// Defines CLOCK_MONOTONIC on Linux #define _GNU_SOURCE #include "ggml.h" #if defined(_MSC_VER) || defined(__MINGW32__) #include // using malloc.h with MSC/MINGW #elif !defined(__FreeBSD__) && !defined(__NetBSD__) && !defined(__OpenBSD__) #include #endif #include #include #include #include #include #include #include #include #include #include #include // 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(_WIN32) #include typedef volatile LONG atomic_int; typedef atomic_int atomic_bool; static void atomic_store(atomic_int* ptr, LONG val) { InterlockedExchange(ptr, val); } static LONG atomic_load(atomic_int* ptr) { return InterlockedCompareExchange(ptr, 0, 0); } static LONG atomic_fetch_add(atomic_int* ptr, LONG inc) { return InterlockedExchangeAdd(ptr, inc); } static LONG atomic_fetch_sub(atomic_int* ptr, LONG dec) { return atomic_fetch_add(ptr, -(dec)); } typedef HANDLE pthread_t; typedef DWORD thread_ret_t; static int pthread_create(pthread_t* out, void* unused, thread_ret_t(*func)(void*), void* arg) { (void) unused; HANDLE handle = CreateThread(NULL, 0, (LPTHREAD_START_ROUTINE) func, arg, 0, NULL); if (handle == NULL) { return EAGAIN; } *out = handle; return 0; } static int pthread_join(pthread_t thread, void* unused) { (void) unused; return (int) WaitForSingleObject(thread, INFINITE); } static int sched_yield (void) { Sleep (0); return 0; } #else #include #include typedef void* thread_ret_t; #endif // __FMA__ and __F16C__ are not defined in MSVC, however they are implied with AVX2/AVX512 #if defined(_MSC_VER) && (defined(__AVX2__) || defined(__AVX512F__)) #ifndef __FMA__ #define __FMA__ #endif #ifndef __F16C__ #define __F16C__ #endif #ifndef __SSE3__ #define __SSE3__ #endif #endif #ifdef __HAIKU__ #define static_assert(cond, msg) _Static_assert(cond, msg) #endif /*#define GGML_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 #if defined(_MSC_VER) || defined(__MINGW32__) #define GGML_ALIGNED_MALLOC(size) _aligned_malloc(size, GGML_MEM_ALIGN) #define GGML_ALIGNED_FREE(ptr) _aligned_free(ptr) #else inline static void* ggml_aligned_malloc(size_t size) { void* aligned_memory = NULL; int result = posix_memalign(&aligned_memory, GGML_MEM_ALIGN, size); if (result != 0) { // Handle allocation failure return NULL; } return aligned_memory; } #define GGML_ALIGNED_MALLOC(size) ggml_aligned_malloc(size) #define GGML_ALIGNED_FREE(ptr) free(ptr) #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) #if defined(GGML_USE_ACCELERATE) #include #elif defined(GGML_USE_OPENBLAS) #include #elif defined(GGML_USE_CUBLAS) #include "ggml-cuda.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 , 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 #define GGML_COMPUTE_FP16_TO_FP32(x) ((float) (x)) #define GGML_COMPUTE_FP32_TO_FP16(x) (x) #define GGML_FP16_TO_FP32(x) ((float) (x)) #define GGML_FP32_TO_FP16(x) (x) #else #ifdef __wasm_simd128__ #include #else #ifdef __POWER9_VECTOR__ #include #undef bool #define bool _Bool #else #include #endif #endif #ifdef __F16C__ #ifdef _MSC_VER #define GGML_COMPUTE_FP16_TO_FP32(x) _mm_cvtss_f32(_mm_cvtph_ps(_mm_cvtsi32_si128(x))) #define GGML_COMPUTE_FP32_TO_FP16(x) _mm_extract_epi16(_mm_cvtps_ph(_mm_set_ss(x), 0), 0) #else #define GGML_COMPUTE_FP16_TO_FP32(x) _cvtsh_ss(x) #define GGML_COMPUTE_FP32_TO_FP16(x) _cvtss_sh(x, 0) #endif #elif defined(__POWER9_VECTOR__) #define GGML_COMPUTE_FP16_TO_FP32(x) ggml_compute_fp16_to_fp32(x) #define GGML_COMPUTE_FP32_TO_FP16(x) ggml_compute_fp32_to_fp16(x) /* the inline asm below is about 12% faster than the lookup method */ #define GGML_FP16_TO_FP32(x) GGML_COMPUTE_FP16_TO_FP32(x) #define GGML_FP32_TO_FP16(x) GGML_COMPUTE_FP32_TO_FP16(x) static inline float ggml_compute_fp16_to_fp32(ggml_fp16_t h) { register float f; register double d; __asm__( "mtfprd %0,%2\n" "xscvhpdp %0,%0\n" "frsp %1,%0\n" : /* temp */ "=d"(d), /* out */ "=f"(f): /* in */ "r"(h)); return f; } static inline ggml_fp16_t ggml_compute_fp32_to_fp16(float f) { register double d; register ggml_fp16_t r; __asm__( /* xscvdphp can work on double or single precision */ "xscvdphp %0,%2\n" "mffprd %1,%0\n" : /* temp */ "=d"(d), /* out */ "=r"(r): /* in */ "f"(f)); return r; } #else // FP16 <-> FP32 // ref: https://github.com/Maratyszcza/FP16 static inline float fp32_from_bits(uint32_t w) { union { uint32_t as_bits; float as_value; } fp32; fp32.as_bits = w; return fp32.as_value; } static inline uint32_t fp32_to_bits(float f) { union { float as_value; uint32_t as_bits; } fp32; fp32.as_value = f; return fp32.as_bits; } static inline float ggml_compute_fp16_to_fp32(ggml_fp16_t h) { const uint32_t w = (uint32_t) h << 16; const uint32_t sign = w & UINT32_C(0x80000000); const uint32_t two_w = w + w; const uint32_t exp_offset = UINT32_C(0xE0) << 23; #if defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 199901L) || defined(__GNUC__) && !defined(__STRICT_ANSI__) const float exp_scale = 0x1.0p-112f; #else const float exp_scale = fp32_from_bits(UINT32_C(0x7800000)); #endif const float normalized_value = fp32_from_bits((two_w >> 4) + exp_offset) * exp_scale; const uint32_t magic_mask = UINT32_C(126) << 23; const float magic_bias = 0.5f; const float denormalized_value = fp32_from_bits((two_w >> 17) | magic_mask) - magic_bias; const uint32_t denormalized_cutoff = UINT32_C(1) << 27; const uint32_t result = sign | (two_w < denormalized_cutoff ? fp32_to_bits(denormalized_value) : fp32_to_bits(normalized_value)); return fp32_from_bits(result); } static inline ggml_fp16_t ggml_compute_fp32_to_fp16(float f) { #if defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 199901L) || defined(__GNUC__) && !defined(__STRICT_ANSI__) const float scale_to_inf = 0x1.0p+112f; const float scale_to_zero = 0x1.0p-110f; #else const float scale_to_inf = fp32_from_bits(UINT32_C(0x77800000)); const float scale_to_zero = fp32_from_bits(UINT32_C(0x08800000)); #endif float base = (fabsf(f) * scale_to_inf) * scale_to_zero; const uint32_t w = fp32_to_bits(f); const uint32_t shl1_w = w + w; const uint32_t sign = w & UINT32_C(0x80000000); uint32_t bias = shl1_w & UINT32_C(0xFF000000); if (bias < UINT32_C(0x71000000)) { bias = UINT32_C(0x71000000); } base = fp32_from_bits((bias >> 1) + UINT32_C(0x07800000)) + base; const uint32_t bits = fp32_to_bits(base); const uint32_t exp_bits = (bits >> 13) & UINT32_C(0x00007C00); const uint32_t mantissa_bits = bits & UINT32_C(0x00000FFF); const uint32_t nonsign = exp_bits + mantissa_bits; return (sign >> 16) | (shl1_w > UINT32_C(0xFF000000) ? UINT16_C(0x7E00) : nonsign); } #define GGML_COMPUTE_FP16_TO_FP32(x) ggml_compute_fp16_to_fp32(x) #define GGML_COMPUTE_FP32_TO_FP16(x) ggml_compute_fp32_to_fp16(x) #endif // __F16C__ #endif // __ARM_NEON // // global data // // precomputed gelu table for f16 (128 KB) static ggml_fp16_t table_gelu_f16[1 << 16]; // precomputed silu table for f16 (128 KB) static ggml_fp16_t table_silu_f16[1 << 16]; // precomputed exp table for f16 (128 KB) static ggml_fp16_t table_exp_f16[1 << 16]; // precomputed f32 table for f16 (256 KB) static float table_f32_f16[1 << 16]; // On ARM NEON, it's quicker to directly convert x -> x instead of calling into ggml_lookup_fp16_to_fp32, // so we define GGML_FP16_TO_FP32 and GGML_FP32_TO_FP16 elsewhere for NEON. // This is also true for POWER9. #if !defined(GGML_FP16_TO_FP32) || !defined(GGML_FP32_TO_FP16) inline static float ggml_lookup_fp16_to_fp32(ggml_fp16_t f) { uint16_t s; memcpy(&s, &f, sizeof(uint16_t)); return table_f32_f16[s]; } #define GGML_FP16_TO_FP32(x) ggml_lookup_fp16_to_fp32(x) #define GGML_FP32_TO_FP16(x) GGML_COMPUTE_FP32_TO_FP16(x) #endif // note: do not use these inside ggml.c // these are meant to be used via the ggml.h API float ggml_fp16_to_fp32(ggml_fp16_t x) { return (float) GGML_FP16_TO_FP32(x); } ggml_fp16_t ggml_fp32_to_fp16(float x) { return GGML_FP32_TO_FP16(x); } // // timing // #if defined(_MSC_VER) || defined(__MINGW32__) static int64_t timer_freq; void ggml_time_init(void) { LARGE_INTEGER frequency; QueryPerformanceFrequency(&frequency); timer_freq = frequency.QuadPart; } int64_t ggml_time_ms(void) { LARGE_INTEGER t; QueryPerformanceCounter(&t); return (t.QuadPart * 1000) / timer_freq; } int64_t ggml_time_us(void) { LARGE_INTEGER t; QueryPerformanceCounter(&t); return (t.QuadPart * 1000000) / timer_freq; } #else void ggml_time_init(void) {} int64_t ggml_time_ms(void) { struct timespec ts; clock_gettime(CLOCK_MONOTONIC, &ts); return (int64_t)ts.tv_sec*1000 + (int64_t)ts.tv_nsec/1000000; } int64_t ggml_time_us(void) { struct timespec ts; clock_gettime(CLOCK_MONOTONIC, &ts); return (int64_t)ts.tv_sec*1000000 + (int64_t)ts.tv_nsec/1000; } #endif int64_t ggml_cycles(void) { return clock(); } int64_t ggml_cycles_per_ms(void) { return CLOCKS_PER_SEC/1000; } #ifdef GGML_PERF #define ggml_perf_time_ms() ggml_time_ms() #define ggml_perf_time_us() ggml_time_us() #define ggml_perf_cycles() ggml_cycles() #define ggml_perf_cycles_per_ms() ggml_cycles_per_ms() #else #define ggml_perf_time_ms() 0 #define ggml_perf_time_us() 0 #define ggml_perf_cycles() 0 #define ggml_perf_cycles_per_ms() 0 #endif // // cache line // #if defined(__cpp_lib_hardware_interference_size) #define CACHE_LINE_SIZE hardware_destructive_interference_size #else #if defined(__POWER9_VECTOR__) #define CACHE_LINE_SIZE 128 #else #define CACHE_LINE_SIZE 64 #endif #endif static const size_t CACHE_LINE_SIZE_F32 = CACHE_LINE_SIZE/sizeof(float); // // quantization // #if __AVX__ || __AVX2__ || __AVX512F__ // Unpack 16 4-bit fields into 16 bytes // The output vector contains 16 bytes, each one in [ 0 .. 15 ] interval static inline __m128i bytes_from_nibbles_16(const uint8_t * rsi) { // Load 8 bytes from memory __m128i tmp = _mm_loadu_si64( ( const __m128i* )rsi ); // Expand bytes into uint16_t values __m128i bytes = _mm_cvtepu8_epi16( tmp ); // Unpack values into individual bytes const __m128i lowMask = _mm_set1_epi8( 0xF ); __m128i high = _mm_andnot_si128( lowMask, bytes ); __m128i low = _mm_and_si128( lowMask, bytes ); high = _mm_slli_epi16( high, 4 ); bytes = _mm_or_si128( low, high ); return bytes; } // horizontally add 8 floats static inline float hsum_float_8(const __m256 x) { __m128 res = _mm256_extractf128_ps(x, 1); res = _mm_add_ps(res, _mm256_castps256_ps128(x)); res = _mm_add_ps(res, _mm_movehl_ps(res, res)); res = _mm_add_ss(res, _mm_movehdup_ps(res)); return _mm_cvtss_f32(res); } // horizontally add 8 int32_t static inline int hsum_i32_8(const __m256i a) { const __m128i sum128 = _mm_add_epi32(_mm256_castsi256_si128(a), _mm256_extractf128_si256(a, 1)); const __m128i hi64 = _mm_unpackhi_epi64(sum128, sum128); const __m128i sum64 = _mm_add_epi32(hi64, sum128); const __m128i hi32 = _mm_shuffle_epi32(sum64, _MM_SHUFFLE(2, 3, 0, 1)); return _mm_cvtsi128_si32(_mm_add_epi32(sum64, hi32)); } // horizontally add 4 int32_t static inline int hsum_i32_4(const __m128i a) { const __m128i hi64 = _mm_unpackhi_epi64(a, a); const __m128i sum64 = _mm_add_epi32(hi64, a); const __m128i hi32 = _mm_shuffle_epi32(sum64, _MM_SHUFFLE(2, 3, 0, 1)); return _mm_cvtsi128_si32(_mm_add_epi32(sum64, hi32)); } #if __AVX2__ || __AVX512F__ // Unpack 32 4-bit fields into 32 bytes // The output vector contains 32 bytes, each one in [ 0 .. 15 ] interval static inline __m256i bytes_from_nibbles_32(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; } // add int16_t pairwise and return as float vector static inline __m256 sum_i16_pairs_float(const __m256i x) { const __m256i ones = _mm256_set1_epi16(1); const __m256i summed_pairs = _mm256_madd_epi16(ones, x); return _mm256_cvtepi32_ps(summed_pairs); } // multiply int8_t, add results pairwise twice and return as float vector static inline __m256 mul_sum_i8_pairs_float(const __m256i x, const __m256i y) { // Get absolute values of x vectors const __m256i ax = _mm256_sign_epi8(x, x); // Sign the values of the y vectors const __m256i sy = _mm256_sign_epi8(y, x); // Perform multiplication and create 16-bit values const __m256i dot = _mm256_maddubs_epi16(ax, sy); return sum_i16_pairs_float(dot); } static inline __m128i packNibbles( __m256i bytes ) { // Move bits within 16-bit lanes from 0000_abcd_0000_efgh into 0000_0000_abcd_efgh const __m256i lowByte = _mm256_set1_epi16( 0xFF ); __m256i high = _mm256_andnot_si256( lowByte, bytes ); __m256i low = _mm256_and_si256( lowByte, bytes ); high = _mm256_srli_epi16( high, 4 ); bytes = _mm256_or_si256( low, high ); // Compress uint16_t lanes into bytes __m128i r0 = _mm256_castsi256_si128( bytes ); __m128i r1 = _mm256_extracti128_si256( bytes, 1 ); return _mm_packus_epi16( r0, r1 ); } #else static inline __m128i packNibbles( __m128i bytes1, __m128i bytes2 ) { // Move bits within 16-bit lanes from 0000_abcd_0000_efgh into 0000_0000_abcd_efgh const __m128i lowByte = _mm_set1_epi16( 0xFF ); __m128i high = _mm_andnot_si128( lowByte, bytes1 ); __m128i low = _mm_and_si128( lowByte, bytes1 ); high = _mm_srli_epi16( high, 4 ); bytes1 = _mm_or_si128( low, high ); high = _mm_andnot_si128( lowByte, bytes2 ); low = _mm_and_si128( lowByte, bytes2 ); high = _mm_srli_epi16( high, 4 ); bytes2 = _mm_or_si128( low, high ); return _mm_packus_epi16( bytes1, bytes2); } #endif #endif // __AVX__ || __AVX2__ || __AVX512F__ #if __ARM_NEON #if !defined(__aarch64__) inline static uint16_t vaddvq_u8(uint8x16_t v) { return (uint16_t)vgetq_lane_u8(v, 0) + (uint16_t)vgetq_lane_u8(v, 1) + (uint16_t)vgetq_lane_u8(v, 2) + (uint16_t)vgetq_lane_u8(v, 3) + (uint16_t)vgetq_lane_u8(v, 4) + (uint16_t)vgetq_lane_u8(v, 5) + (uint16_t)vgetq_lane_u8(v, 6) + (uint16_t)vgetq_lane_u8(v, 7) + (uint16_t)vgetq_lane_u8(v, 8) + (uint16_t)vgetq_lane_u8(v, 9) + (uint16_t)vgetq_lane_u8(v, 10) + (uint16_t)vgetq_lane_u8(v, 11) + (uint16_t)vgetq_lane_u8(v, 12) + (uint16_t)vgetq_lane_u8(v, 13) + (uint16_t)vgetq_lane_u8(v, 14) + (uint16_t)vgetq_lane_u8(v, 15); } inline static int16_t vaddvq_s8(int8x16_t v) { return (int16_t)vgetq_lane_s8(v, 0) + (int16_t)vgetq_lane_s8(v, 1) + (int16_t)vgetq_lane_s8(v, 2) + (int16_t)vgetq_lane_s8(v, 3) + (int16_t)vgetq_lane_s8(v, 4) + (int16_t)vgetq_lane_s8(v, 5) + (int16_t)vgetq_lane_s8(v, 6) + (int16_t)vgetq_lane_s8(v, 7) + (int16_t)vgetq_lane_s8(v, 8) + (int16_t)vgetq_lane_s8(v, 9) + (int16_t)vgetq_lane_s8(v, 10) + (int16_t)vgetq_lane_s8(v, 11) + (int16_t)vgetq_lane_s8(v, 12) + (int16_t)vgetq_lane_s8(v, 13) + (int16_t)vgetq_lane_s8(v, 14) + (int16_t)vgetq_lane_s8(v, 15); } inline static int32_t vaddvq_s16(int16x8_t v) { return (int32_t)vgetq_lane_s16(v, 0) + (int32_t)vgetq_lane_s16(v, 1) + (int32_t)vgetq_lane_s16(v, 2) + (int32_t)vgetq_lane_s16(v, 3) + (int32_t)vgetq_lane_s16(v, 4) + (int32_t)vgetq_lane_s16(v, 5) + (int32_t)vgetq_lane_s16(v, 6) + (int32_t)vgetq_lane_s16(v, 7); } inline static uint32_t vaddvq_u16(uint16x8_t v) { return (uint32_t)vgetq_lane_u16(v, 0) + (uint32_t)vgetq_lane_u16(v, 1) + (uint32_t)vgetq_lane_u16(v, 2) + (uint32_t)vgetq_lane_u16(v, 3) + (uint32_t)vgetq_lane_u16(v, 4) + (uint32_t)vgetq_lane_u16(v, 5) + (uint32_t)vgetq_lane_u16(v, 6) + (uint32_t)vgetq_lane_u16(v, 7); } inline static int32_t vaddvq_s32(int32x4_t v) { return vgetq_lane_s32(v, 0) + vgetq_lane_s32(v, 1) + vgetq_lane_s32(v, 2) + vgetq_lane_s32(v, 3); } inline static float vaddvq_f32(float32x4_t v) { return vgetq_lane_f32(v, 0) + vgetq_lane_f32(v, 1) + vgetq_lane_f32(v, 2) + vgetq_lane_f32(v, 3); } float vminvq_f32(float32x4_t v) { return MIN(MIN(vgetq_lane_f32(v, 0), vgetq_lane_f32(v, 1)), MIN(vgetq_lane_f32(v, 2), vgetq_lane_f32(v, 3))); } float vmaxvq_f32(float32x4_t v) { return MAX(MAX(vgetq_lane_f32(v, 0), vgetq_lane_f32(v, 1)), MAX(vgetq_lane_f32(v, 2), vgetq_lane_f32(v, 3))); } int8x8_t vzip1_s8(int8x8_t a, int8x8_t b) { return vget_low_s8(vcombine_s8(a, b)); } int8x8_t vzip2_s8(int8x8_t a, int8x8_t b) { return vget_high_s8(vcombine_s8(a, b)); } uint8x8_t vzip1_u8(uint8x8_t a, uint8x8_t b) { return vget_low_u8(vcombine_u8(a, b)); } uint8x8_t vzip2_u8(uint8x8_t a, uint8x8_t b) { return vget_high_u8(vcombine_u8(a, b)); } #endif #endif #define QK4_0 32 typedef struct { float d; // delta uint8_t qs[QK4_0 / 2]; // nibbles / quants } block_q4_0; static_assert(sizeof(block_q4_0) == sizeof(float) + QK4_0 / 2, "wrong q4_0 block size/padding"); #define QK4_1 32 typedef struct { float d; // delta float m; // min uint8_t qs[QK4_1 / 2]; // nibbles / quants } block_q4_1; static_assert(sizeof(block_q4_1) == 2 * sizeof(float) + QK4_1 / 2, "wrong q4_1 block size/padding"); #define QK4_2 16 typedef struct { ggml_fp16_t d; // delta uint8_t qs[QK4_2 / 2]; // nibbles / quants } block_q4_2; static_assert(sizeof(block_q4_2) == sizeof(ggml_fp16_t) + QK4_2 / 2, "wrong q4_2 block size/padding"); #define QK4_3 16 typedef struct { ggml_fp16_t d; // delta ggml_fp16_t m; // min uint8_t qs[QK4_3 / 2]; // nibbles / quants } block_q4_3; static_assert(sizeof(block_q4_3) == 2 * sizeof(ggml_fp16_t) + QK4_3 / 2, "wrong q4_3 block size/padding"); #define QK8_0 32 typedef struct { float d; // delta float s0; // d * sum(qs[i]) low float s1; // d * sum(qs[i]) high int8_t qs[QK8_0]; // quants } block_q8_0; static_assert(sizeof(block_q8_0) == 3*sizeof(float) + QK8_0, "wrong q8_0 block size/padding"); #ifndef GGML_NO_RMSE // Stuff for RMSE-minimizing quantization static inline int nearest_int(float fval) { assert(fval <= 4194303.f); float val = fval + 12582912.f; int i; memcpy(&i, &val, sizeof(int)); return (i & 0x007fffff) - 0x00400000; } static float kquantize_q4_with_bounds(int n, int nmin, int nmax, const float * restrict X, int nCandidates, const float * restrict candidates, int8_t * restrict L) { assert (nmin >= INT8_MIN); assert (nmax <= INT8_MAX); float amax = 0; for (int i=0; i sumlxM2*suml2P) { if (sumlxP2 > best*suml2P) { best = sumlxP2/suml2P; bestScale = iscale; } } else { if (sumlxM2 > best*suml2M) { best = sumlxM2/suml2M; bestScale = -iscale; } } } float sumlx = 0; int suml2 = 0; for (int i=0; i best*suml2) { best = sumlx2/suml2; bestScale = iscale; } } float sumlx = 0; int suml2 = 0; for (int i=0; i max) max = v; } const float d = (max - min) / ((1 << 4) - 1); const float id = d ? 1.0f/d : 0.0f; y[i].d = d; y[i].m = min; for (int l = 0; l < QK4_1; l += 2) { const float v0 = (x[i*QK4_1 + l + 0] - min)*id; const float v1 = (x[i*QK4_1 + l + 1] - min)*id; const uint8_t vi0 = roundf(v0); const uint8_t vi1 = roundf(v1); assert(vi0 < 16); assert(vi1 < 16); pp[l/2] = vi0 | (vi1 << 4); } memcpy(y[i].qs, pp, sizeof(pp)); } } static void quantize_row_q4_1(const float * restrict x, void * restrict vy, int k) { assert(k % QK4_1 == 0); const int nb = k / QK4_1; block_q4_1 * restrict y = vy; #ifndef GGML_NO_RMSE quantize_row_q4_1_rmse(x, y, k); return; #endif #if defined(__AVX2__) for (int i = 0; i < nb; i++) { // Load elements into 4 AVX vectors __m256 v0 = _mm256_loadu_ps( x ); __m256 v1 = _mm256_loadu_ps( x + 8 ); __m256 v2 = _mm256_loadu_ps( x + 16 ); __m256 v3 = _mm256_loadu_ps( x + 24 ); x += 32; // Compute max for the block __m256 vmax; vmax = _mm256_max_ps( v0, v1 ); vmax = _mm256_max_ps( vmax, v2 ); vmax = _mm256_max_ps( vmax, v3 ); __m128 max4 = _mm_max_ps( _mm256_extractf128_ps( vmax, 1 ), _mm256_castps256_ps128( vmax ) ); max4 = _mm_max_ps( max4, _mm_movehl_ps( max4, max4 ) ); max4 = _mm_max_ss( max4, _mm_movehdup_ps( max4 ) ); const float maxScalar = _mm_cvtss_f32( max4 ); // Compute min for the block __m256 vmin; vmin = _mm256_min_ps( v0, v1 ); vmin = _mm256_min_ps( vmin, v2 ); vmin = _mm256_min_ps( vmin, v3 ); __m128 min4 = _mm_min_ps( _mm256_extractf128_ps( vmin, 1 ), _mm256_castps256_ps128( vmin ) ); min4 = _mm_min_ps( min4, _mm_movehl_ps( min4, min4 ) ); min4 = _mm_min_ss( min4, _mm_movehdup_ps( min4 ) ); const float minScalar = _mm_cvtss_f32( min4 ); // Quantize these floats const float d = (maxScalar - minScalar) / ((1 << 4) - 1); const float id = d ? 1.0f/d : 0.0f; y[i].m = minScalar; y[i].d = d; // x = (x-min)*id const __m256 mul = _mm256_set1_ps( id ); const __m256 off = _mm256_set1_ps( minScalar ); v0 = _mm256_mul_ps( _mm256_sub_ps( v0, off ), mul ); v1 = _mm256_mul_ps( _mm256_sub_ps( v1, off ), mul ); v2 = _mm256_mul_ps( _mm256_sub_ps( v2, off ), mul ); v3 = _mm256_mul_ps( _mm256_sub_ps( v3, off ), mul ); // Round to nearest integer v0 = _mm256_round_ps( v0, _MM_ROUND_NEAREST ); v1 = _mm256_round_ps( v1, _MM_ROUND_NEAREST ); v2 = _mm256_round_ps( v2, _MM_ROUND_NEAREST ); v3 = _mm256_round_ps( v3, _MM_ROUND_NEAREST ); // Convert floats to integers __m256i i0 = _mm256_cvtps_epi32( v0 ); __m256i i1 = _mm256_cvtps_epi32( v1 ); __m256i i2 = _mm256_cvtps_epi32( v2 ); __m256i i3 = _mm256_cvtps_epi32( v3 ); // Convert int32 to int16 i0 = _mm256_packs_epi32( i0, i1 ); // 0, 1, 2, 3, 8, 9, 10, 11, 4, 5, 6, 7, 12, 13, 14, 15 i2 = _mm256_packs_epi32( i2, i3 ); // 16, 17, 18, 19, 24, 25, 26, 27, 20, 21, 22, 23, 28, 29, 30, 31 // Convert int16 to int8 i0 = _mm256_packs_epi16( i0, i2 ); // 0, 1, 2, 3, 8, 9, 10, 11, 16, 17, 18, 19, 24, 25, 26, 27, 4, 5, 6, 7, 12, 13, 14, 15, 20, 21, 22, 23, 28, 29, 30, 31 // We got our precious signed bytes, but the order is now wrong // These AVX2 pack instructions process 16-byte pieces independently // The following instruction is fixing the order const __m256i perm = _mm256_setr_epi32( 0, 4, 1, 5, 2, 6, 3, 7 ); i0 = _mm256_permutevar8x32_epi32( i0, perm ); // Compress the vector into 4 bit/value, and store __m128i res = packNibbles( i0 ); _mm_storeu_si128( ( __m128i* )y[i].qs, res ); } #elif __ARM_NEON for (int i = 0; i < nb; i++) { float32x4_t srcv[8]; float32x4_t minv[8]; float32x4_t maxv[8]; for (int l = 0; l < 8; l++) srcv[l] = vld1q_f32(x + i*QK4_1 + 4*l); for (int l = 0; l < 4; l++) minv[2*l] = vminq_f32(srcv[2*l], srcv[2*l + 1]); for (int l = 0; l < 2; l++) minv[4*l] = vminq_f32(minv[4*l], minv[4*l + 2]); for (int l = 0; l < 1; l++) minv[8*l] = vminq_f32(minv[8*l], minv[8*l + 4]); for (int l = 0; l < 4; l++) maxv[2*l] = vmaxq_f32(srcv[2*l], srcv[2*l + 1]); for (int l = 0; l < 2; l++) maxv[4*l] = vmaxq_f32(maxv[4*l], maxv[4*l + 2]); for (int l = 0; l < 1; l++) maxv[8*l] = vmaxq_f32(maxv[8*l], maxv[8*l + 4]); const float min = vminvq_f32(minv[0]); const float max = vmaxvq_f32(maxv[0]); const float d = (max - min) / ((1 << 4) - 1); const float id = d ? 1.0f/d : 0.0f; y[i].d = d; y[i].m = min; const float32x4_t minv0 = vdupq_n_f32(min); for (int l = 0; l < 8; l++) { const float32x4_t v = vmulq_n_f32(vsubq_f32(srcv[l], minv0), id); const float32x4_t vf = vaddq_f32(v, vdupq_n_f32(0.5f)); // needed to round to nearest const int32x4_t vi = vcvtq_s32_f32(vf); y[i].qs[2*l + 0] = vgetq_lane_s32(vi, 0) | (vgetq_lane_s32(vi, 1) << 4); y[i].qs[2*l + 1] = vgetq_lane_s32(vi, 2) | (vgetq_lane_s32(vi, 3) << 4); } } #else // scalar quantize_row_q4_1_reference(x, vy, k); #endif } // reference implementation for deterministic creation of model files static void quantize_row_q4_2_reference(const float * restrict x, block_q4_2 * restrict y, int k) { assert(k % QK4_2 == 0); #ifndef GGML_NO_RMSE quantize_row_q4_2_rmse(x, y, k); return; #endif const int nb = k / QK4_2; for (int i = 0; i < nb; i++) { float amax = 0.0f; // absolute max for (int l = 0; l < QK4_2; l++) { const float v = x[i*QK4_2 + l]; amax = MAX(amax, fabsf(v)); } const float d = amax / ((1 << 3) - 1); const float id = d ? 1.0f/d : 0.0f; y[i].d = GGML_FP32_TO_FP16(d); for (int l = 0; l < QK4_2; l += 2) { const float v0 = x[i*QK4_2 + l + 0]*id; const float v1 = x[i*QK4_2 + l + 1]*id; const uint8_t vi0 = (uint8_t)(v0 + 8.5f); const uint8_t vi1 = (uint8_t)(v1 + 8.5f); assert(vi0 < 16); assert(vi1 < 16); y[i].qs[l/2] = vi0 | (vi1 << 4); } } } #ifndef GGML_NO_RMSE static void quantize_row_q41_helper(int n, const float * restrict x, int8_t * restrict L, float * restrict tmp_x, float * result_a, float * result_b) { #define CANDIDATE_COUNT 20 static const float candidates[CANDIDATE_COUNT] = { +18.5f, +17.75f, +17.25f, +16.75f, +16.25f, +15.75f, +15.25f, +15.0f, +14.75f, +14.25f, +13.75f, +13.25f, +12.75f, +12.25, +11.75f, +11.25f, +10.75f, +10.25f, +9.25, +8.25 }; const float epsilon = 1e-5; float min = x[0], max = x[0]; for (int j=1; j max) max = x[j]; } if (max == min) { *result_a = min; *result_b = 1.f; for (int j=0; j 0 && fabsf(a - aold) < epsilon*fabsf(aold) && fabsf(b - bold) < epsilon*fabsf(bold)) break; } float err = 0; for (int j=0; j simple_err) { a = min; b = (max - min)/15; for (int j=0; j max) max = v; } const float d = (max - min) / ((1 << 4) - 1); const float id = d ? 1.0f/d : 0.0f; y[i].d = GGML_FP32_TO_FP16(d); y[i].m = GGML_FP32_TO_FP16(min); for (int l = 0; l < QK4_3; l += 2) { const float v0 = (x[i*QK4_3 + l + 0] - min)*id; const float v1 = (x[i*QK4_3 + l + 1] - min)*id; const uint8_t vi0 = (int) (v0 + 0.5f); const uint8_t vi1 = (int) (v1 + 0.5f); assert(vi0 < 16); assert(vi1 < 16); y[i].qs[l/2] = vi0 | (vi1 << 4); } } } static void quantize_row_q4_3(const float * restrict x, void * restrict vy, int k) { assert(k % QK4_3 == 0); block_q4_3 * restrict y = vy; quantize_row_q4_3_reference(x, y, k); } // reference implementation for deterministic creation of model files static void quantize_row_q8_0_reference(const float * restrict x, block_q8_0 * restrict y, int k) { assert(k % QK8_0 == 0); const int nb = k / QK8_0; for (int i = 0; i < nb; i++) { float amax = 0.0f; // absolute max for (int l = 0; l < QK8_0; l++) { const float v = x[i*QK8_0 + l]; amax = MAX(amax, fabsf(v)); } const float d = amax / ((1 << 7) - 1); const float id = d ? 1.0f/d : 0.0f; y[i].d = d; int sum0 = 0; int sum1 = 0; for (int l = 0; l < QK8_0/2; ++l) { const float v0 = x[i*QK8_0 + l]*id; const float v1 = x[i*QK8_0 + QK8_0/2 + l]*id; y[i].qs[ l] = roundf(v0); y[i].qs[QK8_0/2 + l] = roundf(v1); sum0 += y[i].qs[ l]; sum1 += y[i].qs[QK8_0/2 + l]; } y[i].s0 = d * sum0; y[i].s1 = d * sum1; } } static void quantize_row_q8_0(const float * restrict x, void * restrict vy, int k) { assert(k % QK8_0 == 0); const int nb = k / QK8_0; block_q8_0 * restrict y = vy; #if defined(__ARM_NEON) for (int i = 0; i < nb; i++) { float32x4_t srcv [8]; float32x4_t asrcv[8]; float32x4_t amaxv[8]; for (int 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]); const float amax = vmaxvq_f32(amaxv[0]); const float d = amax / ((1 << 7) - 1); const float id = d ? 1.0f/d : 0.0f; y[i].d = d; int32x4_t accv0 = vdupq_n_s32(0); int32x4_t accv1 = vdupq_n_s32(0); // low half for (int l = 0; l < 4; l++) { const float32x4_t v = vmulq_n_f32(srcv[l], id); const int32x4_t vi = vcvtnq_s32_f32(v); y[i].qs[4*l + 0] = vgetq_lane_s32(vi, 0); y[i].qs[4*l + 1] = vgetq_lane_s32(vi, 1); y[i].qs[4*l + 2] = vgetq_lane_s32(vi, 2); y[i].qs[4*l + 3] = vgetq_lane_s32(vi, 3); accv0 = vaddq_s32(accv0, vi); } // high half for (int l = 4; l < 8; l++) { const float32x4_t v = vmulq_n_f32(srcv[l], id); const int32x4_t vi = vcvtnq_s32_f32(v); y[i].qs[4*l + 0] = vgetq_lane_s32(vi, 0); y[i].qs[4*l + 1] = vgetq_lane_s32(vi, 1); y[i].qs[4*l + 2] = vgetq_lane_s32(vi, 2); y[i].qs[4*l + 3] = vgetq_lane_s32(vi, 3); accv1 = vaddq_s32(accv1, vi); } const int32_t sum0 = vaddvq_s32(accv0); const int32_t sum1 = vaddvq_s32(accv1); y[i].s0 = d * sum0; y[i].s1 = d * sum1; } #elif defined(__AVX2__) || defined(__AVX__) for (int i = 0; i < nb; i++) { // Load elements into 4 AVX vectors __m256 v0 = _mm256_loadu_ps( x ); __m256 v1 = _mm256_loadu_ps( x + 8 ); __m256 v2 = _mm256_loadu_ps( x + 16 ); __m256 v3 = _mm256_loadu_ps( x + 24 ); x += 32; // Compute max(abs(e)) for the block const __m256 signBit = _mm256_set1_ps( -0.0f ); __m256 maxAbs = _mm256_andnot_ps( signBit, v0 ); maxAbs = _mm256_max_ps( maxAbs, _mm256_andnot_ps( signBit, v1 ) ); maxAbs = _mm256_max_ps( maxAbs, _mm256_andnot_ps( signBit, v2 ) ); maxAbs = _mm256_max_ps( maxAbs, _mm256_andnot_ps( signBit, v3 ) ); __m128 max4 = _mm_max_ps( _mm256_extractf128_ps( maxAbs, 1 ), _mm256_castps256_ps128( maxAbs ) ); max4 = _mm_max_ps( max4, _mm_movehl_ps( max4, max4 ) ); max4 = _mm_max_ss( max4, _mm_movehdup_ps( max4 ) ); const float maxScalar = _mm_cvtss_f32( max4 ); // Quantize these floats const float d = maxScalar / 127.f; y[i].d = d; const float id = ( maxScalar != 0.0f ) ? 127.f / maxScalar : 0.0f; const __m256 mul = _mm256_set1_ps( id ); // Apply the multiplier v0 = _mm256_mul_ps( v0, mul ); v1 = _mm256_mul_ps( v1, mul ); v2 = _mm256_mul_ps( v2, mul ); v3 = _mm256_mul_ps( v3, mul ); // Round to nearest integer v0 = _mm256_round_ps( v0, _MM_ROUND_NEAREST ); v1 = _mm256_round_ps( v1, _MM_ROUND_NEAREST ); v2 = _mm256_round_ps( v2, _MM_ROUND_NEAREST ); v3 = _mm256_round_ps( v3, _MM_ROUND_NEAREST ); // Convert floats to integers __m256i i0 = _mm256_cvtps_epi32( v0 ); __m256i i1 = _mm256_cvtps_epi32( v1 ); __m256i i2 = _mm256_cvtps_epi32( v2 ); __m256i i3 = _mm256_cvtps_epi32( v3 ); #if defined(__AVX2__) // Compute the sum of the quants and set y[i].s //y[i].s = d * hsum_i32_8(_mm256_add_epi32(_mm256_add_epi32(i0, i1), _mm256_add_epi32(i2, i3))); y[i].s0 = d * hsum_i32_8(_mm256_add_epi32(i0, i1)); y[i].s1 = d * hsum_i32_8(_mm256_add_epi32(i2, i3)); // Convert int32 to int16 i0 = _mm256_packs_epi32( i0, i1 ); // 0, 1, 2, 3, 8, 9, 10, 11, 4, 5, 6, 7, 12, 13, 14, 15 i2 = _mm256_packs_epi32( i2, i3 ); // 16, 17, 18, 19, 24, 25, 26, 27, 20, 21, 22, 23, 28, 29, 30, 31 // Convert int16 to int8 i0 = _mm256_packs_epi16( i0, i2 ); // 0, 1, 2, 3, 8, 9, 10, 11, 16, 17, 18, 19, 24, 25, 26, 27, 4, 5, 6, 7, 12, 13, 14, 15, 20, 21, 22, 23, 28, 29, 30, 31 // We got our precious signed bytes, but the order is now wrong // These AVX2 pack instructions process 16-byte pieces independently // The following instruction is fixing the order const __m256i perm = _mm256_setr_epi32( 0, 4, 1, 5, 2, 6, 3, 7 ); i0 = _mm256_permutevar8x32_epi32( i0, perm ); _mm256_storeu_si256((__m256i *)y[i].qs, i0); #else // Since we don't have in AVX some necessary functions, // we split the registers in half and call AVX2 analogs from SSE __m128i ni0 = _mm256_castsi256_si128( i0 ); __m128i ni1 = _mm256_extractf128_si256( i0, 1); __m128i ni2 = _mm256_castsi256_si128( i1 ); __m128i ni3 = _mm256_extractf128_si256( i1, 1); __m128i ni4 = _mm256_castsi256_si128( i2 ); __m128i ni5 = _mm256_extractf128_si256( i2, 1); __m128i ni6 = _mm256_castsi256_si128( i3 ); __m128i ni7 = _mm256_extractf128_si256( i3, 1); // Compute the sum of the quants and set y[i].s const __m128i s0 = _mm_add_epi32(_mm_add_epi32(ni0, ni1), _mm_add_epi32(ni2, ni3)); const __m128i s1 = _mm_add_epi32(_mm_add_epi32(ni4, ni5), _mm_add_epi32(ni6, ni7)); y[i].s0 = d * hsum_i32_4(s0); y[i].s1 = d * hsum_i32_4(s1); // Convert int32 to int16 ni0 = _mm_packs_epi32( ni0, ni1 ); ni2 = _mm_packs_epi32( ni2, ni3 ); ni4 = _mm_packs_epi32( ni4, ni5 ); ni6 = _mm_packs_epi32( ni6, ni7 ); // Convert int16 to int8 ni0 = _mm_packs_epi16( ni0, ni2 ); ni4 = _mm_packs_epi16( ni4, ni6 ); _mm_storeu_si128((__m128i *)(y[i].qs + 0), ni0); _mm_storeu_si128((__m128i *)(y[i].qs + 16), ni4); #endif } #else // scalar quantize_row_q8_0_reference(x, y, k); #endif } static void dequantize_row_q4_0(const void * restrict vx, float * restrict y, int k) { assert(k % QK4_0 == 0); const int nb = k / QK4_0; const block_q4_0 * restrict x = vx; #if defined(__AVX2__) for (int i = 0; i < nb; i++) { // scale factor const __m256 d_v = _mm256_broadcast_ss(&x[i].d); const uint8_t * restrict pp = x[i].qs; for (int l = 0; l < QK4_0; l += 32) { // Load 32x4-bit integers into 32x8-bit integers __m256i vx8 = bytes_from_nibbles_32(pp+l/2); // Subtract 8 from the integers vx8 = _mm256_sub_epi8(vx8, _mm256_set1_epi8(8)); // Convert to 16-bit int const __m256i vx16_lo = _mm256_cvtepi8_epi16(_mm256_extracti128_si256(vx8, 0)); const __m256i vx16_hi = _mm256_cvtepi8_epi16(_mm256_extracti128_si256(vx8, 1)); // Convert to 32-bit int -> float 32 const __m256 vf[4] = { _mm256_cvtepi32_ps(_mm256_cvtepi16_epi32(_mm256_extracti128_si256(vx16_lo, 0))), _mm256_cvtepi32_ps(_mm256_cvtepi16_epi32(_mm256_extracti128_si256(vx16_lo, 1))), _mm256_cvtepi32_ps(_mm256_cvtepi16_epi32(_mm256_extracti128_si256(vx16_hi, 0))), _mm256_cvtepi32_ps(_mm256_cvtepi16_epi32(_mm256_extracti128_si256(vx16_hi, 1))) }; // Scale and store for (int j = 0; j < 4; j++) { const __m256 result = _mm256_mul_ps(vf[j], d_v); _mm256_storeu_ps(y + i * QK4_0 + l + j*8, result); } } } #elif defined(__ARM_NEON) for (int i = 0; i < nb; i++) { const float32x4_t vd = vdupq_n_f32(x[i].d); const uint8_t * restrict pp = x[i].qs; for (int l = 0; l < QK4_0; l += 16) { // Load 16x4-bit integers into 8x8-bit integers const uint8x8_t v8 = vld1_u8(pp + l/2); // Expand 4-bit qs to 8-bit bytes const uint8x8_t v0 = vand_u8(v8, vdup_n_u8(0x0f)); const uint8x8_t v1 = vshr_n_u8(v8, 4); // Convert to signed 8-bit integers const int8x8_t vs_0 = vreinterpret_s8_u8(v0); const int8x8_t vs_1 = vreinterpret_s8_u8(v1); // Subtract 8 from each byte const int8x8_t vb_0 = vsub_s8(vs_0, vdup_n_s8(8)); const int8x8_t vb_1 = vsub_s8(vs_1, vdup_n_s8(8)); // Interleave and combine const int8x8_t vx_0 = vzip1_s8(vb_0, vb_1); const int8x8_t vx_1 = vzip2_s8(vb_0, vb_1); const int8x16_t vq = vcombine_s8(vx_0, vx_1); // convert to 2x int16x8_t const int16x8_t vi_0 = vmovl_s8(vget_low_s8 (vq)); const int16x8_t vi_1 = vmovl_s8(vget_high_s8(vq)); // convert to 4x float32x4_t const float32x4_t vf_0 = vcvtq_f32_s32(vmovl_s16(vget_low_s16 (vi_0))); const float32x4_t vf_1 = vcvtq_f32_s32(vmovl_s16(vget_high_s16(vi_0))); const float32x4_t vf_2 = vcvtq_f32_s32(vmovl_s16(vget_low_s16 (vi_1))); const float32x4_t vf_3 = vcvtq_f32_s32(vmovl_s16(vget_high_s16(vi_1))); // Multiply by d const float32x4_t r0 = vmulq_f32(vf_0, vd); const float32x4_t r1 = vmulq_f32(vf_1, vd); const float32x4_t r2 = vmulq_f32(vf_2, vd); const float32x4_t r3 = vmulq_f32(vf_3, vd); // Store vst1q_f32(y + i*QK4_0 + l + 0, r0); vst1q_f32(y + i*QK4_0 + l + 4, r1); vst1q_f32(y + i*QK4_0 + l + 8, r2); vst1q_f32(y + i*QK4_0 + l + 12, r3); } } #else // scalar for (int i = 0; i < nb; i++) { const float d = x[i].d; const uint8_t * restrict pp = x[i].qs; for (int l = 0; l < QK4_0; l += 2) { const uint8_t vi = pp[l/2]; const int8_t vi0 = vi & 0xf; const int8_t vi1 = vi >> 4; const float v0 = (vi0 - 8)*d; const float v1 = (vi1 - 8)*d; //printf("d = %f, vi = %d, vi0 = %d, vi1 = %d, v0 = %f, v1 = %f\n", d, vi, vi0, vi1, v0, v1); y[i*QK4_0 + l + 0] = v0; y[i*QK4_0 + l + 1] = v1; assert(!isnan(y[i*QK4_0 + l + 0])); assert(!isnan(y[i*QK4_0 + l + 1])); } } #endif } static void dequantize_row_q4_1(const void * restrict vx, float * restrict y, int k) { assert(k % QK4_1 == 0); const int nb = k / QK4_1; const block_q4_1 * restrict x = vx; #if defined(__AVX2__) for (int i = 0; i < nb; i++) { const __m256 d_v = _mm256_broadcast_ss(&x[i].d); const __m256 d_m = _mm256_broadcast_ss(&x[i].m); const uint8_t * restrict pp = x[i].qs; for (int l = 0; l < QK4_1; l += 32) { // Load 32x4-bit integers into 32x8-bit integers __m256i vx8 = bytes_from_nibbles_32(pp+l/2); // Convert to 16-bit int const __m256i vx16_lo = _mm256_cvtepi8_epi16(_mm256_extracti128_si256(vx8, 0)); const __m256i vx16_hi = _mm256_cvtepi8_epi16(_mm256_extracti128_si256(vx8, 1)); // Convert to 32-bit int -> float 32 const __m256 vf[4] = { _mm256_cvtepi32_ps(_mm256_cvtepi16_epi32(_mm256_extracti128_si256(vx16_lo, 0))), _mm256_cvtepi32_ps(_mm256_cvtepi16_epi32(_mm256_extracti128_si256(vx16_lo, 1))), _mm256_cvtepi32_ps(_mm256_cvtepi16_epi32(_mm256_extracti128_si256(vx16_hi, 0))), _mm256_cvtepi32_ps(_mm256_cvtepi16_epi32(_mm256_extracti128_si256(vx16_hi, 1))) }; // Scale, add m and store for (int j = 0; j < 4; j++) { const __m256 result = _mm256_add_ps(_mm256_mul_ps(vf[j], d_v), d_m); _mm256_storeu_ps(y + i * QK4_1 + l + j*8, result); } } } #elif defined(__ARM_NEON) for (int i = 0; i < nb; i++) { const float32x4_t vd = vdupq_n_f32(x[i].d); const float32x4_t vm = vdupq_n_f32(x[i].m); const uint8_t * restrict pp = x[i].qs; for (int l = 0; l < QK4_1; l += 16) { // Load 16x4-bit integers into 8x8-bit integers const uint8x8_t v8 = vld1_u8(pp + l/2); // Expand 4-bit qs to 8-bit bytes const uint8x8_t v0 = vand_u8(v8, vdup_n_u8(0x0f)); const uint8x8_t v1 = vshr_n_u8(v8, 4); // Interleave and combine const uint8x8_t vx_0 = vzip1_u8(v0, v1); const uint8x8_t vx_1 = vzip2_u8(v0, v1); const uint8x16_t vq = vcombine_u8(vx_0, vx_1); // convert to 2x uint16x8_t const uint16x8_t vi_0 = vmovl_u8(vget_low_u8 (vq)); const uint16x8_t vi_1 = vmovl_u8(vget_high_u8(vq)); // convert to 4x float32x4_t const float32x4_t vf_0 = vcvtq_f32_u32(vmovl_u16(vget_low_u16 (vi_0))); const float32x4_t vf_1 = vcvtq_f32_u32(vmovl_u16(vget_high_u16(vi_0))); const float32x4_t vf_2 = vcvtq_f32_u32(vmovl_u16(vget_low_u16 (vi_1))); const float32x4_t vf_3 = vcvtq_f32_u32(vmovl_u16(vget_high_u16(vi_1))); // multiply by d and add m const float32x4_t r0 = vmlaq_f32(vm, vf_0, vd); const float32x4_t r1 = vmlaq_f32(vm, vf_1, vd); const float32x4_t r2 = vmlaq_f32(vm, vf_2, vd); const float32x4_t r3 = vmlaq_f32(vm, vf_3, vd); // Store vst1q_f32(y + i*QK4_1 + l + 0, r0); vst1q_f32(y + i*QK4_1 + l + 4, r1); vst1q_f32(y + i*QK4_1 + l + 8, r2); vst1q_f32(y + i*QK4_1 + l + 12, r3); } } #else for (int i = 0; i < nb; i++) { const float d = x[i].d; const float m = x[i].m; const uint8_t * restrict pp = x[i].qs; for (int l = 0; l < QK4_1; 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*QK4_1 + l + 0] = v0; y[i*QK4_1 + l + 1] = v1; assert(!isnan(y[i*QK4_1 + l + 0])); assert(!isnan(y[i*QK4_1 + l + 1])); } } #endif } static void dequantize_row_q4_2(const void * restrict vx, float * restrict y, int k) { assert(k % QK4_2 == 0); const int nb = k / QK4_2; const block_q4_2 * restrict x = vx; for (int i = 0; i < nb; i++) { const float d = GGML_FP16_TO_FP32(x[i].d); const uint8_t * restrict pp = x[i].qs; for (int l = 0; l < QK4_2; 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*QK4_2 + l + 0] = v0; y[i*QK4_2 + l + 1] = v1; assert(!isnan(y[i*QK4_2 + l + 0])); assert(!isnan(y[i*QK4_2 + l + 1])); } } } static void dequantize_row_q4_3(const void * restrict vx, float * restrict y, int k) { assert(k % QK4_3 == 0); const int nb = k / QK4_3; const block_q4_3 * restrict x = vx; for (int i = 0; i < nb; i++) { const float d = GGML_FP16_TO_FP32(x[i].d); const float m = GGML_FP16_TO_FP32(x[i].m); const uint8_t * restrict pp = x[i].qs; for (int l = 0; l < QK4_3; 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*QK4_3 + l + 0] = v0; y[i*QK4_3 + l + 1] = v1; assert(!isnan(y[i*QK4_3 + l + 0])); assert(!isnan(y[i*QK4_3 + l + 1])); } } } static void ggml_vec_dot_q4_0_q8_0(const int n, float * restrict s, const void * restrict vx, const void * restrict vy); static void ggml_vec_dot_q4_1_q8_0(const int n, float * restrict s, const void * restrict vx, const void * restrict vy); static void ggml_vec_dot_q4_2_q8_0(const int n, float * restrict s, const void * restrict vx, const void * restrict vy); static void ggml_vec_dot_q4_3_q8_0(const int n, float * restrict s, const void * restrict vx, const void * restrict vy); static const quantize_fns_t quantize_fns[GGML_TYPE_COUNT] = { [GGML_TYPE_Q4_0] = { .dequantize_row_q = dequantize_row_q4_0, .quantize_row_q = quantize_row_q4_0, .quantize_row_q_reference = (quantize_row_q_t) quantize_row_q4_0_reference, .quantize_row_q_dot = quantize_row_q8_0, .vec_dot_q = ggml_vec_dot_q4_0_q8_0, }, [GGML_TYPE_Q4_1] = { .dequantize_row_q = dequantize_row_q4_1, .quantize_row_q = (quantize_row_q_t) quantize_row_q4_1, .quantize_row_q_reference = (quantize_row_q_t) quantize_row_q4_1_reference, .quantize_row_q_dot = quantize_row_q8_0, .vec_dot_q = ggml_vec_dot_q4_1_q8_0, }, [GGML_TYPE_Q4_2] = { .dequantize_row_q = dequantize_row_q4_2, .quantize_row_q = quantize_row_q4_2, .quantize_row_q_reference = (quantize_row_q_t) quantize_row_q4_2_reference, .quantize_row_q_dot = quantize_row_q8_0, .vec_dot_q = ggml_vec_dot_q4_2_q8_0, }, [GGML_TYPE_Q4_3] = { .dequantize_row_q = dequantize_row_q4_3, .quantize_row_q = quantize_row_q4_3, .quantize_row_q_reference = (quantize_row_q_t) quantize_row_q4_3_reference, .quantize_row_q_dot = quantize_row_q8_0, .vec_dot_q = ggml_vec_dot_q4_3_q8_0, }, [GGML_TYPE_Q8_0] = { .dequantize_row_q = NULL, // TODO .quantize_row_q = quantize_row_q8_0, .quantize_row_q_reference = (quantize_row_q_t) quantize_row_q8_0_reference, .quantize_row_q_dot = quantize_row_q8_0, .vec_dot_q = NULL, // TODO }, }; // For internal test use quantize_fns_t ggml_internal_get_quantize_fn(size_t i) { GGML_ASSERT(i < GGML_TYPE_COUNT); return quantize_fns[i]; } // // simd mappings // // we define a common set of C macros which map to specific intrinsics based on the current architecture // we then implement the fundamental computation operations below using only these macros // adding support for new architectures requires to define the corresponding SIMD macros // // GGML_F32_STEP / GGML_F16_STEP // number of elements to process in a single step // // GGML_F32_EPR / GGML_F16_EPR // number of elements to fit in a single register // #if defined(__ARM_NEON) && defined(__ARM_FEATURE_FMA) #define GGML_SIMD // F32 NEON #define GGML_F32_STEP 16 #define GGML_F32_EPR 4 #define GGML_F32x4 float32x4_t #define GGML_F32x4_ZERO vdupq_n_f32(0.0f) #define GGML_F32x4_SET1(x) vdupq_n_f32(x) #define GGML_F32x4_LOAD vld1q_f32 #define GGML_F32x4_STORE vst1q_f32 #define GGML_F32x4_FMA(a, b, c) vfmaq_f32(a, b, c) #define GGML_F32x4_ADD vaddq_f32 #define GGML_F32x4_MUL vmulq_f32 #define GGML_F32x4_REDUCE_ONE(x) vaddvq_f32(x) #define GGML_F32x4_REDUCE(res, x) \ { \ for (int i = 0; i < GGML_F32_ARR/2; ++i) { \ x[2*i] = vaddq_f32(x[2*i], x[2*i+1]); \ } \ for (int i = 0; i < GGML_F32_ARR/4; ++i) { \ x[4*i] = vaddq_f32(x[4*i], x[4*i+2]); \ } \ for (int i = 0; i < GGML_F32_ARR/8; ++i) { \ x[8*i] = vaddq_f32(x[8*i], x[8*i+4]); \ } \ res = GGML_F32x4_REDUCE_ONE(x[0]); \ } #define GGML_F32_VEC GGML_F32x4 #define GGML_F32_VEC_ZERO GGML_F32x4_ZERO #define GGML_F32_VEC_SET1 GGML_F32x4_SET1 #define GGML_F32_VEC_LOAD GGML_F32x4_LOAD #define GGML_F32_VEC_STORE GGML_F32x4_STORE #define GGML_F32_VEC_FMA GGML_F32x4_FMA #define GGML_F32_VEC_ADD GGML_F32x4_ADD #define GGML_F32_VEC_MUL GGML_F32x4_MUL #define GGML_F32_VEC_REDUCE GGML_F32x4_REDUCE // F16 NEON #if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) #define GGML_F16_STEP 32 #define GGML_F16_EPR 8 #define GGML_F16x8 float16x8_t #define GGML_F16x8_ZERO vdupq_n_f16(0.0f) #define GGML_F16x8_SET1(x) vdupq_n_f16(x) #define GGML_F16x8_LOAD vld1q_f16 #define GGML_F16x8_STORE vst1q_f16 #define GGML_F16x8_FMA(a, b, c) vfmaq_f16(a, b, c) #define GGML_F16x8_ADD vaddq_f16 #define GGML_F16x8_MUL vmulq_f16 #define GGML_F16x8_REDUCE(res, x) \ { \ for (int i = 0; i < GGML_F16_ARR/2; ++i) { \ x[2*i] = vaddq_f16(x[2*i], x[2*i+1]); \ } \ for (int i = 0; i < GGML_F16_ARR/4; ++i) { \ x[4*i] = vaddq_f16(x[4*i], x[4*i+2]); \ } \ for (int i = 0; i < GGML_F16_ARR/8; ++i) { \ x[8*i] = vaddq_f16(x[8*i], x[8*i+4]); \ } \ const float32x4_t t0 = vcvt_f32_f16(vget_low_f16 (x[0])); \ const float32x4_t t1 = vcvt_f32_f16(vget_high_f16(x[0])); \ res = (ggml_float) vaddvq_f32(vaddq_f32(t0, t1)); \ } #define GGML_F16_VEC GGML_F16x8 #define GGML_F16_VEC_ZERO GGML_F16x8_ZERO #define GGML_F16_VEC_SET1 GGML_F16x8_SET1 #define GGML_F16_VEC_LOAD(p, i) GGML_F16x8_LOAD(p) #define GGML_F16_VEC_STORE(p, r, i) GGML_F16x8_STORE(p, r[i]) #define GGML_F16_VEC_FMA GGML_F16x8_FMA #define GGML_F16_VEC_ADD GGML_F16x8_ADD #define GGML_F16_VEC_MUL GGML_F16x8_MUL #define GGML_F16_VEC_REDUCE GGML_F16x8_REDUCE #else // if FP16 vector arithmetic is not supported, we use FP32 instead // and take advantage of the vcvt_ functions to convert to/from FP16 #define GGML_F16_STEP 16 #define GGML_F16_EPR 4 #define GGML_F32Cx4 float32x4_t #define GGML_F32Cx4_ZERO vdupq_n_f32(0.0f) #define GGML_F32Cx4_SET1(x) vdupq_n_f32(x) #define GGML_F32Cx4_LOAD(x) vcvt_f32_f16(vld1_f16(x)) #define GGML_F32Cx4_STORE(x, y) vst1_f16(x, vcvt_f16_f32(y)) #define GGML_F32Cx4_FMA(a, b, c) vfmaq_f32(a, b, c) #define GGML_F32Cx4_ADD vaddq_f32 #define GGML_F32Cx4_MUL vmulq_f32 #define GGML_F32Cx4_REDUCE GGML_F32x4_REDUCE #define GGML_F16_VEC GGML_F32Cx4 #define GGML_F16_VEC_ZERO GGML_F32Cx4_ZERO #define GGML_F16_VEC_SET1 GGML_F32Cx4_SET1 #define GGML_F16_VEC_LOAD(p, i) GGML_F32Cx4_LOAD(p) #define GGML_F16_VEC_STORE(p, r, i) GGML_F32Cx4_STORE(p, r[i]) #define GGML_F16_VEC_FMA GGML_F32Cx4_FMA #define GGML_F16_VEC_ADD GGML_F32Cx4_ADD #define GGML_F16_VEC_MUL GGML_F32Cx4_MUL #define GGML_F16_VEC_REDUCE GGML_F32Cx4_REDUCE #endif #elif defined(__AVX__) #define GGML_SIMD // F32 AVX #define GGML_F32_STEP 32 #define GGML_F32_EPR 8 #define GGML_F32x8 __m256 #define GGML_F32x8_ZERO _mm256_setzero_ps() #define GGML_F32x8_SET1(x) _mm256_set1_ps(x) #define GGML_F32x8_LOAD _mm256_loadu_ps #define GGML_F32x8_STORE _mm256_storeu_ps #if defined(__FMA__) #define GGML_F32x8_FMA(a, b, c) _mm256_fmadd_ps(b, c, a) #else #define GGML_F32x8_FMA(a, b, c) _mm256_add_ps(_mm256_mul_ps(b, c), a) #endif #define GGML_F32x8_ADD _mm256_add_ps #define GGML_F32x8_MUL _mm256_mul_ps #define GGML_F32x8_REDUCE(res, x) \ { \ for (int i = 0; i < GGML_F32_ARR/2; ++i) { \ x[2*i] = _mm256_add_ps(x[2*i], x[2*i+1]); \ } \ for (int i = 0; i < GGML_F32_ARR/4; ++i) { \ x[4*i] = _mm256_add_ps(x[4*i], x[4*i+2]); \ } \ for (int i = 0; i < GGML_F32_ARR/8; ++i) { \ x[8*i] = _mm256_add_ps(x[8*i], x[8*i+4]); \ } \ const __m128 t0 = _mm_add_ps(_mm256_castps256_ps128(x[0]), \ _mm256_extractf128_ps(x[0], 1)); \ const __m128 t1 = _mm_hadd_ps(t0, t0); \ res = _mm_cvtss_f32(_mm_hadd_ps(t1, t1)); \ } // TODO: is this optimal ? #define GGML_F32_VEC GGML_F32x8 #define GGML_F32_VEC_ZERO GGML_F32x8_ZERO #define GGML_F32_VEC_SET1 GGML_F32x8_SET1 #define GGML_F32_VEC_LOAD GGML_F32x8_LOAD #define GGML_F32_VEC_STORE GGML_F32x8_STORE #define GGML_F32_VEC_FMA GGML_F32x8_FMA #define GGML_F32_VEC_ADD GGML_F32x8_ADD #define GGML_F32_VEC_MUL GGML_F32x8_MUL #define GGML_F32_VEC_REDUCE GGML_F32x8_REDUCE // F16 AVX #define GGML_F16_STEP 32 #define GGML_F16_EPR 8 // F16 arithmetic is not supported by AVX, so we use F32 instead #define GGML_F32Cx8 __m256 #define GGML_F32Cx8_ZERO _mm256_setzero_ps() #define GGML_F32Cx8_SET1(x) _mm256_set1_ps(x) #if defined(__F16C__) // the _mm256_cvt intrinsics require F16C #define GGML_F32Cx8_LOAD(x) _mm256_cvtph_ps(_mm_loadu_si128((__m128i *)(x))) #define GGML_F32Cx8_STORE(x, y) _mm_storeu_si128((__m128i *)(x), _mm256_cvtps_ph(y, 0)) #else static inline __m256 __avx_f32cx8_load(ggml_fp16_t *x) { float tmp[8]; for (int i = 0; i < 8; i++) tmp[i] = GGML_FP16_TO_FP32(x[i]); return _mm256_loadu_ps(tmp); } static inline void __avx_f32cx8_store(ggml_fp16_t *x, __m256 y) { float arr[8]; _mm256_storeu_ps(arr, y); for (int i = 0; i < 8; i++) x[i] = GGML_FP32_TO_FP16(arr[i]); } #define GGML_F32Cx8_LOAD(x) __avx_f32cx8_load(x) #define GGML_F32Cx8_STORE(x, y) __avx_f32cx8_store(x, y) #endif #define GGML_F32Cx8_FMA GGML_F32x8_FMA #define GGML_F32Cx8_ADD _mm256_add_ps #define GGML_F32Cx8_MUL _mm256_mul_ps #define GGML_F32Cx8_REDUCE GGML_F32x8_REDUCE #define GGML_F16_VEC GGML_F32Cx8 #define GGML_F16_VEC_ZERO GGML_F32Cx8_ZERO #define GGML_F16_VEC_SET1 GGML_F32Cx8_SET1 #define GGML_F16_VEC_LOAD(p, i) GGML_F32Cx8_LOAD(p) #define GGML_F16_VEC_STORE(p, r, i) GGML_F32Cx8_STORE(p, r[i]) #define GGML_F16_VEC_FMA GGML_F32Cx8_FMA #define GGML_F16_VEC_ADD GGML_F32Cx8_ADD #define GGML_F16_VEC_MUL GGML_F32Cx8_MUL #define GGML_F16_VEC_REDUCE GGML_F32Cx8_REDUCE #elif defined(__POWER9_VECTOR__) #define GGML_SIMD // F32 POWER9 #define GGML_F32_STEP 32 #define GGML_F32_EPR 4 #define GGML_F32x4 vector float #define GGML_F32x4_ZERO 0.0f #define GGML_F32x4_SET1 vec_splats #define GGML_F32x4_LOAD(p) vec_xl(0, p) #define GGML_F32x4_STORE(p, r) vec_xst(r, 0, p) #define GGML_F32x4_FMA(a, b, c) vec_madd(b, c, a) #define GGML_F32x4_ADD vec_add #define GGML_F32x4_MUL vec_mul #define GGML_F32x4_REDUCE(res, x) \ { \ for (int i = 0; i < GGML_F32_ARR/2; ++i) { \ x[2*i] = vec_add(x[2*i], x[2*i+1]); \ } \ for (int i = 0; i < GGML_F32_ARR/4; ++i) { \ x[4*i] = vec_add(x[4*i], x[4*i+2]); \ } \ for (int i = 0; i < GGML_F32_ARR/8; ++i) { \ x[8*i] = vec_add(x[8*i], x[8*i+4]); \ } \ res = vec_extract(x[0], 0) + \ vec_extract(x[0], 1) + \ vec_extract(x[0], 2) + \ vec_extract(x[0], 3); \ } #define GGML_F32_VEC GGML_F32x4 #define GGML_F32_VEC_ZERO GGML_F32x4_ZERO #define GGML_F32_VEC_SET1 GGML_F32x4_SET1 #define GGML_F32_VEC_LOAD GGML_F32x4_LOAD #define GGML_F32_VEC_STORE GGML_F32x4_STORE #define GGML_F32_VEC_FMA GGML_F32x4_FMA #define GGML_F32_VEC_ADD GGML_F32x4_ADD #define GGML_F32_VEC_MUL GGML_F32x4_MUL #define GGML_F32_VEC_REDUCE GGML_F32x4_REDUCE // F16 POWER9 #define GGML_F16_STEP GGML_F32_STEP #define GGML_F16_EPR GGML_F32_EPR #define GGML_F16_VEC GGML_F32x4 #define GGML_F16_VEC_ZERO GGML_F32x4_ZERO #define GGML_F16_VEC_SET1 GGML_F32x4_SET1 #define GGML_F16_VEC_FMA GGML_F32x4_FMA #define GGML_F16_VEC_REDUCE GGML_F32x4_REDUCE // Use vec_xl, not vec_ld, in case the load address is not aligned. #define GGML_F16_VEC_LOAD(p, i) (i & 0x1) ? \ vec_extract_fp32_from_shorth(vec_xl(0, p - GGML_F16_EPR)) : \ vec_extract_fp32_from_shortl(vec_xl(0, p)) #define GGML_ENDIAN_BYTE(i) ((unsigned char *)&(uint16_t){1})[i] #define GGML_F16_VEC_STORE(p, r, i) \ if (i & 0x1) \ vec_xst(vec_pack_to_short_fp32(r[i - GGML_ENDIAN_BYTE(1)], \ r[i - GGML_ENDIAN_BYTE(0)]), \ 0, p - GGML_F16_EPR) #elif defined(__wasm_simd128__) #define GGML_SIMD // F32 WASM #define GGML_F32_STEP 16 #define GGML_F32_EPR 4 #define GGML_F32x4 v128_t #define GGML_F32x4_ZERO wasm_f32x4_splat(0.0f) #define GGML_F32x4_SET1(x) wasm_f32x4_splat(x) #define GGML_F32x4_LOAD wasm_v128_load #define GGML_F32x4_STORE wasm_v128_store #define GGML_F32x4_FMA(a, b, c) wasm_f32x4_add(wasm_f32x4_mul(b, c), a) #define GGML_F32x4_ADD wasm_f32x4_add #define GGML_F32x4_MUL wasm_f32x4_mul #define GGML_F32x4_REDUCE(res, x) \ { \ for (int i = 0; i < GGML_F32_ARR/2; ++i) { \ x[2*i] = wasm_f32x4_add(x[2*i], x[2*i+1]); \ } \ for (int i = 0; i < GGML_F32_ARR/4; ++i) { \ x[4*i] = wasm_f32x4_add(x[4*i], x[4*i+2]); \ } \ for (int i = 0; i < GGML_F32_ARR/8; ++i) { \ x[8*i] = wasm_f32x4_add(x[8*i], x[8*i+4]); \ } \ res = wasm_f32x4_extract_lane(x[0], 0) + \ wasm_f32x4_extract_lane(x[0], 1) + \ wasm_f32x4_extract_lane(x[0], 2) + \ wasm_f32x4_extract_lane(x[0], 3); \ } #define GGML_F32_VEC GGML_F32x4 #define GGML_F32_VEC_ZERO GGML_F32x4_ZERO #define GGML_F32_VEC_SET1 GGML_F32x4_SET1 #define GGML_F32_VEC_LOAD GGML_F32x4_LOAD #define GGML_F32_VEC_STORE GGML_F32x4_STORE #define GGML_F32_VEC_FMA GGML_F32x4_FMA #define GGML_F32_VEC_ADD GGML_F32x4_ADD #define GGML_F32_VEC_MUL GGML_F32x4_MUL #define GGML_F32_VEC_REDUCE GGML_F32x4_REDUCE // F16 WASM #define GGML_F16_STEP 16 #define GGML_F16_EPR 4 inline static v128_t __wasm_f16x4_load(const ggml_fp16_t * p) { float tmp[4]; tmp[0] = GGML_FP16_TO_FP32(p[0]); tmp[1] = GGML_FP16_TO_FP32(p[1]); tmp[2] = GGML_FP16_TO_FP32(p[2]); tmp[3] = GGML_FP16_TO_FP32(p[3]); return wasm_v128_load(tmp); } inline static void __wasm_f16x4_store(ggml_fp16_t * p, v128_t x) { float tmp[4]; wasm_v128_store(tmp, x); p[0] = GGML_FP32_TO_FP16(tmp[0]); p[1] = GGML_FP32_TO_FP16(tmp[1]); p[2] = GGML_FP32_TO_FP16(tmp[2]); p[3] = GGML_FP32_TO_FP16(tmp[3]); } #define GGML_F16x4 v128_t #define GGML_F16x4_ZERO wasm_f32x4_splat(0.0f) #define GGML_F16x4_SET1(x) wasm_f32x4_splat(x) #define GGML_F16x4_LOAD(x) __wasm_f16x4_load(x) #define GGML_F16x4_STORE(x, y) __wasm_f16x4_store(x, y) #define GGML_F16x4_FMA GGML_F32x4_FMA #define GGML_F16x4_ADD wasm_f32x4_add #define GGML_F16x4_MUL wasm_f32x4_mul #define GGML_F16x4_REDUCE(res, x) \ { \ for (int i = 0; i < GGML_F16_ARR/2; ++i) { \ x[2*i] = wasm_f32x4_add(x[2*i], x[2*i+1]); \ } \ for (int i = 0; i < GGML_F16_ARR/4; ++i) { \ x[4*i] = wasm_f32x4_add(x[4*i], x[4*i+2]); \ } \ for (int i = 0; i < GGML_F16_ARR/8; ++i) { \ x[8*i] = wasm_f32x4_add(x[8*i], x[8*i+4]); \ } \ res = wasm_f32x4_extract_lane(x[0], 0) + \ wasm_f32x4_extract_lane(x[0], 1) + \ wasm_f32x4_extract_lane(x[0], 2) + \ wasm_f32x4_extract_lane(x[0], 3); \ } #define GGML_F16_VEC GGML_F16x4 #define GGML_F16_VEC_ZERO GGML_F16x4_ZERO #define GGML_F16_VEC_SET1 GGML_F16x4_SET1 #define GGML_F16_VEC_LOAD(p, i) GGML_F16x4_LOAD(p) #define GGML_F16_VEC_STORE(p, r, i) GGML_F16x4_STORE(p, r[i]) #define GGML_F16_VEC_FMA GGML_F16x4_FMA #define GGML_F16_VEC_ADD GGML_F16x4_ADD #define GGML_F16_VEC_MUL GGML_F16x4_MUL #define GGML_F16_VEC_REDUCE GGML_F16x4_REDUCE #elif defined(__SSE3__) #define GGML_SIMD // F32 SSE #define GGML_F32_STEP 32 #define GGML_F32_EPR 4 #define GGML_F32x4 __m128 #define GGML_F32x4_ZERO _mm_setzero_ps() #define GGML_F32x4_SET1(x) _mm_set1_ps(x) #define GGML_F32x4_LOAD _mm_loadu_ps #define GGML_F32x4_STORE _mm_storeu_ps #if defined(__FMA__) // TODO: Does this work? #define GGML_F32x4_FMA(a, b, c) _mm_fmadd_ps(b, c, a) #else #define GGML_F32x4_FMA(a, b, c) _mm_add_ps(_mm_mul_ps(b, c), a) #endif #define GGML_F32x4_ADD _mm_add_ps #define GGML_F32x4_MUL _mm_mul_ps #define GGML_F32x4_REDUCE(res, x) \ { \ for (int i = 0; i < GGML_F32_ARR/2; ++i) { \ x[2*i] = _mm_add_ps(x[2*i], x[2*i+1]); \ } \ for (int i = 0; i < GGML_F32_ARR/4; ++i) { \ x[4*i] = _mm_add_ps(x[4*i], x[4*i+2]); \ } \ for (int i = 0; i < GGML_F32_ARR/8; ++i) { \ x[8*i] = _mm_add_ps(x[8*i], x[8*i+4]); \ } \ const __m128 t0 = _mm_hadd_ps(x[0], x[0]); \ res = _mm_cvtss_f32(_mm_hadd_ps(t0, t0)); \ } // TODO: is this optimal ? #define GGML_F32_VEC GGML_F32x4 #define GGML_F32_VEC_ZERO GGML_F32x4_ZERO #define GGML_F32_VEC_SET1 GGML_F32x4_SET1 #define GGML_F32_VEC_LOAD GGML_F32x4_LOAD #define GGML_F32_VEC_STORE GGML_F32x4_STORE #define GGML_F32_VEC_FMA GGML_F32x4_FMA #define GGML_F32_VEC_ADD GGML_F32x4_ADD #define GGML_F32_VEC_MUL GGML_F32x4_MUL #define GGML_F32_VEC_REDUCE GGML_F32x4_REDUCE // F16 SSE #define GGML_F16_STEP 32 #define GGML_F16_EPR 4 static inline __m128 __sse_f16x4_load(ggml_fp16_t *x) { float tmp[4]; tmp[0] = GGML_FP16_TO_FP32(x[0]); tmp[1] = GGML_FP16_TO_FP32(x[1]); tmp[2] = GGML_FP16_TO_FP32(x[2]); tmp[3] = GGML_FP16_TO_FP32(x[3]); return _mm_loadu_ps(tmp); } static inline void __sse_f16x4_store(ggml_fp16_t *x, __m128 y) { float arr[4]; _mm_storeu_ps(arr, y); x[0] = GGML_FP32_TO_FP16(arr[0]); x[1] = GGML_FP32_TO_FP16(arr[1]); x[2] = GGML_FP32_TO_FP16(arr[2]); x[3] = GGML_FP32_TO_FP16(arr[3]); } #define GGML_F32Cx4 __m128 #define GGML_F32Cx4_ZERO _mm_setzero_ps() #define GGML_F32Cx4_SET1(x) _mm_set1_ps(x) #define GGML_F32Cx4_LOAD(x) __sse_f16x4_load(x) #define GGML_F32Cx4_STORE(x, y) __sse_f16x4_store(x, y) #define GGML_F32Cx4_FMA GGML_F32x4_FMA #define GGML_F32Cx4_ADD _mm_add_ps #define GGML_F32Cx4_MUL _mm_mul_ps #define GGML_F32Cx4_REDUCE GGML_F32x4_REDUCE #define GGML_F16_VEC GGML_F32Cx4 #define GGML_F16_VEC_ZERO GGML_F32Cx4_ZERO #define GGML_F16_VEC_SET1 GGML_F32Cx4_SET1 #define GGML_F16_VEC_LOAD(p, i) GGML_F32Cx4_LOAD(p) #define GGML_F16_VEC_STORE(p, r, i) GGML_F32Cx4_STORE(p, r[i]) #define GGML_F16_VEC_FMA GGML_F32Cx4_FMA #define GGML_F16_VEC_ADD GGML_F32Cx4_ADD #define GGML_F16_VEC_MUL GGML_F32Cx4_MUL #define GGML_F16_VEC_REDUCE GGML_F32Cx4_REDUCE #endif // GGML_F32_ARR / GGML_F16_ARR // number of registers to use per step #ifdef GGML_SIMD #define GGML_F32_ARR (GGML_F32_STEP/GGML_F32_EPR) #define GGML_F16_ARR (GGML_F16_STEP/GGML_F16_EPR) #endif // // fundamental operations // inline static void ggml_vec_set_i8(const int n, int8_t * x, const int8_t v) { for (int i = 0; i < n; ++i) x[i] = v; } inline static void ggml_vec_set_i16(const int n, int16_t * x, const int16_t v) { for (int i = 0; i < n; ++i) x[i] = v; } inline static void ggml_vec_set_i32(const int n, int32_t * x, const int32_t v) { for (int i = 0; i < n; ++i) x[i] = v; } inline static void ggml_vec_set_f16(const int n, ggml_fp16_t * x, const int32_t v) { for (int i = 0; i < n; ++i) x[i] = v; } inline static void ggml_vec_add_f32 (const int n, float * z, const float * x, const float * y) { for (int i = 0; i < n; ++i) z[i] = x[i] + y[i]; } inline static void ggml_vec_acc_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] += x[i]; } inline static void ggml_vec_acc1_f32(const int n, float * y, const float v) { for (int i = 0; i < n; ++i) y[i] += v; } inline static void ggml_vec_sub_f32 (const int n, float * z, const float * x, const float * y) { for (int i = 0; i < n; ++i) z[i] = x[i] - y[i]; } inline static void ggml_vec_set_f32 (const int n, float * x, const float v) { for (int i = 0; i < n; ++i) x[i] = v; } inline static void ggml_vec_cpy_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = x[i]; } inline static void ggml_vec_neg_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = -x[i]; } inline static void ggml_vec_mul_f32 (const int n, float * z, const float * x, const float * y) { for (int i = 0; i < n; ++i) z[i] = x[i]*y[i]; } inline static void ggml_vec_div_f32 (const int n, float * z, const float * x, const float * y) { for (int i = 0; i < n; ++i) z[i] = x[i]/y[i]; } inline static void ggml_vec_dot_f32(const int n, float * restrict s, const float * restrict x, const float * restrict y) { #ifdef GGML_SIMD float sumf = 0.0f; const int np = (n & ~(GGML_F32_STEP - 1)); GGML_F32_VEC sum[GGML_F32_ARR] = { GGML_F32_VEC_ZERO }; GGML_F32_VEC ax[GGML_F32_ARR]; GGML_F32_VEC ay[GGML_F32_ARR]; for (int i = 0; i < np; i += GGML_F32_STEP) { for (int j = 0; j < GGML_F32_ARR; j++) { ax[j] = GGML_F32_VEC_LOAD(x + i + j*GGML_F32_EPR); ay[j] = GGML_F32_VEC_LOAD(y + i + j*GGML_F32_EPR); sum[j] = GGML_F32_VEC_FMA(sum[j], ax[j], ay[j]); } } // reduce sum0..sum3 to sum0 GGML_F32_VEC_REDUCE(sumf, sum); // leftovers for (int i = np; i < n; ++i) { sumf += x[i]*y[i]; } #else // scalar ggml_float sumf = 0.0; for (int i = 0; i < n; ++i) { sumf += (ggml_float)(x[i]*y[i]); } #endif *s = sumf; } inline static void ggml_vec_dot_f16(const int n, float * restrict s, ggml_fp16_t * restrict x, ggml_fp16_t * restrict y) { ggml_float sumf = 0.0; #if defined(GGML_SIMD) const int np = (n & ~(GGML_F16_STEP - 1)); GGML_F16_VEC sum[GGML_F16_ARR] = { GGML_F16_VEC_ZERO }; GGML_F16_VEC ax[GGML_F16_ARR]; GGML_F16_VEC ay[GGML_F16_ARR]; for (int i = 0; i < np; i += GGML_F16_STEP) { for (int j = 0; j < GGML_F16_ARR; j++) { ax[j] = GGML_F16_VEC_LOAD(x + i + j*GGML_F16_EPR, j); ay[j] = GGML_F16_VEC_LOAD(y + i + j*GGML_F16_EPR, j); sum[j] = GGML_F16_VEC_FMA(sum[j], ax[j], ay[j]); } } // reduce sum0..sum3 to sum0 GGML_F16_VEC_REDUCE(sumf, sum); // leftovers for (int i = np; i < n; ++i) { sumf += (ggml_float)(GGML_FP16_TO_FP32(x[i])*GGML_FP16_TO_FP32(y[i])); } #else for (int i = 0; i < n; ++i) { sumf += (ggml_float)(GGML_FP16_TO_FP32(x[i])*GGML_FP16_TO_FP32(y[i])); } #endif *s = sumf; } static void ggml_vec_dot_q4_0_q8_0(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) { const int nb = n / QK8_0; assert(n % QK8_0 == 0); assert(nb % 2 == 0); const block_q4_0 * restrict x = vx; const block_q8_0 * restrict y = vy; #if defined(__ARM_NEON) float32x4_t sumv0 = vdupq_n_f32(0.0f); float32x4_t sumv1 = vdupq_n_f32(0.0f); float sum8 = 0; for (int i = 0; i < nb; i += 2) { const block_q4_0 * restrict x0 = &x[i + 0]; const block_q4_0 * restrict x1 = &x[i + 1]; const block_q8_0 * restrict y0 = &y[i + 0]; const block_q8_0 * restrict y1 = &y[i + 1]; sum8 += x0->d * (y0->s0 + y0->s1) + x1->d * (y1->s0 + y1->s1); const uint8x16_t m4b = vdupq_n_u8(0xf); const uint8x16_t v0_0 = vld1q_u8(x0->qs); const uint8x16_t v0_1 = vld1q_u8(x1->qs); // 4-bit -> 8-bit const int8x16_t v0_0l = vreinterpretq_s8_u8(vandq_u8 (v0_0, m4b)); const int8x16_t v0_0h = vreinterpretq_s8_u8(vshrq_n_u8(v0_0, 4)); const int8x16_t v0_1l = vreinterpretq_s8_u8(vandq_u8 (v0_1, m4b)); const int8x16_t v0_1h = vreinterpretq_s8_u8(vshrq_n_u8(v0_1, 4)); // load y const int8x16_t v1_0l = vld1q_s8(y0->qs); const int8x16_t v1_0h = vld1q_s8(y0->qs + 16); const int8x16_t v1_1l = vld1q_s8(y1->qs); const int8x16_t v1_1h = vld1q_s8(y1->qs + 16); // interleave const int8x16_t v1_0ls = vuzp1q_s8(v1_0l, v1_0h); const int8x16_t v1_0hs = vuzp2q_s8(v1_0l, v1_0h); const int8x16_t v1_1ls = vuzp1q_s8(v1_1l, v1_1h); const int8x16_t v1_1hs = vuzp2q_s8(v1_1l, v1_1h); #if defined(__ARM_FEATURE_DOTPROD) // dot product into int32x4_t const int32x4_t p_0 = vdotq_s32(vdotq_s32(vdupq_n_s32(0), v0_0l, v1_0ls), v0_0h, v1_0hs); const int32x4_t p_1 = vdotq_s32(vdotq_s32(vdupq_n_s32(0), v0_1l, v1_1ls), v0_1h, v1_1hs); sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(p_0), x0->d*y0->d); sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(p_1), x1->d*y1->d); #else const int16x8_t pl0l = vmull_s8(vget_low_s8 (v0_0l), vget_low_s8 (v1_0ls)); const int16x8_t pl0h = vmull_s8(vget_high_s8(v0_0l), vget_high_s8(v1_0ls)); const int16x8_t ph0l = vmull_s8(vget_low_s8 (v0_0h), vget_low_s8 (v1_0hs)); const int16x8_t ph0h = vmull_s8(vget_high_s8(v0_0h), vget_high_s8(v1_0hs)); const int16x8_t pl1l = vmull_s8(vget_low_s8 (v0_1l), vget_low_s8 (v1_1ls)); const int16x8_t pl1h = vmull_s8(vget_high_s8(v0_1l), vget_high_s8(v1_1ls)); const int16x8_t ph1l = vmull_s8(vget_low_s8 (v0_1h), vget_low_s8 (v1_1hs)); const int16x8_t ph1h = vmull_s8(vget_high_s8(v0_1h), vget_high_s8(v1_1hs)); const int32x4_t pl0 = vaddq_s32(vpaddlq_s16(pl0l), vpaddlq_s16(pl0h)); const int32x4_t ph0 = vaddq_s32(vpaddlq_s16(ph0l), vpaddlq_s16(ph0h)); const int32x4_t pl1 = vaddq_s32(vpaddlq_s16(pl1l), vpaddlq_s16(pl1h)); const int32x4_t ph1 = vaddq_s32(vpaddlq_s16(ph1l), vpaddlq_s16(ph1h)); sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(pl0, ph0)), x0->d*y0->d); sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(pl1, ph1)), x1->d*y1->d); #endif } *s = vaddvq_f32(sumv0) + vaddvq_f32(sumv1) - 8 * sum8; #elif defined(__AVX2__) // Initialize accumulator with zeros __m256 acc = _mm256_setzero_ps(); // Main loop for (int i = 0; i < nb; ++i) { /* Compute combined scale for the block */ const __m256 d = _mm256_mul_ps( _mm256_broadcast_ss( &x[i].d ), _mm256_broadcast_ss( &y[i].d ) ); __m256i bx = bytes_from_nibbles_32(x[i].qs); // Now we have a vector with bytes in [ 0 .. 15 ] interval. Offset them into [ -8 .. +7 ] interval. const __m256i off = _mm256_set1_epi8( 8 ); bx = _mm256_sub_epi8( bx, off ); __m256i by = _mm256_loadu_si256((const __m256i *)y[i].qs); const __m256 q = mul_sum_i8_pairs_float(bx, by); /* Multiply q with scale and accumulate */ acc = _mm256_fmadd_ps( d, q, acc ); } *s = hsum_float_8(acc); #elif defined(__AVX__) // Initialize accumulator with zeros __m256 acc = _mm256_setzero_ps(); // Main loop for (int i = 0; i < nb; ++i) { // Compute combined scale for the block const __m256 d = _mm256_mul_ps( _mm256_broadcast_ss( &x[i].d ), _mm256_broadcast_ss( &y[i].d ) ); __m128i i32[2]; for (int j = 0; j < 2; ++j) { // Load 8 bytes, and unpack 4 bit fields into bytes, making 16 bytes __m128i bx = bytes_from_nibbles_16(x[i].qs + 8*j); __m128i by = _mm_loadu_si128((const __m128i *)(y[i].qs + 16*j)); // Now we have a vector with bytes in [ 0 .. 15 ] interval. Offset them into [ -8 .. +7 ] interval. const __m128i off = _mm_set1_epi8( 8 ); bx = _mm_sub_epi8( bx, off ); // Get absolute values of x vectors const __m128i ax = _mm_sign_epi8(bx, bx); // Sign the values of the y vectors const __m128i sy = _mm_sign_epi8(by, bx); // Perform multiplication and create 16-bit values const __m128i dot = _mm_maddubs_epi16(ax, sy); const __m128i ones = _mm_set1_epi16(1); i32[j] = _mm_madd_epi16(ones, dot); } // Convert int32_t to float __m256 p = _mm256_cvtepi32_ps( _mm256_set_m128i( i32[0], i32[1] )); // Apply the scale, and accumulate acc = _mm256_add_ps(_mm256_mul_ps( d, p ), acc); } *s = hsum_float_8(acc); #else // scalar float sumf = 0.0; for (int i = 0; i < nb; i++) { const float d0 = x[i].d; const float d1 = y[i].d; const uint8_t * restrict p0 = x[i].qs; const int8_t * restrict p1 = y[i].qs; int sumi = 0; for (int j = 0; j < QK8_0/2; j++) { const uint8_t v0 = p0[j]; const int i0 = (int8_t) (v0 & 0xf) - 8; const int i1 = (int8_t) (v0 >> 4) - 8; const int i2 = p1[2*j + 0]; const int i3 = p1[2*j + 1]; sumi += i0*i2 + i1*i3; } sumf += d0*d1*sumi; } *s = sumf; #endif } static void ggml_vec_dot_q4_1_q8_0(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) { const int nb = n / QK8_0; assert(n % QK8_0 == 0); assert(nb % 2 == 0); const block_q4_1 * restrict x = vx; const block_q8_0 * restrict y = vy; // TODO: add AVX / WASM SIMD / etc #if defined(__ARM_NEON) float32x4_t sumv0 = vdupq_n_f32(0.0f); float32x4_t sumv1 = vdupq_n_f32(0.0f); float summs = 0; for (int i = 0; i < nb; i += 2) { const block_q4_1 * restrict x0 = &x[i + 0]; const block_q4_1 * restrict x1 = &x[i + 1]; const block_q8_0 * restrict y0 = &y[i + 0]; const block_q8_0 * restrict y1 = &y[i + 1]; summs += x0->m * (y0->s0 + y0->s1) + x1->m * (y1->s0 + y1->s1); const uint8x16_t m4b = vdupq_n_u8(0xf); const uint8x16_t v0_0 = vld1q_u8(x0->qs); const uint8x16_t v0_1 = vld1q_u8(x1->qs); // 4-bit -> 8-bit const int8x16_t v0_0l = vreinterpretq_s8_u8(vandq_u8 (v0_0, m4b)); const int8x16_t v0_0h = vreinterpretq_s8_u8(vshrq_n_u8(v0_0, 4)); const int8x16_t v0_1l = vreinterpretq_s8_u8(vandq_u8 (v0_1, m4b)); const int8x16_t v0_1h = vreinterpretq_s8_u8(vshrq_n_u8(v0_1, 4)); // interleave const int8x16_t v0_0lz = vzip1q_s8(v0_0l, v0_0h); const int8x16_t v0_0hz = vzip2q_s8(v0_0l, v0_0h); const int8x16_t v0_1lz = vzip1q_s8(v0_1l, v0_1h); const int8x16_t v0_1hz = vzip2q_s8(v0_1l, v0_1h); // load y const int8x16_t v1_0l = vld1q_s8(y0->qs); const int8x16_t v1_0h = vld1q_s8(y0->qs + 16); const int8x16_t v1_1l = vld1q_s8(y1->qs); const int8x16_t v1_1h = vld1q_s8(y1->qs + 16); #if defined(__ARM_FEATURE_DOTPROD) // dot product into int32x4_t const int32x4_t p_0 = vdotq_s32(vdotq_s32(vdupq_n_s32(0), v0_0lz, v1_0l), v0_0hz, v1_0h); const int32x4_t p_1 = vdotq_s32(vdotq_s32(vdupq_n_s32(0), v0_1lz, v1_1l), v0_1hz, v1_1h); sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(p_0), x0->d*y0->d); sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(p_1), x1->d*y1->d); #else const int16x8_t pl0l = vmull_s8(vget_low_s8 (v0_0lz), vget_low_s8 (v1_0l)); const int16x8_t pl0h = vmull_s8(vget_high_s8(v0_0lz), vget_high_s8(v1_0l)); const int16x8_t ph0l = vmull_s8(vget_low_s8 (v0_0hz), vget_low_s8 (v1_0h)); const int16x8_t ph0h = vmull_s8(vget_high_s8(v0_0hz), vget_high_s8(v1_0h)); const int16x8_t pl1l = vmull_s8(vget_low_s8 (v0_1lz), vget_low_s8 (v1_1l)); const int16x8_t pl1h = vmull_s8(vget_high_s8(v0_1lz), vget_high_s8(v1_1l)); const int16x8_t ph1l = vmull_s8(vget_low_s8 (v0_1hz), vget_low_s8 (v1_1h)); const int16x8_t ph1h = vmull_s8(vget_high_s8(v0_1hz), vget_high_s8(v1_1h)); const int32x4_t pl0 = vaddq_s32(vpaddlq_s16(pl0l), vpaddlq_s16(pl0h)); const int32x4_t ph0 = vaddq_s32(vpaddlq_s16(ph0l), vpaddlq_s16(ph0h)); const int32x4_t pl1 = vaddq_s32(vpaddlq_s16(pl1l), vpaddlq_s16(pl1h)); const int32x4_t ph1 = vaddq_s32(vpaddlq_s16(ph1l), vpaddlq_s16(ph1h)); sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(pl0, ph0)), x0->d*y0->d); sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(pl1, ph1)), x1->d*y1->d); #endif } *s = vaddvq_f32(sumv0) + vaddvq_f32(sumv1) + summs; #elif defined(__AVX2__) // Initialize accumulator with zeros __m256 acc = _mm256_setzero_ps(); float summs = 0; // Main loop for (int i = 0; i < nb; ++i) { const float * d0 = &x[i].d; const float * d1 = &y[i].d; summs += x[i].m * (y[i].s0 + y[i].s1); const __m256 d0v = _mm256_broadcast_ss( d0 ); const __m256 d1v = _mm256_broadcast_ss( d1 ); // Compute combined scales const __m256 d0d1 = _mm256_mul_ps( d0v, d1v ); // Load 16 bytes, and unpack 4 bit fields into bytes, making 32 bytes const __m256i bx = bytes_from_nibbles_32(x[i].qs); const __m256i by = _mm256_loadu_si256( (const __m256i *)y[i].qs ); const __m256 xy = mul_sum_i8_pairs_float(bx, by); // Accumulate d0*d1*x*y acc = _mm256_fmadd_ps( d0d1, xy, acc ); } *s = hsum_float_8(acc) + summs; #else // scalar float sumf = 0.0; for (int i = 0; i < nb; i++) { const float d0 = x[i].d; const float m0 = x[i].m; const float d1 = y[i].d; const uint8_t * restrict p0 = x[i].qs; const int8_t * restrict p1 = y[i].qs; // TODO: this is very slow .. for (int j = 0; j < QK8_0/2; j++) { const uint8_t v0 = p0[j]; const float f0 = d0*(v0 & 0xf) + m0; const float f1 = d0*(v0 >> 4) + m0; const float f2 = d1*p1[2*j + 0]; const float f3 = d1*p1[2*j + 1]; sumf += f0*f2 + f1*f3; } } *s = sumf; #endif } static void ggml_vec_dot_q4_2_q8_0(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) { const int nb = n / QK8_0; assert(n % QK8_0 == 0); assert(nb % 2 == 0); assert(QK8_0 == 2*QK4_2); const block_q4_2 * restrict x = vx; const block_q8_0 * restrict y = vy; #if defined(__ARM_NEON) float32x4_t sumv0 = vdupq_n_f32(0.0f); float32x4_t sumv1 = vdupq_n_f32(0.0f); for (int i = 0; i < nb; i += 2) { const block_q4_2 * restrict x0_0 = &x[2*(i + 0) + 0]; const block_q4_2 * restrict x0_1 = &x[2*(i + 0) + 1]; const block_q4_2 * restrict x1_0 = &x[2*(i + 1) + 0]; const block_q4_2 * restrict x1_1 = &x[2*(i + 1) + 1]; const block_q8_0 * restrict y0 = &y[i + 0]; const block_q8_0 * restrict y1 = &y[i + 1]; const uint8x16_t m4b = vdupq_n_u8(0xf); const int8x16_t s8b = vdupq_n_s8(0x8); const uint8x16_t v0_0 = vcombine_u8(vld1_u8(x0_0->qs), vld1_u8(x0_1->qs)); const uint8x16_t v0_1 = vcombine_u8(vld1_u8(x1_0->qs), vld1_u8(x1_1->qs)); // 4-bit -> 8-bit const int8x16_t v0_0l = vreinterpretq_s8_u8(vandq_u8 (v0_0, m4b)); const int8x16_t v0_0h = vreinterpretq_s8_u8(vshrq_n_u8(v0_0, 4)); const int8x16_t v0_1l = vreinterpretq_s8_u8(vandq_u8 (v0_1, m4b)); const int8x16_t v0_1h = vreinterpretq_s8_u8(vshrq_n_u8(v0_1, 4)); // sub 8 const int8x16_t v0_0ls = vsubq_s8(v0_0l, s8b); const int8x16_t v0_0hs = vsubq_s8(v0_0h, s8b); const int8x16_t v0_1ls = vsubq_s8(v0_1l, s8b); const int8x16_t v0_1hs = vsubq_s8(v0_1h, s8b); // interleave const int8x16_t v0_0lz = vzip1q_s8(v0_0ls, v0_0hs); const int8x16_t v0_0hz = vzip2q_s8(v0_0ls, v0_0hs); const int8x16_t v0_1lz = vzip1q_s8(v0_1ls, v0_1hs); const int8x16_t v0_1hz = vzip2q_s8(v0_1ls, v0_1hs); // load y const int8x16_t v1_0l = vld1q_s8(y0->qs); const int8x16_t v1_0h = vld1q_s8(y0->qs + 16); const int8x16_t v1_1l = vld1q_s8(y1->qs); const int8x16_t v1_1h = vld1q_s8(y1->qs + 16); #if defined(__ARM_FEATURE_DOTPROD) sumv0 = vmlaq_n_f32(sumv0, vaddq_f32( vmulq_n_f32(vcvtq_f32_s32(vdotq_s32(vdupq_n_s32(0), v0_0lz, v1_0l)), GGML_FP16_TO_FP32(x0_0->d)), vmulq_n_f32(vcvtq_f32_s32(vdotq_s32(vdupq_n_s32(0), v0_0hz, v1_0h)), GGML_FP16_TO_FP32(x0_1->d))), y0->d); sumv1 = vmlaq_n_f32(sumv1, vaddq_f32( vmulq_n_f32(vcvtq_f32_s32(vdotq_s32(vdupq_n_s32(0), v0_1lz, v1_1l)), GGML_FP16_TO_FP32(x1_0->d)), vmulq_n_f32(vcvtq_f32_s32(vdotq_s32(vdupq_n_s32(0), v0_1hz, v1_1h)), GGML_FP16_TO_FP32(x1_1->d))), y1->d); #else const int16x8_t pl0l = vmull_s8(vget_low_s8 (v0_0lz), vget_low_s8 (v1_0l)); const int16x8_t pl0h = vmull_s8(vget_high_s8(v0_0lz), vget_high_s8(v1_0l)); const int16x8_t ph0l = vmull_s8(vget_low_s8 (v0_0hz), vget_low_s8 (v1_0h)); const int16x8_t ph0h = vmull_s8(vget_high_s8(v0_0hz), vget_high_s8(v1_0h)); const int16x8_t pl1l = vmull_s8(vget_low_s8 (v0_1lz), vget_low_s8 (v1_1l)); const int16x8_t pl1h = vmull_s8(vget_high_s8(v0_1lz), vget_high_s8(v1_1l)); const int16x8_t ph1l = vmull_s8(vget_low_s8 (v0_1hz), vget_low_s8 (v1_1h)); const int16x8_t ph1h = vmull_s8(vget_high_s8(v0_1hz), vget_high_s8(v1_1h)); const int32x4_t pl0 = vaddq_s32(vpaddlq_s16(pl0l), vpaddlq_s16(pl0h)); const int32x4_t ph0 = vaddq_s32(vpaddlq_s16(ph0l), vpaddlq_s16(ph0h)); const int32x4_t pl1 = vaddq_s32(vpaddlq_s16(pl1l), vpaddlq_s16(pl1h)); const int32x4_t ph1 = vaddq_s32(vpaddlq_s16(ph1l), vpaddlq_s16(ph1h)); sumv0 = vmlaq_n_f32(sumv0, vaddq_f32( vmulq_n_f32(vcvtq_f32_s32(pl0), GGML_FP16_TO_FP32(x0_0->d)), vmulq_n_f32(vcvtq_f32_s32(ph0), GGML_FP16_TO_FP32(x0_1->d))), y0->d); sumv1 = vmlaq_n_f32(sumv1, vaddq_f32( vmulq_n_f32(vcvtq_f32_s32(pl1), GGML_FP16_TO_FP32(x1_0->d)), vmulq_n_f32(vcvtq_f32_s32(ph1), GGML_FP16_TO_FP32(x1_1->d))), y1->d); #endif } *s = vaddvq_f32(sumv0) + vaddvq_f32(sumv1); #elif defined(__AVX2__) // Initialize accumulator with zeros __m256 acc = _mm256_setzero_ps(); // Main loop for (int i = 0; i < nb; i++) { /* Compute combined scale for the block */ const __m128 d0 = _mm_set1_ps(GGML_FP16_TO_FP32(x[2*i + 0].d)); const __m128 d1 = _mm_set1_ps(GGML_FP16_TO_FP32(x[2*i + 1].d)); const __m256 d = _mm256_mul_ps(_mm256_set_m128(d1, d0), _mm256_broadcast_ss(&y[i].d)); __m128i bx0 = bytes_from_nibbles_16(x[2*i + 0].qs); __m128i bx1 = bytes_from_nibbles_16(x[2*i + 1].qs); __m256i bx = _mm256_set_m128i(bx1, bx0); // Now we have a vector with bytes in [ 0 .. 15 ] interval. Offset them into [ -8 .. +7 ] interval. const __m256i off = _mm256_set1_epi8(8); bx = _mm256_sub_epi8(bx, off); __m256i by = _mm256_loadu_si256((const __m256i *)y[i].qs); const __m256 q = mul_sum_i8_pairs_float(bx, by); /* Multiply q with scale and accumulate */ acc = _mm256_fmadd_ps(d, q, acc); } *s = hsum_float_8(acc); #else // scalar float sumf = 0.0; for (int i = 0; i < nb; i++) { const uint8_t * restrict x0 = x[2*i + 0].qs; const uint8_t * restrict x1 = x[2*i + 1].qs; const int8_t * restrict y0 = y[i].qs; const float d0 = GGML_FP16_TO_FP32(x[2*i + 0].d); const float d1 = GGML_FP16_TO_FP32(x[2*i + 1].d); int sumi_0 = 0; int sumi_1 = 0; for (int j = 0; j < QK8_0/4; j++) { const uint8_t v0 = x0[j]; const uint8_t v1 = x1[j]; const int i0_0 = (int8_t) (v0 & 0xf) - 8; const int i1_0 = (int8_t) (v0 >> 4) - 8; const int i0_1 = (int8_t) (v1 & 0xf) - 8; const int i1_1 = (int8_t) (v1 >> 4) - 8; const int i2_0 = y0[2*j + 0]; const int i3_0 = y0[2*j + 1]; const int i2_1 = y0[2*(j + QK8_0/4) + 0]; const int i3_1 = y0[2*(j + QK8_0/4) + 1]; sumi_0 += i0_0*i2_0 + i1_0*i3_0; sumi_1 += i0_1*i2_1 + i1_1*i3_1; } sumf += (d0 * y[i].d) * sumi_0; sumf += (d1 * y[i].d) * sumi_1; } *s = sumf; #endif } static void ggml_vec_dot_q4_3_q8_0(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) { const int nb = n / QK8_0; assert(n % QK8_0 == 0); assert(nb % 2 == 0); assert(QK8_0 == 2*QK4_2); const block_q4_3 * restrict x = vx; const block_q8_0 * restrict y = vy; #if defined(__ARM_NEON) float32x4_t sumv0 = vdupq_n_f32(0.0f); float32x4_t sumv1 = vdupq_n_f32(0.0f); float summs0 = 0.0f; float summs1 = 0.0f; for (int i = 0; i < nb; ++i) { const block_q4_3 * restrict x0_0 = &x[2*(i + 0) + 0]; const block_q4_3 * restrict x0_1 = &x[2*(i + 0) + 1]; const block_q8_0 * restrict y0 = &y[i + 0]; summs0 += GGML_FP16_TO_FP32(x0_0->m) * y0->s0; summs1 += GGML_FP16_TO_FP32(x0_1->m) * y0->s1; const uint8x16_t v0_0 = vcombine_u8(vld1_u8(x0_0->qs), vld1_u8(x0_1->qs)); // 4-bit -> 8-bit const int8x16_t v0_0l = vreinterpretq_s8_u8(vandq_u8 (v0_0, vdupq_n_u8(0xf))); const int8x16_t v0_0h = vreinterpretq_s8_u8(vshrq_n_u8(v0_0, 4)); // interleave const int8x16_t v0_0lz = vzip1q_s8(v0_0l, v0_0h); const int8x16_t v0_0hz = vzip2q_s8(v0_0l, v0_0h); // load y const int8x16_t v1_0l = vld1q_s8(y0->qs); const int8x16_t v1_0h = vld1q_s8(y0->qs + 16); const float x0_0d = GGML_FP16_TO_FP32(x0_0->d); const float x0_1d = GGML_FP16_TO_FP32(x0_1->d); #if defined(__ARM_FEATURE_DOTPROD) sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vdotq_s32(vdupq_n_s32(0), v0_0lz, v1_0l)), x0_0d*y0->d); sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vdotq_s32(vdupq_n_s32(0), v0_0hz, v1_0h)), x0_1d*y0->d); #else const int16x8_t pl0l = vmull_s8(vget_low_s8 (v0_0lz), vget_low_s8 (v1_0l)); const int16x8_t pl0h = vmull_s8(vget_high_s8(v0_0lz), vget_high_s8(v1_0l)); const int16x8_t ph0l = vmull_s8(vget_low_s8 (v0_0hz), vget_low_s8 (v1_0h)); const int16x8_t ph0h = vmull_s8(vget_high_s8(v0_0hz), vget_high_s8(v1_0h)); const int32x4_t pl0 = vaddq_s32(vpaddlq_s16(pl0l), vpaddlq_s16(pl0h)); const int32x4_t ph0 = vaddq_s32(vpaddlq_s16(ph0l), vpaddlq_s16(ph0h)); sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(pl0), x0_0d*y0->d); sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(ph0), x0_1d*y0->d); #endif } *s = vaddvq_f32(vaddq_f32(sumv0, sumv1)) + summs0 + summs1; #elif defined(__AVX2__) // Initialize accumulator with zeros __m256 acc = _mm256_setzero_ps(); // Main loop for (int i = 0; i < nb; i++) { const __m128 d0 = _mm_set1_ps(GGML_FP16_TO_FP32(x[2*i + 0].d)); const __m128 d1 = _mm_set1_ps(GGML_FP16_TO_FP32(x[2*i + 1].d)); const __m256 dx = _mm256_set_m128(d1, d0); const __m128 m0 = _mm_set1_ps(GGML_FP16_TO_FP32(x[2*i + 0].m)); const __m128 m1 = _mm_set1_ps(GGML_FP16_TO_FP32(x[2*i + 1].m)); const __m256 mx = _mm256_set_m128(m1, m0); const __m128i bx0 = bytes_from_nibbles_16(x[2*i + 0].qs); const __m128i bx1 = bytes_from_nibbles_16(x[2*i + 1].qs); const __m256i bx = _mm256_set_m128i(bx1, bx0); const __m256 dy = _mm256_broadcast_ss(&y[i].d); const __m256i by = _mm256_loadu_si256((const __m256i *)y[i].qs); const __m256i syi = _mm256_maddubs_epi16(_mm256_set1_epi8(1), by); const __m256 syf = sum_i16_pairs_float(syi); const __m256 q = mul_sum_i8_pairs_float(bx, by); const __m256 sxy = _mm256_fmadd_ps(q, dx, _mm256_mul_ps(mx, syf)); acc = _mm256_fmadd_ps(sxy, dy, acc); } *s = hsum_float_8(acc); #else // scalar float sumf = 0.0; for (int i = 0; i < nb; i++) { const uint8_t * restrict x0 = x[2*i + 0].qs; const uint8_t * restrict x1 = x[2*i + 1].qs; const int8_t * restrict y0 = y[i].qs; const float d0 = GGML_FP16_TO_FP32(x[2*i + 0].d); const float m0 = GGML_FP16_TO_FP32(x[2*i + 0].m); const float d1 = GGML_FP16_TO_FP32(x[2*i + 1].d); const float m1 = GGML_FP16_TO_FP32(x[2*i + 1].m); int sxy_0 = 0; int sxy_1 = 0; for (int j = 0; j < QK8_0/4; j++) { const uint8_t v0 = x0[j]; const uint8_t v1 = x1[j]; const int x0_0 = v0 & 0xf; const int x1_0 = v0 >> 4; const int x0_1 = v1 & 0xf; const int x1_1 = v1 >> 4; const int y0_0 = y0[2*j + 0]; const int y1_0 = y0[2*j + 1]; const int y0_1 = y0[2*(j + QK8_0/4) + 0]; const int y1_1 = y0[2*(j + QK8_0/4) + 1]; sxy_0 += x0_0*y0_0 + x1_0*y1_0; sxy_1 += x0_1*y0_1 + x1_1*y1_1; } sumf += (d0*sxy_0 + d1*sxy_1)*y[i].d + m0*y[i].s0 + m1*y[i].s1; } *s = sumf; #endif } // compute GGML_VEC_DOT_UNROLL dot products at once // xs - x row stride in bytes inline static void ggml_vec_dot_f16_unroll(const int n, const int xs, float * restrict s, void * restrict xv, ggml_fp16_t * restrict y) { ggml_float sumf[GGML_VEC_DOT_UNROLL] = { 0.0 }; ggml_fp16_t * restrict x[GGML_VEC_DOT_UNROLL]; for (int i = 0; i < GGML_VEC_DOT_UNROLL; ++i) { x[i] = (ggml_fp16_t *) ((char *) xv + i*xs); } #if defined(GGML_SIMD) const int np = (n & ~(GGML_F16_STEP - 1)); GGML_F16_VEC sum[GGML_VEC_DOT_UNROLL][GGML_F16_ARR] = { { GGML_F16_VEC_ZERO } }; GGML_F16_VEC ax[GGML_F16_ARR]; GGML_F16_VEC ay[GGML_F16_ARR]; for (int i = 0; i < np; i += GGML_F16_STEP) { for (int j = 0; j < GGML_F16_ARR; j++) { ay[j] = GGML_F16_VEC_LOAD(y + i + j*GGML_F16_EPR, j); for (int k = 0; k < GGML_VEC_DOT_UNROLL; ++k) { ax[j] = GGML_F16_VEC_LOAD(x[k] + i + j*GGML_F16_EPR, j); sum[k][j] = GGML_F16_VEC_FMA(sum[k][j], ax[j], ay[j]); } } } // reduce sum0..sum3 to sum0 for (int k = 0; k < GGML_VEC_DOT_UNROLL; ++k) { GGML_F16_VEC_REDUCE(sumf[k], sum[k]); } // leftovers for (int i = np; i < n; ++i) { for (int j = 0; j < GGML_VEC_DOT_UNROLL; ++j) { sumf[j] += (ggml_float)(GGML_FP16_TO_FP32(x[j][i])*GGML_FP16_TO_FP32(y[i])); } } #else for (int i = 0; i < n; ++i) { for (int j = 0; j < GGML_VEC_DOT_UNROLL; ++j) { sumf[j] += (ggml_float)(GGML_FP16_TO_FP32(x[j][i])*GGML_FP16_TO_FP32(y[i])); } } #endif for (int i = 0; i < GGML_VEC_DOT_UNROLL; ++i) { s[i] = sumf[i]; } } inline static void ggml_vec_mad_f32(const int n, float * restrict y, const float * restrict x, const float v) { #if defined(GGML_SIMD) const int np = (n & ~(GGML_F32_STEP - 1)); GGML_F32_VEC vx = GGML_F32_VEC_SET1(v); GGML_F32_VEC ax[GGML_F32_ARR]; GGML_F32_VEC ay[GGML_F32_ARR]; for (int i = 0; i < np; i += GGML_F32_STEP) { for (int j = 0; j < GGML_F32_ARR; j++) { ax[j] = GGML_F32_VEC_LOAD(x + i + j*GGML_F32_EPR); ay[j] = GGML_F32_VEC_LOAD(y + i + j*GGML_F32_EPR); ay[j] = GGML_F32_VEC_FMA(ay[j], ax[j], vx); GGML_F32_VEC_STORE(y + i + j*GGML_F32_EPR, ay[j]); } } // leftovers for (int i = np; i < n; ++i) { y[i] += x[i]*v; } #else // scalar for (int i = 0; i < n; ++i) { y[i] += x[i]*v; } #endif } //inline static void ggml_vec_scale_f32(const int n, float * y, const float v) { for (int i = 0; i < n; ++i) y[i] *= v; } inline static void ggml_vec_scale_f32(const int n, float * y, const float v) { #if defined(GGML_SIMD) const int np = (n & ~(GGML_F32_STEP - 1)); GGML_F32_VEC vx = GGML_F32_VEC_SET1(v); GGML_F32_VEC ay[GGML_F32_ARR]; for (int i = 0; i < np; i += GGML_F32_STEP) { for (int j = 0; j < GGML_F32_ARR; j++) { ay[j] = GGML_F32_VEC_LOAD(y + i + j*GGML_F32_EPR); ay[j] = GGML_F32_VEC_MUL(ay[j], vx); GGML_F32_VEC_STORE(y + i + j*GGML_F32_EPR, ay[j]); } } // leftovers for (int i = np; i < n; ++i) { y[i] *= v; } #else // scalar for (int i = 0; i < n; ++i) { y[i] *= v; } #endif } inline static void ggml_vec_norm_f32 (const int n, float * s, const float * x) { ggml_vec_dot_f32(n, s, x, x); *s = sqrtf(*s); } inline static void ggml_vec_sqr_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = x[i]*x[i]; } inline static void ggml_vec_sqrt_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = sqrtf(x[i]); } inline static void ggml_vec_abs_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = fabsf(x[i]); } inline static void ggml_vec_sgn_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? 1.f : ((x[i] < 0.f) ? -1.f : 0.f); } inline static void ggml_vec_step_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? 1.f : 0.f; } inline static void ggml_vec_relu_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? x[i] : 0.f; } static const float GELU_COEF_A = 0.044715f; static const float SQRT_2_OVER_PI = 0.79788456080286535587989211986876f; inline static float ggml_gelu_f32(float x) { return 0.5f*x*(1.0f + tanhf(SQRT_2_OVER_PI*x*(1.0f + GELU_COEF_A*x*x))); } inline static void ggml_vec_gelu_f16(const int n, ggml_fp16_t * y, const ggml_fp16_t * x) { const uint16_t * i16 = (const uint16_t *) x; for (int i = 0; i < n; ++i) { y[i] = table_gelu_f16[i16[i]]; } } #ifdef GGML_GELU_FP16 inline static void ggml_vec_gelu_f32(const int n, float * y, const float * x) { uint16_t t; for (int i = 0; i < n; ++i) { ggml_fp16_t fp16 = GGML_FP32_TO_FP16(x[i]); memcpy(&t, &fp16, sizeof(uint16_t)); y[i] = GGML_FP16_TO_FP32(table_gelu_f16[t]); } } #else inline static void ggml_vec_gelu_f32(const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) { y[i] = ggml_gelu_f32(x[i]); } } #endif // Sigmoid Linear Unit (SiLU) function inline static float ggml_silu_f32(float x) { return x/(1.0f + expf(-x)); } inline static void ggml_vec_silu_f16(const int n, ggml_fp16_t * y, const ggml_fp16_t * x) { const uint16_t * i16 = (const uint16_t *) x; for (int i = 0; i < n; ++i) { y[i] = table_silu_f16[i16[i]]; } } #ifdef GGML_SILU_FP16 inline static void ggml_vec_silu_f32(const int n, float * y, const float * x) { uint16_t t; for (int i = 0; i < n; ++i) { ggml_fp16_t fp16 = GGML_FP32_TO_FP16(x[i]); memcpy(&t, &fp16, sizeof(uint16_t)); y[i] = GGML_FP16_TO_FP32(table_silu_f16[t]); } } #else inline static void ggml_vec_silu_f32(const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) { y[i] = ggml_silu_f32(x[i]); } } #endif inline static void ggml_vec_sum_f32(const int n, float * s, const float * x) { #ifndef GGML_USE_ACCELERATE ggml_float sum = 0.0; for (int i = 0; i < n; ++i) { sum += (ggml_float)x[i]; } *s = sum; #else vDSP_sve(x, 1, s, n); #endif } inline static void ggml_vec_max_f32(const int n, float * s, const float * x) { #ifndef GGML_USE_ACCELERATE float max = -INFINITY; for (int i = 0; i < n; ++i) { max = MAX(max, x[i]); } *s = max; #else vDSP_maxv(x, 1, s, n); #endif } inline static void ggml_vec_norm_inv_f32(const int n, float * s, const float * x) { ggml_vec_norm_f32(n, s, x); *s = 1.f/(*s); } // // logging // #if (GGML_DEBUG >= 1) #define GGML_PRINT_DEBUG(...) printf(__VA_ARGS__) #else #define GGML_PRINT_DEBUG(...) #endif #if (GGML_DEBUG >= 5) #define GGML_PRINT_DEBUG_5(...) printf(__VA_ARGS__) #else #define GGML_PRINT_DEBUG_5(...) #endif #if (GGML_DEBUG >= 10) #define GGML_PRINT_DEBUG_10(...) printf(__VA_ARGS__) #else #define GGML_PRINT_DEBUG_10(...) #endif #define GGML_PRINT(...) printf(__VA_ARGS__) // // data types // static const int GGML_BLCK_SIZE[GGML_TYPE_COUNT] = { [GGML_TYPE_F32] = 1, [GGML_TYPE_F16] = 1, [GGML_TYPE_Q4_0] = QK4_0, [GGML_TYPE_Q4_1] = QK4_1, [GGML_TYPE_Q4_2] = QK4_2, [GGML_TYPE_Q4_3] = QK4_3, [GGML_TYPE_Q8_0] = QK8_0, [GGML_TYPE_I8] = 1, [GGML_TYPE_I16] = 1, [GGML_TYPE_I32] = 1, }; static_assert(GGML_TYPE_COUNT == 10, "GGML_BLCK_SIZE is outdated"); static const size_t GGML_TYPE_SIZE[GGML_TYPE_COUNT] = { [GGML_TYPE_F32] = sizeof(float), [GGML_TYPE_F16] = sizeof(ggml_fp16_t), [GGML_TYPE_Q4_0] = sizeof(block_q4_0), [GGML_TYPE_Q4_1] = sizeof(block_q4_1), [GGML_TYPE_Q4_2] = sizeof(block_q4_2), [GGML_TYPE_Q4_3] = sizeof(block_q4_3), [GGML_TYPE_Q8_0] = sizeof(block_q8_0), [GGML_TYPE_I8] = sizeof(int8_t), [GGML_TYPE_I16] = sizeof(int16_t), [GGML_TYPE_I32] = sizeof(int32_t), }; static_assert(GGML_TYPE_COUNT == 10, "GGML_TYPE_SIZE is outdated"); static const char * GGML_TYPE_NAME[GGML_TYPE_COUNT] = { [GGML_TYPE_F32] = "f32", [GGML_TYPE_F16] = "f16", [GGML_TYPE_Q4_0] = "q4_0", [GGML_TYPE_Q4_1] = "q4_1", [GGML_TYPE_Q4_2] = "q4_2", [GGML_TYPE_Q4_3] = "q4_3", [GGML_TYPE_Q8_0] = "q8_0", [GGML_TYPE_I8] = "i8", [GGML_TYPE_I16] = "i16", [GGML_TYPE_I32] = "i32", }; static_assert(GGML_TYPE_COUNT == 10, "GGML_TYPE_NAME is outdated"); static bool GGML_IS_QUANTIZED[GGML_TYPE_COUNT] = { [GGML_TYPE_F32] = false, [GGML_TYPE_F16] = false, [GGML_TYPE_Q4_0] = true, [GGML_TYPE_Q4_1] = true, [GGML_TYPE_Q4_2] = true, [GGML_TYPE_Q4_3] = true, [GGML_TYPE_Q8_0] = true, [GGML_TYPE_I8] = false, [GGML_TYPE_I16] = false, [GGML_TYPE_I32] = false, }; static_assert(GGML_TYPE_COUNT == 10, "GGML_IS_QUANTIZED is outdated"); static const char * GGML_OP_LABEL[GGML_OP_COUNT] = { "NONE", "DUP", "ADD", "SUB", "MUL", "DIV", "SQR", "SQRT", "SUM", "MEAN", "REPEAT", "ABS", "SGN", "NEG", "STEP", "RELU", "GELU", "SILU", "NORM", "RMS_NORM", "MUL_MAT", "SCALE", "CPY", "CONT", "RESHAPE", "VIEW", "PERMUTE", "TRANSPOSE", "GET_ROWS", "DIAG_MASK_INF", "SOFT_MAX", "ROPE", "CONV_1D_1S", "CONV_1D_2S", "FLASH_ATTN", "FLASH_FF", "MAP_UNARY", "MAP_BINARY", }; static_assert(GGML_OP_COUNT == 38, "GGML_OP_COUNT != 38"); static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = { "none", "x", "x+y", "x-y", "x*y", "x/y", "x^2", "√x", "Σx", "Σx/n", "repeat(x)", "abs(x)", "sgn(x)", "-x", "step(x)", "relu(x)", "gelu(x)", "silu(x)", "norm(x)", "rms_norm(x)", "X*Y", "x*v", "x-\\>y", "cont(x)", "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)", "f(x)", "f(x,y)", }; static_assert(GGML_OP_COUNT == 38, "GGML_OP_COUNT != 38"); static_assert(sizeof(struct ggml_object)%GGML_MEM_ALIGN == 0, "ggml_object size must be a multiple of GGML_MEM_ALIGN"); static_assert(sizeof(struct ggml_tensor)%GGML_MEM_ALIGN == 0, "ggml_tensor size must be a multiple of GGML_MEM_ALIGN"); // // ggml context // struct ggml_context { size_t mem_size; void * mem_buffer; bool mem_buffer_owned; bool no_alloc; int n_objects; struct ggml_object * objects_begin; struct ggml_object * objects_end; struct ggml_scratch scratch; struct ggml_scratch scratch_save; }; struct ggml_context_container { bool used; struct ggml_context context; }; // // compute types // enum ggml_task_type { GGML_TASK_INIT = 0, GGML_TASK_COMPUTE, GGML_TASK_FINALIZE, }; struct ggml_compute_params { enum ggml_task_type type; int ith, nth; // work buffer for all threads size_t wsize; void * wdata; }; // // ggml state // struct ggml_state { struct ggml_context_container contexts[GGML_MAX_CONTEXTS]; }; // global state static struct ggml_state g_state; static atomic_int g_state_barrier = 0; // barrier via spin lock inline static void ggml_critical_section_start(void) { int processing = atomic_fetch_add(&g_state_barrier, 1); while (processing > 0) { // wait for other threads to finish atomic_fetch_sub(&g_state_barrier, 1); sched_yield(); // TODO: reconsider this processing = atomic_fetch_add(&g_state_barrier, 1); } } // TODO: make this somehow automatically executed // some sort of "sentry" mechanism inline static void ggml_critical_section_end(void) { atomic_fetch_sub(&g_state_barrier, 1); } //////////////////////////////////////////////////////////////////////////////// void ggml_print_object(const struct ggml_object * obj) { GGML_PRINT(" - ggml_object: offset = %zu, size = %zu, next = %p\n", obj->offs, obj->size, (const void *) obj->next); } void ggml_print_objects(const struct ggml_context * ctx) { struct ggml_object * obj = ctx->objects_begin; GGML_PRINT("%s: objects in context %p:\n", __func__, (const void *) ctx); while (obj != NULL) { ggml_print_object(obj); obj = obj->next; } GGML_PRINT("%s: --- end ---\n", __func__); } int64_t ggml_nelements(const struct ggml_tensor * tensor) { static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); return tensor->ne[0]*tensor->ne[1]*tensor->ne[2]*tensor->ne[3]; } int ggml_nrows(const struct ggml_tensor * tensor) { static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); return tensor->ne[1]*tensor->ne[2]*tensor->ne[3]; } size_t ggml_nbytes(const struct ggml_tensor * tensor) { static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); return (ggml_nelements(tensor)*GGML_TYPE_SIZE[tensor->type])/GGML_BLCK_SIZE[tensor->type]; } int ggml_blck_size(enum ggml_type type) { return GGML_BLCK_SIZE[type]; } size_t ggml_type_size(enum ggml_type type) { return GGML_TYPE_SIZE[type]; } float ggml_type_sizef(enum ggml_type type) { return ((float)(GGML_TYPE_SIZE[type]))/GGML_BLCK_SIZE[type]; } const char * ggml_type_name(enum ggml_type type) { return GGML_TYPE_NAME[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]); } bool ggml_is_quantized(enum ggml_type type) { return GGML_IS_QUANTIZED[type]; } static inline bool ggml_is_transposed(const struct ggml_tensor * tensor) { return tensor->nb[0] > tensor->nb[1]; } static inline bool ggml_is_contiguous(const struct ggml_tensor * tensor) { static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); return tensor->nb[0] == GGML_TYPE_SIZE[tensor->type] && tensor->nb[1] == (tensor->nb[0]*tensor->ne[0])/GGML_BLCK_SIZE[tensor->type] && tensor->nb[2] == tensor->nb[1]*tensor->ne[1] && tensor->nb[3] == tensor->nb[2]*tensor->ne[2]; } static inline bool ggml_is_padded_1d(const struct ggml_tensor * tensor) { static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); return tensor->nb[0] == GGML_TYPE_SIZE[tensor->type] && tensor->nb[2] == tensor->nb[1]*tensor->ne[1] && tensor->nb[3] == tensor->nb[2]*tensor->ne[2]; } static inline bool ggml_are_same_shape(const struct ggml_tensor * t0, const struct ggml_tensor * t1) { static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); return (t0->ne[0] == t1->ne[0] ) && (t0->ne[1] == t1->ne[1] ) && (t0->ne[2] == t1->ne[2] ) && (t0->ne[3] == t1->ne[3] ); } // check if t1 can be represented as a repeatition of t0 static inline bool ggml_can_repeat(const struct ggml_tensor * t0, const struct ggml_tensor * t1) { static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); return (t1->ne[0]%t0->ne[0] == 0) && (t1->ne[1]%t0->ne[1] == 0) && (t1->ne[2]%t0->ne[2] == 0) && (t1->ne[3]%t0->ne[3] == 0); } static inline int ggml_up32(int n) { return (n + 31) & ~31; } static inline int ggml_up64(int n) { return (n + 63) & ~63; } static inline int ggml_up(int n, int m) { // assert m is a power of 2 GGML_ASSERT((m & (m - 1)) == 0); return (n + m - 1) & ~(m - 1); } // assert that pointer is aligned to GGML_MEM_ALIGN #define ggml_assert_aligned(ptr) \ GGML_ASSERT(((uintptr_t) (ptr))%GGML_MEM_ALIGN == 0) //////////////////////////////////////////////////////////////////////////////// struct ggml_context * ggml_init(struct ggml_init_params params) { // make this function thread safe ggml_critical_section_start(); static bool is_first_call = true; if (is_first_call) { // initialize time system (required on Windows) ggml_time_init(); // initialize GELU, SILU and EXP F32 tables { const uint64_t t_start = ggml_time_us(); UNUSED(t_start); ggml_fp16_t ii; for (int i = 0; i < (1 << 16); ++i) { uint16_t ui = i; memcpy(&ii, &ui, sizeof(ii)); const float f = table_f32_f16[i] = GGML_COMPUTE_FP16_TO_FP32(ii); table_gelu_f16[i] = GGML_FP32_TO_FP16(ggml_gelu_f32(f)); table_silu_f16[i] = GGML_FP32_TO_FP16(ggml_silu_f32(f)); table_exp_f16[i] = GGML_FP32_TO_FP16(expf(f)); } const uint64_t t_end = ggml_time_us(); UNUSED(t_end); GGML_PRINT_DEBUG("%s: GELU, SILU and EXP tables initialized in %f ms\n", __func__, (t_end - t_start)/1000.0f); } // initialize g_state { const uint64_t t_start = ggml_time_us(); UNUSED(t_start); g_state = (struct ggml_state) { /*.contexts =*/ { { 0 } }, }; for (int i = 0; i < GGML_MAX_CONTEXTS; ++i) { g_state.contexts[i].used = false; } const uint64_t t_end = ggml_time_us(); UNUSED(t_end); GGML_PRINT_DEBUG("%s: g_state initialized in %f ms\n", __func__, (t_end - t_start)/1000.0f); } // initialize cuBLAS #if defined(GGML_USE_CUBLAS) ggml_init_cublas(); #endif 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; } const size_t mem_size = (params.mem_size + GGML_MEM_ALIGN - 1) & ~(GGML_MEM_ALIGN - 1); *ctx = (struct ggml_context) { /*.mem_size =*/ mem_size, /*.mem_buffer =*/ params.mem_buffer ? params.mem_buffer : GGML_ALIGNED_MALLOC(mem_size), /*.mem_buffer_owned =*/ params.mem_buffer ? false : true, /*.no_alloc =*/ params.no_alloc, /*.n_objects =*/ 0, /*.objects_begin =*/ NULL, /*.objects_end =*/ NULL, /*.scratch =*/ { 0, 0, NULL, }, /*.scratch_save =*/ { 0, 0, NULL, }, }; GGML_ASSERT(ctx->mem_buffer != NULL); ggml_assert_aligned(ctx->mem_buffer); GGML_PRINT_DEBUG("%s: context initialized\n", __func__); ggml_critical_section_end(); return ctx; } void ggml_free(struct ggml_context * ctx) { // make this function thread safe ggml_critical_section_start(); bool found = false; for (int i = 0; i < GGML_MAX_CONTEXTS; i++) { if (&g_state.contexts[i].context == ctx) { g_state.contexts[i].used = false; GGML_PRINT_DEBUG("%s: context %d 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) { GGML_ALIGNED_FREE(ctx->mem_buffer); } found = true; break; } } if (!found) { GGML_PRINT_DEBUG("%s: context not found\n", __func__); } ggml_critical_section_end(); } size_t ggml_used_mem(const struct ggml_context * ctx) { return ctx->objects_end->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 int64_t* ne, void* data) { // always insert objects at the end of the context's memory pool struct ggml_object * obj_cur = ctx->objects_end; const size_t cur_offs = obj_cur == NULL ? 0 : obj_cur->offs; const size_t cur_size = obj_cur == NULL ? 0 : obj_cur->size; const size_t cur_end = cur_offs + cur_size; size_t size_needed = 0; if (data == NULL && !ctx->no_alloc) { size_needed += GGML_TYPE_SIZE[type]*(ne[0]/GGML_BLCK_SIZE[type]); for (int i = 1; i < n_dims; i++) { size_needed *= ne[i]; } // align to GGML_MEM_ALIGN size_needed = ((size_needed + GGML_MEM_ALIGN - 1)/GGML_MEM_ALIGN)*GGML_MEM_ALIGN; } char * const mem_buffer = ctx->mem_buffer; struct ggml_object * const obj_new = (struct ggml_object *)(mem_buffer + cur_end); if (ctx->scratch.data == NULL || data != NULL) { size_needed += sizeof(struct ggml_tensor); if (cur_end + size_needed + GGML_OBJECT_SIZE > ctx->mem_size) { GGML_PRINT("%s: not enough space in the context's memory pool (needed %zu, available %zu)\n", __func__, cur_end + size_needed + GGML_OBJECT_SIZE, ctx->mem_size); assert(false); return NULL; } *obj_new = (struct ggml_object) { .offs = cur_end + GGML_OBJECT_SIZE, .size = size_needed, .next = NULL, }; } else { if (ctx->scratch.offs + size_needed > ctx->scratch.size) { GGML_PRINT("%s: not enough space in the scratch memory\n", __func__); assert(false); return NULL; } if (cur_end + sizeof(struct ggml_tensor) + GGML_OBJECT_SIZE > ctx->mem_size) { GGML_PRINT("%s: not enough space in the context's memory pool (needed %zu, available %zu)\n", __func__, cur_end + sizeof(struct ggml_tensor) + GGML_OBJECT_SIZE, ctx->mem_size); assert(false); return NULL; } data = (char * const) ctx->scratch.data + ctx->scratch.offs; *obj_new = (struct ggml_object) { .offs = cur_end + GGML_OBJECT_SIZE, .size = sizeof(struct ggml_tensor), .next = NULL, }; //printf("scratch offs = %zu, size_needed = %zu\n", ctx->scratch.offs, size_needed); ctx->scratch.offs += size_needed; } if (obj_cur != NULL) { obj_cur->next = obj_new; } else { // this is the first object in this context ctx->objects_begin = obj_new; } ctx->objects_end = obj_new; //printf("%s: inserted new object at %zu, size = %zu\n", __func__, cur_end, obj_new->size); struct ggml_tensor * const result = (struct ggml_tensor *)(mem_buffer + obj_new->offs); ggml_assert_aligned(result); *result = (struct ggml_tensor) { /*.type =*/ type, /*.n_dims =*/ n_dims, /*.ne =*/ { 1, 1, 1, 1 }, /*.nb =*/ { 0, 0, 0, 0 }, /*.op =*/ GGML_OP_NONE, /*.is_param =*/ false, /*.grad =*/ NULL, /*.src0 =*/ NULL, /*.src1 =*/ NULL, /*.opt =*/ { NULL }, /*.n_tasks =*/ 0, /*.perf_runs =*/ 0, /*.perf_cycles =*/ 0, /*.perf_time_us =*/ 0, /*.data =*/ (data == NULL && !ctx->no_alloc) ? (void *)(result + 1) : data, /*.pad =*/ { 0 }, }; // TODO: this should not be needed as long as we don't rely on aligned SIMD loads //ggml_assert_aligned(result->data); for (int i = 0; i < n_dims; i++) { result->ne[i] = ne[i]; } result->nb[0] = GGML_TYPE_SIZE[type]; result->nb[1] = result->nb[0]*(result->ne[0]/GGML_BLCK_SIZE[type]); for (int i = 2; i < GGML_MAX_DIMS; i++) { result->nb[i] = result->nb[i - 1]*result->ne[i - 1]; } ctx->n_objects++; return result; } struct ggml_tensor * ggml_new_tensor( struct ggml_context * ctx, enum ggml_type type, int n_dims, const int64_t * ne) { return ggml_new_tensor_impl(ctx, type, n_dims, ne, NULL); } struct ggml_tensor * ggml_new_tensor_1d( struct ggml_context * ctx, enum ggml_type type, int64_t ne0) { return ggml_new_tensor(ctx, type, 1, &ne0); } struct ggml_tensor * ggml_new_tensor_2d( struct ggml_context * ctx, enum ggml_type type, int64_t ne0, int64_t ne1) { const int64_t ne[2] = { ne0, ne1 }; return ggml_new_tensor(ctx, type, 2, ne); } struct ggml_tensor * ggml_new_tensor_3d( struct ggml_context * ctx, enum ggml_type type, int64_t ne0, int64_t ne1, int64_t ne2) { const int64_t ne[3] = { ne0, ne1, ne2 }; return ggml_new_tensor(ctx, type, 3, ne); } struct ggml_tensor * ggml_new_tensor_4d( struct ggml_context * ctx, enum ggml_type type, int64_t ne0, int64_t ne1, int64_t ne2, int64_t ne3) { const int64_t ne[4] = { ne0, ne1, ne2, ne3 }; return ggml_new_tensor(ctx, type, 4, ne); } struct ggml_tensor * ggml_new_i32(struct ggml_context * ctx, int32_t value) { ctx->scratch_save = ctx->scratch; ctx->scratch.data = NULL; struct ggml_tensor * result = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 1); ctx->scratch = ctx->scratch_save; ggml_set_i32(result, value); return result; } struct ggml_tensor * ggml_new_f32(struct ggml_context * ctx, float value) { ctx->scratch_save = ctx->scratch; ctx->scratch.data = NULL; struct ggml_tensor * result = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 1); ctx->scratch = ctx->scratch_save; ggml_set_f32(result, value); return result; } struct ggml_tensor * ggml_dup_tensor(struct ggml_context * ctx, const struct ggml_tensor * src) { return ggml_new_tensor_impl(ctx, src->type, src->n_dims, src->ne, NULL); } struct ggml_tensor * ggml_set_zero(struct ggml_tensor * tensor) { memset(tensor->data, 0, ggml_nbytes(tensor)); return tensor; } struct ggml_tensor * ggml_set_i32 (struct ggml_tensor * tensor, int32_t value) { const int n = ggml_nrows(tensor); const int nc = tensor->ne[0]; const size_t n1 = tensor->nb[1]; char * const data = tensor->data; switch (tensor->type) { case GGML_TYPE_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; default: { GGML_ASSERT(false); } break; } return tensor; } struct ggml_tensor * ggml_set_f32(struct ggml_tensor * tensor, float value) { const int n = ggml_nrows(tensor); const int nc = tensor->ne[0]; const size_t n1 = tensor->nb[1]; char * const data = tensor->data; switch (tensor->type) { case GGML_TYPE_I8: { assert(tensor->nb[0] == sizeof(int8_t)); for (int i = 0; i < n; i++) { ggml_vec_set_i8(nc, (int8_t *)(data + i*n1), value); } } break; case GGML_TYPE_I16: { assert(tensor->nb[0] == sizeof(int16_t)); for (int i = 0; i < n; i++) { ggml_vec_set_i16(nc, (int16_t *)(data + i*n1), value); } } break; case GGML_TYPE_I32: { assert(tensor->nb[0] == sizeof(int32_t)); for (int i = 0; i < n; i++) { ggml_vec_set_i32(nc, (int32_t *)(data + i*n1), value); } } break; case GGML_TYPE_F16: { assert(tensor->nb[0] == sizeof(ggml_fp16_t)); for (int i = 0; i < n; i++) { ggml_vec_set_f16(nc, (ggml_fp16_t *)(data + i*n1), value); } } break; case GGML_TYPE_F32: { assert(tensor->nb[0] == sizeof(float)); for (int i = 0; i < n; i++) { ggml_vec_set_f32(nc, (float *)(data + i*n1), value); } } break; default: { GGML_ASSERT(false); } break; } return tensor; } int32_t ggml_get_i32_1d(const struct ggml_tensor * tensor, int i) { switch (tensor->type) { case GGML_TYPE_I8: { GGML_ASSERT(tensor->nb[0] == sizeof(int8_t)); return ((int8_t *)(tensor->data))[i]; } break; case GGML_TYPE_I16: { GGML_ASSERT(tensor->nb[0] == sizeof(int16_t)); return ((int16_t *)(tensor->data))[i]; } break; case GGML_TYPE_I32: { GGML_ASSERT(tensor->nb[0] == sizeof(int32_t)); return ((int32_t *)(tensor->data))[i]; } break; case GGML_TYPE_F16: { GGML_ASSERT(tensor->nb[0] == sizeof(ggml_fp16_t)); return GGML_FP16_TO_FP32(((ggml_fp16_t *)(tensor->data))[i]); } break; case GGML_TYPE_F32: { GGML_ASSERT(tensor->nb[0] == sizeof(float)); return ((float *)(tensor->data))[i]; } break; default: { GGML_ASSERT(false); } break; } return 0.0f; } void ggml_set_i32_1d(const struct ggml_tensor * tensor, int i, int32_t value) { switch (tensor->type) { case GGML_TYPE_I8: { GGML_ASSERT(tensor->nb[0] == sizeof(int8_t)); ((int8_t *)(tensor->data))[i] = value; } break; case GGML_TYPE_I16: { GGML_ASSERT(tensor->nb[0] == sizeof(int16_t)); ((int16_t *)(tensor->data))[i] = value; } break; case GGML_TYPE_I32: { GGML_ASSERT(tensor->nb[0] == sizeof(int32_t)); ((int32_t *)(tensor->data))[i] = value; } break; case GGML_TYPE_F16: { GGML_ASSERT(tensor->nb[0] == sizeof(ggml_fp16_t)); ((ggml_fp16_t *)(tensor->data))[i] = GGML_FP32_TO_FP16(value); } break; case GGML_TYPE_F32: { GGML_ASSERT(tensor->nb[0] == sizeof(float)); ((float *)(tensor->data))[i] = value; } break; default: { GGML_ASSERT(false); } break; } } float ggml_get_f32_1d(const struct ggml_tensor * tensor, int i) { switch (tensor->type) { case GGML_TYPE_I8: { GGML_ASSERT(tensor->nb[0] == sizeof(int8_t)); return ((int8_t *)(tensor->data))[i]; } break; case GGML_TYPE_I16: { GGML_ASSERT(tensor->nb[0] == sizeof(int16_t)); return ((int16_t *)(tensor->data))[i]; } break; case GGML_TYPE_I32: { GGML_ASSERT(tensor->nb[0] == sizeof(int32_t)); return ((int32_t *)(tensor->data))[i]; } break; case GGML_TYPE_F16: { GGML_ASSERT(tensor->nb[0] == sizeof(ggml_fp16_t)); return GGML_FP16_TO_FP32(((ggml_fp16_t *)(tensor->data))[i]); } break; case GGML_TYPE_F32: { GGML_ASSERT(tensor->nb[0] == sizeof(float)); return ((float *)(tensor->data))[i]; } break; default: { GGML_ASSERT(false); } break; } return 0.0f; } void ggml_set_f32_1d(const struct ggml_tensor * tensor, int i, float value) { switch (tensor->type) { case GGML_TYPE_I8: { GGML_ASSERT(tensor->nb[0] == sizeof(int8_t)); ((int8_t *)(tensor->data))[i] = value; } break; case GGML_TYPE_I16: { GGML_ASSERT(tensor->nb[0] == sizeof(int16_t)); ((int16_t *)(tensor->data))[i] = value; } break; case GGML_TYPE_I32: { GGML_ASSERT(tensor->nb[0] == sizeof(int32_t)); ((int32_t *)(tensor->data))[i] = value; } break; case GGML_TYPE_F16: { GGML_ASSERT(tensor->nb[0] == sizeof(ggml_fp16_t)); ((ggml_fp16_t *)(tensor->data))[i] = GGML_FP32_TO_FP16(value); } break; case GGML_TYPE_F32: { GGML_ASSERT(tensor->nb[0] == sizeof(float)); ((float *)(tensor->data))[i] = value; } break; default: { GGML_ASSERT(false); } break; } } void * ggml_get_data(const struct ggml_tensor * tensor) { return tensor->data; } float * ggml_get_data_f32(const struct ggml_tensor * tensor) { assert(tensor->type == GGML_TYPE_F32); return (float *)(tensor->data); } struct ggml_tensor * ggml_view_tensor( struct ggml_context * ctx, const struct ggml_tensor * src) { struct ggml_tensor * result = ggml_new_tensor_impl(ctx, src->type, src->n_dims, src->ne, src->data); result->nb[0] = src->nb[0]; result->nb[1] = src->nb[1]; result->nb[2] = src->nb[2]; result->nb[3] = src->nb[3]; return result; } //////////////////////////////////////////////////////////////////////////////// // ggml_dup struct ggml_tensor * ggml_dup_impl( struct ggml_context * ctx, struct ggml_tensor * a, bool inplace) { bool is_node = false; if (!inplace && (a->grad)) { is_node = true; } struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); result->op = GGML_OP_DUP; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src0 = a; result->src1 = NULL; return result; } struct ggml_tensor * ggml_dup( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_dup_impl(ctx, a, false); } struct ggml_tensor * ggml_dup_inplace( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_dup_impl(ctx, a, true); } // ggml_add struct ggml_tensor * ggml_add_impl( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, bool inplace) { GGML_ASSERT(ggml_are_same_shape(a, b)); bool is_node = false; if (!inplace && (a->grad || b->grad)) { is_node = true; } struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); result->op = GGML_OP_ADD; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src0 = a; result->src1 = b; return result; } struct ggml_tensor * ggml_add( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) { return ggml_add_impl(ctx, a, b, false); } struct ggml_tensor * ggml_add_inplace( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) { return ggml_add_impl(ctx, a, b, true); } // ggml_sub struct ggml_tensor * ggml_sub_impl( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, bool inplace) { GGML_ASSERT(ggml_are_same_shape(a, b)); bool is_node = false; if (!inplace && (a->grad || b->grad)) { is_node = true; } struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); result->op = GGML_OP_SUB; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src0 = a; result->src1 = b; return result; } struct ggml_tensor * ggml_sub( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) { return ggml_sub_impl(ctx, a, b, false); } struct ggml_tensor * ggml_sub_inplace( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) { return ggml_sub_impl(ctx, a, b, true); } // ggml_mul struct ggml_tensor * ggml_mul_impl( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, bool inplace) { GGML_ASSERT(ggml_are_same_shape(a, b)); bool is_node = false; if (!inplace && (a->grad || b->grad)) { is_node = true; } if (inplace) { GGML_ASSERT(is_node == false); } struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); result->op = GGML_OP_MUL; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src0 = a; result->src1 = b; return result; } struct ggml_tensor * ggml_mul( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) { return ggml_mul_impl(ctx, a, b, false); } struct ggml_tensor * ggml_mul_inplace( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) { return ggml_mul_impl(ctx, a, b, true); } // ggml_div struct ggml_tensor * ggml_div_impl( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, bool inplace) { GGML_ASSERT(ggml_are_same_shape(a, b)); bool is_node = false; if (!inplace && (a->grad || b->grad)) { is_node = true; } if (inplace) { GGML_ASSERT(is_node == false); } struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); result->op = GGML_OP_DIV; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src0 = a; result->src1 = b; return result; } struct ggml_tensor * ggml_div( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) { return ggml_div_impl(ctx, a, b, false); } struct ggml_tensor * ggml_div_inplace( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) { return ggml_div_impl(ctx, a, b, true); } // ggml_sqr struct ggml_tensor * ggml_sqr_impl( struct ggml_context * ctx, struct ggml_tensor * a, bool inplace) { bool is_node = false; if (!inplace && (a->grad)) { is_node = true; } struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); result->op = GGML_OP_SQR; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src0 = a; result->src1 = NULL; return result; } struct ggml_tensor * ggml_sqr( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_sqr_impl(ctx, a, false); } struct ggml_tensor * ggml_sqr_inplace( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_sqr_impl(ctx, a, true); } // ggml_sqrt struct ggml_tensor * ggml_sqrt_impl( struct ggml_context * ctx, struct ggml_tensor * a, bool inplace) { bool is_node = false; if (!inplace && (a->grad)) { is_node = true; } struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); result->op = GGML_OP_SQRT; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src0 = a; result->src1 = NULL; return result; } struct ggml_tensor * ggml_sqrt( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_sqrt_impl(ctx, a, false); } struct ggml_tensor * ggml_sqrt_inplace( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_sqrt_impl(ctx, a, true); } // ggml_sum struct ggml_tensor * ggml_sum( struct ggml_context * ctx, struct ggml_tensor * a) { bool is_node = false; if (a->grad) { is_node = true; } struct ggml_tensor * result = ggml_new_tensor_1d(ctx, a->type, 1); result->op = GGML_OP_SUM; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src0 = a; result->src1 = NULL; return result; } // ggml_mean struct ggml_tensor * ggml_mean( struct ggml_context * ctx, struct ggml_tensor * a) { bool is_node = false; if (a->grad) { GGML_ASSERT(false); // TODO: implement is_node = true; } int64_t ne[GGML_MAX_DIMS] = { 1, a->ne[1], a->ne[2], a->ne[3] }; struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, a->n_dims, ne); result->op = GGML_OP_MEAN; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src0 = a; result->src1 = NULL; return result; } // ggml_repeat struct ggml_tensor * ggml_repeat( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) { GGML_ASSERT(ggml_can_repeat(a, b)); bool is_node = false; if (a->grad) { is_node = true; } if (ggml_are_same_shape(a, b) && !is_node) { return a; } struct ggml_tensor * result = ggml_new_tensor(ctx, a->type, b->n_dims, b->ne); result->op = GGML_OP_REPEAT; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src0 = a; result->src1 = b; return result; } // ggml_abs struct ggml_tensor * ggml_abs_impl( struct ggml_context * ctx, struct ggml_tensor * a, bool inplace) { bool is_node = false; if (!inplace && (a->grad)) { is_node = true; } struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); result->op = GGML_OP_ABS; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src0 = a; result->src1 = NULL; return result; } struct ggml_tensor * ggml_abs( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_abs_impl(ctx, a, false); } struct ggml_tensor * ggml_abs_inplace( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_abs_impl(ctx, a, true); } // ggml_sgn struct ggml_tensor * ggml_sgn_impl( struct ggml_context * ctx, struct ggml_tensor * a, bool inplace) { bool is_node = false; if (!inplace && (a->grad)) { is_node = true; } struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); result->op = GGML_OP_SGN; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src0 = a; result->src1 = NULL; return result; } struct ggml_tensor * ggml_sgn( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_sgn_impl(ctx, a, false); } struct ggml_tensor * ggml_sgn_inplace( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_sgn_impl(ctx, a, true); } // ggml_neg struct ggml_tensor * ggml_neg_impl( struct ggml_context * ctx, struct ggml_tensor * a, bool inplace) { bool is_node = false; if (!inplace && (a->grad)) { is_node = true; } struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); result->op = GGML_OP_NEG; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src0 = a; result->src1 = NULL; return result; } struct ggml_tensor * ggml_neg( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_neg_impl(ctx, a, false); } struct ggml_tensor * ggml_neg_inplace( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_neg_impl(ctx, a, true); } // ggml_step struct ggml_tensor * ggml_step_impl( struct ggml_context * ctx, struct ggml_tensor * a, bool inplace) { bool is_node = false; if (!inplace && (a->grad)) { is_node = true; } struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); result->op = GGML_OP_STEP; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src0 = a; result->src1 = NULL; return result; } struct ggml_tensor * ggml_step( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_step_impl(ctx, a, false); } struct ggml_tensor * ggml_step_inplace( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_step_impl(ctx, a, true); } // ggml_relu struct ggml_tensor * ggml_relu_impl( struct ggml_context * ctx, struct ggml_tensor * a, bool inplace) { bool is_node = false; if (!inplace && (a->grad)) { is_node = true; } struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); result->op = GGML_OP_RELU; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src0 = a; result->src1 = NULL; return result; } struct ggml_tensor * ggml_relu( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_relu_impl(ctx, a, false); } struct ggml_tensor * ggml_relu_inplace( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_relu_impl(ctx, a, true); } // ggml_gelu struct ggml_tensor * ggml_gelu_impl( struct ggml_context * ctx, struct ggml_tensor * a, bool inplace) { bool is_node = false; if (!inplace && (a->grad)) { is_node = true; } struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); result->op = GGML_OP_GELU; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src0 = a; result->src1 = NULL; return result; } struct ggml_tensor * ggml_gelu( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_gelu_impl(ctx, a, false); } struct ggml_tensor * ggml_gelu_inplace( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_gelu_impl(ctx, a, true); } // ggml_silu struct ggml_tensor * ggml_silu_impl( struct ggml_context * ctx, struct ggml_tensor * a, bool inplace) { bool is_node = false; if (!inplace && (a->grad)) { is_node = true; } struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); result->op = GGML_OP_SILU; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src0 = a; result->src1 = NULL; return result; } struct ggml_tensor * ggml_silu( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_silu_impl(ctx, a, false); } struct ggml_tensor * ggml_silu_inplace( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_silu_impl(ctx, a, true); } // ggml_norm struct ggml_tensor * ggml_norm_impl( struct ggml_context * ctx, struct ggml_tensor * a, bool inplace) { bool is_node = false; if (!inplace && (a->grad)) { GGML_ASSERT(false); // TODO: implement backward is_node = true; } struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); result->op = GGML_OP_NORM; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src0 = a; result->src1 = NULL; // TODO: maybe store epsilon here? return result; } struct ggml_tensor * ggml_norm( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_norm_impl(ctx, a, false); } struct ggml_tensor * ggml_norm_inplace( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_norm_impl(ctx, a, true); } struct ggml_tensor * ggml_rms_norm_impl( struct ggml_context * ctx, struct ggml_tensor * a, bool inplace) { bool is_node = false; if (!inplace && (a->grad)) { GGML_ASSERT(false); // TODO: implement backward is_node = true; } struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); result->op = GGML_OP_RMS_NORM; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src0 = a; result->src1 = NULL; // TODO: maybe store epsilon here? return result; } struct ggml_tensor * ggml_rms_norm( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_rms_norm_impl(ctx, a, false); } struct ggml_tensor * ggml_rms_norm_inplace( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_rms_norm_impl(ctx, a, true); } // ggml_mul_mat struct ggml_tensor * ggml_mul_mat( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) { GGML_ASSERT(ggml_can_mul_mat(a, b)); GGML_ASSERT(!ggml_is_transposed(a)); bool is_node = false; if (a->grad || b->grad) { is_node = true; } const int64_t ne[4] = { a->ne[1], b->ne[1], a->ne[2], b->ne[3] }; struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, MIN(a->n_dims, b->n_dims), ne); result->op = GGML_OP_MUL_MAT; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src0 = a; result->src1 = b; return result; } // ggml_scale struct ggml_tensor * ggml_scale_impl( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, bool inplace) { GGML_ASSERT(ggml_is_scalar(b)); GGML_ASSERT(ggml_is_padded_1d(a)); bool is_node = false; if (!inplace && (a->grad || b->grad)) { GGML_ASSERT(false); // TODO: implement backward is_node = true; } // TODO: when implement backward, fix this: //struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); struct ggml_tensor * result = ggml_view_tensor(ctx, a); result->op = GGML_OP_SCALE; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src0 = a; result->src1 = b; return result; } struct ggml_tensor * ggml_scale( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) { return ggml_scale_impl(ctx, a, b, false); } struct ggml_tensor * ggml_scale_inplace( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) { return ggml_scale_impl(ctx, a, b, true); } // ggml_cpy struct ggml_tensor * ggml_cpy_impl( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, bool inplace) { GGML_ASSERT(ggml_nelements(a) == ggml_nelements(b)); bool is_node = false; if (!inplace && (a->grad || b->grad)) { GGML_ASSERT(false); // TODO: implement backward is_node = true; } // make a view of the destination struct ggml_tensor * result = ggml_view_tensor(ctx, b); result->op = GGML_OP_CPY; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src0 = a; result->src1 = b; return result; } struct ggml_tensor * ggml_cpy( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) { return ggml_cpy_impl(ctx, a, b, false); } struct ggml_tensor * ggml_cpy_inplace( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) { return ggml_cpy_impl(ctx, a, b, true); } // ggml_cont struct ggml_tensor * ggml_cont_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_CONT; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src0 = a; result->src1 = NULL; return result; } struct ggml_tensor * ggml_cont( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_cont_impl(ctx, a, false); } struct ggml_tensor * ggml_cont_inplace( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_cont_impl(ctx, a, true); } // ggml_reshape struct ggml_tensor * ggml_reshape( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) { GGML_ASSERT(ggml_is_contiguous(a)); GGML_ASSERT(ggml_is_contiguous(b)); GGML_ASSERT(ggml_nelements(a) == ggml_nelements(b)); bool is_node = false; if (a->grad || b->grad) { GGML_ASSERT(false); // TODO: implement backward is_node = true; } struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, b->n_dims, b->ne, a->data); result->op = GGML_OP_RESHAPE; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src0 = a; result->src1 = NULL; return result; } struct ggml_tensor * ggml_reshape_2d( struct ggml_context * ctx, struct ggml_tensor * a, int64_t ne0, int64_t ne1) { GGML_ASSERT(ggml_is_contiguous(a)); GGML_ASSERT(ggml_nelements(a) == ne0*ne1); bool is_node = false; if (a->grad) { GGML_ASSERT(false); // TODO: implement backward is_node = true; } const int64_t ne[2] = { ne0, ne1 }; struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, 2, ne, a->data); result->op = GGML_OP_RESHAPE; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src0 = a; result->src1 = NULL; return result; } struct ggml_tensor * ggml_reshape_3d( struct ggml_context * ctx, struct ggml_tensor * a, int64_t ne0, int64_t ne1, int64_t ne2) { GGML_ASSERT(ggml_is_contiguous(a)); GGML_ASSERT(ggml_nelements(a) == ne0*ne1*ne2); bool is_node = false; if (a->grad) { GGML_ASSERT(false); // TODO: implement backward is_node = true; } const int64_t ne[3] = { ne0, ne1, ne2 }; struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, 3, ne, a->data); result->op = GGML_OP_RESHAPE; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src0 = a; result->src1 = NULL; return result; } // ggml_view_1d struct ggml_tensor * ggml_view_1d( struct ggml_context * ctx, struct ggml_tensor * a, int64_t ne0, size_t offset) { if (a->grad) { GGML_ASSERT(false); // gradient propagation is not supported } struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, 1, &ne0, (char *) a->data + offset); result->op = GGML_OP_VIEW; result->grad = NULL; result->src0 = a; result->src1 = NULL; // TODO: maybe store the offset here? return result; } // ggml_view_2d struct ggml_tensor * ggml_view_2d( struct ggml_context * ctx, struct ggml_tensor * a, int64_t ne0, int64_t ne1, size_t nb1, size_t offset) { if (a->grad) { GGML_ASSERT(false); // gradient propagation is not supported } const int64_t ne[GGML_MAX_DIMS] = { ne0, ne1, 1, 1 }; struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, 2, ne, (char *) a->data + offset); result->nb[1] = nb1; result->nb[2] = result->nb[1]*ne1; result->nb[3] = result->nb[2]; result->op = GGML_OP_VIEW; result->grad = NULL; result->src0 = a; result->src1 = NULL; // TODO: maybe store the offset here? return result; } // ggml_view_3d struct ggml_tensor * ggml_view_3d( struct ggml_context * ctx, struct ggml_tensor * a, int64_t ne0, int64_t ne1, int64_t ne2, size_t nb1, size_t nb2, size_t offset) { if (a->grad) { GGML_ASSERT(false); // gradient propagation is not supported } const int64_t ne[GGML_MAX_DIMS] = { ne0, ne1, ne2, 1 }; struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, 3, ne, (char *) a->data + offset); result->nb[1] = nb1; result->nb[2] = nb2; result->nb[3] = result->nb[2]*ne2; result->op = GGML_OP_VIEW; result->grad = NULL; result->src0 = a; result->src1 = NULL; // TODO: maybe store the offset here? return result; } // ggml_permute struct ggml_tensor * ggml_permute( struct ggml_context * ctx, struct ggml_tensor * a, int axis0, int axis1, int axis2, int axis3) { GGML_ASSERT(axis0 >= 0 && axis0 < GGML_MAX_DIMS); GGML_ASSERT(axis1 >= 0 && axis1 < GGML_MAX_DIMS); GGML_ASSERT(axis2 >= 0 && axis2 < GGML_MAX_DIMS); GGML_ASSERT(axis3 >= 0 && axis3 < GGML_MAX_DIMS); GGML_ASSERT(axis0 != axis1); GGML_ASSERT(axis0 != axis2); GGML_ASSERT(axis0 != axis3); GGML_ASSERT(axis1 != axis2); GGML_ASSERT(axis1 != axis3); GGML_ASSERT(axis2 != axis3); bool is_node = false; if (a->grad) { GGML_ASSERT(false); // TODO: implement backward is_node = true; } struct ggml_tensor * result = ggml_view_tensor(ctx, a); int ne[GGML_MAX_DIMS]; int nb[GGML_MAX_DIMS]; ne[axis0] = a->ne[0]; ne[axis1] = a->ne[1]; ne[axis2] = a->ne[2]; ne[axis3] = a->ne[3]; nb[axis0] = a->nb[0]; nb[axis1] = a->nb[1]; nb[axis2] = a->nb[2]; nb[axis3] = a->nb[3]; result->ne[0] = ne[0]; result->ne[1] = ne[1]; result->ne[2] = ne[2]; result->ne[3] = ne[3]; result->nb[0] = nb[0]; result->nb[1] = nb[1]; result->nb[2] = nb[2]; result->nb[3] = nb[3]; result->op = GGML_OP_PERMUTE; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src0 = a; result->src1 = NULL; // TODO: maybe store the permutation here? return result; } // ggml_transpose struct ggml_tensor * ggml_transpose( struct ggml_context * ctx, struct ggml_tensor * a) { bool is_node = false; if (a->grad) { GGML_ASSERT(false); // TODO: implement backward is_node = true; } struct ggml_tensor * result = ggml_view_tensor(ctx, a); result->ne[0] = a->ne[1]; result->ne[1] = a->ne[0]; result->nb[0] = a->nb[1]; result->nb[1] = a->nb[0]; result->op = GGML_OP_TRANSPOSE; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src0 = a; result->src1 = NULL; return result; } // ggml_get_rows struct ggml_tensor * ggml_get_rows( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) { GGML_ASSERT(ggml_is_matrix(a) && ggml_is_vector(b) && b->type == GGML_TYPE_I32); bool is_node = false; if (a->grad || b->grad) { GGML_ASSERT(false); // TODO: implement backward is_node = true; } // TODO: implement non F32 return //struct ggml_tensor * result = ggml_new_tensor_2d(ctx, a->type, a->ne[0], b->ne[0]); struct ggml_tensor * result = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, a->ne[0], b->ne[0]); result->op = GGML_OP_GET_ROWS; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src0 = a; result->src1 = b; return result; } // ggml_diag_mask_inf struct ggml_tensor * ggml_diag_mask_inf( struct ggml_context * ctx, struct ggml_tensor * a, int n_past) { bool is_node = false; if (a->grad) { GGML_ASSERT(false); // TODO: implement backward is_node = true; } // TODO: when implement backward, fix this: //struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); struct ggml_tensor * result = ggml_view_tensor(ctx, a); struct ggml_tensor * b = ggml_new_i32(ctx, n_past); result->op = GGML_OP_DIAG_MASK_INF; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src0 = a; result->src1 = b; return result; } // ggml_soft_max struct ggml_tensor * ggml_soft_max( struct ggml_context * ctx, struct ggml_tensor * a) { bool is_node = false; if (a->grad) { GGML_ASSERT(false); // TODO: implement backward is_node = true; } // TODO: when implement backward, fix this: //struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); struct ggml_tensor * result = ggml_view_tensor(ctx, a); result->op = GGML_OP_SOFT_MAX; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src0 = a; result->src1 = NULL; return result; } // ggml_rope struct ggml_tensor * ggml_rope( struct ggml_context * ctx, struct ggml_tensor * a, int n_past, int n_dims, int mode) { GGML_ASSERT(n_past >= 0); bool is_node = false; if (a->grad) { GGML_ASSERT(false); // TODO: implement backward is_node = true; } // TODO: when implement backward, fix this: //struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); struct ggml_tensor * result = ggml_view_tensor(ctx, a); struct ggml_tensor * b = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 3); ((int32_t *) b->data)[0] = n_past; ((int32_t *) b->data)[1] = n_dims; ((int32_t *) b->data)[2] = mode; result->op = GGML_OP_ROPE; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src0 = a; result->src1 = b; return result; } // ggml_conv_1d_1s struct ggml_tensor * ggml_conv_1d_1s( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) { GGML_ASSERT(ggml_is_matrix(b)); GGML_ASSERT(a->ne[1] == b->ne[1]); GGML_ASSERT(a->ne[3] == 1); bool is_node = false; if (a->grad || b->grad) { GGML_ASSERT(false); // TODO: implement backward is_node = true; } const int64_t ne[4] = { b->ne[0], a->ne[2], 1, 1, }; struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 2, ne); result->op = GGML_OP_CONV_1D_1S; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src0 = a; result->src1 = b; return result; } // ggml_conv_1d_2s struct ggml_tensor * ggml_conv_1d_2s( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) { GGML_ASSERT(ggml_is_matrix(b)); GGML_ASSERT(a->ne[1] == b->ne[1]); GGML_ASSERT(a->ne[3] == 1); bool is_node = false; if (a->grad || b->grad) { GGML_ASSERT(false); // TODO: implement backward is_node = true; } const int64_t ne[4] = { b->ne[0]/2, a->ne[2], 1, 1, }; struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 2, ne); result->op = GGML_OP_CONV_1D_2S; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src0 = a; result->src1 = b; return result; } // ggml_flash_attn struct ggml_tensor * ggml_flash_attn( struct ggml_context * ctx, struct ggml_tensor * q, struct ggml_tensor * k, struct ggml_tensor * v, bool masked) { GGML_ASSERT(ggml_can_mul_mat(k, q)); // TODO: check if vT can be multiplied by (k*qT) bool is_node = false; if (q->grad || k->grad || v->grad) { GGML_ASSERT(false); // TODO: implement backward is_node = true; } //struct ggml_tensor * result = ggml_dup_tensor(ctx, q); struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, q->ne); result->op = GGML_OP_FLASH_ATTN; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src0 = q; result->src1 = k; result->opt[0] = v; result->opt[1] = ggml_new_i32(ctx, masked ? 1 : 0); return result; } // ggml_flash_ff struct ggml_tensor * ggml_flash_ff( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b0, struct ggml_tensor * b1, struct ggml_tensor * c0, struct ggml_tensor * c1) { GGML_ASSERT(ggml_can_mul_mat(b0, a)); // TODO: more checks bool is_node = false; if (a->grad || b0->grad || b1->grad || c0->grad || c1->grad) { GGML_ASSERT(false); // TODO: implement backward is_node = true; } //struct ggml_tensor * result = ggml_dup_tensor(ctx, a); struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, a->ne); result->op = GGML_OP_FLASH_FF; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src0 = a; result->src1 = b0; result->opt[0] = b1; result->opt[1] = c0; result->opt[2] = c1; return result; } // ggml_map_unary struct ggml_tensor * ggml_map_unary_impl_f32( struct ggml_context * ctx, struct ggml_tensor * a, const ggml_unary_op_f32_t fun, bool inplace) { bool is_node = false; if (!inplace && a->grad) { is_node = true; } struct ggml_tensor * addr_tensor = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, sizeof(void *) / sizeof(int32_t)); *((void (**)(void))addr_tensor->data) = (void (*)(void))fun; struct ggml_tensor *result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); result->op = GGML_OP_MAP_UNARY; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src0 = a; result->opt[0] = addr_tensor; return result; } struct ggml_tensor * ggml_map_unary_f32( struct ggml_context * ctx, struct ggml_tensor * a, const ggml_unary_op_f32_t fun) { return ggml_map_unary_impl_f32(ctx, a, fun, false); } struct ggml_tensor * ggml_map_unary_inplace_f32( struct ggml_context * ctx, struct ggml_tensor * a, const ggml_unary_op_f32_t fun) { return ggml_map_unary_impl_f32(ctx, a, fun, true); } // ggml_map_binary struct ggml_tensor * ggml_map_binary_impl_f32( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, const ggml_binary_op_f32_t fun, bool inplace) { GGML_ASSERT(ggml_are_same_shape(a, b)); bool is_node = false; if (!inplace && (a->grad || b->grad)) { is_node = true; } struct ggml_tensor * addr_tensor = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, sizeof(void *) / sizeof(int32_t)); *((void (**)(void))addr_tensor->data) = (void (*)(void))fun; struct ggml_tensor *result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); result->op = GGML_OP_MAP_BINARY; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src0 = a; result->src1 = b; result->opt[0] = addr_tensor; return result; } struct ggml_tensor * ggml_map_binary_f32( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, const ggml_binary_op_f32_t fun) { return ggml_map_binary_impl_f32(ctx, a, b, fun, false); } struct ggml_tensor * ggml_map_binary_inplace_f32( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, const ggml_binary_op_f32_t fun) { return ggml_map_binary_impl_f32(ctx, a, b, fun, true); } //////////////////////////////////////////////////////////////////////////////// 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(ggml_nelements(dst) == ggml_nelements(src0)); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } const int64_t ne00 = src0->ne[0]; const int64_t ne01 = src0->ne[1]; const int64_t ne02 = src0->ne[2]; const int64_t ne03 = src0->ne[3]; const int64_t ne0 = dst->ne[0]; const int64_t ne1 = dst->ne[1]; const int64_t ne2 = dst->ne[2]; const int64_t ne3 = dst->ne[3]; const size_t nb00 = src0->nb[0]; const size_t nb01 = src0->nb[1]; const size_t nb02 = src0->nb[2]; const size_t nb03 = src0->nb[3]; const size_t nb0 = dst->nb[0]; const size_t nb1 = dst->nb[1]; const size_t nb2 = dst->nb[2]; const size_t nb3 = dst->nb[3]; const int ith = params->ith; // thread index const int nth = params->nth; // number of threads if (ggml_is_contiguous(src0) && ggml_is_contiguous(dst) && src0->type == dst->type) { // parallelize by elements const int ne = ggml_nelements(dst); const int dr = (ne + nth - 1) / nth; const int ie0 = dr * ith; const int ie1 = MIN(ie0 + dr, ne); memcpy( ((char *) dst->data + ie0*nb0), ((char *) src0->data + ie0*nb00), (ie1 - ie0) * GGML_TYPE_SIZE[src0->type]); return; } // parallelize by rows const int nr = ne01; // number of rows per thread const int dr = (nr + nth - 1) / nth; // row range for this thread const int ir0 = dr * ith; const int ir1 = MIN(ir0 + dr, nr); if (src0->type == dst->type && ne00 == ne0 && nb00 == GGML_TYPE_SIZE[src0->type] && nb0 == GGML_TYPE_SIZE[dst->type]) { // copy by rows const size_t rs = ne00*nb00; for (int64_t i03 = 0; i03 < ne03; i03++) { for (int64_t i02 = 0; i02 < ne02; i02++) { for (int64_t i01 = ir0; i01 < ir1; i01++) { memcpy( ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3), ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03), rs); } } } return; } // TODO: add more special-case implementations for tensor shapes/strides that can benefit from memcpy if (ggml_is_contiguous(dst)) { if (nb00 == sizeof(ggml_fp16_t)) { if (dst->type == GGML_TYPE_F16) { size_t id = 0; const size_t rs = ne00 * nb00; char * dst_ptr = (char *) dst->data; for (int i03 = 0; i03 < ne03; i03++) { for (int i02 = 0; i02 < ne02; i02++) { id += rs * ir0; for (int i01 = ir0; i01 < ir1; i01++) { const char * src0_ptr = (char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03; memcpy(dst_ptr + id, src0_ptr, rs); id += rs; } id += rs * (ne01 - ir1); } } } else if (dst->type == GGML_TYPE_F32) { size_t id = 0; float * dst_ptr = (float *) dst->data; for (int i03 = 0; i03 < ne03; i03++) { for (int i02 = 0; i02 < ne02; i02++) { id += ne00 * ir0; for (int i01 = ir0; i01 < ir1; i01++) { const ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); for (int i00 = 0; i00 < ne00; i00++) { dst_ptr[id] = GGML_FP16_TO_FP32(src0_ptr[i00]); id++; } } id += ne00 * (ne01 - ir1); } } } else if (ggml_is_quantized(dst->type)) { quantize_row_q_t const quantize_row_q = quantize_fns[dst->type].quantize_row_q; float * src0_f32 = (float *) params->wdata + (ne00 + CACHE_LINE_SIZE_F32) * ith; size_t id = 0; size_t rs = nb0 * (ne00 / GGML_BLCK_SIZE[dst->type]); char * dst_ptr = (char *) dst->data; for (int i03 = 0; i03 < ne03; i03++) { for (int i02 = 0; i02 < ne02; i02++) { id += rs * ir0; for (int i01 = ir0; i01 < ir1; i01++) { const ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); for (int i00 = 0; i00 < ne00; i00++) { src0_f32[i00] = GGML_FP16_TO_FP32(src0_ptr[i00]); } quantize_row_q(src0_f32, dst_ptr + id, ne00); id += rs; } id += rs * (ne01 - ir1); } } } else { GGML_ASSERT(false); // TODO: implement } } else { //printf("%s: this is not optimal - fix me\n", __func__); if (dst->type == GGML_TYPE_F32) { size_t id = 0; float * dst_ptr = (float *) dst->data; for (int i03 = 0; i03 < ne03; i03++) { for (int i02 = 0; i02 < ne02; i02++) { id += ne00 * ir0; for (int i01 = ir0; i01 < ir1; i01++) { for (int i00 = 0; i00 < ne00; i00++) { const ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); dst_ptr[id] = GGML_FP16_TO_FP32(*src0_ptr); id++; } } id += ne00 * (ne01 - ir1); } } } else if (dst->type == GGML_TYPE_F16) { size_t id = 0; ggml_fp16_t * dst_ptr = (ggml_fp16_t *) dst->data; for (int i03 = 0; i03 < ne03; i03++) { for (int i02 = 0; i02 < ne02; i02++) { id += ne00 * ir0; for (int i01 = ir0; i01 < ir1; i01++) { for (int i00 = 0; i00 < ne00; i00++) { const ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); dst_ptr[id] = *src0_ptr; id++; } } id += ne00 * (ne01 - ir1); } } } else { GGML_ASSERT(false); // TODO: implement } } return; } // dst counters int64_t i10 = 0; int64_t i11 = 0; int64_t i12 = 0; int64_t i13 = 0; if (dst->type == GGML_TYPE_F16) { for (int64_t i03 = 0; i03 < ne03; i03++) { for (int64_t i02 = 0; i02 < ne02; i02++) { i10 += ne00 * ir0; while (i10 >= ne0) { i10 -= ne0; if (++i11 == ne1) { i11 = 0; if (++i12 == ne2) { i12 = 0; if (++i13 == ne3) { i13 = 0; } } } } for (int64_t i01 = ir0; i01 < ir1; i01++) { for (int64_t i00 = 0; i00 < ne00; i00++) { const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); char * dst_ptr = ((char *) dst->data + i10*nb0 + i11*nb1 + i12*nb2 + i13*nb3); memcpy(dst_ptr, src0_ptr, sizeof(ggml_fp16_t)); if (++i10 == ne00) { i10 = 0; if (++i11 == ne01) { i11 = 0; if (++i12 == ne02) { i12 = 0; if (++i13 == ne03) { i13 = 0; } } } } } } i10 += ne00 * (ne01 - ir1); while (i10 >= ne0) { i10 -= ne0; if (++i11 == ne1) { i11 = 0; if (++i12 == ne2) { i12 = 0; if (++i13 == ne3) { i13 = 0; } } } } } } } else if (dst->type == GGML_TYPE_F32) { for (int64_t i03 = 0; i03 < ne03; i03++) { for (int64_t i02 = 0; i02 < ne02; i02++) { i10 += ne00 * ir0; while (i10 >= ne0) { i10 -= ne0; if (++i11 == ne1) { i11 = 0; if (++i12 == ne2) { i12 = 0; if (++i13 == ne3) { i13 = 0; } } } } for (int64_t i01 = ir0; i01 < ir1; i01++) { for (int64_t i00 = 0; i00 < ne00; i00++) { const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); char * dst_ptr = ((char *) dst->data + i10*nb0 + i11*nb1 + i12*nb2 + i13*nb3); *(float *) dst_ptr = GGML_FP16_TO_FP32(*(const ggml_fp16_t *) src0_ptr); if (++i10 == ne0) { i10 = 0; if (++i11 == ne1) { i11 = 0; if (++i12 == ne2) { i12 = 0; if (++i13 == ne3) { i13 = 0; } } } } } } i10 += ne00 * (ne01 - ir1); while (i10 >= ne0) { i10 -= ne0; if (++i11 == ne1) { i11 = 0; if (++i12 == ne2) { i12 = 0; if (++i13 == ne3) { i13 = 0; } } } } } } } else { GGML_ASSERT(false); // TODO: implement } } static void ggml_compute_forward_dup_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0)); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } const int64_t ne00 = src0->ne[0]; const int64_t ne01 = src0->ne[1]; const int64_t ne02 = src0->ne[2]; const int64_t ne03 = src0->ne[3]; const int64_t ne0 = dst->ne[0]; const int64_t ne1 = dst->ne[1]; const int64_t ne2 = dst->ne[2]; const int64_t ne3 = dst->ne[3]; const size_t nb00 = src0->nb[0]; const size_t nb01 = src0->nb[1]; const size_t nb02 = src0->nb[2]; const size_t nb03 = src0->nb[3]; const size_t nb0 = dst->nb[0]; const size_t nb1 = dst->nb[1]; const size_t nb2 = dst->nb[2]; const size_t nb3 = dst->nb[3]; const int ith = params->ith; // thread index const int nth = params->nth; // number of threads if (ggml_is_contiguous(src0) && ggml_is_contiguous(dst) && src0->type == dst->type) { // parallelize by elements const int ne = ggml_nelements(dst); const int dr = (ne + nth - 1) / nth; const int ie0 = dr * ith; const int ie1 = MIN(ie0 + dr, ne); memcpy( ((char *) dst->data + ie0*nb0), ((char *) src0->data + ie0*nb00), (ie1 - ie0) * GGML_TYPE_SIZE[src0->type]); return; } // parallelize by rows const int nr = ne01; // number of rows per thread const int dr = (nr + nth - 1) / nth; // row range for this thread const int ir0 = dr * ith; const int ir1 = MIN(ir0 + dr, nr); if (src0->type == dst->type && ne00 == ne0 && nb00 == GGML_TYPE_SIZE[src0->type] && nb0 == GGML_TYPE_SIZE[dst->type]) { // copy by rows const size_t rs = ne00*nb00; for (int64_t i03 = 0; i03 < ne03; i03++) { for (int64_t i02 = 0; i02 < ne02; i02++) { for (int64_t i01 = ir0; i01 < ir1; i01++) { memcpy( ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3), ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03), rs); } } } return; } if (ggml_is_contiguous(dst)) { // TODO: simplify if (nb00 == sizeof(float)) { if (dst->type == GGML_TYPE_F32) { size_t id = 0; const size_t rs = ne00 * nb00; char * dst_ptr = (char *) dst->data; for (int i03 = 0; i03 < ne03; i03++) { for (int i02 = 0; i02 < ne02; i02++) { id += rs * ir0; for (int i01 = ir0; i01 < ir1; i01++) { const char * src0_ptr = (char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03; memcpy(dst_ptr + id, src0_ptr, rs); id += rs; } id += rs * (ne01 - ir1); } } } else if (dst->type == GGML_TYPE_F16) { size_t id = 0; ggml_fp16_t * dst_ptr = (ggml_fp16_t *) dst->data; for (int i03 = 0; i03 < ne03; i03++) { for (int i02 = 0; i02 < ne02; i02++) { id += ne00 * ir0; for (int i01 = ir0; i01 < ir1; i01++) { for (int i00 = 0; i00 < ne00; i00++) { const float * src0_ptr = (float *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); dst_ptr[id] = GGML_FP32_TO_FP16(*src0_ptr); id++; } } id += ne00 * (ne01 - ir1); } } } else if (ggml_is_quantized(dst->type)) { quantize_row_q_t const quantize_row_q = quantize_fns[dst->type].quantize_row_q; size_t id = 0; size_t rs = nb0 * (ne00 / GGML_BLCK_SIZE[dst->type]); char * dst_ptr = (char *) dst->data; for (int i03 = 0; i03 < ne03; i03++) { for (int i02 = 0; i02 < ne02; i02++) { id += rs * ir0; for (int i01 = ir0; i01 < ir1; i01++) { const float * src0_ptr = (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); quantize_row_q(src0_ptr, dst_ptr + id, ne00); id += rs; } id += rs * (ne01 - ir1); } } } else { GGML_ASSERT(false); // TODO: implement } } else { //printf("%s: this is not optimal - fix me\n", __func__); if (dst->type == GGML_TYPE_F32) { size_t id = 0; float * dst_ptr = (float *) dst->data; for (int i03 = 0; i03 < ne03; i03++) { for (int i02 = 0; i02 < ne02; i02++) { id += ne00 * ir0; for (int i01 = ir0; i01 < ir1; i01++) { for (int i00 = 0; i00 < ne00; i00++) { const float * src0_ptr = (float *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); dst_ptr[id] = *src0_ptr; id++; } } id += ne00 * (ne01 - ir1); } } } else if (dst->type == GGML_TYPE_F16) { size_t id = 0; ggml_fp16_t * dst_ptr = (ggml_fp16_t *) dst->data; for (int i03 = 0; i03 < ne03; i03++) { for (int i02 = 0; i02 < ne02; i02++) { id += ne00 * ir0; for (int i01 = ir0; i01 < ir1; i01++) { for (int i00 = 0; i00 < ne00; i00++) { const float * src0_ptr = (float *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); dst_ptr[id] = GGML_FP32_TO_FP16(*src0_ptr); id++; } } id += ne00 * (ne01 - ir1); } } } else { GGML_ASSERT(false); // TODO: implement } } return; } // dst counters int64_t i10 = 0; int64_t i11 = 0; int64_t i12 = 0; int64_t i13 = 0; if (dst->type == GGML_TYPE_F32) { for (int64_t i03 = 0; i03 < ne03; i03++) { for (int64_t i02 = 0; i02 < ne02; i02++) { i10 += ne00 * ir0; while (i10 >= ne0) { i10 -= ne0; if (++i11 == ne1) { i11 = 0; if (++i12 == ne2) { i12 = 0; if (++i13 == ne3) { i13 = 0; } } } } for (int64_t i01 = ir0; i01 < ir1; i01++) { for (int64_t i00 = 0; i00 < ne00; i00++) { const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); char * dst_ptr = ((char *) dst->data + i10*nb0 + i11*nb1 + i12*nb2 + i13*nb3); memcpy(dst_ptr, src0_ptr, sizeof(float)); if (++i10 == ne0) { i10 = 0; if (++i11 == ne1) { i11 = 0; if (++i12 == ne2) { i12 = 0; if (++i13 == ne3) { i13 = 0; } } } } } } i10 += ne00 * (ne01 - ir1); while (i10 >= ne0) { i10 -= ne0; if (++i11 == ne1) { i11 = 0; if (++i12 == ne2) { i12 = 0; if (++i13 == ne3) { i13 = 0; } } } } } } } else if (dst->type == GGML_TYPE_F16) { for (int64_t i03 = 0; i03 < ne03; i03++) { for (int64_t i02 = 0; i02 < ne02; i02++) { i10 += ne00 * ir0; while (i10 >= ne0) { i10 -= ne0; if (++i11 == ne1) { i11 = 0; if (++i12 == ne2) { i12 = 0; if (++i13 == ne3) { i13 = 0; } } } } for (int64_t i01 = ir0; i01 < ir1; i01++) { for (int64_t i00 = 0; i00 < ne00; i00++) { const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); char * dst_ptr = ((char *) dst->data + i10*nb0 + i11*nb1 + i12*nb2 + i13*nb3); *(ggml_fp16_t *) dst_ptr = GGML_FP32_TO_FP16(*(const float *) src0_ptr); if (++i10 == ne0) { i10 = 0; if (++i11 == ne1) { i11 = 0; if (++i12 == ne2) { i12 = 0; if (++i13 == ne3) { i13 = 0; } } } } } } i10 += ne00 * (ne01 - ir1); while (i10 >= ne0) { i10 -= ne0; if (++i11 == ne1) { i11 = 0; if (++i12 == ne2) { i12 = 0; if (++i13 == ne3) { i13 = 0; } } } } } } } else { GGML_ASSERT(false); // TODO: implement } } static void ggml_compute_forward_dup( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F16: { ggml_compute_forward_dup_f16(params, src0, dst); } break; case GGML_TYPE_F32: { ggml_compute_forward_dup_f32(params, src0, dst); } break; default: { GGML_ASSERT(false); } break; } } // ggml_compute_forward_add static void ggml_compute_forward_add_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { GGML_ASSERT(ggml_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)) { for (int j = ith; j < n; j += nth) { #ifdef GGML_USE_ACCELERATE vDSP_vadd( (float *) ((char *) src0->data + j*nb01), 1, (float *) ((char *) src1->data + j*nb11), 1, (float *) ((char *) dst->data + j*nb1), 1, nc); #else ggml_vec_add_f32(nc, (float *) ((char *) dst->data + j*nb1), (float *) ((char *) src0->data + j*nb01), (float *) ((char *) src1->data + j*nb11)); #endif } } 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_f16_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { GGML_ASSERT(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } const int ith = params->ith; const int nth = params->nth; const int 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(src0->type == GGML_TYPE_F16); GGML_ASSERT(src1->type == GGML_TYPE_F32); GGML_ASSERT(dst->type == GGML_TYPE_F16); GGML_ASSERT( nb0 == sizeof(ggml_fp16_t)); GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); if (nb10 == sizeof(float)) { for (int j = ith; j < n; j += nth) { ggml_fp16_t * dst_ptr = (ggml_fp16_t *) ((char *) dst->data + j*nb1); ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((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] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(src0_ptr[i]) + *src1_ptr); } } } else { // src1 is not contiguous GGML_ASSERT(false); } } static void ggml_compute_forward_add_f16_f16( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { GGML_ASSERT(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } const int ith = params->ith; const int nth = params->nth; const int 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(src0->type == GGML_TYPE_F16); GGML_ASSERT(src1->type == GGML_TYPE_F16); GGML_ASSERT(dst->type == GGML_TYPE_F16); GGML_ASSERT( nb0 == sizeof(ggml_fp16_t)); GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); if (nb10 == sizeof(ggml_fp16_t)) { for (int j = ith; j < n; j += nth) { ggml_fp16_t * dst_ptr = (ggml_fp16_t *) ((char *) dst->data + j*nb1); ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + j*nb01); for (int i = 0; i < nc; i++) { ggml_fp16_t * src1_ptr = (ggml_fp16_t *) ((char *) src1->data + j*nb11 + i*nb10); dst_ptr[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(src0_ptr[i]) + GGML_FP16_TO_FP32(*src1_ptr)); } } } else { // src1 is not contiguous GGML_ASSERT(false); } } static void ggml_compute_forward_add_q_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { GGML_ASSERT(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } const int64_t ne00 = src0->ne[0]; const int64_t ne01 = src0->ne[1]; const int64_t ne02 = src0->ne[2]; const int64_t ne03 = src0->ne[3]; //const int64_t ne10 = src1->ne[0]; //const int64_t ne11 = src1->ne[1]; const int64_t ne12 = src1->ne[2]; const int64_t ne13 = src1->ne[3]; //const int64_t ne0 = dst->ne[0]; //const int64_t ne1 = dst->ne[1]; const int64_t ne2 = dst->ne[2]; const int64_t ne3 = dst->ne[3]; const int nb00 = src0->nb[0]; const int nb01 = src0->nb[1]; const int nb02 = src0->nb[2]; const int nb03 = src0->nb[3]; const int nb10 = src1->nb[0]; const int nb11 = src1->nb[1]; const int nb12 = src1->nb[2]; const int nb13 = src1->nb[3]; const int nb0 = dst->nb[0]; const int nb1 = dst->nb[1]; const int nb2 = dst->nb[2]; const int nb3 = dst->nb[3]; const int ith = params->ith; const int nth = params->nth; GGML_ASSERT(ne02 == ne12); GGML_ASSERT(ne03 == ne13); GGML_ASSERT(ne2 == ne12); GGML_ASSERT(ne3 == ne13); const enum ggml_type type = src0->type; dequantize_row_q_t const dequantize_row_q = quantize_fns[type].dequantize_row_q; quantize_row_q_t const quantize_row_q = quantize_fns[type].quantize_row_q; // we don't support permuted src0 or src1 GGML_ASSERT(nb00 == (int) GGML_TYPE_SIZE[type]); GGML_ASSERT(nb10 == sizeof(float)); // dst cannot be transposed or permuted GGML_ASSERT(nb0 <= nb1); GGML_ASSERT(nb1 <= nb2); GGML_ASSERT(nb2 <= nb3); GGML_ASSERT(ggml_is_quantized(src0->type)); GGML_ASSERT(dst->type == src0->type); GGML_ASSERT(src1->type == GGML_TYPE_F32); // 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); float * wdata = (float *) params->wdata + (ne00 + CACHE_LINE_SIZE_F32) * ith; for (int ir = ir0; ir < ir1; ++ir) { // src0 indices const int i03 = ir/(ne02*ne01); const int i02 = (ir - i03*ne02*ne01)/ne01; const int i01 = (ir - i03*ne02*ne01 - i02*ne01); // src1 and dst are same shape as src0 => same indices const int i13 = i03; const int i12 = i02; const int i11 = i01; const int i3 = i03; const int i2 = i02; const int i1 = i01; void * src0_row = (void *) ((char *) src0->data + (i01*nb01 + i02*nb02 + i03*nb03)); float * src1_row = (float *)((char *) src1->data + (i11*nb11 + i12*nb12 + i13*nb13)); void * dst_row = (void *) ((char *) dst->data + ( i1*nb1 + i2*nb2 + i3*nb0)); assert(ne00 % 32 == 0); // unquantize row from src0 to temp buffer dequantize_row_q(src0_row, wdata, ne00); // add src1 ggml_vec_acc_f32(ne00, wdata, src1_row); // quantize row to dst quantize_row_q(wdata, dst_row, ne00); } } static void ggml_compute_forward_add( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_add_f32(params, src0, src1, dst); } break; case GGML_TYPE_F16: { if (src1->type == GGML_TYPE_F16) { ggml_compute_forward_add_f16_f16(params, src0, src1, dst); } else if (src1->type == GGML_TYPE_F32) { ggml_compute_forward_add_f16_f32(params, src0, src1, dst); } else { GGML_ASSERT(false); } } break; case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_1: case GGML_TYPE_Q4_2: case GGML_TYPE_Q4_3: { ggml_compute_forward_add_q_f32(params, src0, src1, dst); } break; default: { GGML_ASSERT(false); } break; } } // ggml_compute_forward_sub static void ggml_compute_forward_sub_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { assert(params->ith == 0); assert(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } const int 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; default: { GGML_ASSERT(false); } break; } } // ggml_compute_forward_mul static void ggml_compute_forward_mul_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { 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; default: { GGML_ASSERT(false); } break; } } // ggml_compute_forward_div static void ggml_compute_forward_div_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { assert(params->ith == 0); assert(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } const int 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; default: { GGML_ASSERT(false); } break; } } // ggml_compute_forward_sqr static void ggml_compute_forward_sqr_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { assert(params->ith == 0); assert(ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } const int n = ggml_nrows(src0); const int nc = src0->ne[0]; assert( dst->nb[0] == sizeof(float)); assert(src0->nb[0] == sizeof(float)); for (int i = 0; i < n; i++) { ggml_vec_sqr_f32(nc, (float *) ((char *) dst->data + i*( dst->nb[1])), (float *) ((char *) src0->data + i*(src0->nb[1]))); } } static void ggml_compute_forward_sqr( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_sqr_f32(params, src0, dst); } break; default: { GGML_ASSERT(false); } break; } } // ggml_compute_forward_sqrt static void ggml_compute_forward_sqrt_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { assert(params->ith == 0); assert(ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } const int n = ggml_nrows(src0); const int nc = src0->ne[0]; assert( dst->nb[0] == sizeof(float)); assert(src0->nb[0] == sizeof(float)); for (int i = 0; i < n; i++) { ggml_vec_sqrt_f32(nc, (float *) ((char *) dst->data + i*( dst->nb[1])), (float *) ((char *) src0->data + i*(src0->nb[1]))); } } static void ggml_compute_forward_sqrt( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_sqrt_f32(params, src0, dst); } break; default: { GGML_ASSERT(false); } break; } } // ggml_compute_forward_sum static void ggml_compute_forward_sum_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { assert(params->ith == 0); assert(ggml_is_scalar(dst)); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } assert(ggml_is_scalar(dst)); assert(src0->nb[0] == sizeof(float)); const int64_t ne00 = src0->ne[0]; const int64_t ne01 = src0->ne[1]; const int64_t ne02 = src0->ne[2]; const int64_t ne03 = src0->ne[3]; const size_t nb01 = src0->nb[1]; const size_t nb02 = src0->nb[2]; const size_t nb03 = src0->nb[3]; for (int64_t i03 = 0; i03 < ne03; i03++) { for (int64_t i02 = 0; i02 < ne02; i02++) { for (int64_t i01 = 0; i01 < ne01; i01++) { ggml_vec_sum_f32(ne00, (float *) (dst->data), (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03)); } } } } static void ggml_compute_forward_sum( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_sum_f32(params, src0, dst); } break; default: { GGML_ASSERT(false); } break; } } // ggml_compute_forward_mean static void ggml_compute_forward_mean_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { assert(params->ith == 0); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } assert(src0->nb[0] == sizeof(float)); const int64_t ne00 = src0->ne[0]; const int64_t ne01 = src0->ne[1]; const int64_t ne02 = src0->ne[2]; const int64_t ne03 = src0->ne[3]; const size_t nb01 = src0->nb[1]; const size_t nb02 = src0->nb[2]; const size_t nb03 = src0->nb[3]; const int64_t ne0 = dst->ne[0]; const int64_t ne1 = dst->ne[1]; const int64_t ne2 = dst->ne[2]; const int64_t ne3 = dst->ne[3]; assert(ne0 == 1); assert(ne1 == ne01); assert(ne2 == ne02); assert(ne3 == ne03); UNUSED(ne0); UNUSED(ne1); UNUSED(ne2); UNUSED(ne3); const size_t nb1 = dst->nb[1]; const size_t nb2 = dst->nb[2]; const size_t nb3 = dst->nb[3]; for (int64_t i03 = 0; i03 < ne03; i03++) { for (int64_t i02 = 0; i02 < ne02; i02++) { for (int64_t i01 = 0; i01 < ne01; i01++) { ggml_vec_sum_f32(ne00, (float *) ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3), (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03)); *(float *) ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3) /= (float) ne00; } } } } static void ggml_compute_forward_mean( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_mean_f32(params, src0, dst); } break; default: { GGML_ASSERT(false); } break; } } // ggml_compute_forward_repeat static void ggml_compute_forward_repeat_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { 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; default: { GGML_ASSERT(false); } break; } } // ggml_compute_forward_abs static void ggml_compute_forward_abs_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { assert(params->ith == 0); assert(ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } const int n = ggml_nrows(src0); const int nc = src0->ne[0]; assert(dst->nb[0] == sizeof(float)); assert(src0->nb[0] == sizeof(float)); for (int i = 0; i < n; i++) { ggml_vec_abs_f32(nc, (float *) ((char *) dst->data + i*( dst->nb[1])), (float *) ((char *) src0->data + i*(src0->nb[1]))); } } static void ggml_compute_forward_abs( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_abs_f32(params, src0, dst); } break; default: { GGML_ASSERT(false); } break; } } // ggml_compute_forward_sgn static void ggml_compute_forward_sgn_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { assert(params->ith == 0); assert(ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } const int n = ggml_nrows(src0); const int nc = src0->ne[0]; assert(dst->nb[0] == sizeof(float)); assert(src0->nb[0] == sizeof(float)); for (int i = 0; i < n; i++) { ggml_vec_sgn_f32(nc, (float *) ((char *) dst->data + i*( dst->nb[1])), (float *) ((char *) src0->data + i*(src0->nb[1]))); } } static void ggml_compute_forward_sgn( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_sgn_f32(params, src0, dst); } break; default: { GGML_ASSERT(false); } break; } } // ggml_compute_forward_neg static void ggml_compute_forward_neg_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { assert(params->ith == 0); assert(ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } const int n = ggml_nrows(src0); const int nc = src0->ne[0]; assert(dst->nb[0] == sizeof(float)); assert(src0->nb[0] == sizeof(float)); for (int i = 0; i < n; i++) { ggml_vec_neg_f32(nc, (float *) ((char *) dst->data + i*( dst->nb[1])), (float *) ((char *) src0->data + i*(src0->nb[1]))); } } static void ggml_compute_forward_neg( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_neg_f32(params, src0, dst); } break; default: { GGML_ASSERT(false); } break; } } // ggml_compute_forward_step static void ggml_compute_forward_step_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { assert(params->ith == 0); assert(ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } const int n = ggml_nrows(src0); const int nc = src0->ne[0]; assert(dst->nb[0] == sizeof(float)); assert(src0->nb[0] == sizeof(float)); for (int i = 0; i < n; i++) { ggml_vec_step_f32(nc, (float *) ((char *) dst->data + i*( dst->nb[1])), (float *) ((char *) src0->data + i*(src0->nb[1]))); } } static void ggml_compute_forward_step( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_step_f32(params, src0, dst); } break; default: { GGML_ASSERT(false); } break; } } // ggml_compute_forward_relu static void ggml_compute_forward_relu_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { assert(params->ith == 0); assert(ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } const int n = ggml_nrows(src0); const int nc = src0->ne[0]; assert(dst->nb[0] == sizeof(float)); assert(src0->nb[0] == sizeof(float)); for (int i = 0; i < n; i++) { ggml_vec_relu_f32(nc, (float *) ((char *) dst->data + i*( dst->nb[1])), (float *) ((char *) src0->data + i*(src0->nb[1]))); } } static void ggml_compute_forward_relu( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_relu_f32(params, src0, dst); } break; default: { GGML_ASSERT(false); } break; } } // ggml_compute_forward_gelu static void ggml_compute_forward_gelu_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { GGML_ASSERT(ggml_is_contiguous(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; default: { 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; default: { GGML_ASSERT(false); } break; } } // ggml_compute_forward_norm static void ggml_compute_forward_norm_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { GGML_ASSERT(ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } GGML_ASSERT(src0->nb[0] == sizeof(float)); const int ith = params->ith; const int nth = params->nth; const int64_t ne00 = src0->ne[0]; const int64_t ne01 = src0->ne[1]; const int64_t ne02 = src0->ne[2]; const int64_t ne03 = src0->ne[3]; const size_t nb01 = src0->nb[1]; const size_t nb02 = src0->nb[2]; const size_t nb03 = src0->nb[3]; const size_t nb1 = dst->nb[1]; const size_t nb2 = dst->nb[2]; const size_t nb3 = dst->nb[3]; const float eps = 1e-5f; // TODO: make this a parameter // TODO: optimize for (int64_t i03 = 0; i03 < ne03; i03++) { for (int64_t i02 = 0; i02 < ne02; i02++) { for (int64_t i01 = ith; i01 < ne01; i01 += nth) { const float * x = (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); ggml_float sum = 0.0; for (int64_t i00 = 0; i00 < ne00; i00++) { sum += (ggml_float)x[i00]; } float mean = sum/ne00; float * y = (float *) ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3); ggml_float sum2 = 0.0; for (int64_t i00 = 0; i00 < ne00; i00++) { float v = x[i00] - mean; y[i00] = v; sum2 += (ggml_float)(v*v); } float variance = sum2/ne00; const float scale = 1.0f/sqrtf(variance + eps); ggml_vec_scale_f32(ne00, y, scale); } } } } static void ggml_compute_forward_norm( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_norm_f32(params, src0, dst); } break; default: { GGML_ASSERT(false); } break; } } static void ggml_compute_forward_rms_norm_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { GGML_ASSERT(ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } GGML_ASSERT(src0->nb[0] == sizeof(float)); const int ith = params->ith; const int nth = params->nth; const int64_t ne00 = src0->ne[0]; const int64_t ne01 = src0->ne[1]; const int64_t ne02 = src0->ne[2]; const int64_t ne03 = src0->ne[3]; const size_t nb01 = src0->nb[1]; const size_t nb02 = src0->nb[2]; const size_t nb03 = src0->nb[3]; const size_t nb1 = dst->nb[1]; const size_t nb2 = dst->nb[2]; const size_t nb3 = dst->nb[3]; const float eps = 1e-6f; // TODO: make this a parameter // TODO: optimize for (int64_t i03 = 0; i03 < ne03; i03++) { for (int64_t i02 = 0; i02 < ne02; i02++) { for (int64_t i01 = ith; i01 < ne01; i01 += nth) { const float * x = (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); ggml_float sum = 0.0; for (int64_t i00 = 0; i00 < ne00; i00++) { sum += (ggml_float)(x[i00] * x[i00]); } float mean = sum/ne00; float * y = (float *) ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3); memcpy(y, x, ne00 * sizeof(float)); // for (int i00 = 0; i00 < ne00; i00++) { // y[i00] = x[i00]; // } const float scale = 1.0f/sqrtf(mean + eps); ggml_vec_scale_f32(ne00, y, scale); } } } } static void ggml_compute_forward_rms_norm( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_rms_norm_f32(params, src0, dst); } break; default: { GGML_ASSERT(false); } break; } } // ggml_compute_forward_mul_mat #if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CUBLAS) // helper function to determine if it is better to use BLAS or not // for large matrices, BLAS is faster static bool ggml_compute_forward_mul_mat_use_blas( const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { //const int64_t ne00 = src0->ne[0]; //const int64_t ne01 = src0->ne[1]; const int64_t ne10 = src1->ne[0]; const int64_t ne0 = dst->ne[0]; const int64_t ne1 = dst->ne[1]; // TODO: find the optimal values for these if (ggml_is_contiguous(src0) && ggml_is_contiguous(src1) && ((ne0 >= 32 && ne1 >= 32 && ne10 >= 32))) { /*printf("BLAS: %d %d %d %d %d\n", ne0, ne1, ne10, ne00, ne01);*/ return true; } return false; } #endif static void ggml_compute_forward_mul_mat_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { int64_t t0 = ggml_perf_time_us(); UNUSED(t0); const int64_t ne00 = src0->ne[0]; const int64_t ne01 = src0->ne[1]; const int64_t ne02 = src0->ne[2]; const int64_t ne03 = src0->ne[3]; #if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CUBLAS) const int64_t ne10 = src1->ne[0]; #endif const int64_t ne11 = src1->ne[1]; #ifndef NDEBUG const int64_t ne12 = src1->ne[2]; const int64_t ne13 = src1->ne[3]; const int64_t ne0 = dst->ne[0]; const int64_t ne1 = dst->ne[1]; const int64_t ne2 = dst->ne[2]; const int64_t ne3 = dst->ne[3]; const int nb00 = src0->nb[0]; #endif const int nb01 = src0->nb[1]; const int nb02 = src0->nb[2]; const int nb03 = src0->nb[3]; #ifndef NDEBUG const int nb10 = src1->nb[0]; #endif const int nb11 = src1->nb[1]; const int nb12 = src1->nb[2]; const int nb13 = src1->nb[3]; const int nb0 = dst->nb[0]; const int nb1 = dst->nb[1]; const int nb2 = dst->nb[2]; const int nb3 = dst->nb[3]; const int ith = params->ith; const int nth = params->nth; assert(ne02 == ne12); assert(ne03 == ne13); assert(ne2 == ne12); assert(ne3 == ne13); // we don't support permuted src0 or src1 assert(nb00 == sizeof(float)); assert(nb10 == sizeof(float)); // dst cannot be transposed or permuted assert(nb0 == sizeof(float)); assert(nb0 <= nb1); assert(nb1 <= nb2); assert(nb2 <= nb3); assert(ne0 == ne01); assert(ne1 == ne11); assert(ne2 == ne02); assert(ne3 == ne03); // nb01 >= nb00 - src0 is not transposed // compute by src0 rows #if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CUBLAS) if (ggml_compute_forward_mul_mat_use_blas(src0, src1, dst)) { if (params->ith != 0) { return; } if (params->type == GGML_TASK_INIT) { return; } if (params->type == GGML_TASK_FINALIZE) { return; } #if defined(GGML_USE_CUBLAS) const float alpha = 1.0f; const float beta = 0.0f; const int x_ne = ne01 * ne10; const int y_ne = ne11 * ne10; const int d_ne = ne11 * ne01; size_t x_size, y_size, d_size; float *d_X = ggml_cuda_pool_malloc(sizeof(float) * x_ne, &x_size); float *d_Y = ggml_cuda_pool_malloc(sizeof(float) * y_ne, &y_size); float *d_D = ggml_cuda_pool_malloc(sizeof(float) * d_ne, &d_size); #endif for (int64_t i03 = 0; i03 < ne03; i03++) { for (int64_t i02 = 0; i02 < ne02; i02++) { const float * x = (float *) ((char *) src0->data + i02*nb02 + i03*nb03); const float * y = (float *) ((char *) src1->data + i02*nb12 + i03*nb13); float * d = (float *) ((char *) dst->data + i02*nb2 + i03*nb3); #if defined(GGML_USE_CUBLAS) // copy data to device CUDA_CHECK(cudaMemcpyAsync(d_X, x, sizeof(float) * x_ne, cudaMemcpyHostToDevice, g_cudaStream)); CUDA_CHECK(cudaMemcpyAsync(d_Y, y, sizeof(float) * y_ne, cudaMemcpyHostToDevice, g_cudaStream)); // compute CUBLAS_CHECK( cublasSgemm(g_cublasH, CUBLAS_OP_T, CUBLAS_OP_N, ne01, ne11, ne10, &alpha, d_X, ne00, d_Y, ne10, &beta, d_D, ne01)); // copy data to host CUDA_CHECK(cudaMemcpyAsync(d, d_D, sizeof(float) * d_ne, cudaMemcpyDeviceToHost, g_cudaStream)); #else // zT = y * xT cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasTrans, ne11, ne01, ne10, 1.0f, y, ne10, x, ne00, 0.0f, d, ne01); #endif } } #if defined(GGML_USE_CUBLAS) CUDA_CHECK(cudaStreamSynchronize(g_cudaStream)); ggml_cuda_pool_free(d_X, x_size); ggml_cuda_pool_free(d_Y, y_size); ggml_cuda_pool_free(d_D, d_size); #endif //printf("CBLAS F32 = %f ms, %d x %d x %d x %d\n", (ggml_perf_time_us() - t0)/1000.0, ne0, ne1, ne2, ne3); return; } #endif if (params->type == GGML_TASK_INIT) { return; } if (params->type == GGML_TASK_FINALIZE) { return; } // parallelize by src0 rows using ggml_vec_dot_f32 // total rows in src0 const int nr = ne01*ne02*ne03; // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); for (int ir = ir0; ir < ir1; ++ir) { // src0 indices const int i03 = ir/(ne02*ne01); const int i02 = (ir - i03*ne02*ne01)/ne01; const int i01 = (ir - i03*ne02*ne01 - i02*ne01); for (int64_t ic = 0; ic < ne11; ++ic) { // src1 indices const int i13 = i03; const int i12 = i02; const int i11 = ic; // dst indices const int i0 = i01; const int i1 = i11; const int i2 = i02; const int i3 = i03; ggml_vec_dot_f32(ne00, (float *) ((char *) dst->data + (i0*nb0 + i1*nb1 + i2*nb2 + i3*nb3)), (float *) ((char *) src0->data + (i01*nb01 + i02*nb02 + i03*nb03)), (float *) ((char *) src1->data + (i11*nb11 + i12*nb12 + i13*nb13))); } } //int64_t t1 = ggml_perf_time_us(); //static int64_t acc = 0; //acc += t1 - t0; //if (t1 - t0 > 10) { // printf("\n"); // printf("ne00 = %5d, ne01 = %5d, ne02 = %5d, ne03 = %5d\n", ne00, ne01, ne02, ne03); // printf("nb00 = %5d, nb01 = %5d, nb02 = %5d, nb03 = %5d\n", nb00, nb01, nb02, nb03); // printf("ne10 = %5d, ne11 = %5d, ne12 = %5d, ne13 = %5d\n", ne10, ne11, ne12, ne13); // printf("nb10 = %5d, nb11 = %5d, nb12 = %5d, nb13 = %5d\n", nb10, nb11, nb12, nb13); // printf("XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX task %d/%d: %d us, acc = %d\n", ith, nth, (int) (t1 - t0), (int) acc); //} } static void ggml_compute_forward_mul_mat_f16_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { int64_t t0 = ggml_perf_time_us(); UNUSED(t0); const int64_t ne00 = src0->ne[0]; const int64_t ne01 = src0->ne[1]; const int64_t ne02 = src0->ne[2]; const int64_t ne03 = src0->ne[3]; const int64_t ne10 = src1->ne[0]; const int64_t ne11 = src1->ne[1]; const int64_t ne12 = src1->ne[2]; const int64_t ne13 = src1->ne[3]; const int64_t ne0 = dst->ne[0]; const int64_t ne1 = dst->ne[1]; const int64_t ne2 = dst->ne[2]; const int64_t ne3 = dst->ne[3]; //const int64_t ne = ne0*ne1*ne2*ne3; const int nb00 = src0->nb[0]; const int nb01 = src0->nb[1]; const int nb02 = src0->nb[2]; const int nb03 = src0->nb[3]; const int nb10 = src1->nb[0]; const int nb11 = src1->nb[1]; const int nb12 = src1->nb[2]; const int nb13 = src1->nb[3]; const int nb0 = dst->nb[0]; const int nb1 = dst->nb[1]; const int nb2 = dst->nb[2]; const int nb3 = dst->nb[3]; const int ith = params->ith; const int nth = params->nth; GGML_ASSERT(ne02 == ne12); GGML_ASSERT(ne03 == ne13); GGML_ASSERT(ne2 == ne12); GGML_ASSERT(ne3 == ne13); // TODO: we don't support permuted src0 GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); // dst cannot be transposed or permuted GGML_ASSERT(nb0 == sizeof(float)); GGML_ASSERT(nb0 <= nb1); GGML_ASSERT(nb1 <= nb2); GGML_ASSERT(nb2 <= nb3); GGML_ASSERT(ne0 == ne01); GGML_ASSERT(ne1 == ne11); GGML_ASSERT(ne2 == ne02); GGML_ASSERT(ne3 == ne03); // nb01 >= nb00 - src0 is not transposed // compute by src0 rows #if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CUBLAS) 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; } #if defined(GGML_USE_CUBLAS) ggml_fp16_t * const wdata = params->wdata; const float alpha = 1.0f; const float beta = 0.0f; const int x_ne = ne01 * ne10; const int y_ne = ne11 * ne10; const int d_ne = ne11 * ne01; size_t x_size, y_size, d_size; float *d_X = ggml_cuda_pool_malloc(sizeof(float) * x_ne, &x_size); float *d_Y = ggml_cuda_pool_malloc(sizeof(float) * y_ne, &y_size); float *d_D = ggml_cuda_pool_malloc(sizeof(float) * d_ne, &d_size); #else float * const wdata = params->wdata; #endif for (int64_t i03 = 0; i03 < ne03; i03++) { for (int64_t i02 = 0; i02 < ne02; i02++) { #if defined(GGML_USE_CUBLAS) // with cuBlAS, instead of converting src0 to fp32, we convert src1 to fp16 { size_t id = 0; for (int64_t i01 = 0; i01 < ne11; ++i01) { for (int64_t i00 = 0; i00 < ne10; ++i00) { wdata[id++] = GGML_FP32_TO_FP16(*(float *) ((char *) src1->data + i03*nb13 + i02*nb12 + i01*nb11 + i00*nb10)); } } } #else { size_t id = 0; for (int64_t i01 = 0; i01 < ne01; ++i01) { for (int64_t i00 = 0; i00 < ne00; ++i00) { wdata[id++] = GGML_FP16_TO_FP32(*(ggml_fp16_t *) ((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01 + i00*nb00)); } } } #endif #if defined(GGML_USE_CUBLAS) const ggml_fp16_t * x = (ggml_fp16_t *) ((char *) src0->data + i02*nb02 + i03*nb03); const ggml_fp16_t * y = (ggml_fp16_t *) wdata; float * d = (float *) ((char *) dst->data + i02*nb2 + i03*nb3); // copy data to device CUDA_CHECK(cudaMemcpyAsync(d_X, x, sizeof(ggml_fp16_t) * x_ne, cudaMemcpyHostToDevice, g_cudaStream)); CUDA_CHECK(cudaMemcpyAsync(d_Y, y, sizeof(ggml_fp16_t) * y_ne, cudaMemcpyHostToDevice, g_cudaStream)); // compute CUBLAS_CHECK( cublasGemmEx(g_cublasH, CUBLAS_OP_T, CUBLAS_OP_N, ne01, ne11, ne10, &alpha, d_X, CUDA_R_16F, ne00, d_Y, CUDA_R_16F, ne10, &beta, d_D, CUDA_R_32F, ne01, CUBLAS_COMPUTE_32F, CUBLAS_GEMM_DEFAULT)); // copy data to host CUDA_CHECK(cudaMemcpyAsync(d, d_D, sizeof(float) * d_ne, cudaMemcpyDeviceToHost, g_cudaStream)); #else const float * x = wdata; const float * y = (float *) ((char *) src1->data + i02*nb12 + i03*nb13); float * d = (float *) ((char *) dst->data + i02*nb2 + i03*nb3); // zT = y * xT cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasTrans, ne11, ne01, ne10, 1.0f, y, ne10, x, ne00, 0.0f, d, ne01); #endif } } #if defined(GGML_USE_CUBLAS) CUDA_CHECK(cudaStreamSynchronize(g_cudaStream)); ggml_cuda_pool_free(d_X, x_size); ggml_cuda_pool_free(d_Y, y_size); ggml_cuda_pool_free(d_D, d_size); #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) { ggml_fp16_t * const wdata = params->wdata; size_t id = 0; for (int64_t i13 = 0; i13 < ne13; ++i13) { for (int64_t i12 = 0; i12 < ne12; ++i12) { for (int64_t i11 = 0; i11 < ne11; ++i11) { for (int64_t i10 = 0; i10 < ne10; ++i10) { wdata[id++] = GGML_FP32_TO_FP16(*(float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11 + i10*nb10)); } } } } GGML_ASSERT(id*sizeof(ggml_fp16_t) <= params->wsize); return; } if (params->type == GGML_TASK_FINALIZE) { return; } // fp16 -> half the size, so divide by 2 // TODO: do not support transposed src1 assert(nb10/2 == sizeof(ggml_fp16_t)); // parallelize by src0 rows using ggml_vec_dot_f16 // total rows in src0 const int nr = ne01*ne02*ne03; // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); ggml_fp16_t * wdata = params->wdata; for (int ir = ir0; ir < ir1; ++ir) { // src0 indices const int i03 = ir/(ne02*ne01); const int i02 = (ir - i03*ne02*ne01)/ne01; const int i01 = (ir - i03*ne02*ne01 - i02*ne01); const int i13 = i03; const int i12 = i02; const int i0 = i01; const int i2 = i02; const int i3 = i03; ggml_fp16_t * src0_row = (ggml_fp16_t *) ((char *) src0->data + (i01*nb01 + i02*nb02 + i03*nb03)); ggml_fp16_t * src1_col = wdata + ( 0 + i12*ne11 + i13*ne12*ne11)*ne00; float * dst_col = (float *) ((char *) dst->data + (i0*nb0 + 0*nb1 + i2*nb2 + i3*nb3)); for (int64_t ic = 0; ic < ne11; ++ic) { ggml_vec_dot_f16(ne00, &dst_col[ic*ne0], src0_row, src1_col + ic*ne00); } } //int64_t t1 = ggml_time_us(); //static int64_t acc = 0; //acc += t1 - t0; //if (t1 - t0 > 10) { // printf("\n"); // printf("ne00 = %5d, ne01 = %5d, ne02 = %5d, ne03 = %5d\n", ne00, ne01, ne02, ne03); // printf("nb00 = %5d, nb01 = %5d, nb02 = %5d, nb03 = %5d\n", nb00, nb01, nb02, nb03); // printf("ne10 = %5d, ne11 = %5d, ne12 = %5d, ne13 = %5d\n", ne10, ne11, ne12, ne13); // printf("XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX task %d/%d: %d us, acc = %d\n", ith, nth, (int) (t1 - t0), (int) acc); //} } static void ggml_compute_forward_mul_mat_q_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { int64_t t0 = ggml_perf_time_us(); UNUSED(t0); const int64_t ne00 = src0->ne[0]; const int64_t ne01 = src0->ne[1]; const int64_t ne02 = src0->ne[2]; const int64_t ne03 = src0->ne[3]; const int64_t ne10 = src1->ne[0]; const int64_t ne11 = src1->ne[1]; const int64_t ne12 = src1->ne[2]; const int64_t ne13 = src1->ne[3]; const int64_t ne0 = dst->ne[0]; const int64_t ne1 = dst->ne[1]; const int64_t ne2 = dst->ne[2]; const int64_t ne3 = dst->ne[3]; const int nb00 = src0->nb[0]; const int nb01 = src0->nb[1]; const int nb02 = src0->nb[2]; const int nb03 = src0->nb[3]; const int nb10 = src1->nb[0]; const int nb11 = src1->nb[1]; const int nb12 = src1->nb[2]; const int nb13 = src1->nb[3]; const int nb0 = dst->nb[0]; const int nb1 = dst->nb[1]; const int nb2 = dst->nb[2]; const int nb3 = dst->nb[3]; const int ith = params->ith; const int nth = params->nth; GGML_ASSERT(ne02 == ne12); GGML_ASSERT(ne03 == ne13); GGML_ASSERT(ne2 == ne12); GGML_ASSERT(ne3 == ne13); const enum ggml_type type = src0->type; quantize_row_q_t const quantize_row_q_dot = quantize_fns[type].quantize_row_q_dot; vec_dot_q_t const vec_dot_q = quantize_fns[type].vec_dot_q; // we don't support permuted src0 or src1 GGML_ASSERT(nb00 == (int) GGML_TYPE_SIZE[type]); GGML_ASSERT(nb10 == sizeof(float)); // dst cannot be transposed or permuted GGML_ASSERT(nb0 == sizeof(float)); GGML_ASSERT(nb0 <= nb1); GGML_ASSERT(nb1 <= nb2); GGML_ASSERT(nb2 <= nb3); GGML_ASSERT(ne0 == ne01); GGML_ASSERT(ne1 == ne11); GGML_ASSERT(ne2 == ne02); GGML_ASSERT(ne3 == ne03); // nb01 >= nb00 - src0 is not transposed // compute by src0 rows #if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CUBLAS) if (ggml_compute_forward_mul_mat_use_blas(src0, src1, dst)) { if (params->ith != 0) { return; } if (params->type == GGML_TASK_INIT) { return; } if (params->type == GGML_TASK_FINALIZE) { return; } #if defined(GGML_USE_CUBLAS) const float alpha = 1.0f; const float beta = 0.0f; const int x_ne = ne01 * ne10; const int y_ne = ne11 * ne10; const int d_ne = ne11 * ne01; size_t x_size, y_size, d_size, q_size; float *d_X = ggml_cuda_pool_malloc(sizeof(float) * x_ne, &x_size); float *d_Y = ggml_cuda_pool_malloc(sizeof(float) * y_ne, &y_size); float *d_D = ggml_cuda_pool_malloc(sizeof(float) * d_ne, &d_size); float *d_Q = ggml_cuda_pool_malloc(GGML_TYPE_SIZE[type] * x_ne / GGML_BLCK_SIZE[type], &q_size); void (*dequantize_row_q_cuda)(const void * x, float * y, int k, cudaStream_t stream) = NULL; if (type == GGML_TYPE_Q4_0) { dequantize_row_q_cuda = dequantize_row_q4_0_cuda; } else if (type == GGML_TYPE_Q4_1) { dequantize_row_q_cuda = dequantize_row_q4_1_cuda; } else if (type == GGML_TYPE_Q4_2) { dequantize_row_q_cuda = dequantize_row_q4_2_cuda; } else if (type == GGML_TYPE_Q4_3) { dequantize_row_q_cuda = dequantize_row_q4_3_cuda; } else { GGML_ASSERT(false); } #else float * const wdata = params->wdata; dequantize_row_q_t const dequantize_row_q = quantize_fns[type].dequantize_row_q; #endif for (int64_t i03 = 0; i03 < ne03; i03++) { for (int64_t i02 = 0; i02 < ne02; i02++) { const float * y = (float *) ((char *) src1->data + i02*nb12 + i03*nb13); float * d = (float *) ((char *) dst->data + i02*nb2 + i03*nb3); #if defined(GGML_USE_CUBLAS) // copy and dequantize on device CUDA_CHECK( cudaMemcpyAsync(d_Q, (char *) src0->data + i03*nb03 + i02*nb02, GGML_TYPE_SIZE[type] * x_ne / GGML_BLCK_SIZE[type], cudaMemcpyHostToDevice, g_cudaStream)); dequantize_row_q_cuda(d_Q, d_X, ne01 * ne00, g_cudaStream); CUDA_CHECK(cudaGetLastError()); #else { size_t id = 0; for (int64_t i01 = 0; i01 < ne01; ++i01) { dequantize_row_q((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01, wdata + id, ne00); id += ne00; } } const float * x = wdata; #endif #if defined(GGML_USE_CUBLAS) // copy data to device CUDA_CHECK(cudaMemcpyAsync(d_Y, y, sizeof(float) * y_ne, cudaMemcpyHostToDevice, g_cudaStream)); // compute CUBLAS_CHECK( cublasSgemm(g_cublasH, CUBLAS_OP_T, CUBLAS_OP_N, ne01, ne11, ne10, &alpha, d_X, ne00, d_Y, ne10, &beta, d_D, ne01)); // copy data to host CUDA_CHECK(cudaMemcpyAsync(d, d_D, sizeof(float) * d_ne, cudaMemcpyDeviceToHost, g_cudaStream)); #else // zT = y * xT cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasTrans, ne11, ne01, ne10, 1.0f, y, ne10, x, ne00, 0.0f, d, ne01); #endif } } #if defined(GGML_USE_CUBLAS) CUDA_CHECK(cudaStreamSynchronize(g_cudaStream)); ggml_cuda_pool_free(d_X, x_size); ggml_cuda_pool_free(d_Y, y_size); ggml_cuda_pool_free(d_D, d_size); ggml_cuda_pool_free(d_Q, q_size); #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) { char * wdata = params->wdata; const size_t row_size = ne10*GGML_TYPE_SIZE[GGML_TYPE_Q8_0]/GGML_BLCK_SIZE[GGML_TYPE_Q8_0]; for (int64_t i13 = 0; i13 < ne13; ++i13) { for (int64_t i12 = 0; i12 < ne12; ++i12) { for (int64_t i11 = 0; i11 < ne11; ++i11) { quantize_row_q_dot((float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11), (void *) wdata, ne10); wdata += row_size; } } } return; } if (params->type == GGML_TASK_FINALIZE) { return; } // parallelize by src0 rows using ggml_vec_dot_q // total rows in src0 const int nr = ne01*ne02*ne03; // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); void * wdata = params->wdata; const size_t row_size = ne00*GGML_TYPE_SIZE[GGML_TYPE_Q8_0]/GGML_BLCK_SIZE[GGML_TYPE_Q8_0]; for (int ir = ir0; ir < ir1; ++ir) { // src0 indices const int i03 = ir/(ne02*ne01); const int i02 = (ir - i03*ne02*ne01)/ne01; const int i01 = (ir - i03*ne02*ne01 - i02*ne01); const int i13 = i03; const int i12 = i02; const int i0 = i01; const int i2 = i02; const int i3 = i03; void * src0_row = (void *) ((char *) src0->data + (i01*nb01 + i02*nb02 + i03*nb03)); char * src1_col = ((char *) wdata + ( (0 + i12*ne11 + i13*ne12*ne11)*row_size)); float * dst_col = (float *) ((char *) dst->data + (i0*nb0 + 0*nb1 + i2*nb2 + i3*nb3)); assert(ne00 % 32 == 0); for (int64_t ic = 0; ic < ne11; ++ic) { vec_dot_q(ne00, &dst_col[ic*ne0], src0_row, (void *) (src1_col + ic*row_size)); } } //int64_t t1 = ggml_time_us(); //static int64_t acc = 0; //acc += t1 - t0; //if (t1 - t0 > 10) { // printf("\n"); // printf("ne00 = %5d, ne01 = %5d, ne02 = %5d, ne03 = %5d\n", ne00, ne01, ne02, ne03); // printf("nb00 = %5d, nb01 = %5d, nb02 = %5d, nb03 = %5d\n", nb00, nb01, nb02, nb03); // printf("ne10 = %5d, ne11 = %5d, ne12 = %5d, ne13 = %5d\n", ne10, ne11, ne12, ne13); // printf("XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX task %d/%d: %d us, acc = %d\n", ith, nth, (int) (t1 - t0), (int) acc); //} } static void ggml_compute_forward_mul_mat( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_1: case GGML_TYPE_Q4_2: case GGML_TYPE_Q4_3: case GGML_TYPE_Q8_0: { ggml_compute_forward_mul_mat_q_f32(params, src0, src1, dst); } break; case GGML_TYPE_F16: { ggml_compute_forward_mul_mat_f16_f32(params, src0, src1, dst); } break; case GGML_TYPE_F32: { ggml_compute_forward_mul_mat_f32(params, src0, src1, dst); } break; default: { GGML_ASSERT(false); } break; } } // ggml_compute_forward_scale static void ggml_compute_forward_scale_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { GGML_ASSERT(ggml_is_contiguous(src0)); GGML_ASSERT(ggml_is_contiguous(dst)); GGML_ASSERT(ggml_are_same_shape(src0, dst)); GGML_ASSERT(ggml_is_scalar(src1)); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } // scale factor const float v = *(float *) src1->data; const int ith = params->ith; const int nth = params->nth; const int nc = src0->ne[0]; const int nr = ggml_nrows(src0); // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); 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; default: { GGML_ASSERT(false); } break; } } // ggml_compute_forward_cpy static void ggml_compute_forward_cpy( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { ggml_compute_forward_dup(params, src0, dst); } // ggml_compute_forward_cont static void ggml_compute_forward_cont( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { ggml_compute_forward_dup(params, src0, dst); } // ggml_compute_forward_reshape static void ggml_compute_forward_reshape( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { // NOP UNUSED(params); UNUSED(src0); UNUSED(dst); } // ggml_compute_forward_view static void ggml_compute_forward_view( const struct ggml_compute_params * params, const struct ggml_tensor * src0) { // NOP UNUSED(params); UNUSED(src0); } // ggml_compute_forward_permute static void ggml_compute_forward_permute( const struct ggml_compute_params * params, const struct ggml_tensor * src0) { // NOP UNUSED(params); UNUSED(src0); } // ggml_compute_forward_transpose static void ggml_compute_forward_transpose( const struct ggml_compute_params * params, const struct ggml_tensor * src0) { // NOP UNUSED(params); UNUSED(src0); } // ggml_compute_forward_get_rows static void ggml_compute_forward_get_rows_q( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { assert(params->ith == 0); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } const int nc = src0->ne[0]; const int nr = ggml_nelements(src1); const enum ggml_type type = src0->type; dequantize_row_q_t const dequantize_row_q = quantize_fns[type].dequantize_row_q; assert( dst->ne[0] == nc); assert( dst->ne[1] == nr); assert(src0->nb[0] == GGML_TYPE_SIZE[type]); for (int i = 0; i < nr; ++i) { const int r = ((int32_t *) src1->data)[i]; dequantize_row_q( (const void *) ((char *) src0->data + r*src0->nb[1]), (float *) ((char *) dst->data + i*dst->nb[1]), nc); } } static void ggml_compute_forward_get_rows_f16( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { assert(params->ith == 0); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } const int nc = src0->ne[0]; const int nr = ggml_nelements(src1); assert( dst->ne[0] == nc); assert( dst->ne[1] == nr); assert(src0->nb[0] == sizeof(ggml_fp16_t)); for (int i = 0; i < nr; ++i) { const int r = ((int32_t *) src1->data)[i]; for (int j = 0; j < nc; ++j) { ggml_fp16_t v = ((ggml_fp16_t *) ((char *) src0->data + r*src0->nb[1]))[j]; ((float *) ((char *) dst->data + i*dst->nb[1]))[j] = GGML_FP16_TO_FP32(v); } } } static void ggml_compute_forward_get_rows_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { assert(params->ith == 0); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } const int nc = src0->ne[0]; const int nr = ggml_nelements(src1); assert( dst->ne[0] == nc); assert( dst->ne[1] == nr); assert(src0->nb[0] == sizeof(float)); for (int i = 0; i < nr; ++i) { const int r = ((int32_t *) src1->data)[i]; ggml_vec_cpy_f32(nc, (float *) ((char *) dst->data + i*dst->nb[1]), (float *) ((char *) src0->data + r*src0->nb[1])); } } static void ggml_compute_forward_get_rows( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_1: case GGML_TYPE_Q4_2: case GGML_TYPE_Q4_3: case GGML_TYPE_Q8_0: { ggml_compute_forward_get_rows_q(params, src0, src1, dst); } break; case GGML_TYPE_F16: { ggml_compute_forward_get_rows_f16(params, src0, src1, dst); } break; case GGML_TYPE_F32: { ggml_compute_forward_get_rows_f32(params, src0, src1, dst); } break; default: { GGML_ASSERT(false); } break; } //static bool first = true; //printf("ne0 = %d, ne1 = %d, ne2 = %d\n", dst->ne[0], dst->ne[1], dst->ne[2]); //if (first) { // first = false; //} else { // for (int k = 0; k < dst->ne[1]; ++k) { // for (int j = 0; j < dst->ne[0]/16; ++j) { // for (int i = 0; i < 16; ++i) { // printf("%8.4f ", ((float *) dst->data)[k*dst->ne[0] + j*16 + i]); // } // printf("\n"); // } // printf("\n"); // } // printf("\n"); // exit(0); //} } // ggml_compute_forward_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; default: { GGML_ASSERT(false); } break; } } // ggml_compute_forward_soft_max static void ggml_compute_forward_soft_max_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { GGML_ASSERT(ggml_is_contiguous(src0)); GGML_ASSERT(ggml_is_contiguous(dst)); GGML_ASSERT(ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } // TODO: handle transposed/permuted matrices const int ith = params->ith; const int nth = params->nth; const int nc = src0->ne[0]; const int nr = ggml_nrows(src0); // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); for (int i1 = ir0; i1 < ir1; i1++) { float *p = (float *)((char *) dst->data + i1*dst->nb[1]); #ifndef NDEBUG for (int i = 0; i < nc; ++i) { //printf("p[%d] = %f\n", i, p[i]); assert(!isnan(p[i])); } #endif float max = -INFINITY; ggml_vec_max_f32(nc, &max, p); ggml_float sum = 0.0; uint16_t scvt; for (int i = 0; i < nc; i++) { if (p[i] == -INFINITY) { p[i] = 0.0f; } else { //const float val = (p[i] == -INFINITY) ? 0.0 : exp(p[i] - max); ggml_fp16_t s = GGML_FP32_TO_FP16(p[i] - max); memcpy(&scvt, &s, sizeof(scvt)); const float val = GGML_FP16_TO_FP32(table_exp_f16[scvt]); sum += (ggml_float)val; p[i] = val; } } assert(sum > 0.0); sum = 1.0/sum; ggml_vec_scale_f32(nc, p, sum); #ifndef NDEBUG for (int i = 0; i < nc; ++i) { assert(!isnan(p[i])); assert(!isinf(p[i])); } #endif } } static void ggml_compute_forward_soft_max( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_soft_max_f32(params, src0, dst); } break; default: { GGML_ASSERT(false); } break; } } // ggml_compute_forward_rope static void ggml_compute_forward_rope_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { assert(src1->type == GGML_TYPE_I32); assert(ggml_nelements(src1) == 3); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } const int n_past = ((int32_t *) src1->data)[0]; const int n_dims = ((int32_t *) src1->data)[1]; const int mode = ((int32_t *) src1->data)[2]; //const int64_t ne0 = src0->ne[0]; const int64_t ne1 = src0->ne[1]; const int64_t ne2 = src0->ne[2]; const int64_t ne3 = src0->ne[3]; const int nb0 = src0->nb[0]; const int nb1 = src0->nb[1]; const int nb2 = src0->nb[2]; const int nb3 = src0->nb[3]; //printf("ne0: %d, ne1: %d, ne2: %d, ne3: %d\n", ne0, ne1, ne2, ne3); //printf("n_past = %d, ne2 = %d\n", n_past, ne2); assert(nb0 == sizeof(float)); const int ith = params->ith; const int nth = params->nth; const int nr = ggml_nrows(src0); // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); // row index used to determine which thread to use int ir = 0; const float theta_scale = powf(10000.0, -2.0f/n_dims); const bool is_neox = mode & 2; for (int64_t i3 = 0; i3 < ne3; i3++) { for (int64_t i2 = ((mode & 1) == 0 ? 0 : n_past); i2 < ne2; i2++) { const int p = ((mode & 1) == 0 ? n_past + i2 : i2); for (int64_t i1 = 0; i1 < ne1; i1++) { if (ir++ < ir0) continue; if (ir > ir1) break; float theta = (float)p; for (int i0 = 0; i0 < n_dims; i0 += 2) { const float cos_theta = cosf(theta); const float sin_theta = sinf(theta); theta *= theta_scale; if (!is_neox) { const float * const src = (float *)((char *) src0->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); float * dst_data = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); const float x0 = src[0]; const float x1 = src[1]; dst_data[0] = x0*cos_theta - x1*sin_theta; dst_data[1] = x0*sin_theta + x1*cos_theta; } else { const float * const src = (float *)((char *) src0->data + i3*nb3 + i2*nb2 + i1*nb1 + (i0/2)*nb0); float * dst_data = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + (i0/2)*nb0); const float x0 = src[0]; const float x1 = src[n_dims/2]; dst_data[0] = x0*cos_theta - x1*sin_theta; dst_data[n_dims/2] = x0*sin_theta + x1*cos_theta; } } } } } } static void ggml_compute_forward_rope_f16( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { assert(src1->type == GGML_TYPE_I32); assert(ggml_nelements(src1) == 3); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } const int n_past = ((int32_t *) src1->data)[0]; const int n_dims = ((int32_t *) src1->data)[1]; const int mode = ((int32_t *) src1->data)[2]; //const int64_t ne0 = src0->ne[0]; const int64_t ne1 = src0->ne[1]; const int64_t ne2 = src0->ne[2]; const int64_t ne3 = src0->ne[3]; const int nb0 = src0->nb[0]; const int nb1 = src0->nb[1]; const int nb2 = src0->nb[2]; const int nb3 = src0->nb[3]; //printf("ne0: %d, ne1: %d, ne2: %d, ne3: %d\n", ne0, ne1, ne2, ne3); //printf("n_past = %d, ne2 = %d\n", n_past, ne2); assert(nb0 == sizeof(ggml_fp16_t)); const int ith = params->ith; const int nth = params->nth; const int nr = ggml_nrows(src0); // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); // row index used to determine which thread to use int ir = 0; const float theta_scale = powf(10000.0, -2.0f/n_dims); const bool is_neox = mode & 2; for (int64_t i3 = 0; i3 < ne3; i3++) { for (int64_t i2 = ((mode & 1) == 0 ? 0 : n_past); i2 < ne2; i2++) { const int p = ((mode & 1) == 0 ? n_past + i2 : i2); for (int64_t i1 = 0; i1 < ne1; i1++) { if (ir++ < ir0) continue; if (ir > ir1) break; float theta = (float)p; for (int i0 = 0; i0 < n_dims; i0 += 2) { const float cos_theta = cosf(theta); const float sin_theta = sinf(theta); theta *= theta_scale; if (!is_neox) { const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); ggml_fp16_t * dst_data = (ggml_fp16_t *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); const float x0 = GGML_FP16_TO_FP32(src[0]); const float x1 = GGML_FP16_TO_FP32(src[1]); dst_data[0] = GGML_FP32_TO_FP16(x0*cos_theta - x1*sin_theta); dst_data[1] = GGML_FP32_TO_FP16(x0*sin_theta + x1*cos_theta); } else { const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb3 + i2*nb2 + i1*nb1 + (i0/2)*nb0); ggml_fp16_t * dst_data = (ggml_fp16_t *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + (i0/2)*nb0); const float x0 = GGML_FP16_TO_FP32(src[0]); const float x1 = GGML_FP16_TO_FP32(src[n_dims/2]); dst_data[0] = GGML_FP32_TO_FP16(x0*cos_theta - x1*sin_theta); dst_data[n_dims/2] = GGML_FP32_TO_FP16(x0*sin_theta + x1*cos_theta); } } } } } } static void ggml_compute_forward_rope( const struct ggml_compute_params * params, const struct ggml_tensor * src0, 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; default: { GGML_ASSERT(false); } break; } } // ggml_compute_forward_conv_1d_1s static void ggml_compute_forward_conv_1d_1s_f16_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { GGML_ASSERT(src0->type == GGML_TYPE_F16); GGML_ASSERT(src1->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); int64_t t0 = ggml_perf_time_us(); UNUSED(t0); const int64_t ne00 = src0->ne[0]; const int64_t ne01 = src0->ne[1]; const int64_t ne02 = src0->ne[2]; //const int64_t ne03 = src0->ne[3]; const int64_t ne10 = src1->ne[0]; const int64_t ne11 = src1->ne[1]; //const int64_t ne12 = src1->ne[2]; //const int64_t ne13 = src1->ne[3]; //const int64_t ne0 = dst->ne[0]; //const int64_t ne1 = dst->ne[1]; //const int64_t ne2 = dst->ne[2]; //const int64_t ne3 = dst->ne[3]; //const int64_t ne = ne0*ne1*ne2*ne3; const int nb00 = src0->nb[0]; const int nb01 = src0->nb[1]; const int nb02 = src0->nb[2]; //const int nb03 = src0->nb[3]; const int nb10 = src1->nb[0]; const int nb11 = src1->nb[1]; //const int nb12 = src1->nb[2]; //const int nb13 = src1->nb[3]; //const int nb0 = dst->nb[0]; const int nb1 = dst->nb[1]; //const int nb2 = dst->nb[2]; //const int nb3 = dst->nb[3]; const int ith = params->ith; const int nth = params->nth; const int nk = ne00; const int nh = nk/2; const int ew0 = ggml_up32(ne01); GGML_ASSERT(ne00 % 2 == 1); // TODO: support even kernel sizes GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); GGML_ASSERT(nb10 == sizeof(float)); if (params->type == GGML_TASK_INIT) { // TODO: fix this memset (wsize is overestimated) memset(params->wdata, 0, params->wsize); // prepare kernel data (src0) { ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + 0; for (int64_t i02 = 0; i02 < ne02; i02++) { for (int64_t i01 = 0; i01 < ne01; i01++) { const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i02*nb02 + i01*nb01); ggml_fp16_t * dst_data = wdata + i02*ew0*ne00; for (int64_t i00 = 0; i00 < ne00; i00++) { dst_data[i00*ew0 + i01] = src[i00]; } } } } // prepare source data (src1) { ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + ne02*ew0*ne00; for (int64_t i11 = 0; i11 < ne11; i11++) { const float * const src = (float *)((char *) src1->data + i11*nb11); ggml_fp16_t * dst_data = wdata; for (int64_t i10 = 0; i10 < ne10; i10++) { dst_data[(i10 + nh)*ew0 + i11] = GGML_FP32_TO_FP16(src[i10]); } } } return; } if (params->type == GGML_TASK_FINALIZE) { return; } // total rows in dst const int nr = ne02; // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); for (int i1 = ir0; i1 < ir1; i1++) { float * dst_data = (float *)((char *) dst->data + i1*nb1); for (int64_t i0 = 0; i0 < ne10; ++i0) { dst_data[i0] = 0; for (int k = -nh; k <= nh; k++) { float v = 0.0f; ggml_vec_dot_f16(ew0, &v, (ggml_fp16_t *) params->wdata + i1*ew0*ne00 + (nh + k)*ew0, (ggml_fp16_t *) params->wdata + ne02*ew0*ne00 + (i0 + nh + k)*ew0); dst_data[i0] += v; } } } } static void ggml_compute_forward_conv_1d_1s_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT(src1->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); int64_t t0 = ggml_perf_time_us(); UNUSED(t0); const int64_t ne00 = src0->ne[0]; const int64_t ne01 = src0->ne[1]; const int64_t ne02 = src0->ne[2]; //const int64_t ne03 = src0->ne[3]; const int64_t ne10 = src1->ne[0]; const int64_t ne11 = src1->ne[1]; //const int64_t ne12 = src1->ne[2]; //const int64_t ne13 = src1->ne[3]; //const int64_t ne0 = dst->ne[0]; //const int64_t ne1 = dst->ne[1]; //const int64_t ne2 = dst->ne[2]; //const int64_t ne3 = dst->ne[3]; //const int64_t ne = ne0*ne1*ne2*ne3; const int nb00 = src0->nb[0]; const int nb01 = src0->nb[1]; const int nb02 = src0->nb[2]; //const int nb03 = src0->nb[3]; const int nb10 = src1->nb[0]; const int nb11 = src1->nb[1]; //const int nb12 = src1->nb[2]; //const int nb13 = src1->nb[3]; //const int nb0 = dst->nb[0]; const int nb1 = dst->nb[1]; //const int nb2 = dst->nb[2]; //const int nb3 = dst->nb[3]; const int ith = params->ith; const int nth = params->nth; const int nk = ne00; const int nh = nk/2; const int ew0 = ggml_up32(ne01); GGML_ASSERT(ne00 % 2 == 1); // TODO: support even kernel sizes GGML_ASSERT(nb00 == sizeof(float)); GGML_ASSERT(nb10 == sizeof(float)); if (params->type == GGML_TASK_INIT) { // TODO: fix this memset (wsize is overestimated) memset(params->wdata, 0, params->wsize); // prepare kernel data (src0) { float * const wdata = (float *) params->wdata + 0; for (int64_t i02 = 0; i02 < ne02; i02++) { for (int64_t i01 = 0; i01 < ne01; i01++) { const float * const src = (float *)((char *) src0->data + i02*nb02 + i01*nb01); float * dst_data = wdata + i02*ew0*ne00; for (int64_t i00 = 0; i00 < ne00; i00++) { dst_data[i00*ew0 + i01] = src[i00]; } } } } // prepare source data (src1) { float * const wdata = (float *) params->wdata + ne02*ew0*ne00; for (int64_t i11 = 0; i11 < ne11; i11++) { const float * const src = (float *)((char *) src1->data + i11*nb11); float * dst_data = wdata; for (int64_t i10 = 0; i10 < ne10; i10++) { dst_data[(i10 + nh)*ew0 + i11] = src[i10]; } } } return; } if (params->type == GGML_TASK_FINALIZE) { return; } // total rows in dst const int nr = ne02; // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); for (int i1 = ir0; i1 < ir1; i1++) { float * dst_data = (float *)((char *) dst->data + i1*nb1); for (int64_t i0 = 0; i0 < ne10; ++i0) { dst_data[i0] = 0; for (int k = -nh; k <= nh; k++) { float v = 0.0f; ggml_vec_dot_f32(ew0, &v, (float *) params->wdata + i1*ew0*ne00 + (nh + k)*ew0, (float *) params->wdata + ne02*ew0*ne00 + (i0 + nh + k)*ew0); dst_data[i0] += v; } } } } static void ggml_compute_forward_conv_1d_1s( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F16: { ggml_compute_forward_conv_1d_1s_f16_f32(params, src0, src1, dst); } break; case GGML_TYPE_F32: { ggml_compute_forward_conv_1d_1s_f32(params, src0, src1, dst); } break; default: { GGML_ASSERT(false); } break; } } // ggml_compute_forward_conv_1d_2s static void ggml_compute_forward_conv_1d_2s_f16_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { GGML_ASSERT(src0->type == GGML_TYPE_F16); GGML_ASSERT(src1->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); int64_t t0 = ggml_perf_time_us(); UNUSED(t0); const int64_t ne00 = src0->ne[0]; const int64_t ne01 = src0->ne[1]; const int64_t ne02 = src0->ne[2]; //const int64_t ne03 = src0->ne[3]; const int64_t ne10 = src1->ne[0]; const int64_t ne11 = src1->ne[1]; //const int64_t ne12 = src1->ne[2]; //const int64_t ne13 = src1->ne[3]; //const int64_t ne0 = dst->ne[0]; //const int64_t ne1 = dst->ne[1]; //const int64_t ne2 = dst->ne[2]; //const int64_t ne3 = dst->ne[3]; //const int64_t ne = ne0*ne1*ne2*ne3; const int nb00 = src0->nb[0]; const int nb01 = src0->nb[1]; const int nb02 = src0->nb[2]; //const int nb03 = src0->nb[3]; const int nb10 = src1->nb[0]; const int nb11 = src1->nb[1]; //const int nb12 = src1->nb[2]; //const int nb13 = src1->nb[3]; //const int nb0 = dst->nb[0]; const int nb1 = dst->nb[1]; //const int nb2 = dst->nb[2]; //const int nb3 = dst->nb[3]; const int ith = params->ith; const int nth = params->nth; const int nk = ne00; const int nh = nk/2; const int ew0 = ggml_up32(ne01); GGML_ASSERT(ne00 % 2 == 1); // TODO: support even kernel sizes GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); GGML_ASSERT(nb10 == sizeof(float)); if (params->type == GGML_TASK_INIT) { // TODO: fix this memset (wsize is overestimated) memset(params->wdata, 0, params->wsize); // prepare kernel data (src0) { ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + 0; for (int64_t i02 = 0; i02 < ne02; i02++) { for (int64_t i01 = 0; i01 < ne01; i01++) { const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i02*nb02 + i01*nb01); ggml_fp16_t * dst_data = wdata + i02*ew0*ne00; for (int64_t i00 = 0; i00 < ne00; i00++) { dst_data[i00*ew0 + i01] = src[i00]; } } } } // prepare source data (src1) { ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + ne02*ew0*ne00; for (int64_t i11 = 0; i11 < ne11; i11++) { const float * const src = (float *)((char *) src1->data + i11*nb11); ggml_fp16_t * dst_data = wdata; for (int64_t i10 = 0; i10 < ne10; i10++) { dst_data[(i10 + nh)*ew0 + i11] = GGML_FP32_TO_FP16(src[i10]); } } } return; } if (params->type == GGML_TASK_FINALIZE) { return; } // total rows in dst const int nr = ne02; // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); for (int i1 = ir0; i1 < ir1; i1++) { float * dst_data = (float *)((char *) dst->data + i1*nb1); for (int64_t i0 = 0; i0 < ne10; i0 += 2) { dst_data[i0/2] = 0; for (int k = -nh; k <= nh; k++) { float v = 0.0f; ggml_vec_dot_f16(ew0, &v, (ggml_fp16_t *) params->wdata + i1*ew0*ne00 + (nh + k)*ew0, (ggml_fp16_t *) params->wdata + ne02*ew0*ne00 + (i0 + nh + k)*ew0); dst_data[i0/2] += v; } } } } static void ggml_compute_forward_conv_1d_2s_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT(src1->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); int64_t t0 = ggml_perf_time_us(); UNUSED(t0); const int64_t ne00 = src0->ne[0]; const int64_t ne01 = src0->ne[1]; const int64_t ne02 = src0->ne[2]; //const int64_t ne03 = src0->ne[3]; const int64_t ne10 = src1->ne[0]; const int64_t ne11 = src1->ne[1]; //const int64_t ne12 = src1->ne[2]; //const int64_t ne13 = src1->ne[3]; //const int64_t ne0 = dst->ne[0]; //const int64_t ne1 = dst->ne[1]; //const int64_t ne2 = dst->ne[2]; //const int64_t ne3 = dst->ne[3]; //const int64_t ne = ne0*ne1*ne2*ne3; const int nb00 = src0->nb[0]; const int nb01 = src0->nb[1]; const int nb02 = src0->nb[2]; //const int nb03 = src0->nb[3]; const int nb10 = src1->nb[0]; const int nb11 = src1->nb[1]; //const int nb12 = src1->nb[2]; //const int nb13 = src1->nb[3]; //const int nb0 = dst->nb[0]; const int nb1 = dst->nb[1]; //const int nb2 = dst->nb[2]; //const int nb3 = dst->nb[3]; const int ith = params->ith; const int nth = params->nth; const int nk = ne00; const int nh = nk/2; const int ew0 = ggml_up32(ne01); GGML_ASSERT(ne00 % 2 == 1); // TODO: support even kernel sizes GGML_ASSERT(nb00 == sizeof(float)); GGML_ASSERT(nb10 == sizeof(float)); if (params->type == GGML_TASK_INIT) { // TODO: fix this memset (wsize is overestimated) memset(params->wdata, 0, params->wsize); // prepare kernel data (src0) { float * const wdata = (float *) params->wdata + 0; for (int64_t i02 = 0; i02 < ne02; i02++) { for (int64_t i01 = 0; i01 < ne01; i01++) { const float * const src = (float *)((char *) src0->data + i02*nb02 + i01*nb01); float * dst_data = wdata + i02*ew0*ne00; for (int64_t i00 = 0; i00 < ne00; i00++) { dst_data[i00*ew0 + i01] = src[i00]; } } } } // prepare source data (src1) { float * const wdata = (float *) params->wdata + ne02*ew0*ne00; for (int64_t i11 = 0; i11 < ne11; i11++) { const float * const src = (float *)((char *) src1->data + i11*nb11); float * dst_data = wdata; for (int64_t i10 = 0; i10 < ne10; i10++) { dst_data[(i10 + nh)*ew0 + i11] = src[i10]; } } } return; } if (params->type == GGML_TASK_FINALIZE) { return; } // total rows in dst const int nr = ne02; // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); for (int i1 = ir0; i1 < ir1; i1++) { float * dst_data = (float *)((char *) dst->data + i1*nb1); for (int64_t i0 = 0; i0 < ne10; i0 += 2) { dst_data[i0/2] = 0; for (int k = -nh; k <= nh; k++) { float v = 0.0f; ggml_vec_dot_f32(ew0, &v, (float *) params->wdata + i1*ew0*ne00 + (nh + k)*ew0, (float *) params->wdata + ne02*ew0*ne00 + (i0 + nh + k)*ew0); dst_data[i0/2] += v; } } } } static void ggml_compute_forward_conv_1d_2s( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F16: { ggml_compute_forward_conv_1d_2s_f16_f32(params, src0, src1, dst); } break; case GGML_TYPE_F32: { ggml_compute_forward_conv_1d_2s_f32(params, src0, src1, dst); } break; default: { GGML_ASSERT(false); } break; } } // ggml_compute_forward_flash_attn static void ggml_compute_forward_flash_attn_f32( const struct ggml_compute_params * params, const struct ggml_tensor * q, const struct ggml_tensor * k, const struct ggml_tensor * v, const bool masked, struct ggml_tensor * dst) { int64_t t0 = ggml_perf_time_us(); UNUSED(t0); const int64_t neq0 = q->ne[0]; const int64_t neq1 = q->ne[1]; const int64_t neq2 = q->ne[2]; const int64_t neq3 = q->ne[3]; const int64_t nek0 = k->ne[0]; const int64_t nek1 = k->ne[1]; //const int64_t nek2 = k->ne[2]; //const int64_t nek3 = k->ne[3]; //const int64_t nev0 = v->ne[0]; const int64_t nev1 = v->ne[1]; //const int64_t nev2 = v->ne[2]; //const int64_t nev3 = v->ne[3]; const int64_t ne0 = dst->ne[0]; const int64_t ne1 = dst->ne[1]; //const int64_t ne2 = dst->ne[2]; //const int64_t ne3 = dst->ne[3]; const int nbk0 = k->nb[0]; const int nbk1 = k->nb[1]; const int nbk2 = k->nb[2]; const int nbk3 = k->nb[3]; const int nbq0 = q->nb[0]; const int nbq1 = q->nb[1]; const int nbq2 = q->nb[2]; const int nbq3 = q->nb[3]; const int nbv0 = v->nb[0]; const int nbv1 = v->nb[1]; const int nbv2 = v->nb[2]; const int nbv3 = v->nb[3]; const int nb0 = dst->nb[0]; const int nb1 = dst->nb[1]; const int nb2 = dst->nb[2]; const int nb3 = dst->nb[3]; const int ith = params->ith; const int nth = params->nth; const int64_t D = neq0; const int64_t N = neq1; const int64_t P = nek1 - N; const int64_t M = P + N; const int Mup = ggml_up(M, GGML_SOFT_MAX_UNROLL); GGML_ASSERT(ne0 == D); GGML_ASSERT(ne1 == N); GGML_ASSERT(P >= 0); GGML_ASSERT(nbq0 == sizeof(float)); GGML_ASSERT(nbk0 == sizeof(float)); GGML_ASSERT(nbv0 == sizeof(float)); GGML_ASSERT(neq0 == D); GGML_ASSERT(nek0 == D); GGML_ASSERT(nev1 == D); GGML_ASSERT(neq1 == N); GGML_ASSERT(nek1 == N + P); GGML_ASSERT(nev1 == D); // dst cannot be transposed or permuted GGML_ASSERT(nb0 == sizeof(float)); GGML_ASSERT(nb0 <= nb1); GGML_ASSERT(nb1 <= nb2); GGML_ASSERT(nb2 <= nb3); if (params->type == GGML_TASK_INIT) { return; } if (params->type == GGML_TASK_FINALIZE) { return; } // parallelize by q rows using ggml_vec_dot_f32 // total rows in q const int nr = neq1*neq2*neq3; // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); const float scale = 1.0f/sqrtf(D); //printf("P=%d N=%d D=%d ir0=%d ir1=%d scale = %f\n", P, N, D, ir0, ir1, scale); for (int ir = ir0; ir < ir1; ++ir) { // q indices const int iq3 = ir/(neq2*neq1); const int iq2 = (ir - iq3*neq2*neq1)/neq1; const int iq1 = (ir - iq3*neq2*neq1 - iq2*neq1); float * S = (float *) params->wdata + ith*(Mup + CACHE_LINE_SIZE_F32); for (int i = M; i < Mup; ++i) { S[i] = -INFINITY; } for (int64_t ic = 0; ic < nek1; ++ic) { // k indices const int ik3 = iq3; const int ik2 = iq2; const int ik1 = ic; // S indices const int i1 = ik1; ggml_vec_dot_f32(neq0, S + i1, (float *) ((char *) k->data + (ik1*nbk1 + ik2*nbk2 + ik3*nbk3)), (float *) ((char *) q->data + (iq1*nbq1 + iq2*nbq2 + iq3*nbq3))); } // scale ggml_vec_scale_f32(nek1, S, scale); if (masked) { for (int64_t i = P; i < M; i++) { if (i > P + iq1) { S[i] = -INFINITY; } } } // softmax { float max = -INFINITY; ggml_vec_max_f32(M, &max, S); ggml_float sum = 0.0; { #ifdef GGML_SOFT_MAX_ACCELERATE max = -max; vDSP_vsadd(S, 1, &max, S, 1, Mup); vvexpf(S, S, &Mup); ggml_vec_sum_f32(Mup, &sum, S); #else uint16_t scvt[GGML_SOFT_MAX_UNROLL]; ggml_float sump[GGML_SOFT_MAX_UNROLL] = { 0.0 }; for (int i = 0; i < Mup; i += GGML_SOFT_MAX_UNROLL) { float * SS = S + i; for (int j = 0; j < GGML_SOFT_MAX_UNROLL; ++j) { if (SS[j] == -INFINITY) { SS[j] = 0.0f; } else { ggml_fp16_t s = GGML_FP32_TO_FP16(SS[j] - max); memcpy(&scvt[j], &s, sizeof(uint16_t)); const float val = GGML_FP16_TO_FP32(table_exp_f16[scvt[j]]); sump[j] += (ggml_float)val; SS[j] = val; } } } for (int i = 0; i < GGML_SOFT_MAX_UNROLL; i++) { sum += sump[i]; } #endif } assert(sum > 0.0); sum = 1.0/sum; ggml_vec_scale_f32(M, S, sum); #ifndef NDEBUG for (int i = 0; i < M; ++i) { assert(!isnan(S[i])); assert(!isinf(S[i])); } #endif } for (int64_t ic = 0; ic < nev1; ++ic) { // dst indices const int i1 = iq1; const int i2 = iq2; const int i3 = iq3; ggml_vec_dot_f32(nek1, (float *) ((char *) dst->data + (ic*nb0 + i1*nb1 + i2*nb2 + i3*nb3)), (float *) ((char *) v->data + ( ic*nbv1 + i2*nbv2 + i3*nbv3)), S); } } } static void ggml_compute_forward_flash_attn_f16( const struct ggml_compute_params * params, const struct ggml_tensor * q, const struct ggml_tensor * k, const struct ggml_tensor * v, const bool masked, struct ggml_tensor * dst) { int64_t t0 = ggml_perf_time_us(); UNUSED(t0); const int64_t neq0 = q->ne[0]; const int64_t neq1 = q->ne[1]; const int64_t neq2 = q->ne[2]; const int64_t neq3 = q->ne[3]; const int64_t nek0 = k->ne[0]; const int64_t nek1 = k->ne[1]; //const int64_t nek2 = k->ne[2]; //const int64_t nek3 = k->ne[3]; //const int64_t nev0 = v->ne[0]; const int64_t nev1 = v->ne[1]; //const int64_t nev2 = v->ne[2]; //const int64_t nev3 = v->ne[3]; const int64_t ne0 = dst->ne[0]; const int64_t ne1 = dst->ne[1]; //const int64_t ne2 = dst->ne[2]; //const int64_t ne3 = dst->ne[3]; const int nbk0 = k->nb[0]; const int nbk1 = k->nb[1]; const int nbk2 = k->nb[2]; const int nbk3 = k->nb[3]; const int nbq0 = q->nb[0]; const int nbq1 = q->nb[1]; const int nbq2 = q->nb[2]; const int nbq3 = q->nb[3]; const int nbv0 = v->nb[0]; const int nbv1 = v->nb[1]; const int nbv2 = v->nb[2]; const int nbv3 = v->nb[3]; const int nb0 = dst->nb[0]; const int nb1 = dst->nb[1]; const int nb2 = dst->nb[2]; const int nb3 = dst->nb[3]; const int ith = params->ith; const int nth = params->nth; const int64_t D = neq0; const int64_t N = neq1; const int64_t P = nek1 - N; const int64_t M = P + N; const int Mup = ggml_up(M, GGML_SOFT_MAX_UNROLL); GGML_ASSERT(ne0 == D); GGML_ASSERT(ne1 == N); GGML_ASSERT(P >= 0); GGML_ASSERT(nbq0 == sizeof(ggml_fp16_t)); GGML_ASSERT(nbk0 == sizeof(ggml_fp16_t)); GGML_ASSERT(nbv0 == sizeof(ggml_fp16_t)); GGML_ASSERT(neq0 == D); GGML_ASSERT(nek0 == D); GGML_ASSERT(nev1 == D); GGML_ASSERT(neq1 == N); GGML_ASSERT(nek1 == N + P); GGML_ASSERT(nev1 == D); // dst cannot be transposed or permuted GGML_ASSERT(nb0 == sizeof(float)); GGML_ASSERT(nb0 <= nb1); GGML_ASSERT(nb1 <= nb2); GGML_ASSERT(nb2 <= nb3); if (params->type == GGML_TASK_INIT) { return; } if (params->type == GGML_TASK_FINALIZE) { return; } // parallelize by q rows using ggml_vec_dot_f32 // total rows in q const int nr = neq1*neq2*neq3; // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); const float scale = 1.0f/sqrtf(D); //printf("P=%d N=%d D=%d ir0=%d ir1=%d scale = %f\n", P, N, D, ir0, ir1, scale); for (int ir = ir0; ir < ir1; ++ir) { // q indices const int iq3 = ir/(neq2*neq1); const int iq2 = (ir - iq3*neq2*neq1)/neq1; const int iq1 = (ir - iq3*neq2*neq1 - iq2*neq1); float * S = (float *) params->wdata + ith*(2*Mup + CACHE_LINE_SIZE_F32); for (int i = M; i < Mup; ++i) { S[i] = -INFINITY; } if (GGML_VEC_DOT_UNROLL > 2 || nek1 % GGML_VEC_DOT_UNROLL != 0) { for (int64_t ic = 0; ic < nek1; ++ic) { // k indices const int ik3 = iq3; const int ik2 = iq2; const int ik1 = ic; // S indices const int i1 = ik1; ggml_vec_dot_f16(neq0, S + i1, (ggml_fp16_t *) ((char *) k->data + (ik1*nbk1 + ik2*nbk2 + ik3*nbk3)), (ggml_fp16_t *) ((char *) q->data + (iq1*nbq1 + iq2*nbq2 + iq3*nbq3))); } } else { for (int64_t ic = 0; ic < nek1; ic += GGML_VEC_DOT_UNROLL) { // k indices const int ik3 = iq3; const int ik2 = iq2; const int ik1 = ic; // S indices const int i1 = ik1; ggml_vec_dot_f16_unroll(neq0, nbk1, S + i1, ((char *) k->data + (ik1*nbk1 + ik2*nbk2 + ik3*nbk3)), (ggml_fp16_t *) ((char *) q->data + (iq1*nbq1 + iq2*nbq2 + iq3*nbq3))); } } // scale ggml_vec_scale_f32(nek1, S, scale); if (masked) { for (int64_t i = P; i < M; i++) { if (i > P + iq1) { S[i] = -INFINITY; } } } // softmax { float max = -INFINITY; ggml_vec_max_f32(M, &max, S); ggml_float sum = 0.0; { #ifdef GGML_SOFT_MAX_ACCELERATE max = -max; vDSP_vsadd(S, 1, &max, S, 1, Mup); vvexpf(S, S, &Mup); ggml_vec_sum_f32(Mup, &sum, S); #else uint16_t scvt[GGML_SOFT_MAX_UNROLL]; ggml_float sump[GGML_SOFT_MAX_UNROLL] = { 0.0 }; for (int i = 0; i < Mup; i += GGML_SOFT_MAX_UNROLL) { float * SS = S + i; for (int j = 0; j < GGML_SOFT_MAX_UNROLL; ++j) { if (SS[j] == -INFINITY) { SS[j] = 0.0f; } else { ggml_fp16_t s = GGML_FP32_TO_FP16(SS[j] - max); memcpy(&scvt[j], &s, sizeof(uint16_t)); const float val = GGML_FP16_TO_FP32(table_exp_f16[scvt[j]]); sump[j] += (ggml_float)val; SS[j] = val; } } } for (int i = 0; i < GGML_SOFT_MAX_UNROLL; i++) { sum += sump[i]; } #endif } assert(sum > 0.0); sum = 1.0/sum; ggml_vec_scale_f32(M, S, sum); #ifndef NDEBUG for (int i = 0; i < M; ++i) { assert(!isnan(S[i])); assert(!isinf(S[i])); } #endif } ggml_fp16_t * S16 = (ggml_fp16_t *) ((float *) params->wdata + ith*(2*Mup + CACHE_LINE_SIZE_F32) + Mup); for (int64_t i = 0; i < M; i++) { S16[i] = GGML_FP32_TO_FP16(S[i]); } if (GGML_VEC_DOT_UNROLL == 1 || (nev1 % GGML_VEC_DOT_UNROLL != 0)) { for (int64_t ic = 0; ic < nev1; ++ic) { // dst indices const int i1 = iq1; const int i2 = iq2; const int i3 = iq3; ggml_vec_dot_f16(nek1, (float *) ((char *) dst->data + (ic*nb0 + i1*nb1 + i2*nb2 + i3*nb3)), (ggml_fp16_t *) ((char *) v->data + ( ic*nbv1 + i2*nbv2 + i3*nbv3)), S16); } } else { for (int64_t ic = 0; ic < nev1; ic += GGML_VEC_DOT_UNROLL) { // dst indices const int i1 = iq1; const int i2 = iq2; const int i3 = iq3; ggml_vec_dot_f16_unroll(nek1, nbv1, (float *) ((char *) dst->data + (ic*nb0 + i1*nb1 + i2*nb2 + i3*nb3)), ((char *) v->data + ( ic*nbv1 + i2*nbv2 + i3*nbv3)), S16); } } } } static void ggml_compute_forward_flash_attn( const struct ggml_compute_params * params, const struct ggml_tensor * q, const struct ggml_tensor * k, const struct ggml_tensor * v, const bool masked, struct ggml_tensor * dst) { switch (q->type) { case GGML_TYPE_F16: { ggml_compute_forward_flash_attn_f16(params, q, k, v, masked, dst); } break; case GGML_TYPE_F32: { ggml_compute_forward_flash_attn_f32(params, q, k, v, masked, dst); } break; default: { GGML_ASSERT(false); } break; } } // ggml_compute_forward_flash_ff static void ggml_compute_forward_flash_ff_f16( const struct ggml_compute_params * params, const struct ggml_tensor * a, // F16 const struct ggml_tensor * b0, // F16 fc_w const struct ggml_tensor * b1, // F32 fc_b const struct ggml_tensor * c0, // F16 proj_w const struct ggml_tensor * c1, // F32 proj_b struct ggml_tensor * dst) { int64_t t0 = ggml_perf_time_us(); UNUSED(t0); const int64_t nea0 = a->ne[0]; const int64_t nea1 = a->ne[1]; const int64_t nea2 = a->ne[2]; const int64_t nea3 = a->ne[3]; const int64_t neb00 = b0->ne[0]; const int64_t neb01 = b0->ne[1]; //const int64_t neb02 = b0->ne[2]; //const int64_t neb03 = b0->ne[3]; const int64_t neb10 = b1->ne[0]; const int64_t neb11 = b1->ne[1]; //const int64_t neb12 = b1->ne[2]; //const int64_t neb13 = b1->ne[3]; const int64_t nec00 = c0->ne[0]; const int64_t nec01 = c0->ne[1]; //const int64_t nec02 = c0->ne[2]; //const int64_t nec03 = c0->ne[3]; const int64_t nec10 = c1->ne[0]; const int64_t nec11 = c1->ne[1]; //const int64_t nec12 = c1->ne[2]; //const int64_t nec13 = c1->ne[3]; const int64_t ne0 = dst->ne[0]; const int64_t ne1 = dst->ne[1]; const int64_t ne2 = dst->ne[2]; //const int64_t ne3 = dst->ne[3]; const int nba0 = a->nb[0]; const int nba1 = a->nb[1]; const int nba2 = a->nb[2]; const int nba3 = a->nb[3]; const int nbb00 = b0->nb[0]; const int nbb01 = b0->nb[1]; const int nbb02 = b0->nb[2]; const int nbb03 = b0->nb[3]; const int nbb10 = b1->nb[0]; //const int nbb11 = b1->nb[1]; //const int nbb12 = b1->nb[2]; //const int nbb13 = b1->nb[3]; const int nbc00 = c0->nb[0]; const int nbc01 = c0->nb[1]; const int nbc02 = c0->nb[2]; const int nbc03 = c0->nb[3]; const int nbc10 = c1->nb[0]; //const int nbc11 = c1->nb[1]; //const int nbc12 = c1->nb[2]; //const int nbc13 = c1->nb[3]; const int nb0 = dst->nb[0]; const int nb1 = dst->nb[1]; const int nb2 = dst->nb[2]; const int nb3 = dst->nb[3]; const int ith = params->ith; const int nth = params->nth; const int64_t D = nea0; //const int64_t N = nea1; const int64_t M = neb01; GGML_ASSERT(ne0 == nea0); GGML_ASSERT(ne1 == nea1); GGML_ASSERT(ne2 == nea2); GGML_ASSERT(nba0 == sizeof(ggml_fp16_t)); GGML_ASSERT(nbb00 == sizeof(ggml_fp16_t)); GGML_ASSERT(nbb10 == sizeof(float)); GGML_ASSERT(nbc00 == sizeof(ggml_fp16_t)); GGML_ASSERT(nbc10 == sizeof(float)); GGML_ASSERT(neb00 == D); GGML_ASSERT(neb01 == M); GGML_ASSERT(neb10 == M); GGML_ASSERT(neb11 == 1); GGML_ASSERT(nec00 == M); GGML_ASSERT(nec01 == D); GGML_ASSERT(nec10 == D); GGML_ASSERT(nec11 == 1); // dst cannot be transposed or permuted GGML_ASSERT(nb0 == sizeof(float)); GGML_ASSERT(nb0 <= nb1); GGML_ASSERT(nb1 <= nb2); GGML_ASSERT(nb2 <= nb3); if (params->type == GGML_TASK_INIT) { return; } if (params->type == GGML_TASK_FINALIZE) { return; } // parallelize by a rows using ggml_vec_dot_f32 // total rows in a const int nr = nea1*nea2*nea3; // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); for (int ir = ir0; ir < ir1; ++ir) { // a indices const int ia3 = ir/(nea2*nea1); const int ia2 = (ir - ia3*nea2*nea1)/nea1; const int ia1 = (ir - ia3*nea2*nea1 - ia2*nea1); float * S = (float *) params->wdata + ith*(2*M + CACHE_LINE_SIZE_F32); for (int64_t ic = 0; ic < neb01; ++ic) { // b0 indices const int ib03 = ia3; const int ib02 = ia2; const int ib01 = ic; // S indices const int i1 = ib01; ggml_vec_dot_f16(nea0, S + i1, (ggml_fp16_t *) ((char *) b0->data + (ib01*nbb01 + ib02*nbb02 + ib03*nbb03)), (ggml_fp16_t *) ((char *) a->data + ( ia1*nba1 + ia2*nba2 + ia3*nba3))); } ggml_vec_add_f32(neb01, S, S, (float *) b1->data); //ggml_vec_gelu_f32(neb01, S, S); ggml_fp16_t * S16 = (ggml_fp16_t *) ((float *) params->wdata + ith*(2*M + CACHE_LINE_SIZE_F32) + M); for (int64_t i = 0; i < M; i++) { S16[i] = GGML_FP32_TO_FP16(S[i]); } ggml_vec_gelu_f16(neb01, S16, S16); { // dst indices const int i1 = ia1; const int i2 = ia2; const int i3 = ia3; for (int64_t ic = 0; ic < nec01; ++ic) { ggml_vec_dot_f16(neb01, (float *) ((char *) dst->data + (ic*nb0 + i1*nb1 + i2*nb2 + i3*nb3)), (ggml_fp16_t *) ((char *) c0->data + ( ic*nbc01 + i2*nbc02 + i3*nbc03)), S16); } ggml_vec_add_f32(nec01, (float *) ((char *) dst->data + (i1*nb1 + i2*nb2 + i3*nb3)), (float *) ((char *) dst->data + (i1*nb1 + i2*nb2 + i3*nb3)), (float *) c1->data); } } } static void ggml_compute_forward_flash_ff( const struct ggml_compute_params * params, const struct ggml_tensor * a, const struct ggml_tensor * b0, const struct ggml_tensor * b1, const struct ggml_tensor * c0, const struct ggml_tensor * c1, struct ggml_tensor * dst) { switch (b0->type) { case GGML_TYPE_F16: { ggml_compute_forward_flash_ff_f16(params, a, b0, b1, c0, c1, dst); } break; case GGML_TYPE_F32: { GGML_ASSERT(false); // TODO } break; default: { GGML_ASSERT(false); } break; } } // ggml_compute_forward_map_unary static void ggml_compute_forward_map_unary_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst, const ggml_unary_op_f32_t fun) { GGML_ASSERT(ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } const int n = ggml_nrows(src0); const int nc = src0->ne[0]; assert( dst->nb[0] == sizeof(float)); assert(src0->nb[0] == sizeof(float)); for (int i = 0; i < n; i++) { fun(nc, (float *) ((char *) dst->data + i*( dst->nb[1])), (float *) ((char *) src0->data + i*(src0->nb[1]))); } } static void ggml_compute_forward_map_unary( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst, const ggml_unary_op_f32_t fun) { switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_map_unary_f32(params, src0, dst, fun); } break; default: { GGML_ASSERT(false); } break; } } // ggml_compute_forward_map_binary static void ggml_compute_forward_map_binary_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst, const ggml_binary_op_f32_t fun) { assert(params->ith == 0); assert(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } const int n = ggml_nrows(src0); const int nc = src0->ne[0]; assert( dst->nb[0] == sizeof(float)); assert(src0->nb[0] == sizeof(float)); assert(src1->nb[0] == sizeof(float)); for (int i = 0; i < n; i++) { fun(nc, (float *) ((char *) dst->data + i*( dst->nb[1])), (float *) ((char *) src0->data + i*(src0->nb[1])), (float *) ((char *) src1->data + i*(src1->nb[1]))); } } static void ggml_compute_forward_map_binary( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst, const ggml_binary_op_f32_t fun) { switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_map_binary_f32(params, src0, src1, dst, fun); } break; default: { GGML_ASSERT(false); } break; } } ///////////////////////////////// static void ggml_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor) { GGML_ASSERT(params); switch (tensor->op) { case GGML_OP_DUP: { ggml_compute_forward_dup(params, tensor->src0, tensor); } break; case GGML_OP_ADD: { ggml_compute_forward_add(params, tensor->src0, tensor->src1, tensor); } break; case GGML_OP_SUB: { ggml_compute_forward_sub(params, tensor->src0, tensor->src1, tensor); } break; case GGML_OP_MUL: { ggml_compute_forward_mul(params, tensor->src0, tensor->src1, tensor); } break; case GGML_OP_DIV: { ggml_compute_forward_div(params, tensor->src0, tensor->src1, tensor); } break; case GGML_OP_SQR: { ggml_compute_forward_sqr(params, tensor->src0, tensor); } break; case GGML_OP_SQRT: { ggml_compute_forward_sqrt(params, tensor->src0, tensor); } break; case GGML_OP_SUM: { ggml_compute_forward_sum(params, tensor->src0, tensor); } break; case GGML_OP_MEAN: { ggml_compute_forward_mean(params, tensor->src0, tensor); } break; case GGML_OP_REPEAT: { ggml_compute_forward_repeat(params, tensor->src0, tensor); } break; case GGML_OP_ABS: { ggml_compute_forward_abs(params, tensor->src0, tensor); } break; case GGML_OP_SGN: { ggml_compute_forward_sgn(params, tensor->src0, tensor); } break; case GGML_OP_NEG: { ggml_compute_forward_neg(params, tensor->src0, tensor); } break; case GGML_OP_STEP: { ggml_compute_forward_step(params, tensor->src0, tensor); } break; case GGML_OP_RELU: { ggml_compute_forward_relu(params, tensor->src0, tensor); } break; case GGML_OP_GELU: { ggml_compute_forward_gelu(params, tensor->src0, tensor); } break; case GGML_OP_SILU: { ggml_compute_forward_silu(params, tensor->src0, tensor); } break; case GGML_OP_NORM: { ggml_compute_forward_norm(params, tensor->src0, tensor); } break; case GGML_OP_RMS_NORM: { ggml_compute_forward_rms_norm(params, tensor->src0, tensor); } break; case GGML_OP_MUL_MAT: { ggml_compute_forward_mul_mat(params, tensor->src0, tensor->src1, tensor); } break; case GGML_OP_SCALE: { ggml_compute_forward_scale(params, tensor->src0, tensor->src1, tensor); } break; case GGML_OP_CPY: { ggml_compute_forward_cpy(params, tensor->src0, tensor); } break; case GGML_OP_CONT: { ggml_compute_forward_cont(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_MAP_UNARY: { const ggml_unary_op_f32_t fun = *((ggml_unary_op_f32_t *)tensor->opt[0]->data); ggml_compute_forward_map_unary(params, tensor->src0, tensor, fun); } break; case GGML_OP_MAP_BINARY: { const ggml_binary_op_f32_t fun = *((ggml_binary_op_f32_t *)tensor->opt[0]->data); ggml_compute_forward_map_binary(params, tensor->src0, tensor->src1, tensor, fun); } break; case GGML_OP_NONE: { // nop } break; case GGML_OP_COUNT: { GGML_ASSERT(false); } break; } } //////////////////////////////////////////////////////////////////////////////// static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor * tensor, bool inplace) { struct ggml_tensor * src0 = tensor->src0; struct ggml_tensor * src1 = tensor->src1; switch (tensor->op) { case GGML_OP_DUP: { if (src0->grad) { src0->grad = ggml_add_impl(ctx, src0->grad, tensor->grad, inplace); } } break; case GGML_OP_ADD: { if (src0->grad) { src0->grad = ggml_add_impl(ctx, src0->grad, tensor->grad, inplace); } if (src1->grad) { src1->grad = ggml_add_impl(ctx, src1->grad, tensor->grad, inplace); } } break; case GGML_OP_SUB: { if (src0->grad) { src0->grad = ggml_add_impl(ctx, src0->grad, tensor->grad, inplace); } if (src1->grad) { src1->grad = ggml_sub_impl(ctx, src1->grad, tensor->grad, inplace); } } break; case GGML_OP_MUL: { if (src0->grad) { src0->grad = ggml_add_impl(ctx, src0->grad, ggml_mul(ctx, src1, tensor->grad), inplace); } if (src1->grad) { src1->grad = ggml_add_impl(ctx, src1->grad, ggml_mul(ctx, src0, tensor->grad), inplace); } } break; case GGML_OP_DIV: { if (src0->grad) { src0->grad = ggml_add_impl(ctx, src0->grad, ggml_div(ctx, tensor->grad, src1), inplace); } if (src1->grad) { src1->grad = ggml_sub_impl(ctx, src1->grad, ggml_mul(ctx, tensor->grad, ggml_div(ctx, tensor, src1)), inplace); } } break; case GGML_OP_SQR: { if (src0->grad) { src0->grad = ggml_add_impl(ctx, src0->grad, ggml_mul(ctx, ggml_mul(ctx, src0, tensor->grad), ggml_repeat(ctx, ggml_new_f32(ctx, 2.0f), src0)), inplace); } } break; case GGML_OP_SQRT: { if (src0->grad) { src0->grad = ggml_add_impl(ctx, src0->grad, ggml_div(ctx, ggml_repeat(ctx, ggml_new_f32(ctx, 0.5f), tensor), tensor), inplace); } } break; case GGML_OP_SUM: { if (src0->grad) { src0->grad = ggml_add_impl(ctx, src0->grad, ggml_repeat(ctx, tensor->grad, src0->grad), inplace); } } break; case GGML_OP_MEAN: { GGML_ASSERT(false); // TODO: implement } break; case GGML_OP_REPEAT: { if (src0->grad) { src0->grad = ggml_add_impl(ctx, src0->grad, ggml_sum(ctx, tensor->grad), inplace); } } break; case GGML_OP_ABS: { if (src0->grad) { src0->grad = ggml_add_impl(ctx, src0->grad, ggml_mul(ctx, ggml_sgn(ctx, src0), tensor->grad), inplace); } } break; case GGML_OP_SGN: { if (src0->grad) { // noop } } break; case GGML_OP_NEG: { if (src0->grad) { src0->grad = ggml_sub_impl(ctx, src0->grad, tensor->grad, inplace); } } break; case GGML_OP_STEP: { if (src0->grad) { // noop } } break; case GGML_OP_RELU: { if (src0->grad) { src0->grad = ggml_sub_impl(ctx, src0->grad, ggml_mul(ctx, ggml_step(ctx, src0), tensor->grad), inplace); } } break; case GGML_OP_GELU: { GGML_ASSERT(false); // TODO: not implemented } break; case GGML_OP_SILU: { GGML_ASSERT(false); // TODO: not implemented } break; case GGML_OP_NORM: { GGML_ASSERT(false); // TODO: not implemented } break; case GGML_OP_RMS_NORM: { GGML_ASSERT(false); // TODO: not implemented } break; case GGML_OP_MUL_MAT: { if (src0->grad) { // TODO: this requires outer product - ggml_out_prod(ctx, src1, tensor->grad); GGML_ASSERT(false); } if (src1->grad) { src1->grad = ggml_add_impl(ctx, src1->grad, ggml_mul_mat(ctx, ggml_cont(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_CONT: { 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_MAP_UNARY: case GGML_OP_MAP_BINARY: { 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 =*/ GGML_DEFAULT_N_THREADS, /*.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 // //typedef os_unfair_lock ggml_lock_t; // //#define ggml_lock_init(x) UNUSED(x) //#define ggml_lock_destroy(x) UNUSED(x) //#define ggml_lock_lock os_unfair_lock_lock //#define ggml_lock_unlock os_unfair_lock_unlock // //#define GGML_LOCK_INITIALIZER OS_UNFAIR_LOCK_INIT typedef int ggml_lock_t; #define ggml_lock_init(x) UNUSED(x) #define ggml_lock_destroy(x) UNUSED(x) #define ggml_lock_lock(x) UNUSED(x) #define ggml_lock_unlock(x) UNUSED(x) #define GGML_LOCK_INITIALIZER 0 typedef pthread_t ggml_thread_t; #define ggml_thread_create pthread_create #define ggml_thread_join pthread_join #else //typedef pthread_spinlock_t ggml_lock_t; //#define ggml_lock_init(x) pthread_spin_init(x, PTHREAD_PROCESS_PRIVATE) //#define ggml_lock_destroy pthread_spin_destroy //#define ggml_lock_lock pthread_spin_lock //#define ggml_lock_unlock pthread_spin_unlock typedef int ggml_lock_t; #define ggml_lock_init(x) UNUSED(x) #define ggml_lock_destroy(x) UNUSED(x) #define ggml_lock_lock(x) UNUSED(x) #define ggml_lock_unlock(x) UNUSED(x) #define GGML_LOCK_INITIALIZER 0 typedef pthread_t ggml_thread_t; #define ggml_thread_create pthread_create #define ggml_thread_join pthread_join #endif struct ggml_compute_state_shared { ggml_lock_t spin; int n_threads; // synchronization primitives atomic_int n_ready; atomic_bool has_work; atomic_bool stop; // stop all threads }; struct ggml_compute_state { ggml_thread_t thrd; struct ggml_compute_params params; struct ggml_tensor * node; struct ggml_compute_state_shared * shared; }; static thread_ret_t ggml_graph_compute_thread(void * data) { struct ggml_compute_state * state = (struct ggml_compute_state *) data; const int n_threads = state->shared->n_threads; while (true) { if (atomic_fetch_add(&state->shared->n_ready, 1) == n_threads - 1) { atomic_store(&state->shared->has_work, false); } else { while (atomic_load(&state->shared->has_work)) { if (atomic_load(&state->shared->stop)) { return 0; } ggml_lock_lock (&state->shared->spin); ggml_lock_unlock(&state->shared->spin); } } atomic_fetch_sub(&state->shared->n_ready, 1); // wait for work while (!atomic_load(&state->shared->has_work)) { if (atomic_load(&state->shared->stop)) { return 0; } ggml_lock_lock (&state->shared->spin); ggml_lock_unlock(&state->shared->spin); } // check if we should stop if (atomic_load(&state->shared->stop)) { break; } if (state->node) { if (state->params.ith < state->params.nth) { ggml_compute_forward(&state->params, state->node); } state->node = NULL; } else { break; } } return 0; } void ggml_graph_compute(struct ggml_context * ctx, struct ggml_cgraph * cgraph) { const int n_threads = cgraph->n_threads; struct ggml_compute_state_shared state_shared = { /*.spin =*/ GGML_LOCK_INITIALIZER, /*.n_threads =*/ n_threads, /*.n_ready =*/ 0, /*.has_work =*/ false, /*.stop =*/ false, }; struct ggml_compute_state * workers = n_threads > 1 ? alloca(sizeof(struct ggml_compute_state)*(n_threads - 1)) : NULL; // create thread pool if (n_threads > 1) { ggml_lock_init(&state_shared.spin); atomic_store(&state_shared.has_work, true); for (int j = 0; j < n_threads - 1; j++) { workers[j] = (struct ggml_compute_state) { .thrd = 0, .params = { .type = GGML_TASK_COMPUTE, .ith = j + 1, .nth = n_threads, .wsize = cgraph->work ? ggml_nbytes(cgraph->work) : 0, .wdata = cgraph->work ? cgraph->work->data : NULL, }, .node = NULL, .shared = &state_shared, }; int rc = ggml_thread_create(&workers[j].thrd, NULL, ggml_graph_compute_thread, &workers[j]); GGML_ASSERT(rc == 0); UNUSED(rc); } } // initialize tasks + work buffer { size_t work_size = 0; // thread scheduling for the different operations for (int i = 0; i < cgraph->n_nodes; i++) { struct ggml_tensor * node = cgraph->nodes[i]; switch (node->op) { case GGML_OP_CPY: case GGML_OP_DUP: { node->n_tasks = n_threads; size_t cur = 0; if (ggml_is_quantized(node->type)) { cur = GGML_TYPE_SIZE[GGML_TYPE_F32] * node->ne[0] * n_threads; } work_size = MAX(work_size, cur); } break; case GGML_OP_ADD: { node->n_tasks = n_threads; size_t cur = 0; if (ggml_is_quantized(node->src0->type)) { cur = GGML_TYPE_SIZE[GGML_TYPE_F32] * node->src0->ne[0] * n_threads; } work_size = MAX(work_size, cur); } break; case GGML_OP_SUB: case GGML_OP_MUL: case GGML_OP_DIV: case GGML_OP_SQR: case GGML_OP_SQRT: case GGML_OP_SUM: case GGML_OP_MEAN: case GGML_OP_REPEAT: case GGML_OP_ABS: case GGML_OP_SGN: case GGML_OP_NEG: case GGML_OP_STEP: case GGML_OP_RELU: { node->n_tasks = 1; } break; case GGML_OP_GELU: { node->n_tasks = n_threads; } break; case GGML_OP_SILU: { node->n_tasks = n_threads; } break; case GGML_OP_NORM: case GGML_OP_RMS_NORM: { node->n_tasks = n_threads; } break; case GGML_OP_MUL_MAT: { node->n_tasks = n_threads; // TODO: use different scheduling for different matrix sizes //const int nr0 = ggml_nrows(node->src0); //const int nr1 = ggml_nrows(node->src1); //node->n_tasks = MIN(n_threads, MAX(1, nr0/128)); //printf("nr0 = %8d, nr1 = %8d, nr0*nr1 = %8d, n_tasks = %d\n", nr0, nr1, nr0*nr1, node->n_tasks); size_t cur = 0; if (node->src0->type == GGML_TYPE_F16 && node->src1->type == GGML_TYPE_F32) { #if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CUBLAS) 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 (ggml_is_quantized(node->src0->type) && node->src1->type == GGML_TYPE_F32) { #if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CUBLAS) if (ggml_compute_forward_mul_mat_use_blas(node->src0, node->src1, node)) { node->n_tasks = 1; cur = GGML_TYPE_SIZE[GGML_TYPE_F32]*(node->src0->ne[0]*node->src0->ne[1]); } else #endif { cur = GGML_TYPE_SIZE[GGML_TYPE_Q8_0]*ggml_nelements(node->src1)/GGML_BLCK_SIZE[GGML_TYPE_Q8_0]; } } else { GGML_ASSERT(false); } work_size = MAX(work_size, cur); } break; case GGML_OP_SCALE: { node->n_tasks = n_threads; } break; case GGML_OP_CONT: case GGML_OP_RESHAPE: case GGML_OP_VIEW: case GGML_OP_PERMUTE: case GGML_OP_TRANSPOSE: case GGML_OP_GET_ROWS: case GGML_OP_DIAG_MASK_INF: { node->n_tasks = 1; } break; case GGML_OP_SOFT_MAX: { node->n_tasks = n_threads; } break; case GGML_OP_ROPE: { node->n_tasks = n_threads; } break; case GGML_OP_CONV_1D_1S: case GGML_OP_CONV_1D_2S: { node->n_tasks = n_threads; GGML_ASSERT(node->src0->ne[3] == 1); GGML_ASSERT(node->src1->ne[2] == 1); GGML_ASSERT(node->src1->ne[3] == 1); size_t cur = 0; const int nk = node->src0->ne[0]; if (node->src0->type == GGML_TYPE_F16 && node->src1->type == GGML_TYPE_F32) { cur = sizeof(ggml_fp16_t)*( nk*ggml_up32(node->src0->ne[1])*node->src0->ne[2] + ( 2*(nk/2) + node->src1->ne[0])*node->src1->ne[1] ); } else if (node->src0->type == GGML_TYPE_F32 && node->src1->type == GGML_TYPE_F32) { cur = sizeof(float)*( nk*ggml_up32(node->src0->ne[1])*node->src0->ne[2] + ( 2*(nk/2) + node->src1->ne[0])*node->src1->ne[1] ); } else { GGML_ASSERT(false); } work_size = MAX(work_size, cur); } break; case GGML_OP_FLASH_ATTN: { node->n_tasks = n_threads; size_t cur = 0; const int64_t ne11 = ggml_up(node->src1->ne[1], GGML_SOFT_MAX_UNROLL); if (node->src1->type == GGML_TYPE_F32) { cur = sizeof(float)*ne11*node->n_tasks; // TODO: this can become (n_tasks-1) cur += sizeof(float)*ne11*node->n_tasks; // this is overestimated by x2 } if (node->src1->type == GGML_TYPE_F16) { cur = sizeof(float)*ne11*node->n_tasks; // TODO: this can become (n_tasks-1) cur += sizeof(float)*ne11*node->n_tasks; // this is overestimated by x2 } work_size = MAX(work_size, cur); } break; case GGML_OP_FLASH_FF: { node->n_tasks = n_threads; size_t cur = 0; if (node->src1->type == GGML_TYPE_F32) { cur = sizeof(float)*node->src1->ne[1]*node->n_tasks; // TODO: this can become (n_tasks-1) cur += sizeof(float)*node->src1->ne[1]*node->n_tasks; // this is overestimated by x2 } if (node->src1->type == GGML_TYPE_F16) { cur = sizeof(float)*node->src1->ne[1]*node->n_tasks; // TODO: this can become (n_tasks-1) cur += sizeof(float)*node->src1->ne[1]*node->n_tasks; // this is overestimated by x2 } work_size = MAX(work_size, cur); } break; case GGML_OP_MAP_UNARY: case GGML_OP_MAP_BINARY: { node->n_tasks = 1; } 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(¶ms, 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(¶ms, 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(¶ms, node); // wait for thread pool if (node->n_tasks > 1) { if (atomic_fetch_add(&state_shared.n_ready, 1) == n_threads - 1) { atomic_store(&state_shared.has_work, false); } while (atomic_load(&state_shared.has_work)) { ggml_lock_lock (&state_shared.spin); ggml_lock_unlock(&state_shared.spin); } atomic_fetch_sub(&state_shared.n_ready, 1); while (atomic_load(&state_shared.n_ready) != 0) { ggml_lock_lock (&state_shared.spin); ggml_lock_unlock(&state_shared.spin); } } // performance stats (node) { int64_t perf_cycles_cur = ggml_perf_cycles() - perf_node_start_cycles; int64_t perf_time_us_cur = ggml_perf_time_us() - perf_node_start_time_us; node->perf_runs++; node->perf_cycles += perf_cycles_cur; node->perf_time_us += perf_time_us_cur; } } // join thread pool if (n_threads > 1) { atomic_store(&state_shared.stop, true); atomic_store(&state_shared.has_work, true); for (int j = 0; j < n_threads - 1; j++) { int rc = ggml_thread_join(workers[j].thrd, NULL); GGML_ASSERT(rc == 0); UNUSED(rc); } ggml_lock_destroy(&state_shared.spin); } // performance stats (graph) { int64_t perf_cycles_cur = ggml_perf_cycles() - perf_start_cycles; int64_t perf_time_us_cur = ggml_perf_time_us() - perf_start_time_us; cgraph->perf_runs++; cgraph->perf_cycles += perf_cycles_cur; cgraph->perf_time_us += perf_time_us_cur; GGML_PRINT_DEBUG("%s: perf (%d) - cpu = %.3f / %.3f ms, wall = %.3f / %.3f ms\n", __func__, cgraph->perf_runs, (double) perf_cycles_cur / (double) ggml_cycles_per_ms(), (double) cgraph->perf_cycles / (double) ggml_cycles_per_ms() / (double) cgraph->perf_runs, (double) perf_time_us_cur / 1000.0, (double) cgraph->perf_time_us / 1000.0 / cgraph->perf_runs); } } void ggml_graph_reset(struct ggml_cgraph * cgraph) { for (int i = 0; i < cgraph->n_nodes; i++) { struct ggml_tensor * grad = cgraph->grads[i]; if (grad) { ggml_set_zero(grad); } } } void ggml_graph_print(const struct ggml_cgraph * cgraph) { int64_t perf_total_per_op_us[GGML_OP_COUNT] = {0}; GGML_PRINT("=== GRAPH ===\n"); GGML_PRINT_DEBUG("n_threads = %d\n", cgraph->n_threads); GGML_PRINT_DEBUG("total work size = %zu bytes\n", cgraph->work_size); GGML_PRINT("n_nodes = %d\n", cgraph->n_nodes); for (int i = 0; i < cgraph->n_nodes; i++) { struct ggml_tensor * node = cgraph->nodes[i]; perf_total_per_op_us[node->op] += node->perf_time_us; GGML_PRINT(" - %3d: [ %" PRId64 ", %" PRId64 ", %" PRId64 "] %16s %s (%3d) cpu = %7.3f / %7.3f ms, wall = %7.3f / %7.3f ms\n", i, node->ne[0], node->ne[1], node->ne[2], GGML_OP_LABEL[node->op], node->is_param ? "x" : node->grad ? "g" : " ", node->perf_runs, (double) node->perf_cycles / (double) ggml_cycles_per_ms(), (double) node->perf_cycles / (double) ggml_cycles_per_ms() / (double) node->perf_runs, (double) node->perf_time_us / 1000.0, (double) node->perf_time_us / 1000.0 / node->perf_runs); } GGML_PRINT("n_leafs = %d\n", cgraph->n_leafs); for (int i = 0; i < cgraph->n_leafs; i++) { struct ggml_tensor * node = cgraph->leafs[i]; GGML_PRINT(" - %3d: [ %" PRId64 ", %" PRId64 "] %8s\n", i, node->ne[0], node->ne[1], GGML_OP_LABEL[node->op]); } for (int i = 0; i < GGML_OP_COUNT; i++) { GGML_PRINT("perf_total_per_op_us[%16s] = %7.3f ms\n", GGML_OP_LABEL[i], (double) perf_total_per_op_us[i] / 1000.0); } GGML_PRINT("========================================\n"); } // check if node is part of the graph static bool ggml_graph_find(const struct ggml_cgraph * cgraph, const struct ggml_tensor * node) { if (cgraph == NULL) { return true; } for (int i = 0; i < cgraph->n_nodes; i++) { if (cgraph->nodes[i] == node) { return true; } } return false; } static struct ggml_tensor * ggml_graph_get_parent(const struct ggml_cgraph * cgraph, const struct ggml_tensor * node) { for (int i = 0; i < cgraph->n_nodes; i++) { struct ggml_tensor * parent = cgraph->nodes[i]; if (parent->grad == node) { return parent; } } return NULL; } void ggml_graph_dump_dot(const struct ggml_cgraph * gb, const struct ggml_cgraph * gf, const char * filename) { char color[16]; FILE * fp = fopen(filename, "w"); GGML_ASSERT(fp); fprintf(fp, "digraph G {\n"); fprintf(fp, " newrank = true;\n"); fprintf(fp, " rankdir = LR;\n"); for (int i = 0; i < gb->n_nodes; i++) { struct ggml_tensor * node = gb->nodes[i]; if (ggml_graph_get_parent(gb, node) != NULL) { continue; } if (node->is_param) { snprintf(color, sizeof(color), "yellow"); } else if (node->grad) { if (ggml_graph_find(gf, node)) { snprintf(color, sizeof(color), "green"); } else { snprintf(color, sizeof(color), "lightblue"); } } else { snprintf(color, sizeof(color), "white"); } fprintf(fp, " \"%p\" [ \ style = filled; fillcolor = %s; shape = record; \ label=\"%d [%" PRId64 ", %" PRId64 "] | %s", (void *) node, color, i, node->ne[0], node->ne[1], GGML_OP_SYMBOL[node->op]); if (node->grad) { fprintf(fp, " | %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=\"%.1e\"; ]\n", (void *) node, color, (double)ggml_get_f32_1d(node, 0)); } else { fprintf(fp, " \"%p\" [ \ style = filled; fillcolor = %s; shape = record; \ label=\"CONST %d [%" PRId64 ", %" PRId64 "]\"; ]\n", (void *) node, color, i, node->ne[0], node->ne[1]); } } for (int i = 0; i < gb->n_nodes; i++) { struct ggml_tensor * node = gb->nodes[i]; struct ggml_tensor * parent = ggml_graph_get_parent(gb, node); if (node->src0) { struct ggml_tensor * parent0 = ggml_graph_get_parent(gb, node->src0); fprintf(fp, " \"%p\":%s -> \"%p\":%s [ arrowhead = %s; style = %s; label = \"x\"; ]\n", parent0 ? (void *) parent0 : (void *) node->src0, parent0 ? "g" : "x", parent ? (void *) parent : (void *) node, parent ? "g" : "x", parent ? "empty" : "vee", parent ? "dashed" : "solid"); } if (node->src1) { struct ggml_tensor * parent1 = ggml_graph_get_parent(gb, node->src1); fprintf(fp, " \"%p\":%s -> \"%p\":%s [ arrowhead = %s; style = %s; label = \"y\"; ]\n", parent1 ? (void *) parent1 : (void *) node->src1, parent1 ? "g" : "x", parent ? (void *) parent : (void *) node, parent ? "g" : "x", parent ? "empty" : "vee", parent ? "dashed" : "solid"); } } for (int i = 0; i < gb->n_leafs; i++) { struct ggml_tensor * node = gb->leafs[i]; if (node->src0) { fprintf(fp, " \"%p\":%s -> \"%p\":%s [ label = \"x\"; ]\n", (void *) node->src0, "x", (void *) node, "x"); } if (node->src1) { fprintf(fp, " \"%p\":%s -> \"%p\":%s [ label = \"y\"; ]\n", (void *) node->src1, "x", (void *) node, "x"); } } fprintf(fp, "}\n"); fclose(fp); GGML_PRINT("%s: dot -Tpng %s -o %s.png && open %s.png\n", __func__, filename, filename, filename); } //////////////////////////////////////////////////////////////////////////////// static void ggml_opt_set_params(int np, struct ggml_tensor * const ps[], const float * x) { int i = 0; for (int p = 0; p < np; ++p) { const int64_t ne = ggml_nelements(ps[p]) ; // TODO: add function to set tensor from array for (int64_t j = 0; j < ne; ++j) { ggml_set_f32_1d(ps[p], j, x[i++]); } } } static void ggml_opt_get_params(int np, struct ggml_tensor * const ps[], float * x) { int i = 0; for (int p = 0; p < np; ++p) { const int64_t ne = ggml_nelements(ps[p]) ; // TODO: add function to get all elements at once for (int64_t j = 0; j < ne; ++j) { x[i++] = ggml_get_f32_1d(ps[p], j); } } } static void ggml_opt_get_grad(int np, struct ggml_tensor * const ps[], float * g) { int i = 0; for (int p = 0; p < np; ++p) { const int64_t ne = ggml_nelements(ps[p]) ; // TODO: add function to get all elements at once for (int64_t j = 0; j < ne; ++j) { g[i++] = ggml_get_f32_1d(ps[p]->grad, j); } } } // // ADAM // // ref: https://arxiv.org/pdf/1412.6980.pdf // static enum ggml_opt_result ggml_opt_adam( struct ggml_context * ctx, struct ggml_opt_params params, struct ggml_tensor * f, struct ggml_cgraph * gf, struct ggml_cgraph * gb) { GGML_ASSERT(ggml_is_scalar(f)); gf->n_threads = params.n_threads; gb->n_threads = params.n_threads; // these will store the parameters we want to optimize struct ggml_tensor * ps[GGML_MAX_PARAMS]; int np = 0; int nx = 0; for (int i = 0; i < gf->n_nodes; ++i) { if (gf->nodes[i]->is_param) { GGML_PRINT_DEBUG("found param %d: grad->op = %d\n", np, gf->nodes[i]->grad->op); GGML_ASSERT(np < GGML_MAX_PARAMS); ps[np++] = gf->nodes[i]; nx += ggml_nelements(gf->nodes[i]); } } // constants const float alpha = params.adam.alpha; const float beta1 = params.adam.beta1; const float beta2 = params.adam.beta2; const float eps = params.adam.eps; float * x = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx)->data; // view of the parameters float * g1 = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx)->data; // gradient float * g2 = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx)->data; // gradient squared float * m = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx)->data; // first moment float * v = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx)->data; // second moment float * mh = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx)->data; // first moment hat float * vh = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx)->data; // second moment hat float * pf = params.past > 0 ? ggml_new_tensor_1d(ctx, GGML_TYPE_F32, params.past)->data : NULL; // past function values // initialize ggml_vec_set_f32(nx, m, 0.0f); ggml_vec_set_f32(nx, v, 0.0f); // update view ggml_opt_get_params(np, ps, x); // compute the function value ggml_graph_reset (gf); ggml_set_f32 (f->grad, 1.0f); ggml_graph_compute(ctx, gb); float fx_prev = ggml_get_f32_1d(f, 0); if (pf) { pf[0] = fx_prev; } int n_no_improvement = 0; float fx_best = fx_prev; // run the optimizer for (int t = 0; t < params.adam.n_iter; ++t) { GGML_PRINT_DEBUG ("=== iter %d ===\n", t); GGML_PRINT_DEBUG ("f = %10.6f\n", ggml_get_f32_1d(f, 0)); GGML_PRINT_DEBUG_5("df/dx0 = %10.6f\n", ggml_get_f32_1d(ps[0]->grad, 0)); GGML_PRINT_DEBUG_5("df/dx1 = %10.6f\n", ggml_get_f32_1d(ps[1]->grad, 0)); for (int i = 0; i < np; ++i) { GGML_PRINT_DEBUG("param %d: %10.6f, g = %10.6f\n", i, ggml_get_f32_1d(ps[i], 0), ggml_get_f32_1d(ps[i]->grad, 0)); } const int64_t t_start_wall = ggml_time_us(); const int64_t t_start_cpu = ggml_cycles(); UNUSED(t_start_wall); UNUSED(t_start_cpu); { // update the gradient ggml_opt_get_grad(np, ps, g1); // m_t = beta1*m_t-1 + (1 - beta1)*g_t ggml_vec_scale_f32(nx, m, beta1); ggml_vec_mad_f32 (nx, m, g1, 1.0f - beta1); // g2 = g1^2 ggml_vec_sqr_f32 (nx, g2, g1); // v_t = beta2*v_t-1 + (1 - beta2)*g_t^2 ggml_vec_scale_f32(nx, v, beta2); ggml_vec_mad_f32 (nx, v, g2, 1.0f - beta2); // m^hat = m_t / (1 - beta1^t) // v^hat = v_t / (1 - beta2^t) // x_t = x_t-1 - alpha*m^hat/(sqrt(v^hat) + eps) ggml_vec_cpy_f32 (nx, mh, m); ggml_vec_cpy_f32 (nx, vh, v); ggml_vec_scale_f32(nx, mh, alpha/(1.0f - powf(beta1, t + 1))); ggml_vec_scale_f32(nx, vh, 1.0f/(1.0f - powf(beta2, t + 1))); ggml_vec_sqrt_f32 (nx, vh, vh); ggml_vec_acc1_f32 (nx, vh, eps); ggml_vec_div_f32 (nx, mh, mh, vh); ggml_vec_sub_f32 (nx, x, x, mh); // update the parameters ggml_opt_set_params(np, ps, x); } ggml_graph_reset (gf); ggml_set_f32 (f->grad, 1.0f); ggml_graph_compute(ctx, gb); const float fx = ggml_get_f32_1d(f, 0); // check convergence if (fabsf(fx - fx_prev)/fx < params.adam.eps_f) { GGML_PRINT_DEBUG("converged\n"); return GGML_OPT_OK; } // delta-based convergence test if (pf != NULL) { // need at least params.past iterations to start checking for convergence if (params.past <= t) { const float rate = (pf[t%params.past] - fx)/fx; if (fabsf(rate) < params.delta) { return GGML_OPT_OK; } } pf[t%params.past] = fx; } // check for improvement if (params.max_no_improvement > 0) { if (fx_best > fx) { fx_best = fx; n_no_improvement = 0; } else { ++n_no_improvement; if (n_no_improvement >= params.max_no_improvement) { return GGML_OPT_OK; } } } fx_prev = fx; { const int64_t t_end_cpu = ggml_cycles(); GGML_PRINT_DEBUG("time iter: %5.3f s\n", ((float)(t_end_cpu - t_start_cpu))/CLOCKS_PER_SEC); UNUSED(t_end_cpu); const int64_t t_end_wall = ggml_time_us(); GGML_PRINT_DEBUG("wall time iter: %5.3f s\n", (t_end_wall - t_start_wall)/1e6); UNUSED(t_end_wall); } } return GGML_OPT_DID_NOT_CONVERGE; } // // L-BFGS // // the L-BFGS implementation below is based on the following implementation: // // https://github.com/chokkan/liblbfgs // struct ggml_lbfgs_iteration_data { float alpha; float ys; float * s; float * y; }; static enum ggml_opt_result linesearch_backtracking( struct ggml_context * ctx, const struct ggml_opt_params * params, int nx, float * x, float * fx, float * g, float * d, float * step, const float * xp, struct ggml_tensor * f, struct ggml_cgraph * gf, struct ggml_cgraph * gb, const int np, struct ggml_tensor * ps[]) { int count = 0; float width = 0.0f; float dg = 0.0f; float finit = 0.0f; float dginit = 0.0f; float dgtest = 0.0f; const float dec = 0.5f; const float inc = 2.1f; if (*step <= 0.f) { return GGML_LINESEARCH_INVALID_PARAMETERS; } // compute the initial gradient in the search direction ggml_vec_dot_f32(nx, &dginit, g, d); // make sure that d points to a descent direction if (0 < dginit) { return GGML_LINESEARCH_FAIL; } // initialize local variables finit = *fx; dgtest = params->lbfgs.ftol*dginit; while (true) { ggml_vec_cpy_f32(nx, x, xp); ggml_vec_mad_f32(nx, x, d, *step); // evaluate the function and gradient values { ggml_opt_set_params(np, ps, x); ggml_graph_reset (gf); ggml_set_f32 (f->grad, 1.0f); ggml_graph_compute(ctx, gb); ggml_opt_get_grad(np, ps, g); *fx = ggml_get_f32_1d(f, 0); } ++count; if (*fx > finit + (*step)*dgtest) { width = dec; } else { // Armijo condition is satisfied if (params->lbfgs.linesearch == GGML_LINESEARCH_BACKTRACKING_ARMIJO) { return count; } ggml_vec_dot_f32(nx, &dg, g, d); // check the Wolfe condition if (dg < params->lbfgs.wolfe * dginit) { width = inc; } else { if(params->lbfgs.linesearch == GGML_LINESEARCH_BACKTRACKING_WOLFE) { // regular Wolfe conditions return count; } if(dg > -params->lbfgs.wolfe*dginit) { width = dec; } else { // strong Wolfe condition (GGML_LINESEARCH_BACKTRACKING_STRONG_WOLFE) return count; } return count; } } if (*step < params->lbfgs.min_step) { return GGML_LINESEARCH_MINIMUM_STEP; } if (*step > params->lbfgs.max_step) { return GGML_LINESEARCH_MAXIMUM_STEP; } if (params->lbfgs.max_linesearch <= count) { return GGML_LINESEARCH_MAXIMUM_ITERATIONS; } (*step) *= width; } return GGML_LINESEARCH_FAIL; } static enum ggml_opt_result ggml_opt_lbfgs( struct ggml_context * ctx, struct ggml_opt_params params, struct ggml_tensor * f, struct ggml_cgraph * gf, struct ggml_cgraph * gb) { if (params.lbfgs.linesearch == GGML_LINESEARCH_BACKTRACKING_WOLFE || params.lbfgs.linesearch == GGML_LINESEARCH_BACKTRACKING_STRONG_WOLFE) { if (params.lbfgs.wolfe <= params.lbfgs.ftol || 1.f <= params.lbfgs.wolfe) { return GGML_OPT_INVALID_WOLFE; } } gf->n_threads = params.n_threads; gb->n_threads = params.n_threads; const int m = params.lbfgs.m; // these will store the parameters we want to optimize struct ggml_tensor * ps[GGML_MAX_PARAMS]; int np = 0; int nx = 0; for (int i = 0; i < gf->n_nodes; ++i) { if (gf->nodes[i]->is_param) { GGML_PRINT_DEBUG("found param %d: grad->op = %d\n", np, gf->nodes[i]->grad->op); GGML_ASSERT(np < GGML_MAX_PARAMS); ps[np++] = gf->nodes[i]; nx += ggml_nelements(gf->nodes[i]); } } float * x = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx)->data; // current parameters float * xp = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx)->data; // previous parameters float * g = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx)->data; // current gradient float * gp = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx)->data; // previous gradient float * d = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx)->data; // search direction float * pf = params.past > 0 ? ggml_new_tensor_1d(ctx, GGML_TYPE_F32, params.past)->data : NULL; // past function values float fx = 0.0f; // cost function value float xnorm = 0.0f; // ||x|| float gnorm = 0.0f; // ||g|| float step = 0.0f; // initialize x from the graph nodes ggml_opt_get_params(np, ps, x); // the L-BFGS memory struct ggml_lbfgs_iteration_data * lm = alloca(sizeof(struct ggml_lbfgs_iteration_data)*m); for (int i = 0; i < m; ++i) { lm[i].alpha = 0.0f; lm[i].ys = 0.0f; lm[i].s = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx)->data; lm[i].y = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx)->data; } // evaluate the function value and its gradient { ggml_opt_set_params(np, ps, x); ggml_graph_reset (gf); ggml_set_f32 (f->grad, 1.0f); ggml_graph_compute(ctx, gb); ggml_opt_get_grad(np, ps, g); fx = ggml_get_f32_1d(f, 0); } if (pf) { pf[0] = fx; } float fx_best = fx; // search direction = -gradient ggml_vec_neg_f32(nx, d, g); // ||x||, ||g|| ggml_vec_norm_f32(nx, &xnorm, x); ggml_vec_norm_f32(nx, &gnorm, g); if (xnorm < 1.0f) { xnorm = 1.0f; } // already optimized if (gnorm/xnorm <= params.lbfgs.eps) { return GGML_OPT_OK; } // initial step ggml_vec_norm_inv_f32(nx, &step, d); int j = 0; int k = 1; int ls = 0; int end = 0; int bound = 0; int n_no_improvement = 0; float ys = 0.0f; float yy = 0.0f; float beta = 0.0f; while (true) { // store the current position and gradient vectors ggml_vec_cpy_f32(nx, xp, x); ggml_vec_cpy_f32(nx, gp, g); ls = linesearch_backtracking(ctx, ¶ms, nx, x, &fx, g, d, &step, xp, f, gf, gb, np, ps); if (ls < 0) { // linesearch failed - go back to the previous point and return ggml_vec_cpy_f32(nx, x, xp); ggml_vec_cpy_f32(nx, g, gp); return ls; } ggml_vec_norm_f32(nx, &xnorm, x); ggml_vec_norm_f32(nx, &gnorm, g); GGML_PRINT_DEBUG("f = %10.6f\n", ggml_get_f32_1d(f, 0)); if (xnorm < 1.0f) { xnorm = 1.0f; } if (gnorm/xnorm <= params.lbfgs.eps) { // converged return GGML_OPT_OK; } // delta-based convergence test if (pf != NULL) { // need at least params.past iterations to start checking for convergence if (params.past <= k) { const float rate = (pf[k%params.past] - fx)/fx; if (fabsf(rate) < params.delta) { return GGML_OPT_OK; } } pf[k%params.past] = fx; } // check for improvement if (params.max_no_improvement > 0) { if (fx < fx_best) { fx_best = fx; n_no_improvement = 0; } else { n_no_improvement++; if (n_no_improvement >= params.max_no_improvement) { return GGML_OPT_OK; } } } if (params.lbfgs.n_iter != 0 && params.lbfgs.n_iter < k + 1) { // reached the maximum number of iterations return GGML_OPT_DID_NOT_CONVERGE; } // update vectors s and y: // s_{k+1} = x_{k+1} - x_{k} = \step * d_{k}. // y_{k+1} = g_{k+1} - g_{k}. // ggml_vec_sub_f32(nx, lm[end].s, x, xp); ggml_vec_sub_f32(nx, lm[end].y, g, gp); // compute scalars ys and yy: // ys = y^t \cdot s -> 1 / \rho. // yy = y^t \cdot y. // ggml_vec_dot_f32(nx, &ys, lm[end].y, lm[end].s); ggml_vec_dot_f32(nx, &yy, lm[end].y, lm[end].y); lm[end].ys = ys; // find new search direction // ref: https://en.wikipedia.org/wiki/Limited-memory_BFGS bound = (m <= k) ? m : k; k++; end = (end + 1)%m; // initialize search direction with -g ggml_vec_neg_f32(nx, d, g); j = end; for (int i = 0; i < bound; ++i) { j = (j + m - 1) % m; // \alpha_{j} = \rho_{j} s^{t}_{j} \cdot q_{k+1} ggml_vec_dot_f32(nx, &lm[j].alpha, lm[j].s, d); lm[j].alpha /= lm[j].ys; // q_{i} = q_{i+1} - \alpha_{i} y_{i} ggml_vec_mad_f32(nx, d, lm[j].y, -lm[j].alpha); } ggml_vec_scale_f32(nx, d, ys/yy); for (int i = 0; i < bound; ++i) { // \beta_{j} = \rho_{j} y^t_{j} \cdot \gamma_{i} ggml_vec_dot_f32(nx, &beta, lm[j].y, d); beta /= lm[j].ys; // \gamma_{i+1} = \gamma_{i} + (\alpha_{j} - \beta_{j}) s_{j} ggml_vec_mad_f32(nx, d, lm[j].s, lm[j].alpha - beta); j = (j + 1)%m; } step = 1.0; } return GGML_OPT_DID_NOT_CONVERGE; } struct ggml_opt_params ggml_opt_default_params(enum ggml_opt_type type) { struct ggml_opt_params result; switch (type) { case GGML_OPT_ADAM: { result = (struct ggml_opt_params) { .type = GGML_OPT_ADAM, .n_threads = 1, .past = 0, .delta = 1e-5f, .max_no_improvement = 100, .print_forward_graph = true, .print_backward_graph = true, .adam = { .n_iter = 10000, .alpha = 0.001f, .beta1 = 0.9f, .beta2 = 0.999f, .eps = 1e-8f, .eps_f = 1e-5f, .eps_g = 1e-3f, }, }; } break; case GGML_OPT_LBFGS: { result = (struct ggml_opt_params) { .type = GGML_OPT_LBFGS, .n_threads = 1, .past = 0, .delta = 1e-5f, .max_no_improvement = 0, .print_forward_graph = true, .print_backward_graph = true, .lbfgs = { .m = 6, .n_iter = 100, .max_linesearch = 20, .eps = 1e-5f, .ftol = 1e-4f, .wolfe = 0.9f, .min_step = 1e-20f, .max_step = 1e+20f, .linesearch = GGML_LINESEARCH_DEFAULT, }, }; } break; } return result; } enum ggml_opt_result ggml_opt( struct ggml_context * ctx, struct ggml_opt_params params, struct ggml_tensor * f) { bool free_ctx = false; if (ctx == NULL) { struct ggml_init_params params_ctx = { .mem_size = 16*1024*1024, .mem_buffer = NULL, .no_alloc = false, }; ctx = ggml_init(params_ctx); if (ctx == NULL) { return GGML_OPT_NO_CONTEXT; } free_ctx = true; } enum ggml_opt_result result = GGML_OPT_OK; // build forward + backward compute graphs struct ggml_cgraph gf = ggml_build_forward (f); struct ggml_cgraph gb = ggml_build_backward(ctx, &gf, false); switch (params.type) { case GGML_OPT_ADAM: { result = ggml_opt_adam(ctx, params, f, &gf, &gb); } break; case GGML_OPT_LBFGS: { result = ggml_opt_lbfgs(ctx, params, f, &gf, &gb); } break; } if (params.print_forward_graph) { ggml_graph_print (&gf); ggml_graph_dump_dot(&gf, NULL, "opt-forward.dot"); } if (params.print_backward_graph) { ggml_graph_print (&gb); ggml_graph_dump_dot(&gb, &gf, "opt-backward.dot"); } if (free_ctx) { ggml_free(ctx); } return result; } //////////////////////////////////////////////////////////////////////////////// static void collect_quant_histogram(int n, const uint8_t * restrict qs, int64_t * hist) { for (int l = 0; l < n; l += 2) { const uint8_t vi0 = qs[l/2] & 0xF; const uint8_t vi1 = qs[l/2] >> 4; hist[vi0]++; hist[vi1]++; } } size_t ggml_quantize_q4_0(const float * src, void * dst, int n, int k, int64_t * hist) { assert(k % QK4_0 == 0); const int nb = k / QK4_0; for (int j = 0; j < n; j += k) { block_q4_0 * restrict y = (block_q4_0 *)dst + j/QK4_0; quantize_row_q4_0_reference(src + j, y, k); for (int i = 0; i < nb; i++) { collect_quant_histogram(QK4_0, y[i].qs, hist); } } return (n/QK4_0*sizeof(block_q4_0)); } size_t ggml_quantize_q4_1(const float * src, void * dst, int n, int k, int64_t * hist) { assert(k % QK4_1 == 0); const int nb = k / QK4_1; for (int j = 0; j < n; j += k) { block_q4_1 * restrict y = (block_q4_1 *)dst + j/QK4_1; quantize_row_q4_1_reference(src + j, y, k); for (int i = 0; i < nb; i++) { collect_quant_histogram(QK4_1, y[i].qs, hist); } } return (n/QK4_1*sizeof(block_q4_1)); } size_t ggml_quantize_q4_2(const float * src, void * dst, int n, int k, int64_t * hist) { assert(k % QK4_2 == 0); const int nb = k / QK4_2; for (int j = 0; j < n; j += k) { block_q4_2 * restrict y = (block_q4_2 *)dst + j/QK4_2; quantize_row_q4_2_reference(src + j, y, k); for (int i = 0; i < nb; i++) { collect_quant_histogram(QK4_2, y[i].qs, hist); } } return (n/QK4_2*sizeof(block_q4_2)); } size_t ggml_quantize_q4_3(const float * src, void * dst, int n, int k, int64_t * hist) { assert(k % QK4_3 == 0); const int nb = k / QK4_3; for (int j = 0; j < n; j += k) { block_q4_3 * restrict y = (block_q4_3 *)dst + j/QK4_3; quantize_row_q4_3_reference(src + j, y, k); for (int i = 0; i < nb; i++) { collect_quant_histogram(QK4_3, y[i].qs, hist); } } return (n/QK4_3*sizeof(block_q4_3)); } size_t ggml_quantize_chunk(enum ggml_type type, const float * src, void * dst, int start, int n, int64_t * hist) { size_t result = 0; switch (type) { case GGML_TYPE_Q4_0: { GGML_ASSERT(start % QK4_0 == 0); block_q4_0 * block = (block_q4_0*)dst + start / QK4_0; result = ggml_quantize_q4_0(src + start, block, n, n, hist); } break; case GGML_TYPE_Q4_1: { GGML_ASSERT(start % QK4_1 == 0); block_q4_1 * block = (block_q4_1*)dst + start / QK4_1; result = ggml_quantize_q4_1(src + start, block, n, n, hist); } break; case GGML_TYPE_Q4_2: { GGML_ASSERT(start % QK4_2 == 0); block_q4_2 * block = (block_q4_2*)dst + start / QK4_2; result = ggml_quantize_q4_2(src + start, block, n, n, hist); } break; case GGML_TYPE_Q4_3: { GGML_ASSERT(start % QK4_3 == 0); block_q4_3 * block = (block_q4_3*)dst + start / QK4_3; result = ggml_quantize_q4_3(src + start, block, n, n, hist); } break; default: assert(false); } 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_avx512_vbmi(void) { #if defined(__AVX512VBMI__) return 1; #else return 0; #endif } int ggml_cpu_has_avx512_vnni(void) { #if defined(__AVX512VNNI__) return 1; #else return 0; #endif } int ggml_cpu_has_fma(void) { #if defined(__FMA__) return 1; #else return 0; #endif } int ggml_cpu_has_neon(void) { #if defined(__ARM_NEON) return 1; #else return 0; #endif } int ggml_cpu_has_arm_fma(void) { #if defined(__ARM_FEATURE_FMA) return 1; #else return 0; #endif } int ggml_cpu_has_f16c(void) { #if defined(__F16C__) return 1; #else return 0; #endif } int ggml_cpu_has_fp16_va(void) { #if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) return 1; #else return 0; #endif } int ggml_cpu_has_wasm_simd(void) { #if defined(__wasm_simd128__) return 1; #else return 0; #endif } int ggml_cpu_has_blas(void) { #if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CUBLAS) return 1; #else return 0; #endif } int ggml_cpu_has_cublas(void) { #if defined(GGML_USE_CUBLAS) return 1; #else return 0; #endif } int ggml_cpu_has_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 } ////////////////////////////////////////////////////////////////////////////////