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llama.cpp/ggml.c

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Initial release
2023-03-10 19:40:58 +01:00
#include "ggml.h"
#if defined(_MSC_VER) || defined(__MINGW32__)
#include <malloc.h> // using malloc.h with MSC/MINGW
#elif !defined(__FreeBSD__)
#include <alloca.h>
#endif
#include <assert.h>
#include <time.h>
#include <math.h>
#include <stdlib.h>
#include <string.h>
#include <stdint.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>
#include <errno.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
#ifdef __HAIKU__
#define static_assert(cond, msg) _Static_assert(cond, msg)
#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) (x)
#define GGML_COMPUTE_FP32_TO_FP16(x) (x)
#define GGML_FP16_TO_FP32(x) (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__
#define GGML_COMPUTE_FP16_TO_FP32(x) _cvtsh_ss(x)
#define GGML_COMPUTE_FP32_TO_FP16(x) _cvtss_sh(x, 0)
#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.
#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 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
Add AVX2 support for x86 architectures thanks to @Const-me !
2023-03-11 16:58:18 +01:00
// AVX routines provided by GH user Const-me
// ref: https://github.com/ggerganov/ggml/pull/27#issuecomment-1464934600
#if __AVX2__
// Unpack 32 4-bit fields into 32 bytes
// The output vector contains 32 bytes, each one in [ 0 .. 15 ] interval
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;
}
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 );
}
#endif
Initial release
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// 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)
void quantize_row_q4_0(const float * restrict x, void * restrict y, int k) {
assert(k % QK == 0);
const int nb = k / QK;
Support all LLaMA models + change Q4_0 quantization storage
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const size_t bs = sizeof(float) + QK/2;
Initial release
2023-03-10 19:40:58 +01:00
Windows fixes (#31) * Apply fixes suggested to build on windows Issue: https://github.com/ggerganov/llama.cpp/issues/22 * Remove unsupported VLAs * MSVC: Remove features that are only available on MSVC C++20. * Fix zero initialization of the other fields. * Change the use of vector for stack allocations.
2023-03-12 21:15:00 +01:00
uint8_t * restrict pd = ((uint8_t *)y + 0*bs);
uint8_t * restrict pb = ((uint8_t *)y + 0*bs + sizeof(float));
Initial release
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uint8_t pp[QK/2];
#if __ARM_NEON
#if QK == 32
for (int i = 0; i < nb; i++) {
float amax = 0.0f; // absolute max
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]);
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 ? 1.0/d : 0.0;
Support all LLaMA models + change Q4_0 quantization storage
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*(float *)pd = d;
pd += bs;
Initial release
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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);
pp[2*l + 0] = vgetq_lane_s32(vi, 0) | (vgetq_lane_s32(vi, 1) << 4);
pp[2*l + 1] = vgetq_lane_s32(vi, 2) | (vgetq_lane_s32(vi, 3) << 4);
}
Support all LLaMA models + change Q4_0 quantization storage
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memcpy(pb, pp, sizeof(pp));
pb += bs;
Initial release
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}
#else
#error "not implemented for QK"
#endif
Add AVX2 support for x86 architectures thanks to @Const-me !
2023-03-11 16:58:18 +01:00
#elif defined(__AVX2__)
#if QK == 32
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;
*(float *)pd = d;
pd += bs;
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* )pb, res );
pb += bs;
}
#else
#error "not implemented for QK"
#endif
Initial release
2023-03-10 19:40:58 +01:00
#elif defined(__wasm_simd128__)
#if QK == 32
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;
Support all LLaMA models + change Q4_0 quantization storage
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*(float *)pd = d;
pd += bs;
Initial release
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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);
pp[2*l + 0] = wasm_i32x4_extract_lane(vi, 0) | (wasm_i32x4_extract_lane(vi, 1) << 4);
pp[2*l + 1] = wasm_i32x4_extract_lane(vi, 2) | (wasm_i32x4_extract_lane(vi, 3) << 4);
}
Support all LLaMA models + change Q4_0 quantization storage
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memcpy(pb, pp, sizeof(pp));
pb += bs;
Initial release
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}
#else
#error "not implemented for QK"
#endif
#else
// scalar
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;
Support all LLaMA models + change Q4_0 quantization storage
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*(float *)pd = d;
pd += bs;
Initial release
2023-03-10 19:40:58 +01:00
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) (round(v0))) + 8;
const uint8_t vi1 = ((int8_t) (round(v1))) + 8;
assert(vi0 >= 0 && vi0 < 16);
assert(vi1 >= 0 && vi1 < 16);
pp[l/2] = vi0 | (vi1 << 4);
}
Support all LLaMA models + change Q4_0 quantization storage
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memcpy(pb, pp, sizeof(pp));
pb += bs;
Initial release
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}
#endif
}
// method 4
// blocks of QK elements
// represented with 2 floats (min + delta) and QK/2 8-bit ints (i.e QK 4-bit unsigned integer factors)
void quantize_row_q4_1(const float * restrict x, void * restrict y, int k) {
assert(k % QK == 0);
const int nb = k / QK;
float * restrict pm = (float *) (y);
float * restrict pd = (float *) (pm + nb);
uint8_t * restrict pb = (uint8_t *) (pd + nb);
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;
pm[i] = min;
pd[i] = d;
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 = round(v0);
const uint8_t vi1 = round(v1);
assert(vi0 >= 0 && vi0 < 16);
assert(vi1 >= 0 && vi1 < 16);
pp[l/2] = vi0 | (vi1 << 4);
}
memcpy(pb + i*QK/2, pp, sizeof(pp));
}
}
// TODO: vectorize
void dequantize_row_q4_0(const void * restrict x, float * restrict y, int k) {
assert(k % QK == 0);
const int nb = k / QK;
Support all LLaMA models + change Q4_0 quantization storage
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const size_t bs = sizeof(float) + QK/2;
Initial release
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Windows fixes (#31) * Apply fixes suggested to build on windows Issue: https://github.com/ggerganov/llama.cpp/issues/22 * Remove unsupported VLAs * MSVC: Remove features that are only available on MSVC C++20. * Fix zero initialization of the other fields. * Change the use of vector for stack allocations.
2023-03-12 21:15:00 +01:00
const uint8_t * restrict pd = ((const uint8_t *)x + 0*bs);
const uint8_t * restrict pb = ((const uint8_t *)x + 0*bs + sizeof(float));
Initial release
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// scalar
for (int i = 0; i < nb; i++) {
Support all LLaMA models + change Q4_0 quantization storage
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const float d = *(const float *) (pd + i*bs);
Initial release
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Support all LLaMA models + change Q4_0 quantization storage
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const uint8_t * restrict pp = pb + i*bs;
Initial release
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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;
Support all LLaMA models + change Q4_0 quantization storage
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//printf("d = %f, vi = %d, vi0 = %d, vi1 = %d, v0 = %f, v1 = %f\n", d, vi, vi0, vi1, v0, v1);
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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]));
}
}
}
void dequantize_row_q4_1(const void * restrict x, float * restrict y, int k) {
assert(k % QK == 0);
const int nb = k / QK;
const float * restrict pm = (const float *) (x);
const float * restrict pd = (const float *) (pm + nb);
const uint8_t * restrict pb = (const uint8_t *) (pd + nb);
for (int i = 0; i < nb; i++) {
const float m = pm[i];
const float d = pd[i];
const uint8_t * restrict pp = pb + i*QK/2;
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]));
}
}
}
//
// 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 = 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
// we take advantage of the _mm256_cvt intrinsics to convert F16 <-> F32
#define GGML_F32Cx8 __m256
#define GGML_F32Cx8_ZERO _mm256_setzero_ps()
#define GGML_F32Cx8_SET1(x) _mm256_set1_ps(x)
#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))
#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) {
ggml_float sumf = 0.0;
#ifdef GGML_SIMD
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
for (int i = 0; i < n; ++i) {
sumf += x[i]*y[i];
}
#endif
*s = sumf;
}
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_FP16_TO_FP32(x[i])*GGML_FP16_TO_FP32(y[i]);
}
#else
for (int i = 0; i < n; ++i) {
sumf += GGML_FP16_TO_FP32(x[i])*GGML_FP16_TO_FP32(y[i]);
}
#endif
*s = sumf;
}
inline static void ggml_vec_dot_q4_0(const int n, float * restrict s, const void * restrict x, const void * restrict y) {
const int nb = n / QK;
assert(n % QK == 0);
assert(nb % 2 == 0);
Support all LLaMA models + change Q4_0 quantization storage
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const size_t bs = sizeof(float) + QK/2;
Initial release
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Windows fixes (#31) * Apply fixes suggested to build on windows Issue: https://github.com/ggerganov/llama.cpp/issues/22 * Remove unsupported VLAs * MSVC: Remove features that are only available on MSVC C++20. * Fix zero initialization of the other fields. * Change the use of vector for stack allocations.
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const uint8_t * restrict pd0 = ((const uint8_t *)x + 0*bs);
const uint8_t * restrict pd1 = ((const uint8_t *)y + 0*bs);
Support all LLaMA models + change Q4_0 quantization storage
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Windows fixes (#31) * Apply fixes suggested to build on windows Issue: https://github.com/ggerganov/llama.cpp/issues/22 * Remove unsupported VLAs * MSVC: Remove features that are only available on MSVC C++20. * Fix zero initialization of the other fields. * Change the use of vector for stack allocations.
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const uint8_t * restrict pb0 = ((const uint8_t *)x + 0*bs + sizeof(float));
const uint8_t * restrict pb1 = ((const uint8_t *)y + 0*bs + sizeof(float));
Initial release
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float sumf = 0.0;
#ifdef __ARM_NEON
#if QK == 32
float sum0 = 0.0f;
float sum1 = 0.0f;
for (int i = 0; i < nb; i += 2) {
Support all LLaMA models + change Q4_0 quantization storage
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const float d0_0 = *(const float *) (pd0 + i*bs);
const float d1_0 = *(const float *) (pd1 + i*bs);
const float d0_1 = *(const float *) (pd0 + (i + 1)*bs);
const float d1_1 = *(const float *) (pd1 + (i + 1)*bs);
Initial release
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//printf("d0_0: %f, d1_0: %f, d0_1: %f, d1_1: %f\n", d0_0, d1_0, d0_1, d1_1);
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const uint8_t * restrict p0 = pb0 + i*bs;
const uint8_t * restrict p1 = pb1 + i*bs;
Initial release
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const uint8x16_t m4b = vdupq_n_u8(0xf);
const int8x16_t s8b = vdupq_n_s8(0x8);
const uint8x16_t v0_0 = vld1q_u8(p0);
const uint8x16_t v1_0 = vld1q_u8(p1);
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const uint8x16_t v0_1 = vld1q_u8(p0 + bs);
const uint8x16_t v1_1 = vld1q_u8(p1 + bs);
Initial release
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// 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);
// dot product into int16x8_t
Use vdotq_s32 to improve performance (#67) * 10% performance boost on ARM * Back to original change
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// assume that vdotq_s32 is always available, if not, should check for __ARM_FEATURE_DOTPROD
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);
Check for vdotq_s32 availability
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Use vdotq_s32 to improve performance (#67) * 10% performance boost on ARM * Back to original change
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p_0 = vdotq_s32(p_0, v0_0hs, v1_0hs);
p_1 = vdotq_s32(p_1, v0_1hs, v1_1hs);
Check for vdotq_s32 availability
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// scalar
#if defined(__ARM_FEATURE_QRDMX)
Use vdotq_s32 to improve performance (#67) * 10% performance boost on ARM * Back to original change
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sum0 += d0_0*d1_0*vaddvq_s32(p_0);
sum1 += d0_1*d1_1*vaddvq_s32(p_1);
Check for vdotq_s32 availability
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#else
Use vdotq_s32 to improve performance (#67) * 10% performance boost on ARM * Back to original change
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sum0 += d0_0*d1_0*(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 += d0_1*d1_1*(vgetq_lane_s32(p_1, 0) + vgetq_lane_s32(p_1, 1) + vgetq_lane_s32(p_1, 2) + vgetq_lane_s32(p_1, 3));
Initial release
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#endif
}
sumf = sum0 + sum1;
#else
#error "not implemented for QK"
#endif
Add AVX2 support for x86 architectures thanks to @Const-me !
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#elif defined(__AVX2__)
#if QK == 32
const size_t countBlocks = nb;
// Initialize accumulator with zeros
__m256 acc = _mm256_setzero_ps();
// Main loop
for (int i = 0; i < nb; ++i) {
const float * d0_0 = (const float *) (pd0 + i*bs);
const float * d1_0 = (const float *) (pd1 + i*bs);
const uint8_t * restrict p0 = pb0 + i*bs;
const uint8_t * restrict p1 = pb1 + i*bs;
// Compute combined scale for the block
const __m256 scale = _mm256_mul_ps( _mm256_broadcast_ss( d0_0 ), _mm256_broadcast_ss( d1_0 ) );
// Load 16 bytes, and unpack 4 bit fields into bytes, making 32 bytes
__m256i bx = bytesFromNibbles( p0 );
__m256i by = bytesFromNibbles( p1 );
// 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 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
x16 = _mm256_cvtepi8_epi16( _mm256_extracti128_si256( bx, 1 ) );
y16 = _mm256_cvtepi8_epi16( _mm256_extracti128_si256( by, 1 ) );
// Accumulate products of int16_t integers
i32 = _mm256_add_epi32( i32, _mm256_madd_epi16( x16, y16 ) );
// Convert int32_t to float
__m256 p = _mm256_cvtepi32_ps( i32 );
// Apply the scale, and accumulate
acc = _mm256_fmadd_ps( scale, 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 );
#else
#error "not implemented for QK"
#endif
Initial release
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#elif defined(__wasm_simd128__)
#if QK == 32
// wasm simd
float sum0 = 0.0f;
float sum1 = 0.0f;
for (int i = 0; i < nb; i += 2) {
Support all LLaMA models + change Q4_0 quantization storage
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const float d0_0 = *(const float *) (pd0 + i*bs);
const float d1_0 = *(const float *) (pd1 + i*bs);
const float d0_1 = *(const float *) (pd0 + (i + 1)*bs);
const float d1_1 = *(const float *) (pd1 + (i + 1)*bs);
Initial release
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Support all LLaMA models + change Q4_0 quantization storage
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const uint8_t * restrict p0 = pb0 + i*bs;
const uint8_t * restrict p1 = pb1 + i*bs;
Initial release
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const v128_t m4b = wasm_u8x16_splat(0xf);
const v128_t s8b = wasm_i8x16_splat(0x8);
const v128_t v0_0 = wasm_v128_load(p0);
Support all LLaMA models + change Q4_0 quantization storage
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const v128_t v0_1 = wasm_v128_load(p0 + bs);
Initial release
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const v128_t v1_0 = wasm_v128_load(p1);
Support all LLaMA models + change Q4_0 quantization storage
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const v128_t v1_1 = wasm_v128_load(p1 + bs);
Initial release
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// 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 += d0_0*d1_0*(
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 += d0_1*d1_1*(
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
#error "not implemented for QK"
#endif
#else
// scalar
for (int i = 0; i < nb; i++) {
Support all LLaMA models + change Q4_0 quantization storage
2023-03-11 09:47:09 +01:00
const float d0 = *(const float *) (pd0 + i*bs);
const float d1 = *(const float *) (pd1 + i*bs);
Initial release
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Support all LLaMA models + change Q4_0 quantization storage
2023-03-11 09:47:09 +01:00
const uint8_t * restrict p0 = pb0 + i*bs;
const uint8_t * restrict p1 = pb1 + i*bs;
Initial release
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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;
}
inline static void ggml_vec_dot_q4_1(const int n, float * restrict s, const void * restrict x, const void * restrict y) {
const int nb = n / QK;
const float * restrict pm0 = (const float *) x;
const float * restrict pm1 = (const float *) y;
const float * restrict pd0 = (const float *) (pm0 + nb);
const float * restrict pd1 = (const float *) (pm1 + nb);
const uint8_t * restrict pb0 = (const uint8_t *) (pd0 + nb);
const uint8_t * restrict pb1 = (const uint8_t *) (pd1 + nb);
float sumf = 0.0;
#if 1
// scalar
for (int i = 0; i < nb; i++) {
const float m0 = pm0[i];
const float m1 = pm1[i];
const float d0 = pd0[i];
const float d1 = pd1[i];
const uint8_t * restrict p0 = pb0 + i*QK/2;
const uint8_t * restrict p1 = pb1 + i*QK/2;
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_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_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_mad_f16(const int n, ggml_fp16_t * restrict y, ggml_fp16_t * restrict x, const float v) {
#if defined(GGML_SIMD)
const int np = (n & ~(GGML_F16_STEP - 1));
GGML_F16_VEC vx = GGML_F16_VEC_SET1(v);
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);
ay[j] = GGML_F16_VEC_FMA(ay[j], ax[j], vx);
GGML_F16_VEC_STORE(y + i + j*GGML_F16_EPR, ay, j);
}
}
// leftovers
for (int i = np; i < n; ++i) {
GGML_ASSERT(false);
y[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(y[i]) + GGML_FP16_TO_FP32(x[i])*v);
}
#else
for (int i = 0; i < n; ++i) {
y[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(y[i]) + GGML_FP16_TO_FP32(x[i])*v);
}
#endif
}
inline static void ggml_vec_mad_q4_0(const int n, float * restrict y, void * restrict x, const float v) {
assert(n % QK == 0);
const int nb = n / QK;
Support all LLaMA models + change Q4_0 quantization storage
2023-03-11 09:47:09 +01:00
const size_t bs = sizeof(float) + QK/2;
Initial release
2023-03-10 19:40:58 +01:00
Windows fixes (#31) * Apply fixes suggested to build on windows Issue: https://github.com/ggerganov/llama.cpp/issues/22 * Remove unsupported VLAs * MSVC: Remove features that are only available on MSVC C++20. * Fix zero initialization of the other fields. * Change the use of vector for stack allocations.
2023-03-12 21:15:00 +01:00
const uint8_t * restrict pd = ((const uint8_t *)x + 0*bs);
const uint8_t * restrict pb = ((const uint8_t *)x + 0*bs + sizeof(float));
Initial release
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#if __ARM_NEON
#if QK == 32
for (int i = 0; i < nb; ++i) {
Support all LLaMA models + change Q4_0 quantization storage
2023-03-11 09:47:09 +01:00
const float d0 = v*(*(const float *) (pd + i*bs));
Initial release
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Support all LLaMA models + change Q4_0 quantization storage
2023-03-11 09:47:09 +01:00
const uint8_t * restrict pp = pb + i*bs;
Initial release
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const uint8x8_t m4b = vdup_n_u8(0xf);
const int8x8_t s8b = vdup_n_s8(0x8);
const float32x4_t vd = vdupq_n_f32(d0);
for (int j = 0; j < 2; j++) {
const uint8x8_t vx = vld1_u8(pp + j*8);
const int8x8_t vxl = vreinterpret_s8_u8(vand_u8(vx, m4b));
const int8x8_t vxh = vreinterpret_s8_u8(vshr_n_u8(vx, 4));
// sub 8
const int8x8_t vxls = vsub_s8(vxl, s8b);
const int8x8_t vxhs = vsub_s8(vxh, s8b);
//const int8x8_t vxlt = vzip_s8(vxls, vxhs)[0];
//const int8x8_t vxht = vzip_s8(vxls, vxhs)[1];
const int8x8_t vxlt = vzip1_s8(vxls, vxhs);
const int8x8_t vxht = vzip2_s8(vxls, vxhs);
const int8x16_t vxq = vcombine_s8(vxlt, vxht);
// convert to 2x int16x8_t
const int16x8_t vxq0 = vmovl_s8(vget_low_s8 (vxq));
const int16x8_t vxq1 = vmovl_s8(vget_high_s8(vxq));
// convert to 4x float32x4_t
const float32x4_t vx0 = vcvtq_f32_s32(vmovl_s16(vget_low_s16 (vxq0)));
const float32x4_t vx1 = vcvtq_f32_s32(vmovl_s16(vget_high_s16(vxq0)));
const float32x4_t vx2 = vcvtq_f32_s32(vmovl_s16(vget_low_s16 (vxq1)));
const float32x4_t vx3 = vcvtq_f32_s32(vmovl_s16(vget_high_s16(vxq1)));
const float32x4_t vy0 = vld1q_f32(y + i*32 + j*16 + 0);
const float32x4_t vy1 = vld1q_f32(y + i*32 + j*16 + 4);
const float32x4_t vy2 = vld1q_f32(y + i*32 + j*16 + 8);
const float32x4_t vy3 = vld1q_f32(y + i*32 + j*16 + 12);
const float32x4_t vr0 = vfmaq_f32(vy0, vx0, vd);
const float32x4_t vr1 = vfmaq_f32(vy1, vx1, vd);
const float32x4_t vr2 = vfmaq_f32(vy2, vx2, vd);
const float32x4_t vr3 = vfmaq_f32(vy3, vx3, vd);
vst1q_f32(y + i*32 + j*16 + 0, vr0);
vst1q_f32(y + i*32 + j*16 + 4, vr1);
vst1q_f32(y + i*32 + j*16 + 8, vr2);
vst1q_f32(y + i*32 + j*16 + 12, vr3);
}
}
#endif
#else
// scalar
for (int i = 0; i < nb; i++) {
Support all LLaMA models + change Q4_0 quantization storage
2023-03-11 09:47:09 +01:00
const float d = *(const float *) (pd + i*bs);
Initial release
2023-03-10 19:40:58 +01:00
Support all LLaMA models + change Q4_0 quantization storage
2023-03-11 09:47:09 +01:00
const uint8_t * restrict pp = pb + i*bs;
Initial release
2023-03-10 19:40:58 +01:00
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;
y[i*QK + l + 0] += v0*v;
y[i*QK + l + 1] += v1*v;
assert(!isnan(y[i*QK + l + 0]));
assert(!isnan(y[i*QK + l + 1]));
assert(!isinf(y[i*QK + l + 0]));
assert(!isinf(y[i*QK + l + 1]));
}
}
#endif
}
inline static void ggml_vec_mad_q4_1(const int n, float * restrict y, void * restrict x, const float v) {
assert(n % QK == 0);
const int nb = n / QK;
const float * restrict pm = (const float *) (x);
const float * restrict pd = (const float *) (pm + nb);
const uint8_t * restrict pb = (const uint8_t *) (pd + nb);
for (int i = 0; i < nb; i++) {
const float m = pm[i];
const float d = pd[i];
const uint8_t * restrict pp = pb + i*QK/2;
for (int l = 0; l < QK; l += 2) {
const uint8_t vi = pp[l/2];
const uint8_t vi0 = vi & 0xf;
const uint8_t vi1 = vi >> 4;
const float v0 = d*vi0 + m;
const float v1 = d*vi1 + m;
y[i*QK + l + 0] += v0*v;
y[i*QK + l + 1] += v1*v;
assert(!isnan(y[i*QK + l + 0]));
assert(!isnan(y[i*QK + l + 1]));
assert(!isinf(y[i*QK + l + 0]));
assert(!isinf(y[i*QK + l + 1]));
//printf("mad: v0 %f v1 %f, i = %d, l = %d, d = %f, vi = %d, vi0 = %d, vi1 = %d\n", v0, v1, i, l, d, vi, vi0, vi1);
}
}
}
//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 = sqrt(*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] = sqrt(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 ggml_float GELU_COEF_A = 0.044715;
static const ggml_float SQRT_2_OVER_PI = 0.79788456080286535587989211986876;
inline static float ggml_gelu_f32(float x) {
return 0.5*x*(1.0 + tanh(SQRT_2_OVER_PI*x*(1.0 + 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/(1.0 + exp(-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 += 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
ggml_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./(*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(float ) + QK/2,
sizeof(float )*2 + QK/2,
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",
"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 == 34, "GGML_OP_COUNT != 34");
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)",
"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 == 34, "GGML_OP_COUNT != 34");
//
// 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;
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__);
}
int 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_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) \
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 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(exp(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,
/*.n_objects =*/ 0,
/*.objects_begin =*/ NULL,
/*.objects_end =*/ NULL,
/*.scratch =*/ { 0, 0, NULL, },
/*.scratch_save =*/ { 0, 0, NULL, },
};
ggml_assert_aligned(ctx->mem_buffer);
GGML_PRINT_DEBUG("%s: context initialized\n", __func__);
ggml_critical_section_end();
return ctx;
}
void ggml_free(struct ggml_context * ctx) {
// make this function thread safe
ggml_critical_section_start();
bool found = false;
for (int i = 0; i < GGML_MAX_CONTEXTS; i++) {
if (&g_state.contexts[i].context == ctx) {
g_state.contexts[i].used = false;
GGML_PRINT_DEBUG("%s: context %d with %d objects has been freed. memory used = %zu\n",
__func__, i, ctx->n_objects, ctx->objects_end->offs + ctx->objects_end->size);
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;
}
////////////////////////////////////////////////////////////////////////////////
struct ggml_tensor * ggml_new_tensor_impl(
struct ggml_context * ctx,
enum ggml_type type,
int n_dims,
const int* 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) {
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 ? (void *)(result + 1) : data,
/*.pad =*/ { 0 },
};
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 int * 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,
int ne0) {
return ggml_new_tensor(ctx, type, 1, &ne0);
}
struct ggml_tensor * ggml_new_tensor_2d(
struct ggml_context * ctx,
enum ggml_type type,
int ne0,
int ne1) {
const int 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,
int ne0,
int ne1,
int ne2) {
const int 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,
int ne0,
int ne1,
int ne2,
int ne3) {
const int 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) {
return ggml_new_tensor_impl(ctx, src->type, src->n_dims, src->ne, src->data);
}
////////////////////////////////////////////////////////////////////////////////
// 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;
}
int 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);
}
// 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));
bool is_node = false;
if (a->grad || b->grad) {
is_node = true;
}
const int 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,
int ne0,
int 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 int 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,
int ne0,
int ne1,
int 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 int 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,
int 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,
int ne0,
int ne1,
size_t nb1,
size_t offset) {
if (a->grad) {
GGML_ASSERT(false); // gradient propagation is not supported
}
const int 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_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 int 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 int 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_is_contiguous(dst));
GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0));
if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
return;
}
const int ne00 = src0->ne[0];
const int ne01 = src0->ne[1];
const int ne02 = src0->ne[2];
const int 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];
if (ggml_is_contiguous(src0) && src0->type == dst->type) {
memcpy(dst->data, src0->data, ggml_nelements(dst) * GGML_TYPE_SIZE[src0->type]);
return;
}
if (src0->nb[0] == sizeof(ggml_fp16_t)) {
if (dst->type == GGML_TYPE_F16) {
int id = 0;
const size_t rs = ne00*nb00;
for (int i03 = 0; i03 < ne03; i03++) {
for (int i02 = 0; i02 < ne02; i02++) {
for (int i01 = 0; i01 < ne01; i01++) {
const char * src0_ptr = (char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03;
char * dst_ptr = (char *) dst->data + id*rs;
memcpy(dst_ptr, src0_ptr, rs);
id++;
}
}
}
} else if (dst->type == GGML_TYPE_F32) {
int id = 0;
float * dst_ptr = (float *) dst->data;
for (int i03 = 0; i03 < ne03; i03++) {
for (int i02 = 0; i02 < ne02; i02++) {
for (int i01 = 0; i01 < ne01; i01++) {
for (int i00 = 0; i00 < ne00; i00++) {
const ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03);
dst_ptr[id] = GGML_FP16_TO_FP32(*src0_ptr);
id++;
}
}
}
}
} else {
GGML_ASSERT(false); // TODO: implement
}
} else {
//printf("%s: this is not optimal - fix me\n", __func__);
if (dst->type == GGML_TYPE_F32) {
int id = 0;
float * dst_ptr = (float *) dst->data;
for (int i03 = 0; i03 < ne03; i03++) {
for (int i02 = 0; i02 < ne02; i02++) {
for (int i01 = 0; i01 < ne01; i01++) {
for (int i00 = 0; i00 < ne00; i00++) {
const ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03);
dst_ptr[id] = GGML_FP16_TO_FP32(*src0_ptr);
id++;
}
}
}
}
} else if (dst->type == GGML_TYPE_F16) {
int id = 0;
ggml_fp16_t * dst_ptr = (ggml_fp16_t *) dst->data;
for (int i03 = 0; i03 < ne03; i03++) {
for (int i02 = 0; i02 < ne02; i02++) {
for (int i01 = 0; i01 < ne01; i01++) {
for (int i00 = 0; i00 < ne00; i00++) {
const ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03);
dst_ptr[id] = *src0_ptr;
id++;
}
}
}
}
} 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_is_contiguous(dst));
GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0));
if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
return;
}
const int ne00 = src0->ne[0];
const int ne01 = src0->ne[1];
const int ne02 = src0->ne[2];
const int 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];
if (ggml_is_contiguous(src0) && src0->type == dst->type) {
memcpy(dst->data, src0->data, ggml_nelements(dst) * GGML_TYPE_SIZE[src0->type]);
return;
}
if (src0->nb[0] == sizeof(float)) {
if (dst->type == GGML_TYPE_F32) {
int id = 0;
const size_t rs = ne00*nb00;
for (int i03 = 0; i03 < ne03; i03++) {
for (int i02 = 0; i02 < ne02; i02++) {
for (int i01 = 0; i01 < ne01; i01++) {
const char * src0_ptr = (char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03;
char * dst_ptr = (char *) dst->data + id*rs;
memcpy(dst_ptr, src0_ptr, rs);
id++;
}
}
}
} else if (dst->type == GGML_TYPE_F16) {
int id = 0;
ggml_fp16_t * dst_ptr = (ggml_fp16_t *) dst->data;
for (int i03 = 0; i03 < ne03; i03++) {
for (int i02 = 0; i02 < ne02; i02++) {
for (int i01 = 0; i01 < ne01; i01++) {
for (int i00 = 0; i00 < ne00; i00++) {
const float * src0_ptr = (float *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03);
dst_ptr[id] = GGML_FP32_TO_FP16(*src0_ptr);
id++;
}
}
}
}
} else {
GGML_ASSERT(false); // TODO: implement
}
} else {
//printf("%s: this is not optimal - fix me\n", __func__);
if (dst->type == GGML_TYPE_F32) {
int id = 0;
float * dst_ptr = (float *) dst->data;
for (int i03 = 0; i03 < ne03; i03++) {
for (int i02 = 0; i02 < ne02; i02++) {
for (int i01 = 0; i01 < ne01; i01++) {
for (int i00 = 0; i00 < ne00; i00++) {
const float * src0_ptr = (float *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03);
dst_ptr[id] = *src0_ptr;
id++;
}
}
}
}
} else if (dst->type == GGML_TYPE_F16) {
int id = 0;
ggml_fp16_t * dst_ptr = (ggml_fp16_t *) dst->data;
for (int i03 = 0; i03 < ne03; i03++) {
for (int i02 = 0; i02 < ne02; i02++) {
for (int i01 = 0; i01 < ne01; i01++) {
for (int i00 = 0; i00 < ne00; i00++) {
const float * src0_ptr = (float *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03);
dst_ptr[id] = GGML_FP32_TO_FP16(*src0_ptr);
id++;
}
}
}
}
} 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 int ne00 = src0->ne[0];
const int ne01 = src0->ne[1];
const int ne02 = src0->ne[2];
const int 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 (int i03 = 0; i03 < ne03; i03++) {
for (int i02 = 0; i02 < ne02; i02++) {
for (int 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 int ne00 = src0->ne[0];
const int ne01 = src0->ne[1];
const int ne02 = src0->ne[2];
const int 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 int ne0 = dst->ne[0];
const int ne1 = dst->ne[1];
const int ne2 = dst->ne[2];
const int 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 (int i03 = 0; i03 < ne03; i03++) {
for (int i02 = 0; i02 < ne02; i02++) {
for (int 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 int ne00 = src0->ne[0];
const int ne01 = src0->ne[1];
const int ne02 = src0->ne[2];
const int 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 ggml_float eps = 1e-5f; // TODO: make this a parameter
// TODO: optimize
for (int i03 = 0; i03 < ne03; i03++) {
for (int i02 = 0; i02 < ne02; i02++) {
for (int i01 = ith; i01 < ne01; i01 += nth) {
const float * x = (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03);
ggml_float mean = 0.0;
for (int i00 = 0; i00 < ne00; i00++) {
mean += x[i00];
}
mean /= ne00;
float * y = (float *) ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3);
ggml_float sum2 = 0.0;
for (int i00 = 0; i00 < ne00; i00++) {
ggml_float v = x[i00] - mean;
y[i00] = v;
sum2 += v*v;
}
const float scale = 1.0/sqrt(sum2/ne00 + 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;
}
}
// 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) {
UNUSED(src0);
const int ne10 = src1->ne[0];
const int ne0 = dst->ne[0];
const int 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\n", ne0, ne1, ne10);
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 int ne00 = src0->ne[0];
const int ne01 = src0->ne[1];
const int ne02 = src0->ne[2];
const int ne03 = src0->ne[3];
const int ne10 = src1->ne[0];
const int ne11 = src1->ne[1];
const int ne12 = src1->ne[2];
const int ne13 = src1->ne[3];
const int ne0 = dst->ne[0];
const int ne1 = dst->ne[1];
const int ne2 = dst->ne[2];
const int ne3 = dst->ne[3];
const int 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;
assert(ne02 == ne12);
assert(ne03 == ne13);
assert(ne2 == ne12);
assert(ne3 == ne13);
// TODO: we don't support permuted src0
assert(nb00 == sizeof(float) || nb01 == 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
//
// nb00 < nb01 - src0 is transposed
// compute by src0 columns
#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;
}
for (int i03 = 0; i03 < ne03; i03++) {
for (int i02 = 0; i02 < ne02; i02++) {
const float * x = (float *) (src0->data);
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) {
if (nb01 >= nb00) {
return;
}
// TODO: fix this memset (wsize is overestimated)
memset(params->wdata, 0, params->wsize);
return;
}
if (params->type == GGML_TASK_FINALIZE) {
if (nb01 >= nb00) {
return;
}
// TODO: fix this memset (wsize is overestimated)
//assert(params->wsize == (ggml_nbytes(dst) + CACHE_LINE_SIZE)*nth);
float * const wdata = params->wdata;
// cols per thread
const int dc = (ne + nth - 1)/nth;
// col range for this thread
const int ic0 = dc*ith;
const int ic1 = MIN(ic0 + dc, ne);
ggml_vec_cpy_f32(ic1 - ic0, (float *) dst->data + ic0, wdata + ic0);
for (int k = 1; k < nth; k++) {
ggml_vec_acc_f32(ic1 - ic0, (float *) dst->data + ic0, wdata + (ne + CACHE_LINE_SIZE_F32)*k + ic0);
}
return;
}
if (nb01 >= nb00) {
// TODO: do not support transposed src1
assert(nb10 == sizeof(float));
// 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 (int 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)));
}
}
} else {
// parallelize by src1 columns using ggml_vec_mad_f32
// each thread has its own work data
// during FINALIZE we accumulate all work data into dst
// total columns in src1
const int nc = ne10;
// columns per thread
const int dc = (nc + nth - 1)/nth;
// column range for this thread
const int ic0 = dc*ith;
const int ic1 = MIN(ic0 + dc, nc);
// work data for thread
const int wo = (ne + CACHE_LINE_SIZE_F32)*ith;
float * const wdata = params->wdata;
for (int i13 = 0; i13 < ne13; ++i13) {
for (int i12 = 0; i12 < ne12; ++i12) {
for (int i11 = 0; i11 < ne11; ++i11) {
for (int ic = ic0; ic < ic1; ++ic) {
// src1 indices
const int i10 = ic;
// src0 indices
const int i03 = i13;
const int i02 = i12;
const int i00 = ic;
// dst indices
const int i1 = i11;
const int i2 = i12;
const int i3 = i13;
assert(sizeof(float)*(wo + i3*ne2*ne1*ne0 + i2*ne1*ne0 + i1*ne0 + ne01) <= params->wsize);
ggml_vec_mad_f32(ne01,
(float *) (wdata + wo + i3*ne2*ne1*ne0 + i2*ne1*ne0 + i1*ne0),
(float *) ((char *) src0->data + (i00*nb00 + i02*nb02 + i03*nb03)),
*(float *) ((char *) src1->data + (i10*nb10 + 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 int ne00 = src0->ne[0];
const int ne01 = src0->ne[1];
const int ne02 = src0->ne[2];
const int ne03 = src0->ne[3];
const int ne10 = src1->ne[0];
const int ne11 = src1->ne[1];
const int ne12 = src1->ne[2];
const int ne13 = src1->ne[3];
const int ne0 = dst->ne[0];
const int ne1 = dst->ne[1];
const int ne2 = dst->ne[2];
const int ne3 = dst->ne[3];
const int 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) || nb01 == 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
//
// nb00 < nb01 - src0 is transposed
// compute by src0 columns
#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 (int i03 = 0; i03 < ne03; i03++) {
for (int i02 = 0; i02 < ne02; i02++) {
{
int id = 0;
for (int i01 = 0; i01 < ne01; ++i01) {
for (int 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 * z = wdata + ne00*ne01;
// z = x * yT
//{
// cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasTrans,
// ne01, ne11, ne00,
// 1.0f, x, ne00,
// y, ne00,
// 0.0f, z, ne11);
//}
float * d = (float *) ((char *) dst->data + i02*nb2 + i03*nb3);
// transpose z
//for (int j = 0; j < ne11; ++j) {
// for (int i = 0; i < ne01; ++i) {
// d[j*ne01 + i] = z[i*ne11 + j];
// }
//}
{
#if 1
// zT = y * xT
cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasTrans,
ne11, ne01, ne10,
1.0f, y, ne00,
x, ne00,
0.0f, d, ne01);
#else
// zT = (xT * y)T
cblas_sgemm(CblasColMajor, CblasTrans, CblasNoTrans,
ne01, ne11, ne10,
1.0f, x, ne00,
y, ne00,
0.0f, d, ne01);
#endif
}
}
}
/*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) {
if (nb01 >= nb00) {
ggml_fp16_t * const wdata = params->wdata;
int id = 0;
for (int i13 = 0; i13 < ne13; ++i13) {
for (int i12 = 0; i12 < ne12; ++i12) {
for (int i11 = 0; i11 < ne11; ++i11) {
for (int 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;
}
// TODO: fix this memset (wsize is overestimated)
memset(params->wdata, 0, params->wsize);
return;
}
if (params->type == GGML_TASK_FINALIZE) {
if (nb01 >= nb00) {
return;
}
// TODO: fix this memset (wsize is overestimated)
//assert(params->wsize == (ggml_nbytes(dst) + CACHE_LINE_SIZE)*nth);
ggml_fp16_t * const wdata = params->wdata;
// cols per thread
const int dc = (ne + nth - 1)/nth;
// col range for this thread
const int ic0 = dc*ith;
const int ic1 = MIN(ic0 + dc, ne);
for (int i = ic0; i < ic1; ++i) {
((float *) dst->data)[i] = GGML_FP16_TO_FP32(wdata[i]);
}
for (int k = 1; k < nth; k++) {
for (int i = ic0; i < ic1; ++i) {
((float *) dst->data)[i] += GGML_FP16_TO_FP32(wdata[(ne + CACHE_LINE_SIZE_F32)*k + i]);
}
}
return;
}
if (nb01 >= nb00) {
// 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));
assert(ne00 % 32 == 0);
for (int ic = 0; ic < ne11; ++ic) {
ggml_vec_dot_f16(ne00, &dst_col[ic*ne0], src0_row, src1_col + ic*ne00);
}
}
} else {
// parallelize by src1 columns using ggml_vec_mad_f16
// each thread has its own work data
// during FINALIZE we accumulate all work data into dst
// total columns in src1
const int nc = ne10;
// columns per thread
const int dc = (nc + nth - 1)/nth;
// column range for this thread
const int ic0 = dc*ith;
const int ic1 = MIN(ic0 + dc, nc);
// work data for thread
const int wo = (ne + CACHE_LINE_SIZE_F32)*ith;
ggml_fp16_t * const wdata = params->wdata;
for (int i13 = 0; i13 < ne13; ++i13) {
for (int i12 = 0; i12 < ne12; ++i12) {
for (int i11 = 0; i11 < ne11; ++i11) {
// dst indices
const int i1 = i11;
const int i2 = i12;
const int i3 = i13;
ggml_fp16_t * dst_row = wdata + wo + i3*ne2*ne1*ne0 + i2*ne1*ne0 + i1*ne0;
for (int ic = ic0; ic < ic1; ++ic) {
// src1 indices
const int i10 = ic;
// src0 indices
const int i03 = i13;
const int i02 = i12;
const int i00 = ic;
assert(sizeof(ggml_fp16_t)*(wo + i3*ne2*ne1*ne0 + i2*ne1*ne0 + i1*ne0 + ne01) <= params->wsize);
ggml_fp16_t * src0_col = (ggml_fp16_t *) ((char *) src0->data + (i00*nb00 + i02*nb02 + i03*nb03));
float src1_val = * (float *) ((char *) src1->data + (i10*nb10 + i11*nb11 + i12*nb12 + i13*nb13));
ggml_vec_mad_f16(ne01, dst_row, src0_col, src1_val);
}
}
}
}
}
//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_q4_0_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 int ne00 = src0->ne[0];
const int ne01 = src0->ne[1];
const int ne02 = src0->ne[2];
const int ne03 = src0->ne[3];
const int ne10 = src1->ne[0];
const int ne11 = src1->ne[1];
const int ne12 = src1->ne[2];
const int ne13 = src1->ne[3];
const int ne0 = dst->ne[0];
const int ne1 = dst->ne[1];
const int ne2 = dst->ne[2];
const int ne3 = dst->ne[3];
const int 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 == (int) GGML_TYPE_SIZE[GGML_TYPE_Q4_0] || nb01 == (int) GGML_TYPE_SIZE[GGML_TYPE_Q4_0]);
// 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
//
// nb00 < nb01 - src0 is transposed
// compute by src0 columns
#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 (int i03 = 0; i03 < ne03; i03++) {
for (int i02 = 0; i02 < ne02; i02++) {
{
int id = 0;
for (int i01 = 0; i01 < ne01; ++i01) {
//for (int 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));
//}
dequantize_row_q4_0((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 * z = wdata + ne00*ne01;
// z = x * yT
//{
// cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasTrans,
// ne01, ne11, ne00,
// 1.0f, x, ne00,
// y, ne00,
// 0.0f, z, ne11);
//}
float * d = (float *) ((char *) dst->data + i02*nb2 + i03*nb3);
// transpose z
//for (int j = 0; j < ne11; ++j) {
// for (int i = 0; i < ne01; ++i) {
// d[j*ne01 + i] = z[i*ne11 + j];
// }
//}
{
#if 1
// zT = y * xT
cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasTrans,
ne11, ne01, ne10,
1.0f, y, ne00,
x, ne00,
0.0f, d, ne01);
#else
// zT = (xT * y)T
cblas_sgemm(CblasColMajor, CblasTrans, CblasNoTrans,
ne01, ne11, ne10,
1.0f, x, ne00,
y, ne00,
0.0f, d, ne01);
#endif
}
}
}
/*printf("CBLAS Q4_0 = %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) {
//printf("HHHHHHHHH ith = %d, nth = %d\n", ith, nth);
if (nb01 >= nb00) {
char * wdata = params->wdata;
for (int i13 = 0; i13 < ne13; ++i13) {
for (int i12 = 0; i12 < ne12; ++i12) {
for (int i11 = 0; i11 < ne11; ++i11) {
//for (int i10 = 0; i10 < ne10; ++i10) {
// wdata[id++] = GGML_FP32_TO_FP16(*(float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11 + i10*nb10));
//}
quantize_row_q4_0((float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11), (void *) wdata, ne10);
wdata += (ne10*GGML_TYPE_SIZE[GGML_TYPE_Q4_0])/GGML_BLCK_SIZE[GGML_TYPE_Q4_0];
}
}
}
return;
}
// TODO: fix this memset (wsize is overestimated)
memset(params->wdata, 0, params->wsize);
return;
}
if (params->type == GGML_TASK_FINALIZE) {
if (nb01 >= nb00) {
return;
}
float * const wdata = params->wdata;
// cols per thread
const int dc = (ne + nth - 1)/nth;
// col range for this thread
const int ic0 = dc*ith;
const int ic1 = MIN(ic0 + dc, ne);
ggml_vec_cpy_f32(ic1 - ic0, (float *) dst->data + ic0, wdata + ic0);
for (int k = 1; k < nth; k++) {
ggml_vec_acc_f32(ic1 - ic0, (float *) dst->data + ic0, wdata + (ne + CACHE_LINE_SIZE_F32)*k + ic0);
}
return;
}
if (nb01 >= nb00) {
// TODO: do not support transposed src1
// parallelize by src0 rows using ggml_vec_dot_q4_0
// 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;
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)*ne00*GGML_TYPE_SIZE[GGML_TYPE_Q4_0])/GGML_BLCK_SIZE[GGML_TYPE_Q4_0]);
float * dst_col = (float *) ((char *) dst->data + (i0*nb0 + 0*nb1 + i2*nb2 + i3*nb3));
assert(ne00 % 32 == 0);
for (int ic = 0; ic < ne11; ++ic) {
ggml_vec_dot_q4_0(ne00, &dst_col[ic*ne0], src0_row, ((void *) (src1_col + (ic*ne00*GGML_TYPE_SIZE[GGML_TYPE_Q4_0])/GGML_BLCK_SIZE[GGML_TYPE_Q4_0])));
}
}
} else {
//printf("AAAAA ith = %d, nth = %d\n", ith, nth);
// parallelize by src1 columns using ggml_vec_mad_q4_0
// each thread has its own work data
// during FINALIZE we accumulate all work data into dst
// total columns in src1
const int nc = ne10;
// columns per thread
const int dc = (nc + nth - 1)/nth;
// column range for this thread
const int ic0 = dc*ith;
const int ic1 = MIN(ic0 + dc, nc);
// work data for thread
const int wo = (ne + CACHE_LINE_SIZE_F32)*ith;
float * const wdata = params->wdata;
for (int i13 = 0; i13 < ne13; ++i13) {
for (int i12 = 0; i12 < ne12; ++i12) {
for (int i11 = 0; i11 < ne11; ++i11) {
// dst indices
const int i1 = i11;
const int i2 = i12;
const int i3 = i13;
float * dst_row = wdata + wo + i3*ne2*ne1*ne0 + i2*ne1*ne0 + i1*ne0;
for (int ic = ic0; ic < ic1; ++ic) {
// src1 indices
const int i10 = ic;
// src0 indices
const int i03 = i13;
const int i02 = i12;
const int i00 = ic;
assert(sizeof(float)*(wo + i3*ne2*ne1*ne0 + i2*ne1*ne0 + i1*ne0 + ne01) <= params->wsize);
void * src0_col = (void *) ((char *) src0->data + (i00*nb00 + i02*nb02 + i03*nb03));
float src1_val = *(float *) ((char *) src1->data + (i10*nb10 + i11*nb11 + i12*nb12 + i13*nb13));
ggml_vec_mad_q4_0(ne01, dst_row, src0_col, src1_val);
}
}
}
}
}
//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_q4_1_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 int ne00 = src0->ne[0];
const int ne01 = src0->ne[1];
const int ne02 = src0->ne[2];
const int ne03 = src0->ne[3];
const int ne10 = src1->ne[0];
const int ne11 = src1->ne[1];
const int ne12 = src1->ne[2];
const int ne13 = src1->ne[3];
const int ne0 = dst->ne[0];
const int ne1 = dst->ne[1];
const int ne2 = dst->ne[2];
const int ne3 = dst->ne[3];
const int 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 == (int) GGML_TYPE_SIZE[GGML_TYPE_Q4_1] || nb01 == (int) GGML_TYPE_SIZE[GGML_TYPE_Q4_1]);
// 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
//
// nb00 < nb01 - src0 is transposed
// compute by src0 columns
#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 (int i03 = 0; i03 < ne03; i03++) {
for (int i02 = 0; i02 < ne02; i02++) {
{
int id = 0;
for (int i01 = 0; i01 < ne01; ++i01) {
//for (int 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));
//}
dequantize_row_q4_1((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 * z = wdata + ne00*ne01;
// z = x * yT
//{
// cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasTrans,
// ne01, ne11, ne00,
// 1.0f, x, ne00,
// y, ne00,
// 0.0f, z, ne11);
//}
float * d = (float *) ((char *) dst->data + i02*nb2 + i03*nb3);
// transpose z
//for (int j = 0; j < ne11; ++j) {
// for (int i = 0; i < ne01; ++i) {
// d[j*ne01 + i] = z[i*ne11 + j];
// }
//}
{
#if 1
// zT = y * xT
cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasTrans,
ne11, ne01, ne10,
1.0f, y, ne00,
x, ne00,
0.0f, d, ne01);
#else
// zT = (xT * y)T
cblas_sgemm(CblasColMajor, CblasTrans, CblasNoTrans,
ne01, ne11, ne10,
1.0f, x, ne00,
y, ne00,
0.0f, d, ne01);
#endif
}
}
}
//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) {
//printf("HHHHHHHHH ith = %d, nth = %d\n", ith, nth);
if (nb01 >= nb00) {
char * wdata = params->wdata;
for (int i13 = 0; i13 < ne13; ++i13) {
for (int i12 = 0; i12 < ne12; ++i12) {
for (int i11 = 0; i11 < ne11; ++i11) {
//for (int i10 = 0; i10 < ne10; ++i10) {
// wdata[id++] = GGML_FP32_TO_FP16(*(float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11 + i10*nb10));
//}
quantize_row_q4_1((float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11), (void *) wdata, ne10);
wdata += (ne10*GGML_TYPE_SIZE[GGML_TYPE_Q4_1])/GGML_BLCK_SIZE[GGML_TYPE_Q4_1];
}
}
}
return;
}
// TODO: fix this memset (wsize is overestimated)
memset(params->wdata, 0, params->wsize);
return;
}
if (params->type == GGML_TASK_FINALIZE) {
if (nb01 >= nb00) {
return;
}
float * const wdata = params->wdata;
// cols per thread
const int dc = (ne + nth - 1)/nth;
// col range for this thread
const int ic0 = dc*ith;
const int ic1 = MIN(ic0 + dc, ne);
ggml_vec_cpy_f32(ic1 - ic0, (float *) dst->data + ic0, wdata + ic0);
for (int k = 1; k < nth; k++) {
ggml_vec_acc_f32(ic1 - ic0, (float *) dst->data + ic0, wdata + (ne + CACHE_LINE_SIZE_F32)*k + ic0);
}
return;
}
if (nb01 >= nb00) {
// TODO: do not support transposed src1
// parallelize by src0 rows using ggml_vec_dot_q4_1
// 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;
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)*ne00*GGML_TYPE_SIZE[GGML_TYPE_Q4_1])/GGML_BLCK_SIZE[GGML_TYPE_Q4_1]);
float * dst_col = (float *) ((char *) dst->data + (i0*nb0 + 0*nb1 + i2*nb2 + i3*nb3));
assert(ne00 % 32 == 0);
for (int ic = 0; ic < ne11; ++ic) {
ggml_vec_dot_q4_1(ne00, &dst_col[ic*ne0], src0_row, ((void *) (src1_col + (ic*ne00*GGML_TYPE_SIZE[GGML_TYPE_Q4_1])/GGML_BLCK_SIZE[GGML_TYPE_Q4_1])));
}
}
} else {
//printf("AAAAA ith = %d, nth = %d\n", ith, nth);
// parallelize by src1 columns using ggml_vec_mad_q4_1
// each thread has its own work data
// during FINALIZE we accumulate all work data into dst
// total columns in src1
const int nc = ne10;
// columns per thread
const int dc = (nc + nth - 1)/nth;
// column range for this thread
const int ic0 = dc*ith;
const int ic1 = MIN(ic0 + dc, nc);
// work data for thread
const int wo = (ne + CACHE_LINE_SIZE_F32)*ith;
float * const wdata = params->wdata;
for (int i13 = 0; i13 < ne13; ++i13) {
for (int i12 = 0; i12 < ne12; ++i12) {
for (int i11 = 0; i11 < ne11; ++i11) {
// dst indices
const int i1 = i11;
const int i2 = i12;
const int i3 = i13;
float * dst_row = wdata + wo + i3*ne2*ne1*ne0 + i2*ne1*ne0 + i1*ne0;
for (int ic = ic0; ic < ic1; ++ic) {
// src1 indices
const int i10 = ic;
// src0 indices
const int i03 = i13;
const int i02 = i12;
const int i00 = ic;
assert(sizeof(float)*(wo + i3*ne2*ne1*ne0 + i2*ne1*ne0 + i1*ne0 + ne01) <= params->wsize);
void * src0_col = (void *) ((char *) src0->data + (i00*nb00 + i02*nb02 + i03*nb03));
float src1_val = *(float *) ((char *) src1->data + (i10*nb10 + i11*nb11 + i12*nb12 + i13*nb13));
ggml_vec_mad_q4_1(ne01, dst_row, src0_col, src1_val);
}
}
}
}
}
//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:
{
ggml_compute_forward_mul_mat_q4_0_f32(params, src0, src1, dst);
} break;
case GGML_TYPE_Q4_1:
{
ggml_compute_forward_mul_mat_q4_1_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_q4_0(
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] == GGML_TYPE_SIZE[GGML_TYPE_Q4_0]);
for (int i = 0; i < nr; ++i) {
const int r = ((int32_t *) src1->data)[i];
dequantize_row_q4_0(
(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_q4_1(
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] == GGML_TYPE_SIZE[GGML_TYPE_Q4_1]);
for (int i = 0; i < nr; ++i) {
const int r = ((int32_t *) src1->data)[i];
dequantize_row_q4_1(
(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:
{
ggml_compute_forward_get_rows_q4_0(params, src0, src1, dst);
} break;
case GGML_TYPE_Q4_1:
{
ggml_compute_forward_get_rows_q4_1(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 += val;
p[i] = val;
}
}
assert(sum > 0.0f);
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(params->ith == 0);
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 int ne0 = src0->ne[0];
const int ne1 = src0->ne[1];
const int ne2 = src0->ne[2];
const int 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));
// TODO: optimize
for (int i3 = 0; i3 < ne3; i3++) {
for (int i2 = (mode == 0 ? 0 : n_past); i2 < ne2; i2++) {
const int p = (mode == 0 ? n_past + i2 : i2);
for (int i1 = 0; i1 < ne1; i1++) {
for (int i0 = 0; i0 < n_dims; i0 += 2) {
const double theta = pow(10000.0, ((double)-i0)/n_dims);
const double cos_theta = cos(p*theta);
const double sin_theta = sin(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);
double x0 = src[0];
double 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(params->ith == 0);
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 int ne0 = src0->ne[0];
const int ne1 = src0->ne[1];
const int ne2 = src0->ne[2];
const int 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));
for (int i3 = 0; i3 < ne3; i3++) {
for (int i2 = (mode == 0 ? 0 : n_past); i2 < ne2; i2++) {
const int p = (mode == 0 ? n_past + i2 : i2);
for (int i1 = 0; i1 < ne1; i1++) {
for (int i0 = 0; i0 < n_dims; i0 += 2) {
const double theta = pow(10000.0, ((double)-i0)/n_dims);
const double cos_theta = cos(p*theta);
const double sin_theta = sin(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);
double x0 = ggml_fp16_to_fp32(src[0]);
double 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 int ne00 = src0->ne[0];
const int ne01 = src0->ne[1];
const int ne02 = src0->ne[2];
//const int ne03 = src0->ne[3];
const int ne10 = src1->ne[0];
const int ne11 = src1->ne[1];
//const int ne12 = src1->ne[2];
//const int ne13 = src1->ne[3];
//const int ne0 = dst->ne[0];
//const int ne1 = dst->ne[1];
//const int ne2 = dst->ne[2];
//const int ne3 = dst->ne[3];
//const int 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 (int i02 = 0; i02 < ne02; i02++) {
for (int 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 (int 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 (int i11 = 0; i11 < ne11; i11++) {
const float * const src = (float *)((char *) src1->data + i11*nb11);
ggml_fp16_t * dst_data = wdata;
for (int 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 (int 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 int ne00 = src0->ne[0];
const int ne01 = src0->ne[1];
const int ne02 = src0->ne[2];
//const int ne03 = src0->ne[3];
const int ne10 = src1->ne[0];
const int ne11 = src1->ne[1];
//const int ne12 = src1->ne[2];
//const int ne13 = src1->ne[3];
//const int ne0 = dst->ne[0];
//const int ne1 = dst->ne[1];
//const int ne2 = dst->ne[2];
//const int ne3 = dst->ne[3];
//const int 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 (int i02 = 0; i02 < ne02; i02++) {
for (int 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 (int 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 (int i11 = 0; i11 < ne11; i11++) {
const float * const src = (float *)((char *) src1->data + i11*nb11);
float * dst_data = wdata;
for (int 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 (int 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 int ne00 = src0->ne[0];
const int ne01 = src0->ne[1];
const int ne02 = src0->ne[2];
//const int ne03 = src0->ne[3];
const int ne10 = src1->ne[0];
const int ne11 = src1->ne[1];
//const int ne12 = src1->ne[2];
//const int ne13 = src1->ne[3];
//const int ne0 = dst->ne[0];
//const int ne1 = dst->ne[1];
//const int ne2 = dst->ne[2];
//const int ne3 = dst->ne[3];
//const int 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 (int i02 = 0; i02 < ne02; i02++) {
for (int 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 (int 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 (int i11 = 0; i11 < ne11; i11++) {
const float * const src = (float *)((char *) src1->data + i11*nb11);
ggml_fp16_t * dst_data = wdata;
for (int 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 (int 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 int ne00 = src0->ne[0];
const int ne01 = src0->ne[1];
const int ne02 = src0->ne[2];
//const int ne03 = src0->ne[3];
const int ne10 = src1->ne[0];
const int ne11 = src1->ne[1];
//const int ne12 = src1->ne[2];
//const int ne13 = src1->ne[3];
//const int ne0 = dst->ne[0];
//const int ne1 = dst->ne[1];
//const int ne2 = dst->ne[2];
//const int ne3 = dst->ne[3];
//const int 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 (int i02 = 0; i02 < ne02; i02++) {
for (int 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 (int 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 (int i11 = 0; i11 < ne11; i11++) {
const float * const src = (float *)((char *) src1->data + i11*nb11);
float * dst_data = wdata;
for (int 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 (int 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 int neq0 = q->ne[0];
const int neq1 = q->ne[1];
const int neq2 = q->ne[2];
const int neq3 = q->ne[3];
const int nek0 = k->ne[0];
const int nek1 = k->ne[1];
//const int nek2 = k->ne[2];
//const int nek3 = k->ne[3];
//const int nev0 = v->ne[0];
const int nev1 = v->ne[1];
//const int nev2 = v->ne[2];
//const int nev3 = v->ne[3];
const int ne0 = dst->ne[0];
const int ne1 = dst->ne[1];
//const int ne2 = dst->ne[2];
//const int 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 int D = neq0;
const int N = neq1;
const int P = nek1 - N;
const int 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 = 1.0/sqrt((double) 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 (int 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 (int i = P; i < M; i++) {
if (i > P + iq1) {
S[i] = -INFINITY;
}
}
}
// softmax
{
float max = -INFINITY;
ggml_vec_max_f32(M, &max, S);
float sum = 0.0f;
{
#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] += val;
SS[j] = val;
}
}
}
for (int i = 0; i < GGML_SOFT_MAX_UNROLL; i++) {
sum += sump[i];
}
#endif
}
assert(sum > 0.0f);
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 (int 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 int neq0 = q->ne[0];
const int neq1 = q->ne[1];
const int neq2 = q->ne[2];
const int neq3 = q->ne[3];
const int nek0 = k->ne[0];
const int nek1 = k->ne[1];
//const int nek2 = k->ne[2];
//const int nek3 = k->ne[3];
//const int nev0 = v->ne[0];
const int nev1 = v->ne[1];
//const int nev2 = v->ne[2];
//const int nev3 = v->ne[3];
const int ne0 = dst->ne[0];
const int ne1 = dst->ne[1];
//const int ne2 = dst->ne[2];
//const int 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 int D = neq0;
const int N = neq1;
const int P = nek1 - N;
const int 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 = 1.0/sqrt((double) 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 (int 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 (int 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 (int i = P; i < M; i++) {
if (i > P + iq1) {
S[i] = -INFINITY;
}
}
}
// softmax
{
float max = -INFINITY;
ggml_vec_max_f32(M, &max, S);
float sum = 0.0f;
{
#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] += val;
SS[j] = val;
}
}
}
for (int i = 0; i < GGML_SOFT_MAX_UNROLL; i++) {
sum += sump[i];
}
#endif
}
assert(sum > 0.0f);
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 (int 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 (int 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 (int 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 int nea0 = a->ne[0];
const int nea1 = a->ne[1];
const int nea2 = a->ne[2];
const int nea3 = a->ne[3];
const int neb00 = b0->ne[0];
const int neb01 = b0->ne[1];
//const int neb02 = b0->ne[2];
//const int neb03 = b0->ne[3];
const int neb10 = b1->ne[0];
const int neb11 = b1->ne[1];
//const int neb12 = b1->ne[2];
//const int neb13 = b1->ne[3];
const int nec00 = c0->ne[0];
const int nec01 = c0->ne[1];
//const int nec02 = c0->ne[2];
//const int nec03 = c0->ne[3];
const int nec10 = c1->ne[0];
const int nec11 = c1->ne[1];
//const int nec12 = c1->ne[2];
//const int nec13 = c1->ne[3];
const int ne0 = dst->ne[0];
const int ne1 = dst->ne[1];
const int ne2 = dst->ne[2];
//const int 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 int D = nea0;
//const int N = nea1;
const int 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 (int 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 (int 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 (int 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_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_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) {
if (cgraph->n_threads <= 0) {
cgraph->n_threads = 8;
}
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:
{
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;
// TODO: better way to determine if the matrix is transposed
if (node->src0->nb[1] < node->src0->nb[0]) {
cur = ggml_nbytes(node)*node->n_tasks; // TODO: this can become (n_tasks-1)
// TODO: overestimated by factor of x2 for FP16
} else {
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 (node->src0->type == GGML_TYPE_Q4_0 &&
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 {
cur = (GGML_TYPE_SIZE[GGML_TYPE_Q4_0]*ggml_nelements(node->src1))/GGML_BLCK_SIZE[GGML_TYPE_Q4_0];
}
#else
cur = (GGML_TYPE_SIZE[GGML_TYPE_Q4_0]*ggml_nelements(node->src1))/GGML_BLCK_SIZE[GGML_TYPE_Q4_0];
#endif
} else if (node->src0->type == GGML_TYPE_Q4_1 &&
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 {
cur = (GGML_TYPE_SIZE[GGML_TYPE_Q4_1]*ggml_nelements(node->src1))/GGML_BLCK_SIZE[GGML_TYPE_Q4_1];
}
#else
cur = (GGML_TYPE_SIZE[GGML_TYPE_Q4_1]*ggml_nelements(node->src1))/GGML_BLCK_SIZE[GGML_TYPE_Q4_1];
#endif
} 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 = 1;
} 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 int 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: [ %6d, %6d, %6d] %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: [ %6d, %6d] %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 [%d, %d] | <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, ggml_get_f32_1d(node, 0));
} else {
fprintf(fp, " \"%p\" [ \
style = filled; fillcolor = %s; shape = record; \
label=\"<x>CONST %d [%d, %d]\"; ]\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 int ne = ggml_nelements(ps[p]) ;
// TODO: add function to set tensor from array
for (int 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 int ne = ggml_nelements(ps[p]) ;
// TODO: add function to get all elements at once
for (int 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 int ne = ggml_nelements(ps[p]) ;
// TODO: add function to get all elements at once
for (int 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 (fabs(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.) {
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. <= 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 < 1.0) {
xnorm = 1.0;
}
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 (fabs(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,
};
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;
}
////////////////////////////////////////////////////////////////////////////////
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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