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llama.cpp/ggml.c
unbounded 62cfc54f77
Add quantize-stats command for testing quantization (#728) 
Command that calculates some statistics over the errors introduced by
quantization, like mean square error, max error and some percentile errors for layer
weights. Should be useful for testing quantization improvements.

Exposes some internal state from ggml and llama for testing
2023-04-08 00:09:18 +02:00

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// Defines CLOCK_MONOTONIC and asprintf on Linux
#define _GNU_SOURCE
#include "ggml.h"
#if defined(_MSC_VER) || defined(__MINGW32__)
#include <malloc.h> // using malloc.h with MSC/MINGW
#elif !defined(__FreeBSD__) && !defined(__NetBSD__) && !defined(__OpenBSD__)
#include <alloca.h>
#endif
#include <assert.h>
#include <errno.h>
#include <time.h>
#include <math.h>
#include <stdlib.h>
#include <string.h>
#include <stdint.h>
#include <inttypes.h>
#include <stdio.h>
#include <float.h>
// if C99 - static_assert is noop
// ref: https://stackoverflow.com/a/53923785/4039976
#ifndef static_assert
#define static_assert(cond, msg) struct global_scope_noop_trick
#endif
#if defined _MSC_VER || defined(__MINGW32__)
#if !defined(__MINGW32__)
#include <Windows.h>
#else
// ref: https://github.com/ggerganov/whisper.cpp/issues/168
#include <windows.h>
#endif
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) {
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) {
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;
#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
#ifdef __HAIKU__
#define static_assert(cond, msg) _Static_assert(cond, msg)
#endif
#define GGML_MLOCK_SUPPORT 0
#ifdef __has_include
#if __has_include(<sys/mman.h>)
#undef GGML_MLOCK_SUPPORT
#define GGML_MLOCK_SUPPORT 1
#include <sys/mman.h>
#endif
#endif
/*#define GGML_PERF*/
#define GGML_DEBUG 0
#define GGML_GELU_FP16
#define GGML_SILU_FP16
#define GGML_SOFT_MAX_UNROLL 4
#define GGML_VEC_DOT_UNROLL 2
#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
#if UINTPTR_MAX == 0xFFFFFFFF
#define GGML_MEM_ALIGN 4
#else
#define GGML_MEM_ALIGN 16
#endif
#define UNUSED(x) (void)(x)
#define SWAP(x, y, T) do { T SWAP = x; x = y; y = SWAP; } while (0)
#define GGML_ASSERT(x) \
do { \
if (!(x)) { \
fprintf(stderr, "GGML_ASSERT: %s:%d: %s\n", __FILE__, __LINE__, #x); \
abort(); \
} \
} while (0)
#ifdef GGML_USE_ACCELERATE
#include <Accelerate/Accelerate.h>
#elif GGML_USE_OPENBLAS
#include <cblas.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
#include <immintrin.h>
#endif
#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 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];
// 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);
}
//
// timing
//
#if defined(_MSC_VER) || defined(__MINGW32__)
static int64_t timer_freq;
void ggml_time_init(void) {
LARGE_INTEGER frequency;
QueryPerformanceFrequency(&frequency);
timer_freq = frequency.QuadPart;
}
int64_t ggml_time_ms(void) {
LARGE_INTEGER t;
QueryPerformanceCounter(&t);
return (t.QuadPart * 1000) / timer_freq;
}
int64_t ggml_time_us(void) {
LARGE_INTEGER t;
QueryPerformanceCounter(&t);
return (t.QuadPart * 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 QK 32
// AVX routines provided by GH user Const-me
// ref: https://github.com/ggerganov/ggml/pull/27#issuecomment-1464934600
#if __AVX2__ || __AVX512F__
// Unpack 32 4-bit fields into 32 bytes
// The output vector contains 32 bytes, each one in [ 0 .. 15 ] interval
static inline __m256i bytesFromNibbles( const uint8_t* rsi )
{
// Load 16 bytes from memory
__m128i tmp = _mm_loadu_si128( ( const __m128i* )rsi );
// Expand bytes into uint16_t values
__m256i bytes = _mm256_cvtepu8_epi16( tmp );
// Unpack values into individual bytes
const __m256i lowMask = _mm256_set1_epi8( 0xF );
__m256i high = _mm256_andnot_si256( lowMask, bytes );
__m256i low = _mm256_and_si256( lowMask, bytes );
high = _mm256_slli_epi16( high, 4 );
bytes = _mm256_or_si256( low, high );
return bytes;
}
static inline __m128i packNibbles( __m256i bytes )
{
// Move bits within 16-bit lanes from 0000_abcd_0000_efgh into 0000_0000_abcd_efgh
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 );
}
#elif __AVX__
static inline __m128i bytesFromNibbles( const uint8_t* rsi )
{
// Load 8 bytes from memory
__m128i tmp = _mm_loadu_si64( ( const __m128i* )rsi );
// Expand bytes into uint16_t values
__m128i bytes = _mm_cvtepu8_epi16( tmp );
// Unpack values into individual bytes
const __m128i lowMask = _mm_set1_epi8( 0xF );
__m128i high = _mm_andnot_si128( lowMask, bytes );
__m128i low = _mm_and_si128( lowMask, bytes );
high = _mm_slli_epi16( high, 4 );
bytes = _mm_or_si128( low, high );
return bytes;
}
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
// method 5
// blocks of QK elements
// represented with a single float (delta) and QK/2 8-bit ints (i.e QK 4-bit signed integer factors)
typedef struct {
float d; // delta
uint8_t qs[QK / 2]; // nibbles / quants
} block_q4_0;
static_assert(sizeof(block_q4_0) == sizeof(float) + QK / 2, "wrong q4_0 block size/padding");
// method 4
// blocks of QK elements
// represented with 2 floats (delta + min) and QK/2 8-bit ints (i.e QK 4-bit unsigned integer factors)
typedef struct {
float d;
float m;
uint8_t qs[QK / 2]; // nibbles / quants
} block_q4_1;
static_assert(sizeof(block_q4_1) == sizeof(float) * 2 + QK / 2, "wrong q4_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) {
assert(k % QK == 0);
const int nb = k / QK;
uint8_t pp[QK/2];
for (int i = 0; i < nb; i++) {
float amax = 0.0f; // absolute max
for (int l = 0; l < QK; l++) {
const float v = x[i*QK + l];
amax = MAX(amax, fabsf(v));
}
const float d = amax / ((1 << 3) - 1);
const float id = d ? 1.0f/d : 0.0f;
y[i].d = d;
for (int l = 0; l < QK; l += 2) {
const float v0 = x[i*QK + l + 0]*id;
const float v1 = x[i*QK + l + 1]*id;
const uint8_t vi0 = (int8_t)roundf(v0) + 8;
const uint8_t vi1 = (int8_t)roundf(v1) + 8;
assert(vi0 < 16);
assert(vi1 < 16);
pp[l/2] = vi0 | (vi1 << 4);
}
memcpy(y[i].qs, pp, sizeof(pp));
}
}
static void quantize_row_q4_0(const float * restrict x, void * restrict vy, int k) {
assert(k % QK == 0);
const int nb = k / QK;
block_q4_0 * restrict y = vy;
#if defined(__POWER9_VECTOR__)
const vector float v85 = vec_splats(8.5f);
for (int i = 0; i < nb; i++) {
float amax = 0.0f; // absolute max
vector float srcv [8];
vector float asrcv[8];
vector float amaxv[8];
for (int l = 0; l < 8; l++) srcv[l] = *(vector float *)(x + i*32 + 4*l);
for (int l = 0; l < 8; l++) asrcv[l] = vec_abs(srcv[l]);
for (int l = 0; l < 4; l++) amaxv[2*l] = vec_max(asrcv[2*l], asrcv[2*l+1]);
//for (int l = 0; l < 2; l++) amaxv[4*l] = vec_max(amaxv[4*l], amaxv[4*l+2]);
amaxv[0] = vec_max(amaxv[0], amaxv[2]);
amaxv[4] = vec_max(amaxv[4], amaxv[6]);
//for (int l = 0; l < 1; l++) amaxv[8*l] = vec_max(amaxv[8*l], amaxv[8*l+4]);
amaxv[0] = vec_max(amaxv[0], amaxv[4]);
amax = MAX(
MAX(vec_extract(amaxv[0], 0), vec_extract(amaxv[0], 1)),
MAX(vec_extract(amaxv[0], 2), vec_extract(amaxv[0], 3)));
const float d = amax / ((1 << 3) - 1);
const float id = d ? 1.0/d : 0.0;
y[i].d = d;
const vector float vid = vec_splats(id);
uint8_t * restrict pb = y[i].qs;
for (int l = 0; l < 8; l++) {
const vector float vf = vec_madd(srcv[l], vid, v85);
const vector signed int vi = vec_signed(vf);
pb[2*l + 0] = vec_extract(vi, 0) | (vec_extract(vi, 1) << 4);
pb[2*l + 1] = vec_extract(vi, 2) | (vec_extract(vi, 3) << 4);
}
}
#elif __ARM_NEON
for (int i = 0; i < nb; i++) {
float32x4_t srcv [8];
float32x4_t asrcv[8];
float32x4_t amaxv[8];
for (int l = 0; l < 8; l++) srcv[l] = vld1q_f32(x + i*32 + 4*l);
for (int l = 0; l < 8; l++) asrcv[l] = vabsq_f32(srcv[l]);
for (int l = 0; l < 4; l++) amaxv[2*l] = vmaxq_f32(asrcv[2*l], asrcv[2*l+1]);
for (int l = 0; l < 2; l++) amaxv[4*l] = vmaxq_f32(amaxv[4*l], amaxv[4*l+2]);
for (int l = 0; l < 1; l++) amaxv[8*l] = vmaxq_f32(amaxv[8*l], amaxv[8*l+4]);
// absolute max
const float amax = MAX(
MAX(vgetq_lane_f32(amaxv[0], 0), vgetq_lane_f32(amaxv[0], 1)),
MAX(vgetq_lane_f32(amaxv[0], 2), vgetq_lane_f32(amaxv[0], 3)));
const float d = amax / ((1 << 3) - 1);
const float id = d ? 1.0f/d : 0.0f;
y[i].d = d;
for (int l = 0; l < 8; l++) {
const float32x4_t v = vmulq_n_f32(srcv[l], id);
const float32x4_t vf = vaddq_f32(v, vdupq_n_f32(8.5f));
const int32x4_t vi = vcvtq_s32_f32(vf);
y[i].qs[2*l + 0] = vgetq_lane_s32(vi, 0) | (vgetq_lane_s32(vi, 1) << 4);
y[i].qs[2*l + 1] = vgetq_lane_s32(vi, 2) | (vgetq_lane_s32(vi, 3) << 4);
}
}
#elif defined(__AVX2__)
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 / 7.0f;
y[i].d = d;
const float id = ( maxScalar != 0.0f ) ? 7.0f / 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 );
// 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 );
// Apply offset to translate the range from [ -7 .. +7 ] into [ +1 .. +15 ]
const __m256i off = _mm256_set1_epi8( 8 );
i0 = _mm256_add_epi8( i0, off );
// Compress the vector into 4 bit/value, and store
__m128i res = packNibbles( i0 );
_mm_storeu_si128( ( __m128i* )y[i].qs, res );
}
#elif 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 / 7.0f;
y[i].d = d;
const float id = ( maxScalar != 0.0f ) ? 7.0f / 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 );
// 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 );
// Apply offset to translate the range from [ -7 .. +7 ] into [ +1 .. +15 ]
const __m128i off = _mm_set1_epi8( 8);
ni0 = _mm_add_epi8( ni0, off );
ni4 = _mm_add_epi8( ni4, off );
// Compress the vector into 4 bit/value, and store
__m128i res = packNibbles( ni0, ni4 );
_mm_storeu_si128( ( __m128i* )y[i].qs, res );
}
#elif defined(__wasm_simd128__)
for (int i = 0; i < nb; i++) {
float amax = 0.0f; // absolute max
v128_t srcv [8];
v128_t asrcv[8];
v128_t amaxv[8];
for (int l = 0; l < 8; l++) srcv[l] = wasm_v128_load(x + i*32 + 4*l);
for (int l = 0; l < 8; l++) asrcv[l] = wasm_f32x4_abs(srcv[l]);
for (int l = 0; l < 4; l++) amaxv[2*l] = wasm_f32x4_max(asrcv[2*l], asrcv[2*l+1]);
for (int l = 0; l < 2; l++) amaxv[4*l] = wasm_f32x4_max(amaxv[4*l], amaxv[4*l+2]);
for (int l = 0; l < 1; l++) amaxv[8*l] = wasm_f32x4_max(amaxv[8*l], amaxv[8*l+4]);
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 << 3) - 1);
const float id = d ? 1.0/d : 0.0;
y[i].d = d;
for (int l = 0; l < 8; l++) {
const v128_t v = wasm_f32x4_mul(srcv[l], wasm_f32x4_splat(id));
const v128_t vf = wasm_f32x4_add(v, wasm_f32x4_splat(8.5f));
const v128_t vi = wasm_i32x4_trunc_sat_f32x4(vf);
y[i].qs[2*l + 0] = wasm_i32x4_extract_lane(vi, 0) | (wasm_i32x4_extract_lane(vi, 1) << 4);
y[i].qs[2*l + 1] = wasm_i32x4_extract_lane(vi, 2) | (wasm_i32x4_extract_lane(vi, 3) << 4);
}
}
#else
// scalar
quantize_row_q4_0_reference(x, y, k);
#endif
}
static void quantize_row_q4_1_reference(const float * restrict x, void * restrict vy, int k) {
assert(k % QK == 0);
const int nb = k / QK;
block_q4_1 * restrict y = vy;
uint8_t pp[QK/2];
for (int i = 0; i < nb; i++) {
float min = FLT_MAX;
float max = -FLT_MAX;
for (int l = 0; l < QK; l++) {
const float v = x[i*QK + l];
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 = d;
y[i].m = min;
for (int l = 0; l < QK; l += 2) {
const float v0 = (x[i*QK + l + 0] - min)*id;
const float v1 = (x[i*QK + l + 1] - min)*id;
const uint8_t vi0 = roundf(v0);
const uint8_t vi1 = roundf(v1);
assert(vi0 < 16);
assert(vi1 < 16);
pp[l/2] = vi0 | (vi1 << 4);
}
memcpy(y[i].qs, pp, sizeof(pp));
}
}
static void quantize_row_q4_1(const float * restrict x, void * restrict vy, int k) {
assert(k % QK == 0);
const int nb = k / QK;
block_q4_1 * restrict y = vy;
#if defined(__AVX2__)
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 for the block
__m256 vmax;
vmax = _mm256_max_ps( v0, v1 );
vmax = _mm256_max_ps( vmax, v2 );
vmax = _mm256_max_ps( vmax, v3 );
__m128 max4 = _mm_max_ps( _mm256_extractf128_ps( vmax, 1 ), _mm256_castps256_ps128( vmax ) );
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 );
// Compute min for the block
__m256 vmin;
vmin = _mm256_min_ps( v0, v1 );
vmin = _mm256_min_ps( vmin, v2 );
vmin = _mm256_min_ps( vmin, v3 );
__m128 min4 = _mm_min_ps( _mm256_extractf128_ps( vmin, 1 ), _mm256_castps256_ps128( vmin ) );
min4 = _mm_min_ps( min4, _mm_movehl_ps( min4, min4 ) );
min4 = _mm_min_ss( min4, _mm_movehdup_ps( min4 ) );
const float minScalar = _mm_cvtss_f32( min4 );
// Quantize these floats
const float d = (maxScalar - minScalar) / ((1 << 4) - 1);
const float id = d ? 1.0f/d : 0.0f;
y[i].m = minScalar;
y[i].d = d;
// x = (x-min)*id
const __m256 mul = _mm256_set1_ps( id );
const __m256 off = _mm256_set1_ps( minScalar );
v0 = _mm256_mul_ps( _mm256_sub_ps( v0, off ), mul );
v1 = _mm256_mul_ps( _mm256_sub_ps( v1, off ), mul );
v2 = _mm256_mul_ps( _mm256_sub_ps( v2, off ), mul );
v3 = _mm256_mul_ps( _mm256_sub_ps( v3, off ), 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 );
// 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 );
// Compress the vector into 4 bit/value, and store
__m128i res = packNibbles( i0 );
_mm_storeu_si128( ( __m128i* )y[i].qs, res );
}
#elif __ARM_NEON
for (int i = 0; i < nb; i++) {
float32x4_t srcv[8];
float32x4_t minv[8];
float32x4_t maxv[8];
for (int l = 0; l < 8; l++) srcv[l] = vld1q_f32(x + i*32 + 4*l);
for (int l = 0; l < 4; l++) minv[2*l] = vminq_f32(srcv[2*l], srcv[2*l + 1]);
for (int l = 0; l < 2; l++) minv[4*l] = vminq_f32(minv[4*l], minv[4*l + 2]);
for (int l = 0; l < 1; l++) minv[8*l] = vminq_f32(minv[8*l], minv[8*l + 4]);
for (int l = 0; l < 4; l++) maxv[2*l] = vmaxq_f32(srcv[2*l], srcv[2*l + 1]);
for (int l = 0; l < 2; l++) maxv[4*l] = vmaxq_f32(maxv[4*l], maxv[4*l + 2]);
for (int l = 0; l < 1; l++) maxv[8*l] = vmaxq_f32(maxv[8*l], maxv[8*l + 4]);
const float min = vminvq_f32(minv[0]);
const float max = vmaxvq_f32(maxv[0]);
const float d = (max - min) / ((1 << 4) - 1);
const float id = d ? 1.0f/d : 0.0f;
y[i].d = d;
y[i].m = min;
const float32x4_t minv0 = vdupq_n_f32(min);
for (int l = 0; l < 8; l++) {
const float32x4_t v = vmulq_n_f32(vsubq_f32(srcv[l], minv0), id);
const int32x4_t vi = vcvtq_s32_f32(v);
y[i].qs[2*l + 0] = vgetq_lane_s32(vi, 0) | (vgetq_lane_s32(vi, 1) << 4);
y[i].qs[2*l + 1] = vgetq_lane_s32(vi, 2) | (vgetq_lane_s32(vi, 3) << 4);
}
}
#else
// scalar
quantize_row_q4_1_reference(x, vy, k);
#endif
}
static void dequantize_row_q4_0(const void * restrict vx, float * restrict y, int k) {
assert(k % QK == 0);
const int nb = k / QK;
const block_q4_0 * restrict x = vx;
#if defined(__AVX2__)
for (int i = 0; i < nb; i++) {
// scale factor
const __m256 d_v = _mm256_broadcast_ss(&x[i].d);
const uint8_t * restrict pp = x[i].qs;
for (int l = 0; l < QK; l += 32) {
// Load 32x4-bit integers into 32x8-bit integers
__m256i vx8 = bytesFromNibbles(pp+l/2);
// Subtract 8 from the integers
vx8 = _mm256_sub_epi8(vx8, _mm256_set1_epi8(8));
// Convert to 16-bit int
const __m256i vx16_lo = _mm256_cvtepi8_epi16(_mm256_extracti128_si256(vx8, 0));
const __m256i vx16_hi = _mm256_cvtepi8_epi16(_mm256_extracti128_si256(vx8, 1));
// Convert to 32-bit int -> float 32
const __m256 vf[4] = {
_mm256_cvtepi32_ps(_mm256_cvtepi16_epi32(_mm256_extracti128_si256(vx16_lo, 0))),
_mm256_cvtepi32_ps(_mm256_cvtepi16_epi32(_mm256_extracti128_si256(vx16_lo, 1))),
_mm256_cvtepi32_ps(_mm256_cvtepi16_epi32(_mm256_extracti128_si256(vx16_hi, 0))),
_mm256_cvtepi32_ps(_mm256_cvtepi16_epi32(_mm256_extracti128_si256(vx16_hi, 1)))
};
// Scale and store
for (int j = 0; j < 4; j++) {
const __m256 result = _mm256_mul_ps(vf[j], d_v);
_mm256_storeu_ps(y + i * QK + l + j*8, result);
}
}
}
#elif defined(__ARM_NEON)
for (int i = 0; i < nb; i++) {
const float32x4_t vd = vdupq_n_f32(x[i].d);
const uint8_t * restrict pp = x[i].qs;
for (int l = 0; l < QK; l += 16) {
// Load 16x4-bit integers into 8x8-bit integers
const uint8x8_t v8 = vld1_u8(pp + l/2);
// Expand 4-bit qs to 8-bit bytes
const uint8x8_t v0 = vand_u8(v8, vdup_n_u8(0x0f));
const uint8x8_t v1 = vshr_n_u8(v8, 4);
// Convert to signed 8-bit integers
const int8x8_t vs_0 = vreinterpret_s8_u8(v0);
const int8x8_t vs_1 = vreinterpret_s8_u8(v1);
// Subtract 8 from each byte
const int8x8_t vb_0 = vsub_s8(vs_0, vdup_n_s8(8));
const int8x8_t vb_1 = vsub_s8(vs_1, vdup_n_s8(8));
// Interleave and combine
const int8x8_t vx_0 = vzip1_s8(vb_0, vb_1);
const int8x8_t vx_1 = vzip2_s8(vb_0, vb_1);
const int8x16_t vq = vcombine_s8(vx_0, vx_1);
// convert to 2x int16x8_t
const int16x8_t vi_0 = vmovl_s8(vget_low_s8 (vq));
const int16x8_t vi_1 = vmovl_s8(vget_high_s8(vq));
// convert to 4x float32x4_t
const float32x4_t vf_0 = vcvtq_f32_s32(vmovl_s16(vget_low_s16 (vi_0)));
const float32x4_t vf_1 = vcvtq_f32_s32(vmovl_s16(vget_high_s16(vi_0)));
const float32x4_t vf_2 = vcvtq_f32_s32(vmovl_s16(vget_low_s16 (vi_1)));
const float32x4_t vf_3 = vcvtq_f32_s32(vmovl_s16(vget_high_s16(vi_1)));
// Multiply by d
const float32x4_t r0 = vmulq_f32(vf_0, vd);
const float32x4_t r1 = vmulq_f32(vf_1, vd);
const float32x4_t r2 = vmulq_f32(vf_2, vd);
const float32x4_t r3 = vmulq_f32(vf_3, vd);
// Store
vst1q_f32(y + i*QK + l + 0, r0);
vst1q_f32(y + i*QK + l + 4, r1);
vst1q_f32(y + i*QK + l + 8, r2);
vst1q_f32(y + i*QK + l + 12, r3);
}
}
#else
// scalar
for (int i = 0; i < nb; i++) {
const float d = x[i].d;
const uint8_t * restrict pp = x[i].qs;
for (int l = 0; l < QK; l += 2) {
const uint8_t vi = pp[l/2];
const int8_t vi0 = vi & 0xf;
const int8_t vi1 = vi >> 4;
const float v0 = (vi0 - 8)*d;
const float v1 = (vi1 - 8)*d;
//printf("d = %f, vi = %d, vi0 = %d, vi1 = %d, v0 = %f, v1 = %f\n", d, vi, vi0, vi1, v0, v1);
y[i*QK + l + 0] = v0;
y[i*QK + l + 1] = v1;
assert(!isnan(y[i*QK + l + 0]));
assert(!isnan(y[i*QK + l + 1]));
}
}
#endif
}
static void dequantize_row_q4_1(const void * restrict vx, float * restrict y, int k) {
assert(k % QK == 0);
const int nb = k / QK;
const block_q4_1 * restrict x = vx;
#if defined(__AVX2__)
for (int i = 0; i < nb; i++) {
const __m256 d_v = _mm256_broadcast_ss(&x[i].d);
const __m256 d_m = _mm256_broadcast_ss(&x[i].m);
const uint8_t * restrict pp = x[i].qs;
for (int l = 0; l < QK; l += 32) {
// Load 32x4-bit integers into 32x8-bit integers
__m256i vx8 = bytesFromNibbles(pp+l/2);
// Convert to 16-bit int
const __m256i vx16_lo = _mm256_cvtepi8_epi16(_mm256_extracti128_si256(vx8, 0));
const __m256i vx16_hi = _mm256_cvtepi8_epi16(_mm256_extracti128_si256(vx8, 1));
// Convert to 32-bit int -> float 32
const __m256 vf[4] = {
_mm256_cvtepi32_ps(_mm256_cvtepi16_epi32(_mm256_extracti128_si256(vx16_lo, 0))),
_mm256_cvtepi32_ps(_mm256_cvtepi16_epi32(_mm256_extracti128_si256(vx16_lo, 1))),
_mm256_cvtepi32_ps(_mm256_cvtepi16_epi32(_mm256_extracti128_si256(vx16_hi, 0))),
_mm256_cvtepi32_ps(_mm256_cvtepi16_epi32(_mm256_extracti128_si256(vx16_hi, 1)))
};
// Scale, add m and store
for (int j = 0; j < 4; j++) {
const __m256 result = _mm256_add_ps(_mm256_mul_ps(vf[j], d_v), d_m);
_mm256_storeu_ps(y + i * QK + l + j*8, result);
}
}
}
#elif defined(__ARM_NEON)
for (int i = 0; i < nb; i++) {
const float32x4_t vd = vdupq_n_f32(x[i].d);
const float32x4_t vm = vdupq_n_f32(x[i].m);
const uint8_t * restrict pp = x[i].qs;
for (int l = 0; l < QK; l += 16) {
// Load 16x4-bit integers into 8x8-bit integers
const uint8x8_t v8 = vld1_u8(pp + l/2);
// Expand 4-bit qs to 8-bit bytes
const uint8x8_t v0 = vand_u8(v8, vdup_n_u8(0x0f));
const uint8x8_t v1 = vshr_n_u8(v8, 4);
// Interleave and combine
const uint8x8_t vx_0 = vzip1_u8(v0, v1);
const uint8x8_t vx_1 = vzip2_u8(v0, v1);
const uint8x16_t vq = vcombine_u8(vx_0, vx_1);
// convert to 2x uint16x8_t
const uint16x8_t vi_0 = vmovl_u8(vget_low_u8 (vq));
const uint16x8_t vi_1 = vmovl_u8(vget_high_u8(vq));
// convert to 4x float32x4_t
const float32x4_t vf_0 = vcvtq_f32_u32(vmovl_u16(vget_low_u16 (vi_0)));
const float32x4_t vf_1 = vcvtq_f32_u32(vmovl_u16(vget_high_u16(vi_0)));
const float32x4_t vf_2 = vcvtq_f32_u32(vmovl_u16(vget_low_u16 (vi_1)));
const float32x4_t vf_3 = vcvtq_f32_u32(vmovl_u16(vget_high_u16(vi_1)));
// multiply by d and add m
const float32x4_t r0 = vmlaq_f32(vm, vf_0, vd);
const float32x4_t r1 = vmlaq_f32(vm, vf_1, vd);
const float32x4_t r2 = vmlaq_f32(vm, vf_2, vd);
const float32x4_t r3 = vmlaq_f32(vm, vf_3, vd);
// Store
vst1q_f32(y + i*QK + l + 0, r0);
vst1q_f32(y + i*QK + l + 4, r1);
vst1q_f32(y + i*QK + l + 8, r2);
vst1q_f32(y + i*QK + l + 12, r3);
}
}
#else
for (int i = 0; i < nb; i++) {
const float d = x[i].d;
const float m = x[i].m;
const uint8_t * restrict pp = x[i].qs;
for (int l = 0; l < QK; l += 2) {
const uint8_t vi = pp[l/2];
const int8_t vi0 = vi & 0xf;
const int8_t vi1 = vi >> 4;
const float v0 = vi0*d + m;
const float v1 = vi1*d + m;
y[i*QK + l + 0] = v0;
y[i*QK + l + 1] = v1;
assert(!isnan(y[i*QK + l + 0]));
assert(!isnan(y[i*QK + l + 1]));
}
}
#endif
}
//
// 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
#if defined(__ARM_FEATURE_QRDMX)
#define GGML_F32x4_REDUCE_ONE(x) vaddvq_f32(x)
#else
#define GGML_F32x4_REDUCE_ONE(x) \
(vgetq_lane_f32(x, 0) + \
vgetq_lane_f32(x, 1) + \
vgetq_lane_f32(x, 2) + \
vgetq_lane_f32(x, 3))
#endif
#define GGML_F32x4_REDUCE(res, x) \
{ \
for (int i = 0; i < GGML_F32_ARR/2; ++i) { \
x[2*i] = vaddq_f32(x[2*i], x[2*i+1]); \
} \
for (int i = 0; i < GGML_F32_ARR/4; ++i) { \
x[4*i] = vaddq_f32(x[4*i], x[4*i+2]); \
} \
for (int i = 0; i < GGML_F32_ARR/8; ++i) { \
x[8*i] = vaddq_f32(x[8*i], x[8*i+4]); \
} \
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) \
{ \
for (int i = 0; i < GGML_F16_ARR/2; ++i) { \
x[2*i] = vaddq_f16(x[2*i], x[2*i+1]); \
} \
for (int i = 0; i < GGML_F16_ARR/4; ++i) { \
x[4*i] = vaddq_f16(x[4*i], x[4*i+2]); \
} \
for (int i = 0; i < GGML_F16_ARR/8; ++i) { \
x[8*i] = vaddq_f16(x[8*i], x[8*i+4]); \
} \
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) \
{ \
for (int i = 0; i < GGML_F32_ARR/2; ++i) { \
x[2*i] = _mm256_add_ps(x[2*i], x[2*i+1]); \
} \
for (int i = 0; i < GGML_F32_ARR/4; ++i) { \
x[4*i] = _mm256_add_ps(x[4*i], x[4*i+2]); \
} \
for (int i = 0; i < GGML_F32_ARR/8; ++i) { \
x[8*i] = _mm256_add_ps(x[8*i], x[8*i+4]); \
} \
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) \
{ \
for (int i = 0; i < GGML_F32_ARR/2; ++i) { \
x[2*i] = vec_add(x[2*i], x[2*i+1]); \
} \
for (int i = 0; i < GGML_F32_ARR/4; ++i) { \
x[4*i] = vec_add(x[4*i], x[4*i+2]); \
} \
for (int i = 0; i < GGML_F32_ARR/8; ++i) { \
x[8*i] = vec_add(x[8*i], x[8*i+4]); \
} \
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) \
{ \
for (int i = 0; i < GGML_F32_ARR/2; ++i) { \
x[2*i] = wasm_f32x4_add(x[2*i], x[2*i+1]); \
} \
for (int i = 0; i < GGML_F32_ARR/4; ++i) { \
x[4*i] = wasm_f32x4_add(x[4*i], x[4*i+2]); \
} \
for (int i = 0; i < GGML_F32_ARR/8; ++i) { \
x[8*i] = wasm_f32x4_add(x[8*i], x[8*i+4]); \
} \
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) \
{ \
for (int i = 0; i < GGML_F16_ARR/2; ++i) { \
x[2*i] = wasm_f32x4_add(x[2*i], x[2*i+1]); \
} \
for (int i = 0; i < GGML_F16_ARR/4; ++i) { \
x[4*i] = wasm_f32x4_add(x[4*i], x[4*i+2]); \
} \
for (int i = 0; i < GGML_F16_ARR/8; ++i) { \
x[8*i] = wasm_f32x4_add(x[8*i], x[8*i+4]); \
} \
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) \
{ \
for (int i = 0; i < GGML_F32_ARR/2; ++i) { \
x[2*i] = _mm_add_ps(x[2*i], x[2*i+1]); \
} \
for (int i = 0; i < GGML_F32_ARR/4; ++i) { \
x[4*i] = _mm_add_ps(x[4*i], x[4*i+2]); \
} \
for (int i = 0; i < GGML_F32_ARR/8; ++i) { \
x[8*i] = _mm_add_ps(x[8*i], x[8*i+4]); \
} \
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_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]; }
inline 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;
}
#if __AVX512F__ && QK == 32
static inline __m512 dot_q4_0_oneblock_avx512(
__m512 acc,
const block_q4_0 * restrict x,
const block_q4_0 * restrict y,
int i
) {
// Compute combined scale for the block
__m512 d = _mm512_set1_ps( x[i].d * y[i].d );
__m256i bx = bytesFromNibbles( x[i].qs );
__m256i by = bytesFromNibbles( y[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 );
by = _mm256_sub_epi8( by, off );
// Sign-extend 16 signed bytes into int16_t
__m512i x32 = _mm512_cvtepi8_epi16( bx );
__m512i y32 = _mm512_cvtepi8_epi16( by );
// Compute products of int16_t integers, add pairwise
__m512i i64 = _mm512_madd_epi16( x32, y32 );
// Convert int32_t to float
__m512 p = _mm512_cvtepi32_ps( i64 );
// Apply the scale, and accumulate
return _mm512_fmadd_ps( d, p, acc );
}
#endif
inline 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(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) {
const int nb = n / QK;
assert(n % QK == 0);
assert(nb % 2 == 0);
const block_q4_0 * restrict x = vx;
const block_q4_0 * restrict y = vy;
float sumf = 0.0;
#if defined(__ARM_NEON)
float sum0 = 0.0f;
float sum1 = 0.0f;
for (int i = 0; i < nb; i += 2) {
const block_q4_0 * restrict x0 = &x[i + 0];
const block_q4_0 * restrict y0 = &y[i + 0];
const block_q4_0 * restrict x1 = &x[i + 1];
const block_q4_0 * restrict y1 = &y[i + 1];
const uint8x16_t m4b = vdupq_n_u8(0xf);
const int8x16_t s8b = vdupq_n_s8(0x8);
const uint8x16_t v0_0 = vld1q_u8(x0->qs);
const uint8x16_t v1_0 = vld1q_u8(y0->qs);
const uint8x16_t v0_1 = vld1q_u8(x1->qs);
const uint8x16_t v1_1 = vld1q_u8(y1->qs);
// 4-bit -> 8-bit
const int8x16_t v0_0l = vreinterpretq_s8_u8(vandq_u8(v0_0, m4b));
const int8x16_t v1_0l = vreinterpretq_s8_u8(vandq_u8(v1_0, m4b));
const int8x16_t v0_0h = vreinterpretq_s8_u8(vshrq_n_u8(v0_0, 4));
const int8x16_t v1_0h = vreinterpretq_s8_u8(vshrq_n_u8(v1_0, 4));
const int8x16_t v0_1l = vreinterpretq_s8_u8(vandq_u8(v0_1, m4b));
const int8x16_t v1_1l = vreinterpretq_s8_u8(vandq_u8(v1_1, m4b));
const int8x16_t v0_1h = vreinterpretq_s8_u8(vshrq_n_u8(v0_1, 4));
const int8x16_t v1_1h = vreinterpretq_s8_u8(vshrq_n_u8(v1_1, 4));
// sub 8
const int8x16_t v0_0ls = vsubq_s8(v0_0l, s8b);
const int8x16_t v1_0ls = vsubq_s8(v1_0l, s8b);
const int8x16_t v0_0hs = vsubq_s8(v0_0h, s8b);
const int8x16_t v1_0hs = vsubq_s8(v1_0h, s8b);
const int8x16_t v0_1ls = vsubq_s8(v0_1l, s8b);
const int8x16_t v1_1ls = vsubq_s8(v1_1l, s8b);
const int8x16_t v0_1hs = vsubq_s8(v0_1h, s8b);
const int8x16_t v1_1hs = vsubq_s8(v1_1h, s8b);
#if defined(__ARM_FEATURE_DOTPROD)
// dot product into int16x8_t
int32x4_t p_0 = vdotq_s32(vdupq_n_s32(0), v0_0ls, v1_0ls);
int32x4_t p_1 = vdotq_s32(vdupq_n_s32(0), v0_1ls, v1_1ls);
p_0 = vdotq_s32(p_0, v0_0hs, v1_0hs);
p_1 = vdotq_s32(p_1, v0_1hs, v1_1hs);
// scalar
#if defined(__ARM_FEATURE_QRDMX)
sum0 += x0->d * y0->d * vaddvq_s32(p_0);
sum1 += x1->d * y1->d * vaddvq_s32(p_1);
#else
sum0 += x0->d * y0->d * (vgetq_lane_s32(p_0, 0) + vgetq_lane_s32(p_0, 1) + vgetq_lane_s32(p_0, 2) + vgetq_lane_s32(p_0, 3));
sum1 += x1->d * y1->d * (vgetq_lane_s32(p_1, 0) + vgetq_lane_s32(p_1, 1) + vgetq_lane_s32(p_1, 2) + vgetq_lane_s32(p_1, 3));
#endif
#else
const int16x8_t pl0l = vmull_s8(vget_low_s8 (v0_0ls), vget_low_s8 (v1_0ls));
const int16x8_t pl0h = vmull_s8(vget_high_s8(v0_0ls), vget_high_s8(v1_0ls));
const int16x8_t ph0l = vmull_s8(vget_low_s8 (v0_0hs), vget_low_s8 (v1_0hs));
const int16x8_t ph0h = vmull_s8(vget_high_s8(v0_0hs), vget_high_s8(v1_0hs));
const int16x8_t pl1l = vmull_s8(vget_low_s8 (v0_1ls), vget_low_s8 (v1_1ls));
const int16x8_t pl1h = vmull_s8(vget_high_s8(v0_1ls), vget_high_s8(v1_1ls));
const int16x8_t ph1l = vmull_s8(vget_low_s8 (v0_1hs), vget_low_s8 (v1_1hs));
const int16x8_t ph1h = vmull_s8(vget_high_s8(v0_1hs), vget_high_s8(v1_1hs));
const int16x8_t pl_0 = vaddq_s16(pl0l, pl0h);
const int16x8_t ph_0 = vaddq_s16(ph0l, ph0h);
const int16x8_t pl_1 = vaddq_s16(pl1l, pl1h);
const int16x8_t ph_1 = vaddq_s16(ph1l, ph1h);
const int16x8_t p_0 = vaddq_s16(pl_0, ph_0);
const int16x8_t p_1 = vaddq_s16(pl_1, ph_1);
// scalar
#if defined(__ARM_FEATURE_QRDMX)
sum0 += x0->d * y0->d * vaddvq_s16(p_0);
sum1 += x1->d * y1->d * vaddvq_s16(p_1);
#else
sum0 += x0->d * y0->d * (vgetq_lane_s16(p_0, 0) + vgetq_lane_s16(p_0, 1) + vgetq_lane_s16(p_0, 2) + vgetq_lane_s16(p_0, 3) + vgetq_lane_s16(p_0, 4) + vgetq_lane_s16(p_0, 5) + vgetq_lane_s16(p_0, 6) + vgetq_lane_s16(p_0, 7));
sum1 += x1->d * y1->d * (vgetq_lane_s16(p_1, 0) + vgetq_lane_s16(p_1, 1) + vgetq_lane_s16(p_1, 2) + vgetq_lane_s16(p_1, 3) + vgetq_lane_s16(p_1, 4) + vgetq_lane_s16(p_1, 5) + vgetq_lane_s16(p_1, 6) + vgetq_lane_s16(p_1, 7));
#endif
#endif
}
sumf = sum0 + sum1;
#elif defined(__AVX512F__)
// Initialize accumulator with zeros
__m512 acc0 = _mm512_setzero_ps();
__m512 acc1 = _mm512_setzero_ps();
const int superblock_size = 8;
const int superblock_count = nb / superblock_size;
for (int superblock_ix = 0; superblock_ix < superblock_count; superblock_ix += 1) {
int i = superblock_ix * superblock_size;
acc0 = dot_q4_0_oneblock_avx512( acc0, x, y, i+0 );
acc1 = dot_q4_0_oneblock_avx512( acc1, x, y, i+1 );
acc0 = dot_q4_0_oneblock_avx512( acc0, x, y, i+2 );
acc1 = dot_q4_0_oneblock_avx512( acc1, x, y, i+3 );
acc0 = dot_q4_0_oneblock_avx512( acc0, x, y, i+4 );
acc1 = dot_q4_0_oneblock_avx512( acc1, x, y, i+5 );
acc0 = dot_q4_0_oneblock_avx512( acc0, x, y, i+6 );
acc1 = dot_q4_0_oneblock_avx512( acc1, x, y, i+7 );
}
// Remainders
for (int i = superblock_count * superblock_size; i < nb; ++i) {
acc0 = dot_q4_0_oneblock_avx512( acc0, x, y, i );
}
// Horizontal sum of all lanes of the accumulator
sumf = _mm512_reduce_add_ps( acc0 ) + _mm512_reduce_add_ps( acc1 );
#elif defined(__AVX2__)
// Initialize accumulator with zeros
__m256 acc = _mm256_setzero_ps();
/* Prepare the constants we will need during execution */
const __m256i lowMask = _mm256_set1_epi8( 0xF );
const __m256i offset_8 = _mm256_set1_epi16( 8 );
#define UNROLL_COUNT 8
// make sure we only unroll multiples of the block count
assert(nb % UNROLL_COUNT == 0);
// Main loop
for (int i = 0; i < nb; i+=UNROLL_COUNT) {
// This loop will be unrolled by the compiler
for (int u=0;u<UNROLL_COUNT;u++) {
/* Compute combined scale for the block */
const __m256 scale = _mm256_mul_ps(
_mm256_broadcast_ss( &x[i+u].d ),
_mm256_broadcast_ss( &y[i+u].d ) );
/* get input from x
Input: 32 Nibbles (16 bytes) at *x[i+u]
Output: 2 vectors with 16 values of type int16_t (x_high_q, x_low_q) */
/* Load 16 bytes from memory */
const __m128i tmp_x = _mm_loadu_si128( ( const __m128i* ) x[i+u].qs);
/* Expand bytes into uint16_t values */
const __m256i bytes_x = _mm256_cvtepu8_epi16(tmp_x);
/* Unpack values into individual bytes */
__m256i x_low_q = _mm256_and_si256( lowMask, bytes_x );
const __m256i pre_shift_x_high_q = _mm256_andnot_si256( lowMask, bytes_x );
__m256i x_high_q = _mm256_srli_epi16( pre_shift_x_high_q, 4 );
/* Now we have two vectors with bytes in [ 0 .. 15 ] interval. Offset them into [ -8 .. +7 ] interval. */
x_high_q = _mm256_sub_epi16( x_high_q, offset_8 );
x_low_q = _mm256_sub_epi16( x_low_q, offset_8 );
/* get input from y
Input: 32 Nibbles (16 bytes) at *y[i+u]
Output: 2 vectors with 16 values of type int16_t (y_high_q, y_low_q) */
/* Load 16 bytes from memory */
const __m128i tmp_y = _mm_loadu_si128( (const __m128i* ) y[i+u].qs);
/* Expand bytes into uint16_t values */
const __m256i bytes_y = _mm256_cvtepu8_epi16(tmp_y);
/* Unpack values into individual bytes */
const __m256i pre_shift_y_high_q = _mm256_andnot_si256( lowMask, bytes_y );
__m256i y_high_q = _mm256_srli_epi16( pre_shift_y_high_q, 4 );
__m256i y_low_q = _mm256_and_si256( lowMask, bytes_y );
/* Now we have two vectors with bytes in [ 0 .. 15 ] interval. Offset them into [ -8 .. +7 ] interval. */
y_high_q = _mm256_sub_epi16( y_high_q, offset_8 );
y_low_q = _mm256_sub_epi16( y_low_q, offset_8 );
/* Compute products of int16_t integers, add pairwise, store as int32_t */
__m256i xy_high_q = _mm256_madd_epi16( x_high_q, y_high_q );
__m256i xy_low_q = _mm256_madd_epi16( x_low_q, y_low_q );
/* Accumulate the products of int32_t integers -> we now have a vector of 8 int_32t */
__m256i xy_q = _mm256_add_epi32( xy_high_q, xy_low_q );
/* Convert to vectore of 8 int32_t to 8 floats */
__m256 q = _mm256_cvtepi32_ps( xy_q );
/* Multiply q with scale and accumulate */
acc = _mm256_fmadd_ps( scale, q, acc );
}
}
// Return horizontal sum of the acc vector
__m128 res = _mm256_extractf128_ps( acc, 1 );
res = _mm_add_ps( res, _mm256_castps256_ps128( acc ) );
res = _mm_add_ps( res, _mm_movehl_ps( res, res ) );
res = _mm_add_ss( res, _mm_movehdup_ps( res ) );
sumf = _mm_cvtss_f32( res );
#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_mul_ps( _mm256_broadcast_ss( &x[i].d ), _mm256_broadcast_ss( &y[i].d ) );
__m128i i32[2];
for (int j = 0; j < 2; ++j) {
// Load 8 bytes, and unpack 4 bit fields into bytes, making 16 bytes
__m128i bx = bytesFromNibbles( x[i].qs + 8*j );
__m128i by = bytesFromNibbles( y[i].qs + 8*j );
// Now we have a vector with bytes in [ 0 .. 15 ] interval. Offset them into [ -8 .. +7 ] interval.
const __m128i off = _mm_set1_epi8( 8 );
bx = _mm_sub_epi8( bx, off );
by = _mm_sub_epi8( by, off );
// Get absolute values of x vectors
const __m128i ax = _mm_sign_epi8(bx, bx);
// Sign the values of the y vectors
const __m128i sy = _mm_sign_epi8(by, bx);
// Perform multiplication and create 16-bit values
const __m128i dot = _mm_maddubs_epi16(ax, sy);
const __m128i ones = _mm_set1_epi16(1);
i32[j] = _mm_madd_epi16(ones, dot);
}
// 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);
}
// Return horizontal sum of the acc vector
__m128 res = _mm256_extractf128_ps( acc, 1 );
res = _mm_add_ps( res, _mm256_castps256_ps128( acc ) );
res = _mm_add_ps( res, _mm_movehl_ps( res, res ) );
res = _mm_add_ss( res, _mm_movehdup_ps( res ) );
sumf = _mm_cvtss_f32( res );
#elif defined(__wasm_simd128__)
// wasm simd
float sum0 = 0.0f;
float sum1 = 0.0f;
for (int i = 0; i < nb; i += 2) {
const block_q4_0 * restrict x0 = &px[i + 0];
const block_q4_0 * restrict y0 = &py[i + 0];
const block_q4_0 * restrict x1 = &px[i + 1];
const block_q4_0 * restrict y1 = &py[i + 1];
const v128_t m4b = wasm_u8x16_splat(0xf);
const v128_t s8b = wasm_i8x16_splat(0x8);
const v128_t v0_0 = wasm_v128_load(x0.qs);
const v128_t v0_1 = wasm_v128_load(y0.qs);
const v128_t v1_0 = wasm_v128_load(x1.qs);
const v128_t v1_1 = wasm_v128_load(y1.qs);
// 4-bit -> 8-bit
const v128_t v0_0l = wasm_v128_and(v0_0, m4b);
const v128_t v1_0l = wasm_v128_and(v1_0, m4b);
const v128_t v0_0h = wasm_u8x16_shr(v0_0, 4);
const v128_t v1_0h = wasm_u8x16_shr(v1_0, 4);
const v128_t v0_1l = wasm_v128_and(v0_1, m4b);
const v128_t v1_1l = wasm_v128_and(v1_1, m4b);
const v128_t v0_1h = wasm_u8x16_shr(v0_1, 4);
const v128_t v1_1h = wasm_u8x16_shr(v1_1, 4);
// sub 8
const v128_t v0_0ls = wasm_i8x16_sub(v0_0l, s8b);
const v128_t v1_0ls = wasm_i8x16_sub(v1_0l, s8b);
const v128_t v0_0hs = wasm_i8x16_sub(v0_0h, s8b);
const v128_t v1_0hs = wasm_i8x16_sub(v1_0h, s8b);
const v128_t v0_1ls = wasm_i8x16_sub(v0_1l, s8b);
const v128_t v1_1ls = wasm_i8x16_sub(v1_1l, s8b);
const v128_t v0_1hs = wasm_i8x16_sub(v0_1h, s8b);
const v128_t v1_1hs = wasm_i8x16_sub(v1_1h, s8b);
// dot product into int16x8_t
const v128_t pl0l = wasm_i16x8_mul(wasm_i16x8_extend_low_i8x16(v0_0ls), wasm_i16x8_extend_low_i8x16(v1_0ls));
const v128_t pl0h = wasm_i16x8_mul(wasm_i16x8_extend_high_i8x16(v0_0ls), wasm_i16x8_extend_high_i8x16(v1_0ls));
const v128_t ph0l = wasm_i16x8_mul(wasm_i16x8_extend_low_i8x16(v0_0hs), wasm_i16x8_extend_low_i8x16(v1_0hs));
const v128_t ph0h = wasm_i16x8_mul(wasm_i16x8_extend_high_i8x16(v0_0hs), wasm_i16x8_extend_high_i8x16(v1_0hs));
const v128_t pl1l = wasm_i16x8_mul(wasm_i16x8_extend_low_i8x16(v0_1ls), wasm_i16x8_extend_low_i8x16(v1_1ls));
const v128_t pl1h = wasm_i16x8_mul(wasm_i16x8_extend_high_i8x16(v0_1ls), wasm_i16x8_extend_high_i8x16(v1_1ls));
const v128_t ph1l = wasm_i16x8_mul(wasm_i16x8_extend_low_i8x16(v0_1hs), wasm_i16x8_extend_low_i8x16(v1_1hs));
const v128_t ph1h = wasm_i16x8_mul(wasm_i16x8_extend_high_i8x16(v0_1hs), wasm_i16x8_extend_high_i8x16(v1_1hs));
const v128_t pl_0 = wasm_i16x8_add(pl0l, pl0h);
const v128_t ph_0 = wasm_i16x8_add(ph0l, ph0h);
const v128_t pl_1 = wasm_i16x8_add(pl1l, pl1h);
const v128_t ph_1 = wasm_i16x8_add(ph1l, ph1h);
const v128_t p_0 = wasm_i16x8_add(pl_0, ph_0);
const v128_t p_1 = wasm_i16x8_add(pl_1, ph_1);
sum0 += x0->d * y0->d * (
wasm_i16x8_extract_lane(p_0, 0) + wasm_i16x8_extract_lane(p_0, 1) +
wasm_i16x8_extract_lane(p_0, 2) + wasm_i16x8_extract_lane(p_0, 3) +
wasm_i16x8_extract_lane(p_0, 4) + wasm_i16x8_extract_lane(p_0, 5) +
wasm_i16x8_extract_lane(p_0, 6) + wasm_i16x8_extract_lane(p_0, 7));
sum1 += x1->d * y1->d * (
wasm_i16x8_extract_lane(p_1, 0) + wasm_i16x8_extract_lane(p_1, 1) +
wasm_i16x8_extract_lane(p_1, 2) + wasm_i16x8_extract_lane(p_1, 3) +
wasm_i16x8_extract_lane(p_1, 4) + wasm_i16x8_extract_lane(p_1, 5) +
wasm_i16x8_extract_lane(p_1, 6) + wasm_i16x8_extract_lane(p_1, 7));
}
sumf = sum0 + sum1;
#else
// scalar
for (int i = 0; i < nb; i++) {
const float d0 = x[i].d;
const float d1 = y[i].d;
const uint8_t * restrict p0 = x[i].qs;
const uint8_t * restrict p1 = y[i].qs;
for (int j = 0; j < QK/2; j++) {
const uint8_t v0 = p0[j];
const uint8_t v1 = p1[j];
const float f0 = d0*((int8_t) (v0 & 0xf) - 8);
const float f1 = d0*((int8_t) (v0 >> 4) - 8);
const float f2 = d1*((int8_t) (v1 & 0xf) - 8);
const float f3 = d1*((int8_t) (v1 >> 4) - 8);
sumf += f0*f2 + f1*f3;
}
}
#endif
*s = sumf;
}
static void ggml_vec_dot_q4_1(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) {
const int nb = n / QK;
const block_q4_1 * restrict x = vx;
const block_q4_1 * restrict y = vy;
float sumf = 0.0;
#if defined(__AVX2__)
// Initialize accumulator with zeros
__m256 acc = _mm256_setzero_ps();
// Accumulator for constant offsets
float acc_offset = 0.0f;
// Main loop
for (int i = 0; i < nb; ++i) {
const float * d0 = &x[i].d;
const float * d1 = &y[i].d;
const float * m0 = &x[i].m;
const float * m1 = &y[i].m;
const __m256 d0v = _mm256_broadcast_ss( d0 );
const __m256 d1v = _mm256_broadcast_ss( d1 );
const __m256 m0v = _mm256_broadcast_ss( m0 );
const __m256 m1v = _mm256_broadcast_ss( m1 );
// Compute combined scale for the block
const __m256 scale_01 = _mm256_mul_ps( d0v, d1v );
// Compute cross scales for the block
const __m256 scale_0 = _mm256_mul_ps( d0v, m1v );
const __m256 scale_1 = _mm256_mul_ps( m0v, d1v );
const __m256 cross_scales = _mm256_blend_ps( scale_0, scale_1, 0xAA /* 0b10101010 */ );
// Load 16 bytes, and unpack 4 bit fields into bytes, making 32 bytes
__m256i bx = bytesFromNibbles( x[i].qs );
__m256i by = bytesFromNibbles( y[i].qs );
// Now we have a vector with bytes in [ 0 .. 15 ] interval.
// Sign-extend first 16 signed bytes into int16_t
__m256i x16 = _mm256_cvtepi8_epi16( _mm256_castsi256_si128( bx ) );
__m256i y16 = _mm256_cvtepi8_epi16( _mm256_castsi256_si128( by ) );
// Compute products of int16_t integers, add pairwise
__m256i i32 = _mm256_madd_epi16( x16, y16 );
// Sign-extend last 16 signed bytes into int16_t vectors
__m256i x16_h = _mm256_cvtepi8_epi16( _mm256_extracti128_si256( bx, 1 ) );
__m256i y16_h = _mm256_cvtepi8_epi16( _mm256_extracti128_si256( by, 1 ) );
// Accumulate products of int16_t integers
i32 = _mm256_add_epi32( i32, _mm256_madd_epi16( x16_h, y16_h ) );
// compute sums of unsigned bytes in bx, by in blocks of 8.
// This results in a layout like X100 0000 X200 0000 X300 0000 X400 0000,
// which we then interleave as X100 Y100 X200 Y200 X300 Y300 X400 Y400.
// so if we then cast to 8 singles, we get 8 floats like [ x0_7, y0_7, x8_15, y8_15, x16_23, y16_23, x24_31, y24_31 ]
__m256i xsumi = _mm256_sad_epu8( bx, _mm256_setzero_si256() );
__m256i ysumi = _mm256_sad_epu8( by, _mm256_setzero_si256() );
__m256i sumsi = _mm256_or_si256( xsumi, _mm256_slli_si256( ysumi, 4 ) );
__m256 sums = _mm256_cvtepi32_ps( sumsi );
// Convert int32_t to float
__m256 p = _mm256_cvtepi32_ps( i32 );
// Apply the scale, and accumulate
// acc += d0*d1*x*y + d0*m1*x + d1*m0*y
acc = _mm256_fmadd_ps( scale_01, p, acc );
acc = _mm256_fmadd_ps( cross_scales, sums, acc );
// acc_offset += m0*m1 (for each entry in the block)
acc_offset += (*m0)*(*m1);
}
// Return horizontal sum of the acc vector
__m128 res = _mm256_extractf128_ps( acc, 1 );
res = _mm_add_ps( res, _mm256_castps256_ps128( acc ) );
res = _mm_add_ps( res, _mm_movehl_ps( res, res ) );
res = _mm_add_ss( res, _mm_movehdup_ps( res ) );
sumf = _mm_cvtss_f32( res ) + acc_offset * QK;
#elif defined(__ARM_NEON)
float sum00 = 0.0f;
float sum01 = 0.0f;
float sum10 = 0.0f;
float sum11 = 0.0f;
for (int i = 0; i < nb; ++i) {
const block_q4_1 * restrict x0 = &x[i + 0];
const block_q4_1 * restrict y0 = &y[i + 0];
const uint8x16_t m4b = vdupq_n_u8(0xf);
const uint8x16_t v0_0 = vld1q_u8(x0->qs);
const uint8x16_t v1_0 = vld1q_u8(y0->qs);
// and with 0xf
const uint8x16_t v0_0l = vandq_u8(v0_0, m4b);
const uint8x16_t v1_0l = vandq_u8(v1_0, m4b);
const uint8x16_t v0_0h = vshrq_n_u8(v0_0, 4);
const uint8x16_t v1_0h = vshrq_n_u8(v1_0, 4);
// dot product into uint16x8_t
const uint16x8_t pl0l = vmull_u8(vget_low_u8 (v0_0l), vget_low_u8 (v1_0l));
const uint16x8_t pl0h = vmull_u8(vget_high_u8(v0_0l), vget_high_u8(v1_0l));
const uint16x8_t ph0l = vmull_u8(vget_low_u8 (v0_0h), vget_low_u8 (v1_0h));
const uint16x8_t ph0h = vmull_u8(vget_high_u8(v0_0h), vget_high_u8(v1_0h));
const uint16x8_t pl0 = vaddq_u16(pl0l, pl0h);
const uint16x8_t ph0 = vaddq_u16(ph0l, ph0h);
sum00 += x0->m*y0->m;
sum01 += y0->m*x0->d*(vaddvq_u8(v0_0l) + vaddvq_u8(v0_0h));
sum10 += x0->m*y0->d*(vaddvq_u8(v1_0l) + vaddvq_u8(v1_0h));
sum11 += x0->d*y0->d*vaddvq_u16(vaddq_u16(pl0, ph0));
}
sumf = QK*sum00 + sum01 + sum10 + sum11;
#else
// scalar
for (int i = 0; i < nb; i++) {
const float d0 = x[i].d;
const float d1 = y[i].d;
const float m0 = x[i].m;
const float m1 = y[i].m;
const uint8_t * restrict p0 = x[i].qs;
const uint8_t * restrict p1 = y[i].qs;
for (int j = 0; j < QK/2; j++) {
const uint8_t v0 = p0[j];
const uint8_t v1 = p1[j];
const float f0 = d0*(v0 & 0xf) + m0;
const float f1 = d0*(v0 >> 4) + m0;
const float f2 = d1*(v1 & 0xf) + m1;
const float f3 = d1*(v1 >> 4) + m1;
sumf += f0*f2 + f1*f3;
}
}
#endif
*s = sumf;
}
// 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_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_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_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 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
// 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 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_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);
}
//
// 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__)
//
// data types
//
static const int GGML_BLCK_SIZE[GGML_TYPE_COUNT] = {
QK,
QK,
1,
1,
1,
1,
1,
};
static_assert(GGML_TYPE_COUNT == 7, "GGML_TYPE_COUNT != 5");
static const size_t GGML_TYPE_SIZE[GGML_TYPE_COUNT] = {
sizeof(block_q4_0),
sizeof(block_q4_1),
sizeof(int8_t ),
sizeof(int16_t),
sizeof(int32_t),
sizeof(ggml_fp16_t),
sizeof(float ),
};
// don't forget to update the array above when adding new types
static_assert(GGML_TYPE_COUNT == 7, "GGML_TYPE_COUNT != 5");
static const char * GGML_OP_LABEL[GGML_OP_COUNT] = {
"NONE",
"DUP",
"ADD",
"SUB",
"MUL",
"DIV",
"SQR",
"SQRT",
"SUM",
"MEAN",
"REPEAT",
"ABS",
"SGN",
"NEG",
"STEP",
"RELU",
"GELU",
"SILU",
"NORM",
"RMS_NORM",
"MUL_MAT",
"SCALE",
"CPY",
"RESHAPE",
"VIEW",
"PERMUTE",
"TRANSPOSE",
"GET_ROWS",
"DIAG_MASK_INF",
"SOFT_MAX",
"ROPE",
"CONV_1D_1S",
"CONV_1D_2S",
"FLASH_ATTN",
"FLASH_FF",
};
static_assert(GGML_OP_COUNT == 35, "GGML_OP_COUNT != 35");
static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
"none",
"x",
"x+y",
"x-y",
"x*y",
"x/y",
"x^2",
"√x",
"Σx",
"Σx/n",
"repeat(x)",
"abs(x)",
"sgn(x)",
"-x",
"step(x)",
"relu(x)",
"gelu(x)",
"silu(x)",
"norm(x)",
"rms_norm(x)",
"X*Y",
"x*v",
"x-\\>y",
"reshape(x)",
"view(x)",
"permute(x)",
"transpose(x)",
"get_rows(x)",
"diag_mask_inf(x)",
"soft_max(x)",
"rope(x)",
"conv_1d_1s(x)",
"conv_1d_2s(x)",
"flash_attn(x)",
"flash_ff(x)",
};
static_assert(GGML_OP_COUNT == 35, "GGML_OP_COUNT != 35");
//
// ggml object
//
struct ggml_object {
size_t offs;
size_t size;
struct ggml_object * next;
char padding[8];
};
static const size_t GGML_OBJECT_SIZE = sizeof(struct ggml_object);
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");
//
// ggml context
//
struct ggml_context {
size_t mem_size;
void * mem_buffer;
bool mem_buffer_owned;
bool mem_buffer_mlocked;
bool no_alloc;
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;
};
//
// compute types
//
enum ggml_task_type {
GGML_TASK_INIT = 0,
GGML_TASK_COMPUTE,
GGML_TASK_FINALIZE,
};
struct ggml_compute_params {
enum ggml_task_type type;
int ith, nth;
// work buffer for all threads
size_t wsize;
void * wdata;
};
//
// ggml state
//
struct ggml_state {
struct ggml_context_container contexts[GGML_MAX_CONTEXTS];
};
// 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_print_object(const struct ggml_object * obj) {
GGML_PRINT(" - ggml_object: offset = %zu, size = %zu, next = %p\n",
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];
}
int 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) {
static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
return (ggml_nelements(tensor)*GGML_TYPE_SIZE[tensor->type])/GGML_BLCK_SIZE[tensor->type];
}
int ggml_blck_size(enum ggml_type type) {
return GGML_BLCK_SIZE[type];
}
size_t ggml_type_size(enum ggml_type type) {
return GGML_TYPE_SIZE[type];
}
float ggml_type_sizef(enum ggml_type type) {
return ((float)(GGML_TYPE_SIZE[type]))/GGML_BLCK_SIZE[type];
}
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]) &&
(t0->ne[2] == t1->ne[2]) &&
(t0->ne[3] == t1->ne[3]);
}
static inline bool ggml_is_transposed(const struct ggml_tensor * tensor) {
return tensor->nb[0] > tensor->nb[1];
}
static inline 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_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];
}
static inline 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 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, 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_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, 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 } },
};
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);
}
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;
}
*ctx = (struct ggml_context) {
/*.mem_size =*/ params.mem_size,
/*.mem_buffer =*/ params.mem_buffer ? params.mem_buffer : malloc(params.mem_size),
/*.mem_buffer_owned =*/ params.mem_buffer ? false : true,
/*.mem_buffer_mlocked =*/ false,
/*.no_alloc =*/ 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); // check for allocation failure
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 with %d objects has been freed. memory used = %zu\n",
__func__, i, ctx->n_objects, ctx->objects_end->offs + ctx->objects_end->size);
#if GGML_MLOCK_SUPPORT
if (ctx->mem_buffer_mlocked) {
if (munlock(ctx->mem_buffer, ctx->mem_size)) {
fprintf(stderr, "%s: failed to munlock buffer: %s\n", __func__, strerror(errno));
}
}
#endif
if (ctx->mem_buffer_owned) {
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->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;
}
#ifdef __APPLE__
#define MLOCK_SUGGESTION \
"Try increasing the sysctl values 'vm.user_wire_limit' and 'vm.global_user_wire_limit' and/or " \
"decreasing 'vm.global_no_user_wire_amount'. Also try increasing RLIMIT_MLOCK (ulimit -l).\n"
#else
#define MLOCK_SUGGESTION \
"Try increasing RLIMIT_MLOCK ('ulimit -l' as root).\n"
#endif
bool ggml_mlock_supported(void) {
return GGML_MLOCK_SUPPORT;
}
bool ggml_mlock(
struct ggml_context * ctx,
const void *opt_extra_addr,
size_t opt_extra_len,
char **err_p) {
// TODO: Use SetProcessWorkingSetSize() + VirtualLock() on WIN32
#if GGML_MLOCK_SUPPORT
if (ctx->mem_buffer_mlocked) {
return true;
}
if (mlock(ctx->mem_buffer, ctx->mem_size) ||
(opt_extra_len &&
mlock(opt_extra_addr, opt_extra_len))) {
if ((*err_p = malloc(1024))) {
snprintf(*err_p, 1024,
"failed to mlock %zu-byte buffer: %s\n" MLOCK_SUGGESTION,
ctx->mem_size + opt_extra_len,
strerror(errno));
}
return false;
}
ctx->mem_buffer_mlocked = true;
return true;
#else // GGML_MLOCK_SUPPORT
*err_p = strdup("can't mlock because it's not supported on this system");
return false;
#endif // GGML_MLOCK_SUPPORT
}
////////////////////////////////////////////////////////////////////////////////
struct ggml_tensor * ggml_new_tensor_impl(
struct ggml_context * ctx,
enum ggml_type type,
int n_dims,
const int64_t* ne,
void* data) {
// 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;
size_t size_needed = 0;
if (data == NULL && !ctx->no_alloc) {
size_needed += GGML_TYPE_SIZE[type]*(ne[0]/GGML_BLCK_SIZE[type]);
for (int i = 1; i < n_dims; i++) {
size_needed *= ne[i];
}
// align to GGML_MEM_ALIGN
size_needed = ((size_needed + GGML_MEM_ALIGN - 1)/GGML_MEM_ALIGN)*GGML_MEM_ALIGN;
}
char * const mem_buffer = ctx->mem_buffer;
struct ggml_object * const obj_new = (struct ggml_object *)(mem_buffer + cur_end);
if (ctx->scratch.data == NULL || data != NULL) {
size_needed += sizeof(struct ggml_tensor);
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 + GGML_OBJECT_SIZE, ctx->mem_size);
assert(false);
return NULL;
}
*obj_new = (struct ggml_object) {
.offs = cur_end + GGML_OBJECT_SIZE,
.size = size_needed,
.next = NULL,
};
} else {
if (ctx->scratch.offs + size_needed > ctx->scratch.size) {
GGML_PRINT("%s: not enough space in the scratch memory\n", __func__);
assert(false);
return NULL;
}
if (cur_end + sizeof(struct ggml_tensor) + 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 + sizeof(struct ggml_tensor) + GGML_OBJECT_SIZE, ctx->mem_size);
assert(false);
return NULL;
}
data = (char * const) ctx->scratch.data + ctx->scratch.offs;
*obj_new = (struct ggml_object) {
.offs = cur_end + GGML_OBJECT_SIZE,
.size = sizeof(struct ggml_tensor),
.next = NULL,
};
//printf("scratch offs = %zu, size_needed = %zu\n", ctx->scratch.offs, size_needed);
ctx->scratch.offs += size_needed;
}
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);
struct ggml_tensor * const result = (struct ggml_tensor *)(mem_buffer + obj_new->offs);
ggml_assert_aligned(result);
*result = (struct ggml_tensor) {
/*.type =*/ type,
/*.n_dims =*/ n_dims,
/*.ne =*/ { 1, 1, 1, 1 },
/*.nb =*/ { 0, 0, 0, 0 },
/*.op =*/ GGML_OP_NONE,
/*.is_param =*/ false,
/*.grad =*/ NULL,
/*.src0 =*/ NULL,
/*.src1 =*/ NULL,
/*.opt =*/ { NULL },
/*.n_tasks =*/ 0,
/*.perf_runs =*/ 0,
/*.perf_cycles =*/ 0,
/*.perf_time_us =*/ 0,
/*.data =*/ (data == NULL && !ctx->no_alloc) ? (void *)(result + 1) : data,
/*.pad =*/ { 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);
}
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) {
ctx->scratch_save = ctx->scratch;
ctx->scratch.data = NULL;
struct ggml_tensor * result = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 1);
ctx->scratch = ctx->scratch_save;
ggml_set_i32(result, value);
return result;
}
struct ggml_tensor * ggml_new_f32(struct ggml_context * ctx, float value) {
ctx->scratch_save = ctx->scratch;
ctx->scratch.data = NULL;
struct ggml_tensor * result = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 1);
ctx->scratch = ctx->scratch_save;
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_impl(ctx, src->type, src->n_dims, src->ne, NULL);
}
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_Q4_0:
{
GGML_ASSERT(false);
} break;
case GGML_TYPE_Q4_1:
{
GGML_ASSERT(false);
} break;
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), 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;
case GGML_TYPE_COUNT:
{
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_Q4_0:
{
GGML_ASSERT(false);
} break;
case GGML_TYPE_Q4_1:
{
GGML_ASSERT(false);
} break;
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), 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;
case GGML_TYPE_COUNT:
{
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_Q4_0:
{
GGML_ASSERT(false);
} break;
case GGML_TYPE_Q4_1:
{
GGML_ASSERT(false);
} break;
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;
case GGML_TYPE_COUNT:
{
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_Q4_0:
{
GGML_ASSERT(false);
} break;
case GGML_TYPE_Q4_1:
{
GGML_ASSERT(false);
} break;
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;
case GGML_TYPE_COUNT:
{
GGML_ASSERT(false);
} break;
}
}
float ggml_get_f32_1d(const struct ggml_tensor * tensor, int i) {
switch (tensor->type) {
case GGML_TYPE_Q4_0:
{
GGML_ASSERT(false);
} break;
case GGML_TYPE_Q4_1:
{
GGML_ASSERT(false);
} break;
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;
case GGML_TYPE_COUNT:
{
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_Q4_0:
{
GGML_ASSERT(false);
} break;
case GGML_TYPE_Q4_1:
{
GGML_ASSERT(false);
} break;
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;
case GGML_TYPE_COUNT:
{
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);
}
struct ggml_tensor * ggml_view_tensor(
struct ggml_context * ctx,
const struct ggml_tensor * src) {
struct ggml_tensor * result = ggml_new_tensor_impl(ctx, src->type, src->n_dims, src->ne, src->data);
result->nb[0] = src->nb[0];
result->nb[1] = src->nb[1];
result->nb[2] = src->nb[2];
result->nb[3] = src->nb[3];
return result;
}
////////////////////////////////////////////////////////////////////////////////
// ggml_dup
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->src0 = a;
result->src1 = NULL;
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
struct ggml_tensor * ggml_add_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_ADD;
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
result->src0 = a;
result->src1 = 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_sub
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->src0 = a;
result->src1 = 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
struct ggml_tensor * ggml_mul_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 == false);
}
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->src0 = a;
result->src1 = 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
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 == false);
}
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->src0 = a;
result->src1 = 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
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->src0 = a;
result->src1 = NULL;
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
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->src0 = a;
result->src1 = NULL;
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_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->src0 = a;
result->src1 = NULL;
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->src0 = a;
result->src1 = NULL;
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;
}
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;
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
result->src0 = a;
result->src1 = b;
return result;
}
// ggml_abs
struct ggml_tensor * ggml_abs_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_ABS;
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
result->src0 = a;
result->src1 = NULL;
return result;
}
struct ggml_tensor * ggml_abs(
struct ggml_context * ctx,
struct ggml_tensor * a) {
return ggml_abs_impl(ctx, a, false);
}
struct ggml_tensor * ggml_abs_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a) {
return ggml_abs_impl(ctx, a, true);
}
// ggml_sgn
struct ggml_tensor * ggml_sgn_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_SGN;
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
result->src0 = a;
result->src1 = NULL;
return result;
}
struct ggml_tensor * ggml_sgn(
struct ggml_context * ctx,
struct ggml_tensor * a) {
return ggml_sgn_impl(ctx, a, false);
}
struct ggml_tensor * ggml_sgn_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a) {
return ggml_sgn_impl(ctx, a, true);
}
// ggml_neg
struct ggml_tensor * ggml_neg_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_NEG;
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
result->src0 = a;
result->src1 = NULL;
return result;
}
struct ggml_tensor * ggml_neg(
struct ggml_context * ctx,
struct ggml_tensor * a) {
return ggml_neg_impl(ctx, a, false);
}
struct ggml_tensor * ggml_neg_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a) {
return ggml_neg_impl(ctx, a, true);
}
// ggml_step
struct ggml_tensor * ggml_step_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_STEP;
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
result->src0 = a;
result->src1 = NULL;
return result;
}
struct ggml_tensor * ggml_step(
struct ggml_context * ctx,
struct ggml_tensor * a) {
return ggml_step_impl(ctx, a, false);
}
struct ggml_tensor * ggml_step_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a) {
return ggml_step_impl(ctx, a, true);
}
// ggml_relu
struct ggml_tensor * ggml_relu_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_RELU;
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
result->src0 = a;
result->src1 = NULL;
return result;
}
struct ggml_tensor * ggml_relu(
struct ggml_context * ctx,
struct ggml_tensor * a) {
return ggml_relu_impl(ctx, a, false);
}
struct ggml_tensor * ggml_relu_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a) {
return ggml_relu_impl(ctx, a, true);
}
// ggml_gelu
struct ggml_tensor * ggml_gelu_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_GELU;
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
result->src0 = a;
result->src1 = NULL;
return result;
}
struct ggml_tensor * ggml_gelu(
struct ggml_context * ctx,
struct ggml_tensor * a) {
return ggml_gelu_impl(ctx, a, false);
}
struct ggml_tensor * ggml_gelu_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a) {
return ggml_gelu_impl(ctx, a, true);
}
// ggml_silu
struct ggml_tensor * ggml_silu_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_SILU;
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
result->src0 = a;
result->src1 = NULL;
return result;
}
struct ggml_tensor * ggml_silu(
struct ggml_context * ctx,
struct ggml_tensor * a) {
return ggml_silu_impl(ctx, a, false);
}
struct ggml_tensor * ggml_silu_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a) {
return ggml_silu_impl(ctx, a, true);
}
// ggml_norm
struct ggml_tensor * ggml_norm_impl(
struct ggml_context * ctx,
struct ggml_tensor * a,
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_NORM;
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
result->src0 = a;
result->src1 = NULL; // TODO: maybe store epsilon here?
return result;
}
struct ggml_tensor * ggml_norm(
struct ggml_context * ctx,
struct ggml_tensor * a) {
return ggml_norm_impl(ctx, a, false);
}
struct ggml_tensor * ggml_norm_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a) {
return ggml_norm_impl(ctx, a, true);
}
struct ggml_tensor * ggml_rms_norm_impl(
struct ggml_context * ctx,
struct ggml_tensor * a,
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_RMS_NORM;
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
result->src0 = a;
result->src1 = NULL; // TODO: maybe store epsilon here?
return result;
}
struct ggml_tensor * ggml_rms_norm(
struct ggml_context * ctx,
struct ggml_tensor * a) {
return ggml_rms_norm_impl(ctx, a, false);
}
struct ggml_tensor * ggml_rms_norm_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a) {
return ggml_rms_norm_impl(ctx, a, 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], 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_MUL_MAT;
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
result->src0 = a;
result->src1 = b;
return result;
}
// ggml_scale
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 (!inplace && (a->grad || b->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);
result->op = GGML_OP_SCALE;
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
result->src0 = a;
result->src1 = 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_cpy
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)) {
GGML_ASSERT(false); // TODO: implement backward
is_node = true;
}
// make a view of the destination
struct ggml_tensor * result = ggml_view_tensor(ctx, b);
result->op = GGML_OP_CPY;
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
result->src0 = a;
result->src1 = 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_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 || b->grad) {
GGML_ASSERT(false); // TODO: implement backward
is_node = true;
}
struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, b->n_dims, b->ne, a->data);
result->op = GGML_OP_RESHAPE;
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
result->src0 = a;
result->src1 = NULL;
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) {
GGML_ASSERT(false); // TODO: implement backward
is_node = true;
}
const int64_t ne[2] = { ne0, ne1 };
struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, 2, ne, a->data);
result->op = GGML_OP_RESHAPE;
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
result->src0 = a;
result->src1 = NULL;
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) {
GGML_ASSERT(false); // TODO: implement backward
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->data);
result->op = GGML_OP_RESHAPE;
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
result->src0 = a;
result->src1 = NULL;
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) {
if (a->grad) {
GGML_ASSERT(false); // gradient propagation is not supported
}
struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, 1, &ne0, (char *) a->data + offset);
result->op = GGML_OP_VIEW;
result->grad = NULL;
result->src0 = a;
result->src1 = NULL; // TODO: maybe store the offset here?
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) {
if (a->grad) {
GGML_ASSERT(false); // gradient propagation is not supported
}
const int64_t ne[GGML_MAX_DIMS] = { ne0, ne1, 1, 1 };
struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, 2, ne, (char *) a->data + offset);
result->nb[1] = nb1;
result->nb[2] = result->nb[1]*ne1;
result->nb[3] = result->nb[2];
result->op = GGML_OP_VIEW;
result->grad = NULL;
result->src0 = a;
result->src1 = NULL; // TODO: maybe store the offset here?
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) {
if (a->grad) {
GGML_ASSERT(false); // gradient propagation is not supported
}
const int64_t ne[GGML_MAX_DIMS] = { ne0, ne1, ne2, 1 };
struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, 3, ne, (char *) a->data + offset);
result->nb[1] = nb1;
result->nb[2] = nb2;
result->nb[3] = result->nb[2]*ne2;
result->op = GGML_OP_VIEW;
result->grad = NULL;
result->src0 = a;
result->src1 = NULL; // TODO: maybe store the offset here?
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) {
GGML_ASSERT(false); // TODO: implement backward
is_node = true;
}
struct ggml_tensor * result = ggml_view_tensor(ctx, a);
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->src0 = a;
result->src1 = NULL; // TODO: maybe store the permutation here?
return result;
}
// ggml_transpose
struct ggml_tensor * ggml_transpose(
struct ggml_context * ctx,
struct ggml_tensor * a) {
bool is_node = false;
if (a->grad) {
GGML_ASSERT(false); // TODO: implement backward
is_node = true;
}
struct ggml_tensor * result = ggml_view_tensor(ctx, a);
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->src0 = a;
result->src1 = NULL;
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) {
GGML_ASSERT(false); // TODO: implement backward
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->src0 = a;
result->src1 = b;
return result;
}
// ggml_diag_mask_inf
struct ggml_tensor * ggml_diag_mask_inf(
struct ggml_context * ctx,
struct ggml_tensor * a,
int n_past) {
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);
struct ggml_tensor * b = ggml_new_i32(ctx, n_past);
result->op = GGML_OP_DIAG_MASK_INF;
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
result->src0 = a;
result->src1 = b;
return result;
}
// ggml_soft_max
struct ggml_tensor * ggml_soft_max(
struct ggml_context * ctx,
struct ggml_tensor * a) {
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);
result->op = GGML_OP_SOFT_MAX;
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
result->src0 = a;
result->src1 = NULL;
return result;
}
// ggml_rope
struct ggml_tensor * ggml_rope(
struct ggml_context * ctx,
struct ggml_tensor * a,
int n_past,
int n_dims,
int mode) {
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);
struct ggml_tensor * b = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 3);
((int32_t *) b->data)[0] = n_past;
((int32_t *) b->data)[1] = n_dims;
((int32_t *) b->data)[2] = mode;
result->op = GGML_OP_ROPE;
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
result->src0 = a;
result->src1 = b;
return result;
}
// ggml_conv_1d_1s
struct ggml_tensor * ggml_conv_1d_1s(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b) {
GGML_ASSERT(ggml_is_matrix(b));
GGML_ASSERT(a->ne[1] == b->ne[1]);
GGML_ASSERT(a->ne[3] == 1);
bool is_node = false;
if (a->grad || b->grad) {
GGML_ASSERT(false); // TODO: implement backward
is_node = true;
}
const int64_t ne[4] = { b->ne[0], a->ne[2], 1, 1, };
struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 2, ne);
result->op = GGML_OP_CONV_1D_1S;
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
result->src0 = a;
result->src1 = b;
return result;
}
// ggml_conv_1d_2s
struct ggml_tensor * ggml_conv_1d_2s(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b) {
GGML_ASSERT(ggml_is_matrix(b));
GGML_ASSERT(a->ne[1] == b->ne[1]);
GGML_ASSERT(a->ne[3] == 1);
bool is_node = false;
if (a->grad || b->grad) {
GGML_ASSERT(false); // TODO: implement backward
is_node = true;
}
const int64_t ne[4] = { b->ne[0]/2, a->ne[2], 1, 1, };
struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 2, ne);
result->op = GGML_OP_CONV_1D_2S;
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
result->src0 = a;
result->src1 = b;
return result;
}
// 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) {
GGML_ASSERT(false); // TODO: implement backward
is_node = true;
}
//struct ggml_tensor * result = ggml_dup_tensor(ctx, q);
struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, q->ne);
result->op = GGML_OP_FLASH_ATTN;
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
result->src0 = q;
result->src1 = k;
result->opt[0] = v;
result->opt[1] = ggml_new_i32(ctx, masked ? 1 : 0);
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) {
GGML_ASSERT(false); // TODO: implement backward
is_node = true;
}
//struct ggml_tensor * result = ggml_dup_tensor(ctx, a);
struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, a->ne);
result->op = GGML_OP_FLASH_FF;
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
result->src0 = a;
result->src1 = b0;
result->opt[0] = b1;
result->opt[1] = c0;
result->opt[2] = c1;
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_f16(
const struct ggml_compute_params * params,
const struct ggml_tensor * src0,
struct ggml_tensor * dst) {
GGML_ASSERT(params->ith == 0);
GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0));
if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
return;
}
const int64_t ne00 = src0->ne[0];
const int64_t ne01 = src0->ne[1];
const int64_t ne02 = src0->ne[2];
const int64_t ne03 = src0->ne[3];
const size_t nb00 = src0->nb[0];
const size_t nb01 = src0->nb[1];
const size_t nb02 = src0->nb[2];
const size_t nb03 = src0->nb[3];
const size_t nb0 = dst->nb[0];
const size_t nb1 = dst->nb[1];
const size_t nb2 = dst->nb[2];
const size_t nb3 = dst->nb[3];
if (ggml_is_contiguous(src0) && ggml_is_contiguous(dst) && src0->type == dst->type) {
memcpy(dst->data, src0->data, ggml_nelements(dst) * GGML_TYPE_SIZE[src0->type]);
return;
}
if (src0->type == dst->type &&
src0->ne[0] == dst->ne[0] &&
src0->nb[0] == GGML_TYPE_SIZE[src0->type] && dst->nb[0] == 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 = 0; i01 < ne01; 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
// 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++) {
for (int64_t i01 = 0; i01 < ne01; 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;
}
}
}
}
}
}
}
}
} else if (dst->type == GGML_TYPE_F32) {
for (int64_t i03 = 0; i03 < ne03; i03++) {
for (int64_t i02 = 0; i02 < ne02; i02++) {
for (int64_t i01 = 0; i01 < ne01; 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 == ne00) {
i10 = 0;
if (++i11 == ne01) {
i11 = 0;
if (++i12 == ne02) {
i12 = 0;
if (++i13 == ne03) {
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(params->ith == 0);
GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0));
if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
return;
}
const int64_t ne00 = src0->ne[0];
const int64_t ne01 = src0->ne[1];
const int64_t ne02 = src0->ne[2];
const int64_t ne03 = src0->ne[3];
const size_t nb00 = src0->nb[0];
const size_t nb01 = src0->nb[1];
const size_t nb02 = src0->nb[2];
const size_t nb03 = src0->nb[3];
const size_t nb0 = dst->nb[0];
const size_t nb1 = dst->nb[1];
const size_t nb2 = dst->nb[2];
const size_t nb3 = dst->nb[3];
if (ggml_is_contiguous(src0) && ggml_is_contiguous(dst) && src0->type == dst->type) {
memcpy(dst->data, src0->data, ggml_nelements(dst) * GGML_TYPE_SIZE[src0->type]);
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++) {
for (int64_t i01 = 0; i01 < ne01; 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 == dst->ne[0]) {
i10 = 0;
if (++i11 == dst->ne[1]) {
i11 = 0;
if (++i12 == dst->ne[2]) {
i12 = 0;
if (++i13 == dst->ne[3]) {
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++) {
for (int64_t i01 = 0; i01 < ne01; 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 == dst->ne[0]) {
i10 = 0;
if (++i11 == dst->ne[1]) {
i11 = 0;
if (++i12 == dst->ne[2]) {
i12 = 0;
if (++i13 == dst->ne[3]) {
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) {
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;
case GGML_TYPE_Q4_0:
case GGML_TYPE_Q4_1:
case GGML_TYPE_I8:
case GGML_TYPE_I16:
case GGML_TYPE_I32:
case GGML_TYPE_COUNT:
{
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_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 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 nb10 = src1->nb[0];
const size_t nb11 = src1->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));
if (nb10 == sizeof(float)) {
const int j0 = (n/nth)*ith;
const int j1 = ith == nth - 1 ? n : (n/nth)*(ith + 1);
for (int j = j0; j < j1; j++) {
ggml_vec_add_f32(nc,
(float *) ((char *) dst->data + j*nb1),
(float *) ((char *) src0->data + j*nb01),
(float *) ((char *) src1->data + j*nb11));
}
} else {
// src1 is not contiguous
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++) {
float * src1_ptr = (float *) ((char *) src1->data + j*nb11 + i*nb10);
dst_ptr[i] = src0_ptr[i] + *src1_ptr;
}
}
}
}
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_Q4_0:
case GGML_TYPE_Q4_1:
case GGML_TYPE_I8:
case GGML_TYPE_I16:
case GGML_TYPE_I32:
case GGML_TYPE_F16:
case GGML_TYPE_COUNT:
{
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 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++) {
ggml_vec_sub_f32(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_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;
case GGML_TYPE_Q4_0:
case GGML_TYPE_Q4_1:
case GGML_TYPE_I8:
case GGML_TYPE_I16:
case GGML_TYPE_I32:
case GGML_TYPE_F16:
case GGML_TYPE_COUNT:
{
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) {
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++) {
ggml_vec_mul_f32(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_mul(
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_mul_f32(params, src0, src1, dst);
} break;
case GGML_TYPE_Q4_0:
case GGML_TYPE_Q4_1:
case GGML_TYPE_I8:
case GGML_TYPE_I16:
case GGML_TYPE_I32:
case GGML_TYPE_F16:
case GGML_TYPE_COUNT:
{
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 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++) {
ggml_vec_div_f32(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_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;
case GGML_TYPE_Q4_0:
case GGML_TYPE_Q4_1:
case GGML_TYPE_I8:
case GGML_TYPE_I16:
case GGML_TYPE_I32:
case GGML_TYPE_F16:
case GGML_TYPE_COUNT:
{
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;
case GGML_TYPE_Q4_0:
case GGML_TYPE_Q4_1:
case GGML_TYPE_I8:
case GGML_TYPE_I16:
case GGML_TYPE_I32:
case GGML_TYPE_F16:
case GGML_TYPE_COUNT:
{
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;
case GGML_TYPE_Q4_0:
case GGML_TYPE_Q4_1:
case GGML_TYPE_I8:
case GGML_TYPE_I16:
case GGML_TYPE_I32:
case GGML_TYPE_F16:
case GGML_TYPE_COUNT:
{
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));
const int64_t ne00 = src0->ne[0];
const int64_t ne01 = src0->ne[1];
const int64_t ne02 = src0->ne[2];
const int64_t ne03 = src0->ne[3];
const size_t nb01 = src0->nb[1];
const size_t nb02 = src0->nb[2];
const size_t nb03 = src0->nb[3];
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 *) (dst->data),
(float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03));
}
}
}
}
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_Q4_0:
case GGML_TYPE_Q4_1:
case GGML_TYPE_I8:
case GGML_TYPE_I16:
case GGML_TYPE_I32:
case GGML_TYPE_F16:
case GGML_TYPE_COUNT:
{
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));
const int64_t ne00 = src0->ne[0];
const int64_t ne01 = src0->ne[1];
const int64_t ne02 = src0->ne[2];
const int64_t ne03 = src0->ne[3];
const size_t nb01 = src0->nb[1];
const size_t nb02 = src0->nb[2];
const size_t nb03 = src0->nb[3];
const int64_t ne0 = dst->ne[0];
const int64_t ne1 = dst->ne[1];
const int64_t ne2 = dst->ne[2];
const int64_t ne3 = dst->ne[3];
assert(ne0 == 1);
assert(ne1 == ne01);
assert(ne2 == ne02);
assert(ne3 == ne03);
UNUSED(ne0);
UNUSED(ne1);
UNUSED(ne2);
UNUSED(ne3);
const size_t nb1 = dst->nb[1];
const size_t nb2 = dst->nb[2];
const size_t nb3 = dst->nb[3];
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;
case GGML_TYPE_Q4_0:
case GGML_TYPE_Q4_1:
case GGML_TYPE_I8:
case GGML_TYPE_I16:
case GGML_TYPE_I32:
case GGML_TYPE_F16:
case GGML_TYPE_COUNT:
{
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) {
assert(params->ith == 0);
assert(ggml_can_repeat(src0, dst));
if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
return;
}
// TODO: implement support for rank > 2 tensors
assert(src0->ne[2] == 1);
assert(src0->ne[3] == 1);
assert( dst->ne[2] == 1);
assert( dst->ne[3] == 1);
const int nc = dst->ne[0];
const int nr = dst->ne[1];
const int nc0 = src0->ne[0];
const int nr0 = src0->ne[1];
const int ncr = nc/nc0; // guaranteed to be an integer due to the check in ggml_can_repeat
const int nrr = nr/nr0; // guaranteed to be an integer due to the check in ggml_can_repeat
// TODO: support for transposed / permuted tensors
assert( dst->nb[0] == sizeof(float));
assert(src0->nb[0] == sizeof(float));
// TODO: maybe this is not optimal?
for (int i = 0; i < nrr; i++) {
for (int j = 0; j < ncr; j++) {
for (int k = 0; k < nr0; k++) {
ggml_vec_cpy_f32(nc0,
(float *) ((char *) dst->data + (i*nr0 + k)*( dst->nb[1]) + j*nc0*( dst->nb[0])),
(float *) ((char *) src0->data + ( k)*(src0->nb[1])));
}
}
}
}
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;
case GGML_TYPE_Q4_0:
case GGML_TYPE_Q4_1:
case GGML_TYPE_I8:
case GGML_TYPE_I16:
case GGML_TYPE_I32:
case GGML_TYPE_F16:
case GGML_TYPE_COUNT:
{
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;
case GGML_TYPE_Q4_0:
case GGML_TYPE_Q4_1:
case GGML_TYPE_I8:
case GGML_TYPE_I16:
case GGML_TYPE_I32:
case GGML_TYPE_F16:
case GGML_TYPE_COUNT:
{
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;
case GGML_TYPE_Q4_0:
case GGML_TYPE_Q4_1:
case GGML_TYPE_I8:
case GGML_TYPE_I16:
case GGML_TYPE_I32:
case GGML_TYPE_F16:
case GGML_TYPE_COUNT:
{
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;
case GGML_TYPE_Q4_0:
case GGML_TYPE_Q4_1:
case GGML_TYPE_I8:
case GGML_TYPE_I16:
case GGML_TYPE_I32:
case GGML_TYPE_F16:
case GGML_TYPE_COUNT:
{
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;
case GGML_TYPE_Q4_0:
case GGML_TYPE_Q4_1:
case GGML_TYPE_I8:
case GGML_TYPE_I16:
case GGML_TYPE_I32:
case GGML_TYPE_F16:
case GGML_TYPE_COUNT:
{
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;
case GGML_TYPE_Q4_0:
case GGML_TYPE_Q4_1:
case GGML_TYPE_I8:
case GGML_TYPE_I16:
case GGML_TYPE_I32:
case GGML_TYPE_F16:
case GGML_TYPE_COUNT:
{
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(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;
}
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;
case GGML_TYPE_Q4_0:
case GGML_TYPE_Q4_1:
case GGML_TYPE_I8:
case GGML_TYPE_I16:
case GGML_TYPE_I32:
case GGML_TYPE_F16:
case GGML_TYPE_COUNT:
{
GGML_ASSERT(false);
} break;
}
//printf("XXXXXXXX gelu\n");
}
// 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(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;
}
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;
case GGML_TYPE_Q4_0:
case GGML_TYPE_Q4_1:
case GGML_TYPE_I8:
case GGML_TYPE_I16:
case GGML_TYPE_I32:
case GGML_TYPE_F16:
case GGML_TYPE_COUNT:
{
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;
const int64_t ne00 = src0->ne[0];
const int64_t ne01 = src0->ne[1];
const int64_t ne02 = src0->ne[2];
const int64_t ne03 = src0->ne[3];
const size_t nb01 = src0->nb[1];
const size_t nb02 = src0->nb[2];
const size_t nb03 = src0->nb[3];
const size_t nb1 = dst->nb[1];
const size_t nb2 = dst->nb[2];
const size_t nb3 = dst->nb[3];
const float eps = 1e-5f; // TODO: make this a parameter
// 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;
case GGML_TYPE_Q4_0:
case GGML_TYPE_Q4_1:
case GGML_TYPE_I8:
case GGML_TYPE_I16:
case GGML_TYPE_I32:
case GGML_TYPE_F16:
case GGML_TYPE_COUNT:
{
GGML_ASSERT(false);
} break;
}
}
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;
const int64_t ne00 = src0->ne[0];
const int64_t ne01 = src0->ne[1];
const int64_t ne02 = src0->ne[2];
const int64_t ne03 = src0->ne[3];
const size_t nb01 = src0->nb[1];
const size_t nb02 = src0->nb[2];
const size_t nb03 = src0->nb[3];
const size_t nb1 = dst->nb[1];
const size_t nb2 = dst->nb[2];
const size_t nb3 = dst->nb[3];
const float eps = 1e-6f; // TODO: make this a parameter
// 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]);
}
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;
case GGML_TYPE_Q4_0:
case GGML_TYPE_Q4_1:
case GGML_TYPE_I8:
case GGML_TYPE_I16:
case GGML_TYPE_I32:
case GGML_TYPE_F16:
case GGML_TYPE_COUNT:
{
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_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);
const int64_t ne00 = src0->ne[0];
const int64_t ne01 = src0->ne[1];
const int64_t ne02 = src0->ne[2];
const int64_t ne03 = src0->ne[3];
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
const int64_t ne10 = src1->ne[0];
#endif
const int64_t ne11 = src1->ne[1];
#ifndef NDEBUG
const int64_t ne12 = src1->ne[2];
const int64_t ne13 = src1->ne[3];
const int64_t ne0 = dst->ne[0];
const int64_t ne1 = dst->ne[1];
const int64_t ne2 = dst->ne[2];
const int64_t ne3 = dst->ne[3];
const int nb00 = src0->nb[0];
#endif
const int nb01 = src0->nb[1];
const int nb02 = src0->nb[2];
const int nb03 = src0->nb[3];
#ifndef NDEBUG
const int nb10 = src1->nb[0];
#endif
const int nb11 = src1->nb[1];
const int nb12 = src1->nb[2];
const int nb13 = src1->nb[3];
const int nb0 = dst->nb[0];
const int nb1 = dst->nb[1];
const int nb2 = dst->nb[2];
const int nb3 = dst->nb[3];
const int ith = params->ith;
const int nth = params->nth;
assert(ne02 == ne12);
assert(ne03 == ne13);
assert(ne2 == ne12);
assert(ne3 == ne13);
// we don't support permuted src0 or src1
assert(nb00 == sizeof(float));
assert(nb10 == sizeof(float));
// dst cannot be transposed or permuted
assert(nb0 == sizeof(float));
assert(nb0 <= nb1);
assert(nb1 <= nb2);
assert(nb2 <= nb3);
assert(ne0 == ne01);
assert(ne1 == ne11);
assert(ne2 == ne02);
assert(ne3 == ne03);
// nb01 >= nb00 - src0 is not transposed
// compute by src0 rows
#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 i03 = 0; i03 < ne03; i03++) {
for (int64_t i02 = 0; i02 < ne02; i02++) {
const float * x = (float *) ((char *) src0->data + i02*nb02 + i03*nb03);
const float * y = (float *) ((char *) src1->data + i02*nb12 + i03*nb13);
float * d = (float *) ((char *) dst->data + i02*nb2 + i03*nb3);
// zT = y * xT
cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasTrans,
ne11, ne01, ne10,
1.0f, y, ne10,
x, ne10,
0.0f, d, ne01);
}
}
//printf("CBLAS F32 = %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) {
return;
}
if (params->type == GGML_TASK_FINALIZE) {
return;
}
// parallelize by src0 rows using ggml_vec_dot_f32
// total rows in src0
const int nr = ne01*ne02*ne03;
// 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 indices
const int i03 = ir/(ne02*ne01);
const int i02 = (ir - i03*ne02*ne01)/ne01;
const int i01 = (ir - i03*ne02*ne01 - i02*ne01);
for (int64_t ic = 0; ic < ne11; ++ic) {
// src1 indices
const int i13 = i03;
const int i12 = i02;
const int i11 = ic;
// dst indices
const int i0 = i01;
const int i1 = i11;
const int i2 = i02;
const int i3 = i03;
ggml_vec_dot_f32(ne00,
(float *) ((char *) dst->data + (i0*nb0 + i1*nb1 + i2*nb2 + i3*nb3)),
(float *) ((char *) src0->data + (i01*nb01 + i02*nb02 + i03*nb03)),
(float *) ((char *) src1->data + (i11*nb11 + i12*nb12 + i13*nb13)));
}
}
//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_mul_mat_f16_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);
const int64_t ne00 = src0->ne[0];
const int64_t ne01 = src0->ne[1];
const int64_t ne02 = src0->ne[2];
const int64_t ne03 = src0->ne[3];
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 int64_t ne0 = dst->ne[0];
const int64_t ne1 = dst->ne[1];
const int64_t ne2 = dst->ne[2];
const int64_t ne3 = dst->ne[3];
//const int64_t ne = ne0*ne1*ne2*ne3;
const int nb00 = src0->nb[0];
const int nb01 = src0->nb[1];
const int nb02 = src0->nb[2];
const int nb03 = src0->nb[3];
const int nb10 = src1->nb[0];
const int nb11 = src1->nb[1];
const int nb12 = src1->nb[2];
const int nb13 = src1->nb[3];
const int nb0 = dst->nb[0];
const int nb1 = dst->nb[1];
const int nb2 = dst->nb[2];
const int nb3 = dst->nb[3];
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);
// TODO: we don't support permuted src0
GGML_ASSERT(nb00 == sizeof(ggml_fp16_t));
// 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 == ne01);
GGML_ASSERT(ne1 == ne11);
GGML_ASSERT(ne2 == ne02);
GGML_ASSERT(ne3 == ne03);
// nb01 >= nb00 - src0 is not transposed
// compute by src0 rows
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
if (ggml_compute_forward_mul_mat_use_blas(src0, src1, dst)) {
GGML_ASSERT(nb10 == sizeof(float));
if (params->ith != 0) {
return;
}
if (params->type == GGML_TASK_INIT) {
return;
}
if (params->type == GGML_TASK_FINALIZE) {
return;
}
float * const wdata = params->wdata;
for (int64_t i03 = 0; i03 < ne03; i03++) {
for (int64_t i02 = 0; i02 < ne02; i02++) {
{
size_t id = 0;
for (int64_t i01 = 0; i01 < ne01; ++i01) {
for (int64_t i00 = 0; i00 < ne00; ++i00) {
wdata[id++] = GGML_FP16_TO_FP32(*(ggml_fp16_t *) ((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01 + i00*nb00));
}
}
}
const float * x = wdata;
const float * y = (float *) ((char *) src1->data + i02*nb12 + i03*nb13);
float * d = (float *) ((char *) dst->data + i02*nb2 + i03*nb3);
// zT = y * xT
cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasTrans,
ne11, ne01, ne10,
1.0f, y, ne10,
x, ne10,
0.0f, d, ne01);
}
}
/*printf("CBLAS F16 = %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) {
ggml_fp16_t * const wdata = params->wdata;
size_t id = 0;
for (int64_t i13 = 0; i13 < ne13; ++i13) {
for (int64_t i12 = 0; i12 < ne12; ++i12) {
for (int64_t i11 = 0; i11 < ne11; ++i11) {
for (int64_t i10 = 0; i10 < ne10; ++i10) {
wdata[id++] = GGML_FP32_TO_FP16(*(float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11 + i10*nb10));
}
}
}
}
GGML_ASSERT(id*sizeof(ggml_fp16_t) <= params->wsize);
return;
}
if (params->type == GGML_TASK_FINALIZE) {
return;
}
// fp16 -> half the size, so divide by 2
// TODO: do not support transposed src1
assert(nb10/2 == sizeof(ggml_fp16_t));
// parallelize by src0 rows using ggml_vec_dot_f16
// total rows in src0
const int nr = ne01*ne02*ne03;
// 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);
ggml_fp16_t * wdata = params->wdata;
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);
const int i13 = i03;
const int i12 = i02;
const int i0 = i01;
const int i2 = i02;
const int i3 = i03;
ggml_fp16_t * src0_row = (ggml_fp16_t *) ((char *) src0->data + (i01*nb01 + i02*nb02 + i03*nb03));
ggml_fp16_t * src1_col = wdata + ( 0 + i12*ne11 + i13*ne12*ne11)*ne00;
float * dst_col = (float *) ((char *) dst->data + (i0*nb0 + 0*nb1 + i2*nb2 + i3*nb3));
for (int64_t ic = 0; ic < ne11; ++ic) {
ggml_vec_dot_f16(ne00, &dst_col[ic*ne0], src0_row, src1_col + ic*ne00);
}
}
//int64_t t1 = ggml_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("XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX task %d/%d: %d us, acc = %d\n", ith, nth, (int) (t1 - t0), (int) acc);
//}
}
static const quantize_fns_t quantize_fns[GGML_TYPE_COUNT] = {
[GGML_TYPE_Q4_0] = {
.dequantize_row_q = dequantize_row_q4_0,
.quantize_row_q = quantize_row_q4_0,
.quantize_row_q_reference = (quantize_row_q_t) quantize_row_q4_0_reference,
.vec_dot_q = ggml_vec_dot_q4_0,
},
[GGML_TYPE_Q4_1] = {
.dequantize_row_q = dequantize_row_q4_1,
.quantize_row_q = quantize_row_q4_1,
.quantize_row_q_reference = (quantize_row_q_t) quantize_row_q4_1_reference,
.vec_dot_q = ggml_vec_dot_q4_1,
},
};
// For internal test use
quantize_fns_t ggml_internal_get_quantize_fn(size_t i) {
GGML_ASSERT(i < GGML_TYPE_COUNT);
return quantize_fns[i];
}
static void ggml_compute_forward_mul_mat_q_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);
const int64_t ne00 = src0->ne[0];
const int64_t ne01 = src0->ne[1];
const int64_t ne02 = src0->ne[2];
const int64_t ne03 = src0->ne[3];
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 int64_t ne0 = dst->ne[0];
const int64_t ne1 = dst->ne[1];
const int64_t ne2 = dst->ne[2];
const int64_t ne3 = dst->ne[3];
const int nb00 = src0->nb[0];
const int nb01 = src0->nb[1];
const int nb02 = src0->nb[2];
const int nb03 = src0->nb[3];
const int nb10 = src1->nb[0];
const int nb11 = src1->nb[1];
const int nb12 = src1->nb[2];
const int nb13 = src1->nb[3];
const int nb0 = dst->nb[0];
const int nb1 = dst->nb[1];
const int nb2 = dst->nb[2];
const int nb3 = dst->nb[3];
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);
const enum ggml_type type = src0->type;
quantize_row_q_t const quantize_row_q = quantize_fns[type].quantize_row_q;
vec_dot_q_t const vec_dot_q = quantize_fns[type].vec_dot_q;
// we don't support permuted src0 or src1
GGML_ASSERT(nb00 == (int) 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);
GGML_ASSERT(ne0 == ne01);
GGML_ASSERT(ne1 == ne11);
GGML_ASSERT(ne2 == ne02);
GGML_ASSERT(ne3 == ne03);
// nb01 >= nb00 - src0 is not transposed
// compute by src0 rows
#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;
}
float * const wdata = params->wdata;
dequantize_row_q_t const dequantize_row_q = quantize_fns[type].dequantize_row_q;
for (int64_t i03 = 0; i03 < ne03; i03++) {
for (int64_t i02 = 0; i02 < ne02; i02++) {
{
size_t id = 0;
for (int64_t i01 = 0; i01 < ne01; ++i01) {
dequantize_row_q((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01, wdata + id, ne00);
id += ne00;
}
}
const float * x = wdata;
const float * y = (float *) ((char *) src1->data + i02*nb12 + i03*nb13);
float * d = (float *) ((char *) dst->data + i02*nb2 + i03*nb3);
// zT = y * xT
cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasTrans,
ne11, ne01, ne10,
1.0f, y, ne10,
x, ne10,
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) {
char * wdata = params->wdata;
const size_t row_size = ne10*GGML_TYPE_SIZE[type]/GGML_BLCK_SIZE[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) {
quantize_row_q((float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11), (void *) wdata, ne10);
wdata += row_size;
}
}
}
return;
}
if (params->type == GGML_TASK_FINALIZE) {
return;
}
// parallelize by src0 rows using ggml_vec_dot_q
// total rows in src0
const int nr = ne01*ne02*ne03;
// 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);
void * wdata = params->wdata;
const size_t row_size = ne00*GGML_TYPE_SIZE[type]/GGML_BLCK_SIZE[type];
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);
const int i13 = i03;
const int i12 = i02;
const int i0 = i01;
const int i2 = i02;
const int i3 = i03;
void * src0_row = (void *) ((char *) src0->data + (i01*nb01 + i02*nb02 + i03*nb03));
char * src1_col = ((char *) wdata + ( (0 + i12*ne11 + i13*ne12*ne11)*row_size));
float * dst_col = (float *) ((char *) dst->data + (i0*nb0 + 0*nb1 + i2*nb2 + i3*nb3));
assert(ne00 % 32 == 0);
for (int64_t ic = 0; ic < ne11; ++ic) {
vec_dot_q(ne00, &dst_col[ic*ne0], src0_row, (void *) (src1_col + ic*row_size));
}
}
//int64_t t1 = ggml_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("XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX task %d/%d: %d us, acc = %d\n", ith, nth, (int) (t1 - t0), (int) acc);
//}
}
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) {
switch (src0->type) {
case GGML_TYPE_Q4_0:
case GGML_TYPE_Q4_1:
{
ggml_compute_forward_mul_mat_q_f32(params, src0, src1, dst);
} break;
case GGML_TYPE_F16:
{
ggml_compute_forward_mul_mat_f16_f32(params, src0, src1, dst);
} break;
case GGML_TYPE_F32:
{
ggml_compute_forward_mul_mat_f32(params, src0, src1, dst);
} break;
case GGML_TYPE_I8:
case GGML_TYPE_I16:
case GGML_TYPE_I32:
case GGML_TYPE_COUNT:
{
GGML_ASSERT(false);
} break;
}
#if 0
if (src0->type == GGML_TYPE_F16 || src0->type == GGML_TYPE_Q4_1) {
static int first = 8;
printf("src0: ne0 = %5d, ne1 = %5d, ne2 = %5d\n", src0->ne[0], src0->ne[1], src0->ne[2]);
printf("src1: ne0 = %5d, ne1 = %5d, ne2 = %5d\n", src1->ne[0], src1->ne[1], src1->ne[2]);
printf("dst: ne0 = %5d, ne1 = %5d, ne2 = %5d\n", dst->ne[0], dst->ne[1], dst->ne[2]);
if (first) {
--first;
} 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);
}
} else {
printf("aaaa src0: ne0 = %5d, ne1 = %5d, ne2 = %5d\n", src0->ne[0], src0->ne[1], src0->ne[2]);
printf("aaaa src1: ne0 = %5d, ne1 = %5d, ne2 = %5d\n", src1->ne[0], src1->ne[1], src1->ne[2]);
printf("aaaa dst: ne0 = %5d, ne1 = %5d, ne2 = %5d\n", dst->ne[0], dst->ne[1], dst->ne[2]);
}
#endif
}
// 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);
for (int i1 = ir0; i1 < ir1; i1++) {
ggml_vec_scale_f32(nc, (float *) ((char *) dst->data + i1*(dst->nb[1])), 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;
case GGML_TYPE_Q4_0:
case GGML_TYPE_Q4_1:
case GGML_TYPE_I8:
case GGML_TYPE_I16:
case GGML_TYPE_I32:
case GGML_TYPE_F16:
case GGML_TYPE_COUNT:
{
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_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;
dequantize_row_q_t const dequantize_row_q = quantize_fns[type].dequantize_row_q;
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:
{
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;
case GGML_TYPE_I8:
case GGML_TYPE_I16:
case GGML_TYPE_I32:
case GGML_TYPE_COUNT:
{
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_mask_inf
static void ggml_compute_forward_diag_mask_inf_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(src1->type == GGML_TYPE_I32);
assert(ggml_nelements(src1) == 1);
if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
return;
}
const int n_past = ((int32_t *) src1->data)[0];
// 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;
assert( dst->nb[0] == sizeof(float));
assert(src0->nb[0] == sizeof(float));
for (int k = 0; k < nz; k++) {
for (int j = 0; j < nr; j++) {
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]) = -INFINITY;
}
}
}
}
}
static void ggml_compute_forward_diag_mask_inf(
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_diag_mask_inf_f32(params, src0, src1, dst);
} break;
case GGML_TYPE_Q4_0:
case GGML_TYPE_Q4_1:
case GGML_TYPE_I8:
case GGML_TYPE_I16:
case GGML_TYPE_I32:
case GGML_TYPE_F16:
case GGML_TYPE_COUNT:
{
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 *p = (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(p[i]));
}
#endif
float max = -INFINITY;
ggml_vec_max_f32(nc, &max, p);
ggml_float sum = 0.0;
uint16_t scvt;
for (int i = 0; i < nc; i++) {
if (p[i] == -INFINITY) {
p[i] = 0.0f;
} else {
//const float val = (p[i] == -INFINITY) ? 0.0 : exp(p[i] - max);
ggml_fp16_t s = GGML_FP32_TO_FP16(p[i] - max);
memcpy(&scvt, &s, sizeof(scvt));
const float val = GGML_FP16_TO_FP32(table_exp_f16[scvt]);
sum += (ggml_float)val;
p[i] = val;
}
}
assert(sum > 0.0);
sum = 1.0/sum;
ggml_vec_scale_f32(nc, p, sum);
#ifndef NDEBUG
for (int i = 0; i < nc; ++i) {
assert(!isnan(p[i]));
assert(!isinf(p[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;
case GGML_TYPE_Q4_0:
case GGML_TYPE_Q4_1:
case GGML_TYPE_I8:
case GGML_TYPE_I16:
case GGML_TYPE_I32:
case GGML_TYPE_F16:
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,
const struct ggml_tensor * src1,
struct ggml_tensor * dst) {
assert(src1->type == GGML_TYPE_I32);
assert(ggml_nelements(src1) == 3);
if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
return;
}
const int n_past = ((int32_t *) src1->data)[0];
const int n_dims = ((int32_t *) src1->data)[1];
const int mode = ((int32_t *) src1->data)[2];
//const int64_t ne0 = src0->ne[0];
const int64_t ne1 = src0->ne[1];
const int64_t ne2 = src0->ne[2];
const int64_t ne3 = src0->ne[3];
const int nb0 = src0->nb[0];
const int nb1 = src0->nb[1];
const int nb2 = src0->nb[2];
const int nb3 = src0->nb[3];
//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(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);
// row index used to determine which thread to use
int ir = 0;
for (int64_t i3 = 0; i3 < ne3; i3++) {
for (int64_t i2 = (mode == 0 ? 0 : n_past); i2 < ne2; i2++) {
const int p = (mode == 0 ? n_past + i2 : i2);
for (int64_t i1 = 0; i1 < ne1; i1++) {
if (ir++ < ir0) continue;
if (ir > ir1) break;
for (int i0 = 0; i0 < n_dims; i0 += 2) {
const float theta = powf(10000.0, ((float)-i0)/n_dims);
const float cos_theta = cosf(p*theta);
const float sin_theta = sinf(p*theta);
const float * const src = (float *)((char *) src0->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
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 - x1*sin_theta;
dst_data[1] = x0*sin_theta + x1*cos_theta;
}
}
}
}
}
static void ggml_compute_forward_rope_f16(
const struct ggml_compute_params * params,
const struct ggml_tensor * src0,
const struct ggml_tensor * src1,
struct ggml_tensor * dst) {
assert(src1->type == GGML_TYPE_I32);
assert(ggml_nelements(src1) == 3);
if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
return;
}
const int n_past = ((int32_t *) src1->data)[0];
const int n_dims = ((int32_t *) src1->data)[1];
const int mode = ((int32_t *) src1->data)[2];
//const int64_t ne0 = src0->ne[0];
const int64_t ne1 = src0->ne[1];
const int64_t ne2 = src0->ne[2];
const int64_t ne3 = src0->ne[3];
const int nb0 = src0->nb[0];
const int nb1 = src0->nb[1];
const int nb2 = src0->nb[2];
const int nb3 = src0->nb[3];
//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(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);
// row index used to determine which thread to use
int ir = 0;
for (int64_t i3 = 0; i3 < ne3; i3++) {
for (int64_t i2 = (mode == 0 ? 0 : n_past); i2 < ne2; i2++) {
const int p = (mode == 0 ? n_past + i2 : i2);
for (int64_t i1 = 0; i1 < ne1; i1++) {
if (ir++ < ir0) continue;
if (ir > ir1) break;
for (int i0 = 0; i0 < n_dims; i0 += 2) {
const float theta = powf(10000.0, ((float)-i0)/n_dims);
const float cos_theta = cosf(p*theta);
const float sin_theta = sinf(p*theta);
const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
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);
}
}
}
}
}
static void ggml_compute_forward_rope(
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_rope_f16(params, src0, src1, dst);
} break;
case GGML_TYPE_F32:
{
ggml_compute_forward_rope_f32(params, src0, src1, dst);
} break;
case GGML_TYPE_Q4_0:
case GGML_TYPE_Q4_1:
case GGML_TYPE_I8:
case GGML_TYPE_I16:
case GGML_TYPE_I32:
case GGML_TYPE_COUNT:
{
GGML_ASSERT(false);
} break;
}
}
// ggml_compute_forward_conv_1d_1s
static void ggml_compute_forward_conv_1d_1s_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);
const int64_t ne00 = src0->ne[0];
const int64_t ne01 = src0->ne[1];
const int64_t ne02 = src0->ne[2];
//const int64_t ne03 = src0->ne[3];
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 int64_t ne0 = dst->ne[0];
//const int64_t ne1 = dst->ne[1];
//const int64_t ne2 = dst->ne[2];
//const int64_t ne3 = dst->ne[3];
//const int64_t ne = ne0*ne1*ne2*ne3;
const int nb00 = src0->nb[0];
const int nb01 = src0->nb[1];
const int nb02 = src0->nb[2];
//const int nb03 = src0->nb[3];
const int nb10 = src1->nb[0];
const int nb11 = src1->nb[1];
//const int nb12 = src1->nb[2];
//const int nb13 = src1->nb[3];
//const int nb0 = dst->nb[0];
const int nb1 = dst->nb[1];
//const int nb2 = dst->nb[2];
//const int nb3 = dst->nb[3];
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_1s_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);
const int64_t ne00 = src0->ne[0];
const int64_t ne01 = src0->ne[1];
const int64_t ne02 = src0->ne[2];
//const int64_t ne03 = src0->ne[3];
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 int64_t ne0 = dst->ne[0];
//const int64_t ne1 = dst->ne[1];
//const int64_t ne2 = dst->ne[2];
//const int64_t ne3 = dst->ne[3];
//const int64_t ne = ne0*ne1*ne2*ne3;
const int nb00 = src0->nb[0];
const int nb01 = src0->nb[1];
const int nb02 = src0->nb[2];
//const int nb03 = src0->nb[3];
const int nb10 = src1->nb[0];
const int nb11 = src1->nb[1];
//const int nb12 = src1->nb[2];
//const int nb13 = src1->nb[3];
//const int nb0 = dst->nb[0];
const int nb1 = dst->nb[1];
//const int nb2 = dst->nb[2];
//const int nb3 = dst->nb[3];
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_1s(
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_1s_f16_f32(params, src0, src1, dst);
} break;
case GGML_TYPE_F32:
{
ggml_compute_forward_conv_1d_1s_f32(params, src0, src1, dst);
} break;
case GGML_TYPE_Q4_0:
case GGML_TYPE_Q4_1:
case GGML_TYPE_I8:
case GGML_TYPE_I16:
case GGML_TYPE_I32:
case GGML_TYPE_COUNT:
{
GGML_ASSERT(false);
} break;
}
}
// ggml_compute_forward_conv_1d_2s
static void ggml_compute_forward_conv_1d_2s_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);
const int64_t ne00 = src0->ne[0];
const int64_t ne01 = src0->ne[1];
const int64_t ne02 = src0->ne[2];
//const int64_t ne03 = src0->ne[3];
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 int64_t ne0 = dst->ne[0];
//const int64_t ne1 = dst->ne[1];
//const int64_t ne2 = dst->ne[2];
//const int64_t ne3 = dst->ne[3];
//const int64_t ne = ne0*ne1*ne2*ne3;
const int nb00 = src0->nb[0];
const int nb01 = src0->nb[1];
const int nb02 = src0->nb[2];
//const int nb03 = src0->nb[3];
const int nb10 = src1->nb[0];
const int nb11 = src1->nb[1];
//const int nb12 = src1->nb[2];
//const int nb13 = src1->nb[3];
//const int nb0 = dst->nb[0];
const int nb1 = dst->nb[1];
//const int nb2 = dst->nb[2];
//const int nb3 = dst->nb[3];
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_2s_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);
const int64_t ne00 = src0->ne[0];
const int64_t ne01 = src0->ne[1];
const int64_t ne02 = src0->ne[2];
//const int64_t ne03 = src0->ne[3];
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 int64_t ne0 = dst->ne[0];
//const int64_t ne1 = dst->ne[1];
//const int64_t ne2 = dst->ne[2];
//const int64_t ne3 = dst->ne[3];
//const int64_t ne = ne0*ne1*ne2*ne3;
const int nb00 = src0->nb[0];
const int nb01 = src0->nb[1];
const int nb02 = src0->nb[2];
//const int nb03 = src0->nb[3];
const int nb10 = src1->nb[0];
const int nb11 = src1->nb[1];
//const int nb12 = src1->nb[2];
//const int nb13 = src1->nb[3];
//const int nb0 = dst->nb[0];
const int nb1 = dst->nb[1];
//const int nb2 = dst->nb[2];
//const int nb3 = dst->nb[3];
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_2s(
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_2s_f16_f32(params, src0, src1, dst);
} break;
case GGML_TYPE_F32:
{
ggml_compute_forward_conv_1d_2s_f32(params, src0, src1, dst);
} break;
case GGML_TYPE_Q4_0:
case GGML_TYPE_Q4_1:
case GGML_TYPE_I8:
case GGML_TYPE_I16:
case GGML_TYPE_I32:
case GGML_TYPE_COUNT:
{
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);
const int64_t neq0 = q->ne[0];
const int64_t neq1 = q->ne[1];
const int64_t neq2 = q->ne[2];
const int64_t neq3 = q->ne[3];
const int64_t nek0 = k->ne[0];
const int64_t nek1 = k->ne[1];
//const int64_t nek2 = k->ne[2];
//const int64_t nek3 = k->ne[3];
//const int64_t nev0 = v->ne[0];
const int64_t nev1 = v->ne[1];
//const int64_t nev2 = v->ne[2];
//const int64_t nev3 = v->ne[3];
const int64_t ne0 = dst->ne[0];
const int64_t ne1 = dst->ne[1];
//const int64_t ne2 = dst->ne[2];
//const int64_t ne3 = dst->ne[3];
const int nbk0 = k->nb[0];
const int nbk1 = k->nb[1];
const int nbk2 = k->nb[2];
const int nbk3 = k->nb[3];
const int nbq0 = q->nb[0];
const int nbq1 = q->nb[1];
const int nbq2 = q->nb[2];
const int nbq3 = q->nb[3];
const int nbv0 = v->nb[0];
const int nbv1 = v->nb[1];
const int nbv2 = v->nb[2];
const int nbv3 = v->nb[3];
const int nb0 = dst->nb[0];
const int nb1 = dst->nb[1];
const int nb2 = dst->nb[2];
const int nb3 = dst->nb[3];
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];
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
}
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);
const int64_t neq0 = q->ne[0];
const int64_t neq1 = q->ne[1];
const int64_t neq2 = q->ne[2];
const int64_t neq3 = q->ne[3];
const int64_t nek0 = k->ne[0];
const int64_t nek1 = k->ne[1];
//const int64_t nek2 = k->ne[2];
//const int64_t nek3 = k->ne[3];
//const int64_t nev0 = v->ne[0];
const int64_t nev1 = v->ne[1];
//const int64_t nev2 = v->ne[2];
//const int64_t nev3 = v->ne[3];
const int64_t ne0 = dst->ne[0];
const int64_t ne1 = dst->ne[1];
//const int64_t ne2 = dst->ne[2];
//const int64_t ne3 = dst->ne[3];
const int nbk0 = k->nb[0];
const int nbk1 = k->nb[1];
const int nbk2 = k->nb[2];
const int nbk3 = k->nb[3];
const int nbq0 = q->nb[0];
const int nbq1 = q->nb[1];
const int nbq2 = q->nb[2];
const int nbq3 = q->nb[3];
const int nbv0 = v->nb[0];
const int nbv1 = v->nb[1];
const int nbv2 = v->nb[2];
const int nbv3 = v->nb[3];
const int nb0 = dst->nb[0];
const int nb1 = dst->nb[1];
const int nb2 = dst->nb[2];
const int nb3 = dst->nb[3];
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;
case GGML_TYPE_Q4_0:
case GGML_TYPE_Q4_1:
case GGML_TYPE_I8:
case GGML_TYPE_I16:
case GGML_TYPE_I32:
case GGML_TYPE_COUNT:
{
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);
const int64_t nea0 = a->ne[0];
const int64_t nea1 = a->ne[1];
const int64_t nea2 = a->ne[2];
const int64_t nea3 = a->ne[3];
const int64_t neb00 = b0->ne[0];
const int64_t neb01 = b0->ne[1];
//const int64_t neb02 = b0->ne[2];
//const int64_t neb03 = b0->ne[3];
const int64_t neb10 = b1->ne[0];
const int64_t neb11 = b1->ne[1];
//const int64_t neb12 = b1->ne[2];
//const int64_t neb13 = b1->ne[3];
const int64_t nec00 = c0->ne[0];
const int64_t nec01 = c0->ne[1];
//const int64_t nec02 = c0->ne[2];
//const int64_t nec03 = c0->ne[3];
const int64_t nec10 = c1->ne[0];
const int64_t nec11 = c1->ne[1];
//const int64_t nec12 = c1->ne[2];
//const int64_t nec13 = c1->ne[3];
const int64_t ne0 = dst->ne[0];
const int64_t ne1 = dst->ne[1];
const int64_t ne2 = dst->ne[2];
//const int64_t ne3 = dst->ne[3];
const int nba0 = a->nb[0];
const int nba1 = a->nb[1];
const int nba2 = a->nb[2];
const int nba3 = a->nb[3];
const int nbb00 = b0->nb[0];
const int nbb01 = b0->nb[1];
const int nbb02 = b0->nb[2];
const int nbb03 = b0->nb[3];
const int nbb10 = b1->nb[0];
//const int nbb11 = b1->nb[1];
//const int nbb12 = b1->nb[2];
//const int nbb13 = b1->nb[3];
const int nbc00 = c0->nb[0];
const int nbc01 = c0->nb[1];
const int nbc02 = c0->nb[2];
const int nbc03 = c0->nb[3];
const int nbc10 = c1->nb[0];
//const int nbc11 = c1->nb[1];
//const int nbc12 = c1->nb[2];
//const int nbc13 = c1->nb[3];
const int nb0 = dst->nb[0];
const int nb1 = dst->nb[1];
const int nb2 = dst->nb[2];
const int nb3 = dst->nb[3];
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;
case GGML_TYPE_Q4_0:
case GGML_TYPE_Q4_1:
case GGML_TYPE_I8:
case GGML_TYPE_I16:
case GGML_TYPE_I32:
case GGML_TYPE_COUNT:
{
GGML_ASSERT(false);
} break;
}
}
/////////////////////////////////
static void ggml_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor) {
GGML_ASSERT(params);
switch (tensor->op) {
case GGML_OP_DUP:
{
ggml_compute_forward_dup(params, tensor->src0, tensor);
} break;
case GGML_OP_ADD:
{
ggml_compute_forward_add(params, tensor->src0, tensor->src1, tensor);
} break;
case GGML_OP_SUB:
{
ggml_compute_forward_sub(params, tensor->src0, tensor->src1, tensor);
} break;
case GGML_OP_MUL:
{
ggml_compute_forward_mul(params, tensor->src0, tensor->src1, tensor);
} break;
case GGML_OP_DIV:
{
ggml_compute_forward_div(params, tensor->src0, tensor->src1, tensor);
} break;
case GGML_OP_SQR:
{
ggml_compute_forward_sqr(params, tensor->src0, tensor);
} break;
case GGML_OP_SQRT:
{
ggml_compute_forward_sqrt(params, tensor->src0, tensor);
} break;
case GGML_OP_SUM:
{
ggml_compute_forward_sum(params, tensor->src0, tensor);
} break;
case GGML_OP_MEAN:
{
ggml_compute_forward_mean(params, tensor->src0, tensor);
} break;
case GGML_OP_REPEAT:
{
ggml_compute_forward_repeat(params, tensor->src0, tensor);
} break;
case GGML_OP_ABS:
{
ggml_compute_forward_abs(params, tensor->src0, tensor);
} break;
case GGML_OP_SGN:
{
ggml_compute_forward_sgn(params, tensor->src0, tensor);
} break;
case GGML_OP_NEG:
{
ggml_compute_forward_neg(params, tensor->src0, tensor);
} break;
case GGML_OP_STEP:
{
ggml_compute_forward_step(params, tensor->src0, tensor);
} break;
case GGML_OP_RELU:
{
ggml_compute_forward_relu(params, tensor->src0, tensor);
} break;
case GGML_OP_GELU:
{
ggml_compute_forward_gelu(params, tensor->src0, tensor);
} break;
case GGML_OP_SILU:
{
ggml_compute_forward_silu(params, tensor->src0, tensor);
} break;
case GGML_OP_NORM:
{
ggml_compute_forward_norm(params, tensor->src0, tensor);
} break;
case GGML_OP_RMS_NORM:
{
ggml_compute_forward_rms_norm(params, tensor->src0, tensor);
} break;
case GGML_OP_MUL_MAT:
{
ggml_compute_forward_mul_mat(params, tensor->src0, tensor->src1, tensor);
} break;
case GGML_OP_SCALE:
{
ggml_compute_forward_scale(params, tensor->src0, tensor->src1, tensor);
} break;
case GGML_OP_CPY:
{
ggml_compute_forward_cpy(params, tensor->src0, tensor);
} break;
case GGML_OP_RESHAPE:
{
ggml_compute_forward_reshape(params, tensor->src0, tensor);
} break;
case GGML_OP_VIEW:
{
ggml_compute_forward_view(params, tensor->src0);
} break;
case GGML_OP_PERMUTE:
{
ggml_compute_forward_permute(params, tensor->src0);
} break;
case GGML_OP_TRANSPOSE:
{
ggml_compute_forward_transpose(params, tensor->src0);
} break;
case GGML_OP_GET_ROWS:
{
ggml_compute_forward_get_rows(params, tensor->src0, tensor->src1, tensor);
} break;
case GGML_OP_DIAG_MASK_INF:
{
ggml_compute_forward_diag_mask_inf(params, tensor->src0, tensor->src1, tensor);
} break;
case GGML_OP_SOFT_MAX:
{
ggml_compute_forward_soft_max(params, tensor->src0, tensor);
} break;
case GGML_OP_ROPE:
{
ggml_compute_forward_rope(params, tensor->src0, tensor->src1, tensor);
} break;
case GGML_OP_CONV_1D_1S:
{
ggml_compute_forward_conv_1d_1s(params, tensor->src0, tensor->src1, tensor);
} break;
case GGML_OP_CONV_1D_2S:
{
ggml_compute_forward_conv_1d_2s(params, tensor->src0, tensor->src1, tensor);
} break;
case GGML_OP_FLASH_ATTN:
{
int32_t t = ggml_get_i32_1d(tensor->opt[1], 0);
GGML_ASSERT(t == 0 || t == 1);
bool masked = t != 0;
ggml_compute_forward_flash_attn(params, tensor->src0, tensor->src1, tensor->opt[0], masked, tensor);
} break;
case GGML_OP_FLASH_FF:
{
ggml_compute_forward_flash_ff(params, tensor->src0, tensor->src1, tensor->opt[0], tensor->opt[1], tensor->opt[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->src0;
struct ggml_tensor * src1 = tensor->src1;
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_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_mul(ctx,
ggml_mul(ctx, src0, tensor->grad),
ggml_repeat(ctx, ggml_new_f32(ctx, 2.0f), src0)),
inplace);
}
} break;
case GGML_OP_SQRT:
{
if (src0->grad) {
src0->grad =
ggml_add_impl(ctx,
src0->grad,
ggml_div(ctx,
ggml_repeat(ctx, ggml_new_f32(ctx, 0.5f), tensor),
tensor),
inplace);
}
} break;
case GGML_OP_SUM:
{
if (src0->grad) {
src0->grad =
ggml_add_impl(ctx,
src0->grad,
ggml_repeat(ctx, tensor->grad, src0->grad),
inplace);
}
} break;
case GGML_OP_MEAN:
{
GGML_ASSERT(false); // TODO: implement
} break;
case GGML_OP_REPEAT:
{
if (src0->grad) {
src0->grad =
ggml_add_impl(ctx,
src0->grad,
ggml_sum(ctx, tensor->grad),
inplace);
}
} break;
case GGML_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_OP_SGN:
{
if (src0->grad) {
// noop
}
} break;
case GGML_OP_NEG:
{
if (src0->grad) {
src0->grad = ggml_sub_impl(ctx, src0->grad, tensor->grad, inplace);
}
} break;
case GGML_OP_STEP:
{
if (src0->grad) {
// noop
}
} break;
case GGML_OP_RELU:
{
if (src0->grad) {
src0->grad = ggml_sub_impl(ctx,
src0->grad,
ggml_mul(ctx,
ggml_step(ctx, src0),
tensor->grad),
inplace);
}
} break;
case GGML_OP_GELU:
{
GGML_ASSERT(false); // TODO: not implemented
} break;
case GGML_OP_SILU:
{
GGML_ASSERT(false); // TODO: not implemented
} break;
case GGML_OP_NORM:
{
GGML_ASSERT(false); // TODO: not implemented
} break;
case GGML_OP_RMS_NORM:
{
GGML_ASSERT(false); // TODO: not implemented
} break;
case GGML_OP_MUL_MAT:
{
if (src0->grad) {
// TODO: this requires outer product - ggml_out_prod(ctx, src1, tensor->grad);
GGML_ASSERT(false);
}
if (src1->grad) {
src1->grad =
ggml_add_impl(ctx,
src1->grad,
// TODO: fix transpose, the node will break the graph connections
ggml_mul_mat(ctx, ggml_transpose(ctx, src0), tensor->grad),
inplace);
}
} break;
case GGML_OP_SCALE:
{
GGML_ASSERT(false); // TODO: not implemented
} break;
case GGML_OP_CPY:
{
GGML_ASSERT(false); // TODO: not implemented
} break;
case GGML_OP_RESHAPE:
{
GGML_ASSERT(false); // TODO: not implemented
} break;
case GGML_OP_VIEW:
{
GGML_ASSERT(false); // not supported
} break;
case GGML_OP_PERMUTE:
{
GGML_ASSERT(false); // TODO: not implemented
} break;
case GGML_OP_TRANSPOSE:
{
GGML_ASSERT(false); // TODO: not implemented
} break;
case GGML_OP_GET_ROWS:
{
GGML_ASSERT(false); // TODO: not implemented
} break;
case GGML_OP_DIAG_MASK_INF:
{
GGML_ASSERT(false); // TODO: not implemented
} break;
case GGML_OP_SOFT_MAX:
{
GGML_ASSERT(false); // TODO: not implemented
} break;
case GGML_OP_ROPE:
{
GGML_ASSERT(false); // TODO: not implemented
} break;
case GGML_OP_CONV_1D_1S:
{
GGML_ASSERT(false); // TODO: not implemented
} break;
case GGML_OP_CONV_1D_2S:
{
GGML_ASSERT(false); // TODO: not implemented
} break;
case GGML_OP_FLASH_ATTN:
{
GGML_ASSERT(false); // not supported
} break;
case GGML_OP_FLASH_FF:
{
GGML_ASSERT(false); // not supported
} break;
case GGML_OP_NONE:
{
// nop
} break;
case GGML_OP_COUNT:
{
GGML_ASSERT(false);
} break;
}
}
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
for (int i = 0; i < cgraph->n_nodes; i++) {
if (cgraph->nodes[i] == node) {
return;
}
}
for (int i = 0; i < cgraph->n_leafs; i++) {
if (cgraph->leafs[i] == node) {
return;
}
}
if (node->src0) {
ggml_visit_parents(cgraph, node->src0);
}
if (node->src1) {
ggml_visit_parents(cgraph, node->src1);
}
for (int i = 0; i < GGML_MAX_OPT; ++i) {
if (node->opt[i]) {
ggml_visit_parents(cgraph, node->opt[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);
cgraph->leafs[cgraph->n_leafs] = node;
cgraph->n_leafs++;
} else {
GGML_ASSERT(cgraph->n_nodes < GGML_MAX_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,
/*.n_threads =*/ 0,
/*.work_size =*/ 0,
/*.work =*/ NULL,
/*.nodes =*/ { NULL },
/*.grads =*/ { NULL },
/*.leafs =*/ { NULL },
/*.perf_runs =*/ 0,
/*.perf_cycles =*/ 0,
/*.perf_time_us =*/ 0,
};
ggml_build_forward_impl(&result, tensor, false);
return result;
}
struct ggml_cgraph ggml_build_backward(struct ggml_context * ctx, struct ggml_cgraph * gf, bool keep) {
struct ggml_cgraph result = *gf;
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 = gf->n_nodes - 1; i >= 0; 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_impl(&result, node->grad, true);
}
}
return result;
}
//
// 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)
#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
#endif
struct ggml_compute_state_shared {
ggml_lock_t spin;
int n_threads;
// synchronization primitives
atomic_int n_ready;
atomic_bool has_work;
atomic_bool stop; // stop all threads
};
struct ggml_compute_state {
ggml_thread_t thrd;
struct ggml_compute_params params;
struct ggml_tensor * node;
struct ggml_compute_state_shared * shared;
};
static thread_ret_t ggml_graph_compute_thread(void * data) {
struct ggml_compute_state * state = (struct ggml_compute_state *) data;
const int n_threads = state->shared->n_threads;
while (true) {
if (atomic_fetch_add(&state->shared->n_ready, 1) == n_threads - 1) {
atomic_store(&state->shared->has_work, false);
} else {
while (atomic_load(&state->shared->has_work)) {
if (atomic_load(&state->shared->stop)) {
return 0;
}
ggml_lock_lock (&state->shared->spin);
ggml_lock_unlock(&state->shared->spin);
}
}
atomic_fetch_sub(&state->shared->n_ready, 1);
// wait for work
while (!atomic_load(&state->shared->has_work)) {
if (atomic_load(&state->shared->stop)) {
return 0;
}
ggml_lock_lock (&state->shared->spin);
ggml_lock_unlock(&state->shared->spin);
}
// check if we should stop
if (atomic_load(&state->shared->stop)) {
break;
}
if (state->node) {
if (state->params.ith < state->params.nth) {
ggml_compute_forward(&state->params, state->node);
}
state->node = NULL;
} else {
break;
}
}
return 0;
}
void ggml_graph_compute(struct ggml_context * ctx, struct ggml_cgraph * cgraph) {
const int n_threads = cgraph->n_threads;
struct ggml_compute_state_shared state_shared = {
/*.spin =*/ GGML_LOCK_INITIALIZER,
/*.n_threads =*/ n_threads,
/*.n_ready =*/ 0,
/*.has_work =*/ false,
/*.stop =*/ false,
};
struct ggml_compute_state * workers = n_threads > 1 ? alloca(sizeof(struct ggml_compute_state)*(n_threads - 1)) : NULL;
// create thread pool
if (n_threads > 1) {
ggml_lock_init(&state_shared.spin);
atomic_store(&state_shared.has_work, true);
for (int j = 0; j < n_threads - 1; j++) {
workers[j] = (struct ggml_compute_state) {
.thrd = 0,
.params = {
.type = GGML_TASK_COMPUTE,
.ith = j + 1,
.nth = n_threads,
.wsize = cgraph->work ? ggml_nbytes(cgraph->work) : 0,
.wdata = cgraph->work ? cgraph->work->data : NULL,
},
.node = NULL,
.shared = &state_shared,
};
int rc = ggml_thread_create(&workers[j].thrd, NULL, ggml_graph_compute_thread, &workers[j]);
GGML_ASSERT(rc == 0);
UNUSED(rc);
}
}
// initialize tasks + work buffer
{
size_t work_size = 0;
// thread scheduling for the different operations
for (int i = 0; i < cgraph->n_nodes; i++) {
struct ggml_tensor * node = cgraph->nodes[i];
switch (node->op) {
case GGML_OP_DUP:
{
node->n_tasks = 1;
} break;
case GGML_OP_ADD:
{
node->n_tasks = n_threads;
} break;
case GGML_OP_SUB:
case GGML_OP_MUL:
case GGML_OP_DIV:
case GGML_OP_SQR:
case GGML_OP_SQRT:
case GGML_OP_SUM:
case GGML_OP_MEAN:
case GGML_OP_REPEAT:
case GGML_OP_ABS:
case GGML_OP_SGN:
case GGML_OP_NEG:
case GGML_OP_STEP:
case GGML_OP_RELU:
{
node->n_tasks = 1;
} break;
case GGML_OP_GELU:
{
node->n_tasks = n_threads;
} break;
case GGML_OP_SILU:
{
node->n_tasks = n_threads;
} break;
case GGML_OP_NORM:
case GGML_OP_RMS_NORM:
{
node->n_tasks = n_threads;
} break;
case GGML_OP_MUL_MAT:
{
node->n_tasks = n_threads;
// TODO: use different scheduling for different matrix sizes
//const int nr0 = ggml_nrows(node->src0);
//const int nr1 = ggml_nrows(node->src1);
//node->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, node->n_tasks);
size_t cur = 0;
if (node->src0->type == GGML_TYPE_F16 && node->src1->type == GGML_TYPE_F32) {
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
if (ggml_compute_forward_mul_mat_use_blas(node->src0, node->src1, node)) {
node->n_tasks = 1; // TODO: this actually is doing nothing
// the threads are still spinning
cur = GGML_TYPE_SIZE[GGML_TYPE_F32]*(node->src0->ne[0]*node->src0->ne[1]);
//printf("src0: ne0 = %d, ne1 = %d, ne = %d\n", node->src0->ne[0], node->src0->ne[1], node->src0->ne[0]*node->src0->ne[1]);
//printf("src1: ne0 = %d, ne1 = %d, ne = %d\n", node->src1->ne[0], node->src1->ne[1], node->src1->ne[0]*node->src1->ne[1]);
//printf("cur = %zu\n", cur);
} else {
cur = GGML_TYPE_SIZE[GGML_TYPE_F16]*ggml_nelements(node->src1);
}
#else
cur = GGML_TYPE_SIZE[GGML_TYPE_F16]*ggml_nelements(node->src1);
#endif
} else if (node->src0->type == GGML_TYPE_F32 && node->src1->type == GGML_TYPE_F32) {
cur = 0;
} else if (quantize_fns[node->src0->type].vec_dot_q && node->src1->type == GGML_TYPE_F32) {
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
if (ggml_compute_forward_mul_mat_use_blas(node->src0, node->src1, node)) {
node->n_tasks = 1;
cur = GGML_TYPE_SIZE[GGML_TYPE_F32]*(node->src0->ne[0]*node->src0->ne[1]);
} else
#endif
{
cur = GGML_TYPE_SIZE[node->src0->type]*ggml_nelements(node->src1)/GGML_BLCK_SIZE[node->src0->type];
}
} else {
GGML_ASSERT(false);
}
work_size = MAX(work_size, cur);
} break;
case GGML_OP_SCALE:
{
node->n_tasks = n_threads;
} break;
case GGML_OP_CPY:
case GGML_OP_RESHAPE:
case GGML_OP_VIEW:
case GGML_OP_PERMUTE:
case GGML_OP_TRANSPOSE:
case GGML_OP_GET_ROWS:
case GGML_OP_DIAG_MASK_INF:
{
node->n_tasks = 1;
} break;
case GGML_OP_SOFT_MAX:
{
node->n_tasks = n_threads;
} break;
case GGML_OP_ROPE:
{
node->n_tasks = n_threads;
} break;
case GGML_OP_CONV_1D_1S:
case GGML_OP_CONV_1D_2S:
{
node->n_tasks = n_threads;
GGML_ASSERT(node->src0->ne[3] == 1);
GGML_ASSERT(node->src1->ne[2] == 1);
GGML_ASSERT(node->src1->ne[3] == 1);
size_t cur = 0;
const int nk = node->src0->ne[0];
if (node->src0->type == GGML_TYPE_F16 &&
node->src1->type == GGML_TYPE_F32) {
cur = sizeof(ggml_fp16_t)*(
nk*ggml_up32(node->src0->ne[1])*node->src0->ne[2] +
( 2*(nk/2) + node->src1->ne[0])*node->src1->ne[1]
);
} else if (node->src0->type == GGML_TYPE_F32 &&
node->src1->type == GGML_TYPE_F32) {
cur = sizeof(float)*(
nk*ggml_up32(node->src0->ne[1])*node->src0->ne[2] +
( 2*(nk/2) + node->src1->ne[0])*node->src1->ne[1]
);
} else {
GGML_ASSERT(false);
}
work_size = MAX(work_size, cur);
} break;
case GGML_OP_FLASH_ATTN:
{
node->n_tasks = n_threads;
size_t cur = 0;
const int64_t ne11 = ggml_up(node->src1->ne[1], GGML_SOFT_MAX_UNROLL);
if (node->src1->type == GGML_TYPE_F32) {
cur = sizeof(float)*ne11*node->n_tasks; // TODO: this can become (n_tasks-1)
cur += sizeof(float)*ne11*node->n_tasks; // this is overestimated by x2
}
if (node->src1->type == GGML_TYPE_F16) {
cur = sizeof(float)*ne11*node->n_tasks; // TODO: this can become (n_tasks-1)
cur += sizeof(float)*ne11*node->n_tasks; // this is overestimated by x2
}
work_size = MAX(work_size, cur);
} break;
case GGML_OP_FLASH_FF:
{
node->n_tasks = n_threads;
size_t cur = 0;
if (node->src1->type == GGML_TYPE_F32) {
cur = sizeof(float)*node->src1->ne[1]*node->n_tasks; // TODO: this can become (n_tasks-1)
cur += sizeof(float)*node->src1->ne[1]*node->n_tasks; // this is overestimated by x2
}
if (node->src1->type == GGML_TYPE_F16) {
cur = sizeof(float)*node->src1->ne[1]*node->n_tasks; // TODO: this can become (n_tasks-1)
cur += sizeof(float)*node->src1->ne[1]*node->n_tasks; // this is overestimated by x2
}
work_size = MAX(work_size, cur);
} break;
case GGML_OP_NONE:
{
node->n_tasks = 1;
} break;
case GGML_OP_COUNT:
{
GGML_ASSERT(false);
} break;
}
}
if (cgraph->work != NULL && work_size > cgraph->work_size) {
GGML_ASSERT(false); // TODO: better handling
}
if (work_size > 0 && cgraph->work == NULL) {
cgraph->work_size = work_size + CACHE_LINE_SIZE*(n_threads - 1);
GGML_PRINT_DEBUG("%s: allocating work buffer for graph (%zu bytes)\n", __func__, cgraph->work_size);
cgraph->work = ggml_new_tensor_1d(ctx, GGML_TYPE_I8, cgraph->work_size);
}
}
const int64_t perf_start_cycles = ggml_perf_cycles();
const int64_t perf_start_time_us = ggml_perf_time_us();
for (int i = 0; i < cgraph->n_nodes; i++) {
GGML_PRINT_DEBUG_5("%s: %d/%d\n", __func__, i, cgraph->n_nodes);
struct ggml_tensor * node = cgraph->nodes[i];
// TODO: this could be used to avoid unnecessary computations, but it needs to be improved
//if (node->grad == NULL && node->perf_runs > 0) {
// continue;
//}
const int64_t perf_node_start_cycles = ggml_perf_cycles();
const int64_t perf_node_start_time_us = ggml_perf_time_us();
// INIT
struct ggml_compute_params params = {
/*.type =*/ GGML_TASK_INIT,
/*.ith =*/ 0,
/*.nth =*/ node->n_tasks,
/*.wsize =*/ cgraph->work ? ggml_nbytes(cgraph->work) : 0,
/*.wdata =*/ cgraph->work ? cgraph->work->data : NULL,
};
ggml_compute_forward(&params, node);
// COMPUTE
if (node->n_tasks > 1) {
if (atomic_fetch_add(&state_shared.n_ready, 1) == n_threads - 1) {
atomic_store(&state_shared.has_work, false);
}
while (atomic_load(&state_shared.has_work)) {
ggml_lock_lock (&state_shared.spin);
ggml_lock_unlock(&state_shared.spin);
}
// launch thread pool
for (int j = 0; j < n_threads - 1; j++) {
workers[j].params = (struct ggml_compute_params) {
.type = GGML_TASK_COMPUTE,
.ith = j + 1,
.nth = node->n_tasks,
.wsize = cgraph->work ? ggml_nbytes(cgraph->work) : 0,
.wdata = cgraph->work ? cgraph->work->data : NULL,
};
workers[j].node = node;
}
atomic_fetch_sub(&state_shared.n_ready, 1);
while (atomic_load(&state_shared.n_ready) > 0) {
ggml_lock_lock (&state_shared.spin);
ggml_lock_unlock(&state_shared.spin);
}
atomic_store(&state_shared.has_work, true);
}
params.type = GGML_TASK_COMPUTE;
ggml_compute_forward(&params, node);
// wait for thread pool
if (node->n_tasks > 1) {
if (atomic_fetch_add(&state_shared.n_ready, 1) == n_threads - 1) {
atomic_store(&state_shared.has_work, false);
}
while (atomic_load(&state_shared.has_work)) {
ggml_lock_lock (&state_shared.spin);
ggml_lock_unlock(&state_shared.spin);
}
atomic_fetch_sub(&state_shared.n_ready, 1);
while (atomic_load(&state_shared.n_ready) != 0) {
ggml_lock_lock (&state_shared.spin);
ggml_lock_unlock(&state_shared.spin);
}
}
// FINALIZE
if (node->n_tasks > 1) {
if (atomic_fetch_add(&state_shared.n_ready, 1) == n_threads - 1) {
atomic_store(&state_shared.has_work, false);
}
while (atomic_load(&state_shared.has_work)) {
ggml_lock_lock (&state_shared.spin);
ggml_lock_unlock(&state_shared.spin);
}
// launch thread pool
for (int j = 0; j < n_threads - 1; j++) {
workers[j].params = (struct ggml_compute_params) {
.type = GGML_TASK_FINALIZE,
.ith = j + 1,
.nth = node->n_tasks,
.wsize = cgraph->work ? ggml_nbytes(cgraph->work) : 0,
.wdata = cgraph->work ? cgraph->work->data : NULL,
};
workers[j].node = node;
}
atomic_fetch_sub(&state_shared.n_ready, 1);
while (atomic_load(&state_shared.n_ready) > 0) {
ggml_lock_lock (&state_shared.spin);
ggml_lock_unlock(&state_shared.spin);
}
atomic_store(&state_shared.has_work, true);
}
params.type = GGML_TASK_FINALIZE;
ggml_compute_forward(&params, node);
// wait for thread pool
if (node->n_tasks > 1) {
if (atomic_fetch_add(&state_shared.n_ready, 1) == n_threads - 1) {
atomic_store(&state_shared.has_work, false);
}
while (atomic_load(&state_shared.has_work)) {
ggml_lock_lock (&state_shared.spin);
ggml_lock_unlock(&state_shared.spin);
}
atomic_fetch_sub(&state_shared.n_ready, 1);
while (atomic_load(&state_shared.n_ready) != 0) {
ggml_lock_lock (&state_shared.spin);
ggml_lock_unlock(&state_shared.spin);
}
}
// performance stats (node)
{
int64_t perf_cycles_cur = ggml_perf_cycles() - perf_node_start_cycles;
int64_t perf_time_us_cur = ggml_perf_time_us() - perf_node_start_time_us;
node->perf_runs++;
node->perf_cycles += perf_cycles_cur;
node->perf_time_us += perf_time_us_cur;
}
}
// join thread pool
if (n_threads > 1) {
atomic_store(&state_shared.stop, true);
atomic_store(&state_shared.has_work, true);
for (int j = 0; j < n_threads - 1; j++) {
int rc = ggml_thread_join(workers[j].thrd, NULL);
GGML_ASSERT(rc == 0);
UNUSED(rc);
}
ggml_lock_destroy(&state_shared.spin);
}
// 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);
}
}
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_print(const struct ggml_cgraph * cgraph) {
int64_t perf_total_per_op_us[GGML_OP_COUNT] = {0};
GGML_PRINT("=== GRAPH ===\n");
GGML_PRINT_DEBUG("n_threads = %d\n", cgraph->n_threads);
GGML_PRINT_DEBUG("total work size = %zu bytes\n",cgraph->work_size);
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] += node->perf_time_us;
GGML_PRINT(" - %3d: [ %" PRId64 ", %" PRId64 ", %" 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_LABEL[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: [ %" PRId64 ", %" PRId64 "] %8s\n",
i,
node->ne[0], node->ne[1],
GGML_OP_LABEL[node->op]);
}
for (int i = 0; i < GGML_OP_COUNT; i++) {
GGML_PRINT("perf_total_per_op_us[%16s] = %7.3f ms\n", GGML_OP_LABEL[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;
}
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=\"%d [%" PRId64 ", %" PRId64 "] | <x>%s",
(void *) node, color,
i, node->ne[0], node->ne[1],
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");
if (ggml_nelements(node) == 1) {
fprintf(fp, " \"%p\" [ \
style = filled; fillcolor = %s; shape = record; \
label=\"<x>%.1e\"; ]\n",
(void *) node, color, (double)ggml_get_f32_1d(node, 0));
} else {
fprintf(fp, " \"%p\" [ \
style = filled; fillcolor = %s; shape = record; \
label=\"<x>CONST %d [%" PRId64 ", %" PRId64 "]\"; ]\n",
(void *) node, color,
i, node->ne[0], node->ne[1]);
}
}
for (int i = 0; i < gb->n_nodes; i++) {
struct ggml_tensor * node = gb->nodes[i];
struct ggml_tensor * parent = ggml_graph_get_parent(gb, node);
if (node->src0) {
struct ggml_tensor * parent0 = ggml_graph_get_parent(gb, node->src0);
fprintf(fp, " \"%p\":%s -> \"%p\":%s [ arrowhead = %s; style = %s; label = \"x\"; ]\n",
parent0 ? (void *) parent0 : (void *) node->src0,
parent0 ? "g" : "x",
parent ? (void *) parent : (void *) node,
parent ? "g" : "x",
parent ? "empty" : "vee",
parent ? "dashed" : "solid");
}
if (node->src1) {
struct ggml_tensor * parent1 = ggml_graph_get_parent(gb, node->src1);
fprintf(fp, " \"%p\":%s -> \"%p\":%s [ arrowhead = %s; style = %s; label = \"y\"; ]\n",
parent1 ? (void *) parent1 : (void *) node->src1,
parent1 ? "g" : "x",
parent ? (void *) parent : (void *) node,
parent ? "g" : "x",
parent ? "empty" : "vee",
parent ? "dashed" : "solid");
}
}
for (int i = 0; i < gb->n_leafs; i++) {
struct ggml_tensor * node = gb->leafs[i];
if (node->src0) {
fprintf(fp, " \"%p\":%s -> \"%p\":%s [ label = \"x\"; ]\n",
(void *) node->src0, "x",
(void *) node, "x");
}
if (node->src1) {
fprintf(fp, " \"%p\":%s -> \"%p\":%s [ label = \"y\"; ]\n",
(void *) node->src1, "x",
(void *) node, "x");
}
}
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_params params,
struct ggml_tensor * f,
struct ggml_cgraph * gf,
struct ggml_cgraph * gb) {
GGML_ASSERT(ggml_is_scalar(f));
gf->n_threads = params.n_threads;
gb->n_threads = params.n_threads;
// 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]);
}
}
// constants
const float alpha = params.adam.alpha;
const float beta1 = params.adam.beta1;
const float beta2 = params.adam.beta2;
const float eps = params.adam.eps;
float * x = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx)->data; // view of the parameters
float * g1 = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx)->data; // gradient
float * g2 = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx)->data; // gradient squared
float * m = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx)->data; // first moment
float * v = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx)->data; // second moment
float * mh = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx)->data; // first moment hat
float * vh = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx)->data; // second moment hat
float * pf = params.past > 0 ? ggml_new_tensor_1d(ctx, GGML_TYPE_F32, params.past)->data : NULL; // past function values
// initialize
ggml_vec_set_f32(nx, m, 0.0f);
ggml_vec_set_f32(nx, v, 0.0f);
// update view
ggml_opt_get_params(np, ps, x);
// compute the function value
ggml_graph_reset (gf);
ggml_set_f32 (f->grad, 1.0f);
ggml_graph_compute(ctx, gb);
float fx_prev = ggml_get_f32_1d(f, 0);
if (pf) {
pf[0] = fx_prev;
}
int n_no_improvement = 0;
float fx_best = fx_prev;
// run the optimizer
for (int t = 0; t < params.adam.n_iter; ++t) {
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);
{
// update the gradient
ggml_opt_get_grad(np, ps, g1);
// m_t = beta1*m_t-1 + (1 - beta1)*g_t
ggml_vec_scale_f32(nx, m, beta1);
ggml_vec_mad_f32 (nx, m, g1, 1.0f - beta1);
// g2 = g1^2
ggml_vec_sqr_f32 (nx, g2, g1);
// v_t = beta2*v_t-1 + (1 - beta2)*g_t^2
ggml_vec_scale_f32(nx, v, beta2);
ggml_vec_mad_f32 (nx, v, g2, 1.0f - beta2);
// m^hat = m_t / (1 - beta1^t)
// v^hat = v_t / (1 - beta2^t)
// x_t = x_t-1 - alpha*m^hat/(sqrt(v^hat) + eps)
ggml_vec_cpy_f32 (nx, mh, m);
ggml_vec_cpy_f32 (nx, vh, v);
ggml_vec_scale_f32(nx, mh, alpha/(1.0f - powf(beta1, t + 1)));
ggml_vec_scale_f32(nx, vh, 1.0f/(1.0f - powf(beta2, t + 1)));
ggml_vec_sqrt_f32 (nx, vh, vh);
ggml_vec_acc1_f32 (nx, vh, eps);
ggml_vec_div_f32 (nx, mh, mh, vh);
ggml_vec_sub_f32 (nx, x, x, mh);
// update the parameters
ggml_opt_set_params(np, ps, x);
}
ggml_graph_reset (gf);
ggml_set_f32 (f->grad, 1.0f);
ggml_graph_compute(ctx, gb);
const float fx = ggml_get_f32_1d(f, 0);
// check convergence
if (fabsf(fx - fx_prev)/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 <= t) {
const float rate = (pf[t%params.past] - fx)/fx;
if (fabsf(rate) < params.delta) {
return GGML_OPT_OK;
}
}
pf[t%params.past] = fx;
}
// check for improvement
if (params.max_no_improvement > 0) {
if (fx_best > fx) {
fx_best = fx;
n_no_improvement = 0;
} else {
++n_no_improvement;
if (n_no_improvement >= params.max_no_improvement) {
return GGML_OPT_OK;
}
}
}
fx_prev = 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(
struct ggml_context * ctx,
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,
const int np,
struct ggml_tensor * ps[]) {
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) {
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(ctx, gb);
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;
}
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_params params,
struct ggml_tensor * f,
struct ggml_cgraph * gf,
struct ggml_cgraph * gb) {
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;
}
}
gf->n_threads = params.n_threads;
gb->n_threads = params.n_threads;
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]);
}
}
float * x = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx)->data; // current parameters
float * xp = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx)->data; // previous parameters
float * g = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx)->data; // current gradient
float * gp = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx)->data; // previous gradient
float * d = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx)->data; // search direction
float * pf = params.past > 0 ? ggml_new_tensor_1d(ctx, GGML_TYPE_F32, params.past)->data : NULL; // past function values
float fx = 0.0f; // cost function value
float xnorm = 0.0f; // ||x||
float gnorm = 0.0f; // ||g||
float step = 0.0f;
// initialize x from the graph nodes
ggml_opt_get_params(np, ps, x);
// the L-BFGS memory
struct ggml_lbfgs_iteration_data * lm = alloca(sizeof(struct ggml_lbfgs_iteration_data)*m);
for (int i = 0; i < m; ++i) {
lm[i].alpha = 0.0f;
lm[i].ys = 0.0f;
lm[i].s = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx)->data;
lm[i].y = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx)->data;
}
// 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(ctx, gb);
ggml_opt_get_grad(np, ps, g);
fx = ggml_get_f32_1d(f, 0);
}
if (pf) {
pf[0] = fx;
}
float fx_best = 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;
}
// initial step
ggml_vec_norm_inv_f32(nx, &step, d);
int j = 0;
int k = 1;
int ls = 0;
int end = 0;
int bound = 0;
int n_no_improvement = 0;
float ys = 0.0f;
float yy = 0.0f;
float beta = 0.0f;
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(ctx, &params, nx, x, &fx, g, d, &step, xp, f, gf, gb, np, ps);
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;
}
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) {
const float rate = (pf[k%params.past] - fx)/fx;
if (fabsf(rate) < params.delta) {
return GGML_OPT_OK;
}
}
pf[k%params.past] = fx;
}
// check for improvement
if (params.max_no_improvement > 0) {
if (fx < fx_best) {
fx_best = fx;
n_no_improvement = 0;
} else {
n_no_improvement++;
if (n_no_improvement >= params.max_no_improvement) {
return GGML_OPT_OK;
}
}
}
if (params.lbfgs.n_iter != 0 && params.lbfgs.n_iter < k + 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[end].s, x, xp);
ggml_vec_sub_f32(nx, lm[end].y, 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[end].y, lm[end].s);
ggml_vec_dot_f32(nx, &yy, lm[end].y, lm[end].y);
lm[end].ys = ys;
// find new search direction
// ref: https://en.wikipedia.org/wiki/Limited-memory_BFGS
bound = (m <= k) ? m : k;
k++;
end = (end + 1)%m;
// initialize search direction with -g
ggml_vec_neg_f32(nx, d, g);
j = end;
for (int i = 0; i < bound; ++i) {
j = (j + m - 1) % m;
// \alpha_{j} = \rho_{j} s^{t}_{j} \cdot q_{k+1}
ggml_vec_dot_f32(nx, &lm[j].alpha, lm[j].s, d);
lm[j].alpha /= lm[j].ys;
// q_{i} = q_{i+1} - \alpha_{i} y_{i}
ggml_vec_mad_f32(nx, d, lm[j].y, -lm[j].alpha);
}
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[j].y, d);
beta /= lm[j].ys;
// \gamma_{i+1} = \gamma_{i} + (\alpha_{j} - \beta_{j}) s_{j}
ggml_vec_mad_f32(nx, d, lm[j].s, lm[j].alpha - beta);
j = (j + 1)%m;
}
step = 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,
.alpha = 0.001f,
.beta1 = 0.9f,
.beta2 = 0.999f,
.eps = 1e-8f,
.eps_f = 1e-5f,
.eps_g = 1e-3f,
},
};
} 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;
}
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;
// build forward + backward compute graphs
struct ggml_cgraph gf = ggml_build_forward (f);
struct ggml_cgraph gb = ggml_build_backward(ctx, &gf, false);
switch (params.type) {
case GGML_OPT_ADAM:
{
result = ggml_opt_adam(ctx, params, f, &gf, &gb);
} break;
case GGML_OPT_LBFGS:
{
result = ggml_opt_lbfgs(ctx, params, f, &gf, &gb);
} break;
}
if (params.print_forward_graph) {
ggml_graph_print (&gf);
ggml_graph_dump_dot(&gf, NULL, "opt-forward.dot");
}
if (params.print_backward_graph) {
ggml_graph_print (&gb);
ggml_graph_dump_dot(&gb, &gf, "opt-backward.dot");
}
if (free_ctx) {
ggml_free(ctx);
}
return result;
}
////////////////////////////////////////////////////////////////////////////////
size_t ggml_quantize_q4_0(const float * src, void * dst, int n, int k, int64_t * hist) {
assert(k % QK == 0);
const int nb = k / QK;
for (int j = 0; j < n; j += k) {
block_q4_0 * restrict y = (block_q4_0 *)dst + j/QK;
quantize_row_q4_0_reference(src + j, y, k);
for (int i = 0; i < nb; i++) {
for (int l = 0; l < QK; l += 2) {
const uint8_t vi0 = y[i].qs[l/2] & 0xF;
const uint8_t vi1 = y[i].qs[l/2] >> 4;
hist[vi0]++;
hist[vi1]++;
}
}
}
return (n/QK*sizeof(block_q4_0));
}
size_t ggml_quantize_q4_1(const float * src, void * dst, int n, int k, int64_t * hist) {
assert(k % QK == 0);
const int nb = k / QK;
for (int j = 0; j < n; j += k) {
block_q4_1 * restrict y = (block_q4_1 *)dst + j/QK;
quantize_row_q4_1_reference(src + j, y, k);
for (int i = 0; i < nb; i++) {
for (int l = 0; l < QK; l += 2) {
const uint8_t vi0 = y[i].qs[l/2] & 0xF;
const uint8_t vi1 = y[i].qs[l/2] >> 4;
hist[vi0]++;
hist[vi1]++;
}
}
}
return (n/QK*sizeof(block_q4_1));
}
////////////////////////////////////////////////////////////////////////////////
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_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_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)
return 1;
#else
return 0;
#endif
}
int ggml_cpu_has_sse3(void) {
#if defined(__SSE3__)
return 1;
#else
return 0;
#endif
}
int ggml_cpu_has_vsx(void) {
#if defined(__POWER9_VECTOR__)
return 1;
#else
return 0;
#endif
}
////////////////////////////////////////////////////////////////////////////////
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