGN)
#define GGML_ALIGNED_FREE(ptr) _aligned_free(ptr)
#else
inline static void * ggml_aligned_malloc(size_t size) {
    if (size == 0) {
        GGML_PRINT("WARNING: Behavior may be unexpected when allocating 0 bytes for ggml_aligned_malloc!\n");
        return NULL;
    }
    void * aligned_memory = NULL;
#ifdef GGML_USE_CPU_HBM
    int result = hbw_posix_memalign(&aligned_memory, 16, size);
#elif GGML_USE_METAL
    int result = posix_memalign(&aligned_memory, sysconf(_SC_PAGESIZE), size);
#else
    int result = posix_memalign(&aligned_memory, GGML_MEM_ALIGN, size);
#endif
    if (result != 0) {
        // Handle allocation failure
        const char * error_desc = "unknown allocation error";
        switch (result) {
        case EINVAL:
            error_desc = "invalid alignment value";
            break;
        case ENOMEM:
            error_desc = "insufficient memory";
            break;
        }
        GGML_PRINT("%s: %s (attempted to allocate %6.2f MB)\n", __func__, error_desc, size / (1024.0 * 1024.0));
        return NULL;
    }
    return aligned_memory;
}
#define GGML_ALIGNED_MALLOC(size) ggml_aligned_malloc(size)
#ifdef GGML_USE_CPU_HBM
#define GGML_ALIGNED_FREE(ptr)                                                                                                 \
    if (NULL != ptr)                                                                                                           \
    hbw_free(ptr)
#else
#define GGML_ALIGNED_FREE(ptr) free(ptr)
#endif
#endif

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

//
// tensor access macros
//

#define GGML_TENSOR_UNARY_OP_LOCALS                                                                                            \
    GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne);                                                                                \
    GGML_TENSOR_LOCALS(size_t, nb0, src0, nb);                                                                                 \
    GGML_TENSOR_LOCALS(int64_t, ne, dst, ne);                                                                                  \
    GGML_TENSOR_LOCALS(size_t, nb, dst, nb);

#define GGML_TENSOR_BINARY_OP_LOCALS                                                                                           \
    GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne);                                                                                \
    GGML_TENSOR_LOCALS(size_t, nb0, src0, nb);                                                                                 \
    GGML_TENSOR_LOCALS(int64_t, ne1, src1, ne);                                                                                \
    GGML_TENSOR_LOCALS(size_t, nb1, src1, nb);                                                                                 \
    GGML_TENSOR_LOCALS(int64_t, ne, dst, ne);                                                                                  \
    GGML_TENSOR_LOCALS(size_t, nb, dst, nb);

#if defined(GGML_USE_ACCELERATE)
#include <Accelerate/Accelerate.h>
#if defined(GGML_USE_CLBLAST) // allow usage of CLBlast alongside Accelerate functions
#include "ggml-opencl.h"
#endif
#elif defined(GGML_USE_OPENBLAS)
#if defined(GGML_BLAS_USE_MKL)
#include <mkl.h>
#else
#include <cblas.h>
#endif
#elif defined(GGML_USE_CUBLAS)
#include "ggml-cuda.h"
#elif defined(GGML_USE_CLBLAST)
#include "ggml-opencl.h"
#endif

#undef MIN
#undef MAX
#define MIN(a, b) ((a) < (b) ? (a) : (b))
#define MAX(a, b) ((a) > (b) ? (a) : (b))

// floating point type used to accumulate sums
typedef double ggml_float;

// 16-bit float
// on Arm, we use __fp16
// on x86, we use uint16_t
#ifdef __ARM_NEON

// if YCM cannot find <arm_neon.h>, make a symbolic link to it, for example:
//
//   $ ln -sfn /Library/Developer/CommandLineTools/usr/lib/clang/13.1.6/include/arm_neon.h ./src/
//
#include <arm_neon.h>

#define GGML_COMPUTE_FP16_TO_FP32(x) ((float)(x))
#define GGML_COMPUTE_FP32_TO_FP16(x) (x)

#define GGML_FP16_TO_FP32(x) ((float)(x))
#define GGML_FP32_TO_FP16(x) (x)

#else

#ifdef __wasm_simd128__
#include <wasm_simd128.h>
#else
#ifdef __POWER9_VECTOR__
#include <altivec.h>
#undef bool
#define bool _Bool
#else
#if defined(_MSC_VER) || defined(__MINGW32__)
#include <intrin.h>
#else
#if defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) || defined(__SSSE3__) || defined(__SSE3__)
#if !defined(__riscv)
#include <immintrin.h>
#endif
#endif
#endif
#endif
#endif

#ifdef __riscv_v_intrinsic
#include <riscv_vector.h>
#endif

#ifdef __F16C__

#ifdef _MSC_VER
#define GGML_COMPUTE_FP16_TO_FP32(x) _mm_cvtss_f32(_mm_cvtph_ps(_mm_cvtsi32_si128(x)))
#define GGML_COMPUTE_FP32_TO_FP16(x) _mm_extract_epi16(_mm_cvtps_ph(_mm_set_ss(x), 0), 0)
#else
#define GGML_COMPUTE_FP16_TO_FP32(x) _cvtsh_ss(x)
#define GGML_COMPUTE_FP32_TO_FP16(x) _cvtss_sh(x, 0)
#endif

#elif defined(__POWER9_VECTOR__)

#define GGML_COMPUTE_FP16_TO_FP32(x) ggml_compute_fp16_to_fp32(x)
#define GGML_COMPUTE_FP32_TO_FP16(x) ggml_compute_fp32_to_fp16(x)
/* the inline asm below is about 12% faster than the lookup method */
#define GGML_FP16_TO_FP32(x) GGML_COMPUTE_FP16_TO_FP32(x)
#define GGML_FP32_TO_FP16(x) GGML_COMPUTE_FP32_TO_FP16(x)

static inline float ggml_compute_fp16_to_fp32(ggml_fp16_t h) {
    register float f;
    register double d;
    __asm__("mtfprd %0,%2\n"
            "xscvhpdp %0,%0\n"
            "frsp %1,%0\n"
            :
            /* temp */ "=d"(d),
            /* out */ "=f"(f)
            :
            /* in */ "r"(h));
    return f;
}

static inline ggml_fp16_t ggml_compute_fp32_to_fp16(float f) {
    register double d;
    register ggml_fp16_t r;
    __asm__(/* xscvdphp can work on double or single precision */
            "xscvdphp %0,%2\n"
            "mffprd %1,%0\n"
            :
            /* temp */ "=d"(d),
            /* out */ "=r"(r)
            :
            /* in */ "f"(f));
    return r;
}

#else

// FP16 <-> FP32
// ref: https://github.com/Maratyszcza/FP16

static inline float fp32_from_bits(uint32_t w) {
    union {
        uint32_t as_bits;
        float as_value;
    } fp32;
    fp32.as_bits = w;
    return fp32.as_value;
}

static inline uint32_t fp32_to_bits(float f) {
    union {
        float as_value;
        uint32_t as_bits;
    } fp32;
    fp32.as_value = f;
    return fp32.as_bits;
}

static inline float ggml_compute_fp16_to_fp32(ggml_fp16_t h) {
    const uint32_t w = (uint32_t)h << 16;
    const uint32_t sign = w & UINT32_C(0x80000000);
    const uint32_t two_w = w + w;

    const uint32_t exp_offset = UINT32_C(0xE0) << 23;
#if defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 199901L) || defined(__GNUC__) && !defined(__STRICT_ANSI__)
    const float exp_scale = 0x1.0p-112f;
#else
    const float exp_scale = fp32_from_bits(UINT32_C(0x7800000));
#endif
    const float normalized_value = fp32_from_bits((two_w >> 4) + exp_offset) * exp_scale;

    const uint32_t magic_mask = UINT32_C(126) << 23;
    const float magic_bias = 0.5f;
    const float denormalized_value = fp32_from_bits((two_w >> 17) | magic_mask) - magic_bias;

    const uint32_t denormalized_cutoff = UINT32_C(1) << 27;
    const uint32_t result =
        sign | (two_w < denormalized_cutoff ? fp32_to_bits(denormalized_value) : fp32_to_bits(normalized_value));
    return fp32_from_bits(result);
}

static inline ggml_fp16_t ggml_compute_fp32_to_fp16(float f) {
#if defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 199901L) || defined(__GNUC__) && !defined(__STRICT_ANSI__)
    const float scale_to_inf = 0x1.0p+112f;
    const float scale_to_zero = 0x1.0p-110f;
#else
    const float scale_to_inf = fp32_from_bits(UINT32_C(0x77800000));
    const float scale_to_zero = fp32_from_bits(UINT32_C(0x08800000));
#endif
    float base = (fabsf(f) * scale_to_inf) * scale_to_zero;

    const uint32_t w = fp32_to_bits(f);
    const uint32_t shl1_w = w + w;
    const uint32_t sign = w & UINT32_C(0x80000000);
    uint32_t bias = shl1_w & UINT32_C(0xFF000000);
    if (bias < UINT32_C(0x71000000)) {
        bias = UINT32_C(0x71000000);
    }

    base = fp32_from_bits((bias >> 1) + UINT32_C(0x07800000)) + base;
    const uint32_t bits = fp32_to_bits(base);
    const uint32_t exp_bits = (bits >> 13) & UINT32_C(0x00007C00);
    const uint32_t mantissa_bits = bits & UINT32_C(0x00000FFF);
    const uint32_t nonsign = exp_bits + mantissa_bits;
    return (sign >> 16) | (shl1_w > UINT32_C(0xFF000000) ? UINT16_C(0x7E00) : nonsign);
}

#define GGML_COMPUTE_FP16_TO_FP32(x) ggml_compute_fp16_to_fp32(x)
#define GGML_COMPUTE_FP32_TO_FP16(x) ggml_compute_fp32_to_fp16(x)

#endif // __F16C__

#endif // __ARM_NEON

//
// global data
//

// precomputed gelu table for f16 (128 KB)
static ggml_fp16_t table_gelu_f16[1 << 16];

// precomputed quick gelu table for f16 (128 KB)
static ggml_fp16_t table_gelu_quick_f16[1 << 16];

// precomputed silu table for f16 (128 KB)
static ggml_fp16_t table_silu_f16[1 << 16];

// precomputed exp table for f16 (128 KB)
static ggml_fp16_t table_exp_f16[1 << 16];

// precomputed f32 table for f16 (256 KB)
static float table_f32_f16[1 << 16];

#if defined(__ARM_NEON) || defined(__wasm_simd128__)
#define B1(c, s, n) 0x##n##c, 0x##n##s
#define B2(c, s, n) B1(c, s, n##c), B1(c, s, n##s)
#define B3(c, s, n) B2(c, s, n##c), B2(c, s, n##s)
#define B4(c, s, n) B3(c, s, n##c), B3(c, s, n##s)
#define B5(c, s, n) B4(c, s, n##c), B4(c, s, n##s)
#define B6(c, s, n) B5(c, s, n##c), B5(c, s, n##s)
#define B7(c, s, n) B6(c, s, n##c), B6(c, s, n##s)
#define B8(c, s) B7(c, s, c), B7(c, s, s)

// precomputed tables for expanding 8bits to 8 bytes:
static const uint64_t table_b2b_0[1 << 8] = {B8(00, 10)}; // ( b) << 4
static const uint64_t table_b2b_1[1 << 8] = {B8(10, 00)}; // (!b) << 4
#endif

// On ARM NEON, it's quicker to directly convert x -> x instead of calling into ggml_lookup_fp16_to_fp32,
// so we define GGML_FP16_TO_FP32 and GGML_FP32_TO_FP16 elsewhere for NEON.
// This is also true for POWER9.
#if !defined(GGML_FP16_TO_FP32) || !defined(GGML_FP32_TO_FP16)

inline static float ggml_lookup_fp16_to_fp32(ggml_fp16_t f) {
    uint16_t s;
    memcpy(&s, &f, sizeof(uint16_t));
    return table_f32_f16[s];
}

#define GGML_FP16_TO_FP32(x) ggml_lookup_fp16_to_fp32(x)
#define GGML_FP32_TO_FP16(x) GGML_COMPUTE_FP32_TO_FP16(x)

#endif

// note: do not use these inside ggml.c
// these are meant to be used via the ggml.h API
float ggml_fp16_to_fp32(ggml_fp16_t x) { return (float)GGML_FP16_TO_FP32(x); }

ggml_fp16_t ggml_fp32_to_fp16(float x) { return GGML_FP32_TO_FP16(x); }

void ggml_fp16_to_fp32_row(const ggml_fp16_t * x, float * y, int n) {
    for (int i = 0; i < n; i++) {
        y[i] = GGML_FP16_TO_FP32(x[i]);
    }
}

void ggml_fp32_to_fp16_row(const float * x, ggml_fp16_t * y, int n) {
    int i = 0;
#if defined(__F16C__)
    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);
    }
#endif
    for (; i < n; i++) {
        y[i] = GGML_FP32_TO_FP16(x[i]);
    }
}

//
// timing
//

#if defined(_MSC_VER) || defined(__MINGW32__)
static int64_t timer_freq, timer_start;
void ggml_time_init(void) {
    LARGE_INTEGER t;
    QueryPerformanceFrequency(&t);
    timer_freq = t.QuadPart;

    // The multiplication by 1000 or 1000000 below can cause an overflow if timer_freq
    // and the uptime is high enough.
    // We subtract the program start time to reduce the likelihood of that happening.
    QueryPerformanceCounter(&t);
    timer_start = t.QuadPart;
}
int64_t ggml_time_ms(void) {
    LARGE_INTEGER t;
    QueryPerformanceCounter(&t);
    return ((t.QuadPart - timer_start) * 1000) / timer_freq;
}
int64_t ggml_time_us(void) {
    LARGE_INTEGER t;
    QueryPerformanceCounter(&t);
    return ((t.QuadPart - timer_start) * 1000000) / timer_freq;
}
#else
void ggml_time_init(void) {}
int64_t ggml_time_ms(void) {
    struct timespec ts;
    clock_gettime(CLOCK_MONOTONIC, &ts);
    return (int64_t)ts.tv_sec * 1000 + (int64_t)ts.tv_nsec / 1000000;
}

int64_t ggml_time_us(void) {
    struct timespec ts;
    clock_gettime(CLOCK_MONOTONIC, &ts);
    return (int64_t)ts.tv_sec * 1000000 + (int64_t)ts.tv_nsec / 1000;
}
#endif

int64_t ggml_cycles(void) { return clock(); }

int64_t ggml_cycles_per_ms(void) { return CLOCKS_PER_SEC / 1000; }

#ifdef GGML_PERF
#define ggml_perf_time_ms() ggml_time_ms()
#define ggml_perf_time_us() ggml_time_us()
#define ggml_perf_cycles() ggml_cycles()
#define ggml_perf_cycles_per_ms() ggml_cycles_per_ms()
#else
#define ggml_perf_time_ms() 0
#define ggml_perf_time_us() 0
#define ggml_perf_cycles() 0
#define ggml_perf_cycles_per_ms() 0
#endif

//
// cache line
//

#if defined(__cpp_lib_hardware_interference_size)
#define CACHE_LINE_SIZE hardware_destructive_interference_size
#else
#if defined(__POWER9_VECTOR__)
#define CACHE_LINE_SIZE 128
#else
#define CACHE_LINE_SIZE 64
#endif
#endif

static const size_t CACHE_LINE_SIZE_F32 = CACHE_LINE_SIZE / sizeof(float);

//
// quantization
//

#define MM256_SET_M128I(a, b) _mm256_insertf128_si256(_mm256_castsi128_si256(b), (a), 1)

#if defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) || defined(__SSSE3__)
// multiply int8_t, add results pairwise twice
static inline __m128i mul_sum_i8_pairs(const __m128i x, const __m128i y) {
    // Get absolute values of x vectors
    const __m128i ax = _mm_sign_epi8(x, x);
    // Sign the values of the y vectors
    const __m128i sy = _mm_sign_epi8(y, x);
    // Perform multiplication and create 16-bit values
    const __m128i dot = _mm_maddubs_epi16(ax, sy);
    const __m128i ones = _mm_set1_epi16(1);
    return _mm_madd_epi16(ones, dot);
}

#if __AVX__ || __AVX2__ || __AVX512F__
// horizontally add 8 floats
static inline float hsum_float_8(const __m256 x) {
    __m128 res = _mm256_extractf128_ps(x, 1);
    res = _mm_add_ps(res, _mm256_castps256_ps128(x));
    res = _mm_add_ps(res, _mm_movehl_ps(res, res));
    res = _mm_add_ss(res, _mm_movehdup_ps(res));
    return _mm_cvtss_f32(res);
}

// horizontally add 8 int32_t
static inline int hsum_i32_8(const __m256i a) {
    const __m128i sum128 = _mm_add_epi32(_mm256_castsi256_si128(a), _mm256_extractf128_si256(a, 1));
    const __m128i hi64 = _mm_unpackhi_epi64(sum128, sum128);
    const __m128i sum64 = _mm_add_epi32(hi64, sum128);
    const __m128i hi32 = _mm_shuffle_epi32(sum64, _MM_SHUFFLE(2, 3, 0, 1));
    return _mm_cvtsi128_si32(_mm_add_epi32(sum64, hi32));
}

// horizontally add 4 int32_t
static inline int hsum_i32_4(const __m128i a) {
    const __m128i hi64 = _mm_unpackhi_epi64(a, a);
    const __m128i sum64 = _mm_add_epi32(hi64, a);
    const __m128i hi32 = _mm_shuffle_epi32(sum64, _MM_SHUFFLE(2, 3, 0, 1));
    return _mm_cvtsi128_si32(_mm_add_epi32(sum64, hi32));
}

#if defined(__AVX2__) || defined(__AVX512F__)
// spread 32 bits to 32 bytes { 0x00, 0xFF }
static inline __m256i bytes_from_bits_32(const uint8_t * x) {
    uint32_t x32;
    memcpy(&x32, x, sizeof(uint32_t));
    const __m256i shuf_mask = _mm256_set_epi64x(0x0303030303030303, 0x0202020202020202, 0x0101010101010101, 0x0000000000000000);
    __m256i bytes = _mm256_shuffle_epi8(_mm256_set1_epi32(x32), shuf_mask);
    const __m256i bit_mask = _mm256_set1_epi64x(0x7fbfdfeff7fbfdfe);
    bytes = _mm256_or_si256(bytes, bit_mask);
    return _mm256_cmpeq_epi8(bytes, _mm256_set1_epi64x(-1));
}

// Unpack 32 4-bit fields into 32 bytes
// The output vector contains 32 bytes, each one in [ 0 .. 15 ] interval
static inline __m256i bytes_from_nibbles_32(const uint8_t * rsi) {
    const __m128i tmp = _mm_loadu_si128((const __m128i *)rsi);
    const __m256i bytes = MM256_SET_M128I(_mm_srli_epi16(tmp, 4), tmp);
    const __m256i lowMask = _mm256_set1_epi8(0xF);
    return _mm256_and_si256(lowMask, bytes);
}

// add int16_t pairwise and return as float vector
static inline __m256 sum_i16_pairs_float(const __m256i x) {
    const __m256i ones = _mm256_set1_epi16(1);
    const __m256i summed_pairs = _mm256_madd_epi16(ones, x);
    return _mm256_cvtepi32_ps(summed_pairs);
}

static inline __m256 mul_sum_us8_pairs_float(const __m256i ax, const __m256i sy) {
#if __AVXVNNI__
    const __m256i zero = _mm256_setzero_si256();
    const __m256i summed_pairs = _mm256_dpbusd_epi32(zero, ax, sy);
    return _mm256_cvtepi32_ps(summed_pairs);
#else
    // Perform multiplication and create 16-bit values
    const __m256i dot = _mm256_maddubs_epi16(ax, sy);
    return sum_i16_pairs_float(dot);
#endif
}

// multiply int8_t, add results pairwise twice and return as float vector
static inline __m256 mul_sum_i8_pairs_float(const __m256i x, const __m256i y) {
#if __AVXVNNIINT8__
    const __m256i zero = _mm256_setzero_si256();
    const __m256i summed_pairs = _mm256_dpbssd_epi32(zero, x, y);
    return _mm256_cvtepi32_ps(summed_pairs);
#else
    // Get absolute values of x vectors
    const __m256i ax = _mm256_sign_epi8(x, x);
    // Sign the values of the y vectors
    const __m256i sy = _mm256_sign_epi8(y, x);
    return mul_sum_us8_pairs_float(ax, sy);
#endif
}

static inline __m128i packNibbles(__m256i bytes) {
    // Move bits within 16-bit lanes from 0000_abcd_0000_efgh into 0000_0000_abcd_efgh
#if __AVX512F__
    const __m256i bytes_srli_4 = _mm256_srli_epi16(bytes, 4); // 0000_0000_abcd_0000
    bytes = _mm256_or_si256(bytes, bytes_srli_4);             // 0000_abcd_abcd_efgh
    return _mm256_cvtepi16_epi8(bytes);                       // abcd_efgh
#else
    const __m256i lowByte = _mm256_set1_epi16(0xFF);
    __m256i high = _mm256_andnot_si256(lowByte, bytes);
    __m256i low = _mm256_and_si256(lowByte, bytes);
    high = _mm256_srli_epi16(high, 4);
    bytes = _mm256_or_si256(low, high);

    // Compress uint16_t lanes into bytes
    __m128i r0 = _mm256_castsi256_si128(bytes);
    __m128i r1 = _mm256_extracti128_si256(bytes, 1);
    return _mm_packus_epi16(r0, r1);
#endif
}
#elif defined(__AVX__)
// spread 32 bits to 32 bytes { 0x00, 0xFF }
static inline __m256i bytes_from_bits_32(const uint8_t * x) {
    uint32_t x32;
    memcpy(&x32, x, sizeof(uint32_t));
    const __m128i shuf_maskl = _mm_set_epi64x(0x0101010101010101, 0x0000000000000000);
    const __m128i shuf_maskh = _mm_set_epi64x(0x0303030303030303, 0x0202020202020202);
    __m128i bytesl = _mm_shuffle_epi8(_mm_set1_epi32(x32), shuf_maskl);
    __m128i bytesh = _mm_shuffle_epi8(_mm_set1_epi32(x32), shuf_maskh);
    const __m128i bit_mask = _mm_set1_epi64x(0x7fbfdfeff7fbfdfe);
    bytesl = _mm_or_si128(bytesl, bit_mask);
    bytesh = _mm_or_si128(bytesh, bit_mask);
    bytesl = _mm_cmpeq_epi8(bytesl, _mm_set1_epi64x(-1));
    bytesh = _mm_cmpeq_epi8(bytesh, _mm_set1_epi64x(-1));
    return MM256_SET_M128I(bytesh, bytesl);
}

// Unpack 32 4-bit fields into 32 bytes
// The output vector contains 32 bytes, each one in [ 0 .. 15 ] interval
static inline __m256i bytes_from_nibbles_32(const uint8_t * rsi) {
    // Load 16 bytes from memory
    __m128i tmpl = _mm_loadu_si128((const __m128i *)rsi);
    __m128i tmph = _mm_srli_epi16(tmpl, 4);
    const __m128i lowMask = _mm_set1_epi8(0xF);
    tmpl = _mm_and_si128(lowMask, tmpl);
    tmph = _mm_and_si128(lowMask, tmph);
    return MM256_SET_M128I(tmph, tmpl);
}

// add int16_t pairwise and return as float vector
static inline __m256 sum_i16_pairs_float(const __m128i xh, const __m128i xl) {
    const __m128i ones = _mm_set1_epi16(1);
    const __m128i summed_pairsl = _mm_madd_epi16(ones, xl);
    const __m128i summed_pairsh = _mm_madd_epi16(ones, xh);
    const __m256i summed_pairs = MM256_SET_M128I(summed_pairsh, summed_pairsl);
    return _mm256_cvtepi32_ps(summed_pairs);
}

static inline __m256 mul_sum_us8_pairs_float(const __m256i ax, const __m256i sy) {
    const __m128i axl = _mm256_castsi256_si128(ax);
    const __m128i axh = _mm256_extractf128_si256(ax, 1);
    const __m128i syl = _mm256_castsi256_si128(sy);
    const __m128i syh = _mm256_extractf128_si256(sy, 1);
    // Perform multiplication and create 16-bit values
    const __m128i dotl = _mm_maddubs_epi16(axl, syl);
    const __m128i doth = _mm_maddubs_epi16(axh, syh);
    return sum_i16_pairs_float(doth, dotl);
}

// multiply int8_t, add results pairwise twice and return as float vector
static inline __m256 mul_sum_i8_pairs_float(const __m256i x, const __m256i y) {
    const __m128i xl = _mm256_castsi256_si128(x);
    const __m128i xh = _mm256_extractf128_si256(x, 1);
    const __m128i yl = _mm256_castsi256_si128(y);
    const __m128i yh = _mm256_extractf128_si256(y, 1);
    // Get absolute values of x vectors
    const __m128i axl = _mm_sign_epi8(xl, xl);
    const __m128i axh = _mm_sign_epi8(xh, xh);
    // Sign the values of the y vectors
    const __m128i syl = _mm_sign_epi8(yl, xl);
    const __m128i syh = _mm_sign_epi8(yh, xh);
    // Perform multiplication and create 16-bit values
    const __m128i dotl = _mm_maddubs_epi16(axl, syl);
    const __m128i doth = _mm_maddubs_epi16(axh, syh);
    return sum_i16_pairs_float(doth, dotl);
}

static inline __m128i packNibbles(__m128i bytes1, __m128i bytes2) {
    // Move bits within 16-bit lanes from 0000_abcd_0000_efgh into 0000_0000_abcd_efgh
    const __m128i lowByte = _mm_set1_epi16(0xFF);
    __m128i high = _mm_andnot_si128(lowByte, bytes1);
    __m128i low = _mm_and_si128(lowByte, bytes1);
    high = _mm_srli_epi16(high, 4);
    bytes1 = _mm_or_si128(low, high);
    high = _mm_andnot_si128(lowByte, bytes2);
    low = _mm_and_si128(lowByte, bytes2);
    high = _mm_srli_epi16(high, 4);
    bytes2 = _mm_or_si128(low, high);

    return _mm_packus_epi16(bytes1, bytes2);
}
#endif
#elif defined(__SSSE3__)
// horizontally add 4x4 floats
static inline float hsum_float_4x4(const __m128 a, const __m128 b, const __m128 c, const __m128 d) {
    __m128 res_0 = _mm_hadd_ps(a, b);
    __m128 res_1 = _mm_hadd_ps(c, d);
    __m128 res = _mm_hadd_ps(res_0, res_1);
    res = _mm_hadd_ps(res, res);
    res = _mm_hadd_ps(res, res);

    return _mm_cvtss_f32(res);
}
#endif // __AVX__ || __AVX2__ || __AVX512F__
#endif // defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) || defined(__SSSE3__)

#if defined(__ARM_NEON)

#if !defined(__aarch64__)

inline static int32_t vaddvq_s32(int32x4_t v) {
    return vgetq_lane_s32(v, 0) + vgetq_lane_s32(v, 1) + vgetq_lane_s32(v, 2) + vgetq_lane_s32(v, 3);
}

inline static float vaddvq_f32(float32x4_t v) {
    return vgetq_lane_f32(v, 0) + vgetq_lane_f32(v, 1) + vgetq_lane_f32(v, 2) + vgetq_lane_f32(v, 3);
}

inline static float vmaxvq_f32(float32x4_t v) {
    return MAX(MAX(vgetq_lane_f32(v, 0), vgetq_lane_f32(v, 1)), MAX(vgetq_lane_f32(v, 2), vgetq_lane_f32(v, 3)));
}

inline static int32x4_t vcvtnq_s32_f32(float32x4_t v) {
    int32x4_t res;

    res[0] = roundf(vgetq_lane_f32(v, 0));
    res[1] = roundf(vgetq_lane_f32(v, 1));
    res[2] = roundf(vgetq_lane_f32(v, 2));
    res[3] = roundf(vgetq_lane_f32(v, 3));

    return res;
}

#endif
#endif

#define QK4_0 32
typedef struct {
    ggml_fp16_t d;         // delta
    uint8_t qs[QK4_0 / 2]; // nibbles / quants
} block_q4_0;
static_assert(sizeof(block_q4_0) == sizeof(ggml_fp16_t) + QK4_0 / 2, "wrong q4_0 block size/padding");

#define QK4_1 32
typedef struct {
    ggml_fp16_t d;         // delta
    ggml_fp16_t m;         // min
    uint8_t qs[QK4_1 / 2]; // nibbles / quants
} block_q4_1;
static_assert(sizeof(block_q4_1) == 2 * sizeof(ggml_fp16_t) + QK4_1 / 2, "wrong q4_1 block size/padding");

#define QK5_0 32
typedef struct {
    ggml_fp16_t d;         // delta
    uint8_t qh[4];         // 5-th bit of quants
    uint8_t qs[QK5_0 / 2]; // nibbles / quants
} block_q5_0;
static_assert(sizeof(block_q5_0) == sizeof(ggml_fp16_t) + sizeof(uint32_t) + QK5_0 / 2, "wrong q5_0 block size/padding");

#define QK5_1 32
typedef struct {
    ggml_fp16_t d;         // delta
    ggml_fp16_t m;         // min
    uint8_t qh[4];         // 5-th bit of quants
    uint8_t qs[QK5_1 / 2]; // nibbles / quants
} block_q5_1;
static_assert(sizeof(block_q5_1) == 2 * sizeof(ggml_fp16_t) + sizeof(uint32_t) + QK5_1 / 2, "wrong q5_1 block size/padding");

#define QK8_0 32
typedef struct {
    ggml_fp16_t d;    // delta
    int8_t qs[QK8_0]; // quants
} block_q8_0;
static_assert(sizeof(block_q8_0) == sizeof(ggml_fp16_t) + QK8_0, "wrong q8_0 block size/padding");

#define QK8_1 32
typedef struct {
    float d;          // delta
    float s;          // d * sum(qs[i])
    int8_t qs[QK8_1]; // quants
} block_q8_1;
static_assert(sizeof(block_q8_1) == 2 * sizeof(float) + QK8_1, "wrong q8_1 block size/padding");

// reference implementation for deterministic creation of model files
static void quantize_row_q4_0_reference(const float * restrict x, block_q4_0 * restrict y, int k) {
    static const int qk = QK4_0;

    assert(k % qk == 0);

    const int nb = k / qk;

    for (int i = 0; i < nb; i++) {
        float amax = 0.0f; // absolute max
        float max = 0.0f;

        for (int j = 0; j < qk; j++) {
            const float v = x[i * qk + j];
            if (amax < fabsf(v)) {
                amax = fabsf(v);
                max = v;
            }
        }

        const float d = max / -8;
        const float id = d ? 1.0f / d : 0.0f;

        y[i].d = GGML_FP32_TO_FP16(d);

        for (int j = 0; j < qk / 2; ++j) {
            const float x0 = x[i * qk + 0 + j] * id;
            const float x1 = x[i * qk + qk / 2 + j] * id;

            const uint8_t xi0 = MIN(15, (int8_t)(x0 + 8.5f));
            const uint8_t xi1 = MIN(15, (int8_t)(x1 + 8.5f));

            y[i].qs[j] = xi0;
            y[i].qs[j] |= xi1 << 4;
        }
    }
}

static void quantize_row_q4_0(const float * restrict x, void * restrict y, int k) { quantize_row_q4_0_reference(x, y, k); }

static void quantize_row_q4_1_reference(const float * restrict x, block_q4_1 * restrict y, int k) {
    const int qk = QK4_1;

    assert(k % qk == 0);

    const int nb = k / qk;

    for (int i = 0; i < nb; i++) {
        float min = FLT_MAX;
        float max = -FLT_MAX;

        for (int j = 0; j < qk; j++) {
            const float v = x[i * qk + j];

            if (v < min)
                min = v;
            if (v > max)
                max = v;
        }

        const float d = (max - min) / ((1 << 4) - 1);
        const float id = d ? 1.0f / d : 0.0f;

        y[i].d = GGML_FP32_TO_FP16(d);
        y[i].m = GGML_FP32_TO_FP16(min);

        for (int j = 0; j < qk / 2; ++j) {
            const float x0 = (x[i * qk + 0 + j] - min) * id;
            const float x1 = (x[i * qk + qk / 2 + j] - min) * id;

            const uint8_t xi0 = MIN(15, (int8_t)(x0 + 0.5f));
            const uint8_t xi1 = MIN(15, (int8_t)(x1 + 0.5f));

            y[i].qs[j] = xi0;
            y[i].qs[j] |= xi1 << 4;
        }
    }
}

static void quantize_row_q4_1(const float * restrict x, void * restrict y, int k) { quantize_row_q4_1_reference(x, y, k); }

static void quantize_row_q5_0_reference(const float * restrict x, block_q5_0 * restrict y, int k) {
    static const int qk = QK5_0;

    assert(k % qk == 0);

    const int nb = k / qk;

    for (int i = 0; i < nb; i++) {
        float amax = 0.0f; // absolute max
        float max = 0.0f;

        for (int j = 0; j < qk; j++) {
            const float v = x[i * qk + j];
            if (amax < fabsf(v)) {
                amax = fabsf(v);
                max = v;
            }
        }

        const float d = max / -16;
        const float id = d ? 1.0f / d : 0.0f;

        y[i].d = GGML_FP32_TO_FP16(d);

        uint32_t qh = 0;

        for (int j = 0; j < qk / 2; ++j) {
            const float x0 = x[i * qk + 0 + j] * id;
            const float x1 = x[i * qk + qk / 2 + j] * id;

            const uint8_t xi0 = MIN(31, (int8_t)(x0 + 16.5f));
            const uint8_t xi1 = MIN(31, (int8_t)(x1 + 16.5f));

            y[i].qs[j] = (xi0 & 0x0F) | ((xi1 & 0x0F) << 4);

            // get the 5-th bit and store it in qh at the right position
            qh |= ((xi0 & 0x10) >> 4) << (j + 0);
            qh |= ((xi1 & 0x10) >> 4) << (j + qk / 2);
        }

        memcpy(&y[i].qh, &qh, sizeof(qh));
    }
}

static void quantize_row_q5_0(const float * restrict x, void * restrict y, int k) { quantize_row_q5_0_reference(x, y, k); }

static void quantize_row_q5_1_reference(const float * restrict x, block_q5_1 * restrict y, int k) {
    const int qk = QK5_1;

    assert(k % qk == 0);

    const int nb = k / qk;

    for (int i = 0; i < nb; i++) {
        float min = FLT_MAX;
        float max = -FLT_MAX;

        for (int j = 0; j < qk; j++) {
            const float v = x[i * qk + j];

            if (v < min)
                min = v;
            if (v > max)
                max = v;
        }

        const float d = (max - min) / ((1 << 5) - 1);
        const float id = d ? 1.0f / d : 0.0f;

        y[i].d = GGML_FP32_TO_FP16(d);
        y[i].m = GGML_FP32_TO_FP16(min);

        uint32_t qh = 0;

        for (int j = 0; j < qk / 2; ++j) {
            const float x0 = (x[i * qk + 0 + j] - min) * id;
            const float x1 = (x[i * qk + qk / 2 + j] - min) * id;

            const uint8_t xi0 = (uint8_t)(x0 + 0.5f);
            const uint8_t xi1 = (uint8_t)(x1 + 0.5f);

            y[i].qs[j] = (xi0 & 0x0F) | ((xi1 & 0x0F) << 4);

            // get the 5-th bit and store it in qh at the right position
            qh |= ((xi0 & 0x10) >> 4) << (j + 0);
            qh |= ((xi1 & 0x10) >> 4) << (j + qk / 2);
        }

        memcpy(&y[i].qh, &qh, sizeof(y[i].qh));
    }
}

static void quantize_row_q5_1(const float * restrict x, void * restrict y, int k) { quantize_row_q5_1_reference(x, y, k); }

// reference implementation for deterministic creation of model files
static void quantize_row_q8_0_reference(const float * restrict x, block_q8_0 * restrict y, int k) {
    assert(k % QK8_0 == 0);
    const int nb = k / QK8_0;

    for (int i = 0; i < nb; i++) {
        float amax = 0.0f; // absolute max

        for (int j = 0; j < QK8_0; j++) {
            const float v = x[i * QK8_0 + j];
            amax = MAX(amax, fabsf(v));
        }

        const float d = amax / ((1 << 7) - 1);
        const float id = d ? 1.0f / d : 0.0f;

        y[i].d = GGML_FP32_TO_FP16(d);

        for (int j = 0; j < QK8_0; ++j) {
            const float x0 = x[i * QK8_0 + j] * id;

            y[i].qs[j] = roundf(x0);
        }
    }
}

static void quantize_row_q8_0(const float * restrict x, void * restrict vy, int k) {
    assert(QK8_0 == 32);
    assert(k % QK8_0 == 0);
    const int nb = k / QK8_0;

    block_q8_0 * restrict y = vy;

#if defined(__ARM_NEON)
    for (int i = 0; i < nb; i++) {
        float32x4_t srcv[8];
        float32x4_t asrcv[8];
        float32x4_t amaxv[8];

        for (int j = 0; j < 8; j++)
            srcv[j] = vld1q_f32(x + i * 32 + 4 * j);
        for (int j = 0; j < 8; j++)
            asrcv[j] = vabsq_f32(srcv[j]);

        for (int j = 0; j < 4; j++)
            amaxv[2 * j] = vmaxq_f32(asrcv[2 * j], asrcv[2 * j + 1]);
        for (int j = 0; j < 2; j++)
            amaxv[4 * j] = vmaxq_f32(amaxv[4 * j], amaxv[4 * j + 2]);
        for (int j = 0; j < 1; j++)
            amaxv[8 * j] = vmaxq_f32(amaxv[8 * j], amaxv[8 * j + 4]);

        const float amax = vmaxvq_f32(amaxv[0]);

        const float d = amax / ((1 << 7) - 1);
        const float id = d ? 1.0f / d : 0.0f;

        y[i].d = GGML_FP32_TO_FP16(d);

        for (int j = 0; j < 8; j++) {
            const float32x4_t v = vmulq_n_f32(srcv[j], id);
            const int32x4_t vi = vcvtnq_s32_f32(v);

            y[i].qs[4 * j + 0] = vgetq_lane_s32(vi, 0);
            y[i].qs[4 * j + 1] = vgetq_lane_s32(vi, 1);
            y[i].qs[4 * j + 2] = vgetq_lane_s32(vi, 2);
            y[i].qs[4 * j + 3] = vgetq_lane_s32(vi, 3);
        }
    }
#elif defined(__wasm_simd128__)
    for (int i = 0; i < nb; i++) {
        v128_t srcv[8];
        v128_t asrcv[8];
        v128_t amaxv[8];

        for (int j = 0; j < 8; j++)
            srcv[j] = wasm_v128_load(x + i * 32 + 4 * j);
        for (int j = 0; j < 8; j++)
            asrcv[j] = wasm_f32x4_abs(srcv[j]);

        for (int j = 0; j < 4; j++)
            amaxv[2 * j] = wasm_f32x4_max(asrcv[2 * j], asrcv[2 * j + 1]);
        for (int j = 0; j < 2; j++)
            amaxv[4 * j] = wasm_f32x4_max(amaxv[4 * j], amaxv[4 * j + 2]);
        for (int j = 0; j < 1; j++)
            amaxv[8 * j] = wasm_f32x4_max(amaxv[8 * j], amaxv[8 * j + 4]);

        const float amax = MAX(MAX(wasm_f32x4_extract_lane(amaxv[0], 0), wasm_f32x4_extract_lane(amaxv[0], 1)),
                               MAX(wasm_f32x4_extract_lane(amaxv[0], 2), wasm_f32x4_extract_lane(amaxv[0], 3)));

        const float d = amax / ((1 << 7) - 1);
        const float id = d ? 1.0f / d : 0.0f;

        y[i].d = GGML_FP32_TO_FP16(d);

        for (int j = 0; j < 8; j++) {
            const v128_t v = wasm_f32x4_mul(srcv[j], wasm_f32x4_splat(id));
            const v128_t vi = wasm_i32x4_trunc_sat_f32x4(v);

            y[i].qs[4 * j + 0] = wasm_i32x4_extract_lane(vi, 0);
            y[i].qs[4 * j + 1] = wasm_i32x4_extract_lane(vi, 1);
            y[i].qs[4 * j + 2] = wasm_i32x4_extract_lane(vi, 2);
            y[i].qs[4 * j + 3] = wasm_i32x4_extract_lane(vi, 3);
        }
    }
#elif defined(__AVX2__) || defined(__AVX__)
    for (int i = 0; i < nb; i++) {
        // Load elements into 4 AVX vectors
        __m256 v0 = _mm256_loadu_ps(x);
        __m256 v1 = _mm256_loadu_ps(x + 8);
        __m256 v2 = _mm256_loadu_ps(x + 16);
        __m256 v3 = _mm256_loadu_ps(x + 24);
        x += 32;

        // Compute max(abs(e)) for the block
        const __m256 signBit = _mm256_set1_ps(-0.0f);
        __m256 maxAbs = _mm256_andnot_ps(signBit, v0);
        maxAbs = _mm256_max_ps(maxAbs, _mm256_andnot_ps(signBit, v1));
        maxAbs = _mm256_max_ps(maxAbs, _mm256_andnot_ps(signBit, v2));
        maxAbs = _mm256_max_ps(maxAbs, _mm256_andnot_ps(signBit, v3));

        __m128 max4 = _mm_max_ps(_mm256_extractf128_ps(maxAbs, 1), _mm256_castps256_ps128(maxAbs));
        max4 = _mm_max_ps(max4, _mm_movehl_ps(max4, max4));
        max4 = _mm_max_ss(max4, _mm_movehdup_ps(max4));
        const float maxScalar = _mm_cvtss_f32(max4);

        // Quantize these floats
        const float d = maxScalar / 127.f;
        y[i].d = GGML_FP32_TO_FP16(d);
        const float id = (maxScalar != 0.0f) ? 127.f / maxScalar : 0.0f;
        const __m256 mul = _mm256_set1_ps(id);

        // Apply the multiplier
        v0 = _mm256_mul_ps(v0, mul);
        v1 = _mm256_mul_ps(v1, mul);
        v2 = _mm256_mul_ps(v2, mul);
        v3 = _mm256_mul_ps(v3, mul);

        // Round to nearest integer
        v0 = _mm256_round_ps(v0, _MM_ROUND_NEAREST);
        v1 = _mm256_round_ps(v1, _MM_ROUND_NEAREST);
        v2 = _mm256_round_ps(v2, _MM_ROUND_NEAREST);
        v3 = _mm256_round_ps(v3, _MM_ROUND_NEAREST);

        // Convert floats to integers
        __m256i i0 = _mm256_cvtps_epi32(v0);
        __m256i i1 = _mm256_cvtps_epi32(v1);
        __m256i i2 = _mm256_cvtps_epi32(v2);
        __m256i i3 = _mm256_cvtps_epi32(v3);

#if defined(__AVX2__)
        // Convert int32 to int16
        i0 = _mm256_packs_epi32(i0, i1); // 0, 1, 2, 3,  8, 9, 10, 11,  4, 5, 6, 7, 12, 13, 14, 15
        i2 = _mm256_packs_epi32(i2, i3); // 16, 17, 18, 19,  24, 25, 26, 27,  20, 21, 22, 23, 28, 29, 30, 31
                                         // Convert int16 to int8
        i0 = _mm256_packs_epi16(i0, i2); // 0, 1, 2, 3,  8, 9, 10, 11,  16, 17, 18, 19,  24, 25, 26, 27,  4, 5, 6, 7, 12, 13,
                                         // 14, 15, 20, 21, 22, 23, 28, 29, 30, 31

        // We got our precious signed bytes, but the order is now wrong
        // These AVX2 pack instructions process 16-byte pieces independently
        // The following instruction is fixing the order
        const __m256i perm = _mm256_setr_epi32(0, 4, 1, 5, 2, 6, 3, 7);
        i0 = _mm256_permutevar8x32_epi32(i0, perm);

        _mm256_storeu_si256((__m256i *)y[i].qs, i0);
#else
        // Since we don't have in AVX some necessary functions,
        // we split the registers in half and call AVX2 analogs from SSE
        __m128i ni0 = _mm256_castsi256_si128(i0);
        __m128i ni1 = _mm256_extractf128_si256(i0, 1);
        __m128i ni2 = _mm256_castsi256_si128(i1);
        __m128i ni3 = _mm256_extractf128_si256(i1, 1);
        __m128i ni4 = _mm256_castsi256_si128(i2);
        __m128i ni5 = _mm256_extractf128_si256(i2, 1);
        __m128i ni6 = _mm256_castsi256_si128(i3);
        __m128i ni7 = _mm256_extractf128_si256(i3, 1);

        // Convert int32 to int16
        ni0 = _mm_packs_epi32(ni0, ni1);
        ni2 = _mm_packs_epi32(ni2, ni3);
        ni4 = _mm_packs_epi32(ni4, ni5);
        ni6 = _mm_packs_epi32(ni6, ni7);
        // Convert int16 to int8
        ni0 = _mm_packs_epi16(ni0, ni2);
        ni4 = _mm_packs_epi16(ni4, ni6);

        _mm_storeu_si128((__m128i *)(y[i].qs + 0), ni0);
        _mm_storeu_si128((__m128i *)(y[i].qs + 16), ni4);
#endif
    }
#else
    // scalar
    quantize_row_q8_0_reference(x, y, k);
#endif
}

// reference implementation for deterministic creation of model files
static void quantize_row_q8_1_reference(const float * restrict x, block_q8_1 * restrict y, int k) {
    assert(QK8_1 == 32);
    assert(k % QK8_1 == 0);
    const int nb = k / QK8_1;

    for (int i = 0; i < nb; i++) {
        float amax = 0.0f; // absolute max

        for (int j = 0; j < QK8_1; j++) {
            const float v = x[i * QK8_1 + j];
            amax = MAX(amax, fabsf(v));
        }

        const float d = amax / ((1 << 7) - 1);
        const float id = d ? 1.0f / d : 0.0f;

        y[i].d = d;

        int sum = 0;

        for (int j = 0; j < QK8_1 / 2; ++j) {
            const float v0 = x[i * QK8_1 + j] * id;
            const float v1 = x[i * QK8_1 + QK8_1 / 2 + j] * id;

            y[i].qs[j] = roundf(v0);
            y[i].qs[QK8_1 / 2 + j] = roundf(v1);

            sum += y[i].qs[j];
            sum += y[i].qs[QK8_1 / 2 + j];
        }

        y[i].s = sum * d;
    }
}

static void quantize_row_q8_1(const float * restrict x, void * restrict vy, int k) {
    assert(k % QK8_1 == 0);
    const int nb = k / QK8_1;

    block_q8_1 * restrict y = vy;

#if defined(__ARM_NEON)
    for (int i = 0; i < nb; i++) {
        float32x4_t srcv[8];
        float32x4_t asrcv[8];
        float32x4_t amaxv[8];

        for (int j = 0; j < 8; j++)
            srcv[j] = vld1q_f32(x + i * 32 + 4 * j);
        for (int j = 0; j < 8; j++)
            asrcv[j] = vabsq_f32(srcv[j]);

        for (int j = 0; j < 4; j++)
            amaxv[2 * j] = vmaxq_f32(asrcv[2 * j], asrcv[2 * j + 1]);
        for (int j = 0; j < 2; j++)
            amaxv[4 * j] = vmaxq_f32(amaxv[4 * j], amaxv[4 * j + 2]);
        for (int j = 0; j < 1; j++)
            amaxv[8 * j] = vmaxq_f32(amaxv[8 * j], amaxv[8 * j + 4]);

        const float amax = vmaxvq_f32(amaxv[0]);

        const float d = amax / ((1 << 7) - 1);
        const float id = d ? 1.0f / d : 0.0f;

        y[i].d = d;

        int32x4_t accv = vdupq_n_s32(0);

        for (int j = 0; j < 8; j++) {
            const float32x4_t v = vmulq_n_f32(srcv[j], id);
            const int32x4_t vi = vcvtnq_s32_f32(v);

            y[i].qs[4 * j + 0] = vgetq_lane_s32(vi, 0);
            y[i].qs[4 * j + 1] = vgetq_lane_s32(vi, 1);
            y[i].qs[4 * j + 2] = vgetq_lane_s32(vi, 2);
            y[i].qs[4 * j + 3] = vgetq_lane_s32(vi, 3);

            accv = vaddq_s32(accv, vi);
        }

        y[i].s = d * vaddvq_s32(accv);
    }
#elif defined(__wasm_simd128__)
    for (int i = 0; i < nb; i++) {
        v128_t srcv[8];
        v128_t asrcv[8];
        v128_t amaxv[8];

        for (int j = 0; j < 8; j++)
            srcv[j] = wasm_v128_load(x + i * 32 + 4 * j);
        for (int j = 0; j < 8; j++)
            asrcv[j] = wasm_f32x4_abs(srcv[j]);

        for (int j = 0; j < 4; j++)
            amaxv[2 * j] = wasm_f32x4_max(asrcv[2 * j], asrcv[2 * j + 1]);
        for (int j = 0; j < 2; j++)
            amaxv[4 * j] = wasm_f32x4_max(amaxv[4 * j], amaxv[4 * j + 2]);
        for (int j = 0; j < 1; j++)
            amaxv[8 * j] = wasm_f32x4_max(amaxv[8 * j], amaxv[8 * j + 4]);

        const float amax = MAX(MAX(wasm_f32x4_extract_lane(amaxv[0], 0), wasm_f32x4_extract_lane(amaxv[0], 1)),
                               MAX(wasm_f32x4_extract_lane(amaxv[0], 2), wasm_f32x4_extract_lane(amaxv[0], 3)));

        const float d = amax / ((1 << 7) - 1);
        const float id = d ? 1.0f / d : 0.0f;

        y[i].d = d;

        v128_t accv = wasm_i32x4_splat(0);

        for (int j = 0; j < 8; j++) {
            const v128_t v = wasm_f32x4_mul(srcv[j], wasm_f32x4_splat(id));
            const v128_t vi = wasm_i32x4_trunc_sat_f32x4(v);

            y[i].qs[4 * j + 0] = wasm_i32x4_extract_lane(vi, 0);
            y[i].qs[4 * j + 1] = wasm_i32x4_extract_lane(vi, 1);
            y[i].qs[4 * j + 2] = wasm_i32x4_extract_lane(vi, 2);
            y[i].qs[4 * j + 3] = wasm_i32x4_extract_lane(vi, 3);

            accv = wasm_i32x4_add(accv, vi);
        }

        y[i].s = d * (wasm_i32x4_extract_lane(accv, 0) + wasm_i32x4_extract_lane(accv, 1) + wasm_i32x4_extract_lane(accv, 2) +
                      wasm_i32x4_extract_lane(accv, 3));
    }
#elif defined(__AVX2__) || defined(__AVX__)
    for (int i = 0; i < nb; i++) {
        // Load elements into 4 AVX vectors
        __m256 v0 = _mm256_loadu_ps(x);
        __m256 v1 = _mm256_loadu_ps(x + 8);
        __m256 v2 = _mm256_loadu_ps(x + 16);
        __m256 v3 = _mm256_loadu_ps(x + 24);
        x += 32;

        // Compute max(abs(e)) for the block
        const __m256 signBit = _mm256_set1_ps(-0.0f);
        __m256 maxAbs = _mm256_andnot_ps(signBit, v0);
        maxAbs = _mm256_max_ps(maxAbs, _mm256_andnot_ps(signBit, v1));
        maxAbs = _mm256_max_ps(maxAbs, _mm256_andnot_ps(signBit, v2));
        maxAbs = _mm256_max_ps(maxAbs, _mm256_andnot_ps(signBit, v3));

        __m128 max4 = _mm_max_ps(_mm256_extractf128_ps(maxAbs, 1), _mm256_castps256_ps128(maxAbs));
        max4 = _mm_max_ps(max4, _mm_movehl_ps(max4, max4));
        max4 = _mm_max_ss(max4, _mm_movehdup_ps(max4));
        const float maxScalar = _mm_cvtss_f32(max4);

        // Quantize these floats
        const float d = maxScalar / 127.f;
        y[i].d = d;
        const float id = (maxScalar != 0.0f) ? 127.f / maxScalar : 0.0f;
        const __m256 mul = _mm256_set1_ps(id);

        // Apply the multiplier
        v0 = _mm256_mul_ps(v0, mul);
        v1 = _mm256_mul_ps(v1, mul);
        v2 = _mm256_mul_ps(v2, mul);
        v3 = _mm256_mul_ps(v3, mul);

        // Round to nearest integer
        v0 = _mm256_round_ps(v0, _MM_ROUND_NEAREST);
        v1 = _mm256_round_ps(v1, _MM_ROUND_NEAREST);
        v2 = _mm256_round_ps(v2, _MM_ROUND_NEAREST);
        v3 = _mm256_round_ps(v3, _MM_ROUND_NEAREST);

        // Convert floats to integers
        __m256i i0 = _mm256_cvtps_epi32(v0);
        __m256i i1 = _mm256_cvtps_epi32(v1);
        __m256i i2 = _mm256_cvtps_epi32(v2);
        __m256i i3 = _mm256_cvtps_epi32(v3);

#if defined(__AVX2__)
        // Compute the sum of the quants and set y[i].s
        y[i].s = d * hsum_i32_8(_mm256_add_epi32(_mm256_add_epi32(i0, i1), _mm256_add_epi32(i2, i3)));

        // Convert int32 to int16
        i0 = _mm256_packs_epi32(i0, i1); // 0, 1, 2, 3,  8, 9, 10, 11,  4, 5, 6, 7, 12, 13, 14, 15
        i2 = _mm256_packs_epi32(i2, i3); // 16, 17, 18, 19,  24, 25, 26, 27,  20, 21, 22, 23, 28, 29, 30, 31
                                         // Convert int16 to int8
        i0 = _mm256_packs_epi16(i0, i2); // 0, 1, 2, 3,  8, 9, 10, 11,  16, 17, 18, 19,  24, 25, 26, 27,  4, 5, 6, 7, 12, 13,
                                         // 14, 15, 20, 21, 22, 23, 28, 29, 30, 31

        // We got our precious signed bytes, but the order is now wrong
        // These AVX2 pack instructions process 16-byte pieces independently
        // The following instruction is fixing the order
        const __m256i perm = _mm256_setr_epi32(0, 4, 1, 5, 2, 6, 3, 7);
        i0 = _mm256_permutevar8x32_epi32(i0, perm);

        _mm256_storeu_si256((__m256i *)y[i].qs, i0);
#else
        // Since we don't have in AVX some necessary functions,
        // we split the registers in half and call AVX2 analogs from SSE
        __m128i ni0 = _mm256_castsi256_si128(i0);
        __m128i ni1 = _mm256_extractf128_si256(i0, 1);
        __m128i ni2 = _mm256_castsi256_si128(i1);
        __m128i ni3 = _mm256_extractf128_si256(i1, 1);
        __m128i ni4 = _mm256_castsi256_si128(i2);
        __m128i ni5 = _mm256_extractf128_si256(i2, 1);
        __m128i ni6 = _mm256_castsi256_si128(i3);
        __m128i ni7 = _mm256_extractf128_si256(i3, 1);

        // Compute the sum of the quants and set y[i].s
        const __m128i s0 = _mm_add_epi32(_mm_add_epi32(ni0, ni1), _mm_add_epi32(ni2, ni3));
        const __m128i s1 = _mm_add_epi32(_mm_add_epi32(ni4, ni5), _mm_add_epi32(ni6, ni7));
        y[i].s = d * hsum_i32_4(_mm_add_epi32(s0, s1));

        // Convert int32 to int16
        ni0 = _mm_packs_epi32(ni0, ni1);
        ni2 = _mm_packs_epi32(ni2, ni3);
        ni4 = _mm_packs_epi32(ni4, ni5);
        ni6 = _mm_packs_epi32(ni6, ni7);
        // Convert int16 to int8
        ni0 = _mm_packs_epi16(ni0, ni2);
        ni4 = _mm_packs_epi16(ni4, ni6);

        _mm_storeu_si128((__m128i *)(y[i].qs + 0), ni0);
        _mm_storeu_si128((__m128i *)(y[i].qs + 16), ni4);
#endif
    }
#else
    // scalar
    quantize_row_q8_1_reference(x, y, k);
#endif
}

static void dequantize_row_q4_0(const block_q4_0 * restrict x, float * restrict y, int k) {
    static const int qk = QK4_0;

    assert(k % qk == 0);

    const int nb = k / qk;

    for (int i = 0; i < nb; i++) {
        const float d = GGML_FP16_TO_FP32(x[i].d);

        for (int j = 0; j < qk / 2; ++j) {
            const int x0 = (x[i].qs[j] & 0x0F) - 8;
            const int x1 = (x[i].qs[j] >> 4) - 8;

            y[i * qk + j + 0] = x0 * d;
            y[i * qk + j + qk / 2] = x1 * d;
        }
    }
}

static void dequantize_row_q4_1(const block_q4_1 * restrict x, float * restrict y, int k) {
    static const int qk = QK4_1;

    assert(k % qk == 0);

    const int nb = k / qk;

    for (int i = 0; i < nb; i++) {
        const float d = GGML_FP16_TO_FP32(x[i].d);
        const float m = GGML_FP16_TO_FP32(x[i].m);

        for (int j = 0; j < qk / 2; ++j) {
            const int x0 = (x[i].qs[j] & 0x0F);
            const int x1 = (x[i].qs[j] >> 4);

            y[i * qk + j + 0] = x0 * d + m;
            y[i * qk + j + qk / 2] = x1 * d + m;
        }
    }
}

static void dequantize_row_q5_0(const block_q5_0 * restrict x, float * restrict y, int k) {
    static const int qk = QK5_0;

    assert(k % qk == 0);

    const int nb = k / qk;

    for (int i = 0; i < nb; i++) {
        const float d = GGML_FP16_TO_FP32(x[i].d);

        uint32_t qh;
        memcpy(&qh, x[i].qh, sizeof(qh));

        for (int j = 0; j < qk / 2; ++j) {
            const uint8_t xh_0 = ((qh >> (j + 0)) << 4) & 0x10;
            const uint8_t xh_1 = ((qh >> (j + 12))) & 0x10;

            const int32_t x0 = ((x[i].qs[j] & 0x0F) | xh_0) - 16;
            const int32_t x1 = ((x[i].qs[j] >> 4) | xh_1) - 16;

            y[i * qk + j + 0] = x0 * d;
            y[i * qk + j + qk / 2] = x1 * d;
        }
    }
}

static void dequantize_row_q5_1(const block_q5_1 * restrict x, float * restrict y, int k) {
    static const int qk = QK5_1;

    assert(k % qk == 0);

    const int nb = k / qk;

    for (int i = 0; i < nb; i++) {
        const float d = GGML_FP16_TO_FP32(x[i].d);
        const float m = GGML_FP16_TO_FP32(x[i].m);

        uint32_t qh;
        memcpy(&qh, x[i].qh, sizeof(qh));

        for (int j = 0; j < qk / 2; ++j) {
            const uint8_t xh_0 = ((qh >> (j + 0)) << 4) & 0x10;
            const uint8_t xh_1 = ((qh >> (j + 12))) & 0x10;

            const int x0 = (x[i].qs[j] & 0x0F) | xh_0;
            const int x1 = (x[i].qs[j] >> 4) | xh_1;

            y[i * qk + j + 0] = x0 * d + m;
            y[i * qk + j + qk / 2] = x1 * d + m;
        }
    }
}

static void dequantize_row_q8_0(const void * restrict vx, float * restrict y, int k) {
    static const int qk = QK8_0;

    assert(k % qk == 0);

    const int nb = k / qk;

    const block_q8_0 * restrict x = vx;

    for (int i = 0; i < nb; i++) {
        const float d = GGML_FP16_TO_FP32(x[i].d);

        for (int j = 0; j < qk; ++j) {
            y[i * qk + j] = x[i].qs[j] * d;
        }
    }
}

static void ggml_vec_dot_f32(const int n, float * restrict s, const float * restrict x, const float * restrict y);
static void ggml_vec_dot_f16(const int n, float * restrict s, ggml_fp16_t * restrict x, ggml_fp16_t * restrict y);
static void ggml_vec_dot_q4_0_q8_0(const int n, float * restrict s, const void * restrict vx, const void * restrict vy);
static void ggml_vec_dot_q4_1_q8_1(const int n, float * restrict s, const void * restrict vx, const void * restrict vy);
static void ggml_vec_dot_q5_0_q8_0(const int n, float * restrict s, const void * restrict vx, const void * restrict vy);
static void ggml_vec_dot_q5_1_q8_1(const int n, float * restrict s, const void * restrict vx, const void * restrict vy);
static void ggml_vec_dot_q8_0_q8_0(const int n, float * restrict s, const void * restrict vx, const void * restrict vy);

static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
    [GGML_TYPE_I8] =
        {
            .type_name = "i8",
            .blck_size = 1,
            .type_size = sizeof(int8_t),
            .is_quantized = false,
        },
    [GGML_TYPE_I16] =
        {
            .type_name = "i16",
            .blck_size = 1,
            .type_size = sizeof(int16_t),
            .is_quantized = false,
        },
    [GGML_TYPE_I32] =
        {
            .type_name = "i32",
            .blck_size = 1,
            .type_size = sizeof(int32_t),
            .is_quantized = false,
        },
    [GGML_TYPE_F32] =
        {
            .type_name = "f32",
            .blck_size = 1,
            .type_size = sizeof(float),
            .is_quantized = false,
            .vec_dot = (ggml_vec_dot_t)ggml_vec_dot_f32,
            .vec_dot_type = GGML_TYPE_F32,
        },
    [GGML_TYPE_F16] =
        {
            .type_name = "f16",
            .blck_size = 1,
            .type_size = sizeof(ggml_fp16_t),
            .is_quantized = false,
            .to_float = (ggml_to_float_t)ggml_fp16_to_fp32_row,
            .from_float = (ggml_from_float_t)ggml_fp32_to_fp16_row,
            .from_float_reference = (ggml_from_float_t)ggml_fp32_to_fp16_row,
            .vec_dot = (ggml_vec_dot_t)ggml_vec_dot_f16,
            .vec_dot_type = GGML_TYPE_F16,
        },
    [GGML_TYPE_Q4_0] =
        {
            .type_name = "q4_0",
            .blck_size = QK4_0,
            .type_size = sizeof(block_q4_0),
            .is_quantized = true,
            .to_float = (ggml_to_float_t)dequantize_row_q4_0,
            .from_float = quantize_row_q4_0,
            .from_float_reference = (ggml_from_float_t)quantize_row_q4_0_reference,
            .vec_dot = ggml_vec_dot_q4_0_q8_0,
            .vec_dot_type = GGML_TYPE_Q8_0,
        },
    [GGML_TYPE_Q4_1] =
        {
            .type_name = "q4_1",
            .blck_size = QK4_1,
            .type_size = sizeof(block_q4_1),
            .is_quantized = true,
            .to_float = (ggml_to_float_t)dequantize_row_q4_1,
            .from_float = quantize_row_q4_1,
            .from_float_reference = (ggml_from_float_t)quantize_row_q4_1_reference,
            .vec_dot = ggml_vec_dot_q4_1_q8_1,
            .vec_dot_type = GGML_TYPE_Q8_1,
        },
    [GGML_TYPE_Q5_0] =
        {
            .type_name = "q5_0",
            .blck_size = QK5_0,
            .type_size = sizeof(block_q5_0),
            .is_quantized = true,
            .to_float = (ggml_to_float_t)dequantize_row_q5_0,
            .from_float = quantize_row_q5_0,
            .from_float_reference = (ggml_from_float_t)quantize_row_q5_0_reference,
            .vec_dot = ggml_vec_dot_q5_0_q8_0,
            .vec_dot_type = GGML_TYPE_Q8_0,
        },
    [GGML_TYPE_Q5_1] =
        {
            .type_name = "q5_1",
            .blck_size = QK5_1,
            .type_size = sizeof(block_q5_1),
            .is_quantized = true,
            .to_float = (ggml_to_float_t)dequantize_row_q5_1,
            .from_float = quantize_row_q5_1,
            .from_float_reference = (ggml_from_float_t)quantize_row_q5_1_reference,
            .vec_dot = ggml_vec_dot_q5_1_q8_1,
            .vec_dot_type = GGML_TYPE_Q8_1,
        },
    [GGML_TYPE_Q8_0] =
        {
            .type_name = "q8_0",
            .blck_size = QK8_0,
            .type_size = sizeof(block_q8_0),
            .is_quantized = true,
            .to_float = dequantize_row_q8_0,
            .from_float = quantize_row_q8_0,
            .from_float_reference = (ggml_from_float_t)quantize_row_q8_0_reference,
            .vec_dot = ggml_vec_dot_q8_0_q8_0,
            .vec_dot_type = GGML_TYPE_Q8_0,
        },
    [GGML_TYPE_Q8_1] =
        {
            .type_name = "q8_1",
            .blck_size = QK8_1,
            .type_size = sizeof(block_q8_1),
            .is_quantized = true,
            .from_float = quantize_row_q8_1,
            .from_float_reference = (ggml_from_float_t)quantize_row_q8_1_reference,
            .vec_dot_type = GGML_TYPE_Q8_1,
        },
#ifdef GGML_USE_K_QUANTS
    [GGML_TYPE_Q2_K] =
        {
            .type_name = "q2_K",
            .blck_size = QK_K,
            .type_size = sizeof(block_q2_K),
            .is_quantized = true,
            .to_float = (ggml_to_float_t)dequantize_row_q2_K,
            .from_float = quantize_row_q2_K,
            .from_float_reference = (ggml_from_float_t)quantize_row_q2_K_reference,
            .vec_dot = ggml_vec_dot_q2_K_q8_K,
            .vec_dot_type = GGML_TYPE_Q8_K,
        },
    [GGML_TYPE_Q3_K] =
        {
            .type_name = "q3_K",
            .blck_size = QK_K,
            .type_size = sizeof(block_q3_K),
            .is_quantized = true,
            .to_float = (ggml_to_float_t)dequantize_row_q3_K,
            .from_float = quantize_row_q3_K,
            .from_float_reference = (ggml_from_float_t)quantize_row_q3_K_reference,
            .vec_dot = ggml_vec_dot_q3_K_q8_K,
            .vec_dot_type = GGML_TYPE_Q8_K,
        },
    [GGML_TYPE_Q4_K] =
        {
            .type_name = "q4_K",
            .blck_size = QK_K,
            .type_size = sizeof(block_q4_K),
            .is_quantized = true,
            .to_float = (ggml_to_float_t)dequantize_row_q4_K,
            .from_float = quantize_row_q4_K,
            .from_float_reference = (ggml_from_float_t)quantize_row_q4_K_reference,
            .vec_dot = ggml_vec_dot_q4_K_q8_K,
            .vec_dot_type = GGML_TYPE_Q8_K,
        },
    [GGML_TYPE_Q5_K] =
        {
            .type_name = "q5_K",
            .blck_size = QK_K,
            .type_size = sizeof(block_q5_K),
            .is_quantized = true,
            .to_float = (ggml_to_float_t)dequantize_row_q5_K,
            .from_float = quantize_row_q5_K,
            .from_float_reference = (ggml_from_float_t)quantize_row_q5_K_reference,
            .vec_dot = ggml_vec_dot_q5_K_q8_K,
            .vec_dot_type = GGML_TYPE_Q8_K,
        },
    [GGML_TYPE_Q6_K] =
        {
            .type_name = "q6_K",
            .blck_size = QK_K,
            .type_size = sizeof(block_q6_K),
            .is_quantized = true,
            .to_float = (ggml_to_float_t)dequantize_row_q6_K,
            .from_float = quantize_row_q6_K,
            .from_float_reference = (ggml_from_float_t)quantize_row_q6_K_reference,
            .vec_dot = ggml_vec_dot_q6_K_q8_K,
            .vec_dot_type = GGML_TYPE_Q8_K,
        },
    [GGML_TYPE_Q8_K] =
        {
            .type_name = "q8_K",
            .blck_size = QK_K,
            .type_size = sizeof(block_q8_K),
            .is_quantized = true,
            .from_float = quantize_row_q8_K,
        }
#endif
};

// For internal test use
ggml_type_traits_t ggml_internal_get_type_traits(enum ggml_type type) {
    GGML_ASSERT(type < GGML_TYPE_COUNT);
    return type_traits[type];
}

//
// simd mappings
//

// we define a common set of C macros which map to specific intrinsics based on the current architecture
// we then implement the fundamental computation operations below using only these macros
// adding support for new architectures requires to define the corresponding SIMD macros
//
// GGML_F32_STEP / GGML_F16_STEP
//   number of elements to process in a single step
//
// GGML_F32_EPR / GGML_F16_EPR
//   number of elements to fit in a single register
//

#if defined(__ARM_NEON) && defined(__ARM_FEATURE_FMA)

#define GGML_SIMD

// F32 NEON

#define GGML_F32_STEP 16
#define GGML_F32_EPR 4

#define GGML_F32x4 float32x4_t
#define GGML_F32x4_ZERO vdupq_n_f32(0.0f)
#define GGML_F32x4_SET1(x) vdupq_n_f32(x)
#define GGML_F32x4_LOAD vld1q_f32
#define GGML_F32x4_STORE vst1q_f32
#define GGML_F32x4_FMA(a, b, c) vfmaq_f32(a, b, c)
#define GGML_F32x4_ADD vaddq_f32
#define GGML_F32x4_MUL vmulq_f32
#define GGML_F32x4_REDUCE_ONE(x) vaddvq_f32(x)
#define GGML_F32x4_REDUCE(res, x)                                                                                              \
    {                                                                                                                          \
        int offset = GGML_F32_ARR >> 1;                                                                                        \
        for (int i = 0; i < offset; ++i) {                                                                                     \
            x[i] = vaddq_f32(x[i], x[offset + i]);                                                                             \
        }                                                                                                                      \
        offset >>= 1;                                                                                                          \
        for (int i = 0; i < offset; ++i) {                                                                                     \
            x[i] = vaddq_f32(x[i], x[offset + i]);                                                                             \
        }                                                                                                                      \
        offset >>= 1;                                                                                                          \
        for (int i = 0; i < offset; ++i) {                                                                                     \
            x[i] = vaddq_f32(x[i], x[offset + i]);                                                                             \
        }                                                                                                                      \
        res = GGML_F32x4_REDUCE_ONE(x[0]);                                                                                     \
    }

#define GGML_F32_VEC GGML_F32x4
#define GGML_F32_VEC_ZERO GGML_F32x4_ZERO
#define GGML_F32_VEC_SET1 GGML_F32x4_SET1
#define GGML_F32_VEC_LOAD GGML_F32x4_LOAD
#define GGML_F32_VEC_STORE GGML_F32x4_STORE
#define GGML_F32_VEC_FMA GGML_F32x4_FMA
#define GGML_F32_VEC_ADD GGML_F32x4_ADD
#define GGML_F32_VEC_MUL GGML_F32x4_MUL
#define GGML_F32_VEC_REDUCE GGML_F32x4_REDUCE

// F16 NEON

#if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC)
#define GGML_F16_STEP 32
#define GGML_F16_EPR 8

#define GGML_F16x8 float16x8_t
#define GGML_F16x8_ZERO vdupq_n_f16(0.0f)
#define GGML_F16x8_SET1(x) vdupq_n_f16(x)
#define GGML_F16x8_LOAD vld1q_f16
#define GGML_F16x8_STORE vst1q_f16
#define GGML_F16x8_FMA(a, b, c) vfmaq_f16(a, b, c)
#define GGML_F16x8_ADD vaddq_f16
#define GGML_F16x8_MUL vmulq_f16
#define GGML_F16x8_REDUCE(res, x)                                                                                              \
    {                                                                                                                          \
        int offset = GGML_F16_ARR >> 1;                                                                                        \
        for (int i = 0; i < offset; ++i) {                                                                                     \
            x[i] = vaddq_f16(x[i], x[offset + i]);                                                                             \
        }                                                                                                                      \
        offset >>= 1;                                                                                                          \
        for (int i = 0; i < offset; ++i) {                                                                                     \
            x[i] = vaddq_f16(x[i], x[offset + i]);                                                                             \
        }                                                                                                                      \
        offset >>= 1;                                                                                                          \
        for (int i = 0; i < offset; ++i) {                                                                                     \
            x[i] = vaddq_f16(x[i], x[offset + i]);                                                                             \
        }                                                                                                                      \
        const float32x4_t t0 = vcvt_f32_f16(vget_low_f16(x[0]));                                                               \
        const float32x4_t t1 = vcvt_f32_f16(vget_high_f16(x[0]));                                                              \
        res = (ggml_float)vaddvq_f32(vaddq_f32(t0, t1));                                                                       \
    }

#define GGML_F16_VEC GGML_F16x8
#define GGML_F16_VEC_ZERO GGML_F16x8_ZERO
#define GGML_F16_VEC_SET1 GGML_F16x8_SET1
#define GGML_F16_VEC_LOAD(p, i) GGML_F16x8_LOAD(p)
#define GGML_F16_VEC_STORE(p, r, i) GGML_F16x8_STORE(p, r[i])
#define GGML_F16_VEC_FMA GGML_F16x8_FMA
#define GGML_F16_VEC_ADD GGML_F16x8_ADD
#define GGML_F16_VEC_MUL GGML_F16x8_MUL
#define GGML_F16_VEC_REDUCE GGML_F16x8_REDUCE
#else
// if FP16 vector arithmetic is not supported, we use FP32 instead
// and take advantage of the vcvt_ functions to convert to/from FP16

#define GGML_F16_STEP 16
#define GGML_F16_EPR 4

#define GGML_F32Cx4 float32x4_t
#define GGML_F32Cx4_ZERO vdupq_n_f32(0.0f)
#define GGML_F32Cx4_SET1(x) vdupq_n_f32(x)
#define GGML_F32Cx4_LOAD(x) vcvt_f32_f16(vld1_f16(x))
#define GGML_F32Cx4_STORE(x, y) vst1_f16(x, vcvt_f16_f32(y))
#define GGML_F32Cx4_FMA(a, b, c) vfmaq_f32(a, b, c)
#define GGML_F32Cx4_ADD vaddq_f32
#define GGML_F32Cx4_MUL vmulq_f32
#define GGML_F32Cx4_REDUCE GGML_F32x4_REDUCE

#define GGML_F16_VEC GGML_F32Cx4
#define GGML_F16_VEC_ZERO GGML_F32Cx4_ZERO
#define GGML_F16_VEC_SET1 GGML_F32Cx4_SET1
#define GGML_F16_VEC_LOAD(p, i) GGML_F32Cx4_LOAD(p)
#define GGML_F16_VEC_STORE(p, r, i) GGML_F32Cx4_STORE(p, r[i])
#define GGML_F16_VEC_FMA GGML_F32Cx4_FMA
#define GGML_F16_VEC_ADD GGML_F32Cx4_ADD
#define GGML_F16_VEC_MUL GGML_F32Cx4_MUL
#define GGML_F16_VEC_REDUCE GGML_F32Cx4_REDUCE
#endif

#elif defined(__AVX__)

#define GGML_SIMD

// F32 AVX

#define GGML_F32_STEP 32
#define GGML_F32_EPR 8

#define GGML_F32x8 __m256
#define GGML_F32x8_ZERO _mm256_setzero_ps()
#define GGML_F32x8_SET1(x) _mm256_set1_ps(x)
#define GGML_F32x8_LOAD _mm256_loadu_ps
#define GGML_F32x8_STORE _mm256_storeu_ps
#if defined(__FMA__)
#define GGML_F32x8_FMA(a, b, c) _mm256_fmadd_ps(b, c, a)
#else
#define GGML_F32x8_FMA(a, b, c) _mm256_add_ps(_mm256_mul_ps(b, c), a)
#endif
#define GGML_F32x8_ADD _mm256_add_ps
#define GGML_F32x8_MUL _mm256_mul_ps
#define GGML_F32x8_REDUCE(res, x)                                                                                              \
    {                                                                                                                          \
        int offset = GGML_F32_ARR >> 1;                                                                                        \
        for (int i = 0; i < offset; ++i) {                                                                                     \
            x[i] = _mm256_add_ps(x[i], x[offset + i]);                                                                         \
        }                                                                                                                      \
        offset >>= 1;                                                                                                          \
        for (int i = 0; i < offset; ++i) {                                                                                     \
            x[i] = _mm256_add_ps(x[i], x[offset + i]);                                                                         \
        }                                                                                                                      \
        offset >>= 1;                                                                                                          \
        for (int i = 0; i < offset; ++i) {                                                                                     \
            x[i] = _mm256_add_ps(x[i], x[offset + i]);                                                                         \
        }                                                                                                                      \
        const __m128 t0 = _mm_add_ps(_mm256_castps256_ps128(x[0]), _mm256_extractf128_ps(x[0], 1));                            \
        const __m128 t1 = _mm_hadd_ps(t0, t0);                                                                                 \
        res = _mm_cvtss_f32(_mm_hadd_ps(t1, t1));                                                                              \
    }
// TODO: is this optimal ?

#define GGML_F32_VEC GGML_F32x8
#define GGML_F32_VEC_ZERO GGML_F32x8_ZERO
#define GGML_F32_VEC_SET1 GGML_F32x8_SET1
#define GGML_F32_VEC_LOAD GGML_F32x8_LOAD
#define GGML_F32_VEC_STORE GGML_F32x8_STORE
#define GGML_F32_VEC_FMA GGML_F32x8_FMA
#define GGML_F32_VEC_ADD GGML_F32x8_ADD
#define GGML_F32_VEC_MUL GGML_F32x8_MUL
#define GGML_F32_VEC_REDUCE GGML_F32x8_REDUCE

// F16 AVX

#define GGML_F16_STEP 32
#define GGML_F16_EPR 8

// F16 arithmetic is not supported by AVX, so we use F32 instead

#define GGML_F32Cx8 __m256
#define GGML_F32Cx8_ZERO _mm256_setzero_ps()
#define GGML_F32Cx8_SET1(x) _mm256_set1_ps(x)

#if defined(__F16C__)
// the  _mm256_cvt intrinsics require F16C
#define GGML_F32Cx8_LOAD(x) _mm256_cvtph_ps(_mm_loadu_si128((__m128i *)(x)))
#define GGML_F32Cx8_STORE(x, y) _mm_storeu_si128((__m128i *)(x), _mm256_cvtps_ph(y, 0))
#else
static inline __m256 __avx_f32cx8_load(ggml_fp16_t * x) {
    float tmp[8];

    for (int i = 0; i < 8; i++) {
        tmp[i] = GGML_FP16_TO_FP32(x[i]);
    }

    return _mm256_loadu_ps(tmp);
}
static inline void __avx_f32cx8_store(ggml_fp16_t * x, __m256 y) {
    float arr[8];

    _mm256_storeu_ps(arr, y);

    for (int i = 0; i < 8; i++)
        x[i] = GGML_FP32_TO_FP16(arr[i]);
}
#define GGML_F32Cx8_LOAD(x) __avx_f32cx8_load(x)
#define GGML_F32Cx8_STORE(x, y) __avx_f32cx8_store(x, y)
#endif

#define GGML_F32Cx8_FMA GGML_F32x8_FMA
#define GGML_F32Cx8_ADD _mm256_add_ps
#define GGML_F32Cx8_MUL _mm256_mul_ps
#define GGML_F32Cx8_REDUCE GGML_F32x8_REDUCE

#define GGML_F16_VEC GGML_F32Cx8
#define GGML_F16_VEC_ZERO GGML_F32Cx8_ZERO
#define GGML_F16_VEC_SET1 GGML_F32Cx8_SET1
#define GGML_F16_VEC_LOAD(p, i) GGML_F32Cx8_LOAD(p)
#define GGML_F16_VEC_STORE(p, r, i) GGML_F32Cx8_STORE(p, r[i])
#define GGML_F16_VEC_FMA GGML_F32Cx8_FMA
#define GGML_F16_VEC_ADD GGML_F32Cx8_ADD
#define GGML_F16_VEC_MUL GGML_F32Cx8_MUL
#define GGML_F16_VEC_REDUCE GGML_F32Cx8_REDUCE

#elif defined(__POWER9_VECTOR__)

#define GGML_SIMD

// F32 POWER9

#define GGML_F32_STEP 32
#define GGML_F32_EPR 4

#define GGML_F32x4 vector float
#define GGML_F32x4_ZERO 0.0f
#define GGML_F32x4_SET1 vec_splats
#define GGML_F32x4_LOAD(p) vec_xl(0, p)
#define GGML_F32x4_STORE(p, r) vec_xst(r, 0, p)
#define GGML_F32x4_FMA(a, b, c) vec_madd(b, c, a)
#define GGML_F32x4_ADD vec_add
#define GGML_F32x4_MUL vec_mul
#define GGML_F32x4_REDUCE(res, x)                                                                                              \
    {                                                                                                                          \
        int offset = GGML_F32_ARR >> 1;                                                                                        \
        for (int i = 0; i < offset; ++i) {                                                                                     \
            x[i] = vec_add(x[i], x[offset + i]);                                                                               \
        }                                                                                                                      \
        offset >>= 1;                                                                                                          \
        for (int i = 0; i < offset; ++i) {                                                                                     \
            x[i] = vec_add(x[i], x[offset + i]);                                                                               \
        }                                                                                                                      \
        offset >>= 1;                                                                                                          \
        for (int i = 0; i < offset; ++i) {                                                                                     \
            x[i] = vec_add(x[i], x[offset + i]);                                                                               \
        }                                                                                                                      \
        res = vec_extract(x[0], 0) + vec_extract(x[0], 1) + vec_extract(x[0], 2) + vec_extract(x[0], 3);                       \
    }

#define GGML_F32_VEC GGML_F32x4
#define GGML_F32_VEC_ZERO GGML_F32x4_ZERO
#define GGML_F32_VEC_SET1 GGML_F32x4_SET1
#define GGML_F32_VEC_LOAD GGML_F32x4_LOAD
#define GGML_F32_VEC_STORE GGML_F32x4_STORE
#define GGML_F32_VEC_FMA GGML_F32x4_FMA
#define GGML_F32_VEC_ADD GGML_F32x4_ADD
#define GGML_F32_VEC_MUL GGML_F32x4_MUL
#define GGML_F32_VEC_REDUCE GGML_F32x4_REDUCE

// F16 POWER9
#define GGML_F16_STEP GGML_F32_STEP
#define GGML_F16_EPR GGML_F32_EPR
#define GGML_F16_VEC GGML_F32x4
#define GGML_F16_VEC_ZERO GGML_F32x4_ZERO
#define GGML_F16_VEC_SET1 GGML_F32x4_SET1
#define GGML_F16_VEC_FMA GGML_F32x4_FMA
#define GGML_F16_VEC_REDUCE GGML_F32x4_REDUCE
// Use vec_xl, not vec_ld, in case the load address is not aligned.
#define GGML_F16_VEC_LOAD(p, i)                                                                                                \
    (i & 0x1) ? vec_extract_fp32_from_shorth(vec_xl(0, p - GGML_F16_EPR)) : vec_extract_fp32_from_shortl(vec_xl(0, p))
#define GGML_ENDIAN_BYTE(i) ((unsigned char *)&(uint16_t){1})[i]
#define GGML_F16_VEC_STORE(p, r, i)                                                                                            \
    if (i & 0x1)                                                                                                               \
    vec_xst(vec_pack_to_short_fp32(r[i - GGML_ENDIAN_BYTE(1)], r[i - GGML_ENDIAN_BYTE(0)]), 0, p - GGML_F16_EPR)

#elif defined(__wasm_simd128__)

#define GGML_SIMD

// F32 WASM

#define GGML_F32_STEP 16
#define GGML_F32_EPR 4

#define GGML_F32x4 v128_t
#define GGML_F32x4_ZERO wasm_f32x4_splat(0.0f)
#define GGML_F32x4_SET1(x) wasm_f32x4_splat(x)
#define GGML_F32x4_LOAD wasm_v128_load
#define GGML_F32x4_STORE wasm_v128_store
#define GGML_F32x4_FMA(a, b, c) wasm_f32x4_add(wasm_f32x4_mul(b, c), a)
#define GGML_F32x4_ADD wasm_f32x4_add
#define GGML_F32x4_MUL wasm_f32x4_mul
#define GGML_F32x4_REDUCE(res, x)                                                                                              \
    {                                                                                                                          \
        int offset = GGML_F32_ARR >> 1;                                                                                        \
        for (int i = 0; i < offset; ++i) {                                                                                     \
            x[i] = wasm_f32x4_add(x[i], x[offset + i]);                                                                        \
        }                                                                                                                      \
        offset >>= 1;                                                                                                          \
        for (int i = 0; i < offset; ++i) {                                                                                     \
            x[i] = wasm_f32x4_add(x[i], x[offset + i]);                                                                        \
        }                                                                                                                      \
        offset >>= 1;                                                                                                          \
        for (int i = 0; i < offset; ++i) {                                                                                     \
            x[i] = wasm_f32x4_add(x[i], x[offset + i]);                                                                        \
        }                                                                                                                      \
        res = wasm_f32x4_extract_lane(x[0], 0) + wasm_f32x4_extract_lane(x[0], 1) + wasm_f32x4_extract_lane(x[0], 2) +         \
              wasm_f32x4_extract_lane(x[0], 3);                                                                                \
    }

#define GGML_F32_VEC GGML_F32x4
#define GGML_F32_VEC_ZERO GGML_F32x4_ZERO
#define GGML_F32_VEC_SET1 GGML_F32x4_SET1
#define GGML_F32_VEC_LOAD GGML_F32x4_LOAD
#define GGML_F32_VEC_STORE GGML_F32x4_STORE
#define GGML_F32_VEC_FMA GGML_F32x4_FMA
#define GGML_F32_VEC_ADD GGML_F32x4_ADD
#define GGML_F32_VEC_MUL GGML_F32x4_MUL
#define GGML_F32_VEC_REDUCE GGML_F32x4_REDUCE

// F16 WASM

#define GGML_F16_STEP 16
#define GGML_F16_EPR 4

inline static v128_t __wasm_f16x4_load(const ggml_fp16_t * p) {
    float tmp[4];

    tmp[0] = GGML_FP16_TO_FP32(p[0]);
    tmp[1] = GGML_FP16_TO_FP32(p[1]);
    tmp[2] = GGML_FP16_TO_FP32(p[2]);
    tmp[3] = GGML_FP16_TO_FP32(p[3]);

    return wasm_v128_load(tmp);
}

inline static void __wasm_f16x4_store(ggml_fp16_t * p, v128_t x) {
    float tmp[4];

    wasm_v128_store(tmp, x);

    p[0] = GGML_FP32_TO_FP16(tmp[0]);
    p[1] = GGML_FP32_TO_FP16(tmp[1]);
    p[2] = GGML_FP32_TO_FP16(tmp[2]);
    p[3] = GGML_FP32_TO_FP16(tmp[3]);
}

#define GGML_F16x4 v128_t
#define GGML_F16x4_ZERO wasm_f32x4_splat(0.0f)
#define GGML_F16x4_SET1(x) wasm_f32x4_splat(x)
#define GGML_F16x4_LOAD(x) __wasm_f16x4_load(x)
#define GGML_F16x4_STORE(x, y) __wasm_f16x4_store(x, y)
#define GGML_F16x4_FMA GGML_F32x4_FMA
#define GGML_F16x4_ADD wasm_f32x4_add
#define GGML_F16x4_MUL wasm_f32x4_mul
#define GGML_F16x4_REDUCE(res, x)                                                                                              \
    {                                                                                                                          \
        int offset = GGML_F16_ARR >> 1;                                                                                        \
        for (int i = 0; i < offset; ++i) {                                                                                     \
            x[i] = wasm_f32x4_add(x[i], x[offset + i]);                                                                        \
        }                                                                                                                      \
        offset >>= 1;                                                                                                          \
        for (int i = 0; i < offset; ++i) {                                                                                     \
            x[i] = wasm_f32x4_add(x[i], x[offset + i]);                                                                        \
        }                                                                                                                      \
        offset >>= 1;                                                                                                          \
        for (int i = 0; i < offset; ++i) {                                                                                     \
            x[i] = wasm_f32x4_add(x[i], x[offset + i]);                                                                        \
        }                                                                                                                      \
        res = wasm_f32x4_extract_lane(x[0], 0) + wasm_f32x4_extract_lane(x[0], 1) + wasm_f32x4_extract_lane(x[0], 2) +         \
              wasm_f32x4_extract_lane(x[0], 3);                                                                                \
    }

#define GGML_F16_VEC GGML_F16x4
#define GGML_F16_VEC_ZERO GGML_F16x4_ZERO
#define GGML_F16_VEC_SET1 GGML_F16x4_SET1
#define GGML_F16_VEC_LOAD(p, i) GGML_F16x4_LOAD(p)
#define GGML_F16_VEC_STORE(p, r, i) GGML_F16x4_STORE(p, r[i])
#define GGML_F16_VEC_FMA GGML_F16x4_FMA
#define GGML_F16_VEC_ADD GGML_F16x4_ADD
#define GGML_F16_VEC_MUL GGML_F16x4_MUL
#define GGML_F16_VEC_REDUCE GGML_F16x4_REDUCE

#elif defined(__SSE3__)

#define GGML_SIMD

// F32 SSE

#define GGML_F32_STEP 32
#define GGML_F32_EPR 4

#define GGML_F32x4 __m128
#define GGML_F32x4_ZERO _mm_setzero_ps()
#define GGML_F32x4_SET1(x) _mm_set1_ps(x)
#define GGML_F32x4_LOAD _mm_loadu_ps
#define GGML_F32x4_STORE _mm_storeu_ps
#if defined(__FMA__)
// TODO: Does this work?
#define GGML_F32x4_FMA(a, b, c) _mm_fmadd_ps(b, c, a)
#else
#define GGML_F32x4_FMA(a, b, c) _mm_add_ps(_mm_mul_ps(b, c), a)
#endif
#define GGML_F32x4_ADD _mm_add_ps
#define GGML_F32x4_MUL _mm_mul_ps
#define GGML_F32x4_REDUCE(res, x)                                                                                              \
    {                                                                                                                          \
        int offset = GGML_F32_ARR >> 1;                                                                                        \
        for (int i = 0; i < offset; ++i) {                                                                                     \
            x[i] = _mm_add_ps(x[i], x[offset + i]);                                                                            \
        }                                                                                                                      \
        offset >>= 1;                                                                                                          \
        for (int i = 0; i < offset; ++i) {                                                                                     \
            x[i] = _mm_add_ps(x[i], x[offset + i]);                                                                            \
        }                                                                                                                      \
        offset >>= 1;                                                                                                          \
        for (int i = 0; i < offset; ++i) {                                                                                     \
            x[i] = _mm_add_ps(x[i], x[offset + i]);                                                                            \
        }                                                                                                                      \
        const __m128 t0 = _mm_hadd_ps(x[0], x[0]);                                                                             \
        res = _mm_cvtss_f32(_mm_hadd_ps(t0, t0));                                                                              \
    }
// TODO: is this optimal ?

#define GGML_F32_VEC GGML_F32x4
#define GGML_F32_VEC_ZERO GGML_F32x4_ZERO
#define GGML_F32_VEC_SET1 GGML_F32x4_SET1
#define GGML_F32_VEC_LOAD GGML_F32x4_LOAD
#define GGML_F32_VEC_STORE GGML_F32x4_STORE
#define GGML_F32_VEC_FMA GGML_F32x4_FMA
#define GGML_F32_VEC_ADD GGML_F32x4_ADD
#define GGML_F32_VEC_MUL GGML_F32x4_MUL
#define GGML_F32_VEC_REDUCE GGML_F32x4_REDUCE

// F16 SSE

#define GGML_F16_STEP 32
#define GGML_F16_EPR 4

static inline __m128 __sse_f16x4_load(ggml_fp16_t * x) {
    float tmp[4];

    tmp[0] = GGML_FP16_TO_FP32(x[0]);
    tmp[1] = GGML_FP16_TO_FP32(x[1]);
    tmp[2] = GGML_FP16_TO_FP32(x[2]);
    tmp[3] = GGML_FP16_TO_FP32(x[3]);

    return _mm_loadu_ps(tmp);
}

static inline void __sse_f16x4_store(ggml_fp16_t * x, __m128 y) {
    float arr[4];

    _mm_storeu_ps(arr, y);

    x[0] = GGML_FP32_TO_FP16(arr[0]);
    x[1] = GGML_FP32_TO_FP16(arr[1]);
    x[2] = GGML_FP32_TO_FP16(arr[2]);
    x[3] = GGML_FP32_TO_FP16(arr[3]);
}

#define GGML_F32Cx4 __m128
#define GGML_F32Cx4_ZERO _mm_setzero_ps()
#define GGML_F32Cx4_SET1(x) _mm_set1_ps(x)
#define GGML_F32Cx4_LOAD(x) __sse_f16x4_load(x)
#define GGML_F32Cx4_STORE(x, y) __sse_f16x4_store(x, y)
#define GGML_F32Cx4_FMA GGML_F32x4_FMA
#define GGML_F32Cx4_ADD _mm_add_ps
#define GGML_F32Cx4_MUL _mm_mul_ps
#define GGML_F32Cx4_REDUCE GGML_F32x4_REDUCE

#define GGML_F16_VEC GGML_F32Cx4
#define GGML_F16_VEC_ZERO GGML_F32Cx4_ZERO
#define GGML_F16_VEC_SET1 GGML_F32Cx4_SET1
#define GGML_F16_VEC_LOAD(p, i) GGML_F32Cx4_LOAD(p)
#define GGML_F16_VEC_STORE(p, r, i) GGML_F32Cx4_STORE(p, r[i])
#define GGML_F16_VEC_FMA GGML_F32Cx4_FMA
#define GGML_F16_VEC_ADD GGML_F32Cx4_ADD
#define GGML_F16_VEC_MUL GGML_F32Cx4_MUL
#define GGML_F16_VEC_REDUCE GGML_F32Cx4_REDUCE

#endif

// GGML_F32_ARR / GGML_F16_ARR
//   number of registers to use per step
#ifdef GGML_SIMD
#define GGML_F32_ARR (GGML_F32_STEP / GGML_F32_EPR)
#define GGML_F16_ARR (GGML_F16_STEP / GGML_F16_EPR)
#endif

//
// fundamental operations
//

inline static void ggml_vec_set_i8(const int n, int8_t * x, const int8_t v) {
    for (int i = 0; i < n; ++i)
        x[i] = v;
}

inline static void ggml_vec_set_i16(const int n, int16_t * x, const int16_t v) {
    for (int i = 0; i < n; ++i)
        x[i] = v;
}

inline static void ggml_vec_set_i32(const int n, int32_t * x, const int32_t v) {
    for (int i = 0; i < n; ++i)
        x[i] = v;
}

inline static void ggml_vec_set_f16(const int n, ggml_fp16_t * x, const int32_t v) {
    for (int i = 0; i < n; ++i)
        x[i] = v;
}

inline static void ggml_vec_add_f32(const int n, float * z, const float * x, const float * y) {
    for (int i = 0; i < n; ++i)
        z[i] = x[i] + y[i];
}
inline static void ggml_vec_add1_f32(const int n, float * z, const float * x, const float v) {
    for (int i = 0; i < n; ++i)
        z[i] = x[i] + v;
}
inline static void ggml_vec_acc_f32(const int n, float * y, const float * x) {
    for (int i = 0; i < n; ++i)
        y[i] += x[i];
}
inline static void ggml_vec_acc1_f32(const int n, float * y, const float v) {
    for (int i = 0; i < n; ++i)
        y[i] += v;
}
inline static void ggml_vec_sub_f32(const int n, float * z, const float * x, const float * y) {
    for (int i = 0; i < n; ++i)
        z[i] = x[i] - y[i];
}
inline static void ggml_vec_set_f32(const int n, float * x, const float v) {
    for (int i = 0; i < n; ++i)
        x[i] = v;
}
inline static void ggml_vec_cpy_f32(const int n, float * y, const float * x) {
    for (int i = 0; i < n; ++i)
        y[i] = x[i];
}
inline static void ggml_vec_neg_f32(const int n, float * y, const float * x) {
    for (int i = 0; i < n; ++i)
        y[i] = -x[i];
}
inline static void ggml_vec_mul_f32(const int n, float * z, const float * x, const float * y) {
    for (int i = 0; i < n; ++i)
        z[i] = x[i] * y[i];
}
inline static void ggml_vec_div_f32(const int n, float * z, const float * x, const float * y) {
    for (int i = 0; i < n; ++i)
        z[i] = x[i] / y[i];
}

static void ggml_vec_dot_f32(const int n, float * restrict s, const float * restrict x, const float * restrict y) {
#ifdef GGML_SIMD
    float sumf = 0.0f;
    const int np = (n & ~(GGML_F32_STEP - 1));

    GGML_F32_VEC sum[GGML_F32_ARR] = {GGML_F32_VEC_ZERO};

    GGML_F32_VEC ax[GGML_F32_ARR];
    GGML_F32_VEC ay[GGML_F32_ARR];

    for (int i = 0; i < np; i += GGML_F32_STEP) {
        for (int j = 0; j < GGML_F32_ARR; j++) {
            ax[j] = GGML_F32_VEC_LOAD(x + i + j * GGML_F32_EPR);
            ay[j] = GGML_F32_VEC_LOAD(y + i + j * GGML_F32_EPR);

            sum[j] = GGML_F32_VEC_FMA(sum[j], ax[j], ay[j]);
        }
    }

    // reduce sum0..sum3 to sum0
    GGML_F32_VEC_REDUCE(sumf, sum);

    // leftovers
    for (int i = np; i < n; ++i) {
        sumf += x[i] * y[i];
    }
#else
    // scalar
    ggml_float sumf = 0.0;
    for (int i = 0; i < n; ++i) {
        sumf += (ggml_float)(x[i] * y[i]);
    }
#endif

    *s = sumf;
}

static void ggml_vec_dot_f16(const int n, float * restrict s, ggml_fp16_t * restrict x, ggml_fp16_t * restrict y) {
    ggml_float sumf = 0.0;

#if defined(GGML_SIMD)
    const int np = (n & ~(GGML_F16_STEP - 1));

    GGML_F16_VEC sum[GGML_F16_ARR] = {GGML_F16_VEC_ZERO};

    GGML_F16_VEC ax[GGML_F16_ARR];
    GGML_F16_VEC ay[GGML_F16_ARR];

    for (int i = 0; i < np; i += GGML_F16_STEP) {
        for (int j = 0; j < GGML_F16_ARR; j++) {
            ax[j] = GGML_F16_VEC_LOAD(x + i + j * GGML_F16_EPR, j);
            ay[j] = GGML_F16_VEC_LOAD(y + i + j * GGML_F16_EPR, j);

            sum[j] = GGML_F16_VEC_FMA(sum[j], ax[j], ay[j]);
        }
    }

    // reduce sum0..sum3 to sum0
    GGML_F16_VEC_REDUCE(sumf, sum);

    // leftovers
    for (int i = np; i < n; ++i) {
        sumf += (ggml_float)(GGML_FP16_TO_FP32(x[i]) * GGML_FP16_TO_FP32(y[i]));
    }
#else
    for (int i = 0; i < n; ++i) {
        sumf += (ggml_float)(GGML_FP16_TO_FP32(x[i]) * GGML_FP16_TO_FP32(y[i]));
    }
#endif

    *s = sumf;
}

static void ggml_vec_dot_q4_0_q8_0(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) {
    const int qk = QK8_0;
    const int nb = n / qk;

    assert(n % qk == 0);

    const block_q4_0 * restrict x = vx;
    const block_q8_0 * restrict y = vy;

#if defined(__ARM_NEON)
    float32x4_t sumv0 = vdupq_n_f32(0.0f);
    float32x4_t sumv1 = vdupq_n_f32(0.0f);

    GGML_ASSERT(nb % 2 == 0); // TODO: handle odd nb
    for (int i = 0; i < nb; i += 2) {
        const block_q4_0 * restrict x0 = &x[i + 0];
        const block_q4_0 * restrict x1 = &x[i + 1];
        const block_q8_0 * restrict y0 = &y[i + 0];
        const block_q8_0 * restrict y1 = &y[i + 1];

        const uint8x16_t m4b = vdupq_n_u8(0x0F);
        const int8x16_t s8b = vdupq_n_s8(0x8);

        const uint8x16_t v0_0 = vld1q_u8(x0->qs);
        const uint8x16_t v0_1 = vld1q_u8(x1->qs);

        // 4-bit -> 8-bit
        const int8x16_t v0_0l = vreinterpretq_s8_u8(vandq_u8(v0_0, m4b));
        const int8x16_t v0_0h = vreinterpretq_s8_u8(vshrq_n_u8(v0_0, 4));
        const int8x16_t v0_1l = vreinterpretq_s8_u8(vandq_u8(v0_1, m4b));
        const int8x16_t v0_1h = vreinterpretq_s8_u8(vshrq_n_u8(v0_1, 4));

        // sub 8
        const int8x16_t v0_0ls = vsubq_s8(v0_0l, s8b);
        const int8x16_t v0_0hs = vsubq_s8(v0_0h, s8b);
        const int8x16_t v0_1ls = vsubq_s8(v0_1l, s8b);
        const int8x16_t v0_1hs = vsubq_s8(v0_1h, s8b);

        // load y
        const int8x16_t v1_0l = vld1q_s8(y0->qs);
        const int8x16_t v1_0h = vld1q_s8(y0->qs + 16);
        const int8x16_t v1_1l = vld1q_s8(y1->qs);
        const int8x16_t v1_1h = vld1q_s8(y1->qs + 16);

#if defined(__ARM_FEATURE_DOTPROD)
        // dot product into int32x4_t
        const int32x4_t p_0 = vdotq_s32(vdotq_s32(vdupq_n_s32(0), v0_0ls, v1_0l), v0_0hs, v1_0h);
        const int32x4_t p_1 = vdotq_s32(vdotq_s32(vdupq_n_s32(0), v0_1ls, v1_1l), v0_1hs, v1_1h);

        sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(p_0), GGML_FP16_TO_FP32(x0->d) * GGML_FP16_TO_FP32(y0->d));
        sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(p_1), GGML_FP16_TO_FP32(x1->d) * GGML_FP16_TO_FP32(y1->d));
#else
        const int16x8_t pl0l = vmull_s8(vget_low_s8(v0_0ls), vget_low_s8(v1_0l));
        const int16x8_t pl0h = vmull_s8(vget_high_s8(v0_0ls), vget_high_s8(v1_0l));
        const int16x8_t ph0l = vmull_s8(vget_low_s8(v0_0hs), vget_low_s8(v1_0h));
        const int16x8_t ph0h = vmull_s8(vget_high_s8(v0_0hs), vget_high_s8(v1_0h));

        const int16x8_t pl1l = vmull_s8(vget_low_s8(v0_1ls), vget_low_s8(v1_1l));
        const int16x8_t pl1h = vmull_s8(vget_high_s8(v0_1ls), vget_high_s8(v1_1l));
        const int16x8_t ph1l = vmull_s8(vget_low_s8(v0_1hs), vget_low_s8(v1_1h));
        const int16x8_t ph1h = vmull_s8(vget_high_s8(v0_1hs), vget_high_s8(v1_1h));

        const int32x4_t pl0 = vaddq_s32(vpaddlq_s16(pl0l), vpaddlq_s16(pl0h));
        const int32x4_t ph0 = vaddq_s32(vpaddlq_s16(ph0l), vpaddlq_s16(ph0h));
        const int32x4_t pl1 = vaddq_s32(vpaddlq_s16(pl1l), vpaddlq_s16(pl1h));
        const int32x4_t ph1 = vaddq_s32(vpaddlq_s16(ph1l), vpaddlq_s16(ph1h));

        sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(pl0, ph0)), GGML_FP16_TO_FP32(x0->d) * GGML_FP16_TO_FP32(y0->d));
        sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(pl1, ph1)), GGML_FP16_TO_FP32(x1->d) * GGML_FP16_TO_FP32(y1->d));
#endif
    }

    *s = vaddvq_f32(sumv0) + vaddvq_f32(sumv1);
#elif defined(__AVX2__)
    // Initialize accumulator with zeros
    __m256 acc = _mm256_setzero_ps();

    // Main loop
    for (int i = 0; i < nb; ++i) {
        /* Compute combined scale for the block */
        const __m256 d = _mm256_set1_ps(GGML_FP16_TO_FP32(x[i].d) * GGML_FP16_TO_FP32(y[i].d));

        __m256i bx = bytes_from_nibbles_32(x[i].qs);

        // Now we have a vector with bytes in [ 0 .. 15 ] interval. Offset them into [ -8 .. +7 ] interval.
        const __m256i off = _mm256_set1_epi8(8);
        bx = _mm256_sub_epi8(bx, off);

        __m256i by = _mm256_loadu_si256((const __m256i *)y[i].qs);

        const __m256 q = mul_sum_i8_pairs_float(bx, by);

        /* Multiply q with scale and accumulate */
        acc = _mm256_fmadd_ps(d, q, acc);
    }

    *s = hsum_float_8(acc);
#elif defined(__AVX__)
    // Initialize accumulator with zeros
    __m256 acc = _mm256_setzero_ps();

    // Main loop
    for (int i = 0; i < nb; ++i) {
        // Compute combined scale for the block
        const __m256 d = _mm256_set1_ps(GGML_FP16_TO_FP32(x[i].d) * GGML_FP16_TO_FP32(y[i].d));

        const __m128i lowMask = _mm_set1_epi8(0xF);
        const __m128i off = _mm_set1_epi8(8);

        const __m128i tmp = _mm_loadu_si128((const __m128i *)x[i].qs);

        __m128i bx = _mm_and_si128(lowMask, tmp);
        __m128i by = _mm_loadu_si128((const __m128i *)y[i].qs);
        bx = _mm_sub_epi8(bx, off);
        const __m128i i32_0 = mul_sum_i8_pairs(bx, by);

        bx = _mm_and_si128(lowMask, _mm_srli_epi64(tmp, 4));
        by = _mm_loadu_si128((const __m128i *)(y[i].qs + 16));
        bx = _mm_sub_epi8(bx, off);
        const __m128i i32_1 = mul_sum_i8_pairs(bx, by);

        // Convert int32_t to float
        __m256 p = _mm256_cvtepi32_ps(MM256_SET_M128I(i32_0, i32_1));

        // Apply the scale, and accumulate
        acc = _mm256_add_ps(_mm256_mul_ps(d, p), acc);
    }

    *s = hsum_float_8(acc);
#elif defined(__SSSE3__)
    // set constants
    const __m128i lowMask = _mm_set1_epi8(0xF);
    const __m128i off = _mm_set1_epi8(8);

    // Initialize accumulator with zeros
    __m128 acc_0 = _mm_setzero_ps();
    __m128 acc_1 = _mm_setzero_ps();
    __m128 acc_2 = _mm_setzero_ps();
    __m128 acc_3 = _mm_setzero_ps();

    // First round without accumulation
    {
        _mm_prefetch(&x[0] + sizeof(block_q4_0), _MM_HINT_T0);
        _mm_prefetch(&y[0] + sizeof(block_q8_0), _MM_HINT_T0);

        // Compute combined scale for the block 0 and 1
        const __m128 d_0_1 = _mm_set1_ps(GGML_FP16_TO_FP32(x[0].d) * GGML_FP16_TO_FP32(y[0].d));

        const __m128i tmp_0_1 = _mm_loadu_si128((const __m128i *)x[0].qs);

        __m128i bx_0 = _mm_and_si128(lowMask, tmp_0_1);
        __m128i by_0 = _mm_loadu_si128((const __m128i *)y[0].qs);
        bx_0 = _mm_sub_epi8(bx_0, off);
        const __m128i i32_0 = mul_sum_i8_pairs(bx_0, by_0);

        __m128i bx_1 = _mm_and_si128(lowMask, _mm_srli_epi64(tmp_0_1, 4));
        __m128i by_1 = _mm_loadu_si128((const __m128i *)(y[0].qs + 16));
        bx_1 = _mm_sub_epi8(bx_1, off);
        const __m128i i32_1 = mul_sum_i8_pairs(bx_1, by_1);

        _mm_prefetch(&x[1] + sizeof(block_q4_0), _MM_HINT_T0);
        _mm_prefetch(&y[1] + sizeof(block_q8_0), _MM_HINT_T0);

        // Compute combined scale for the block 2 and 3
        const __m128 d_2_3 = _mm_set1_ps(GGML_FP16_TO_FP32(x[1].d) * GGML_FP16_TO_FP32(y[1].d));

        const __m128i tmp_2_3 = _mm_loadu_si128((const __m128i *)x[1].qs);

        __m128i bx_2 = _mm_and_si128(lowMask, tmp_2_3);
        __m128i by_2 = _mm_loadu_si128((const __m128i *)y[1].qs);
        bx_2 = _mm_sub_epi8(bx_2, off);
        const __m128i i32_2 = mul_sum_i8_pairs(bx_2, by_2);

        __m128i bx_3 = _mm_and_si128(lowMask, _mm_srli_epi64(tmp_2_3, 4));
        __m128i by_3 = _mm_loadu_si128((const __m128i *)(y[1].qs + 16));
        bx_3 = _mm_sub_epi8(bx_3, off);
        const __m128i i32_3 = mul_sum_i8_pairs(bx_3, by_3);

        // Convert int32_t to float
        __m128 p0 = _mm_cvtepi32_ps(i32_0);
        __m128 p1 = _mm_cvtepi32_ps(i32_1);
        __m128 p2 = _mm_cvtepi32_ps(i32_2);
        __m128 p3 = _mm_cvtepi32_ps(i32_3);

        // Apply the scale
        acc_0 = _mm_mul_ps(d_0_1, p0);
        acc_1 = _mm_mul_ps(d_0_1, p1);
        acc_2 = _mm_mul_ps(d_2_3, p2);
        acc_3 = _mm_mul_ps(d_2_3, p3);
    }

    // Main loop
    GGML_ASSERT(nb % 2 == 0); // TODO: handle odd nb
    for (int i = 2; i < nb; i += 2) {
        _mm_prefetch(&x[i] + sizeof(block_q4_0), _MM_HINT_T0);
        _mm_prefetch(&y[i] + sizeof(block_q8_0), _MM_HINT_T0);

        // Compute combined scale for the block 0 and 1
        const __m128 d_0_1 = _mm_set1_ps(GGML_FP16_TO_FP32(x[i].d) * GGML_FP16_TO_FP32(y[i].d));

        const __m128i tmp_0_1 = _mm_loadu_si128((const __m128i *)x[i].qs);

        __m128i bx_0 = _mm_and_si128(lowMask, tmp_0_1);
        __m128i by_0 = _mm_loadu_si128((const __m128i *)y[i].qs);
        bx_0 = _mm_sub_epi8(bx_0, off);
        const __m128i i32_0 = mul_sum_i8_pairs(bx_0, by_0);

        __m128i bx_1 = _mm_and_si128(lowMask, _mm_srli_epi64(tmp_0_1, 4));
        __m128i by_1 = _mm_loadu_si128((const __m128i *)(y[i].qs + 16));
        bx_1 = _mm_sub_epi8(bx_1, off);
        const __m128i i32_1 = mul_sum_i8_pairs(bx_1, by_1);

        _mm_prefetch(&x[i] + 2 * sizeof(block_q4_0), _MM_HINT_T0);
        _mm_prefetch(&y[i] + 2 * sizeof(block_q8_0), _MM_HINT_T0);

        // Compute combined scale for the block 2 and 3
        const __m128 d_2_3 = _mm_set1_ps(GGML_FP16_TO_FP32(x[i + 1].d) * GGML_FP16_TO_FP32(y[i + 1].d));

        const __m128i tmp_2_3 = _mm_loadu_si128((const __m128i *)x[i + 1].qs);

        __m128i bx_2 = _mm_and_si128(lowMask, tmp_2_3);
        __m128i by_2 = _mm_loadu_si128((const __m128i *)y[i + 1].qs);
        bx_2 = _mm_sub_epi8(bx_2, off);
        const __m128i i32_2 = mul_sum_i8_pairs(bx_2, by_2);

        __m128i bx_3 = _mm_and_si128(lowMask, _mm_srli_epi64(tmp_2_3, 4));
        __m128i by_3 = _mm_loadu_si128((const __m128i *)(y[i + 1].qs + 16));
        bx_3 = _mm_sub_epi8(bx_3, off);
        const __m128i i32_3 = mul_sum_i8_pairs(bx_3, by_3);

        // Convert int32_t to float
        __m128 p0 = _mm_cvtepi32_ps(i32_0);
        __m128 p1 = _mm_cvtepi32_ps(i32_1);
        __m128 p2 = _mm_cvtepi32_ps(i32_2);
        __m128 p3 = _mm_cvtepi32_ps(i32_3);

        // Apply the scale
        __m128 p0_d = _mm_mul_ps(d_0_1, p0);
        __m128 p1_d = _mm_mul_ps(d_0_1, p1);
        __m128 p2_d = _mm_mul_ps(d_2_3, p2);
        __m128 p3_d = _mm_mul_ps(d_2_3, p3);

        // Acummulate
        acc_0 = _mm_add_ps(p0_d, acc_0);
        acc_1 = _mm_add_ps(p1_d, acc_1);
        acc_2 = _mm_add_ps(p2_d, acc_2);
        acc_3 = _mm_add_ps(p3_d, acc_3);
    }

    *s = hsum_float_4x4(acc_0, acc_1, acc_2, acc_3);
#elif defined(__riscv_v_intrinsic)
    float sumf = 0.0;

    size_t vl = __riscv_vsetvl_e8m1(qk / 2);

    for (int i = 0; i < nb; i++) {
        vuint8m1_t tx = __riscv_vle8_v_u8m1(x[i].qs, vl);

        vint8m1_t y0 = __riscv_vle8_v_i8m1(y[i].qs, vl);
        vint8m1_t y1 = __riscv_vle8_v_i8m1(y[i].qs + 16, vl);

        vuint8m1_t x_a = __riscv_vand_vx_u8m1(tx, 0x0F, vl);
        vuint8m1_t x_l = __riscv_vsrl_vx_u8m1(tx, 0x04, vl);

        vint8m1_t x_ai = __riscv_vreinterpret_v_u8m1_i8m1(x_a);
        vint8m1_t x_li = __riscv_vreinterpret_v_u8m1_i8m1(x_l);

        vint8m1_t v0 = __riscv_vsub_vx_i8m1(x_ai, 8, vl);
        vint8m1_t v1 = __riscv_vsub_vx_i8m1(x_li, 8, vl);

        vint16m2_t vec_mul1 = __riscv_vwmul_vv_i16m2(v0, y0, vl);
        vint16m2_t vec_mul2 = __riscv_vwmul_vv_i16m2(v1, y1, vl);

        vint32m1_t vec_zero = __riscv_vmv_v_x_i32m1(0, vl);

        vint32m1_t vs1 = __riscv_vwredsum_vs_i16m2_i32m1(vec_mul1, vec_zero, vl);
        vint32m1_t vs2 = __riscv_vwredsum_vs_i16m2_i32m1(vec_mul2, vec_zero, vl);

        int sumi = __riscv_vmv_x_s_i32m1_i32(vs1);
        sumi += __riscv_vmv_x_s_i32m1_i32(vs2);

        sumf += sumi * GGML_FP16_TO_FP32(x[i].d) * GGML_FP16_TO_FP32(y[i].d);
    }

    *s = sumf;
#else
    // scalar
    float sumf = 0.0;

    for (int i = 0; i < nb; i++) {
        int sumi = 0;

        for (int j = 0; j < qk / 2; ++j) {
            const int v0 = (x[i].qs[j] & 0x0F) - 8;
            const int v1 = (x[i].qs[j] >> 4) - 8;

            sumi += (v0 * y[i].qs[j]) + (v1 * y[i].qs[j + qk / 2]);
        }

        sumf += sumi * GGML_FP16_TO_FP32(x[i].d) * GGML_FP16_TO_FP32(y[i].d);
    }

    *s = sumf;
#endif
}

static void ggml_vec_dot_q4_1_q8_1(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) {
    const int qk = QK8_1;
    const int nb = n / qk;

    assert(n % qk == 0);

    const block_q4_1 * restrict x = vx;
    const block_q8_1 * restrict y = vy;

    // TODO: add WASM SIMD
#if defined(__ARM_NEON)
    float32x4_t sumv0 = vdupq_n_f32(0.0f);
    float32x4_t sumv1 = vdupq_n_f32(0.0f);

    float summs = 0;

    GGML_ASSERT(nb % 2 == 0); // TODO: handle odd nb
    for (int i = 0; i < nb; i += 2) {
        const block_q4_1 * restrict x0 = &x[i + 0];
        const block_q4_1 * restrict x1 = &x[i + 1];
        const block_q8_1 * restrict y0 = &y[i + 0];
        const block_q8_1 * restrict y1 = &y[i + 1];

        summs += GGML_FP16_TO_FP32(x0->m) * y0->s + GGML_FP16_TO_FP32(x1->m) * y1->s;

        const uint8x16_t m4b = vdupq_n_u8(0x0F);

        const uint8x16_t v0_0 = vld1q_u8(x0->qs);
        const uint8x16_t v0_1 = vld1q_u8(x1->qs);

        // 4-bit -> 8-bit
        const int8x16_t v0_0l = vreinterpretq_s8_u8(vandq_u8(v0_0, m4b));
        const int8x16_t v0_0h = vreinterpretq_s8_u8(vshrq_n_u8(v0_0, 4));
        const int8x16_t v0_1l = vreinterpretq_s8_u8(vandq_u8(v0_1, m4b));
        const int8x16_t v0_1h = vreinterpretq_s8_u8(vshrq_n_u8(v0_1, 4));

        // load y
        const int8x16_t v1_0l = vld1q_s8(y0->qs);
        const int8x16_t v1_0h = vld1q_s8(y0->qs + 16);
        const int8x16_t v1_1l = vld1q_s8(y1->qs);
        const int8x16_t v1_1h = vld1q_s8(y1->qs + 16);

#if defined(__ARM_FEATURE_DOTPROD)
        // dot product into int32x4_t
        const int32x4_t p_0 = vdotq_s32(vdotq_s32(vdupq_n_s32(0), v0_0l, v1_0l), v0_0h, v1_0h);
        const int32x4_t p_1 = vdotq_s32(vdotq_s32(vdupq_n_s32(0), v0_1l, v1_1l), v0_1h, v1_1h);

        sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(p_0), GGML_FP16_TO_FP32(x0->d) * y0->d);
        sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(p_1), GGML_FP16_TO_FP32(x1->d) * y1->d);
#else
        const int16x8_t pl0l = vmull_s8(vget_low_s8(v0_0l), vget_low_s8(v1_0l));
        const int16x8_t pl0h = vmull_s8(vget_high_s8(v0_0l), vget_high_s8(v1_0l));
        const int16x8_t ph0l = vmull_s8(vget_low_s8(v0_0h), vget_low_s8(v1_0h));
        const int16x8_t ph0h = vmull_s8(vget_high_s8(v0_0h), vget_high_s8(v1_0h));

        const int16x8_t pl1l = vmull_s8(vget_low_s8(v0_1l), vget_low_s8(v1_1l));
        const int16x8_t pl1h = vmull_s8(vget_high_s8(v0_1l), vget_high_s8(v1_1l));
        const int16x8_t ph1l = vmull_s8(vget_low_s8(v0_1h), vget_low_s8(v1_1h));
        const int16x8_t ph1h = vmull_s8(vget_high_s8(v0_1h), vget_high_s8(v1_1h));

        const int32x4_t pl0 = vaddq_s32(vpaddlq_s16(pl0l), vpaddlq_s16(pl0h));
        const int32x4_t ph0 = vaddq_s32(vpaddlq_s16(ph0l), vpaddlq_s16(ph0h));
        const int32x4_t pl1 = vaddq_s32(vpaddlq_s16(pl1l), vpaddlq_s16(pl1h));
        const int32x4_t ph1 = vaddq_s32(vpaddlq_s16(ph1l), vpaddlq_s16(ph1h));

        sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(pl0, ph0)), GGML_FP16_TO_FP32(x0->d) * y0->d);
        sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(pl1, ph1)), GGML_FP16_TO_FP32(x1->d) * y1->d);
#endif
    }

    *s = vaddvq_f32(sumv0) + vaddvq_f32(sumv1) + summs;
#elif defined(__AVX2__) || defined(__AVX__)
    // Initialize accumulator with zeros
    __m256 acc = _mm256_setzero_ps();

    float summs = 0;

    // Main loop
    for (int i = 0; i < nb; ++i) {
        const float d0 = GGML_FP16_TO_FP32(x[i].d);
        const float d1 = y[i].d;

        summs += GGML_FP16_TO_FP32(x[i].m) * y[i].s;

        const __m256 d0v = _mm256_set1_ps(d0);
        const __m256 d1v = _mm256_set1_ps(d1);

        // Compute combined scales
        const __m256 d0d1 = _mm256_mul_ps(d0v, d1v);

        // Load 16 bytes, and unpack 4 bit fields into bytes, making 32 bytes
        const __m256i bx = bytes_from_nibbles_32(x[i].qs);
        const __m256i by = _mm256_loadu_si256((const __m256i *)y[i].qs);

        const __m256 xy = mul_sum_us8_pairs_float(bx, by);

        // Accumulate d0*d1*x*y
#if defined(__AVX2__)
        acc = _mm256_fmadd_ps(d0d1, xy, acc);
#else
        acc = _mm256_add_ps(_mm256_mul_ps(d0d1, xy), acc);
#endif
    }

    *s = hsum_float_8(acc) + summs;
#elif defined(__riscv_v_intrinsic)
    float sumf = 0.0;

    size_t vl = __riscv_vsetvl_e8m1(qk / 2);

    for (int i = 0; i < nb; i++) {
        vuint8m1_t tx = __riscv_vle8_v_u8m1(x[i].qs, vl);

        vint8m1_t y0 = __riscv_vle8_v_i8m1(y[i].qs, vl);
        vint8m1_t y1 = __riscv_vle8_v_i8m1(y[i].qs + 16, vl);

        vuint8m1_t x_a = __riscv_vand_vx_u8m1(tx, 0x0F, vl);
        vuint8m1_t x_l = __riscv_vsrl_vx_u8m1(tx, 0x04, vl);

        vint8m1_t v0 = __riscv_vreinterpret_v_u8m1_i8m1(x_a);
        vint8m1_t v1 = __riscv_vreinterpret_v_u8m1_i8m1(x_l);

        vint16m2_t vec_mul1 = __riscv_vwmul_vv_i16m2(v0, y0, vl);
        vint16m2_t vec_mul2 = __riscv_vwmul_vv_i16m2(v1, y1, vl);

        vint32m1_t vec_zero = __riscv_vmv_v_x_i32m1(0, vl);

        vint32m1_t vs1 = __riscv_vwredsum_vs_i16m2_i32m1(vec_mul1, vec_zero, vl);
        vint32m1_t vs2 = __riscv_vwredsum_vs_i16m2_i32m1(vec_mul2, vec_zero, vl);

        int sumi = __riscv_vmv_x_s_i32m1_i32(vs1);
        sumi += __riscv_vmv_x_s_i32m1_i32(vs2);

        sumf += (GGML_FP16_TO_FP32(x[i].d) * y[i].d) * sumi + GGML_FP16_TO_FP32(x[i].m) * y[i].s;
    }

    *s = sumf;
#else
    // scalar
    float sumf = 0.0;

    for (int i = 0; i < nb; i++) {
        int sumi = 0;

        for (int j = 0; j < qk / 2; ++j) {
            const int v0 = (x[i].qs[j] & 0x0F);
            const int v1 = (x[i].qs[j] >> 4);

            sumi += (v0 * y[i].qs[j]) + (v1 * y[i].qs[j + qk / 2]);
        }

        sumf += (GGML_FP16_TO_FP32(x[i].d) * y[i].d) * sumi + GGML_FP16_TO_FP32(x[i].m) * y[i].s;
    }

    *s = sumf;
#endif
}

static void ggml_vec_dot_q5_0_q8_0(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) {
    const int qk = QK8_0;
    const int nb = n / qk;

    assert(n % qk == 0);
    assert(qk == QK5_0);

    const block_q5_0 * restrict x = vx;
    const block_q8_0 * restrict y = vy;

#if defined(__ARM_NEON)
    float32x4_t sumv0 = vdupq_n_f32(0.0f);
    float32x4_t sumv1 = vdupq_n_f32(0.0f);

    uint32_t qh0;
    uint32_t qh1;

    uint64_t tmp0[4];
    uint64_t tmp1[4];

    GGML_ASSERT(nb % 2 == 0); // TODO: handle odd nb
    for (int i = 0; i < nb; i += 2) {
        const block_q5_0 * restrict x0 = &x[i];
        const block_q5_0 * restrict x1 = &x[i + 1];
        const block_q8_0 * restrict y0 = &y[i];
        const block_q8_0 * restrict y1 = &y[i + 1];

        const uint8x16_t m4b = vdupq_n_u8(0x0F);

        // extract the 5th bit via lookup table ((!b) << 4)
        memcpy(&qh0, x0->qh, sizeof(qh0));
        memcpy(&qh1, x1->qh, sizeof(qh1));

        tmp0[0] = table_b2b_1[(qh0 >> 0) & 0xFF];
        tmp0[1] = table_b2b_1[(qh0 >> 8) & 0xFF];
        tmp0[2] = table_b2b_1[(qh0 >> 16) & 0xFF];
        tmp0[3] = table_b2b_1[(qh0 >> 24)];

        tmp1[0] = table_b2b_1[(qh1 >> 0) & 0xFF];
        tmp1[1] = table_b2b_1[(qh1 >> 8) & 0xFF];
        tmp1[2] = table_b2b_1[(qh1 >> 16) & 0xFF];
        tmp1[3] = table_b2b_1[(qh1 >> 24)];

        const int8x16_t qhl0 = vld1q_s8((const int8_t *)(tmp0 + 0));
        const int8x16_t qhh0 = vld1q_s8((const int8_t *)(tmp0 + 2));
        const int8x16_t qhl1 = vld1q_s8((const int8_t *)(tmp1 + 0));
        const int8x16_t qhh1 = vld1q_s8((const int8_t *)(tmp1 + 2));

        const uint8x16_t v0_0 = vld1q_u8(x0->qs);
        const uint8x16_t v0_1 = vld1q_u8(x1->qs);

        // 4-bit -> 8-bit
        int8x16_t v0_0l = vreinterpretq_s8_u8(vandq_u8(v0_0, m4b));
        int8x16_t v0_0h = vreinterpretq_s8_u8(vshrq_n_u8(v0_0, 4));
        int8x16_t v0_1l = vreinterpretq_s8_u8(vandq_u8(v0_1, m4b));
        int8x16_t v0_1h = vreinterpretq_s8_u8(vshrq_n_u8(v0_1, 4));

        // add high bit and sub 16 (equivalent to sub 0x10 when bit is zero)
        const int8x16_t v0_0lf = vsubq_s8(v0_0l, qhl0);
        const int8x16_t v0_0hf = vsubq_s8(v0_0h, qhh0);
        const int8x16_t v0_1lf = vsubq_s8(v0_1l, qhl1);
        const int8x16_t v0_1hf = vsubq_s8(v0_1h, qhh1);

        // load y
        const int8x16_t v1_0l = vld1q_s8(y0->qs);
        const int8x16_t v1_0h = vld1q_s8(y0->qs + 16);
        const int8x16_t v1_1l = vld1q_s8(y1->qs);
        const int8x16_t v1_1h = vld1q_s8(y1->qs + 16);

#if defined(__ARM_FEATURE_DOTPROD)
        sumv0 = vmlaq_n_f32(
            sumv0, vcvtq_f32_s32(vaddq_s32(vdotq_s32(vdupq_n_s32(0), v0_0lf, v1_0l), vdotq_s32(vdupq_n_s32(0), v0_0hf, v1_0h))),
            GGML_FP16_TO_FP32(x0->d) * GGML_FP16_TO_FP32(y0->d));
        sumv1 = vmlaq_n_f32(
            sumv1, vcvtq_f32_s32(vaddq_s32(vdotq_s32(vdupq_n_s32(0), v0_1lf, v1_1l), vdotq_s32(vdupq_n_s32(0), v0_1hf, v1_1h))),
            GGML_FP16_TO_FP32(x1->d) * GGML_FP16_TO_FP32(y1->d));
#else
        const int16x8_t pl0l = vmull_s8(vget_low_s8(v0_0lf), vget_low_s8(v1_0l));
        const int16x8_t pl0h = vmull_s8(vget_high_s8(v0_0lf), vget_high_s8(v1_0l));
        const int16x8_t ph0l = vmull_s8(vget_low_s8(v0_0hf), vget_low_s8(v1_0h));
        const int16x8_t ph0h = vmull_s8(vget_high_s8(v0_0hf), vget_high_s8(v1_0h));

        const int16x8_t pl1l = vmull_s8(vget_low_s8(v0_1lf), vget_low_s8(v1_1l));
        const int16x8_t pl1h = vmull_s8(vget_high_s8(v0_1lf), vget_high_s8(v1_1l));
        const int16x8_t ph1l = vmull_s8(vget_low_s8(v0_1hf), vget_low_s8(v1_1h));
        const int16x8_t ph1h = vmull_s8(vget_high_s8(v0_1hf), vget_high_s8(v1_1h));

        const int32x4_t pl0 = vaddq_s32(vpaddlq_s16(pl0l), vpaddlq_s16(pl0h));
        const int32x4_t ph0 = vaddq_s32(vpaddlq_s16(ph0l), vpaddlq_s16(ph0h));
        const int32x4_t pl1 = vaddq_s32(vpaddlq_s16(pl1l), vpaddlq_s16(pl1h));
        const int32x4_t ph1 = vaddq_s32(vpaddlq_s16(ph1l), vpaddlq_s16(ph1h));

        sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(pl0, ph0)), GGML_FP16_TO_FP32(x0->d) * GGML_FP16_TO_FP32(y0->d));
        sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(pl1, ph1)), GGML_FP16_TO_FP32(x1->d) * GGML_FP16_TO_FP32(y1->d));
#endif
    }

    *s = vaddvq_f32(sumv0) + vaddvq_f32(sumv1);
#elif defined(__wasm_simd128__)
    v128_t sumv = wasm_f32x4_splat(0.0f);

    uint32_t qh;
    uint64_t tmp[4];

    // TODO: check if unrolling this is better
    for (int i = 0; i < nb; ++i) {
        const block_q5_0 * restrict x0 = &x[i];
        const block_q8_0 * restrict y0 = &y[i];

        const v128_t m4b = wasm_i8x16_splat(0x0F);

        // extract the 5th bit
        memcpy(&qh, x0->qh, sizeof(qh));

        tmp[0] = table_b2b_1[(qh >> 0) & 0xFF];
        tmp[1] = table_b2b_1[(qh >> 8) & 0xFF];
        tmp[2] = table_b2b_1[(qh >> 16) & 0xFF];
        tmp[3] = table_b2b_1[(qh >> 24)];

        const v128_t qhl = wasm_v128_load(tmp + 0);
        const v128_t qhh = wasm_v128_load(tmp + 2);

        const v128_t v0 = wasm_v128_load(x0->qs);

        // 4-bit -> 8-bit
        const v128_t v0l = wasm_v128_and(v0, m4b);
        const v128_t v0h = wasm_u8x16_shr(v0, 4);

        // add high bit and sub 16 (equivalent to sub 0x10 when bit is zero)
        const v128_t v0lf = wasm_i8x16_sub(v0l, qhl);
        const v128_t v0hf = wasm_i8x16_sub(v0h, qhh);

        // load y
        const v128_t v1l = wasm_v128_load(y0->qs);
        const v128_t v1h = wasm_v128_load(y0->qs + 16);

        // int8x16 -> int16x8
        const v128_t v0lfl = wasm_i16x8_extend_low_i8x16(v0lf);
        const v128_t v0lfh = wasm_i16x8_extend_high_i8x16(v0lf);
        const v128_t v0hfl = wasm_i16x8_extend_low_i8x16(v0hf);
        const v128_t v0hfh = wasm_i16x8_extend_high_i8x16(v0hf);

        const v128_t v1ll = wasm_i16x8_extend_low_i8x16(v1l);
        const v128_t v1lh = wasm_i16x8_extend_high_i8x16(v1l);
        const v128_t v1hl = wasm_i16x8_extend_low_i8x16(v1h);
        const v128_t v1hh = wasm_i16x8_extend_high_i8x16(v1h);

        // dot product
        sumv = wasm_f32x4_add(
            sumv, wasm_f32x4_mul(wasm_f32x4_convert_i32x4(wasm_i32x4_add(
                                     wasm_i32x4_add(wasm_i32x4_dot_i16x8(v0lfl, v1ll), wasm_i32x4_dot_i16x8(v0lfh, v1lh)),
                                     wasm_i32x4_add(wasm_i32x4_dot_i16x8(v0hfl, v1hl), wasm_i32x4_dot_i16x8(v0hfh, v1hh)))),
                                 wasm_f32x4_splat(GGML_FP16_TO_FP32(x0->d) * GGML_FP16_TO_FP32(y0->d))));
    }

    *s = wasm_f32x4_extract_lane(sumv, 0) + wasm_f32x4_extract_lane(sumv, 1) + wasm_f32x4_extract_lane(sumv, 2) +
         wasm_f32x4_extract_lane(sumv, 3);
#elif defined(__AVX2__)
    // Initialize accumulator with zeros
    __m256 acc = _mm256_setzero_ps();

    // Main loop
    for (int i = 0; i < nb; i++) {
        /* Compute combined scale for the block */
        const __m256 d = _mm256_set1_ps(GGML_FP16_TO_FP32(x[i].d) * GGML_FP16_TO_FP32(y[i].d));

        __m256i bx = bytes_from_nibbles_32(x[i].qs);
        __m256i bxhi = bytes_from_bits_32(x[i].qh);
        bxhi = _mm256_andnot_si256(bxhi, _mm256_set1_epi8((char)0xF0));
        bx = _mm256_or_si256(bx, bxhi);

        __m256i by = _mm256_loadu_si256((const __m256i *)y[i].qs);

        const __m256 q = mul_sum_i8_pairs_float(bx, by);

        /* Multiply q with scale and accumulate */
        acc = _mm256_fmadd_ps(d, q, acc);
    }

    *s = hsum_float_8(acc);
#elif defined(__AVX__)
    // Initialize accumulator with zeros
    __m256 acc = _mm256_setzero_ps();
    __m128i mask = _mm_set1_epi8((char)0xF0);

    // Main loop
    for (int i = 0; i < nb; i++) {
        /* Compute combined scale for the block */
        const __m256 d = _mm256_set1_ps(GGML_FP16_TO_FP32(x[i].d) * GGML_FP16_TO_FP32(y[i].d));

        __m256i bx = bytes_from_nibbles_32(x[i].qs);
        const __m256i bxhi = bytes_from_bits_32(x[i].qh);
        __m128i bxhil = _mm256_castsi256_si128(bxhi);
        __m128i bxhih = _mm256_extractf128_si256(bxhi, 1);
        bxhil = _mm_andnot_si128(bxhil, mask);
        bxhih = _mm_andnot_si128(bxhih, mask);
        __m128i bxl = _mm256_castsi256_si128(bx);
        __m128i bxh = _mm256_extractf128_si256(bx, 1);
        bxl = _mm_or_si128(bxl, bxhil);
        bxh = _mm_or_si128(bxh, bxhih);
        bx = MM256_SET_M128I(bxh, bxl);

        const __m256i by = _mm256_loadu_si256((const __m256i *)y[i].qs);

        const __m256 q = mul_sum_i8_pairs_float(bx, by);

        /* Multiply q with scale and accumulate */
        acc = _mm256_add_ps(_mm256_mul_ps(d, q), acc);
    }

    *s = hsum_float_8(acc);
#elif defined(__riscv_v_intrinsic)
    float sumf = 0.0;

    uint32_t qh;

    // These temp values are for masking and shift operations
    uint32_t temp_1[16] = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15};
    uint32_t temp_2[16] = {0x1,   0x2,   0x4,   0x8,   0x10,   0x20,   0x40,   0x80,
                           0x100, 0x200, 0x400, 0x800, 0x1000, 0x2000, 0x4000, 0x8000};

    size_t vl = __riscv_vsetvl_e8m1(qk / 2);

    for (int i = 0; i < nb; i++) {
        memcpy(&qh, x[i].qh, sizeof(uint32_t));

        // temporary registers
        vuint32m4_t vt_1 = __riscv_vle32_v_u32m4(temp_2, vl);
        vuint32m4_t vt_2 = __riscv_vle32_v_u32m4(temp_1, vl);
        vuint32m4_t vt_3 = __riscv_vsll_vx_u32m4(vt_1, 16, vl);
        vuint32m4_t vt_4 = __riscv_vadd_vx_u32m4(vt_2, 12, vl);

        // ((qh & (1u << (j + 0 ))) >> (j + 0 )) << 4;
        vuint32m4_t xha_0 = __riscv_vand_vx_u32m4(vt_1, qh, vl);
        vuint32m4_t xhr_0 = __riscv_vsrl_vv_u32m4(xha_0, vt_2, vl);
        vuint32m4_t xhl_0 = __riscv_vsll_vx_u32m4(xhr_0, 4, vl);

        // ((qh & (1u << (j + 16))) >> (j + 12));
        vuint32m4_t xha_1 = __riscv_vand_vx_u32m4(vt_3, qh, vl);
        vuint32m4_t xhl_1 = __riscv_vsrl_vv_u32m4(xha_1, vt_4, vl);

        // narrowing
        vuint16m2_t xhc_0 = __riscv_vncvt_x_x_w_u16m2(xhl_0, vl);
        vuint8m1_t xh_0 = __riscv_vncvt_x_x_w_u8m1(xhc_0, vl);

        vuint16m2_t xhc_1 = __riscv_vncvt_x_x_w_u16m2(xhl_1, vl);
        vuint8m1_t xh_1 = __riscv_vncvt_x_x_w_u8m1(xhc_1, vl);

        // load
        vuint8m1_t tx = __riscv_vle8_v_u8m1(x[i].qs, vl);

        vint8m1_t y0 = __riscv_vle8_v_i8m1(y[i].qs, vl);
        vint8m1_t y1 = __riscv_vle8_v_i8m1(y[i].qs + 16, vl);

        vuint8m1_t x_at = __riscv_vand_vx_u8m1(tx, 0x0F, vl);
        vuint8m1_t x_lt = __riscv_vsrl_vx_u8m1(tx, 0x04, vl);

        vuint8m1_t x_a = __riscv_vor_vv_u8m1(x_at, xh_0, vl);
        vuint8m1_t x_l = __riscv_vor_vv_u8m1(x_lt, xh_1, vl);

        vint8m1_t x_ai = __riscv_vreinterpret_v_u8m1_i8m1(x_a);
        vint8m1_t x_li = __riscv_vreinterpret_v_u8m1_i8m1(x_l);

        vint8m1_t v0 = __riscv_vsub_vx_i8m1(x_ai, 16, vl);
        vint8m1_t v1 = __riscv_vsub_vx_i8m1(x_li, 16, vl);

        vint16m2_t vec_mul1 = __riscv_vwmul_vv_i16m2(v0, y0, vl);
        vint16m2_t vec_mul2 = __riscv_vwmul_vv_i16m2(v1, y1, vl);

        vint32m1_t vec_zero = __riscv_vmv_v_x_i32m1(0, vl);

        vint32m1_t vs1 = __riscv_vwredsum_vs_i16m2_i32m1(vec_mul1, vec_zero, vl);
        vint32m1_t vs2 = __riscv_vwredsum_vs_i16m2_i32m1(vec_mul2, vec_zero, vl);

        int sumi = __riscv_vmv_x_s_i32m1_i32(vs1);
        sumi += __riscv_vmv_x_s_i32m1_i32(vs2);

        sumf += (GGML_FP16_TO_FP32(x[i].d) * GGML_FP16_TO_FP32(y[i].d)) * sumi;
    }

    *s = sumf;
#else
    // scalar
    float sumf = 0.0;

    for (int i = 0; i < nb; i++) {
        uint32_t qh;
        memcpy(&qh, x[i].qh, sizeof(qh));

        int sumi = 0;

        for (int j = 0; j < qk / 2; ++j) {
            const uint8_t xh_0 = ((qh & (1u << (j + 0))) >> (j + 0)) << 4;
            const uint8_t xh_1 = ((qh & (1u << (j + 16))) >> (j + 12));

            const int32_t x0 = ((x[i].qs[j] & 0x0F) | xh_0) - 16;
            const int32_t x1 = ((x[i].qs[j] >> 4) | xh_1) - 16;

            sumi += (x0 * y[i].qs[j]) + (x1 * y[i].qs[j + qk / 2]);
        }

        sumf += (GGML_FP16_TO_FP32(x[i].d) * GGML_FP16_TO_FP32(y[i].d)) * sumi;
    }

    *s = sumf;
#endif
}

static void ggml_vec_dot_q5_1_q8_1(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) {
    const int qk = QK8_1;
    const int nb = n / qk;

    assert(n % qk == 0);
    assert(qk == QK5_1);

    const block_q5_1 * restrict x = vx;
    const block_q8_1 * restrict y = vy;

#if defined(__ARM_NEON)
    float32x4_t sumv0 = vdupq_n_f32(0.0f);
    float32x4_t sumv1 = vdupq_n_f32(0.0f);

    float summs0 = 0.0f;
    float summs1 = 0.0f;

    uint32_t qh0;
    uint32_t qh1;

    uint64_t tmp0[4];
    uint64_t tmp1[4];

    GGML_ASSERT(nb % 2 == 0); // TODO: handle odd nb
    for (int i = 0; i < nb; i += 2) {
        const block_q5_1 * restrict x0 = &x[i];
        const block_q5_1 * restrict x1 = &x[i + 1];
        const block_q8_1 * restrict y0 = &y[i];
        const block_q8_1 * restrict y1 = &y[i + 1];

        const uint8x16_t m4b = vdupq_n_u8(0x0F);

        summs0 += GGML_FP16_TO_FP32(x0->m) * y0->s;
        summs1 += GGML_FP16_TO_FP32(x1->m) * y1->s;

        // extract the 5th bit via lookup table ((b) << 4)
        memcpy(&qh0, x0->qh, sizeof(qh0));
        memcpy(&qh1, x1->qh, sizeof(qh1));

        tmp0[0] = table_b2b_0[(qh0 >> 0) & 0xFF];
        tmp0[1] = table_b2b_0[(qh0 >> 8) & 0xFF];
        tmp0[2] = table_b2b_0[(qh0 >> 16) & 0xFF];
        tmp0[3] = table_b2b_0[(qh0 >> 24)];

        tmp1[0] = table_b2b_0[(qh1 >> 0) & 0xFF];
        tmp1[1] = table_b2b_0[(qh1 >> 8) & 0xFF];
        tmp1[2] = table_b2b_0[(qh1 >> 16) & 0xFF];
        tmp1[3] = table_b2b_0[(qh1 >> 24)];

        const int8x16_t qhl0 = vld1q_s8((const int8_t *)(tmp0 + 0));
        const int8x16_t qhh0 = vld1q_s8((const int8_t *)(tmp0 + 2));
        const int8x16_t qhl1 = vld1q_s8((const int8_t *)(tmp1 + 0));
        const int8x16_t qhh1 = vld1q_s8((const int8_t *)(tmp1 + 2));

        const uint8x16_t v0_0 = vld1q_u8(x0->qs);
        const uint8x16_t v0_1 = vld1q_u8(x1->qs);

        // 4-bit -> 8-bit
        const int8x16_t v0_0l = vreinterpretq_s8_u8(vandq_u8(v0_0, m4b));
        const int8x16_t v0_0h = vreinterpretq_s8_u8(vshrq_n_u8(v0_0, 4));
        const int8x16_t v0_1l = vreinterpretq_s8_u8(vandq_u8(v0_1, m4b));
        const int8x16_t v0_1h = vreinterpretq_s8_u8(vshrq_n_u8(v0_1, 4));

        // add high bit
        const int8x16_t v0_0lf = vorrq_s8(v0_0l, qhl0);
        const int8x16_t v0_0hf = vorrq_s8(v0_0h, qhh0);
        const int8x16_t v0_1lf = vorrq_s8(v0_1l, qhl1);
        const int8x16_t v0_1hf = vorrq_s8(v0_1h, qhh1);

        // load y
        const int8x16_t v1_0l = vld1q_s8(y0->qs);
        const int8x16_t v1_0h = vld1q_s8(y0->qs + 16);
        const int8x16_t v1_1l = vld1q_s8(y1->qs);
        const int8x16_t v1_1h = vld1q_s8(y1->qs + 16);

#if defined(__ARM_FEATURE_DOTPROD)
        sumv0 = vmlaq_n_f32(
            sumv0, vcvtq_f32_s32(vaddq_s32(vdotq_s32(vdupq_n_s32(0), v0_0lf, v1_0l), vdotq_s32(vdupq_n_s32(0), v0_0hf, v1_0h))),
            GGML_FP16_TO_FP32(x0->d) * y0->d);
        sumv1 = vmlaq_n_f32(
            sumv1, vcvtq_f32_s32(vaddq_s32(vdotq_s32(vdupq_n_s32(0), v0_1lf, v1_1l), vdotq_s32(vdupq_n_s32(0), v0_1hf, v1_1h))),
            GGML_FP16_TO_FP32(x1->d) * y1->d);
#else
        const int16x8_t pl0l = vmull_s8(vget_low_s8(v0_0lf), vget_low_s8(v1_0l));
        const int16x8_t pl0h = vmull_s8(vget_high_s8(v0_0lf), vget_high_s8(v1_0l));
        const int16x8_t ph0l = vmull_s8(vget_low_s8(v0_0hf), vget_low_s8(v1_0h));
        const int16x8_t ph0h = vmull_s8(vget_high_s8(v0_0hf), vget_high_s8(v1_0h));

        const int16x8_t pl1l = vmull_s8(vget_low_s8(v0_1lf), vget_low_s8(v1_1l));
        const int16x8_t pl1h = vmull_s8(vget_high_s8(v0_1lf), vget_high_s8(v1_1l));
        const int16x8_t ph1l = vmull_s8(vget_low_s8(v0_1hf), vget_low_s8(v1_1h));
        const int16x8_t ph1h = vmull_s8(vget_high_s8(v0_1hf), vget_high_s8(v1_1h));

        const int32x4_t pl0 = vaddq_s32(vpaddlq_s16(pl0l), vpaddlq_s16(pl0h));
        const int32x4_t ph0 = vaddq_s32(vpaddlq_s16(ph0l), vpaddlq_s16(ph0h));
        const int32x4_t pl1 = vaddq_s32(vpaddlq_s16(pl1l), vpaddlq_s16(pl1h));
        const int32x4_t ph1 = vaddq_s32(vpaddlq_s16(ph1l), vpaddlq_s16(ph1h));

        sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(pl0, ph0)), GGML_FP16_TO_FP32(x0->d) * y0->d);
        sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(pl1, ph1)), GGML_FP16_TO_FP32(x1->d) * y1->d);
#endif
    }

    *s = vaddvq_f32(sumv0) + vaddvq_f32(sumv1) + summs0 + summs1;
#elif defined(__wasm_simd128__)
    v128_t sumv = wasm_f32x4_splat(0.0f);

    float summs = 0.0f;

    uint32_t qh;
    uint64_t tmp[4];

    // TODO: check if unrolling this is better
    for (int i = 0; i < nb; ++i) {
        const block_q5_1 * restrict x0 = &x[i];
        const block_q8_1 * restrict y0 = &y[i];

        summs += GGML_FP16_TO_FP32(x0->m) * y0->s;

        const v128_t m4b = wasm_i8x16_splat(0x0F);

        // extract the 5th bit
        memcpy(&qh, x0->qh, sizeof(qh));

        tmp[0] = table_b2b_0[(qh >> 0) & 0xFF];
        tmp[1] = table_b2b_0[(qh >> 8) & 0xFF];
        tmp[2] = table_b2b_0[(qh >> 16) & 0xFF];
        tmp[3] = table_b2b_0[(qh >> 24)];

        const v128_t qhl = wasm_v128_load(tmp + 0);
        const v128_t qhh = wasm_v128_load(tmp + 2);

        const v128_t v0 = wasm_v128_load(x0->qs);

        // 4-bit -> 8-bit
        const v128_t v0l = wasm_v128_and(v0, m4b);
        const v128_t v0h = wasm_u8x16_shr(v0, 4);

        // add high bit
        const v128_t v0lf = wasm_v128_or(v0l, qhl);
        const v128_t v0hf = wasm_v128_or(v0h, qhh);

        // load y
        const v128_t v1l = wasm_v128_load(y0->qs);
        const v128_t v1h = wasm_v128_load(y0->qs + 16);

        // int8x16 -> int16x8
        const v128_t v0lfl = wasm_i16x8_extend_low_i8x16(v0lf);
        const v128_t v0lfh = wasm_i16x8_extend_high_i8x16(v0lf);
        const v128_t v0hfl = wasm_i16x8_extend_low_i8x16(v0hf);
        const v128_t v0hfh = wasm_i16x8_extend_high_i8x16(v0hf);

        const v128_t v1ll = wasm_i16x8_extend_low_i8x16(v1l);
        const v128_t v1lh = wasm_i16x8_extend_high_i8x16(v1l);
        const v128_t v1hl = wasm_i16x8_extend_low_i8x16(v1h);
        const v128_t v1hh = wasm_i16x8_extend_high_i8x16(v1h);

        // dot product
        sumv = wasm_f32x4_add(
            sumv, wasm_f32x4_mul(wasm_f32x4_convert_i32x4(wasm_i32x4_add(
                                     wasm_i32x4_add(wasm_i32x4_dot_i16x8(v0lfl, v1ll), wasm_i32x4_dot_i16x8(v0lfh, v1lh)),
                                     wasm_i32x4_add(wasm_i32x4_dot_i16x8(v0hfl, v1hl), wasm_i32x4_dot_i16x8(v0hfh, v1hh)))),
                                 wasm_f32x4_splat(GGML_FP16_TO_FP32(x0->d) * y0->d)));
    }

    *s = wasm_f32x4_extract_lane(sumv, 0) + wasm_f32x4_extract_lane(sumv, 1) + wasm_f32x4_extract_lane(sumv, 2) +
         wasm_f32x4_extract_lane(sumv, 3) + summs;
#elif defined(__AVX2__)
    // Initialize accumulator with zeros
    __m256 acc = _mm256_setzero_ps();

    float summs = 0.0f;

    // Main loop
    for (int i = 0; i < nb; i++) {
        const __m256 dx = _mm256_set1_ps(GGML_FP16_TO_FP32(x[i].d));

        summs += GGML_FP16_TO_FP32(x[i].m) * y[i].s;

        __m256i bx = bytes_from_nibbles_32(x[i].qs);
        __m256i bxhi = bytes_from_bits_32(x[i].qh);
        bxhi = _mm256_and_si256(bxhi, _mm256_set1_epi8(0x10));
        bx = _mm256_or_si256(bx, bxhi);

        const __m256 dy = _mm256_set1_ps(y[i].d);
        const __m256i by = _mm256_loadu_si256((const __m256i *)y[i].qs);

        const __m256 q = mul_sum_us8_pairs_float(bx, by);

        acc = _mm256_fmadd_ps(q, _mm256_mul_ps(dx, dy), acc);
    }

    *s = hsum_float_8(acc) + summs;
#elif defined(__AVX__)
    // Initialize accumulator with zeros
    __m256 acc = _mm256_setzero_ps();
    __m128i mask = _mm_set1_epi8(0x10);

    float summs = 0.0f;

    // Main loop
    for (int i = 0; i < nb; i++) {
        const __m256 dx = _mm256_set1_ps(GGML_FP16_TO_FP32(x[i].d));

        summs += GGML_FP16_TO_FP32(x[i].m) * y[i].s;

        __m256i bx = bytes_from_nibbles_32(x[i].qs);
        const __m256i bxhi = bytes_from_bits_32(x[i].qh);
        __m128i bxhil = _mm256_castsi256_si128(bxhi);
        __m128i bxhih = _mm256_extractf128_si256(bxhi, 1);
        bxhil = _mm_and_si128(bxhil, mask);
        bxhih = _mm_and_si128(bxhih, mask);
        __m128i bxl = _mm256_castsi256_si128(bx);
        __m128i bxh = _mm256_extractf128_si256(bx, 1);
        bxl = _mm_or_si128(bxl, bxhil);
        bxh = _mm_or_si128(bxh, bxhih);
        bx = MM256_SET_M128I(bxh, bxl);

        const __m256 dy = _mm256_set1_ps(y[i].d);
        const __m256i by = _mm256_loadu_si256((const __m256i *)y[i].qs);

        const __m256 q = mul_sum_us8_pairs_float(bx, by);

        acc = _mm256_add_ps(_mm256_mul_ps(q, _mm256_mul_ps(dx, dy)), acc);
    }

    *s = hsum_float_8(acc) + summs;
#elif defined(__riscv_v_intrinsic)
    float sumf = 0.0;

    uint32_t qh;

    // These temp values are for shift operations
    uint32_t temp_1[16] = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15};

    size_t vl = __riscv_vsetvl_e8m1(qk / 2);

    for (int i = 0; i < nb; i++) {
        memcpy(&qh, x[i].qh, sizeof(uint32_t));

        // temporary registers
        vuint32m4_t vt_1 = __riscv_vle32_v_u32m4(temp_1, vl);
        vuint32m4_t vt_2 = __riscv_vadd_vx_u32m4(vt_1, 12, vl);

        // load qh
        vuint32m4_t vqh = __riscv_vmv_v_x_u32m4(qh, vl);

        // ((qh >> (j +  0)) << 4) & 0x10;
        vuint32m4_t xhr_0 = __riscv_vsrl_vv_u32m4(vqh, vt_1, vl);
        vuint32m4_t xhl_0 = __riscv_vsll_vx_u32m4(xhr_0, 4, vl);
        vuint32m4_t xha_0 = __riscv_vand_vx_u32m4(xhl_0, 0x10, vl);

        // ((qh >> (j + 12))     ) & 0x10;
        vuint32m4_t xhr_1 = __riscv_vsrl_vv_u32m4(vqh, vt_2, vl);
        vuint32m4_t xha_1 = __riscv_vand_vx_u32m4(xhr_1, 0x10, vl);

        // narrowing
        vuint16m2_t xhc_0 = __riscv_vncvt_x_x_w_u16m2(xha_0, vl);
        vuint8m1_t xh_0 = __riscv_vncvt_x_x_w_u8m1(xhc_0, vl);

        vuint16m2_t xhc_1 = __riscv_vncvt_x_x_w_u16m2(xha_1, vl);
        vuint8m1_t xh_1 = __riscv_vncvt_x_x_w_u8m1(xhc_1, vl);

        // load
        vuint8m1_t tx = __riscv_vle8_v_u8m1(x[i].qs, vl);

        vint8m1_t y0 = __riscv_vle8_v_i8m1(y[i].qs, vl);
        vint8m1_t y1 = __riscv_vle8_v_i8m1(y[i].qs + 16, vl);

        vuint8m1_t x_at = __riscv_vand_vx_u8m1(tx, 0x0F, vl);
        vuint8m1_t x_lt = __riscv_vsrl_vx_u8m1(tx, 0x04, vl);

        vuint8m1_t x_a = __riscv_vor_vv_u8m1(x_at, xh_0, vl);
        vuint8m1_t x_l = __riscv_vor_vv_u8m1(x_lt, xh_1, vl);

        vint8m1_t v0 = __riscv_vreinterpret_v_u8m1_i8m1(x_a);
        vint8m1_t v1 = __riscv_vreinterpret_v_u8m1_i8m1(x_l);

        vint16m2_t vec_mul1 = __riscv_vwmul_vv_i16m2(v0, y0, vl);
        vint16m2_t vec_mul2 = __riscv_vwmul_vv_i16m2(v1, y1, vl);

        vint32m1_t vec_zero = __riscv_vmv_v_x_i32m1(0, vl);

        vint32m1_t vs1 = __riscv_vwredsum_vs_i16m2_i32m1(vec_mul1, vec_zero, vl);
        vint32m1_t vs2 = __riscv_vwredsum_vs_i16m2_i32m1(vec_mul2, vec_zero, vl);

        int sumi = __riscv_vmv_x_s_i32m1_i32(vs1);
        sumi += __riscv_vmv_x_s_i32m1_i32(vs2);

        sumf += (GGML_FP16_TO_FP32(x[i].d) * y[i].d) * sumi + GGML_FP16_TO_FP32(x[i].m) * y[i].s;
    }

    *s = sumf;
#else
    // scalar
    float sumf = 0.0;

    for (int i = 0; i < nb; i++) {
        uint32_t qh;
        memcpy(&qh, x[i].qh, sizeof(qh));

        int sumi = 0;

        for (int j = 0; j < qk / 2; ++j) {
            const uint8_t xh_0 = ((qh >> (j + 0)) << 4) & 0x10;
            const uint8_t xh_1 = ((qh >> (j + 12))) & 0x10;

            const int32_t x0 = (x[i].qs[j] & 0xF) | xh_0;
            const int32_t x1 = (x[i].qs[j] >> 4) | xh_1;

            sumi += (x0 * y[i].qs[j]) + (x1 * y[i].qs[j + qk / 2]);
        }

        sumf += (GGML_FP16_TO_FP32(x[i].d) * y[i].d) * sumi + GGML_FP16_TO_FP32(x[i].m) * y[i].s;
    }

    *s = sumf;
#endif
}

static void ggml_vec_dot_q8_0_q8_0(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) {
    const int qk = QK8_0;
    const int nb = n / qk;

    assert(n % qk == 0);

    const block_q8_0 * restrict x = vx;
    const block_q8_0 * restrict y = vy;

#if defined(__ARM_NEON)
    float32x4_t sumv0 = vdupq_n_f32(0.0f);
    float32x4_t sumv1 = vdupq_n_f32(0.0f);

    GGML_ASSERT(nb % 2 == 0); // TODO: handle odd nb
    for (int i = 0; i < nb; i += 2) {
        const block_q8_0 * restrict x0 = &x[i + 0];
        const block_q8_0 * restrict x1 = &x[i + 1];
        const block_q8_0 * restrict y0 = &y[i + 0];
        const block_q8_0 * restrict y1 = &y[i + 1];

        const int8x16_t x0_0 = vld1q_s8(x0->qs);
        const int8x16_t x0_1 = vld1q_s8(x0->qs + 16);
        const int8x16_t x1_0 = vld1q_s8(x1->qs);
        const int8x16_t x1_1 = vld1q_s8(x1->qs + 16);

        // load y
        const int8x16_t y0_0 = vld1q_s8(y0->qs);
        const int8x16_t y0_1 = vld1q_s8(y0->qs + 16);
        const int8x16_t y1_0 = vld1q_s8(y1->qs);
        const int8x16_t y1_1 = vld1q_s8(y1->qs + 16);

#if defined(__ARM_FEATURE_DOTPROD)
        sumv0 = vmlaq_n_f32(
            sumv0, vcvtq_f32_s32(vaddq_s32(vdotq_s32(vdupq_n_s32(0), x0_0, y0_0), vdotq_s32(vdupq_n_s32(0), x0_1, y0_1))),
            GGML_FP16_TO_FP32(x0->d) * GGML_FP16_TO_FP32(y0->d));

        sumv1 = vmlaq_n_f32(
            sumv1, vcvtq_f32_s32(vaddq_s32(vdotq_s32(vdupq_n_s32(0), x1_0, y1_0), vdotq_s32(vdupq_n_s32(0), x1_1, y1_1))),
            GGML_FP16_TO_FP32(x1->d) * GGML_FP16_TO_FP32(y1->d));

#else
        const int16x8_t p0_0 = vmull_s8(vget_low_s8(x0_0), vget_low_s8(y0_0));
        const int16x8_t p0_1 = vmull_s8(vget_high_s8(x0_0), vget_high_s8(y0_0));
        const int16x8_t p0_2 = vmull_s8(vget_low_s8(x0_1), vget_low_s8(y0_1));
        const int16x8_t p0_3 = vmull_s8(vget_high_s8(x0_1), vget_high_s8(y0_1));

        const int16x8_t p1_0 = vmull_s8(vget_low_s8(x1_0), vget_low_s8(y1_0));
        const int16x8_t p1_1 = vmull_s8(vget_high_s8(x1_0), vget_high_s8(y1_0));
        const int16x8_t p1_2 = vmull_s8(vget_low_s8(x1_1), vget_low_s8(y1_1));
        const int16x8_t p1_3 = vmull_s8(vget_high_s8(x1_1), vget_high_s8(y1_1));

        const int32x4_t p0 = vaddq_s32(vpaddlq_s16(p0_0), vpaddlq_s16(p0_1));
        const int32x4_t p1 = vaddq_s32(vpaddlq_s16(p0_2), vpaddlq_s16(p0_3));
        const int32x4_t p2 = vaddq_s32(vpaddlq_s16(p1_0), vpaddlq_s16(p1_1));
        const int32x4_t p3 = vaddq_s32(vpaddlq_s16(p1_2), vpaddlq_s16(p1_3));

        sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(p0, p1)), GGML_FP16_TO_FP32(x0->d) * GGML_FP16_TO_FP32(y0->d));
        sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(p2, p3)), GGML_FP16_TO_FP32(x1->d) * GGML_FP16_TO_FP32(y1->d));
#endif
    }

    *s = vaddvq_f32(sumv0) + vaddvq_f32(sumv1);
#elif defined(__AVX2__) || defined(__AVX__)
    // Initialize accumulator with zeros
    __m256 acc = _mm256_setzero_ps();

    // Main loop
    for (int i = 0; i < nb; ++i) {
        // Compute combined scale for the block
        const __m256 d = _mm256_set1_ps(GGML_FP16_TO_FP32(x[i].d) * GGML_FP16_TO_FP32(y[i].d));
        __m256i bx = _mm256_loadu_si256((const __m256i *)x[i].qs);
        __m256i by = _mm256_loadu_si256((const __m256i *)y[i].qs);

        const __m256 q = mul_sum_i8_pairs_float(bx, by);

        // Multiply q with scale and accumulate
#if defined(__AVX2__)
        acc = _mm256_fmadd_ps(d, q, acc);
#else
        acc = _mm256_add_ps(_mm256_mul_ps(d, q), acc);
#endif
    }

    *s = hsum_float_8(acc);
#elif defined(__riscv_v_intrinsic)
    float sumf = 0.0;
    size_t vl = __riscv_vsetvl_e8m1(qk);

    for (int i = 0; i < nb; i++) {
        // load elements
        vint8m1_t bx = __riscv_vle8_v_i8m1(x[i].qs, vl);
        vint8m1_t by = __riscv_vle8_v_i8m1(y[i].qs, vl);

        vint16m2_t vw_mul = __riscv_vwmul_vv_i16m2(bx, by, vl);

        vint32m1_t v_zero = __riscv_vmv_v_x_i32m1(0, vl);
        vint32m1_t v_sum = __riscv_vwredsum_vs_i16m2_i32m1(vw_mul, v_zero, vl);

        int sumi = __riscv_vmv_x_s_i32m1_i32(v_sum);

        sumf += sumi * (GGML_FP16_TO_FP32(x[i].d) * GGML_FP16_TO_FP32(y[i].d));
    }

    *s = sumf;
#else
    // scalar
    float sumf = 0.0;

    for (int i = 0; i < nb; i++) {
        int sumi = 0;

        for (int j = 0; j < qk; j++) {
            sumi += x[i].qs[j] * y[i].qs[j];
        }

        sumf += sumi * (GGML_FP16_TO_FP32(x[i].d) * GGML_FP16_TO_FP32(y[i].d));
    }

    *s = sumf;
#endif
}

// compute GGML_VEC_DOT_UNROLL dot products at once
// xs - x row stride in bytes
inline static void ggml_vec_dot_f16_unroll(const int n, const int xs, float * restrict s, void * restrict xv,
                                           ggml_fp16_t * restrict y) {
    ggml_float sumf[GGML_VEC_DOT_UNROLL] = {0.0};

    ggml_fp16_t * restrict x[GGML_VEC_DOT_UNROLL];

    for (int i = 0; i < GGML_VEC_DOT_UNROLL; ++i) {
        x[i] = (ggml_fp16_t *)((char *)xv + i * xs);
    }

#if defined(GGML_SIMD)
    const int np = (n & ~(GGML_F16_STEP - 1));

    GGML_F16_VEC sum[GGML_VEC_DOT_UNROLL][GGML_F16_ARR] = {{GGML_F16_VEC_ZERO}};

    GGML_F16_VEC ax[GGML_F16_ARR];
    GGML_F16_VEC ay[GGML_F16_ARR];

    for (int i = 0; i < np; i += GGML_F16_STEP) {
        for (int j = 0; j < GGML_F16_ARR; j++) {
            ay[j] = GGML_F16_VEC_LOAD(y + i + j * GGML_F16_EPR, j);

            for (int k = 0; k < GGML_VEC_DOT_UNROLL; ++k) {
                ax[j] = GGML_F16_VEC_LOAD(x[k] + i + j * GGML_F16_EPR, j);

                sum[k][j] = GGML_F16_VEC_FMA(sum[k][j], ax[j], ay[j]);
            }
        }
    }

    // reduce sum0..sum3 to sum0
    for (int k = 0; k < GGML_VEC_DOT_UNROLL; ++k) {
        GGML_F16_VEC_REDUCE(sumf[k], sum[k]);
    }

    // leftovers
    for (int i = np; i < n; ++i) {
        for (int j = 0; j < GGML_VEC_DOT_UNROLL; ++j) {
            sumf[j] += (ggml_float)(GGML_FP16_TO_FP32(x[j][i]) * GGML_FP16_TO_FP32(y[i]));
        }
    }
#else
    for (int i = 0; i < n; ++i) {
        for (int j = 0; j < GGML_VEC_DOT_UNROLL; ++j) {
            sumf[j] += (ggml_float)(GGML_FP16_TO_FP32(x[j][i]) * GGML_FP16_TO_FP32(y[i]));
        }
    }
#endif

    for (int i = 0; i < GGML_VEC_DOT_UNROLL; ++i) {
        s[i] = sumf[i];
    }
}

inline static void ggml_vec_mad_f32(const int n, float * restrict y, const float * restrict x, const float v) {
#if defined(GGML_SIMD)
    const int np = (n & ~(GGML_F32_STEP - 1));

    GGML_F32_VEC vx = GGML_F32_VEC_SET1(v);

    GGML_F32_VEC ax[GGML_F32_ARR];
    GGML_F32_VEC ay[GGML_F32_ARR];

    for (int i = 0; i < np; i += GGML_F32_STEP) {
        for (int j = 0; j < GGML_F32_ARR; j++) {
            ax[j] = GGML_F32_VEC_LOAD(x + i + j * GGML_F32_EPR);
            ay[j] = GGML_F32_VEC_LOAD(y + i + j * GGML_F32_EPR);
            ay[j] = GGML_F32_VEC_FMA(ay[j], ax[j], vx);

            GGML_F32_VEC_STORE(y + i + j * GGML_F32_EPR, ay[j]);
        }
    }

    // leftovers
    for (int i = np; i < n; ++i) {
        y[i] += x[i] * v;
    }
#else
    // scalar
    for (int i = 0; i < n; ++i) {
        y[i] += x[i] * v;
    }
#endif
}

// inline static void ggml_vec_scale_f32(const int n, float * y, const float   v) { for (int i = 0; i < n; ++i) y[i] *= v; }
inline static void ggml_vec_scale_f32(const int n, float * y, const float v) {
#if defined(GGML_USE_ACCELERATE)
    vDSP_vsmul(y, 1, &v, y, 1, n);
#elif defined(GGML_SIMD)
    const int np = (n & ~(GGML_F32_STEP - 1));

    GGML_F32_VEC vx = GGML_F32_VEC_SET1(v);

    GGML_F32_VEC ay[GGML_F32_ARR];

    for (int i = 0; i < np; i += GGML_F32_STEP) {
        for (int j = 0; j < GGML_F32_ARR; j++) {
            ay[j] = GGML_F32_VEC_LOAD(y + i + j * GGML_F32_EPR);
            ay[j] = GGML_F32_VEC_MUL(ay[j], vx);

            GGML_F32_VEC_STORE(y + i + j * GGML_F32_EPR, ay[j]);
        }
    }

    // leftovers
    for (int i = np; i < n; ++i) {
        y[i] *= v;
    }
#else
    // scalar
    for (int i = 0; i < n; ++i) {
        y[i] *= v;
    }
#endif
}

inline static void ggml_vec_norm_f32(const int n, float * s, const float * x) {
    ggml_vec_dot_f32(n, s, x, x);
    *s = sqrtf(*s);
}
inline static void ggml_vec_sqr_f32(const int n, float * y, const float * x) {
    for (int i = 0; i < n; ++i)
        y[i] = x[i] * x[i];
}
inline static void ggml_vec_sqrt_f32(const int n, float * y, const float * x) {
    for (int i = 0; i < n; ++i)
        y[i] = sqrtf(x[i]);
}
inline static void ggml_vec_log_f32(const int n, float * y, const float * x) {
    for (int i = 0; i < n; ++i)
        y[i] = logf(x[i]);
}
inline static void ggml_vec_abs_f32(const int n, float * y, const float * x) {
    for (int i = 0; i < n; ++i)
        y[i] = fabsf(x[i]);
}
inline static void ggml_vec_sgn_f32(const int n, float * y, const float * x) {
    for (int i = 0; i < n; ++i)
        y[i] = (x[i] > 0.f) ? 1.f : ((x[i] < 0.f) ? -1.f : 0.f);
}
inline static void ggml_vec_step_f32(const int n, float * y, const float * x) {
    for (int i = 0; i < n; ++i)
        y[i] = (x[i] > 0.f) ? 1.f : 0.f;
}
inline static void ggml_vec_tanh_f32(const int n, float * y, const float * x) {
    for (int i = 0; i < n; ++i)
        y[i] = tanhf(x[i]);
}
inline static void ggml_vec_elu_f32(const int n, float * y, const float * x) {
    for (int i = 0; i < n; ++i)
        y[i] = (x[i] > 0.f) ? x[i] : expf(x[i]) - 1;
}
inline static void ggml_vec_relu_f32(const int n, float * y, const float * x) {
    for (int i = 0; i < n; ++i)
        y[i] = (x[i] > 0.f) ? x[i] : 0.f;
}

static const float GELU_COEF_A = 0.044715f;
static const float GELU_QUICK_COEF = -1.702f;
static const float SQRT_2_OVER_PI = 0.79788456080286535587989211986876f;

inline static float ggml_gelu_f32(float x) {
    return 0.5f * x * (1.0f + tanhf(SQRT_2_OVER_PI * x * (1.0f + GELU_COEF_A * x * x)));
}

inline static void ggml_vec_gelu_f16(const int n, ggml_fp16_t * y, const ggml_fp16_t * x) {
    const uint16_t * i16 = (const uint16_t *)x;
    for (int i = 0; i < n; ++i) {
        y[i] = table_gelu_f16[i16[i]];
    }
}

#ifdef GGML_GELU_FP16
inline static void ggml_vec_gelu_f32(const int n, float * y, const float * x) {
    uint16_t t;
    for (int i = 0; i < n; ++i) {
        ggml_fp16_t fp16 = GGML_FP32_TO_FP16(x[i]);
        memcpy(&t, &fp16, sizeof(uint16_t));
        y[i] = GGML_FP16_TO_FP32(table_gelu_f16[t]);
    }
}
#else
inline static void ggml_vec_gelu_f32(const int n, float * y, const float * x) {
    for (int i = 0; i < n; ++i) {
        y[i] = ggml_gelu_f32(x[i]);
    }
}
#endif

inline static float ggml_gelu_quick_f32(float x) { return x * (1.0f / (1.0f + expf(GELU_QUICK_COEF * x))); }

// inline static void ggml_vec_gelu_quick_f16(const int n, ggml_fp16_t * y, const ggml_fp16_t * x) {
//     const uint16_t * i16 = (const uint16_t *) x;
//     for (int i = 0; i < n; ++i) {
//         y[i] = table_gelu_quick_f16[i16[i]];
//     }
// }

#ifdef GGML_GELU_QUICK_FP16
inline static void ggml_vec_gelu_quick_f32(const int n, float * y, const float * x) {
    uint16_t t;
    for (int i = 0; i < n; ++i) {
        ggml_fp16_t fp16 = GGML_FP32_TO_FP16(x[i]);
        memcpy(&t, &fp16, sizeof(uint16_t));
        y[i] = GGML_FP16_TO_FP32(table_gelu_quick_f16[t]);
    }
}
#else
inline static void ggml_vec_gelu_quick_f32(const int n, float * y, const float * x) {
    for (int i = 0; i < n; ++i) {
        y[i] = ggml_gelu_quick_f32(x[i]);
    }
}
#endif

// Sigmoid Linear Unit (SiLU) function
inline static float ggml_silu_f32(float x) { return x / (1.0f + expf(-x)); }

// inline static void ggml_vec_silu_f16(const int n, ggml_fp16_t * y, const ggml_fp16_t * x) {
//     const uint16_t * i16 = (const uint16_t *) x;
//     for (int i = 0; i < n; ++i) {
//         y[i] = table_silu_f16[i16[i]];
//     }
// }

#ifdef GGML_SILU_FP16
inline static void ggml_vec_silu_f32(const int n, float * y, const float * x) {
    uint16_t t;
    for (int i = 0; i < n; ++i) {
        ggml_fp16_t fp16 = GGML_FP32_TO_FP16(x[i]);
        memcpy(&t, &fp16, sizeof(uint16_t));
        y[i] = GGML_FP16_TO_FP32(table_silu_f16[t]);
    }
}
#else
inline static void ggml_vec_silu_f32(const int n, float * y, const float * x) {
    for (int i = 0; i < n; ++i) {
        y[i] = ggml_silu_f32(x[i]);
    }
}
#endif

inline static float ggml_silu_backward_f32(float x, float dy) {
    const float s = 1.0f / (1.0f + expf(-x));
    return dy * s * (1.0f + x * (1.0f - s));
}

#ifdef GGML_SILU_FP16
inline static void ggml_vec_silu_backward_f32(const int n, float * dx, const float * x, const float * dy) {
    for (int i = 0; i < n; ++i) {
        // we did not use x[i] to compute forward silu but its f16 equivalent
        // take derivative at f16 of x[i]:
        ggml_fp16_t fp16 = GGML_FP32_TO_FP16(x[i]);
        float usedx = GGML_FP16_TO_FP32(fp16);
        dx[i] = ggml_silu_backward_f32(usedx, dy[i]);
    }
}
#else
inline static void ggml_vec_silu_backward_f32(const int n, float * dx, const float * x, const float * dy) {
    for (int i = 0; i < n; ++i) {
        dx[i] = ggml_silu_backward_f32(x[i], dy[i]);
    }
}
#endif

inline static void ggml_vec_sum_f32(const int n, float * s, const float * x) {
#ifndef GGML_USE_ACCELERATE
    ggml_float sum = 0.0;
    for (int i = 0; i < n; ++i) {
        sum += (ggml_float)x[i];
    }
    *s = sum;
#else
    vDSP_sve(x, 1, s, n);
#endif
}

inline static void ggml_vec_sum_f32_ggf(const int n, ggml_float * s, const float * x) {
    ggml_float sum = 0.0;
    for (int i = 0; i < n; ++i) {
        sum += (ggml_float)x[i];
    }
    *s = sum;
}

inline static void ggml_vec_sum_f16_ggf(const int n, float * s, const ggml_fp16_t * x) {
    float sum = 0.0f;
    for (int i = 0; i < n; ++i) {
        sum += GGML_FP16_TO_FP32(x[i]);
    }
    *s = sum;
}

inline static void ggml_vec_max_f32(const int n, float * s, const float * x) {
#ifndef GGML_USE_ACCELERATE
    float max = -INFINITY;
    for (int i = 0; i < n; ++i) {
        max = MAX(max, x[i]);
    }
    *s = max;
#else
    vDSP_maxv(x, 1, s, n);
#endif
}

inline static void ggml_vec_norm_inv_f32(const int n, float * s, const float * x) {
    ggml_vec_norm_f32(n, s, x);
    *s = 1.f / (*s);
}

inline static void ggml_vec_argmax_f32(const int n, int * s, const float * x) {
    float max = -INFINITY;
    int idx = 0;
    for (int i = 0; i < n; ++i) {
        max = MAX(max, x[i]);
        if (max == x[i]) {
            idx = i;
        }
    }
    *s = idx;
}

//
// data types
//

static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
    "NONE",

    "DUP",
    "ADD",
    "ADD1",
    "ACC",
    "SUB",
    "MUL",
    "DIV",
    "SQR",
    "SQRT",
    "LOG",
    "SUM",
    "SUM_ROWS",
    "MEAN",
    "ARGMAX",
    "REPEAT",
    "REPEAT_BACK",
    "CONCAT",
    "SILU_BACK",
    "NORM",
    "RMS_NORM",
    "RMS_NORM_BACK",
    "GROUP_NORM",

    "MUL_MAT",
    "OUT_PROD",

    "SCALE",
    "SET",
    "CPY",
    "CONT",
    "RESHAPE",
    "VIEW",
    "PERMUTE",
    "TRANSPOSE",
    "GET_ROWS",
    "GET_ROWS_BACK",
    "DIAG",
    "DIAG_MASK_INF",
    "DIAG_MASK_ZERO",
    "SOFT_MAX",
    "SOFT_MAX_BACK",
    "ROPE",
    "ROPE_BACK",
    "ALIBI",
    "CLAMP",
    "CONV_1D",
    "CONV_2D",
    "CONV_TRANSPOSE_2D",
    "POOL_1D",
    "POOL_2D",
    "UPSCALE",

    "FLASH_ATTN",
    "FLASH_FF",
    "FLASH_ATTN_BACK",
    "WIN_PART",
    "WIN_UNPART",
    "GET_REL_POS",
    "ADD_REL_POS",

    "UNARY",

    "MAP_UNARY",
    "MAP_BINARY",

    "MAP_CUSTOM1_F32",
    "MAP_CUSTOM2_F32",
    "MAP_CUSTOM3_F32",

    "MAP_CUSTOM1",
    "MAP_CUSTOM2",
    "MAP_CUSTOM3",

    "CROSS_ENTROPY_LOSS",
    "CROSS_ENTROPY_LOSS_BACK",
};

static_assert(GGML_OP_COUNT == 68, "GGML_OP_COUNT != 68");

static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
    "none",

    "x",
    "x+y",
    "x+y",
    "view(x,nb,offset)+=y->x",
    "x-y",
    "x*y",
    "x/y",
    "x^2",
    "√x",
    "log(x)",
    "Σx",
    "Σx_k",
    "Σx/n",
    "argmax(x)",
    "repeat(x)",
    "repeat_back(x)",
    "concat(x, y)",
    "silu_back(x)",
    "norm(x)",
    "rms_norm(x)",
    "rms_norm_back(x)",
    "group_norm(x)",

    "X*Y",
    "X*Y",

    "x*v",
    "y-\\>view(x)",
    "x-\\>y",
    "cont(x)",
    "reshape(x)",
    "view(x)",
    "permute(x)",
    "transpose(x)",
    "get_rows(x)",
    "get_rows_back(x)",
    "diag(x)",
    "diag_mask_inf(x)",
    "diag_mask_zero(x)",
    "soft_max(x)",
    "soft_max_back(x)",
    "rope(x)",
    "rope_back(x)",
    "alibi(x)",
    "clamp(x)",
    "conv_1d(x)",
    "conv_2d(x)",
    "conv_transpose_2d(x)",
    "pool_1d(x)",
    "pool_2d(x)",
    "upscale(x)",

    "flash_attn(x)",
    "flash_ff(x)",
    "flash_attn_back(x)",
    "win_part(x)",
    "win_unpart(x)",
    "get_rel_pos(x)",
    "add_rel_pos(x)",

    "unary(x)",

    "f(x)",
    "f(x,y)",

    "custom_f32(x)",
    "custom_f32(x,y)",
    "custom_f32(x,y,z)",

    "custom(x)",
    "custom(x,y)",
    "custom(x,y,z)",

    "cross_entropy_loss(x,y)",
    "cross_entropy_loss_back(x,y)",
};

static_assert(GGML_OP_COUNT == 68, "GGML_OP_COUNT != 68");

static_assert(GGML_OP_POOL_COUNT == 2, "GGML_OP_POOL_COUNT != 2");

static_assert(sizeof(struct ggml_object) % GGML_MEM_ALIGN == 0, "ggml_object size must be a multiple of GGML_MEM_ALIGN");
static_assert(sizeof(struct ggml_tensor) % GGML_MEM_ALIGN == 0, "ggml_tensor size must be a multiple of GGML_MEM_ALIGN");

// WARN:
// Mis-confguration can lead to problem that's hard to reason about:
// * At best  it crash or talks nosense.
// * At worst it talks slightly difference but hard to perceive.
//
// An op has to enable INIT or FINALIZE when any of it's branch needs that pass.
// Take care about compile options (e.g., GGML_USE_xxx).
static bool GGML_OP_HAS_INIT[GGML_OP_COUNT] = {0};
static bool GGML_OP_HAS_FINALIZE[GGML_OP_COUNT] = {0};

static void ggml_setup_op_has_task_pass(void) {
    { // INIT
        bool * p = GGML_OP_HAS_INIT;

        p[GGML_OP_ACC] = true;
        p[GGML_OP_MUL_MAT] = true;
        p[GGML_OP_OUT_PROD] = true;
        p[GGML_OP_SET] = true;
        p[GGML_OP_GET_ROWS_BACK] = true;
        p[GGML_OP_DIAG_MASK_INF] = true;
        p[GGML_OP_DIAG_MASK_ZERO] = true;
        p[GGML_OP_CONV_1D] = true;
        p[GGML_OP_CONV_2D] = true;
        p[GGML_OP_CONV_TRANSPOSE_2D] = true;
        p[GGML_OP_FLASH_ATTN_BACK] = true;
        p[GGML_OP_CROSS_ENTROPY_LOSS] = true;
        p[GGML_OP_ADD_REL_POS] = true;
    }

    { // FINALIZE
        bool * p = GGML_OP_HAS_FINALIZE;

        p[GGML_OP_CROSS_ENTROPY_LOSS] = true;
    }
}

//
// ggml context
//

struct ggml_context {
    size_t mem_size;
    void * mem_buffer;
    bool mem_buffer_owned;
    bool no_alloc;
    bool no_alloc_save; // this is used to save the no_alloc state when using scratch buffers

    int n_objects;

    struct ggml_object * objects_begin;
    struct ggml_object * objects_end;

    struct ggml_scratch scratch;
    struct ggml_scratch scratch_save;
};

struct ggml_context_container {
    bool used;

    struct ggml_context context;
};

//
// 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 {
    struct ggml_numa_node nodes[GGML_NUMA_MAX_NODES];
    uint32_t n_nodes;
    uint32_t total_cpus; // hardware threads on system
};

//
// ggml state
//

struct ggml_state {
    struct ggml_context_container contexts[GGML_MAX_CONTEXTS];
    struct ggml_numa_nodes numa;
};

// global state
static struct ggml_state g_state;
static atomic_int g_state_barrier = 0;

// barrier via spin lock
inline static void ggml_critical_section_start(void) {
    int processing = atomic_fetch_add(&g_state_barrier, 1);

    while (processing > 0) {
        // wait for other threads to finish
        atomic_fetch_sub(&g_state_barrier, 1);
        sched_yield(); // TODO: reconsider this
        processing = atomic_fetch_add(&g_state_barrier, 1);
    }
}

// TODO: make this somehow automatically executed
//       some sort of "sentry" mechanism
inline static void ggml_critical_section_end(void) { atomic_fetch_sub(&g_state_barrier, 1); }

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

        return;
    }

#ifdef __linux__
    struct stat st;
    char path[256];
    int rv;

    // 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);

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

    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_PRINT(
                    "WARNING: /proc/sys/kernel/numa_balancing is enabled, this has been observed to impair performance\n");
            }
            fclose(fptr);
        }
    }
#else
    // TODO
#endif
}

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

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

void ggml_print_object(const struct ggml_object * obj) {
    GGML_PRINT(" - ggml_object: type = %d, offset = %zu, size = %zu, next = %p\n", obj->type, obj->offs, obj->size,
               (const void *)obj->next);
}

void ggml_print_objects(const struct ggml_context * ctx) {
    struct ggml_object * obj = ctx->objects_begin;

    GGML_PRINT("%s: objects in context %p:\n", __func__, (const void *)ctx);

    while (obj != NULL) {
        ggml_print_object(obj);
        obj = obj->next;
    }

    GGML_PRINT("%s: --- end ---\n", __func__);
}

int64_t ggml_nelements(const struct ggml_tensor * tensor) {
    static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");

    return tensor->ne[0] * tensor->ne[1] * tensor->ne[2] * tensor->ne[3];
}

int64_t ggml_nrows(const struct ggml_tensor * tensor) {
    static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");

    return tensor->ne[1] * tensor->ne[2] * tensor->ne[3];
}

size_t ggml_nbytes(const struct ggml_tensor * tensor) {
    size_t nbytes;
    size_t blck_size = ggml_blck_size(tensor->type);
    if (blck_size == 1) {
        nbytes = ggml_type_size(tensor->type);
        for (int i = 0; i < GGML_MAX_DIMS; ++i) {
            nbytes += (tensor->ne[i] - 1) * tensor->nb[i];
        }
    } else {
        nbytes = tensor->ne[0] * tensor->nb[0] / blck_size;
        for (int i = 1; i < GGML_MAX_DIMS; ++i) {
            nbytes += (tensor->ne[i] - 1) * tensor->nb[i];
        }
    }

    return nbytes;
}

size_t ggml_nbytes_pad(const struct ggml_tensor * tensor) { return GGML_PAD(ggml_nbytes(tensor), GGML_MEM_ALIGN); }

size_t ggml_nbytes_split(const struct ggml_tensor * tensor, int nrows_split) {
    static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");

    return (nrows_split * tensor->ne[0] * ggml_type_size(tensor->type)) / ggml_blck_size(tensor->type);
}

int ggml_blck_size(enum ggml_type type) { return type_traits[type].blck_size; }

size_t ggml_type_size(enum ggml_type type) { return type_traits[type].type_size; }

float ggml_type_sizef(enum ggml_type type) { return ((float)(type_traits[type].type_size)) / type_traits[type].blck_size; }

const char * ggml_type_name(enum ggml_type type) { return type_traits[type].type_name; }

bool ggml_is_quantized(enum ggml_type type) { return type_traits[type].is_quantized; }

const char * ggml_op_name(enum ggml_op op) { return GGML_OP_NAME[op]; }

const char * ggml_op_symbol(enum ggml_op op) { return GGML_OP_SYMBOL[op]; }

size_t ggml_element_size(const struct ggml_tensor * tensor) { return ggml_type_size(tensor->type); }

static inline bool ggml_is_scalar(const struct ggml_tensor * tensor) {
    static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");

    return tensor->ne[0] == 1 && tensor->ne[1] == 1 && tensor->ne[2] == 1 && tensor->ne[3] == 1;
}

static inline bool ggml_is_vector(const struct ggml_tensor * tensor) {
    static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");

    return tensor->ne[1] == 1 && tensor->ne[2] == 1 && tensor->ne[3] == 1;
}

static inline bool ggml_is_matrix(const struct ggml_tensor * tensor) {
    static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");

    return tensor->ne[2] == 1 && tensor->ne[3] == 1;
}

static inline bool ggml_can_mul_mat(const struct ggml_tensor * t0, const struct ggml_tensor * t1) {
    static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");

    return (t0->ne[0] == t1->ne[0]) && (t1->ne[2] % t0->ne[2] == 0) && // verify t0 is broadcastable
           (t1->ne[3] % t0->ne[3] == 0);
}

static inline bool ggml_can_out_prod(const struct ggml_tensor * t0, const struct ggml_tensor * t1) {
    static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");

    return (t0->ne[1] == t1->ne[1]) && (t0->ne[2] == t1->ne[2]) && (t0->ne[3] == t1->ne[3]);
}

enum ggml_type ggml_ftype_to_ggml_type(enum ggml_ftype ftype) {
    enum ggml_type wtype = GGML_TYPE_COUNT;

    switch (ftype) {
    case GGML_FTYPE_ALL_F32:
        wtype = GGML_TYPE_F32;
        break;
    case GGML_FTYPE_MOSTLY_F16:
        wtype = GGML_TYPE_F16;
        break;
    case GGML_FTYPE_MOSTLY_Q4_0:
        wtype = GGML_TYPE_Q4_0;
        break;
    case GGML_FTYPE_MOSTLY_Q4_1:
        wtype = GGML_TYPE_Q4_1;
        break;
    case GGML_FTYPE_MOSTLY_Q5_0:
        wtype = GGML_TYPE_Q5_0;
        break;
    case GGML_FTYPE_MOSTLY_Q5_1:
        wtype = GGML_TYPE_Q5_1;
        break;
    case GGML_FTYPE_MOSTLY_Q8_0:
        wtype = GGML_TYPE_Q8_0;
        break;
    case GGML_FTYPE_MOSTLY_Q2_K:
        wtype = GGML_TYPE_Q2_K;
        break;
    case GGML_FTYPE_MOSTLY_Q3_K:
        wtype = GGML_TYPE_Q3_K;
        break;
    case GGML_FTYPE_MOSTLY_Q4_K:
        wtype = GGML_TYPE_Q4_K;
        break;
    case GGML_FTYPE_MOSTLY_Q5_K:
        wtype = GGML_TYPE_Q5_K;
        break;
    case GGML_FTYPE_MOSTLY_Q6_K:
        wtype = GGML_TYPE_Q6_K;
        break;
    case GGML_FTYPE_UNKNOWN:
        wtype = GGML_TYPE_COUNT;
        break;
    case GGML_FTYPE_MOSTLY_Q4_1_SOME_F16:
        wtype = GGML_TYPE_COUNT;
        break;
    }

    GGML_ASSERT(wtype != GGML_TYPE_COUNT);

    return wtype;
}

size_t ggml_tensor_overhead(void) { return GGML_OBJECT_SIZE + GGML_TENSOR_SIZE; }

bool ggml_is_transposed(const struct ggml_tensor * tensor) { return tensor->nb[0] > tensor->nb[1]; }

bool ggml_is_contiguous(const struct ggml_tensor * tensor) {
    static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");

    return tensor->nb[0] == ggml_type_size(tensor->type) &&
           tensor->nb[1] == (tensor->nb[0] * tensor->ne[0]) / ggml_blck_size(tensor->type) &&
           tensor->nb[2] == tensor->nb[1] * tensor->ne[1] && tensor->nb[3] == tensor->nb[2] * tensor->ne[2];
}

static inline bool ggml_is_contiguous_except_dim_1(const struct ggml_tensor * tensor) {
    static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");

    return tensor->nb[0] == ggml_type_size(tensor->type) && tensor->nb[2] == tensor->nb[1] * tensor->ne[1] &&
           tensor->nb[3] == tensor->nb[2] * tensor->ne[2];
}

bool ggml_is_permuted(const struct ggml_tensor * tensor) {
    static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");

    return tensor->nb[0] > tensor->nb[1] || tensor->nb[1] > tensor->nb[2] || tensor->nb[2] > tensor->nb[3];
}

static inline bool ggml_is_padded_1d(const struct ggml_tensor * tensor) {
    static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");

    return tensor->nb[0] == ggml_type_size(tensor->type) && tensor->nb[2] == tensor->nb[1] * tensor->ne[1] &&
           tensor->nb[3] == tensor->nb[2] * tensor->ne[2];
}

bool ggml_are_same_shape(const struct ggml_tensor * t0, const struct ggml_tensor * t1) {
    static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");

    return (t0->ne[0] == t1->ne[0]) && (t0->ne[1] == t1->ne[1]) && (t0->ne[2] == t1->ne[2]) && (t0->ne[3] == t1->ne[3]);
}

// check if t1 can be represented as a repeatition of t0
static inline bool ggml_can_repeat(const struct ggml_tensor * t0, const struct ggml_tensor * t1) {
    static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");

    return (t1->ne[0] % t0->ne[0] == 0) && (t1->ne[1] % t0->ne[1] == 0) && (t1->ne[2] % t0->ne[2] == 0) &&
           (t1->ne[3] % t0->ne[3] == 0);
}

static inline bool ggml_can_repeat_rows(const struct ggml_tensor * t0, const struct ggml_tensor * t1) {
    static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");

    return (t0->ne[0] == t1->ne[0]) && ggml_can_repeat(t0, t1);
}

static inline int ggml_up32(int n) { return (n + 31) & ~31; }

// static inline int ggml_up64(int n) {
//     return (n + 63) & ~63;
// }

static inline int ggml_up(int n, int m) {
    // assert m is a power of 2
    GGML_ASSERT((m & (m - 1)) == 0);
    return (n + m - 1) & ~(m - 1);
}

// assert that pointer is aligned to GGML_MEM_ALIGN
#define ggml_assert_aligned(ptr) GGML_ASSERT(((uintptr_t)(ptr)) % GGML_MEM_ALIGN == 0)

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

struct ggml_context * ggml_init(struct ggml_init_params params) {
    // make this function thread safe
    ggml_critical_section_start();

    static bool is_first_call = true;

    if (is_first_call) {
        // initialize time system (required on Windows)
        ggml_time_init();

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

            ggml_fp16_t ii;
            for (int i = 0; i < (1 << 16); ++i) {
                uint16_t ui = i;
                memcpy(&ii, &ui, sizeof(ii));
                const float f = table_f32_f16[i] = GGML_COMPUTE_FP16_TO_FP32(ii);
                table_gelu_f16[i] = GGML_FP32_TO_FP16(ggml_gelu_f32(f));
                table_gelu_quick_f16[i] = GGML_FP32_TO_FP16(ggml_gelu_quick_f32(f));
                table_silu_f16[i] = GGML_FP32_TO_FP16(ggml_silu_f32(f));
                table_exp_f16[i] = GGML_FP32_TO_FP16(expf(f));
            }

            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) / 1000.0f);
        }

        // initialize g_state
        {
            const uint64_t t_start = ggml_time_us();
            UNUSED(t_start);

            g_state = (struct ggml_state){
                /*.contexts =*/{{0}},
                /*.numa =*/
                {
                    .n_nodes = 0,
                    .total_cpus = 0,
                },
            };

            for (int i = 0; i < GGML_MAX_CONTEXTS; ++i) {
                g_state.contexts[i].used = false;
            }

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

            GGML_PRINT_DEBUG("%s: g_state initialized in %f ms\n", __func__, (t_end - t_start) / 1000.0f);
        }

#if defined(GGML_USE_CUBLAS)
        ggml_init_cublas();
#elif defined(GGML_USE_CLBLAST)
        ggml_cl_init();
#endif

        ggml_setup_op_has_task_pass();

        is_first_call = false;
    }

    // find non-used context in g_state
    struct ggml_context * ctx = NULL;

    for (int i = 0; i < GGML_MAX_CONTEXTS; i++) {
        if (!g_state.contexts[i].used) {
            g_state.contexts[i].used = true;
            ctx = &g_state.contexts[i].context;

            GGML_PRINT_DEBUG("%s: found unused context %d\n", __func__, i);
            break;
        }
    }

    if (ctx == NULL) {
        GGML_PRINT_DEBUG("%s: no unused context found\n", __func__);

        ggml_critical_section_end();

        return NULL;
    }

    // allow to call ggml_init with 0 size
    if (params.mem_size == 0) {
        params.mem_size = GGML_MEM_ALIGN;
    }

    const size_t mem_size = params.mem_buffer ? params.mem_size : GGML_PAD(params.mem_size, GGML_MEM_ALIGN);

    *ctx = (struct ggml_context){
        /*.mem_size           =*/mem_size,
        /*.mem_buffer         =*/params.mem_buffer ? params.mem_buffer : GGML_ALIGNED_MALLOC(mem_size),
        /*.mem_buffer_owned   =*/params.mem_buffer ? false : true,
        /*.no_alloc           =*/params.no_alloc,
        /*.no_alloc_save      =*/params.no_alloc,
        /*.n_objects          =*/0,
        /*.objects_begin      =*/NULL,
        /*.objects_end        =*/NULL,
        /*.scratch            =*/
        {
            0,
            0,
            NULL,
        },
        /*.scratch_save       =*/
        {
            0,
            0,
            NULL,
        },
    };

    GGML_ASSERT(ctx->mem_buffer != NULL);

    ggml_assert_aligned(ctx->mem_buffer);

    GGML_PRINT_DEBUG("%s: context initialized\n", __func__);

    ggml_critical_section_end();

    return ctx;
}

void ggml_free(struct ggml_context * ctx) {
    // make this function thread safe
    ggml_critical_section_start();

    bool found = false;

    for (int i = 0; i < GGML_MAX_CONTEXTS; i++) {
        if (&g_state.contexts[i].context == ctx) {
            g_state.contexts[i].used = false;

            GGML_PRINT_DEBUG("%s: context %d has been freed. memory used = %zu\n", __func__, i, ggml_used_mem(ctx));

            if (ctx->mem_buffer_owned) {
                GGML_ALIGNED_FREE(ctx->mem_buffer);
            }

            found = true;
            break;
        }
    }

    if (!found) {
        GGML_PRINT_DEBUG("%s: context not found\n", __func__);
    }

    ggml_critical_section_end();
}

size_t ggml_used_mem(const struct ggml_context * ctx) {
    return ctx->objects_end == NULL ? 0 : ctx->objects_end->offs + ctx->objects_end->size;
}

size_t ggml_set_scratch(struct ggml_context * ctx, struct ggml_scratch scratch) {
    const size_t result = ctx->scratch.data ? ctx->scratch.offs : 0;

    ctx->scratch = scratch;

    return result;
}

bool ggml_get_no_alloc(struct ggml_context * ctx) { return ctx->no_alloc; }

void ggml_set_no_alloc(struct ggml_context * ctx, bool no_alloc) { ctx->no_alloc = no_alloc; }

void * ggml_get_mem_buffer(const struct ggml_context * ctx) { return ctx->mem_buffer; }

size_t ggml_get_mem_size(const struct ggml_context * ctx) { return ctx->mem_size; }

size_t ggml_get_max_tensor_size(const struct ggml_context * ctx) {
    size_t max_size = 0;

    struct ggml_object * obj = ctx->objects_begin;

    while (obj != NULL) {
        if (obj->type == GGML_OBJECT_TENSOR) {
            struct ggml_tensor * tensor = (struct ggml_tensor *)((char *)ctx->mem_buffer + obj->offs);

            const size_t size = ggml_nbytes(tensor);

            if (max_size < size) {
                max_size = size;
            }
        }

        obj = obj->next;
    }

    return max_size;
}

// IMPORTANT:
// when creating "opt" tensors, always save and load the scratch buffer
// this is an error prone process, but it is necessary to support inplace
// operators when using scratch buffers
// TODO: implement a better way
static void ggml_scratch_save(struct ggml_context * ctx) {
    // this is needed to allow opt tensors to store their data
    // TODO: again, need to find a better way
    ctx->no_alloc_save = ctx->no_alloc;
    ctx->no_alloc = false;

    ctx->scratch_save = ctx->scratch;
    ctx->scratch.data = NULL;
}

static void ggml_scratch_load(struct ggml_context * ctx) {
    ctx->no_alloc = ctx->no_alloc_save;

    ctx->scratch = ctx->scratch_save;
}

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

static struct ggml_object * ggml_new_object(struct ggml_context * ctx, enum ggml_object_type type, size_t size) {
    // always insert objects at the end of the context's memory pool
    struct ggml_object * obj_cur = ctx->objects_end;

    const size_t cur_offs = obj_cur == NULL ? 0 : obj_cur->offs;
    const size_t cur_size = obj_cur == NULL ? 0 : obj_cur->size;
    const size_t cur_end = cur_offs + cur_size;

    // align to GGML_MEM_ALIGN
    size_t size_needed = GGML_PAD(size, GGML_MEM_ALIGN);

    char * const mem_buffer = ctx->mem_buffer;
    struct ggml_object * const obj_new = (struct ggml_object *)(mem_buffer + cur_end);

    if (cur_end + size_needed + GGML_OBJECT_SIZE > ctx->mem_size) {
        GGML_PRINT("%s: not enough space in the context's memory pool (needed %zu, available %zu)\n", __func__,
                   cur_end + size_needed, ctx->mem_size);
        assert(false);
        return NULL;
    }

    *obj_new = (struct ggml_object){
        .offs = cur_end + GGML_OBJECT_SIZE,
        .size = size_needed,
        .next = NULL,
        .type = type,
    };

    ggml_assert_aligned(mem_buffer + obj_new->offs);

    if (obj_cur != NULL) {
        obj_cur->next = obj_new;
    } else {
        // this is the first object in this context
        ctx->objects_begin = obj_new;
    }

    ctx->objects_end = obj_new;

    // printf("%s: inserted new object at %zu, size = %zu\n", __func__, cur_end, obj_new->size);

    return obj_new;
}

static struct ggml_tensor * ggml_new_tensor_impl(struct ggml_context * ctx, enum ggml_type type, int n_dims, const int64_t * ne,
                                                 struct ggml_tensor * view_src, size_t view_offs) {

    assert(n_dims >= 1 && n_dims <= GGML_MAX_DIMS);

    // find the base tensor and absolute offset
    if (view_src != NULL && view_src->view_src != NULL) {
        view_offs += view_src->view_offs;
        view_src = view_src->view_src;
    }

    size_t data_size = ggml_type_size(type) * (ne[0] / ggml_blck_size(type));
    for (int i = 1; i < n_dims; i++) {
        data_size *= ne[i];
    }

    GGML_ASSERT(view_src == NULL || data_size + view_offs <= ggml_nbytes(view_src));

    void * data = view_src != NULL ? view_src->data : NULL;
    if (data != NULL) {
        data = (char *)data + view_offs;
    }

    size_t obj_alloc_size = 0;

    if (view_src == NULL && !ctx->no_alloc) {
        if (ctx->scratch.data != NULL) {
            // allocate tensor data in the scratch buffer
            if (ctx->scratch.offs + data_size > ctx->scratch.size) {
                GGML_PRINT("%s: not enough space in the scratch memory pool (needed %zu, available %zu)\n", __func__,
                           ctx->scratch.offs + data_size, ctx->scratch.size);
                assert(false);
                return NULL;
            }

            data = (char * const)ctx->scratch.data + ctx->scratch.offs;

            ctx->scratch.offs += data_size;
        } else {
            // allocate tensor data in the context's memory pool
            obj_alloc_size = data_size;
        }
    }

    struct ggml_object * const obj_new = ggml_new_object(ctx, GGML_OBJECT_TENSOR, GGML_TENSOR_SIZE + obj_alloc_size);

    // TODO: for recoverable errors, we would need to free the data allocated from the scratch buffer here

    struct ggml_tensor * const result = (struct ggml_tensor *)((char *)ctx->mem_buffer + obj_new->offs);

    *result = (struct ggml_tensor){
        /*.type         =*/type,
        /*.backend      =*/GGML_BACKEND_CPU,
        /*.n_dims       =*/n_dims,
        /*.ne           =*/{1, 1, 1, 1},
        /*.nb           =*/{0, 0, 0, 0},
        /*.op           =*/GGML_OP_NONE,
        /*.op_params    =*/{0},
        /*.is_param     =*/false,
        /*.grad         =*/NULL,
        /*.src          =*/{NULL},
        /*.perf_runs    =*/0,
        /*.perf_cycles  =*/0,
        /*.perf_time_us =*/0,
        /*.view_src     =*/view_src,
        /*.view_offs    =*/view_offs,
        /*.data         =*/obj_alloc_size > 0 ? (void *)(result + 1) : data,
        /*.name         =*/{0},
        /*.extra        =*/NULL,
        /*.padding      =*/{0},
    };

    // TODO: this should not be needed as long as we don't rely on aligned SIMD loads
    // ggml_assert_aligned(result->data);

    for (int i = 0; i < n_dims; i++) {
        result->ne[i] = ne[i];
    }

    result->nb[0] = ggml_type_size(type);
    result->nb[1] = result->nb[0] * (result->ne[0] / ggml_blck_size(type));
    for (int i = 2; i < GGML_MAX_DIMS; i++) {
        result->nb[i] = result->nb[i - 1] * result->ne[i - 1];
    }

    ctx->n_objects++;

    return result;
}

struct ggml_tensor * ggml_new_tensor(struct ggml_context * ctx, enum ggml_type type, int n_dims, const int64_t * ne) {
    return ggml_new_tensor_impl(ctx, type, n_dims, ne, NULL, 0);
}

struct ggml_tensor * ggml_new_tensor_1d(struct ggml_context * ctx, enum ggml_type type, int64_t ne0) {
    return ggml_new_tensor(ctx, type, 1, &ne0);
}

struct ggml_tensor * ggml_new_tensor_2d(struct ggml_context * ctx, enum ggml_type type, int64_t ne0, int64_t ne1) {
    const int64_t ne[2] = {ne0, ne1};
    return ggml_new_tensor(ctx, type, 2, ne);
}

struct ggml_tensor * ggml_new_tensor_3d(struct ggml_context * ctx, enum ggml_type type, int64_t ne0, int64_t ne1, int64_t ne2) {
    const int64_t ne[3] = {ne0, ne1, ne2};
    return ggml_new_tensor(ctx, type, 3, ne);
}

struct ggml_tensor * ggml_new_tensor_4d(struct ggml_context * ctx, enum ggml_type type, int64_t ne0, int64_t ne1, int64_t ne2,
                                        int64_t ne3) {
    const int64_t ne[4] = {ne0, ne1, ne2, ne3};
    return ggml_new_tensor(ctx, type, 4, ne);
}

struct ggml_tensor * ggml_new_i32(struct ggml_context * ctx, int32_t value) {
    ggml_scratch_save(ctx);

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

    ggml_scratch_load(ctx);

    ggml_set_i32(result, value);

    return result;
}

struct ggml_tensor * ggml_new_f32(struct ggml_context * ctx, float value) {
    ggml_scratch_save(ctx);

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

    ggml_scratch_load(ctx);

    ggml_set_f32(result, value);

    return result;
}

struct ggml_tensor * ggml_dup_tensor(struct ggml_context * ctx, const struct ggml_tensor * src) {
    return ggml_new_tensor(ctx, src->type, src->n_dims, src->ne);
}

static void ggml_set_op_params(struct ggml_tensor * tensor, const void * params, size_t params_size) {
    GGML_ASSERT(tensor != NULL); // silence -Warray-bounds warnings
    assert(params_size <= GGML_MAX_OP_PARAMS);
    memcpy(tensor->op_params, params, params_size);
}

static int32_t ggml_get_op_params_i32(const struct ggml_tensor * tensor, uint32_t i) {
    assert(i < GGML_MAX_OP_PARAMS / sizeof(int32_t));
    return ((const int32_t *)(tensor->op_params))[i];
}

static void ggml_set_op_params_i32(struct ggml_tensor * tensor, uint32_t i, int32_t value) {
    assert(i < GGML_MAX_OP_PARAMS / sizeof(int32_t));
    ((int32_t *)(tensor->op_params))[i] = value;
}

struct ggml_tensor * ggml_set_zero(struct ggml_tensor * tensor) {
    memset(tensor->data, 0, ggml_nbytes(tensor));
    return tensor;
}

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 + i * n1), 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 + i * n1), 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 + i * n1), 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 + i * n1), GGML_FP32_TO_FP16(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 + i * n1), value);
        }
    } break;
    default: {
        GGML_ASSERT(false);
    } break;
    }

    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 + i * n1), 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 + i * n1), 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 + i * n1), 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 + i * n1), GGML_FP32_TO_FP16(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 + i * n1), value);
        }
    } break;
    default: {
        GGML_ASSERT(false);
    } break;
    }

    return tensor;
}

int32_t ggml_get_i32_1d(const struct ggml_tensor * tensor, int i) {
    switch (tensor->type) {
    case GGML_TYPE_I8: {
        GGML_ASSERT(tensor->nb[0] == sizeof(int8_t));
        return ((int8_t *)(tensor->data))[i];
    } break;
    case GGML_TYPE_I16: {
        GGML_ASSERT(tensor->nb[0] == sizeof(int16_t));
        return ((int16_t *)(tensor->data))[i];
    } break;
    case GGML_TYPE_I32: {
        GGML_ASSERT(tensor->nb[0] == sizeof(int32_t));
        return ((int32_t *)(tensor->data))[i];
    } break;
    case GGML_TYPE_F16: {
        GGML_ASSERT(tensor->nb[0] == sizeof(ggml_fp16_t));
        return GGML_FP16_TO_FP32(((ggml_fp16_t *)(tensor->data))[i]);
    } break;
    case GGML_TYPE_F32: {
        GGML_ASSERT(tensor->nb[0] == sizeof(float));
        return ((float *)(tensor->data))[i];
    } break;
    default: {
        GGML_ASSERT(false);
    } break;
    }

    return 0.0f;
}

void ggml_set_i32_1d(const struct ggml_tensor * tensor, int i, int32_t value) {
    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_FP32_TO_FP16(value);
    } break;
    case GGML_TYPE_F32: {
        GGML_ASSERT(tensor->nb[0] == sizeof(float));
        ((float *)(tensor->data))[i] = value;
    } break;
    default: {
        GGML_ASSERT(false);
    } break;
    }
}

float ggml_get_f32_1d(const struct ggml_tensor * tensor, int i) {
    switch (tensor->type) {
    case GGML_TYPE_I8: {
        GGML_ASSERT(tensor->nb[0] == sizeof(int8_t));
        return ((int8_t *)(tensor->data))[i];
    } break;
    case GGML_TYPE_I16: {
        GGML_ASSERT(tensor->nb[0] == sizeof(int16_t));
        return ((int16_t *)(tensor->data))[i];
    } break;
    case GGML_TYPE_I32: {
        GGML_ASSERT(tensor->nb[0] == sizeof(int32_t));
        return ((int32_t *)(tensor->data))[i];
    } break;
    case GGML_TYPE_F16: {
        GGML_ASSERT(tensor->nb[0] == sizeof(ggml_fp16_t));
        return GGML_FP16_TO_FP32(((ggml_fp16_t *)(tensor->data))[i]);
    } break;
    case GGML_TYPE_F32: {
        GGML_ASSERT(tensor->nb[0] == sizeof(float));
        return ((float *)(tensor->data))[i];
    } break;
    default: {
        GGML_ASSERT(false);
    } break;
    }

    return 0.0f;
}

void ggml_set_f32_1d(const struct ggml_tensor * tensor, int i, float value) {
    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_FP32_TO_FP16(value);
    } break;
    case GGML_TYPE_F32: {
        GGML_ASSERT(tensor->nb[0] == sizeof(float));
        ((float *)(tensor->data))[i] = value;
    } break;
    default: {
        GGML_ASSERT(false);
    } break;
    }
}

void * ggml_get_data(const struct ggml_tensor * tensor) { return tensor->data; }

float * ggml_get_data_f32(const struct ggml_tensor * tensor) {
    assert(tensor->type == GGML_TYPE_F32);
    return (float *)(tensor->data);
}

enum ggml_unary_op ggml_get_unary_op(const struct ggml_tensor * tensor) {
    GGML_ASSERT(tensor->op == GGML_OP_UNARY);
    return (enum ggml_unary_op)ggml_get_op_params_i32(tensor, 0);
}

const char * ggml_get_name(const struct ggml_tensor * tensor) { return tensor->name; }

struct ggml_tensor * ggml_set_name(struct ggml_tensor * tensor, const char * name) {
    strncpy(tensor->name, name, sizeof(tensor->name));
    tensor->name[sizeof(tensor->name) - 1] = '\0';
    return tensor;
}

struct ggml_tensor * ggml_format_name(struct ggml_tensor * tensor, const char * fmt, ...) {
    va_list args;
    va_start(args, fmt);
    vsnprintf(tensor->name, sizeof(tensor->name), fmt, args);
    va_end(args);
    return tensor;
}

struct ggml_tensor * ggml_view_tensor(struct ggml_context * ctx, struct ggml_tensor * src) {
    struct ggml_tensor * result = ggml_new_tensor_impl(ctx, src->type, src->n_dims, src->ne, src, 0);
    ggml_format_name(result, "%s (view)", src->name);

    for (int i = 0; i < GGML_MAX_DIMS; i++) {
        result->nb[i] = src->nb[i];
    }

    return result;
}

struct ggml_tensor * ggml_get_tensor(struct ggml_context * ctx, const char * name) {
    struct ggml_object * obj = ctx->objects_begin;

    char * const mem_buffer = ctx->mem_buffer;

    while (obj != NULL) {
        if (obj->type == GGML_OBJECT_TENSOR) {
            struct ggml_tensor * cur = (struct ggml_tensor *)(mem_buffer + obj->offs);
            if (strcmp(cur->name, name) == 0) {
                return cur;
            }
        }

        obj = obj->next;
    }

    return NULL;
}

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

// ggml_dup

static struct ggml_tensor * ggml_dup_impl(struct ggml_context * ctx, struct ggml_tensor * a, bool inplace) {
    bool is_node = false;

    if (!inplace && (a->grad)) {
        is_node = true;
    }

    struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);

    result->op = GGML_OP_DUP;
    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
    result->src[0] = a;

    return result;
}

struct ggml_tensor * ggml_dup(struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_dup_impl(ctx, a, false); }

struct ggml_tensor * ggml_dup_inplace(struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_dup_impl(ctx, a, true); }

// ggml_add

static struct ggml_tensor * ggml_add_impl(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b,
                                          bool inplace) {
    // TODO: support less-strict constraint
    //       GGML_ASSERT(ggml_can_repeat(b, a));
    GGML_ASSERT(ggml_can_repeat_rows(b, a));

    bool is_node = false;

    if (!inplace && (a->grad || b->grad)) {
        // TODO: support backward pass for broadcasting
        GGML_ASSERT(ggml_are_same_shape(a, b));
        is_node = true;
    }

    struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);

    result->op = GGML_OP_ADD;
    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
    result->src[0] = a;
    result->src[1] = b;

    return result;
}

struct ggml_tensor * ggml_add(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) {
    return ggml_add_impl(ctx, a, b, false);
}

struct ggml_tensor * ggml_add_inplace(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) {
    return ggml_add_impl(ctx, a, b, true);
}

// ggml_add1

static struct ggml_tensor * ggml_add1_impl(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b,
                                           bool inplace) {
    GGML_ASSERT(ggml_is_scalar(b));
    GGML_ASSERT(ggml_is_padded_1d(a));

    bool is_node = false;

    if (a->grad || b->grad) {
        is_node = true;
    }

    struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);

    result->op = GGML_OP_ADD1;
    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
    result->src[0] = a;
    result->src[1] = b;

    return result;
}

struct ggml_tensor * ggml_add1(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) {
    return ggml_add1_impl(ctx, a, b, false);
}

struct ggml_tensor * ggml_add1_inplace(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) {
    return ggml_add1_impl(ctx, a, b, true);
}

// ggml_acc

static struct ggml_tensor * ggml_acc_impl(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, size_t nb1,
                                          size_t nb2, size_t nb3, size_t offset, bool inplace) {
    GGML_ASSERT(ggml_nelements(b) <= ggml_nelements(a));
    GGML_ASSERT(ggml_is_contiguous(a));
    GGML_ASSERT(a->type == GGML_TYPE_F32);
    GGML_ASSERT(b->type == GGML_TYPE_F32);

    bool is_node = false;

    if (!inplace && (a->grad || b->grad)) {
        is_node = true;
    }

    struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);

    int32_t params[] = {nb1, nb2, nb3, offset, inplace ? 1 : 0};
    ggml_set_op_params(result, params, sizeof(params));

    result->op = GGML_OP_ACC;
    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
    result->src[0] = a;
    result->src[1] = b;

    return result;
}

struct ggml_tensor * ggml_acc(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, size_t nb1, size_t nb2,
                              size_t nb3, size_t offset) {
    return ggml_acc_impl(ctx, a, b, nb1, nb2, nb3, offset, false);
}

struct ggml_tensor * ggml_acc_inplace(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, size_t nb1,
                                      size_t nb2, size_t nb3, size_t offset) {
    return ggml_acc_impl(ctx, a, b, nb1, nb2, nb3, offset, true);
}

// ggml_sub

static struct ggml_tensor * ggml_sub_impl(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b,
                                          bool inplace) {
    GGML_ASSERT(ggml_are_same_shape(a, b));

    bool is_node = false;

    if (!inplace && (a->grad || b->grad)) {
        is_node = true;
    }

    struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);

    result->op = GGML_OP_SUB;
    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
    result->src[0] = a;
    result->src[1] = b;

    return result;
}

struct ggml_tensor * ggml_sub(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) {
    return ggml_sub_impl(ctx, a, b, false);
}

struct ggml_tensor * ggml_sub_inplace(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) {
    return ggml_sub_impl(ctx, a, b, true);
}

// ggml_mul

static struct ggml_tensor * ggml_mul_impl(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b,
                                          bool inplace) {
    // TODO: support less-strict constraint
    //       GGML_ASSERT(ggml_can_repeat(b, a));
    GGML_ASSERT(ggml_can_repeat_rows(b, a));

    bool is_node = false;

    if (!inplace && (a->grad || b->grad)) {
        // TODO: support backward pass for broadcasting
        GGML_ASSERT(ggml_are_same_shape(a, b));
        is_node = true;
    }

    if (inplace) {
        GGML_ASSERT(!is_node);
    }

    struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);

    result->op = GGML_OP_MUL;
    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
    result->src[0] = a;
    result->src[1] = b;

    return result;
}

struct ggml_tensor * ggml_mul(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) {
    return ggml_mul_impl(ctx, a, b, false);
}

struct ggml_tensor * ggml_mul_inplace(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) {
    return ggml_mul_impl(ctx, a, b, true);
}

// ggml_div

static struct ggml_tensor * ggml_div_impl(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b,
                                          bool inplace) {
    GGML_ASSERT(ggml_are_same_shape(a, b));

    bool is_node = false;

    if (!inplace && (a->grad || b->grad)) {
        is_node = true;
    }

    if (inplace) {
        GGML_ASSERT(!is_node);
    }

    struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);

    result->op = GGML_OP_DIV;
    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
    result->src[0] = a;
    result->src[1] = b;

    return result;
}

struct ggml_tensor * ggml_div(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) {
    return ggml_div_impl(ctx, a, b, false);
}

struct ggml_tensor * ggml_div_inplace(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) {
    return ggml_div_impl(ctx, a, b, true);
}

// ggml_sqr

static struct ggml_tensor * ggml_sqr_impl(struct ggml_context * ctx, struct ggml_tensor * a, bool inplace) {
    bool is_node = false;

    if (!inplace && (a->grad)) {
        is_node = true;
    }

    struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);

    result->op = GGML_OP_SQR;
    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
    result->src[0] = a;

    return result;
}

struct ggml_tensor * ggml_sqr(struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_sqr_impl(ctx, a, false); }

struct ggml_tensor * ggml_sqr_inplace(struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_sqr_impl(ctx, a, true); }

// ggml_sqrt

static struct ggml_tensor * ggml_sqrt_impl(struct ggml_context * ctx, struct ggml_tensor * a, bool inplace) {
    bool is_node = false;

    if (!inplace && (a->grad)) {
        is_node = true;
    }

    struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);

    result->op = GGML_OP_SQRT;
    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
    result->src[0] = a;

    return result;
}

struct ggml_tensor * ggml_sqrt(struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_sqrt_impl(ctx, a, false); }

struct ggml_tensor * ggml_sqrt_inplace(struct ggml_context * ctx, struct ggml_tensor * a) {
    return ggml_sqrt_impl(ctx, a, true);
}

// ggml_log

static struct ggml_tensor * ggml_log_impl(struct ggml_context * ctx, struct ggml_tensor * a, bool inplace) {
    bool is_node = false;

    if (!inplace && (a->grad)) {
        is_node = true;
    }

    struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);

    result->op = GGML_OP_LOG;
    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
    result->src[0] = a;

    return result;
}

struct ggml_tensor * ggml_log(struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_log_impl(ctx, a, false); }

struct ggml_tensor * ggml_log_inplace(struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_log_impl(ctx, a, true); }

// ggml_sum

struct ggml_tensor * ggml_sum(struct ggml_context * ctx, struct ggml_tensor * a) {
    bool is_node = false;

    if (a->grad) {
        is_node = true;
    }

    struct ggml_tensor * result = ggml_new_tensor_1d(ctx, a->type, 1);

    result->op = GGML_OP_SUM;
    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
    result->src[0] = a;

    return result;
}

// ggml_sum_rows

struct ggml_tensor * ggml_sum_rows(struct ggml_context * ctx, struct ggml_tensor * a) {
    bool is_node = false;

    if (a->grad) {
        is_node = true;
    }

    int64_t ne[4] = {1, 1, 1, 1};
    for (int i = 1; i < a->n_dims; ++i) {
        ne[i] = a->ne[i];
    }

    struct ggml_tensor * result = ggml_new_tensor(ctx, a->type, a->n_dims, ne);

    result->op = GGML_OP_SUM_ROWS;
    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
    result->src[0] = a;

    return result;
}

// ggml_mean

struct ggml_tensor * ggml_mean(struct ggml_context * ctx, struct ggml_tensor * a) {
    bool is_node = false;

    if (a->grad) {
        GGML_ASSERT(false); // TODO: implement
        is_node = true;
    }

    int64_t ne[GGML_MAX_DIMS] = {1, a->ne[1], a->ne[2], a->ne[3]};
    struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, a->n_dims, ne);

    result->op = GGML_OP_MEAN;
    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
    result->src[0] = a;

    return result;
}

// ggml_argmax

struct ggml_tensor * ggml_argmax(struct ggml_context * ctx, struct ggml_tensor * a) {
    GGML_ASSERT(ggml_is_matrix(a));
    bool is_node = false;

    if (a->grad) {
        GGML_ASSERT(false);
        is_node = true;
    }

    int64_t ne[GGML_MAX_DIMS] = {a->ne[1], 1, 1, 1};
    struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_I32, a->n_dims, ne);

    result->op = GGML_OP_ARGMAX;
    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
    result->src[0] = a;

    return result;
}

// ggml_repeat

struct ggml_tensor * ggml_repeat(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) {
    GGML_ASSERT(ggml_can_repeat(a, b));

    bool is_node = false;

    if (a->grad) {
        is_node = true;
    }

    struct ggml_tensor * result = ggml_new_tensor(ctx, a->type, b->n_dims, b->ne);

    result->op = GGML_OP_REPEAT;
    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
    result->src[0] = a;
    result->src[1] = b;

    return result;
}

// ggml_repeat_back

struct ggml_tensor * ggml_repeat_back(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) {
    GGML_ASSERT(ggml_can_repeat(b, a));

    bool is_node = false;

    if (a->grad) {
        is_node = true;
    }

    if (ggml_are_same_shape(a, b) && !is_node) {
        return a;
    }

    struct ggml_tensor * result = ggml_new_tensor(ctx, a->type, b->n_dims, b->ne);

    result->op = GGML_OP_REPEAT_BACK;
    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
    result->src[0] = a;
    result->src[1] = b;

    return result;
}

// ggml_concat

struct ggml_tensor * ggml_concat(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) {
    GGML_ASSERT(a->ne[0] == b->ne[0] && a->ne[1] == b->ne[1] && a->ne[3] == b->ne[3]);

    bool is_node = false;

    if (a->grad || b->grad) {
        is_node = true;
    }

    struct ggml_tensor * result = ggml_new_tensor_4d(ctx, a->type, a->ne[0], a->ne[1], a->ne[2] + b->ne[2], a->ne[3]);

    result->op = GGML_OP_CONCAT;
    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
    result->src[0] = a;
    result->src[1] = b;

    return result;
}

// ggml_abs

struct ggml_tensor * ggml_abs(struct ggml_context * ctx, struct ggml_tensor * a) {
    return ggml_unary(ctx, a, GGML_UNARY_OP_ABS);
}

struct ggml_tensor * ggml_abs_inplace(struct ggml_context * ctx, struct ggml_tensor * a) {
    return ggml_unary_inplace(ctx, a, GGML_UNARY_OP_ABS);
}

// ggml_sgn

struct ggml_tensor * ggml_sgn(struct ggml_context * ctx, struct ggml_tensor * a) {
    return ggml_unary(ctx, a, GGML_UNARY_OP_SGN);
}

struct ggml_tensor * ggml_sgn_inplace(struct ggml_context * ctx, struct ggml_tensor * a) {
    return ggml_unary_inplace(ctx, a, GGML_UNARY_OP_SGN);
}

// ggml_neg

struct ggml_tensor * ggml_neg(struct ggml_context * ctx, struct ggml_tensor * a) {
    return ggml_unary(ctx, a, GGML_UNARY_OP_NEG);
}

struct ggml_tensor * ggml_neg_inplace(struct ggml_context * ctx, struct ggml_tensor * a) {
    return ggml_unary_inplace(ctx, a, GGML_UNARY_OP_NEG);
}

// ggml_step

struct ggml_tensor * ggml_step(struct ggml_context * ctx, struct ggml_tensor * a) {
    return ggml_unary(ctx, a, GGML_UNARY_OP_STEP);
}

struct ggml_tensor * ggml_step_inplace(struct ggml_context * ctx, struct ggml_tensor * a) {
    return ggml_unary_inplace(ctx, a, GGML_UNARY_OP_STEP);
}

// ggml_tanh

struct ggml_tensor * ggml_tanh(struct ggml_context * ctx, struct ggml_tensor * a) {
    return ggml_unary(ctx, a, GGML_UNARY_OP_TANH);
}

struct ggml_tensor * ggml_tanh_inplace(struct ggml_context * ctx, struct ggml_tensor * a) {
    return ggml_unary_inplace(ctx, a, GGML_UNARY_OP_TANH);
}

// ggml_elu

struct ggml_tensor * ggml_elu(struct ggml_context * ctx, struct ggml_tensor * a) {
    return ggml_unary(ctx, a, GGML_UNARY_OP_ELU);
}

struct ggml_tensor * ggml_elu_inplace(struct ggml_context * ctx, struct ggml_tensor * a) {
    return ggml_unary_inplace(ctx, a, GGML_UNARY_OP_ELU);
}

// ggml_relu

struct ggml_tensor * ggml_relu(struct ggml_context * ctx, struct ggml_tensor * a) {
    return ggml_unary(ctx, a, GGML_UNARY_OP_RELU);
}

struct ggml_tensor * ggml_relu_inplace(struct ggml_context * ctx, struct ggml_tensor * a) {
    return ggml_unary_inplace(ctx, a, GGML_UNARY_OP_RELU);
}

// ggml_gelu

struct ggml_tensor * ggml_gelu(struct ggml_context * ctx, struct ggml_tensor * a) {
    return ggml_unary(ctx, a, GGML_UNARY_OP_GELU);
}

struct ggml_tensor * ggml_gelu_inplace(struct ggml_context * ctx, struct ggml_tensor * a) {
    return ggml_unary_inplace(ctx, a, GGML_UNARY_OP_GELU);
}

// ggml_gelu_quick

struct ggml_tensor * ggml_gelu_quick(struct ggml_context * ctx, struct ggml_tensor * a) {
    return ggml_unary(ctx, a, GGML_UNARY_OP_GELU_QUICK);
}

struct ggml_tensor * ggml_gelu_quick_inplace(struct ggml_context * ctx, struct ggml_tensor * a) {
    return ggml_unary_inplace(ctx, a, GGML_UNARY_OP_GELU_QUICK);
}

// ggml_silu

struct ggml_tensor * ggml_silu(struct ggml_context * ctx, struct ggml_tensor * a) {
    return ggml_unary(ctx, a, GGML_UNARY_OP_SILU);
}

struct ggml_tensor * ggml_silu_inplace(struct ggml_context * ctx, struct ggml_tensor * a) {
    return ggml_unary_inplace(ctx, a, GGML_UNARY_OP_SILU);
}

// ggml_silu_back

struct ggml_tensor * ggml_silu_back(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) {
    bool is_node = false;

    if (a->grad || b->grad) {
        // TODO: implement backward
        is_node = true;
    }

    struct ggml_tensor * result = ggml_dup_tensor(ctx, a);

    result->op = GGML_OP_SILU_BACK;
    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
    result->src[0] = a;
    result->src[1] = b;

    return result;
}

// ggml_norm

static struct ggml_tensor * ggml_norm_impl(struct ggml_context * ctx, struct ggml_tensor * a, float eps, bool inplace) {
    bool is_node = false;

    if (!inplace && (a->grad)) {
        GGML_ASSERT(false); // TODO: implement backward
        is_node = true;
    }

    struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);

    ggml_set_op_params(result, &eps, sizeof(eps));

    result->op = GGML_OP_NORM;
    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
    result->src[0] = a;

    return result;
}

struct ggml_tensor * ggml_norm(struct ggml_context * ctx, struct ggml_tensor * a, float eps) {
    return ggml_norm_impl(ctx, a, eps, false);
}

struct ggml_tensor * ggml_norm_inplace(struct ggml_context * ctx, struct ggml_tensor * a, float eps) {
    return ggml_norm_impl(ctx, a, eps, true);
}

// ggml_rms_norm

static struct ggml_tensor * ggml_rms_norm_impl(struct ggml_context * ctx, struct ggml_tensor * a, float eps, bool inplace) {
    bool is_node = false;

    if (!inplace && (a->grad)) {
        is_node = true;
    }

    struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);

    ggml_set_op_params(result, &eps, sizeof(eps));

    result->op = GGML_OP_RMS_NORM;
    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
    result->src[0] = a;

    return result;
}

struct ggml_tensor * ggml_rms_norm(struct ggml_context * ctx, struct ggml_tensor * a, float eps) {
    return ggml_rms_norm_impl(ctx, a, eps, false);
}

struct ggml_tensor * ggml_rms_norm_inplace(struct ggml_context * ctx, struct ggml_tensor * a, float eps) {
    return ggml_rms_norm_impl(ctx, a, eps, true);
}

// ggml_rms_norm_back

struct ggml_tensor * ggml_rms_norm_back(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, float eps) {
    bool is_node = false;

    if (a->grad) {
        // TODO: implement backward
        is_node = true;
    }

    struct ggml_tensor * result = ggml_dup_tensor(ctx, a);

    ggml_set_op_params(result, &eps, sizeof(eps));

    result->op = GGML_OP_RMS_NORM_BACK;
    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
    result->src[0] = a;
    result->src[1] = b;

    return result;
}

// ggml_group_norm

static struct ggml_tensor * ggml_group_norm_impl(struct ggml_context * ctx, struct ggml_tensor * a, int n_groups,
                                                 bool inplace) {

    bool is_node = false;
    if (!inplace && (a->grad)) {
        GGML_ASSERT(false); // TODO: implement backward
        is_node = true;
    }

    struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);

    result->op = GGML_OP_GROUP_NORM;
    result->op_params[0] = n_groups;
    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
    result->src[0] = a;
    result->src[1] = NULL; // TODO: maybe store epsilon here?

    return result;
}

struct ggml_tensor * ggml_group_norm(struct ggml_context * ctx, struct ggml_tensor * a, int n_groups) {
    return ggml_group_norm_impl(ctx, a, n_groups, false);
}

struct ggml_tensor * ggml_group_norm_inplace(struct ggml_context * ctx, struct ggml_tensor * a, int n_groups) {
    return ggml_group_norm_impl(ctx, a, n_groups, true);
}

// ggml_mul_mat

struct ggml_tensor * ggml_mul_mat(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) {
    GGML_ASSERT(ggml_can_mul_mat(a, b));
    GGML_ASSERT(!ggml_is_transposed(a));

    bool is_node = false;

    if (a->grad || b->grad) {
        is_node = true;
    }

    const int64_t ne[4] = {a->ne[1], b->ne[1], b->ne[2], b->ne[3]};
    struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, MAX(a->n_dims, b->n_dims), ne);

    result->op = GGML_OP_MUL_MAT;
    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
    result->src[0] = a;
    result->src[1] = b;

    return result;
}

// ggml_out_prod

struct ggml_tensor * ggml_out_prod(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) {
    GGML_ASSERT(ggml_can_out_prod(a, b));
    GGML_ASSERT(!ggml_is_transposed(a));

    bool is_node = false;

    if (a->grad || b->grad) {
        is_node = true;
    }

    const int64_t ne[4] = {a->ne[0], b->ne[0], a->ne[2], b->ne[3]};
    struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, MIN(a->n_dims, b->n_dims), ne);

    result->op = GGML_OP_OUT_PROD;
    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
    result->src[0] = a;
    result->src[1] = b;

    return result;
}

// ggml_scale

static struct ggml_tensor * ggml_scale_impl(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b,
                                            bool inplace) {
    GGML_ASSERT(ggml_is_scalar(b));
    GGML_ASSERT(ggml_is_padded_1d(a));

    bool is_node = false;

    if (a->grad || b->grad) {
        is_node = true;
    }

    struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);

    result->op = GGML_OP_SCALE;
    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
    result->src[0] = a;
    result->src[1] = b;

    return result;
}

struct ggml_tensor * ggml_scale(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) {
    return ggml_scale_impl(ctx, a, b, false);
}

struct ggml_tensor * ggml_scale_inplace(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) {
    return ggml_scale_impl(ctx, a, b, true);
}

// ggml_set

static struct ggml_tensor * ggml_set_impl(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, size_t nb1,
                                          size_t nb2, size_t nb3, size_t offset, bool inplace) {
    GGML_ASSERT(ggml_nelements(a) >= ggml_nelements(b));

    bool is_node = false;

    if (a->grad || b->grad) {
        is_node = true;
    }

    // make a view of the destination
    struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);

    int32_t params[] = {nb1, nb2, nb3, offset, inplace ? 1 : 0};
    ggml_set_op_params(result, params, sizeof(params));

    result->op = GGML_OP_SET;
    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
    result->src[0] = a;
    result->src[1] = b;

    return result;
}

struct ggml_tensor * ggml_set(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, size_t nb1, size_t nb2,
                              size_t nb3, size_t offset) {
    return ggml_set_impl(ctx, a, b, nb1, nb2, nb3, offset, false);
}

struct ggml_tensor * ggml_set_inplace(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, size_t nb1,
                                      size_t nb2, size_t nb3, size_t offset) {
    return ggml_set_impl(ctx, a, b, nb1, nb2, nb3, offset, true);
}

struct ggml_tensor * ggml_set_1d(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, size_t offset) {
    return ggml_set_impl(ctx, a, b, a->nb[1], a->nb[2], a->nb[3], offset, false);
}

struct ggml_tensor * ggml_set_1d_inplace(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b,
                                         size_t offset) {
    return ggml_set_impl(ctx, a, b, a->nb[1], a->nb[2], a->nb[3], offset, true);
}

struct ggml_tensor * ggml_set_2d(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, size_t nb1,
                                 size_t offset) {
    return ggml_set_impl(ctx, a, b, nb1, a->nb[2], a->nb[3], offset, false);
}

struct ggml_tensor * ggml_set_2d_inplace(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, size_t nb1,
                                         size_t offset) {
    return ggml_set_impl(ctx, a, b, nb1, a->nb[2], a->nb[3], offset, false);
}

// ggml_cpy

static struct ggml_tensor * ggml_cpy_impl(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b,
                                          bool inplace) {
    GGML_ASSERT(ggml_nelements(a) == ggml_nelements(b));

    bool is_node = false;

    if (!inplace && (a->grad || b->grad)) {
        is_node = true;
    }

    // make a view of the destination
    struct ggml_tensor * result = ggml_view_tensor(ctx, b);
    if (strlen(b->name) > 0) {
        ggml_format_name(result, "%s (copy of %s)", b->name, a->name);
    } else {
        ggml_format_name(result, "%s (copy)", a->name);
    }

    result->op = GGML_OP_CPY;
    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
    result->src[0] = a;
    result->src[1] = b;

    return result;
}

struct ggml_tensor * ggml_cpy(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) {
    return ggml_cpy_impl(ctx, a, b, false);
}

struct ggml_tensor * ggml_cpy_inplace(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) {
    return ggml_cpy_impl(ctx, a, b, true);
}

// ggml_cont

static struct ggml_tensor * ggml_cont_impl(struct ggml_context * ctx, struct ggml_tensor * a, bool inplace) {
    bool is_node = false;

    if (!inplace && a->grad) {
        is_node = true;
    }

    struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
    ggml_format_name(result, "%s (cont)", a->name);

    result->op = GGML_OP_CONT;
    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
    result->src[0] = a;

    return result;
}

struct ggml_tensor * ggml_cont(struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_cont_impl(ctx, a, false); }

struct ggml_tensor * ggml_cont_inplace(struct ggml_context * ctx, struct ggml_tensor * a) {
    return ggml_cont_impl(ctx, a, true);
}

// ggml_reshape

struct ggml_tensor * ggml_reshape(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) {
    GGML_ASSERT(ggml_is_contiguous(a));
    GGML_ASSERT(ggml_is_contiguous(b));
    GGML_ASSERT(ggml_nelements(a) == ggml_nelements(b));

    bool is_node = false;

    if (a->grad) {
        is_node = true;
    }

    if (b->grad) {
        // gradient propagation is not supported
        // GGML_ASSERT(false);
    }

    struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, b->n_dims, b->ne, a, 0);
    ggml_format_name(result, "%s (reshaped)", a->name);

    result->op = GGML_OP_RESHAPE;
    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
    result->src[0] = a;

    return result;
}

struct ggml_tensor * ggml_reshape_1d(struct ggml_context * ctx, struct ggml_tensor * a, int64_t ne0) {
    GGML_ASSERT(ggml_is_contiguous(a));
    GGML_ASSERT(ggml_nelements(a) == ne0);

    bool is_node = false;

    if (a->grad) {
        is_node = true;
    }

    const int64_t ne[1] = {ne0};
    struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, 1, ne, a, 0);
    ggml_format_name(result, "%s (reshaped)", a->name);

    result->op = GGML_OP_RESHAPE;
    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
    result->src[0] = a;

    return result;
}

struct ggml_tensor * ggml_reshape_2d(struct ggml_context * ctx, struct ggml_tensor * a, int64_t ne0, int64_t ne1) {
    GGML_ASSERT(ggml_is_contiguous(a));
    GGML_ASSERT(ggml_nelements(a) == ne0 * ne1);

    bool is_node = false;

    if (a->grad) {
        is_node = true;
    }

    const int64_t ne[2] = {ne0, ne1};
    struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, 2, ne, a, 0);
    ggml_format_name(result, "%s (reshaped)", a->name);

    result->op = GGML_OP_RESHAPE;
    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
    result->src[0] = a;

    return result;
}

struct ggml_tensor * ggml_reshape_3d(struct ggml_context * ctx, struct ggml_tensor * a, int64_t ne0, int64_t ne1, int64_t ne2) {
    GGML_ASSERT(ggml_is_contiguous(a));
    GGML_ASSERT(ggml_nelements(a) == ne0 * ne1 * ne2);

    bool is_node = false;

    if (a->grad) {
        is_node = true;
    }

    const int64_t ne[3] = {ne0, ne1, ne2};
    struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, 3, ne, a, 0);
    ggml_format_name(result, "%s (reshaped)", a->name);

    result->op = GGML_OP_RESHAPE;
    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
    result->src[0] = a;

    return result;
}

struct ggml_tensor * ggml_reshape_4d(struct ggml_context * ctx, struct ggml_tensor * a, int64_t ne0, int64_t ne1, int64_t ne2,
                                     int64_t ne3) {
    GGML_ASSERT(ggml_is_contiguous(a));
    GGML_ASSERT(ggml_nelements(a) == ne0 * ne1 * ne2 * ne3);

    bool is_node = false;

    if (a->grad) {
        is_node = true;
    }

    const int64_t ne[4] = {ne0, ne1, ne2, ne3};
    struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, 4, ne, a, 0);
    ggml_format_name(result, "%s (reshaped)", a->name);

    result->op = GGML_OP_RESHAPE;
    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
    result->src[0] = a;

    return result;
}

static struct ggml_tensor * ggml_view_impl(struct ggml_context * ctx, struct ggml_tensor * a, int n_dims, const int64_t * ne,
                                           size_t offset) {

    bool is_node = false;

    if (a->grad) {
        is_node = true;
    }

    struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, n_dims, ne, a, offset);
    ggml_format_name(result, "%s (view)", a->name);

    ggml_set_op_params(result, &offset, sizeof(offset));

    result->op = GGML_OP_VIEW;
    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
    result->src[0] = a;

    return result;
}

// ggml_view_1d

struct ggml_tensor * ggml_view_1d(struct ggml_context * ctx, struct ggml_tensor * a, int64_t ne0, size_t offset) {

    struct ggml_tensor * result = ggml_view_impl(ctx, a, 1, &ne0, offset);

    return result;
}

// ggml_view_2d

struct ggml_tensor * ggml_view_2d(struct ggml_context * ctx, struct ggml_tensor * a, int64_t ne0, int64_t ne1, size_t nb1,
                                  size_t offset) {

    const int64_t ne[2] = {ne0, ne1};

    struct ggml_tensor * result = ggml_view_impl(ctx, a, 2, ne, offset);

    result->nb[1] = nb1;
    result->nb[2] = result->nb[1] * ne1;
    result->nb[3] = result->nb[2];

    return result;
}

// ggml_view_3d

struct ggml_tensor * ggml_view_3d(struct ggml_context * ctx, struct ggml_tensor * a, int64_t ne0, int64_t ne1, int64_t ne2,
                                  size_t nb1, size_t nb2, size_t offset) {

    const int64_t ne[3] = {ne0, ne1, ne2};

    struct ggml_tensor * result = ggml_view_impl(ctx, a, 3, ne, offset);

    result->nb[1] = nb1;
    result->nb[2] = nb2;
    result->nb[3] = result->nb[2] * ne2;

    return result;
}

// ggml_view_4d

struct ggml_tensor * ggml_view_4d(struct ggml_context * ctx, struct ggml_tensor * a, int64_t ne0, int64_t ne1, int64_t ne2,
                                  int64_t ne3, size_t nb1, size_t nb2, size_t nb3, size_t offset) {

    const int64_t ne[4] = {ne0, ne1, ne2, ne3};

    struct ggml_tensor * result = ggml_view_impl(ctx, a, 4, ne, offset);

    result->nb[1] = nb1;
    result->nb[2] = nb2;
    result->nb[3] = nb3;

    return result;
}

// ggml_permute

struct ggml_tensor * ggml_permute(struct ggml_context * ctx, struct ggml_tensor * a, int axis0, int axis1, int axis2,
                                  int axis3) {
    GGML_ASSERT(axis0 >= 0 && axis0 < GGML_MAX_DIMS);
    GGML_ASSERT(axis1 >= 0 && axis1 < GGML_MAX_DIMS);
    GGML_ASSERT(axis2 >= 0 && axis2 < GGML_MAX_DIMS);
    GGML_ASSERT(axis3 >= 0 && axis3 < GGML_MAX_DIMS);

    GGML_ASSERT(axis0 != axis1);
    GGML_ASSERT(axis0 != axis2);
    GGML_ASSERT(axis0 != axis3);
    GGML_ASSERT(axis1 != axis2);
    GGML_ASSERT(axis1 != axis3);
    GGML_ASSERT(axis2 != axis3);

    bool is_node = false;

    if (a->grad) {
        is_node = true;
    }

    struct ggml_tensor * result = ggml_view_tensor(ctx, a);
    ggml_format_name(result, "%s (permuted)", a->name);

    int ne[GGML_MAX_DIMS];
    int nb[GGML_MAX_DIMS];

    ne[axis0] = a->ne[0];
    ne[axis1] = a->ne[1];
    ne[axis2] = a->ne[2];
    ne[axis3] = a->ne[3];

    nb[axis0] = a->nb[0];
    nb[axis1] = a->nb[1];
    nb[axis2] = a->nb[2];
    nb[axis3] = a->nb[3];

    result->ne[0] = ne[0];
    result->ne[1] = ne[1];
    result->ne[2] = ne[2];
    result->ne[3] = ne[3];

    result->nb[0] = nb[0];
    result->nb[1] = nb[1];
    result->nb[2] = nb[2];
    result->nb[3] = nb[3];

    result->op = GGML_OP_PERMUTE;
    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
    result->src[0] = a;

    int32_t params[] = {axis0, axis1, axis2, axis3};
    ggml_set_op_params(result, params, sizeof(params));

    return result;
}

// ggml_transpose

struct ggml_tensor * ggml_transpose(struct ggml_context * ctx, struct ggml_tensor * a) {
    bool is_node = false;

    if (a->grad) {
        is_node = true;
    }

    struct ggml_tensor * result = ggml_view_tensor(ctx, a);
    ggml_format_name(result, "%s (transposed)", a->name);

    result->ne[0] = a->ne[1];
    result->ne[1] = a->ne[0];

    result->nb[0] = a->nb[1];
    result->nb[1] = a->nb[0];

    result->op = GGML_OP_TRANSPOSE;
    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
    result->src[0] = a;

    return result;
}

// ggml_get_rows

struct ggml_tensor * ggml_get_rows(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) {
    GGML_ASSERT(ggml_is_matrix(a) && ggml_is_vector(b) && b->type == GGML_TYPE_I32);

    bool is_node = false;

    if (a->grad || b->grad) {
        is_node = true;
    }

    // TODO: implement non F32 return
    // struct ggml_tensor * result = ggml_new_tensor_2d(ctx, a->type, a->ne[0], b->ne[0]);
    struct ggml_tensor * result = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, a->ne[0], b->ne[0]);

    result->op = GGML_OP_GET_ROWS;
    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
    result->src[0] = a;
    result->src[1] = b;

    return result;
}

// ggml_get_rows_back

struct ggml_tensor * ggml_get_rows_back(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b,
                                        struct ggml_tensor * c) {
    GGML_ASSERT(ggml_is_matrix(a) && ggml_is_vector(b) && b->type == GGML_TYPE_I32);
    GGML_ASSERT(ggml_is_matrix(c) && (a->ne[0] == c->ne[0]));

    bool is_node = false;

    if (a->grad || b->grad) {
        is_node = true;
    }

    // TODO: implement non F32 return
    // struct ggml_tensor * result = ggml_new_tensor_2d(ctx, a->type, a->ne[0], b->ne[0]);
    struct ggml_tensor * result = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, c->ne[0], c->ne[1]);

    result->op = GGML_OP_GET_ROWS_BACK;
    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
    result->src[0] = a;
    result->src[1] = b;
    result->src[2] = c;

    return result;
}

// ggml_diag

struct ggml_tensor * ggml_diag(struct ggml_context * ctx, struct ggml_tensor * a) {
    GGML_ASSERT(a->ne[1] == 1);
    bool is_node = false;

    if (a->grad) {
        is_node = true;
    }

    const int64_t ne[4] = {a->ne[0], a->ne[0], a->ne[2], a->ne[3]};
    struct ggml_tensor * result = ggml_new_tensor(ctx, a->type, MAX(a->n_dims, 2), ne);

    result->op = GGML_OP_DIAG;
    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
    result->src[0] = a;

    return result;
}

// ggml_diag_mask_inf

static struct ggml_tensor * ggml_diag_mask_inf_impl(struct ggml_context * ctx, struct ggml_tensor * a, int n_past,
                                                    bool inplace) {
    bool is_node = false;

    if (a->grad) {
        is_node = true;
    }

    struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);

    int32_t params[] = {n_past};
    ggml_set_op_params(result, params, sizeof(params));

    result->op = GGML_OP_DIAG_MASK_INF;
    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
    result->src[0] = a;

    return result;
}

struct ggml_tensor * ggml_diag_mask_inf(struct ggml_context * ctx, struct ggml_tensor * a, int n_past) {
    return ggml_diag_mask_inf_impl(ctx, a, n_past, false);
}

struct ggml_tensor * ggml_diag_mask_inf_inplace(struct ggml_context * ctx, struct ggml_tensor * a, int n_past) {
    return ggml_diag_mask_inf_impl(ctx, a, n_past, true);
}

// ggml_diag_mask_zero

static struct ggml_tensor * ggml_diag_mask_zero_impl(struct ggml_context * ctx, struct ggml_tensor * a, int n_past,
                                                     bool inplace) {
    bool is_node = false;

    if (a->grad) {
        is_node = true;
    }

    struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);

    int32_t params[] = {n_past};
    ggml_set_op_params(result, params, sizeof(params));

    result->op = GGML_OP_DIAG_MASK_ZERO;
    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
    result->src[0] = a;

    return result;
}

struct ggml_tensor * ggml_diag_mask_zero(struct ggml_context * ctx, struct ggml_tensor * a, int n_past) {
    return ggml_diag_mask_zero_impl(ctx, a, n_past, false);
}

struct ggml_tensor * ggml_diag_mask_zero_inplace(struct ggml_context * ctx, struct ggml_tensor * a, int n_past) {
    return ggml_diag_mask_zero_impl(ctx, a, n_past, true);
}

// ggml_soft_max

static struct ggml_tensor * ggml_soft_max_impl(struct ggml_context * ctx, struct ggml_tensor * a, bool inplace) {
    bool is_node = false;

    if (a->grad) {
        is_node = true;
    }

    struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);

    result->op = GGML_OP_SOFT_MAX;
    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
    result->src[0] = a;

    return result;
}

struct ggml_tensor * ggml_soft_max(struct ggml_context * ctx, struct ggml_tensor * a) {
    return ggml_soft_max_impl(ctx, a, false);
}

struct ggml_tensor * ggml_soft_max_inplace(struct ggml_context * ctx, struct ggml_tensor * a) {
    return ggml_soft_max_impl(ctx, a, true);
}

// ggml_soft_max_back

static struct ggml_tensor * ggml_soft_max_back_impl(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b,
                                                    bool inplace) {
    bool is_node = false;

    if (a->grad || b->grad) {
        is_node = true; // TODO : implement backward pass
    }

    struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);

    result->op = GGML_OP_SOFT_MAX_BACK;
    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
    result->src[0] = a;
    result->src[1] = b;

    return result;
}

struct ggml_tensor * ggml_soft_max_back(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) {
    return ggml_soft_max_back_impl(ctx, a, b, false);
}

struct ggml_tensor * ggml_soft_max_back_inplace(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) {
    return ggml_soft_max_back_impl(ctx, a, b, true);
}

// ggml_rope

static struct ggml_tensor * ggml_rope_impl(struct ggml_context * ctx, struct ggml_tensor * a, int n_past, int n_dims, int mode,
                                           int n_ctx, float freq_base, float freq_scale, float xpos_base, bool xpos_down,
                                           bool inplace) {
    GGML_ASSERT(n_past >= 0);
    bool is_node = false;

    if (a->grad) {
        is_node = true;
    }

    struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);

    int32_t params[8] = {n_past, n_dims, mode, n_ctx};
    memcpy(params + 4, &freq_base, sizeof(float));
    memcpy(params + 5, &freq_scale, sizeof(float));
    memcpy(params + 6, &xpos_base, sizeof(float));
    memcpy(params + 7, &xpos_down, sizeof(bool));
    ggml_set_op_params(result, params, sizeof(params));

    result->op = GGML_OP_ROPE;
    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
    result->src[0] = a;

    return result;
}

struct ggml_tensor * ggml_rope(struct ggml_context * ctx, struct ggml_tensor * a, int n_past, int n_dims, int mode, int n_ctx) {
    return ggml_rope_impl(ctx, a, n_past, n_dims, mode, n_ctx, 10000.0f, 1.0f, 0.0f, false, false);
}

struct ggml_tensor * ggml_rope_inplace(struct ggml_context * ctx, struct ggml_tensor * a, int n_past, int n_dims, int mode,
                                       int n_ctx) {
    return ggml_rope_impl(ctx, a, n_past, n_dims, mode, n_ctx, 10000.0f, 1.0f, 0.0f, false, true);
}

struct ggml_tensor * ggml_rope_custom(struct ggml_context * ctx, struct ggml_tensor * a, int n_past, int n_dims, int mode,
                                      int n_ctx, float freq_base, float freq_scale) {
    return ggml_rope_impl(ctx, a, n_past, n_dims, mode, n_ctx, freq_base, freq_scale, 0.0f, false, false);
}

struct ggml_tensor * ggml_rope_custom_inplace(struct ggml_context * ctx, struct ggml_tensor * a, int n_past, int n_dims,
                                              int mode, int n_ctx, float freq_base, float freq_scale) {
    return ggml_rope_impl(ctx, a, n_past, n_dims, mode, n_ctx, freq_base, freq_scale, 0.0f, false, true);
}

struct ggml_tensor * ggml_rope_xpos_inplace(struct ggml_context * ctx, struct ggml_tensor * a, int n_past, int n_dims,
                                            float base, bool down) {
    return ggml_rope_impl(ctx, a, n_past, n_dims, 0, 0, 10000.0f, 1.0f, base, down, true);
}

// ggml_rope_back

struct ggml_tensor * ggml_rope_back(struct ggml_context * ctx, struct ggml_tensor * a, int n_past, int n_dims, int mode,
                                    int n_ctx, float freq_base, float freq_scale, float xpos_base, bool xpos_down) {
    GGML_ASSERT(n_past >= 0);
    GGML_ASSERT((mode & 4) == 0 && "ggml_rope_back() for ChatGLM not implemented yet");

    bool is_node = false;

    if (a->grad) {
        is_node = false; // TODO: implement backward
    }

    struct ggml_tensor * result = ggml_dup_tensor(ctx, a);

    int32_t params[8] = {n_past, n_dims, mode, n_ctx};
    memcpy(params + 4, &freq_base, sizeof(float));
    memcpy(params + 5, &freq_scale, sizeof(float));
    memcpy(params + 6, &xpos_base, sizeof(float));
    memcpy(params + 7, &xpos_down, sizeof(bool));
    ggml_set_op_params(result, params, sizeof(params));

    result->op = GGML_OP_ROPE_BACK;
    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
    result->src[0] = a;

    return result;
}

// ggml_alibi

struct ggml_tensor * ggml_alibi(struct ggml_context * ctx, struct ggml_tensor * a, int n_past, int n_head, float bias_max) {
    GGML_ASSERT(n_past >= 0);
    bool is_node = false;

    if (a->grad) {
        GGML_ASSERT(false); // TODO: implement backward
        is_node = true;
    }

    // TODO: when implement backward, fix this:
    // struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
    struct ggml_tensor * result = ggml_view_tensor(ctx, a);

    int32_t op_params[3] = {n_past, n_head};
    memcpy(op_params + 2, &bias_max, sizeof(float));
    ggml_set_op_params(result, op_params, sizeof(op_params));

    result->op = GGML_OP_ALIBI;
    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
    result->src[0] = a;

    return result;
}

// ggml_clamp

struct ggml_tensor * ggml_clamp(struct ggml_context * ctx, struct ggml_tensor * a, float min, float max) {
    bool is_node = false;

    if (a->grad) {
        GGML_ASSERT(false); // TODO: implement backward
        is_node = true;
    }

    // TODO: when implement backward, fix this:
    struct ggml_tensor * result = ggml_view_tensor(ctx, a);

    float params[] = {min, max};
    ggml_set_op_params(result, params, sizeof(params));

    result->op = GGML_OP_CLAMP;
    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
    result->src[0] = a;

    return result;
}

// ggml_conv_1d

static int64_t ggml_calc_conv_output_size(int64_t ins, int64_t ks, int s, int p, int d) {
    return (ins + 2 * p - d * (ks - 1) - 1) / s + 1;
}

GGML_API struct ggml_tensor * ggml_conv_1d(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, int s0,
                                           int p0, int d0) {
    GGML_ASSERT(ggml_is_matrix(b));
    GGML_ASSERT(a->ne[1] == b->ne[1]);
    bool is_node = false;

    if (a->grad || b->grad) {
        GGML_ASSERT(false); // TODO: implement backward
        is_node = true;
    }

    const int64_t ne[4] = {
        ggml_calc_conv_output_size(b->ne[0], a->ne[0], s0, p0, d0),
        a->ne[2],
        1,
        1,
    };
    struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 2, ne);

    int32_t params[] = {s0, p0, d0};
    ggml_set_op_params(result, params, sizeof(params));

    result->op = GGML_OP_CONV_1D;
    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
    result->src[0] = a;
    result->src[1] = b;

    return result;
}

// ggml_conv_1d_ph

struct ggml_tensor * ggml_conv_1d_ph(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, int s, int d) {
    return ggml_conv_1d(ctx, a, b, s, a->ne[0] / 2, d);
}

// ggml_conv_2d

struct ggml_tensor * ggml_conv_2d(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, int s0, int s1,
                                  int p0, int p1, int d0, int d1) {

    GGML_ASSERT(a->ne[2] == b->ne[2]);
    bool is_node = false;

    if (a->grad || b->grad) {
        GGML_ASSERT(false); // TODO: implement backward
        is_node = true;
    }

    const int64_t ne[4] = {
        ggml_calc_conv_output_size(b->ne[0], a->ne[0], s0, p0, d0),
        ggml_calc_conv_output_size(b->ne[1], a->ne[1], s1, p1, d1),
        a->ne[3],
        b->ne[3],
    };
    struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne);

    int32_t params[] = {s0, s1, p0, p1, d0, d1};
    ggml_set_op_params(result, params, sizeof(params));

    result->op = GGML_OP_CONV_2D;
    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
    result->src[0] = a;
    result->src[1] = b;

    return result;
}

// ggml_conv_2d_sk_p0

struct ggml_tensor * ggml_conv_2d_sk_p0(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) {
    return ggml_conv_2d(ctx, a, b, a->ne[0], a->ne[1], 0, 0, 1, 1);
}

// ggml_conv_2d_s1_ph

struct ggml_tensor * ggml_conv_2d_s1_ph(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) {
    return ggml_conv_2d(ctx, a, b, 1, 1, a->ne[0] / 2, a->ne[1] / 2, 1, 1);
}

// ggml_conv_transpose_2d_p0

static int64_t ggml_calc_conv_transpose_output_size(int64_t ins, int64_t ks, int s, int p) {
    return (ins - 1) * s - 2 * p + ks;
}

struct ggml_tensor * ggml_conv_transpose_2d_p0(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b,
                                               int stride) {
    GGML_ASSERT(a->ne[3] == b->ne[2]);

    bool is_node = false;

    if (a->grad || b->grad) {
        GGML_ASSERT(false); // TODO: implement backward
        is_node = true;
    }

    const int64_t ne[4] = {
        ggml_calc_conv_transpose_output_size(b->ne[0], a->ne[0], stride, 0 /*p0*/),
        ggml_calc_conv_transpose_output_size(b->ne[1], a->ne[1], stride, 0 /*p1*/),
        a->ne[2],
        b->ne[3],
    };

    struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne);

    ggml_set_op_params_i32(result, 0, stride);

    result->op = GGML_OP_CONV_TRANSPOSE_2D;
    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
    result->src[0] = a;
    result->src[1] = b;

    return result;
}

// ggml_pool_*

static int64_t ggml_calc_pool_output_size(int64_t ins, int ks, int s, int p) { return (ins + 2 * p - ks) / s + 1; }

// ggml_pool_1d

struct ggml_tensor * ggml_pool_1d(struct ggml_context * ctx, struct ggml_tensor * a, enum ggml_op_pool op, int k0, int s0,
                                  int p0) {

    bool is_node = false;

    if (a->grad) {
        GGML_ASSERT(false); // TODO: implement backward
        is_node = true;
    }

    const int64_t ne[3] = {
        ggml_calc_pool_output_size(a->ne[0], k0, s0, p0),
        a->ne[1],
    };
    struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 2, ne);

    int32_t params[] = {op, k0, s0, p0};
    ggml_set_op_params(result, params, sizeof(params));

    result->op = GGML_OP_POOL_1D;
    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
    result->src[0] = a;

    return result;
}

// ggml_pool_2d

struct ggml_tensor * ggml_pool_2d(struct ggml_context * ctx, struct ggml_tensor * a, enum ggml_op_pool op, int k0, int k1,
                                  int s0, int s1, int p0, int p1) {

    bool is_node = false;

    if (a->grad) {
        GGML_ASSERT(false); // TODO: implement backward
        is_node = true;
    }

    const int64_t ne[3] = {
        ggml_calc_pool_output_size(a->ne[0], k0, s0, p0),
        ggml_calc_pool_output_size(a->ne[1], k1, s1, p1),
        a->ne[2],
    };
    struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 3, ne);

    int32_t params[] = {op, k0, k1, s0, s1, p0, p1};
    ggml_set_op_params(result, params, sizeof(params));

    result->op = GGML_OP_POOL_2D;
    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
    result->src[0] = a;

    return result;
}

// ggml_upscale

static struct ggml_tensor * ggml_upscale_impl(struct ggml_context * ctx, struct ggml_tensor * a, int scale_factor) {
    bool is_node = false;

    if (a->grad) {
        GGML_ASSERT(false); // TODO: implement backward
        is_node = true;
    }

    struct ggml_tensor * result =
        ggml_new_tensor_4d(ctx, a->type, a->ne[0] * scale_factor, a->ne[1] * scale_factor, a->ne[2], a->ne[3]);

    result->op = GGML_OP_UPSCALE;
    result->op_params[0] = scale_factor;
    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
    result->src[0] = a;
    result->src[1] = NULL;

    return result;
}

struct ggml_tensor * ggml_upscale(struct ggml_context * ctx, struct ggml_tensor * a, int scale_factor) {
    return ggml_upscale_impl(ctx, a, scale_factor);
}

// ggml_flash_attn

struct ggml_tensor * ggml_flash_attn(struct ggml_context * ctx, struct ggml_tensor * q, struct ggml_tensor * k,
                                     struct ggml_tensor * v, bool masked) {
    GGML_ASSERT(ggml_can_mul_mat(k, q));
    // TODO: check if vT can be multiplied by (k*qT)

    bool is_node = false;

    if (q->grad || k->grad || v->grad) {
        is_node = true;
    }

    // struct ggml_tensor * result = ggml_dup_tensor(ctx, q);
    struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, q->n_dims, q->ne);

    int32_t t = masked ? 1 : 0;
    ggml_set_op_params(result, &t, sizeof(t));

    result->op = GGML_OP_FLASH_ATTN;
    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
    result->src[0] = q;
    result->src[1] = k;
    result->src[2] = v;

    return result;
}

// ggml_flash_ff

struct ggml_tensor * ggml_flash_ff(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b0,
                                   struct ggml_tensor * b1, struct ggml_tensor * c0, struct ggml_tensor * c1) {
    GGML_ASSERT(ggml_can_mul_mat(b0, a));
    // TODO: more checks

    bool is_node = false;

    if (a->grad || b0->grad || b1->grad || c0->grad || c1->grad) {
        is_node = true;
    }

    // struct ggml_tensor * result = ggml_dup_tensor(ctx, a);
    struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, a->n_dims, a->ne);

    result->op = GGML_OP_FLASH_FF;
    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
    result->src[0] = a;
    result->src[1] = b0;
    result->src[2] = b1;
    result->src[3] = c0;
    result->src[4] = c1;

    return result;
}

// ggml_flash_attn_back

struct ggml_tensor * ggml_flash_attn_back(struct ggml_context * ctx, struct ggml_tensor * q, struct ggml_tensor * k,
                                          struct ggml_tensor * v, struct ggml_tensor * d, bool masked) {
    GGML_ASSERT(ggml_can_mul_mat(k, q));
    // TODO: check if vT can be multiplied by (k*qT)

    // d shape [D,N,ne2,ne3]
    // q shape [D,N,ne2,ne3]
    // k shape [D,M,ne2,ne3]
    // v shape [M,D,ne2,ne3]

    const int64_t D = q->ne[0];
    const int64_t N = q->ne[1];
    const int64_t M = k->ne[1];
    const int64_t ne2 = q->ne[2];
    const int64_t ne3 = q->ne[3];

    GGML_ASSERT(k->ne[0] == D);
    GGML_ASSERT(v->ne[0] == M);
    GGML_ASSERT(v->ne[1] == D);
    GGML_ASSERT(d->ne[0] == D);
    GGML_ASSERT(d->ne[1] == N);
    GGML_ASSERT(k->ne[2] == ne2);
    GGML_ASSERT(k->ne[3] == ne3);
    GGML_ASSERT(v->ne[2] == ne2);
    GGML_ASSERT(v->ne[3] == ne3);
    GGML_ASSERT(d->ne[2] == ne2);
    GGML_ASSERT(d->ne[3] == ne3);

    bool is_node = false;

    if (q->grad || k->grad || v->grad) {
        // when using this operation (in backwards pass) these grads are set.
        // we don't want to create (big) grad of our result, so is_node is false.
        is_node = false;
    }

    // store gradients of q, k and v as continuous tensors concatenated in result.
    // q shape[D,N,ne2,ne3] ; k shape [D,M,ne2,ne3] ; v shape [M,D,ne2,ne3]
    // gradq->data = result->data
    // gradk->data = result->data + nb0*D*N*ne2*ne3
    // gradv->data = result->data + nb0*D*N*ne2*ne3 + nb0*D*M*ne2*ne3
    // note: v and gradv are actually transposed, i.e. v->ne[0] != D.
    int64_t ne[4] = {D, M + N + M, ne2, ne3};

    struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne);

    int32_t masked_i = masked ? 1 : 0;
    ggml_set_op_params(result, &masked_i, sizeof(masked_i));

    result->op = GGML_OP_FLASH_ATTN_BACK;
    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
    result->src[0] = q;
    result->src[1] = k;
    result->src[2] = v;
    result->src[3] = d;

    return result;
}

// ggml_win_part

struct ggml_tensor * ggml_win_part(struct ggml_context * ctx, struct ggml_tensor * a, int w) {
    GGML_ASSERT(a->ne[3] == 1);
    GGML_ASSERT(a->type == GGML_TYPE_F32);

    bool is_node = false;

    if (a->grad) {
        GGML_ASSERT(false); // TODO: implement backward
        is_node = true;
    }

    // padding
    const int px = (w - a->ne[1] % w) % w;
    const int py = (w - a->ne[2] % w) % w;

    const int npx = (px + a->ne[1]) / w;
    const int npy = (py + a->ne[2]) / w;
    const int np = npx * npy;

    const int64_t ne[4] = {
        a->ne[0],
        w,
        w,
        np,
    };

    struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne);

    int32_t params[] = {npx, npy, w};
    ggml_set_op_params(result, params, sizeof(params));

    result->op = GGML_OP_WIN_PART;
    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
    result->src[0] = a;

    return result;
}

// ggml_win_unpart

struct ggml_tensor * ggml_win_unpart(struct ggml_context * ctx, struct ggml_tensor * a, int w0, int h0, int w) {
    GGML_ASSERT(a->type == GGML_TYPE_F32);

    bool is_node = false;

    if (a->grad) {
        GGML_ASSERT(false); // TODO: implement backward
        is_node = true;
    }

    const int64_t ne[4] = {
        a->ne[0],
        w0,
        h0,
        1,
    };
    struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 3, ne);

    int32_t params[] = {w};
    ggml_set_op_params(result, params, sizeof(params));

    result->op = GGML_OP_WIN_UNPART;
    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
    result->src[0] = a;

    return result;
}

// ggml_get_rel_pos

struct ggml_tensor * ggml_get_rel_pos(struct ggml_context * ctx, struct ggml_tensor * a, int qh, int kh) {
    GGML_ASSERT(qh == kh);
    GGML_ASSERT(2 * MAX(qh, kh) - 1 == a->ne[1]);

    bool is_node = false;

    if (a->grad) {
        GGML_ASSERT(false); // TODO: implement backward
        is_node = true;
    }

    const int64_t ne[4] = {
        a->ne[0],
        kh,
        qh,
        1,
    };
    struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F16, 3, ne);

    result->op = GGML_OP_GET_REL_POS;
    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
    result->src[0] = a;
    result->src[1] = NULL;

    return result;
}

// ggml_add_rel_pos

static struct ggml_tensor * ggml_add_rel_pos_impl(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * pw,
                                                  struct ggml_tensor * ph, bool inplace) {
    GGML_ASSERT(ggml_are_same_shape(pw, ph));
    GGML_ASSERT(ggml_is_contiguous(a));
    GGML_ASSERT(ggml_is_contiguous(pw));
    GGML_ASSERT(ggml_is_contiguous(ph));
    GGML_ASSERT(ph->type == GGML_TYPE_F32);
    GGML_ASSERT(pw->type == GGML_TYPE_F32);
    GGML_ASSERT(pw->ne[3] == a->ne[2]);
    GGML_ASSERT(pw->ne[0] * pw->ne[0] == a->ne[0]);
    GGML_ASSERT(pw->ne[1] * pw->ne[2] == a->ne[1]);

    bool is_node = false;

    if (!inplace && (a->grad || pw->grad || ph->grad)) {
        is_node = true;
    }

    struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
    ggml_set_op_params_i32(result, 0, inplace ? 1 : 0);

    result->op = GGML_OP_ADD_REL_POS;
    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
    result->src[0] = a;
    result->src[1] = pw;
    result->src[2] = ph;

    return result;
}

struct ggml_tensor * ggml_add_rel_pos(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * pw,
                                      struct ggml_tensor * ph) {
    return ggml_add_rel_pos_impl(ctx, a, pw, ph, false);
}

struct ggml_tensor * ggml_add_rel_pos_inplace(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * pw,
                                              struct ggml_tensor * ph) {
    return ggml_add_rel_pos_impl(ctx, a, pw, ph, true);
}

// gmml_unary

static struct ggml_tensor * ggml_unary_impl(struct ggml_context * ctx, struct ggml_tensor * a, enum ggml_unary_op op,
                                            bool inplace) {
    bool is_node = false;

    if (!inplace && (a->grad)) {
        is_node = true;
    }

    struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);

    ggml_set_op_params_i32(result, 0, (int32_t)op);

    result->op = GGML_OP_UNARY;
    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
    result->src[0] = a;

    return result;
}

struct ggml_tensor * ggml_unary(struct ggml_context * ctx, struct ggml_tensor * a, enum ggml_unary_op op) {
    return ggml_unary_impl(ctx, a, op, false);
}

struct ggml_tensor * ggml_unary_inplace(struct ggml_context * ctx, struct ggml_tensor * a, enum ggml_unary_op op) {
    return ggml_unary_impl(ctx, a, op, true);
}

// ggml_map_unary

static struct ggml_tensor * ggml_map_unary_impl_f32(struct ggml_context * ctx, struct ggml_tensor * a,
                                                    const ggml_unary_op_f32_t fun, bool inplace) {
    bool is_node = false;

    if (!inplace && a->grad) {
        is_node = true;
    }

    struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);

    ggml_set_op_params(result, (const void *)&fun, sizeof(fun));

    result->op = GGML_OP_MAP_UNARY;
    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
    result->src[0] = a;

    return result;
}

struct ggml_tensor * ggml_map_unary_f32(struct ggml_context * ctx, struct ggml_tensor * a, const ggml_unary_op_f32_t fun) {
    return ggml_map_unary_impl_f32(ctx, a, fun, false);
}

struct ggml_tensor * ggml_map_unary_inplace_f32(struct ggml_context * ctx, struct ggml_tensor * a,
                                                const ggml_unary_op_f32_t fun) {
    return ggml_map_unary_impl_f32(ctx, a, fun, true);
}

// ggml_map_binary

static struct ggml_tensor * ggml_map_binary_impl_f32(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b,
                                                     const ggml_binary_op_f32_t fun, bool inplace) {
    GGML_ASSERT(ggml_are_same_shape(a, b));

    bool is_node = false;

    if (!inplace && (a->grad || b->grad)) {
        is_node = true;
    }

    struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);

    ggml_set_op_params(result, (const void *)&fun, sizeof(fun));

    result->op = GGML_OP_MAP_BINARY;
    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
    result->src[0] = a;
    result->src[1] = b;

    return result;
}

struct ggml_tensor * ggml_map_binary_f32(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b,
                                         const ggml_binary_op_f32_t fun) {
    return ggml_map_binary_impl_f32(ctx, a, b, fun, false);
}

struct ggml_tensor * ggml_map_binary_inplace_f32(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b,
                                                 const ggml_binary_op_f32_t fun) {
    return ggml_map_binary_impl_f32(ctx, a, b, fun, true);
}

// ggml_map_custom1_f32

static struct ggml_tensor * ggml_map_custom1_impl_f32(struct ggml_context * ctx, struct ggml_tensor * a,
                                                      const ggml_custom1_op_f32_t fun, bool inplace) {
    bool is_node = false;

    if (!inplace && a->grad) {
        is_node = true;
    }

    struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);

    ggml_set_op_params(result, (const void *)&fun, sizeof(fun));

    result->op = GGML_OP_MAP_CUSTOM1_F32;
    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
    result->src[0] = a;

    return result;
}

struct ggml_tensor * ggml_map_custom1_f32(struct ggml_context * ctx, struct ggml_tensor * a, const ggml_custom1_op_f32_t fun) {
    return ggml_map_custom1_impl_f32(ctx, a, fun, false);
}

struct ggml_tensor * ggml_map_custom1_inplace_f32(struct ggml_context * ctx, struct ggml_tensor * a,
                                                  const ggml_custom1_op_f32_t fun) {
    return ggml_map_custom1_impl_f32(ctx, a, fun, true);
}

// ggml_map_custom2_f32

static struct ggml_tensor * ggml_map_custom2_impl_f32(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b,
                                                      const ggml_custom2_op_f32_t fun, bool inplace) {
    bool is_node = false;

    if (!inplace && (a->grad || b->grad)) {
        is_node = true;
    }

    struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);

    ggml_set_op_params(result, (const void *)&fun, sizeof(fun));

    result->op = GGML_OP_MAP_CUSTOM2_F32;
    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
    result->src[0] = a;
    result->src[1] = b;

    return result;
}

struct ggml_tensor * ggml_map_custom2_f32(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b,
                                          const ggml_custom2_op_f32_t fun) {
    return ggml_map_custom2_impl_f32(ctx, a, b, fun, false);
}

struct ggml_tensor * ggml_map_custom2_inplace_f32(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b,
                                                  const ggml_custom2_op_f32_t fun) {
    return ggml_map_custom2_impl_f32(ctx, a, b, fun, true);
}

// ggml_map_custom3_f32

static struct ggml_tensor * ggml_map_custom3_impl_f32(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b,
                                                      struct ggml_tensor * c, const ggml_custom3_op_f32_t fun, bool inplace) {
    bool is_node = false;

    if (!inplace && (a->grad || b->grad || c->grad)) {
        is_node = true;
    }

    struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);

    ggml_set_op_params(result, (const void *)&fun, sizeof(fun));

    result->op = GGML_OP_MAP_CUSTOM3_F32;
    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
    result->src[0] = a;
    result->src[1] = b;
    result->src[2] = c;

    return result;
}

struct ggml_tensor * ggml_map_custom3_f32(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b,
                                          struct ggml_tensor * c, const ggml_custom3_op_f32_t fun) {
    return ggml_map_custom3_impl_f32(ctx, a, b, c, fun, false);
}

struct ggml_tensor * ggml_map_custom3_inplace_f32(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b,
                                                  struct ggml_tensor * c, const ggml_custom3_op_f32_t fun) {
    return ggml_map_custom3_impl_f32(ctx, a, b, c, fun, true);
}

// ggml_map_custom1
struct ggml_map_custom1_op_params {
    ggml_custom1_op_t fun;
    int n_tasks;
    void * userdata;
};

static struct ggml_tensor * ggml_map_custom1_impl(struct ggml_context * ctx, struct ggml_tensor * a,
                                                  const ggml_custom1_op_t fun, int n_tasks, void * userdata, bool inplace) {
    GGML_ASSERT(n_tasks == GGML_N_TASKS_MAX || n_tasks > 0);

    bool is_node = false;

    if (!inplace && a->grad) {
        is_node = true;
    }

    struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);

    struct ggml_map_custom1_op_params params = {/*.fun      =*/fun,
                                                /*.n_tasks  =*/n_tasks,
                                                /*.userdata =*/userdata};
    ggml_set_op_params(result, (const void *)&params, sizeof(params));

    result->op = GGML_OP_MAP_CUSTOM1;
    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
    result->src[0] = a;

    return result;
}

struct ggml_tensor * ggml_map_custom1(struct ggml_context * ctx, struct ggml_tensor * a, const ggml_custom1_op_t fun,
                                      int n_tasks, void * userdata) {
    return ggml_map_custom1_impl(ctx, a, fun, n_tasks, userdata, false);
}

struct ggml_tensor * ggml_map_custom1_inplace(struct ggml_context * ctx, struct ggml_tensor * a, const ggml_custom1_op_t fun,
                                              int n_tasks, void * userdata) {
    return ggml_map_custom1_impl(ctx, a, fun, n_tasks, userdata, true);
}

// ggml_map_custom2

struct ggml_map_custom2_op_params {
    ggml_custom2_op_t fun;
    int n_tasks;
    void * userdata;
};

static struct ggml_tensor * ggml_map_custom2_impl(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b,
                                                  const ggml_custom2_op_t fun, int n_tasks, void * userdata, bool inplace) {
    GGML_ASSERT(n_tasks == GGML_N_TASKS_MAX || n_tasks > 0);

    bool is_node = false;

    if (!inplace && (a->grad || b->grad)) {
        is_node = true;
    }

    struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);

    struct ggml_map_custom2_op_params params = {/*.fun      =*/fun,
                                                /*.n_tasks  =*/n_tasks,
                                                /*.userdata =*/userdata};
    ggml_set_op_params(result, (const void *)&params, sizeof(params));

    result->op = GGML_OP_MAP_CUSTOM2;
    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
    result->src[0] = a;
    result->src[1] = b;

    return result;
}

struct ggml_tensor * ggml_map_custom2(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b,
                                      const ggml_custom2_op_t fun, int n_tasks, void * userdata) {
    return ggml_map_custom2_impl(ctx, a, b, fun, n_tasks, userdata, false);
}

struct ggml_tensor * ggml_map_custom2_inplace(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b,
                                              const ggml_custom2_op_t fun, int n_tasks, void * userdata) {
    return ggml_map_custom2_impl(ctx, a, b, fun, n_tasks, userdata, true);
}

// ggml_map_custom3

struct ggml_map_custom3_op_params {
    ggml_custom3_op_t fun;
    int n_tasks;
    void * userdata;
};

static struct ggml_tensor * ggml_map_custom3_impl(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b,
                                                  struct ggml_tensor * c, const ggml_custom3_op_t fun, int n_tasks,
                                                  void * userdata, bool inplace) {
    GGML_ASSERT(n_tasks == GGML_N_TASKS_MAX || n_tasks > 0);

    bool is_node = false;

    if (!inplace && (a->grad || b->grad || c->grad)) {
        is_node = true;
    }

    struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);

    struct ggml_map_custom3_op_params params = {/*.fun      =*/fun,
                                                /*.n_tasks  =*/n_tasks,
                                                /*.userdata =*/userdata};
    ggml_set_op_params(result, (const void *)&params, sizeof(params));

    result->op = GGML_OP_MAP_CUSTOM3;
    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
    result->src[0] = a;
    result->src[1] = b;
    result->src[2] = c;

    return result;
}

struct ggml_tensor * ggml_map_custom3(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b,
                                      struct ggml_tensor * c, const ggml_custom3_op_t fun, int n_tasks, void * userdata) {
    return ggml_map_custom3_impl(ctx, a, b, c, fun, n_tasks, userdata, false);
}

struct ggml_tensor * ggml_map_custom3_inplace(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b,
                                              struct ggml_tensor * c, const ggml_custom3_op_t fun, int n_tasks,
                                              void * userdata) {
    return ggml_map_custom3_impl(ctx, a, b, c, fun, n_tasks, userdata, true);
}

// ggml_cross_entropy_loss

struct ggml_tensor * ggml_cross_entropy_loss(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) {
    GGML_ASSERT(ggml_are_same_shape(a, b));
    bool is_node = false;

    if (a->grad || b->grad) {
        is_node = true;
    }

    struct ggml_tensor * result = ggml_new_tensor_1d(ctx, a->type, 1);

    result->op = GGML_OP_CROSS_ENTROPY_LOSS;
    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
    result->src[0] = a;
    result->src[1] = b;

    return result;
}

// ggml_cross_entropy_loss_back

struct ggml_tensor * ggml_cross_entropy_loss_back(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b,
                                                  struct ggml_tensor * c) {
    GGML_ASSERT(ggml_are_same_shape(a, b));
    GGML_ASSERT(ggml_is_scalar(c));

    struct ggml_tensor * result = ggml_dup_tensor(ctx, a);

    result->op = GGML_OP_CROSS_ENTROPY_LOSS_BACK;
    result->grad = NULL;
    result->src[0] = a;
    result->src[1] = b;
    result->src[2] = c;

    return result;
}

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

void ggml_set_param(struct ggml_context * ctx, struct ggml_tensor * tensor) {
    tensor->is_param = true;

    GGML_ASSERT(tensor->grad == NULL);
    tensor->grad = ggml_dup_tensor(ctx, tensor);
}

// ggml_compute_forward_dup

static void ggml_compute_forward_dup_same_cont(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                               struct ggml_tensor * dst) {
    GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0));
    GGML_ASSERT(ggml_is_contiguous(dst) && ggml_is_contiguous(src0));
    GGML_ASSERT(src0->type == dst->type);

    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
        return;
    }

    const size_t nb00 = src0->nb[0];
    const size_t nb0 = dst->nb[0];

    const int ith = params->ith; // thread index
    const int nth = params->nth; // number of threads

    // parallelize by elements
    const int ne = ggml_nelements(dst);
    const int dr = (ne + nth - 1) / nth;
    const int ie0 = dr * ith;
    const int ie1 = MIN(ie0 + dr, ne);

    if (ie0 < ie1) {
        memcpy(((char *)dst->data + ie0 * nb0), ((char *)src0->data + ie0 * nb00), (ie1 - ie0) * ggml_type_size(src0->type));
    }
}
static void ggml_compute_forward_dup_f16(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                         struct ggml_tensor * dst) {
    GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0));

    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
        return;
    }

    GGML_TENSOR_UNARY_OP_LOCALS;

    const int ith = params->ith; // thread index
    const int nth = params->nth; // number of threads

    if (ggml_is_contiguous(src0) && ggml_is_contiguous(dst) && src0->type == dst->type) {
        ggml_compute_forward_dup_same_cont(params, src0, dst);
        return;
    }

    // parallelize by rows
    const int nr = ne01;
    // number of rows per thread
    const int dr = (nr + nth - 1) / nth;
    // row range for this thread
    const int ir0 = dr * ith;
    const int ir1 = MIN(ir0 + dr, nr);

    if (src0->type == dst->type && ne00 == ne0 && nb00 == ggml_type_size(src0->type) && nb0 == ggml_type_size(dst->type)) {
        // copy by rows
        const size_t rs = ne00 * nb00;
        for (int64_t i03 = 0; i03 < ne03; i03++) {
            for (int64_t i02 = 0; i02 < ne02; i02++) {
                for (int64_t i01 = ir0; i01 < ir1; i01++) {
                    memcpy(((char *)dst->data + i01 * nb1 + i02 * nb2 + i03 * nb3),
                           ((char *)src0->data + i01 * nb01 + i02 * nb02 + i03 * nb03), rs);
                }
            }
        }
        return;
    }

    // TODO: add more special-case implementations for tensor shapes/strides that can benefit from memcpy

    if (ggml_is_contiguous(dst)) {
        if (nb00 == sizeof(ggml_fp16_t)) {
            if (dst->type == GGML_TYPE_F16) {
                size_t id = 0;
                const size_t rs = ne00 * nb00;
                char * dst_ptr = (char *)dst->data;

                for (int i03 = 0; i03 < ne03; i03++) {
                    for (int i02 = 0; i02 < ne02; i02++) {
                        id += rs * ir0;
                        for (int i01 = ir0; i01 < ir1; i01++) {
                            const char * src0_ptr = (char *)src0->data + i01 * nb01 + i02 * nb02 + i03 * nb03;
                            memcpy(dst_ptr + id, src0_ptr, rs);
                            id += rs;
                        }
                        id += rs * (ne01 - ir1);
                    }
                }
            } else if (dst->type == GGML_TYPE_F32) {
                size_t id = 0;
                float * dst_ptr = (float *)dst->data;

                for (int i03 = 0; i03 < ne03; i03++) {
                    for (int i02 = 0; i02 < ne02; i02++) {
                        id += ne00 * ir0;
                        for (int i01 = ir0; i01 < ir1; i01++) {
                            const ggml_fp16_t * src0_ptr =
                                (ggml_fp16_t *)((char *)src0->data + i01 * nb01 + i02 * nb02 + i03 * nb03);
                            for (int i00 = 0; i00 < ne00; i00++) {
                                dst_ptr[id] = GGML_FP16_TO_FP32(src0_ptr[i00]);
                                id++;
                            }
                        }
                        id += ne00 * (ne01 - ir1);
                    }
                }
            } else if (type_traits[dst->type].from_float) {
                ggml_from_float_t const quantize_row_q = type_traits[dst->type].from_float;
                float * src0_f32 = (float *)params->wdata + (ne00 + CACHE_LINE_SIZE_F32) * ith;

                size_t id = 0;
                size_t rs = nb0 * (ne00 / ggml_blck_size(dst->type));
                char * dst_ptr = (char *)dst->data;

                for (int i03 = 0; i03 < ne03; i03++) {
                    for (int i02 = 0; i02 < ne02; i02++) {
                        id += rs * ir0;
                        for (int i01 = ir0; i01 < ir1; i01++) {
                            const ggml_fp16_t * src0_ptr =
                                (ggml_fp16_t *)((char *)src0->data + i01 * nb01 + i02 * nb02 + i03 * nb03);

                            for (int i00 = 0; i00 < ne00; i00++) {
                                src0_f32[i00] = GGML_FP16_TO_FP32(src0_ptr[i00]);
                            }

                            quantize_row_q(src0_f32, dst_ptr + id, ne00);
                            id += rs;
                        }
                        id += rs * (ne01 - ir1);
                    }
                }
            } else {
                GGML_ASSERT(false); // TODO: implement
            }
        } else {
            // printf("%s: this is not optimal - fix me\n", __func__);

            if (dst->type == GGML_TYPE_F32) {
                size_t id = 0;
                float * dst_ptr = (float *)dst->data;

                for (int i03 = 0; i03 < ne03; i03++) {
                    for (int i02 = 0; i02 < ne02; i02++) {
                        id += ne00 * ir0;
                        for (int i01 = ir0; i01 < ir1; i01++) {
                            for (int i00 = 0; i00 < ne00; i00++) {
                                const ggml_fp16_t * src0_ptr =
                                    (ggml_fp16_t *)((char *)src0->data + i00 * nb00 + i01 * nb01 + i02 * nb02 + i03 * nb03);

                                dst_ptr[id] = GGML_FP16_TO_FP32(*src0_ptr);
                                id++;
                            }
                        }
                        id += ne00 * (ne01 - ir1);
                    }
                }
            } else if (dst->type == GGML_TYPE_F16) {
                size_t id = 0;
                ggml_fp16_t * dst_ptr = (ggml_fp16_t *)dst->data;

                for (int i03 = 0; i03 < ne03; i03++) {
                    for (int i02 = 0; i02 < ne02; i02++) {
                        id += ne00 * ir0;
                        for (int i01 = ir0; i01 < ir1; i01++) {
                            for (int i00 = 0; i00 < ne00; i00++) {
                                const ggml_fp16_t * src0_ptr =
                                    (ggml_fp16_t *)((char *)src0->data + i00 * nb00 + i01 * nb01 + i02 * nb02 + i03 * nb03);

                                dst_ptr[id] = *src0_ptr;
                                id++;
                            }
                        }
                        id += ne00 * (ne01 - ir1);
                    }
                }
            } else {
                GGML_ASSERT(false); // TODO: implement
            }
        }
        return;
    }

    // dst counters
    int64_t i10 = 0;
    int64_t i11 = 0;
    int64_t i12 = 0;
    int64_t i13 = 0;

    if (dst->type == GGML_TYPE_F16) {
        for (int64_t i03 = 0; i03 < ne03; i03++) {
            for (int64_t i02 = 0; i02 < ne02; i02++) {
                i10 += ne00 * ir0;
                while (i10 >= ne0) {
                    i10 -= ne0;
                    if (++i11 == ne1) {
                        i11 = 0;
                        if (++i12 == ne2) {
                            i12 = 0;
                            if (++i13 == ne3) {
                                i13 = 0;
                            }
                        }
                    }
                }
                for (int64_t i01 = ir0; i01 < ir1; i01++) {
                    for (int64_t i00 = 0; i00 < ne00; i00++) {
                        const char * src0_ptr = ((char *)src0->data + i00 * nb00 + i01 * nb01 + i02 * nb02 + i03 * nb03);
                        char * dst_ptr = ((char *)dst->data + i10 * nb0 + i11 * nb1 + i12 * nb2 + i13 * nb3);

                        memcpy(dst_ptr, src0_ptr, sizeof(ggml_fp16_t));

                        if (++i10 == ne00) {
                            i10 = 0;
                            if (++i11 == ne01) {
                                i11 = 0;
                                if (++i12 == ne02) {
                                    i12 = 0;
                                    if (++i13 == ne03) {
                                        i13 = 0;
                                    }
                                }
                            }
                        }
                    }
                }
                i10 += ne00 * (ne01 - ir1);
                while (i10 >= ne0) {
                    i10 -= ne0;
                    if (++i11 == ne1) {
                        i11 = 0;
                        if (++i12 == ne2) {
                            i12 = 0;
                            if (++i13 == ne3) {
                                i13 = 0;
                            }
                        }
                    }
                }
            }
        }
    } else if (dst->type == GGML_TYPE_F32) {
        for (int64_t i03 = 0; i03 < ne03; i03++) {
            for (int64_t i02 = 0; i02 < ne02; i02++) {
                i10 += ne00 * ir0;
                while (i10 >= ne0) {
                    i10 -= ne0;
                    if (++i11 == ne1) {
                        i11 = 0;
                        if (++i12 == ne2) {
                            i12 = 0;
                            if (++i13 == ne3) {
                                i13 = 0;
                            }
                        }
                    }
                }
                for (int64_t i01 = ir0; i01 < ir1; i01++) {
                    for (int64_t i00 = 0; i00 < ne00; i00++) {
                        const char * src0_ptr = ((char *)src0->data + i00 * nb00 + i01 * nb01 + i02 * nb02 + i03 * nb03);
                        char * dst_ptr = ((char *)dst->data + i10 * nb0 + i11 * nb1 + i12 * nb2 + i13 * nb3);

                        *(float *)dst_ptr = GGML_FP16_TO_FP32(*(const ggml_fp16_t *)src0_ptr);

                        if (++i10 == ne0) {
                            i10 = 0;
                            if (++i11 == ne1) {
                                i11 = 0;
                                if (++i12 == ne2) {
                                    i12 = 0;
                                    if (++i13 == ne3) {
                                        i13 = 0;
                                    }
                                }
                            }
                        }
                    }
                }
                i10 += ne00 * (ne01 - ir1);
                while (i10 >= ne0) {
                    i10 -= ne0;
                    if (++i11 == ne1) {
                        i11 = 0;
                        if (++i12 == ne2) {
                            i12 = 0;
                            if (++i13 == ne3) {
                                i13 = 0;
                            }
                        }
                    }
                }
            }
        }
    } else {
        GGML_ASSERT(false); // TODO: implement
    }
}

static void ggml_compute_forward_dup_f32(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                         struct ggml_tensor * dst) {
    GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0));

    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
        return;
    }

    GGML_TENSOR_UNARY_OP_LOCALS;

    const int ith = params->ith; // thread index
    const int nth = params->nth; // number of threads

    if (ggml_is_contiguous(src0) && ggml_is_contiguous(dst) && src0->type == dst->type) {
        ggml_compute_forward_dup_same_cont(params, src0, dst);
        return;
    }

    // parallelize by rows
    const int nr = ne01;
    // number of rows per thread
    const int dr = (nr + nth - 1) / nth;
    // row range for this thread
    const int ir0 = dr * ith;
    const int ir1 = MIN(ir0 + dr, nr);

    if (src0->type == dst->type && ne00 == ne0 && nb00 == ggml_type_size(src0->type) && nb0 == ggml_type_size(dst->type)) {
        // copy by rows
        const size_t rs = ne00 * nb00;
        for (int64_t i03 = 0; i03 < ne03; i03++) {
            for (int64_t i02 = 0; i02 < ne02; i02++) {
                for (int64_t i01 = ir0; i01 < ir1; i01++) {
                    memcpy(((char *)dst->data + i01 * nb1 + i02 * nb2 + i03 * nb3),
                           ((char *)src0->data + i01 * nb01 + i02 * nb02 + i03 * nb03), rs);
                }
            }
        }
        return;
    }

    if (ggml_is_contiguous(dst)) {
        // TODO: simplify
        if (nb00 == sizeof(float)) {
            if (dst->type == GGML_TYPE_F32) {
                size_t id = 0;
                const size_t rs = ne00 * nb00;
                char * dst_ptr = (char *)dst->data;

                for (int i03 = 0; i03 < ne03; i03++) {
                    for (int i02 = 0; i02 < ne02; i02++) {
                        id += rs * ir0;
                        for (int i01 = ir0; i01 < ir1; i01++) {
                            const char * src0_ptr = (char *)src0->data + i01 * nb01 + i02 * nb02 + i03 * nb03;
                            memcpy(dst_ptr + id, src0_ptr, rs);
                            id += rs;
                        }
                        id += rs * (ne01 - ir1);
                    }
                }
            } else if (type_traits[dst->type].from_float) {
                ggml_from_float_t const quantize_row_q = type_traits[dst->type].from_float;

                size_t id = 0;
                size_t rs = nb0 * (ne00 / ggml_blck_size(dst->type));
                char * dst_ptr = (char *)dst->data;

                for (int i03 = 0; i03 < ne03; i03++) {
                    for (int i02 = 0; i02 < ne02; i02++) {
                        id += rs * ir0;
                        for (int i01 = ir0; i01 < ir1; i01++) {
                            const float * src0_ptr = (float *)((char *)src0->data + i01 * nb01 + i02 * nb02 + i03 * nb03);
                            quantize_row_q(src0_ptr, dst_ptr + id, ne00);
                            id += rs;
                        }
                        id += rs * (ne01 - ir1);
                    }
                }
            } else {
                GGML_ASSERT(false); // TODO: implement
            }
        } else {
            // printf("%s: this is not optimal - fix me\n", __func__);

            if (dst->type == GGML_TYPE_F32) {
                size_t id = 0;
                float * dst_ptr = (float *)dst->data;

                for (int i03 = 0; i03 < ne03; i03++) {
                    for (int i02 = 0; i02 < ne02; i02++) {
                        id += ne00 * ir0;
                        for (int i01 = ir0; i01 < ir1; i01++) {
                            for (int i00 = 0; i00 < ne00; i00++) {
                                const float * src0_ptr =
                                    (float *)((char *)src0->data + i00 * nb00 + i01 * nb01 + i02 * nb02 + i03 * nb03);

                                dst_ptr[id] = *src0_ptr;
                                id++;
                            }
                        }
                        id += ne00 * (ne01 - ir1);
                    }
                }
            } else if (dst->type == GGML_TYPE_F16) {
                size_t id = 0;
                ggml_fp16_t * dst_ptr = (ggml_fp16_t *)dst->data;

                for (int i03 = 0; i03 < ne03; i03++) {
                    for (int i02 = 0; i02 < ne02; i02++) {
                        id += ne00 * ir0;
                        for (int i01 = ir0; i01 < ir1; i01++) {
                            for (int i00 = 0; i00 < ne00; i00++) {
                                const float * src0_ptr =
                                    (float *)((char *)src0->data + i00 * nb00 + i01 * nb01 + i02 * nb02 + i03 * nb03);

                                dst_ptr[id] = GGML_FP32_TO_FP16(*src0_ptr);
                                id++;
                            }
                        }
                        id += ne00 * (ne01 - ir1);
                    }
                }
            } else {
                GGML_ASSERT(false); // TODO: implement
            }
        }

        return;
    }

    // dst counters

    int64_t i10 = 0;
    int64_t i11 = 0;
    int64_t i12 = 0;
    int64_t i13 = 0;

    if (dst->type == GGML_TYPE_F32) {
        for (int64_t i03 = 0; i03 < ne03; i03++) {
            for (int64_t i02 = 0; i02 < ne02; i02++) {
                i10 += ne00 * ir0;
                while (i10 >= ne0) {
                    i10 -= ne0;
                    if (++i11 == ne1) {
                        i11 = 0;
                        if (++i12 == ne2) {
                            i12 = 0;
                            if (++i13 == ne3) {
                                i13 = 0;
                            }
                        }
                    }
                }
                for (int64_t i01 = ir0; i01 < ir1; i01++) {
                    for (int64_t i00 = 0; i00 < ne00; i00++) {
                        const char * src0_ptr = ((char *)src0->data + i00 * nb00 + i01 * nb01 + i02 * nb02 + i03 * nb03);
                        char * dst_ptr = ((char *)dst->data + i10 * nb0 + i11 * nb1 + i12 * nb2 + i13 * nb3);

                        memcpy(dst_ptr, src0_ptr, sizeof(float));

                        if (++i10 == ne0) {
                            i10 = 0;
                            if (++i11 == ne1) {
                                i11 = 0;
                                if (++i12 == ne2) {
                                    i12 = 0;
                                    if (++i13 == ne3) {
                                        i13 = 0;
                                    }
                                }
                            }
                        }
                    }
                }
                i10 += ne00 * (ne01 - ir1);
                while (i10 >= ne0) {
                    i10 -= ne0;
                    if (++i11 == ne1) {
                        i11 = 0;
                        if (++i12 == ne2) {
                            i12 = 0;
                            if (++i13 == ne3) {
                                i13 = 0;
                            }
                        }
                    }
                }
            }
        }
    } else if (dst->type == GGML_TYPE_F16) {
        for (int64_t i03 = 0; i03 < ne03; i03++) {
            for (int64_t i02 = 0; i02 < ne02; i02++) {
                i10 += ne00 * ir0;
                while (i10 >= ne0) {
                    i10 -= ne0;
                    if (++i11 == ne1) {
                        i11 = 0;
                        if (++i12 == ne2) {
                            i12 = 0;
                            if (++i13 == ne3) {
                                i13 = 0;
                            }
                        }
                    }
                }
                for (int64_t i01 = ir0; i01 < ir1; i01++) {
                    for (int64_t i00 = 0; i00 < ne00; i00++) {
                        const char * src0_ptr = ((char *)src0->data + i00 * nb00 + i01 * nb01 + i02 * nb02 + i03 * nb03);
                        char * dst_ptr = ((char *)dst->data + i10 * nb0 + i11 * nb1 + i12 * nb2 + i13 * nb3);

                        *(ggml_fp16_t *)dst_ptr = GGML_FP32_TO_FP16(*(const float *)src0_ptr);

                        if (++i10 == ne0) {
                            i10 = 0;
                            if (++i11 == ne1) {
                                i11 = 0;
                                if (++i12 == ne2) {
                                    i12 = 0;
                                    if (++i13 == ne3) {
                                        i13 = 0;
                                    }
                                }
                            }
                        }
                    }
                }
                i10 += ne00 * (ne01 - ir1);
                while (i10 >= ne0) {
                    i10 -= ne0;
                    if (++i11 == ne1) {
                        i11 = 0;
                        if (++i12 == ne2) {
                            i12 = 0;
                            if (++i13 == ne3) {
                                i13 = 0;
                            }
                        }
                    }
                }
            }
        }
    } else {
        GGML_ASSERT(false); // TODO: implement
    }
}

static void ggml_compute_forward_dup(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                     struct ggml_tensor * dst) {
    if (ggml_is_contiguous(src0) && ggml_is_contiguous(dst) && src0->type == dst->type) {
        ggml_compute_forward_dup_same_cont(params, src0, dst);
        return;
    }
    switch (src0->type) {
    case GGML_TYPE_F16: {
        ggml_compute_forward_dup_f16(params, src0, dst);
    } break;
    case GGML_TYPE_F32: {
        ggml_compute_forward_dup_f32(params, src0, dst);
    } break;
    default: {
        GGML_ASSERT(false);
    } break;
    }
}

// ggml_compute_forward_add

static void ggml_compute_forward_add_f32(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                         const struct ggml_tensor * src1, struct ggml_tensor * dst) {
    GGML_ASSERT(ggml_can_repeat_rows(src1, src0) && ggml_are_same_shape(src0, dst));

    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
        return;
    }

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

    const int nr = ggml_nrows(src0);

    GGML_TENSOR_BINARY_OP_LOCALS;

    GGML_ASSERT(nb0 == sizeof(float));
    GGML_ASSERT(nb00 == sizeof(float));

    // rows per thread
    const int dr = (nr + nth - 1) / nth;

    // row range for this thread
    const int ir0 = dr * ith;
    const int ir1 = MIN(ir0 + dr, nr);

    if (nb10 == sizeof(float)) {
        for (int ir = ir0; ir < ir1; ++ir) {
            // src1 is broadcastable across src0 and dst in i1, i2, i3
            const int64_t i03 = ir / (ne02 * ne01);
            const int64_t i02 = (ir - i03 * ne02 * ne01) / ne01;
            const int64_t i01 = (ir - i03 * ne02 * ne01 - i02 * ne01);

            const int64_t i13 = i03 % ne13;
            const int64_t i12 = i02 % ne12;
            const int64_t i11 = i01 % ne11;

            float * dst_ptr = (float *)((char *)dst->data + i03 * nb3 + i02 * nb2 + i01 * nb1);
            float * src0_ptr = (float *)((char *)src0->data + i03 * nb03 + i02 * nb02 + i01 * nb01);
            float * src1_ptr = (float *)((char *)src1->data + i13 * nb13 + i12 * nb12 + i11 * nb11);

#ifdef GGML_USE_ACCELERATE
            vDSP_vadd(src0_ptr, 1, src1_ptr, 1, dst_ptr, 1, ne00);
#else
            ggml_vec_add_f32(ne00, dst_ptr, src0_ptr, src1_ptr);
#endif
            // }
            // }
        }
    } else {
        // src1 is not contiguous
        for (int ir = ir0; ir < ir1; ++ir) {
            // src1 is broadcastable across src0 and dst in i1, i2, i3
            const int64_t i03 = ir / (ne02 * ne01);
            const int64_t i02 = (ir - i03 * ne02 * ne01) / ne01;
            const int64_t i01 = (ir - i03 * ne02 * ne01 - i02 * ne01);

            const int64_t i13 = i03 % ne13;
            const int64_t i12 = i02 % ne12;
            const int64_t i11 = i01 % ne11;

            float * dst_ptr = (float *)((char *)dst->data + i03 * nb3 + i02 * nb2 + i01 * nb1);
            float * src0_ptr = (float *)((char *)src0->data + i03 * nb03 + i02 * nb02 + i01 * nb01);

            for (int i0 = 0; i0 < ne0; i0++) {
                float * src1_ptr = (float *)((char *)src1->data + i13 * nb13 + i12 * nb12 + i11 * nb11 + i0 * nb10);

                dst_ptr[i0] = src0_ptr[i0] + *src1_ptr;
            }
        }
    }
}

static void ggml_compute_forward_add_f16_f32(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                             const struct ggml_tensor * src1, struct ggml_tensor * dst) {
    GGML_ASSERT(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst));

    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
        return;
    }

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

    const int nr = ggml_nrows(src0);

    GGML_TENSOR_BINARY_OP_LOCALS;

    GGML_ASSERT(src0->type == GGML_TYPE_F16);
    GGML_ASSERT(src1->type == GGML_TYPE_F32);
    GGML_ASSERT(dst->type == GGML_TYPE_F16);

    GGML_ASSERT(nb0 == sizeof(ggml_fp16_t));
    GGML_ASSERT(nb00 == sizeof(ggml_fp16_t));

    // rows per thread
    const int dr = (nr + nth - 1) / nth;

    // row range for this thread
    const int ir0 = dr * ith;
    const int ir1 = MIN(ir0 + dr, nr);

    if (nb10 == sizeof(float)) {
        for (int ir = ir0; ir < ir1; ++ir) {
            // src0, src1 and dst are same shape => same indices
            const int i3 = ir / (ne2 * ne1);
            const int i2 = (ir - i3 * ne2 * ne1) / ne1;
            const int i1 = (ir - i3 * ne2 * ne1 - i2 * ne1);

            ggml_fp16_t * dst_ptr = (ggml_fp16_t *)((char *)dst->data + i3 * nb3 + i2 * nb2 + i1 * nb1);
            ggml_fp16_t * src0_ptr = (ggml_fp16_t *)((char *)src0->data + i3 * nb03 + i2 * nb02 + i1 * nb01);
            float * src1_ptr = (float *)((char *)src1->data + i3 * nb13 + i2 * nb12 + i1 * nb11);

            for (int i = 0; i < ne0; i++) {
                dst_ptr[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(src0_ptr[i]) + src1_ptr[i]);
            }
        }
    } else {
        // src1 is not contiguous
        GGML_ASSERT(false);
    }
}

static void ggml_compute_forward_add_f16_f16(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                             const struct ggml_tensor * src1, struct ggml_tensor * dst) {
    GGML_ASSERT(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst));

    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
        return;
    }

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

    const int nr = ggml_nrows(src0);

    GGML_TENSOR_BINARY_OP_LOCALS;

    GGML_ASSERT(src0->type == GGML_TYPE_F16);
    GGML_ASSERT(src1->type == GGML_TYPE_F16);
    GGML_ASSERT(dst->type == GGML_TYPE_F16);

    GGML_ASSERT(nb0 == sizeof(ggml_fp16_t));
    GGML_ASSERT(nb00 == sizeof(ggml_fp16_t));

    // rows per thread
    const int dr = (nr + nth - 1) / nth;

    // row range for this thread
    const int ir0 = dr * ith;
    const int ir1 = MIN(ir0 + dr, nr);

    if (nb10 == sizeof(ggml_fp16_t)) {
        for (int ir = ir0; ir < ir1; ++ir) {
            // src0, src1 and dst are same shape => same indices
            const int i3 = ir / (ne2 * ne1);
            const int i2 = (ir - i3 * ne2 * ne1) / ne1;
            const int i1 = (ir - i3 * ne2 * ne1 - i2 * ne1);

            ggml_fp16_t * dst_ptr = (ggml_fp16_t *)((char *)dst->data + i3 * nb3 + i2 * nb2 + i1 * nb1);
            ggml_fp16_t * src0_ptr = (ggml_fp16_t *)((char *)src0->data + i3 * nb03 + i2 * nb02 + i1 * nb01);
            ggml_fp16_t * src1_ptr = (ggml_fp16_t *)((char *)src1->data + i3 * nb13 + i2 * nb12 + i1 * nb11);

            for (int i = 0; i < ne0; i++) {
                dst_ptr[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(src0_ptr[i]) + GGML_FP16_TO_FP32(src1_ptr[i]));
            }
        }
    } else {
        // src1 is not contiguous
        GGML_ASSERT(false);
    }
}

static void ggml_compute_forward_add_q_f32(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                           const struct ggml_tensor * src1, struct ggml_tensor * dst) {
    GGML_ASSERT(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst));

    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
        return;
    }

    const int nr = ggml_nrows(src0);

    GGML_TENSOR_BINARY_OP_LOCALS;

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

    const enum ggml_type type = src0->type;
    ggml_to_float_t const dequantize_row_q = type_traits[type].to_float;
    ggml_from_float_t const quantize_row_q = type_traits[type].from_float;

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

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

    GGML_ASSERT(ggml_is_quantized(src0->type));
    GGML_ASSERT(dst->type == src0->type);
    GGML_ASSERT(src1->type == GGML_TYPE_F32);

    // rows per thread
    const int dr = (nr + nth - 1) / nth;

    // row range for this thread
    const int ir0 = dr * ith;
    const int ir1 = MIN(ir0 + dr, nr);

    float * wdata = (float *)params->wdata + (ne00 + CACHE_LINE_SIZE_F32) * ith;

    for (int ir = ir0; ir < ir1; ++ir) {
        // src0 indices
        const int i03 = ir / (ne02 * ne01);
        const int i02 = (ir - i03 * ne02 * ne01) / ne01;
        const int i01 = (ir - i03 * ne02 * ne01 - i02 * ne01);

        // src1 and dst are same shape as src0 => same indices
        const int i13 = i03;
        const int i12 = i02;
        const int i11 = i01;

        const int i3 = i03;
        const int i2 = i02;
        const int i1 = i01;

        void * src0_row = (void *)((char *)src0->data + (i01 * nb01 + i02 * nb02 + i03 * nb03));
        float * src1_row = (float *)((char *)src1->data + (i11 * nb11 + i12 * nb12 + i13 * nb13));
        void * dst_row = (void *)((char *)dst->data + (i1 * nb1 + i2 * nb2 + i3 * nb3));

        assert(ne00 % 32 == 0);

        // unquantize row from src0 to temp buffer
        dequantize_row_q(src0_row, wdata, ne00);
        // add src1
        ggml_vec_acc_f32(ne00, wdata, src1_row);
        // quantize row to dst
        quantize_row_q(wdata, dst_row, ne00);
    }
}

static void ggml_compute_forward_add(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                     const struct ggml_tensor * src1, struct ggml_tensor * dst) {
    switch (src0->type) {
    case GGML_TYPE_F32: {
        ggml_compute_forward_add_f32(params, src0, src1, dst);
    } break;
    case GGML_TYPE_F16: {
        if (src1->type == GGML_TYPE_F16) {
            ggml_compute_forward_add_f16_f16(params, src0, src1, dst);
        } else if (src1->type == GGML_TYPE_F32) {
            ggml_compute_forward_add_f16_f32(params, src0, src1, dst);
        } else {
            GGML_ASSERT(false);
        }
    } break;
    case GGML_TYPE_Q4_0:
    case GGML_TYPE_Q4_1:
    case GGML_TYPE_Q5_0:
    case GGML_TYPE_Q5_1:
    case GGML_TYPE_Q8_0:
    case GGML_TYPE_Q2_K:
    case GGML_TYPE_Q3_K:
    case GGML_TYPE_Q4_K:
    case GGML_TYPE_Q5_K:
    case GGML_TYPE_Q6_K: {
        ggml_compute_forward_add_q_f32(params, src0, src1, dst);
    } break;
    default: {
        GGML_ASSERT(false);
    } break;
    }
}

// ggml_compute_forward_add1

static void ggml_compute_forward_add1_f32(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                          const struct ggml_tensor * src1, struct ggml_tensor * dst) {
    GGML_ASSERT(ggml_are_same_shape(src0, dst));
    GGML_ASSERT(ggml_is_scalar(src1));

    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
        return;
    }

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

    const int nr = ggml_nrows(src0);

    GGML_TENSOR_UNARY_OP_LOCALS;

    GGML_ASSERT(nb0 == sizeof(float));
    GGML_ASSERT(nb00 == sizeof(float));

    // rows per thread
    const int dr = (nr + nth - 1) / nth;

    // row range for this thread
    const int ir0 = dr * ith;
    const int ir1 = MIN(ir0 + dr, nr);

    for (int ir = ir0; ir < ir1; ++ir) {
        // src0 and dst are same shape => same indices
        const int i3 = ir / (ne2 * ne1);
        const int i2 = (ir - i3 * ne2 * ne1) / ne1;
        const int i1 = (ir - i3 * ne2 * ne1 - i2 * ne1);

#ifdef GGML_USE_ACCELERATE
        UNUSED(ggml_vec_add1_f32);

        vDSP_vadd((float *)((char *)src0->data + i3 * nb03 + i2 * nb02 + i1 * nb01), 1, (float *)((char *)src1->data), 0,
                  (float *)((char *)dst->data + i3 * nb3 + i2 * nb2 + i1 * nb1), 1, ne0);
#else
        ggml_vec_add1_f32(ne0, (float *)((char *)dst->data + i3 * nb3 + i2 * nb2 + i1 * nb1),
                          (float *)((char *)src0->data + i3 * nb03 + i2 * nb02 + i1 * nb01), *(float *)src1->data);
#endif
    }
}

static void ggml_compute_forward_add1_f16_f32(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                              const struct ggml_tensor * src1, struct ggml_tensor * dst) {
    GGML_ASSERT(ggml_are_same_shape(src0, dst));
    GGML_ASSERT(ggml_is_scalar(src1));

    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
        return;
    }

    // scalar to add
    const float v = *(float *)src1->data;

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

    const int nr = ggml_nrows(src0);

    GGML_TENSOR_UNARY_OP_LOCALS;

    GGML_ASSERT(src0->type == GGML_TYPE_F16);
    GGML_ASSERT(src1->type == GGML_TYPE_F32);
    GGML_ASSERT(dst->type == GGML_TYPE_F16);

    GGML_ASSERT(nb0 == sizeof(ggml_fp16_t));
    GGML_ASSERT(nb00 == sizeof(ggml_fp16_t));

    // rows per thread
    const int dr = (nr + nth - 1) / nth;

    // row range for this thread
    const int ir0 = dr * ith;
    const int ir1 = MIN(ir0 + dr, nr);

    for (int ir = ir0; ir < ir1; ++ir) {
        // src0 and dst are same shape => same indices
        const int i3 = ir / (ne2 * ne1);
        const int i2 = (ir - i3 * ne2 * ne1) / ne1;
        const int i1 = (ir - i3 * ne2 * ne1 - i2 * ne1);

        ggml_fp16_t * dst_ptr = (ggml_fp16_t *)((char *)dst->data + i3 * nb3 + i2 * nb2 + i1 * nb1);
        ggml_fp16_t * src0_ptr = (ggml_fp16_t *)((char *)src0->data + i3 * nb03 + i2 * nb02 + i1 * nb01);
        for (int i = 0; i < ne0; i++) {
            dst_ptr[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(src0_ptr[i]) + v);
        }
    }
}

static void ggml_compute_forward_add1_f16_f16(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                              const struct ggml_tensor * src1, struct ggml_tensor * dst) {
    GGML_ASSERT(ggml_are_same_shape(src0, dst));
    GGML_ASSERT(ggml_is_scalar(src1));

    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
        return;
    }

    // scalar to add
    const float v = GGML_FP16_TO_FP32(*(ggml_fp16_t *)src1->data);

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

    const int nr = ggml_nrows(src0);

    GGML_TENSOR_UNARY_OP_LOCALS;

    GGML_ASSERT(src0->type == GGML_TYPE_F16);
    GGML_ASSERT(src1->type == GGML_TYPE_F16);
    GGML_ASSERT(dst->type == GGML_TYPE_F16);

    GGML_ASSERT(nb0 == sizeof(ggml_fp16_t));
    GGML_ASSERT(nb00 == sizeof(ggml_fp16_t));

    // rows per thread
    const int dr = (nr + nth - 1) / nth;

    // row range for this thread
    const int ir0 = dr * ith;
    const int ir1 = MIN(ir0 + dr, nr);

    for (int ir = ir0; ir < ir1; ++ir) {
        // src0 and dst are same shape => same indices
        const int i3 = ir / (ne2 * ne1);
        const int i2 = (ir - i3 * ne2 * ne1) / ne1;
        const int i1 = (ir - i3 * ne2 * ne1 - i2 * ne1);

        ggml_fp16_t * dst_ptr = (ggml_fp16_t *)((char *)dst->data + i3 * nb3 + i2 * nb2 + i1 * nb1);
        ggml_fp16_t * src0_ptr = (ggml_fp16_t *)((char *)src0->data + i3 * nb03 + i2 * nb02 + i1 * nb01);
        for (int i = 0; i < ne0; i++) {
            dst_ptr[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(src0_ptr[i]) + v);
        }
    }
}

static void ggml_compute_forward_add1_q_f32(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                            const struct ggml_tensor * src1, struct ggml_tensor * dst) {
    GGML_ASSERT(ggml_are_same_shape(src0, dst));
    GGML_ASSERT(ggml_is_scalar(src1));

    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
        return;
    }

    // scalar to add
    const float v = *(float *)src1->data;

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

    const int nr = ggml_nrows(src0);

    GGML_TENSOR_UNARY_OP_LOCALS;

    const enum ggml_type type = src0->type;
    ggml_to_float_t const dequantize_row_q = type_traits[type].to_float;
    ggml_from_float_t const quantize_row_q = type_traits[type].from_float;

    // we don't support permuted src0
    GGML_ASSERT(nb00 == ggml_type_size(type));

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

    GGML_ASSERT(ggml_is_quantized(src0->type));
    GGML_ASSERT(dst->type == src0->type);
    GGML_ASSERT(src1->type == GGML_TYPE_F32);

    // rows per thread
    const int dr = (nr + nth - 1) / nth;

    // row range for this thread
    const int ir0 = dr * ith;
    const int ir1 = MIN(ir0 + dr, nr);

    float * wdata = (float *)params->wdata + (ne0 + CACHE_LINE_SIZE_F32) * ith;

    for (int ir = ir0; ir < ir1; ++ir) {
        // src0 and dst are same shape => same indices
        const int i3 = ir / (ne2 * ne1);
        const int i2 = (ir - i3 * ne2 * ne1) / ne1;
        const int i1 = (ir - i3 * ne2 * ne1 - i2 * ne1);

        void * src0_row = (void *)((char *)src0->data + (i1 * nb01 + i2 * nb02 + i3 * nb03));
        void * dst_row = (void *)((char *)dst->data + (i1 * nb1 + i2 * nb2 + i3 * nb0));

        assert(ne0 % 32 == 0);

        // unquantize row from src0 to temp buffer
        dequantize_row_q(src0_row, wdata, ne0);
        // add src1
        ggml_vec_acc1_f32(ne0, wdata, v);
        // quantize row to dst
        quantize_row_q(wdata, dst_row, ne0);
    }
}

static void ggml_compute_forward_add1(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                      const struct ggml_tensor * src1, struct ggml_tensor * dst) {
    switch (src0->type) {
    case GGML_TYPE_F32: {
        ggml_compute_forward_add1_f32(params, src0, src1, dst);
    } break;
    case GGML_TYPE_F16: {
        if (src1->type == GGML_TYPE_F16) {
            ggml_compute_forward_add1_f16_f16(params, src0, src1, dst);
        } else if (src1->type == GGML_TYPE_F32) {
            ggml_compute_forward_add1_f16_f32(params, src0, src1, dst);
        } else {
            GGML_ASSERT(false);
        }
    } break;
    case GGML_TYPE_Q4_0:
    case GGML_TYPE_Q4_1:
    case GGML_TYPE_Q5_0:
    case GGML_TYPE_Q5_1:
    case GGML_TYPE_Q8_0:
    case GGML_TYPE_Q8_1:
    case GGML_TYPE_Q2_K:
    case GGML_TYPE_Q3_K:
    case GGML_TYPE_Q4_K:
    case GGML_TYPE_Q5_K:
    case GGML_TYPE_Q6_K: {
        ggml_compute_forward_add1_q_f32(params, src0, src1, dst);
    } break;
    default: {
        GGML_ASSERT(false);
    } break;
    }
}

// ggml_compute_forward_acc

static void ggml_compute_forward_acc_f32(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                         const struct ggml_tensor * src1, struct ggml_tensor * dst) {
    GGML_ASSERT(ggml_are_same_shape(src0, dst));
    GGML_ASSERT(ggml_is_contiguous(dst) && ggml_is_contiguous(src0));

    // view src0 and dst with these strides and data offset inbytes during acc
    // nb0 is implicitely element_size because src0 and dst are contiguous
    size_t nb1 = ((int32_t *)dst->op_params)[0];
    size_t nb2 = ((int32_t *)dst->op_params)[1];
    size_t nb3 = ((int32_t *)dst->op_params)[2];
    size_t offset = ((int32_t *)dst->op_params)[3];
    bool inplace = (bool)((int32_t *)dst->op_params)[4];

    if (!inplace && (params->type == GGML_TASK_INIT)) {
        // memcpy needs to be synchronized across threads to avoid race conditions.
        // => do it in INIT phase
        memcpy(((char *)dst->data), ((char *)src0->data), ggml_nbytes(dst));
    }

    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
        return;
    }

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

    const int nr = ggml_nrows(src1);
    const int nc = src1->ne[0];

    GGML_TENSOR_LOCALS(int64_t, ne1, src1, ne);
    GGML_TENSOR_LOCALS(size_t, nb1, src1, nb);

    // src0 and dst as viewed during acc
    const size_t nb0 = ggml_element_size(src0);

    const size_t nb00 = nb0;
    const size_t nb01 = nb1;
    const size_t nb02 = nb2;
    const size_t nb03 = nb3;

    GGML_ASSERT(offset + (ne10 == 0 ? 0 : ne10 - 1) * nb0 + (ne11 == 0 ? 0 : ne11 - 1) * nb1 +
                    (ne12 == 0 ? 0 : ne12 - 1) * nb2 + (ne13 == 0 ? 0 : ne13 - 1) * nb3 <
                ggml_nbytes(dst));
    GGML_ASSERT(offset + (ne10 == 0 ? 0 : ne10 - 1) * nb00 + (ne11 == 0 ? 0 : ne11 - 1) * nb01 +
                    (ne12 == 0 ? 0 : ne12 - 1) * nb02 + (ne13 == 0 ? 0 : ne13 - 1) * nb03 <
                ggml_nbytes(src0));

    GGML_ASSERT(nb10 == sizeof(float));

    // rows per thread
    const int dr = (nr + nth - 1) / nth;

    // row range for this thread
    const int ir0 = dr * ith;
    const int ir1 = MIN(ir0 + dr, nr);

    for (int ir = ir0; ir < ir1; ++ir) {
        // src0 and dst are viewed with shape of src1 and offset
        // => same indices
        const int i3 = ir / (ne12 * ne11);
        const int i2 = (ir - i3 * ne12 * ne11) / ne11;
        const int i1 = (ir - i3 * ne12 * ne11 - i2 * ne11);

#ifdef GGML_USE_ACCELERATE
        vDSP_vadd((float *)((char *)src0->data + i3 * nb03 + i2 * nb02 + i1 * nb01 + offset), 1,
                  (float *)((char *)src1->data + i3 * nb13 + i2 * nb12 + i1 * nb11), 1,
                  (float *)((char *)dst->data + i3 * nb3 + i2 * nb2 + i1 * nb1 + offset), 1, nc);
#else
        ggml_vec_add_f32(nc, (float *)((char *)dst->data + i3 * nb3 + i2 * nb2 + i1 * nb1 + offset),
                         (float *)((char *)src0->data + i3 * nb03 + i2 * nb02 + i1 * nb01 + offset),
                         (float *)((char *)src1->data + i3 * nb13 + i2 * nb12 + i1 * nb11));
#endif
    }
}

static void ggml_compute_forward_acc(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                     const struct ggml_tensor * src1, struct ggml_tensor * dst) {

    switch (src0->type) {
    case GGML_TYPE_F32: {
        ggml_compute_forward_acc_f32(params, src0, src1, dst);
    } break;
    case GGML_TYPE_F16:
    case GGML_TYPE_Q4_0:
    case GGML_TYPE_Q4_1:
    case GGML_TYPE_Q5_0:
    case GGML_TYPE_Q5_1:
    case GGML_TYPE_Q8_0:
    case GGML_TYPE_Q8_1:
    case GGML_TYPE_Q2_K:
    case GGML_TYPE_Q3_K:
    case GGML_TYPE_Q4_K:
    case GGML_TYPE_Q5_K:
    case GGML_TYPE_Q6_K:
    default: {
        GGML_ASSERT(false);
    } break;
    }
}

// ggml_compute_forward_sub

static void ggml_compute_forward_sub_f32(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                         const struct ggml_tensor * src1, struct ggml_tensor * dst) {
    assert(params->ith == 0);
    assert(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst));

    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
        return;
    }

    const int nr = ggml_nrows(src0);

    GGML_TENSOR_BINARY_OP_LOCALS;

    GGML_ASSERT(nb0 == sizeof(float));
    GGML_ASSERT(nb00 == sizeof(float));

    if (nb10 == sizeof(float)) {
        for (int ir = 0; ir < nr; ++ir) {
            // src0, src1 and dst are same shape => same indices
            const int i3 = ir / (ne2 * ne1);
            const int i2 = (ir - i3 * ne2 * ne1) / ne1;
            const int i1 = (ir - i3 * ne2 * ne1 - i2 * ne1);

#ifdef GGML_USE_ACCELERATE
            vDSP_vsub((float *)((char *)src1->data + i3 * nb13 + i2 * nb12 + i1 * nb11), 1,
                      (float *)((char *)src0->data + i3 * nb03 + i2 * nb02 + i1 * nb01), 1,
                      (float *)((char *)dst->data + i3 * nb3 + i2 * nb2 + i1 * nb1), 1, ne0);
#else
            ggml_vec_sub_f32(ne0, (float *)((char *)dst->data + i3 * nb3 + i2 * nb2 + i1 * nb1),
                             (float *)((char *)src0->data + i3 * nb03 + i2 * nb02 + i1 * nb01),
                             (float *)((char *)src1->data + i3 * nb13 + i2 * nb12 + i1 * nb11));
#endif
            // }
            // }
        }
    } else {
        // src1 is not contiguous
        for (int ir = 0; ir < nr; ++ir) {
            // src0, src1 and dst are same shape => same indices
            const int i3 = ir / (ne2 * ne1);
            const int i2 = (ir - i3 * ne2 * ne1) / ne1;
            const int i1 = (ir - i3 * ne2 * ne1 - i2 * ne1);

            float * dst_ptr = (float *)((char *)dst->data + i3 * nb3 + i2 * nb2 + i1 * nb1);
            float * src0_ptr = (float *)((char *)src0->data + i3 * nb03 + i2 * nb02 + i1 * nb01);
            for (int i0 = 0; i0 < ne0; i0++) {
                float * src1_ptr = (float *)((char *)src1->data + i3 * nb13 + i2 * nb12 + i1 * nb11 + i0 * nb10);

                dst_ptr[i0] = src0_ptr[i0] - *src1_ptr;
            }
        }
    }
}

static void ggml_compute_forward_sub(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                     const struct ggml_tensor * src1, struct ggml_tensor * dst) {
    switch (src0->type) {
    case GGML_TYPE_F32: {
        ggml_compute_forward_sub_f32(params, src0, src1, dst);
    } break;
    default: {
        GGML_ASSERT(false);
    } break;
    }
}

// ggml_compute_forward_mul

static void ggml_compute_forward_mul_f32(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                         const struct ggml_tensor * src1, struct ggml_tensor * dst) {
    GGML_ASSERT(ggml_can_repeat_rows(src1, src0) && ggml_are_same_shape(src0, dst));

    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
        return;
    }
    const int ith = params->ith;
    const int nth = params->nth;

#ifdef GGML_USE_CLBLAST
    if (src1->backend == GGML_BACKEND_GPU) {
        if (ith == 0) {
            ggml_cl_mul(src0, src1, dst);
        }
        return;
    }
#endif

    const int64_t nr = ggml_nrows(src0);

    GGML_TENSOR_BINARY_OP_LOCALS;

    GGML_ASSERT(nb0 == sizeof(float));
    GGML_ASSERT(nb00 == sizeof(float));
    GGML_ASSERT(ne00 == ne10);

    if (nb10 == sizeof(float)) {
        for (int64_t ir = ith; ir < nr; ir += nth) {
            // src0 and dst are same shape => same indices
            const int64_t i03 = ir / (ne02 * ne01);
            const int64_t i02 = (ir - i03 * ne02 * ne01) / ne01;
            const int64_t i01 = (ir - i03 * ne02 * ne01 - i02 * ne01);

            const int64_t i13 = i03 % ne13;
            const int64_t i12 = i02 % ne12;
            const int64_t i11 = i01 % ne11;

            float * dst_ptr = (float *)((char *)dst->data + i03 * nb3 + i02 * nb2 + i01 * nb1);
            float * src0_ptr = (float *)((char *)src0->data + i03 * nb03 + i02 * nb02 + i01 * nb01);
            float * src1_ptr = (float *)((char *)src1->data + i13 * nb13 + i12 * nb12 + i11 * nb11);

#ifdef GGML_USE_ACCELERATE
            UNUSED(ggml_vec_mul_f32);

            vDSP_vmul(src0_ptr, 1, src1_ptr, 1, dst_ptr, 1, ne00);
#else
            ggml_vec_mul_f32(ne00, dst_ptr, src0_ptr, src1_ptr);
#endif
            // }
            // }
        }
    } else {
        // src1 is not contiguous
        for (int64_t ir = ith; ir < nr; ir += nth) {
            // src0 and dst are same shape => same indices
            // src1 is broadcastable across src0 and dst in i1, i2, i3
            const int64_t i03 = ir / (ne02 * ne01);
            const int64_t i02 = (ir - i03 * ne02 * ne01) / ne01;
            const int64_t i01 = (ir - i03 * ne02 * ne01 - i02 * ne01);

            const int64_t i13 = i03 % ne13;
            const int64_t i12 = i02 % ne12;
            const int64_t i11 = i01 % ne11;

            float * dst_ptr = (float *)((char *)dst->data + i03 * nb3 + i02 * nb2 + i01 * nb1);
            float * src0_ptr = (float *)((char *)src0->data + i03 * nb03 + i02 * nb02 + i01 * nb01);

            for (int64_t i0 = 0; i0 < ne00; i0++) {
                float * src1_ptr = (float *)((char *)src1->data + i13 * nb13 + i12 * nb12 + i11 * nb11 + i0 * nb10);

                dst_ptr[i0] = src0_ptr[i0] * (*src1_ptr);
            }
        }
    }
}

static void ggml_compute_forward_mul(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                     const struct ggml_tensor * src1, struct ggml_tensor * dst) {
    GGML_ASSERT(src1->type == GGML_TYPE_F32 && "only f32 src1 supported for now");

    switch (src0->type) {
    case GGML_TYPE_F32: {
        ggml_compute_forward_mul_f32(params, src0, src1, dst);
    } break;
    default: {
        GGML_ASSERT(false);
    } break;
    }
}

// ggml_compute_forward_div

static void ggml_compute_forward_div_f32(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                         const struct ggml_tensor * src1, struct ggml_tensor * dst) {
    assert(params->ith == 0);
    assert(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst));

    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
        return;
    }

    const int nr = ggml_nrows(src0);

    GGML_TENSOR_BINARY_OP_LOCALS;

    GGML_ASSERT(nb0 == sizeof(float));
    GGML_ASSERT(nb00 == sizeof(float));

    if (nb10 == sizeof(float)) {
        for (int ir = 0; ir < nr; ++ir) {
            // src0, src1 and dst are same shape => same indices
            const int i3 = ir / (ne2 * ne1);
            const int i2 = (ir - i3 * ne2 * ne1) / ne1;
            const int i1 = (ir - i3 * ne2 * ne1 - i2 * ne1);

#ifdef GGML_USE_ACCELERATE
            UNUSED(ggml_vec_div_f32);

            vDSP_vdiv((float *)((char *)src1->data + i3 * nb13 + i2 * nb12 + i1 * nb11), 1,
                      (float *)((char *)src0->data + i3 * nb03 + i2 * nb02 + i1 * nb01), 1,
                      (float *)((char *)dst->data + i3 * nb3 + i2 * nb2 + i1 * nb1), 1, ne0);
#else
            ggml_vec_div_f32(ne0, (float *)((char *)dst->data + i3 * nb3 + i2 * nb2 + i1 * nb1),
                             (float *)((char *)src0->data + i3 * nb03 + i2 * nb02 + i1 * nb01),
                             (float *)((char *)src1->data + i3 * nb13 + i2 * nb12 + i1 * nb11));
#endif
            // }
            // }
        }
    } else {
        // src1 is not contiguous
        for (int ir = 0; ir < nr; ++ir) {
            // src0, src1 and dst are same shape => same indices
            const int i3 = ir / (ne2 * ne1);
            const int i2 = (ir - i3 * ne2 * ne1) / ne1;
            const int i1 = (ir - i3 * ne2 * ne1 - i2 * ne1);

            float * dst_ptr = (float *)((char *)dst->data + i3 * nb3 + i2 * nb2 + i1 * nb1);
            float * src0_ptr = (float *)((char *)src0->data + i3 * nb03 + i2 * nb02 + i1 * nb01);
            for (int i0 = 0; i0 < ne0; i0++) {
                float * src1_ptr = (float *)((char *)src1->data + i3 * nb13 + i2 * nb12 + i1 * nb11 + i0 * nb10);

                dst_ptr[i0] = src0_ptr[i0] / (*src1_ptr);
            }
        }
    }
}

static void ggml_compute_forward_div(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                     const struct ggml_tensor * src1, struct ggml_tensor * dst) {
    switch (src0->type) {
    case GGML_TYPE_F32: {
        ggml_compute_forward_div_f32(params, src0, src1, dst);
    } break;
    default: {
        GGML_ASSERT(false);
    } break;
    }
}

// ggml_compute_forward_sqr

static void ggml_compute_forward_sqr_f32(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                         struct ggml_tensor * dst) {
    assert(params->ith == 0);
    assert(ggml_are_same_shape(src0, dst));

    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
        return;
    }

    const int n = ggml_nrows(src0);
    const int nc = src0->ne[0];

    assert(dst->nb[0] == sizeof(float));
    assert(src0->nb[0] == sizeof(float));

    for (int i = 0; i < n; i++) {
        ggml_vec_sqr_f32(nc, (float *)((char *)dst->data + i * (dst->nb[1])),
                         (float *)((char *)src0->data + i * (src0->nb[1])));
    }
}

static void ggml_compute_forward_sqr(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                     struct ggml_tensor * dst) {
    switch (src0->type) {
    case GGML_TYPE_F32: {
        ggml_compute_forward_sqr_f32(params, src0, dst);
    } break;
    default: {
        GGML_ASSERT(false);
    } break;
    }
}

// ggml_compute_forward_sqrt

static void ggml_compute_forward_sqrt_f32(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                          struct ggml_tensor * dst) {
    assert(params->ith == 0);
    assert(ggml_are_same_shape(src0, dst));

    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
        return;
    }

    const int n = ggml_nrows(src0);
    const int nc = src0->ne[0];

    assert(dst->nb[0] == sizeof(float));
    assert(src0->nb[0] == sizeof(float));

    for (int i = 0; i < n; i++) {
        ggml_vec_sqrt_f32(nc, (float *)((char *)dst->data + i * (dst->nb[1])),
                          (float *)((char *)src0->data + i * (src0->nb[1])));
    }
}

static void ggml_compute_forward_sqrt(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                      struct ggml_tensor * dst) {
    switch (src0->type) {
    case GGML_TYPE_F32: {
        ggml_compute_forward_sqrt_f32(params, src0, dst);
    } break;
    default: {
        GGML_ASSERT(false);
    } break;
    }
}

// ggml_compute_forward_log

static void ggml_compute_forward_log_f32(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                         struct ggml_tensor * dst) {
    GGML_ASSERT(params->ith == 0);
    GGML_ASSERT(ggml_are_same_shape(src0, dst));

    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
        return;
    }

    const int n = ggml_nrows(src0);
    const int nc = src0->ne[0];

    GGML_ASSERT(dst->nb[0] == sizeof(float));
    GGML_ASSERT(src0->nb[0] == sizeof(float));

    for (int i = 0; i < n; i++) {
        ggml_vec_log_f32(nc, (float *)((char *)dst->data + i * (dst->nb[1])),
                         (float *)((char *)src0->data + i * (src0->nb[1])));
    }
}

static void ggml_compute_forward_log(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                     struct ggml_tensor * dst) {
    switch (src0->type) {
    case GGML_TYPE_F32: {
        ggml_compute_forward_log_f32(params, src0, dst);
    } break;
    default: {
        GGML_ASSERT(false);
    } break;
    }
}

// ggml_compute_forward_sum

static void ggml_compute_forward_sum_f32(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                         struct ggml_tensor * dst) {
    assert(params->ith == 0);
    assert(ggml_is_scalar(dst));

    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
        return;
    }

    assert(ggml_is_scalar(dst));
    assert(src0->nb[0] == sizeof(float));

    GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne);
    GGML_TENSOR_LOCALS(size_t, nb0, src0, nb);

    ggml_float sum = 0;
    ggml_float row_sum = 0;

    for (int64_t i03 = 0; i03 < ne03; i03++) {
        for (int64_t i02 = 0; i02 < ne02; i02++) {
            for (int64_t i01 = 0; i01 < ne01; i01++) {
                ggml_vec_sum_f32_ggf(ne00, &row_sum, (float *)((char *)src0->data + i01 * nb01 + i02 * nb02 + i03 * nb03));
                sum += row_sum;
            }
        }
    }
    ((float *)dst->data)[0] = sum;
}

static void ggml_compute_forward_sum_f16(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                         struct ggml_tensor * dst) {
    assert(params->ith == 0);
    assert(ggml_is_scalar(dst));

    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
        return;
    }

    assert(src0->nb[0] == sizeof(ggml_fp16_t));

    GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne);
    GGML_TENSOR_LOCALS(size_t, nb0, src0, nb);

    float sum = 0;
    float row_sum = 0;

    for (int64_t i03 = 0; i03 < ne03; i03++) {
        for (int64_t i02 = 0; i02 < ne02; i02++) {
            for (int64_t i01 = 0; i01 < ne01; i01++) {
                ggml_vec_sum_f16_ggf(ne00, &row_sum,
                                     (ggml_fp16_t *)((char *)src0->data + i01 * nb01 + i02 * nb02 + i03 * nb03));
                sum += row_sum;
            }
        }
    }
    ((ggml_fp16_t *)dst->data)[0] = GGML_FP32_TO_FP16(sum);
}

static void ggml_compute_forward_sum(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                     struct ggml_tensor * dst) {
    switch (src0->type) {
    case GGML_TYPE_F32: {
        ggml_compute_forward_sum_f32(params, src0, dst);
    } break;
    case GGML_TYPE_F16: {
        ggml_compute_forward_sum_f16(params, src0, dst);
    } break;
    default: {
        GGML_ASSERT(false);
    } break;
    }
}

// ggml_compute_forward_sum_rows

static void ggml_compute_forward_sum_rows_f32(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                              struct ggml_tensor * dst) {
    GGML_ASSERT(params->ith == 0);

    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
        return;
    }

    GGML_ASSERT(src0->nb[0] == sizeof(float));
    GGML_ASSERT(dst->nb[0] == sizeof(float));

    GGML_TENSOR_UNARY_OP_LOCALS;

    GGML_ASSERT(ne0 == 1);
    GGML_ASSERT(ne1 == ne01);
    GGML_ASSERT(ne2 == ne02);
    GGML_ASSERT(ne3 == ne03);

    for (int64_t i3 = 0; i3 < ne03; i3++) {
        for (int64_t i2 = 0; i2 < ne02; i2++) {
            for (int64_t i1 = 0; i1 < ne01; i1++) {
                float * src_row = (float *)((char *)src0->data + i1 * nb01 + i2 * nb02 + i3 * nb03);
                float * dst_row = (float *)((char *)dst->data + i1 * nb1 + i2 * nb2 + i3 * nb3);
                float row_sum = 0;
                ggml_vec_sum_f32(ne00, &row_sum, src_row);
                dst_row[0] = row_sum;
            }
        }
    }
}

static void ggml_compute_forward_sum_rows(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                          struct ggml_tensor * dst) {
    switch (src0->type) {
    case GGML_TYPE_F32: {
        ggml_compute_forward_sum_rows_f32(params, src0, dst);
    } break;
    default: {
        GGML_ASSERT(false);
    } break;
    }
}

// ggml_compute_forward_mean

static void ggml_compute_forward_mean_f32(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                          struct ggml_tensor * dst) {
    assert(params->ith == 0);

    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
        return;
    }

    assert(src0->nb[0] == sizeof(float));

    GGML_TENSOR_UNARY_OP_LOCALS;

    assert(ne0 == 1);
    assert(ne1 == ne01);
    assert(ne2 == ne02);
    assert(ne3 == ne03);

    UNUSED(ne0);
    UNUSED(ne1);
    UNUSED(ne2);
    UNUSED(ne3);

    for (int64_t i03 = 0; i03 < ne03; i03++) {
        for (int64_t i02 = 0; i02 < ne02; i02++) {
            for (int64_t i01 = 0; i01 < ne01; i01++) {
                ggml_vec_sum_f32(ne00, (float *)((char *)dst->data + i01 * nb1 + i02 * nb2 + i03 * nb3),
                                 (float *)((char *)src0->data + i01 * nb01 + i02 * nb02 + i03 * nb03));

                *(float *)((char *)dst->data + i01 * nb1 + i02 * nb2 + i03 * nb3) /= (float)ne00;
            }
        }
    }
}

static void ggml_compute_forward_mean(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                      struct ggml_tensor * dst) {
    switch (src0->type) {
    case GGML_TYPE_F32: {
        ggml_compute_forward_mean_f32(params, src0, dst);
    } break;
    default: {
        GGML_ASSERT(false);
    } break;
    }
}

// ggml_compute_forward_argmax

static void ggml_compute_forward_argmax_f32(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                            struct ggml_tensor * dst) {
    assert(params->ith == 0);

    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
        return;
    }

    assert(src0->nb[0] == sizeof(float));
    assert(dst->nb[0] == sizeof(float));

    const int64_t ne00 = src0->ne[0];
    const int64_t ne01 = src0->ne[1];

    const size_t nb01 = src0->nb[1];
    const size_t nb0 = dst->nb[0];

    for (int64_t i1 = 0; i1 < ne01; i1++) {
        float * src = (float *)((char *)src0->data + i1 * nb01);
        int32_t * dst_ = (int32_t *)((char *)dst->data + i1 * nb0);
        int v = 0;
        ggml_vec_argmax_f32(ne00, &v, src);
        dst_[0] = v;
    }
}

static void ggml_compute_forward_argmax(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                        struct ggml_tensor * dst) {
    switch (src0->type) {
    case GGML_TYPE_F32: {
        ggml_compute_forward_argmax_f32(params, src0, dst);
    } break;
    default: {
        GGML_ASSERT(false);
    } break;
    }
}

// ggml_compute_forward_repeat

static void ggml_compute_forward_repeat_f32(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                            struct ggml_tensor * dst) {
    GGML_ASSERT(params->ith == 0);
    GGML_ASSERT(ggml_can_repeat(src0, dst));

    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
        return;
    }

    GGML_TENSOR_UNARY_OP_LOCALS;

    // guaranteed to be an integer due to the check in ggml_can_repeat
    const int nr0 = (int)(ne0 / ne00);
    const int nr1 = (int)(ne1 / ne01);
    const int nr2 = (int)(ne2 / ne02);
    const int nr3 = (int)(ne3 / ne03);

    // TODO: support for transposed / permuted tensors
    GGML_ASSERT(nb0 == sizeof(float));
    GGML_ASSERT(nb00 == sizeof(float));

    // TODO: maybe this is not optimal?
    for (int i3 = 0; i3 < nr3; i3++) {
        for (int k3 = 0; k3 < ne03; k3++) {
            for (int i2 = 0; i2 < nr2; i2++) {
                for (int k2 = 0; k2 < ne02; k2++) {
                    for (int i1 = 0; i1 < nr1; i1++) {
                        for (int k1 = 0; k1 < ne01; k1++) {
                            for (int i0 = 0; i0 < nr0; i0++) {
                                ggml_vec_cpy_f32(ne00,
                                                 (float *)((char *)dst->data + (i3 * ne03 + k3) * nb3 + (i2 * ne02 + k2) * nb2 +
                                                           (i1 * ne01 + k1) * nb1 + (i0 * ne00) * nb0),
                                                 (float *)((char *)src0->data + (k3)*nb03 + (k2)*nb02 + (k1)*nb01));
                            }
                        }
                    }
                }
            }
        }
    }
}

static void ggml_compute_forward_repeat(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                        struct ggml_tensor * dst) {
    switch (src0->type) {
    case GGML_TYPE_F32: {
        ggml_compute_forward_repeat_f32(params, src0, dst);
    } break;
    default: {
        GGML_ASSERT(false);
    } break;
    }
}

// ggml_compute_forward_repeat_back

static void ggml_compute_forward_repeat_back_f32(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                                 struct ggml_tensor * dst) {
    GGML_ASSERT(params->ith == 0);
    GGML_ASSERT(ggml_can_repeat(dst, src0));

    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
        return;
    }

    GGML_TENSOR_UNARY_OP_LOCALS;

    // guaranteed to be an integer due to the check in ggml_can_repeat
    const int nr0 = (int)(ne00 / ne0);
    const int nr1 = (int)(ne01 / ne1);
    const int nr2 = (int)(ne02 / ne2);
    const int nr3 = (int)(ne03 / ne3);

    // TODO: support for transposed / permuted tensors
    GGML_ASSERT(nb0 == sizeof(float));
    GGML_ASSERT(nb00 == sizeof(float));

    if (ggml_is_contiguous(dst)) {
        ggml_vec_set_f32(ne0 * ne1 * ne2 * ne3, dst->data, 0);
    } else {
        for (int k3 = 0; k3 < ne3; k3++) {
            for (int k2 = 0; k2 < ne2; k2++) {
                for (int k1 = 0; k1 < ne1; k1++) {
                    ggml_vec_set_f32(ne0, (float *)((char *)dst->data + k1 * nb1 + k2 * nb2 + k3 * nb3), 0);
                }
            }
        }
    }

    // TODO: maybe this is not optimal?
    for (int i3 = 0; i3 < nr3; i3++) {
        for (int k3 = 0; k3 < ne3; k3++) {
            for (int i2 = 0; i2 < nr2; i2++) {
                for (int k2 = 0; k2 < ne2; k2++) {
                    for (int i1 = 0; i1 < nr1; i1++) {
                        for (int k1 = 0; k1 < ne1; k1++) {
                            for (int i0 = 0; i0 < nr0; i0++) {
                                ggml_vec_acc_f32(ne0, (float *)((char *)dst->data + (k3)*nb3 + (k2)*nb2 + (k1)*nb1),
                                                 (float *)((char *)src0->data + (i3 * ne3 + k3) * nb03 +
                                                           (i2 * ne2 + k2) * nb02 + (i1 * ne1 + k1) * nb01 +
                                                           (i0 * ne0) * nb00));
                            }
                        }
                    }
                }
            }
        }
    }
}

static void ggml_compute_forward_repeat_back(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                             struct ggml_tensor * dst) {
    switch (src0->type) {
    case GGML_TYPE_F32: {
        ggml_compute_forward_repeat_back_f32(params, src0, dst);
    } break;
    default: {
        GGML_ASSERT(false);
    } break;
    }
}

// ggml_compute_forward_concat

static void ggml_compute_forward_concat_f32(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                            const struct ggml_tensor * src1, struct ggml_tensor * dst) {

    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
        return;
    }

    GGML_ASSERT(src0->nb[0] == sizeof(float));

    const int ith = params->ith;

    GGML_TENSOR_BINARY_OP_LOCALS;

    // TODO: support for transposed / permuted tensors
    GGML_ASSERT(nb0 == sizeof(float));
    GGML_ASSERT(nb00 == sizeof(float));
    GGML_ASSERT(nb10 == sizeof(float));

    for (int i3 = 0; i3 < ne3; i3++) {
        for (int i2 = ith; i2 < ne2; i2++) {
            if (i2 < ne02) { // src0
                for (int i1 = 0; i1 < ne1; i1++) {
                    for (int i0 = 0; i0 < ne0; i0++) {
                        const float * x = (float *)((char *)src0->data + i0 * nb00 + i1 * nb01 + i2 * nb02 + i3 * nb03);

                        float * y = (float *)((char *)dst->data + i0 * nb0 + i1 * nb1 + i2 * nb2 + i3 * nb3);
                        *y = *x;
                    }
                }
            } // src1
            else {
                for (int i1 = 0; i1 < ne1; i1++) {
                    for (int i0 = 0; i0 < ne0; i0++) {
                        const float * x =
                            (float *)((char *)src1->data + i0 * nb10 + i1 * nb11 + (i2 - ne02) * nb12 + i3 * nb13);

                        float * y = (float *)((char *)dst->data + i0 * nb0 + i1 * nb1 + i2 * nb2 + i3 * nb3);
                        *y = *x;
                    }
                }
            }
        }
    }
}

static void ggml_compute_forward_concat(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                        const struct ggml_tensor * src1, struct ggml_tensor * dst) {
    switch (src0->type) {
    case GGML_TYPE_F32: {
        ggml_compute_forward_concat_f32(params, src0, src1, dst);
    } break;
    default: {
        GGML_ASSERT(false);
    } break;
    }
}

// ggml_compute_forward_abs

static void ggml_compute_forward_abs_f32(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                         struct ggml_tensor * dst) {
    assert(params->ith == 0);
    assert(ggml_are_same_shape(src0, dst));

    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
        return;
    }

    const int n = ggml_nrows(src0);
    const int nc = src0->ne[0];

    assert(dst->nb[0] == sizeof(float));
    assert(src0->nb[0] == sizeof(float));

    for (int i = 0; i < n; i++) {
        ggml_vec_abs_f32(nc, (float *)((char *)dst->data + i * (dst->nb[1])),
                         (float *)((char *)src0->data + i * (src0->nb[1])));
    }
}

static void ggml_compute_forward_abs(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                     struct ggml_tensor * dst) {
    switch (src0->type) {
    case GGML_TYPE_F32: {
        ggml_compute_forward_abs_f32(params, src0, dst);
    } break;
    default: {
        GGML_ASSERT(false);
    } break;
    }
}

// ggml_compute_forward_sgn

static void ggml_compute_forward_sgn_f32(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                         struct ggml_tensor * dst) {
    assert(params->ith == 0);
    assert(ggml_are_same_shape(src0, dst));

    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
        return;
    }

    const int n = ggml_nrows(src0);
    const int nc = src0->ne[0];

    assert(dst->nb[0] == sizeof(float));
    assert(src0->nb[0] == sizeof(float));

    for (int i = 0; i < n; i++) {
        ggml_vec_sgn_f32(nc, (float *)((char *)dst->data + i * (dst->nb[1])),
                         (float *)((char *)src0->data + i * (src0->nb[1])));
    }
}

static void ggml_compute_forward_sgn(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                     struct ggml_tensor * dst) {
    switch (src0->type) {
    case GGML_TYPE_F32: {
        ggml_compute_forward_sgn_f32(params, src0, dst);
    } break;
    default: {
        GGML_ASSERT(false);
    } break;
    }
}

// ggml_compute_forward_neg

static void ggml_compute_forward_neg_f32(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                         struct ggml_tensor * dst) {
    assert(params->ith == 0);
    assert(ggml_are_same_shape(src0, dst));

    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
        return;
    }

    const int n = ggml_nrows(src0);
    const int nc = src0->ne[0];

    assert(dst->nb[0] == sizeof(float));
    assert(src0->nb[0] == sizeof(float));

    for (int i = 0; i < n; i++) {
        ggml_vec_neg_f32(nc, (float *)((char *)dst->data + i * (dst->nb[1])),
                         (float *)((char *)src0->data + i * (src0->nb[1])));
    }
}

static void ggml_compute_forward_neg(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                     struct ggml_tensor * dst) {
    switch (src0->type) {
    case GGML_TYPE_F32: {
        ggml_compute_forward_neg_f32(params, src0, dst);
    } break;
    default: {
        GGML_ASSERT(false);
    } break;
    }
}

// ggml_compute_forward_step

static void ggml_compute_forward_step_f32(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                          struct ggml_tensor * dst) {
    assert(params->ith == 0);
    assert(ggml_are_same_shape(src0, dst));

    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
        return;
    }

    const int n = ggml_nrows(src0);
    const int nc = src0->ne[0];

    assert(dst->nb[0] == sizeof(float));
    assert(src0->nb[0] == sizeof(float));

    for (int i = 0; i < n; i++) {
        ggml_vec_step_f32(nc, (float *)((char *)dst->data + i * (dst->nb[1])),
                          (float *)((char *)src0->data + i * (src0->nb[1])));
    }
}

static void ggml_compute_forward_step(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                      struct ggml_tensor * dst) {
    switch (src0->type) {
    case GGML_TYPE_F32: {
        ggml_compute_forward_step_f32(params, src0, dst);
    } break;
    default: {
        GGML_ASSERT(false);
    } break;
    }
}

// ggml_compute_forward_tanh

static void ggml_compute_forward_tanh_f32(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                          struct ggml_tensor * dst) {
    assert(params->ith == 0);
    assert(ggml_are_same_shape(src0, dst));

    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
        return;
    }

    const int n = ggml_nrows(src0);
    const int nc = src0->ne[0];

    assert(dst->nb[0] == sizeof(float));
    assert(src0->nb[0] == sizeof(float));

    for (int i = 0; i < n; i++) {
        ggml_vec_tanh_f32(nc, (float *)((char *)dst->data + i * (dst->nb[1])),
                          (float *)((char *)src0->data + i * (src0->nb[1])));
    }
}

static void ggml_compute_forward_tanh(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                      struct ggml_tensor * dst) {
    switch (src0->type) {
    case GGML_TYPE_F32: {
        ggml_compute_forward_tanh_f32(params, src0, dst);
    } break;
    default: {
        GGML_ASSERT(false);
    } break;
    }
}

// ggml_compute_forward_elu

static void ggml_compute_forward_elu_f32(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                         struct ggml_tensor * dst) {
    assert(params->ith == 0);
    assert(ggml_are_same_shape(src0, dst));

    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
        return;
    }

    const int n = ggml_nrows(src0);
    const int nc = src0->ne[0];

    assert(dst->nb[0] == sizeof(float));
    assert(src0->nb[0] == sizeof(float));

    for (int i = 0; i < n; i++) {
        ggml_vec_elu_f32(nc, (float *)((char *)dst->data + i * (dst->nb[1])),
                         (float *)((char *)src0->data + i * (src0->nb[1])));
    }
}

static void ggml_compute_forward_elu(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                     struct ggml_tensor * dst) {
    switch (src0->type) {
    case GGML_TYPE_F32: {
        ggml_compute_forward_elu_f32(params, src0, dst);
    } break;
    default: {
        GGML_ASSERT(false);
    } break;
    }
}

// ggml_compute_forward_relu

static void ggml_compute_forward_relu_f32(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                          struct ggml_tensor * dst) {
    assert(params->ith == 0);
    assert(ggml_are_same_shape(src0, dst));

    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
        return;
    }

    const int n = ggml_nrows(src0);
    const int nc = src0->ne[0];

    assert(dst->nb[0] == sizeof(float));
    assert(src0->nb[0] == sizeof(float));

    for (int i = 0; i < n; i++) {
        ggml_vec_relu_f32(nc, (float *)((char *)dst->data + i * (dst->nb[1])),
                          (float *)((char *)src0->data + i * (src0->nb[1])));
    }
}

static void ggml_compute_forward_relu(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                      struct ggml_tensor * dst) {
    switch (src0->type) {
    case GGML_TYPE_F32: {
        ggml_compute_forward_relu_f32(params, src0, dst);
    } break;
    default: {
        GGML_ASSERT(false);
    } break;
    }
}

// ggml_compute_forward_gelu

static void ggml_compute_forward_gelu_f32(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                          struct ggml_tensor * dst) {
    GGML_ASSERT(ggml_is_contiguous_except_dim_1(src0));
    GGML_ASSERT(ggml_is_contiguous_except_dim_1(dst));
    GGML_ASSERT(ggml_are_same_shape(src0, dst));

    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
        return;
    }

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

    const int nc = src0->ne[0];
    const int nr = ggml_nrows(src0);

    // rows per thread
    const int dr = (nr + nth - 1) / nth;

    // row range for this thread
    const int ir0 = dr * ith;
    const int ir1 = MIN(ir0 + dr, nr);

    for (int i1 = ir0; i1 < ir1; i1++) {
        ggml_vec_gelu_f32(nc, (float *)((char *)dst->data + i1 * (dst->nb[1])),
                          (float *)((char *)src0->data + i1 * (src0->nb[1])));

#ifndef NDEBUG
        for (int k = 0; k < nc; k++) {
            const float x = ((float *)((char *)dst->data + i1 * (dst->nb[1])))[k];
            UNUSED(x);
            assert(!isnan(x));
            assert(!isinf(x));
        }
#endif
    }
}

static void ggml_compute_forward_gelu(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                      struct ggml_tensor * dst) {
    switch (src0->type) {
    case GGML_TYPE_F32: {
        ggml_compute_forward_gelu_f32(params, src0, dst);
    } break;
    default: {
        GGML_ASSERT(false);
    } break;
    }
}

// ggml_compute_forward_gelu_quick

static void ggml_compute_forward_gelu_quick_f32(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                                struct ggml_tensor * dst) {
    GGML_ASSERT(ggml_is_contiguous_except_dim_1(src0));
    GGML_ASSERT(ggml_is_contiguous_except_dim_1(dst));
    GGML_ASSERT(ggml_are_same_shape(src0, dst));

    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
        return;
    }

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

    const int nc = src0->ne[0];
    const int nr = ggml_nrows(src0);

    // rows per thread
    const int dr = (nr + nth - 1) / nth;

    // row range for this thread
    const int ir0 = dr * ith;
    const int ir1 = MIN(ir0 + dr, nr);

    for (int i1 = ir0; i1 < ir1; i1++) {
        ggml_vec_gelu_quick_f32(nc, (float *)((char *)dst->data + i1 * (dst->nb[1])),
                                (float *)((char *)src0->data + i1 * (src0->nb[1])));

#ifndef NDEBUG
        for (int k = 0; k < nc; k++) {
            const float x = ((float *)((char *)dst->data + i1 * (dst->nb[1])))[k];
            UNUSED(x);
            assert(!isnan(x));
            assert(!isinf(x));
        }
#endif
    }
}

static void ggml_compute_forward_gelu_quick(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                            struct ggml_tensor * dst) {
    switch (src0->type) {
    case GGML_TYPE_F32: {
        ggml_compute_forward_gelu_quick_f32(params, src0, dst);
    } break;
    default: {
        GGML_ASSERT(false);
    } break;
    }
}

// ggml_compute_forward_silu

static void ggml_compute_forward_silu_f32(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                          struct ggml_tensor * dst) {
    GGML_ASSERT(ggml_is_contiguous_except_dim_1(src0));
    GGML_ASSERT(ggml_is_contiguous_except_dim_1(dst));
    GGML_ASSERT(ggml_are_same_shape(src0, dst));

    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
        return;
    }

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

    const int nc = src0->ne[0];
    const int nr = ggml_nrows(src0);

    // rows per thread
    const int dr = (nr + nth - 1) / nth;

    // row range for this thread
    const int ir0 = dr * ith;
    const int ir1 = MIN(ir0 + dr, nr);

    for (int i1 = ir0; i1 < ir1; i1++) {
        ggml_vec_silu_f32(nc, (float *)((char *)dst->data + i1 * (dst->nb[1])),
                          (float *)((char *)src0->data + i1 * (src0->nb[1])));

#ifndef NDEBUG
        for (int k = 0; k < nc; k++) {
            const float x = ((float *)((char *)dst->data + i1 * (dst->nb[1])))[k];
            UNUSED(x);
            assert(!isnan(x));
            assert(!isinf(x));
        }
#endif
    }
}

static void ggml_compute_forward_silu(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                      struct ggml_tensor * dst) {
    switch (src0->type) {
    case GGML_TYPE_F32: {
        ggml_compute_forward_silu_f32(params, src0, dst);
    } break;
    default: {
        GGML_ASSERT(false);
    } break;
    }
}

// ggml_compute_forward_silu_back

static void ggml_compute_forward_silu_back_f32(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                               const struct ggml_tensor * grad, struct ggml_tensor * dst) {
    GGML_ASSERT(ggml_is_contiguous_except_dim_1(grad));
    GGML_ASSERT(ggml_is_contiguous_except_dim_1(src0));
    GGML_ASSERT(ggml_is_contiguous_except_dim_1(dst));
    GGML_ASSERT(ggml_are_same_shape(src0, dst));
    GGML_ASSERT(ggml_are_same_shape(src0, grad));

    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
        return;
    }

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

    const int nc = src0->ne[0];
    const int nr = ggml_nrows(src0);

    // rows per thread
    const int dr = (nr + nth - 1) / nth;

    // row range for this thread
    const int ir0 = dr * ith;
    const int ir1 = MIN(ir0 + dr, nr);

    for (int i1 = ir0; i1 < ir1; i1++) {
        ggml_vec_silu_backward_f32(nc, (float *)((char *)dst->data + i1 * (dst->nb[1])),
                                   (float *)((char *)src0->data + i1 * (src0->nb[1])),
                                   (float *)((char *)grad->data + i1 * (grad->nb[1])));

#ifndef NDEBUG
        for (int k = 0; k < nc; k++) {
            const float x = ((float *)((char *)dst->data + i1 * (dst->nb[1])))[k];
            UNUSED(x);
            assert(!isnan(x));
            assert(!isinf(x));
        }
#endif
    }
}

static void ggml_compute_forward_silu_back(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                           const struct ggml_tensor * grad, struct ggml_tensor * dst) {
    switch (src0->type) {
    case GGML_TYPE_F32: {
        ggml_compute_forward_silu_back_f32(params, src0, grad, dst);
    } break;
    default: {
        GGML_ASSERT(false);
    } break;
    }
}

// ggml_compute_forward_norm

static void ggml_compute_forward_norm_f32(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                          struct ggml_tensor * dst) {
    GGML_ASSERT(ggml_are_same_shape(src0, dst));

    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
        return;
    }

    GGML_ASSERT(src0->nb[0] == sizeof(float));

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

    GGML_TENSOR_UNARY_OP_LOCALS;

    float eps;
    memcpy(&eps, dst->op_params, sizeof(float));

    // TODO: optimize
    for (int64_t i03 = 0; i03 < ne03; i03++) {
        for (int64_t i02 = 0; i02 < ne02; i02++) {
            for (int64_t i01 = ith; i01 < ne01; i01 += nth) {
                const float * x = (float *)((char *)src0->data + i01 * nb01 + i02 * nb02 + i03 * nb03);

                ggml_float sum = 0.0;
                for (int64_t i00 = 0; i00 < ne00; i00++) {
                    sum += (ggml_float)x[i00];
                }

                float mean = sum / ne00;

                float * y = (float *)((char *)dst->data + i01 * nb1 + i02 * nb2 + i03 * nb3);

                ggml_float sum2 = 0.0;
                for (int64_t i00 = 0; i00 < ne00; i00++) {
                    float v = x[i00] - mean;
                    y[i00] = v;
                    sum2 += (ggml_float)(v * v);
                }

                float variance = sum2 / ne00;
                const float scale = 1.0f / sqrtf(variance + eps);

                ggml_vec_scale_f32(ne00, y, scale);
            }
        }
    }
}

static void ggml_compute_forward_norm(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                      struct ggml_tensor * dst) {
    switch (src0->type) {
    case GGML_TYPE_F32: {
        ggml_compute_forward_norm_f32(params, src0, dst);
    } break;
    default: {
        GGML_ASSERT(false);
    } break;
    }
}

// ggml_compute_forward_group_rms_norm

static void ggml_compute_forward_rms_norm_f32(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                              struct ggml_tensor * dst) {
    GGML_ASSERT(ggml_are_same_shape(src0, dst));

    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
        return;
    }

    GGML_ASSERT(src0->nb[0] == sizeof(float));

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

    GGML_TENSOR_UNARY_OP_LOCALS;

    float eps;
    memcpy(&eps, dst->op_params, sizeof(float));

    // TODO: optimize
    for (int64_t i03 = 0; i03 < ne03; i03++) {
        for (int64_t i02 = 0; i02 < ne02; i02++) {
            for (int64_t i01 = ith; i01 < ne01; i01 += nth) {
                const float * x = (float *)((char *)src0->data + i01 * nb01 + i02 * nb02 + i03 * nb03);

                ggml_float sum = 0.0;
                for (int64_t i00 = 0; i00 < ne00; i00++) {
                    sum += (ggml_float)(x[i00] * x[i00]);
                }

                const float mean = sum / ne00;

                float * y = (float *)((char *)dst->data + i01 * nb1 + i02 * nb2 + i03 * nb3);

                memcpy(y, x, ne00 * sizeof(float));
                // for (int i00 = 0; i00 < ne00; i00++) {
                //     y[i00] = x[i00];
                // }

                const float scale = 1.0f / sqrtf(mean + eps);

                ggml_vec_scale_f32(ne00, y, scale);
            }
        }
    }
}

static void ggml_compute_forward_rms_norm(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                          struct ggml_tensor * dst) {
    switch (src0->type) {
    case GGML_TYPE_F32: {
        ggml_compute_forward_rms_norm_f32(params, src0, dst);
    } break;
    default: {
        GGML_ASSERT(false);
    } break;
    }
}

static void ggml_compute_forward_rms_norm_back_f32(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                                   const struct ggml_tensor * src1, struct ggml_tensor * dst) {
    GGML_ASSERT(ggml_are_same_shape(src0, dst) && ggml_are_same_shape(src0, src1));

    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
        return;
    }

    GGML_ASSERT(src0->nb[0] == sizeof(float));

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

    GGML_TENSOR_BINARY_OP_LOCALS;

    float eps;
    memcpy(&eps, dst->op_params, sizeof(float));

    // TODO: optimize
    for (int64_t i03 = 0; i03 < ne03; i03++) {
        for (int64_t i02 = 0; i02 < ne02; i02++) {
            for (int64_t i01 = ith; i01 < ne01; i01 += nth) {
                // src1 is same shape as src0 => same indices
                const int64_t i11 = i01;
                const int64_t i12 = i02;
                const int64_t i13 = i03;

                const float * x = (float *)((char *)src0->data + i01 * nb01 + i02 * nb02 + i03 * nb03);
                const float * dz = (float *)((char *)src1->data + i11 * nb11 + i12 * nb12 + i13 * nb13);

                ggml_float sum_xx = 0.0;
                ggml_float sum_xdz = 0.0;

                for (int64_t i00 = 0; i00 < ne00; i00++) {
                    sum_xx += (ggml_float)(x[i00] * x[i00]);
                    sum_xdz += (ggml_float)(x[i00] * dz[i00]);
                }

                // const float mean     = (float)(sum_xx)/ne00;
                const float mean_eps = (float)(sum_xx) / ne00 + eps;
                const float sum_eps = (float)(sum_xx) + eps * ne00;
                // const float mean_xdz = (float)(sum_xdz)/ne00;
                //  we could cache rms from forward pass to improve performance.
                //  to do this implement ggml_rms and compose ggml_rms_norm using ggml_rms.
                // const float rms      = sqrtf(mean_eps);
                const float rrms = 1.0f / sqrtf(mean_eps);
                // const float scale    = -rrms/(ne00 * mean_eps); // -1/(n*rms**3)

                {
                    // z = rms_norm(x)
                    //
                    // rms_norm(src0) =
                    //     scale(
                    //         src0,
                    //         div(
                    //             1,
                    //             sqrt(
                    //                 add(
                    //                     scale(
                    //                         sum(
                    //                             sqr(
                    //                                 src0)),
                    //                         (1.0/N)),
                    //                     eps))));

                    // postorder:
                    // ## op    args         grad
                    // 00 param src0         grad[#00]
                    // 01 const 1
                    // 02 sqr   (#00)        grad[#02]
                    // 03 sum   (#02)        grad[#03]
                    // 04 const 1/N
                    // 05 scale (#03, #04)   grad[#05]
                    // 06 const eps
                    // 07 add   (#05, #06)   grad[#07]
                    // 08 sqrt  (#07)        grad[#08]
                    // 09 div   (#01,#08)    grad[#09]
                    // 10 scale (#00,#09)    grad[#10]
                    //
                    // backward pass, given grad[#10]
                    // #10: scale
                    // grad[#00] += scale(grad[#10],#09)
                    // grad[#09] += sum(mul(grad[#10],#00))
                    // #09: div
                    // grad[#08] += neg(mul(grad[#09], div(#09,#08)))
                    // #08: sqrt
                    // grad[#07] += mul(grad[#08], div(0.5, #08))
                    // #07: add
                    // grad[#05] += grad[#07]
                    // #05: scale
                    // grad[#03] += scale(grad[#05],#04)
                    // #03: sum
                    // grad[#02] += repeat(grad[#03], #02)
                    // #02:
                    // grad[#00] += scale(mul(#00, grad[#02]), 2.0)
                    //
                    // substitute and simplify:
                    // grad[#00] = scale(grad(#10), #09) + scale(mul(#00, grad[#02]), 2.0)
                    // grad[#02] = repeat(grad[#03], #02)
                    // grad[#02] = repeat(scale(grad[#05],#04), #02)
                    // grad[#02] = repeat(scale(grad[#07],#04), #02)
                    // grad[#02] = repeat(scale(mul(grad[#08], div(0.5, #08)),#04), #02)
                    // grad[#02] = repeat(scale(mul(neg(mul(grad[#09], div(#09,#08))), div(0.5, #08)),#04), #02)
                    // grad[#02] = repeat(scale(mul(neg(mul(sum(mul(grad[#10],#00)), div(#09,#08))), div(0.5, #08)),#04), #02)
                    // grad[#02] = repeat(-(sum(mul(grad[#10],#00)) * div(#09,#08) * div(0.5, #08) * (1/N)), #02)
                    // grad[#02] = repeat(-(sum(mul(grad[#10],#00)) * div(div(#01,#08),#08) * div(0.5, #08) * (1/N)), #02)
                    // grad[#02] = repeat(-(sum(mul(grad[#10],#00)) * div(1,#08*#08) * div(0.5, #08) * (1/N)), #02)
                    // grad[#02] = repeat(-(sum(mul(grad[#10],#00)) * div(1,#07) * div(0.5, #08) * (1/N)), #02)
                    // grad[#00] = scale(grad(#10), #09) + scale(mul(#00, grad[#02]), 2.0)
                    // grad[#00] = scale(grad(#10), #09) + scale(mul(#00, repeat(-(sum(mul(grad[#10],#00)) * div(1,#07) *
                    // div(0.5, #08) * (1/N)), #02)), 2.0) grad[#00] = scale(grad(#10), #09) + scale(scale(#00,
                    // -(sum(mul(grad[#10],#00)) * div(1,#07) * div(0.5, #08) * (1/N))), 2.0) grad[#00] = scale(grad(#10), #09)
                    // + scale(#00, -(sum(mul(grad[#10],#00)) * div(1,#07) * div(1,#08) * (1/N))) grad[#00] = scale(grad(#10),
                    // #09) + scale(#00, sum(mul(grad[#10],#00)) * div(1,#07*#08) * (-1/N)) grad[#00] = scale(grad(#10), #09) +
                    // scale(#00, sum(mul(grad[#10],#00)) * div(1,#07*#08) * (-1/N)) grad[#00] = scale(grad(#10), #09) +
                    // scale(#00, sum(mul(grad[#10],#00)) * div(1,mean_eps*rms) * (-1/N)) grad[#00] = scale(grad(#10), #09) +
                    // scale(#00, sum(mul(grad[#10],#00)) * div(-1,rms*N*mean_eps)) grad[#00] = scale(grad(#10), #09) +
                    // scale(#00, sum(mul(grad[#10],#00)) * div(-1,rms*N*(sum_xx/N+eps))) grad[#00] = scale(grad(#10), #09) +
                    // scale(#00, sum(mul(grad[#10],#00)) * div(-1,rms*N*sum_xx+rms*N*eps)) grad[#00] = scale(dz, rrms) +
                    // scale(x, sum(mul(dz,x)) * div(-1,rms*N*mean_eps)) grad[#00] = scale(dz, rrms) + scale(x, sum_xdz *
                    // div(-1,rms*N*mean_eps)) a = b*c + d*e a = b*c*f/f + d*e*f/f a = (b*c*f + d*e*f)*(1/f) a = (b*c*(1/c) +
                    // d*e*(1/c))*(1/(1/c)) a = (b + d*e/c)*c b = dz, c = rrms, d = x, e = sum_xdz * div(-1,rms*N*mean_eps) a =
                    // (dz + x*sum_xdz * div(-1,rms*N*mean_eps)/rrms)*rrms a = (dz + x*sum_xdz *
                    // div(-1,rms*N*mean_eps)*rms)*rrms a = (dz + x*sum_xdz * div(-rms,rms*N*mean_eps))*rrms a = (dz + x*sum_xdz
                    // * div(-1,N*mean_eps))*rrms a = (dz + x*div(-sum_xdz,N*mean_eps))*rrms a = (dz +
                    // x*div(-mean_xdz,mean_eps))*rrms grad[#00] = scale(dz + scale(x, div(-mean_xdz,mean_eps)),rrms) grad[#00]
                    // = scale(dz + scale(x, -mean_xdz/mean_eps),rrms) dx = scale(dz + scale(x, -mean_xdz/mean_eps),rrms)
                }
                // dx = scale(dz + scale(x, -mean_xdz/mean_eps),rrms)
                // post-order:
                // dx := x
                // dx := scale(dx,-mean_xdz/mean_eps)
                // dx := add(dx, dz)
                // dx := scale(dx, rrms)
                float * dx = (float *)((char *)dst->data + i01 * nb1 + i02 * nb2 + i03 * nb3);

                ggml_vec_cpy_f32(ne00, dx, x);
                // ggml_vec_scale_f32(ne00, dx, -mean_xdz/mean_eps);
                ggml_vec_scale_f32(ne00, dx, (float)(-sum_xdz) / sum_eps);
                ggml_vec_acc_f32(ne00, dx, dz);
                ggml_vec_scale_f32(ne00, dx, rrms);
            }
        }
    }
}

static void ggml_compute_forward_rms_norm_back(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                               const struct ggml_tensor * src1, struct ggml_tensor * dst) {
    switch (src0->type) {
    case GGML_TYPE_F32: {
        ggml_compute_forward_rms_norm_back_f32(params, src0, src1, dst);
    } break;
    default: {
        GGML_ASSERT(false);
    } break;
    }
}

// ggml_compute_forward_group_norm

static void ggml_compute_forward_group_norm_f32(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                                struct ggml_tensor * dst) {
    GGML_ASSERT(ggml_are_same_shape(src0, dst));

    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
        return;
    }

    GGML_ASSERT(src0->nb[0] == sizeof(float));

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

    GGML_TENSOR_UNARY_OP_LOCALS;

    const float eps = 1e-6f; // TODO: make this a parameter

    // TODO: optimize

    int n_channels = src0->ne[2];
    int n_groups = dst->op_params[0];
    int n_channels_per_group = (n_channels + n_groups - 1) / n_groups;
    for (int i = ith; i < n_groups; i += nth) {
        int start = i * n_channels_per_group;
        int end = start + n_channels_per_group;
        if (end > n_channels) {
            end = n_channels;
        }
        int step = end - start;

        for (int64_t i03 = 0; i03 < ne03; i03++) {
            ggml_float sum = 0.0;
            for (int64_t i02 = start; i02 < end; i02++) {
                for (int64_t i01 = 0; i01 < ne01; i01++) {
                    const float * x = (float *)((char *)src0->data + i01 * nb01 + i02 * nb02 + i03 * nb03);

                    for (int64_t i00 = 0; i00 < ne00; i00++) {
                        sum += (ggml_float)x[i00];
                    }
                }
            }
            float mean = sum / (ne00 * ne01 * step);
            ggml_float sum2 = 0.0;

            for (int64_t i02 = start; i02 < end; i02++) {
                for (int64_t i01 = 0; i01 < ne01; i01++) {
                    const float * x = (float *)((char *)src0->data + i01 * nb01 + i02 * nb02 + i03 * nb03);

                    float * y = (float *)((char *)dst->data + i01 * nb1 + i02 * nb2 + i03 * nb3);

                    for (int64_t i00 = 0; i00 < ne00; i00++) {
                        float v = x[i00] - mean;
                        y[i00] = v;
                        sum2 += (ggml_float)(v * v);
                    }
                }
            }
            float variance = sum2 / (ne00 * ne01 * step);
            const float scale = 1.0f / sqrtf(variance + eps);

            for (int64_t i02 = start; i02 < end; i02++) {
                for (int64_t i01 = 0; i01 < ne01; i01++) {
                    float * y = (float *)((char *)dst->data + i01 * nb1 + i02 * nb2 + i03 * nb3);
                    ggml_vec_scale_f32(ne00, y, scale);
                }
            }
        }
    }
}

static void ggml_compute_forward_group_norm(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                            struct ggml_tensor * dst) {
    switch (src0->type) {
    case GGML_TYPE_F32: {
        ggml_compute_forward_group_norm_f32(params, src0, dst);
    } break;
    default: {
        GGML_ASSERT(false);
    } break;
    }
}

// ggml_compute_forward_mul_mat

#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
// helper function to determine if it is better to use BLAS or not
// for large matrices, BLAS is faster
static bool ggml_compute_forward_mul_mat_use_blas(const struct ggml_tensor * src0, const struct ggml_tensor * src1,
                                                  struct ggml_tensor * dst) {
    // const int64_t ne00 = src0->ne[0];
    // const int64_t ne01 = src0->ne[1];

    const int64_t ne10 = src1->ne[0];

    const int64_t ne0 = dst->ne[0];
    const int64_t ne1 = dst->ne[1];

    // TODO: find the optimal values for these
    if (ggml_is_contiguous(src0) && ggml_is_contiguous(src1) && (ne0 >= 32 && ne1 >= 32 && ne10 >= 32)) {

        /*printf("BLAS: %d %d %d %d %d\n", ne0, ne1, ne10, ne00, ne01);*/
        return true;
    }

    return false;
}
#endif

static void ggml_compute_forward_mul_mat(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                         const struct ggml_tensor * src1, struct ggml_tensor * dst) {
    int64_t t0 = ggml_perf_time_us();
    UNUSED(t0);

    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);

    ggml_vec_dot_t const vec_dot = type_traits[type].vec_dot;
    enum ggml_type const vec_dot_type = type_traits[type].vec_dot_type;
    ggml_from_float_t const from_float_to_vec_dot = type_traits[vec_dot_type].from_float;

    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(type));
    GGML_ASSERT(nb10 == sizeof(float));

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

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

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

#if defined(GGML_USE_CLBLAST)
    if (ggml_cl_can_mul_mat(src0, src1, dst)) {
        // TODO: handle case when src0 is broadcast-able into src1 across 2nd,3rd dimension
        //       ref: https://github.com/ggerganov/ggml/pull/224
        GGML_ASSERT(ne02 == ne12);
        GGML_ASSERT(ne03 == ne13);

        if (params->ith == 0 && params->type == GGML_TASK_COMPUTE) {
            ggml_cl_mul_mat(src0, src1, dst, params->wdata, params->wsize);
        }
        return;
    }
#endif

#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
    if (ggml_compute_forward_mul_mat_use_blas(src0, src1, dst)) {
        if (params->ith != 0) {
            return;
        }

        if (params->type == GGML_TASK_INIT) {
            return;
        }

        if (params->type == GGML_TASK_FINALIZE) {
            return;
        }

        for (int64_t i13 = 0; i13 < ne13; i13++) {
            for (int64_t i12 = 0; i12 < ne12; i12++) {
                // broadcast src0 into src1 across 2nd,3rd dimension
                const int64_t i03 = i13 / r3;
                const int64_t i02 = i12 / r2;

                const void * x = (char *)src0->data + i02 * nb02 + i03 * nb03;
                const float * y = (float *)((char *)src1->data + i12 * nb12 + i13 * nb13);

                float * d = (float *)((char *)dst->data + i12 * nb2 + i13 * nb3);

                if (type != GGML_TYPE_F32) {
                    float * const wdata = params->wdata;
                    ggml_to_float_t const to_float = type_traits[type].to_float;

                    size_t id = 0;
                    for (int64_t i01 = 0; i01 < ne01; ++i01) {
                        to_float((const char *)x + i01 * nb01, wdata + id, ne00);
                        id += ne00;
                    }

                    assert(id * sizeof(float) <= params->wsize);
                    x = wdata;
                }

                cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasTrans, ne11, ne01, ne10, 1.0f, y, ne10, x, ne00, 0.0f, d, ne01);
            }
        }

        // printf("CBLAS = %f ms, %d x %d x %d x %d\n", (ggml_perf_time_us() - t0)/1000.0, ne0, ne1, ne2, ne3);

        return;
    }
#endif

    if (params->type == GGML_TASK_INIT) {
        if (src1->type != vec_dot_type) {
            char * wdata = params->wdata;
            const size_t row_size = ne10 * ggml_type_size(vec_dot_type) / ggml_blck_size(vec_dot_type);

            for (int64_t i13 = 0; i13 < ne13; ++i13) {
                for (int64_t i12 = 0; i12 < ne12; ++i12) {
                    for (int64_t i11 = 0; i11 < ne11; ++i11) {
                        from_float_to_vec_dot((float *)((char *)src1->data + i13 * nb13 + i12 * nb12 + i11 * nb11),
                                              (void *)wdata, ne10);
                        wdata += row_size;
                    }
                }
            }
        }

        return;
    }

    if (params->type == GGML_TASK_FINALIZE) {
        return;
    }

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

    const int64_t nr0 = ne01;               // src0 rows
    const int64_t nr1 = ne11 * ne12 * ne13; // src1 rows

    // printf("nr0 = %lld, nr1 = %lld\n", nr0, nr1);

    // distribute the thread work across the inner or outer loop based on which one is larger

    const int64_t nth0 = nr0 > nr1 ? nth : 1; // parallelize by src0 rows
    const int64_t nth1 = nr0 > nr1 ? 1 : nth; // parallelize by src1 rows

    const int64_t ith0 = ith % nth0;
    const int64_t ith1 = ith / nth0;

    const int64_t dr0 = (nr0 + nth0 - 1) / nth0;
    const int64_t dr1 = (nr1 + nth1 - 1) / nth1;

    const int64_t ir010 = dr0 * ith0;
    const int64_t ir011 = MIN(ir010 + dr0, nr0);

    const int64_t ir110 = dr1 * ith1;
    const int64_t ir111 = MIN(ir110 + dr1, nr1);

    // printf("ir010 = %6lld, ir011 = %6lld, ir110 = %6lld, ir111 = %6lld\n", ir010, ir011, ir110, ir111);

    // threads with no work simply yield (not sure if it helps)
    if (ir010 >= ir011 || ir110 >= ir111) {
        sched_yield();
        return;
    }

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

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

    // attempt to reduce false-sharing (does not seem to make a difference)
    float tmp[16];

    for (int64_t iir1 = ir110; iir1 < ir111; iir1 += blck_1) {
        for (int64_t iir0 = ir010; iir0 < ir011; iir0 += blck_0) {
            for (int64_t ir1 = iir1; ir1 < iir1 + blck_1 && ir1 < ir111; ++ir1) {
                const int64_t i13 = (ir1 / (ne12 * ne11));
                const int64_t i12 = (ir1 - i13 * ne12 * ne11) / ne11;
                const int64_t i11 = (ir1 - i13 * ne12 * ne11 - i12 * ne11);

                // 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 < ir011; ++ir0) {
                //     vec_dot(ne00, &dst_col[ir0], src0_row + ir0*nb01, src1_col);
                // }

                for (int64_t ir0 = iir0; ir0 < iir0 + blck_0 && ir0 < ir011; ++ir0) {
                    vec_dot(ne00, &tmp[ir0 - iir0], src0_row + ir0 * nb01, src1_col);
                }
                memcpy(&dst_col[iir0], tmp, (MIN(iir0 + blck_0, ir011) - iir0) * sizeof(float));
            }
        }
    }
}

// ggml_compute_forward_out_prod

static void ggml_compute_forward_out_prod_f32(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                              const struct ggml_tensor * src1, struct ggml_tensor * dst) {
    int64_t t0 = ggml_perf_time_us();
    UNUSED(t0);

    GGML_TENSOR_BINARY_OP_LOCALS;

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

    GGML_ASSERT(ne02 == ne12);
    GGML_ASSERT(ne03 == ne13);
    GGML_ASSERT(ne2 == ne12);
    GGML_ASSERT(ne3 == ne13);

    // we don't support permuted src0 or src1
    GGML_ASSERT(nb00 == sizeof(float));

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

    GGML_ASSERT(ne0 == ne00);
    GGML_ASSERT(ne1 == ne10);
    GGML_ASSERT(ne2 == ne02);
    GGML_ASSERT(ne3 == ne03);

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

    // TODO: #if defined(GGML_USE_CUBLAS) ggml_cuda_out_prod
    // TODO: #if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CLBLAST)

    if (params->type == GGML_TASK_INIT) {
        ggml_vec_set_f32(ne0 * ne1 * ne2 * ne3, dst->data, 0);
        return;
    }

    if (params->type == GGML_TASK_FINALIZE) {
        return;
    }

    // parallelize by last three dimensions

    // total rows in dst
    const int64_t nr = ne1 * ne2 * ne3;

    // rows per thread
    const int64_t dr = (nr + nth - 1) / nth;

    // row range for this thread
    const int64_t ir0 = dr * ith;
    const int64_t ir1 = MIN(ir0 + dr, nr);

    // dst[:,:,:,:] = 0
    // for i2,i3:
    //   for i1:
    //     for i01:
    //       for i0:
    //         dst[i0,i1,i2,i3] += src0[i0,i01,i2,i3] * src1[i1,i01,i2,i3]

    for (int64_t ir = ir0; ir < ir1; ++ir) {
        // dst indices
        const int64_t i3 = ir / (ne2 * ne1);
        const int64_t i2 = (ir - i3 * ne2 * ne1) / ne1;
        const int64_t i1 = (ir - i3 * ne2 * ne1 - i2 * ne1);

        const int64_t i02 = i2;
        const int64_t i03 = i3;

        // const int64_t i10 = i1;
        const int64_t i12 = i2;
        const int64_t i13 = i3;

        for (int64_t i01 = 0; i01 < ne01; ++i01) {
            const int64_t i11 = i01;

            float * s0 = (float *)((char *)src0->data + (i01 * nb01 + i02 * nb02 + i03 * nb03));
            float * s1 = (float *)((char *)src1->data + (i1 * nb10 + i11 * nb11 + i12 * nb12 + i13 * nb13));
            float * d = (float *)((char *)dst->data + (i1 * nb1 + i2 * nb2 + i3 * nb3));

            ggml_vec_mad_f32(ne0, d, s0, *s1);
            // for (int64_t i0 = 0; i0 < ne0; ++i0) {
            //     d[i0] += s0[i0] * s1[i1];
            // }
        }
    }

    // int64_t t1 = ggml_perf_time_us();
    // static int64_t acc = 0;
    // acc += t1 - t0;
    // if (t1 - t0 > 10) {
    //     printf("\n");
    //     printf("ne00 = %5d, ne01 = %5d, ne02 = %5d, ne03 = %5d\n", ne00, ne01, ne02, ne03);
    //     printf("nb00 = %5d, nb01 = %5d, nb02 = %5d, nb03 = %5d\n", nb00, nb01, nb02, nb03);
    //     printf("ne10 = %5d, ne11 = %5d, ne12 = %5d, ne13 = %5d\n", ne10, ne11, ne12, ne13);
    //     printf("nb10 = %5d, nb11 = %5d, nb12 = %5d, nb13 = %5d\n", nb10, nb11, nb12, nb13);

    //    printf("XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX task %d/%d: %d us, acc = %d\n", ith, nth, (int) (t1 -
    //    t0), (int) acc);
    //}
}

static void ggml_compute_forward_out_prod(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                          const struct ggml_tensor * src1, struct ggml_tensor * dst) {
    switch (src0->type) {
    case GGML_TYPE_Q4_0:
    case GGML_TYPE_Q4_1:
    case GGML_TYPE_Q5_0:
    case GGML_TYPE_Q5_1:
    case GGML_TYPE_Q8_0:
    case GGML_TYPE_Q8_1: {
        GGML_ASSERT(false); // todo
        // ggml_compute_forward_out_prod_q_f32(params, src0, src1, dst);
    } break;
    case GGML_TYPE_F16: {
        GGML_ASSERT(false); // todo
        // ggml_compute_forward_out_prod_f16_f32(params, src0, src1, dst);
    } break;
    case GGML_TYPE_F32: {
        ggml_compute_forward_out_prod_f32(params, src0, src1, dst);
    } break;
    default: {
        GGML_ASSERT(false);
    } break;
    }
}

// ggml_compute_forward_scale

static void ggml_compute_forward_scale_f32(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                           const struct ggml_tensor * src1, struct ggml_tensor * dst) {
    GGML_ASSERT(ggml_is_contiguous(src0));
    GGML_ASSERT(ggml_is_contiguous(dst));
    GGML_ASSERT(ggml_are_same_shape(src0, dst));
    GGML_ASSERT(ggml_is_scalar(src1));

    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
        return;
    }

    // scale factor
    const float v = *(float *)src1->data;

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

    const int nc = src0->ne[0];
    const int nr = ggml_nrows(src0);

    // rows per thread
    const int dr = (nr + nth - 1) / nth;

    // row range for this thread
    const int ir0 = dr * ith;
    const int ir1 = MIN(ir0 + dr, nr);

    const size_t nb01 = src0->nb[1];

    const size_t nb1 = dst->nb[1];

    for (int i1 = ir0; i1 < ir1; i1++) {
        if (dst->data != src0->data) {
            // src0 is same shape as dst => same indices
            memcpy((char *)dst->data + i1 * nb1, (char *)src0->data + i1 * nb01, nc * sizeof(float));
        }
        ggml_vec_scale_f32(nc, (float *)((char *)dst->data + i1 * nb1), v);
    }
}

static void ggml_compute_forward_scale(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                       const struct ggml_tensor * src1, struct ggml_tensor * dst) {
    switch (src0->type) {
    case GGML_TYPE_F32: {
        ggml_compute_forward_scale_f32(params, src0, src1, dst);
    } break;
    default: {
        GGML_ASSERT(false);
    } break;
    }
}

// ggml_compute_forward_set

static void ggml_compute_forward_set_f32(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                         const struct ggml_tensor * src1, struct ggml_tensor * dst) {
    GGML_ASSERT(ggml_are_same_shape(src0, dst));
    GGML_ASSERT(ggml_is_contiguous(dst) && ggml_is_contiguous(src0));

    // view src0 and dst with these strides and data offset inbytes during set
    // nb0 is implicitely element_size because src0 and dst are contiguous
    size_t nb1 = ((int32_t *)dst->op_params)[0];
    size_t nb2 = ((int32_t *)dst->op_params)[1];
    size_t nb3 = ((int32_t *)dst->op_params)[2];
    size_t offset = ((int32_t *)dst->op_params)[3];
    bool inplace = (bool)((int32_t *)dst->op_params)[4];

    if (!inplace && (params->type == GGML_TASK_INIT)) {
        // memcpy needs to be synchronized across threads to avoid race conditions.
        // => do it in INIT phase
        memcpy(((char *)dst->data), ((char *)src0->data), ggml_nbytes(dst));
    }

    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
        return;
    }

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

    const int nr = ggml_nrows(src1);
    const int nc = src1->ne[0];

    GGML_TENSOR_LOCALS(int64_t, ne1, src1, ne);
    GGML_TENSOR_LOCALS(size_t, nb1, src1, nb);

    // src0 and dst as viewed during set
    const size_t nb0 = ggml_element_size(src0);

    const int im0 = (ne10 == 0 ? 0 : ne10 - 1);
    const int im1 = (ne11 == 0 ? 0 : ne11 - 1);
    const int im2 = (ne12 == 0 ? 0 : ne12 - 1);
    const int im3 = (ne13 == 0 ? 0 : ne13 - 1);

    GGML_ASSERT(offset + im0 * nb0 + im1 * nb1 + im2 * nb2 + im3 * nb3 <= ggml_nbytes(dst));

    GGML_ASSERT(nb10 == sizeof(float));

    // rows per thread
    const int dr = (nr + nth - 1) / nth;

    // row range for this thread
    const int ir0 = dr * ith;
    const int ir1 = MIN(ir0 + dr, nr);

    for (int ir = ir0; ir < ir1; ++ir) {
        // src0 and dst are viewed with shape of src1 and offset
        // => same indices
        const int i3 = ir / (ne12 * ne11);
        const int i2 = (ir - i3 * ne12 * ne11) / ne11;
        const int i1 = (ir - i3 * ne12 * ne11 - i2 * ne11);

        ggml_vec_cpy_f32(nc, (float *)((char *)dst->data + i3 * nb3 + i2 * nb2 + i1 * nb1 + offset),
                         (float *)((char *)src1->data + i3 * nb13 + i2 * nb12 + i1 * nb11));
    }
}

static void ggml_compute_forward_set(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                     const struct ggml_tensor * src1, struct ggml_tensor * dst) {

    switch (src0->type) {
    case GGML_TYPE_F32: {
        ggml_compute_forward_set_f32(params, src0, src1, dst);
    } break;
    case GGML_TYPE_F16:
    case GGML_TYPE_Q4_0:
    case GGML_TYPE_Q4_1:
    case GGML_TYPE_Q5_0:
    case GGML_TYPE_Q5_1:
    case GGML_TYPE_Q8_0:
    case GGML_TYPE_Q8_1:
    case GGML_TYPE_Q2_K:
    case GGML_TYPE_Q3_K:
    case GGML_TYPE_Q4_K:
    case GGML_TYPE_Q5_K:
    case GGML_TYPE_Q6_K:
    default: {
        GGML_ASSERT(false);
    } break;
    }
}

// ggml_compute_forward_cpy

static void ggml_compute_forward_cpy(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                     struct ggml_tensor * dst) {
    ggml_compute_forward_dup(params, src0, dst);
}

// ggml_compute_forward_cont

static void ggml_compute_forward_cont(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                      struct ggml_tensor * dst) {
    ggml_compute_forward_dup(params, src0, dst);
}

// ggml_compute_forward_reshape

static void ggml_compute_forward_reshape(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                         struct ggml_tensor * dst) {
    // NOP
    UNUSED(params);
    UNUSED(src0);
    UNUSED(dst);
}

// ggml_compute_forward_view

static void ggml_compute_forward_view(const struct ggml_compute_params * params, const struct ggml_tensor * src0) {
    // NOP
    UNUSED(params);
    UNUSED(src0);
}

// ggml_compute_forward_permute

static void ggml_compute_forward_permute(const struct ggml_compute_params * params, const struct ggml_tensor * src0) {
    // NOP
    UNUSED(params);
    UNUSED(src0);
}

// ggml_compute_forward_transpose

static void ggml_compute_forward_transpose(const struct ggml_compute_params * params, const struct ggml_tensor * src0) {
    // NOP
    UNUSED(params);
    UNUSED(src0);
}

// ggml_compute_forward_get_rows

static void ggml_compute_forward_get_rows_q(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                            const struct ggml_tensor * src1, struct ggml_tensor * dst) {
    assert(params->ith == 0);

    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
        return;
    }

    const int nc = src0->ne[0];
    const int nr = ggml_nelements(src1);
    const enum ggml_type type = src0->type;
    ggml_to_float_t const dequantize_row_q = type_traits[type].to_float;

    assert(dst->ne[0] == nc);
    assert(dst->ne[1] == nr);
    assert(src0->nb[0] == ggml_type_size(type));

    for (int i = 0; i < nr; ++i) {
        const int r = ((int32_t *)src1->data)[i];

        dequantize_row_q((const void *)((char *)src0->data + r * src0->nb[1]), (float *)((char *)dst->data + i * dst->nb[1]),
                         nc);
    }
}

static void ggml_compute_forward_get_rows_f16(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                              const struct ggml_tensor * src1, struct ggml_tensor * dst) {
    assert(params->ith == 0);

    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
        return;
    }

    const int nc = src0->ne[0];
    const int nr = ggml_nelements(src1);

    assert(dst->ne[0] == nc);
    assert(dst->ne[1] == nr);
    assert(src0->nb[0] == sizeof(ggml_fp16_t));

    for (int i = 0; i < nr; ++i) {
        const int r = ((int32_t *)src1->data)[i];

        for (int j = 0; j < nc; ++j) {
            ggml_fp16_t v = ((ggml_fp16_t *)((char *)src0->data + r * src0->nb[1]))[j];
            ((float *)((char *)dst->data + i * dst->nb[1]))[j] = GGML_FP16_TO_FP32(v);
        }
    }
}

static void ggml_compute_forward_get_rows_f32(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                              const struct ggml_tensor * src1, struct ggml_tensor * dst) {
    assert(params->ith == 0);

    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
        return;
    }

    const int nc = src0->ne[0];
    const int nr = ggml_nelements(src1);

    assert(dst->ne[0] == nc);
    assert(dst->ne[1] == nr);
    assert(src0->nb[0] == sizeof(float));

    for (int i = 0; i < nr; ++i) {
        const int r = ((int32_t *)src1->data)[i];

        ggml_vec_cpy_f32(nc, (float *)((char *)dst->data + i * dst->nb[1]), (float *)((char *)src0->data + r * src0->nb[1]));
    }
}

static void ggml_compute_forward_get_rows(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                          const struct ggml_tensor * src1, struct ggml_tensor * dst) {
    switch (src0->type) {
    case GGML_TYPE_Q4_0:
    case GGML_TYPE_Q4_1:
    case GGML_TYPE_Q5_0:
    case GGML_TYPE_Q5_1:
    case GGML_TYPE_Q8_0:
    case GGML_TYPE_Q8_1:
    case GGML_TYPE_Q2_K:
    case GGML_TYPE_Q3_K:
    case GGML_TYPE_Q4_K:
    case GGML_TYPE_Q5_K:
    case GGML_TYPE_Q6_K: {
        ggml_compute_forward_get_rows_q(params, src0, src1, dst);
    } break;
    case GGML_TYPE_F16: {
        ggml_compute_forward_get_rows_f16(params, src0, src1, dst);
    } break;
    case GGML_TYPE_F32: {
        ggml_compute_forward_get_rows_f32(params, src0, src1, dst);
    } break;
    default: {
        GGML_ASSERT(false);
    } break;
    }

    // static bool first = true;
    // printf("ne0 = %d, ne1 = %d, ne2 = %d\n", dst->ne[0], dst->ne[1], dst->ne[2]);
    // if (first) {
    //     first = false;
    // } else {
    //     for (int k = 0; k < dst->ne[1]; ++k) {
    //         for (int j = 0; j < dst->ne[0]/16; ++j) {
    //             for (int i = 0; i < 16; ++i) {
    //                 printf("%8.4f ", ((float *) dst->data)[k*dst->ne[0] + j*16 + i]);
    //             }
    //             printf("\n");
    //         }
    //         printf("\n");
    //     }
    //     printf("\n");
    //     exit(0);
    // }
}

// ggml_compute_forward_get_rows_back

static void ggml_compute_forward_get_rows_back_f32_f16(const struct ggml_compute_params * params,
                                                       const struct ggml_tensor * src0, const struct ggml_tensor * src1,
                                                       const struct ggml_tensor * opt0, struct ggml_tensor * dst) {
    GGML_ASSERT(params->ith == 0);
    GGML_ASSERT(ggml_are_same_shape(opt0, dst));
    GGML_ASSERT(ggml_is_contiguous(opt0));
    GGML_ASSERT(ggml_is_contiguous(dst));

    ggml_compute_forward_dup_same_cont(params, opt0, dst);

    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
        return;
    }

    const int nc = src0->ne[0];
    const int nr = ggml_nelements(src1);

    GGML_ASSERT(dst->ne[0] == nc);
    GGML_ASSERT(src0->nb[0] == sizeof(ggml_fp16_t));

    for (int i = 0; i < nr; ++i) {
        const int r = ((int32_t *)src1->data)[i];

        for (int j = 0; j < nc; ++j) {
            ggml_fp16_t v = ((ggml_fp16_t *)((char *)src0->data + i * src0->nb[1]))[j];
            ((float *)((char *)dst->data + r * dst->nb[1]))[j] += GGML_FP16_TO_FP32(v);
        }
    }
}

static void ggml_compute_forward_get_rows_back_f32(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                                   const struct ggml_tensor * src1, const struct ggml_tensor * opt0,
                                                   struct ggml_tensor * dst) {
    GGML_ASSERT(params->ith == 0);
    GGML_ASSERT(ggml_are_same_shape(opt0, dst));
    GGML_ASSERT(ggml_is_contiguous(opt0));
    GGML_ASSERT(ggml_is_contiguous(dst));

    // ggml_compute_forward_dup_same_cont(params, opt0, dst);

    if (params->type == GGML_TASK_INIT) {
        memset(dst->data, 0, ggml_nbytes(dst));
    }

    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
        return;
    }

    const int nc = src0->ne[0];
    const int nr = ggml_nelements(src1);

    GGML_ASSERT(dst->ne[0] == nc);
    GGML_ASSERT(src0->nb[0] == sizeof(float));

    for (int i = 0; i < nr; ++i) {
        const int r = ((int32_t *)src1->data)[i];

        ggml_vec_add_f32(nc, (float *)((char *)dst->data + r * dst->nb[1]), (float *)((char *)dst->data + r * dst->nb[1]),
                         (float *)((char *)src0->data + i * src0->nb[1]));
    }
}

static void ggml_compute_forward_get_rows_back(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                               const struct ggml_tensor * src1, const struct ggml_tensor * opt0,
                                               struct ggml_tensor * dst) {
    switch (src0->type) {
    case GGML_TYPE_F16: {
        ggml_compute_forward_get_rows_back_f32_f16(params, src0, src1, opt0, dst);
    } break;
    case GGML_TYPE_F32: {
        ggml_compute_forward_get_rows_back_f32(params, src0, src1, opt0, dst);
    } break;
    default: {
        GGML_ASSERT(false);
    } break;
    }

    // static bool first = true;
    // printf("ne0 = %d, ne1 = %d, ne2 = %d\n", dst->ne[0], dst->ne[1], dst->ne[2]);
    // if (first) {
    //     first = false;
    // } else {
    //     for (int k = 0; k < dst->ne[1]; ++k) {
    //         for (int j = 0; j < dst->ne[0]/16; ++j) {
    //             for (int i = 0; i < 16; ++i) {
    //                 printf("%8.4f ", ((float *) dst->data)[k*dst->ne[0] + j*16 + i]);
    //             }
    //             printf("\n");
    //         }
    //         printf("\n");
    //     }
    //     printf("\n");
    //     exit(0);
    // }
}

// ggml_compute_forward_diag

static void ggml_compute_forward_diag_f32(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                          struct ggml_tensor * dst) {
    GGML_ASSERT(params->ith == 0);

    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
        return;
    }

    // TODO: handle transposed/permuted matrices

    GGML_TENSOR_UNARY_OP_LOCALS;

    GGML_ASSERT(ne00 == ne0);
    GGML_ASSERT(ne00 == ne1);
    GGML_ASSERT(ne01 == 1);
    GGML_ASSERT(ne02 == ne2);
    GGML_ASSERT(ne03 == ne3);

    GGML_ASSERT(nb00 == sizeof(float));
    GGML_ASSERT(nb0 == sizeof(float));

    for (int i3 = 0; i3 < ne3; i3++) {
        for (int i2 = 0; i2 < ne2; i2++) {
            for (int i1 = 0; i1 < ne1; i1++) {
                float * d = (float *)((char *)dst->data + i3 * nb3 + i2 * nb2 + i1 * nb1);
                float * s = (float *)((char *)src0->data + i3 * nb03 + i2 * nb02);
                for (int i0 = 0; i0 < i1; i0++) {
                    d[i0] = 0;
                }
                d[i1] = s[i1];
                for (int i0 = i1 + 1; i0 < ne0; i0++) {
                    d[i0] = 0;
                }
            }
        }
    }
}

static void ggml_compute_forward_diag(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                      struct ggml_tensor * dst) {
    switch (src0->type) {
    case GGML_TYPE_F32: {
        ggml_compute_forward_diag_f32(params, src0, dst);
    } break;
    default: {
        GGML_ASSERT(false);
    } break;
    }
}

// ggml_compute_forward_diag_mask_inf

static void ggml_compute_forward_diag_mask_f32(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                               struct ggml_tensor * dst, const float value) {

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

    const int n_past = ((int32_t *)dst->op_params)[0];
    const bool inplace = src0->data == dst->data;

    GGML_ASSERT(n_past >= 0);

    if (!inplace && (params->type == GGML_TASK_INIT)) {
        // memcpy needs to be synchronized across threads to avoid race conditions.
        // => do it in INIT phase
        GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0));
        GGML_ASSERT(ggml_is_contiguous(dst) && ggml_is_contiguous(src0));
        memcpy(((char *)dst->data), ((char *)src0->data), ggml_nbytes(dst));
    }

    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
        return;
    }

    // TODO: handle transposed/permuted matrices

    const int n = ggml_nrows(src0);
    const int nc = src0->ne[0];
    const int nr = src0->ne[1];
    const int nz = n / nr;

    GGML_ASSERT(dst->nb[0] == sizeof(float));
    GGML_ASSERT(src0->nb[0] == sizeof(float));

    for (int k = 0; k < nz; k++) {
        for (int j = ith; j < nr; j += nth) {
            for (int i = n_past; i < nc; i++) {
                if (i > n_past + j) {
                    *(float *)((char *)dst->data + k * dst->nb[2] + j * dst->nb[1] + i * dst->nb[0]) = value;
                }
            }
        }
    }
}

static void ggml_compute_forward_diag_mask_inf(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                               struct ggml_tensor * dst) {
    switch (src0->type) {
    case GGML_TYPE_F32: {
        ggml_compute_forward_diag_mask_f32(params, src0, dst, -INFINITY);
    } break;
    default: {
        GGML_ASSERT(false);
    } break;
    }
}

static void ggml_compute_forward_diag_mask_zero(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                                struct ggml_tensor * dst) {
    switch (src0->type) {
    case GGML_TYPE_F32: {
        ggml_compute_forward_diag_mask_f32(params, src0, dst, 0);
    } break;
    default: {
        GGML_ASSERT(false);
    } break;
    }
}

// ggml_compute_forward_soft_max

static void ggml_compute_forward_soft_max_f32(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                              struct ggml_tensor * dst) {
    GGML_ASSERT(ggml_is_contiguous(src0));
    GGML_ASSERT(ggml_is_contiguous(dst));
    GGML_ASSERT(ggml_are_same_shape(src0, dst));

    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
        return;
    }

    // TODO: handle transposed/permuted matrices

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

    const int nc = src0->ne[0];
    const int nr = ggml_nrows(src0);

    // rows per thread
    const int dr = (nr + nth - 1) / nth;

    // row range for this thread
    const int ir0 = dr * ith;
    const int ir1 = MIN(ir0 + dr, nr);

    for (int i1 = ir0; i1 < ir1; i1++) {
        float * sp = (float *)((char *)src0->data + i1 * src0->nb[1]);
        float * dp = (float *)((char *)dst->data + i1 * dst->nb[1]);

#ifndef NDEBUG
        for (int i = 0; i < nc; ++i) {
            // printf("p[%d] = %f\n", i, p[i]);
            assert(!isnan(sp[i]));
        }
#endif

        float max = -INFINITY;
        ggml_vec_max_f32(nc, &max, sp);

        ggml_float sum = 0.0;

        uint16_t scvt;
        for (int i = 0; i < nc; i++) {
            if (sp[i] == -INFINITY) {
                dp[i] = 0.0f;
            } else {
                // const float val = (sp[i] == -INFINITY) ? 0.0 : exp(sp[i] - max);
                ggml_fp16_t s = GGML_FP32_TO_FP16(sp[i] - max);
                memcpy(&scvt, &s, sizeof(scvt));
                const float val = GGML_FP16_TO_FP32(table_exp_f16[scvt]);
                sum += (ggml_float)val;
                dp[i] = val;
            }
        }

        assert(sum > 0.0);

        sum = 1.0 / sum;
        ggml_vec_scale_f32(nc, dp, sum);

#ifndef NDEBUG
        for (int i = 0; i < nc; ++i) {
            assert(!isnan(dp[i]));
            assert(!isinf(dp[i]));
        }
#endif
    }
}

static void ggml_compute_forward_soft_max(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                          struct ggml_tensor * dst) {
    switch (src0->type) {
    case GGML_TYPE_F32: {
        ggml_compute_forward_soft_max_f32(params, src0, dst);
    } break;
    default: {
        GGML_ASSERT(false);
    } break;
    }
}

// ggml_compute_forward_soft_max_back

static void ggml_compute_forward_soft_max_back_f32(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                                   const struct ggml_tensor * src1, struct ggml_tensor * dst) {
    GGML_ASSERT(ggml_is_contiguous(src0));
    GGML_ASSERT(ggml_is_contiguous(src1));
    GGML_ASSERT(ggml_is_contiguous(dst));
    GGML_ASSERT(ggml_are_same_shape(src0, dst));
    GGML_ASSERT(ggml_are_same_shape(src1, dst));

    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
        return;
    }

    // TODO: handle transposed/permuted matrices

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

    const int nc = src0->ne[0];
    const int nr = ggml_nrows(src0);

    // rows per thread
    const int dr = (nr + nth - 1) / nth;

    // row range for this thread
    const int ir0 = dr * ith;
    const int ir1 = MIN(ir0 + dr, nr);

    for (int i1 = ir0; i1 < ir1; i1++) {
        float * dy = (float *)((char *)src0->data + i1 * src0->nb[1]);
        float * y = (float *)((char *)src1->data + i1 * src1->nb[1]);
        float * dx = (float *)((char *)dst->data + i1 * dst->nb[1]);

#ifndef NDEBUG
        for (int i = 0; i < nc; ++i) {
            // printf("p[%d] = %f\n", i, p[i]);
            assert(!isnan(dy[i]));
            assert(!isnan(y[i]));
        }
#endif
        // Jii = yi - yi*yi
        // Jij = -yi*yj
        // J = diag(y)-y.T*y
        // dx = J * dy
        // dxk = sum_i(Jki * dyi)
        // dxk = sum_i(-yk*yi * dyi) - (-yk*yk)*dyk + (yk - yk*yk)*dyk
        // dxk = sum_i(-yk*yi * dyi) + yk*yk*dyk + yk*dyk - yk*yk*dyk
        // dxk = sum_i(-yk*yi * dyi) + yk*dyk
        // dxk = -yk * sum_i(yi * dyi) + yk*dyk
        // dxk = -yk * dot(y, dy) + yk*dyk
        // dxk = yk * (- dot(y, dy) + dyk)
        // dxk = yk * (dyk - dot(y, dy))
        //
        // post-order:
        // dot_y_dy := dot(y, dy)
        // dx := dy
        // dx := dx - dot_y_dy
        // dx := dx * y

        // linear runtime, no additional memory
        float dot_y_dy = 0;
        ggml_vec_dot_f32(nc, &dot_y_dy, y, dy);
        ggml_vec_cpy_f32(nc, dx, dy);
        ggml_vec_acc1_f32(nc, dx, -dot_y_dy);
        ggml_vec_mul_f32(nc, dx, dx, y);

#ifndef NDEBUG
        for (int i = 0; i < nc; ++i) {
            assert(!isnan(dx[i]));
            assert(!isinf(dx[i]));
        }
#endif
    }
}

static void ggml_compute_forward_soft_max_back(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                               const struct ggml_tensor * src1, struct ggml_tensor * dst) {
    switch (src0->type) {
    case GGML_TYPE_F32: {
        ggml_compute_forward_soft_max_back_f32(params, src0, src1, dst);
    } break;
    default: {
        GGML_ASSERT(false);
    } break;
    }
}

// ggml_compute_forward_alibi

static void ggml_compute_forward_alibi_f32(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                           struct ggml_tensor * dst) {
    assert(params->ith == 0);

    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
        return;
    }

    const int n_past = ((int32_t *)dst->op_params)[0];
    const int n_head = ((int32_t *)dst->op_params)[1];
    float max_bias;
    memcpy(&max_bias, (int32_t *)dst->op_params + 2, sizeof(float));

    assert(n_past >= 0);

    const int ne0 = src0->ne[0]; // all_seq_len = n_past + ne1
    const int ne1 = src0->ne[1]; // seq_len_without_past
    const int ne2 = src0->ne[2]; // n_head -> this is k
    // const int ne3 = src0->ne[3]; // 1 -> bsz

    const int n = ggml_nrows(src0);
    const int ne2_ne3 = n / ne1; // ne2*ne3

    const int nb0 = src0->nb[0];
    const int nb1 = src0->nb[1];
    const int nb2 = src0->nb[2];
    // const int nb3 = src0->nb[3];

    GGML_ASSERT(nb0 == sizeof(float));
    GGML_ASSERT(ne1 + n_past == ne0);
    GGML_ASSERT(n_head == ne2);

    // add alibi to src0 (KQ_scaled)
    const int n_heads_log2_floor = 1 << (int)floor(log2(n_head));

    const float m0 = powf(2.0f, -(max_bias) / n_heads_log2_floor);
    const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_heads_log2_floor);

    for (int i = 0; i < ne0; i++) {
        for (int j = 0; j < ne1; j++) {
            for (int k = 0; k < ne2_ne3; k++) {
                float * const src = (float *)((char *)src0->data + i * nb0 + j * nb1 + k * nb2);
                float * pdst = (float *)((char *)dst->data + i * nb0 + j * nb1 + k * nb2);

                // TODO: k*nb2 or k*nb3

                float m_k;

                if (k < n_heads_log2_floor) {
                    m_k = powf(m0, k + 1);
                } else {
                    m_k = powf(m1, 2 * (k - n_heads_log2_floor) + 1);
                }

                pdst[0] = i * m_k + src[0];
            }
        }
    }
}

static void ggml_compute_forward_alibi_f16(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                           struct ggml_tensor * dst) {
    assert(params->ith == 0);

    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
        return;
    }

    const int n_past = ((int32_t *)dst->op_params)[0];
    const int n_head = ((int32_t *)dst->op_params)[1];
    float max_bias;
    memcpy(&max_bias, (int32_t *)dst->op_params + 2, sizeof(float));

    assert(n_past >= 0);

    const int ne0 = src0->ne[0]; // all_seq_len = n_past + ne1
    const int ne1 = src0->ne[1]; // seq_len_without_past
    const int ne2 = src0->ne[2]; // n_head -> this is k
    // const int ne3 = src0->ne[3]; // 1 -> bsz

    const int n = ggml_nrows(src0);
    const int ne2_ne3 = n / ne1; // ne2*ne3

    const int nb0 = src0->nb[0];
    const int nb1 = src0->nb[1];
    const int nb2 = src0->nb[2];
    // const int nb3 = src0->nb[3];

    GGML_ASSERT(nb0 == sizeof(ggml_fp16_t));
    GGML_ASSERT(ne1 + n_past == ne0);
    (void)n_past;
    GGML_ASSERT(n_head == ne2);

    // add alibi to src0 (KQ_scaled)
    const int n_heads_log2_floor = 1 << (int)floor(log2(n_head));

    const float m0 = powf(2.0f, -(max_bias) / n_heads_log2_floor);
    const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_heads_log2_floor);

    for (int i = 0; i < ne0; i++) {
        for (int j = 0; j < ne1; j++) {
            for (int k = 0; k < ne2_ne3; k++) {
                ggml_fp16_t * const src = (ggml_fp16_t *)((char *)src0->data + i * nb0 + j * nb1 + k * nb2);
                float * pdst = (float *)((char *)dst->data + i * nb0 + j * nb1 + k * nb2);

                // TODO: k*nb2 or k*nb3

                float m_k;

                if (k < n_heads_log2_floor) {
                    m_k = powf(m0, k + 1);
                } else {
                    m_k = powf(m1, 2 * (k - n_heads_log2_floor) + 1);
                }

                // we return F32
                pdst[0] = i * m_k + GGML_FP16_TO_FP32(src[0]);
            }
        }
    }
}

static void ggml_compute_forward_alibi(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                       struct ggml_tensor * dst) {
    switch (src0->type) {
    case GGML_TYPE_F16: {
        ggml_compute_forward_alibi_f16(params, src0, dst);
    } break;
    case GGML_TYPE_F32: {
        ggml_compute_forward_alibi_f32(params, src0, dst);
    } break;
    case GGML_TYPE_Q4_0:
    case GGML_TYPE_Q4_1:
    case GGML_TYPE_Q5_0:
    case GGML_TYPE_Q5_1:
    case GGML_TYPE_Q8_0:
    case GGML_TYPE_Q8_1:
    case GGML_TYPE_Q2_K:
    case GGML_TYPE_Q3_K:
    case GGML_TYPE_Q4_K:
    case GGML_TYPE_Q5_K:
    case GGML_TYPE_Q6_K:
    case GGML_TYPE_Q8_K:
    case GGML_TYPE_I8:
    case GGML_TYPE_I16:
    case GGML_TYPE_I32:
    case GGML_TYPE_COUNT: {
        GGML_ASSERT(false);
    } break;
    }
}

// ggml_compute_forward_clamp

static void ggml_compute_forward_clamp_f32(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                           struct ggml_tensor * dst) {
    assert(params->ith == 0);

    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
        return;
    }

    float min;
    float max;
    memcpy(&min, (float *)dst->op_params + 0, sizeof(float));
    memcpy(&max, (float *)dst->op_params + 1, sizeof(float));

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

    const int n = ggml_nrows(src0);
    const int nc = src0->ne[0];

    const size_t nb00 = src0->nb[0];
    const size_t nb01 = src0->nb[1];

    const size_t nb0 = dst->nb[0];
    const size_t nb1 = dst->nb[1];

    GGML_ASSERT(nb0 == sizeof(float));
    GGML_ASSERT(nb00 == sizeof(float));

    for (int j = ith; j < n; j += nth) {
        float * dst_ptr = (float *)((char *)dst->data + j * nb1);
        float * src0_ptr = (float *)((char *)src0->data + j * nb01);

        for (int i = 0; i < nc; i++) {
            dst_ptr[i] = MAX(MIN(src0_ptr[i], max), min);
        }
    }
}

static void ggml_compute_forward_clamp(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                       struct ggml_tensor * dst) {
    switch (src0->type) {
    case GGML_TYPE_F32: {
        ggml_compute_forward_clamp_f32(params, src0, dst);
    } break;
    case GGML_TYPE_F16:
    case GGML_TYPE_Q4_0:
    case GGML_TYPE_Q4_1:
    case GGML_TYPE_Q5_0:
    case GGML_TYPE_Q5_1:
    case GGML_TYPE_Q8_0:
    case GGML_TYPE_Q8_1:
    case GGML_TYPE_Q2_K:
    case GGML_TYPE_Q3_K:
    case GGML_TYPE_Q4_K:
    case GGML_TYPE_Q5_K:
    case GGML_TYPE_Q6_K:
    case GGML_TYPE_Q8_K:
    case GGML_TYPE_I8:
    case GGML_TYPE_I16:
    case GGML_TYPE_I32:
    case GGML_TYPE_COUNT: {
        GGML_ASSERT(false);
    } break;
    }
}

// ggml_compute_forward_rope

static void ggml_compute_forward_rope_f32(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                          struct ggml_tensor * dst) {

    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
        return;
    }

    float freq_base;
    float freq_scale;

    // these two only relevant for xPos RoPE:
    float xpos_base;
    bool xpos_down;

    const int n_past = ((int32_t *)dst->op_params)[0];
    const int n_dims = ((int32_t *)dst->op_params)[1];
    const int mode = ((int32_t *)dst->op_params)[2];
    const int n_ctx = ((int32_t *)dst->op_params)[3];
    memcpy(&freq_base, (int32_t *)dst->op_params + 4, sizeof(float));
    memcpy(&freq_scale, (int32_t *)dst->op_params + 5, sizeof(float));
    memcpy(&xpos_base, (int32_t *)dst->op_params + 6, sizeof(float));
    memcpy(&xpos_down, (int32_t *)dst->op_params + 7, sizeof(bool));

    assert(n_past >= 0);

    GGML_TENSOR_UNARY_OP_LOCALS;

    // printf("ne0: %d, ne1: %d, ne2: %d, ne3: %d\n", ne0, ne1, ne2, ne3);
    // printf("n_past = %d, ne2 = %d\n", n_past, ne2);

    GGML_ASSERT(nb00 == sizeof(float));

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

    const int nr = ggml_nrows(dst);

    GGML_ASSERT(n_dims <= ne0);
    GGML_ASSERT(n_dims % 2 == 0);

    // rows per thread
    const int dr = (nr + nth - 1) / nth;

    // row range for this thread
    const int ir0 = dr * ith;
    const int ir1 = MIN(ir0 + dr, nr);

    // row index used to determine which thread to use
    int ir = 0;

    const float theta_scale = powf(freq_base, -2.0f / n_dims);

    const bool is_neox = mode & 2;
    const bool is_glm = mode & 4;

    for (int64_t i3 = 0; i3 < ne3; i3++) {
        for (int64_t i2 = ((mode & 1) == 0 ? 0 : n_past); i2 < ne2; i2++) {
            const int64_t p = ((mode & 1) == 0 ? n_past + i2 : i2);
            for (int64_t i1 = 0; i1 < ne1; i1++) {
                if (ir++ < ir0)
                    continue;
                if (ir > ir1)
                    break;

                float theta = freq_scale * (float)p;

                if (is_glm) {
                    theta = MIN(p, n_ctx - 2);
                    float block_theta = MAX(p - (n_ctx - 2), 0);
                    for (int64_t i0 = 0; i0 < ne0 / 4; i0++) {
                        const float cos_theta = cosf(theta);
                        const float sin_theta = sinf(theta);
                        const float cos_block_theta = cosf(block_theta);
                        const float sin_block_theta = sinf(block_theta);

                        theta *= theta_scale;
                        block_theta *= theta_scale;

                        const float * const src = (float *)((char *)src0->data + i3 * nb03 + i2 * nb02 + i1 * nb01 + i0 * nb00);
                        float * dst_data = (float *)((char *)dst->data + i3 * nb3 + i2 * nb2 + i1 * nb1 + i0 * nb0);

                        const float x0 = src[0];
                        const float x1 = src[n_dims / 2];
                        const float x2 = src[n_dims];
                        const float x3 = src[n_dims / 2 * 3];

                        dst_data[0] = x0 * cos_theta - x1 * sin_theta;
                        dst_data[n_dims / 2] = x0 * sin_theta + x1 * cos_theta;
                        dst_data[n_dims] = x2 * cos_block_theta - x3 * sin_block_theta;
                        dst_data[n_dims / 2 * 3] = x2 * sin_block_theta + x3 * cos_block_theta;
                    }
                } else if (!is_neox) {
                    for (int64_t i0 = 0; i0 < ne0; i0 += 2) {
                        const float cos_theta = cosf(theta);
                        const float sin_theta = sinf(theta);
                        // zeta scaling for xPos only:
                        float zeta =
                            xpos_base != 0.0f ? powf((i0 + 0.4f * ne0) / (1.4f * ne0), (n_past + i2) / xpos_base) : 1.0f;
                        if (xpos_down)
                            zeta = 1.0f / zeta;

                        theta *= theta_scale;

                        const float * const src = (float *)((char *)src0->data + i3 * nb03 + i2 * nb02 + i1 * nb01 + i0 * nb00);
                        float * dst_data = (float *)((char *)dst->data + i3 * nb3 + i2 * nb2 + i1 * nb1 + i0 * nb0);

                        const float x0 = src[0];
                        const float x1 = src[1];

                        dst_data[0] = x0 * cos_theta * zeta - x1 * sin_theta * zeta;
                        dst_data[1] = x0 * sin_theta * zeta + x1 * cos_theta * zeta;
                    }
                } else {
                    // TODO: this might be wrong for ne0 != n_dims - need double check
                    // ref:
                    // https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt_neox/modeling_gpt_neox.py#LL251C1-L294C28
                    for (int64_t ib = 0; ib < ne0 / n_dims; ++ib) {
                        for (int64_t ic = 0; ic < n_dims; ic += 2) {
                            const float cos_theta = cosf(theta);
                            const float sin_theta = sinf(theta);

                            theta *= theta_scale;

                            const int64_t i0 = ib * n_dims + ic / 2;

                            const float * const src =
                                (float *)((char *)src0->data + i3 * nb03 + i2 * nb02 + i1 * nb01 + i0 * nb00);
                            float * dst_data = (float *)((char *)dst->data + i3 * nb3 + i2 * nb2 + i1 * nb1 + i0 * nb0);

                            const float x0 = src[0];
                            const float x1 = src[n_dims / 2];

                            dst_data[0] = x0 * cos_theta - x1 * sin_theta;
                            dst_data[n_dims / 2] = x0 * sin_theta + x1 * cos_theta;
                        }
                    }
                }
            }
        }
    }
}

static void ggml_compute_forward_rope_f16(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                          struct ggml_tensor * dst) {

    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
        return;
    }

    float freq_base;
    float freq_scale;

    const int n_past = ((int32_t *)dst->op_params)[0];
    const int n_dims = ((int32_t *)dst->op_params)[1];
    const int mode = ((int32_t *)dst->op_params)[2];
    const int n_ctx = ((int32_t *)dst->op_params)[3];
    memcpy(&freq_base, (int32_t *)dst->op_params + 4, sizeof(float));
    memcpy(&freq_scale, (int32_t *)dst->op_params + 5, sizeof(float));

    assert(n_past >= 0);

    GGML_TENSOR_UNARY_OP_LOCALS;

    // printf("ne0: %d, ne1: %d, ne2: %d, ne3: %d\n", ne0, ne1, ne2, ne3);
    // printf("n_past = %d, ne2 = %d\n", n_past, ne2);

    GGML_ASSERT(nb0 == sizeof(ggml_fp16_t));

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

    const int nr = ggml_nrows(dst);

    GGML_ASSERT(n_dims <= ne0);
    GGML_ASSERT(n_dims % 2 == 0);

    // rows per thread
    const int dr = (nr + nth - 1) / nth;

    // row range for this thread
    const int ir0 = dr * ith;
    const int ir1 = MIN(ir0 + dr, nr);

    // row index used to determine which thread to use
    int ir = 0;

    const float theta_scale = powf(freq_base, -2.0f / n_dims);

    const bool is_neox = mode & 2;
    const bool is_glm = mode & 4;

    for (int64_t i3 = 0; i3 < ne3; i3++) {
        for (int64_t i2 = ((mode & 1) == 0 ? 0 : n_past); i2 < ne2; i2++) {
            const int64_t p = ((mode & 1) == 0 ? n_past + i2 : i2);
            for (int64_t i1 = 0; i1 < ne1; i1++) {
                if (ir++ < ir0)
                    continue;
                if (ir > ir1)
                    break;

                float theta = freq_scale * (float)p;

                if (is_glm) {
                    theta = MIN(p, n_ctx - 2);
                    float block_theta = MAX(p - (n_ctx - 2), 0);
                    for (int64_t i0 = 0; i0 < ne0 / 4; i0++) {
                        const float cos_theta = cosf(theta);
                        const float sin_theta = sinf(theta);
                        const float cos_block_theta = cosf(block_theta);
                        const float sin_block_theta = sinf(block_theta);

                        theta *= theta_scale;
                        block_theta *= theta_scale;

                        const ggml_fp16_t * const src =
                            (ggml_fp16_t *)((char *)src0->data + i3 * nb03 + i2 * nb02 + i1 * nb01 + i0 * nb00);
                        ggml_fp16_t * dst_data = (ggml_fp16_t *)((char *)dst->data + i3 * nb3 + i2 * nb2 + i1 * nb1 + i0 * nb0);

                        const float x0 = GGML_FP16_TO_FP32(src[0]);
                        const float x1 = GGML_FP16_TO_FP32(src[n_dims / 2]);
                        const float x2 = GGML_FP16_TO_FP32(src[n_dims]);
                        const float x3 = GGML_FP16_TO_FP32(src[n_dims / 2 * 3]);

                        dst_data[0] = GGML_FP32_TO_FP16(x0 * cos_theta - x1 * sin_theta);
                        dst_data[n_dims / 2] = GGML_FP32_TO_FP16(x0 * sin_theta + x1 * cos_theta);
                        dst_data[n_dims] = GGML_FP32_TO_FP16(x2 * cos_block_theta - x3 * sin_block_theta);
                        dst_data[n_dims / 2 * 3] = GGML_FP32_TO_FP16(x2 * sin_block_theta + x3 * cos_block_theta);
                    }
                }
                if (!is_neox) {
                    for (int64_t i0 = 0; i0 < ne0; i0 += 2) {
                        const float cos_theta = cosf(theta);
                        const float sin_theta = sinf(theta);

                        theta *= theta_scale;

                        const ggml_fp16_t * const src =
                            (ggml_fp16_t *)((char *)src0->data + i3 * nb03 + i2 * nb02 + i1 * nb01 + i0 * nb00);
                        ggml_fp16_t * dst_data = (ggml_fp16_t *)((char *)dst->data + i3 * nb3 + i2 * nb2 + i1 * nb1 + i0 * nb0);

                        const float x0 = GGML_FP16_TO_FP32(src[0]);
                        const float x1 = GGML_FP16_TO_FP32(src[1]);

                        dst_data[0] = GGML_FP32_TO_FP16(x0 * cos_theta - x1 * sin_theta);
                        dst_data[1] = GGML_FP32_TO_FP16(x0 * sin_theta + x1 * cos_theta);
                    }
                } else {
                    // TODO: this might be wrong for ne0 != n_dims - need double check
                    // ref:
                    // https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt_neox/modeling_gpt_neox.py#LL251C1-L294C28
                    for (int64_t ib = 0; ib < ne0 / n_dims; ++ib) {
                        for (int64_t ic = 0; ic < n_dims; ic += 2) {
                            const float cos_theta = cosf(theta);
                            const float sin_theta = sinf(theta);

                            theta *= theta_scale;

                            const int64_t i0 = ib * n_dims + ic / 2;

                            const ggml_fp16_t * const src =
                                (ggml_fp16_t *)((char *)src0->data + i3 * nb03 + i2 * nb02 + i1 * nb01 + i0 * nb00);
                            ggml_fp16_t * dst_data =
                                (ggml_fp16_t *)((char *)dst->data + i3 * nb3 + i2 * nb2 + i1 * nb1 + i0 * nb0);

                            const float x0 = GGML_FP16_TO_FP32(src[0]);
                            const float x1 = GGML_FP16_TO_FP32(src[n_dims / 2]);

                            dst_data[0] = GGML_FP32_TO_FP16(x0 * cos_theta - x1 * sin_theta);
                            dst_data[n_dims / 2] = GGML_FP32_TO_FP16(x0 * sin_theta + x1 * cos_theta);
                        }
                    }
                }
            }
        }
    }
}

static void ggml_compute_forward_rope(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                      struct ggml_tensor * dst) {
    switch (src0->type) {
    case GGML_TYPE_F16: {
        ggml_compute_forward_rope_f16(params, src0, dst);
    } break;
    case GGML_TYPE_F32: {
        ggml_compute_forward_rope_f32(params, src0, dst);
    } break;
    default: {
        GGML_ASSERT(false);
    } break;
    }
}

// ggml_compute_forward_rope_back

static void ggml_compute_forward_rope_back_f32(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                               struct ggml_tensor * dst) {

    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
        return;
    }

    // y = rope(x, src1)
    // dx = rope_back(dy, src1)
    // src0 is dy, src1 contains options

    float freq_base;
    float freq_scale;

    // these two only relevant for xPos RoPE:
    float xpos_base;
    bool xpos_down;

    const int n_past = ((int32_t *)dst->op_params)[0];
    const int n_dims = ((int32_t *)dst->op_params)[1];
    const int mode = ((int32_t *)dst->op_params)[2];
    const int n_ctx = ((int32_t *)dst->op_params)[3];
    UNUSED(n_ctx);
    memcpy(&freq_base, (int32_t *)dst->op_params + 4, sizeof(float));
    memcpy(&freq_scale, (int32_t *)dst->op_params + 5, sizeof(float));
    memcpy(&xpos_base, (int32_t *)dst->op_params + 6, sizeof(float));
    memcpy(&xpos_down, (int32_t *)dst->op_params + 7, sizeof(bool));

    assert(n_past >= 0);

    GGML_TENSOR_UNARY_OP_LOCALS;

    // printf("ne0: %d, ne1: %d, ne2: %d, ne3: %d\n", ne0, ne1, ne2, ne3);
    // printf("n_past = %d, ne2 = %d\n", n_past, ne2);

    assert(nb0 == sizeof(float));

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

    const int nr = ggml_nrows(dst);

    // rows per thread
    const int dr = (nr + nth - 1) / nth;

    // row range for this thread
    const int ir0 = dr * ith;
    const int ir1 = MIN(ir0 + dr, nr);

    // row index used to determine which thread to use
    int ir = 0;

    const float theta_scale = powf(freq_base, -2.0f / n_dims);

    const bool is_neox = mode & 2;

    for (int64_t i3 = 0; i3 < ne3; i3++) {
        for (int64_t i2 = ((mode & 1) == 0 ? 0 : n_past); i2 < ne2; i2++) {
            const int64_t p = ((mode & 1) == 0 ? n_past + i2 : i2);
            for (int64_t i1 = 0; i1 < ne1; i1++) {
                if (ir++ < ir0)
                    continue;
                if (ir > ir1)
                    break;

                float theta = freq_scale * (float)p;

                if (!is_neox) {
                    for (int64_t i0 = 0; i0 < ne0; i0 += 2) {
                        const float cos_theta = cosf(theta);
                        const float sin_theta = sinf(theta);
                        // zeta scaling for xPos only:
                        float zeta =
                            xpos_base != 0.0f ? powf((i0 + 0.4f * ne0) / (1.4f * ne0), (n_past + i2) / xpos_base) : 1.0f;
                        if (xpos_down)
                            zeta = 1.0f / zeta;

                        theta *= theta_scale;

                        const float * const dy = (float *)((char *)src0->data + i3 * nb03 + i2 * nb02 + i1 * nb01 + i0 * nb00);
                        float * dx = (float *)((char *)dst->data + i3 * nb3 + i2 * nb2 + i1 * nb1 + i0 * nb0);

                        const float dy0 = dy[0];
                        const float dy1 = dy[1];

                        dx[0] = dy0 * cos_theta * zeta + dy1 * sin_theta * zeta;
                        dx[1] = -dy0 * sin_theta * zeta + dy1 * cos_theta * zeta;
                    }
                } else {
                    for (int64_t ib = 0; ib < ne0 / n_dims; ++ib) {
                        for (int64_t ic = 0; ic < n_dims; ic += 2) {
                            const float cos_theta = cosf(theta);
                            const float sin_theta = sinf(theta);

                            theta *= theta_scale;

                            const int64_t i0 = ib * n_dims + ic / 2;

                            const float * const dy =
                                (float *)((char *)src0->data + i3 * nb03 + i2 * nb02 + i1 * nb01 + i0 * nb00);
                            float * dx = (float *)((char *)dst->data + i3 * nb3 + i2 * nb2 + i1 * nb1 + i0 * nb0);

                            const float dy0 = dy[0];
                            const float dy1 = dy[n_dims / 2];

                            dx[0] = dy0 * cos_theta + dy1 * sin_theta;
                            dx[n_dims / 2] = -dy0 * sin_theta + dy1 * cos_theta;
                        }
                    }
                }
            }
        }
    }
}

static void ggml_compute_forward_rope_back_f16(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                               struct ggml_tensor * dst) {

    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
        return;
    }

    // y = rope(x, src1)
    // dx = rope_back(dy, src1)
    // src0 is dy, src1 contains options

    const int n_past = ((int32_t *)dst->op_params)[0];
    const int n_dims = ((int32_t *)dst->op_params)[1];
    const int mode = ((int32_t *)dst->op_params)[2];

    assert(n_past >= 0);

    GGML_TENSOR_UNARY_OP_LOCALS;

    // printf("ne0: %d, ne1: %d, ne2: %d, ne3: %d\n", ne0, ne1, ne2, ne3);
    // printf("n_past = %d, ne2 = %d\n", n_past, ne2);

    assert(nb0 == sizeof(ggml_fp16_t));

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

    const int nr = ggml_nrows(dst);

    // rows per thread
    const int dr = (nr + nth - 1) / nth;

    // row range for this thread
    const int ir0 = dr * ith;
    const int ir1 = MIN(ir0 + dr, nr);

    // row index used to determine which thread to use
    int ir = 0;

    const float theta_scale = powf(10000.0, -2.0f / n_dims);

    const bool is_neox = mode & 2;

    for (int64_t i3 = 0; i3 < ne3; i3++) {
        for (int64_t i2 = ((mode & 1) == 0 ? 0 : n_past); i2 < ne2; i2++) {
            const int64_t p = ((mode & 1) == 0 ? n_past + i2 : i2);
            for (int64_t i1 = 0; i1 < ne1; i1++) {
                if (ir++ < ir0)
                    continue;
                if (ir > ir1)
                    break;

                float theta = (float)p;

                if (!is_neox) {
                    for (int64_t i0 = 0; i0 < ne0; i0 += 2) {
                        const float cos_theta = cosf(theta);
                        const float sin_theta = sinf(theta);

                        theta *= theta_scale;

                        const ggml_fp16_t * const dy =
                            (ggml_fp16_t *)((char *)src0->data + i3 * nb03 + i2 * nb02 + i1 * nb01 + i0 * nb00);
                        ggml_fp16_t * dx = (ggml_fp16_t *)((char *)dst->data + i3 * nb3 + i2 * nb2 + i1 * nb1 + i0 * nb0);

                        const float dy0 = GGML_FP16_TO_FP32(dy[0]);
                        const float dy1 = GGML_FP16_TO_FP32(dy[1]);

                        dx[0] = GGML_FP32_TO_FP16(dy0 * cos_theta + dy1 * sin_theta);
                        dx[1] = GGML_FP32_TO_FP16(-dy0 * sin_theta + dy1 * cos_theta);
                    }
                } else {
                    for (int64_t ib = 0; ib < ne0 / n_dims; ++ib) {
                        for (int64_t ic = 0; ic < n_dims; ic += 2) {
                            const float cos_theta = cosf(theta);
                            const float sin_theta = sinf(theta);

                            theta *= theta_scale;

                            const int64_t i0 = ib * n_dims + ic / 2;

                            const ggml_fp16_t * const dy =
                                (ggml_fp16_t *)((char *)src0->data + i3 * nb03 + i2 * nb02 + i1 * nb01 + i0 * nb00);
                            ggml_fp16_t * dx = (ggml_fp16_t *)((char *)dst->data + i3 * nb3 + i2 * nb2 + i1 * nb1 + i0 * nb0);

                            const float dy0 = GGML_FP16_TO_FP32(dy[0]);
                            const float dy1 = GGML_FP16_TO_FP32(dy[n_dims / 2]);

                            dx[0] = GGML_FP32_TO_FP16(dy0 * cos_theta + dy1 * sin_theta);
                            dx[n_dims / 2] = GGML_FP32_TO_FP16(-dy0 * sin_theta + dy1 * cos_theta);
                        }
                    }
                }
            }
        }
    }
}

static void ggml_compute_forward_rope_back(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                           struct ggml_tensor * dst) {
    switch (src0->type) {
    case GGML_TYPE_F16: {
        ggml_compute_forward_rope_back_f16(params, src0, dst);
    } break;
    case GGML_TYPE_F32: {
        ggml_compute_forward_rope_back_f32(params, src0, dst);
    } break;
    default: {
        GGML_ASSERT(false);
    } break;
    }
}

// ggml_compute_forward_conv_1d

static void ggml_compute_forward_conv_1d_s1_ph_f16_f32(const struct ggml_compute_params * params,
                                                       const struct ggml_tensor * src0, const struct ggml_tensor * src1,
                                                       struct ggml_tensor * dst) {
    GGML_ASSERT(src0->type == GGML_TYPE_F16);
    GGML_ASSERT(src1->type == GGML_TYPE_F32);
    GGML_ASSERT(dst->type == GGML_TYPE_F32);

    int64_t t0 = ggml_perf_time_us();
    UNUSED(t0);

    GGML_TENSOR_BINARY_OP_LOCALS;

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

    const int nk = ne00;
    const int nh = nk / 2;

    const int ew0 = ggml_up32(ne01);

    GGML_ASSERT(ne00 % 2 == 1); // TODO: support even kernel sizes
    GGML_ASSERT(nb00 == sizeof(ggml_fp16_t));
    GGML_ASSERT(nb10 == sizeof(float));

    if (params->type == GGML_TASK_INIT) {
        // TODO: fix this memset (wsize is overestimated)
        memset(params->wdata, 0, params->wsize);

        // prepare kernel data (src0)
        {
            ggml_fp16_t * const wdata = (ggml_fp16_t *)params->wdata + 0;

            for (int64_t i02 = 0; i02 < ne02; i02++) {
                for (int64_t i01 = 0; i01 < ne01; i01++) {
                    const ggml_fp16_t * const src = (ggml_fp16_t *)((char *)src0->data + i02 * nb02 + i01 * nb01);
                    ggml_fp16_t * dst_data = wdata + i02 * ew0 * ne00;
                    for (int64_t i00 = 0; i00 < ne00; i00++) {
                        dst_data[i00 * ew0 + i01] = src[i00];
                    }
                }
            }
        }

        // prepare source data (src1)
        {
            ggml_fp16_t * const wdata = (ggml_fp16_t *)params->wdata + ne02 * ew0 * ne00;

            for (int64_t i11 = 0; i11 < ne11; i11++) {
                const float * const src = (float *)((char *)src1->data + i11 * nb11);
                ggml_fp16_t * dst_data = wdata;
                for (int64_t i10 = 0; i10 < ne10; i10++) {
                    dst_data[(i10 + nh) * ew0 + i11] = GGML_FP32_TO_FP16(src[i10]);
                }
            }
        }

        return;
    }

    if (params->type == GGML_TASK_FINALIZE) {
        return;
    }

    // total rows in dst
    const int nr = ne02;

    // rows per thread
    const int dr = (nr + nth - 1) / nth;

    // row range for this thread
    const int ir0 = dr * ith;
    const int ir1 = MIN(ir0 + dr, nr);

    for (int i1 = ir0; i1 < ir1; i1++) {
        float * dst_data = (float *)((char *)dst->data + i1 * nb1);
        for (int64_t i0 = 0; i0 < ne10; ++i0) {
            dst_data[i0] = 0;
            for (int k = -nh; k <= nh; k++) {
                float v = 0.0f;
                ggml_vec_dot_f16(ew0, &v, (ggml_fp16_t *)params->wdata + i1 * ew0 * ne00 + (nh + k) * ew0,
                                 (ggml_fp16_t *)params->wdata + ne02 * ew0 * ne00 + (i0 + nh + k) * ew0);

                dst_data[i0] += v;
            }
        }
    }
}

static void ggml_compute_forward_conv_1d_s1_ph_f32(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                                   const struct ggml_tensor * src1, struct ggml_tensor * dst) {
    GGML_ASSERT(src0->type == GGML_TYPE_F32);
    GGML_ASSERT(src1->type == GGML_TYPE_F32);
    GGML_ASSERT(dst->type == GGML_TYPE_F32);

    int64_t t0 = ggml_perf_time_us();
    UNUSED(t0);

    GGML_TENSOR_BINARY_OP_LOCALS;

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

    const int nk = ne00;
    const int nh = nk / 2;

    const int ew0 = ggml_up32(ne01);

    GGML_ASSERT(ne00 % 2 == 1); // TODO: support even kernel sizes
    GGML_ASSERT(nb00 == sizeof(float));
    GGML_ASSERT(nb10 == sizeof(float));

    if (params->type == GGML_TASK_INIT) {
        // TODO: fix this memset (wsize is overestimated)
        memset(params->wdata, 0, params->wsize);

        // prepare kernel data (src0)
        {
            float * const wdata = (float *)params->wdata + 0;

            for (int64_t i02 = 0; i02 < ne02; i02++) {
                for (int64_t i01 = 0; i01 < ne01; i01++) {
                    const float * const src = (float *)((char *)src0->data + i02 * nb02 + i01 * nb01);
                    float * dst_data = wdata + i02 * ew0 * ne00;
                    for (int64_t i00 = 0; i00 < ne00; i00++) {
                        dst_data[i00 * ew0 + i01] = src[i00];
                    }
                }
            }
        }

        // prepare source data (src1)
        {
            float * const wdata = (float *)params->wdata + ne02 * ew0 * ne00;

            for (int64_t i11 = 0; i11 < ne11; i11++) {
                const float * const src = (float *)((char *)src1->data + i11 * nb11);
                float * dst_data = wdata;
                for (int64_t i10 = 0; i10 < ne10; i10++) {
                    dst_data[(i10 + nh) * ew0 + i11] = src[i10];
                }
            }
        }

        return;
    }

    if (params->type == GGML_TASK_FINALIZE) {
        return;
    }

    // total rows in dst
    const int nr = ne02;

    // rows per thread
    const int dr = (nr + nth - 1) / nth;

    // row range for this thread
    const int ir0 = dr * ith;
    const int ir1 = MIN(ir0 + dr, nr);

    for (int i1 = ir0; i1 < ir1; i1++) {
        float * dst_data = (float *)((char *)dst->data + i1 * nb1);
        for (int64_t i0 = 0; i0 < ne10; ++i0) {
            dst_data[i0] = 0;
            for (int k = -nh; k <= nh; k++) {
                float v = 0.0f;
                ggml_vec_dot_f32(ew0, &v, (float *)params->wdata + i1 * ew0 * ne00 + (nh + k) * ew0,
                                 (float *)params->wdata + ne02 * ew0 * ne00 + (i0 + nh + k) * ew0);

                dst_data[i0] += v;
            }
        }
    }
}

static void ggml_compute_forward_conv_1d_s1_ph(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                               const struct ggml_tensor * src1, struct ggml_tensor * dst) {
    switch (src0->type) {
    case GGML_TYPE_F16: {
        ggml_compute_forward_conv_1d_s1_ph_f16_f32(params, src0, src1, dst);
    } break;
    case GGML_TYPE_F32: {
        ggml_compute_forward_conv_1d_s1_ph_f32(params, src0, src1, dst);
    } break;
    default: {
        GGML_ASSERT(false);
    } break;
    }
}

static void ggml_compute_forward_conv_1d_s2_ph_f16_f32(const struct ggml_compute_params * params,
                                                       const struct ggml_tensor * src0, const struct ggml_tensor * src1,
                                                       struct ggml_tensor * dst) {
    GGML_ASSERT(src0->type == GGML_TYPE_F16);
    GGML_ASSERT(src1->type == GGML_TYPE_F32);
    GGML_ASSERT(dst->type == GGML_TYPE_F32);

    int64_t t0 = ggml_perf_time_us();
    UNUSED(t0);

    GGML_TENSOR_BINARY_OP_LOCALS;

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

    const int nk = ne00;
    const int nh = nk / 2;

    const int ew0 = ggml_up32(ne01);

    GGML_ASSERT(ne00 % 2 == 1); // TODO: support even kernel sizes
    GGML_ASSERT(nb00 == sizeof(ggml_fp16_t));
    GGML_ASSERT(nb10 == sizeof(float));

    if (params->type == GGML_TASK_INIT) {
        // TODO: fix this memset (wsize is overestimated)
        memset(params->wdata, 0, params->wsize);

        // prepare kernel data (src0)
        {
            ggml_fp16_t * const wdata = (ggml_fp16_t *)params->wdata + 0;

            for (int64_t i02 = 0; i02 < ne02; i02++) {
                for (int64_t i01 = 0; i01 < ne01; i01++) {
                    const ggml_fp16_t * const src = (ggml_fp16_t *)((char *)src0->data + i02 * nb02 + i01 * nb01);
                    ggml_fp16_t * dst_data = wdata + i02 * ew0 * ne00;
                    for (int64_t i00 = 0; i00 < ne00; i00++) {
                        dst_data[i00 * ew0 + i01] = src[i00];
                    }
                }
            }
        }

        // prepare source data (src1)
        {
            ggml_fp16_t * const wdata = (ggml_fp16_t *)params->wdata + ne02 * ew0 * ne00;

            for (int64_t i11 = 0; i11 < ne11; i11++) {
                const float * const src = (float *)((char *)src1->data + i11 * nb11);
                ggml_fp16_t * dst_data = wdata;
                for (int64_t i10 = 0; i10 < ne10; i10++) {
                    dst_data[(i10 + nh) * ew0 + i11] = GGML_FP32_TO_FP16(src[i10]);
                }
            }
        }

        return;
    }

    if (params->type == GGML_TASK_FINALIZE) {
        return;
    }

    // total rows in dst
    const int nr = ne02;

    // rows per thread
    const int dr = (nr + nth - 1) / nth;

    // row range for this thread
    const int ir0 = dr * ith;
    const int ir1 = MIN(ir0 + dr, nr);

    for (int i1 = ir0; i1 < ir1; i1++) {
        float * dst_data = (float *)((char *)dst->data + i1 * nb1);
        for (int64_t i0 = 0; i0 < ne10; i0 += 2) {
            dst_data[i0 / 2] = 0;
            for (int k = -nh; k <= nh; k++) {
                float v = 0.0f;
                ggml_vec_dot_f16(ew0, &v, (ggml_fp16_t *)params->wdata + i1 * ew0 * ne00 + (nh + k) * ew0,
                                 (ggml_fp16_t *)params->wdata + ne02 * ew0 * ne00 + (i0 + nh + k) * ew0);

                dst_data[i0 / 2] += v;
            }
        }
    }
}

static void ggml_compute_forward_conv_1d_s2_ph_f32(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                                   const struct ggml_tensor * src1, struct ggml_tensor * dst) {
    GGML_ASSERT(src0->type == GGML_TYPE_F32);
    GGML_ASSERT(src1->type == GGML_TYPE_F32);
    GGML_ASSERT(dst->type == GGML_TYPE_F32);

    int64_t t0 = ggml_perf_time_us();
    UNUSED(t0);

    GGML_TENSOR_BINARY_OP_LOCALS;

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

    const int nk = ne00;
    const int nh = nk / 2;

    const int ew0 = ggml_up32(ne01);

    GGML_ASSERT(ne00 % 2 == 1); // TODO: support even kernel sizes
    GGML_ASSERT(nb00 == sizeof(float));
    GGML_ASSERT(nb10 == sizeof(float));

    if (params->type == GGML_TASK_INIT) {
        // TODO: fix this memset (wsize is overestimated)
        memset(params->wdata, 0, params->wsize);

        // prepare kernel data (src0)
        {
            float * const wdata = (float *)params->wdata + 0;

            for (int64_t i02 = 0; i02 < ne02; i02++) {
                for (int64_t i01 = 0; i01 < ne01; i01++) {
                    const float * const src = (float *)((char *)src0->data + i02 * nb02 + i01 * nb01);
                    float * dst_data = wdata + i02 * ew0 * ne00;
                    for (int64_t i00 = 0; i00 < ne00; i00++) {
                        dst_data[i00 * ew0 + i01] = src[i00];
                    }
                }
            }
        }

        // prepare source data (src1)
        {
            float * const wdata = (float *)params->wdata + ne02 * ew0 * ne00;

            for (int64_t i11 = 0; i11 < ne11; i11++) {
                const float * const src = (float *)((char *)src1->data + i11 * nb11);
                float * dst_data = wdata;
                for (int64_t i10 = 0; i10 < ne10; i10++) {
                    dst_data[(i10 + nh) * ew0 + i11] = src[i10];
                }
            }
        }

        return;
    }

    if (params->type == GGML_TASK_FINALIZE) {
        return;
    }

    // total rows in dst
    const int nr = ne02;

    // rows per thread
    const int dr = (nr + nth - 1) / nth;

    // row range for this thread
    const int ir0 = dr * ith;
    const int ir1 = MIN(ir0 + dr, nr);

    for (int i1 = ir0; i1 < ir1; i1++) {
        float * dst_data = (float *)((char *)dst->data + i1 * nb1);
        for (int64_t i0 = 0; i0 < ne10; i0 += 2) {
            dst_data[i0 / 2] = 0;
            for (int k = -nh; k <= nh; k++) {
                float v = 0.0f;
                ggml_vec_dot_f32(ew0, &v, (float *)params->wdata + i1 * ew0 * ne00 + (nh + k) * ew0,
                                 (float *)params->wdata + ne02 * ew0 * ne00 + (i0 + nh + k) * ew0);

                dst_data[i0 / 2] += v;
            }
        }
    }
}

static void ggml_compute_forward_conv_1d_s2_ph(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                               const struct ggml_tensor * src1, struct ggml_tensor * dst) {
    switch (src0->type) {
    case GGML_TYPE_F16: {
        ggml_compute_forward_conv_1d_s2_ph_f16_f32(params, src0, src1, dst);
    } break;
    case GGML_TYPE_F32: {
        ggml_compute_forward_conv_1d_s2_ph_f32(params, src0, src1, dst);
    } break;
    default: {
        GGML_ASSERT(false);
    } break;
    }
}

// ggml_compute_forward_conv_1d

static void ggml_compute_forward_conv_1d(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                         const struct ggml_tensor * src1, struct ggml_tensor * dst) {
    const int32_t s0 = ((const int32_t *)(dst->op_params))[0];
    const int32_t p0 = ((const int32_t *)(dst->op_params))[1];
    const int32_t d0 = ((const int32_t *)(dst->op_params))[2];
    GGML_ASSERT(d0 == 1);               // dilation not supported
    GGML_ASSERT(p0 == src0->ne[0] / 2); // only half padding supported
    if (s0 == 1) {
        ggml_compute_forward_conv_1d_s1_ph(params, src0, src1, dst);
    } else if (s0 == 2) {
        ggml_compute_forward_conv_1d_s2_ph(params, src0, src1, dst);
    } else {
        GGML_ASSERT(false); // only stride 1 and 2 supported
    };
}

// ggml_compute_forward_conv_2d

static void ggml_compute_forward_conv_2d_f16_f32(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                                 const struct ggml_tensor * src1, struct ggml_tensor * dst) {
    GGML_ASSERT(src0->type == GGML_TYPE_F16);
    GGML_ASSERT(src1->type == GGML_TYPE_F32);
    GGML_ASSERT(dst->type == GGML_TYPE_F32);

    int64_t t0 = ggml_perf_time_us();
    UNUSED(t0);

    GGML_TENSOR_BINARY_OP_LOCALS;

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

    const int nk0 = ne00;
    const int nk1 = ne01;

    // size of the convolution row - the kernel size unrolled across all channels
    const int ew0 = nk0 * nk1 * ne02;

    const int32_t s0 = ((const int32_t *)(dst->op_params))[0];
    const int32_t s1 = ((const int32_t *)(dst->op_params))[1];
    const int32_t p0 = ((const int32_t *)(dst->op_params))[2];
    const int32_t p1 = ((const int32_t *)(dst->op_params))[3];
    const int32_t d0 = ((const int32_t *)(dst->op_params))[4];
    const int32_t d1 = ((const int32_t *)(dst->op_params))[5];

    GGML_ASSERT(nb00 == sizeof(ggml_fp16_t));
    GGML_ASSERT(nb10 == sizeof(float));

    if (params->type == GGML_TASK_INIT) {
        memset(params->wdata, 0, params->wsize);

        // prepare source data (src1)
        {
            ggml_fp16_t * const wdata = (ggml_fp16_t *)params->wdata + 0;

            for (int i13 = 0; i13 < ne13; i13++) {
                for (int i12 = 0; i12 < ne12; i12++) {
                    const float * const src = (float *)((char *)src1->data + i13 * nb13 + i12 * nb12);
                    ggml_fp16_t * dst_data = wdata + i13 * (ne1 * ne0 * ew0);

                    for (int i1 = 0; i1 < ne1; i1++) {
                        for (int i0 = 0; i0 < ne0; i0++) {
                            for (int ik1 = 0; ik1 < nk1; ik1++) {
                                for (int ik0 = 0; ik0 < nk0; ik0++) {
                                    const int idx0 = i0 * s0 + ik0 * d0 - p0;
                                    const int idx1 = i1 * s1 + ik1 * d1 - p1;

                                    if (!(idx1 < 0 || idx1 >= ne11 || idx0 < 0 || idx0 >= ne10)) {
                                        dst_data[(i1 * ne0 + i0) * ew0 + i12 * (nk0 * nk1) + ik1 * nk0 + ik0] =
                                            GGML_FP32_TO_FP16(src[idx1 * ne10 + idx0]);
                                    }
                                }
                            }
                        }
                    }
                }
            }
        }

        return;
    }

    if (params->type == GGML_TASK_FINALIZE) {
        return;
    }

    // total patches in dst
    const int np = ne2;

    // patches per thread
    const int dp = (np + nth - 1) / nth;

    // patch range for this thread
    const int ip0 = dp * ith;
    const int ip1 = MIN(ip0 + dp, np);

    ggml_fp16_t * const wdata = (ggml_fp16_t *)params->wdata + 0;

    for (int i3 = 0; i3 < ne3; i3++) {
        for (int i2 = ip0; i2 < ip1; i2++) {
            float * dst_data = (float *)((char *)dst->data + i3 * nb3 + i2 * nb2);

            for (int i1 = 0; i1 < ne1; ++i1) {
                for (int i0 = 0; i0 < ne0; ++i0) {
                    ggml_vec_dot_f16(ew0, dst_data + i1 * ne0 + i0, (ggml_fp16_t *)((char *)src0->data + i2 * nb03),
                                     (ggml_fp16_t *)wdata + i3 * nb3 + (i1 * ne0 + i0) * ew0);
                }
            }
        }
    }
}

static void ggml_compute_forward_conv_2d(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                         const struct ggml_tensor * src1, struct ggml_tensor * dst) {
    switch (src0->type) {
    case GGML_TYPE_F16: {
        ggml_compute_forward_conv_2d_f16_f32(params, src0, src1, dst);
    } break;
    case GGML_TYPE_F32: {
        // ggml_compute_forward_conv_2d_f32(params, src0, src1, dst);
        GGML_ASSERT(false);
    } break;
    default: {
        GGML_ASSERT(false);
    } break;
    }
}

// ggml_compute_forward_conv_transpose_2d

static void ggml_compute_forward_conv_transpose_2d(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                                   const struct ggml_tensor * src1, struct ggml_tensor * dst) {
    GGML_ASSERT(src0->type == GGML_TYPE_F16);
    GGML_ASSERT(src1->type == GGML_TYPE_F32);
    GGML_ASSERT(dst->type == GGML_TYPE_F32);

    int64_t t0 = ggml_perf_time_us();
    UNUSED(t0);

    GGML_TENSOR_BINARY_OP_LOCALS;

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

    const int nk = ne00 * ne01 * ne02 * ne03;

    GGML_ASSERT(nb00 == sizeof(ggml_fp16_t));
    GGML_ASSERT(nb10 == sizeof(float));

    if (params->type == GGML_TASK_INIT) {
        memset(params->wdata, 0, params->wsize);

        // permute kernel data (src0) from (Kw x Kh x Cout x Cin) to (Cin x Kw x Kh x Cout)
        {
            ggml_fp16_t * const wdata = (ggml_fp16_t *)params->wdata + 0;

            for (int64_t i03 = 0; i03 < ne03; i03++) {
                for (int64_t i02 = 0; i02 < ne02; i02++) {
                    const ggml_fp16_t * const src = (ggml_fp16_t *)((char *)src0->data + i03 * nb03 + i02 * nb02);
                    ggml_fp16_t * dst_data = wdata + i02 * ne01 * ne00 * ne03;
                    for (int64_t i01 = 0; i01 < ne01; i01++) {
                        for (int64_t i00 = 0; i00 < ne00; i00++) {
                            dst_data[i01 * ne00 * ne03 + i00 * ne03 + i03] = src[i01 * ne00 + i00];
                        }
                    }
                }
            }
        }

        // permute source data (src1) from (Sw x Sh x Cin) to (Cin x Sw x Sh)
        {
            ggml_fp16_t * const wdata = (ggml_fp16_t *)params->wdata + nk;
            for (int i12 = 0; i12 < ne12; i12++) {
                for (int i11 = 0; i11 < ne11; i11++) {
                    const float * const src = (float *)((char *)src1->data + i12 * nb12 + i11 * nb11);
                    ggml_fp16_t * dst_data = wdata + i11 * ne10 * ne12;
                    for (int i10 = 0; i10 < ne10; i10++) {
                        dst_data[i10 * ne12 + i12] = GGML_FP32_TO_FP16(src[i10]);
                    }
                }
            }
        }

        return;
    }

    if (params->type == GGML_TASK_FINALIZE) {
        return;
    }

    const int32_t stride = ggml_get_op_params_i32(dst, 0);

    // total patches in dst
    const int np = ne2;

    // patches per thread
    const int dp = (np + nth - 1) / nth;

    // patch range for this thread
    const int ip0 = dp * ith;
    const int ip1 = MIN(ip0 + dp, np);

    ggml_fp16_t * const wdata = (ggml_fp16_t *)params->wdata + 0;
    ggml_fp16_t * const wdata_src = wdata + nk;

    for (int i2 = ip0; i2 < ip1; i2++) { // Cout
        float * dst_data = (float *)((char *)dst->data + i2 * nb2);
        ggml_fp16_t * wdata_kernel = wdata + i2 * ne01 * ne00 * ne03;
        for (int i11 = 0; i11 < ne11; i11++) {
            for (int i10 = 0; i10 < ne10; i10++) {
                const int i1n = i11 * ne10 * ne12 + i10 * ne12;
                for (int i01 = 0; i01 < ne01; i01++) {
                    for (int i00 = 0; i00 < ne00; i00++) {
                        float v = 0;
                        ggml_vec_dot_f16(ne03, &v, wdata_src + i1n, wdata_kernel + i01 * ne00 * ne03 + i00 * ne03);
                        dst_data[(i11 * stride + i01) * ne0 + i10 * stride + i00] += v;
                    }
                }
            }
        }
    }
}

// ggml_compute_forward_pool_1d_sk_p0

static void ggml_compute_forward_pool_1d_sk_p0(const struct ggml_compute_params * params, const enum ggml_op_pool op,
                                               const struct ggml_tensor * src, const int k, struct ggml_tensor * dst) {
    assert(src->type == GGML_TYPE_F32);
    assert(params->ith == 0);

    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
        return;
    }

    const char * cdata = (const char *)src->data;
    const char * const data_end = cdata + ggml_nbytes(src);
    float * drow = (float *)dst->data;

    const int64_t rs = dst->ne[0];

    while (cdata < data_end) {
        const float * const srow = (const float *)cdata;

        int j = 0;

        for (int64_t i = 0; i < rs; ++i) {
            switch (op) {
            case GGML_OP_POOL_AVG:
                drow[i] = 0;
                break;
            case GGML_OP_POOL_MAX:
                drow[i] = -FLT_MAX;
                break;
            case GGML_OP_POOL_COUNT:
                GGML_ASSERT(false);
                break;
            }
            for (int ki = 0; ki < k; ++ki) {
                switch (op) {
                case GGML_OP_POOL_AVG:
                    drow[i] += srow[j];
                    break;
                case GGML_OP_POOL_MAX:
                    if (srow[j] > drow[i])
                        drow[i] = srow[j];
                    break;
                case GGML_OP_POOL_COUNT:
                    GGML_ASSERT(false);
                    break;
                }
                ++j;
            }
            switch (op) {
            case GGML_OP_POOL_AVG:
                drow[i] /= k;
                break;
            case GGML_OP_POOL_MAX:
                break;
            case GGML_OP_POOL_COUNT:
                GGML_ASSERT(false);
                break;
            }
        }

        cdata += src->nb[1];
        drow += rs;
    }
}

// ggml_compute_forward_pool_1d

static void ggml_compute_forward_pool_1d(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                         struct ggml_tensor * dst) {

    const int32_t * opts = (const int32_t *)dst->op_params;
    enum ggml_op_pool op = opts[0];
    const int k0 = opts[1];
    const int s0 = opts[2];
    const int p0 = opts[3];
    GGML_ASSERT(p0 == 0);  // padding not supported
    GGML_ASSERT(k0 == s0); // only s = k supported

    ggml_compute_forward_pool_1d_sk_p0(params, op, src0, k0, dst);
}

// ggml_compute_forward_pool_2d_sk_p0

static void ggml_compute_forward_pool_2d_sk_p0(const struct ggml_compute_params * params, const enum ggml_op_pool op,
                                               const struct ggml_tensor * src, const int k0, const int k1,
                                               struct ggml_tensor * dst) {
    assert(src->type == GGML_TYPE_F32);
    assert(params->ith == 0);

    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
        return;
    }

    const char * cdata = (const char *)src->data;
    const char * const data_end = cdata + ggml_nbytes(src);

    const int64_t px = dst->ne[0];
    const int64_t py = dst->ne[1];
    const int64_t pa = px * py;

    float * dplane = (float *)dst->data;

    const int ka = k0 * k1;

    while (cdata < data_end) {
        for (int oy = 0; oy < py; ++oy) {
            float * const drow = dplane + oy * px;
            for (int ox = 0; ox < px; ++ox) {
                float * const out = drow + ox;
                switch (op) {
                case GGML_OP_POOL_AVG:
                    *out = 0;
                    break;
                case GGML_OP_POOL_MAX:
                    *out = -FLT_MAX;
                    break;
                case GGML_OP_POOL_COUNT:
                    GGML_ASSERT(false);
                    break;
                }

                const int ix = ox * k0;
                const int iy = oy * k1;

                for (int ky = 0; ky < k1; ++ky) {
                    const float * const srow = (const float *)(cdata + src->nb[1] * (iy + ky));
                    for (int kx = 0; kx < k0; ++kx) {
                        int j = ix + kx;
                        switch (op) {
                        case GGML_OP_POOL_AVG:
                            *out += srow[j];
                            break;
                        case GGML_OP_POOL_MAX:
                            if (srow[j] > *out)
                                *out = srow[j];
                            break;
                        case GGML_OP_POOL_COUNT:
                            GGML_ASSERT(false);
                            break;
                        }
                    }
                }
                switch (op) {
                case GGML_OP_POOL_AVG:
                    *out /= ka;
                    break;
                case GGML_OP_POOL_MAX:
                    break;
                case GGML_OP_POOL_COUNT:
                    GGML_ASSERT(false);
                    break;
                }
            }
        }

        cdata += src->nb[2];
        dplane += pa;
    }
}

// ggml_compute_forward_pool_2d

static void ggml_compute_forward_pool_2d(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                         struct ggml_tensor * dst) {

    const int32_t * opts = (const int32_t *)dst->op_params;
    enum ggml_op_pool op = opts[0];
    const int k0 = opts[1];
    const int k1 = opts[2];
    const int s0 = opts[3];
    const int s1 = opts[4];
    const int p0 = opts[5];
    const int p1 = opts[6];
    GGML_ASSERT(p0 == 0);
    GGML_ASSERT(p1 == 0); // padding not supported
    GGML_ASSERT(k0 == s0);
    GGML_ASSERT(k1 == s1); // only s = k supported

    ggml_compute_forward_pool_2d_sk_p0(params, op, src0, k0, k1, dst);
}

// ggml_compute_forward_upscale

static void ggml_compute_forward_upscale_f32(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                             struct ggml_tensor * dst) {

    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
        return;
    }

    GGML_ASSERT(src0->nb[0] == sizeof(float));

    const int ith = params->ith;

    GGML_TENSOR_UNARY_OP_LOCALS;

    const int scale_factor = dst->op_params[0];

    // TODO: optimize

    for (int i03 = 0; i03 < ne03; i03++) {
        for (int i02 = ith; i02 < ne02; i02++) {
            for (int m = 0; m < dst->ne[1]; m++) {
                int i01 = m / scale_factor;
                for (int n = 0; n < dst->ne[0]; n++) {
                    int i00 = n / scale_factor;

                    const float * x = (float *)((char *)src0->data + i00 * nb00 + i01 * nb01 + i02 * nb02 + i03 * nb03);

                    float * y =
                        (float *)((char *)dst->data + n * dst->nb[0] + m * dst->nb[1] + i02 * dst->nb[2] + i03 * dst->nb[3]);

                    *y = *x;
                }
            }
        }
    }
}

static void ggml_compute_forward_upscale(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                         struct ggml_tensor * dst) {
    switch (src0->type) {
    case GGML_TYPE_F32: {
        ggml_compute_forward_upscale_f32(params, src0, dst);
    } break;
    default: {
        GGML_ASSERT(false);
    } break;
    }
}

// ggml_compute_forward_flash_attn

static void ggml_compute_forward_flash_attn_f32(const struct ggml_compute_params * params, const struct ggml_tensor * q,
                                                const struct ggml_tensor * k, const struct ggml_tensor * v, const bool masked,
                                                struct ggml_tensor * dst) {
    int64_t t0 = ggml_perf_time_us();
    UNUSED(t0);

    GGML_TENSOR_LOCALS(int64_t, neq, q, ne);
    GGML_TENSOR_LOCALS(size_t, nbq, q, nb);
    GGML_TENSOR_LOCALS(int64_t, nek, k, ne);
    GGML_TENSOR_LOCALS(size_t, nbk, k, nb);
    GGML_TENSOR_LOCALS(int64_t, nev, v, ne);
    GGML_TENSOR_LOCALS(size_t, nbv, v, nb);
    GGML_TENSOR_LOCALS(int64_t, ne, dst, ne);
    GGML_TENSOR_LOCALS(size_t, nb, dst, nb);

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

    const int64_t D = neq0;
    const int64_t N = neq1;
    const int64_t P = nek1 - N;
    const int64_t M = P + N;

    const int Mup = ggml_up(M, GGML_SOFT_MAX_UNROLL);

    GGML_ASSERT(ne0 == D);
    GGML_ASSERT(ne1 == N);
    GGML_ASSERT(P >= 0);

    GGML_ASSERT(nbq0 == sizeof(float));
    GGML_ASSERT(nbk0 == sizeof(float));
    GGML_ASSERT(nbv0 == sizeof(float));

    GGML_ASSERT(neq0 == D);
    GGML_ASSERT(nek0 == D);
    GGML_ASSERT(nev1 == D);

    GGML_ASSERT(neq1 == N);
    GGML_ASSERT(nek1 == N + P);
    GGML_ASSERT(nev1 == D);

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

    if (params->type == GGML_TASK_INIT) {
        return;
    }

    if (params->type == GGML_TASK_FINALIZE) {
        return;
    }

    // parallelize by q rows using ggml_vec_dot_f32

    // total rows in q
    const int nr = neq1 * neq2 * neq3;

    // rows per thread
    const int dr = (nr + nth - 1) / nth;

    // row range for this thread
    const int ir0 = dr * ith;
    const int ir1 = MIN(ir0 + dr, nr);

    const float scale = 1.0f / sqrtf(D);

    // printf("P=%d N=%d D=%d ir0=%d ir1=%d scale = %f\n", P, N, D, ir0, ir1, scale);

    for (int ir = ir0; ir < ir1; ++ir) {
        // q indices
        const int iq3 = ir / (neq2 * neq1);
        const int iq2 = (ir - iq3 * neq2 * neq1) / neq1;
        const int iq1 = (ir - iq3 * neq2 * neq1 - iq2 * neq1);

        float * S = (float *)params->wdata + ith * (Mup + CACHE_LINE_SIZE_F32);

        for (int i = M; i < Mup; ++i) {
            S[i] = -INFINITY;
        }

        for (int64_t ic = 0; ic < nek1; ++ic) {
            // k indices
            const int ik3 = iq3;
            const int ik2 = iq2;
            const int ik1 = ic;

            // S indices
            const int i1 = ik1;

            ggml_vec_dot_f32(neq0, S + i1, (float *)((char *)k->data + (ik1 * nbk1 + ik2 * nbk2 + ik3 * nbk3)),
                             (float *)((char *)q->data + (iq1 * nbq1 + iq2 * nbq2 + iq3 * nbq3)));
        }

        // scale
        ggml_vec_scale_f32(nek1, S, scale);

        if (masked) {
            for (int64_t i = P; i < M; i++) {
                if (i > P + iq1) {
                    S[i] = -INFINITY;
                }
            }
        }

        // softmax
        {
            float max = -INFINITY;
            ggml_vec_max_f32(M, &max, S);

            ggml_float sum = 0.0;
            {
#ifdef GGML_SOFT_MAX_ACCELERATE
                max = -max;
                vDSP_vsadd(S, 1, &max, S, 1, Mup);
                vvexpf(S, S, &Mup);
                ggml_vec_sum_f32(Mup, &sum, S);
#else
                uint16_t scvt[GGML_SOFT_MAX_UNROLL];
                UNUSED(scvt);
                ggml_float sump[GGML_SOFT_MAX_UNROLL] = {0.0};

                for (int i = 0; i < Mup; i += GGML_SOFT_MAX_UNROLL) {
                    float * SS = S + i;

                    for (int j = 0; j < GGML_SOFT_MAX_UNROLL; ++j) {
                        if (SS[j] == -INFINITY) {
                            SS[j] = 0.0f;
                        } else {
#ifndef GGML_FLASH_ATTN_EXP_FP16
                            const float val = expf(SS[j] - max);
#else
                            ggml_fp16_t s = GGML_FP32_TO_FP16(SS[j] - max);
                            memcpy(&scvt[j], &s, sizeof(uint16_t));
                            const float val = GGML_FP16_TO_FP32(table_exp_f16[scvt[j]]);
#endif
                            sump[j] += (ggml_float)val;
                            SS[j] = val;
                        }
                    }
                }

                for (int i = 0; i < GGML_SOFT_MAX_UNROLL; i++) {
                    sum += sump[i];
                }
#endif
            }

            assert(sum > 0.0);

            sum = 1.0 / sum;
            ggml_vec_scale_f32(M, S, sum);

#ifndef NDEBUG
            for (int i = 0; i < M; ++i) {
                assert(!isnan(S[i]));
                assert(!isinf(S[i]));
            }
#endif
        }

        for (int64_t ic = 0; ic < nev1; ++ic) {
            // dst indices
            const int i1 = iq1;
            const int i2 = iq2;
            const int i3 = iq3;

            ggml_vec_dot_f32(nek1, (float *)((char *)dst->data + (ic * nb0 + i1 * nb1 + i2 * nb2 + i3 * nb3)),
                             (float *)((char *)v->data + (ic * nbv1 + i2 * nbv2 + i3 * nbv3)), S);
        }
    }
}

static void ggml_compute_forward_flash_attn_f16(const struct ggml_compute_params * params, const struct ggml_tensor * q,
                                                const struct ggml_tensor * k, const struct ggml_tensor * v, const bool masked,
                                                struct ggml_tensor * dst) {
    int64_t t0 = ggml_perf_time_us();
    UNUSED(t0);

    GGML_TENSOR_LOCALS(int64_t, neq, q, ne);
    GGML_TENSOR_LOCALS(size_t, nbq, q, nb);
    GGML_TENSOR_LOCALS(int64_t, nek, k, ne);
    GGML_TENSOR_LOCALS(size_t, nbk, k, nb);
    GGML_TENSOR_LOCALS(int64_t, nev, v, ne);
    GGML_TENSOR_LOCALS(size_t, nbv, v, nb);
    GGML_TENSOR_LOCALS(int64_t, ne, dst, ne);
    GGML_TENSOR_LOCALS(size_t, nb, dst, nb);

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

    const int64_t D = neq0;
    const int64_t N = neq1;
    const int64_t P = nek1 - N;
    const int64_t M = P + N;

    const int Mup = ggml_up(M, GGML_SOFT_MAX_UNROLL);

    GGML_ASSERT(ne0 == D);
    GGML_ASSERT(ne1 == N);
    GGML_ASSERT(P >= 0);

    GGML_ASSERT(nbq0 == sizeof(ggml_fp16_t));
    GGML_ASSERT(nbk0 == sizeof(ggml_fp16_t));
    GGML_ASSERT(nbv0 == sizeof(ggml_fp16_t));

    GGML_ASSERT(neq0 == D);
    GGML_ASSERT(nek0 == D);
    GGML_ASSERT(nev1 == D);

    GGML_ASSERT(neq1 == N);
    GGML_ASSERT(nek1 == N + P);
    GGML_ASSERT(nev1 == D);

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

    if (params->type == GGML_TASK_INIT) {
        return;
    }

    if (params->type == GGML_TASK_FINALIZE) {
        return;
    }

    // parallelize by q rows using ggml_vec_dot_f32

    // total rows in q
    const int nr = neq1 * neq2 * neq3;

    // rows per thread
    const int dr = (nr + nth - 1) / nth;

    // row range for this thread
    const int ir0 = dr * ith;
    const int ir1 = MIN(ir0 + dr, nr);

    const float scale = 1.0f / sqrtf(D);

    // printf("P=%d N=%d D=%d ir0=%d ir1=%d scale = %f\n", P, N, D, ir0, ir1, scale);

    for (int ir = ir0; ir < ir1; ++ir) {
        // q indices
        const int iq3 = ir / (neq2 * neq1);
        const int iq2 = (ir - iq3 * neq2 * neq1) / neq1;
        const int iq1 = (ir - iq3 * neq2 * neq1 - iq2 * neq1);

        float * S = (float *)params->wdata + ith * (2 * Mup + CACHE_LINE_SIZE_F32);

        for (int i = M; i < Mup; ++i) {
            S[i] = -INFINITY;
        }

        if (GGML_VEC_DOT_UNROLL > 2 || nek1 % GGML_VEC_DOT_UNROLL != 0) {
            for (int64_t ic = 0; ic < nek1; ++ic) {
                // k indices
                const int ik3 = iq3;
                const int ik2 = iq2;
                const int ik1 = ic;

                // S indices
                const int i1 = ik1;

                ggml_vec_dot_f16(neq0, S + i1, (ggml_fp16_t *)((char *)k->data + (ik1 * nbk1 + ik2 * nbk2 + ik3 * nbk3)),
                                 (ggml_fp16_t *)((char *)q->data + (iq1 * nbq1 + iq2 * nbq2 + iq3 * nbq3)));
            }
        } else {
            for (int64_t ic = 0; ic < nek1; ic += GGML_VEC_DOT_UNROLL) {
                // k indices
                const int ik3 = iq3;
                const int ik2 = iq2;
                const int ik1 = ic;

                // S indices
                const int i1 = ik1;

                ggml_vec_dot_f16_unroll(neq0, nbk1, S + i1, ((char *)k->data + (ik1 * nbk1 + ik2 * nbk2 + ik3 * nbk3)),
                                        (ggml_fp16_t *)((char *)q->data + (iq1 * nbq1 + iq2 * nbq2 + iq3 * nbq3)));
            }
        }

        // scale
        ggml_vec_scale_f32(nek1, S, scale);

        if (masked) {
            for (int64_t i = P; i < M; i++) {
                if (i > P + iq1) {
                    S[i] = -INFINITY;
                }
            }
        }

        // softmax
        {
            float max = -INFINITY;
            ggml_vec_max_f32(M, &max, S);

            ggml_float sum = 0.0;
            {
#ifdef GGML_SOFT_MAX_ACCELERATE
                max = -max;
                vDSP_vsadd(S, 1, &max, S, 1, Mup);
                vvexpf(S, S, &Mup);
                ggml_vec_sum_f32(Mup, &sum, S);
#else
                uint16_t scvt[GGML_SOFT_MAX_UNROLL];
                ggml_float sump[GGML_SOFT_MAX_UNROLL] = {0.0};

                for (int i = 0; i < Mup; i += GGML_SOFT_MAX_UNROLL) {
                    float * SS = S + i;

                    for (int j = 0; j < GGML_SOFT_MAX_UNROLL; ++j) {
                        if (SS[j] == -INFINITY) {
                            SS[j] = 0.0f;
                        } else {
                            ggml_fp16_t s = GGML_FP32_TO_FP16(SS[j] - max);
                            memcpy(&scvt[j], &s, sizeof(uint16_t));
                            const float val = GGML_FP16_TO_FP32(table_exp_f16[scvt[j]]);
                            sump[j] += (ggml_float)val;
                            SS[j] = val;
                        }
                    }
                }

                for (int i = 0; i < GGML_SOFT_MAX_UNROLL; i++) {
                    sum += sump[i];
                }
#endif
            }

            assert(sum > 0.0);

            sum = 1.0 / sum;
            ggml_vec_scale_f32(M, S, sum);

#ifndef NDEBUG
            for (int i = 0; i < M; ++i) {
                assert(!isnan(S[i]));
                assert(!isinf(S[i]));
            }
#endif
        }

        ggml_fp16_t * S16 = (ggml_fp16_t *)((float *)params->wdata + ith * (2 * Mup + CACHE_LINE_SIZE_F32) + Mup);

        for (int64_t i = 0; i < M; i++) {
            S16[i] = GGML_FP32_TO_FP16(S[i]);
        }

        if (GGML_VEC_DOT_UNROLL == 1 || (nev1 % GGML_VEC_DOT_UNROLL != 0)) {
            for (int64_t ic = 0; ic < nev1; ++ic) {
                // dst indices
                const int i1 = iq1;
                const int i2 = iq2;
                const int i3 = iq3;

                ggml_vec_dot_f16(nek1, (float *)((char *)dst->data + (ic * nb0 + i1 * nb1 + i2 * nb2 + i3 * nb3)),
                                 (ggml_fp16_t *)((char *)v->data + (ic * nbv1 + i2 * nbv2 + i3 * nbv3)), S16);
            }
        } else {
            for (int64_t ic = 0; ic < nev1; ic += GGML_VEC_DOT_UNROLL) {
                // dst indices
                const int i1 = iq1;
                const int i2 = iq2;
                const int i3 = iq3;

                ggml_vec_dot_f16_unroll(nek1, nbv1, (float *)((char *)dst->data + (ic * nb0 + i1 * nb1 + i2 * nb2 + i3 * nb3)),
                                        ((char *)v->data + (ic * nbv1 + i2 * nbv2 + i3 * nbv3)), S16);
            }
        }
    }
}

static void ggml_compute_forward_flash_attn(const struct ggml_compute_params * params, const struct ggml_tensor * q,
                                            const struct ggml_tensor * k, const struct ggml_tensor * v, const bool masked,
                                            struct ggml_tensor * dst) {
    switch (q->type) {
    case GGML_TYPE_F16: {
        ggml_compute_forward_flash_attn_f16(params, q, k, v, masked, dst);
    } break;
    case GGML_TYPE_F32: {
        ggml_compute_forward_flash_attn_f32(params, q, k, v, masked, dst);
    } break;
    default: {
        GGML_ASSERT(false);
    } break;
    }
}

// ggml_compute_forward_flash_ff

static void ggml_compute_forward_flash_ff_f16(const struct ggml_compute_params * params,
                                              const struct ggml_tensor * a,  // F16
                                              const struct ggml_tensor * b0, // F16 fc_w
                                              const struct ggml_tensor * b1, // F32 fc_b
                                              const struct ggml_tensor * c0, // F16 proj_w
                                              const struct ggml_tensor * c1, // F32 proj_b
                                              struct ggml_tensor * dst) {
    int64_t t0 = ggml_perf_time_us();
    UNUSED(t0);

    GGML_TENSOR_LOCALS(int64_t, nea, a, ne);
    GGML_TENSOR_LOCALS(size_t, nba, a, nb);
    GGML_TENSOR_LOCALS(int64_t, neb0, b0, ne);
    GGML_TENSOR_LOCALS(size_t, nbb0, b0, nb);
    GGML_TENSOR_LOCALS(int64_t, neb1, b1, ne);
    GGML_TENSOR_LOCALS(size_t, nbb1, b1, nb);
    GGML_TENSOR_LOCALS(int64_t, nec0, c0, ne);
    GGML_TENSOR_LOCALS(size_t, nbc0, c0, nb);
    GGML_TENSOR_LOCALS(int64_t, nec1, c1, ne);
    GGML_TENSOR_LOCALS(size_t, nbc1, c1, nb);
    GGML_TENSOR_LOCALS(int64_t, ne, dst, ne);
    GGML_TENSOR_LOCALS(size_t, nb, dst, nb);

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

    const int64_t D = nea0;
    // const int64_t N = nea1;
    const int64_t M = neb01;

    GGML_ASSERT(ne0 == nea0);
    GGML_ASSERT(ne1 == nea1);
    GGML_ASSERT(ne2 == nea2);

    GGML_ASSERT(nba0 == sizeof(ggml_fp16_t));
    GGML_ASSERT(nbb00 == sizeof(ggml_fp16_t));
    GGML_ASSERT(nbb10 == sizeof(float));
    GGML_ASSERT(nbc00 == sizeof(ggml_fp16_t));
    GGML_ASSERT(nbc10 == sizeof(float));

    GGML_ASSERT(neb00 == D);
    GGML_ASSERT(neb01 == M);
    GGML_ASSERT(neb10 == M);
    GGML_ASSERT(neb11 == 1);

    GGML_ASSERT(nec00 == M);
    GGML_ASSERT(nec01 == D);
    GGML_ASSERT(nec10 == D);
    GGML_ASSERT(nec11 == 1);

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

    if (params->type == GGML_TASK_INIT) {
        return;
    }

    if (params->type == GGML_TASK_FINALIZE) {
        return;
    }

    // parallelize by a rows using ggml_vec_dot_f32

    // total rows in a
    const int nr = nea1 * nea2 * nea3;

    // rows per thread
    const int dr = (nr + nth - 1) / nth;

    // row range for this thread
    const int ir0 = dr * ith;
    const int ir1 = MIN(ir0 + dr, nr);

    for (int ir = ir0; ir < ir1; ++ir) {
        // a indices
        const int ia3 = ir / (nea2 * nea1);
        const int ia2 = (ir - ia3 * nea2 * nea1) / nea1;
        const int ia1 = (ir - ia3 * nea2 * nea1 - ia2 * nea1);

        float * S = (float *)params->wdata + ith * (2 * M + CACHE_LINE_SIZE_F32);

        for (int64_t ic = 0; ic < neb01; ++ic) {
            // b0 indices
            const int ib03 = ia3;
            const int ib02 = ia2;
            const int ib01 = ic;

            // S indices
            const int i1 = ib01;

            ggml_vec_dot_f16(nea0, S + i1, (ggml_fp16_t *)((char *)b0->data + (ib01 * nbb01 + ib02 * nbb02 + ib03 * nbb03)),
                             (ggml_fp16_t *)((char *)a->data + (ia1 * nba1 + ia2 * nba2 + ia3 * nba3)));
        }

        ggml_vec_add_f32(neb01, S, S, (float *)b1->data);
        // ggml_vec_gelu_f32(neb01, S, S);

        ggml_fp16_t * S16 = (ggml_fp16_t *)((float *)params->wdata + ith * (2 * M + CACHE_LINE_SIZE_F32) + M);

        for (int64_t i = 0; i < M; i++) {
            S16[i] = GGML_FP32_TO_FP16(S[i]);
        }

        ggml_vec_gelu_f16(neb01, S16, S16);

        {
            // dst indices
            const int i1 = ia1;
            const int i2 = ia2;
            const int i3 = ia3;

            for (int64_t ic = 0; ic < nec01; ++ic) {

                ggml_vec_dot_f16(neb01, (float *)((char *)dst->data + (ic * nb0 + i1 * nb1 + i2 * nb2 + i3 * nb3)),
                                 (ggml_fp16_t *)((char *)c0->data + (ic * nbc01 + i2 * nbc02 + i3 * nbc03)), S16);
            }

            ggml_vec_add_f32(nec01, (float *)((char *)dst->data + (i1 * nb1 + i2 * nb2 + i3 * nb3)),
                             (float *)((char *)dst->data + (i1 * nb1 + i2 * nb2 + i3 * nb3)), (float *)c1->data);
        }
    }
}

static void ggml_compute_forward_flash_ff(const struct ggml_compute_params * params, const struct ggml_tensor * a,
                                          const struct ggml_tensor * b0, const struct ggml_tensor * b1,
                                          const struct ggml_tensor * c0, const struct ggml_tensor * c1,
                                          struct ggml_tensor * dst) {
    switch (b0->type) {
    case GGML_TYPE_F16: {
        ggml_compute_forward_flash_ff_f16(params, a, b0, b1, c0, c1, dst);
    } break;
    case GGML_TYPE_F32: {
        GGML_ASSERT(false); // TODO
    } break;
    default: {
        GGML_ASSERT(false);
    } break;
    }
}

// ggml_compute_forward_flash_attn_back

static void ggml_compute_forward_flash_attn_back_f32(const struct ggml_compute_params * params, const struct ggml_tensor * q,
                                                     const struct ggml_tensor * k, const struct ggml_tensor * v,
                                                     const struct ggml_tensor * d, const bool masked,
                                                     struct ggml_tensor * dst) {
    int64_t t0 = ggml_perf_time_us();
    UNUSED(t0);

    GGML_TENSOR_LOCALS(int64_t, neq, q, ne);
    GGML_TENSOR_LOCALS(size_t, nbq, q, nb);
    GGML_TENSOR_LOCALS(int64_t, nek, k, ne);
    GGML_TENSOR_LOCALS(size_t, nbk, k, nb);
    GGML_TENSOR_LOCALS(int64_t, nev, v, ne);
    GGML_TENSOR_LOCALS(size_t, nbv, v, nb);
    GGML_TENSOR_LOCALS(int64_t, ned, d, ne);
    GGML_TENSOR_LOCALS(size_t, nbd, d, nb);
    GGML_TENSOR_LOCALS(int64_t, ne, dst, ne);
    GGML_TENSOR_LOCALS(size_t, nb, dst, nb);

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

    const int64_t D = neq0;
    const int64_t N = neq1;
    const int64_t P = nek1 - N;
    const int64_t M = P + N;

    const int Mup = ggml_up(M, GGML_SOFT_MAX_UNROLL);
    const int mxDM = MAX(D, Mup);

    // GGML_ASSERT(ne0 == D);
    // GGML_ASSERT(ne1 == N);
    GGML_ASSERT(P >= 0);

    GGML_ASSERT(nbq0 == sizeof(float));
    GGML_ASSERT(nbk0 == sizeof(float));
    GGML_ASSERT(nbv0 == sizeof(float));

    GGML_ASSERT(neq0 == D);
    GGML_ASSERT(nek0 == D);
    GGML_ASSERT(nev1 == D);
    GGML_ASSERT(ned0 == D);

    GGML_ASSERT(neq1 == N);
    GGML_ASSERT(nek1 == N + P);
    GGML_ASSERT(nev1 == D);
    GGML_ASSERT(ned1 == N);

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

    if (params->type == GGML_TASK_INIT) {
        if (ith == 0) {
            memset(dst->data, 0, nb0 * ne0 * ne1 * ne2 * ne3);
        }
        return;
    }

    if (params->type == GGML_TASK_FINALIZE) {
        return;
    }

    // parallelize by q rows using ggml_vec_dot_f32

    // total rows in q
    const int nr = neq2 * neq3;

    // rows per thread
    const int dr = (nr + nth - 1) / nth;

    // row range for this thread
    const int ir0 = dr * ith;
    const int ir1 = MIN(ir0 + dr, nr);

    const float scale = 1.0f / sqrtf(D);

    // printf("P=%d N=%d D=%d ir0=%d ir1=%d scale = %f\n", P, N, D, ir0, ir1, scale);

    for (int ir = ir0; ir < ir1; ++ir) {
        // q indices
        const int iq3 = ir / (neq2);
        const int iq2 = ir - iq3 * neq2;
        for (int iq1 = 0; iq1 < neq1; ++iq1) {

            // not sure about CACHE_LINE_SIZE_F32..
            // - maybe it must not be multiplied by 2 and excluded from .. in SM 1*(..) offset?
            float * S = (float *)params->wdata + ith * 2 * (mxDM + CACHE_LINE_SIZE_F32) + 0 * (mxDM + CACHE_LINE_SIZE_F32);
            float * SM = (float *)params->wdata + ith * 2 * (mxDM + CACHE_LINE_SIZE_F32) + 1 * (mxDM + CACHE_LINE_SIZE_F32);

            for (int i = M; i < Mup; ++i) {
                S[i] = -INFINITY;
            }

            for (int64_t ic = 0; ic < nek1; ++ic) {
                // k indices
                const int ik3 = iq3;
                const int ik2 = iq2;
                const int ik1 = ic;

                // S indices
                const int i1 = ik1;

                ggml_vec_dot_f32(neq0, S + i1, (float *)((char *)k->data + (ik1 * nbk1 + ik2 * nbk2 + ik3 * nbk3)),
                                 (float *)((char *)q->data + (iq1 * nbq1 + iq2 * nbq2 + iq3 * nbq3)));
            }

            // scale
            ggml_vec_scale_f32(nek1, S, scale);

            if (masked) {
                for (int64_t i = P; i < M; i++) {
                    if (i > P + iq1) {
                        S[i] = -INFINITY;
                    }
                }
            }

            // softmax
            {
                float max = -INFINITY;
                ggml_vec_max_f32(M, &max, S);

                ggml_float sum = 0.0;
                {
#ifdef GGML_SOFT_MAX_ACCELERATE
                    max = -max;
                    vDSP_vsadd(SM, 1, &max, SM, 1, Mup);
                    vvexpf(SM, SM, &Mup);
                    ggml_vec_sum_f32(Mup, &sum, SM);
#else
                    uint16_t scvt[GGML_SOFT_MAX_UNROLL];
                    UNUSED(scvt);
                    ggml_float sump[GGML_SOFT_MAX_UNROLL] = {0.0};

                    for (int i = 0; i < Mup; i += GGML_SOFT_MAX_UNROLL) {
                        float * SR = S + i;
                        float * SW = SM + i;

                        for (int j = 0; j < GGML_SOFT_MAX_UNROLL; ++j) {
                            if (SR[j] == -INFINITY) {
                                SW[j] = 0.0f;
                            } else {
#ifndef GGML_FLASH_ATTN_EXP_FP16
                                const float val = expf(SR[j] - max);
#else
                                ggml_fp16_t s = GGML_FP32_TO_FP16(SR[j] - max);
                                memcpy(&scvt[j], &s, sizeof(uint16_t));
                                const float val = GGML_FP16_TO_FP32(table_exp_f16[scvt[j]]);
#endif
                                sump[j] += (ggml_float)val;
                                SW[j] = val;
                            }
                        }
                    }

                    for (int i = 0; i < GGML_SOFT_MAX_UNROLL; i++) {
                        sum += sump[i];
                    }
#endif
                }

                assert(sum > 0.0);

                sum = 1.0 / sum;
                ggml_vec_scale_f32(M, SM, sum);
            }

            // step-by-step explanation
            {
                // forward-process                   shape      grads from backward process
                // parallel_for iq2,iq3:
                //  k[:D,:M,:,:]                     [D,M,:,:]  grad[k][:D,:M,iq2,iq3]  += grad[kcur]
                //  q[:D,:N,:,:]                     [D,N,:,:]  grad[q][:D,iq1,iq2,iq3] += grad[qcur]
                //  v[:M,:D,:,:]                     [M,D,:,:]  grad[v][:M,:D,iq2,iq3]  += grad[vcur]
                //  for iq1:
                //   kcur   = k[:D,:M,iq2,iq3]       [D,M,1,1]  grad[kcur] = grad[S1].T @ qcur
                //   qcur   = q[:D,iq1,iq2,iq3]      [D,1,1,1]  grad[qcur] = grad[S1]   @ kcur
                //   vcur   = v[:M,:D,iq2,iq3]       [M,D,1,1]  grad[vcur] = grad[S5].T @ S4
                //   S0     = -Inf                   [D,1,1,1]
                //  ~S1[i]  = dot(kcur[:D,i], qcur)
                //   S1     = qcur @ kcur.T          [M,1,1,1]  grad[S1]   = grad[S2] * scale
                //   S2     = S1 * scale             [M,1,1,1]  grad[S2]   = diag_mask_zero(grad[S3], P)
                //   S3     = diag_mask_inf(S2, P)   [M,1,1,1]  grad[S3]   = S4 * (grad[S4] - dot(S4, grad[S4]))
                //   S4     = softmax(S3)            [M,1,1,1]  grad[S4]   = grad[S5] @ vcur
                //  ~S5[i]  = dot(vcur[:,i], S4)
                //   S5     = S4 @ vcur.T            [D,1,1,1]  grad[S5]   = d[:D,iq1,iq2,iq3]
                //  ~dst[i,iq1,iq2,iq3]  = S5[i]              ^
                //   dst[:D,iq1,iq2,iq3] = S5                 | grad[dst[:D,iq1,iq2,iq3]] = d[:D,iq1,iq2,iq3]
                // dst                               backward-/ grad[dst]                 = d
                //
                // output gradients with their dependencies:
                //
                // grad[kcur] = grad[S1].T @ qcur
                // grad[S1]   = diag_mask_zero(grad[S3], P) * scale
                // grad[S3]   = S4 * (grad[S4] - dot(S4, grad[S4]))
                // grad[S4]   = grad[S5] @ vcur
                // grad[S4]   = d[:D,iq1,iq2,iq3] @ vcur
                // grad[qcur] = grad[S1]   @ kcur
                // grad[vcur] = grad[S5].T @ S4
                // grad[vcur] = d[:D,iq1,iq2,iq3].T @ S4
                //
                // in post-order:
                //
                // S1         = qcur @ kcur.T
                // S2         = S1 * scale
                // S3         = diag_mask_inf(S2, P)
                // S4         = softmax(S3)
                // grad[S4]   = d[:D,iq1,iq2,iq3] @ vcur
                // grad[S3]   = S4 * (grad[S4] - dot(S4, grad[S4]))
                // grad[S1]   = diag_mask_zero(grad[S3], P) * scale
                // grad[qcur] = grad[S1]   @ kcur
                // grad[kcur] = grad[S1].T @ qcur
                // grad[vcur] = d[:D,iq1,iq2,iq3].T @ S4
                //
                // using less variables (SM=S4):
                //
                // S             = diag_mask_inf(qcur @ kcur.T * scale, P)
                // SM            = softmax(S)
                // S             = d[:D,iq1,iq2,iq3] @ vcur
                // dot_SM_gradSM = dot(SM, S)
                // S             = SM * (S - dot(SM, S))
                // S             = diag_mask_zero(S, P) * scale
                //
                // grad[q][:D,iq1,iq2,iq3] += S   @ kcur
                // grad[k][:D,:M,iq2,iq3]  += S.T @ qcur
                // grad[v][:M,:D,iq2,iq3]  += d[:D,iq1,iq2,iq3].T @ SM
            }

            // S = gradSM = d[:D,iq1,iq2,iq3] @ vcur
            // S = d[:D,iq1,iq2,iq3] @ vcur
            // S[:M] += vcur[:M,ic] * d[ic,iq1,iq2,iq3]
            ggml_vec_set_f32(M, S, 0);
            for (int64_t ic = 0; ic < D; ++ic) {
                // dst indices
                const int i1 = iq1;
                const int i2 = iq2;
                const int i3 = iq3;

                ggml_vec_mad_f32(M, S, (float *)((char *)v->data + (ic * nbv1 + i2 * nbv2 + i3 * nbv3)),
                                 *(float *)((char *)d->data + (ic * nbd0 + i1 * nbd1 + i2 * nbd2 + i3 * nbd3)));
            }

            // S = SM * (S - dot(SM, S))
            float dot_SM_gradSM = 0;
            ggml_vec_dot_f32(M, &dot_SM_gradSM, SM, S);
            ggml_vec_acc1_f32(M, S, -dot_SM_gradSM);
            ggml_vec_mul_f32(M, S, S, SM);

            // S = diag_mask_zero(S, P) * scale
            if (masked) {
                // for (int64_t i = P + iq1 + 1; i < M; i++) {
                //     S[i] = 0;
                // }
                for (int64_t i = P; i < M; i++) {
                    if (i > P + iq1) {
                        S[i] = 0;
                    }
                }
            }
            ggml_vec_scale_f32(M, S, scale);

            void * grad_q = (char *)dst->data;
            void * grad_k = (char *)dst->data + nb0 * D * N * neq2 * neq3;
            void * grad_v = (char *)dst->data + nb0 * D * N * neq2 * neq3 + nb0 * D * M * neq2 * neq3;

            const size_t nbgq1 = nb0 * neq0;
            const size_t nbgq2 = nb0 * neq0 * neq1;
            const size_t nbgq3 = nb0 * neq0 * neq1 * neq2;

            const size_t nbgk1 = nb0 * nek0;
            const size_t nbgk2 = nb0 * nek0 * nek1;
            const size_t nbgk3 = nb0 * nek0 * nek1 * neq2;

            const size_t nbgv1 = nb0 * nev0;
            const size_t nbgv2 = nb0 * nev0 * nev1;
            const size_t nbgv3 = nb0 * nev0 * nev1 * neq2;

            // S    shape [M,1]
            // SM   shape [M,1]
            // kcur shape [D,M]
            // qcur shape [D,1]
            // vcur shape [M,D]
            //
            // grad[q][:D,iq1,iq2,iq3] += S @ kcur
            // grad[q][:D,iq1,iq2,iq3] += shape[M,1] @ shape[D,M]
            // grad[q][:D,iq1,iq2,iq3] += S[ic] * kcur[:D,ic]
            //
            //// grad[q][ic,iq1,iq2,iq3] += dot(kcur[:,ic],S.T)
            //// grad[q][ic,iq1,iq2,iq3] += dot(k[:D,ic,iq2,iq3],S.T)
            for (int64_t ic = 0; ic < M; ++ic) {
                // dst indices
                const int i1 = iq1;
                const int i2 = iq2;
                const int i3 = iq3;

                ggml_vec_mad_f32(D, (float *)((char *)grad_q + (i1 * nbgq1 + i2 * nbgq2 + i3 * nbgq3)),
                                 (float *)((char *)k->data + (ic * nbk1 + i2 * nbk2 + i3 * nbk3)), S[ic]);
            }

            // grad[k][:D,:M,iq2,iq3] += S.T       @ qcur
            // grad[k][:D,ic,iq2,iq3] += S.T[0,ic] * qcur[:D,0]
            // grad[k][:D,ic,iq2,iq3] += S[ic]     * qcur[:D,0]
            for (int64_t ic = 0; ic < M; ++ic) {
                // dst indices
                const int i1 = iq1;
                const int i2 = iq2;
                const int i3 = iq3;

                // ggml_vec_set_f32(D,
                //         (float *) ((char *) grad_k  + (ic*nbgk1  + i2*nbgk2  + i3*nbgk3)),
                //         0);
                ggml_vec_mad_f32(D, (float *)((char *)grad_k + (ic * nbgk1 + i2 * nbgk2 + i3 * nbgk3)),
                                 (float *)((char *)q->data + (i1 * nbq1 + i2 * nbq2 + i3 * nbq3)), S[ic]);
            }

            // grad[v][:M,:D,iq2,iq3] += d[:D,iq1,iq2,iq3].T       @ SM
            // grad[v][:M,ic,iq2,iq3] += d[:D,iq1,iq2,iq3].T[0,ic] * SM[:M]
            // grad[v][:M,ic,iq2,iq3] += d[ic,iq1,iq2,iq3]         * SM[:M]
            for (int64_t ic = 0; ic < D; ++ic) {
                // dst indices
                const int i1 = iq1;
                const int i2 = iq2;
                const int i3 = iq3;

                // ggml_vec_set_f32(M,
                //         (float *) ((char *) grad_v   + (          ic*nbgv1 + i2*nbgv2 + i3*nbgv3)),
                //         0);
                ggml_vec_mad_f32(M, (float *)((char *)grad_v + (ic * nbgv1 + i2 * nbgv2 + i3 * nbgv3)), SM,
                                 *(float *)((char *)d->data + (ic * nbd0 + i1 * nbd1 + i2 * nbd2 + i3 * nbd3)));
            }
        }
    }
}

static void ggml_compute_forward_flash_attn_back(const struct ggml_compute_params * params, const struct ggml_tensor * q,
                                                 const struct ggml_tensor * k, const struct ggml_tensor * v,
                                                 const struct ggml_tensor * d, const bool masked, struct ggml_tensor * dst) {
    switch (q->type) {
    case GGML_TYPE_F32: {
        ggml_compute_forward_flash_attn_back_f32(params, q, k, v, d, masked, dst);
    } break;
    default: {
        GGML_ASSERT(false);
    } break;
    }
}

// ggml_compute_forward_win_part

static void ggml_compute_forward_win_part_f32(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                              struct ggml_tensor * dst) {
    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
        return;
    }

    GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne);
    GGML_TENSOR_LOCALS(int64_t, ne, dst, ne);

    const int32_t nep0 = ((const int32_t *)(dst->op_params))[0];
    const int32_t nep1 = ((const int32_t *)(dst->op_params))[1];
    const int32_t w = ((const int32_t *)(dst->op_params))[2];

    assert(ne00 == ne0);
    assert(ne3 == nep0 * nep1);

    // TODO: optimize / multi-thread
    for (int py = 0; py < nep1; ++py) {
        for (int px = 0; px < nep0; ++px) {
            const int64_t i3 = py * nep0 + px;
            for (int64_t i2 = 0; i2 < ne2; ++i2) {
                for (int64_t i1 = 0; i1 < ne1; ++i1) {
                    for (int64_t i0 = 0; i0 < ne0; ++i0) {
                        const int64_t i02 = py * w + i2;
                        const int64_t i01 = px * w + i1;
                        const int64_t i00 = i0;

                        const int64_t i = i3 * ne2 * ne1 * ne0 + i2 * ne1 * ne0 + i1 * ne0 + i0;
                        const int64_t j = i02 * ne01 * ne00 + i01 * ne00 + i00;

                        if (py * w + i2 >= ne02 || px * w + i1 >= ne01) {
                            ((float *)dst->data)[i] = 0.0f;
                        } else {
                            ((float *)dst->data)[i] = ((float *)src0->data)[j];
                        }
                    }
                }
            }
        }
    }
}

static void ggml_compute_forward_win_part(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                          struct ggml_tensor * dst) {
    switch (src0->type) {
    case GGML_TYPE_F32: {
        ggml_compute_forward_win_part_f32(params, src0, dst);
    } break;
    default: {
        GGML_ASSERT(false);
    } break;
    }
}

// ggml_compute_forward_win_unpart

static void ggml_compute_forward_win_unpart_f32(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                                struct ggml_tensor * dst) {
    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
        return;
    }

    GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne);
    GGML_TENSOR_LOCALS(int64_t, ne, dst, ne);

    const int32_t w = ((const int32_t *)(dst->op_params))[0];

    // padding
    const int px = (w - ne1 % w) % w;
    // const int py = (w - ne2%w)%w;

    const int npx = (px + ne1) / w;
    // const int npy = (py + ne2)/w;

    assert(ne0 == ne00);

    // TODO: optimize / multi-thread
    for (int64_t i2 = 0; i2 < ne2; ++i2) {
        for (int64_t i1 = 0; i1 < ne1; ++i1) {
            for (int64_t i0 = 0; i0 < ne0; ++i0) {
                const int ip2 = i2 / w;
                const int ip1 = i1 / w;

                const int64_t i02 = i2 % w;
                const int64_t i01 = i1 % w;
                const int64_t i00 = i0;

                const int64_t i = (ip2 * npx + ip1) * ne02 * ne01 * ne00 + i02 * ne01 * ne00 + i01 * ne00 + i00;
                const int64_t j = i2 * ne1 * ne0 + i1 * ne0 + i0;

                ((float *)dst->data)[j] = ((float *)src0->data)[i];
            }
        }
    }
}

static void ggml_compute_forward_win_unpart(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                            struct ggml_tensor * dst) {
    switch (src0->type) {
    case GGML_TYPE_F32: {
        ggml_compute_forward_win_unpart_f32(params, src0, dst);
    } break;
    default: {
        GGML_ASSERT(false);
    } break;
    }
}

// gmml_compute_forward_unary

static void ggml_compute_forward_unary(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                       struct ggml_tensor * dst) {
    const enum ggml_unary_op op = ggml_get_unary_op(dst);

    switch (op) {
    case GGML_UNARY_OP_ABS: {
        ggml_compute_forward_abs(params, src0, dst);
    } break;
    case GGML_UNARY_OP_SGN: {
        ggml_compute_forward_sgn(params, src0, dst);
    } break;
    case GGML_UNARY_OP_NEG: {
        ggml_compute_forward_neg(params, src0, dst);
    } break;
    case GGML_UNARY_OP_STEP: {
        ggml_compute_forward_step(params, src0, dst);
    } break;
    case GGML_UNARY_OP_TANH: {
        ggml_compute_forward_tanh(params, src0, dst);
    } break;
    case GGML_UNARY_OP_ELU: {
        ggml_compute_forward_elu(params, src0, dst);
    } break;
    case GGML_UNARY_OP_RELU: {
        ggml_compute_forward_relu(params, src0, dst);
    } break;
    case GGML_UNARY_OP_GELU: {
        ggml_compute_forward_gelu(params, src0, dst);
    } break;
    case GGML_UNARY_OP_GELU_QUICK: {
        ggml_compute_forward_gelu_quick(params, src0, dst);
    } break;
    case GGML_UNARY_OP_SILU: {
        ggml_compute_forward_silu(params, src0, dst);
    } break;
    default: {
        GGML_ASSERT(false);
    } break;
    }
}

// ggml_compute_forward_get_rel_pos

static void ggml_compute_forward_get_rel_pos_f16(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                                 struct ggml_tensor * dst) {
    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
        return;
    }

    // ref: https://github.com/facebookresearch/segment-anything/blob/main/segment_anything/modeling/image_encoder.py#L292-L322

    GGML_TENSOR_UNARY_OP_LOCALS;

    const int64_t w = ne1;

    ggml_fp16_t * src0_data = (ggml_fp16_t *)src0->data;
    ggml_fp16_t * dst_data = (ggml_fp16_t *)dst->data;

    for (int64_t i2 = 0; i2 < ne2; ++i2) {
        for (int64_t i1 = 0; i1 < ne1; ++i1) {
            const int64_t pos = (w - i1 - 1) + i2;
            for (int64_t i0 = 0; i0 < ne0; ++i0) {
                dst_data[i2 * ne1 * ne0 + i1 * ne0 + i0] = src0_data[pos * ne00 + i0];
            }
        }
    }
}

static void ggml_compute_forward_get_rel_pos(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                             struct ggml_tensor * dst) {
    switch (src0->type) {
    case GGML_TYPE_F16: {
        ggml_compute_forward_get_rel_pos_f16(params, src0, dst);
    } break;
    default: {
        GGML_ASSERT(false);
    } break;
    }
}

// ggml_compute_forward_add_rel_pos

static void ggml_compute_forward_add_rel_pos_f32(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                                 const struct ggml_tensor * src1, const struct ggml_tensor * src2,
                                                 struct ggml_tensor * dst) {

    const bool inplace = (bool)((int32_t *)dst->op_params)[0];
    if (!inplace && params->type == GGML_TASK_INIT) {
        memcpy((char *)dst->data, (char *)src0->data, ggml_nbytes(dst));
        return;
    }
    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
        return;
    }

    int64_t t0 = ggml_perf_time_us();
    UNUSED(t0);

    // ref: https://github.com/facebookresearch/segment-anything/blob/main/segment_anything/modeling/image_encoder.py#L357-L359

    float * src1_data = (float *)src1->data;
    float * src2_data = (float *)src2->data;
    float * dst_data = (float *)dst->data;

    const int64_t ne10 = src1->ne[0];
    const int64_t ne11 = src1->ne[1];
    const int64_t ne12 = src1->ne[2];
    const int64_t ne13 = src1->ne[3];

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

    // total patches in dst
    const int np = ne13;

    // patches per thread
    const int dp = (np + nth - 1) / nth;

    // patch range for this thread
    const int ip0 = dp * ith;
    const int ip1 = MIN(ip0 + dp, np);

    for (int64_t i13 = ip0; i13 < ip1; ++i13) {
        for (int64_t i12 = 0; i12 < ne12; ++i12) {
            for (int64_t i11 = 0; i11 < ne11; ++i11) {
                const int64_t jp1 = i13 * ne12 * ne11 * ne10 + i12 * ne11 * ne10 + i11 * ne10;
                for (int64_t i10 = 0; i10 < ne10; ++i10) {
                    const int64_t jp0 = jp1 + i10;
                    const float src1_e = src1_data[jp0];
                    const float src2_e = src2_data[jp0];

                    const int64_t jdh = jp0 * ne10;
                    const int64_t jdw = jdh - (ne10 - 1) * i10;

                    for (int64_t j = 0; j < ne10; ++j) {
                        dst_data[jdh + j] += src2_e;
                        dst_data[jdw + j * ne10] += src1_e;
                    }
                }
            }
        }
    }
}

static void ggml_compute_forward_add_rel_pos(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                             const struct ggml_tensor * src1, const struct ggml_tensor * src2,
                                             struct ggml_tensor * dst) {
    switch (src0->type) {
    case GGML_TYPE_F32: {
        ggml_compute_forward_add_rel_pos_f32(params, src0, src1, src2, dst);
    } break;
    default: {
        GGML_ASSERT(false);
    } break;
    }
}

// ggml_compute_forward_map_unary

static void ggml_compute_forward_map_unary_f32(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                               struct ggml_tensor * dst, const ggml_unary_op_f32_t fun) {
    GGML_ASSERT(ggml_are_same_shape(src0, dst));

    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
        return;
    }

    const int n = ggml_nrows(src0);
    const int nc = src0->ne[0];

    assert(dst->nb[0] == sizeof(float));
    assert(src0->nb[0] == sizeof(float));

    for (int i = 0; i < n; i++) {
        fun(nc, (float *)((char *)dst->data + i * (dst->nb[1])), (float *)((char *)src0->data + i * (src0->nb[1])));
    }
}

static void ggml_compute_forward_map_unary(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                           struct ggml_tensor * dst, const ggml_unary_op_f32_t fun) {
    switch (src0->type) {
    case GGML_TYPE_F32: {
        ggml_compute_forward_map_unary_f32(params, src0, dst, fun);
    } break;
    default: {
        GGML_ASSERT(false);
    } break;
    }
}

// ggml_compute_forward_map_binary

static void ggml_compute_forward_map_binary_f32(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                                const struct ggml_tensor * src1, struct ggml_tensor * dst,
                                                const ggml_binary_op_f32_t fun) {
    assert(params->ith == 0);
    assert(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst));

    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
        return;
    }

    const int n = ggml_nrows(src0);
    const int nc = src0->ne[0];

    assert(dst->nb[0] == sizeof(float));
    assert(src0->nb[0] == sizeof(float));
    assert(src1->nb[0] == sizeof(float));

    for (int i = 0; i < n; i++) {
        fun(nc, (float *)((char *)dst->data + i * (dst->nb[1])), (float *)((char *)src0->data + i * (src0->nb[1])),
            (float *)((char *)src1->data + i * (src1->nb[1])));
    }
}

static void ggml_compute_forward_map_binary(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                            const struct ggml_tensor * src1, struct ggml_tensor * dst,
                                            const ggml_binary_op_f32_t fun) {
    switch (src0->type) {
    case GGML_TYPE_F32: {
        ggml_compute_forward_map_binary_f32(params, src0, src1, dst, fun);
    } break;
    default: {
        GGML_ASSERT(false);
    } break;
    }
}

// ggml_compute_forward_map_custom1

static void ggml_compute_forward_map_custom1_f32(const struct ggml_compute_params * params, const struct ggml_tensor * a,
                                                 struct ggml_tensor * dst, const ggml_custom1_op_f32_t fun) {
    assert(params->ith == 0);

    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
        return;
    }

    fun(dst, a);
}

// ggml_compute_forward_map_custom2

static void ggml_compute_forward_map_custom2_f32(const struct ggml_compute_params * params, const struct ggml_tensor * a,
                                                 const struct ggml_tensor * b, struct ggml_tensor * dst,
                                                 const ggml_custom2_op_f32_t fun) {
    assert(params->ith == 0);

    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
        return;
    }

    fun(dst, a, b);
}

// ggml_compute_forward_map_custom3

static void ggml_compute_forward_map_custom3_f32(const struct ggml_compute_params * params, const struct ggml_tensor * a,
                                                 const struct ggml_tensor * b, const struct ggml_tensor * c,
                                                 struct ggml_tensor * dst, const ggml_custom3_op_f32_t fun) {
    assert(params->ith == 0);

    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
        return;
    }

    fun(dst, a, b, c);
}

// ggml_compute_forward_map_custom1

static void ggml_compute_forward_map_custom1(const struct ggml_compute_params * params, const struct ggml_tensor * a,
                                             struct ggml_tensor * dst) {
    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
        return;
    }

    struct ggml_map_custom1_op_params * p = (struct ggml_map_custom1_op_params *)dst->op_params;

    p->fun(dst, a, params->ith, params->nth, p->userdata);
}

// ggml_compute_forward_map_custom2

static void ggml_compute_forward_map_custom2(const struct ggml_compute_params * params, const struct ggml_tensor * a,
                                             const struct ggml_tensor * b, struct ggml_tensor * dst) {
    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
        return;
    }

    struct ggml_map_custom2_op_params * p = (struct ggml_map_custom2_op_params *)dst->op_params;

    p->fun(dst, a, b, params->ith, params->nth, p->userdata);
}

// ggml_compute_forward_map_custom3

static void ggml_compute_forward_map_custom3(const struct ggml_compute_params * params, const struct ggml_tensor * a,
                                             const struct ggml_tensor * b, const struct ggml_tensor * c,
                                             struct ggml_tensor * dst) {
    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
        return;
    }

    struct ggml_map_custom3_op_params * p = (struct ggml_map_custom3_op_params *)dst->op_params;

    p->fun(dst, a, b, c, params->ith, params->nth, p->userdata);
}

// ggml_compute_forward_cross_entropy_loss

static void ggml_compute_forward_cross_entropy_loss_f32(const struct ggml_compute_params * params,
                                                        const struct ggml_tensor * src0, const struct ggml_tensor * src1,
                                                        struct ggml_tensor * dst) {
    GGML_ASSERT(ggml_is_contiguous(src0));
    GGML_ASSERT(ggml_is_contiguous(src1));
    GGML_ASSERT(ggml_is_scalar(dst));
    GGML_ASSERT(ggml_are_same_shape(src0, src1));

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

    float * sums = (float *)params->wdata;

    // TODO: handle transposed/permuted matrices
    const int nc = src0->ne[0];
    const int nr = ggml_nrows(src0);

    GGML_ASSERT(params->wsize >= sizeof(float) * (nth + nth * nc));

    if (params->type == GGML_TASK_INIT) {
        if (ith == 0) {
            memset(sums, 0, sizeof(float) * (nth + nth * nc));
        }
        return;
    }

    if (params->type == GGML_TASK_FINALIZE) {
        if (ith == 0) {
            float * dp = (float *)dst->data;
            ggml_vec_sum_f32(nth, dp, sums);
            dp[0] *= -1.0f / (float)nr;
        }
        return;
    }

    const double eps = 1e-9;

    // rows per thread
    const int dr = (nr + nth - 1) / nth;

    // row range for this thread
    const int ir0 = dr * ith;
    const int ir1 = MIN(ir0 + dr, nr);

    for (int i1 = ir0; i1 < ir1; i1++) {
        float * s0 = (float *)((char *)src0->data + i1 * src0->nb[1]);
        float * s1 = (float *)((char *)src1->data + i1 * src1->nb[1]);
        float * st = ((float *)params->wdata) + nth + ith * nc;

#ifndef NDEBUG
        for (int i = 0; i < nc; ++i) {
            // printf("p[%d] = %f\n", i, p[i]);
            assert(!isnan(s0[i]));
            assert(!isnan(s1[i]));
        }
#endif
        // soft_max
        ggml_float sum = 0.0;
        {
            float max = -INFINITY;
            ggml_vec_max_f32(nc, &max, s0);

            uint16_t scvt;
            UNUSED(scvt);
            for (int i = 0; i < nc; i++) {
                if (s0[i] == -INFINITY) {
                    st[i] = 0.0f;
                } else {
#ifndef GGML_CROSS_ENTROPY_EXP_FP16
                    const float s = s0[i] - max;
                    const float val = expf(s);
#else
                    ggml_fp16_t s = GGML_FP32_TO_FP16(s0[i] - max);
                    memcpy(&scvt, &s, sizeof(scvt));
                    const float val = GGML_FP16_TO_FP32(table_exp_f16[scvt]);
#endif
                    sum += (ggml_float)val;
                    st[i] = val;
                }
            }

            assert(sum > 0.0);
            // sum = 1.0/sum;
        }
        // avoid log(0) by rescaling from [0..1] to [eps..1]
        sum = (1.0 - eps) / sum;
        ggml_vec_scale_f32(nc, st, sum);
        ggml_vec_add1_f32(nc, st, st, eps);
        ggml_vec_log_f32(nc, st, st);
        ggml_vec_mul_f32(nc, st, st, s1);

        float st_sum = 0;
        ggml_vec_sum_f32(nc, &st_sum, st);
        sums[ith] += st_sum;

#ifndef NDEBUG
        for (int i = 0; i < nc; ++i) {
            assert(!isnan(st[i]));
            assert(!isinf(st[i]));
        }
#endif
    }
}

static void ggml_compute_forward_cross_entropy_loss(const struct ggml_compute_params * params, const struct ggml_tensor * src0,
                                                    const struct ggml_tensor * src1, struct ggml_tensor * dst) {
    switch (src0->type) {
    case GGML_TYPE_F32: {
        ggml_compute_forward_cross_entropy_loss_f32(params, src0, src1, dst);
    } break;
    default: {
        GGML_ASSERT(false);
    } break;
    }
}

// ggml_compute_forward_cross_entropy_loss_back

static void ggml_compute_forward_cross_entropy_loss_back_f32(const struct ggml_compute_params * params,
                                                             const struct ggml_tensor * src0, const struct ggml_tensor * src1,
                                                             const struct ggml_tensor * opt0, struct ggml_tensor * dst) {
    GGML_ASSERT(ggml_is_contiguous(dst));
    GGML_ASSERT(ggml_is_contiguous(src0));
    GGML_ASSERT(ggml_is_contiguous(src1));
    GGML_ASSERT(ggml_is_contiguous(opt0));
    GGML_ASSERT(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst));

    const int64_t ith = params->ith;
    const int64_t nth = params->nth;

    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
        return;
    }

    const double eps = 1e-9;

    // TODO: handle transposed/permuted matrices
    const int64_t nc = src0->ne[0];
    const int64_t nr = ggml_nrows(src0);

    // rows per thread
    const int64_t dr = (nr + nth - 1) / nth;

    // row range for this thread
    const int64_t ir0 = dr * ith;
    const int64_t ir1 = MIN(ir0 + dr, nr);

    float * d = (float *)opt0->data;

    for (int64_t i1 = ir0; i1 < ir1; i1++) {
        float * ds0 = (float *)((char *)dst->data + i1 * dst->nb[1]);
        float * s0 = (float *)((char *)src0->data + i1 * src0->nb[1]);
        float * s1 = (float *)((char *)src1->data + i1 * src1->nb[1]);

#ifndef NDEBUG
        for (int i = 0; i < nc; ++i) {
            // printf("p[%d] = %f\n", i, p[i]);
            assert(!isnan(s0[i]));
            assert(!isnan(s1[i]));
        }
#endif

        // soft_max
        ggml_float sum = 0.0;
        {
            float max = -INFINITY;
            ggml_vec_max_f32(nc, &max, s0);

            uint16_t scvt;
            UNUSED(scvt);
            for (int i = 0; i < nc; i++) {
                if (s0[i] == -INFINITY) {
                    ds0[i] = 0.0f;
                } else {
#ifndef GGML_CROSS_ENTROPY_EXP_FP16
                    const float s = s0[i] - max;
                    const float val = expf(s);
#else
                    ggml_fp16_t s = GGML_FP32_TO_FP16(s0[i] - max);
                    memcpy(&scvt, &s, sizeof(scvt));
                    const float val = GGML_FP16_TO_FP32(table_exp_f16[scvt]);
#endif
                    sum += (ggml_float)val;
                    ds0[i] = val;
                }
            }

            assert(sum > 0.0);
            sum = (1.0 - eps) / sum;
        }

        // grad(src0) = (softmax(src0) - src1) * grad(cross_entropy_loss(src0, src1)) / nr
        ggml_vec_scale_f32(nc, ds0, sum);
        ggml_vec_add1_f32(nc, ds0, ds0, eps);
        ggml_vec_sub_f32(nc, ds0, ds0, s1);
        ggml_vec_scale_f32(nc, ds0, d[0] / (float)nr);

#ifndef NDEBUG
        for (int i = 0; i < nc; ++i) {
            assert(!isnan(ds0[i]));
            assert(!isinf(ds0[i]));
        }
#endif
    }
}

static void ggml_compute_forward_cross_entropy_loss_back(const struct ggml_compute_params * params,
                                                         const struct ggml_tensor * src0, const struct ggml_tensor * src1,
                                                         const struct ggml_tensor * opt0, struct ggml_tensor * dst) {
    switch (src0->type) {
    case GGML_TYPE_F32: {
        ggml_compute_forward_cross_entropy_loss_back_f32(params, src0, src1, opt0, dst);
    } break;
    default: {
        GGML_ASSERT(false);
    } break;
    }
}

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

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

#ifdef GGML_USE_CUBLAS
    bool skip_cpu = ggml_cuda_compute_forward(params, tensor);
    if (skip_cpu) {
        return;
    }
    GGML_ASSERT(tensor->src[0] == NULL || tensor->src[0]->backend == GGML_BACKEND_CPU);
    GGML_ASSERT(tensor->src[1] == NULL || tensor->src[1]->backend == GGML_BACKEND_CPU);
#endif // GGML_USE_CUBLAS

    switch (tensor->op) {
    case GGML_OP_DUP: {
        ggml_compute_forward_dup(params, tensor->src[0], tensor);
    } break;
    case GGML_OP_ADD: {
        ggml_compute_forward_add(params, tensor->src[0], tensor->src[1], tensor);
    } break;
    case GGML_OP_ADD1: {
        ggml_compute_forward_add1(params, tensor->src[0], tensor->src[1], tensor);
    } break;
    case GGML_OP_ACC: {
        ggml_compute_forward_acc(params, tensor->src[0], tensor->src[1], tensor);
    } break;
    case GGML_OP_SUB: {
        ggml_compute_forward_sub(params, tensor->src[0], tensor->src[1], tensor);
    } break;
    case GGML_OP_MUL: {
        ggml_compute_forward_mul(params, tensor->src[0], tensor->src[1], tensor);
    } break;
    case GGML_OP_DIV: {
        ggml_compute_forward_div(params, tensor->src[0], tensor->src[1], tensor);
    } break;
    case GGML_OP_SQR: {
        ggml_compute_forward_sqr(params, tensor->src[0], tensor);
    } break;
    case GGML_OP_SQRT: {
        ggml_compute_forward_sqrt(params, tensor->src[0], tensor);
    } break;
    case GGML_OP_LOG: {
        ggml_compute_forward_log(params, tensor->src[0], tensor);
    } break;
    case GGML_OP_SUM: {
        ggml_compute_forward_sum(params, tensor->src[0], tensor);
    } break;
    case GGML_OP_SUM_ROWS: {
        ggml_compute_forward_sum_rows(params, tensor->src[0], tensor);
    } break;
    case GGML_OP_MEAN: {
        ggml_compute_forward_mean(params, tensor->src[0], tensor);
    } break;
    case GGML_OP_ARGMAX: {
        ggml_compute_forward_argmax(params, tensor->src[0], tensor);
    } break;
    case GGML_OP_REPEAT: {
        ggml_compute_forward_repeat(params, tensor->src[0], tensor);
    } break;
    case GGML_OP_REPEAT_BACK: {
        ggml_compute_forward_repeat_back(params, tensor->src[0], tensor);
    } break;
    case GGML_OP_CONCAT: {
        ggml_compute_forward_concat(params, tensor->src[0], tensor->src[1], tensor);
    } break;
    case GGML_OP_SILU_BACK: {
        ggml_compute_forward_silu_back(params, tensor->src[0], tensor->src[1], tensor);
    } break;
    case GGML_OP_NORM: {
        ggml_compute_forward_norm(params, tensor->src[0], tensor);
    } break;
    case GGML_OP_RMS_NORM: {
        ggml_compute_forward_rms_norm(params, tensor->src[0], tensor);
    } break;
    case GGML_OP_RMS_NORM_BACK: {
        ggml_compute_forward_rms_norm_back(params, tensor->src[0], tensor->src[1], tensor);
    } break;
    case GGML_OP_GROUP_NORM: {
        ggml_compute_forward_group_norm(params, tensor->src[0], tensor);
    } break;
    case GGML_OP_MUL_MAT: {
        ggml_compute_forward_mul_mat(params, tensor->src[0], tensor->src[1], tensor);
    } break;
    case GGML_OP_OUT_PROD: {
        ggml_compute_forward_out_prod(params, tensor->src[0], tensor->src[1], tensor);
    } break;
    case GGML_OP_SCALE: {
        ggml_compute_forward_scale(params, tensor->src[0], tensor->src[1], tensor);
    } break;
    case GGML_OP_SET: {
        ggml_compute_forward_set(params, tensor->src[0], tensor->src[1], tensor);
    } break;
    case GGML_OP_CPY: {
        ggml_compute_forward_cpy(params, tensor->src[0], tensor);
    } break;
    case GGML_OP_CONT: {
        ggml_compute_forward_cont(params, tensor->src[0], tensor);
    } break;
    case GGML_OP_RESHAPE: {
        ggml_compute_forward_reshape(params, tensor->src[0], tensor);
    } break;
    case GGML_OP_VIEW: {
        ggml_compute_forward_view(params, tensor->src[0]);
    } break;
    case GGML_OP_PERMUTE: {
        ggml_compute_forward_permute(params, tensor->src[0]);
    } break;
    case GGML_OP_TRANSPOSE: {
        ggml_compute_forward_transpose(params, tensor->src[0]);
    } break;
    case GGML_OP_GET_ROWS: {
        ggml_compute_forward_get_rows(params, tensor->src[0], tensor->src[1], tensor);
    } break;
    case GGML_OP_GET_ROWS_BACK: {
        ggml_compute_forward_get_rows_back(params, tensor->src[0], tensor->src[1], tensor->src[2], tensor);
    } break;
    case GGML_OP_DIAG: {
        ggml_compute_forward_diag(params, tensor->src[0], tensor);
    } break;
    case GGML_OP_DIAG_MASK_INF: {
        ggml_compute_forward_diag_mask_inf(params, tensor->src[0], tensor);
    } break;
    case GGML_OP_DIAG_MASK_ZERO: {
        ggml_compute_forward_diag_mask_zero(params, tensor->src[0], tensor);
    } break;
    case GGML_OP_SOFT_MAX: {
        ggml_compute_forward_soft_max(params, tensor->src[0], tensor);
    } break;
    case GGML_OP_SOFT_MAX_BACK: {
        ggml_compute_forward_soft_max_back(params, tensor->src[0], tensor->src[1], tensor);
    } break;
    case GGML_OP_ROPE: {
        ggml_compute_forward_rope(params, tensor->src[0], tensor);
    } break;
    case GGML_OP_ROPE_BACK: {
        ggml_compute_forward_rope_back(params, tensor->src[0], tensor);
    } break;
    case GGML_OP_ALIBI: {
        ggml_compute_forward_alibi(params, tensor->src[0], tensor);
    } break;
    case GGML_OP_CLAMP: {
        ggml_compute_forward_clamp(params, tensor->src[0], tensor);
    } break;
    case GGML_OP_CONV_1D: {
        ggml_compute_forward_conv_1d(params, tensor->src[0], tensor->src[1], tensor);
    } break;
    case GGML_OP_CONV_2D: {
        ggml_compute_forward_conv_2d(params, tensor->src[0], tensor->src[1], tensor);
    } break;
    case GGML_OP_CONV_TRANSPOSE_2D: {
        ggml_compute_forward_conv_transpose_2d(params, tensor->src[0], tensor->src[1], tensor);
    } break;
    case GGML_OP_POOL_1D: {
        ggml_compute_forward_pool_1d(params, tensor->src[0], tensor);
    } break;
    case GGML_OP_POOL_2D: {
        ggml_compute_forward_pool_2d(params, tensor->src[0], tensor);
    } break;
    case GGML_OP_UPSCALE: {
        ggml_compute_forward_upscale(params, tensor->src[0], tensor);
    } break;
    case GGML_OP_FLASH_ATTN: {
        const int32_t t = ggml_get_op_params_i32(tensor, 0);
        GGML_ASSERT(t == 0 || t == 1);
        const bool masked = t != 0;
        ggml_compute_forward_flash_attn(params, tensor->src[0], tensor->src[1], tensor->src[2], masked, tensor);
    } break;
    case GGML_OP_FLASH_FF: {
        ggml_compute_forward_flash_ff(params, tensor->src[0], tensor->src[1], tensor->src[2], tensor->src[3], tensor->src[4],
                                      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, tensor->src[0], tensor->src[1], tensor->src[2], tensor->src[3], masked,
                                             tensor);
    } break;
    case GGML_OP_WIN_PART: {
        ggml_compute_forward_win_part(params, tensor->src[0], tensor);
    } break;
    case GGML_OP_WIN_UNPART: {
        ggml_compute_forward_win_unpart(params, tensor->src[0], tensor);
    } break;
    case GGML_OP_UNARY: {
        ggml_compute_forward_unary(params, tensor->src[0], tensor);
    } break;
    case GGML_OP_GET_REL_POS: {
        ggml_compute_forward_get_rel_pos(params, tensor->src[0], tensor);
    } break;
    case GGML_OP_ADD_REL_POS: {
        ggml_compute_forward_add_rel_pos(params, tensor->src[0], tensor->src[1], tensor->src[2], tensor);
    } break;
    case GGML_OP_MAP_UNARY: {
        ggml_unary_op_f32_t fun;
        memcpy(&fun, tensor->op_params, sizeof(fun));
        ggml_compute_forward_map_unary(params, tensor->src[0], tensor, fun);
    } break;
    case GGML_OP_MAP_BINARY: {
        ggml_binary_op_f32_t fun;
        memcpy(&fun, tensor->op_params, sizeof(fun));
        ggml_compute_forward_map_binary(params, tensor->src[0], tensor->src[1], tensor, fun);
    } break;
    case GGML_OP_MAP_CUSTOM1_F32: {
        ggml_custom1_op_f32_t fun;
        memcpy(&fun, tensor->op_params, sizeof(fun));
        ggml_compute_forward_map_custom1_f32(params, tensor->src[0], tensor, fun);
    } break;
    case GGML_OP_MAP_CUSTOM2_F32: {
        ggml_custom2_op_f32_t fun;
        memcpy(&fun, tensor->op_params, sizeof(fun));
        ggml_compute_forward_map_custom2_f32(params, tensor->src[0], tensor->src[1], tensor, fun);
    } break;
    case GGML_OP_MAP_CUSTOM3_F32: {
        ggml_custom3_op_f32_t fun;
        memcpy(&fun, tensor->op_params, sizeof(fun));
        ggml_compute_forward_map_custom3_f32(params, tensor->src[0], tensor->src[1], tensor->src[2], tensor, fun);
    } break;
    case GGML_OP_MAP_CUSTOM1: {
        ggml_compute_forward_map_custom1(params, tensor->src[0], tensor);
    } break;
    case GGML_OP_MAP_CUSTOM2: {
        ggml_compute_forward_map_custom2(params, tensor->src[0], tensor->src[1], tensor);
    } break;
    case GGML_OP_MAP_CUSTOM3: {
        ggml_compute_forward_map_custom3(params, tensor->src[0], tensor->src[1], tensor->src[2], tensor);
    } break;
    case GGML_OP_CROSS_ENTROPY_LOSS: {
        ggml_compute_forward_cross_entropy_loss(params, tensor->src[0], tensor->src[1], tensor);
    } break;
    case GGML_OP_CROSS_ENTROPY_LOSS_BACK: {
        ggml_compute_forward_cross_entropy_loss_back(params, tensor->src[0], tensor->src[1], tensor->src[2], tensor);
    } break;
    case GGML_OP_NONE: {
        // nop
    } break;
    case GGML_OP_COUNT: {
        GGML_ASSERT(false);
    } break;
    }
}

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

static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor * tensor, bool inplace) {
    struct ggml_tensor * src0 = tensor->src[0];
    struct ggml_tensor * src1 = tensor->src[1];

    switch (tensor->op) {
    case GGML_OP_DUP: {
        if (src0->grad) {
            src0->grad = ggml_add_impl(ctx, src0->grad, tensor->grad, inplace);
        }
    } break;
    case GGML_OP_ADD: {
        if (src0->grad) {
            src0->grad = ggml_add_impl(ctx, src0->grad, tensor->grad, inplace);
        }
        if (src1->grad) {
            src1->grad = ggml_add_impl(ctx, src1->grad, tensor->grad, inplace);
        }
    } break;
    case GGML_OP_ADD1: {
        if (src0->grad) {
            src0->grad = ggml_add_impl(ctx, src0->grad, tensor->grad, inplace);
        }
        if (src1->grad) {
            src1->grad =
                ggml_add_impl(ctx, src1->grad, ggml_mean(ctx, tensor->grad), // TODO: should probably be sum instead of mean
                              inplace);
        }
    } break;
    case GGML_OP_ACC: {
        if (src0->grad) {
            src0->grad = ggml_add_impl(ctx, src0->grad, tensor->grad, inplace);
        }
        if (src1->grad) {
            const size_t nb1 = ((int32_t *)tensor->op_params)[0];
            const size_t nb2 = ((int32_t *)tensor->op_params)[1];
            const size_t nb3 = ((int32_t *)tensor->op_params)[2];
            const size_t offset = ((int32_t *)tensor->op_params)[3];

            struct ggml_tensor * tensor_grad_view = ggml_view_4d(ctx, tensor->grad, src1->grad->ne[0], src1->grad->ne[1],
                                                                 src1->grad->ne[2], src1->grad->ne[3], nb1, nb2, nb3, offset);

            src1->grad =
                ggml_add_impl(ctx, src1->grad, ggml_reshape(ctx, ggml_cont(ctx, tensor_grad_view), src1->grad), inplace);
        }
    } break;
    case GGML_OP_SUB: {
        if (src0->grad) {
            src0->grad = ggml_add_impl(ctx, src0->grad, tensor->grad, inplace);
        }
        if (src1->grad) {
            src1->grad = ggml_sub_impl(ctx, src1->grad, tensor->grad, inplace);
        }
    } break;
    case GGML_OP_MUL: {
        if (src0->grad) {
            src0->grad = ggml_add_impl(ctx, src0->grad, ggml_mul(ctx, src1, tensor->grad), inplace);
        }
        if (src1->grad) {
            src1->grad = ggml_add_impl(ctx, src1->grad, ggml_mul(ctx, src0, tensor->grad), inplace);
        }
    } break;
    case GGML_OP_DIV: {
        if (src0->grad) {
            src0->grad = ggml_add_impl(ctx, src0->grad, ggml_div(ctx, tensor->grad, src1), inplace);
        }
        if (src1->grad) {
            src1->grad = ggml_sub_impl(ctx, src1->grad, ggml_mul(ctx, tensor->grad, ggml_div(ctx, tensor, src1)), inplace);
        }
    } break;
    case GGML_OP_SQR: {
        if (src0->grad) {
            src0->grad = ggml_add_impl(ctx, src0->grad,
                                       ggml_scale(ctx, ggml_mul(ctx, src0, tensor->grad), ggml_new_f32(ctx, 2.0f)), inplace);
        }
    } break;
    case GGML_OP_SQRT: {
        if (src0->grad) {
            src0->grad = ggml_add_impl(ctx, src0->grad,
                                       ggml_scale(ctx, ggml_div(ctx, tensor->grad, tensor), ggml_new_f32(ctx, 0.5f)), inplace);
        }
    } break;
    case GGML_OP_LOG: {
        if (src0->grad) {
            src0->grad = ggml_add_impl(ctx, src0->grad, ggml_div(ctx, tensor->grad, src0), inplace);
        }
    } break;
    case GGML_OP_SUM: {
        if (src0->grad) {
            src0->grad = ggml_add1_impl(ctx, src0->grad, tensor->grad, inplace);
        }
    } break;
    case GGML_OP_SUM_ROWS: {
        if (src0->grad) {
            src0->grad = ggml_add_impl(ctx, src0->grad, ggml_repeat(ctx, tensor->grad, src0->grad), inplace);
        }
    } break;
    case GGML_OP_MEAN:
    case GGML_OP_ARGMAX: {
        GGML_ASSERT(false); // TODO: implement
    } break;
    case GGML_OP_REPEAT: {
        // necessary for llama
        if (src0->grad) {
            src0->grad = ggml_add_impl(ctx, src0->grad, ggml_repeat_back(ctx, tensor->grad, src0->grad), inplace);
        }
    } break;
    case GGML_OP_REPEAT_BACK: {
        if (src0->grad) {
            // TODO: test this
            src0->grad = ggml_add_impl(ctx, src0->grad, ggml_repeat(ctx, tensor->grad, src0->grad), inplace);
        }
    } break;
    case GGML_OP_CONCAT: {
        GGML_ASSERT(false); // TODO: implement
    } break;
    case GGML_OP_SILU_BACK: {
        GGML_ASSERT(false); // TODO: not implemented
    } break;
    case GGML_OP_NORM: {
        GGML_ASSERT(false); // TODO: not implemented
    } break;
    case GGML_OP_RMS_NORM: {
        // necessary for llama
        if (src0->grad) {
            float eps;
            memcpy(&eps, tensor->op_params, sizeof(float));

            src0->grad = ggml_add_impl(ctx, src0->grad, ggml_rms_norm_back(ctx, src0, tensor->grad, eps), inplace);
        }
    } break;
    case GGML_OP_RMS_NORM_BACK: {
        GGML_ASSERT(false); // TODO: not implemented
    } break;
    case GGML_OP_GROUP_NORM: {
        GGML_ASSERT(false); // TODO: not implemented
    } break;
    case GGML_OP_MUL_MAT: {
        // https://cs231n.github.io/optimization-2/#staged
        // # forward pass
        // s0 = np.random.randn(5, 10)
        // s1 = np.random.randn(10, 3)
        // t = s0.dot(s1)

        // # now suppose we had the gradient on t from above in the circuit
        // dt = np.random.randn(*t.shape) # same shape as t
        // ds0 = dt.dot(s1.T) #.T gives the transpose of the matrix
        // ds1 = t.T.dot(dt)

        // tensor.shape [m,p]
        // src0.shape   [n,m]
        // src1.shape   [n,p]

        // necessary for llama
        if (src0->grad) {
            src0->grad = ggml_add_impl(ctx, src0->grad,
                                       ggml_out_prod(ctx,           // [n,m]
                                                     src1,          // [n,p]
                                                     tensor->grad), // [m,p]
                                       inplace);
        }
        if (src1->grad) {
            src1->grad = ggml_add_impl(ctx, src1->grad,
                                       // ggml_mul_mat(ctx,                   // [n,p]
                                       //     ggml_cont(ctx,                  // [m,n]
                                       //         ggml_transpose(ctx, src0)), // [m,n]
                                       //     tensor->grad),                  // [m,p]

                                       // // when src0 is bigger than tensor->grad (this is mostly the case in llama),
                                       // // avoid transpose of src0, rather transpose smaller tensor->grad
                                       // // and then use ggml_out_prod
                                       ggml_out_prod(ctx,                           // [n,p]
                                                     src0,                          // [n,m]
                                                     ggml_transpose(ctx,            // [p,m]
                                                                    tensor->grad)), // [m,p]
                                       inplace);
        }
    } break;
    case GGML_OP_OUT_PROD: {
        GGML_ASSERT(false); // TODO: not implemented
    } break;
    case GGML_OP_SCALE: {
        // necessary for llama
        if (src0->grad) {
            src0->grad = ggml_add_impl(ctx, src0->grad, ggml_scale_impl(ctx, tensor->grad, src1, false), inplace);
        }
        if (src1->grad) {
            src1->grad = ggml_add_impl(ctx, src1->grad, ggml_sum(ctx, ggml_mul_impl(ctx, tensor->grad, src0, false)), inplace);
        }
    } break;
    case GGML_OP_SET: {
        const size_t nb1 = ((int32_t *)tensor->op_params)[0];
        const size_t nb2 = ((int32_t *)tensor->op_params)[1];
        const size_t nb3 = ((int32_t *)tensor->op_params)[2];
        const size_t offset = ((int32_t *)tensor->op_params)[3];

        struct ggml_tensor * tensor_grad_view = NULL;

        if (src0->grad || src1->grad) {
            GGML_ASSERT(src0->type == tensor->type);
            GGML_ASSERT(tensor->grad->type == tensor->type);
            GGML_ASSERT(tensor->grad->type == src1->grad->type);

            tensor_grad_view = ggml_view_4d(ctx, tensor->grad, src1->grad->ne[0], src1->grad->ne[1], src1->grad->ne[2],
                                            src1->grad->ne[3], nb1, nb2, nb3, offset);
        }

        if (src0->grad) {
            src0->grad = ggml_add_impl(
                ctx, src0->grad,
                ggml_acc_impl(ctx, tensor->grad, ggml_neg(ctx, tensor_grad_view), nb1, nb2, nb3, offset, false), inplace);
        }

        if (src1->grad) {
            src1->grad =
                ggml_add_impl(ctx, src1->grad, ggml_reshape(ctx, ggml_cont(ctx, tensor_grad_view), src1->grad), inplace);
        }
    } break;
    case GGML_OP_CPY: {
        // necessary for llama
        // cpy overwrites value of src1 by src0 and returns view(src1)
        // the overwriting is mathematically equivalent to:
        // tensor = src0 * 1 + src1 * 0
        if (src0->grad) {
            // dsrc0 = dtensor * 1
            src0->grad = ggml_add_impl(ctx, src0->grad, tensor->grad, inplace);
        }
        if (src1->grad) {
            // dsrc1 = dtensor * 0 -> noop
        }
    } break;
    case GGML_OP_CONT: {
        // same as cpy
        if (src0->grad) {
            GGML_ASSERT(ggml_is_contiguous(src0->grad));
            GGML_ASSERT(ggml_is_contiguous(tensor->grad));
            src0->grad = ggml_add_impl(ctx, src0->grad, tensor->grad, inplace);
        }
    } break;
    case GGML_OP_RESHAPE: {
        // necessary for llama
        if (src0->grad) {
            src0->grad = ggml_add_impl(ctx, src0->grad, ggml_reshape(ctx, tensor->grad, src0->grad), inplace);
        }
    } break;
    case GGML_OP_VIEW: {
        // necessary for llama
        if (src0->grad) {
            size_t offset;

            memcpy(&offset, tensor->op_params, sizeof(offset));

            size_t nb1 = tensor->nb[1];
            size_t nb2 = tensor->nb[2];
            size_t nb3 = tensor->nb[3];

            if (src0->type != src0->grad->type) {
                // gradient is typically F32, but src0 could be other type
                size_t ng = ggml_element_size(src0->grad);
                size_t n0 = ggml_element_size(src0);
                GGML_ASSERT(offset % n0 == 0);
                GGML_ASSERT(nb1 % n0 == 0);
                GGML_ASSERT(nb2 % n0 == 0);
                GGML_ASSERT(nb3 % n0 == 0);
                offset = (offset / n0) * ng;
                nb1 = (nb1 / n0) * ng;
                nb2 = (nb2 / n0) * ng;
                nb3 = (nb3 / n0) * ng;
            }

            src0->grad = ggml_acc_impl(ctx, src0->grad, tensor->grad, nb1, nb2, nb3, offset, inplace);
        }
    } break;
    case GGML_OP_PERMUTE: {
        // necessary for llama
        if (src0->grad) {
            int32_t * axes = (int32_t *)tensor->op_params;
            int axis0 = axes[0] & 0x3;
            int axis1 = axes[1] & 0x3;
            int axis2 = axes[2] & 0x3;
            int axis3 = axes[3] & 0x3;
            int axes_backward[4] = {0, 0, 0, 0};
            axes_backward[axis0] = 0;
            axes_backward[axis1] = 1;
            axes_backward[axis2] = 2;
            axes_backward[axis3] = 3;
            src0->grad = ggml_add_impl(
                ctx, src0->grad,
                ggml_permute(ctx, tensor->grad, axes_backward[0], axes_backward[1], axes_backward[2], axes_backward[3]),
                inplace);
        }
    } break;
    case GGML_OP_TRANSPOSE: {
        // necessary for llama
        if (src0->grad) {
            src0->grad = ggml_add_impl(ctx, src0->grad, ggml_transpose(ctx, tensor->grad), inplace);
        }
    } break;
    case GGML_OP_GET_ROWS: {
        // necessary for llama (only for tokenizer)
        if (src0->grad) {
            src0->grad = ggml_add_impl(ctx, src0->grad, ggml_get_rows_back(ctx, tensor->grad, src1, src0->grad), inplace);
        }
        if (src1->grad) {
            // noop
        }
    } break;
    case GGML_OP_GET_ROWS_BACK: {
        GGML_ASSERT(false); // TODO: not implemented
    } break;
    case GGML_OP_DIAG: {
        GGML_ASSERT(false); // TODO: not implemented
    } break;
    case GGML_OP_DIAG_MASK_INF: {
        // necessary for llama
        if (src0->grad) {
            const int n_past = ((int32_t *)tensor->op_params)[0];
            src0->grad = ggml_add_impl(ctx, src0->grad, ggml_diag_mask_zero_impl(ctx, tensor->grad, n_past, false), inplace);
        }
    } break;
    case GGML_OP_DIAG_MASK_ZERO: {
        // necessary for llama
        if (src0->grad) {
            const int n_past = ((int32_t *)tensor->op_params)[0];
            src0->grad = ggml_add_impl(ctx, src0->grad, ggml_diag_mask_zero_impl(ctx, tensor->grad, n_past, false), inplace);
        }
    } break;
    case GGML_OP_SOFT_MAX: {
        // necessary for llama
        if (src0->grad) {
            src0->grad = ggml_add_impl(ctx, src0->grad, ggml_soft_max_back(ctx, tensor->grad, tensor), inplace);
        }

    } break;
    case GGML_OP_SOFT_MAX_BACK: {
        GGML_ASSERT(false); // TODO: not implemented
    } break;
    case GGML_OP_ROPE: {
        // necessary for llama
        if (src0->grad) {
            const int n_past = ((int32_t *)tensor->op_params)[0];
            const int n_dims = ((int32_t *)tensor->op_params)[1];
            const int mode = ((int32_t *)tensor->op_params)[2];
            const int n_ctx = ((int32_t *)tensor->op_params)[3];
            float freq_base;
            float freq_scale;
            float xpos_base;
            bool xpos_down;
            memcpy(&freq_base, (int32_t *)tensor->op_params + 4, sizeof(float));
            memcpy(&freq_scale, (int32_t *)tensor->op_params + 5, sizeof(float));
            memcpy(&xpos_base, (int32_t *)tensor->op_params + 6, sizeof(float));
            memcpy(&xpos_down, (int32_t *)tensor->op_params + 7, sizeof(bool));

            src0->grad = ggml_add_impl(
                ctx, src0->grad,
                ggml_rope_back(ctx, tensor->grad, n_past, n_dims, mode, n_ctx, freq_base, freq_scale, xpos_base, xpos_down),
                inplace);
        }
    } break;
    case GGML_OP_ROPE_BACK: {
        if (src0->grad) {
            const int n_past = ((int32_t *)tensor->op_params)[0];
            const int n_dims = ((int32_t *)tensor->op_params)[1];
            const int mode = ((int32_t *)tensor->op_params)[2];
            const int n_ctx = ((int32_t *)tensor->op_params)[3];
            float freq_base;
            float freq_scale;
            float xpos_base;
            bool xpos_down;
            memcpy(&freq_base, (int32_t *)tensor->op_params + 4, sizeof(float));
            memcpy(&freq_scale, (int32_t *)tensor->op_params + 5, sizeof(float));
            memcpy(&xpos_base, (int32_t *)tensor->op_params + 6, sizeof(float));
            memcpy(&xpos_down, (int32_t *)tensor->op_params + 7, sizeof(bool));

            src0->grad = ggml_add_impl(ctx, src0->grad,
                                       ggml_rope_impl(ctx, tensor->grad, n_past, n_dims, mode, n_ctx, freq_base, freq_scale,
                                                      xpos_base, xpos_down, false),
                                       inplace);
        }
    } break;
    case GGML_OP_ALIBI: {
        GGML_ASSERT(false); // TODO: not implemented
    } break;
    case GGML_OP_CLAMP: {
        GGML_ASSERT(false); // TODO: not implemented
    } break;
    case GGML_OP_CONV_1D: {
        GGML_ASSERT(false); // TODO: not implemented
    } break;
    case GGML_OP_CONV_2D: {
        GGML_ASSERT(false); // TODO: not implemented
    } break;
    case GGML_OP_CONV_TRANSPOSE_2D: {
        GGML_ASSERT(false); // TODO: not implemented
    } break;
    case GGML_OP_POOL_1D: {
        GGML_ASSERT(false); // TODO: not implemented
    } break;
    case GGML_OP_POOL_2D: {
        GGML_ASSERT(false); // TODO: not implemented
    } break;
    case GGML_OP_UPSCALE: {
        GGML_ASSERT(false); // TODO: not implemented
    } break;
    case GGML_OP_FLASH_ATTN: {
        struct ggml_tensor * flash_grad = NULL;
        if (src0->grad || src1->grad || tensor->src[2]->grad) {
            int32_t t = ggml_get_op_params_i32(tensor, 0);
            GGML_ASSERT(t == 0 || t == 1);
            bool masked = t != 0;
            flash_grad = ggml_flash_attn_back(ctx, src0, src1, tensor->src[2], tensor->grad, masked);
        }

        if (src0->grad) {
            struct ggml_tensor * grad_q = NULL;
            const size_t nb0 = flash_grad->nb[0];
            const size_t offset = 0;
            switch (src0->n_dims) {
            case 2: {
                grad_q = ggml_view_2d(ctx, flash_grad, src0->ne[0], src0->ne[1], nb0 * src0->ne[0], offset);
            } break;
            case 3: {
                grad_q = ggml_view_3d(ctx, flash_grad, src0->ne[0], src0->ne[1], src0->ne[2], nb0 * src0->ne[0],
                                      nb0 * src0->ne[0] * src0->ne[1], offset);
            } break;
            case 4: {
                grad_q = ggml_view_4d(ctx, flash_grad, src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3], nb0 * src0->ne[0],
                                      nb0 * src0->ne[0] * src0->ne[1], nb0 * src0->ne[0] * src0->ne[1] * src0->ne[2], offset);
            } break;
            }

            src0->grad = ggml_add_impl(ctx, src0->grad, grad_q, inplace);
        }

        if (src1->grad) {
            struct ggml_tensor * grad_k = NULL;
            const size_t nb0 = flash_grad->nb[0];
            const size_t offset = nb0 * src0->ne[0] * src0->ne[1] * src0->ne[2] * src0->ne[3];
            switch (src1->n_dims) {
            case 2: {
                grad_k = ggml_view_2d(ctx, flash_grad, src1->ne[0], src1->ne[1], nb0 * src1->ne[0], offset);
            } break;
            case 3: {
                grad_k = ggml_view_3d(ctx, flash_grad, src1->ne[0], src1->ne[1], src1->ne[2], nb0 * src1->ne[0],
                                      nb0 * src1->ne[0] * src1->ne[1], offset);
            } break;
            case 4: {
                grad_k = ggml_view_4d(ctx, flash_grad, src1->ne[0], src1->ne[1], src1->ne[2], src1->ne[3], nb0 * src1->ne[0],
                                      nb0 * src1->ne[0] * src1->ne[1], nb0 * src1->ne[0] * src1->ne[1] * src1->ne[2], offset);
            } break;
            }

            src1->grad = ggml_add_impl(ctx, src1->grad, grad_k, inplace);
        }

        struct ggml_tensor * opt0 = tensor->src[2];

        if (opt0->grad) {
            struct ggml_tensor * grad_v = NULL;
            const size_t nb0 = flash_grad->nb[0];
            const size_t offset = nb0 * src0->ne[0] * src0->ne[1] * src0->ne[2] * src0->ne[3] +
                                  nb0 * src1->ne[0] * src1->ne[1] * src1->ne[2] * src1->ne[3];
            switch (opt0->n_dims) {
            case 2: {
                grad_v = ggml_view_2d(ctx, flash_grad, opt0->ne[0], opt0->ne[1], nb0 * opt0->ne[0], offset);
            } break;
            case 3: {
                grad_v = ggml_view_3d(ctx, flash_grad, opt0->ne[0], opt0->ne[1], opt0->ne[2], nb0 * opt0->ne[0],
                                      nb0 * opt0->ne[0] * opt0->ne[1], offset);
            } break;
            case 4: {
                grad_v = ggml_view_4d(ctx, flash_grad, opt0->ne[0], opt0->ne[1], opt0->ne[2], opt0->ne[3], nb0 * opt0->ne[0],
                                      nb0 * opt0->ne[0] * opt0->ne[1], nb0 * opt0->ne[0] * opt0->ne[1] * opt0->ne[2], offset);
            } break;
            }

            opt0->grad = ggml_add_impl(ctx, opt0->grad, grad_v, inplace);
        }
    } break;
    case GGML_OP_FLASH_FF: {
        GGML_ASSERT(false); // not supported
    } break;
    case GGML_OP_FLASH_ATTN_BACK: {
        GGML_ASSERT(false); // not supported
    } break;
    case GGML_OP_WIN_PART:
    case GGML_OP_WIN_UNPART:
    case GGML_OP_UNARY: {
        switch (ggml_get_unary_op(tensor)) {
        case GGML_UNARY_OP_ABS: {
            if (src0->grad) {
                src0->grad = ggml_add_impl(ctx, src0->grad, ggml_mul(ctx, ggml_sgn(ctx, src0), tensor->grad), inplace);
            }
        } break;
        case GGML_UNARY_OP_SGN: {
            if (src0->grad) {
                // noop
            }
        } break;
        case GGML_UNARY_OP_NEG: {
            if (src0->grad) {
                src0->grad = ggml_sub_impl(ctx, src0->grad, tensor->grad, inplace);
            }
        } break;
        case GGML_UNARY_OP_STEP: {
            if (src0->grad) {
                // noop
            }
        } break;
        case GGML_UNARY_OP_TANH: {
            GGML_ASSERT(false); // TODO: not implemented
        } break;
        case GGML_UNARY_OP_ELU: {
            GGML_ASSERT(false); // TODO: not implemented
        } break;
        case GGML_UNARY_OP_RELU: {
            if (src0->grad) {
                src0->grad = ggml_add_impl(ctx, src0->grad, ggml_mul(ctx, ggml_step(ctx, src0), tensor->grad), inplace);
            }
        } break;
        case GGML_UNARY_OP_GELU: {
            GGML_ASSERT(false); // TODO: not implemented
        } break;
        case GGML_UNARY_OP_GELU_QUICK: {
            GGML_ASSERT(false); // TODO: not implemented
        } break;
        case GGML_UNARY_OP_SILU: {
            // necessary for llama
            if (src0->grad) {
                src0->grad = ggml_add_impl(ctx, src0->grad, ggml_silu_back(ctx, src0, tensor->grad), inplace);
            }
        } break;
        default:
            GGML_ASSERT(false);
        }
    } break;
    case GGML_OP_GET_REL_POS:
    case GGML_OP_ADD_REL_POS:
    case GGML_OP_MAP_UNARY:
    case GGML_OP_MAP_BINARY:
    case GGML_OP_MAP_CUSTOM1_F32:
    case GGML_OP_MAP_CUSTOM2_F32:
    case GGML_OP_MAP_CUSTOM3_F32:
    case GGML_OP_MAP_CUSTOM1:
    case GGML_OP_MAP_CUSTOM2:
    case GGML_OP_MAP_CUSTOM3: {
        GGML_ASSERT(false); // not supported
    } break;
    case GGML_OP_CROSS_ENTROPY_LOSS: {
        if (src0->grad) {
            src0->grad = ggml_add_impl(ctx, src0->grad, ggml_cross_entropy_loss_back(ctx, src0, src1, tensor->grad), inplace);
        }
    } break;
    case GGML_OP_CROSS_ENTROPY_LOSS_BACK: {
        GGML_ASSERT(false); // not supported
    } break;
    case GGML_OP_NONE: {
        // nop
    } break;
    case GGML_OP_COUNT: {
        GGML_ASSERT(false);
    } break;
    }
}

static_assert(GGML_GRAPH_HASHTABLE_SIZE > GGML_MAX_NODES * 2, "GGML_GRAPH_HT_SIZE is too small");

static size_t hash(void * p) { return (size_t)p % GGML_GRAPH_HASHTABLE_SIZE; }

static bool hash_insert(void * hash_table[], void * p) {
    size_t h = hash(p);

    // linear probing
    size_t i = h;
    while (hash_table[i] != NULL && hash_table[i] != p) {
        i = (i + 1) % GGML_GRAPH_HASHTABLE_SIZE;
        if (i == h) {
            // hash table is full
            GGML_ASSERT(false);
        }
    }

    if (hash_table[i] == p) {
        return true;
    }

    // insert
    hash_table[i] = p;
    return false;
}

static void ggml_visit_parents(struct ggml_cgraph * cgraph, struct ggml_tensor * node) {
    if (node->grad == NULL) {
        // this usually happens when we generate intermediate nodes from constants in the backward pass
        // it can also happen during forward pass, if the user performs computations with constants
        if (node->op != GGML_OP_NONE) {
            // GGML_PRINT_DEBUG("%s: warning: node %p has no grad, but op %d\n", __func__, (void *) node, node->op);
        }
    }

    // check if already visited
    if (hash_insert(cgraph->visited_hash_table, node)) {
        return;
    }

    for (int i = 0; i < GGML_MAX_SRC; ++i) {
        if (node->src[i]) {
            ggml_visit_parents(cgraph, node->src[i]);
        }
    }

    if (node->op == GGML_OP_NONE && node->grad == NULL) {
        // reached a leaf node, not part of the gradient graph (e.g. a constant)
        GGML_ASSERT(cgraph->n_leafs < GGML_MAX_NODES);

        if (strlen(node->name) == 0) {
            ggml_format_name(node, "leaf_%d", cgraph->n_leafs);
        }

        cgraph->leafs[cgraph->n_leafs] = node;
        cgraph->n_leafs++;
    } else {
        GGML_ASSERT(cgraph->n_nodes < GGML_MAX_NODES);

        if (strlen(node->name) == 0) {
            ggml_format_name(node, "node_%d", cgraph->n_nodes);
        }

        cgraph->nodes[cgraph->n_nodes] = node;
        cgraph->grads[cgraph->n_nodes] = node->grad;
        cgraph->n_nodes++;
    }
}

static void ggml_build_forward_impl(struct ggml_cgraph * cgraph, struct ggml_tensor * tensor, bool expand) {
    if (!expand) {
        cgraph->n_nodes = 0;
        cgraph->n_leafs = 0;
    }

    const int n0 = cgraph->n_nodes;
    UNUSED(n0);

    ggml_visit_parents(cgraph, tensor);

    const int n_new = cgraph->n_nodes - n0;
    GGML_PRINT_DEBUG("%s: visited %d new nodes\n", __func__, n_new);

    if (n_new > 0) {
        // the last added node should always be starting point
        GGML_ASSERT(cgraph->nodes[cgraph->n_nodes - 1] == tensor);
    }
}

void ggml_build_forward_expand(struct ggml_cgraph * cgraph, struct ggml_tensor * tensor) {
    ggml_build_forward_impl(cgraph, tensor, true);
}

struct ggml_cgraph ggml_build_forward(struct ggml_tensor * tensor) {
    struct ggml_cgraph result = {
        /*.n_nodes      =*/0,
        /*.n_leafs      =*/0,
        /*.nodes        =*/{NULL},
        /*.grads        =*/{NULL},
        /*.leafs        =*/{NULL},
        /*.hash_table   =*/{NULL},
        /*.perf_runs    =*/0,
        /*.perf_cycles  =*/0,
        /*.perf_time_us =*/0,
    };

    ggml_build_forward_impl(&result, tensor, false);

    return result;
}

void ggml_build_backward_expand(struct ggml_context * ctx, struct ggml_cgraph * gf, struct ggml_cgraph * gb, bool keep) {
    GGML_ASSERT(gf->n_nodes > 0);

    // if we are keeping the gradient graph, we have to detach the gradient nodes from the original graph
    if (keep) {
        for (int i = 0; i < gf->n_nodes; i++) {
            struct ggml_tensor * node = gf->nodes[i];

            if (node->grad) {
                node->grad = ggml_dup_tensor(ctx, node);
                gf->grads[i] = node->grad;
            }
        }
    }

    for (int i = gf->n_nodes - 1; i >= 0; i--) {
        struct ggml_tensor * node = gf->nodes[i];

        // because we detached the grad nodes from the original graph, we can afford inplace operations
        if (node->grad) {
            ggml_compute_backward(ctx, node, keep);
        }
    }

    for (int i = 0; i < gf->n_nodes; i++) {
        struct ggml_tensor * node = gf->nodes[i];

        if (node->is_param) {
            GGML_PRINT_DEBUG("%s: found root node %p\n", __func__, (void *)node);
            ggml_build_forward_expand(gb, node->grad);
        }
    }
}

struct ggml_cgraph ggml_build_backward(struct ggml_context * ctx, struct ggml_cgraph * gf, bool keep) {
    struct ggml_cgraph result = *gf;
    ggml_build_backward_expand(ctx, gf, &result, keep);
    return result;
}

struct ggml_cgraph * ggml_new_graph(struct ggml_context * ctx) {
    struct ggml_object * obj = ggml_new_object(ctx, GGML_OBJECT_GRAPH, GGML_GRAPH_SIZE);
    struct ggml_cgraph * cgraph = (struct ggml_cgraph *)((char *)ctx->mem_buffer + obj->offs);

    *cgraph = (struct ggml_cgraph){
        /*.n_nodes      =*/0,
        /*.n_leafs      =*/0,
        /*.nodes        =*/{NULL},
        /*.grads        =*/{NULL},
        /*.leafs        =*/{NULL},
        /*.hash_table   =*/{NULL},
        /*.perf_runs    =*/0,
        /*.perf_cycles  =*/0,
        /*.perf_time_us =*/0,
    };

    return cgraph;
}

struct ggml_cgraph * ggml_build_forward_ctx(struct ggml_context * ctx, struct ggml_tensor * tensor) {
    struct ggml_cgraph * cgraph = ggml_new_graph(ctx);
    ggml_build_forward_impl(cgraph, tensor, false);
    return cgraph;
}

size_t ggml_graph_overhead(void) { return GGML_OBJECT_SIZE + GGML_PAD(GGML_GRAPH_SIZE, GGML_MEM_ALIGN); }

//
// thread data
//
// synchronization is done via busy loops
// I tried using spin locks, but not sure how to use them correctly - the things I tried were slower than busy loops
//

#ifdef __APPLE__

// #include <os/lock.h>
//
// typedef os_unfair_lock ggml_lock_t;
//
// #define ggml_lock_init(x)    UNUSED(x)
// #define ggml_lock_destroy(x) UNUSED(x)
// #define ggml_lock_lock       os_unfair_lock_lock
// #define ggml_lock_unlock     os_unfair_lock_unlock
//
// #define GGML_LOCK_INITIALIZER OS_UNFAIR_LOCK_INIT

typedef int ggml_lock_t;

#define ggml_lock_init(x) UNUSED(x)
#define ggml_lock_destroy(x) UNUSED(x)
#define ggml_lock_lock(x) UNUSED(x)
#define ggml_lock_unlock(x) UNUSED(x)

#define GGML_LOCK_INITIALIZER 0

typedef pthread_t ggml_thread_t;

#define ggml_thread_create pthread_create
#define ggml_thread_join pthread_join

#else

// typedef pthread_spinlock_t ggml_lock_t;

// #define ggml_lock_init(x) pthread_spin_init(x, PTHREAD_PROCESS_PRIVATE)
// #define ggml_lock_destroy pthread_spin_destroy
// #define ggml_lock_lock    pthread_spin_lock
// #define ggml_lock_unlock  pthread_spin_unlock

typedef int ggml_lock_t;

#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

typedef pthread_t ggml_thread_t;

#define ggml_thread_create pthread_create
#define ggml_thread_join pthread_join

#endif

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

    // run thread on node_num thread_n / (threads per node)
    const int node_num = thread_n / ((n_threads + g_state.numa.n_nodes - 1) / g_state.numa.n_nodes);
    struct ggml_numa_node * node = &g_state.numa.nodes[node_num];
    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 (size_t i = 0; i < node->n_cpus; ++i) {
        CPU_SET_S(node->cpus[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);
}

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, int n_threads) {
    UNUSED(thread_n);
    UNUSED(n_threads);
}
static void clear_numa_thread_affinity(void) {}
#endif

struct ggml_compute_state_shared {
    const struct ggml_cgraph * cgraph;
    const struct ggml_cplan * cplan;

    int64_t perf_node_start_cycles;
    int64_t perf_node_start_time_us;

    const int n_threads;

    // synchronization primitives
    atomic_int n_active; // num active threads
    atomic_int node_n;   // active graph node

    bool (*abort_callback)(void * data); // abort ggml_graph_compute when true
    void * abort_callback_data;
};

struct ggml_compute_state {
    ggml_thread_t thrd;
    int ith;
    struct ggml_compute_state_shared * shared;
};

static void ggml_graph_compute_perf_stats_node(struct ggml_tensor * node, const struct ggml_compute_state_shared * st) {
    int64_t cycles_cur = ggml_perf_cycles() - st->perf_node_start_cycles;
    int64_t time_us_cur = ggml_perf_time_us() - st->perf_node_start_time_us;

    node->perf_runs++;
    node->perf_cycles += cycles_cur;
    node->perf_time_us += time_us_cur;
}

static thread_ret_t ggml_graph_compute_thread(void * data) {
    struct ggml_compute_state * state = (struct ggml_compute_state *)data;

    const struct ggml_cgraph * cgraph = state->shared->cgraph;
    const struct ggml_cplan * cplan = state->shared->cplan;

    const int * n_tasks_arr = cplan->n_tasks;
    const int n_threads = state->shared->n_threads;

    set_numa_thread_affinity(state->ith, n_threads);

    int node_n = -1;

    while (true) {
        if (cplan->abort_callback && cplan->abort_callback(cplan->abort_callback_data)) {
            state->shared->node_n += 1;
            return (thread_ret_t)GGML_EXIT_ABORTED;
        }
        if (atomic_fetch_sub(&state->shared->n_active, 1) == 1) {
            // all other threads are finished and spinning
            // do finalize and init here so we don't have synchronize again
            struct ggml_compute_params params = {
                /*.type  =*/GGML_TASK_FINALIZE,
                /*.ith   =*/0,
                /*.nth   =*/0,
                /*.wsize =*/cplan->work_size,
                /*.wdata =*/cplan->work_data,
            };

            if (node_n != -1) {
                /* FINALIZE */
                struct ggml_tensor * node = state->shared->cgraph->nodes[node_n];
                if (GGML_OP_HAS_FINALIZE[node->op]) {
                    params.nth = n_tasks_arr[node_n];
                    ggml_compute_forward(&params, node);
                }
                ggml_graph_compute_perf_stats_node(node, state->shared);
            }

            // distribute new work or execute it direct if 1T
            while (++node_n < cgraph->n_nodes) {
                GGML_PRINT_DEBUG_5("%s: %d/%d\n", __func__, node_n, cgraph->n_nodes);

                struct ggml_tensor * node = cgraph->nodes[node_n];
                const int n_tasks = n_tasks_arr[node_n];

                state->shared->perf_node_start_cycles = ggml_perf_cycles();
                state->shared->perf_node_start_time_us = ggml_perf_time_us();

                params.nth = n_tasks;

                /* INIT */
                if (GGML_OP_HAS_INIT[node->op]) {
                    params.type = GGML_TASK_INIT;
                    ggml_compute_forward(&params, node);
                }

                if (n_tasks == 1) {
                    // TODO: maybe push node_n to the atomic but if other threads see n_tasks is 1,
                    // they do something more efficient than spinning (?)
                    params.type = GGML_TASK_COMPUTE;
                    ggml_compute_forward(&params, node);

                    if (GGML_OP_HAS_FINALIZE[node->op]) {
                        params.type = GGML_TASK_FINALIZE;
                        ggml_compute_forward(&params, node);
                    }

                    ggml_graph_compute_perf_stats_node(node, state->shared);
                } else {
                    break;
                }

                if (cplan->abort_callback && cplan->abort_callback(cplan->abort_callback_data)) {
                    break;
                }
            }

            atomic_store(&state->shared->n_active, n_threads);
            atomic_store(&state->shared->node_n, node_n);
        } else {
            // wait for other threads to finish
            const int last = node_n;
            while (true) {
                // TODO: this sched_yield can have significant impact on the performance - either positive or negative
                //       depending on the workload and the operating system.
                //       since it is not clear what is the best approach, it should potentially become user-configurable
                //       ref: https://github.com/ggerganov/ggml/issues/291
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
                sched_yield();
#endif

                node_n = atomic_load(&state->shared->node_n);
                if (node_n != last)
                    break;
            };
        }

        // check if we should stop
        if (node_n >= cgraph->n_nodes)
            break;

        /* COMPUTE */
        struct ggml_tensor * node = cgraph->nodes[node_n];
        const int n_tasks = n_tasks_arr[node_n];

        struct ggml_compute_params params = {
            /*.type  =*/GGML_TASK_COMPUTE,
            /*.ith   =*/state->ith,
            /*.nth   =*/n_tasks,
            /*.wsize =*/cplan->work_size,
            /*.wdata =*/cplan->work_data,
        };

        if (state->ith < n_tasks) {
            ggml_compute_forward(&params, node);
        }
    }

    return GGML_EXIT_SUCCESS;
}

struct ggml_cplan ggml_graph_plan(struct ggml_cgraph * cgraph, int n_threads) {
    if (n_threads <= 0) {
        n_threads = GGML_DEFAULT_N_THREADS;
    }

    size_t work_size = 0;

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

    // thread scheduling for the different operations + work buffer size estimation
    for (int i = 0; i < cgraph->n_nodes; i++) {
        int n_tasks = 1;

        struct ggml_tensor * node = cgraph->nodes[i];

        switch (node->op) {
        case GGML_OP_CPY:
        case GGML_OP_DUP: {
            n_tasks = n_threads;

            size_t cur = 0;
            if (ggml_is_quantized(node->type)) {
                cur = ggml_type_size(GGML_TYPE_F32) * node->ne[0] * n_tasks;
            }

            work_size = MAX(work_size, cur);
        } break;
        case GGML_OP_ADD:
        case GGML_OP_ADD1: {
            n_tasks = n_threads;

            size_t cur = 0;

            if (ggml_is_quantized(node->src[0]->type)) {
                cur = ggml_type_size(GGML_TYPE_F32) * node->src[0]->ne[0] * n_tasks;
            }

            work_size = MAX(work_size, cur);
        } break;
        case GGML_OP_ACC: {
            n_tasks = n_threads;

            size_t cur = 0;

            if (ggml_is_quantized(node->src[0]->type)) {
                cur = ggml_type_size(GGML_TYPE_F32) * node->src[1]->ne[0] * n_tasks;
            }

            work_size = MAX(work_size, cur);
        } break;
        case GGML_OP_SUB:
        case GGML_OP_DIV:
        case GGML_OP_SQR:
        case GGML_OP_SQRT:
        case GGML_OP_LOG:
        case GGML_OP_SUM:
        case GGML_OP_SUM_ROWS:
        case GGML_OP_MEAN:
        case GGML_OP_ARGMAX:
        case GGML_OP_REPEAT:
        case GGML_OP_REPEAT_BACK: {
            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: {
                n_tasks = 1;
            } break;

            case GGML_UNARY_OP_GELU:
            case GGML_UNARY_OP_GELU_QUICK:
            case GGML_UNARY_OP_SILU: {
                n_tasks = n_threads;
            } break;
            }
        } break;
        case GGML_OP_SILU_BACK:
        case GGML_OP_MUL:
        case GGML_OP_NORM:
        case GGML_OP_RMS_NORM:
        case GGML_OP_RMS_NORM_BACK:
        case GGML_OP_GROUP_NORM: {
            n_tasks = n_threads;
        } break;
        case GGML_OP_CONCAT:
        case GGML_OP_MUL_MAT:
        case GGML_OP_OUT_PROD: {
            n_tasks = n_threads;

            // TODO: use different scheduling for different matrix sizes
            // const int nr0 = ggml_nrows(node->src[0]);
            // const int nr1 = ggml_nrows(node->src[1]);

            // n_tasks = MIN(n_threads, MAX(1, nr0/128));
            // printf("nr0 = %8d, nr1 = %8d, nr0*nr1 = %8d, n_tasks%d\n", nr0, nr1, nr0*nr1, n_tasks);

            size_t cur = 0;
            const enum ggml_type vec_dot_type = type_traits[node->src[0]->type].vec_dot_type;

#if defined(GGML_USE_CUBLAS)
            if (ggml_cuda_can_mul_mat(node->src[0], node->src[1], node)) {
                n_tasks = 1; // TODO: this actually is doing nothing
                             //       the threads are still spinning
            } else
#elif defined(GGML_USE_CLBLAST)
            if (ggml_cl_can_mul_mat(node->src[0], node->src[1], node)) {
                n_tasks = 1; // TODO: this actually is doing nothing
                             //       the threads are still spinning
                cur = ggml_cl_mul_mat_get_wsize(node->src[0], node->src[1], node);
            } else
#endif
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
                if (ggml_compute_forward_mul_mat_use_blas(node->src[0], node->src[1], node)) {
                n_tasks = 1; // TODO: this actually is doing nothing
                             //       the threads are still spinning
                if (node->src[0]->type != GGML_TYPE_F32) {
                    // here we need memory just for single 2D matrix from src0
                    cur = ggml_type_size(GGML_TYPE_F32) * (node->src[0]->ne[0] * node->src[0]->ne[1]);
                }
            } else
#endif
                if (node->src[1]->type != vec_dot_type) {
                cur = ggml_type_size(vec_dot_type) * ggml_nelements(node->src[1]) / ggml_blck_size(vec_dot_type);
            } else {
                cur = 0;
            }

            work_size = MAX(work_size, cur);
        } break;
        case GGML_OP_SCALE: {
            n_tasks = 1;
        } break;
        case GGML_OP_SET:
        case GGML_OP_CONT:
        case GGML_OP_RESHAPE:
        case GGML_OP_VIEW:
        case GGML_OP_PERMUTE:
        case GGML_OP_TRANSPOSE:
        case GGML_OP_GET_ROWS:
        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:
        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_ALIBI: {
            n_tasks = 1; // TODO
        } break;
        case GGML_OP_CLAMP: {
            n_tasks = 1; // TODO
        } break;
        case GGML_OP_CONV_1D: {
            n_tasks = n_threads;

            GGML_ASSERT(node->src[0]->ne[3] == 1);
            GGML_ASSERT(node->src[1]->ne[2] == 1);
            GGML_ASSERT(node->src[1]->ne[3] == 1);

            size_t cur = 0;
            const int nk = node->src[0]->ne[0];

            if (node->src[0]->type == GGML_TYPE_F16 && node->src[1]->type == GGML_TYPE_F32) {
                cur = sizeof(ggml_fp16_t) * (nk * ggml_up32(node->src[0]->ne[1]) * node->src[0]->ne[2] +
                                             (2 * (nk / 2) + node->src[1]->ne[0]) * node->src[1]->ne[1]);
            } else if (node->src[0]->type == GGML_TYPE_F32 && node->src[1]->type == GGML_TYPE_F32) {
                cur = sizeof(float) * (nk * ggml_up32(node->src[0]->ne[1]) * node->src[0]->ne[2] +
                                       (2 * (nk / 2) + node->src[1]->ne[0]) * node->src[1]->ne[1]);
            } else {
                GGML_ASSERT(false);
            }

            work_size = MAX(work_size, cur);
        } break;
        case GGML_OP_CONV_2D: {
            n_tasks = n_threads;

            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]; // C
            const int64_t ne03 = node->src[0]->ne[3]; // N

            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]; // C

            const int64_t ne0 = node->ne[0];
            const int64_t ne1 = node->ne[1];
            const int64_t ne2 = node->ne[2];
            const int64_t nk = ne00 * ne01;
            const int64_t ew0 = nk * ne02;

            UNUSED(ne03);
            UNUSED(ne2);

            size_t cur = 0;

            if (node->src[0]->type == GGML_TYPE_F16 && node->src[1]->type == GGML_TYPE_F32) {
                cur = sizeof(ggml_fp16_t) * (ne0 * ne1 * ew0);
            } else if (node->src[0]->type == GGML_TYPE_F32 && node->src[1]->type == GGML_TYPE_F32) {
                cur = sizeof(float) * (ne10 * ne11 * ne12);
            } else {
                GGML_ASSERT(false);
            }

            work_size = MAX(work_size, cur);
        } break;
        case GGML_OP_CONV_TRANSPOSE_2D: {
            n_tasks = n_threads;

            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

            size_t cur = 0;
            cur += sizeof(ggml_fp16_t) * ne00 * ne01 * ne02 * ne03;
            cur += sizeof(ggml_fp16_t) * ne10 * ne11 * ne12;

            work_size = MAX(work_size, cur);
        } break;
        case GGML_OP_POOL_1D:
        case GGML_OP_POOL_2D: {
            n_tasks = 1;
        } break;
        case GGML_OP_UPSCALE: {
            n_tasks = n_threads;
        } break;
        case GGML_OP_FLASH_ATTN: {
            n_tasks = n_threads;

            size_t cur = 0;

            const int64_t ne11 = ggml_up(node->src[1]->ne[1], GGML_SOFT_MAX_UNROLL);

            if (node->src[1]->type == GGML_TYPE_F32) {
                cur = sizeof(float) * ne11 * n_tasks;  // TODO: this can become (n_tasks-1)
                cur += sizeof(float) * ne11 * n_tasks; // this is overestimated by x2
            }

            if (node->src[1]->type == GGML_TYPE_F16) {
                cur = sizeof(float) * ne11 * n_tasks;  // TODO: this can become (n_tasks-1)
                cur += sizeof(float) * ne11 * n_tasks; // this is overestimated by x2
            }

            work_size = MAX(work_size, cur);
        } break;
        case GGML_OP_FLASH_FF: {
            n_tasks = n_threads;

            size_t cur = 0;

            if (node->src[1]->type == GGML_TYPE_F32) {
                cur = sizeof(float) * node->src[1]->ne[1] * n_tasks;  // TODO: this can become (n_tasks-1)
                cur += sizeof(float) * node->src[1]->ne[1] * n_tasks; // this is overestimated by x2
            }

            if (node->src[1]->type == GGML_TYPE_F16) {
                cur = sizeof(float) * node->src[1]->ne[1] * n_tasks;  // TODO: this can become (n_tasks-1)
                cur += sizeof(float) * node->src[1]->ne[1] * n_tasks; // this is overestimated by x2
            }

            work_size = MAX(work_size, cur);
        } break;
        case GGML_OP_FLASH_ATTN_BACK: {
            n_tasks = n_threads;

            size_t cur = 0;

            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) * mxDn * n_tasks;  // TODO: this can become (n_tasks-1)
                cur += sizeof(float) * mxDn * n_tasks; // this is overestimated by x2
            }

            if (node->src[1]->type == GGML_TYPE_F16) {
                cur = sizeof(float) * mxDn * n_tasks;  // TODO: this can become (n_tasks-1)
                cur += sizeof(float) * mxDn * n_tasks; // this is overestimated by x2
            }

            work_size = MAX(work_size, cur);
        } break;
        case GGML_OP_WIN_PART:
        case GGML_OP_WIN_UNPART:
        case GGML_OP_GET_REL_POS:
        case GGML_OP_MAP_UNARY:
        case GGML_OP_MAP_BINARY:
        case GGML_OP_MAP_CUSTOM1_F32:
        case GGML_OP_MAP_CUSTOM2_F32:
        case GGML_OP_MAP_CUSTOM3_F32: {
            n_tasks = 1;
        } break;
        case GGML_OP_MAP_CUSTOM1: {
            struct ggml_map_custom1_op_params * p = (struct ggml_map_custom1_op_params *)node->op_params;
            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 = (struct ggml_map_custom2_op_params *)node->op_params;
            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 = (struct ggml_map_custom3_op_params *)node->op_params;
            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: {
            n_tasks = n_threads;

            size_t cur = ggml_type_size(node->type) * (n_tasks + node->src[0]->ne[0] * n_tasks);

            work_size = MAX(work_size, cur);
        } break;
        case GGML_OP_CROSS_ENTROPY_LOSS_BACK: {
            n_tasks = n_threads;
        } break;
        case GGML_OP_NONE: {
            n_tasks = 1;
        } break;
        case GGML_OP_COUNT: {
            GGML_ASSERT(false);
        } break;
        }

        cplan.n_tasks[i] = n_tasks;
    }

    if (work_size > 0) {
        work_size += CACHE_LINE_SIZE * (n_threads - 1);
    }

    cplan.n_threads = n_threads;
    cplan.work_size = work_size;
    cplan.work_data = NULL;

    return cplan;
}

int ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan) {
    {
        GGML_ASSERT(cplan);
        GGML_ASSERT(cplan->n_threads > 0);

        if (cplan->work_size > 0) {
            GGML_ASSERT(cplan->work_data);
        }

        for (int i = 0; i < cgraph->n_nodes; ++i) {
            if (cgraph->nodes[i]->op != GGML_OP_NONE) {
                GGML_ASSERT(cplan->n_tasks[i] > 0);
            }
        }
    }

    const int n_threads = cplan->n_threads;

    struct ggml_compute_state_shared state_shared = {
        /*.cgraph                  =*/cgraph,
        /*.cgraph_plan             =*/cplan,
        /*.perf_node_start_cycles  =*/0,
        /*.perf_node_start_time_us =*/0,
        /*.n_threads               =*/n_threads,
        /*.n_active                =*/n_threads,
        /*.node_n                  =*/-1,
        /*.abort_callback          =*/NULL,
        /*.abort_callback_data     =*/NULL,
    };
    struct ggml_compute_state * workers = alloca(sizeof(struct ggml_compute_state) * n_threads);

    // create thread pool
    if (n_threads > 1) {
        for (int j = 1; j < n_threads; ++j) {
            workers[j] = (struct ggml_compute_state){
                .thrd = 0,
                .ith = j,
                .shared = &state_shared,
            };

            const int rc = ggml_thread_create(&workers[j].thrd, NULL, ggml_graph_compute_thread, &workers[j]);
            GGML_ASSERT(rc == 0);
            UNUSED(rc);
        }
    }

    workers[0].ith = 0;
    workers[0].shared = &state_shared;

    const int64_t perf_start_cycles = ggml_perf_cycles();
    const int64_t perf_start_time_us = ggml_perf_time_us();

    // this is a work thread too
    int compute_status = (size_t)ggml_graph_compute_thread(&workers[0]);

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

    // join or kill thread pool
    if (n_threads > 1) {
        for (int j = 1; j < n_threads; j++) {
            const int rc = ggml_thread_join(workers[j].thrd, NULL);
            GGML_ASSERT(rc == 0);
        }
    }

    // performance stats (graph)
    {
        int64_t perf_cycles_cur = ggml_perf_cycles() - perf_start_cycles;
        int64_t perf_time_us_cur = ggml_perf_time_us() - perf_start_time_us;

        cgraph->perf_runs++;
        cgraph->perf_cycles += perf_cycles_cur;
        cgraph->perf_time_us += perf_time_us_cur;

        GGML_PRINT_DEBUG("%s: perf (%d) - cpu = %.3f / %.3f ms, wall = %.3f / %.3f ms\n", __func__, cgraph->perf_runs,
                         (double)perf_cycles_cur / (double)ggml_cycles_per_ms(),
                         (double)cgraph->perf_cycles / (double)ggml_cycles_per_ms() / (double)cgraph->perf_runs,
                         (double)perf_time_us_cur / 1000.0, (double)cgraph->perf_time_us / 1000.0 / cgraph->perf_runs);
    }

    return compute_status;
}

void ggml_graph_reset(struct ggml_cgraph * cgraph) {
    for (int i = 0; i < cgraph->n_nodes; i++) {
        struct ggml_tensor * grad = cgraph->grads[i];

        if (grad) {
            ggml_set_zero(grad);
        }
    }
}

void 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);

    struct ggml_object * obj = ggml_new_object(ctx, GGML_OBJECT_WORK_BUFFER, cplan.work_size);

    cplan.work_data = (uint8_t *)ctx->mem_buffer + obj->offs;

    ggml_graph_compute(cgraph, &cplan);
}

struct ggml_tensor * ggml_graph_get_tensor(struct ggml_cgraph * cgraph, const char * name) {
    for (int i = 0; i < cgraph->n_leafs; i++) {
        struct ggml_tensor * leaf = cgraph->leafs[i];

        if (strcmp(leaf->name, name) == 0) {
            return leaf;
        }
    }

    for (int i = 0; i < cgraph->n_nodes; i++) {
        struct ggml_tensor * node = cgraph->nodes[i];

        if (strcmp(node->name, name) == 0) {
            return node;
        }
    }

    return NULL;
}

static void ggml_graph_export_leaf(const struct ggml_tensor * tensor, FILE * fout) {
    const int64_t * ne = tensor->ne;
    const size_t * nb = tensor->nb;

    fprintf(fout, "%-6s %-12s %8d %" PRId64 " %" PRId64 " %" PRId64 " %" PRId64 " %16zu %16zu %16zu %16zu %16p %32s\n",
            ggml_type_name(tensor->type), ggml_op_name(tensor->op), tensor->n_dims, ne[0], ne[1], ne[2], ne[3], nb[0], nb[1],
            nb[2], nb[3], tensor->data, tensor->name);
}

static void ggml_graph_export_node(const struct ggml_tensor * tensor, const char * arg, FILE * fout) {
    const int64_t * ne = tensor->ne;
    const size_t * nb = tensor->nb;

    fprintf(fout, "%-6s %-6s %-12s %8d %" PRId64 " %" PRId64 " %" PRId64 " %" PRId64 " %16zu %16zu %16zu %16zu %16p %32s\n",
            arg, ggml_type_name(tensor->type), ggml_op_name(tensor->op), tensor->n_dims, ne[0], ne[1], ne[2], ne[3], nb[0],
            nb[1], nb[2], nb[3], tensor->data, tensor->name);
}

void ggml_graph_export(const struct ggml_cgraph * cgraph, const char * fname) {
    uint64_t size_eval = 0;

    // compute size of intermediate results
    // TODO: does not take into account scratch buffers !!!!
    for (int i = 0; i < cgraph->n_nodes; ++i) {
        size_eval += ggml_nbytes_pad(cgraph->nodes[i]);
    }

    // print
    {
        FILE * fout = stdout;

        fprintf(fout, "\n");
        fprintf(fout, "%-16s %8x\n", "magic", GGML_FILE_MAGIC);
        fprintf(fout, "%-16s %8d\n", "version", GGML_FILE_VERSION);
        fprintf(fout, "%-16s %8d\n", "leafs", cgraph->n_leafs);
        fprintf(fout, "%-16s %8d\n", "nodes", cgraph->n_nodes);
        fprintf(fout, "%-16s %" PRIu64 "\n", "eval", size_eval);

        // header
        fprintf(fout, "\n");
        fprintf(fout, "%-6s %-12s %8s %8s %8s %8s %8s %16s %16s %16s %16s %16s %16s\n", "TYPE", "OP", "NDIMS", "NE0", "NE1",
                "NE2", "NE3", "NB0", "NB1", "NB2", "NB3", "DATA", "NAME");

        for (int i = 0; i < cgraph->n_leafs; ++i) {
            ggml_graph_export_leaf(cgraph->leafs[i], fout);

            GGML_ASSERT(cgraph->leafs[i]->op == GGML_OP_NONE);
            GGML_ASSERT(cgraph->leafs[i]->src[0] == NULL);
            GGML_ASSERT(cgraph->leafs[i]->src[1] == NULL);
        }

        // header
        fprintf(fout, "\n");
        fprintf(fout, "%-6s %-6s %-12s %8s %8s %8s %8s %8s %16s %16s %16s %16s %8s %16s %16s\n", "ARG", "TYPE", "OP", "NDIMS",
                "NE0", "NE1", "NE2", "NE3", "NB0", "NB1", "NB2", "NB3", "NTASKS", "DATA", "NAME");

        for (int i = 0; i < cgraph->n_nodes; ++i) {
            ggml_graph_export_node(cgraph->nodes[i], "DST", fout);

            for (int j = 0; j < GGML_MAX_SRC; ++j) {
                if (cgraph->nodes[i]->src[j]) {
                    ggml_graph_export_node(cgraph->nodes[i]->src[j], "SRC", fout);
                }
            }

            fprintf(fout, "\n");
        }

        fprintf(fout, "\n");
    }

    // write binary data
    {
        FILE * fout = fopen(fname, "wb");

        if (!fout) {
            fprintf(stderr, "%s: failed to open %s\n", __func__, fname);
            return;
        }

        // header
        {
            const uint32_t magic = GGML_FILE_MAGIC;
            const uint32_t version = GGML_FILE_VERSION;
            const uint32_t n_leafs = cgraph->n_leafs;
            const uint32_t nodes = cgraph->n_nodes;

            fwrite(&magic, sizeof(uint32_t), 1, fout);
            fwrite(&version, sizeof(uint32_t), 1, fout);
            fwrite(&n_leafs, sizeof(uint32_t), 1, fout);
            fwrite(&nodes, sizeof(uint32_t), 1, fout);
            fwrite(&size_eval, sizeof(uint64_t), 1, fout);
        }

        // leafs
        {
            for (int i = 0; i < cgraph->n_leafs; ++i) {
                const struct ggml_tensor * tensor = cgraph->leafs[i];

                const uint32_t type = tensor->type;
                const uint32_t op = tensor->op;
                const uint32_t n_dims = tensor->n_dims;

                fwrite(&type, sizeof(uint32_t), 1, fout);
                fwrite(&op, sizeof(uint32_t), 1, fout);
                fwrite(&n_dims, sizeof(uint32_t), 1, fout);

                for (int j = 0; j < GGML_MAX_DIMS; ++j) {
                    const uint64_t ne = tensor->ne[j];
                    const uint64_t nb = tensor->nb[j];

                    fwrite(&ne, sizeof(uint64_t), 1, fout);
                    fwrite(&nb, sizeof(uint64_t), 1, fout);
                }

                fwrite(tensor->name, sizeof(char), GGML_MAX_NAME, fout);
                fwrite(tensor->op_params, sizeof(char), GGML_MAX_OP_PARAMS, fout);

                // dump the data
                // TODO: pad this to 32 byte boundary
                {
                    const size_t size = ggml_nbytes(tensor);

                    fwrite(tensor->data, sizeof(char), size, fout);
                }
            }
        }

        // nodes
        {
            for (int i = 0; i < cgraph->n_nodes; ++i) {
                const struct ggml_tensor * tensor = cgraph->nodes[i];

                const uint32_t type = tensor->type;
                const uint32_t op = tensor->op;
                const uint32_t n_dims = tensor->n_dims;

                fwrite(&type, sizeof(uint32_t), 1, fout);
                fwrite(&op, sizeof(uint32_t), 1, fout);
                fwrite(&n_dims, sizeof(uint32_t), 1, fout);

                for (int j = 0; j < GGML_MAX_DIMS; ++j) {
                    const uint64_t ne = tensor->ne[j];
                    const uint64_t nb = tensor->nb[j];

                    fwrite(&ne, sizeof(uint64_t), 1, fout);
                    fwrite(&nb, sizeof(uint64_t), 1, fout);
                }

                fwrite(tensor->name, sizeof(char), GGML_MAX_NAME, fout);
                fwrite(tensor->op_params, sizeof(char), GGML_MAX_OP_PARAMS, fout);

                // output the op arguments
                {
                    struct ggml_tensor * args[GGML_MAX_SRC] = {NULL};

                    for (int j = 0; j < GGML_MAX_SRC; ++j) {
                        args[j] = tensor->src[j];
                    }

                    for (int j = 0; j < GGML_MAX_SRC; ++j) {
                        if (args[j]) {
                            int32_t idx = -1;

                            // check if leaf
                            {
                                for (int k = 0; k < cgraph->n_leafs; ++k) {
                                    if (args[j] == cgraph->leafs[k]) {
                                        idx = k;
                                        break;
                                    }
                                }
                            }

                            // check if node
                            if (idx == -1) {
                                for (int k = 0; k < cgraph->n_nodes; ++k) {
                                    if (args[j] == cgraph->nodes[k]) {
                                        idx = GGML_MAX_NODES + k;
                                        break;
                                    }
                                }
                            }

                            if (idx == -1) {
                                fprintf(stderr, "%s: failed to find tensor, arg = %d, node = %d\n", __func__, j, i);
                                return;
                            }

                            fwrite(&idx, sizeof(int32_t), 1, fout);
                        } else {
                            const int32_t nul = -1;

                            fwrite(&nul, sizeof(int32_t), 1, fout);
                        }
                    }
                }
            }
        }

        fclose(fout);
    }
}

struct ggml_cgraph ggml_graph_import(const char * fname, struct ggml_context ** ctx_data, struct ggml_context ** ctx_eval) {
    assert(*ctx_data == NULL);
    assert(*ctx_eval == NULL);

    struct ggml_cgraph result = {0};

    struct ggml_tensor * data = NULL;

    // read file into data
    {
        FILE * fin = fopen(fname, "rb");
        if (!fin) {
            fprintf(stderr, "%s: failed to open %s\n", __func__, fname);
            return result;
        }

        size_t fsize = 0;

        fseek(fin, 0, SEEK_END);
        fsize = ftell(fin);
        fseek(fin, 0, SEEK_SET);

        // create the data context
        {
            const size_t overhead = 1 * ggml_tensor_overhead();

            struct ggml_init_params params = {
                .mem_size = fsize + overhead,
                .mem_buffer = NULL,
                .no_alloc = false,
            };

            *ctx_data = ggml_init(params);

            if (!*ctx_data) {
                fprintf(stderr, "%s: failed to create ggml context\n", __func__);
                fclose(fin);
                return result;
            }
        }

        data = ggml_new_tensor_1d(*ctx_data, GGML_TYPE_I8, fsize);

        {
            const size_t ret = fread(data->data, sizeof(char), fsize, fin);
            if (ret != fsize) {
                fprintf(stderr, "%s: failed to read %s\n", __func__, fname);
                fclose(fin);
                return result;
            }
        }

        fclose(fin);
    }

    // populate result
    {
        char * ptr = (char *)data->data;

        const uint32_t magic = *(const uint32_t *)ptr;
        ptr += sizeof(magic);

        if (magic != GGML_FILE_MAGIC) {
            fprintf(stderr, "%s: invalid magic number, got %08x\n", __func__, magic);
            return result;
        }

        const uint32_t version = *(const uint32_t *)ptr;
        ptr += sizeof(version);

        if (version != GGML_FILE_VERSION) {
            fprintf(stderr, "%s: invalid version number\n", __func__);
            return result;
        }

        const uint32_t n_leafs = *(const uint32_t *)ptr;
        ptr += sizeof(n_leafs);
        const uint32_t n_nodes = *(const uint32_t *)ptr;
        ptr += sizeof(n_nodes);
        const uint64_t size_eval = *(const uint64_t *)ptr;
        ptr += sizeof(size_eval);

        result.n_leafs = n_leafs;
        result.n_nodes = n_nodes;

        // create the data context
        {
            const size_t overhead = (n_leafs + n_nodes) * ggml_tensor_overhead();

            struct ggml_init_params params = {
                .mem_size = size_eval + overhead,
                .mem_buffer = NULL,
                .no_alloc = true,
            };

            *ctx_eval = ggml_init(params);

            if (!*ctx_eval) {
                fprintf(stderr, "%s: failed to create ggml context\n", __func__);
                return result;
            }
        }

        // leafs
        {
            uint32_t type;
            uint32_t op;
            uint32_t n_dims;

            for (uint32_t i = 0; i < n_leafs; ++i) {
                type = *(const uint32_t *)ptr;
                ptr += sizeof(type);
                op = *(const uint32_t *)ptr;
                ptr += sizeof(op);
                n_dims = *(const uint32_t *)ptr;
                ptr += sizeof(n_dims);

                int64_t ne[GGML_MAX_DIMS];
                size_t nb[GGML_MAX_DIMS];

                for (int j = 0; j < GGML_MAX_DIMS; ++j) {
                    uint64_t ne_cur;
                    uint64_t nb_cur;

                    ne_cur = *(const uint64_t *)ptr;
                    ptr += sizeof(ne_cur);
                    nb_cur = *(const uint64_t *)ptr;
                    ptr += sizeof(nb_cur);

                    ne[j] = ne_cur;
                    nb[j] = nb_cur;
                }

                struct ggml_tensor * tensor = ggml_new_tensor(*ctx_eval, (enum ggml_type)type, n_dims, ne);

                tensor->op = (enum ggml_op)op;

                memcpy(tensor->name, ptr, GGML_MAX_NAME);
                ptr += GGML_MAX_NAME;
                memcpy(tensor->op_params, ptr, GGML_MAX_OP_PARAMS);
                ptr += GGML_MAX_OP_PARAMS;

                tensor->data = (void *)ptr;

                for (int j = 0; j < GGML_MAX_DIMS; ++j) {
                    tensor->nb[j] = nb[j];
                }

                result.leafs[i] = tensor;

                ptr += ggml_nbytes(tensor);

                fprintf(stderr, "%s: loaded leaf %d: '%16s', %3d dims, %9zu bytes\n", __func__, i, tensor->name, n_dims,
                        ggml_nbytes(tensor));
            }
        }

        ggml_set_no_alloc(*ctx_eval, false);

        // nodes
        {
            uint32_t type;
            uint32_t op;
            uint32_t n_dims;

            for (uint32_t i = 0; i < n_nodes; ++i) {
                type = *(const uint32_t *)ptr;
                ptr += sizeof(type);
                op = *(const uint32_t *)ptr;
                ptr += sizeof(op);
                n_dims = *(const uint32_t *)ptr;
                ptr += sizeof(n_dims);

                enum ggml_op eop = (enum ggml_op)op;

                int64_t ne[GGML_MAX_DIMS];
                size_t nb[GGML_MAX_DIMS];

                for (int j = 0; j < GGML_MAX_DIMS; ++j) {
                    uint64_t ne_cur;
                    uint64_t nb_cur;

                    ne_cur = *(const uint64_t *)ptr;
                    ptr += sizeof(ne_cur);
                    nb_cur = *(const uint64_t *)ptr;
                    ptr += sizeof(nb_cur);

                    ne[j] = ne_cur;
                    nb[j] = nb_cur;
                }

                const char * ptr_name = ptr;
                ptr += GGML_MAX_NAME;
                const char * ptr_op_params = ptr;
                ptr += GGML_MAX_OP_PARAMS;

                const int32_t * ptr_arg_idx = (const int32_t *)ptr;
                ptr += GGML_MAX_SRC * sizeof(int32_t);

                struct ggml_tensor * args[GGML_MAX_SRC] = {NULL};

                // parse args
                for (int j = 0; j < GGML_MAX_SRC; ++j) {
                    const int32_t arg_idx = ptr_arg_idx[j];

                    if (arg_idx == -1) {
                        continue;
                    }

                    if (arg_idx < GGML_MAX_NODES) {
                        args[j] = result.leafs[arg_idx];
                    } else {
                        args[j] = result.nodes[arg_idx - GGML_MAX_NODES];
                    }
                }

                // create the tensor
                // "view" operations are handled differently
                // TODO: handle inplace ops - currently a copy is always made

                struct ggml_tensor * tensor = NULL;

                switch (eop) {
                // TODO: implement other view ops
                case GGML_OP_RESHAPE: {
                    tensor = ggml_reshape_4d(*ctx_eval, args[0], ne[0], ne[1], ne[2], ne[3]);
                } break;
                case GGML_OP_VIEW: {
                    tensor = ggml_view_4d(*ctx_eval, args[0], ne[0], ne[1], ne[2], ne[3], 0, 0, 0, 0);

                    size_t offs;
                    memcpy(&offs, ptr_op_params, sizeof(offs));

                    tensor->data = ((char *)tensor->data) + offs;
                } break;
                case GGML_OP_TRANSPOSE: {
                    tensor = ggml_transpose(*ctx_eval, args[0]);
                } break;
                case GGML_OP_PERMUTE: {
                    tensor = ggml_view_4d(*ctx_eval, args[0], ne[0], ne[1], ne[2], ne[3], 0, 0, 0, 0);
                } break;
                default: {
                    tensor = ggml_new_tensor(*ctx_eval, (enum ggml_type)type, n_dims, ne);

                    tensor->op = eop;
                } break;
                }

                memcpy(tensor->name, ptr_name, GGML_MAX_NAME);
                memcpy(tensor->op_params, ptr_op_params, GGML_MAX_OP_PARAMS);

                for (int j = 0; j < GGML_MAX_DIMS; ++j) {
                    tensor->nb[j] = nb[j];
                }

                for (int j = 0; j < GGML_MAX_SRC; ++j) {
                    tensor->src[j] = args[j];
                }

                result.nodes[i] = tensor;

                fprintf(stderr, "%s: loaded node %d: '%16s', %3d dims, %9zu bytes\n", __func__, i, tensor->name, n_dims,
                        ggml_nbytes(tensor));
            }
        }
    }

    return result;
}

void ggml_graph_print(const struct ggml_cgraph * cgraph) {
    int64_t perf_total_per_op_us[GGML_OP_COUNT] = {0};

    GGML_PRINT("=== GRAPH ===\n");

    GGML_PRINT("n_nodes = %d\n", cgraph->n_nodes);
    for (int i = 0; i < cgraph->n_nodes; i++) {
        struct ggml_tensor * node = cgraph->nodes[i];

        perf_total_per_op_us[node->op] += MAX(1, node->perf_time_us);

        GGML_PRINT(" - %3d: [ %5" PRId64 ", %5" PRId64 ", %5" PRId64
                   "] %16s %s (%3d) cpu = %7.3f / %7.3f ms, wall = %7.3f / %7.3f ms\n",
                   i, node->ne[0], node->ne[1], node->ne[2], ggml_op_name(node->op),
                   node->is_param ? "x"
                   : node->grad   ? "g"
                                  : " ",
                   node->perf_runs, (double)node->perf_cycles / (double)ggml_cycles_per_ms(),
                   (double)node->perf_cycles / (double)ggml_cycles_per_ms() / (double)node->perf_runs,
                   (double)node->perf_time_us / 1000.0, (double)node->perf_time_us / 1000.0 / node->perf_runs);
    }

    GGML_PRINT("n_leafs = %d\n", cgraph->n_leafs);
    for (int i = 0; i < cgraph->n_leafs; i++) {
        struct ggml_tensor * node = cgraph->leafs[i];

        GGML_PRINT(" - %3d: [ %5" PRId64 ", %5" PRId64 "] %8s %16s\n", i, node->ne[0], node->ne[1], ggml_op_name(node->op),
                   ggml_get_name(node));
    }

    for (int i = 0; i < GGML_OP_COUNT; i++) {
        if (perf_total_per_op_us[i] == 0) {
            continue;
        }

        GGML_PRINT("perf_total_per_op_us[%16s] = %7.3f ms\n", ggml_op_name(i), (double)perf_total_per_op_us[i] / 1000.0);
    }

    GGML_PRINT("========================================\n");
}

// check if node is part of the graph
static bool ggml_graph_find(const struct ggml_cgraph * cgraph, const struct ggml_tensor * node) {
    if (cgraph == NULL) {
        return true;
    }

    for (int i = 0; i < cgraph->n_nodes; i++) {
        if (cgraph->nodes[i] == node) {
            return true;
        }
    }

    return false;
}

static struct ggml_tensor * ggml_graph_get_parent(const struct ggml_cgraph * cgraph, const struct ggml_tensor * node) {
    for (int i = 0; i < cgraph->n_nodes; i++) {
        struct ggml_tensor * parent = cgraph->nodes[i];

        if (parent->grad == node) {
            return parent;
        }
    }

    return NULL;
}

static void ggml_graph_dump_dot_node_edge(FILE * fp, const struct ggml_cgraph * gb, struct ggml_tensor * node,
                                          struct ggml_tensor * parent, const char * label) {
    struct ggml_tensor * gparent = ggml_graph_get_parent(gb, node);
    struct ggml_tensor * gparent0 = ggml_graph_get_parent(gb, parent);
    fprintf(fp, "  \"%p\":%s -> \"%p\":%s [ arrowhead = %s; style = %s; label = \"%s\"; ]\n",
            gparent0 ? (void *)gparent0 : (void *)parent, gparent0 ? "g" : "x", gparent ? (void *)gparent : (void *)node,
            gparent ? "g" : "x", gparent ? "empty" : "vee", gparent ? "dashed" : "solid", label);
}

static void ggml_graph_dump_dot_leaf_edge(FILE * fp, struct ggml_tensor * node, struct ggml_tensor * parent,
                                          const char * label) {
    fprintf(fp, "  \"%p\":%s -> \"%p\":%s [ label = \"%s\"; ]\n", (void *)parent, "x", (void *)node, "x", label);
}

void ggml_graph_dump_dot(const struct ggml_cgraph * gb, const struct ggml_cgraph * gf, const char * filename) {
    char color[16];

    FILE * fp = fopen(filename, "w");
    GGML_ASSERT(fp);

    fprintf(fp, "digraph G {\n");
    fprintf(fp, "  newrank = true;\n");
    fprintf(fp, "  rankdir = LR;\n");

    for (int i = 0; i < gb->n_nodes; i++) {
        struct ggml_tensor * node = gb->nodes[i];

        if (ggml_graph_get_parent(gb, node) != NULL) {
            continue;
        }

        if (node->is_param) {
            snprintf(color, sizeof(color), "yellow");
        } else if (node->grad) {
            if (ggml_graph_find(gf, node)) {
                snprintf(color, sizeof(color), "green");
            } else {
                snprintf(color, sizeof(color), "lightblue");
            }
        } else {
            snprintf(color, sizeof(color), "white");
        }

        fprintf(fp,
                "  \"%p\" [ "
                "style = filled; fillcolor = %s; shape = record; "
                "label=\"",
                (void *)node, color);

        if (strlen(node->name) > 0) {
            fprintf(fp, "%s (%s)|", node->name, ggml_type_name(node->type));
        } else {
            fprintf(fp, "(%s)|", ggml_type_name(node->type));
        }

        if (node->n_dims == 2) {
            fprintf(fp, "%d [%" PRId64 ", %" PRId64 "] | <x>%s", i, node->ne[0], node->ne[1], ggml_op_symbol(node->op));
        } else {
            fprintf(fp, "%d [%" PRId64 ", %" PRId64 ", %" PRId64 "] | <x>%s", i, node->ne[0], node->ne[1], node->ne[2],
                    ggml_op_symbol(node->op));
        }

        if (node->grad) {
            fprintf(fp, " | <g>%s\"; ]\n", ggml_op_symbol(node->grad->op));
        } else {
            fprintf(fp, "\"; ]\n");
        }
    }

    for (int i = 0; i < gb->n_leafs; i++) {
        struct ggml_tensor * node = gb->leafs[i];

        snprintf(color, sizeof(color), "pink");

        fprintf(fp,
                "  \"%p\" [ "
                "style = filled; fillcolor = %s; shape = record; "
                "label=\"<x>",
                (void *)node, color);

        if (strlen(node->name) > 0) {
            fprintf(fp, "%s (%s)|", node->name, ggml_type_name(node->type));
        } else {
            fprintf(fp, "(%s)|", ggml_type_name(node->type));
        }

        fprintf(fp, "CONST %d [%" PRId64 ", %" PRId64 "]", i, node->ne[0], node->ne[1]);
        if (ggml_nelements(node) < 5) {
            fprintf(fp, " | (");
            for (int j = 0; j < ggml_nelements(node); j++) {
                if (node->type == GGML_TYPE_I8 || node->type == GGML_TYPE_I16 || node->type == GGML_TYPE_I32) {
                    fprintf(fp, "%d", ggml_get_i32_1d(node, j));
                } else if (node->type == GGML_TYPE_F32 || node->type == GGML_TYPE_F16) {
                    fprintf(fp, "%.1e", (double)ggml_get_f32_1d(node, j));
                } else {
                    fprintf(fp, "#");
                }
                if (j < ggml_nelements(node) - 1) {
                    fprintf(fp, ", ");
                }
            }
            fprintf(fp, ")");
        }
        fprintf(fp, "\"; ]\n");
    }

    for (int i = 0; i < gb->n_nodes; i++) {
        struct ggml_tensor * node = gb->nodes[i];

        for (int j = 0; j < GGML_MAX_SRC; j++) {
            if (node->src[j]) {
                char label[16];
                snprintf(label, sizeof(label), "src %d", j);
                ggml_graph_dump_dot_node_edge(fp, gb, node, node->src[j], label);
            }
        }
    }

    for (int i = 0; i < gb->n_leafs; i++) {
        struct ggml_tensor * node = gb->leafs[i];

        for (int j = 0; j < GGML_MAX_SRC; j++) {
            if (node->src[j]) {
                char label[16];
                snprintf(label, sizeof(label), "src %d", j);
                ggml_graph_dump_dot_leaf_edge(fp, node, node->src[j], label);
            }
        }
    }

    fprintf(fp, "}\n");

    fclose(fp);

    GGML_PRINT("%s: dot -Tpng %s -o %s.png && open %s.png\n", __func__, filename, filename, filename);
}

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

static void ggml_opt_set_params(int np, struct ggml_tensor * const ps[], const float * x) {
    int i = 0;
    for (int p = 0; p < np; ++p) {
        const int64_t ne = ggml_nelements(ps[p]);
        // TODO: add function to set tensor from array
        for (int64_t j = 0; j < ne; ++j) {
            ggml_set_f32_1d(ps[p], j, x[i++]);
        }
    }
}

static void ggml_opt_get_params(int np, struct ggml_tensor * const ps[], float * x) {
    int i = 0;
    for (int p = 0; p < np; ++p) {
        const int64_t ne = ggml_nelements(ps[p]);
        // TODO: add function to get all elements at once
        for (int64_t j = 0; j < ne; ++j) {
            x[i++] = ggml_get_f32_1d(ps[p], j);
        }
    }
}

static void ggml_opt_get_grad(int np, struct ggml_tensor * const ps[], float * g) {
    int i = 0;
    for (int p = 0; p < np; ++p) {
        const int64_t ne = ggml_nelements(ps[p]);
        // TODO: add function to get all elements at once
        for (int64_t j = 0; j < ne; ++j) {
            g[i++] = ggml_get_f32_1d(ps[p]->grad, j);
        }
    }
}

//
// ADAM
//
//   ref: https://arxiv.org/pdf/1412.6980.pdf
//

static enum ggml_opt_result ggml_opt_adam(struct ggml_context * ctx, struct ggml_opt_context * opt,
                                          struct ggml_opt_params params, struct ggml_tensor * f, struct ggml_cgraph * gf,
                                          struct ggml_cgraph * gb, ggml_opt_callback callback, void * callback_data) {
    GGML_ASSERT(ggml_is_scalar(f));

    // these will store the parameters we want to optimize
    struct ggml_tensor * ps[GGML_MAX_PARAMS];

    int np = 0;
    int64_t nx = 0;
    for (int i = 0; i < gf->n_nodes; ++i) {
        if (gf->nodes[i]->is_param) {
            GGML_PRINT_DEBUG("found param %d: grad->op = %d\n", np, gf->nodes[i]->grad->op);

            GGML_ASSERT(np < GGML_MAX_PARAMS);

            ps[np++] = gf->nodes[i];
            nx += ggml_nelements(gf->nodes[i]);
        }
    }

    if ((opt->params.type != params.type) || (opt->nx != nx) || (opt->params.past != params.past)) {
        int iter = opt->iter;
        ggml_opt_init(opt->ctx, opt, params, nx);
        opt->iter = iter;
    }

    // constants
    float sched = params.adam.sched;
    const float alpha = params.adam.alpha;
    const float decay = params.adam.decay * alpha;
    const float beta1 = params.adam.beta1;
    const float beta2 = params.adam.beta2;
    const float eps = params.adam.eps;
    const float gclip = params.adam.gclip;
    const int decay_min_ndim = params.adam.decay_min_ndim;

    float * m = opt->adam.m->data; // first moment
    float * v = opt->adam.v->data; // second moment

    float * pf = params.past > 0 ? opt->adam.pf->data : NULL; // past function values

    if (callback) {
        callback(callback_data, &sched);
    }

    // compute the function value
    ggml_graph_reset(gf);
    ggml_set_f32(f->grad, 1.0f);

    struct ggml_cplan cplan = ggml_graph_plan(gb, params.n_threads);
    struct ggml_object * obj = ggml_new_object(ctx, GGML_OBJECT_WORK_BUFFER, cplan.work_size);
    cplan.work_data = (uint8_t *)ctx->mem_buffer + obj->offs;
    ggml_graph_compute(gb, &cplan);

    opt->adam.fx_prev = ggml_get_f32_1d(f, 0);
    opt->adam.fx_best = opt->adam.fx_prev;
    if (pf) {
        pf[opt->iter % params.past] = opt->adam.fx_prev;
    }

    opt->loss_before = opt->adam.fx_prev;
    opt->loss_after = opt->adam.fx_prev;

    // initialize
    if (opt->just_initialized) {
        opt->adam.n_no_improvement = 0;
        opt->just_initialized = false;
    }

    float * fx_best = &opt->adam.fx_best;
    float * fx_prev = &opt->adam.fx_prev;
    int * n_no_improvement = &opt->adam.n_no_improvement;

    int iter0 = opt->iter;

    // run the optimizer
    for (int t = 0; t < params.adam.n_iter; ++t) {
        opt->iter = iter0 + t + 1;
        GGML_PRINT_DEBUG("=== iter %d ===\n", t);

        GGML_PRINT_DEBUG("f      = %10.6f\n", ggml_get_f32_1d(f, 0));
        GGML_PRINT_DEBUG_5("df/dx0 = %10.6f\n", ggml_get_f32_1d(ps[0]->grad, 0));
        GGML_PRINT_DEBUG_5("df/dx1 = %10.6f\n", ggml_get_f32_1d(ps[1]->grad, 0));

        for (int i = 0; i < np; ++i) {
            GGML_PRINT_DEBUG("param %d: %10.6f, g = %10.6f\n", i, ggml_get_f32_1d(ps[i], 0), ggml_get_f32_1d(ps[i]->grad, 0));
        }

        const int64_t t_start_wall = ggml_time_us();
        const int64_t t_start_cpu = ggml_cycles();
        UNUSED(t_start_wall);
        UNUSED(t_start_cpu);

        {
            float gnorm = 1.0f;
            if (gclip > 0.0f) {
                // gradient clipping
                ggml_float sum = 0.0;
                for (int p = 0; p < np; ++p) {
                    const int64_t ne = ggml_nelements(ps[p]);
                    for (int64_t j = 0; j < ne; ++j) {
                        float g = ggml_get_f32_1d(ps[p]->grad, j);
                        sum += (ggml_float)(g * g);
                    }
                }
                ggml_float norm = sqrt(sum);
                if (norm > (ggml_float)gclip) {
                    gnorm = (float)((ggml_float)gclip / norm);
                }
            }
            const float beta1h = alpha * sched / (1.0f - powf(beta1, opt->iter));
            const float beta2h = 1.0f / (1.0f - powf(beta2, opt->iter));
            int64_t i = 0;
            for (int p = 0; p < np; ++p) {
                const int64_t ne = ggml_nelements(ps[p]);
                const float p_decay = ((ps[p]->n_dims >= decay_min_ndim) ? decay : 0.0f) * sched;
                for (int64_t j = 0; j < ne; ++j) {
                    float x = ggml_get_f32_1d(ps[p], j);
                    float g = ggml_get_f32_1d(ps[p]->grad, j) * gnorm;
                    m[i] = m[i] * beta1 + g * (1.0f - beta1);
                    v[i] = v[i] * beta2 + g * g * (1.0f - beta2);
                    float mh = m[i] * beta1h;
                    float vh = v[i] * beta2h;
                    vh = sqrtf(vh) + eps;
                    x = x * (1.0f - p_decay) - mh / vh;
                    ggml_set_f32_1d(ps[p], j, x);
                    ++i;
                }
            }
        }

        if (callback) {
            callback(callback_data, &sched);
        }

        ggml_graph_reset(gf);
        ggml_set_f32(f->grad, 1.0f);

        ggml_graph_compute(gb, &cplan);

        const float fx = ggml_get_f32_1d(f, 0);
        opt->loss_after = fx;

        // check convergence
        if (fabsf(fx - fx_prev[0]) / fx < params.adam.eps_f) {
            GGML_PRINT_DEBUG("converged\n");

            return GGML_OPT_OK;
        }

        // delta-based convergence test
        if (pf != NULL) {
            // need at least params.past iterations to start checking for convergence
            if (params.past <= iter0 + t) {
                const float rate = (pf[(iter0 + t) % params.past] - fx) / fx;

                if (fabsf(rate) < params.delta) {
                    return GGML_OPT_OK;
                }
            }

            pf[(iter0 + t) % params.past] = fx;
        }

        // check for improvement
        if (params.max_no_improvement > 0) {
            if (fx_best[0] > fx) {
                fx_best[0] = fx;
                n_no_improvement[0] = 0;
            } else {
                ++n_no_improvement[0];

                if (n_no_improvement[0] >= params.max_no_improvement) {
                    return GGML_OPT_OK;
                }
            }
        }

        fx_prev[0] = fx;

        {
            const int64_t t_end_cpu = ggml_cycles();
            GGML_PRINT_DEBUG("time iter:      %5.3f s\n", ((float)(t_end_cpu - t_start_cpu)) / CLOCKS_PER_SEC);
            UNUSED(t_end_cpu);

            const int64_t t_end_wall = ggml_time_us();
            GGML_PRINT_DEBUG("wall time iter: %5.3f s\n", (t_end_wall - t_start_wall) / 1e6);
            UNUSED(t_end_wall);
        }
    }

    return GGML_OPT_DID_NOT_CONVERGE;
}

//
// L-BFGS
//
// the L-BFGS implementation below is based on the following implementation:
//
//   https://github.com/chokkan/liblbfgs
//

struct ggml_lbfgs_iteration_data {
    float alpha;
    float ys;
    float * s;
    float * y;
};

static enum ggml_opt_result linesearch_backtracking(const struct ggml_opt_params * params, int nx, float * x, float * fx,
                                                    float * g, float * d, float * step, const float * xp,
                                                    struct ggml_tensor * f, struct ggml_cgraph * gf, struct ggml_cgraph * gb,
                                                    struct ggml_cplan * cplan, const int np, struct ggml_tensor * ps[],
                                                    ggml_opt_callback callback, void * callback_data) {
    int count = 0;

    float width = 0.0f;
    float dg = 0.0f;
    float finit = 0.0f;
    float dginit = 0.0f;
    float dgtest = 0.0f;

    const float dec = 0.5f;
    const float inc = 2.1f;

    if (*step <= 0.f) {
        return GGML_LINESEARCH_INVALID_PARAMETERS;
    }

    // compute the initial gradient in the search direction
    ggml_vec_dot_f32(nx, &dginit, g, d);

    // make sure that d points to a descent direction
    if (0 < dginit) {
        return GGML_LINESEARCH_FAIL;
    }

    // initialize local variables
    finit = *fx;
    dgtest = params->lbfgs.ftol * dginit;

    while (true) {
        if (callback) {
            // LBFG-S does not support learning rate -> ignore learning schedule
            float sched = 0;
            callback(callback_data, &sched);
        }

        ggml_vec_cpy_f32(nx, x, xp);
        ggml_vec_mad_f32(nx, x, d, *step);

        // evaluate the function and gradient values
        {
            ggml_opt_set_params(np, ps, x);

            ggml_graph_reset(gf);
            ggml_set_f32(f->grad, 1.0f);

            ggml_graph_compute(gb, cplan);

            ggml_opt_get_grad(np, ps, g);

            *fx = ggml_get_f32_1d(f, 0);
        }

        ++count;

        if (*fx > finit + (*step) * dgtest) {
            width = dec;
        } else {
            // Armijo condition is satisfied
            if (params->lbfgs.linesearch == GGML_LINESEARCH_BACKTRACKING_ARMIJO) {
                return count;
            }

            ggml_vec_dot_f32(nx, &dg, g, d);

            // check the Wolfe condition
            if (dg < params->lbfgs.wolfe * dginit) {
                width = inc;
            } else {
                if (params->lbfgs.linesearch == GGML_LINESEARCH_BACKTRACKING_WOLFE) {
                    // regular Wolfe conditions
                    return count;
                }

                if (dg > -params->lbfgs.wolfe * dginit) {
                    width = dec;
                } else {
                    // strong Wolfe condition (GGML_LINESEARCH_BACKTRACKING_STRONG_WOLFE)
                    return count;
                }
            }
        }

        if (*step < params->lbfgs.min_step) {
            return GGML_LINESEARCH_MINIMUM_STEP;
        }
        if (*step > params->lbfgs.max_step) {
            return GGML_LINESEARCH_MAXIMUM_STEP;
        }
        if (params->lbfgs.max_linesearch <= count) {
            return GGML_LINESEARCH_MAXIMUM_ITERATIONS;
        }

        (*step) *= width;
    }

    return GGML_LINESEARCH_FAIL;
}

static enum ggml_opt_result ggml_opt_lbfgs(struct ggml_context * ctx, struct ggml_opt_context * opt,
                                           struct ggml_opt_params params, struct ggml_tensor * f, struct ggml_cgraph * gf,
                                           struct ggml_cgraph * gb, ggml_opt_callback callback, void * callback_data) {
    if (params.lbfgs.linesearch == GGML_LINESEARCH_BACKTRACKING_WOLFE ||
        params.lbfgs.linesearch == GGML_LINESEARCH_BACKTRACKING_STRONG_WOLFE) {
        if (params.lbfgs.wolfe <= params.lbfgs.ftol || 1.f <= params.lbfgs.wolfe) {
            return GGML_OPT_INVALID_WOLFE;
        }
    }

    const int m = params.lbfgs.m;

    // these will store the parameters we want to optimize
    struct ggml_tensor * ps[GGML_MAX_PARAMS];

    int np = 0;
    int nx = 0;
    for (int i = 0; i < gf->n_nodes; ++i) {
        if (gf->nodes[i]->is_param) {
            GGML_PRINT_DEBUG("found param %d: grad->op = %d\n", np, gf->nodes[i]->grad->op);

            GGML_ASSERT(np < GGML_MAX_PARAMS);

            ps[np++] = gf->nodes[i];
            nx += ggml_nelements(gf->nodes[i]);
        }
    }

    if ((opt->params.type != params.type) || (opt->nx != nx) || (opt->params.past != params.past) ||
        (opt->params.lbfgs.m != params.lbfgs.m)) {
        int iter = opt->iter;
        ggml_opt_init(ctx, opt, params, nx);
        opt->iter = iter;
    }

    struct ggml_cplan cplan = ggml_graph_plan(gb, params.n_threads);
    struct ggml_object * obj = ggml_new_object(ctx, GGML_OBJECT_WORK_BUFFER, cplan.work_size);
    cplan.work_data = (uint8_t *)ctx->mem_buffer + obj->offs;

    float * x = opt->lbfgs.x->data;   // current parameters
    float * xp = opt->lbfgs.xp->data; // previous parameters
    float * g = opt->lbfgs.g->data;   // current gradient
    float * gp = opt->lbfgs.gp->data; // previous gradient
    float * d = opt->lbfgs.d->data;   // search direction

    float * pf = params.past > 0 ? opt->lbfgs.pf->data : NULL; // past function values

    float fx = 0.0f;    // cost function value
    float xnorm = 0.0f; // ||x||
    float gnorm = 0.0f; // ||g||

    // initialize x from the graph nodes
    ggml_opt_get_params(np, ps, x);

    // the L-BFGS memory
    float * lm_alpha = opt->lbfgs.lmal->data;
    float * lm_ys = opt->lbfgs.lmys->data;
    float * lm_s = opt->lbfgs.lms->data;
    float * lm_y = opt->lbfgs.lmy->data;

    if (callback) {
        // LBFG-S does not support learning rate -> ignore learning schedule
        float sched = 0;
        callback(callback_data, &sched);
    }

    // evaluate the function value and its gradient
    {
        ggml_opt_set_params(np, ps, x);

        ggml_graph_reset(gf);
        ggml_set_f32(f->grad, 1.0f);

        ggml_graph_compute(gb, &cplan);

        ggml_opt_get_grad(np, ps, g);

        fx = ggml_get_f32_1d(f, 0);

        opt->loss_before = fx;
        opt->loss_after = fx;
    }

    // search direction = -gradient
    ggml_vec_neg_f32(nx, d, g);

    // ||x||, ||g||
    ggml_vec_norm_f32(nx, &xnorm, x);
    ggml_vec_norm_f32(nx, &gnorm, g);

    if (xnorm < 1.0f) {
        xnorm = 1.0f;
    }

    // already optimized
    if (gnorm / xnorm <= params.lbfgs.eps) {
        return GGML_OPT_OK;
    }

    if (opt->just_initialized) {
        if (pf) {
            pf[0] = fx;
        }
        opt->lbfgs.fx_best = fx;

        // initial step
        ggml_vec_norm_inv_f32(nx, &opt->lbfgs.step, d);
        opt->lbfgs.j = 0;
        opt->lbfgs.k = 1;
        opt->lbfgs.end = 0;
        opt->lbfgs.n_no_improvement = 0;
        opt->just_initialized = false;
    }

    float * fx_best = &opt->lbfgs.fx_best;
    float * step = &opt->lbfgs.step;
    int * j = &opt->lbfgs.j;
    int * k = &opt->lbfgs.k;
    int * end = &opt->lbfgs.end;
    int * n_no_improvement = &opt->lbfgs.n_no_improvement;

    int ls = 0;
    int bound = 0;

    float ys = 0.0f;
    float yy = 0.0f;
    float beta = 0.0f;

    int it = 0;

    while (true) {
        // store the current position and gradient vectors
        ggml_vec_cpy_f32(nx, xp, x);
        ggml_vec_cpy_f32(nx, gp, g);

        ls = linesearch_backtracking(&params, nx, x, &fx, g, d, step, xp, f, gf, gb, &cplan, np, ps, callback, callback_data);

        if (ls < 0) {
            // linesearch failed - go back to the previous point and return
            ggml_vec_cpy_f32(nx, x, xp);
            ggml_vec_cpy_f32(nx, g, gp);

            return ls;
        }

        opt->loss_after = fx;

        ggml_vec_norm_f32(nx, &xnorm, x);
        ggml_vec_norm_f32(nx, &gnorm, g);

        GGML_PRINT_DEBUG("f = %10.6f\n", ggml_get_f32_1d(f, 0));

        if (xnorm < 1.0f) {
            xnorm = 1.0f;
        }
        if (gnorm / xnorm <= params.lbfgs.eps) {
            // converged
            return GGML_OPT_OK;
        }

        // delta-based convergence test
        if (pf != NULL) {
            // need at least params.past iterations to start checking for convergence
            if (params.past <= k[0]) {
                const float rate = (pf[k[0] % params.past] - fx) / fx;

                if (fabsf(rate) < params.delta) {
                    return GGML_OPT_OK;
                }
            }

            pf[k[0] % params.past] = fx;
        }

        // check for improvement
        if (params.max_no_improvement > 0) {
            if (fx < fx_best[0]) {
                fx_best[0] = fx;
                n_no_improvement[0] = 0;
            } else {
                n_no_improvement[0]++;

                if (n_no_improvement[0] >= params.max_no_improvement) {
                    return GGML_OPT_OK;
                }
            }
        }

        if (params.lbfgs.n_iter != 0 && params.lbfgs.n_iter < it + 1) {
            // reached the maximum number of iterations
            return GGML_OPT_DID_NOT_CONVERGE;
        }

        // update vectors s and y:
        //   s_{k+1} = x_{k+1} - x_{k} = \step * d_{k}.
        //   y_{k+1} = g_{k+1} - g_{k}.
        //
        ggml_vec_sub_f32(nx, &lm_s[end[0] * nx], x, xp);
        ggml_vec_sub_f32(nx, &lm_y[end[0] * nx], g, gp);

        // compute scalars ys and yy:
        //     ys = y^t \cdot s    -> 1 / \rho.
        //     yy = y^t \cdot y.
        //
        ggml_vec_dot_f32(nx, &ys, &lm_y[end[0] * nx], &lm_s[end[0] * nx]);
        ggml_vec_dot_f32(nx, &yy, &lm_y[end[0] * nx], &lm_y[end[0] * nx]);

        lm_ys[end[0]] = ys;

        // find new search direction
        //   ref: https://en.wikipedia.org/wiki/Limited-memory_BFGS

        bound = (m <= k[0]) ? m : k[0];
        k[0]++;
        it++;
        end[0] = (end[0] + 1) % m;

        // initialize search direction with -g
        ggml_vec_neg_f32(nx, d, g);

        j[0] = end[0];
        for (int i = 0; i < bound; ++i) {
            j[0] = (j[0] + m - 1) % m;
            // \alpha_{j} = \rho_{j} s^{t}_{j} \cdot q_{k+1}
            ggml_vec_dot_f32(nx, &lm_alpha[j[0]], &lm_s[j[0] * nx], d);
            lm_alpha[j[0]] /= lm_ys[j[0]];
            // q_{i} = q_{i+1} - \alpha_{i} y_{i}
            ggml_vec_mad_f32(nx, d, &lm_y[j[0] * nx], -lm_alpha[j[0]]);
        }

        ggml_vec_scale_f32(nx, d, ys / yy);

        for (int i = 0; i < bound; ++i) {
            // \beta_{j} = \rho_{j} y^t_{j} \cdot \gamma_{i}
            ggml_vec_dot_f32(nx, &beta, &lm_y[j[0] * nx], d);
            beta /= lm_ys[j[0]];
            // \gamma_{i+1} = \gamma_{i} + (\alpha_{j} - \beta_{j}) s_{j}
            ggml_vec_mad_f32(nx, d, &lm_s[j[0] * nx], lm_alpha[j[0]] - beta);
            j[0] = (j[0] + 1) % m;
        }

        step[0] = 1.0;
    }

    return GGML_OPT_DID_NOT_CONVERGE;
}

struct ggml_opt_params ggml_opt_default_params(enum ggml_opt_type type) {
    struct ggml_opt_params result;

    switch (type) {
    case GGML_OPT_ADAM: {
        result = (struct ggml_opt_params){
            .type = GGML_OPT_ADAM,
            .n_threads = 1,
            .past = 0,
            .delta = 1e-5f,

            .max_no_improvement = 100,

            .print_forward_graph = true,
            .print_backward_graph = true,

            .adam =
                {
                    .n_iter = 10000,
                    .sched = 1.000f,
                    .decay = 0.0f,
                    .decay_min_ndim = 2,
                    .alpha = 0.001f,
                    .beta1 = 0.9f,
                    .beta2 = 0.999f,
                    .eps = 1e-8f,
                    .eps_f = 1e-5f,
                    .eps_g = 1e-3f,
                    .gclip = 0.0f,
                },
        };
    } break;
    case GGML_OPT_LBFGS: {
        result = (struct ggml_opt_params){
            .type = GGML_OPT_LBFGS,
            .n_threads = 1,
            .past = 0,
            .delta = 1e-5f,

            .max_no_improvement = 0,

            .print_forward_graph = true,
            .print_backward_graph = true,

            .lbfgs =
                {
                    .m = 6,
                    .n_iter = 100,
                    .max_linesearch = 20,

                    .eps = 1e-5f,
                    .ftol = 1e-4f,
                    .wolfe = 0.9f,
                    .min_step = 1e-20f,
                    .max_step = 1e+20f,

                    .linesearch = GGML_LINESEARCH_DEFAULT,
                },
        };
    } break;
    }

    return result;
}

GGML_API void ggml_opt_init(struct ggml_context * ctx, struct ggml_opt_context * opt, struct ggml_opt_params params,
                            int64_t nx) {
    opt->ctx = ctx;
    opt->params = params;
    opt->iter = 0;
    opt->nx = nx;
    opt->just_initialized = true;
    switch (opt->params.type) {
    case GGML_OPT_ADAM: {
        opt->adam.m = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx);
        opt->adam.v = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx);
        opt->adam.pf = params.past > 0 ? ggml_new_tensor_1d(ctx, GGML_TYPE_F32, params.past) : NULL;
        ggml_set_zero(opt->adam.m);
        ggml_set_zero(opt->adam.v);
        if (opt->adam.pf) {
            ggml_set_zero(opt->adam.pf);
        }
    } break;
    case GGML_OPT_LBFGS: {
        opt->lbfgs.x = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx);
        opt->lbfgs.xp = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx);
        opt->lbfgs.g = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx);
        opt->lbfgs.gp = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx);
        opt->lbfgs.d = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx);
        opt->lbfgs.pf = params.past > 0 ? ggml_new_tensor_1d(ctx, GGML_TYPE_F32, params.past) : NULL;
        opt->lbfgs.lmal = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, params.lbfgs.m);
        opt->lbfgs.lmys = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, params.lbfgs.m);
        opt->lbfgs.lms = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, nx, params.lbfgs.m);
        opt->lbfgs.lmy = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, nx, params.lbfgs.m);
        ggml_set_zero(opt->lbfgs.x);
        ggml_set_zero(opt->lbfgs.xp);
        ggml_set_zero(opt->lbfgs.g);
        ggml_set_zero(opt->lbfgs.gp);
        ggml_set_zero(opt->lbfgs.d);
        if (opt->lbfgs.pf) {
            ggml_set_zero(opt->lbfgs.pf);
        }
        ggml_set_zero(opt->lbfgs.lmal);
        ggml_set_zero(opt->lbfgs.lmys);
        ggml_set_zero(opt->lbfgs.lms);
        ggml_set_zero(opt->lbfgs.lmy);
    } break;
    }
}

enum ggml_opt_result ggml_opt(struct ggml_context * ctx, struct ggml_opt_params params, struct ggml_tensor * f) {
    bool free_ctx = false;
    if (ctx == NULL) {
        struct ggml_init_params params_ctx = {
            .mem_size = 16 * 1024 * 1024,
            .mem_buffer = NULL,
            .no_alloc = false,
        };

        ctx = ggml_init(params_ctx);
        if (ctx == NULL) {
            return GGML_OPT_NO_CONTEXT;
        }

        free_ctx = true;
    }

    enum ggml_opt_result result = GGML_OPT_OK;

    struct ggml_opt_context * opt = (struct ggml_opt_context *)alloca(sizeof(struct ggml_opt_context));

    ggml_opt_init(ctx, opt, params, 0);
    result = ggml_opt_resume(ctx, opt, f);

    if (free_ctx) {
        ggml_free(ctx);
    }

    return result;
}

enum ggml_opt_result ggml_opt_resume(struct ggml_context * ctx, struct ggml_opt_context * opt, struct ggml_tensor * f) {

    // build forward + backward compute graphs
    struct ggml_tensor * gfbuf = ggml_new_tensor_1d(ctx, GGML_TYPE_I32,
                                                    sizeof(struct ggml_cgraph) / ggml_type_size(GGML_TYPE_I32) +
                                                        (sizeof(struct ggml_cgraph) % ggml_type_size(GGML_TYPE_I32) ? 1 : 0));
    struct ggml_tensor * gbbuf = ggml_new_tensor_1d(ctx, GGML_TYPE_I32,
                                                    sizeof(struct ggml_cgraph) / ggml_type_size(GGML_TYPE_I32) +
                                                        (sizeof(struct ggml_cgraph) % ggml_type_size(GGML_TYPE_I32) ? 1 : 0));

    struct ggml_cgraph * gf = (struct ggml_cgraph *)gfbuf->data;
    struct ggml_cgraph * gb = (struct ggml_cgraph *)gbbuf->data;

    *gf = ggml_build_forward(f);
    *gb = ggml_build_backward(ctx, gf, true);

    return ggml_opt_resume_g(ctx, opt, f, gf, gb, NULL, NULL);
}

enum ggml_opt_result ggml_opt_resume_g(struct ggml_context * ctx, struct ggml_opt_context * opt, struct ggml_tensor * f,
                                       struct ggml_cgraph * gf, struct ggml_cgraph * gb, ggml_opt_callback callback,
                                       void * callback_data) {

    // build forward + backward compute graphs
    enum ggml_opt_result result = GGML_OPT_OK;

    switch (opt->params.type) {
    case GGML_OPT_ADAM: {
        result = ggml_opt_adam(ctx, opt, opt->params, f, gf, gb, callback, callback_data);
    } break;
    case GGML_OPT_LBFGS: {
        result = ggml_opt_lbfgs(ctx, opt, opt->params, f, gf, gb, callback, callback_data);
    } break;
    }

    if (opt->params.print_forward_graph) {
        ggml_graph_print(gf);
        ggml_graph_dump_dot(gf, NULL, "opt-forward.dot");
    }

    if (opt->params.print_backward_graph) {
        ggml_graph_print(gb);
        ggml_graph_dump_dot(gb, gf, "opt-backward.dot");
    }

    return result;
}

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

size_t ggml_quantize_q4_0(const float * src, void * dst, int n, int k, int64_t * hist) {
    assert(k % QK4_0 == 0);
    const int nb = k / QK4_0;

    for (int b = 0; b < n; b += k) {
        block_q4_0 * restrict y = (block_q4_0 *)dst + b / QK4_0;

        quantize_row_q4_0_reference(src + b, y, k);

        for (int i = 0; i < nb; i++) {
            for (int j = 0; j < QK4_0; j += 2) {
                const uint8_t vi0 = y[i].qs[j / 2] & 0x0F;
                const uint8_t vi1 = y[i].qs[j / 2] >> 4;

                hist[vi0]++;
                hist[vi1]++;
            }
        }
    }

    return (n / QK4_0 * sizeof(block_q4_0));
}

size_t ggml_quantize_q4_1(const float * src, void * dst, int n, int k, int64_t * hist) {
    assert(k % QK4_1 == 0);
    const int nb = k / QK4_1;

    for (int b = 0; b < n; b += k) {
        block_q4_1 * restrict y = (block_q4_1 *)dst + b / QK4_1;

        quantize_row_q4_1_reference(src + b, y, k);

        for (int i = 0; i < nb; i++) {
            for (int j = 0; j < QK4_1; j += 2) {
                const uint8_t vi0 = y[i].qs[j / 2] & 0x0F;
                const uint8_t vi1 = y[i].qs[j / 2] >> 4;

                hist[vi0]++;
                hist[vi1]++;
            }
        }
    }

    return (n / QK4_1 * sizeof(block_q4_1));
}

size_t ggml_quantize_q5_0(const float * src, void * dst, int n, int k, int64_t * hist) {
    assert(k % QK5_0 == 0);
    const int nb = k / QK5_0;

    for (int b = 0; b < n; b += k) {
        block_q5_0 * restrict y = (block_q5_0 *)dst + b / QK5_0;

        quantize_row_q5_0_reference(src + b, y, k);

        for (int i = 0; i < nb; i++) {
            uint32_t qh;
            memcpy(&qh, &y[i].qh, sizeof(qh));

            for (int j = 0; j < QK5_0; j += 2) {
                const uint8_t vh0 = ((qh & (1u << (j + 0))) >> (j + 0)) << 4;
                const uint8_t vh1 = ((qh & (1u << (j + 16))) >> (j + 12));

                // cast to 16 bins
                const uint8_t vi0 = ((y[i].qs[j / 2] & 0x0F) | vh0) / 2;
                const uint8_t vi1 = ((y[i].qs[j / 2] >> 4) | vh1) / 2;

                hist[vi0]++;
                hist[vi1]++;
            }
        }
    }

    return (n / QK5_0 * sizeof(block_q5_0));
}

size_t ggml_quantize_q5_1(const float * src, void * dst, int n, int k, int64_t * hist) {
    assert(k % QK5_1 == 0);
    const int nb = k / QK5_1;

    for (int b = 0; b < n; b += k) {
        block_q5_1 * restrict y = (block_q5_1 *)dst + b / QK5_1;

        quantize_row_q5_1_reference(src + b, y, k);

        for (int i = 0; i < nb; i++) {
            uint32_t qh;
            memcpy(&qh, &y[i].qh, sizeof(qh));

            for (int j = 0; j < QK5_1; j += 2) {
                const uint8_t vh0 = ((qh & (1u << (j + 0))) >> (j + 0)) << 4;
                const uint8_t vh1 = ((qh & (1u << (j + 16))) >> (j + 12));

                // cast to 16 bins
                const uint8_t vi0 = ((y[i].qs[j / 2] & 0x0F) | vh0) / 2;
                const uint8_t vi1 = ((y[i].qs[j / 2] >> 4) | vh1) / 2;

                hist[vi0]++;
                hist[vi1]++;
            }
        }
    }

    return (n / QK5_1 * sizeof(block_q5_1));
}

size_t ggml_quantize_q8_0(const float * src, void * dst, int n, int k, int64_t * hist) {
    assert(k % QK8_0 == 0);
    const int nb = k / QK8_0;

    for (int b = 0; b < n; b += k) {
        block_q8_0 * restrict y = (block_q8_0 *)dst + b / QK8_0;

        quantize_row_q8_0_reference(src + b, y, k);

        for (int i = 0; i < nb; i++) {
            for (int j = 0; j < QK8_0; ++j) {
                const int8_t vi = y[i].qs[j];

                hist[vi / 16 + 8]++;
            }
        }
    }

    return (n / QK8_0 * sizeof(block_q8_0));
}

size_t ggml_quantize_chunk(enum ggml_type type, const float * src, void * dst, int start, int n, int64_t * hist) {
    size_t result = 0;
    switch (type) {
    case GGML_TYPE_Q4_0: {
        GGML_ASSERT(start % QK4_0 == 0);
        block_q4_0 * block = (block_q4_0 *)dst + start / QK4_0;
        result = ggml_quantize_q4_0(src + start, block, n, n, hist);
    } break;
    case GGML_TYPE_Q4_1: {
        GGML_ASSERT(start % QK4_1 == 0);
        block_q4_1 * block = (block_q4_1 *)dst + start / QK4_1;
        result = ggml_quantize_q4_1(src + start, block, n, n, hist);
    } break;
    case GGML_TYPE_Q5_0: {
        GGML_ASSERT(start % QK5_0 == 0);
        block_q5_0 * block = (block_q5_0 *)dst + start / QK5_0;
        result = ggml_quantize_q5_0(src + start, block, n, n, hist);
    } break;
    case GGML_TYPE_Q5_1: {
        GGML_ASSERT(start % QK5_1 == 0);
        block_q5_1 * block = (block_q5_1 *)dst + start / QK5_1;
        result = ggml_quantize_q5_1(src + start, block, n, n, hist);
    } break;
    case GGML_TYPE_Q8_0: {
        GGML_ASSERT(start % QK8_0 == 0);
        block_q8_0 * block = (block_q8_0 *)dst + start / QK8_0;
        result = ggml_quantize_q8_0(src + start, block, n, n, hist);
    } break;
#ifdef GGML_USE_K_QUANTS
    case GGML_TYPE_Q2_K: {
        GGML_ASSERT(start % QK_K == 0);
        block_q2_K * block = (block_q2_K *)dst + start / QK_K;
        result = ggml_quantize_q2_K(src + start, block, n, n, hist);
    } break;
    case GGML_TYPE_Q3_K: {
        GGML_ASSERT(start % QK_K == 0);
        block_q3_K * block = (block_q3_K *)dst + start / QK_K;
        result = ggml_quantize_q3_K(src + start, block, n, n, hist);
    } break;
    case GGML_TYPE_Q4_K: {
        GGML_ASSERT(start % QK_K == 0);
        block_q4_K * block = (block_q4_K *)dst + start / QK_K;
        result = ggml_quantize_q4_K(src + start, block, n, n, hist);
    } break;
    case GGML_TYPE_Q5_K: {
        GGML_ASSERT(start % QK_K == 0);
        block_q5_K * block = (block_q5_K *)dst + start / QK_K;
        result = ggml_quantize_q5_K(src + start, block, n, n, hist);
    } break;
    case GGML_TYPE_Q6_K: {
        GGML_ASSERT(start % QK_K == 0);
        block_q6_K * block = (block_q6_K *)dst + start / QK_K;
        result = ggml_quantize_q6_K(src + start, block, n, n, hist);
    } break;
#endif
    case GGML_TYPE_F16: {
        int elemsize = sizeof(ggml_fp16_t);
        ggml_fp32_to_fp16_row(src + start, (ggml_fp16_t *)dst + start, n);
        result = n * elemsize;
    } break;
    case GGML_TYPE_F32: {
        int elemsize = sizeof(float);
        result = n * elemsize;
        memcpy((uint8_t *)dst + start * elemsize, src + start, result);
    } break;
    default:
        assert(false);
    }
    return result;
}

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

struct gguf_str {
    uint64_t n; // GGUFv2
    char * data;
};

static const size_t GGUF_TYPE_SIZE[GGUF_TYPE_COUNT] = {
    [GGUF_TYPE_UINT8] = sizeof(uint8_t),
    [GGUF_TYPE_INT8] = sizeof(int8_t),
    [GGUF_TYPE_UINT16] = sizeof(uint16_t),
    [GGUF_TYPE_INT16] = sizeof(int16_t),
    [GGUF_TYPE_UINT32] = sizeof(uint32_t),
    [GGUF_TYPE_INT32] = sizeof(int32_t),
    [GGUF_TYPE_FLOAT32] = sizeof(float),
    [GGUF_TYPE_BOOL] = sizeof(bool),
    [GGUF_TYPE_STRING] = sizeof(struct gguf_str),
    [GGUF_TYPE_UINT64] = sizeof(uint64_t),
    [GGUF_TYPE_INT64] = sizeof(int64_t),
    [GGUF_TYPE_FLOAT64] = sizeof(double),
    [GGUF_TYPE_ARRAY] = 0, // undefined
};
static_assert(GGUF_TYPE_COUNT == 13, "GGUF_TYPE_COUNT != 13");

static const char * GGUF_TYPE_NAME[GGUF_TYPE_COUNT] = {
    [GGUF_TYPE_UINT8] = "u8",    [GGUF_TYPE_INT8] = "i8",   [GGUF_TYPE_UINT16] = "u16",  [GGUF_TYPE_INT16] = "i16",
    [GGUF_TYPE_UINT32] = "u32",  [GGUF_TYPE_INT32] = "i32", [GGUF_TYPE_FLOAT32] = "f32", [GGUF_TYPE_BOOL] = "bool",
    [GGUF_TYPE_STRING] = "str",  [GGUF_TYPE_ARRAY] = "arr", [GGUF_TYPE_UINT64] = "u64",  [GGUF_TYPE_INT64] = "i64",
    [GGUF_TYPE_FLOAT64] = "f64",
};
static_assert(GGUF_TYPE_COUNT == 13, "GGUF_TYPE_COUNT != 13");

union gguf_value {
    uint8_t uint8;
    int8_t int8;
    uint16_t uint16;
    int16_t int16;
    uint32_t uint32;
    int32_t int32;
    float float32;
    uint64_t uint64;
    int64_t int64;
    double float64;
    bool bool_;

    struct gguf_str str;

    struct {
        enum gguf_type type;

        uint64_t n; // GGUFv2
        void * data;
    } arr;
};

struct gguf_kv {
    struct gguf_str key;

    enum gguf_type type;
    union gguf_value value;
};

struct gguf_header {
    uint32_t magic;
    uint32_t version;
    uint64_t n_tensors; // GGUFv2
    uint64_t n_kv;      // GGUFv2
};

struct gguf_tensor_info {
    struct gguf_str name;

    uint32_t n_dims;
    uint64_t ne[GGML_MAX_DIMS];

    enum ggml_type type;

    uint64_t offset; // offset from start of `data`, must be a multiple of `ALIGNMENT`

    // for writing API
    const void * data;
    size_t size;
};

struct gguf_context {
    struct gguf_header header;

    struct gguf_kv * kv;
    struct gguf_tensor_info * infos;

    size_t alignment;
    size_t offset; // offset of `data` from beginning of file
    size_t size;   // size of `data` in bytes

    // uint8_t * padding;
    void * data;
};

static bool gguf_fread_el(FILE * file, void * dst, size_t size, size_t * offset) {
    const size_t n = fread(dst, 1, size, file);
    *offset += n;
    return n == size;
}

// NOTE: temporary handling of GGUFv1 >> remove after Oct 2023
static bool gguf_fread_str_cur(FILE * file, struct gguf_str * p, size_t * offset) {
    p->n = 0;
    p->data = NULL;

    bool ok = true;

    ok = ok && gguf_fread_el(file, &p->n, sizeof(p->n), offset);
    p->data = calloc(p->n + 1, 1);
    ok = ok && gguf_fread_el(file, p->data, p->n, offset);

    return ok;
}

static bool gguf_fread_str_v1(FILE * file, struct gguf_str * p, size_t * offset) {
    p->n = 0;
    p->data = NULL;

    bool ok = true;

    uint32_t n = 0;
    ok = ok && gguf_fread_el(file, &n, sizeof(n), offset);
    p->data = calloc(n + 1, 1);
    p->n = n;
    ok = ok && gguf_fread_el(file, p->data, p->n, offset);

    return ok;
}

struct gguf_context * gguf_init_empty(void) {
    struct gguf_context * ctx = GGML_ALIGNED_MALLOC(sizeof(struct gguf_context));

    ctx->header.magic = GGUF_MAGIC;
    ctx->header.version = GGUF_VERSION;
    ctx->header.n_tensors = 0;
    ctx->header.n_kv = 0;

    ctx->kv = NULL;
    ctx->infos = NULL;

    ctx->alignment = GGUF_DEFAULT_ALIGNMENT;
    ctx->offset = 0;
    ctx->size = 0;

    ctx->data = NULL;

    return ctx;
}

struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_params params) {
    FILE * file = fopen(fname, "rb");
    if (!file) {
        return NULL;
    }

    // offset from start of file
    size_t offset = 0;

    uint32_t magic = 0;

    // check the magic before making allocations
    {
        gguf_fread_el(file, &magic, sizeof(magic), &offset);

        if (magic != GGUF_MAGIC) {
            fprintf(stderr, "%s: invalid magic number %08x\n", __func__, magic);
            fclose(file);
            return NULL;
        }
    }

    bool ok = true;

    struct gguf_context * ctx = GGML_ALIGNED_MALLOC(sizeof(struct gguf_context));

    // read the header
    {
        ctx->header.magic = magic;

        ctx->kv = NULL;
        ctx->infos = NULL;
        ctx->data = NULL;

        ok = ok && gguf_fread_el(file, &ctx->header.version, sizeof(ctx->header.version), &offset);

        if (ctx->header.version == 1) {
            // NOTE: temporary handling of GGUFv1 >> remove after Oct 2023
            uint32_t n_tensors = 0;
            uint32_t n_kv = 0;

            ok = ok && gguf_fread_el(file, &n_tensors, sizeof(n_tensors), &offset);
            ok = ok && gguf_fread_el(file, &n_kv, sizeof(n_kv), &offset);

            ctx->header.n_tensors = n_tensors;
            ctx->header.n_kv = n_kv;
        } else {
            ok = ok && gguf_fread_el(file, &ctx->header.n_tensors, sizeof(ctx->header.n_tensors), &offset);
            ok = ok && gguf_fread_el(file, &ctx->header.n_kv, sizeof(ctx->header.n_kv), &offset);
        }

        if (!ok) {
            fprintf(stderr, "%s: failed to read header\n", __func__);
            fclose(file);
            gguf_free(ctx);
            return NULL;
        }
    }

    // NOTE: temporary handling of GGUFv1 >> remove after Oct 2023
    bool (*gguf_fread_str)(FILE *, struct gguf_str *, size_t *) = gguf_fread_str_cur;
    if (ctx->header.version == 1) {
        gguf_fread_str = gguf_fread_str_v1;
    }

    // read the kv pairs
    {
        ctx->kv = malloc(ctx->header.n_kv * sizeof(struct gguf_kv));

        for (uint32_t i = 0; i < ctx->header.n_kv; ++i) {
            struct gguf_kv * kv = &ctx->kv[i];

            // fprintf(stderr, "%s: reading kv %d\n", __func__, i);

            ok = ok && gguf_fread_str(file, &kv->key, &offset);
            ok = ok && gguf_fread_el(file, &kv->type, sizeof(kv->type), &offset);

            // fprintf(stderr, "%s: reading kv with key %s\n", __func__, kv->key.data);

            switch (kv->type) {
            case GGUF_TYPE_UINT8:
                ok = ok && gguf_fread_el(file, &kv->value.uint8, sizeof(kv->value.uint8), &offset);
                break;
            case GGUF_TYPE_INT8:
                ok = ok && gguf_fread_el(file, &kv->value.int8, sizeof(kv->value.int8), &offset);
                break;
            case GGUF_TYPE_UINT16:
                ok = ok && gguf_fread_el(file, &kv->value.uint16, sizeof(kv->value.uint16), &offset);
                break;
            case GGUF_TYPE_INT16:
                ok = ok && gguf_fread_el(file, &kv->value.int16, sizeof(kv->value.int16), &offset);
                break;
            case GGUF_TYPE_UINT32:
                ok = ok && gguf_fread_el(file, &kv->value.uint32, sizeof(kv->value.uint32), &offset);
                break;
            case GGUF_TYPE_INT32:
                ok = ok && gguf_fread_el(file, &kv->value.int32, sizeof(kv->value.int32), &offset);
                break;
            case GGUF_TYPE_FLOAT32:
                ok = ok && gguf_fread_el(file, &kv->value.float32, sizeof(kv->value.float32), &offset);
                break;
            case GGUF_TYPE_UINT64:
                ok = ok && gguf_fread_el(file, &kv->value.uint64, sizeof(kv->value.uint64), &offset);
                break;
            case GGUF_TYPE_INT64:
                ok = ok && gguf_fread_el(file, &kv->value.int64, sizeof(kv->value.int64), &offset);
                break;
            case GGUF_TYPE_FLOAT64:
                ok = ok && gguf_fread_el(file, &kv->value.float64, sizeof(kv->value.float64), &offset);
                break;
            case GGUF_TYPE_BOOL:
                ok = ok && gguf_fread_el(file, &kv->value.bool_, sizeof(kv->value.bool_), &offset);
                break;
            case GGUF_TYPE_STRING:
                ok = ok && gguf_fread_str(file, &kv->value.str, &offset);
                break;
            case GGUF_TYPE_ARRAY: {
                ok = ok && gguf_fread_el(file, &kv->value.arr.type, sizeof(kv->value.arr.type), &offset);

                if (ctx->header.version == 1) {
                    // NOTE: temporary handling of GGUFv1 >> remove after Oct 2023
                    uint32_t n = 0;
                    ok = ok && gguf_fread_el(file, &n, sizeof(n), &offset);
                    kv->value.arr.n = n;
                } else {
                    ok = ok && gguf_fread_el(file, &kv->value.arr.n, sizeof(kv->value.arr.n), &offset);
                }

                switch (kv->value.arr.type) {
                case GGUF_TYPE_UINT8:
                case GGUF_TYPE_INT8:
                case GGUF_TYPE_UINT16:
                case GGUF_TYPE_INT16:
                case GGUF_TYPE_UINT32:
                case GGUF_TYPE_INT32:
                case GGUF_TYPE_FLOAT32:
                case GGUF_TYPE_UINT64:
                case GGUF_TYPE_INT64:
                case GGUF_TYPE_FLOAT64:
                case GGUF_TYPE_BOOL: {
                    kv->value.arr.data = malloc(kv->value.arr.n * GGUF_TYPE_SIZE[kv->value.arr.type]);
                    ok = ok &&
                         gguf_fread_el(file, kv->value.arr.data, kv->value.arr.n * GGUF_TYPE_SIZE[kv->value.arr.type], &offset);
                } break;
                case GGUF_TYPE_STRING: {
                    kv->value.arr.data = malloc(kv->value.arr.n * sizeof(struct gguf_str));
                    for (uint32_t j = 0; j < kv->value.arr.n; ++j) {
                        ok = ok && gguf_fread_str(file, &((struct gguf_str *)kv->value.arr.data)[j], &offset);
                    }
                } break;
                case GGUF_TYPE_ARRAY:
                case GGUF_TYPE_COUNT:
                    GGML_ASSERT(false && "invalid type");
                    break;
                };
            } break;
            case GGUF_TYPE_COUNT:
                GGML_ASSERT(false && "invalid type");
            };

            if (!ok) {
                break;
            }
        }

        if (!ok) {
            fprintf(stderr, "%s: failed to read key-value pairs\n", __func__);
            fclose(file);
            gguf_free(ctx);
            return NULL;
        }
    }

    // read the tensor infos
    {
        ctx->infos = malloc(ctx->header.n_tensors * sizeof(struct gguf_tensor_info));

        for (uint32_t i = 0; i < ctx->header.n_tensors; ++i) {
            struct gguf_tensor_info * info = &ctx->infos[i];

            for (int j = 0; j < GGML_MAX_DIMS; ++j) {
                info->ne[j] = 1;
            }

            ok = ok && gguf_fread_str(file, &info->name, &offset);
            ok = ok && gguf_fread_el(file, &info->n_dims, sizeof(info->n_dims), &offset);
            for (uint32_t j = 0; j < info->n_dims; ++j) {
                if (ctx->header.version == 1) {
                    // NOTE: temporary handling of GGUFv1 >> remove after Oct 2023
                    uint32_t t = 0;
                    ok = ok && gguf_fread_el(file, &t, sizeof(t), &offset);
                    info->ne[j] = t;
                } else {
                    ok = ok && gguf_fread_el(file, &info->ne[j], sizeof(info->ne[j]), &offset);
                }
            }
            ok = ok && gguf_fread_el(file, &info->type, sizeof(info->type), &offset);
            ok = ok && gguf_fread_el(file, &info->offset, sizeof(info->offset), &offset);

            if (!ok) {
                fprintf(stderr, "%s: failed to read tensor info\n", __func__);
                fclose(file);
                gguf_free(ctx);
                return NULL;
            }
        }
    }

    ctx->alignment = GGUF_DEFAULT_ALIGNMENT;

    int alignment_idx = gguf_find_key(ctx, "general.alignment");
    if (alignment_idx != -1) {
        ctx->alignment = gguf_get_val_u32(ctx, alignment_idx);
    }

    // we require the data section to be aligned, so take into account any padding
    {
        const size_t offset_pad = offset % ctx->alignment;

        if (offset_pad != 0) {
            offset += ctx->alignment - offset_pad;
            fseek(file, offset, SEEK_SET);
        }
    }

    // store the current file offset - this is where the data section starts
    ctx->offset = offset;

    // compute the total size of the data section, taking into account the alignment
    {
        ctx->size = 0;
        for (uint32_t i = 0; i < ctx->header.n_tensors; ++i) {
            struct gguf_tensor_info * info = &ctx->infos[i];

            const int64_t ne = (int64_t)info->ne[0] * (int64_t)info->ne[1] * (int64_t)info->ne[2] * (int64_t)info->ne[3];

            if (ne % ggml_blck_size(info->type) != 0) {
                fprintf(stderr, "%s: tensor '%s' number of elements (%" PRId64 ") is not a multiple of block size (%d)\n",
                        __func__, info->name.data, ne, ggml_blck_size(info->type));
                fclose(file);
                gguf_free(ctx);
                return NULL;
            }

            const size_t size_cur = (ne * ggml_type_size(info->type)) / ggml_blck_size(info->type);

            ctx->size += GGML_PAD(size_cur, ctx->alignment);
        }
    }

    // load the tensor data only if requested
    if (params.ctx != NULL) {
        // if the provided gguf_context is no_alloc, then we create "empty" tensors and do not read the binary blob
        // otherwise, we load the binary blob into the created ggml_context as well, and point the "data" members of
        // the ggml_tensor structs to the appropriate locations in the binary blob

        // compute the exact size needed for the new ggml_context
        const size_t mem_size = params.no_alloc ? (ctx->header.n_tensors) * ggml_tensor_overhead()
                                                : (ctx->header.n_tensors + 1) * ggml_tensor_overhead() + ctx->size;

        struct ggml_init_params pdata = {
            .mem_size = mem_size,
            .mem_buffer = NULL,
            .no_alloc = params.no_alloc,
        };

        *params.ctx = ggml_init(pdata);

        struct ggml_context * ctx_data = *params.ctx;

        struct ggml_tensor * data = NULL;

        if (!params.no_alloc) {
            data = ggml_new_tensor_1d(ctx_data, GGML_TYPE_I8, ctx->size);

            ok = ok && data != NULL;

            // read the binary blob with the tensor data
            ok = ok && gguf_fread_el(file, data->data, ctx->size, &offset);

            if (!ok) {
                fprintf(stderr, "%s: failed to read tensor data\n", __func__);
                fclose(file);
                ggml_free(ctx_data);
                gguf_free(ctx);
                return NULL;
            }

            ctx->data = data->data;
        }

        ggml_set_no_alloc(ctx_data, true);

        // create the tensors
        for (uint32_t i = 0; i < ctx->header.n_tensors; ++i) {
            const int64_t ne[GGML_MAX_DIMS] = {
                ctx->infos[i].ne[0],
                ctx->infos[i].ne[1],
                ctx->infos[i].ne[2],
                ctx->infos[i].ne[3],
            };

            struct ggml_tensor * cur = ggml_new_tensor(ctx_data, ctx->infos[i].type, ctx->infos[i].n_dims, ne);

            ok = ok && cur != NULL;

            ggml_set_name(cur, ctx->infos[i].name.data);

            if (!ok) {
                break;
            }

            // point the data member to the appropriate location in the binary blob using the tensor infos
            if (!params.no_alloc) {
                // cur->data = (char *) data->data + ctx->infos[i].offset - ctx->offset; // offset from start of file
                cur->data = (char *)data->data + ctx->infos[i].offset; // offset from data
            }
        }

        if (!ok) {
            fprintf(stderr, "%s: failed to read the tensor data\n", __func__);
            fclose(file);
            ggml_free(ctx_data);
            gguf_free(ctx);
            return NULL;
        }

        ggml_set_no_alloc(ctx_data, params.no_alloc);
    }

    fclose(file);

    return ctx;
}

void gguf_free(struct gguf_context * ctx) {
    if (ctx == NULL) {
        return;
    }

    if (ctx->kv) {
        // free string memory - not great..
        for (uint32_t i = 0; i < ctx->header.n_kv; ++i) {
            struct gguf_kv * kv = &ctx->kv[i];

            if (kv->key.data) {
                free(kv->key.data);
            }

            if (kv->type == GGUF_TYPE_STRING) {
                if (kv->value.str.data) {
                    free(kv->value.str.data);
                }
            }

            if (kv->type == GGUF_TYPE_ARRAY) {
                if (kv->value.arr.data) {
                    if (kv->value.arr.type == GGUF_TYPE_STRING) {
                        for (uint32_t j = 0; j < kv->value.arr.n; ++j) {
                            struct gguf_str * str = &((struct gguf_str *)kv->value.arr.data)[j];
                            if (str->data) {
                                free(str->data);
                            }
                        }
                    }
                    free(kv->value.arr.data);
                }
            }
        }

        free(ctx->kv);
    }

    if (ctx->infos) {
        for (uint32_t i = 0; i < ctx->header.n_tensors; ++i) {
            struct gguf_tensor_info * info = &ctx->infos[i];

            if (info->name.data) {
                free(info->name.data);
            }
        }

        free(ctx->infos);
    }

    GGML_ALIGNED_FREE(ctx);
}

const char * gguf_type_name(enum gguf_type type) { return GGUF_TYPE_NAME[type]; }

int gguf_get_version(const struct gguf_context * ctx) { return ctx->header.version; }

size_t gguf_get_alignment(const struct gguf_context * ctx) { return ctx->alignment; }

size_t gguf_get_data_offset(const struct gguf_context * ctx) { return ctx->offset; }

void * gguf_get_data(const struct gguf_context * ctx) { return ctx->data; }

int gguf_get_n_kv(const struct gguf_context * ctx) { return ctx->header.n_kv; }

int gguf_find_key(const struct gguf_context * ctx, const char * key) {
    // return -1 if key not found
    int keyfound = -1;

    const int n_kv = gguf_get_n_kv(ctx);

    for (int i = 0; i < n_kv; ++i) {
        if (strcmp(key, gguf_get_key(ctx, i)) == 0) {
            keyfound = i;
            break;
        }
    }

    return keyfound;
}

const char * gguf_get_key(const struct gguf_context * ctx, int i) { return ctx->kv[i].key.data; }

enum gguf_type gguf_get_kv_type(const struct gguf_context * ctx, int i) { return ctx->kv[i].type; }

enum gguf_type gguf_get_arr_type(const struct gguf_context * ctx, int i) { return ctx->kv[i].value.arr.type; }

const void * gguf_get_arr_data(const struct gguf_context * ctx, int i) { return ctx->kv[i].value.arr.data; }

const char * gguf_get_arr_str(const struct gguf_context * ctx, int key_id, int i) {
    struct gguf_kv * kv = &ctx->kv[key_id];
    struct gguf_str * str = &((struct gguf_str *)kv->value.arr.data)[i];
    return str->data;
}

int gguf_get_arr_n(const struct gguf_context * ctx, int i) { return ctx->kv[i].value.arr.n; }

uint8_t gguf_get_val_u8(const struct gguf_context * ctx, int i) { return ctx->kv[i].value.uint8; }

int8_t gguf_get_val_i8(const struct gguf_context * ctx, int i) { return ctx->kv[i].value.int8; }

uint16_t gguf_get_val_u16(const struct gguf_context * ctx, int i) { return ctx->kv[i].value.uint16; }

int16_t gguf_get_val_i16(const struct gguf_context * ctx, int i) { return ctx->kv[i].value.int16; }

uint32_t gguf_get_val_u32(const struct gguf_context * ctx, int i) { return ctx->kv[i].value.uint32; }

int32_t gguf_get_val_i32(const struct gguf_context * ctx, int i) { return ctx->kv[i].value.int32; }

float gguf_get_val_f32(const struct gguf_context * ctx, int i) { return ctx->kv[i].value.float32; }

uint64_t gguf_get_val_u64(const struct gguf_context * ctx, int i) { return ctx->kv[i].value.uint64; }

int64_t gguf_get_val_i64(const struct gguf_context * ctx, int i) { return ctx->kv[i].value.int64; }

double gguf_get_val_f64(const struct gguf_context * ctx, int i) { return ctx->kv[i].value.float64; }

bool gguf_get_val_bool(const struct gguf_context * ctx, int i) { return ctx->kv[i].value.bool_; }

const char * gguf_get_val_str(const struct gguf_context * ctx, int i) { return ctx->kv[i].value.str.data; }

int gguf_get_n_tensors(const struct gguf_context * ctx) { return ctx->header.n_tensors; }

int gguf_find_tensor(const struct gguf_context * ctx, const char * name) {
    // return -1 if tensor not found
    int tensorfound = -1;

    const int n_tensors = gguf_get_n_tensors(ctx);

    for (int i = 0; i < n_tensors; ++i) {
        if (strcmp(name, gguf_get_tensor_name(ctx, i)) == 0) {
            tensorfound = i;
            break;
        }
    }

    return tensorfound;
}

size_t gguf_get_tensor_offset(const struct gguf_context * ctx, int i) { return ctx->infos[i].offset; }

char * gguf_get_tensor_name(const struct gguf_context * ctx, int i) { return ctx->infos[i].name.data; }

// returns the index
static int gguf_get_or_add_key(struct gguf_context * ctx, const char * key) {
    const int idx = gguf_find_key(ctx, key);
    if (idx >= 0) {
        return idx;
    }

    const int n_kv = gguf_get_n_kv(ctx);

    ctx->kv = realloc(ctx->kv, (n_kv + 1) * sizeof(struct gguf_kv));
    ctx->kv[n_kv].key.n = strlen(key);
    ctx->kv[n_kv].key.data = strdup(key);
    ctx->header.n_kv++;

    return n_kv;
}

void gguf_set_val_u8(struct gguf_context * ctx, const char * key, uint8_t val) {
    const int idx = gguf_get_or_add_key(ctx, key);

    ctx->kv[idx].type = GGUF_TYPE_UINT8;
    ctx->kv[idx].value.uint8 = val;
}

void gguf_set_val_i8(struct gguf_context * ctx, const char * key, int8_t val) {
    const int idx = gguf_get_or_add_key(ctx, key);

    ctx->kv[idx].type = GGUF_TYPE_INT8;
    ctx->kv[idx].value.int8 = val;
}

void gguf_set_val_u16(struct gguf_context * ctx, const char * key, uint16_t val) {
    const int idx = gguf_get_or_add_key(ctx, key);

    ctx->kv[idx].type = GGUF_TYPE_UINT16;
    ctx->kv[idx].value.uint16 = val;
}

void gguf_set_val_i16(struct gguf_context * ctx, const char * key, int16_t val) {
    const int idx = gguf_get_or_add_key(ctx, key);

    ctx->kv[idx].type = GGUF_TYPE_INT16;
    ctx->kv[idx].value.int16 = val;
}

void gguf_set_val_u32(struct gguf_context * ctx, const char * key, uint32_t val) {
    const int idx = gguf_get_or_add_key(ctx, key);

    ctx->kv[idx].type = GGUF_TYPE_UINT32;
    ctx->kv[idx].value.uint32 = val;
}

void gguf_set_val_i32(struct gguf_context * ctx, const char * key, int32_t val) {
    const int idx = gguf_get_or_add_key(ctx, key);

    ctx->kv[idx].type = GGUF_TYPE_INT32;
    ctx->kv[idx].value.int32 = val;
}

void gguf_set_val_f32(struct gguf_context * ctx, const char * key, float val) {
    const int idx = gguf_get_or_add_key(ctx, key);

    ctx->kv[idx].type = GGUF_TYPE_FLOAT32;
    ctx->kv[idx].value.float32 = val;
}

void gguf_set_val_u64(struct gguf_context * ctx, const char * key, uint64_t val) {
    const int idx = gguf_get_or_add_key(ctx, key);

    ctx->kv[idx].type = GGUF_TYPE_UINT64;
    ctx->kv[idx].value.uint64 = val;
}

void gguf_set_val_i64(struct gguf_context * ctx, const char * key, int64_t val) {
    const int idx = gguf_get_or_add_key(ctx, key);

    ctx->kv[idx].type = GGUF_TYPE_INT64;
    ctx->kv[idx].value.int64 = val;
}

void gguf_set_val_f64(struct gguf_context * ctx, const char * key, double val) {
    const int idx = gguf_get_or_add_key(ctx, key);

    ctx->kv[idx].type = GGUF_TYPE_FLOAT64;
    ctx->kv[idx].value.float64 = val;
}

void gguf_set_val_bool(struct gguf_context * ctx, const char * key, bool val) {
    const int idx = gguf_get_or_add_key(ctx, key);

    ctx->kv[idx].type = GGUF_TYPE_BOOL;
    ctx->kv[idx].value.bool_ = val;
}

void gguf_set_val_str(struct gguf_context * ctx, const char * key, const char * val) {
    const int idx = gguf_get_or_add_key(ctx, key);

    ctx->kv[idx].type = GGUF_TYPE_STRING;
    ctx->kv[idx].value.str.n = strlen(val);
    ctx->kv[idx].value.str.data = strdup(val);
}

void gguf_set_arr_data(struct gguf_context * ctx, const char * key, enum gguf_type type, const void * data, int n) {
    const int idx = gguf_get_or_add_key(ctx, key);

    ctx->kv[idx].type = GGUF_TYPE_ARRAY;
    ctx->kv[idx].value.arr.type = type;
    ctx->kv[idx].value.arr.n = n;
    ctx->kv[idx].value.arr.data = malloc(n * GGUF_TYPE_SIZE[type]);
    memcpy(ctx->kv[idx].value.arr.data, data, n * GGUF_TYPE_SIZE[type]);
}

void gguf_set_arr_str(struct gguf_context * ctx, const char * key, const char ** data, int n) {
    const int idx = gguf_get_or_add_key(ctx, key);

    ctx->kv[idx].type = GGUF_TYPE_ARRAY;
    ctx->kv[idx].value.arr.type = GGUF_TYPE_STRING;
    ctx->kv[idx].value.arr.n = n;
    ctx->kv[idx].value.arr.data = malloc(n * sizeof(struct gguf_str));
    for (int i = 0; i < n; i++) {
        struct gguf_str * str = &((struct gguf_str *)ctx->kv[idx].value.arr.data)[i];
        str->n = strlen(data[i]);
        str->data = strdup(data[i]);
    }
}

// set or add KV pairs from another context
void gguf_set_kv(struct gguf_context * ctx, struct gguf_context * src) {
    for (uint32_t i = 0; i < src->header.n_kv; i++) {
        switch (src->kv[i].type) {
        case GGUF_TYPE_UINT8:
            gguf_set_val_u8(ctx, src->kv[i].key.data, src->kv[i].value.uint8);
            break;
        case GGUF_TYPE_INT8:
            gguf_set_val_i8(ctx, src->kv[i].key.data, src->kv[i].value.int8);
            break;
        case GGUF_TYPE_UINT16:
            gguf_set_val_u16(ctx, src->kv[i].key.data, src->kv[i].value.uint16);
            break;
        case GGUF_TYPE_INT16:
            gguf_set_val_i16(ctx, src->kv[i].key.data, src->kv[i].value.int16);
            break;
        case GGUF_TYPE_UINT32:
            gguf_set_val_u32(ctx, src->kv[i].key.data, src->kv[i].value.uint32);
            break;
        case GGUF_TYPE_INT32:
            gguf_set_val_i32(ctx, src->kv[i].key.data, src->kv[i].value.int32);
            break;
        case GGUF_TYPE_FLOAT32:
            gguf_set_val_f32(ctx, src->kv[i].key.data, src->kv[i].value.float32);
            break;
        case GGUF_TYPE_UINT64:
            gguf_set_val_u64(ctx, src->kv[i].key.data, src->kv[i].value.uint64);
            break;
        case GGUF_TYPE_INT64:
            gguf_set_val_i64(ctx, src->kv[i].key.data, src->kv[i].value.int64);
            break;
        case GGUF_TYPE_FLOAT64:
            gguf_set_val_f64(ctx, src->kv[i].key.data, src->kv[i].value.float64);
            break;
        case GGUF_TYPE_BOOL:
            gguf_set_val_bool(ctx, src->kv[i].key.data, src->kv[i].value.bool_);
            break;
        case GGUF_TYPE_STRING:
            gguf_set_val_str(ctx, src->kv[i].key.data, src->kv[i].value.str.data);
            break;
        case GGUF_TYPE_ARRAY: {
            if (src->kv[i].value.arr.type == GGUF_TYPE_STRING) {
                const char ** data = malloc(src->kv[i].value.arr.n * sizeof(char *));
                for (uint32_t j = 0; j < src->kv[i].value.arr.n; j++) {
                    data[j] = ((struct gguf_str *)src->kv[i].value.arr.data)[j].data;
                }
                gguf_set_arr_str(ctx, src->kv[i].key.data, data, src->kv[i].value.arr.n);
                free(data);
            } else if (src->kv[i].value.arr.type == GGUF_TYPE_ARRAY) {
                GGML_ASSERT(false && "nested arrays not supported");
            } else {
                gguf_set_arr_data(ctx, src->kv[i].key.data, src->kv[i].value.arr.type, src->kv[i].value.arr.data,
                                  src->kv[i].value.arr.n);
            }
        } break;
        case GGUF_TYPE_COUNT:
            GGML_ASSERT(false && "invalid type");
            break;
        }
    }
}

void gguf_add_tensor(struct gguf_context * ctx, const struct ggml_tensor * tensor) {
    const int idx = ctx->header.n_tensors;
    ctx->infos = realloc(ctx->infos, (idx + 1) * sizeof(struct gguf_tensor_info));

    ctx->infos[idx].name.n = strlen(tensor->name);
    ctx->infos[idx].name.data = strdup(tensor->name);

    for (int i = 0; i < GGML_MAX_DIMS; ++i) {
        ctx->infos[idx].ne[i] = 1;
    }

    ctx->infos[idx].n_dims = tensor->n_dims;
    for (int i = 0; i < tensor->n_dims; i++) {
        ctx->infos[idx].ne[i] = tensor->ne[i];
    }

    ctx->infos[idx].type = tensor->type;
    ctx->infos[idx].offset = 0;
    ctx->infos[idx].data = tensor->data;
    ctx->infos[idx].size = ggml_nbytes(tensor);

    if (ctx->header.n_tensors > 0) {
        ctx->infos[idx].offset = ctx->infos[idx - 1].offset + GGML_PAD(ctx->infos[idx - 1].size, ctx->alignment);
    }

    ctx->header.n_tensors++;
}

void gguf_set_tensor_type(struct gguf_context * ctx, const char * name, enum ggml_type type) {
    const int idx = gguf_find_tensor(ctx, name);
    if (idx < 0) {
        GGML_ASSERT(false && "tensor not found");
    }

    ctx->infos[idx].type = type;
}

void gguf_set_tensor_data(struct gguf_context * ctx, const char * name, const void * data, size_t size) {
    const int idx = gguf_find_tensor(ctx, name);
    if (idx < 0) {
        GGML_ASSERT(false && "tensor not found");
    }

    ctx->infos[idx].data = data;
    ctx->infos[idx].size = size;

    // update offsets
    for (uint32_t i = idx + 1; i < ctx->header.n_tensors; ++i) {
        ctx->infos[i].offset = ctx->infos[i - 1].offset + GGML_PAD(ctx->infos[i - 1].size, ctx->alignment);
    }
}

// static void gguf_fwrite_str(FILE * file, const struct gguf_str * val) {
//     fwrite(&val->n,   sizeof(val->n),    1, file);
//     fwrite(val->data, sizeof(char), val->n, file);
// }
//
// static void gguf_fwrite_el(FILE * file, const void * val, size_t size) {
//     fwrite(val, sizeof(char), size, file);
// }

struct gguf_buf {
    void * data;
    size_t size;
    size_t offset;
};

static struct gguf_buf gguf_buf_init(size_t size) {
    struct gguf_buf buf = {
        /*buf.data   =*/size == 0 ? NULL : malloc(size),
        /*buf.size   =*/size,
        /*buf.offset =*/0,
    };

    return buf;
}

static void gguf_buf_free(struct gguf_buf buf) {
    if (buf.data) {
        free(buf.data);
    }
}

static void gguf_buf_grow(struct gguf_buf * buf, size_t size) {
    if (buf->offset + size > buf->size) {
        buf->size = 1.5 * (buf->offset + size);
        if (buf->data) {
            buf->data = realloc(buf->data, buf->size);
        }
    }
}

static void gguf_bwrite_str(struct gguf_buf * buf, const struct gguf_str * val) {
    gguf_buf_grow(buf, sizeof(val->n) + val->n);

    if (buf->data) {
        memcpy((char *)buf->data + buf->offset, &val->n, sizeof(val->n));
    }
    buf->offset += sizeof(val->n);

    if (buf->data) {
        memcpy((char *)buf->data + buf->offset, val->data, val->n);
    }
    buf->offset += val->n;
}

static void gguf_bwrite_el(struct gguf_buf * buf, const void * val, size_t el_size) {
    gguf_buf_grow(buf, el_size);

    if (buf->data) {
        memcpy((char *)buf->data + buf->offset, val, el_size);
    }
    buf->offset += el_size;
}

static void gguf_write_to_buf(const struct gguf_context * ctx, struct gguf_buf * buf, bool only_meta) {
    // write header
    gguf_bwrite_el(buf, &ctx->header.magic, sizeof(ctx->header.magic));
    gguf_bwrite_el(buf, &ctx->header.version, sizeof(ctx->header.version));
    gguf_bwrite_el(buf, &ctx->header.n_tensors, sizeof(ctx->header.n_tensors));
    gguf_bwrite_el(buf, &ctx->header.n_kv, sizeof(ctx->header.n_kv));

    // write key-value pairs
    for (uint32_t i = 0; i < ctx->header.n_kv; ++i) {
        struct gguf_kv * kv = &ctx->kv[i];

        gguf_bwrite_str(buf, &kv->key);
        gguf_bwrite_el(buf, &kv->type, sizeof(kv->type));

        switch (kv->type) {
        case GGUF_TYPE_UINT8:
            gguf_bwrite_el(buf, &kv->value.uint8, sizeof(kv->value.uint8));
            break;
        case GGUF_TYPE_INT8:
            gguf_bwrite_el(buf, &kv->value.int8, sizeof(kv->value.int8));
            break;
        case GGUF_TYPE_UINT16:
            gguf_bwrite_el(buf, &kv->value.uint16, sizeof(kv->value.uint16));
            break;
        case GGUF_TYPE_INT16:
            gguf_bwrite_el(buf, &kv->value.int16, sizeof(kv->value.int16));
            break;
        case GGUF_TYPE_UINT32:
            gguf_bwrite_el(buf, &kv->value.uint32, sizeof(kv->value.uint32));
            break;
        case GGUF_TYPE_INT32:
            gguf_bwrite_el(buf, &kv->value.int32, sizeof(kv->value.int32));
            break;
        case GGUF_TYPE_FLOAT32:
            gguf_bwrite_el(buf, &kv->value.float32, sizeof(kv->value.float32));
            break;
        case GGUF_TYPE_UINT64:
            gguf_bwrite_el(buf, &kv->value.uint64, sizeof(kv->value.uint64));
            break;
        case GGUF_TYPE_INT64:
            gguf_bwrite_el(buf, &kv->value.int64, sizeof(kv->value.int64));
            break;
        case GGUF_TYPE_FLOAT64:
            gguf_bwrite_el(buf, &kv->value.float64, sizeof(kv->value.float64));
            break;
        case GGUF_TYPE_BOOL:
            gguf_bwrite_el(buf, &kv->value.bool_, sizeof(kv->value.bool_));
            break;
        case GGUF_TYPE_STRING:
            gguf_bwrite_str(buf, &kv->value.str);
            break;
        case GGUF_TYPE_ARRAY: {
            gguf_bwrite_el(buf, &kv->value.arr.type, sizeof(kv->value.arr.type));
            gguf_bwrite_el(buf, &kv->value.arr.n, sizeof(kv->value.arr.n));

            switch (kv->value.arr.type) {
            case GGUF_TYPE_UINT8:
            case GGUF_TYPE_INT8:
            case GGUF_TYPE_UINT16:
            case GGUF_TYPE_INT16:
            case GGUF_TYPE_UINT32:
            case GGUF_TYPE_INT32:
            case GGUF_TYPE_FLOAT32:
            case GGUF_TYPE_UINT64:
            case GGUF_TYPE_INT64:
            case GGUF_TYPE_FLOAT64:
            case GGUF_TYPE_BOOL: {
                gguf_bwrite_el(buf, kv->value.arr.data, kv->value.arr.n * GGUF_TYPE_SIZE[kv->value.arr.type]);
            } break;
            case GGUF_TYPE_STRING: {
                for (uint32_t j = 0; j < kv->value.arr.n; ++j) {
                    gguf_bwrite_str(buf, &((struct gguf_str *)kv->value.arr.data)[j]);
                }
            } break;
            case GGUF_TYPE_ARRAY:
            case GGUF_TYPE_COUNT:
                GGML_ASSERT(false && "invalid type");
                break;
            };
        } break;
        case GGUF_TYPE_COUNT:
            GGML_ASSERT(false && "invalid type");
        };
    }

    // write tensor infos
    for (uint32_t i = 0; i < ctx->header.n_tensors; ++i) {
        struct gguf_tensor_info * info = &ctx->infos[i];

        gguf_bwrite_str(buf, &info->name);
        gguf_bwrite_el(buf, &info->n_dims, sizeof(info->n_dims));
        for (uint32_t j = 0; j < info->n_dims; ++j) {
            gguf_bwrite_el(buf, &info->ne[j], sizeof(info->ne[j]));
        }
        gguf_bwrite_el(buf, &info->type, sizeof(info->type));
        gguf_bwrite_el(buf, &info->offset, sizeof(info->offset));
    }

    // we require the data section to be aligned, so take into account any padding
    {
        const size_t offset = buf->offset;
        const size_t offset_pad = GGML_PAD(offset, ctx->alignment);

        if (offset_pad != offset) {
            uint8_t pad = 0;
            for (size_t i = 0; i < offset_pad - offset; ++i) {
                gguf_bwrite_el(buf, &pad, sizeof(pad));
            }
        }
    }

    if (only_meta) {
        return;
    }

    size_t offset = 0;

    // write tensor data
    for (uint32_t i = 0; i < ctx->header.n_tensors; ++i) {
        struct gguf_tensor_info * info = &ctx->infos[i];

        const size_t size = info->size;
        const size_t size_pad = GGML_PAD(size, ctx->alignment);

        gguf_bwrite_el(buf, info->data, size);

        if (size_pad != size) {
            uint8_t pad = 0;
            for (size_t j = 0; j < size_pad - size; ++j) {
                gguf_bwrite_el(buf, &pad, sizeof(pad));
            }
        }

        GGML_ASSERT(offset == info->offset);

        offset += size_pad;
    }
}

void gguf_write_to_file(const struct gguf_context * ctx, const char * fname, bool only_meta) {
    FILE * file = fopen(fname, "wb");
    if (!file) {
        GGML_ASSERT(false && "failed to open file for writing");
    }

    struct gguf_buf buf = gguf_buf_init(16 * 1024);

    gguf_write_to_buf(ctx, &buf, only_meta);

    fwrite(buf.data, 1, buf.offset, file);

    gguf_buf_free(buf);

    fclose(file);
}

size_t gguf_get_meta_size(const struct gguf_context * ctx) {
    // no allocs - only compute size
    struct gguf_buf buf = gguf_buf_init(0);

    gguf_write_to_buf(ctx, &buf, true);

    return buf.offset;
}

void gguf_get_meta_data(const struct gguf_context * ctx, void * data) {
    struct gguf_buf buf = gguf_buf_init(16 * 1024);

    gguf_write_to_buf(ctx, &buf, true);

    memcpy(data, buf.data, buf.offset);

    gguf_buf_free(buf);
}

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

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

int ggml_cpu_has_neon(void) {
#if defined(__ARM_NEON)
    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_metal(void) {
#if defined(GGML_USE_METAL)
    return 1;
#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_blas(void) {
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST)
    return 1;
#else
    return 0;
#endif
}

int ggml_cpu_has_cublas(void) {
#if defined(GGML_USE_CUBLAS)
    return 1;
#else
    return 0;
#endif
}

int ggml_cpu_has_clblast(void) {
#if defined(GGML_USE_CLBLAST)
    return 1;
#else
    return 0;
#endif
}

int ggml_cpu_has_gpublas(void) { return ggml_cpu_has_cublas() || ggml_cpu_has_clblast(); }

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
}

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