#define _GNU_SOURCE // Defines CLOCK_MONOTONIC on Linux #define _CRT_SECURE_NO_DEPRECATE // Disables ridiculous "unsafe" warnigns on Windows #include "ggml.h" #ifdef GGML_USE_K_QUANTS #include "k_quants.h" #endif #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 #include #include #ifdef GGML_USE_METAL #include #endif // static_assert should be a #define, but if it's not, // fall back to the _Static_assert C11 keyword. // if C99 - static_assert is noop // ref: https://stackoverflow.com/a/53923785/4039976 #ifndef static_assert #if defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 201100L) #define static_assert(cond, msg) _Static_assert(cond, msg) #else #define static_assert(cond, msg) struct global_scope_noop_trick #endif #endif #if defined(_MSC_VER) // disable "possible loss of data" to avoid hundreds of casts // we should just be careful :) #pragma warning(disable: 4244 4267) #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; #include #include #include #endif // __FMA__ and __F16C__ are not defined in MSVC, however they are implied with AVX2/AVX512 #if defined(_MSC_VER) && (defined(__AVX2__) || defined(__AVX512F__)) #ifndef __FMA__ #define __FMA__ #endif #ifndef __F16C__ #define __F16C__ #endif #ifndef __SSE3__ #define __SSE3__ #endif #endif /*#define GGML_PERF*/ #define GGML_DEBUG 0 #define GGML_GELU_FP16 #define GGML_GELU_QUICK_FP16 #define GGML_SILU_FP16 #define GGML_SOFT_MAX_UNROLL 4 #define GGML_VEC_DOT_UNROLL 2 // // logging // #if (GGML_DEBUG >= 1) #define GGML_PRINT_DEBUG(...) printf(__VA_ARGS__) #else #define GGML_PRINT_DEBUG(...) #endif #if (GGML_DEBUG >= 5) #define GGML_PRINT_DEBUG_5(...) printf(__VA_ARGS__) #else #define GGML_PRINT_DEBUG_5(...) #endif #if (GGML_DEBUG >= 10) #define GGML_PRINT_DEBUG_10(...) printf(__VA_ARGS__) #else #define GGML_PRINT_DEBUG_10(...) #endif #define GGML_PRINT(...) printf(__VA_ARGS__) #ifdef GGML_USE_ACCELERATE // uncomment to use vDSP for soft max computation // note: not sure if it is actually faster //#define GGML_SOFT_MAX_ACCELERATE #endif #if UINTPTR_MAX == 0xFFFFFFFF #define GGML_MEM_ALIGN 4 #else #define GGML_MEM_ALIGN 16 #endif // // logging // #if (GGML_DEBUG >= 1) #define GGML_PRINT_DEBUG(...) printf(__VA_ARGS__) #else #define GGML_PRINT_DEBUG(...) #endif #if (GGML_DEBUG >= 5) #define GGML_PRINT_DEBUG_5(...) printf(__VA_ARGS__) #else #define GGML_PRINT_DEBUG_5(...) #endif #if (GGML_DEBUG >= 10) #define GGML_PRINT_DEBUG_10(...) printf(__VA_ARGS__) #else #define GGML_PRINT_DEBUG_10(...) #endif #define GGML_PRINT(...) printf(__VA_ARGS__) // // end of logging block // #if defined(_MSC_VER) || defined(__MINGW32__) #define GGML_ALIGNED_MALLOC(size) _aligned_malloc(size, GGML_MEM_ALIGN) #define GGML_ALIGNED_FREE(ptr) _aligned_free(ptr) #else inline static void * ggml_aligned_malloc(size_t size) { void * aligned_memory = NULL; #ifdef GGML_USE_METAL int result = posix_memalign(&aligned_memory, getpagesize(), size); #else int result = posix_memalign(&aligned_memory, GGML_MEM_ALIGN, size); #endif if (result != 0) { // Handle allocation failure const char *error_desc = "unknown allocation error"; switch (result) { case EINVAL: error_desc = "invalid alignment value"; break; case ENOMEM: error_desc = "insufficient memory"; break; } GGML_PRINT("%s: %s (attempted to allocate %6.2f MB)\n", __func__, error_desc, size/(1024.0*1024.0)); return NULL; } return aligned_memory; } #define GGML_ALIGNED_MALLOC(size) ggml_aligned_malloc(size) #define GGML_ALIGNED_FREE(ptr) free(ptr) #endif #define UNUSED GGML_UNUSED #define SWAP(x, y, T) do { T SWAP = x; x = y; y = SWAP; } while (0) // // tensor access macros // #define GGML_TENSOR_UNARY_OP_LOCALS \ GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne); \ GGML_TENSOR_LOCALS(size_t, nb0, src0, nb); \ GGML_TENSOR_LOCALS(int64_t, ne, dst, ne); \ GGML_TENSOR_LOCALS(size_t, nb, dst, nb); #define GGML_TENSOR_BINARY_OP_LOCALS \ GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne); \ GGML_TENSOR_LOCALS(size_t, nb0, src0, nb); \ GGML_TENSOR_LOCALS(int64_t, ne1, src1, ne); \ GGML_TENSOR_LOCALS(size_t, nb1, src1, nb); \ GGML_TENSOR_LOCALS(int64_t, ne, dst, ne); \ GGML_TENSOR_LOCALS(size_t, nb, dst, nb); #if defined(GGML_USE_ACCELERATE) #include #if defined(GGML_USE_CLBLAST) // allow usage of CLBlast alongside Accelerate functions #include "ggml-opencl.h" #endif #elif defined(GGML_USE_OPENBLAS) #if defined(GGML_BLAS_USE_MKL) #include #else #include #endif #elif defined(GGML_USE_CUBLAS) #include "ggml-cuda.h" #elif defined(GGML_USE_CLBLAST) #include "ggml-opencl.h" #endif #undef MIN #undef MAX #define MIN(a, b) ((a) < (b) ? (a) : (b)) #define MAX(a, b) ((a) > (b) ? (a) : (b)) // floating point type used to accumulate sums typedef double ggml_float; // 16-bit float // on Arm, we use __fp16 // on x86, we use uint16_t #ifdef __ARM_NEON // if YCM cannot find , 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 #if defined(_MSC_VER) || defined(__MINGW32__) #include #else #if !defined(__riscv) #include #endif #endif #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 quick gelu table for f16 (128 KB) static ggml_fp16_t table_gelu_quick_f16[1 << 16]; // precomputed silu table for f16 (128 KB) static ggml_fp16_t table_silu_f16[1 << 16]; // precomputed exp table for f16 (128 KB) static ggml_fp16_t table_exp_f16[1 << 16]; // precomputed f32 table for f16 (256 KB) static float table_f32_f16[1 << 16]; #if defined(__ARM_NEON) || defined(__wasm_simd128__) #define B1(c,s,n) 0x ## n ## c , 0x ## n ## s #define B2(c,s,n) B1(c,s,n ## c), B1(c,s,n ## s) #define B3(c,s,n) B2(c,s,n ## c), B2(c,s,n ## s) #define B4(c,s,n) B3(c,s,n ## c), B3(c,s,n ## s) #define B5(c,s,n) B4(c,s,n ## c), B4(c,s,n ## s) #define B6(c,s,n) B5(c,s,n ## c), B5(c,s,n ## s) #define B7(c,s,n) B6(c,s,n ## c), B6(c,s,n ## s) #define B8(c,s ) B7(c,s, c), B7(c,s, s) // precomputed tables for expanding 8bits to 8 bytes: static const uint64_t table_b2b_0[1 << 8] = { B8(00, 10) }; // ( b) << 4 static const uint64_t table_b2b_1[1 << 8] = { B8(10, 00) }; // (!b) << 4 #endif // On ARM NEON, it's quicker to directly convert x -> x instead of calling into ggml_lookup_fp16_to_fp32, // so we define GGML_FP16_TO_FP32 and GGML_FP32_TO_FP16 elsewhere for NEON. // This is also true for POWER9. #if !defined(GGML_FP16_TO_FP32) || !defined(GGML_FP32_TO_FP16) inline static float ggml_lookup_fp16_to_fp32(ggml_fp16_t f) { uint16_t s; memcpy(&s, &f, sizeof(uint16_t)); return table_f32_f16[s]; } #define GGML_FP16_TO_FP32(x) ggml_lookup_fp16_to_fp32(x) #define GGML_FP32_TO_FP16(x) GGML_COMPUTE_FP32_TO_FP16(x) #endif // note: do not use these inside ggml.c // these are meant to be used via the ggml.h API float ggml_fp16_to_fp32(ggml_fp16_t x) { return (float) GGML_FP16_TO_FP32(x); } ggml_fp16_t ggml_fp32_to_fp16(float x) { return GGML_FP32_TO_FP16(x); } void ggml_fp16_to_fp32_row(const ggml_fp16_t * x, float * y, int n) { for (int i = 0; i < n; i++) { y[i] = GGML_FP16_TO_FP32(x[i]); } } void ggml_fp32_to_fp16_row(const float * x, ggml_fp16_t * y, int n) { int i = 0; #if defined(__F16C__) for (; i + 7 < n; i += 8) { __m256 x_vec = _mm256_loadu_ps(x + i); __m128i y_vec = _mm256_cvtps_ph(x_vec, _MM_FROUND_TO_NEAREST_INT); _mm_storeu_si128((__m128i *)(y + i), y_vec); } for(; i + 3 < n; i += 4) { __m128 x_vec = _mm_loadu_ps(x + i); __m128i y_vec = _mm_cvtps_ph(x_vec, _MM_FROUND_TO_NEAREST_INT); _mm_storel_epi64((__m128i *)(y + i), y_vec); } #endif for (; i < n; i++) { y[i] = GGML_FP32_TO_FP16(x[i]); } } // // timing // #if defined(_MSC_VER) || defined(__MINGW32__) static int64_t timer_freq, timer_start; void ggml_time_init(void) { LARGE_INTEGER t; QueryPerformanceFrequency(&t); timer_freq = t.QuadPart; // The multiplication by 1000 or 1000000 below can cause an overflow if timer_freq // and the uptime is high enough. // We subtract the program start time to reduce the likelihood of that happening. QueryPerformanceCounter(&t); timer_start = t.QuadPart; } int64_t ggml_time_ms(void) { LARGE_INTEGER t; QueryPerformanceCounter(&t); return ((t.QuadPart-timer_start) * 1000) / timer_freq; } int64_t ggml_time_us(void) { LARGE_INTEGER t; QueryPerformanceCounter(&t); return ((t.QuadPart-timer_start) * 1000000) / timer_freq; } #else void ggml_time_init(void) {} int64_t ggml_time_ms(void) { struct timespec ts; clock_gettime(CLOCK_MONOTONIC, &ts); return (int64_t)ts.tv_sec*1000 + (int64_t)ts.tv_nsec/1000000; } int64_t ggml_time_us(void) { struct timespec ts; clock_gettime(CLOCK_MONOTONIC, &ts); return (int64_t)ts.tv_sec*1000000 + (int64_t)ts.tv_nsec/1000; } #endif int64_t ggml_cycles(void) { return clock(); } int64_t ggml_cycles_per_ms(void) { return CLOCKS_PER_SEC/1000; } #ifdef GGML_PERF #define ggml_perf_time_ms() ggml_time_ms() #define ggml_perf_time_us() ggml_time_us() #define ggml_perf_cycles() ggml_cycles() #define ggml_perf_cycles_per_ms() ggml_cycles_per_ms() #else #define ggml_perf_time_ms() 0 #define ggml_perf_time_us() 0 #define ggml_perf_cycles() 0 #define ggml_perf_cycles_per_ms() 0 #endif // // cache line // #if defined(__cpp_lib_hardware_interference_size) #define CACHE_LINE_SIZE hardware_destructive_interference_size #else #if defined(__POWER9_VECTOR__) #define CACHE_LINE_SIZE 128 #else #define CACHE_LINE_SIZE 64 #endif #endif static const size_t CACHE_LINE_SIZE_F32 = CACHE_LINE_SIZE/sizeof(float); // // quantization // #define MM256_SET_M128I(a, b) _mm256_insertf128_si256(_mm256_castsi128_si256(b), (a), 1) #if defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) || defined(__SSSE3__) // multiply int8_t, add results pairwise twice static inline __m128i mul_sum_i8_pairs(const __m128i x, const __m128i y) { // Get absolute values of x vectors const __m128i ax = _mm_sign_epi8(x, x); // Sign the values of the y vectors const __m128i sy = _mm_sign_epi8(y, x); // Perform multiplication and create 16-bit values const __m128i dot = _mm_maddubs_epi16(ax, sy); const __m128i ones = _mm_set1_epi16(1); return _mm_madd_epi16(ones, dot); } #if __AVX__ || __AVX2__ || __AVX512F__ // horizontally add 8 floats static inline float hsum_float_8(const __m256 x) { __m128 res = _mm256_extractf128_ps(x, 1); res = _mm_add_ps(res, _mm256_castps256_ps128(x)); res = _mm_add_ps(res, _mm_movehl_ps(res, res)); res = _mm_add_ss(res, _mm_movehdup_ps(res)); return _mm_cvtss_f32(res); } // horizontally add 8 int32_t static inline int hsum_i32_8(const __m256i a) { const __m128i sum128 = _mm_add_epi32(_mm256_castsi256_si128(a), _mm256_extractf128_si256(a, 1)); const __m128i hi64 = _mm_unpackhi_epi64(sum128, sum128); const __m128i sum64 = _mm_add_epi32(hi64, sum128); const __m128i hi32 = _mm_shuffle_epi32(sum64, _MM_SHUFFLE(2, 3, 0, 1)); return _mm_cvtsi128_si32(_mm_add_epi32(sum64, hi32)); } // horizontally add 4 int32_t static inline int hsum_i32_4(const __m128i a) { const __m128i hi64 = _mm_unpackhi_epi64(a, a); const __m128i sum64 = _mm_add_epi32(hi64, a); const __m128i hi32 = _mm_shuffle_epi32(sum64, _MM_SHUFFLE(2, 3, 0, 1)); return _mm_cvtsi128_si32(_mm_add_epi32(sum64, hi32)); } #if defined(__AVX2__) || defined(__AVX512F__) // spread 32 bits to 32 bytes { 0x00, 0xFF } static inline __m256i bytes_from_bits_32(const uint8_t * x) { uint32_t x32; memcpy(&x32, x, sizeof(uint32_t)); const __m256i shuf_mask = _mm256_set_epi64x( 0x0303030303030303, 0x0202020202020202, 0x0101010101010101, 0x0000000000000000); __m256i bytes = _mm256_shuffle_epi8(_mm256_set1_epi32(x32), shuf_mask); const __m256i bit_mask = _mm256_set1_epi64x(0x7fbfdfeff7fbfdfe); bytes = _mm256_or_si256(bytes, bit_mask); return _mm256_cmpeq_epi8(bytes, _mm256_set1_epi64x(-1)); } // Unpack 32 4-bit fields into 32 bytes // The output vector contains 32 bytes, each one in [ 0 .. 15 ] interval static inline __m256i bytes_from_nibbles_32(const uint8_t * rsi) { const __m128i tmp = _mm_loadu_si128((const __m128i *)rsi); const __m256i bytes = MM256_SET_M128I(_mm_srli_epi16(tmp, 4), tmp); const __m256i lowMask = _mm256_set1_epi8( 0xF ); return _mm256_and_si256(lowMask, bytes); } // add int16_t pairwise and return as float vector static inline __m256 sum_i16_pairs_float(const __m256i x) { const __m256i ones = _mm256_set1_epi16(1); const __m256i summed_pairs = _mm256_madd_epi16(ones, x); return _mm256_cvtepi32_ps(summed_pairs); } static inline __m256 mul_sum_us8_pairs_float(const __m256i ax, const __m256i sy) { #if __AVXVNNI__ const __m256i zero = _mm256_setzero_si256(); const __m256i summed_pairs = _mm256_dpbusd_epi32(zero, ax, sy); return _mm256_cvtepi32_ps(summed_pairs); #else // Perform multiplication and create 16-bit values const __m256i dot = _mm256_maddubs_epi16(ax, sy); return sum_i16_pairs_float(dot); #endif } // multiply int8_t, add results pairwise twice and return as float vector static inline __m256 mul_sum_i8_pairs_float(const __m256i x, const __m256i y) { #if __AVXVNNIINT8__ const __m256i zero = _mm256_setzero_si256(); const __m256i summed_pairs = _mm256_dpbssd_epi32(zero, x, y); return _mm256_cvtepi32_ps(summed_pairs); #else // Get absolute values of x vectors const __m256i ax = _mm256_sign_epi8(x, x); // Sign the values of the y vectors const __m256i sy = _mm256_sign_epi8(y, x); return mul_sum_us8_pairs_float(ax, sy); #endif } static inline __m128i packNibbles( __m256i bytes ) { // Move bits within 16-bit lanes from 0000_abcd_0000_efgh into 0000_0000_abcd_efgh #if __AVX512F__ const __m256i bytes_srli_4 = _mm256_srli_epi16(bytes, 4); // 0000_0000_abcd_0000 bytes = _mm256_or_si256(bytes, bytes_srli_4); // 0000_abcd_abcd_efgh return _mm256_cvtepi16_epi8(bytes); // abcd_efgh #else const __m256i lowByte = _mm256_set1_epi16( 0xFF ); __m256i high = _mm256_andnot_si256( lowByte, bytes ); __m256i low = _mm256_and_si256( lowByte, bytes ); high = _mm256_srli_epi16( high, 4 ); bytes = _mm256_or_si256( low, high ); // Compress uint16_t lanes into bytes __m128i r0 = _mm256_castsi256_si128( bytes ); __m128i r1 = _mm256_extracti128_si256( bytes, 1 ); return _mm_packus_epi16( r0, r1 ); #endif } #elif defined(__AVX__) // spread 32 bits to 32 bytes { 0x00, 0xFF } static inline __m256i bytes_from_bits_32(const uint8_t * x) { uint32_t x32; memcpy(&x32, x, sizeof(uint32_t)); const __m128i shuf_maskl = _mm_set_epi64x(0x0101010101010101, 0x0000000000000000); const __m128i shuf_maskh = _mm_set_epi64x(0x0303030303030303, 0x0202020202020202); __m128i bytesl = _mm_shuffle_epi8(_mm_set1_epi32(x32), shuf_maskl); __m128i bytesh = _mm_shuffle_epi8(_mm_set1_epi32(x32), shuf_maskh); const __m128i bit_mask = _mm_set1_epi64x(0x7fbfdfeff7fbfdfe); bytesl = _mm_or_si128(bytesl, bit_mask); bytesh = _mm_or_si128(bytesh, bit_mask); bytesl = _mm_cmpeq_epi8(bytesl, _mm_set1_epi64x(-1)); bytesh = _mm_cmpeq_epi8(bytesh, _mm_set1_epi64x(-1)); return MM256_SET_M128I(bytesh, bytesl); } // Unpack 32 4-bit fields into 32 bytes // The output vector contains 32 bytes, each one in [ 0 .. 15 ] interval static inline __m256i bytes_from_nibbles_32(const uint8_t * rsi) { // Load 16 bytes from memory __m128i tmpl = _mm_loadu_si128((const __m128i *)rsi); __m128i tmph = _mm_srli_epi16(tmpl, 4); const __m128i lowMask = _mm_set1_epi8(0xF); tmpl = _mm_and_si128(lowMask, tmpl); tmph = _mm_and_si128(lowMask, tmph); return MM256_SET_M128I(tmph, tmpl); } // add int16_t pairwise and return as float vector static inline __m256 sum_i16_pairs_float(const __m128i xh, const __m128i xl) { const __m128i ones = _mm_set1_epi16(1); const __m128i summed_pairsl = _mm_madd_epi16(ones, xl); const __m128i summed_pairsh = _mm_madd_epi16(ones, xh); const __m256i summed_pairs = MM256_SET_M128I(summed_pairsh, summed_pairsl); return _mm256_cvtepi32_ps(summed_pairs); } static inline __m256 mul_sum_us8_pairs_float(const __m256i ax, const __m256i sy) { const __m128i axl = _mm256_castsi256_si128(ax); const __m128i axh = _mm256_extractf128_si256(ax, 1); const __m128i syl = _mm256_castsi256_si128(sy); const __m128i syh = _mm256_extractf128_si256(sy, 1); // Perform multiplication and create 16-bit values const __m128i dotl = _mm_maddubs_epi16(axl, syl); const __m128i doth = _mm_maddubs_epi16(axh, syh); return sum_i16_pairs_float(doth, dotl); } // multiply int8_t, add results pairwise twice and return as float vector static inline __m256 mul_sum_i8_pairs_float(const __m256i x, const __m256i y) { const __m128i xl = _mm256_castsi256_si128(x); const __m128i xh = _mm256_extractf128_si256(x, 1); const __m128i yl = _mm256_castsi256_si128(y); const __m128i yh = _mm256_extractf128_si256(y, 1); // Get absolute values of x vectors const __m128i axl = _mm_sign_epi8(xl, xl); const __m128i axh = _mm_sign_epi8(xh, xh); // Sign the values of the y vectors const __m128i syl = _mm_sign_epi8(yl, xl); const __m128i syh = _mm_sign_epi8(yh, xh); // Perform multiplication and create 16-bit values const __m128i dotl = _mm_maddubs_epi16(axl, syl); const __m128i doth = _mm_maddubs_epi16(axh, syh); return sum_i16_pairs_float(doth, dotl); } static inline __m128i packNibbles( __m128i bytes1, __m128i bytes2 ) { // Move bits within 16-bit lanes from 0000_abcd_0000_efgh into 0000_0000_abcd_efgh const __m128i lowByte = _mm_set1_epi16( 0xFF ); __m128i high = _mm_andnot_si128( lowByte, bytes1 ); __m128i low = _mm_and_si128( lowByte, bytes1 ); high = _mm_srli_epi16( high, 4 ); bytes1 = _mm_or_si128( low, high ); high = _mm_andnot_si128( lowByte, bytes2 ); low = _mm_and_si128( lowByte, bytes2 ); high = _mm_srli_epi16( high, 4 ); bytes2 = _mm_or_si128( low, high ); return _mm_packus_epi16( bytes1, bytes2); } #endif #elif defined(__SSSE3__) // horizontally add 4x4 floats static inline float hsum_float_4x4(const __m128 a, const __m128 b, const __m128 c, const __m128 d) { __m128 res_0 =_mm_hadd_ps(a, b); __m128 res_1 =_mm_hadd_ps(c, d); __m128 res =_mm_hadd_ps(res_0, res_1); res =_mm_hadd_ps(res, res); res =_mm_hadd_ps(res, res); return _mm_cvtss_f32(res); } #endif // __AVX__ || __AVX2__ || __AVX512F__ #endif // defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) || defined(__SSSE3__) #if defined(__ARM_NEON) #if !defined(__aarch64__) inline static 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); } inline static 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))); } inline static float vmaxvq_f32(float32x4_t v) { return MAX(MAX(vgetq_lane_f32(v, 0), vgetq_lane_f32(v, 1)), MAX(vgetq_lane_f32(v, 2), vgetq_lane_f32(v, 3))); } inline static int32x4_t vcvtnq_s32_f32(float32x4_t v) { int32x4_t res; res[0] = roundf(vgetq_lane_f32(v, 0)); res[1] = roundf(vgetq_lane_f32(v, 1)); res[2] = roundf(vgetq_lane_f32(v, 2)); res[3] = roundf(vgetq_lane_f32(v, 3)); return res; } #endif #endif #define QK4_0 32 typedef struct { ggml_fp16_t d; // delta uint8_t qs[QK4_0 / 2]; // nibbles / quants } block_q4_0; static_assert(sizeof(block_q4_0) == sizeof(ggml_fp16_t) + QK4_0 / 2, "wrong q4_0 block size/padding"); #define QK4_1 32 typedef struct { ggml_fp16_t d; // delta ggml_fp16_t m; // min uint8_t qs[QK4_1 / 2]; // nibbles / quants } block_q4_1; static_assert(sizeof(block_q4_1) == 2 * sizeof(ggml_fp16_t) + QK4_1 / 2, "wrong q4_1 block size/padding"); #define QK5_0 32 typedef struct { ggml_fp16_t d; // delta uint8_t qh[4]; // 5-th bit of quants uint8_t qs[QK5_0 / 2]; // nibbles / quants } block_q5_0; static_assert(sizeof(block_q5_0) == sizeof(ggml_fp16_t) + sizeof(uint32_t) + QK5_0 / 2, "wrong q5_0 block size/padding"); #define QK5_1 32 typedef struct { ggml_fp16_t d; // delta ggml_fp16_t m; // min uint8_t qh[4]; // 5-th bit of quants uint8_t qs[QK5_1 / 2]; // nibbles / quants } block_q5_1; static_assert(sizeof(block_q5_1) == 2 * sizeof(ggml_fp16_t) + sizeof(uint32_t) + QK5_1 / 2, "wrong q5_1 block size/padding"); #define QK8_0 32 typedef struct { ggml_fp16_t d; // delta int8_t qs[QK8_0]; // quants } block_q8_0; static_assert(sizeof(block_q8_0) == sizeof(ggml_fp16_t) + QK8_0, "wrong q8_0 block size/padding"); #define QK8_1 32 typedef struct { float d; // delta float s; // d * sum(qs[i]) int8_t qs[QK8_1]; // quants } block_q8_1; static_assert(sizeof(block_q8_1) == 2*sizeof(float) + QK8_1, "wrong q8_1 block size/padding"); // reference implementation for deterministic creation of model files static void quantize_row_q4_0_reference(const float * restrict x, block_q4_0 * restrict y, int k) { static const int qk = QK4_0; assert(k % qk == 0); const int nb = k / qk; for (int i = 0; i < nb; i++) { float amax = 0.0f; // absolute max float max = 0.0f; for (int j = 0; j < qk; j++) { const float v = x[i*qk + j]; if (amax < fabsf(v)) { amax = fabsf(v); max = v; } } const float d = max / -8; const float id = d ? 1.0f/d : 0.0f; y[i].d = GGML_FP32_TO_FP16(d); for (int j = 0; j < qk/2; ++j) { const float x0 = x[i*qk + 0 + j]*id; const float x1 = x[i*qk + qk/2 + j]*id; const uint8_t xi0 = MIN(15, (int8_t)(x0 + 8.5f)); const uint8_t xi1 = MIN(15, (int8_t)(x1 + 8.5f)); y[i].qs[j] = xi0; y[i].qs[j] |= xi1 << 4; } } } static void quantize_row_q4_0(const float * restrict x, void * restrict y, int k) { quantize_row_q4_0_reference(x, y, k); } static void quantize_row_q4_1_reference(const float * restrict x, block_q4_1 * restrict y, int k) { const int qk = QK4_1; assert(k % qk == 0); const int nb = k / qk; for (int i = 0; i < nb; i++) { float min = FLT_MAX; float max = -FLT_MAX; for (int j = 0; j < qk; j++) { const float v = x[i*qk + j]; if (v < min) min = v; if (v > max) max = v; } const float d = (max - min) / ((1 << 4) - 1); const float id = d ? 1.0f/d : 0.0f; y[i].d = GGML_FP32_TO_FP16(d); y[i].m = GGML_FP32_TO_FP16(min); for (int j = 0; j < qk/2; ++j) { const float x0 = (x[i*qk + 0 + j] - min)*id; const float x1 = (x[i*qk + qk/2 + j] - min)*id; const uint8_t xi0 = MIN(15, (int8_t)(x0 + 0.5f)); const uint8_t xi1 = MIN(15, (int8_t)(x1 + 0.5f)); y[i].qs[j] = xi0; y[i].qs[j] |= xi1 << 4; } } } static void quantize_row_q4_1(const float * restrict x, void * restrict y, int k) { quantize_row_q4_1_reference(x, y, k); } static void quantize_row_q5_0_reference(const float * restrict x, block_q5_0 * restrict y, int k) { static const int qk = QK5_0; assert(k % qk == 0); const int nb = k / qk; for (int i = 0; i < nb; i++) { float amax = 0.0f; // absolute max float max = 0.0f; for (int j = 0; j < qk; j++) { const float v = x[i*qk + j]; if (amax < fabsf(v)) { amax = fabsf(v); max = v; } } const float d = max / -16; const float id = d ? 1.0f/d : 0.0f; y[i].d = GGML_FP32_TO_FP16(d); uint32_t qh = 0; for (int j = 0; j < qk/2; ++j) { const float x0 = x[i*qk + 0 + j]*id; const float x1 = x[i*qk + qk/2 + j]*id; const uint8_t xi0 = MIN(31, (int8_t)(x0 + 16.5f)); const uint8_t xi1 = MIN(31, (int8_t)(x1 + 16.5f)); y[i].qs[j] = (xi0 & 0x0F) | ((xi1 & 0x0F) << 4); // get the 5-th bit and store it in qh at the right position qh |= ((xi0 & 0x10) >> 4) << (j + 0); qh |= ((xi1 & 0x10) >> 4) << (j + qk/2); } memcpy(&y[i].qh, &qh, sizeof(qh)); } } static void quantize_row_q5_0(const float * restrict x, void * restrict y, int k) { quantize_row_q5_0_reference(x, y, k); } static void quantize_row_q5_1_reference(const float * restrict x, block_q5_1 * restrict y, int k) { const int qk = QK5_1; assert(k % qk == 0); const int nb = k / qk; for (int i = 0; i < nb; i++) { float min = FLT_MAX; float max = -FLT_MAX; for (int j = 0; j < qk; j++) { const float v = x[i*qk + j]; if (v < min) min = v; if (v > max) max = v; } const float d = (max - min) / ((1 << 5) - 1); const float id = d ? 1.0f/d : 0.0f; y[i].d = GGML_FP32_TO_FP16(d); y[i].m = GGML_FP32_TO_FP16(min); uint32_t qh = 0; for (int j = 0; j < qk/2; ++j) { const float x0 = (x[i*qk + 0 + j] - min)*id; const float x1 = (x[i*qk + qk/2 + j] - min)*id; const uint8_t xi0 = (uint8_t)(x0 + 0.5f); const uint8_t xi1 = (uint8_t)(x1 + 0.5f); y[i].qs[j] = (xi0 & 0x0F) | ((xi1 & 0x0F) << 4); // get the 5-th bit and store it in qh at the right position qh |= ((xi0 & 0x10) >> 4) << (j + 0); qh |= ((xi1 & 0x10) >> 4) << (j + qk/2); } memcpy(&y[i].qh, &qh, sizeof(y[i].qh)); } } static void quantize_row_q5_1(const float * restrict x, void * restrict y, int k) { quantize_row_q5_1_reference(x, y, k); } // reference implementation for deterministic creation of model files static void quantize_row_q8_0_reference(const float * restrict x, block_q8_0 * restrict y, int k) { assert(k % QK8_0 == 0); const int nb = k / QK8_0; for (int i = 0; i < nb; i++) { float amax = 0.0f; // absolute max for (int j = 0; j < QK8_0; j++) { const float v = x[i*QK8_0 + j]; amax = MAX(amax, fabsf(v)); } const float d = amax / ((1 << 7) - 1); const float id = d ? 1.0f/d : 0.0f; y[i].d = GGML_FP32_TO_FP16(d); for (int j = 0; j < QK8_0; ++j) { const float x0 = x[i*QK8_0 + j]*id; y[i].qs[j] = roundf(x0); } } } static void quantize_row_q8_0(const float * restrict x, void * restrict vy, int k) { assert(QK8_0 == 32); assert(k % QK8_0 == 0); const int nb = k / QK8_0; block_q8_0 * restrict y = vy; #if defined(__ARM_NEON) for (int i = 0; i < nb; i++) { float32x4_t srcv [8]; float32x4_t asrcv[8]; float32x4_t amaxv[8]; for (int j = 0; j < 8; j++) srcv[j] = vld1q_f32(x + i*32 + 4*j); for (int j = 0; j < 8; j++) asrcv[j] = vabsq_f32(srcv[j]); for (int j = 0; j < 4; j++) amaxv[2*j] = vmaxq_f32(asrcv[2*j], asrcv[2*j+1]); for (int j = 0; j < 2; j++) amaxv[4*j] = vmaxq_f32(amaxv[4*j], amaxv[4*j+2]); for (int j = 0; j < 1; j++) amaxv[8*j] = vmaxq_f32(amaxv[8*j], amaxv[8*j+4]); const float amax = vmaxvq_f32(amaxv[0]); const float d = amax / ((1 << 7) - 1); const float id = d ? 1.0f/d : 0.0f; y[i].d = GGML_FP32_TO_FP16(d); for (int j = 0; j < 8; j++) { const float32x4_t v = vmulq_n_f32(srcv[j], id); const int32x4_t vi = vcvtnq_s32_f32(v); y[i].qs[4*j + 0] = vgetq_lane_s32(vi, 0); y[i].qs[4*j + 1] = vgetq_lane_s32(vi, 1); y[i].qs[4*j + 2] = vgetq_lane_s32(vi, 2); y[i].qs[4*j + 3] = vgetq_lane_s32(vi, 3); } } #elif defined(__wasm_simd128__) for (int i = 0; i < nb; i++) { v128_t srcv [8]; v128_t asrcv[8]; v128_t amaxv[8]; for (int j = 0; j < 8; j++) srcv[j] = wasm_v128_load(x + i*32 + 4*j); for (int j = 0; j < 8; j++) asrcv[j] = wasm_f32x4_abs(srcv[j]); for (int j = 0; j < 4; j++) amaxv[2*j] = wasm_f32x4_max(asrcv[2*j], asrcv[2*j+1]); for (int j = 0; j < 2; j++) amaxv[4*j] = wasm_f32x4_max(amaxv[4*j], amaxv[4*j+2]); for (int j = 0; j < 1; j++) amaxv[8*j] = wasm_f32x4_max(amaxv[8*j], amaxv[8*j+4]); const float amax = MAX(MAX(wasm_f32x4_extract_lane(amaxv[0], 0), wasm_f32x4_extract_lane(amaxv[0], 1)), MAX(wasm_f32x4_extract_lane(amaxv[0], 2), wasm_f32x4_extract_lane(amaxv[0], 3))); const float d = amax / ((1 << 7) - 1); const float id = d ? 1.0f/d : 0.0f; y[i].d = GGML_FP32_TO_FP16(d); for (int j = 0; j < 8; j++) { const v128_t v = wasm_f32x4_mul(srcv[j], wasm_f32x4_splat(id)); const v128_t vi = wasm_i32x4_trunc_sat_f32x4(v); y[i].qs[4*j + 0] = wasm_i32x4_extract_lane(vi, 0); y[i].qs[4*j + 1] = wasm_i32x4_extract_lane(vi, 1); y[i].qs[4*j + 2] = wasm_i32x4_extract_lane(vi, 2); y[i].qs[4*j + 3] = wasm_i32x4_extract_lane(vi, 3); } } #elif defined(__AVX2__) || defined(__AVX__) for (int i = 0; i < nb; i++) { // Load elements into 4 AVX vectors __m256 v0 = _mm256_loadu_ps( x ); __m256 v1 = _mm256_loadu_ps( x + 8 ); __m256 v2 = _mm256_loadu_ps( x + 16 ); __m256 v3 = _mm256_loadu_ps( x + 24 ); x += 32; // Compute max(abs(e)) for the block const __m256 signBit = _mm256_set1_ps( -0.0f ); __m256 maxAbs = _mm256_andnot_ps( signBit, v0 ); maxAbs = _mm256_max_ps( maxAbs, _mm256_andnot_ps( signBit, v1 ) ); maxAbs = _mm256_max_ps( maxAbs, _mm256_andnot_ps( signBit, v2 ) ); maxAbs = _mm256_max_ps( maxAbs, _mm256_andnot_ps( signBit, v3 ) ); __m128 max4 = _mm_max_ps( _mm256_extractf128_ps( maxAbs, 1 ), _mm256_castps256_ps128( maxAbs ) ); max4 = _mm_max_ps( max4, _mm_movehl_ps( max4, max4 ) ); max4 = _mm_max_ss( max4, _mm_movehdup_ps( max4 ) ); const float maxScalar = _mm_cvtss_f32( max4 ); // Quantize these floats const float d = maxScalar / 127.f; y[i].d = GGML_FP32_TO_FP16(d); const float id = ( maxScalar != 0.0f ) ? 127.f / maxScalar : 0.0f; const __m256 mul = _mm256_set1_ps( id ); // Apply the multiplier v0 = _mm256_mul_ps( v0, mul ); v1 = _mm256_mul_ps( v1, mul ); v2 = _mm256_mul_ps( v2, mul ); v3 = _mm256_mul_ps( v3, mul ); // Round to nearest integer v0 = _mm256_round_ps( v0, _MM_ROUND_NEAREST ); v1 = _mm256_round_ps( v1, _MM_ROUND_NEAREST ); v2 = _mm256_round_ps( v2, _MM_ROUND_NEAREST ); v3 = _mm256_round_ps( v3, _MM_ROUND_NEAREST ); // Convert floats to integers __m256i i0 = _mm256_cvtps_epi32( v0 ); __m256i i1 = _mm256_cvtps_epi32( v1 ); __m256i i2 = _mm256_cvtps_epi32( v2 ); __m256i i3 = _mm256_cvtps_epi32( v3 ); #if defined(__AVX2__) // Convert int32 to int16 i0 = _mm256_packs_epi32( i0, i1 ); // 0, 1, 2, 3, 8, 9, 10, 11, 4, 5, 6, 7, 12, 13, 14, 15 i2 = _mm256_packs_epi32( i2, i3 ); // 16, 17, 18, 19, 24, 25, 26, 27, 20, 21, 22, 23, 28, 29, 30, 31 // Convert int16 to int8 i0 = _mm256_packs_epi16( i0, i2 ); // 0, 1, 2, 3, 8, 9, 10, 11, 16, 17, 18, 19, 24, 25, 26, 27, 4, 5, 6, 7, 12, 13, 14, 15, 20, 21, 22, 23, 28, 29, 30, 31 // We got our precious signed bytes, but the order is now wrong // These AVX2 pack instructions process 16-byte pieces independently // The following instruction is fixing the order const __m256i perm = _mm256_setr_epi32( 0, 4, 1, 5, 2, 6, 3, 7 ); i0 = _mm256_permutevar8x32_epi32( i0, perm ); _mm256_storeu_si256((__m256i *)y[i].qs, i0); #else // Since we don't have in AVX some necessary functions, // we split the registers in half and call AVX2 analogs from SSE __m128i ni0 = _mm256_castsi256_si128( i0 ); __m128i ni1 = _mm256_extractf128_si256( i0, 1); __m128i ni2 = _mm256_castsi256_si128( i1 ); __m128i ni3 = _mm256_extractf128_si256( i1, 1); __m128i ni4 = _mm256_castsi256_si128( i2 ); __m128i ni5 = _mm256_extractf128_si256( i2, 1); __m128i ni6 = _mm256_castsi256_si128( i3 ); __m128i ni7 = _mm256_extractf128_si256( i3, 1); // Convert int32 to int16 ni0 = _mm_packs_epi32( ni0, ni1 ); ni2 = _mm_packs_epi32( ni2, ni3 ); ni4 = _mm_packs_epi32( ni4, ni5 ); ni6 = _mm_packs_epi32( ni6, ni7 ); // Convert int16 to int8 ni0 = _mm_packs_epi16( ni0, ni2 ); ni4 = _mm_packs_epi16( ni4, ni6 ); _mm_storeu_si128((__m128i *)(y[i].qs + 0), ni0); _mm_storeu_si128((__m128i *)(y[i].qs + 16), ni4); #endif } #else // scalar quantize_row_q8_0_reference(x, y, k); #endif } // reference implementation for deterministic creation of model files static void quantize_row_q8_1_reference(const float * restrict x, block_q8_1 * restrict y, int k) { assert(QK8_1 == 32); assert(k % QK8_1 == 0); const int nb = k / QK8_1; for (int i = 0; i < nb; i++) { float amax = 0.0f; // absolute max for (int j = 0; j < QK8_1; j++) { const float v = x[i*QK8_1 + j]; amax = MAX(amax, fabsf(v)); } const float d = amax / ((1 << 7) - 1); const float id = d ? 1.0f/d : 0.0f; y[i].d = d; int sum = 0; for (int j = 0; j < QK8_1/2; ++j) { const float v0 = x[i*QK8_1 + j]*id; const float v1 = x[i*QK8_1 + QK8_1/2 + j]*id; y[i].qs[ j] = roundf(v0); y[i].qs[QK8_1/2 + j] = roundf(v1); sum += y[i].qs[ j]; sum += y[i].qs[QK8_1/2 + j]; } y[i].s = sum*d; } } static void quantize_row_q8_1(const float * restrict x, void * restrict vy, int k) { assert(k % QK8_1 == 0); const int nb = k / QK8_1; block_q8_1 * restrict y = vy; #if defined(__ARM_NEON) for (int i = 0; i < nb; i++) { float32x4_t srcv [8]; float32x4_t asrcv[8]; float32x4_t amaxv[8]; for (int j = 0; j < 8; j++) srcv[j] = vld1q_f32(x + i*32 + 4*j); for (int j = 0; j < 8; j++) asrcv[j] = vabsq_f32(srcv[j]); for (int j = 0; j < 4; j++) amaxv[2*j] = vmaxq_f32(asrcv[2*j], asrcv[2*j+1]); for (int j = 0; j < 2; j++) amaxv[4*j] = vmaxq_f32(amaxv[4*j], amaxv[4*j+2]); for (int j = 0; j < 1; j++) amaxv[8*j] = vmaxq_f32(amaxv[8*j], amaxv[8*j+4]); const float amax = vmaxvq_f32(amaxv[0]); const float d = amax / ((1 << 7) - 1); const float id = d ? 1.0f/d : 0.0f; y[i].d = d; int32x4_t accv = vdupq_n_s32(0); for (int j = 0; j < 8; j++) { const float32x4_t v = vmulq_n_f32(srcv[j], id); const int32x4_t vi = vcvtnq_s32_f32(v); y[i].qs[4*j + 0] = vgetq_lane_s32(vi, 0); y[i].qs[4*j + 1] = vgetq_lane_s32(vi, 1); y[i].qs[4*j + 2] = vgetq_lane_s32(vi, 2); y[i].qs[4*j + 3] = vgetq_lane_s32(vi, 3); accv = vaddq_s32(accv, vi); } y[i].s = d * vaddvq_s32(accv); } #elif defined(__wasm_simd128__) for (int i = 0; i < nb; i++) { v128_t srcv [8]; v128_t asrcv[8]; v128_t amaxv[8]; for (int j = 0; j < 8; j++) srcv[j] = wasm_v128_load(x + i*32 + 4*j); for (int j = 0; j < 8; j++) asrcv[j] = wasm_f32x4_abs(srcv[j]); for (int j = 0; j < 4; j++) amaxv[2*j] = wasm_f32x4_max(asrcv[2*j], asrcv[2*j+1]); for (int j = 0; j < 2; j++) amaxv[4*j] = wasm_f32x4_max(amaxv[4*j], amaxv[4*j+2]); for (int j = 0; j < 1; j++) amaxv[8*j] = wasm_f32x4_max(amaxv[8*j], amaxv[8*j+4]); const float amax = MAX(MAX(wasm_f32x4_extract_lane(amaxv[0], 0), wasm_f32x4_extract_lane(amaxv[0], 1)), MAX(wasm_f32x4_extract_lane(amaxv[0], 2), wasm_f32x4_extract_lane(amaxv[0], 3))); const float d = amax / ((1 << 7) - 1); const float id = d ? 1.0f/d : 0.0f; y[i].d = d; v128_t accv = wasm_i32x4_splat(0); for (int j = 0; j < 8; j++) { const v128_t v = wasm_f32x4_mul(srcv[j], wasm_f32x4_splat(id)); const v128_t vi = wasm_i32x4_trunc_sat_f32x4(v); y[i].qs[4*j + 0] = wasm_i32x4_extract_lane(vi, 0); y[i].qs[4*j + 1] = wasm_i32x4_extract_lane(vi, 1); y[i].qs[4*j + 2] = wasm_i32x4_extract_lane(vi, 2); y[i].qs[4*j + 3] = wasm_i32x4_extract_lane(vi, 3); accv = wasm_i32x4_add(accv, vi); } y[i].s = d * (wasm_i32x4_extract_lane(accv, 0) + wasm_i32x4_extract_lane(accv, 1) + wasm_i32x4_extract_lane(accv, 2) + wasm_i32x4_extract_lane(accv, 3)); } #elif defined(__AVX2__) || defined(__AVX__) for (int i = 0; i < nb; i++) { // Load elements into 4 AVX vectors __m256 v0 = _mm256_loadu_ps( x ); __m256 v1 = _mm256_loadu_ps( x + 8 ); __m256 v2 = _mm256_loadu_ps( x + 16 ); __m256 v3 = _mm256_loadu_ps( x + 24 ); x += 32; // Compute max(abs(e)) for the block const __m256 signBit = _mm256_set1_ps( -0.0f ); __m256 maxAbs = _mm256_andnot_ps( signBit, v0 ); maxAbs = _mm256_max_ps( maxAbs, _mm256_andnot_ps( signBit, v1 ) ); maxAbs = _mm256_max_ps( maxAbs, _mm256_andnot_ps( signBit, v2 ) ); maxAbs = _mm256_max_ps( maxAbs, _mm256_andnot_ps( signBit, v3 ) ); __m128 max4 = _mm_max_ps( _mm256_extractf128_ps( maxAbs, 1 ), _mm256_castps256_ps128( maxAbs ) ); max4 = _mm_max_ps( max4, _mm_movehl_ps( max4, max4 ) ); max4 = _mm_max_ss( max4, _mm_movehdup_ps( max4 ) ); const float maxScalar = _mm_cvtss_f32( max4 ); // Quantize these floats const float d = maxScalar / 127.f; y[i].d = d; const float id = ( maxScalar != 0.0f ) ? 127.f / maxScalar : 0.0f; const __m256 mul = _mm256_set1_ps( id ); // Apply the multiplier v0 = _mm256_mul_ps( v0, mul ); v1 = _mm256_mul_ps( v1, mul ); v2 = _mm256_mul_ps( v2, mul ); v3 = _mm256_mul_ps( v3, mul ); // Round to nearest integer v0 = _mm256_round_ps( v0, _MM_ROUND_NEAREST ); v1 = _mm256_round_ps( v1, _MM_ROUND_NEAREST ); v2 = _mm256_round_ps( v2, _MM_ROUND_NEAREST ); v3 = _mm256_round_ps( v3, _MM_ROUND_NEAREST ); // Convert floats to integers __m256i i0 = _mm256_cvtps_epi32( v0 ); __m256i i1 = _mm256_cvtps_epi32( v1 ); __m256i i2 = _mm256_cvtps_epi32( v2 ); __m256i i3 = _mm256_cvtps_epi32( v3 ); #if defined(__AVX2__) // Compute the sum of the quants and set y[i].s y[i].s = d * hsum_i32_8(_mm256_add_epi32(_mm256_add_epi32(i0, i1), _mm256_add_epi32(i2, i3))); // Convert int32 to int16 i0 = _mm256_packs_epi32( i0, i1 ); // 0, 1, 2, 3, 8, 9, 10, 11, 4, 5, 6, 7, 12, 13, 14, 15 i2 = _mm256_packs_epi32( i2, i3 ); // 16, 17, 18, 19, 24, 25, 26, 27, 20, 21, 22, 23, 28, 29, 30, 31 // Convert int16 to int8 i0 = _mm256_packs_epi16( i0, i2 ); // 0, 1, 2, 3, 8, 9, 10, 11, 16, 17, 18, 19, 24, 25, 26, 27, 4, 5, 6, 7, 12, 13, 14, 15, 20, 21, 22, 23, 28, 29, 30, 31 // We got our precious signed bytes, but the order is now wrong // These AVX2 pack instructions process 16-byte pieces independently // The following instruction is fixing the order const __m256i perm = _mm256_setr_epi32( 0, 4, 1, 5, 2, 6, 3, 7 ); i0 = _mm256_permutevar8x32_epi32( i0, perm ); _mm256_storeu_si256((__m256i *)y[i].qs, i0); #else // Since we don't have in AVX some necessary functions, // we split the registers in half and call AVX2 analogs from SSE __m128i ni0 = _mm256_castsi256_si128( i0 ); __m128i ni1 = _mm256_extractf128_si256( i0, 1); __m128i ni2 = _mm256_castsi256_si128( i1 ); __m128i ni3 = _mm256_extractf128_si256( i1, 1); __m128i ni4 = _mm256_castsi256_si128( i2 ); __m128i ni5 = _mm256_extractf128_si256( i2, 1); __m128i ni6 = _mm256_castsi256_si128( i3 ); __m128i ni7 = _mm256_extractf128_si256( i3, 1); // Compute the sum of the quants and set y[i].s const __m128i s0 = _mm_add_epi32(_mm_add_epi32(ni0, ni1), _mm_add_epi32(ni2, ni3)); const __m128i s1 = _mm_add_epi32(_mm_add_epi32(ni4, ni5), _mm_add_epi32(ni6, ni7)); y[i].s = d * hsum_i32_4(_mm_add_epi32(s0, s1)); // Convert int32 to int16 ni0 = _mm_packs_epi32( ni0, ni1 ); ni2 = _mm_packs_epi32( ni2, ni3 ); ni4 = _mm_packs_epi32( ni4, ni5 ); ni6 = _mm_packs_epi32( ni6, ni7 ); // Convert int16 to int8 ni0 = _mm_packs_epi16( ni0, ni2 ); ni4 = _mm_packs_epi16( ni4, ni6 ); _mm_storeu_si128((__m128i *)(y[i].qs + 0), ni0); _mm_storeu_si128((__m128i *)(y[i].qs + 16), ni4); #endif } #else // scalar quantize_row_q8_1_reference(x, y, k); #endif } static void dequantize_row_q4_0(const block_q4_0 * restrict x, float * restrict y, int k) { static const int qk = QK4_0; assert(k % qk == 0); const int nb = k / qk; for (int i = 0; i < nb; i++) { const float d = GGML_FP16_TO_FP32(x[i].d); for (int j = 0; j < qk/2; ++j) { const int x0 = (x[i].qs[j] & 0x0F) - 8; const int x1 = (x[i].qs[j] >> 4) - 8; y[i*qk + j + 0 ] = x0*d; y[i*qk + j + qk/2] = x1*d; } } } static void dequantize_row_q4_1(const block_q4_1 * restrict x, float * restrict y, int k) { static const int qk = QK4_1; assert(k % qk == 0); const int nb = k / qk; for (int i = 0; i < nb; i++) { const float d = GGML_FP16_TO_FP32(x[i].d); const float m = GGML_FP16_TO_FP32(x[i].m); for (int j = 0; j < qk/2; ++j) { const int x0 = (x[i].qs[j] & 0x0F); const int x1 = (x[i].qs[j] >> 4); y[i*qk + j + 0 ] = x0*d + m; y[i*qk + j + qk/2] = x1*d + m; } } } static void dequantize_row_q5_0(const block_q5_0 * restrict x, float * restrict y, int k) { static const int qk = QK5_0; assert(k % qk == 0); const int nb = k / qk; for (int i = 0; i < nb; i++) { const float d = GGML_FP16_TO_FP32(x[i].d); uint32_t qh; memcpy(&qh, x[i].qh, sizeof(qh)); for (int j = 0; j < qk/2; ++j) { const uint8_t xh_0 = ((qh >> (j + 0)) << 4) & 0x10; const uint8_t xh_1 = ((qh >> (j + 12)) ) & 0x10; const int32_t x0 = ((x[i].qs[j] & 0x0F) | xh_0) - 16; const int32_t x1 = ((x[i].qs[j] >> 4) | xh_1) - 16; y[i*qk + j + 0 ] = x0*d; y[i*qk + j + qk/2] = x1*d; } } } static void dequantize_row_q5_1(const block_q5_1 * restrict x, float * restrict y, int k) { static const int qk = QK5_1; assert(k % qk == 0); const int nb = k / qk; for (int i = 0; i < nb; i++) { const float d = GGML_FP16_TO_FP32(x[i].d); const float m = GGML_FP16_TO_FP32(x[i].m); uint32_t qh; memcpy(&qh, x[i].qh, sizeof(qh)); for (int j = 0; j < qk/2; ++j) { const uint8_t xh_0 = ((qh >> (j + 0)) << 4) & 0x10; const uint8_t xh_1 = ((qh >> (j + 12)) ) & 0x10; const int x0 = (x[i].qs[j] & 0x0F) | xh_0; const int x1 = (x[i].qs[j] >> 4) | xh_1; y[i*qk + j + 0 ] = x0*d + m; y[i*qk + j + qk/2] = x1*d + m; } } } static void dequantize_row_q8_0(const void * restrict vx, float * restrict y, int k) { static const int qk = QK8_0; assert(k % qk == 0); const int nb = k / qk; const block_q8_0 * restrict x = vx; for (int i = 0; i < nb; i++) { const float d = GGML_FP16_TO_FP32(x[i].d); for (int j = 0; j < qk; ++j) { y[i*qk + j] = x[i].qs[j]*d; } } } static void ggml_vec_dot_f32(const int n, float * restrict s, const float * restrict x, const float * restrict y); static void ggml_vec_dot_f16(const int n, float * restrict s, ggml_fp16_t * restrict x, ggml_fp16_t * restrict y); static void ggml_vec_dot_q4_0_q8_0(const int n, float * restrict s, const void * restrict vx, const void * restrict vy); static void ggml_vec_dot_q4_1_q8_1(const int n, float * restrict s, const void * restrict vx, const void * restrict vy); static void ggml_vec_dot_q5_0_q8_0(const int n, float * restrict s, const void * restrict vx, const void * restrict vy); static void ggml_vec_dot_q5_1_q8_1(const int n, float * restrict s, const void * restrict vx, const void * restrict vy); static void ggml_vec_dot_q8_0_q8_0(const int n, float * restrict s, const void * restrict vx, const void * restrict vy); static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = { [GGML_TYPE_F32] = { .vec_dot = (ggml_vec_dot_t) ggml_vec_dot_f32, .vec_dot_type = GGML_TYPE_F32, }, [GGML_TYPE_F16] = { .to_float = (ggml_to_float_t) ggml_fp16_to_fp32_row, .from_float = (ggml_from_float_t) ggml_fp32_to_fp16_row, .from_float_reference = (ggml_from_float_t) ggml_fp32_to_fp16_row, .vec_dot = (ggml_vec_dot_t) ggml_vec_dot_f16, .vec_dot_type = GGML_TYPE_F16, }, [GGML_TYPE_Q4_0] = { .to_float = (ggml_to_float_t) dequantize_row_q4_0, .from_float = quantize_row_q4_0, .from_float_reference = (ggml_from_float_t) quantize_row_q4_0_reference, .vec_dot = ggml_vec_dot_q4_0_q8_0, .vec_dot_type = GGML_TYPE_Q8_0, }, [GGML_TYPE_Q4_1] = { .to_float = (ggml_to_float_t) dequantize_row_q4_1, .from_float = quantize_row_q4_1, .from_float_reference = (ggml_from_float_t) quantize_row_q4_1_reference, .vec_dot = ggml_vec_dot_q4_1_q8_1, .vec_dot_type = GGML_TYPE_Q8_1, }, [GGML_TYPE_Q5_0] = { .to_float = (ggml_to_float_t) dequantize_row_q5_0, .from_float = quantize_row_q5_0, .from_float_reference = (ggml_from_float_t) quantize_row_q5_0_reference, .vec_dot = ggml_vec_dot_q5_0_q8_0, .vec_dot_type = GGML_TYPE_Q8_0, }, [GGML_TYPE_Q5_1] = { .to_float = (ggml_to_float_t) dequantize_row_q5_1, .from_float = quantize_row_q5_1, .from_float_reference = (ggml_from_float_t) quantize_row_q5_1_reference, .vec_dot = ggml_vec_dot_q5_1_q8_1, .vec_dot_type = GGML_TYPE_Q8_1, }, [GGML_TYPE_Q8_0] = { .to_float = dequantize_row_q8_0, .from_float = quantize_row_q8_0, .from_float_reference = (ggml_from_float_t) quantize_row_q8_0_reference, .vec_dot = ggml_vec_dot_q8_0_q8_0, .vec_dot_type = GGML_TYPE_Q8_0, }, [GGML_TYPE_Q8_1] = { .from_float = quantize_row_q8_1, .from_float_reference = (ggml_from_float_t) quantize_row_q8_1_reference, .vec_dot_type = GGML_TYPE_Q8_1, }, #ifdef GGML_USE_K_QUANTS [GGML_TYPE_Q2_K] = { .to_float = (ggml_to_float_t) dequantize_row_q2_K, .from_float = quantize_row_q2_K, .from_float_reference = (ggml_from_float_t) quantize_row_q2_K_reference, .vec_dot = ggml_vec_dot_q2_K_q8_K, .vec_dot_type = GGML_TYPE_Q8_K, }, [GGML_TYPE_Q3_K] = { .to_float = (ggml_to_float_t) dequantize_row_q3_K, .from_float = quantize_row_q3_K, .from_float_reference = (ggml_from_float_t) quantize_row_q3_K_reference, .vec_dot = ggml_vec_dot_q3_K_q8_K, .vec_dot_type = GGML_TYPE_Q8_K, }, [GGML_TYPE_Q4_K] = { .to_float = (ggml_to_float_t) dequantize_row_q4_K, .from_float = quantize_row_q4_K, .from_float_reference = (ggml_from_float_t) quantize_row_q4_K_reference, .vec_dot = ggml_vec_dot_q4_K_q8_K, .vec_dot_type = GGML_TYPE_Q8_K, }, [GGML_TYPE_Q5_K] = { .to_float = (ggml_to_float_t) dequantize_row_q5_K, .from_float = quantize_row_q5_K, .from_float_reference = (ggml_from_float_t) quantize_row_q5_K_reference, .vec_dot = ggml_vec_dot_q5_K_q8_K, .vec_dot_type = GGML_TYPE_Q8_K, }, [GGML_TYPE_Q6_K] = { .to_float = (ggml_to_float_t) dequantize_row_q6_K, .from_float = quantize_row_q6_K, .from_float_reference = (ggml_from_float_t) quantize_row_q6_K_reference, .vec_dot = ggml_vec_dot_q6_K_q8_K, .vec_dot_type = GGML_TYPE_Q8_K, }, [GGML_TYPE_Q8_K] = { .from_float = quantize_row_q8_K, } #endif }; // For internal test use ggml_type_traits_t ggml_internal_get_type_traits(enum ggml_type i) { GGML_ASSERT(i < GGML_TYPE_COUNT); return type_traits[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) \ { \ int offset = GGML_F32_ARR >> 1; \ for (int i = 0; i < offset; ++i) { \ x[i] = vaddq_f32(x[i], x[offset+i]); \ } \ offset >>= 1; \ for (int i = 0; i < offset; ++i) { \ x[i] = vaddq_f32(x[i], x[offset+i]); \ } \ offset >>= 1; \ for (int i = 0; i < offset; ++i) { \ x[i] = vaddq_f32(x[i], x[offset+i]); \ } \ res = GGML_F32x4_REDUCE_ONE(x[0]); \ } #define GGML_F32_VEC GGML_F32x4 #define GGML_F32_VEC_ZERO GGML_F32x4_ZERO #define GGML_F32_VEC_SET1 GGML_F32x4_SET1 #define GGML_F32_VEC_LOAD GGML_F32x4_LOAD #define GGML_F32_VEC_STORE GGML_F32x4_STORE #define GGML_F32_VEC_FMA GGML_F32x4_FMA #define GGML_F32_VEC_ADD GGML_F32x4_ADD #define GGML_F32_VEC_MUL GGML_F32x4_MUL #define GGML_F32_VEC_REDUCE GGML_F32x4_REDUCE // F16 NEON #if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) #define GGML_F16_STEP 32 #define GGML_F16_EPR 8 #define GGML_F16x8 float16x8_t #define GGML_F16x8_ZERO vdupq_n_f16(0.0f) #define GGML_F16x8_SET1(x) vdupq_n_f16(x) #define GGML_F16x8_LOAD vld1q_f16 #define GGML_F16x8_STORE vst1q_f16 #define GGML_F16x8_FMA(a, b, c) vfmaq_f16(a, b, c) #define GGML_F16x8_ADD vaddq_f16 #define GGML_F16x8_MUL vmulq_f16 #define GGML_F16x8_REDUCE(res, x) \ { \ int offset = GGML_F16_ARR >> 1; \ for (int i = 0; i < offset; ++i) { \ x[i] = vaddq_f16(x[i], x[offset+i]); \ } \ offset >>= 1; \ for (int i = 0; i < offset; ++i) { \ x[i] = vaddq_f16(x[i], x[offset+i]); \ } \ offset >>= 1; \ for (int i = 0; i < offset; ++i) { \ x[i] = vaddq_f16(x[i], x[offset+i]); \ } \ const float32x4_t t0 = vcvt_f32_f16(vget_low_f16 (x[0])); \ const float32x4_t t1 = vcvt_f32_f16(vget_high_f16(x[0])); \ res = (ggml_float) vaddvq_f32(vaddq_f32(t0, t1)); \ } #define GGML_F16_VEC GGML_F16x8 #define GGML_F16_VEC_ZERO GGML_F16x8_ZERO #define GGML_F16_VEC_SET1 GGML_F16x8_SET1 #define GGML_F16_VEC_LOAD(p, i) GGML_F16x8_LOAD(p) #define GGML_F16_VEC_STORE(p, r, i) GGML_F16x8_STORE(p, r[i]) #define GGML_F16_VEC_FMA GGML_F16x8_FMA #define GGML_F16_VEC_ADD GGML_F16x8_ADD #define GGML_F16_VEC_MUL GGML_F16x8_MUL #define GGML_F16_VEC_REDUCE GGML_F16x8_REDUCE #else // if FP16 vector arithmetic is not supported, we use FP32 instead // and take advantage of the vcvt_ functions to convert to/from FP16 #define GGML_F16_STEP 16 #define GGML_F16_EPR 4 #define GGML_F32Cx4 float32x4_t #define GGML_F32Cx4_ZERO vdupq_n_f32(0.0f) #define GGML_F32Cx4_SET1(x) vdupq_n_f32(x) #define GGML_F32Cx4_LOAD(x) vcvt_f32_f16(vld1_f16(x)) #define GGML_F32Cx4_STORE(x, y) vst1_f16(x, vcvt_f16_f32(y)) #define GGML_F32Cx4_FMA(a, b, c) vfmaq_f32(a, b, c) #define GGML_F32Cx4_ADD vaddq_f32 #define GGML_F32Cx4_MUL vmulq_f32 #define GGML_F32Cx4_REDUCE GGML_F32x4_REDUCE #define GGML_F16_VEC GGML_F32Cx4 #define GGML_F16_VEC_ZERO GGML_F32Cx4_ZERO #define GGML_F16_VEC_SET1 GGML_F32Cx4_SET1 #define GGML_F16_VEC_LOAD(p, i) GGML_F32Cx4_LOAD(p) #define GGML_F16_VEC_STORE(p, r, i) GGML_F32Cx4_STORE(p, r[i]) #define GGML_F16_VEC_FMA GGML_F32Cx4_FMA #define GGML_F16_VEC_ADD GGML_F32Cx4_ADD #define GGML_F16_VEC_MUL GGML_F32Cx4_MUL #define GGML_F16_VEC_REDUCE GGML_F32Cx4_REDUCE #endif #elif defined(__AVX__) #define GGML_SIMD // F32 AVX #define GGML_F32_STEP 32 #define GGML_F32_EPR 8 #define GGML_F32x8 __m256 #define GGML_F32x8_ZERO _mm256_setzero_ps() #define GGML_F32x8_SET1(x) _mm256_set1_ps(x) #define GGML_F32x8_LOAD _mm256_loadu_ps #define GGML_F32x8_STORE _mm256_storeu_ps #if defined(__FMA__) #define GGML_F32x8_FMA(a, b, c) _mm256_fmadd_ps(b, c, a) #else #define GGML_F32x8_FMA(a, b, c) _mm256_add_ps(_mm256_mul_ps(b, c), a) #endif #define GGML_F32x8_ADD _mm256_add_ps #define GGML_F32x8_MUL _mm256_mul_ps #define GGML_F32x8_REDUCE(res, x) \ { \ int offset = GGML_F32_ARR >> 1; \ for (int i = 0; i < offset; ++i) { \ x[i] = _mm256_add_ps(x[i], x[offset+i]); \ } \ offset >>= 1; \ for (int i = 0; i < offset; ++i) { \ x[i] = _mm256_add_ps(x[i], x[offset+i]); \ } \ offset >>= 1; \ for (int i = 0; i < offset; ++i) { \ x[i] = _mm256_add_ps(x[i], x[offset+i]); \ } \ const __m128 t0 = _mm_add_ps(_mm256_castps256_ps128(x[0]), \ _mm256_extractf128_ps(x[0], 1)); \ const __m128 t1 = _mm_hadd_ps(t0, t0); \ res = _mm_cvtss_f32(_mm_hadd_ps(t1, t1)); \ } // TODO: is this optimal ? #define GGML_F32_VEC GGML_F32x8 #define GGML_F32_VEC_ZERO GGML_F32x8_ZERO #define GGML_F32_VEC_SET1 GGML_F32x8_SET1 #define GGML_F32_VEC_LOAD GGML_F32x8_LOAD #define GGML_F32_VEC_STORE GGML_F32x8_STORE #define GGML_F32_VEC_FMA GGML_F32x8_FMA #define GGML_F32_VEC_ADD GGML_F32x8_ADD #define GGML_F32_VEC_MUL GGML_F32x8_MUL #define GGML_F32_VEC_REDUCE GGML_F32x8_REDUCE // F16 AVX #define GGML_F16_STEP 32 #define GGML_F16_EPR 8 // F16 arithmetic is not supported by AVX, so we use F32 instead #define GGML_F32Cx8 __m256 #define GGML_F32Cx8_ZERO _mm256_setzero_ps() #define GGML_F32Cx8_SET1(x) _mm256_set1_ps(x) #if defined(__F16C__) // the _mm256_cvt intrinsics require F16C #define GGML_F32Cx8_LOAD(x) _mm256_cvtph_ps(_mm_loadu_si128((__m128i *)(x))) #define GGML_F32Cx8_STORE(x, y) _mm_storeu_si128((__m128i *)(x), _mm256_cvtps_ph(y, 0)) #else static inline __m256 __avx_f32cx8_load(ggml_fp16_t *x) { float tmp[8]; for (int i = 0; i < 8; i++) { tmp[i] = GGML_FP16_TO_FP32(x[i]); } return _mm256_loadu_ps(tmp); } static inline void __avx_f32cx8_store(ggml_fp16_t *x, __m256 y) { float arr[8]; _mm256_storeu_ps(arr, y); for (int i = 0; i < 8; i++) x[i] = GGML_FP32_TO_FP16(arr[i]); } #define GGML_F32Cx8_LOAD(x) __avx_f32cx8_load(x) #define GGML_F32Cx8_STORE(x, y) __avx_f32cx8_store(x, y) #endif #define GGML_F32Cx8_FMA GGML_F32x8_FMA #define GGML_F32Cx8_ADD _mm256_add_ps #define GGML_F32Cx8_MUL _mm256_mul_ps #define GGML_F32Cx8_REDUCE GGML_F32x8_REDUCE #define GGML_F16_VEC GGML_F32Cx8 #define GGML_F16_VEC_ZERO GGML_F32Cx8_ZERO #define GGML_F16_VEC_SET1 GGML_F32Cx8_SET1 #define GGML_F16_VEC_LOAD(p, i) GGML_F32Cx8_LOAD(p) #define GGML_F16_VEC_STORE(p, r, i) GGML_F32Cx8_STORE(p, r[i]) #define GGML_F16_VEC_FMA GGML_F32Cx8_FMA #define GGML_F16_VEC_ADD GGML_F32Cx8_ADD #define GGML_F16_VEC_MUL GGML_F32Cx8_MUL #define GGML_F16_VEC_REDUCE GGML_F32Cx8_REDUCE #elif defined(__POWER9_VECTOR__) #define GGML_SIMD // F32 POWER9 #define GGML_F32_STEP 32 #define GGML_F32_EPR 4 #define GGML_F32x4 vector float #define GGML_F32x4_ZERO 0.0f #define GGML_F32x4_SET1 vec_splats #define GGML_F32x4_LOAD(p) vec_xl(0, p) #define GGML_F32x4_STORE(p, r) vec_xst(r, 0, p) #define GGML_F32x4_FMA(a, b, c) vec_madd(b, c, a) #define GGML_F32x4_ADD vec_add #define GGML_F32x4_MUL vec_mul #define GGML_F32x4_REDUCE(res, x) \ { \ int offset = GGML_F32_ARR >> 1; \ for (int i = 0; i < offset; ++i) { \ x[i] = vec_add(x[i], x[offset+i]); \ } \ offset >>= 1; \ for (int i = 0; i < offset; ++i) { \ x[i] = vec_add(x[i], x[offset+i]); \ } \ offset >>= 1; \ for (int i = 0; i < offset; ++i) { \ x[i] = vec_add(x[i], x[offset+i]); \ } \ res = vec_extract(x[0], 0) + \ vec_extract(x[0], 1) + \ vec_extract(x[0], 2) + \ vec_extract(x[0], 3); \ } #define GGML_F32_VEC GGML_F32x4 #define GGML_F32_VEC_ZERO GGML_F32x4_ZERO #define GGML_F32_VEC_SET1 GGML_F32x4_SET1 #define GGML_F32_VEC_LOAD GGML_F32x4_LOAD #define GGML_F32_VEC_STORE GGML_F32x4_STORE #define GGML_F32_VEC_FMA GGML_F32x4_FMA #define GGML_F32_VEC_ADD GGML_F32x4_ADD #define GGML_F32_VEC_MUL GGML_F32x4_MUL #define GGML_F32_VEC_REDUCE GGML_F32x4_REDUCE // F16 POWER9 #define GGML_F16_STEP GGML_F32_STEP #define GGML_F16_EPR GGML_F32_EPR #define GGML_F16_VEC GGML_F32x4 #define GGML_F16_VEC_ZERO GGML_F32x4_ZERO #define GGML_F16_VEC_SET1 GGML_F32x4_SET1 #define GGML_F16_VEC_FMA GGML_F32x4_FMA #define GGML_F16_VEC_REDUCE GGML_F32x4_REDUCE // Use vec_xl, not vec_ld, in case the load address is not aligned. #define GGML_F16_VEC_LOAD(p, i) (i & 0x1) ? \ vec_extract_fp32_from_shorth(vec_xl(0, p - GGML_F16_EPR)) : \ vec_extract_fp32_from_shortl(vec_xl(0, p)) #define GGML_ENDIAN_BYTE(i) ((unsigned char *)&(uint16_t){1})[i] #define GGML_F16_VEC_STORE(p, r, i) \ if (i & 0x1) \ vec_xst(vec_pack_to_short_fp32(r[i - GGML_ENDIAN_BYTE(1)], \ r[i - GGML_ENDIAN_BYTE(0)]), \ 0, p - GGML_F16_EPR) #elif defined(__wasm_simd128__) #define GGML_SIMD // F32 WASM #define GGML_F32_STEP 16 #define GGML_F32_EPR 4 #define GGML_F32x4 v128_t #define GGML_F32x4_ZERO wasm_f32x4_splat(0.0f) #define GGML_F32x4_SET1(x) wasm_f32x4_splat(x) #define GGML_F32x4_LOAD wasm_v128_load #define GGML_F32x4_STORE wasm_v128_store #define GGML_F32x4_FMA(a, b, c) wasm_f32x4_add(wasm_f32x4_mul(b, c), a) #define GGML_F32x4_ADD wasm_f32x4_add #define GGML_F32x4_MUL wasm_f32x4_mul #define GGML_F32x4_REDUCE(res, x) \ { \ int offset = GGML_F32_ARR >> 1; \ for (int i = 0; i < offset; ++i) { \ x[i] = wasm_f32x4_add(x[i], x[offset+i]); \ } \ offset >>= 1; \ for (int i = 0; i < offset; ++i) { \ x[i] = wasm_f32x4_add(x[i], x[offset+i]); \ } \ offset >>= 1; \ for (int i = 0; i < offset; ++i) { \ x[i] = wasm_f32x4_add(x[i], x[offset+i]); \ } \ res = wasm_f32x4_extract_lane(x[0], 0) + \ wasm_f32x4_extract_lane(x[0], 1) + \ wasm_f32x4_extract_lane(x[0], 2) + \ wasm_f32x4_extract_lane(x[0], 3); \ } #define GGML_F32_VEC GGML_F32x4 #define GGML_F32_VEC_ZERO GGML_F32x4_ZERO #define GGML_F32_VEC_SET1 GGML_F32x4_SET1 #define GGML_F32_VEC_LOAD GGML_F32x4_LOAD #define GGML_F32_VEC_STORE GGML_F32x4_STORE #define GGML_F32_VEC_FMA GGML_F32x4_FMA #define GGML_F32_VEC_ADD GGML_F32x4_ADD #define GGML_F32_VEC_MUL GGML_F32x4_MUL #define GGML_F32_VEC_REDUCE GGML_F32x4_REDUCE // F16 WASM #define GGML_F16_STEP 16 #define GGML_F16_EPR 4 inline static v128_t __wasm_f16x4_load(const ggml_fp16_t * p) { float tmp[4]; tmp[0] = GGML_FP16_TO_FP32(p[0]); tmp[1] = GGML_FP16_TO_FP32(p[1]); tmp[2] = GGML_FP16_TO_FP32(p[2]); tmp[3] = GGML_FP16_TO_FP32(p[3]); return wasm_v128_load(tmp); } inline static void __wasm_f16x4_store(ggml_fp16_t * p, v128_t x) { float tmp[4]; wasm_v128_store(tmp, x); p[0] = GGML_FP32_TO_FP16(tmp[0]); p[1] = GGML_FP32_TO_FP16(tmp[1]); p[2] = GGML_FP32_TO_FP16(tmp[2]); p[3] = GGML_FP32_TO_FP16(tmp[3]); } #define GGML_F16x4 v128_t #define GGML_F16x4_ZERO wasm_f32x4_splat(0.0f) #define GGML_F16x4_SET1(x) wasm_f32x4_splat(x) #define GGML_F16x4_LOAD(x) __wasm_f16x4_load(x) #define GGML_F16x4_STORE(x, y) __wasm_f16x4_store(x, y) #define GGML_F16x4_FMA GGML_F32x4_FMA #define GGML_F16x4_ADD wasm_f32x4_add #define GGML_F16x4_MUL wasm_f32x4_mul #define GGML_F16x4_REDUCE(res, x) \ { \ int offset = GGML_F16_ARR >> 1; \ for (int i = 0; i < offset; ++i) { \ x[i] = wasm_f32x4_add(x[i], x[offset+i]); \ } \ offset >>= 1; \ for (int i = 0; i < offset; ++i) { \ x[i] = wasm_f32x4_add(x[i], x[offset+i]); \ } \ offset >>= 1; \ for (int i = 0; i < offset; ++i) { \ x[i] = wasm_f32x4_add(x[i], x[offset+i]); \ } \ res = wasm_f32x4_extract_lane(x[0], 0) + \ wasm_f32x4_extract_lane(x[0], 1) + \ wasm_f32x4_extract_lane(x[0], 2) + \ wasm_f32x4_extract_lane(x[0], 3); \ } #define GGML_F16_VEC GGML_F16x4 #define GGML_F16_VEC_ZERO GGML_F16x4_ZERO #define GGML_F16_VEC_SET1 GGML_F16x4_SET1 #define GGML_F16_VEC_LOAD(p, i) GGML_F16x4_LOAD(p) #define GGML_F16_VEC_STORE(p, r, i) GGML_F16x4_STORE(p, r[i]) #define GGML_F16_VEC_FMA GGML_F16x4_FMA #define GGML_F16_VEC_ADD GGML_F16x4_ADD #define GGML_F16_VEC_MUL GGML_F16x4_MUL #define GGML_F16_VEC_REDUCE GGML_F16x4_REDUCE #elif defined(__SSE3__) #define GGML_SIMD // F32 SSE #define GGML_F32_STEP 32 #define GGML_F32_EPR 4 #define GGML_F32x4 __m128 #define GGML_F32x4_ZERO _mm_setzero_ps() #define GGML_F32x4_SET1(x) _mm_set1_ps(x) #define GGML_F32x4_LOAD _mm_loadu_ps #define GGML_F32x4_STORE _mm_storeu_ps #if defined(__FMA__) // TODO: Does this work? #define GGML_F32x4_FMA(a, b, c) _mm_fmadd_ps(b, c, a) #else #define GGML_F32x4_FMA(a, b, c) _mm_add_ps(_mm_mul_ps(b, c), a) #endif #define GGML_F32x4_ADD _mm_add_ps #define GGML_F32x4_MUL _mm_mul_ps #define GGML_F32x4_REDUCE(res, x) \ { \ int offset = GGML_F32_ARR >> 1; \ for (int i = 0; i < offset; ++i) { \ x[i] = _mm_add_ps(x[i], x[offset+i]); \ } \ offset >>= 1; \ for (int i = 0; i < offset; ++i) { \ x[i] = _mm_add_ps(x[i], x[offset+i]); \ } \ offset >>= 1; \ for (int i = 0; i < offset; ++i) { \ x[i] = _mm_add_ps(x[i], x[offset+i]); \ } \ const __m128 t0 = _mm_hadd_ps(x[0], x[0]); \ res = _mm_cvtss_f32(_mm_hadd_ps(t0, t0)); \ } // TODO: is this optimal ? #define GGML_F32_VEC GGML_F32x4 #define GGML_F32_VEC_ZERO GGML_F32x4_ZERO #define GGML_F32_VEC_SET1 GGML_F32x4_SET1 #define GGML_F32_VEC_LOAD GGML_F32x4_LOAD #define GGML_F32_VEC_STORE GGML_F32x4_STORE #define GGML_F32_VEC_FMA GGML_F32x4_FMA #define GGML_F32_VEC_ADD GGML_F32x4_ADD #define GGML_F32_VEC_MUL GGML_F32x4_MUL #define GGML_F32_VEC_REDUCE GGML_F32x4_REDUCE // F16 SSE #define GGML_F16_STEP 32 #define GGML_F16_EPR 4 static inline __m128 __sse_f16x4_load(ggml_fp16_t *x) { float tmp[4]; tmp[0] = GGML_FP16_TO_FP32(x[0]); tmp[1] = GGML_FP16_TO_FP32(x[1]); tmp[2] = GGML_FP16_TO_FP32(x[2]); tmp[3] = GGML_FP16_TO_FP32(x[3]); return _mm_loadu_ps(tmp); } static inline void __sse_f16x4_store(ggml_fp16_t *x, __m128 y) { float arr[4]; _mm_storeu_ps(arr, y); x[0] = GGML_FP32_TO_FP16(arr[0]); x[1] = GGML_FP32_TO_FP16(arr[1]); x[2] = GGML_FP32_TO_FP16(arr[2]); x[3] = GGML_FP32_TO_FP16(arr[3]); } #define GGML_F32Cx4 __m128 #define GGML_F32Cx4_ZERO _mm_setzero_ps() #define GGML_F32Cx4_SET1(x) _mm_set1_ps(x) #define GGML_F32Cx4_LOAD(x) __sse_f16x4_load(x) #define GGML_F32Cx4_STORE(x, y) __sse_f16x4_store(x, y) #define GGML_F32Cx4_FMA GGML_F32x4_FMA #define GGML_F32Cx4_ADD _mm_add_ps #define GGML_F32Cx4_MUL _mm_mul_ps #define GGML_F32Cx4_REDUCE GGML_F32x4_REDUCE #define GGML_F16_VEC GGML_F32Cx4 #define GGML_F16_VEC_ZERO GGML_F32Cx4_ZERO #define GGML_F16_VEC_SET1 GGML_F32Cx4_SET1 #define GGML_F16_VEC_LOAD(p, i) GGML_F32Cx4_LOAD(p) #define GGML_F16_VEC_STORE(p, r, i) GGML_F32Cx4_STORE(p, r[i]) #define GGML_F16_VEC_FMA GGML_F32Cx4_FMA #define GGML_F16_VEC_ADD GGML_F32Cx4_ADD #define GGML_F16_VEC_MUL GGML_F32Cx4_MUL #define GGML_F16_VEC_REDUCE GGML_F32Cx4_REDUCE #endif // GGML_F32_ARR / GGML_F16_ARR // number of registers to use per step #ifdef GGML_SIMD #define GGML_F32_ARR (GGML_F32_STEP/GGML_F32_EPR) #define GGML_F16_ARR (GGML_F16_STEP/GGML_F16_EPR) #endif // // fundamental operations // inline static void ggml_vec_set_i8(const int n, int8_t * x, const int8_t v) { for (int i = 0; i < n; ++i) x[i] = v; } inline static void ggml_vec_set_i16(const int n, int16_t * x, const int16_t v) { for (int i = 0; i < n; ++i) x[i] = v; } inline static void ggml_vec_set_i32(const int n, int32_t * x, const int32_t v) { for (int i = 0; i < n; ++i) x[i] = v; } inline static void ggml_vec_set_f16(const int n, ggml_fp16_t * x, const int32_t v) { for (int i = 0; i < n; ++i) x[i] = v; } inline static void ggml_vec_add_f32 (const int n, float * z, const float * x, const float * y) { for (int i = 0; i < n; ++i) z[i] = x[i] + y[i]; } inline static void ggml_vec_add1_f32(const int n, float * z, const float * x, const float v) { for (int i = 0; i < n; ++i) z[i] = x[i] + v; } inline static void ggml_vec_acc_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] += x[i]; } inline static void ggml_vec_acc1_f32(const int n, float * y, const float v) { for (int i = 0; i < n; ++i) y[i] += v; } inline static void ggml_vec_sub_f32 (const int n, float * z, const float * x, const float * y) { for (int i = 0; i < n; ++i) z[i] = x[i] - y[i]; } inline static void ggml_vec_set_f32 (const int n, float * x, const float v) { for (int i = 0; i < n; ++i) x[i] = v; } inline static void ggml_vec_cpy_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = x[i]; } inline static void ggml_vec_neg_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = -x[i]; } inline static void ggml_vec_mul_f32 (const int n, float * z, const float * x, const float * y) { for (int i = 0; i < n; ++i) z[i] = x[i]*y[i]; } inline static void ggml_vec_div_f32 (const int n, float * z, const float * x, const float * y) { for (int i = 0; i < n; ++i) z[i] = x[i]/y[i]; } static void ggml_vec_dot_f32(const int n, float * restrict s, const float * restrict x, const float * restrict y) { #ifdef GGML_SIMD float sumf = 0.0f; const int np = (n & ~(GGML_F32_STEP - 1)); GGML_F32_VEC sum[GGML_F32_ARR] = { GGML_F32_VEC_ZERO }; GGML_F32_VEC ax[GGML_F32_ARR]; GGML_F32_VEC ay[GGML_F32_ARR]; for (int i = 0; i < np; i += GGML_F32_STEP) { for (int j = 0; j < GGML_F32_ARR; j++) { ax[j] = GGML_F32_VEC_LOAD(x + i + j*GGML_F32_EPR); ay[j] = GGML_F32_VEC_LOAD(y + i + j*GGML_F32_EPR); sum[j] = GGML_F32_VEC_FMA(sum[j], ax[j], ay[j]); } } // reduce sum0..sum3 to sum0 GGML_F32_VEC_REDUCE(sumf, sum); // leftovers for (int i = np; i < n; ++i) { sumf += x[i]*y[i]; } #else // scalar ggml_float sumf = 0.0; for (int i = 0; i < n; ++i) { sumf += (ggml_float)(x[i]*y[i]); } #endif *s = sumf; } static void ggml_vec_dot_f16(const int n, float * restrict s, ggml_fp16_t * restrict x, ggml_fp16_t * restrict y) { ggml_float sumf = 0.0; #if defined(GGML_SIMD) const int np = (n & ~(GGML_F16_STEP - 1)); GGML_F16_VEC sum[GGML_F16_ARR] = { GGML_F16_VEC_ZERO }; GGML_F16_VEC ax[GGML_F16_ARR]; GGML_F16_VEC ay[GGML_F16_ARR]; for (int i = 0; i < np; i += GGML_F16_STEP) { for (int j = 0; j < GGML_F16_ARR; j++) { ax[j] = GGML_F16_VEC_LOAD(x + i + j*GGML_F16_EPR, j); ay[j] = GGML_F16_VEC_LOAD(y + i + j*GGML_F16_EPR, j); sum[j] = GGML_F16_VEC_FMA(sum[j], ax[j], ay[j]); } } // reduce sum0..sum3 to sum0 GGML_F16_VEC_REDUCE(sumf, sum); // leftovers for (int i = np; i < n; ++i) { sumf += (ggml_float)(GGML_FP16_TO_FP32(x[i])*GGML_FP16_TO_FP32(y[i])); } #else for (int i = 0; i < n; ++i) { sumf += (ggml_float)(GGML_FP16_TO_FP32(x[i])*GGML_FP16_TO_FP32(y[i])); } #endif *s = sumf; } static void ggml_vec_dot_q4_0_q8_0(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) { const int qk = QK8_0; const int nb = n / qk; assert(n % qk == 0); 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); for (int i = 0; i < nb; i += 2) { const block_q4_0 * restrict x0 = &x[i + 0]; const block_q4_0 * restrict x1 = &x[i + 1]; const block_q8_0 * restrict y0 = &y[i + 0]; const block_q8_0 * restrict y1 = &y[i + 1]; const uint8x16_t m4b = vdupq_n_u8(0x0F); const int8x16_t s8b = vdupq_n_s8(0x8); const uint8x16_t v0_0 = vld1q_u8(x0->qs); const uint8x16_t v0_1 = vld1q_u8(x1->qs); // 4-bit -> 8-bit const int8x16_t v0_0l = vreinterpretq_s8_u8(vandq_u8 (v0_0, m4b)); const int8x16_t v0_0h = vreinterpretq_s8_u8(vshrq_n_u8(v0_0, 4)); const int8x16_t v0_1l = vreinterpretq_s8_u8(vandq_u8 (v0_1, m4b)); const int8x16_t v0_1h = vreinterpretq_s8_u8(vshrq_n_u8(v0_1, 4)); // sub 8 const int8x16_t v0_0ls = vsubq_s8(v0_0l, s8b); const int8x16_t v0_0hs = vsubq_s8(v0_0h, s8b); const int8x16_t v0_1ls = vsubq_s8(v0_1l, s8b); const int8x16_t v0_1hs = vsubq_s8(v0_1h, s8b); // load y const int8x16_t v1_0l = vld1q_s8(y0->qs); const int8x16_t v1_0h = vld1q_s8(y0->qs + 16); const int8x16_t v1_1l = vld1q_s8(y1->qs); const int8x16_t v1_1h = vld1q_s8(y1->qs + 16); #if defined(__ARM_FEATURE_DOTPROD) // dot product into int32x4_t const int32x4_t p_0 = vdotq_s32(vdotq_s32(vdupq_n_s32(0), v0_0ls, v1_0l), v0_0hs, v1_0h); const int32x4_t p_1 = vdotq_s32(vdotq_s32(vdupq_n_s32(0), v0_1ls, v1_1l), v0_1hs, v1_1h); sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(p_0), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d)); sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(p_1), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d)); #else const int16x8_t pl0l = vmull_s8(vget_low_s8 (v0_0ls), vget_low_s8 (v1_0l)); const int16x8_t pl0h = vmull_s8(vget_high_s8(v0_0ls), vget_high_s8(v1_0l)); const int16x8_t ph0l = vmull_s8(vget_low_s8 (v0_0hs), vget_low_s8 (v1_0h)); const int16x8_t ph0h = vmull_s8(vget_high_s8(v0_0hs), vget_high_s8(v1_0h)); const int16x8_t pl1l = vmull_s8(vget_low_s8 (v0_1ls), vget_low_s8 (v1_1l)); const int16x8_t pl1h = vmull_s8(vget_high_s8(v0_1ls), vget_high_s8(v1_1l)); const int16x8_t ph1l = vmull_s8(vget_low_s8 (v0_1hs), vget_low_s8 (v1_1h)); const int16x8_t ph1h = vmull_s8(vget_high_s8(v0_1hs), vget_high_s8(v1_1h)); const int32x4_t pl0 = vaddq_s32(vpaddlq_s16(pl0l), vpaddlq_s16(pl0h)); const int32x4_t ph0 = vaddq_s32(vpaddlq_s16(ph0l), vpaddlq_s16(ph0h)); const int32x4_t pl1 = vaddq_s32(vpaddlq_s16(pl1l), vpaddlq_s16(pl1h)); const int32x4_t ph1 = vaddq_s32(vpaddlq_s16(ph1l), vpaddlq_s16(ph1h)); sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(pl0, ph0)), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d)); sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(pl1, ph1)), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d)); #endif } *s = vaddvq_f32(sumv0) + vaddvq_f32(sumv1); #elif defined(__AVX2__) // Initialize accumulator with zeros __m256 acc = _mm256_setzero_ps(); // Main loop for (int i = 0; i < nb; ++i) { /* Compute combined scale for the block */ const __m256 d = _mm256_set1_ps( GGML_FP16_TO_FP32(x[i].d) * GGML_FP16_TO_FP32(y[i].d) ); __m256i bx = bytes_from_nibbles_32(x[i].qs); // Now we have a vector with bytes in [ 0 .. 15 ] interval. Offset them into [ -8 .. +7 ] interval. const __m256i off = _mm256_set1_epi8( 8 ); bx = _mm256_sub_epi8( bx, off ); __m256i by = _mm256_loadu_si256((const __m256i *)y[i].qs); const __m256 q = mul_sum_i8_pairs_float(bx, by); /* Multiply q with scale and accumulate */ acc = _mm256_fmadd_ps( d, q, acc ); } *s = hsum_float_8(acc); #elif defined(__AVX__) // Initialize accumulator with zeros __m256 acc = _mm256_setzero_ps(); // Main loop for (int i = 0; i < nb; ++i) { // Compute combined scale for the block const __m256 d = _mm256_set1_ps( GGML_FP16_TO_FP32(x[i].d) * GGML_FP16_TO_FP32(y[i].d) ); const __m128i lowMask = _mm_set1_epi8(0xF); const __m128i off = _mm_set1_epi8(8); const __m128i tmp = _mm_loadu_si128((const __m128i *)x[i].qs); __m128i bx = _mm_and_si128(lowMask, tmp); __m128i by = _mm_loadu_si128((const __m128i *)y[i].qs); bx = _mm_sub_epi8(bx, off); const __m128i i32_0 = mul_sum_i8_pairs(bx, by); bx = _mm_and_si128(lowMask, _mm_srli_epi64(tmp, 4)); by = _mm_loadu_si128((const __m128i *)(y[i].qs + 16)); bx = _mm_sub_epi8(bx, off); const __m128i i32_1 = mul_sum_i8_pairs(bx, by); // Convert int32_t to float __m256 p = _mm256_cvtepi32_ps(MM256_SET_M128I(i32_0, i32_1)); // Apply the scale, and accumulate acc = _mm256_add_ps(_mm256_mul_ps( d, p ), acc); } *s = hsum_float_8(acc); #elif defined(__SSSE3__) // set constants const __m128i lowMask = _mm_set1_epi8(0xF); const __m128i off = _mm_set1_epi8(8); // Initialize accumulator with zeros __m128 acc_0 = _mm_setzero_ps(); __m128 acc_1 = _mm_setzero_ps(); __m128 acc_2 = _mm_setzero_ps(); __m128 acc_3 = _mm_setzero_ps(); // First round without accumulation { _mm_prefetch(&x[0] + sizeof(block_q4_0), _MM_HINT_T0); _mm_prefetch(&y[0] + sizeof(block_q8_0), _MM_HINT_T0); // Compute combined scale for the block 0 and 1 const __m128 d_0_1 = _mm_set1_ps( GGML_FP16_TO_FP32(x[0].d) * GGML_FP16_TO_FP32(y[0].d) ); const __m128i tmp_0_1 = _mm_loadu_si128((const __m128i *)x[0].qs); __m128i bx_0 = _mm_and_si128(lowMask, tmp_0_1); __m128i by_0 = _mm_loadu_si128((const __m128i *)y[0].qs); bx_0 = _mm_sub_epi8(bx_0, off); const __m128i i32_0 = mul_sum_i8_pairs(bx_0, by_0); __m128i bx_1 = _mm_and_si128(lowMask, _mm_srli_epi64(tmp_0_1, 4)); __m128i by_1 = _mm_loadu_si128((const __m128i *)(y[0].qs + 16)); bx_1 = _mm_sub_epi8(bx_1, off); const __m128i i32_1 = mul_sum_i8_pairs(bx_1, by_1); _mm_prefetch(&x[1] + sizeof(block_q4_0), _MM_HINT_T0); _mm_prefetch(&y[1] + sizeof(block_q8_0), _MM_HINT_T0); // Compute combined scale for the block 2 and 3 const __m128 d_2_3 = _mm_set1_ps( GGML_FP16_TO_FP32(x[1].d) * GGML_FP16_TO_FP32(y[1].d) ); const __m128i tmp_2_3 = _mm_loadu_si128((const __m128i *)x[1].qs); __m128i bx_2 = _mm_and_si128(lowMask, tmp_2_3); __m128i by_2 = _mm_loadu_si128((const __m128i *)y[1].qs); bx_2 = _mm_sub_epi8(bx_2, off); const __m128i i32_2 = mul_sum_i8_pairs(bx_2, by_2); __m128i bx_3 = _mm_and_si128(lowMask, _mm_srli_epi64(tmp_2_3, 4)); __m128i by_3 = _mm_loadu_si128((const __m128i *)(y[1].qs + 16)); bx_3 = _mm_sub_epi8(bx_3, off); const __m128i i32_3 = mul_sum_i8_pairs(bx_3, by_3); // Convert int32_t to float __m128 p0 = _mm_cvtepi32_ps(i32_0); __m128 p1 = _mm_cvtepi32_ps(i32_1); __m128 p2 = _mm_cvtepi32_ps(i32_2); __m128 p3 = _mm_cvtepi32_ps(i32_3); // Apply the scale acc_0 = _mm_mul_ps( d_0_1, p0 ); acc_1 = _mm_mul_ps( d_0_1, p1 ); acc_2 = _mm_mul_ps( d_2_3, p2 ); acc_3 = _mm_mul_ps( d_2_3, p3 ); } // Main loop for (int i = 2; i < nb; i+=2) { _mm_prefetch(&x[i] + sizeof(block_q4_0), _MM_HINT_T0); _mm_prefetch(&y[i] + sizeof(block_q8_0), _MM_HINT_T0); // Compute combined scale for the block 0 and 1 const __m128 d_0_1 = _mm_set1_ps( GGML_FP16_TO_FP32(x[i].d) * GGML_FP16_TO_FP32(y[i].d) ); const __m128i tmp_0_1 = _mm_loadu_si128((const __m128i *)x[i].qs); __m128i bx_0 = _mm_and_si128(lowMask, tmp_0_1); __m128i by_0 = _mm_loadu_si128((const __m128i *)y[i].qs); bx_0 = _mm_sub_epi8(bx_0, off); const __m128i i32_0 = mul_sum_i8_pairs(bx_0, by_0); __m128i bx_1 = _mm_and_si128(lowMask, _mm_srli_epi64(tmp_0_1, 4)); __m128i by_1 = _mm_loadu_si128((const __m128i *)(y[i].qs + 16)); bx_1 = _mm_sub_epi8(bx_1, off); const __m128i i32_1 = mul_sum_i8_pairs(bx_1, by_1); _mm_prefetch(&x[i] + 2 * sizeof(block_q4_0), _MM_HINT_T0); _mm_prefetch(&y[i] + 2 * sizeof(block_q8_0), _MM_HINT_T0); // Compute combined scale for the block 2 and 3 const __m128 d_2_3 = _mm_set1_ps( GGML_FP16_TO_FP32(x[i + 1].d) * GGML_FP16_TO_FP32(y[i + 1].d) ); const __m128i tmp_2_3 = _mm_loadu_si128((const __m128i *)x[i + 1].qs); __m128i bx_2 = _mm_and_si128(lowMask, tmp_2_3); __m128i by_2 = _mm_loadu_si128((const __m128i *)y[i + 1].qs); bx_2 = _mm_sub_epi8(bx_2, off); const __m128i i32_2 = mul_sum_i8_pairs(bx_2, by_2); __m128i bx_3 = _mm_and_si128(lowMask, _mm_srli_epi64(tmp_2_3, 4)); __m128i by_3 = _mm_loadu_si128((const __m128i *)(y[i + 1].qs + 16)); bx_3 = _mm_sub_epi8(bx_3, off); const __m128i i32_3 = mul_sum_i8_pairs(bx_3, by_3); // Convert int32_t to float __m128 p0 = _mm_cvtepi32_ps(i32_0); __m128 p1 = _mm_cvtepi32_ps(i32_1); __m128 p2 = _mm_cvtepi32_ps(i32_2); __m128 p3 = _mm_cvtepi32_ps(i32_3); // Apply the scale __m128 p0_d = _mm_mul_ps( d_0_1, p0 ); __m128 p1_d = _mm_mul_ps( d_0_1, p1 ); __m128 p2_d = _mm_mul_ps( d_2_3, p2 ); __m128 p3_d = _mm_mul_ps( d_2_3, p3 ); // Acummulate acc_0 = _mm_add_ps(p0_d, acc_0); acc_1 = _mm_add_ps(p1_d, acc_1); acc_2 = _mm_add_ps(p2_d, acc_2); acc_3 = _mm_add_ps(p3_d, acc_3); } *s = hsum_float_4x4(acc_0, acc_1, acc_2, acc_3); #else // scalar float sumf = 0.0; for (int i = 0; i < nb; i++) { int sumi = 0; for (int j = 0; j < qk/2; ++j) { const int v0 = (x[i].qs[j] & 0x0F) - 8; const int v1 = (x[i].qs[j] >> 4) - 8; sumi += (v0 * y[i].qs[j]) + (v1 * y[i].qs[j + qk/2]); } sumf += sumi*GGML_FP16_TO_FP32(x[i].d)*GGML_FP16_TO_FP32(y[i].d); } *s = sumf; #endif } static void ggml_vec_dot_q4_1_q8_1(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) { const int qk = QK8_1; const int nb = n / qk; assert(n % qk == 0); assert(nb % 2 == 0); const block_q4_1 * restrict x = vx; const block_q8_1 * restrict y = vy; // TODO: add WASM SIMD #if defined(__ARM_NEON) float32x4_t sumv0 = vdupq_n_f32(0.0f); float32x4_t sumv1 = vdupq_n_f32(0.0f); float summs = 0; for (int i = 0; i < nb; i += 2) { const block_q4_1 * restrict x0 = &x[i + 0]; const block_q4_1 * restrict x1 = &x[i + 1]; const block_q8_1 * restrict y0 = &y[i + 0]; const block_q8_1 * restrict y1 = &y[i + 1]; summs += GGML_FP16_TO_FP32(x0->m) * y0->s + GGML_FP16_TO_FP32(x1->m) * y1->s; const uint8x16_t m4b = vdupq_n_u8(0x0F); const uint8x16_t v0_0 = vld1q_u8(x0->qs); const uint8x16_t v0_1 = vld1q_u8(x1->qs); // 4-bit -> 8-bit const int8x16_t v0_0l = vreinterpretq_s8_u8(vandq_u8 (v0_0, m4b)); const int8x16_t v0_0h = vreinterpretq_s8_u8(vshrq_n_u8(v0_0, 4)); const int8x16_t v0_1l = vreinterpretq_s8_u8(vandq_u8 (v0_1, m4b)); const int8x16_t v0_1h = vreinterpretq_s8_u8(vshrq_n_u8(v0_1, 4)); // load y const int8x16_t v1_0l = vld1q_s8(y0->qs); const int8x16_t v1_0h = vld1q_s8(y0->qs + 16); const int8x16_t v1_1l = vld1q_s8(y1->qs); const int8x16_t v1_1h = vld1q_s8(y1->qs + 16); #if defined(__ARM_FEATURE_DOTPROD) // dot product into int32x4_t const int32x4_t p_0 = vdotq_s32(vdotq_s32(vdupq_n_s32(0), v0_0l, v1_0l), v0_0h, v1_0h); const int32x4_t p_1 = vdotq_s32(vdotq_s32(vdupq_n_s32(0), v0_1l, v1_1l), v0_1h, v1_1h); sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(p_0), GGML_FP16_TO_FP32(x0->d)*y0->d); sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(p_1), GGML_FP16_TO_FP32(x1->d)*y1->d); #else const int16x8_t pl0l = vmull_s8(vget_low_s8 (v0_0l), vget_low_s8 (v1_0l)); const int16x8_t pl0h = vmull_s8(vget_high_s8(v0_0l), vget_high_s8(v1_0l)); const int16x8_t ph0l = vmull_s8(vget_low_s8 (v0_0h), vget_low_s8 (v1_0h)); const int16x8_t ph0h = vmull_s8(vget_high_s8(v0_0h), vget_high_s8(v1_0h)); const int16x8_t pl1l = vmull_s8(vget_low_s8 (v0_1l), vget_low_s8 (v1_1l)); const int16x8_t pl1h = vmull_s8(vget_high_s8(v0_1l), vget_high_s8(v1_1l)); const int16x8_t ph1l = vmull_s8(vget_low_s8 (v0_1h), vget_low_s8 (v1_1h)); const int16x8_t ph1h = vmull_s8(vget_high_s8(v0_1h), vget_high_s8(v1_1h)); const int32x4_t pl0 = vaddq_s32(vpaddlq_s16(pl0l), vpaddlq_s16(pl0h)); const int32x4_t ph0 = vaddq_s32(vpaddlq_s16(ph0l), vpaddlq_s16(ph0h)); const int32x4_t pl1 = vaddq_s32(vpaddlq_s16(pl1l), vpaddlq_s16(pl1h)); const int32x4_t ph1 = vaddq_s32(vpaddlq_s16(ph1l), vpaddlq_s16(ph1h)); sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(pl0, ph0)), GGML_FP16_TO_FP32(x0->d)*y0->d); sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(pl1, ph1)), GGML_FP16_TO_FP32(x1->d)*y1->d); #endif } *s = vaddvq_f32(sumv0) + vaddvq_f32(sumv1) + summs; #elif defined(__AVX2__) || defined(__AVX__) // Initialize accumulator with zeros __m256 acc = _mm256_setzero_ps(); float summs = 0; // Main loop for (int i = 0; i < nb; ++i) { const float d0 = GGML_FP16_TO_FP32(x[i].d); const float d1 = y[i].d; summs += GGML_FP16_TO_FP32(x[i].m) * y[i].s; const __m256 d0v = _mm256_set1_ps( d0 ); const __m256 d1v = _mm256_set1_ps( d1 ); // Compute combined scales const __m256 d0d1 = _mm256_mul_ps( d0v, d1v ); // Load 16 bytes, and unpack 4 bit fields into bytes, making 32 bytes const __m256i bx = bytes_from_nibbles_32(x[i].qs); const __m256i by = _mm256_loadu_si256( (const __m256i *)y[i].qs ); const __m256 xy = mul_sum_us8_pairs_float(bx, by); // Accumulate d0*d1*x*y #if defined(__AVX2__) acc = _mm256_fmadd_ps( d0d1, xy, acc ); #else acc = _mm256_add_ps( _mm256_mul_ps( d0d1, xy ), acc ); #endif } *s = hsum_float_8(acc) + summs; #else // scalar float sumf = 0.0; for (int i = 0; i < nb; i++) { int sumi = 0; for (int j = 0; j < qk/2; ++j) { const int v0 = (x[i].qs[j] & 0x0F); const int v1 = (x[i].qs[j] >> 4); sumi += (v0 * y[i].qs[j]) + (v1 * y[i].qs[j + qk/2]); } sumf += (GGML_FP16_TO_FP32(x[i].d)*y[i].d)*sumi + GGML_FP16_TO_FP32(x[i].m)*y[i].s; } *s = sumf; #endif } static void ggml_vec_dot_q5_0_q8_0(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) { const int qk = QK8_0; const int nb = n / qk; assert(n % qk == 0); assert(nb % 2 == 0); assert(qk == QK5_0); const block_q5_0 * restrict x = vx; const block_q8_0 * restrict y = vy; #if defined(__ARM_NEON) float32x4_t sumv0 = vdupq_n_f32(0.0f); float32x4_t sumv1 = vdupq_n_f32(0.0f); uint32_t qh0; uint32_t qh1; uint64_t tmp0[4]; uint64_t tmp1[4]; for (int i = 0; i < nb; i += 2) { const block_q5_0 * restrict x0 = &x[i]; const block_q5_0 * restrict x1 = &x[i + 1]; const block_q8_0 * restrict y0 = &y[i]; const block_q8_0 * restrict y1 = &y[i + 1]; const uint8x16_t m4b = vdupq_n_u8(0x0F); // extract the 5th bit via lookup table ((!b) << 4) memcpy(&qh0, x0->qh, sizeof(qh0)); memcpy(&qh1, x1->qh, sizeof(qh1)); tmp0[0] = table_b2b_1[(qh0 >> 0) & 0xFF]; tmp0[1] = table_b2b_1[(qh0 >> 8) & 0xFF]; tmp0[2] = table_b2b_1[(qh0 >> 16) & 0xFF]; tmp0[3] = table_b2b_1[(qh0 >> 24) ]; tmp1[0] = table_b2b_1[(qh1 >> 0) & 0xFF]; tmp1[1] = table_b2b_1[(qh1 >> 8) & 0xFF]; tmp1[2] = table_b2b_1[(qh1 >> 16) & 0xFF]; tmp1[3] = table_b2b_1[(qh1 >> 24) ]; const int8x16_t qhl0 = vld1q_s8((const int8_t *)(tmp0 + 0)); const int8x16_t qhh0 = vld1q_s8((const int8_t *)(tmp0 + 2)); const int8x16_t qhl1 = vld1q_s8((const int8_t *)(tmp1 + 0)); const int8x16_t qhh1 = vld1q_s8((const int8_t *)(tmp1 + 2)); const uint8x16_t v0_0 = vld1q_u8(x0->qs); const uint8x16_t v0_1 = vld1q_u8(x1->qs); // 4-bit -> 8-bit int8x16_t v0_0l = vreinterpretq_s8_u8(vandq_u8 (v0_0, m4b)); int8x16_t v0_0h = vreinterpretq_s8_u8(vshrq_n_u8(v0_0, 4)); int8x16_t v0_1l = vreinterpretq_s8_u8(vandq_u8 (v0_1, m4b)); int8x16_t v0_1h = vreinterpretq_s8_u8(vshrq_n_u8(v0_1, 4)); // add high bit and sub 16 (equivalent to sub 0x10 when bit is zero) const int8x16_t v0_0lf = vsubq_s8(v0_0l, qhl0); const int8x16_t v0_0hf = vsubq_s8(v0_0h, qhh0); const int8x16_t v0_1lf = vsubq_s8(v0_1l, qhl1); const int8x16_t v0_1hf = vsubq_s8(v0_1h, qhh1); // load y const int8x16_t v1_0l = vld1q_s8(y0->qs); const int8x16_t v1_0h = vld1q_s8(y0->qs + 16); const int8x16_t v1_1l = vld1q_s8(y1->qs); const int8x16_t v1_1h = vld1q_s8(y1->qs + 16); #if defined(__ARM_FEATURE_DOTPROD) sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32( vdotq_s32(vdupq_n_s32(0), v0_0lf, v1_0l), vdotq_s32(vdupq_n_s32(0), v0_0hf, v1_0h))), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d)); sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32( vdotq_s32(vdupq_n_s32(0), v0_1lf, v1_1l), vdotq_s32(vdupq_n_s32(0), v0_1hf, v1_1h))), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d)); #else const int16x8_t pl0l = vmull_s8(vget_low_s8 (v0_0lf), vget_low_s8 (v1_0l)); const int16x8_t pl0h = vmull_s8(vget_high_s8(v0_0lf), vget_high_s8(v1_0l)); const int16x8_t ph0l = vmull_s8(vget_low_s8 (v0_0hf), vget_low_s8 (v1_0h)); const int16x8_t ph0h = vmull_s8(vget_high_s8(v0_0hf), vget_high_s8(v1_0h)); const int16x8_t pl1l = vmull_s8(vget_low_s8 (v0_1lf), vget_low_s8 (v1_1l)); const int16x8_t pl1h = vmull_s8(vget_high_s8(v0_1lf), vget_high_s8(v1_1l)); const int16x8_t ph1l = vmull_s8(vget_low_s8 (v0_1hf), vget_low_s8 (v1_1h)); const int16x8_t ph1h = vmull_s8(vget_high_s8(v0_1hf), vget_high_s8(v1_1h)); const int32x4_t pl0 = vaddq_s32(vpaddlq_s16(pl0l), vpaddlq_s16(pl0h)); const int32x4_t ph0 = vaddq_s32(vpaddlq_s16(ph0l), vpaddlq_s16(ph0h)); const int32x4_t pl1 = vaddq_s32(vpaddlq_s16(pl1l), vpaddlq_s16(pl1h)); const int32x4_t ph1 = vaddq_s32(vpaddlq_s16(ph1l), vpaddlq_s16(ph1h)); sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(pl0, ph0)), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d)); sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(pl1, ph1)), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d)); #endif } *s = vaddvq_f32(sumv0) + vaddvq_f32(sumv1); #elif defined(__wasm_simd128__) v128_t sumv = wasm_f32x4_splat(0.0f); uint32_t qh; uint64_t tmp[4]; // TODO: check if unrolling this is better for (int i = 0; i < nb; ++i) { const block_q5_0 * restrict x0 = &x[i]; const block_q8_0 * restrict y0 = &y[i]; const v128_t m4b = wasm_i8x16_splat(0x0F); // extract the 5th bit memcpy(&qh, x0->qh, sizeof(qh)); tmp[0] = table_b2b_1[(qh >> 0) & 0xFF]; tmp[1] = table_b2b_1[(qh >> 8) & 0xFF]; tmp[2] = table_b2b_1[(qh >> 16) & 0xFF]; tmp[3] = table_b2b_1[(qh >> 24) ]; const v128_t qhl = wasm_v128_load(tmp + 0); const v128_t qhh = wasm_v128_load(tmp + 2); const v128_t v0 = wasm_v128_load(x0->qs); // 4-bit -> 8-bit const v128_t v0l = wasm_v128_and (v0, m4b); const v128_t v0h = wasm_u8x16_shr(v0, 4); // add high bit and sub 16 (equivalent to sub 0x10 when bit is zero) const v128_t v0lf = wasm_i8x16_sub(v0l, qhl); const v128_t v0hf = wasm_i8x16_sub(v0h, qhh); // load y const v128_t v1l = wasm_v128_load(y0->qs); const v128_t v1h = wasm_v128_load(y0->qs + 16); // int8x16 -> int16x8 const v128_t v0lfl = wasm_i16x8_extend_low_i8x16 (v0lf); const v128_t v0lfh = wasm_i16x8_extend_high_i8x16(v0lf); const v128_t v0hfl = wasm_i16x8_extend_low_i8x16 (v0hf); const v128_t v0hfh = wasm_i16x8_extend_high_i8x16(v0hf); const v128_t v1ll = wasm_i16x8_extend_low_i8x16 (v1l); const v128_t v1lh = wasm_i16x8_extend_high_i8x16(v1l); const v128_t v1hl = wasm_i16x8_extend_low_i8x16 (v1h); const v128_t v1hh = wasm_i16x8_extend_high_i8x16(v1h); // dot product sumv = wasm_f32x4_add(sumv, wasm_f32x4_mul(wasm_f32x4_convert_i32x4( wasm_i32x4_add( wasm_i32x4_add(wasm_i32x4_dot_i16x8(v0lfl, v1ll), wasm_i32x4_dot_i16x8(v0lfh, v1lh)), wasm_i32x4_add(wasm_i32x4_dot_i16x8(v0hfl, v1hl), wasm_i32x4_dot_i16x8(v0hfh, v1hh)))), wasm_f32x4_splat(GGML_FP16_TO_FP32(x0->d) * GGML_FP16_TO_FP32(y0->d)))); } *s = wasm_f32x4_extract_lane(sumv, 0) + wasm_f32x4_extract_lane(sumv, 1) + wasm_f32x4_extract_lane(sumv, 2) + wasm_f32x4_extract_lane(sumv, 3); #elif defined(__AVX2__) // Initialize accumulator with zeros __m256 acc = _mm256_setzero_ps(); // Main loop for (int i = 0; i < nb; i++) { /* Compute combined scale for the block */ const __m256 d = _mm256_set1_ps(GGML_FP16_TO_FP32(x[i].d) * GGML_FP16_TO_FP32(y[i].d)); __m256i bx = bytes_from_nibbles_32(x[i].qs); __m256i bxhi = bytes_from_bits_32(x[i].qh); bxhi = _mm256_andnot_si256(bxhi, _mm256_set1_epi8((char)0xF0)); bx = _mm256_or_si256(bx, bxhi); __m256i by = _mm256_loadu_si256((const __m256i *)y[i].qs); const __m256 q = mul_sum_i8_pairs_float(bx, by); /* Multiply q with scale and accumulate */ acc = _mm256_fmadd_ps(d, q, acc); } *s = hsum_float_8(acc); #elif defined(__AVX__) // Initialize accumulator with zeros __m256 acc = _mm256_setzero_ps(); __m128i mask = _mm_set1_epi8((char)0xF0); // Main loop for (int i = 0; i < nb; i++) { /* Compute combined scale for the block */ const __m256 d = _mm256_set1_ps(GGML_FP16_TO_FP32(x[i].d) * GGML_FP16_TO_FP32(y[i].d)); __m256i bx = bytes_from_nibbles_32(x[i].qs); const __m256i bxhi = bytes_from_bits_32(x[i].qh); __m128i bxhil = _mm256_castsi256_si128(bxhi); __m128i bxhih = _mm256_extractf128_si256(bxhi, 1); bxhil = _mm_andnot_si128(bxhil, mask); bxhih = _mm_andnot_si128(bxhih, mask); __m128i bxl = _mm256_castsi256_si128(bx); __m128i bxh = _mm256_extractf128_si256(bx, 1); bxl = _mm_or_si128(bxl, bxhil); bxh = _mm_or_si128(bxh, bxhih); bx = MM256_SET_M128I(bxh, bxl); const __m256i by = _mm256_loadu_si256((const __m256i *)y[i].qs); const __m256 q = mul_sum_i8_pairs_float(bx, by); /* Multiply q with scale and accumulate */ acc = _mm256_add_ps(_mm256_mul_ps(d, q), acc); } *s = hsum_float_8(acc); #else // scalar float sumf = 0.0; for (int i = 0; i < nb; i++) { uint32_t qh; memcpy(&qh, x[i].qh, sizeof(qh)); int sumi = 0; for (int j = 0; j < qk/2; ++j) { const uint8_t xh_0 = ((qh & (1u << (j + 0 ))) >> (j + 0 )) << 4; const uint8_t xh_1 = ((qh & (1u << (j + 16))) >> (j + 12)); const int32_t x0 = ((x[i].qs[j] & 0x0F) | xh_0) - 16; const int32_t x1 = ((x[i].qs[j] >> 4) | xh_1) - 16; sumi += (x0 * y[i].qs[j]) + (x1 * y[i].qs[j + qk/2]); } sumf += (GGML_FP16_TO_FP32(x[i].d)*GGML_FP16_TO_FP32(y[i].d)) * sumi; } *s = sumf; #endif } static void ggml_vec_dot_q5_1_q8_1(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) { const int qk = QK8_1; const int nb = n / qk; assert(n % qk == 0); assert(nb % 2 == 0); assert(qk == QK5_1); const block_q5_1 * restrict x = vx; const block_q8_1 * restrict y = vy; #if defined(__ARM_NEON) float32x4_t sumv0 = vdupq_n_f32(0.0f); float32x4_t sumv1 = vdupq_n_f32(0.0f); float summs0 = 0.0f; float summs1 = 0.0f; uint32_t qh0; uint32_t qh1; uint64_t tmp0[4]; uint64_t tmp1[4]; for (int i = 0; i < nb; i += 2) { const block_q5_1 * restrict x0 = &x[i]; const block_q5_1 * restrict x1 = &x[i + 1]; const block_q8_1 * restrict y0 = &y[i]; const block_q8_1 * restrict y1 = &y[i + 1]; const uint8x16_t m4b = vdupq_n_u8(0x0F); summs0 += GGML_FP16_TO_FP32(x0->m) * y0->s; summs1 += GGML_FP16_TO_FP32(x1->m) * y1->s; // extract the 5th bit via lookup table ((b) << 4) memcpy(&qh0, x0->qh, sizeof(qh0)); memcpy(&qh1, x1->qh, sizeof(qh1)); tmp0[0] = table_b2b_0[(qh0 >> 0) & 0xFF]; tmp0[1] = table_b2b_0[(qh0 >> 8) & 0xFF]; tmp0[2] = table_b2b_0[(qh0 >> 16) & 0xFF]; tmp0[3] = table_b2b_0[(qh0 >> 24) ]; tmp1[0] = table_b2b_0[(qh1 >> 0) & 0xFF]; tmp1[1] = table_b2b_0[(qh1 >> 8) & 0xFF]; tmp1[2] = table_b2b_0[(qh1 >> 16) & 0xFF]; tmp1[3] = table_b2b_0[(qh1 >> 24) ]; const int8x16_t qhl0 = vld1q_s8((const int8_t *)(tmp0 + 0)); const int8x16_t qhh0 = vld1q_s8((const int8_t *)(tmp0 + 2)); const int8x16_t qhl1 = vld1q_s8((const int8_t *)(tmp1 + 0)); const int8x16_t qhh1 = vld1q_s8((const int8_t *)(tmp1 + 2)); const uint8x16_t v0_0 = vld1q_u8(x0->qs); const uint8x16_t v0_1 = vld1q_u8(x1->qs); // 4-bit -> 8-bit const int8x16_t v0_0l = vreinterpretq_s8_u8(vandq_u8 (v0_0, m4b)); const int8x16_t v0_0h = vreinterpretq_s8_u8(vshrq_n_u8(v0_0, 4)); const int8x16_t v0_1l = vreinterpretq_s8_u8(vandq_u8 (v0_1, m4b)); const int8x16_t v0_1h = vreinterpretq_s8_u8(vshrq_n_u8(v0_1, 4)); // add high bit const int8x16_t v0_0lf = vorrq_s8(v0_0l, qhl0); const int8x16_t v0_0hf = vorrq_s8(v0_0h, qhh0); const int8x16_t v0_1lf = vorrq_s8(v0_1l, qhl1); const int8x16_t v0_1hf = vorrq_s8(v0_1h, qhh1); // load y const int8x16_t v1_0l = vld1q_s8(y0->qs); const int8x16_t v1_0h = vld1q_s8(y0->qs + 16); const int8x16_t v1_1l = vld1q_s8(y1->qs); const int8x16_t v1_1h = vld1q_s8(y1->qs + 16); #if defined(__ARM_FEATURE_DOTPROD) sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32( vdotq_s32(vdupq_n_s32(0), v0_0lf, v1_0l), vdotq_s32(vdupq_n_s32(0), v0_0hf, v1_0h))), GGML_FP16_TO_FP32(x0->d)*y0->d); sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32( vdotq_s32(vdupq_n_s32(0), v0_1lf, v1_1l), vdotq_s32(vdupq_n_s32(0), v0_1hf, v1_1h))), GGML_FP16_TO_FP32(x1->d)*y1->d); #else const int16x8_t pl0l = vmull_s8(vget_low_s8 (v0_0lf), vget_low_s8 (v1_0l)); const int16x8_t pl0h = vmull_s8(vget_high_s8(v0_0lf), vget_high_s8(v1_0l)); const int16x8_t ph0l = vmull_s8(vget_low_s8 (v0_0hf), vget_low_s8 (v1_0h)); const int16x8_t ph0h = vmull_s8(vget_high_s8(v0_0hf), vget_high_s8(v1_0h)); const int16x8_t pl1l = vmull_s8(vget_low_s8 (v0_1lf), vget_low_s8 (v1_1l)); const int16x8_t pl1h = vmull_s8(vget_high_s8(v0_1lf), vget_high_s8(v1_1l)); const int16x8_t ph1l = vmull_s8(vget_low_s8 (v0_1hf), vget_low_s8 (v1_1h)); const int16x8_t ph1h = vmull_s8(vget_high_s8(v0_1hf), vget_high_s8(v1_1h)); const int32x4_t pl0 = vaddq_s32(vpaddlq_s16(pl0l), vpaddlq_s16(pl0h)); const int32x4_t ph0 = vaddq_s32(vpaddlq_s16(ph0l), vpaddlq_s16(ph0h)); const int32x4_t pl1 = vaddq_s32(vpaddlq_s16(pl1l), vpaddlq_s16(pl1h)); const int32x4_t ph1 = vaddq_s32(vpaddlq_s16(ph1l), vpaddlq_s16(ph1h)); sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(pl0, ph0)), GGML_FP16_TO_FP32(x0->d)*y0->d); sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(pl1, ph1)), GGML_FP16_TO_FP32(x1->d)*y1->d); #endif } *s = vaddvq_f32(sumv0) + vaddvq_f32(sumv1) + summs0 + summs1; #elif defined(__wasm_simd128__) v128_t sumv = wasm_f32x4_splat(0.0f); float summs = 0.0f; uint32_t qh; uint64_t tmp[4]; // TODO: check if unrolling this is better for (int i = 0; i < nb; ++i) { const block_q5_1 * restrict x0 = &x[i]; const block_q8_1 * restrict y0 = &y[i]; summs += GGML_FP16_TO_FP32(x0->m) * y0->s; const v128_t m4b = wasm_i8x16_splat(0x0F); // extract the 5th bit memcpy(&qh, x0->qh, sizeof(qh)); tmp[0] = table_b2b_0[(qh >> 0) & 0xFF]; tmp[1] = table_b2b_0[(qh >> 8) & 0xFF]; tmp[2] = table_b2b_0[(qh >> 16) & 0xFF]; tmp[3] = table_b2b_0[(qh >> 24) ]; const v128_t qhl = wasm_v128_load(tmp + 0); const v128_t qhh = wasm_v128_load(tmp + 2); const v128_t v0 = wasm_v128_load(x0->qs); // 4-bit -> 8-bit const v128_t v0l = wasm_v128_and (v0, m4b); const v128_t v0h = wasm_u8x16_shr(v0, 4); // add high bit const v128_t v0lf = wasm_v128_or(v0l, qhl); const v128_t v0hf = wasm_v128_or(v0h, qhh); // load y const v128_t v1l = wasm_v128_load(y0->qs); const v128_t v1h = wasm_v128_load(y0->qs + 16); // int8x16 -> int16x8 const v128_t v0lfl = wasm_i16x8_extend_low_i8x16 (v0lf); const v128_t v0lfh = wasm_i16x8_extend_high_i8x16(v0lf); const v128_t v0hfl = wasm_i16x8_extend_low_i8x16 (v0hf); const v128_t v0hfh = wasm_i16x8_extend_high_i8x16(v0hf); const v128_t v1ll = wasm_i16x8_extend_low_i8x16 (v1l); const v128_t v1lh = wasm_i16x8_extend_high_i8x16(v1l); const v128_t v1hl = wasm_i16x8_extend_low_i8x16 (v1h); const v128_t v1hh = wasm_i16x8_extend_high_i8x16(v1h); // dot product sumv = wasm_f32x4_add(sumv, wasm_f32x4_mul(wasm_f32x4_convert_i32x4(wasm_i32x4_add( wasm_i32x4_add(wasm_i32x4_dot_i16x8(v0lfl, v1ll), wasm_i32x4_dot_i16x8(v0lfh, v1lh)), wasm_i32x4_add(wasm_i32x4_dot_i16x8(v0hfl, v1hl), wasm_i32x4_dot_i16x8(v0hfh, v1hh)))), wasm_f32x4_splat(GGML_FP16_TO_FP32(x0->d) * y0->d))); } *s = wasm_f32x4_extract_lane(sumv, 0) + wasm_f32x4_extract_lane(sumv, 1) + wasm_f32x4_extract_lane(sumv, 2) + wasm_f32x4_extract_lane(sumv, 3) + summs; #elif defined(__AVX2__) // Initialize accumulator with zeros __m256 acc = _mm256_setzero_ps(); float summs = 0.0f; // Main loop for (int i = 0; i < nb; i++) { const __m256 dx = _mm256_set1_ps(GGML_FP16_TO_FP32(x[i].d)); summs += GGML_FP16_TO_FP32(x[i].m) * y[i].s; __m256i bx = bytes_from_nibbles_32(x[i].qs); __m256i bxhi = bytes_from_bits_32(x[i].qh); bxhi = _mm256_and_si256(bxhi, _mm256_set1_epi8(0x10)); bx = _mm256_or_si256(bx, bxhi); const __m256 dy = _mm256_set1_ps(y[i].d); const __m256i by = _mm256_loadu_si256((const __m256i *)y[i].qs); const __m256 q = mul_sum_us8_pairs_float(bx, by); acc = _mm256_fmadd_ps(q, _mm256_mul_ps(dx, dy), acc); } *s = hsum_float_8(acc) + summs; #elif defined(__AVX__) // Initialize accumulator with zeros __m256 acc = _mm256_setzero_ps(); __m128i mask = _mm_set1_epi8(0x10); float summs = 0.0f; // Main loop for (int i = 0; i < nb; i++) { const __m256 dx = _mm256_set1_ps(GGML_FP16_TO_FP32(x[i].d)); summs += GGML_FP16_TO_FP32(x[i].m) * y[i].s; __m256i bx = bytes_from_nibbles_32(x[i].qs); const __m256i bxhi = bytes_from_bits_32(x[i].qh); __m128i bxhil = _mm256_castsi256_si128(bxhi); __m128i bxhih = _mm256_extractf128_si256(bxhi, 1); bxhil = _mm_and_si128(bxhil, mask); bxhih = _mm_and_si128(bxhih, mask); __m128i bxl = _mm256_castsi256_si128(bx); __m128i bxh = _mm256_extractf128_si256(bx, 1); bxl = _mm_or_si128(bxl, bxhil); bxh = _mm_or_si128(bxh, bxhih); bx = MM256_SET_M128I(bxh, bxl); const __m256 dy = _mm256_set1_ps(y[i].d); const __m256i by = _mm256_loadu_si256((const __m256i *)y[i].qs); const __m256 q = mul_sum_us8_pairs_float(bx, by); acc = _mm256_add_ps(_mm256_mul_ps(q, _mm256_mul_ps(dx, dy)), acc); } *s = hsum_float_8(acc) + summs; #else // scalar float sumf = 0.0; for (int i = 0; i < nb; i++) { uint32_t qh; memcpy(&qh, x[i].qh, sizeof(qh)); int sumi = 0; for (int j = 0; j < qk/2; ++j) { const uint8_t xh_0 = ((qh >> (j + 0)) << 4) & 0x10; const uint8_t xh_1 = ((qh >> (j + 12)) ) & 0x10; const int32_t x0 = (x[i].qs[j] & 0xF) | xh_0; const int32_t x1 = (x[i].qs[j] >> 4) | xh_1; sumi += (x0 * y[i].qs[j]) + (x1 * y[i].qs[j + qk/2]); } sumf += (GGML_FP16_TO_FP32(x[i].d)*y[i].d)*sumi + GGML_FP16_TO_FP32(x[i].m)*y[i].s; } *s = sumf; #endif } static void ggml_vec_dot_q8_0_q8_0(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) { const int qk = QK8_0; const int nb = n / qk; assert(n % qk == 0); assert(nb % 2 == 0); const block_q8_0 * restrict x = vx; const block_q8_0 * restrict y = vy; #if defined(__ARM_NEON) float32x4_t sumv0 = vdupq_n_f32(0.0f); float32x4_t sumv1 = vdupq_n_f32(0.0f); for (int i = 0; i < nb; i += 2) { const block_q8_0 * restrict x0 = &x[i + 0]; const block_q8_0 * restrict x1 = &x[i + 1]; const block_q8_0 * restrict y0 = &y[i + 0]; const block_q8_0 * restrict y1 = &y[i + 1]; const int8x16_t x0_0 = vld1q_s8(x0->qs); const int8x16_t x0_1 = vld1q_s8(x0->qs + 16); const int8x16_t x1_0 = vld1q_s8(x1->qs); const int8x16_t x1_1 = vld1q_s8(x1->qs + 16); // load y const int8x16_t y0_0 = vld1q_s8(y0->qs); const int8x16_t y0_1 = vld1q_s8(y0->qs + 16); const int8x16_t y1_0 = vld1q_s8(y1->qs); const int8x16_t y1_1 = vld1q_s8(y1->qs + 16); #if defined(__ARM_FEATURE_DOTPROD) sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32( vdotq_s32(vdupq_n_s32(0), x0_0, y0_0), vdotq_s32(vdupq_n_s32(0), x0_1, y0_1))), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d)); sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32( vdotq_s32(vdupq_n_s32(0), x1_0, y1_0), vdotq_s32(vdupq_n_s32(0), x1_1, y1_1))), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d)); #else const int16x8_t p0_0 = vmull_s8(vget_low_s8 (x0_0), vget_low_s8 (y0_0)); const int16x8_t p0_1 = vmull_s8(vget_high_s8(x0_0), vget_high_s8(y0_0)); const int16x8_t p0_2 = vmull_s8(vget_low_s8 (x0_1), vget_low_s8 (y0_1)); const int16x8_t p0_3 = vmull_s8(vget_high_s8(x0_1), vget_high_s8(y0_1)); const int16x8_t p1_0 = vmull_s8(vget_low_s8 (x1_0), vget_low_s8 (y1_0)); const int16x8_t p1_1 = vmull_s8(vget_high_s8(x1_0), vget_high_s8(y1_0)); const int16x8_t p1_2 = vmull_s8(vget_low_s8 (x1_1), vget_low_s8 (y1_1)); const int16x8_t p1_3 = vmull_s8(vget_high_s8(x1_1), vget_high_s8(y1_1)); const int32x4_t p0 = vaddq_s32(vpaddlq_s16(p0_0), vpaddlq_s16(p0_1)); const int32x4_t p1 = vaddq_s32(vpaddlq_s16(p0_2), vpaddlq_s16(p0_3)); const int32x4_t p2 = vaddq_s32(vpaddlq_s16(p1_0), vpaddlq_s16(p1_1)); const int32x4_t p3 = vaddq_s32(vpaddlq_s16(p1_2), vpaddlq_s16(p1_3)); sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(p0, p1)), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d)); sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(p2, p3)), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d)); #endif } *s = vaddvq_f32(sumv0) + vaddvq_f32(sumv1); #elif defined(__AVX2__) || defined(__AVX__) // Initialize accumulator with zeros __m256 acc = _mm256_setzero_ps(); // Main loop for (int i = 0; i < nb; ++i) { // Compute combined scale for the block const __m256 d = _mm256_set1_ps(GGML_FP16_TO_FP32(x[i].d) * GGML_FP16_TO_FP32(y[i].d)); __m256i bx = _mm256_loadu_si256((const __m256i *)x[i].qs); __m256i by = _mm256_loadu_si256((const __m256i *)y[i].qs); const __m256 q = mul_sum_i8_pairs_float(bx, by); // Multiply q with scale and accumulate #if defined(__AVX2__) acc = _mm256_fmadd_ps( d, q, acc ); #else acc = _mm256_add_ps( _mm256_mul_ps( d, q ), acc ); #endif } *s = hsum_float_8(acc); #else // scalar float sumf = 0.0; for (int i = 0; i < nb; i++) { int sumi = 0; for (int j = 0; j < qk; j++) { sumi += x[i].qs[j]*y[i].qs[j]; } sumf += sumi*(GGML_FP16_TO_FP32(x[i].d)*GGML_FP16_TO_FP32(y[i].d)); } *s = sumf; #endif } // compute GGML_VEC_DOT_UNROLL dot products at once // xs - x row stride in bytes inline static void ggml_vec_dot_f16_unroll(const int n, const int xs, float * restrict s, void * restrict xv, ggml_fp16_t * restrict y) { ggml_float sumf[GGML_VEC_DOT_UNROLL] = { 0.0 }; ggml_fp16_t * restrict x[GGML_VEC_DOT_UNROLL]; for (int i = 0; i < GGML_VEC_DOT_UNROLL; ++i) { x[i] = (ggml_fp16_t *) ((char *) xv + i*xs); } #if defined(GGML_SIMD) const int np = (n & ~(GGML_F16_STEP - 1)); GGML_F16_VEC sum[GGML_VEC_DOT_UNROLL][GGML_F16_ARR] = { { GGML_F16_VEC_ZERO } }; GGML_F16_VEC ax[GGML_F16_ARR]; GGML_F16_VEC ay[GGML_F16_ARR]; for (int i = 0; i < np; i += GGML_F16_STEP) { for (int j = 0; j < GGML_F16_ARR; j++) { ay[j] = GGML_F16_VEC_LOAD(y + i + j*GGML_F16_EPR, j); for (int k = 0; k < GGML_VEC_DOT_UNROLL; ++k) { ax[j] = GGML_F16_VEC_LOAD(x[k] + i + j*GGML_F16_EPR, j); sum[k][j] = GGML_F16_VEC_FMA(sum[k][j], ax[j], ay[j]); } } } // reduce sum0..sum3 to sum0 for (int k = 0; k < GGML_VEC_DOT_UNROLL; ++k) { GGML_F16_VEC_REDUCE(sumf[k], sum[k]); } // leftovers for (int i = np; i < n; ++i) { for (int j = 0; j < GGML_VEC_DOT_UNROLL; ++j) { sumf[j] += (ggml_float)(GGML_FP16_TO_FP32(x[j][i])*GGML_FP16_TO_FP32(y[i])); } } #else for (int i = 0; i < n; ++i) { for (int j = 0; j < GGML_VEC_DOT_UNROLL; ++j) { sumf[j] += (ggml_float)(GGML_FP16_TO_FP32(x[j][i])*GGML_FP16_TO_FP32(y[i])); } } #endif for (int i = 0; i < GGML_VEC_DOT_UNROLL; ++i) { s[i] = sumf[i]; } } inline static void ggml_vec_mad_f32(const int n, float * restrict y, const float * restrict x, const float v) { #if defined(GGML_SIMD) const int np = (n & ~(GGML_F32_STEP - 1)); GGML_F32_VEC vx = GGML_F32_VEC_SET1(v); GGML_F32_VEC ax[GGML_F32_ARR]; GGML_F32_VEC ay[GGML_F32_ARR]; for (int i = 0; i < np; i += GGML_F32_STEP) { for (int j = 0; j < GGML_F32_ARR; j++) { ax[j] = GGML_F32_VEC_LOAD(x + i + j*GGML_F32_EPR); ay[j] = GGML_F32_VEC_LOAD(y + i + j*GGML_F32_EPR); ay[j] = GGML_F32_VEC_FMA(ay[j], ax[j], vx); GGML_F32_VEC_STORE(y + i + j*GGML_F32_EPR, ay[j]); } } // leftovers for (int i = np; i < n; ++i) { y[i] += x[i]*v; } #else // scalar for (int i = 0; i < n; ++i) { y[i] += x[i]*v; } #endif } //inline static void ggml_vec_scale_f32(const int n, float * y, const float v) { for (int i = 0; i < n; ++i) y[i] *= v; } inline static void ggml_vec_scale_f32(const int n, float * y, const float v) { #if defined(GGML_USE_ACCELERATE) vDSP_vsmul(y, 1, &v, y, 1, n); #elif defined(GGML_SIMD) const int np = (n & ~(GGML_F32_STEP - 1)); GGML_F32_VEC vx = GGML_F32_VEC_SET1(v); GGML_F32_VEC ay[GGML_F32_ARR]; for (int i = 0; i < np; i += GGML_F32_STEP) { for (int j = 0; j < GGML_F32_ARR; j++) { ay[j] = GGML_F32_VEC_LOAD(y + i + j*GGML_F32_EPR); ay[j] = GGML_F32_VEC_MUL(ay[j], vx); GGML_F32_VEC_STORE(y + i + j*GGML_F32_EPR, ay[j]); } } // leftovers for (int i = np; i < n; ++i) { y[i] *= v; } #else // scalar for (int i = 0; i < n; ++i) { y[i] *= v; } #endif } inline static void ggml_vec_norm_f32 (const int n, float * s, const float * x) { ggml_vec_dot_f32(n, s, x, x); *s = sqrtf(*s); } inline static void ggml_vec_sqr_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = x[i]*x[i]; } inline static void ggml_vec_sqrt_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = sqrtf(x[i]); } inline static void ggml_vec_log_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = logf(x[i]); } inline static void ggml_vec_abs_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = fabsf(x[i]); } inline static void ggml_vec_sgn_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? 1.f : ((x[i] < 0.f) ? -1.f : 0.f); } inline static void ggml_vec_step_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? 1.f : 0.f; } inline static void ggml_vec_tanh_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = tanhf(x[i]); } inline static void ggml_vec_elu_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? x[i] : expf(x[i])-1; } inline static void ggml_vec_relu_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? x[i] : 0.f; } static const float GELU_COEF_A = 0.044715f; static const float GELU_QUICK_COEF = -1.702f; static const float SQRT_2_OVER_PI = 0.79788456080286535587989211986876f; inline static float ggml_gelu_f32(float x) { return 0.5f*x*(1.0f + tanhf(SQRT_2_OVER_PI*x*(1.0f + GELU_COEF_A*x*x))); } inline static void ggml_vec_gelu_f16(const int n, ggml_fp16_t * y, const ggml_fp16_t * x) { const uint16_t * i16 = (const uint16_t *) x; for (int i = 0; i < n; ++i) { y[i] = table_gelu_f16[i16[i]]; } } #ifdef GGML_GELU_FP16 inline static void ggml_vec_gelu_f32(const int n, float * y, const float * x) { uint16_t t; for (int i = 0; i < n; ++i) { ggml_fp16_t fp16 = GGML_FP32_TO_FP16(x[i]); memcpy(&t, &fp16, sizeof(uint16_t)); y[i] = GGML_FP16_TO_FP32(table_gelu_f16[t]); } } #else inline static void ggml_vec_gelu_f32(const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) { y[i] = ggml_gelu_f32(x[i]); } } #endif inline static float ggml_gelu_quick_f32(float x) { return x*(1.0f/(1.0f+expf(GELU_QUICK_COEF*x))); } //inline static void ggml_vec_gelu_quick_f16(const int n, ggml_fp16_t * y, const ggml_fp16_t * x) { // const uint16_t * i16 = (const uint16_t *) x; // for (int i = 0; i < n; ++i) { // y[i] = table_gelu_quick_f16[i16[i]]; // } //} #ifdef GGML_GELU_QUICK_FP16 inline static void ggml_vec_gelu_quick_f32(const int n, float * y, const float * x) { uint16_t t; for (int i = 0; i < n; ++i) { ggml_fp16_t fp16 = GGML_FP32_TO_FP16(x[i]); memcpy(&t, &fp16, sizeof(uint16_t)); y[i] = GGML_FP16_TO_FP32(table_gelu_quick_f16[t]); } } #else inline static void ggml_vec_gelu_quick_f32(const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) { y[i] = ggml_gelu_quick_f32(x[i]); } } #endif // Sigmoid Linear Unit (SiLU) function inline static float ggml_silu_f32(float x) { return x/(1.0f + expf(-x)); } //inline static void ggml_vec_silu_f16(const int n, ggml_fp16_t * y, const ggml_fp16_t * x) { // const uint16_t * i16 = (const uint16_t *) x; // for (int i = 0; i < n; ++i) { // y[i] = table_silu_f16[i16[i]]; // } //} #ifdef GGML_SILU_FP16 inline static void ggml_vec_silu_f32(const int n, float * y, const float * x) { uint16_t t; for (int i = 0; i < n; ++i) { ggml_fp16_t fp16 = GGML_FP32_TO_FP16(x[i]); memcpy(&t, &fp16, sizeof(uint16_t)); y[i] = GGML_FP16_TO_FP32(table_silu_f16[t]); } } #else inline static void ggml_vec_silu_f32(const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) { y[i] = ggml_silu_f32(x[i]); } } #endif inline static float ggml_silu_backward_f32(float x, float dy) { const float s = 1.0f/(1.0f + expf(-x)); return dy*s*(1.0f + x*(1.0f - s)); } #ifdef GGML_SILU_FP16 inline static void ggml_vec_silu_backward_f32(const int n, float * dx, const float * x, const float * dy) { for (int i = 0; i < n; ++i) { // we did not use x[i] to compute forward silu but its f16 equivalent // take derivative at f16 of x[i]: ggml_fp16_t fp16 = GGML_FP32_TO_FP16(x[i]); float usedx = GGML_FP16_TO_FP32(fp16); dx[i] = ggml_silu_backward_f32(usedx, dy[i]); } } #else inline static void ggml_vec_silu_backward_f32(const int n, float * dx, const float * x, const float * dy) { for (int i = 0; i < n; ++i) { dx[i] = ggml_silu_backward_f32(x[i], dy[i]); } } #endif inline static void ggml_vec_sum_f32(const int n, float * s, const float * x) { #ifndef GGML_USE_ACCELERATE ggml_float sum = 0.0; for (int i = 0; i < n; ++i) { sum += (ggml_float)x[i]; } *s = sum; #else vDSP_sve(x, 1, s, n); #endif } inline static void ggml_vec_sum_f32_ggf(const int n, ggml_float * s, const float * x) { ggml_float sum = 0.0; for (int i = 0; i < n; ++i) { sum += (ggml_float)x[i]; } *s = sum; } inline static void ggml_vec_sum_f16_ggf(const int n, float * s, const ggml_fp16_t * x) { float sum = 0.0f; for (int i = 0; i < n; ++i) { sum += GGML_FP16_TO_FP32(x[i]); } *s = sum; } inline static void ggml_vec_max_f32(const int n, float * s, const float * x) { #ifndef GGML_USE_ACCELERATE float max = -INFINITY; for (int i = 0; i < n; ++i) { max = MAX(max, x[i]); } *s = max; #else vDSP_maxv(x, 1, s, n); #endif } inline static void ggml_vec_norm_inv_f32(const int n, float * s, const float * x) { ggml_vec_norm_f32(n, s, x); *s = 1.f/(*s); } inline static void ggml_vec_argmax_f32(const int n, int * s, const float * x) { float max = -INFINITY; int idx = 0; for (int i = 0; i < n; ++i) { max = MAX(max, x[i]); if (max == x[i]) { idx = i; } } *s = idx; } // // data types // static const 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_Q5_0] = QK5_0, [GGML_TYPE_Q5_1] = QK5_1, [GGML_TYPE_Q8_0] = QK8_0, [GGML_TYPE_Q8_1] = QK8_1, #ifdef GGML_USE_K_QUANTS [GGML_TYPE_Q2_K] = QK_K, [GGML_TYPE_Q3_K] = QK_K, [GGML_TYPE_Q4_K] = QK_K, [GGML_TYPE_Q5_K] = QK_K, [GGML_TYPE_Q6_K] = QK_K, [GGML_TYPE_Q8_K] = QK_K, #endif [GGML_TYPE_I8] = 1, [GGML_TYPE_I16] = 1, [GGML_TYPE_I32] = 1, }; static_assert(GGML_TYPE_COUNT == 19, "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_Q5_0] = sizeof(block_q5_0), [GGML_TYPE_Q5_1] = sizeof(block_q5_1), [GGML_TYPE_Q8_0] = sizeof(block_q8_0), [GGML_TYPE_Q8_1] = sizeof(block_q8_1), #ifdef GGML_USE_K_QUANTS [GGML_TYPE_Q2_K] = sizeof(block_q2_K), [GGML_TYPE_Q3_K] = sizeof(block_q3_K), [GGML_TYPE_Q4_K] = sizeof(block_q4_K), [GGML_TYPE_Q5_K] = sizeof(block_q5_K), [GGML_TYPE_Q6_K] = sizeof(block_q6_K), [GGML_TYPE_Q8_K] = sizeof(block_q8_K), #endif [GGML_TYPE_I8] = sizeof(int8_t), [GGML_TYPE_I16] = sizeof(int16_t), [GGML_TYPE_I32] = sizeof(int32_t), }; static_assert(GGML_TYPE_COUNT == 19, "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_Q5_0] = "q5_0", [GGML_TYPE_Q5_1] = "q5_1", [GGML_TYPE_Q8_0] = "q8_0", [GGML_TYPE_Q8_1] = "q8_1", [GGML_TYPE_Q2_K] = "q2_K", [GGML_TYPE_Q3_K] = "q3_K", [GGML_TYPE_Q4_K] = "q4_K", [GGML_TYPE_Q5_K] = "q5_K", [GGML_TYPE_Q6_K] = "q6_K", [GGML_TYPE_Q8_K] = "q8_K", [GGML_TYPE_I8] = "i8", [GGML_TYPE_I16] = "i16", [GGML_TYPE_I32] = "i32", }; static_assert(GGML_TYPE_COUNT == 19, "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_Q5_0] = true, [GGML_TYPE_Q5_1] = true, [GGML_TYPE_Q8_0] = true, [GGML_TYPE_Q8_1] = true, [GGML_TYPE_Q2_K] = true, [GGML_TYPE_Q3_K] = true, [GGML_TYPE_Q4_K] = true, [GGML_TYPE_Q5_K] = true, [GGML_TYPE_Q6_K] = true, [GGML_TYPE_Q8_K] = true, [GGML_TYPE_I8] = false, [GGML_TYPE_I16] = false, [GGML_TYPE_I32] = false, }; static_assert(GGML_TYPE_COUNT == 19, "GGML_IS_QUANTIZED is outdated"); static const char * GGML_OP_NAME[GGML_OP_COUNT] = { "NONE", "DUP", "ADD", "ADD1", "ACC", "SUB", "MUL", "DIV", "SQR", "SQRT", "LOG", "SUM", "SUM_ROWS", "MEAN", "ARGMAX", "REPEAT", "REPEAT_BACK", "SILU_BACK", "NORM", "RMS_NORM", "RMS_NORM_BACK", "MUL_MAT", "OUT_PROD", "SCALE", "SET", "CPY", "CONT", "RESHAPE", "VIEW", "PERMUTE", "TRANSPOSE", "GET_ROWS", "GET_ROWS_BACK", "DIAG", "DIAG_MASK_INF", "DIAG_MASK_ZERO", "SOFT_MAX", "SOFT_MAX_BACK", "ROPE", "ROPE_BACK", "ALIBI", "CLAMP", "CONV_1D", "CONV_2D", "POOL_1D", "POOL_2D", "FLASH_ATTN", "FLASH_FF", "FLASH_ATTN_BACK", "WIN_PART", "WIN_UNPART", "UNARY", "MAP_UNARY", "MAP_BINARY", "MAP_CUSTOM1", "MAP_CUSTOM2", "MAP_CUSTOM3", "CROSS_ENTROPY_LOSS", "CROSS_ENTROPY_LOSS_BACK", }; static_assert(GGML_OP_COUNT == 62, "GGML_OP_COUNT != 62"); static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = { "none", "x", "x+y", "x+y", "view(x,nb,offset)+=y->x", "x-y", "x*y", "x/y", "x^2", "√x", "log(x)", "Σx", "Σx_k", "Σx/n", "argmax(x)", "repeat(x)", "repeat_back(x)", "silu_back(x)", "norm(x)", "rms_norm(x)", "rms_norm_back(x)", "X*Y", "X*Y", "x*v", "y-\\>view(x)", "x-\\>y", "cont(x)", "reshape(x)", "view(x)", "permute(x)", "transpose(x)", "get_rows(x)", "get_rows_back(x)", "diag(x)", "diag_mask_inf(x)", "diag_mask_zero(x)", "soft_max(x)", "soft_max_back(x)", "rope(x)", "rope_back(x)", "alibi(x)", "clamp(x)", "conv_1d(x)", "conv_2d(x)", "pool_1d(x)", "pool_2d(x)", "flash_attn(x)", "flash_ff(x)", "flash_attn_back(x)", "win_part(x)", "win_unpart(x)", "unary(x)", "f(x)", "f(x,y)", "custom(x)", "custom(x,y)", "custom(x,y,z)", "cross_entropy_loss(x,y)", "cross_entropy_loss_back(x,y)", }; static_assert(GGML_OP_COUNT == 62, "GGML_OP_COUNT != 62"); static_assert(GGML_OP_POOL_COUNT == 2, "GGML_OP_POOL_COUNT != 2"); static_assert(sizeof(struct ggml_object)%GGML_MEM_ALIGN == 0, "ggml_object size must be a multiple of GGML_MEM_ALIGN"); static_assert(sizeof(struct ggml_tensor)%GGML_MEM_ALIGN == 0, "ggml_tensor size must be a multiple of GGML_MEM_ALIGN"); // WARN: // Mis-confguration can lead to problem that's hard to reason about: // * At best it crash or talks nosense. // * At worst it talks slightly difference but hard to perceive. // // An op has to enable INIT or FINALIZE when any of it's branch needs that pass. // Take care about compile options (e.g., GGML_USE_xxx). static bool GGML_OP_HAS_INIT [GGML_OP_COUNT] = { 0 }; static bool GGML_OP_HAS_FINALIZE[GGML_OP_COUNT] = { 0 }; static void ggml_setup_op_has_task_pass(void) { { // INIT bool * p = GGML_OP_HAS_INIT; p[GGML_OP_ACC ] = true; p[GGML_OP_MUL_MAT ] = true; p[GGML_OP_OUT_PROD ] = true; p[GGML_OP_SET ] = true; p[GGML_OP_GET_ROWS_BACK ] = true; p[GGML_OP_DIAG_MASK_INF ] = true; p[GGML_OP_DIAG_MASK_ZERO ] = true; p[GGML_OP_CONV_1D ] = true; p[GGML_OP_CONV_2D ] = true; p[GGML_OP_FLASH_ATTN_BACK ] = true; p[GGML_OP_CROSS_ENTROPY_LOSS ] = true; } { // FINALIZE bool * p = GGML_OP_HAS_FINALIZE; p[GGML_OP_CROSS_ENTROPY_LOSS ] = true; } } // // ggml context // struct ggml_context { size_t mem_size; void * mem_buffer; bool mem_buffer_owned; bool no_alloc; bool no_alloc_save; // this is used to save the no_alloc state when using scratch buffers int n_objects; struct ggml_object * objects_begin; struct ggml_object * objects_end; struct ggml_scratch scratch; struct ggml_scratch scratch_save; }; struct ggml_context_container { bool used; struct ggml_context context; }; // // NUMA support // #define GGML_NUMA_MAX_NODES 8 #define GGML_NUMA_MAX_CPUS 512 struct ggml_numa_node { uint32_t cpus[GGML_NUMA_MAX_CPUS]; // hardware threads on this node uint32_t n_cpus; }; struct ggml_numa_nodes { struct ggml_numa_node nodes[GGML_NUMA_MAX_NODES]; uint32_t n_nodes; uint32_t total_cpus; // hardware threads on system }; // // ggml state // struct ggml_state { struct ggml_context_container contexts[GGML_MAX_CONTEXTS]; struct ggml_numa_nodes numa; }; // global state static struct ggml_state g_state; static atomic_int g_state_barrier = 0; // barrier via spin lock inline static void ggml_critical_section_start(void) { int processing = atomic_fetch_add(&g_state_barrier, 1); while (processing > 0) { // wait for other threads to finish atomic_fetch_sub(&g_state_barrier, 1); sched_yield(); // TODO: reconsider this processing = atomic_fetch_add(&g_state_barrier, 1); } } // TODO: make this somehow automatically executed // some sort of "sentry" mechanism inline static void ggml_critical_section_end(void) { atomic_fetch_sub(&g_state_barrier, 1); } void ggml_numa_init(void) { if (g_state.numa.n_nodes > 0) { fprintf(stderr, "ggml_numa_init: NUMA already initialized\n"); return; } #ifdef __linux__ struct stat st; char path[256]; int rv; // enumerate nodes while (g_state.numa.n_nodes < GGML_NUMA_MAX_NODES) { rv = snprintf(path, sizeof(path), "/sys/devices/system/node/node%u", g_state.numa.n_nodes); GGML_ASSERT(rv > 0 && (unsigned)rv < sizeof(path)); if (stat(path, &st) != 0) { break; } ++g_state.numa.n_nodes; } // enumerate CPUs while (g_state.numa.total_cpus < GGML_NUMA_MAX_CPUS) { rv = snprintf(path, sizeof(path), "/sys/devices/system/cpu/cpu%u", g_state.numa.total_cpus); GGML_ASSERT(rv > 0 && (unsigned)rv < sizeof(path)); if (stat(path, &st) != 0) { break; } ++g_state.numa.total_cpus; } GGML_PRINT_DEBUG("found %u numa nodes, %u CPUs\n", g_state.numa.n_nodes, g_state.numa.total_cpus); if (g_state.numa.n_nodes < 1 || g_state.numa.total_cpus < 1) { g_state.numa.n_nodes = 0; return; } for (uint32_t n = 0; n < g_state.numa.n_nodes; ++n) { struct ggml_numa_node * node = &g_state.numa.nodes[n]; GGML_PRINT_DEBUG("CPUs on node %u:", n); node->n_cpus = 0; for (uint32_t c = 0; c < g_state.numa.total_cpus; ++c) { rv = snprintf(path, sizeof(path), "/sys/devices/system/node/node%u/cpu%u", n, c); GGML_ASSERT(rv > 0 && (unsigned)rv < sizeof(path)); if (stat(path, &st) == 0) { node->cpus[node->n_cpus++] = c; GGML_PRINT_DEBUG(" %u", c); } } GGML_PRINT_DEBUG("\n"); } if (ggml_is_numa()) { FILE *fptr = fopen("/proc/sys/kernel/numa_balancing", "r"); if (fptr != NULL) { char buf[42]; if (fgets(buf, sizeof(buf), fptr) && strncmp(buf, "0\n", sizeof(buf)) != 0) { GGML_PRINT("WARNING: /proc/sys/kernel/numa_balancing is enabled, this has been observed to impair performance\n"); } fclose(fptr); } } #else // TODO #endif } bool ggml_is_numa(void) { return g_state.numa.n_nodes > 1; } //////////////////////////////////////////////////////////////////////////////// void ggml_print_object(const struct ggml_object * obj) { GGML_PRINT(" - ggml_object: type = %d, offset = %zu, size = %zu, next = %p\n", obj->type, obj->offs, obj->size, (const void *) obj->next); } void ggml_print_objects(const struct ggml_context * ctx) { struct ggml_object * obj = ctx->objects_begin; GGML_PRINT("%s: objects in context %p:\n", __func__, (const void *) ctx); while (obj != NULL) { ggml_print_object(obj); obj = obj->next; } GGML_PRINT("%s: --- end ---\n", __func__); } int64_t ggml_nelements(const struct ggml_tensor * tensor) { static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); return tensor->ne[0]*tensor->ne[1]*tensor->ne[2]*tensor->ne[3]; } int64_t ggml_nrows(const struct ggml_tensor * tensor) { static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); return tensor->ne[1]*tensor->ne[2]*tensor->ne[3]; } size_t ggml_nbytes(const struct ggml_tensor * tensor) { static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); // this should handle cases where the tensor is not contiguous in memory // probaby just: // // return tensor->ne[3]*tensor->nb[3] // // is enough, but just in case, adding the second part return GGML_PAD(MAX(tensor->ne[3]*tensor->nb[3], (ggml_nelements(tensor)*GGML_TYPE_SIZE[tensor->type])/GGML_BLCK_SIZE[tensor->type]), GGML_MEM_ALIGN); } size_t ggml_nbytes_split(const struct ggml_tensor * tensor, int nrows_split) { static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); return (nrows_split*tensor->ne[0]*GGML_TYPE_SIZE[tensor->type])/GGML_BLCK_SIZE[tensor->type]; } int ggml_blck_size(enum ggml_type type) { return 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]; } const char * ggml_op_name(enum ggml_op op) { return GGML_OP_NAME[op]; } const char * ggml_op_symbol(enum ggml_op op) { return GGML_OP_SYMBOL[op]; } size_t ggml_element_size(const struct ggml_tensor * tensor) { return GGML_TYPE_SIZE[tensor->type]; } static inline bool ggml_is_scalar(const struct ggml_tensor * tensor) { static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); return tensor->ne[0] == 1 && tensor->ne[1] == 1 && tensor->ne[2] == 1 && tensor->ne[3] == 1; } static inline bool ggml_is_vector(const struct ggml_tensor * tensor) { static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); return tensor->ne[1] == 1 && tensor->ne[2] == 1 && tensor->ne[3] == 1; } static inline bool ggml_is_matrix(const struct ggml_tensor * tensor) { static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); return tensor->ne[2] == 1 && tensor->ne[3] == 1; } static inline bool ggml_can_mul_mat(const struct ggml_tensor * t0, const struct ggml_tensor * t1) { static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); return (t0->ne[0] == t1->ne[0]) && (t1->ne[2]%t0->ne[2] == 0) && // verify t0 is broadcastable (t1->ne[3]%t0->ne[3] == 0); } static inline bool ggml_can_out_prod(const struct ggml_tensor * t0, const struct ggml_tensor * t1) { static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); return (t0->ne[1] == t1->ne[1]) && (t0->ne[2] == t1->ne[2]) && (t0->ne[3] == t1->ne[3]); } bool ggml_is_quantized(enum ggml_type type) { return GGML_IS_QUANTIZED[type]; } enum ggml_type ggml_ftype_to_ggml_type(enum ggml_ftype ftype) { enum ggml_type wtype = GGML_TYPE_COUNT; switch (ftype) { case GGML_FTYPE_ALL_F32: wtype = GGML_TYPE_F32; break; case GGML_FTYPE_MOSTLY_F16: wtype = GGML_TYPE_F16; break; case GGML_FTYPE_MOSTLY_Q4_0: wtype = GGML_TYPE_Q4_0; break; case GGML_FTYPE_MOSTLY_Q4_1: wtype = GGML_TYPE_Q4_1; break; case GGML_FTYPE_MOSTLY_Q5_0: wtype = GGML_TYPE_Q5_0; break; case GGML_FTYPE_MOSTLY_Q5_1: wtype = GGML_TYPE_Q5_1; break; case GGML_FTYPE_MOSTLY_Q8_0: wtype = GGML_TYPE_Q8_0; break; case GGML_FTYPE_MOSTLY_Q2_K: wtype = GGML_TYPE_Q2_K; break; case GGML_FTYPE_MOSTLY_Q3_K: wtype = GGML_TYPE_Q3_K; break; case GGML_FTYPE_MOSTLY_Q4_K: wtype = GGML_TYPE_Q4_K; break; case GGML_FTYPE_MOSTLY_Q5_K: wtype = GGML_TYPE_Q5_K; break; case GGML_FTYPE_MOSTLY_Q6_K: wtype = GGML_TYPE_Q6_K; break; case GGML_FTYPE_UNKNOWN: wtype = GGML_TYPE_COUNT; break; case GGML_FTYPE_MOSTLY_Q4_1_SOME_F16: wtype = GGML_TYPE_COUNT; break; } GGML_ASSERT(wtype != GGML_TYPE_COUNT); return wtype; } size_t ggml_tensor_overhead(void) { return GGML_OBJECT_SIZE + GGML_TENSOR_SIZE; } bool ggml_is_transposed(const struct ggml_tensor * tensor) { return tensor->nb[0] > tensor->nb[1]; } bool ggml_is_contiguous(const struct ggml_tensor * tensor) { static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); return tensor->nb[0] == GGML_TYPE_SIZE[tensor->type] && tensor->nb[1] == (tensor->nb[0]*tensor->ne[0])/GGML_BLCK_SIZE[tensor->type] && tensor->nb[2] == tensor->nb[1]*tensor->ne[1] && tensor->nb[3] == tensor->nb[2]*tensor->ne[2]; } static inline bool ggml_is_contiguous_except_dim_1(const struct ggml_tensor * tensor) { static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); return tensor->nb[0] == GGML_TYPE_SIZE[tensor->type] && tensor->nb[2] == tensor->nb[1]*tensor->ne[1] && tensor->nb[3] == tensor->nb[2]*tensor->ne[2]; } bool ggml_is_permuted(const struct ggml_tensor * tensor) { static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); return tensor->nb[0] > tensor->nb[1] || tensor->nb[1] > tensor->nb[2] || tensor->nb[2] > tensor->nb[3]; } static inline bool ggml_is_padded_1d(const struct ggml_tensor * tensor) { static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); return tensor->nb[0] == GGML_TYPE_SIZE[tensor->type] && tensor->nb[2] == tensor->nb[1]*tensor->ne[1] && tensor->nb[3] == tensor->nb[2]*tensor->ne[2]; } bool ggml_are_same_shape(const struct ggml_tensor * t0, const struct ggml_tensor * t1) { static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); return (t0->ne[0] == t1->ne[0] ) && (t0->ne[1] == t1->ne[1] ) && (t0->ne[2] == t1->ne[2] ) && (t0->ne[3] == t1->ne[3] ); } // check if t1 can be represented as a repeatition of t0 static inline bool ggml_can_repeat(const struct ggml_tensor * t0, const struct ggml_tensor * t1) { static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); return (t1->ne[0]%t0->ne[0] == 0) && (t1->ne[1]%t0->ne[1] == 0) && (t1->ne[2]%t0->ne[2] == 0) && (t1->ne[3]%t0->ne[3] == 0); } static inline bool ggml_can_repeat_rows(const struct ggml_tensor * t0, const struct ggml_tensor * t1) { static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); return (t0->ne[0] == t1->ne[0]) && ggml_can_repeat(t0, t1); } static inline int ggml_up32(int n) { return (n + 31) & ~31; } //static inline int ggml_up64(int n) { // return (n + 63) & ~63; //} static inline int ggml_up(int n, int m) { // assert m is a power of 2 GGML_ASSERT((m & (m - 1)) == 0); return (n + m - 1) & ~(m - 1); } // assert that pointer is aligned to GGML_MEM_ALIGN #define ggml_assert_aligned(ptr) \ GGML_ASSERT(((uintptr_t) (ptr))%GGML_MEM_ALIGN == 0) //////////////////////////////////////////////////////////////////////////////// struct ggml_context * ggml_init(struct ggml_init_params params) { // make this function thread safe ggml_critical_section_start(); static bool is_first_call = true; if (is_first_call) { // initialize time system (required on Windows) ggml_time_init(); // initialize GELU, Quick GELU, SILU and EXP F32 tables { const uint64_t t_start = ggml_time_us(); UNUSED(t_start); ggml_fp16_t ii; for (int i = 0; i < (1 << 16); ++i) { uint16_t ui = i; memcpy(&ii, &ui, sizeof(ii)); const float f = table_f32_f16[i] = GGML_COMPUTE_FP16_TO_FP32(ii); table_gelu_f16[i] = GGML_FP32_TO_FP16(ggml_gelu_f32(f)); table_gelu_quick_f16[i] = GGML_FP32_TO_FP16(ggml_gelu_quick_f32(f)); table_silu_f16[i] = GGML_FP32_TO_FP16(ggml_silu_f32(f)); table_exp_f16[i] = GGML_FP32_TO_FP16(expf(f)); } const uint64_t t_end = ggml_time_us(); UNUSED(t_end); GGML_PRINT_DEBUG("%s: GELU, Quick GELU, SILU and EXP tables initialized in %f ms\n", __func__, (t_end - t_start)/1000.0f); } // initialize g_state { const uint64_t t_start = ggml_time_us(); UNUSED(t_start); g_state = (struct ggml_state) { /*.contexts =*/ { { 0 } }, /*.numa =*/ { .n_nodes = 0, .total_cpus = 0, }, }; for (int i = 0; i < GGML_MAX_CONTEXTS; ++i) { g_state.contexts[i].used = false; } const uint64_t t_end = ggml_time_us(); UNUSED(t_end); GGML_PRINT_DEBUG("%s: g_state initialized in %f ms\n", __func__, (t_end - t_start)/1000.0f); } #if defined(GGML_USE_CUBLAS) ggml_init_cublas(); #elif defined(GGML_USE_CLBLAST) ggml_cl_init(); #endif ggml_setup_op_has_task_pass(); is_first_call = false; } // find non-used context in g_state struct ggml_context * ctx = NULL; for (int i = 0; i < GGML_MAX_CONTEXTS; i++) { if (!g_state.contexts[i].used) { g_state.contexts[i].used = true; ctx = &g_state.contexts[i].context; GGML_PRINT_DEBUG("%s: found unused context %d\n", __func__, i); break; } } if (ctx == NULL) { GGML_PRINT_DEBUG("%s: no unused context found\n", __func__); ggml_critical_section_end(); return NULL; } const size_t mem_size = params.mem_buffer ? params.mem_size : GGML_PAD(params.mem_size, GGML_MEM_ALIGN); *ctx = (struct ggml_context) { /*.mem_size =*/ mem_size, /*.mem_buffer =*/ params.mem_buffer ? params.mem_buffer : GGML_ALIGNED_MALLOC(mem_size), /*.mem_buffer_owned =*/ params.mem_buffer ? false : true, /*.no_alloc =*/ params.no_alloc, /*.no_alloc_save =*/ params.no_alloc, /*.n_objects =*/ 0, /*.objects_begin =*/ NULL, /*.objects_end =*/ NULL, /*.scratch =*/ { 0, 0, NULL, }, /*.scratch_save =*/ { 0, 0, NULL, }, }; GGML_ASSERT(ctx->mem_buffer != NULL); ggml_assert_aligned(ctx->mem_buffer); GGML_PRINT_DEBUG("%s: context initialized\n", __func__); ggml_critical_section_end(); return ctx; } void ggml_free(struct ggml_context * ctx) { // make this function thread safe ggml_critical_section_start(); bool found = false; for (int i = 0; i < GGML_MAX_CONTEXTS; i++) { if (&g_state.contexts[i].context == ctx) { g_state.contexts[i].used = false; GGML_PRINT_DEBUG("%s: context %d has been freed. memory used = %zu\n", __func__, i, ggml_used_mem(ctx)); if (ctx->mem_buffer_owned) { GGML_ALIGNED_FREE(ctx->mem_buffer); } found = true; break; } } if (!found) { GGML_PRINT_DEBUG("%s: context not found\n", __func__); } ggml_critical_section_end(); } size_t ggml_used_mem(const struct ggml_context * ctx) { return ctx->objects_end == NULL ? 0 : ctx->objects_end->offs + ctx->objects_end->size; } size_t ggml_set_scratch(struct ggml_context * ctx, struct ggml_scratch scratch) { const size_t result = ctx->scratch.data ? ctx->scratch.offs : 0; ctx->scratch = scratch; return result; } bool ggml_get_no_alloc(struct ggml_context * ctx) { return ctx->no_alloc; } void ggml_set_no_alloc(struct ggml_context * ctx, bool no_alloc) { ctx->no_alloc = no_alloc; } void * ggml_get_mem_buffer(const struct ggml_context * ctx) { return ctx->mem_buffer; } size_t ggml_get_mem_size(const struct ggml_context * ctx) { return ctx->mem_size; } size_t ggml_get_max_tensor_size(const struct ggml_context * ctx) { size_t max_size = 0; struct ggml_object * obj = ctx->objects_begin; while (obj != NULL) { if (obj->type == GGML_OBJECT_TENSOR) { struct ggml_tensor * tensor = (struct ggml_tensor *) ((char *) ctx->mem_buffer + obj->offs); const size_t size = ggml_nbytes(tensor); if (max_size < size) { max_size = size; } } obj = obj->next; } return max_size; } // IMPORTANT: // when creating "opt" tensors, always save and load the scratch buffer // this is an error prone process, but it is necessary to support inplace // operators when using scratch buffers // TODO: implement a better way static void ggml_scratch_save(struct ggml_context * ctx) { // this is needed to allow opt tensors to store their data // TODO: again, need to find a better way ctx->no_alloc_save = ctx->no_alloc; ctx->no_alloc = false; ctx->scratch_save = ctx->scratch; ctx->scratch.data = NULL; } static void ggml_scratch_load(struct ggml_context * ctx) { ctx->no_alloc = ctx->no_alloc_save; ctx->scratch = ctx->scratch_save; } //////////////////////////////////////////////////////////////////////////////// static struct ggml_object * ggml_new_object(struct ggml_context * ctx, enum ggml_object_type type, size_t size) { // always insert objects at the end of the context's memory pool struct ggml_object * obj_cur = ctx->objects_end; const size_t cur_offs = obj_cur == NULL ? 0 : obj_cur->offs; const size_t cur_size = obj_cur == NULL ? 0 : obj_cur->size; const size_t cur_end = cur_offs + cur_size; // align to GGML_MEM_ALIGN size_t size_needed = GGML_PAD(size, GGML_MEM_ALIGN); char * const mem_buffer = ctx->mem_buffer; struct ggml_object * const obj_new = (struct ggml_object *)(mem_buffer + cur_end); if (cur_end + size_needed + GGML_OBJECT_SIZE > ctx->mem_size) { GGML_PRINT("%s: not enough space in the context's memory pool (needed %zu, available %zu)\n", __func__, cur_end + size_needed, ctx->mem_size); assert(false); return NULL; } *obj_new = (struct ggml_object) { .offs = cur_end + GGML_OBJECT_SIZE, .size = size_needed, .next = NULL, .type = type, }; ggml_assert_aligned(mem_buffer + obj_new->offs); if (obj_cur != NULL) { obj_cur->next = obj_new; } else { // this is the first object in this context ctx->objects_begin = obj_new; } ctx->objects_end = obj_new; //printf("%s: inserted new object at %zu, size = %zu\n", __func__, cur_end, obj_new->size); return obj_new; } static struct ggml_tensor * ggml_new_tensor_impl( struct ggml_context * ctx, enum ggml_type type, int n_dims, const int64_t * ne, void * data) { assert(n_dims >= 1 && n_dims <= GGML_MAX_DIMS); size_t data_size = 0; if (data == NULL && !ctx->no_alloc) { data_size += GGML_TYPE_SIZE[type]*(ne[0]/GGML_BLCK_SIZE[type]); for (int i = 1; i < n_dims; i++) { data_size *= ne[i]; } } if (ctx->scratch.data != NULL && data == NULL) { // allocate tensor data in the scratch buffer if (ctx->scratch.offs + data_size > ctx->scratch.size) { GGML_PRINT("%s: not enough space in the scratch memory pool (needed %zu, available %zu)\n", __func__, ctx->scratch.offs + data_size, ctx->scratch.size); assert(false); return NULL; } data = (char * const) ctx->scratch.data + ctx->scratch.offs; ctx->scratch.offs += data_size; data_size = 0; } struct ggml_object * const obj_new = ggml_new_object(ctx, GGML_OBJECT_TENSOR, GGML_TENSOR_SIZE + data_size); // TODO: for recoverable errors, we would need to free the data allocated from the scratch buffer here struct ggml_tensor * const result = (struct ggml_tensor *)((char *)ctx->mem_buffer + obj_new->offs); *result = (struct ggml_tensor) { /*.type =*/ type, /*.backend =*/ GGML_BACKEND_CPU, /*.n_dims =*/ n_dims, /*.ne =*/ { 1, 1, 1, 1 }, /*.nb =*/ { 0, 0, 0, 0 }, /*.op =*/ GGML_OP_NONE, /*.op_params =*/ { 0 }, /*.is_param =*/ false, /*.grad =*/ NULL, /*.src =*/ { NULL }, /*.perf_runs =*/ 0, /*.perf_cycles =*/ 0, /*.perf_time_us =*/ 0, /*.data =*/ (data == NULL && !ctx->no_alloc) ? (void *)(result + 1) : data, /*.name =*/ { 0 }, /*.extra =*/ NULL, /*.padding =*/ { 0 }, }; // TODO: this should not be needed as long as we don't rely on aligned SIMD loads //ggml_assert_aligned(result->data); for (int i = 0; i < n_dims; i++) { result->ne[i] = ne[i]; } result->nb[0] = GGML_TYPE_SIZE[type]; result->nb[1] = result->nb[0]*(result->ne[0]/GGML_BLCK_SIZE[type]); for (int i = 2; i < GGML_MAX_DIMS; i++) { result->nb[i] = result->nb[i - 1]*result->ne[i - 1]; } ctx->n_objects++; return result; } static void ggml_set_op_params(struct ggml_tensor * tensor, const void * params, size_t params_size) { GGML_ASSERT(tensor != NULL); // silence -Warray-bounds warnings assert(params_size <= GGML_MAX_OP_PARAMS); memcpy(tensor->op_params, params, params_size); } static int32_t ggml_get_op_params_i32(const struct ggml_tensor * tensor, uint32_t i) { assert(i < GGML_MAX_OP_PARAMS / sizeof(int32_t)); return ((const int32_t *)(tensor->op_params))[i]; } static void ggml_set_op_params_i32(struct ggml_tensor * tensor, uint32_t i, int32_t value) { assert(i < GGML_MAX_OP_PARAMS / sizeof(int32_t)); ((int32_t *)(tensor->op_params))[i] = value; } struct ggml_tensor * ggml_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) { ggml_scratch_save(ctx); struct ggml_tensor * result = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 1); ggml_scratch_load(ctx); ggml_set_i32(result, value); return result; } struct ggml_tensor * ggml_new_f32(struct ggml_context * ctx, float value) { ggml_scratch_save(ctx); struct ggml_tensor * result = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 1); ggml_scratch_load(ctx); ggml_set_f32(result, value); return result; } struct ggml_tensor * ggml_dup_tensor(struct ggml_context * ctx, const struct ggml_tensor * src) { return ggml_new_tensor_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), GGML_FP32_TO_FP16(value)); } } break; case GGML_TYPE_F32: { assert(tensor->nb[0] == sizeof(float)); for (int i = 0; i < n; i++) { ggml_vec_set_f32(nc, (float *)(data + i*n1), value); } } break; default: { GGML_ASSERT(false); } break; } return tensor; } struct ggml_tensor * ggml_set_f32(struct ggml_tensor * tensor, float value) { const int n = ggml_nrows(tensor); const int nc = tensor->ne[0]; const size_t n1 = tensor->nb[1]; char * const data = tensor->data; switch (tensor->type) { case GGML_TYPE_I8: { assert(tensor->nb[0] == sizeof(int8_t)); for (int i = 0; i < n; i++) { ggml_vec_set_i8(nc, (int8_t *)(data + i*n1), value); } } break; case GGML_TYPE_I16: { assert(tensor->nb[0] == sizeof(int16_t)); for (int i = 0; i < n; i++) { ggml_vec_set_i16(nc, (int16_t *)(data + i*n1), value); } } break; case GGML_TYPE_I32: { assert(tensor->nb[0] == sizeof(int32_t)); for (int i = 0; i < n; i++) { ggml_vec_set_i32(nc, (int32_t *)(data + i*n1), value); } } break; case GGML_TYPE_F16: { assert(tensor->nb[0] == sizeof(ggml_fp16_t)); for (int i = 0; i < n; i++) { ggml_vec_set_f16(nc, (ggml_fp16_t *)(data + i*n1), GGML_FP32_TO_FP16(value)); } } break; case GGML_TYPE_F32: { assert(tensor->nb[0] == sizeof(float)); for (int i = 0; i < n; i++) { ggml_vec_set_f32(nc, (float *)(data + i*n1), value); } } break; default: { GGML_ASSERT(false); } break; } return tensor; } int32_t ggml_get_i32_1d(const struct ggml_tensor * tensor, int i) { switch (tensor->type) { case GGML_TYPE_I8: { GGML_ASSERT(tensor->nb[0] == sizeof(int8_t)); return ((int8_t *)(tensor->data))[i]; } break; case GGML_TYPE_I16: { GGML_ASSERT(tensor->nb[0] == sizeof(int16_t)); return ((int16_t *)(tensor->data))[i]; } break; case GGML_TYPE_I32: { GGML_ASSERT(tensor->nb[0] == sizeof(int32_t)); return ((int32_t *)(tensor->data))[i]; } break; case GGML_TYPE_F16: { GGML_ASSERT(tensor->nb[0] == sizeof(ggml_fp16_t)); return GGML_FP16_TO_FP32(((ggml_fp16_t *)(tensor->data))[i]); } break; case GGML_TYPE_F32: { GGML_ASSERT(tensor->nb[0] == sizeof(float)); return ((float *)(tensor->data))[i]; } break; default: { GGML_ASSERT(false); } break; } return 0.0f; } void ggml_set_i32_1d(const struct ggml_tensor * tensor, int i, int32_t value) { switch (tensor->type) { case GGML_TYPE_I8: { GGML_ASSERT(tensor->nb[0] == sizeof(int8_t)); ((int8_t *)(tensor->data))[i] = value; } break; case GGML_TYPE_I16: { GGML_ASSERT(tensor->nb[0] == sizeof(int16_t)); ((int16_t *)(tensor->data))[i] = value; } break; case GGML_TYPE_I32: { GGML_ASSERT(tensor->nb[0] == sizeof(int32_t)); ((int32_t *)(tensor->data))[i] = value; } break; case GGML_TYPE_F16: { GGML_ASSERT(tensor->nb[0] == sizeof(ggml_fp16_t)); ((ggml_fp16_t *)(tensor->data))[i] = GGML_FP32_TO_FP16(value); } break; case GGML_TYPE_F32: { GGML_ASSERT(tensor->nb[0] == sizeof(float)); ((float *)(tensor->data))[i] = value; } break; default: { GGML_ASSERT(false); } break; } } float ggml_get_f32_1d(const struct ggml_tensor * tensor, int i) { switch (tensor->type) { case GGML_TYPE_I8: { GGML_ASSERT(tensor->nb[0] == sizeof(int8_t)); return ((int8_t *)(tensor->data))[i]; } break; case GGML_TYPE_I16: { GGML_ASSERT(tensor->nb[0] == sizeof(int16_t)); return ((int16_t *)(tensor->data))[i]; } break; case GGML_TYPE_I32: { GGML_ASSERT(tensor->nb[0] == sizeof(int32_t)); return ((int32_t *)(tensor->data))[i]; } break; case GGML_TYPE_F16: { GGML_ASSERT(tensor->nb[0] == sizeof(ggml_fp16_t)); return GGML_FP16_TO_FP32(((ggml_fp16_t *)(tensor->data))[i]); } break; case GGML_TYPE_F32: { GGML_ASSERT(tensor->nb[0] == sizeof(float)); return ((float *)(tensor->data))[i]; } break; default: { GGML_ASSERT(false); } break; } return 0.0f; } void ggml_set_f32_1d(const struct ggml_tensor * tensor, int i, float value) { switch (tensor->type) { case GGML_TYPE_I8: { GGML_ASSERT(tensor->nb[0] == sizeof(int8_t)); ((int8_t *)(tensor->data))[i] = value; } break; case GGML_TYPE_I16: { GGML_ASSERT(tensor->nb[0] == sizeof(int16_t)); ((int16_t *)(tensor->data))[i] = value; } break; case GGML_TYPE_I32: { GGML_ASSERT(tensor->nb[0] == sizeof(int32_t)); ((int32_t *)(tensor->data))[i] = value; } break; case GGML_TYPE_F16: { GGML_ASSERT(tensor->nb[0] == sizeof(ggml_fp16_t)); ((ggml_fp16_t *)(tensor->data))[i] = GGML_FP32_TO_FP16(value); } break; case GGML_TYPE_F32: { GGML_ASSERT(tensor->nb[0] == sizeof(float)); ((float *)(tensor->data))[i] = value; } break; default: { GGML_ASSERT(false); } break; } } void * ggml_get_data(const struct ggml_tensor * tensor) { return tensor->data; } float * ggml_get_data_f32(const struct ggml_tensor * tensor) { assert(tensor->type == GGML_TYPE_F32); return (float *)(tensor->data); } enum ggml_unary_op ggml_get_unary_op(const struct ggml_tensor * tensor) { GGML_ASSERT(tensor->op == GGML_OP_UNARY); return (enum ggml_unary_op) ggml_get_op_params_i32(tensor, 0); } const char * ggml_get_name(const struct ggml_tensor * tensor) { return tensor->name; } struct ggml_tensor * ggml_set_name(struct ggml_tensor * tensor, const char * name) { strncpy(tensor->name, name, sizeof(tensor->name)); tensor->name[sizeof(tensor->name) - 1] = '\0'; return tensor; } struct ggml_tensor * ggml_format_name(struct ggml_tensor * tensor, const char * fmt, ...) { va_list args; va_start(args, fmt); vsnprintf(tensor->name, sizeof(tensor->name), fmt, args); va_end(args); return tensor; } struct ggml_tensor * ggml_view_tensor( struct ggml_context * ctx, const struct ggml_tensor * src) { struct ggml_tensor * result = ggml_new_tensor_impl(ctx, src->type, src->n_dims, src->ne, src->data); ggml_format_name(result, "%s (view)", src->name); 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; } struct ggml_tensor * ggml_get_tensor(struct ggml_context * ctx, const char * name) { struct ggml_object * obj = ctx->objects_begin; char * const mem_buffer = ctx->mem_buffer; while (obj != NULL) { if (obj->type == GGML_OBJECT_TENSOR) { struct ggml_tensor * cur = (struct ggml_tensor *)(mem_buffer + obj->offs); if (strcmp(cur->name, name) == 0) { return cur; } } obj = obj->next; } return NULL; } //////////////////////////////////////////////////////////////////////////////// // ggml_dup static struct ggml_tensor * ggml_dup_impl( struct ggml_context * ctx, struct ggml_tensor * a, bool inplace) { bool is_node = false; if (!inplace && (a->grad)) { is_node = true; } struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); result->op = GGML_OP_DUP; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; return result; } struct ggml_tensor * ggml_dup( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_dup_impl(ctx, a, false); } struct ggml_tensor * ggml_dup_inplace( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_dup_impl(ctx, a, true); } // ggml_add static struct ggml_tensor * ggml_add_impl( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, bool inplace) { // TODO: support less-strict constraint // GGML_ASSERT(ggml_can_repeat(b, a)); GGML_ASSERT(ggml_can_repeat_rows(b, a)); bool is_node = false; if (!inplace && (a->grad || b->grad)) { // TODO: support backward pass for broadcasting GGML_ASSERT(ggml_are_same_shape(a, b)); is_node = true; } struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); result->op = GGML_OP_ADD; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; result->src[1] = b; return result; } struct ggml_tensor * ggml_add( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) { return ggml_add_impl(ctx, a, b, false); } struct ggml_tensor * ggml_add_inplace( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) { return ggml_add_impl(ctx, a, b, true); } // ggml_add1 static struct ggml_tensor * ggml_add1_impl( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, bool inplace) { GGML_ASSERT(ggml_is_scalar(b)); GGML_ASSERT(ggml_is_padded_1d(a)); bool is_node = false; if (a->grad || b->grad) { is_node = true; } struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); result->op = GGML_OP_ADD1; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; result->src[1] = b; return result; } struct ggml_tensor * ggml_add1( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) { return ggml_add1_impl(ctx, a, b, false); } struct ggml_tensor * ggml_add1_inplace( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) { return ggml_add1_impl(ctx, a, b, true); } // ggml_acc static struct ggml_tensor * ggml_acc_impl( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, size_t nb1, size_t nb2, size_t nb3, size_t offset, bool inplace) { GGML_ASSERT(ggml_nelements(b) <= ggml_nelements(a)); GGML_ASSERT(ggml_is_contiguous(a)); GGML_ASSERT(a->type == GGML_TYPE_F32); GGML_ASSERT(b->type == GGML_TYPE_F32); bool is_node = false; if (!inplace && (a->grad || b->grad)) { is_node = true; } struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); int32_t params[] = { nb1, nb2, nb3, offset, inplace ? 1 : 0 }; ggml_set_op_params(result, params, sizeof(params)); result->op = GGML_OP_ACC; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; result->src[1] = b; return result; } struct ggml_tensor * ggml_acc( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, size_t nb1, size_t nb2, size_t nb3, size_t offset) { return ggml_acc_impl(ctx, a, b, nb1, nb2, nb3, offset, false); } struct ggml_tensor * ggml_acc_inplace( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, size_t nb1, size_t nb2, size_t nb3, size_t offset) { return ggml_acc_impl(ctx, a, b, nb1, nb2, nb3, offset, true); } // ggml_sub static struct ggml_tensor * ggml_sub_impl( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, bool inplace) { GGML_ASSERT(ggml_are_same_shape(a, b)); bool is_node = false; if (!inplace && (a->grad || b->grad)) { is_node = true; } struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); result->op = GGML_OP_SUB; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; result->src[1] = b; return result; } struct ggml_tensor * ggml_sub( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) { return ggml_sub_impl(ctx, a, b, false); } struct ggml_tensor * ggml_sub_inplace( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) { return ggml_sub_impl(ctx, a, b, true); } // ggml_mul static struct ggml_tensor * ggml_mul_impl( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, bool inplace) { // TODO: support less-strict constraint // GGML_ASSERT(ggml_can_repeat(b, a)); GGML_ASSERT(ggml_can_repeat_rows(b, a)); bool is_node = false; if (!inplace && (a->grad || b->grad)) { // TODO: support backward pass for broadcasting GGML_ASSERT(ggml_are_same_shape(a, b)); is_node = true; } if (inplace) { GGML_ASSERT(is_node == 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->src[0] = a; result->src[1] = b; return result; } struct ggml_tensor * ggml_mul( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) { return ggml_mul_impl(ctx, a, b, false); } struct ggml_tensor * ggml_mul_inplace( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) { return ggml_mul_impl(ctx, a, b, true); } // ggml_div static struct ggml_tensor * ggml_div_impl( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, bool inplace) { GGML_ASSERT(ggml_are_same_shape(a, b)); bool is_node = false; if (!inplace && (a->grad || b->grad)) { is_node = true; } if (inplace) { GGML_ASSERT(is_node == 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->src[0] = a; result->src[1] = b; return result; } struct ggml_tensor * ggml_div( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) { return ggml_div_impl(ctx, a, b, false); } struct ggml_tensor * ggml_div_inplace( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) { return ggml_div_impl(ctx, a, b, true); } // ggml_sqr static struct ggml_tensor * ggml_sqr_impl( struct ggml_context * ctx, struct ggml_tensor * a, bool inplace) { bool is_node = false; if (!inplace && (a->grad)) { is_node = true; } struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); result->op = GGML_OP_SQR; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; return result; } struct ggml_tensor * ggml_sqr( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_sqr_impl(ctx, a, false); } struct ggml_tensor * ggml_sqr_inplace( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_sqr_impl(ctx, a, true); } // ggml_sqrt static struct ggml_tensor * ggml_sqrt_impl( struct ggml_context * ctx, struct ggml_tensor * a, bool inplace) { bool is_node = false; if (!inplace && (a->grad)) { is_node = true; } struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); result->op = GGML_OP_SQRT; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; return result; } struct ggml_tensor * ggml_sqrt( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_sqrt_impl(ctx, a, false); } struct ggml_tensor * ggml_sqrt_inplace( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_sqrt_impl(ctx, a, true); } // ggml_log static struct ggml_tensor * ggml_log_impl( struct ggml_context * ctx, struct ggml_tensor * a, bool inplace) { bool is_node = false; if (!inplace && (a->grad)) { is_node = true; } struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); result->op = GGML_OP_LOG; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; return result; } struct ggml_tensor * ggml_log( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_log_impl(ctx, a, false); } struct ggml_tensor * ggml_log_inplace( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_log_impl(ctx, a, true); } // ggml_sum struct ggml_tensor * ggml_sum( struct ggml_context * ctx, struct ggml_tensor * a) { bool is_node = false; if (a->grad) { is_node = true; } struct ggml_tensor * result = ggml_new_tensor_1d(ctx, a->type, 1); result->op = GGML_OP_SUM; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; return result; } // ggml_sum_rows struct ggml_tensor * ggml_sum_rows( struct ggml_context * ctx, struct ggml_tensor * a) { bool is_node = false; if (a->grad) { is_node = true; } int64_t ne[4] = {1,1,1,1}; for (int i=1; in_dims; ++i) { ne[i] = a->ne[i]; } struct ggml_tensor * result = ggml_new_tensor(ctx, a->type, a->n_dims, ne); result->op = GGML_OP_SUM_ROWS; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; return result; } // ggml_mean struct ggml_tensor * ggml_mean( struct ggml_context * ctx, struct ggml_tensor * a) { bool is_node = false; if (a->grad) { GGML_ASSERT(false); // TODO: implement is_node = true; } int64_t ne[GGML_MAX_DIMS] = { 1, a->ne[1], a->ne[2], a->ne[3] }; struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, a->n_dims, ne); result->op = GGML_OP_MEAN; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; return result; } // ggml_argmax struct ggml_tensor * ggml_argmax( struct ggml_context * ctx, struct ggml_tensor * a) { GGML_ASSERT(ggml_is_matrix(a)); bool is_node = false; if (a->grad) { GGML_ASSERT(false); is_node = true; } int64_t ne[GGML_MAX_DIMS] = { a->ne[1], 1, 1, 1 }; struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_I32, a->n_dims, ne); result->op = GGML_OP_ARGMAX; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; return result; } // ggml_repeat struct ggml_tensor * ggml_repeat( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) { GGML_ASSERT(ggml_can_repeat(a, b)); bool is_node = false; if (a->grad) { is_node = true; } 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->src[0] = a; result->src[1] = b; return result; } // ggml_repeat_back struct ggml_tensor * ggml_repeat_back( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) { GGML_ASSERT(ggml_can_repeat(b, a)); bool is_node = false; if (a->grad) { is_node = true; } if (ggml_are_same_shape(a, b) && !is_node) { return a; } struct ggml_tensor * result = ggml_new_tensor(ctx, a->type, b->n_dims, b->ne); result->op = GGML_OP_REPEAT_BACK; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; result->src[1] = b; return result; } // ggml_abs struct ggml_tensor * ggml_abs( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_unary(ctx, a, GGML_UNARY_OP_ABS); } struct ggml_tensor * ggml_abs_inplace( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_unary_inplace(ctx, a, GGML_UNARY_OP_ABS); } // ggml_sgn struct ggml_tensor * ggml_sgn( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_unary(ctx, a, GGML_UNARY_OP_SGN); } struct ggml_tensor * ggml_sgn_inplace( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_unary_inplace(ctx, a, GGML_UNARY_OP_SGN); } // ggml_neg struct ggml_tensor * ggml_neg( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_unary(ctx, a, GGML_UNARY_OP_NEG); } struct ggml_tensor * ggml_neg_inplace( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_unary_inplace(ctx, a, GGML_UNARY_OP_NEG); } // ggml_step struct ggml_tensor * ggml_step( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_unary(ctx, a, GGML_UNARY_OP_STEP); } struct ggml_tensor * ggml_step_inplace( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_unary_inplace(ctx, a, GGML_UNARY_OP_STEP); } // ggml_tanh struct ggml_tensor * ggml_tanh( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_unary(ctx, a, GGML_UNARY_OP_TANH); } struct ggml_tensor * ggml_tanh_inplace( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_unary_inplace(ctx, a, GGML_UNARY_OP_TANH); } // ggml_elu struct ggml_tensor * ggml_elu( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_unary(ctx, a, GGML_UNARY_OP_ELU); } struct ggml_tensor * ggml_elu_inplace( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_unary_inplace(ctx, a, GGML_UNARY_OP_ELU); } // ggml_relu struct ggml_tensor * ggml_relu( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_unary(ctx, a, GGML_UNARY_OP_RELU); } struct ggml_tensor * ggml_relu_inplace( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_unary_inplace(ctx, a, GGML_UNARY_OP_RELU); } // ggml_gelu struct ggml_tensor * ggml_gelu( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_unary(ctx, a, GGML_UNARY_OP_GELU); } struct ggml_tensor * ggml_gelu_inplace( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_unary_inplace(ctx, a, GGML_UNARY_OP_GELU); } // ggml_gelu_quick struct ggml_tensor * ggml_gelu_quick( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_unary(ctx, a, GGML_UNARY_OP_GELU_QUICK); } struct ggml_tensor * ggml_gelu_quick_inplace( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_unary_inplace(ctx, a, GGML_UNARY_OP_GELU_QUICK); } // ggml_silu struct ggml_tensor * ggml_silu( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_unary(ctx, a, GGML_UNARY_OP_SILU); } struct ggml_tensor * ggml_silu_inplace( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_unary_inplace(ctx, a, GGML_UNARY_OP_SILU); } // ggml_silu_back struct ggml_tensor * ggml_silu_back( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) { bool is_node = false; if (a->grad || b->grad) { // TODO: implement backward is_node = true; } struct ggml_tensor * result = ggml_dup_tensor(ctx, a); result->op = GGML_OP_SILU_BACK; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; result->src[1] = b; return result; } // ggml_norm static struct ggml_tensor * ggml_norm_impl( struct ggml_context * ctx, struct ggml_tensor * a, 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); // TODO: maybe store epsilon here? result->op = GGML_OP_NORM; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; return result; } struct ggml_tensor * ggml_norm( struct ggml_context * ctx, struct ggml_tensor * a) { 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); } static struct ggml_tensor * ggml_rms_norm_impl( struct ggml_context * ctx, struct ggml_tensor * a, float eps, bool inplace) { bool is_node = false; if (!inplace && (a->grad)) { is_node = true; } struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); ggml_set_op_params(result, &eps, sizeof(eps)); result->op = GGML_OP_RMS_NORM; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; return result; } struct ggml_tensor * ggml_rms_norm( struct ggml_context * ctx, struct ggml_tensor * a, float eps) { return ggml_rms_norm_impl(ctx, a, eps, false); } struct ggml_tensor * ggml_rms_norm_inplace( struct ggml_context * ctx, struct ggml_tensor * a, float eps) { return ggml_rms_norm_impl(ctx, a, eps, true); } struct ggml_tensor * ggml_rms_norm_back( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) { bool is_node = false; if (a->grad) { // TODO: implement backward is_node = true; } struct ggml_tensor * result = ggml_dup_tensor(ctx, a); result->op = GGML_OP_RMS_NORM_BACK; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; result->src[1] = b; return result; } // ggml_mul_mat struct ggml_tensor * ggml_mul_mat( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) { GGML_ASSERT(ggml_can_mul_mat(a, b)); GGML_ASSERT(!ggml_is_transposed(a)); bool is_node = false; if (a->grad || b->grad) { is_node = true; } const int64_t ne[4] = { a->ne[1], b->ne[1], b->ne[2], b->ne[3] }; struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, MAX(a->n_dims, b->n_dims), ne); result->op = GGML_OP_MUL_MAT; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; result->src[1] = b; return result; } // ggml_out_prod struct ggml_tensor * ggml_out_prod( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) { GGML_ASSERT(ggml_can_out_prod(a, b)); GGML_ASSERT(!ggml_is_transposed(a)); bool is_node = false; if (a->grad || b->grad) { is_node = true; } const int64_t ne[4] = { a->ne[0], b->ne[0], a->ne[2], b->ne[3] }; struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, MIN(a->n_dims, b->n_dims), ne); result->op = GGML_OP_OUT_PROD; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; result->src[1] = b; return result; } // ggml_scale static struct ggml_tensor * ggml_scale_impl( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, bool inplace) { GGML_ASSERT(ggml_is_scalar(b)); GGML_ASSERT(ggml_is_padded_1d(a)); bool is_node = false; if (a->grad || b->grad) { is_node = true; } struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); result->op = GGML_OP_SCALE; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; result->src[1] = b; return result; } struct ggml_tensor * ggml_scale( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) { return ggml_scale_impl(ctx, a, b, false); } struct ggml_tensor * ggml_scale_inplace( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) { return ggml_scale_impl(ctx, a, b, true); } // ggml_set static struct ggml_tensor * ggml_set_impl( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, size_t nb1, size_t nb2, size_t nb3, size_t offset, bool inplace) { GGML_ASSERT(ggml_nelements(a) >= ggml_nelements(b)); bool is_node = false; if (a->grad || b->grad) { is_node = true; } // make a view of the destination struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); int32_t params[] = { nb1, nb2, nb3, offset, inplace ? 1 : 0 }; ggml_set_op_params(result, params, sizeof(params)); result->op = GGML_OP_SET; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; result->src[1] = b; return result; } struct ggml_tensor * ggml_set( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, size_t nb1, size_t nb2, size_t nb3, size_t offset) { return ggml_set_impl(ctx, a, b, nb1, nb2, nb3, offset, false); } struct ggml_tensor * ggml_set_inplace( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, size_t nb1, size_t nb2, size_t nb3, size_t offset) { return ggml_set_impl(ctx, a, b, nb1, nb2, nb3, offset, true); } struct ggml_tensor * ggml_set_1d( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, size_t offset) { return ggml_set_impl(ctx, a, b, a->nb[1], a->nb[2], a->nb[3], offset, false); } struct ggml_tensor * ggml_set_1d_inplace( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, size_t offset) { return ggml_set_impl(ctx, a, b, a->nb[1], a->nb[2], a->nb[3], offset, true); } struct ggml_tensor * ggml_set_2d( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, size_t nb1, size_t offset) { return ggml_set_impl(ctx, a, b, nb1, a->nb[2], a->nb[3], offset, false); } struct ggml_tensor * ggml_set_2d_inplace( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, size_t nb1, size_t offset) { return ggml_set_impl(ctx, a, b, nb1, a->nb[2], a->nb[3], offset, false); } // ggml_cpy static struct ggml_tensor * ggml_cpy_impl( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, bool inplace) { GGML_ASSERT(ggml_nelements(a) == ggml_nelements(b)); bool is_node = false; if (!inplace && (a->grad || b->grad)) { is_node = true; } // make a view of the destination struct ggml_tensor * result = ggml_view_tensor(ctx, b); if (strlen(b->name) > 0) { ggml_format_name(result, "%s (copy of %s)", b->name, a->name); } else { ggml_format_name(result, "%s (copy)", a->name); } result->op = GGML_OP_CPY; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; result->src[1] = b; return result; } struct ggml_tensor * ggml_cpy( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) { return ggml_cpy_impl(ctx, a, b, false); } struct ggml_tensor * ggml_cpy_inplace( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) { return ggml_cpy_impl(ctx, a, b, true); } // ggml_cont static struct ggml_tensor * ggml_cont_impl( struct ggml_context * ctx, struct ggml_tensor * a, bool inplace) { bool is_node = false; if (!inplace && a->grad) { is_node = true; } struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); ggml_format_name(result, "%s (cont)", a->name); result->op = GGML_OP_CONT; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; return result; } struct ggml_tensor * ggml_cont( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_cont_impl(ctx, a, false); } struct ggml_tensor * ggml_cont_inplace( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_cont_impl(ctx, a, true); } // ggml_reshape struct ggml_tensor * ggml_reshape( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) { GGML_ASSERT(ggml_is_contiguous(a)); GGML_ASSERT(ggml_is_contiguous(b)); GGML_ASSERT(ggml_nelements(a) == ggml_nelements(b)); bool is_node = false; if (a->grad) { is_node = true; } if (b->grad) { // gradient propagation is not supported //GGML_ASSERT(false); } struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, b->n_dims, b->ne, a->data); ggml_format_name(result, "%s (reshaped)", a->name); result->op = GGML_OP_RESHAPE; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; return result; } struct ggml_tensor * ggml_reshape_1d( struct ggml_context * ctx, struct ggml_tensor * a, int64_t ne0) { GGML_ASSERT(ggml_is_contiguous(a)); GGML_ASSERT(ggml_nelements(a) == ne0); bool is_node = false; if (a->grad) { is_node = true; } const int64_t ne[1] = { ne0 }; struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, 1, ne, a->data); ggml_format_name(result, "%s (reshaped)", a->name); result->op = GGML_OP_RESHAPE; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; return result; } struct ggml_tensor * ggml_reshape_2d( struct ggml_context * ctx, struct ggml_tensor * a, int64_t ne0, int64_t ne1) { GGML_ASSERT(ggml_is_contiguous(a)); GGML_ASSERT(ggml_nelements(a) == ne0*ne1); bool is_node = false; if (a->grad) { is_node = true; } const int64_t ne[2] = { ne0, ne1 }; struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, 2, ne, a->data); ggml_format_name(result, "%s (reshaped)", a->name); result->op = GGML_OP_RESHAPE; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; return result; } struct ggml_tensor * ggml_reshape_3d( struct ggml_context * ctx, struct ggml_tensor * a, int64_t ne0, int64_t ne1, int64_t ne2) { GGML_ASSERT(ggml_is_contiguous(a)); GGML_ASSERT(ggml_nelements(a) == ne0*ne1*ne2); bool is_node = false; if (a->grad) { is_node = true; } const int64_t ne[3] = { ne0, ne1, ne2 }; struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, 3, ne, a->data); ggml_format_name(result, "%s (reshaped)", a->name); result->op = GGML_OP_RESHAPE; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; return result; } struct ggml_tensor * ggml_reshape_4d( struct ggml_context * ctx, struct ggml_tensor * a, int64_t ne0, int64_t ne1, int64_t ne2, int64_t ne3) { GGML_ASSERT(ggml_is_contiguous(a)); GGML_ASSERT(ggml_nelements(a) == ne0*ne1*ne2*ne3); bool is_node = false; if (a->grad) { is_node = true; } const int64_t ne[4] = { ne0, ne1, ne2, ne3 }; struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, 4, ne, a->data); ggml_format_name(result, "%s (reshaped)", a->name); result->op = GGML_OP_RESHAPE; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; return result; } // ggml_view_1d static struct ggml_tensor * ggml_view_tensor_offset( struct ggml_context * ctx, struct ggml_tensor * a, int n_dims, const int64_t * ne, size_t offset) { // don't calculate an offset from an unallocated tensor void * data = NULL; if (a->data != NULL) { data = (char *) a->data + offset; } struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, n_dims, ne, data); ggml_format_name(result, "%s (view)", a->name); ggml_set_op_params(result, &offset, sizeof(offset)); return result; } struct ggml_tensor * ggml_view_1d( struct ggml_context * ctx, struct ggml_tensor * a, int64_t ne0, size_t offset) { bool is_node = false; if (a->grad) { is_node = true; } struct ggml_tensor * result = ggml_view_tensor_offset(ctx, a, 1, &ne0, offset); result->op = GGML_OP_VIEW; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; 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) { bool is_node = false; if (a->grad) { is_node = true; } const int64_t ne[GGML_MAX_DIMS] = { ne0, ne1, 1, 1 }; struct ggml_tensor * result = ggml_view_tensor_offset(ctx, a, 2, ne, 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 = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; 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) { bool is_node = false; if (a->grad) { is_node = true; } const int64_t ne[GGML_MAX_DIMS] = { ne0, ne1, ne2, 1 }; struct ggml_tensor * result = ggml_view_tensor_offset(ctx, a, 3, ne, offset); result->nb[1] = nb1; result->nb[2] = nb2; result->nb[3] = result->nb[2]*ne2; result->op = GGML_OP_VIEW; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; return result; } // ggml_view_4d struct ggml_tensor * ggml_view_4d( struct ggml_context * ctx, struct ggml_tensor * a, int64_t ne0, int64_t ne1, int64_t ne2, int64_t ne3, size_t nb1, size_t nb2, size_t nb3, size_t offset) { bool is_node = false; if (a->grad) { is_node = true; } const int64_t ne[GGML_MAX_DIMS] = { ne0, ne1, ne2, ne3 }; struct ggml_tensor * result = ggml_view_tensor_offset(ctx, a, 4, ne, offset); result->nb[1] = nb1; result->nb[2] = nb2; result->nb[3] = nb3; result->op = GGML_OP_VIEW; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; return result; } // ggml_permute struct ggml_tensor * ggml_permute( struct ggml_context * ctx, struct ggml_tensor * a, int axis0, int axis1, int axis2, int axis3) { GGML_ASSERT(axis0 >= 0 && axis0 < GGML_MAX_DIMS); GGML_ASSERT(axis1 >= 0 && axis1 < GGML_MAX_DIMS); GGML_ASSERT(axis2 >= 0 && axis2 < GGML_MAX_DIMS); GGML_ASSERT(axis3 >= 0 && axis3 < GGML_MAX_DIMS); GGML_ASSERT(axis0 != axis1); GGML_ASSERT(axis0 != axis2); GGML_ASSERT(axis0 != axis3); GGML_ASSERT(axis1 != axis2); GGML_ASSERT(axis1 != axis3); GGML_ASSERT(axis2 != axis3); bool is_node = false; if (a->grad) { is_node = true; } struct ggml_tensor * result = ggml_view_tensor(ctx, a); ggml_format_name(result, "%s (permuted)", a->name); int ne[GGML_MAX_DIMS]; int nb[GGML_MAX_DIMS]; ne[axis0] = a->ne[0]; ne[axis1] = a->ne[1]; ne[axis2] = a->ne[2]; ne[axis3] = a->ne[3]; nb[axis0] = a->nb[0]; nb[axis1] = a->nb[1]; nb[axis2] = a->nb[2]; nb[axis3] = a->nb[3]; result->ne[0] = ne[0]; result->ne[1] = ne[1]; result->ne[2] = ne[2]; result->ne[3] = ne[3]; result->nb[0] = nb[0]; result->nb[1] = nb[1]; result->nb[2] = nb[2]; result->nb[3] = nb[3]; result->op = GGML_OP_PERMUTE; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; int32_t params[] = { axis0, axis1, axis2, axis3 }; ggml_set_op_params(result, params, sizeof(params)); return result; } // ggml_transpose struct ggml_tensor * ggml_transpose( struct ggml_context * ctx, struct ggml_tensor * a) { bool is_node = false; if (a->grad) { is_node = true; } struct ggml_tensor * result = ggml_view_tensor(ctx, a); ggml_format_name(result, "%s (transposed)", a->name); result->ne[0] = a->ne[1]; result->ne[1] = a->ne[0]; result->nb[0] = a->nb[1]; result->nb[1] = a->nb[0]; result->op = GGML_OP_TRANSPOSE; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; return result; } // ggml_get_rows struct ggml_tensor * ggml_get_rows( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) { GGML_ASSERT(ggml_is_matrix(a) && ggml_is_vector(b) && b->type == GGML_TYPE_I32); bool is_node = false; if (a->grad || b->grad) { is_node = true; } // TODO: implement non F32 return //struct ggml_tensor * result = ggml_new_tensor_2d(ctx, a->type, a->ne[0], b->ne[0]); struct ggml_tensor * result = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, a->ne[0], b->ne[0]); result->op = GGML_OP_GET_ROWS; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; result->src[1] = b; return result; } // ggml_get_rows_back struct ggml_tensor * ggml_get_rows_back( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, struct ggml_tensor * c) { GGML_ASSERT(ggml_is_matrix(a) && ggml_is_vector(b) && b->type == GGML_TYPE_I32); GGML_ASSERT(ggml_is_matrix(c) && (a->ne[0] == c->ne[0])); bool is_node = false; if (a->grad || b->grad) { is_node = true; } // TODO: implement non F32 return //struct ggml_tensor * result = ggml_new_tensor_2d(ctx, a->type, a->ne[0], b->ne[0]); struct ggml_tensor * result = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, c->ne[0], c->ne[1]); result->op = GGML_OP_GET_ROWS_BACK; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; result->src[1] = b; result->src[2] = c; return result; } // ggml_diag struct ggml_tensor * ggml_diag( struct ggml_context * ctx, struct ggml_tensor * a) { GGML_ASSERT(a->ne[1] == 1); bool is_node = false; if (a->grad) { is_node = true; } const int64_t ne[4] = { a->ne[0], a->ne[0], a->ne[2], a->ne[3] }; struct ggml_tensor * result = ggml_new_tensor(ctx, a->type, MAX(a->n_dims, 2), ne); result->op = GGML_OP_DIAG; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; return result; } // ggml_diag_mask_inf static struct ggml_tensor * ggml_diag_mask_inf_impl( struct ggml_context * ctx, struct ggml_tensor * a, int n_past, bool inplace) { bool is_node = false; if (a->grad) { is_node = true; } struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); int32_t params[] = { n_past, inplace ? 1 : 0 }; ggml_set_op_params(result, params, sizeof(params)); result->op = GGML_OP_DIAG_MASK_INF; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; return result; } struct ggml_tensor * ggml_diag_mask_inf( struct ggml_context * ctx, struct ggml_tensor * a, int n_past) { return ggml_diag_mask_inf_impl(ctx, a, n_past, false); } struct ggml_tensor * ggml_diag_mask_inf_inplace( struct ggml_context * ctx, struct ggml_tensor * a, int n_past) { return ggml_diag_mask_inf_impl(ctx, a, n_past, true); } // ggml_diag_mask_zero static struct ggml_tensor * ggml_diag_mask_zero_impl( struct ggml_context * ctx, struct ggml_tensor * a, int n_past, bool inplace) { bool is_node = false; if (a->grad) { is_node = true; } struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); int32_t params[] = { n_past, inplace ? 1 : 0 }; ggml_set_op_params(result, params, sizeof(params)); result->op = GGML_OP_DIAG_MASK_ZERO; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; return result; } struct ggml_tensor * ggml_diag_mask_zero( struct ggml_context * ctx, struct ggml_tensor * a, int n_past) { return ggml_diag_mask_zero_impl(ctx, a, n_past, false); } struct ggml_tensor * ggml_diag_mask_zero_inplace( struct ggml_context * ctx, struct ggml_tensor * a, int n_past) { return ggml_diag_mask_zero_impl(ctx, a, n_past, true); } // ggml_soft_max static struct ggml_tensor * ggml_soft_max_impl( struct ggml_context * ctx, struct ggml_tensor * a, bool inplace) { bool is_node = false; if (a->grad) { is_node = true; } struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); result->op = GGML_OP_SOFT_MAX; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; return result; } struct ggml_tensor * ggml_soft_max( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_soft_max_impl(ctx, a, false); } struct ggml_tensor * ggml_soft_max_inplace( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_soft_max_impl(ctx, a, true); } // ggml_soft_max_back static struct ggml_tensor * ggml_soft_max_back_impl( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, bool inplace) { bool is_node = false; if (a->grad || b->grad) { is_node = true; // TODO : implement backward pass } struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); result->op = GGML_OP_SOFT_MAX_BACK; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; result->src[1] = b; return result; } struct ggml_tensor * ggml_soft_max_back( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) { return ggml_soft_max_back_impl(ctx, a, b, false); } struct ggml_tensor * ggml_soft_max_back_inplace( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) { return ggml_soft_max_back_impl(ctx, a, b, true); } // ggml_rope static struct ggml_tensor * ggml_rope_impl( struct ggml_context * ctx, struct ggml_tensor * a, int n_past, int n_dims, int mode, int n_ctx, float freq_base, float freq_scale, bool inplace) { GGML_ASSERT(n_past >= 0); bool is_node = false; if (a->grad) { is_node = true; } struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); int32_t params[6] = { n_past, n_dims, mode, n_ctx }; memcpy(params + 4, &freq_base, sizeof(float)); memcpy(params + 5, &freq_scale, sizeof(float)); ggml_set_op_params(result, params, sizeof(params)); result->op = GGML_OP_ROPE; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; return result; } struct ggml_tensor * ggml_rope( struct ggml_context * ctx, struct ggml_tensor * a, int n_past, int n_dims, int mode, int n_ctx) { return ggml_rope_impl(ctx, a, n_past, n_dims, mode, n_ctx, 10000.0f, 1.0f, false); } struct ggml_tensor * ggml_rope_inplace( struct ggml_context * ctx, struct ggml_tensor * a, int n_past, int n_dims, int mode, int n_ctx) { return ggml_rope_impl(ctx, a, n_past, n_dims, mode, n_ctx, 10000.0f, 1.0f, true); } struct ggml_tensor * ggml_rope_custom( struct ggml_context * ctx, struct ggml_tensor * a, int n_past, int n_dims, int mode, int n_ctx, float freq_base, float freq_scale) { return ggml_rope_impl(ctx, a, n_past, n_dims, mode, n_ctx, freq_base, freq_scale, false); } struct ggml_tensor * ggml_rope_custom_inplace( struct ggml_context * ctx, struct ggml_tensor * a, int n_past, int n_dims, int mode, int n_ctx, float freq_base, float freq_scale) { return ggml_rope_impl(ctx, a, n_past, n_dims, mode, n_ctx, freq_base, freq_scale, true); } // ggml_rope_back struct ggml_tensor * ggml_rope_back( struct ggml_context * ctx, struct ggml_tensor * a, int n_past, int n_dims, int mode, int n_ctx) { GGML_ASSERT(n_past >= 0); GGML_ASSERT((mode & 4) == 0 && "ggml_rope_back() for ChatGLM not implemented yet"); bool is_node = false; if (a->grad) { is_node = false; // TODO: implement backward } struct ggml_tensor * result = ggml_dup_tensor(ctx, a); int32_t params[] = { n_past, n_dims, mode, n_ctx }; ggml_set_op_params(result, params, sizeof(params)); result->op = GGML_OP_ROPE_BACK; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; return result; } // ggml_alibi struct ggml_tensor * ggml_alibi( struct ggml_context * ctx, struct ggml_tensor * a, int n_past, int n_head, float bias_max) { GGML_ASSERT(n_past >= 0); bool is_node = false; if (a->grad) { GGML_ASSERT(false); // TODO: implement backward is_node = true; } // TODO: when implement backward, fix this: //struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); struct ggml_tensor * result = ggml_view_tensor(ctx, a); int32_t op_params[3] = { n_past, n_head }; memcpy(op_params + 2, &bias_max, sizeof(float)); ggml_set_op_params(result, op_params, sizeof(op_params)); result->op = GGML_OP_ALIBI; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; return result; } // ggml_clamp struct ggml_tensor * ggml_clamp( struct ggml_context * ctx, struct ggml_tensor * a, float min, float max) { bool is_node = false; if (a->grad) { GGML_ASSERT(false); // TODO: implement backward is_node = true; } // TODO: when implement backward, fix this: struct ggml_tensor * result = ggml_view_tensor(ctx, a); float params[] = { min, max }; ggml_set_op_params(result, params, sizeof(params)); result->op = GGML_OP_CLAMP; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; return result; } // ggml_conv_1d static int64_t ggml_calc_conv_output_size(int64_t ins, int64_t ks, int s, int p, int d) { return (ins + 2 * p - d * (ks - 1) - 1) / s + 1; } GGML_API struct ggml_tensor * ggml_conv_1d( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, int s0, int p0, int d0) { GGML_ASSERT(ggml_is_matrix(b)); GGML_ASSERT(a->ne[1] == b->ne[1]); bool is_node = false; if (a->grad || b->grad) { GGML_ASSERT(false); // TODO: implement backward is_node = true; } const int64_t ne[4] = { ggml_calc_conv_output_size(b->ne[0], a->ne[0], s0, p0, d0), a->ne[2], 1, 1, }; struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 2, ne); int32_t params[] = { s0, p0, d0 }; ggml_set_op_params(result, params, sizeof(params)); result->op = GGML_OP_CONV_1D; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; result->src[1] = b; return result; } // ggml_conv_2d struct ggml_tensor * ggml_conv_2d( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, int s0, int s1, int p0, int p1, int d0, int d1) { GGML_ASSERT(a->ne[2] == b->ne[2]); bool is_node = false; if (a->grad || b->grad) { GGML_ASSERT(false); // TODO: implement backward is_node = true; } const int64_t ne[4] = { ggml_calc_conv_output_size(b->ne[0], a->ne[0], s0, p0, d0), ggml_calc_conv_output_size(b->ne[1], a->ne[1], s1, p1, d1), a->ne[3], b->ne[3], }; struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne); int32_t params[] = { s0, s1, p0, p1, d0, d1 }; ggml_set_op_params(result, params, sizeof(params)); result->op = GGML_OP_CONV_2D; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; result->src[1] = b; return result; } // ggml_conv_1d_ph struct ggml_tensor * ggml_conv_1d_ph( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, int s, int d) { return ggml_conv_1d(ctx, a, b, s, a->ne[0] / 2, d); } // ggml_pool_* static int64_t ggml_calc_pool_output_size(int64_t ins, int ks, int s, int p) { return (ins + 2 * p - ks) / s + 1; } // ggml_pool_1d struct ggml_tensor * ggml_pool_1d( struct ggml_context * ctx, struct ggml_tensor * a, enum ggml_op_pool op, int k0, int s0, int p0) { bool is_node = false; if (a->grad) { GGML_ASSERT(false); // TODO: implement backward is_node = true; } const int64_t ne[3] = { ggml_calc_pool_output_size(a->ne[0], k0, s0, p0), a->ne[1], }; struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 2, ne); int32_t params[] = { op, k0, s0, p0 }; ggml_set_op_params(result, params, sizeof(params)); result->op = GGML_OP_POOL_1D; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; return result; } // ggml_pool_2d struct ggml_tensor * ggml_pool_2d( struct ggml_context * ctx, struct ggml_tensor * a, enum ggml_op_pool op, int k0, int k1, int s0, int s1, int p0, int p1) { bool is_node = false; if (a->grad) { GGML_ASSERT(false); // TODO: implement backward is_node = true; } const int64_t ne[3] = { ggml_calc_pool_output_size(a->ne[0], k0, s0, p0), ggml_calc_pool_output_size(a->ne[1], k1, s1, p1), a->ne[2], }; struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 3, ne); int32_t params[] = { op, k0, k1, s0, s1, p0, p1 }; ggml_set_op_params(result, params, sizeof(params)); result->op = GGML_OP_POOL_2D; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; return result; } // ggml_flash_attn struct ggml_tensor * ggml_flash_attn( struct ggml_context * ctx, struct ggml_tensor * q, struct ggml_tensor * k, struct ggml_tensor * v, bool masked) { GGML_ASSERT(ggml_can_mul_mat(k, q)); // TODO: check if vT can be multiplied by (k*qT) bool is_node = false; if (q->grad || k->grad || v->grad) { is_node = true; } //struct ggml_tensor * result = ggml_dup_tensor(ctx, q); struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, q->n_dims, q->ne); int32_t t = masked ? 1 : 0; ggml_set_op_params(result, &t, sizeof(t)); result->op = GGML_OP_FLASH_ATTN; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = q; result->src[1] = k; result->src[2] = v; return result; } // ggml_flash_ff struct ggml_tensor * ggml_flash_ff( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b0, struct ggml_tensor * b1, struct ggml_tensor * c0, struct ggml_tensor * c1) { GGML_ASSERT(ggml_can_mul_mat(b0, a)); // TODO: more checks bool is_node = false; if (a->grad || b0->grad || b1->grad || c0->grad || c1->grad) { is_node = true; } //struct ggml_tensor * result = ggml_dup_tensor(ctx, a); struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, a->n_dims, a->ne); result->op = GGML_OP_FLASH_FF; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; result->src[1] = b0; result->src[2] = b1; result->src[3] = c0; result->src[4] = c1; return result; } // ggml_flash_attn_back struct ggml_tensor * ggml_flash_attn_back( struct ggml_context * ctx, struct ggml_tensor * q, struct ggml_tensor * k, struct ggml_tensor * v, struct ggml_tensor * d, bool masked) { GGML_ASSERT(ggml_can_mul_mat(k, q)); // TODO: check if vT can be multiplied by (k*qT) // d shape [D,N,ne2,ne3] // q shape [D,N,ne2,ne3] // k shape [D,M,ne2,ne3] // v shape [M,D,ne2,ne3] const int64_t D = q->ne[0]; const int64_t N = q->ne[1]; const int64_t M = k->ne[1]; const int64_t ne2 = q->ne[2]; const int64_t ne3 = q->ne[3]; GGML_ASSERT(k->ne[0] == D); GGML_ASSERT(v->ne[0] == M); GGML_ASSERT(v->ne[1] == D); GGML_ASSERT(d->ne[0] == D); GGML_ASSERT(d->ne[1] == N); GGML_ASSERT(k->ne[2] == ne2); GGML_ASSERT(k->ne[3] == ne3); GGML_ASSERT(v->ne[2] == ne2); GGML_ASSERT(v->ne[3] == ne3); GGML_ASSERT(d->ne[2] == ne2); GGML_ASSERT(d->ne[3] == ne3); bool is_node = false; if (q->grad || k->grad || v->grad) { // when using this operation (in backwards pass) these grads are set. // we don't want to create (big) grad of our result, so is_node is false. is_node = false; } // store gradients of q, k and v as continuous tensors concatenated in result. // q shape[D,N,ne2,ne3] ; k shape [D,M,ne2,ne3] ; v shape [M,D,ne2,ne3] // gradq->data = result->data // gradk->data = result->data + nb0*D*N*ne2*ne3 // gradv->data = result->data + nb0*D*N*ne2*ne3 + nb0*D*M*ne2*ne3 // note: v and gradv are actually transposed, i.e. v->ne[0] != D. int64_t ne[4] = {D,M+N+M,ne2,ne3}; struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne); int32_t masked_i = masked ? 1 : 0; ggml_set_op_params(result, &masked_i, sizeof(masked_i)); result->op = GGML_OP_FLASH_ATTN_BACK; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = q; result->src[1] = k; result->src[2] = v; result->src[3] = d; return result; } // ggml_win_part struct ggml_tensor * ggml_win_part( struct ggml_context * ctx, struct ggml_tensor * a, int w) { GGML_ASSERT(a->ne[3] == 1); GGML_ASSERT(a->type == GGML_TYPE_F32); bool is_node = false; if (a->grad) { GGML_ASSERT(false); // TODO: implement backward is_node = true; } // padding const int px = (w - a->ne[1]%w)%w; const int py = (w - a->ne[2]%w)%w; const int npx = (px + a->ne[1])/w; const int npy = (py + a->ne[2])/w; const int np = npx*npy; const int64_t ne[4] = { a->ne[0], w, w, np, }; struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne); int32_t params[] = { npx, npy, w }; ggml_set_op_params(result, params, sizeof(params)); result->op = GGML_OP_WIN_PART; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; return result; } // ggml_win_unpart struct ggml_tensor * ggml_win_unpart( struct ggml_context * ctx, struct ggml_tensor * a, int w0, int h0, int w) { GGML_ASSERT(a->type == GGML_TYPE_F32); bool is_node = false; if (a->grad) { GGML_ASSERT(false); // TODO: implement backward is_node = true; } const int64_t ne[4] = { a->ne[0], w0, h0, 1, }; struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 3, ne); int32_t params[] = { w }; ggml_set_op_params(result, params, sizeof(params)); result->op = GGML_OP_WIN_UNPART; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; return result; } // gmml_unary static struct ggml_tensor * ggml_unary_impl( struct ggml_context * ctx, struct ggml_tensor * a, enum ggml_unary_op op, bool inplace) { bool is_node = false; if (!inplace && (a->grad)) { is_node = true; } struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); ggml_set_op_params_i32(result, 0, (int32_t) op); result->op = GGML_OP_UNARY; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; return result; } struct ggml_tensor * ggml_unary( struct ggml_context * ctx, struct ggml_tensor * a, enum ggml_unary_op op) { return ggml_unary_impl(ctx, a, op, false); } struct ggml_tensor * ggml_unary_inplace( struct ggml_context * ctx, struct ggml_tensor * a, enum ggml_unary_op op) { return ggml_unary_impl(ctx, a, op, true); } // ggml_map_unary static struct ggml_tensor * ggml_map_unary_impl_f32( struct ggml_context * ctx, struct ggml_tensor * a, const ggml_unary_op_f32_t fun, bool inplace) { bool is_node = false; if (!inplace && a->grad) { is_node = true; } struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); ggml_set_op_params(result, (const void *) &fun, sizeof(fun)); result->op = GGML_OP_MAP_UNARY; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; return result; } struct ggml_tensor * ggml_map_unary_f32( struct ggml_context * ctx, struct ggml_tensor * a, const ggml_unary_op_f32_t fun) { return ggml_map_unary_impl_f32(ctx, a, fun, false); } struct ggml_tensor * ggml_map_unary_inplace_f32( struct ggml_context * ctx, struct ggml_tensor * a, const ggml_unary_op_f32_t fun) { return ggml_map_unary_impl_f32(ctx, a, fun, true); } // ggml_map_binary static struct ggml_tensor * ggml_map_binary_impl_f32( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, const ggml_binary_op_f32_t fun, bool inplace) { GGML_ASSERT(ggml_are_same_shape(a, b)); bool is_node = false; if (!inplace && (a->grad || b->grad)) { is_node = true; } struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); ggml_set_op_params(result, (const void *) &fun, sizeof(fun)); result->op = GGML_OP_MAP_BINARY; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; result->src[1] = b; return result; } struct ggml_tensor * ggml_map_binary_f32( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, const ggml_binary_op_f32_t fun) { return ggml_map_binary_impl_f32(ctx, a, b, fun, false); } struct ggml_tensor * ggml_map_binary_inplace_f32( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, const ggml_binary_op_f32_t fun) { return ggml_map_binary_impl_f32(ctx, a, b, fun, true); } // ggml_map_custom1_f32 static struct ggml_tensor * ggml_map_custom1_impl_f32( struct ggml_context * ctx, struct ggml_tensor * a, const ggml_custom1_op_f32_t fun, bool inplace) { bool is_node = false; if (!inplace && a->grad) { is_node = true; } struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); ggml_set_op_params(result, (const void *) &fun, sizeof(fun)); result->op = GGML_OP_MAP_CUSTOM1_F32; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; return result; } struct ggml_tensor * ggml_map_custom1_f32( struct ggml_context * ctx, struct ggml_tensor * a, const ggml_custom1_op_f32_t fun) { return ggml_map_custom1_impl_f32(ctx, a, fun, false); } struct ggml_tensor * ggml_map_custom1_inplace_f32( struct ggml_context * ctx, struct ggml_tensor * a, const ggml_custom1_op_f32_t fun) { return ggml_map_custom1_impl_f32(ctx, a, fun, true); } // ggml_map_custom2_f32 static struct ggml_tensor * ggml_map_custom2_impl_f32( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, const ggml_custom2_op_f32_t fun, bool inplace) { bool is_node = false; if (!inplace && (a->grad || b->grad)) { is_node = true; } struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); ggml_set_op_params(result, (const void *) &fun, sizeof(fun)); result->op = GGML_OP_MAP_CUSTOM2_F32; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; result->src[1] = b; return result; } struct ggml_tensor * ggml_map_custom2_f32( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, const ggml_custom2_op_f32_t fun) { return ggml_map_custom2_impl_f32(ctx, a, b, fun, false); } struct ggml_tensor * ggml_map_custom2_inplace_f32( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, const ggml_custom2_op_f32_t fun) { return ggml_map_custom2_impl_f32(ctx, a, b, fun, true); } // ggml_map_custom3_f32 static struct ggml_tensor * ggml_map_custom3_impl_f32( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, struct ggml_tensor * c, const ggml_custom3_op_f32_t fun, bool inplace) { bool is_node = false; if (!inplace && (a->grad || b->grad || c->grad)) { is_node = true; } struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); ggml_set_op_params(result, (const void *) &fun, sizeof(fun)); result->op = GGML_OP_MAP_CUSTOM3_F32; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; result->src[1] = b; result->src[2] = c; return result; } struct ggml_tensor * ggml_map_custom3_f32( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, struct ggml_tensor * c, const ggml_custom3_op_f32_t fun) { return ggml_map_custom3_impl_f32(ctx, a, b, c, fun, false); } struct ggml_tensor * ggml_map_custom3_inplace_f32( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, struct ggml_tensor * c, const ggml_custom3_op_f32_t fun) { return ggml_map_custom3_impl_f32(ctx, a, b, c, fun, true); } // ggml_map_custom1 struct ggml_map_custom1_op_params { ggml_custom1_op_t fun; int n_tasks; void * userdata; }; static struct ggml_tensor * ggml_map_custom1_impl( struct ggml_context * ctx, struct ggml_tensor * a, const ggml_custom1_op_t fun, int n_tasks, void * userdata, bool inplace) { GGML_ASSERT(n_tasks == GGML_N_TASKS_MAX || n_tasks > 0); bool is_node = false; if (!inplace && a->grad) { is_node = true; } struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); struct ggml_map_custom1_op_params params = { /*.fun =*/ fun, /*.n_tasks =*/ n_tasks, /*.userdata =*/ userdata }; ggml_set_op_params(result, (const void *) ¶ms, sizeof(params)); result->op = GGML_OP_MAP_CUSTOM1; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; return result; } struct ggml_tensor * ggml_map_custom1( struct ggml_context * ctx, struct ggml_tensor * a, const ggml_custom1_op_t fun, int n_tasks, void * userdata) { return ggml_map_custom1_impl(ctx, a, fun, n_tasks, userdata, false); } struct ggml_tensor * ggml_map_custom1_inplace( struct ggml_context * ctx, struct ggml_tensor * a, const ggml_custom1_op_t fun, int n_tasks, void * userdata) { return ggml_map_custom1_impl(ctx, a, fun, n_tasks, userdata, true); } // ggml_map_custom2 struct ggml_map_custom2_op_params { ggml_custom2_op_t fun; int n_tasks; void * userdata; }; static struct ggml_tensor * ggml_map_custom2_impl( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, const ggml_custom2_op_t fun, int n_tasks, void * userdata, bool inplace) { GGML_ASSERT(n_tasks == GGML_N_TASKS_MAX || n_tasks > 0); bool is_node = false; if (!inplace && (a->grad || b->grad)) { is_node = true; } struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); struct ggml_map_custom2_op_params params = { /*.fun =*/ fun, /*.n_tasks =*/ n_tasks, /*.userdata =*/ userdata }; ggml_set_op_params(result, (const void *) ¶ms, sizeof(params)); result->op = GGML_OP_MAP_CUSTOM2; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; result->src[1] = b; return result; } struct ggml_tensor * ggml_map_custom2( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, const ggml_custom2_op_t fun, int n_tasks, void * userdata) { return ggml_map_custom2_impl(ctx, a, b, fun, n_tasks, userdata, false); } struct ggml_tensor * ggml_map_custom2_inplace( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, const ggml_custom2_op_t fun, int n_tasks, void * userdata) { return ggml_map_custom2_impl(ctx, a, b, fun, n_tasks, userdata, true); } // ggml_map_custom3 struct ggml_map_custom3_op_params { ggml_custom3_op_t fun; int n_tasks; void * userdata; }; static struct ggml_tensor * ggml_map_custom3_impl( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, struct ggml_tensor * c, const ggml_custom3_op_t fun, int n_tasks, void * userdata, bool inplace) { GGML_ASSERT(n_tasks == GGML_N_TASKS_MAX || n_tasks > 0); bool is_node = false; if (!inplace && (a->grad || b->grad || c->grad)) { is_node = true; } struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); struct ggml_map_custom3_op_params params = { /*.fun =*/ fun, /*.n_tasks =*/ n_tasks, /*.userdata =*/ userdata }; ggml_set_op_params(result, (const void *) ¶ms, sizeof(params)); result->op = GGML_OP_MAP_CUSTOM3; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; result->src[1] = b; result->src[2] = c; return result; } struct ggml_tensor * ggml_map_custom3( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, struct ggml_tensor * c, const ggml_custom3_op_t fun, int n_tasks, void * userdata) { return ggml_map_custom3_impl(ctx, a, b, c, fun, n_tasks, userdata, false); } struct ggml_tensor * ggml_map_custom3_inplace( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, struct ggml_tensor * c, const ggml_custom3_op_t fun, int n_tasks, void * userdata) { return ggml_map_custom3_impl(ctx, a, b, c, fun, n_tasks, userdata, true); } // ggml_cross_entropy_loss struct ggml_tensor * ggml_cross_entropy_loss( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) { GGML_ASSERT(ggml_are_same_shape(a, b)); bool is_node = false; if (a->grad || b->grad) { is_node = true; } struct ggml_tensor * result = ggml_new_tensor_1d(ctx, a->type, 1); result->op = GGML_OP_CROSS_ENTROPY_LOSS; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; result->src[1] = b; return result; } // ggml_cross_entropy_loss_back struct ggml_tensor * ggml_cross_entropy_loss_back( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, struct ggml_tensor * c) { GGML_ASSERT(ggml_are_same_shape(a, b)); GGML_ASSERT(ggml_is_scalar(c)); struct ggml_tensor * result = ggml_dup_tensor(ctx, a); result->op = GGML_OP_CROSS_ENTROPY_LOSS_BACK; result->grad = NULL; result->src[0] = a; result->src[1] = b; result->src[2] = c; return result; } //////////////////////////////////////////////////////////////////////////////// void ggml_set_param( struct ggml_context * ctx, struct ggml_tensor * tensor) { tensor->is_param = true; GGML_ASSERT(tensor->grad == NULL); tensor->grad = ggml_dup_tensor(ctx, tensor); } // ggml_compute_forward_dup static void ggml_compute_forward_dup_same_cont( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0)); GGML_ASSERT(ggml_is_contiguous(dst) && ggml_is_contiguous(src0)); GGML_ASSERT(src0->type == dst->type); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } const size_t nb00 = src0->nb[0]; const size_t nb0 = dst->nb[0]; const int ith = params->ith; // thread index const int nth = params->nth; // number of threads // parallelize by elements const int ne = ggml_nelements(dst); const int dr = (ne + nth - 1) / nth; const int ie0 = dr * ith; const int ie1 = MIN(ie0 + dr, ne); if (ie0 < ie1) { memcpy( ((char *) dst->data + ie0*nb0), ((char *) src0->data + ie0*nb00), (ie1 - ie0) * GGML_TYPE_SIZE[src0->type]); } } static void ggml_compute_forward_dup_f16( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0)); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } GGML_TENSOR_UNARY_OP_LOCALS; const int ith = params->ith; // thread index const int nth = params->nth; // number of threads if (ggml_is_contiguous(src0) && ggml_is_contiguous(dst) && src0->type == dst->type) { ggml_compute_forward_dup_same_cont(params, src0, dst); return; } // parallelize by rows const int nr = ne01; // number of rows per thread const int dr = (nr + nth - 1) / nth; // row range for this thread const int ir0 = dr * ith; const int ir1 = MIN(ir0 + dr, nr); if (src0->type == dst->type && ne00 == ne0 && nb00 == GGML_TYPE_SIZE[src0->type] && nb0 == GGML_TYPE_SIZE[dst->type]) { // copy by rows const size_t rs = ne00*nb00; for (int64_t i03 = 0; i03 < ne03; i03++) { for (int64_t i02 = 0; i02 < ne02; i02++) { for (int64_t i01 = ir0; i01 < ir1; i01++) { memcpy( ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3), ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03), rs); } } } return; } // TODO: add more special-case implementations for tensor shapes/strides that can benefit from memcpy if (ggml_is_contiguous(dst)) { if (nb00 == sizeof(ggml_fp16_t)) { if (dst->type == GGML_TYPE_F16) { size_t id = 0; const size_t rs = ne00 * nb00; char * dst_ptr = (char *) dst->data; for (int i03 = 0; i03 < ne03; i03++) { for (int i02 = 0; i02 < ne02; i02++) { id += rs * ir0; for (int i01 = ir0; i01 < ir1; i01++) { const char * src0_ptr = (char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03; memcpy(dst_ptr + id, src0_ptr, rs); id += rs; } id += rs * (ne01 - ir1); } } } else if (dst->type == GGML_TYPE_F32) { size_t id = 0; float * dst_ptr = (float *) dst->data; for (int i03 = 0; i03 < ne03; i03++) { for (int i02 = 0; i02 < ne02; i02++) { id += ne00 * ir0; for (int i01 = ir0; i01 < ir1; i01++) { const ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); for (int i00 = 0; i00 < ne00; i00++) { dst_ptr[id] = GGML_FP16_TO_FP32(src0_ptr[i00]); id++; } } id += ne00 * (ne01 - ir1); } } } else if (type_traits[dst->type].from_float) { ggml_from_float_t const quantize_row_q = type_traits[dst->type].from_float; float * src0_f32 = (float *) params->wdata + (ne00 + CACHE_LINE_SIZE_F32) * ith; size_t id = 0; size_t rs = nb0 * (ne00 / GGML_BLCK_SIZE[dst->type]); char * dst_ptr = (char *) dst->data; for (int i03 = 0; i03 < ne03; i03++) { for (int i02 = 0; i02 < ne02; i02++) { id += rs * ir0; for (int i01 = ir0; i01 < ir1; i01++) { const ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); for (int i00 = 0; i00 < ne00; i00++) { src0_f32[i00] = GGML_FP16_TO_FP32(src0_ptr[i00]); } quantize_row_q(src0_f32, dst_ptr + id, ne00); id += rs; } id += rs * (ne01 - ir1); } } } else { GGML_ASSERT(false); // TODO: implement } } else { //printf("%s: this is not optimal - fix me\n", __func__); if (dst->type == GGML_TYPE_F32) { size_t id = 0; float * dst_ptr = (float *) dst->data; for (int i03 = 0; i03 < ne03; i03++) { for (int i02 = 0; i02 < ne02; i02++) { id += ne00 * ir0; for (int i01 = ir0; i01 < ir1; i01++) { for (int i00 = 0; i00 < ne00; i00++) { const ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); dst_ptr[id] = GGML_FP16_TO_FP32(*src0_ptr); id++; } } id += ne00 * (ne01 - ir1); } } } else if (dst->type == GGML_TYPE_F16) { size_t id = 0; ggml_fp16_t * dst_ptr = (ggml_fp16_t *) dst->data; for (int i03 = 0; i03 < ne03; i03++) { for (int i02 = 0; i02 < ne02; i02++) { id += ne00 * ir0; for (int i01 = ir0; i01 < ir1; i01++) { for (int i00 = 0; i00 < ne00; i00++) { const ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); dst_ptr[id] = *src0_ptr; id++; } } id += ne00 * (ne01 - ir1); } } } else { GGML_ASSERT(false); // TODO: implement } } return; } // dst counters int64_t i10 = 0; int64_t i11 = 0; int64_t i12 = 0; int64_t i13 = 0; if (dst->type == GGML_TYPE_F16) { for (int64_t i03 = 0; i03 < ne03; i03++) { for (int64_t i02 = 0; i02 < ne02; i02++) { i10 += ne00 * ir0; while (i10 >= ne0) { i10 -= ne0; if (++i11 == ne1) { i11 = 0; if (++i12 == ne2) { i12 = 0; if (++i13 == ne3) { i13 = 0; } } } } for (int64_t i01 = ir0; i01 < ir1; i01++) { for (int64_t i00 = 0; i00 < ne00; i00++) { const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); char * dst_ptr = ((char *) dst->data + i10*nb0 + i11*nb1 + i12*nb2 + i13*nb3); memcpy(dst_ptr, src0_ptr, sizeof(ggml_fp16_t)); if (++i10 == ne00) { i10 = 0; if (++i11 == ne01) { i11 = 0; if (++i12 == ne02) { i12 = 0; if (++i13 == ne03) { i13 = 0; } } } } } } i10 += ne00 * (ne01 - ir1); while (i10 >= ne0) { i10 -= ne0; if (++i11 == ne1) { i11 = 0; if (++i12 == ne2) { i12 = 0; if (++i13 == ne3) { i13 = 0; } } } } } } } else if (dst->type == GGML_TYPE_F32) { for (int64_t i03 = 0; i03 < ne03; i03++) { for (int64_t i02 = 0; i02 < ne02; i02++) { i10 += ne00 * ir0; while (i10 >= ne0) { i10 -= ne0; if (++i11 == ne1) { i11 = 0; if (++i12 == ne2) { i12 = 0; if (++i13 == ne3) { i13 = 0; } } } } for (int64_t i01 = ir0; i01 < ir1; i01++) { for (int64_t i00 = 0; i00 < ne00; i00++) { const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); char * dst_ptr = ((char *) dst->data + i10*nb0 + i11*nb1 + i12*nb2 + i13*nb3); *(float *) dst_ptr = GGML_FP16_TO_FP32(*(const ggml_fp16_t *) src0_ptr); if (++i10 == ne0) { i10 = 0; if (++i11 == ne1) { i11 = 0; if (++i12 == ne2) { i12 = 0; if (++i13 == ne3) { i13 = 0; } } } } } } i10 += ne00 * (ne01 - ir1); while (i10 >= ne0) { i10 -= ne0; if (++i11 == ne1) { i11 = 0; if (++i12 == ne2) { i12 = 0; if (++i13 == ne3) { i13 = 0; } } } } } } } else { GGML_ASSERT(false); // TODO: implement } } static void ggml_compute_forward_dup_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0)); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } GGML_TENSOR_UNARY_OP_LOCALS; const int ith = params->ith; // thread index const int nth = params->nth; // number of threads if (ggml_is_contiguous(src0) && ggml_is_contiguous(dst) && src0->type == dst->type) { ggml_compute_forward_dup_same_cont(params, src0, dst); return; } // parallelize by rows const int nr = ne01; // number of rows per thread const int dr = (nr + nth - 1) / nth; // row range for this thread const int ir0 = dr * ith; const int ir1 = MIN(ir0 + dr, nr); if (src0->type == dst->type && ne00 == ne0 && nb00 == GGML_TYPE_SIZE[src0->type] && nb0 == GGML_TYPE_SIZE[dst->type]) { // copy by rows const size_t rs = ne00*nb00; for (int64_t i03 = 0; i03 < ne03; i03++) { for (int64_t i02 = 0; i02 < ne02; i02++) { for (int64_t i01 = ir0; i01 < ir1; i01++) { memcpy( ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3), ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03), rs); } } } return; } if (ggml_is_contiguous(dst)) { // TODO: simplify if (nb00 == sizeof(float)) { if (dst->type == GGML_TYPE_F32) { size_t id = 0; const size_t rs = ne00 * nb00; char * dst_ptr = (char *) dst->data; for (int i03 = 0; i03 < ne03; i03++) { for (int i02 = 0; i02 < ne02; i02++) { id += rs * ir0; for (int i01 = ir0; i01 < ir1; i01++) { const char * src0_ptr = (char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03; memcpy(dst_ptr + id, src0_ptr, rs); id += rs; } id += rs * (ne01 - ir1); } } } else if (type_traits[dst->type].from_float) { ggml_from_float_t const quantize_row_q = type_traits[dst->type].from_float; size_t id = 0; size_t rs = nb0 * (ne00 / GGML_BLCK_SIZE[dst->type]); char * dst_ptr = (char *) dst->data; for (int i03 = 0; i03 < ne03; i03++) { for (int i02 = 0; i02 < ne02; i02++) { id += rs * ir0; for (int i01 = ir0; i01 < ir1; i01++) { const float * src0_ptr = (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); quantize_row_q(src0_ptr, dst_ptr + id, ne00); id += rs; } id += rs * (ne01 - ir1); } } } else { GGML_ASSERT(false); // TODO: implement } } else { //printf("%s: this is not optimal - fix me\n", __func__); if (dst->type == GGML_TYPE_F32) { size_t id = 0; float * dst_ptr = (float *) dst->data; for (int i03 = 0; i03 < ne03; i03++) { for (int i02 = 0; i02 < ne02; i02++) { id += ne00 * ir0; for (int i01 = ir0; i01 < ir1; i01++) { for (int i00 = 0; i00 < ne00; i00++) { const float * src0_ptr = (float *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); dst_ptr[id] = *src0_ptr; id++; } } id += ne00 * (ne01 - ir1); } } } else if (dst->type == GGML_TYPE_F16) { size_t id = 0; ggml_fp16_t * dst_ptr = (ggml_fp16_t *) dst->data; for (int i03 = 0; i03 < ne03; i03++) { for (int i02 = 0; i02 < ne02; i02++) { id += ne00 * ir0; for (int i01 = ir0; i01 < ir1; i01++) { for (int i00 = 0; i00 < ne00; i00++) { const float * src0_ptr = (float *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); dst_ptr[id] = GGML_FP32_TO_FP16(*src0_ptr); id++; } } id += ne00 * (ne01 - ir1); } } } else { GGML_ASSERT(false); // TODO: implement } } return; } // dst counters int64_t i10 = 0; int64_t i11 = 0; int64_t i12 = 0; int64_t i13 = 0; if (dst->type == GGML_TYPE_F32) { for (int64_t i03 = 0; i03 < ne03; i03++) { for (int64_t i02 = 0; i02 < ne02; i02++) { i10 += ne00 * ir0; while (i10 >= ne0) { i10 -= ne0; if (++i11 == ne1) { i11 = 0; if (++i12 == ne2) { i12 = 0; if (++i13 == ne3) { i13 = 0; } } } } for (int64_t i01 = ir0; i01 < ir1; i01++) { for (int64_t i00 = 0; i00 < ne00; i00++) { const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); char * dst_ptr = ((char *) dst->data + i10*nb0 + i11*nb1 + i12*nb2 + i13*nb3); memcpy(dst_ptr, src0_ptr, sizeof(float)); if (++i10 == ne0) { i10 = 0; if (++i11 == ne1) { i11 = 0; if (++i12 == ne2) { i12 = 0; if (++i13 == ne3) { i13 = 0; } } } } } } i10 += ne00 * (ne01 - ir1); while (i10 >= ne0) { i10 -= ne0; if (++i11 == ne1) { i11 = 0; if (++i12 == ne2) { i12 = 0; if (++i13 == ne3) { i13 = 0; } } } } } } } else if (dst->type == GGML_TYPE_F16) { for (int64_t i03 = 0; i03 < ne03; i03++) { for (int64_t i02 = 0; i02 < ne02; i02++) { i10 += ne00 * ir0; while (i10 >= ne0) { i10 -= ne0; if (++i11 == ne1) { i11 = 0; if (++i12 == ne2) { i12 = 0; if (++i13 == ne3) { i13 = 0; } } } } for (int64_t i01 = ir0; i01 < ir1; i01++) { for (int64_t i00 = 0; i00 < ne00; i00++) { const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); char * dst_ptr = ((char *) dst->data + i10*nb0 + i11*nb1 + i12*nb2 + i13*nb3); *(ggml_fp16_t *) dst_ptr = GGML_FP32_TO_FP16(*(const float *) src0_ptr); if (++i10 == ne0) { i10 = 0; if (++i11 == ne1) { i11 = 0; if (++i12 == ne2) { i12 = 0; if (++i13 == ne3) { i13 = 0; } } } } } } i10 += ne00 * (ne01 - ir1); while (i10 >= ne0) { i10 -= ne0; if (++i11 == ne1) { i11 = 0; if (++i12 == ne2) { i12 = 0; if (++i13 == ne3) { i13 = 0; } } } } } } } else { GGML_ASSERT(false); // TODO: implement } } static void ggml_compute_forward_dup( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { if (ggml_is_contiguous(src0) && ggml_is_contiguous(dst) && src0->type == dst->type) { ggml_compute_forward_dup_same_cont(params, src0, dst); return; } switch (src0->type) { case GGML_TYPE_F16: { ggml_compute_forward_dup_f16(params, src0, dst); } break; case GGML_TYPE_F32: { ggml_compute_forward_dup_f32(params, src0, dst); } break; default: { GGML_ASSERT(false); } break; } } // ggml_compute_forward_add static void ggml_compute_forward_add_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { GGML_ASSERT(ggml_can_repeat_rows(src1, src0) && ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } const int ith = params->ith; const int nth = params->nth; const int nr = ggml_nrows(src0); GGML_TENSOR_BINARY_OP_LOCALS; GGML_ASSERT( nb0 == sizeof(float)); GGML_ASSERT(nb00 == sizeof(float)); // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); if (nb10 == sizeof(float)) { for (int ir = ir0; ir < ir1; ++ir) { // src1 is broadcastable across src0 and dst in i1, i2, i3 const int64_t i03 = ir/(ne02*ne01); const int64_t i02 = (ir - i03*ne02*ne01)/ne01; const int64_t i01 = (ir - i03*ne02*ne01 - i02*ne01); const int64_t i13 = i03 % ne13; const int64_t i12 = i02 % ne12; const int64_t i11 = i01 % ne11; float * dst_ptr = (float *) ((char *) dst->data + i03*nb3 + i02*nb2 + i01*nb1 ); float * src0_ptr = (float *) ((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01); float * src1_ptr = (float *) ((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11); #ifdef GGML_USE_ACCELERATE vDSP_vadd(src0_ptr, 1, src1_ptr, 1, dst_ptr, 1, ne00); #else ggml_vec_add_f32(ne00, dst_ptr, src0_ptr, src1_ptr); #endif // } // } } } else { // src1 is not contiguous for (int ir = ir0; ir < ir1; ++ir) { // src1 is broadcastable across src0 and dst in i1, i2, i3 const int64_t i03 = ir/(ne02*ne01); const int64_t i02 = (ir - i03*ne02*ne01)/ne01; const int64_t i01 = (ir - i03*ne02*ne01 - i02*ne01); const int64_t i13 = i03 % ne13; const int64_t i12 = i02 % ne12; const int64_t i11 = i01 % ne11; float * dst_ptr = (float *) ((char *) dst->data + i03*nb3 + i02*nb2 + i01*nb1 ); float * src0_ptr = (float *) ((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01); for (int i0 = 0; i0 < ne0; i0++) { float * src1_ptr = (float *) ((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11 + i0*nb10); dst_ptr[i0] = src0_ptr[i0] + *src1_ptr; } } } } static void ggml_compute_forward_add_f16_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { GGML_ASSERT(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } const int ith = params->ith; const int nth = params->nth; const int nr = ggml_nrows(src0); GGML_TENSOR_BINARY_OP_LOCALS; GGML_ASSERT(src0->type == GGML_TYPE_F16); GGML_ASSERT(src1->type == GGML_TYPE_F32); GGML_ASSERT(dst->type == GGML_TYPE_F16); GGML_ASSERT( nb0 == sizeof(ggml_fp16_t)); GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); if (nb10 == sizeof(float)) { for (int ir = ir0; ir < ir1; ++ir) { // src0, src1 and dst are same shape => same indices const int i3 = ir/(ne2*ne1); const int i2 = (ir - i3*ne2*ne1)/ne1; const int i1 = (ir - i3*ne2*ne1 - i2*ne1); ggml_fp16_t * dst_ptr = (ggml_fp16_t *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1); ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01); float * src1_ptr = (float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11); for (int i = 0; i < ne0; i++) { dst_ptr[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(src0_ptr[i]) + src1_ptr[i]); } } } else { // src1 is not contiguous GGML_ASSERT(false); } } static void ggml_compute_forward_add_f16_f16( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { GGML_ASSERT(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } const int ith = params->ith; const int nth = params->nth; const int nr = ggml_nrows(src0); GGML_TENSOR_BINARY_OP_LOCALS; GGML_ASSERT(src0->type == GGML_TYPE_F16); GGML_ASSERT(src1->type == GGML_TYPE_F16); GGML_ASSERT(dst->type == GGML_TYPE_F16); GGML_ASSERT( nb0 == sizeof(ggml_fp16_t)); GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); if (nb10 == sizeof(ggml_fp16_t)) { for (int ir = ir0; ir < ir1; ++ir) { // src0, src1 and dst are same shape => same indices const int i3 = ir/(ne2*ne1); const int i2 = (ir - i3*ne2*ne1)/ne1; const int i1 = (ir - i3*ne2*ne1 - i2*ne1); ggml_fp16_t * dst_ptr = (ggml_fp16_t *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1); ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01); ggml_fp16_t * src1_ptr = (ggml_fp16_t *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11); for (int i = 0; i < ne0; i++) { dst_ptr[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(src0_ptr[i]) + GGML_FP16_TO_FP32(src1_ptr[i])); } } } else { // src1 is not contiguous GGML_ASSERT(false); } } static void ggml_compute_forward_add_q_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { GGML_ASSERT(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } const int nr = ggml_nrows(src0); GGML_TENSOR_BINARY_OP_LOCALS; const int ith = params->ith; const int nth = params->nth; const enum ggml_type type = src0->type; ggml_to_float_t const dequantize_row_q = type_traits[type].to_float; ggml_from_float_t const quantize_row_q = type_traits[type].from_float; // we don't support permuted src0 or src1 GGML_ASSERT(nb00 == GGML_TYPE_SIZE[type]); GGML_ASSERT(nb10 == sizeof(float)); // dst cannot be transposed or permuted GGML_ASSERT(nb0 <= nb1); GGML_ASSERT(nb1 <= nb2); GGML_ASSERT(nb2 <= nb3); GGML_ASSERT(ggml_is_quantized(src0->type)); GGML_ASSERT(dst->type == src0->type); GGML_ASSERT(src1->type == GGML_TYPE_F32); // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); float * wdata = (float *) params->wdata + (ne00 + CACHE_LINE_SIZE_F32) * ith; for (int ir = ir0; ir < ir1; ++ir) { // src0 indices const int i03 = ir/(ne02*ne01); const int i02 = (ir - i03*ne02*ne01)/ne01; const int i01 = (ir - i03*ne02*ne01 - i02*ne01); // src1 and dst are same shape as src0 => same indices const int i13 = i03; const int i12 = i02; const int i11 = i01; const int i3 = i03; const int i2 = i02; const int i1 = i01; void * src0_row = (void *) ((char *) src0->data + (i01*nb01 + i02*nb02 + i03*nb03)); float * src1_row = (float *)((char *) src1->data + (i11*nb11 + i12*nb12 + i13*nb13)); void * dst_row = (void *) ((char *) dst->data + ( i1*nb1 + i2*nb2 + i3*nb3)); assert(ne00 % 32 == 0); // unquantize row from src0 to temp buffer dequantize_row_q(src0_row, wdata, ne00); // add src1 ggml_vec_acc_f32(ne00, wdata, src1_row); // quantize row to dst quantize_row_q(wdata, dst_row, ne00); } } static void ggml_compute_forward_add( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_add_f32(params, src0, src1, dst); } break; case GGML_TYPE_F16: { if (src1->type == GGML_TYPE_F16) { ggml_compute_forward_add_f16_f16(params, src0, src1, dst); } else if (src1->type == GGML_TYPE_F32) { ggml_compute_forward_add_f16_f32(params, src0, src1, dst); } else { GGML_ASSERT(false); } } break; case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_1: case GGML_TYPE_Q5_0: case GGML_TYPE_Q5_1: case GGML_TYPE_Q8_0: case GGML_TYPE_Q2_K: case GGML_TYPE_Q3_K: case GGML_TYPE_Q4_K: case GGML_TYPE_Q5_K: case GGML_TYPE_Q6_K: { ggml_compute_forward_add_q_f32(params, src0, src1, dst); } break; default: { GGML_ASSERT(false); } break; } } // ggml_compute_forward_add1 static void ggml_compute_forward_add1_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { GGML_ASSERT(ggml_are_same_shape(src0, dst)); GGML_ASSERT(ggml_is_scalar(src1)); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } const int ith = params->ith; const int nth = params->nth; const int nr = ggml_nrows(src0); GGML_TENSOR_UNARY_OP_LOCALS; GGML_ASSERT( nb0 == sizeof(float)); GGML_ASSERT(nb00 == sizeof(float)); // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); for (int ir = ir0; ir < ir1; ++ir) { // src0 and dst are same shape => same indices const int i3 = ir/(ne2*ne1); const int i2 = (ir - i3*ne2*ne1)/ne1; const int i1 = (ir - i3*ne2*ne1 - i2*ne1); #ifdef GGML_USE_ACCELERATE UNUSED(ggml_vec_add1_f32); vDSP_vadd( (float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01), 1, (float *) ((char *) src1->data), 0, (float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 ), 1, ne0); #else ggml_vec_add1_f32(ne0, (float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 ), (float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01), *(float *) src1->data); #endif } } static void ggml_compute_forward_add1_f16_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { GGML_ASSERT(ggml_are_same_shape(src0, dst)); GGML_ASSERT(ggml_is_scalar(src1)); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } // scalar to add const float v = *(float *) src1->data; const int ith = params->ith; const int nth = params->nth; const int nr = ggml_nrows(src0); GGML_TENSOR_UNARY_OP_LOCALS; GGML_ASSERT(src0->type == GGML_TYPE_F16); GGML_ASSERT(src1->type == GGML_TYPE_F32); GGML_ASSERT(dst->type == GGML_TYPE_F16); GGML_ASSERT( nb0 == sizeof(ggml_fp16_t)); GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); for (int ir = ir0; ir < ir1; ++ir) { // src0 and dst are same shape => same indices const int i3 = ir/(ne2*ne1); const int i2 = (ir - i3*ne2*ne1)/ne1; const int i1 = (ir - i3*ne2*ne1 - i2*ne1); ggml_fp16_t * dst_ptr = (ggml_fp16_t *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 ); ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01); for (int i = 0; i < ne0; i++) { dst_ptr[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(src0_ptr[i]) + v); } } } static void ggml_compute_forward_add1_f16_f16( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { GGML_ASSERT(ggml_are_same_shape(src0, dst)); GGML_ASSERT(ggml_is_scalar(src1)); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } // scalar to add const float v = GGML_FP16_TO_FP32(*(ggml_fp16_t *) src1->data); const int ith = params->ith; const int nth = params->nth; const int nr = ggml_nrows(src0); GGML_TENSOR_UNARY_OP_LOCALS; GGML_ASSERT(src0->type == GGML_TYPE_F16); GGML_ASSERT(src1->type == GGML_TYPE_F16); GGML_ASSERT(dst->type == GGML_TYPE_F16); GGML_ASSERT( nb0 == sizeof(ggml_fp16_t)); GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); for (int ir = ir0; ir < ir1; ++ir) { // src0 and dst are same shape => same indices const int i3 = ir/(ne2*ne1); const int i2 = (ir - i3*ne2*ne1)/ne1; const int i1 = (ir - i3*ne2*ne1 - i2*ne1); ggml_fp16_t * dst_ptr = (ggml_fp16_t *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 ); ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01); for (int i = 0; i < ne0; i++) { dst_ptr[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(src0_ptr[i]) + v); } } } static void ggml_compute_forward_add1_q_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { GGML_ASSERT(ggml_are_same_shape(src0, dst)); GGML_ASSERT(ggml_is_scalar(src1)); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } // scalar to add const float v = *(float *) src1->data; const int ith = params->ith; const int nth = params->nth; const int nr = ggml_nrows(src0); GGML_TENSOR_UNARY_OP_LOCALS; const enum ggml_type type = src0->type; ggml_to_float_t const dequantize_row_q = type_traits[type].to_float; ggml_from_float_t const quantize_row_q = type_traits[type].from_float; // we don't support permuted src0 GGML_ASSERT(nb00 == GGML_TYPE_SIZE[type]); // dst cannot be transposed or permuted GGML_ASSERT(nb0 <= nb1); GGML_ASSERT(nb1 <= nb2); GGML_ASSERT(nb2 <= nb3); GGML_ASSERT(ggml_is_quantized(src0->type)); GGML_ASSERT(dst->type == src0->type); GGML_ASSERT(src1->type == GGML_TYPE_F32); // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); float * wdata = (float *) params->wdata + (ne0 + CACHE_LINE_SIZE_F32) * ith; for (int ir = ir0; ir < ir1; ++ir) { // src0 and dst are same shape => same indices const int i3 = ir/(ne2*ne1); const int i2 = (ir - i3*ne2*ne1)/ne1; const int i1 = (ir - i3*ne2*ne1 - i2*ne1); void * src0_row = (void *) ((char *) src0->data + (i1*nb01 + i2*nb02 + i3*nb03)); void * dst_row = (void *) ((char *) dst->data + (i1*nb1 + i2*nb2 + i3*nb0 )); assert(ne0 % 32 == 0); // unquantize row from src0 to temp buffer dequantize_row_q(src0_row, wdata, ne0); // add src1 ggml_vec_acc1_f32(ne0, wdata, v); // quantize row to dst quantize_row_q(wdata, dst_row, ne0); } } static void ggml_compute_forward_add1( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_add1_f32(params, src0, src1, dst); } break; case GGML_TYPE_F16: { if (src1->type == GGML_TYPE_F16) { ggml_compute_forward_add1_f16_f16(params, src0, src1, dst); } else if (src1->type == GGML_TYPE_F32) { ggml_compute_forward_add1_f16_f32(params, src0, src1, dst); } else { GGML_ASSERT(false); } } break; case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_1: case GGML_TYPE_Q5_0: case GGML_TYPE_Q5_1: case GGML_TYPE_Q8_0: case GGML_TYPE_Q8_1: case GGML_TYPE_Q2_K: case GGML_TYPE_Q3_K: case GGML_TYPE_Q4_K: case GGML_TYPE_Q5_K: case GGML_TYPE_Q6_K: { ggml_compute_forward_add1_q_f32(params, src0, src1, dst); } break; default: { GGML_ASSERT(false); } break; } } // ggml_compute_forward_acc static void ggml_compute_forward_acc_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { GGML_ASSERT(ggml_are_same_shape(src0, dst)); GGML_ASSERT(ggml_is_contiguous(dst) && ggml_is_contiguous(src0)); // view src0 and dst with these strides and data offset inbytes during acc // nb0 is implicitely element_size because src0 and dst are contiguous size_t nb1 = ((int32_t *) dst->op_params)[0]; size_t nb2 = ((int32_t *) dst->op_params)[1]; size_t nb3 = ((int32_t *) dst->op_params)[2]; size_t offset = ((int32_t *) dst->op_params)[3]; bool inplace = (bool) ((int32_t *) dst->op_params)[4]; if (!inplace && (params->type == GGML_TASK_INIT)) { // memcpy needs to be synchronized across threads to avoid race conditions. // => do it in INIT phase memcpy( ((char *) dst->data), ((char *) src0->data), ggml_nbytes(dst)); } if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } const int ith = params->ith; const int nth = params->nth; const int nr = ggml_nrows(src1); const int nc = src1->ne[0]; GGML_TENSOR_LOCALS(int64_t, ne1, src1, ne); GGML_TENSOR_LOCALS(size_t, nb1, src1, nb); // src0 and dst as viewed during acc const size_t nb0 = ggml_element_size(src0); const size_t nb00 = nb0; const size_t nb01 = nb1; const size_t nb02 = nb2; const size_t nb03 = nb3; GGML_ASSERT(offset + (ne10 == 0 ? 0 : ne10-1)*nb0 + (ne11 == 0 ? 0 : ne11-1)*nb1 + (ne12 == 0 ? 0 : ne12-1)*nb2 + (ne13 == 0 ? 0 : ne13-1)*nb3 < ggml_nbytes(dst)); GGML_ASSERT(offset + (ne10 == 0 ? 0 : ne10-1)*nb00 + (ne11 == 0 ? 0 : ne11-1)*nb01 + (ne12 == 0 ? 0 : ne12-1)*nb02 + (ne13 == 0 ? 0 : ne13-1)*nb03 < ggml_nbytes(src0)); GGML_ASSERT(nb10 == sizeof(float)); // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); for (int ir = ir0; ir < ir1; ++ir) { // src0 and dst are viewed with shape of src1 and offset // => same indices const int i3 = ir/(ne12*ne11); const int i2 = (ir - i3*ne12*ne11)/ne11; const int i1 = (ir - i3*ne12*ne11 - i2*ne11); #ifdef GGML_USE_ACCELERATE vDSP_vadd( (float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + offset), 1, (float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11), 1, (float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + offset), 1, nc); #else ggml_vec_add_f32(nc, (float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + offset), (float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + offset), (float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11)); #endif } } static void ggml_compute_forward_acc( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_acc_f32(params, src0, src1, dst); } break; case GGML_TYPE_F16: case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_1: case GGML_TYPE_Q5_0: case GGML_TYPE_Q5_1: case GGML_TYPE_Q8_0: case GGML_TYPE_Q8_1: case GGML_TYPE_Q2_K: case GGML_TYPE_Q3_K: case GGML_TYPE_Q4_K: case GGML_TYPE_Q5_K: case GGML_TYPE_Q6_K: default: { GGML_ASSERT(false); } break; } } // ggml_compute_forward_sub static void ggml_compute_forward_sub_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { assert(params->ith == 0); assert(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } const int nr = ggml_nrows(src0); GGML_TENSOR_BINARY_OP_LOCALS; GGML_ASSERT( nb0 == sizeof(float)); GGML_ASSERT(nb00 == sizeof(float)); if (nb10 == sizeof(float)) { for (int ir = 0; ir < nr; ++ir) { // src0, src1 and dst are same shape => same indices const int i3 = ir/(ne2*ne1); const int i2 = (ir - i3*ne2*ne1)/ne1; const int i1 = (ir - i3*ne2*ne1 - i2*ne1); #ifdef GGML_USE_ACCELERATE vDSP_vsub( (float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11), 1, (float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01), 1, (float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 ), 1, ne0); #else ggml_vec_sub_f32(ne0, (float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 ), (float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01), (float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11)); #endif // } // } } } else { // src1 is not contiguous for (int ir = 0; ir < nr; ++ir) { // src0, src1 and dst are same shape => same indices const int i3 = ir/(ne2*ne1); const int i2 = (ir - i3*ne2*ne1)/ne1; const int i1 = (ir - i3*ne2*ne1 - i2*ne1); float * dst_ptr = (float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 ); float * src0_ptr = (float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01); for (int i0 = 0; i0 < ne0; i0++) { float * src1_ptr = (float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11 + i0*nb10); dst_ptr[i0] = src0_ptr[i0] - *src1_ptr; } } } } static void ggml_compute_forward_sub( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_sub_f32(params, src0, src1, dst); } break; default: { GGML_ASSERT(false); } break; } } // ggml_compute_forward_mul static void ggml_compute_forward_mul_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { GGML_ASSERT(ggml_can_repeat_rows(src1, src0) && ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } const int ith = params->ith; const int nth = params->nth; #ifdef GGML_USE_CLBLAST if (src1->backend == GGML_BACKEND_GPU) { if (ith == 0) { ggml_cl_mul(src0, src1, dst); } return; } #endif const int64_t nr = ggml_nrows(src0); GGML_TENSOR_BINARY_OP_LOCALS; GGML_ASSERT( nb0 == sizeof(float)); GGML_ASSERT(nb00 == sizeof(float)); GGML_ASSERT(ne00 == ne10); if (nb10 == sizeof(float)) { for (int64_t ir = ith; ir < nr; ir += nth) { // src0 and dst are same shape => same indices const int64_t i03 = ir/(ne02*ne01); const int64_t i02 = (ir - i03*ne02*ne01)/ne01; const int64_t i01 = (ir - i03*ne02*ne01 - i02*ne01); const int64_t i13 = i03 % ne13; const int64_t i12 = i02 % ne12; const int64_t i11 = i01 % ne11; float * dst_ptr = (float *) ((char *) dst->data + i03*nb3 + i02*nb2 + i01*nb1 ); float * src0_ptr = (float *) ((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01); float * src1_ptr = (float *) ((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11); #ifdef GGML_USE_ACCELERATE UNUSED(ggml_vec_mul_f32); vDSP_vmul( src0_ptr, 1, src1_ptr, 1, dst_ptr, 1, ne00); #else ggml_vec_mul_f32(ne00, dst_ptr, src0_ptr, src1_ptr); #endif // } // } } } else { // src1 is not contiguous for (int64_t ir = ith; ir < nr; ir += nth) { // src0 and dst are same shape => same indices // src1 is broadcastable across src0 and dst in i1, i2, i3 const int64_t i03 = ir/(ne02*ne01); const int64_t i02 = (ir - i03*ne02*ne01)/ne01; const int64_t i01 = (ir - i03*ne02*ne01 - i02*ne01); const int64_t i13 = i03 % ne13; const int64_t i12 = i02 % ne12; const int64_t i11 = i01 % ne11; float * dst_ptr = (float *) ((char *) dst->data + i03*nb3 + i02*nb2 + i01*nb1 ); float * src0_ptr = (float *) ((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01); for (int64_t i0 = 0; i0 < ne00; i0++) { float * src1_ptr = (float *) ((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11 + i0*nb10); dst_ptr[i0] = src0_ptr[i0] * (*src1_ptr); } } } } static void ggml_compute_forward_mul( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_mul_f32(params, src0, src1, dst); } break; default: { GGML_ASSERT(false); } break; } } // ggml_compute_forward_div static void ggml_compute_forward_div_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { assert(params->ith == 0); assert(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } const int nr = ggml_nrows(src0); GGML_TENSOR_BINARY_OP_LOCALS; GGML_ASSERT( nb0 == sizeof(float)); GGML_ASSERT(nb00 == sizeof(float)); if (nb10 == sizeof(float)) { for (int ir = 0; ir < nr; ++ir) { // src0, src1 and dst are same shape => same indices const int i3 = ir/(ne2*ne1); const int i2 = (ir - i3*ne2*ne1)/ne1; const int i1 = (ir - i3*ne2*ne1 - i2*ne1); #ifdef GGML_USE_ACCELERATE vDSP_vdiv( (float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11), 1, (float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01), 1, (float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 ), 1, ne0); #else ggml_vec_div_f32(ne0, (float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 ), (float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01), (float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11)); #endif // } // } } } else { // src1 is not contiguous for (int ir = 0; ir < nr; ++ir) { // src0, src1 and dst are same shape => same indices const int i3 = ir/(ne2*ne1); const int i2 = (ir - i3*ne2*ne1)/ne1; const int i1 = (ir - i3*ne2*ne1 - i2*ne1); float * dst_ptr = (float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 ); float * src0_ptr = (float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01); for (int i0 = 0; i0 < ne0; i0++) { float * src1_ptr = (float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11 + i0*nb10); dst_ptr[i0] = src0_ptr[i0] / (*src1_ptr); } } } } static void ggml_compute_forward_div( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_div_f32(params, src0, src1, dst); } break; default: { GGML_ASSERT(false); } break; } } // ggml_compute_forward_sqr static void ggml_compute_forward_sqr_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { assert(params->ith == 0); assert(ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } const int n = ggml_nrows(src0); const int nc = src0->ne[0]; assert( dst->nb[0] == sizeof(float)); assert(src0->nb[0] == sizeof(float)); for (int i = 0; i < n; i++) { ggml_vec_sqr_f32(nc, (float *) ((char *) dst->data + i*( dst->nb[1])), (float *) ((char *) src0->data + i*(src0->nb[1]))); } } static void ggml_compute_forward_sqr( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_sqr_f32(params, src0, dst); } break; default: { GGML_ASSERT(false); } break; } } // ggml_compute_forward_sqrt static void ggml_compute_forward_sqrt_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { assert(params->ith == 0); assert(ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } const int n = ggml_nrows(src0); const int nc = src0->ne[0]; assert( dst->nb[0] == sizeof(float)); assert(src0->nb[0] == sizeof(float)); for (int i = 0; i < n; i++) { ggml_vec_sqrt_f32(nc, (float *) ((char *) dst->data + i*( dst->nb[1])), (float *) ((char *) src0->data + i*(src0->nb[1]))); } } static void ggml_compute_forward_sqrt( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_sqrt_f32(params, src0, dst); } break; default: { GGML_ASSERT(false); } break; } } // ggml_compute_forward_log static void ggml_compute_forward_log_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { GGML_ASSERT(params->ith == 0); GGML_ASSERT(ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } const int n = ggml_nrows(src0); const int nc = src0->ne[0]; GGML_ASSERT( dst->nb[0] == sizeof(float)); GGML_ASSERT(src0->nb[0] == sizeof(float)); for (int i = 0; i < n; i++) { ggml_vec_log_f32(nc, (float *) ((char *) dst->data + i*( dst->nb[1])), (float *) ((char *) src0->data + i*(src0->nb[1]))); } } static void ggml_compute_forward_log( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_log_f32(params, src0, dst); } break; default: { GGML_ASSERT(false); } break; } } // ggml_compute_forward_sum static void ggml_compute_forward_sum_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { assert(params->ith == 0); assert(ggml_is_scalar(dst)); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } assert(ggml_is_scalar(dst)); assert(src0->nb[0] == sizeof(float)); GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne); GGML_TENSOR_LOCALS(size_t, nb0, src0, nb); ggml_float sum = 0; ggml_float row_sum = 0; for (int64_t i03 = 0; i03 < ne03; i03++) { for (int64_t i02 = 0; i02 < ne02; i02++) { for (int64_t i01 = 0; i01 < ne01; i01++) { ggml_vec_sum_f32_ggf(ne00, &row_sum, (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03)); sum += row_sum; } } } ((float *) dst->data)[0] = sum; } static void ggml_compute_forward_sum_f16( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { assert(params->ith == 0); assert(ggml_is_scalar(dst)); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } assert(src0->nb[0] == sizeof(ggml_fp16_t)); GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne); GGML_TENSOR_LOCALS(size_t, nb0, src0, nb); float sum = 0; float row_sum = 0; for (int64_t i03 = 0; i03 < ne03; i03++) { for (int64_t i02 = 0; i02 < ne02; i02++) { for (int64_t i01 = 0; i01 < ne01; i01++) { ggml_vec_sum_f16_ggf(ne00, &row_sum, (ggml_fp16_t *) ((char *) src0->data + i01 * nb01 + i02 * nb02 + i03 * nb03)); sum += row_sum; } } } ((ggml_fp16_t *) dst->data)[0] = GGML_FP32_TO_FP16(sum); } static void ggml_compute_forward_sum( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_sum_f32(params, src0, dst); } break; case GGML_TYPE_F16: { ggml_compute_forward_sum_f16(params, src0, dst); } break; default: { GGML_ASSERT(false); } break; } } // ggml_compute_forward_sum_rows static void ggml_compute_forward_sum_rows_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { GGML_ASSERT(params->ith == 0); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } GGML_ASSERT(src0->nb[0] == sizeof(float)); GGML_ASSERT(dst->nb[0] == sizeof(float)); GGML_TENSOR_UNARY_OP_LOCALS; GGML_ASSERT(ne0 == 1); GGML_ASSERT(ne1 == ne01); GGML_ASSERT(ne2 == ne02); GGML_ASSERT(ne3 == ne03); for (int64_t i3 = 0; i3 < ne03; i3++) { for (int64_t i2 = 0; i2 < ne02; i2++) { for (int64_t i1 = 0; i1 < ne01; i1++) { float * src_row = (float *) ((char *) src0->data + i1*nb01 + i2*nb02 + i3*nb03); float * dst_row = (float *) ((char *) dst->data + i1*nb1 + i2*nb2 + i3*nb3); float row_sum = 0; ggml_vec_sum_f32(ne00, &row_sum, src_row); dst_row[0] = row_sum; } } } } static void ggml_compute_forward_sum_rows( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_sum_rows_f32(params, src0, dst); } break; default: { GGML_ASSERT(false); } break; } } // ggml_compute_forward_mean static void ggml_compute_forward_mean_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { assert(params->ith == 0); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } assert(src0->nb[0] == sizeof(float)); GGML_TENSOR_UNARY_OP_LOCALS; assert(ne0 == 1); assert(ne1 == ne01); assert(ne2 == ne02); assert(ne3 == ne03); UNUSED(ne0); UNUSED(ne1); UNUSED(ne2); UNUSED(ne3); for (int64_t i03 = 0; i03 < ne03; i03++) { for (int64_t i02 = 0; i02 < ne02; i02++) { for (int64_t i01 = 0; i01 < ne01; i01++) { ggml_vec_sum_f32(ne00, (float *) ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3), (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03)); *(float *) ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3) /= (float) ne00; } } } } static void ggml_compute_forward_mean( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_mean_f32(params, src0, dst); } break; default: { GGML_ASSERT(false); } break; } } // ggml_compute_forward_argmax static void ggml_compute_forward_argmax_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { assert(params->ith == 0); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } assert(src0->nb[0] == sizeof(float)); assert(dst->nb[0] == sizeof(float)); const int64_t ne00 = src0->ne[0]; const int64_t ne01 = src0->ne[1]; const size_t nb01 = src0->nb[1]; const size_t nb0 = dst->nb[0]; for (int64_t i1 = 0; i1 < ne01; i1++) { float * src = (float *) ((char *) src0->data + i1*nb01); int32_t * dst_ = (int32_t *) ((char *) dst->data + i1*nb0); int v = 0; ggml_vec_argmax_f32(ne00, &v, src); dst_[0] = v; } } static void ggml_compute_forward_argmax( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_argmax_f32(params, src0, dst); } break; default: { GGML_ASSERT(false); } break; } } // ggml_compute_forward_repeat static void ggml_compute_forward_repeat_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { GGML_ASSERT(params->ith == 0); GGML_ASSERT(ggml_can_repeat(src0, dst)); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } GGML_TENSOR_UNARY_OP_LOCALS; // guaranteed to be an integer due to the check in ggml_can_repeat const int nr0 = (int)(ne0/ne00); const int nr1 = (int)(ne1/ne01); const int nr2 = (int)(ne2/ne02); const int nr3 = (int)(ne3/ne03); // TODO: support for transposed / permuted tensors GGML_ASSERT(nb0 == sizeof(float)); GGML_ASSERT(nb00 == sizeof(float)); // TODO: maybe this is not optimal? for (int i3 = 0; i3 < nr3; i3++) { for (int k3 = 0; k3 < ne03; k3++) { for (int i2 = 0; i2 < nr2; i2++) { for (int k2 = 0; k2 < ne02; k2++) { for (int i1 = 0; i1 < nr1; i1++) { for (int k1 = 0; k1 < ne01; k1++) { for (int i0 = 0; i0 < nr0; i0++) { ggml_vec_cpy_f32(ne00, (float *) ((char *) dst->data + (i3*ne03 + k3)*nb3 + (i2*ne02 + k2)*nb2 + (i1*ne01 + k1)*nb1 + (i0*ne00)*nb0), (float *) ((char *) src0->data + ( k3)*nb03 + ( k2)*nb02 + ( k1)*nb01)); } } } } } } } } static void ggml_compute_forward_repeat( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_repeat_f32(params, src0, dst); } break; default: { GGML_ASSERT(false); } break; } } // ggml_compute_forward_repeat_back static void ggml_compute_forward_repeat_back_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { GGML_ASSERT(params->ith == 0); GGML_ASSERT(ggml_can_repeat(dst, src0)); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } GGML_TENSOR_UNARY_OP_LOCALS; // guaranteed to be an integer due to the check in ggml_can_repeat const int nr0 = (int)(ne00/ne0); const int nr1 = (int)(ne01/ne1); const int nr2 = (int)(ne02/ne2); const int nr3 = (int)(ne03/ne3); // TODO: support for transposed / permuted tensors GGML_ASSERT(nb0 == sizeof(float)); GGML_ASSERT(nb00 == sizeof(float)); if (ggml_is_contiguous(dst)) { ggml_vec_set_f32(ne0*ne1*ne2*ne3, dst->data, 0); } else { for (int k3 = 0; k3 < ne3; k3++) { for (int k2 = 0; k2 < ne2; k2++) { for (int k1 = 0; k1 < ne1; k1++) { ggml_vec_set_f32(ne0, (float *) ((char *) dst->data + k1*nb1 + k2*nb2 + k3*nb3), 0); } } } } // TODO: maybe this is not optimal? for (int i3 = 0; i3 < nr3; i3++) { for (int k3 = 0; k3 < ne3; k3++) { for (int i2 = 0; i2 < nr2; i2++) { for (int k2 = 0; k2 < ne2; k2++) { for (int i1 = 0; i1 < nr1; i1++) { for (int k1 = 0; k1 < ne1; k1++) { for (int i0 = 0; i0 < nr0; i0++) { ggml_vec_acc_f32(ne0, (float *) ((char *) dst->data + ( k3)*nb3 + ( k2)*nb2 + ( k1)*nb1), (float *) ((char *) src0->data + (i3*ne3 + k3)*nb03 + (i2*ne2 + k2)*nb02 + (i1*ne1 + k1)*nb01 + (i0*ne0)*nb00)); } } } } } } } } static void ggml_compute_forward_repeat_back( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_repeat_back_f32(params, src0, dst); } break; default: { GGML_ASSERT(false); } break; } } // ggml_compute_forward_abs static void ggml_compute_forward_abs_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { assert(params->ith == 0); assert(ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } const int n = ggml_nrows(src0); const int nc = src0->ne[0]; assert(dst->nb[0] == sizeof(float)); assert(src0->nb[0] == sizeof(float)); for (int i = 0; i < n; i++) { ggml_vec_abs_f32(nc, (float *) ((char *) dst->data + i*( dst->nb[1])), (float *) ((char *) src0->data + i*(src0->nb[1]))); } } static void ggml_compute_forward_abs( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_abs_f32(params, src0, dst); } break; default: { GGML_ASSERT(false); } break; } } // ggml_compute_forward_sgn static void ggml_compute_forward_sgn_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { assert(params->ith == 0); assert(ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } const int n = ggml_nrows(src0); const int nc = src0->ne[0]; assert(dst->nb[0] == sizeof(float)); assert(src0->nb[0] == sizeof(float)); for (int i = 0; i < n; i++) { ggml_vec_sgn_f32(nc, (float *) ((char *) dst->data + i*( dst->nb[1])), (float *) ((char *) src0->data + i*(src0->nb[1]))); } } static void ggml_compute_forward_sgn( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_sgn_f32(params, src0, dst); } break; default: { GGML_ASSERT(false); } break; } } // ggml_compute_forward_neg static void ggml_compute_forward_neg_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { assert(params->ith == 0); assert(ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } const int n = ggml_nrows(src0); const int nc = src0->ne[0]; assert(dst->nb[0] == sizeof(float)); assert(src0->nb[0] == sizeof(float)); for (int i = 0; i < n; i++) { ggml_vec_neg_f32(nc, (float *) ((char *) dst->data + i*( dst->nb[1])), (float *) ((char *) src0->data + i*(src0->nb[1]))); } } static void ggml_compute_forward_neg( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_neg_f32(params, src0, dst); } break; default: { GGML_ASSERT(false); } break; } } // ggml_compute_forward_step static void ggml_compute_forward_step_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { assert(params->ith == 0); assert(ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } const int n = ggml_nrows(src0); const int nc = src0->ne[0]; assert(dst->nb[0] == sizeof(float)); assert(src0->nb[0] == sizeof(float)); for (int i = 0; i < n; i++) { ggml_vec_step_f32(nc, (float *) ((char *) dst->data + i*( dst->nb[1])), (float *) ((char *) src0->data + i*(src0->nb[1]))); } } static void ggml_compute_forward_step( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_step_f32(params, src0, dst); } break; default: { GGML_ASSERT(false); } break; } } // ggml_compute_forward_tanh static void ggml_compute_forward_tanh_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { assert(params->ith == 0); assert(ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } const int n = ggml_nrows(src0); const int nc = src0->ne[0]; assert(dst->nb[0] == sizeof(float)); assert(src0->nb[0] == sizeof(float)); for (int i = 0; i < n; i++) { ggml_vec_tanh_f32(nc, (float *) ((char *) dst->data + i*( dst->nb[1])), (float *) ((char *) src0->data + i*(src0->nb[1]))); } } static void ggml_compute_forward_tanh( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_tanh_f32(params, src0, dst); } break; default: { GGML_ASSERT(false); } break; } } // ggml_compute_forward_elu static void ggml_compute_forward_elu_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { assert(params->ith == 0); assert(ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } const int n = ggml_nrows(src0); const int nc = src0->ne[0]; assert(dst->nb[0] == sizeof(float)); assert(src0->nb[0] == sizeof(float)); for (int i = 0; i < n; i++) { ggml_vec_elu_f32(nc, (float *) ((char *) dst->data + i*( dst->nb[1])), (float *) ((char *) src0->data + i*(src0->nb[1]))); } } static void ggml_compute_forward_elu( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_elu_f32(params, src0, dst); } break; default: { GGML_ASSERT(false); } break; } } // ggml_compute_forward_relu static void ggml_compute_forward_relu_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { assert(params->ith == 0); assert(ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } const int n = ggml_nrows(src0); const int nc = src0->ne[0]; assert(dst->nb[0] == sizeof(float)); assert(src0->nb[0] == sizeof(float)); for (int i = 0; i < n; i++) { ggml_vec_relu_f32(nc, (float *) ((char *) dst->data + i*( dst->nb[1])), (float *) ((char *) src0->data + i*(src0->nb[1]))); } } static void ggml_compute_forward_relu( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_relu_f32(params, src0, dst); } break; default: { GGML_ASSERT(false); } break; } } // ggml_compute_forward_gelu static void ggml_compute_forward_gelu_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { GGML_ASSERT(ggml_is_contiguous_except_dim_1(src0)); GGML_ASSERT(ggml_is_contiguous_except_dim_1(dst)); GGML_ASSERT(ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } const int ith = params->ith; const int nth = params->nth; const int nc = src0->ne[0]; const int nr = ggml_nrows(src0); // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); for (int i1 = ir0; i1 < ir1; i1++) { ggml_vec_gelu_f32(nc, (float *) ((char *) dst->data + i1*( dst->nb[1])), (float *) ((char *) src0->data + i1*(src0->nb[1]))); #ifndef NDEBUG for (int k = 0; k < nc; k++) { const float x = ((float *) ((char *) dst->data + i1*( dst->nb[1])))[k]; UNUSED(x); assert(!isnan(x)); assert(!isinf(x)); } #endif } } static void ggml_compute_forward_gelu( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_gelu_f32(params, src0, dst); } break; default: { GGML_ASSERT(false); } break; } } // ggml_compute_forward_gelu_quick static void ggml_compute_forward_gelu_quick_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { GGML_ASSERT(ggml_is_contiguous_except_dim_1(src0)); GGML_ASSERT(ggml_is_contiguous_except_dim_1(dst)); GGML_ASSERT(ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } const int ith = params->ith; const int nth = params->nth; const int nc = src0->ne[0]; const int nr = ggml_nrows(src0); // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); for (int i1 = ir0; i1 < ir1; i1++) { ggml_vec_gelu_quick_f32(nc, (float *) ((char *) dst->data + i1*( dst->nb[1])), (float *) ((char *) src0->data + i1*(src0->nb[1]))); #ifndef NDEBUG for (int k = 0; k < nc; k++) { const float x = ((float *) ((char *) dst->data + i1*( dst->nb[1])))[k]; UNUSED(x); assert(!isnan(x)); assert(!isinf(x)); } #endif } } static void ggml_compute_forward_gelu_quick( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_gelu_quick_f32(params, src0, dst); } break; default: { GGML_ASSERT(false); } break; } } // ggml_compute_forward_silu static void ggml_compute_forward_silu_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { GGML_ASSERT(ggml_is_contiguous_except_dim_1(src0)); GGML_ASSERT(ggml_is_contiguous_except_dim_1(dst)); GGML_ASSERT(ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } const int ith = params->ith; const int nth = params->nth; const int nc = src0->ne[0]; const int nr = ggml_nrows(src0); // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); for (int i1 = ir0; i1 < ir1; i1++) { ggml_vec_silu_f32(nc, (float *) ((char *) dst->data + i1*( dst->nb[1])), (float *) ((char *) src0->data + i1*(src0->nb[1]))); #ifndef NDEBUG for (int k = 0; k < nc; k++) { const float x = ((float *) ((char *) dst->data + i1*( dst->nb[1])))[k]; UNUSED(x); assert(!isnan(x)); assert(!isinf(x)); } #endif } } static void ggml_compute_forward_silu( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_silu_f32(params, src0, dst); } break; default: { GGML_ASSERT(false); } break; } } // ggml_compute_forward_silu_back static void ggml_compute_forward_silu_back_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * grad, struct ggml_tensor * dst) { GGML_ASSERT(ggml_is_contiguous_except_dim_1(grad)); GGML_ASSERT(ggml_is_contiguous_except_dim_1(src0)); GGML_ASSERT(ggml_is_contiguous_except_dim_1(dst)); GGML_ASSERT(ggml_are_same_shape(src0, dst)); GGML_ASSERT(ggml_are_same_shape(src0, grad)); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } const int ith = params->ith; const int nth = params->nth; const int nc = src0->ne[0]; const int nr = ggml_nrows(src0); // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); for (int i1 = ir0; i1 < ir1; i1++) { ggml_vec_silu_backward_f32(nc, (float *) ((char *) dst->data + i1*( dst->nb[1])), (float *) ((char *) src0->data + i1*(src0->nb[1])), (float *) ((char *) grad->data + i1*(grad->nb[1]))); #ifndef NDEBUG for (int k = 0; k < nc; k++) { const float x = ((float *) ((char *) dst->data + i1*( dst->nb[1])))[k]; UNUSED(x); assert(!isnan(x)); assert(!isinf(x)); } #endif } } static void ggml_compute_forward_silu_back( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * grad, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_silu_back_f32(params, src0, grad, dst); } break; default: { GGML_ASSERT(false); } break; } } // ggml_compute_forward_norm static void ggml_compute_forward_norm_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { GGML_ASSERT(ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } GGML_ASSERT(src0->nb[0] == sizeof(float)); const int ith = params->ith; const int nth = params->nth; GGML_TENSOR_UNARY_OP_LOCALS; 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; GGML_TENSOR_UNARY_OP_LOCALS; float eps; memcpy(&eps, dst->op_params, sizeof(float)); // TODO: optimize for (int64_t i03 = 0; i03 < ne03; i03++) { for (int64_t i02 = 0; i02 < ne02; i02++) { for (int64_t i01 = ith; i01 < ne01; i01 += nth) { const float * x = (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); ggml_float sum = 0.0; for (int64_t i00 = 0; i00 < ne00; i00++) { sum += (ggml_float)(x[i00] * x[i00]); } const float mean = sum/ne00; float * y = (float *) ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3); memcpy(y, x, ne00 * sizeof(float)); // for (int i00 = 0; i00 < ne00; i00++) { // y[i00] = x[i00]; // } const float scale = 1.0f/sqrtf(mean + eps); ggml_vec_scale_f32(ne00, y, scale); } } } } static void ggml_compute_forward_rms_norm( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_rms_norm_f32(params, src0, dst); } break; default: { GGML_ASSERT(false); } break; } } static void ggml_compute_forward_rms_norm_back_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { GGML_ASSERT(ggml_are_same_shape(src0, dst) && ggml_are_same_shape(src0, src1)); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } GGML_ASSERT(src0->nb[0] == sizeof(float)); const int ith = params->ith; const int nth = params->nth; GGML_TENSOR_BINARY_OP_LOCALS; 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) { // src1 is same shape as src0 => same indices const int64_t i11 = i01; const int64_t i12 = i02; const int64_t i13 = i03; const float * x = (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); const float * dz = (float *) ((char *) src1->data + i11*nb11 + i12*nb12 + i13*nb13); ggml_float sum_xx = 0.0; ggml_float sum_xdz = 0.0; for (int64_t i00 = 0; i00 < ne00; i00++) { sum_xx += (ggml_float)(x[i00] * x[i00]); sum_xdz += (ggml_float)(x[i00] * dz[i00]); } //const float mean = (float)(sum_xx)/ne00; const float mean_eps = (float)(sum_xx)/ne00 + eps; const float sum_eps = (float)(sum_xx) + eps*ne00; //const float mean_xdz = (float)(sum_xdz)/ne00; // we could cache rms from forward pass to improve performance. // to do this implement ggml_rms and compose ggml_rms_norm using ggml_rms. //const float rms = sqrtf(mean_eps); const float rrms = 1.0f / sqrtf(mean_eps); //const float scale = -rrms/(ne00 * mean_eps); // -1/(n*rms**3) { // z = rms_norm(x) // // rms_norm(src0) = // scale( // src0, // div( // 1, // sqrt( // add( // scale( // sum( // sqr( // src0)), // (1.0/N)), // eps)))); // postorder: // ## op args grad // 00 param src0 grad[#00] // 01 const 1 // 02 sqr (#00) grad[#02] // 03 sum (#02) grad[#03] // 04 const 1/N // 05 scale (#03, #04) grad[#05] // 06 const eps // 07 add (#05, #06) grad[#07] // 08 sqrt (#07) grad[#08] // 09 div (#01,#08) grad[#09] // 10 scale (#00,#09) grad[#10] // // backward pass, given grad[#10] // #10: scale // grad[#00] += scale(grad[#10],#09) // grad[#09] += sum(mul(grad[#10],#00)) // #09: div // grad[#08] += neg(mul(grad[#09], div(#09,#08))) // #08: sqrt // grad[#07] += mul(grad[#08], div(0.5, #08)) // #07: add // grad[#05] += grad[#07] // #05: scale // grad[#03] += scale(grad[#05],#04) // #03: sum // grad[#02] += repeat(grad[#03], #02) // #02: // grad[#00] += scale(mul(#00, grad[#02]), 2.0) // // substitute and simplify: // grad[#00] = scale(grad(#10), #09) + scale(mul(#00, grad[#02]), 2.0) // grad[#02] = repeat(grad[#03], #02) // grad[#02] = repeat(scale(grad[#05],#04), #02) // grad[#02] = repeat(scale(grad[#07],#04), #02) // grad[#02] = repeat(scale(mul(grad[#08], div(0.5, #08)),#04), #02) // grad[#02] = repeat(scale(mul(neg(mul(grad[#09], div(#09,#08))), div(0.5, #08)),#04), #02) // grad[#02] = repeat(scale(mul(neg(mul(sum(mul(grad[#10],#00)), div(#09,#08))), div(0.5, #08)),#04), #02) // grad[#02] = repeat(-(sum(mul(grad[#10],#00)) * div(#09,#08) * div(0.5, #08) * (1/N)), #02) // grad[#02] = repeat(-(sum(mul(grad[#10],#00)) * div(div(#01,#08),#08) * div(0.5, #08) * (1/N)), #02) // grad[#02] = repeat(-(sum(mul(grad[#10],#00)) * div(1,#08*#08) * div(0.5, #08) * (1/N)), #02) // grad[#02] = repeat(-(sum(mul(grad[#10],#00)) * div(1,#07) * div(0.5, #08) * (1/N)), #02) // grad[#00] = scale(grad(#10), #09) + scale(mul(#00, grad[#02]), 2.0) // grad[#00] = scale(grad(#10), #09) + scale(mul(#00, repeat(-(sum(mul(grad[#10],#00)) * div(1,#07) * div(0.5, #08) * (1/N)), #02)), 2.0) // grad[#00] = scale(grad(#10), #09) + scale(scale(#00, -(sum(mul(grad[#10],#00)) * div(1,#07) * div(0.5, #08) * (1/N))), 2.0) // grad[#00] = scale(grad(#10), #09) + scale(#00, -(sum(mul(grad[#10],#00)) * div(1,#07) * div(1,#08) * (1/N))) // grad[#00] = scale(grad(#10), #09) + scale(#00, sum(mul(grad[#10],#00)) * div(1,#07*#08) * (-1/N)) // grad[#00] = scale(grad(#10), #09) + scale(#00, sum(mul(grad[#10],#00)) * div(1,#07*#08) * (-1/N)) // grad[#00] = scale(grad(#10), #09) + scale(#00, sum(mul(grad[#10],#00)) * div(1,mean_eps*rms) * (-1/N)) // grad[#00] = scale(grad(#10), #09) + scale(#00, sum(mul(grad[#10],#00)) * div(-1,rms*N*mean_eps)) // grad[#00] = scale(grad(#10), #09) + scale(#00, sum(mul(grad[#10],#00)) * div(-1,rms*N*(sum_xx/N+eps))) // grad[#00] = scale(grad(#10), #09) + scale(#00, sum(mul(grad[#10],#00)) * div(-1,rms*N*sum_xx+rms*N*eps)) // grad[#00] = scale(dz, rrms) + scale(x, sum(mul(dz,x)) * div(-1,rms*N*mean_eps)) // grad[#00] = scale(dz, rrms) + scale(x, sum_xdz * div(-1,rms*N*mean_eps)) // a = b*c + d*e // a = b*c*f/f + d*e*f/f // a = (b*c*f + d*e*f)*(1/f) // a = (b*c*(1/c) + d*e*(1/c))*(1/(1/c)) // a = (b + d*e/c)*c // b = dz, c = rrms, d = x, e = sum_xdz * div(-1,rms*N*mean_eps) // a = (dz + x*sum_xdz * div(-1,rms*N*mean_eps)/rrms)*rrms // a = (dz + x*sum_xdz * div(-1,rms*N*mean_eps)*rms)*rrms // a = (dz + x*sum_xdz * div(-rms,rms*N*mean_eps))*rrms // a = (dz + x*sum_xdz * div(-1,N*mean_eps))*rrms // a = (dz + x*div(-sum_xdz,N*mean_eps))*rrms // a = (dz + x*div(-mean_xdz,mean_eps))*rrms // grad[#00] = scale(dz + scale(x, div(-mean_xdz,mean_eps)),rrms) // grad[#00] = scale(dz + scale(x, -mean_xdz/mean_eps),rrms) // dx = scale(dz + scale(x, -mean_xdz/mean_eps),rrms) } // dx = scale(dz + scale(x, -mean_xdz/mean_eps),rrms) // post-order: // dx := x // dx := scale(dx,-mean_xdz/mean_eps) // dx := add(dx, dz) // dx := scale(dx, rrms) float * dx = (float *) ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3); ggml_vec_cpy_f32 (ne00, dx, x); // ggml_vec_scale_f32(ne00, dx, -mean_xdz/mean_eps); ggml_vec_scale_f32(ne00, dx, (float)(-sum_xdz)/sum_eps); ggml_vec_acc_f32 (ne00, dx, dz); ggml_vec_scale_f32(ne00, dx, rrms); } } } } static void ggml_compute_forward_rms_norm_back( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_rms_norm_back_f32(params, src0, src1, dst); } break; default: { GGML_ASSERT(false); } break; } } // ggml_compute_forward_mul_mat #if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) // helper function to determine if it is better to use BLAS or not // for large matrices, BLAS is faster static bool ggml_compute_forward_mul_mat_use_blas( const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { //const int64_t ne00 = src0->ne[0]; //const int64_t ne01 = src0->ne[1]; const int64_t ne10 = src1->ne[0]; const int64_t ne0 = dst->ne[0]; const int64_t ne1 = dst->ne[1]; // TODO: find the optimal values for these if (ggml_is_contiguous(src0) && ggml_is_contiguous(src1) && (ne0 >= 32 && ne1 >= 32 && ne10 >= 32)) { /*printf("BLAS: %d %d %d %d %d\n", ne0, ne1, ne10, ne00, ne01);*/ return true; } return false; } #endif static void ggml_compute_forward_mul_mat( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { int64_t t0 = ggml_perf_time_us(); UNUSED(t0); GGML_TENSOR_BINARY_OP_LOCALS; const int ith = params->ith; const int nth = params->nth; const enum ggml_type type = src0->type; const bool src1_cont = ggml_is_contiguous(src1); ggml_vec_dot_t const vec_dot = type_traits[type].vec_dot; enum ggml_type const vec_dot_type = type_traits[type].vec_dot_type; ggml_from_float_t const from_float_to_vec_dot = type_traits[vec_dot_type].from_float; GGML_ASSERT(ne0 == ne01); GGML_ASSERT(ne1 == ne11); GGML_ASSERT(ne2 == ne12); GGML_ASSERT(ne3 == ne13); // we don't support permuted src0 or src1 GGML_ASSERT(nb00 == GGML_TYPE_SIZE[type]); GGML_ASSERT(nb10 == sizeof(float)); // dst cannot be transposed or permuted GGML_ASSERT(nb0 == sizeof(float)); GGML_ASSERT(nb0 <= nb1); GGML_ASSERT(nb1 <= nb2); GGML_ASSERT(nb2 <= nb3); // nb01 >= nb00 - src0 is not transposed // compute by src0 rows #if defined(GGML_USE_CLBLAST) if (ggml_cl_can_mul_mat(src0, src1, dst)) { // TODO: handle case when src0 is broadcast-able into src1 across 2nd,3rd dimension // ref: https://github.com/ggerganov/ggml/pull/224 GGML_ASSERT(ne02 == ne12); GGML_ASSERT(ne03 == ne13); if (params->ith == 0 && params->type == GGML_TASK_COMPUTE) { ggml_cl_mul_mat(src0, src1, dst, params->wdata, params->wsize); } return; } #endif #if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) if (ggml_compute_forward_mul_mat_use_blas(src0, src1, dst)) { // TODO: handle case when src0 is broadcast-able into src1 across 2nd,3rd dimension // ref: https://github.com/ggerganov/ggml/pull/224 GGML_ASSERT(ne02 == ne12); GGML_ASSERT(ne03 == ne13); if (params->ith != 0) { return; } if (params->type == GGML_TASK_INIT) { return; } if (params->type == GGML_TASK_FINALIZE) { return; } for (int64_t i03 = 0; i03 < ne03; i03++) { for (int64_t i02 = 0; i02 < ne02; i02++) { const void * x = (char *) src0->data + i03*nb03 + i02*nb02; const float * y = (float *) ((char *) src1->data + i02*nb12 + i03*nb13); float * d = (float *) ((char *) dst->data + i02*nb2 + i03*nb3); if (type != GGML_TYPE_F32) { float * const wdata = params->wdata; ggml_to_float_t const to_float = type_traits[type].to_float; size_t id = 0; for (int64_t i01 = 0; i01 < ne01; ++i01) { to_float((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01, wdata + id, ne00); id += ne00; } assert(id*sizeof(float) <= params->wsize); x = wdata; } cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasTrans, ne11, ne01, ne10, 1.0f, y, ne10, x, ne00, 0.0f, d, ne01); } } //printf("CBLAS = %f ms, %d x %d x %d x %d\n", (ggml_perf_time_us() - t0)/1000.0, ne0, ne1, ne2, ne3); return; } #endif if (params->type == GGML_TASK_INIT) { if (src1->type != vec_dot_type) { char * wdata = params->wdata; const size_t row_size = ne10*GGML_TYPE_SIZE[vec_dot_type]/GGML_BLCK_SIZE[vec_dot_type]; for (int64_t i13 = 0; i13 < ne13; ++i13) { for (int64_t i12 = 0; i12 < ne12; ++i12) { for (int64_t i11 = 0; i11 < ne11; ++i11) { from_float_to_vec_dot((float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11), (void *) wdata, ne10); wdata += row_size; } } } } return; } if (params->type == GGML_TASK_FINALIZE) { return; } const void * wdata = (src1->type == vec_dot_type) ? src1->data : params->wdata; const size_t row_size = ne10*GGML_TYPE_SIZE[vec_dot_type]/GGML_BLCK_SIZE[vec_dot_type]; const int64_t nr0 = ne01; // src0 rows const int64_t nr1 = ne11*ne12*ne13; // src1 rows //printf("nr0 = %lld, nr1 = %lld\n", nr0, nr1); // distribute the thread work across the inner or outer loop based on which one is larger const int64_t nth0 = nr0 > nr1 ? nth : 1; // parallelize by src0 rows const int64_t nth1 = nr0 > nr1 ? 1 : nth; // parallelize by src1 rows const int64_t ith0 = ith % nth0; const int64_t ith1 = ith / nth0; const int64_t dr0 = (nr0 + nth0 - 1)/nth0; const int64_t dr1 = (nr1 + nth1 - 1)/nth1; const int64_t ir010 = dr0*ith0; const int64_t ir011 = MIN(ir010 + dr0, nr0); const int64_t ir110 = dr1*ith1; const int64_t ir111 = MIN(ir110 + dr1, nr1); //printf("ir010 = %6lld, ir011 = %6lld, ir110 = %6lld, ir111 = %6lld\n", ir010, ir011, ir110, ir111); // threads with no work simply yield (not sure if it helps) if (ir010 >= ir011 || ir110 >= ir111) { sched_yield(); return; } assert(ne12 % ne02 == 0); assert(ne13 % ne03 == 0); // broadcast factors const int64_t r2 = ne12/ne02; const int64_t r3 = ne13/ne03; // block-tiling attempt const int64_t blck_0 = 16; const int64_t blck_1 = 16; // attempt to reduce false-sharing (does not seem to make a difference) float tmp[16]; for (int64_t iir1 = ir110; iir1 < ir111; iir1 += blck_1) { for (int64_t iir0 = ir010; iir0 < ir011; iir0 += blck_0) { for (int64_t ir1 = iir1; ir1 < iir1 + blck_1 && ir1 < ir111; ++ir1) { const int64_t i13 = (ir1/(ne12*ne11)); const int64_t i12 = (ir1 - i13*ne12*ne11)/ne11; const int64_t i11 = (ir1 - i13*ne12*ne11 - i12*ne11); // broadcast src0 into src1 const int64_t i03 = i13/r3; const int64_t i02 = i12/r2; const int64_t i1 = i11; const int64_t i2 = i12; const int64_t i3 = i13; const char * src0_row = (const char *) src0->data + (0 + i02*nb02 + i03*nb03); // desc: when src1 is not a contiguous memory block we have to calculate the offset using the strides // if it is, then we have either copied the data to params->wdata and made it contiguous or we are using // the original src1 data pointer, so we should index using the indices directly // TODO: this is a bit of a hack, we should probably have a better way to handle this const char * src1_col = (const char *) wdata + (src1_cont || src1->type != vec_dot_type ? (i11 + i12*ne11 + i13*ne12*ne11)*row_size : (i11*nb11 + i12*nb12 + i13*nb13)); float * dst_col = (float *) ((char *) dst->data + (i1*nb1 + i2*nb2 + i3*nb3)); //for (int64_t ir0 = iir0; ir0 < iir0 + blck_0 && ir0 < ir011; ++ir0) { // vec_dot(ne00, &dst_col[ir0], src0_row + ir0*nb01, src1_col); //} for (int64_t ir0 = iir0; ir0 < iir0 + blck_0 && ir0 < ir011; ++ir0) { vec_dot(ne00, &tmp[ir0 - iir0], src0_row + ir0*nb01, src1_col); } memcpy(&dst_col[iir0], tmp, (MIN(iir0 + blck_0, ir011) - iir0)*sizeof(float)); } } } } // ggml_compute_forward_out_prod static void ggml_compute_forward_out_prod_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { int64_t t0 = ggml_perf_time_us(); UNUSED(t0); GGML_TENSOR_BINARY_OP_LOCALS; const int ith = params->ith; const int nth = params->nth; GGML_ASSERT(ne02 == ne12); GGML_ASSERT(ne03 == ne13); GGML_ASSERT(ne2 == ne12); GGML_ASSERT(ne3 == ne13); // we don't support permuted src0 or src1 GGML_ASSERT(nb00 == sizeof(float)); // dst cannot be transposed or permuted GGML_ASSERT(nb0 == sizeof(float)); // GGML_ASSERT(nb0 <= nb1); // GGML_ASSERT(nb1 <= nb2); // GGML_ASSERT(nb2 <= nb3); GGML_ASSERT(ne0 == ne00); GGML_ASSERT(ne1 == ne10); GGML_ASSERT(ne2 == ne02); GGML_ASSERT(ne3 == ne03); // nb01 >= nb00 - src0 is not transposed // compute by src0 rows // TODO: #if defined(GGML_USE_CUBLAS) ggml_cuda_out_prod // TODO: #if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CLBLAST) if (params->type == GGML_TASK_INIT) { ggml_vec_set_f32(ne0*ne1*ne2*ne3, dst->data, 0); return; } if (params->type == GGML_TASK_FINALIZE) { return; } // parallelize by last three dimensions // total rows in dst const int64_t nr = ne1*ne2*ne3; // rows per thread const int64_t dr = (nr + nth - 1)/nth; // row range for this thread const int64_t ir0 = dr*ith; const int64_t ir1 = MIN(ir0 + dr, nr); // dst[:,:,:,:] = 0 // for i2,i3: // for i1: // for i01: // for i0: // dst[i0,i1,i2,i3] += src0[i0,i01,i2,i3] * src1[i1,i01,i2,i3] for (int64_t ir = ir0; ir < ir1; ++ir) { // dst indices const int64_t i3 = ir/(ne2*ne1); const int64_t i2 = (ir - i3*ne2*ne1)/ne1; const int64_t i1 = (ir - i3*ne2*ne1 - i2*ne1); const int64_t i02 = i2; const int64_t i03 = i3; //const int64_t i10 = i1; const int64_t i12 = i2; const int64_t i13 = i3; for (int64_t i01 = 0; i01 < ne01; ++i01) { const int64_t i11 = i01; float * s0 = (float *) ((char *) src0->data + ( i01*nb01 + i02*nb02 + i03*nb03)); float * s1 = (float *) ((char *) src1->data + (i1*nb10 + i11*nb11 + i12*nb12 + i13*nb13)); float * d = (float *) ((char *) dst->data + ( i1*nb1 + i2*nb2 + i3*nb3)); ggml_vec_mad_f32(ne0, d, s0, *s1); // for (int64_t i0 = 0; i0 < ne0; ++i0) { // d[i0] += s0[i0] * s1[i1]; // } } } //int64_t t1 = ggml_perf_time_us(); //static int64_t acc = 0; //acc += t1 - t0; //if (t1 - t0 > 10) { // printf("\n"); // printf("ne00 = %5d, ne01 = %5d, ne02 = %5d, ne03 = %5d\n", ne00, ne01, ne02, ne03); // printf("nb00 = %5d, nb01 = %5d, nb02 = %5d, nb03 = %5d\n", nb00, nb01, nb02, nb03); // printf("ne10 = %5d, ne11 = %5d, ne12 = %5d, ne13 = %5d\n", ne10, ne11, ne12, ne13); // printf("nb10 = %5d, nb11 = %5d, nb12 = %5d, nb13 = %5d\n", nb10, nb11, nb12, nb13); // printf("XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX task %d/%d: %d us, acc = %d\n", ith, nth, (int) (t1 - t0), (int) acc); //} } static void ggml_compute_forward_out_prod( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_1: case GGML_TYPE_Q5_0: case GGML_TYPE_Q5_1: case GGML_TYPE_Q8_0: case GGML_TYPE_Q8_1: { GGML_ASSERT(false); // todo // ggml_compute_forward_out_prod_q_f32(params, src0, src1, dst); } break; case GGML_TYPE_F16: { GGML_ASSERT(false); // todo // ggml_compute_forward_out_prod_f16_f32(params, src0, src1, dst); } break; case GGML_TYPE_F32: { ggml_compute_forward_out_prod_f32(params, src0, src1, dst); } break; default: { GGML_ASSERT(false); } break; } } // ggml_compute_forward_scale static void ggml_compute_forward_scale_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { GGML_ASSERT(ggml_is_contiguous(src0)); GGML_ASSERT(ggml_is_contiguous(dst)); GGML_ASSERT(ggml_are_same_shape(src0, dst)); GGML_ASSERT(ggml_is_scalar(src1)); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } // scale factor const float v = *(float *) src1->data; const int ith = params->ith; const int nth = params->nth; const int nc = src0->ne[0]; const int nr = ggml_nrows(src0); // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); const size_t nb01 = src0->nb[1]; const size_t nb1 = dst->nb[1]; for (int i1 = ir0; i1 < ir1; i1++) { if (dst->data != src0->data) { // src0 is same shape as dst => same indices memcpy((char *)dst->data + i1*nb1, (char *)src0->data + i1*nb01, nc * sizeof(float)); } ggml_vec_scale_f32(nc, (float *) ((char *) dst->data + i1*nb1), v); } } static void ggml_compute_forward_scale( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_scale_f32(params, src0, src1, dst); } break; default: { GGML_ASSERT(false); } break; } } // ggml_compute_forward_set static void ggml_compute_forward_set_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { GGML_ASSERT(ggml_are_same_shape(src0, dst)); GGML_ASSERT(ggml_is_contiguous(dst) && ggml_is_contiguous(src0)); // view src0 and dst with these strides and data offset inbytes during set // nb0 is implicitely element_size because src0 and dst are contiguous size_t nb1 = ((int32_t *) dst->op_params)[0]; size_t nb2 = ((int32_t *) dst->op_params)[1]; size_t nb3 = ((int32_t *) dst->op_params)[2]; size_t offset = ((int32_t *) dst->op_params)[3]; bool inplace = (bool) ((int32_t *) dst->op_params)[4]; if (!inplace && (params->type == GGML_TASK_INIT)) { // memcpy needs to be synchronized across threads to avoid race conditions. // => do it in INIT phase memcpy( ((char *) dst->data), ((char *) src0->data), ggml_nbytes(dst)); } if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } const int ith = params->ith; const int nth = params->nth; const int nr = ggml_nrows(src1); const int nc = src1->ne[0]; GGML_TENSOR_LOCALS(int64_t, ne1, src1, ne); GGML_TENSOR_LOCALS(size_t, nb1, src1, nb); // src0 and dst as viewed during set const size_t nb0 = ggml_element_size(src0); const int im0 = (ne10 == 0 ? 0 : ne10-1); const int im1 = (ne11 == 0 ? 0 : ne11-1); const int im2 = (ne12 == 0 ? 0 : ne12-1); const int im3 = (ne13 == 0 ? 0 : ne13-1); GGML_ASSERT(offset + im0*nb0 + im1*nb1 + im2*nb2 + im3*nb3 <= ggml_nbytes(dst)); GGML_ASSERT(nb10 == sizeof(float)); // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); for (int ir = ir0; ir < ir1; ++ir) { // src0 and dst are viewed with shape of src1 and offset // => same indices const int i3 = ir/(ne12*ne11); const int i2 = (ir - i3*ne12*ne11)/ne11; const int i1 = (ir - i3*ne12*ne11 - i2*ne11); ggml_vec_cpy_f32(nc, (float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + offset), (float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11)); } } static void ggml_compute_forward_set( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_set_f32(params, src0, src1, dst); } break; case GGML_TYPE_F16: case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_1: case GGML_TYPE_Q5_0: case GGML_TYPE_Q5_1: case GGML_TYPE_Q8_0: case GGML_TYPE_Q8_1: case GGML_TYPE_Q2_K: case GGML_TYPE_Q3_K: case GGML_TYPE_Q4_K: case GGML_TYPE_Q5_K: case GGML_TYPE_Q6_K: default: { GGML_ASSERT(false); } break; } } // ggml_compute_forward_cpy static void ggml_compute_forward_cpy( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { ggml_compute_forward_dup(params, src0, dst); } // ggml_compute_forward_cont static void ggml_compute_forward_cont( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { ggml_compute_forward_dup(params, src0, dst); } // ggml_compute_forward_reshape static void ggml_compute_forward_reshape( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { // NOP UNUSED(params); UNUSED(src0); UNUSED(dst); } // ggml_compute_forward_view static void ggml_compute_forward_view( const struct ggml_compute_params * params, const struct ggml_tensor * src0) { // NOP UNUSED(params); UNUSED(src0); } // ggml_compute_forward_permute static void ggml_compute_forward_permute( const struct ggml_compute_params * params, const struct ggml_tensor * src0) { // NOP UNUSED(params); UNUSED(src0); } // ggml_compute_forward_transpose static void ggml_compute_forward_transpose( const struct ggml_compute_params * params, const struct ggml_tensor * src0) { // NOP UNUSED(params); UNUSED(src0); } // ggml_compute_forward_get_rows static void ggml_compute_forward_get_rows_q( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { assert(params->ith == 0); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } const int nc = src0->ne[0]; const int nr = ggml_nelements(src1); const enum ggml_type type = src0->type; ggml_to_float_t const dequantize_row_q = type_traits[type].to_float; assert( dst->ne[0] == nc); assert( dst->ne[1] == nr); assert(src0->nb[0] == GGML_TYPE_SIZE[type]); for (int i = 0; i < nr; ++i) { const int r = ((int32_t *) src1->data)[i]; dequantize_row_q( (const void *) ((char *) src0->data + r*src0->nb[1]), (float *) ((char *) dst->data + i*dst->nb[1]), nc); } } static void ggml_compute_forward_get_rows_f16( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { assert(params->ith == 0); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } const int nc = src0->ne[0]; const int nr = ggml_nelements(src1); assert( dst->ne[0] == nc); assert( dst->ne[1] == nr); assert(src0->nb[0] == sizeof(ggml_fp16_t)); for (int i = 0; i < nr; ++i) { const int r = ((int32_t *) src1->data)[i]; for (int j = 0; j < nc; ++j) { ggml_fp16_t v = ((ggml_fp16_t *) ((char *) src0->data + r*src0->nb[1]))[j]; ((float *) ((char *) dst->data + i*dst->nb[1]))[j] = GGML_FP16_TO_FP32(v); } } } static void ggml_compute_forward_get_rows_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { assert(params->ith == 0); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } const int nc = src0->ne[0]; const int nr = ggml_nelements(src1); assert( dst->ne[0] == nc); assert( dst->ne[1] == nr); assert(src0->nb[0] == sizeof(float)); for (int i = 0; i < nr; ++i) { const int r = ((int32_t *) src1->data)[i]; ggml_vec_cpy_f32(nc, (float *) ((char *) dst->data + i*dst->nb[1]), (float *) ((char *) src0->data + r*src0->nb[1])); } } static void ggml_compute_forward_get_rows( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_1: case GGML_TYPE_Q5_0: case GGML_TYPE_Q5_1: case GGML_TYPE_Q8_0: case GGML_TYPE_Q8_1: case GGML_TYPE_Q2_K: case GGML_TYPE_Q3_K: case GGML_TYPE_Q4_K: case GGML_TYPE_Q5_K: case GGML_TYPE_Q6_K: { ggml_compute_forward_get_rows_q(params, src0, src1, dst); } break; case GGML_TYPE_F16: { ggml_compute_forward_get_rows_f16(params, src0, src1, dst); } break; case GGML_TYPE_F32: { ggml_compute_forward_get_rows_f32(params, src0, src1, dst); } break; default: { GGML_ASSERT(false); } break; } //static bool first = true; //printf("ne0 = %d, ne1 = %d, ne2 = %d\n", dst->ne[0], dst->ne[1], dst->ne[2]); //if (first) { // first = false; //} else { // for (int k = 0; k < dst->ne[1]; ++k) { // for (int j = 0; j < dst->ne[0]/16; ++j) { // for (int i = 0; i < 16; ++i) { // printf("%8.4f ", ((float *) dst->data)[k*dst->ne[0] + j*16 + i]); // } // printf("\n"); // } // printf("\n"); // } // printf("\n"); // exit(0); //} } // ggml_compute_forward_get_rows_back static void ggml_compute_forward_get_rows_back_f32_f16( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, const struct ggml_tensor * opt0, struct ggml_tensor * dst) { GGML_ASSERT(params->ith == 0); GGML_ASSERT(ggml_are_same_shape(opt0, dst)); GGML_ASSERT(ggml_is_contiguous(opt0)); GGML_ASSERT(ggml_is_contiguous(dst)); ggml_compute_forward_dup_same_cont(params, opt0, dst); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } const int nc = src0->ne[0]; const int nr = ggml_nelements(src1); GGML_ASSERT( dst->ne[0] == nc); GGML_ASSERT(src0->nb[0] == sizeof(ggml_fp16_t)); for (int i = 0; i < nr; ++i) { const int r = ((int32_t *) src1->data)[i]; for (int j = 0; j < nc; ++j) { ggml_fp16_t v = ((ggml_fp16_t *) ((char *) src0->data + i*src0->nb[1]))[j]; ((float *) ((char *) dst->data + r*dst->nb[1]))[j] += GGML_FP16_TO_FP32(v); } } } static void ggml_compute_forward_get_rows_back_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, const struct ggml_tensor * opt0, struct ggml_tensor * dst) { GGML_ASSERT(params->ith == 0); GGML_ASSERT(ggml_are_same_shape(opt0, dst)); GGML_ASSERT(ggml_is_contiguous(opt0)); GGML_ASSERT(ggml_is_contiguous(dst)); // ggml_compute_forward_dup_same_cont(params, opt0, dst); if (params->type == GGML_TASK_INIT) { memset(dst->data, 0, ggml_nbytes(dst)); } if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } const int nc = src0->ne[0]; const int nr = ggml_nelements(src1); GGML_ASSERT( dst->ne[0] == nc); GGML_ASSERT(src0->nb[0] == sizeof(float)); for (int i = 0; i < nr; ++i) { const int r = ((int32_t *) src1->data)[i]; ggml_vec_add_f32(nc, (float *) ((char *) dst->data + r*dst->nb[1]), (float *) ((char *) dst->data + r*dst->nb[1]), (float *) ((char *) src0->data + i*src0->nb[1])); } } static void ggml_compute_forward_get_rows_back( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, const struct ggml_tensor * opt0, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F16: { ggml_compute_forward_get_rows_back_f32_f16(params, src0, src1, opt0, dst); } break; case GGML_TYPE_F32: { ggml_compute_forward_get_rows_back_f32(params, src0, src1, opt0, dst); } break; default: { GGML_ASSERT(false); } break; } //static bool first = true; //printf("ne0 = %d, ne1 = %d, ne2 = %d\n", dst->ne[0], dst->ne[1], dst->ne[2]); //if (first) { // first = false; //} else { // for (int k = 0; k < dst->ne[1]; ++k) { // for (int j = 0; j < dst->ne[0]/16; ++j) { // for (int i = 0; i < 16; ++i) { // printf("%8.4f ", ((float *) dst->data)[k*dst->ne[0] + j*16 + i]); // } // printf("\n"); // } // printf("\n"); // } // printf("\n"); // exit(0); //} } // ggml_compute_forward_diag static void ggml_compute_forward_diag_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { GGML_ASSERT(params->ith == 0); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } // TODO: handle transposed/permuted matrices GGML_TENSOR_UNARY_OP_LOCALS; GGML_ASSERT(ne00 == ne0); GGML_ASSERT(ne00 == ne1); GGML_ASSERT(ne01 == 1); GGML_ASSERT(ne02 == ne2); GGML_ASSERT(ne03 == ne3); GGML_ASSERT(nb00 == sizeof(float)); GGML_ASSERT(nb0 == sizeof(float)); for (int i3 = 0; i3 < ne3; i3++) { for (int i2 = 0; i2 < ne2; i2++) { for (int i1 = 0; i1 < ne1; i1++) { float * d = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1); float * s = (float *)((char *) src0->data + i3*nb03 + i2*nb02); for (int i0 = 0; i0 < i1; i0++) { d[i0] = 0; } d[i1] = s[i1]; for (int i0 = i1+1; i0 < ne0; i0++) { d[i0] = 0; } } } } } static void ggml_compute_forward_diag( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_diag_f32(params, src0, dst); } break; default: { GGML_ASSERT(false); } break; } } // ggml_compute_forward_diag_mask_inf static void ggml_compute_forward_diag_mask_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst, const float value) { const int ith = params->ith; const int nth = params->nth; const int n_past = ((int32_t *) dst->op_params)[0]; const bool inplace = (bool)((int32_t *) dst->op_params)[1]; GGML_ASSERT(n_past >= 0); if (!inplace && (params->type == GGML_TASK_INIT)) { // memcpy needs to be synchronized across threads to avoid race conditions. // => do it in INIT phase GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0)); GGML_ASSERT(ggml_is_contiguous(dst) && ggml_is_contiguous(src0)); memcpy( ((char *) dst->data), ((char *) src0->data), ggml_nbytes(dst)); } if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } // TODO: handle transposed/permuted matrices const int n = ggml_nrows(src0); const int nc = src0->ne[0]; const int nr = src0->ne[1]; const int nz = n/nr; GGML_ASSERT( dst->nb[0] == sizeof(float)); GGML_ASSERT(src0->nb[0] == sizeof(float)); for (int k = 0; k < nz; k++) { for (int j = ith; j < nr; j += nth) { for (int i = n_past; i < nc; i++) { if (i > n_past + j) { *(float *)((char *) dst->data + k*dst->nb[2] + j*dst->nb[1] + i*dst->nb[0]) = value; } } } } } static void ggml_compute_forward_diag_mask_inf( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_diag_mask_f32(params, src0, dst, -INFINITY); } break; default: { GGML_ASSERT(false); } break; } } static void ggml_compute_forward_diag_mask_zero( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_diag_mask_f32(params, src0, dst, 0); } break; default: { GGML_ASSERT(false); } break; } } // ggml_compute_forward_soft_max static void ggml_compute_forward_soft_max_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { GGML_ASSERT(ggml_is_contiguous(src0)); GGML_ASSERT(ggml_is_contiguous(dst)); GGML_ASSERT(ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } // TODO: handle transposed/permuted matrices const int ith = params->ith; const int nth = params->nth; const int nc = src0->ne[0]; const int nr = ggml_nrows(src0); // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); for (int i1 = ir0; i1 < ir1; i1++) { float *sp = (float *)((char *) src0->data + i1*src0->nb[1]); float *dp = (float *)((char *) dst->data + i1*dst->nb[1]); #ifndef NDEBUG for (int i = 0; i < nc; ++i) { //printf("p[%d] = %f\n", i, p[i]); assert(!isnan(sp[i])); } #endif float max = -INFINITY; ggml_vec_max_f32(nc, &max, sp); ggml_float sum = 0.0; uint16_t scvt; for (int i = 0; i < nc; i++) { if (sp[i] == -INFINITY) { dp[i] = 0.0f; } else { // const float val = (sp[i] == -INFINITY) ? 0.0 : exp(sp[i] - max); ggml_fp16_t s = GGML_FP32_TO_FP16(sp[i] - max); memcpy(&scvt, &s, sizeof(scvt)); const float val = GGML_FP16_TO_FP32(table_exp_f16[scvt]); sum += (ggml_float)val; dp[i] = val; } } assert(sum > 0.0); sum = 1.0/sum; ggml_vec_scale_f32(nc, dp, sum); #ifndef NDEBUG for (int i = 0; i < nc; ++i) { assert(!isnan(dp[i])); assert(!isinf(dp[i])); } #endif } } static void ggml_compute_forward_soft_max( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_soft_max_f32(params, src0, dst); } break; default: { GGML_ASSERT(false); } break; } } // ggml_compute_forward_soft_max_back static void ggml_compute_forward_soft_max_back_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { GGML_ASSERT(ggml_is_contiguous(src0)); GGML_ASSERT(ggml_is_contiguous(src1)); GGML_ASSERT(ggml_is_contiguous(dst)); GGML_ASSERT(ggml_are_same_shape(src0, dst)); GGML_ASSERT(ggml_are_same_shape(src1, dst)); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } // TODO: handle transposed/permuted matrices const int ith = params->ith; const int nth = params->nth; const int nc = src0->ne[0]; const int nr = ggml_nrows(src0); // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); for (int i1 = ir0; i1 < ir1; i1++) { float *dy = (float *)((char *) src0->data + i1*src0->nb[1]); float *y = (float *)((char *) src1->data + i1*src1->nb[1]); float *dx = (float *)((char *) dst->data + i1*dst->nb[1]); #ifndef NDEBUG for (int i = 0; i < nc; ++i) { //printf("p[%d] = %f\n", i, p[i]); assert(!isnan(dy[i])); assert(!isnan(y[i])); } #endif // Jii = yi - yi*yi // Jij = -yi*yj // J = diag(y)-y.T*y // dx = J * dy // dxk = sum_i(Jki * dyi) // dxk = sum_i(-yk*yi * dyi) - (-yk*yk)*dyk + (yk - yk*yk)*dyk // dxk = sum_i(-yk*yi * dyi) + yk*dyk // dxk = -yk * sum_i(yi * dyi) + yk*dyk // dxk = -yk * dot(y, dy) + yk*dyk // dxk = yk * (- dot(y, dy) + dyk) // dxk = yk * (dyk - dot(y, dy)) // // post-order: // dot_y_dy := dot(y, dy) // dx := dy // dx := dx - dot_y_dy // dx := dx * y // linear runtime, no additional memory float dot_y_dy = 0; ggml_vec_dot_f32 (nc, &dot_y_dy, y, dy); ggml_vec_cpy_f32 (nc, dx, dy); ggml_vec_acc1_f32(nc, dx, -dot_y_dy); ggml_vec_mul_f32 (nc, dx, dx, y); #ifndef NDEBUG for (int i = 0; i < nc; ++i) { assert(!isnan(dx[i])); assert(!isinf(dx[i])); } #endif } } static void ggml_compute_forward_soft_max_back( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_soft_max_back_f32(params, src0, src1, dst); } break; default: { GGML_ASSERT(false); } break; } } // ggml_compute_forward_alibi static void ggml_compute_forward_alibi_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { assert(params->ith == 0); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } const int n_past = ((int32_t *) dst->op_params)[0]; const int n_head = ((int32_t *) dst->op_params)[1]; float max_bias; memcpy(&max_bias, (int32_t *) dst->op_params + 2, sizeof(float)); assert(n_past >= 0); const int ne0 = src0->ne[0]; // all_seq_len = n_past + ne1 const int ne1 = src0->ne[1]; // seq_len_without_past const int ne2 = src0->ne[2]; // n_head -> this is k //const int ne3 = src0->ne[3]; // 1 -> bsz const int n = ggml_nrows(src0); const int ne2_ne3 = n/ne1; // ne2*ne3 const int nb0 = src0->nb[0]; const int nb1 = src0->nb[1]; const int nb2 = src0->nb[2]; //const int nb3 = src0->nb[3]; GGML_ASSERT(nb0 == sizeof(float)); GGML_ASSERT(ne1 + n_past == ne0); GGML_ASSERT(n_head == ne2); // add alibi to src0 (KQ_scaled) const int n_heads_log2_floor = 1 << (int) floor(log2(n_head)); const float m0 = powf(2.0f, -(max_bias) / n_heads_log2_floor); const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_heads_log2_floor); for (int i = 0; i < ne0; i++) { for (int j = 0; j < ne1; j++) { for (int k = 0; k < ne2_ne3; k++) { float * const src = (float *)((char *) src0->data + i*nb0 + j*nb1 + k*nb2); float * pdst = (float *)((char *) dst->data + i*nb0 + j*nb1 + k*nb2); // TODO: k*nb2 or k*nb3 float m_k; if (k < n_heads_log2_floor) { m_k = powf(m0, k + 1); } else { m_k = powf(m1, 2 * (k - n_heads_log2_floor) + 1); } pdst[0] = i * m_k + src[0]; } } } } static void ggml_compute_forward_alibi_f16( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { assert(params->ith == 0); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } const int n_past = ((int32_t *) dst->op_params)[0]; const int n_head = ((int32_t *) dst->op_params)[1]; float max_bias; memcpy(&max_bias, (int32_t *) dst->op_params + 2, sizeof(float)); assert(n_past >= 0); const int ne0 = src0->ne[0]; // all_seq_len = n_past + ne1 const int ne1 = src0->ne[1]; // seq_len_without_past const int ne2 = src0->ne[2]; // n_head -> this is k //const int ne3 = src0->ne[3]; // 1 -> bsz const int n = ggml_nrows(src0); const int ne2_ne3 = n/ne1; // ne2*ne3 const int nb0 = src0->nb[0]; const int nb1 = src0->nb[1]; const int nb2 = src0->nb[2]; //const int nb3 = src0->nb[3]; GGML_ASSERT(nb0 == sizeof(ggml_fp16_t)); GGML_ASSERT(ne1 + n_past == ne0); (void) n_past; GGML_ASSERT(n_head == ne2); // add alibi to src0 (KQ_scaled) const int n_heads_log2_floor = 1 << (int) floor(log2(n_head)); const float m0 = powf(2.0f, -(max_bias) / n_heads_log2_floor); const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_heads_log2_floor); for (int i = 0; i < ne0; i++) { for (int j = 0; j < ne1; j++) { for (int k = 0; k < ne2_ne3; k++) { ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i*nb0 + j*nb1 + k*nb2); float * pdst = (float *)((char *) dst->data + i*nb0 + j*nb1 + k*nb2); // TODO: k*nb2 or k*nb3 float m_k; if (k < n_heads_log2_floor) { m_k = powf(m0, k + 1); } else { m_k = powf(m1, 2 * (k - n_heads_log2_floor) + 1); } // we return F32 pdst[0] = i * m_k + GGML_FP16_TO_FP32(src[0]); } } } } static void ggml_compute_forward_alibi( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F16: { ggml_compute_forward_alibi_f16(params, src0, dst); } break; case GGML_TYPE_F32: { ggml_compute_forward_alibi_f32(params, src0, dst); } break; case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_1: case GGML_TYPE_Q5_0: case GGML_TYPE_Q5_1: case GGML_TYPE_Q8_0: case GGML_TYPE_Q8_1: case GGML_TYPE_Q2_K: case GGML_TYPE_Q3_K: case GGML_TYPE_Q4_K: case GGML_TYPE_Q5_K: case GGML_TYPE_Q6_K: case GGML_TYPE_Q8_K: case GGML_TYPE_I8: case GGML_TYPE_I16: case GGML_TYPE_I32: case GGML_TYPE_COUNT: { GGML_ASSERT(false); } break; } } // ggml_compute_forward_clamp static void ggml_compute_forward_clamp_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { assert(params->ith == 0); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } float min; float max; memcpy(&min, (float *) dst->op_params + 0, sizeof(float)); memcpy(&max, (float *) dst->op_params + 1, sizeof(float)); const int ith = params->ith; const int nth = params->nth; const int n = ggml_nrows(src0); const int nc = src0->ne[0]; const size_t nb00 = src0->nb[0]; const size_t nb01 = src0->nb[1]; const size_t nb0 = dst->nb[0]; const size_t nb1 = dst->nb[1]; GGML_ASSERT( nb0 == sizeof(float)); GGML_ASSERT(nb00 == sizeof(float)); for (int j = ith; j < n; j += nth) { float * dst_ptr = (float *) ((char *) dst->data + j*nb1); float * src0_ptr = (float *) ((char *) src0->data + j*nb01); for (int i = 0; i < nc; i++) { dst_ptr[i] = MAX(MIN(src0_ptr[i], max), min); } } } static void ggml_compute_forward_clamp( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_clamp_f32(params, src0, dst); } break; case GGML_TYPE_F16: case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_1: case GGML_TYPE_Q5_0: case GGML_TYPE_Q5_1: case GGML_TYPE_Q8_0: case GGML_TYPE_Q8_1: case GGML_TYPE_Q2_K: case GGML_TYPE_Q3_K: case GGML_TYPE_Q4_K: case GGML_TYPE_Q5_K: case GGML_TYPE_Q6_K: case GGML_TYPE_Q8_K: case GGML_TYPE_I8: case GGML_TYPE_I16: case GGML_TYPE_I32: case GGML_TYPE_COUNT: { GGML_ASSERT(false); } break; } } // ggml_compute_forward_rope static void ggml_compute_forward_rope_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } float freq_base; float freq_scale; const int n_past = ((int32_t *) dst->op_params)[0]; const int n_dims = ((int32_t *) dst->op_params)[1]; const int mode = ((int32_t *) dst->op_params)[2]; const int n_ctx = ((int32_t *) dst->op_params)[3]; memcpy(&freq_base, (int32_t *) dst->op_params + 4, sizeof(float)); memcpy(&freq_scale, (int32_t *) dst->op_params + 5, sizeof(float)); assert(n_past >= 0); GGML_TENSOR_UNARY_OP_LOCALS; //printf("ne0: %d, ne1: %d, ne2: %d, ne3: %d\n", ne0, ne1, ne2, ne3); //printf("n_past = %d, ne2 = %d\n", n_past, ne2); GGML_ASSERT(nb00 == sizeof(float)); const int ith = params->ith; const int nth = params->nth; const int nr = ggml_nrows(dst); GGML_ASSERT(n_dims <= ne0); GGML_ASSERT(n_dims % 2 == 0); // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); // row index used to determine which thread to use int ir = 0; const float theta_scale = powf(freq_base, -2.0f/n_dims); const bool is_neox = mode & 2; const bool is_glm = mode & 4; for (int64_t i3 = 0; i3 < ne3; i3++) { for (int64_t i2 = ((mode & 1) == 0 ? 0 : n_past); i2 < ne2; i2++) { const int64_t p = ((mode & 1) == 0 ? n_past + i2 : i2); for (int64_t i1 = 0; i1 < ne1; i1++) { if (ir++ < ir0) continue; if (ir > ir1) break; float theta = freq_scale * (float)p; if (is_glm) { theta = MIN(p, n_ctx - 2); float block_theta = MAX(p - (n_ctx - 2), 0); for (int64_t i0 = 0; i0 < ne0 / 4; i0++) { const float cos_theta = cosf(theta); const float sin_theta = sinf(theta); const float cos_block_theta = cosf(block_theta); const float sin_block_theta = sinf(block_theta); theta *= theta_scale; block_theta *= theta_scale; const float * const src = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); float * dst_data = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); const float x0 = src[0]; const float x1 = src[n_dims/2]; const float x2 = src[n_dims]; const float x3 = src[n_dims/2*3]; dst_data[0] = x0*cos_theta - x1*sin_theta; dst_data[n_dims/2] = x0*sin_theta + x1*cos_theta; dst_data[n_dims] = x2*cos_block_theta - x3*sin_block_theta; dst_data[n_dims/2*3] = x2*sin_block_theta + x3*cos_block_theta; } } else if (!is_neox) { for (int64_t i0 = 0; i0 < ne0; i0 += 2) { const float cos_theta = cosf(theta); const float sin_theta = sinf(theta); theta *= theta_scale; const float * const src = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); float * dst_data = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); const float x0 = src[0]; const float x1 = src[1]; dst_data[0] = x0*cos_theta - x1*sin_theta; dst_data[1] = x0*sin_theta + x1*cos_theta; } } else { // TODO: this is probably wrong, but I can't figure it out .. // ref: https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt_neox/modeling_gpt_neox.py#LL251C1-L294C28 for (int64_t ib = 0; ib < ne0/n_dims; ++ib) { for (int64_t ic = 0; ic < n_dims; ic += 2) { const float cos_theta = cosf(theta); const float sin_theta = sinf(theta); theta *= theta_scale; const int64_t i0 = ib*n_dims + ic/2; const float * const src = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); float * dst_data = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); const float x0 = src[0]; const float x1 = src[n_dims/2]; dst_data[0] = x0*cos_theta - x1*sin_theta; dst_data[n_dims/2] = x0*sin_theta + x1*cos_theta; } } } } } } } static void ggml_compute_forward_rope_f16( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } float freq_base; float freq_scale; const int n_past = ((int32_t *) dst->op_params)[0]; const int n_dims = ((int32_t *) dst->op_params)[1]; const int mode = ((int32_t *) dst->op_params)[2]; const int n_ctx = ((int32_t *) dst->op_params)[3]; memcpy(&freq_base, (int32_t *) dst->op_params + 4, sizeof(float)); memcpy(&freq_scale, (int32_t *) dst->op_params + 5, sizeof(float)); assert(n_past >= 0); GGML_TENSOR_UNARY_OP_LOCALS; //printf("ne0: %d, ne1: %d, ne2: %d, ne3: %d\n", ne0, ne1, ne2, ne3); //printf("n_past = %d, ne2 = %d\n", n_past, ne2); GGML_ASSERT(nb0 == sizeof(ggml_fp16_t)); const int ith = params->ith; const int nth = params->nth; const int nr = ggml_nrows(dst); GGML_ASSERT(n_dims <= ne0); GGML_ASSERT(n_dims % 2 == 0); // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); // row index used to determine which thread to use int ir = 0; const float theta_scale = powf(freq_base, -2.0f/n_dims); const bool is_neox = mode & 2; const bool is_glm = mode & 4; for (int64_t i3 = 0; i3 < ne3; i3++) { for (int64_t i2 = ((mode & 1) == 0 ? 0 : n_past); i2 < ne2; i2++) { const int64_t p = ((mode & 1) == 0 ? n_past + i2 : i2); for (int64_t i1 = 0; i1 < ne1; i1++) { if (ir++ < ir0) continue; if (ir > ir1) break; float theta = freq_scale * (float)p; if (is_glm) { theta = MIN(p, n_ctx - 2); float block_theta = MAX(p - (n_ctx - 2), 0); for (int64_t i0 = 0; i0 < ne0 / 4; i0++) { const float cos_theta = cosf(theta); const float sin_theta = sinf(theta); const float cos_block_theta = cosf(block_theta); const float sin_block_theta = sinf(block_theta); theta *= theta_scale; block_theta *= theta_scale; const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); ggml_fp16_t * dst_data = (ggml_fp16_t *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); const float x0 = GGML_FP16_TO_FP32(src[0]); const float x1 = GGML_FP16_TO_FP32(src[n_dims/2]); const float x2 = GGML_FP16_TO_FP32(src[n_dims]); const float x3 = GGML_FP16_TO_FP32(src[n_dims/2*3]); dst_data[0] = GGML_FP32_TO_FP16(x0*cos_theta - x1*sin_theta); dst_data[n_dims/2] = GGML_FP32_TO_FP16(x0*sin_theta + x1*cos_theta); dst_data[n_dims] = GGML_FP32_TO_FP16(x2*cos_block_theta - x3*sin_block_theta); dst_data[n_dims/2*3] = GGML_FP32_TO_FP16(x2*sin_block_theta + x3*cos_block_theta); } } if (!is_neox) { for (int64_t i0 = 0; i0 < ne0; i0 += 2) { const float cos_theta = cosf(theta); const float sin_theta = sinf(theta); theta *= theta_scale; const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); ggml_fp16_t * dst_data = (ggml_fp16_t *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); const float x0 = GGML_FP16_TO_FP32(src[0]); const float x1 = GGML_FP16_TO_FP32(src[1]); dst_data[0] = GGML_FP32_TO_FP16(x0*cos_theta - x1*sin_theta); dst_data[1] = GGML_FP32_TO_FP16(x0*sin_theta + x1*cos_theta); } } else { // TODO: this is probably wrong, but I can't figure it out .. // ref: https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt_neox/modeling_gpt_neox.py#LL251C1-L294C28 for (int64_t ib = 0; ib < ne0/n_dims; ++ib) { for (int64_t ic = 0; ic < n_dims; ic += 2) { const float cos_theta = cosf(theta); const float sin_theta = sinf(theta); theta *= theta_scale; const int64_t i0 = ib*n_dims + ic/2; const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); ggml_fp16_t * dst_data = (ggml_fp16_t *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); const float x0 = GGML_FP16_TO_FP32(src[0]); const float x1 = GGML_FP16_TO_FP32(src[n_dims/2]); dst_data[0] = GGML_FP32_TO_FP16(x0*cos_theta - x1*sin_theta); dst_data[n_dims/2] = GGML_FP32_TO_FP16(x0*sin_theta + x1*cos_theta); } } } } } } } static void ggml_compute_forward_rope( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F16: { ggml_compute_forward_rope_f16(params, src0, dst); } break; case GGML_TYPE_F32: { ggml_compute_forward_rope_f32(params, src0, dst); } break; default: { GGML_ASSERT(false); } break; } } // ggml_compute_forward_rope_back static void ggml_compute_forward_rope_back_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } // y = rope(x, src1) // dx = rope_back(dy, src1) // src0 is dy, src1 contains options const int n_past = ((int32_t *) dst->op_params)[0]; const int n_dims = ((int32_t *) dst->op_params)[1]; const int mode = ((int32_t *) dst->op_params)[2]; assert(n_past >= 0); GGML_TENSOR_UNARY_OP_LOCALS; //printf("ne0: %d, ne1: %d, ne2: %d, ne3: %d\n", ne0, ne1, ne2, ne3); //printf("n_past = %d, ne2 = %d\n", n_past, ne2); assert(nb0 == sizeof(float)); const int ith = params->ith; const int nth = params->nth; const int nr = ggml_nrows(dst); // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); // row index used to determine which thread to use int ir = 0; const float theta_scale = powf(10000.0, -2.0f/n_dims); const bool is_neox = mode & 2; for (int64_t i3 = 0; i3 < ne3; i3++) { for (int64_t i2 = ((mode & 1) == 0 ? 0 : n_past); i2 < ne2; i2++) { const int64_t p = ((mode & 1) == 0 ? n_past + i2 : i2); for (int64_t i1 = 0; i1 < ne1; i1++) { if (ir++ < ir0) continue; if (ir > ir1) break; float theta = (float)p; if (!is_neox) { for (int64_t i0 = 0; i0 < ne0; i0 += 2) { const float cos_theta = cosf(theta); const float sin_theta = sinf(theta); theta *= theta_scale; const float * const dy = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); float * dx = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); const float dy0 = dy[0]; const float dy1 = dy[1]; dx[0] = dy0*cos_theta + dy1*sin_theta; dx[1] = - dy0*sin_theta + dy1*cos_theta; } } else { for (int64_t ib = 0; ib < ne0/n_dims; ++ib) { for (int64_t ic = 0; ic < n_dims; ic += 2) { const float cos_theta = cosf(theta); const float sin_theta = sinf(theta); theta *= theta_scale; const int64_t i0 = ib*n_dims + ic/2; const float * const dy = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); float * dx = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); const float dy0 = dy[0]; const float dy1 = dy[n_dims/2]; dx[0] = dy0*cos_theta + dy1*sin_theta; dx[n_dims/2] = - dy0*sin_theta + dy1*cos_theta; } } } } } } } static void ggml_compute_forward_rope_back_f16( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } // y = rope(x, src1) // dx = rope_back(dy, src1) // src0 is dy, src1 contains options const int n_past = ((int32_t *) dst->op_params)[0]; const int n_dims = ((int32_t *) dst->op_params)[1]; const int mode = ((int32_t *) dst->op_params)[2]; assert(n_past >= 0); GGML_TENSOR_UNARY_OP_LOCALS; //printf("ne0: %d, ne1: %d, ne2: %d, ne3: %d\n", ne0, ne1, ne2, ne3); //printf("n_past = %d, ne2 = %d\n", n_past, ne2); assert(nb0 == sizeof(ggml_fp16_t)); const int ith = params->ith; const int nth = params->nth; const int nr = ggml_nrows(dst); // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); // row index used to determine which thread to use int ir = 0; const float theta_scale = powf(10000.0, -2.0f/n_dims); const bool is_neox = mode & 2; for (int64_t i3 = 0; i3 < ne3; i3++) { for (int64_t i2 = ((mode & 1) == 0 ? 0 : n_past); i2 < ne2; i2++) { const int64_t p = ((mode & 1) == 0 ? n_past + i2 : i2); for (int64_t i1 = 0; i1 < ne1; i1++) { if (ir++ < ir0) continue; if (ir > ir1) break; float theta = (float)p; if (!is_neox) { for (int64_t i0 = 0; i0 < ne0; i0 += 2) { const float cos_theta = cosf(theta); const float sin_theta = sinf(theta); theta *= theta_scale; const ggml_fp16_t * const dy = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); ggml_fp16_t * dx = (ggml_fp16_t *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); const float dy0 = GGML_FP16_TO_FP32(dy[0]); const float dy1 = GGML_FP16_TO_FP32(dy[1]); dx[0] = GGML_FP32_TO_FP16( dy0*cos_theta + dy1*sin_theta); dx[1] = GGML_FP32_TO_FP16(-dy0*sin_theta + dy1*cos_theta); } } else { for (int64_t ib = 0; ib < ne0/n_dims; ++ib) { for (int64_t ic = 0; ic < n_dims; ic += 2) { const float cos_theta = cosf(theta); const float sin_theta = sinf(theta); theta *= theta_scale; const int64_t i0 = ib*n_dims + ic/2; const ggml_fp16_t * const dy = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); ggml_fp16_t * dx = (ggml_fp16_t *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); const float dy0 = GGML_FP16_TO_FP32(dy[0]); const float dy1 = GGML_FP16_TO_FP32(dy[n_dims/2]); dx[0] = GGML_FP32_TO_FP16( dy0*cos_theta + dy1*sin_theta); dx[n_dims/2] = GGML_FP32_TO_FP16(-dy0*sin_theta + dy1*cos_theta); } } } } } } } static void ggml_compute_forward_rope_back( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F16: { ggml_compute_forward_rope_back_f16(params, src0, dst); } break; case GGML_TYPE_F32: { ggml_compute_forward_rope_back_f32(params, src0, dst); } break; default: { GGML_ASSERT(false); } break; } } // ggml_compute_forward_conv_1d static void ggml_compute_forward_conv_1d_s1_ph_f16_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { GGML_ASSERT(src0->type == GGML_TYPE_F16); GGML_ASSERT(src1->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); int64_t t0 = ggml_perf_time_us(); UNUSED(t0); GGML_TENSOR_BINARY_OP_LOCALS; const int ith = params->ith; const int nth = params->nth; const int nk = ne00; const int nh = nk/2; const int ew0 = ggml_up32(ne01); GGML_ASSERT(ne00 % 2 == 1); // TODO: support even kernel sizes GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); GGML_ASSERT(nb10 == sizeof(float)); if (params->type == GGML_TASK_INIT) { // TODO: fix this memset (wsize is overestimated) memset(params->wdata, 0, params->wsize); // prepare kernel data (src0) { ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + 0; for (int64_t i02 = 0; i02 < ne02; i02++) { for (int64_t i01 = 0; i01 < ne01; i01++) { const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i02*nb02 + i01*nb01); ggml_fp16_t * dst_data = wdata + i02*ew0*ne00; for (int64_t i00 = 0; i00 < ne00; i00++) { dst_data[i00*ew0 + i01] = src[i00]; } } } } // prepare source data (src1) { ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + ne02*ew0*ne00; for (int64_t i11 = 0; i11 < ne11; i11++) { const float * const src = (float *)((char *) src1->data + i11*nb11); ggml_fp16_t * dst_data = wdata; for (int64_t i10 = 0; i10 < ne10; i10++) { dst_data[(i10 + nh)*ew0 + i11] = GGML_FP32_TO_FP16(src[i10]); } } } return; } if (params->type == GGML_TASK_FINALIZE) { return; } // total rows in dst const int nr = ne02; // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); for (int i1 = ir0; i1 < ir1; i1++) { float * dst_data = (float *)((char *) dst->data + i1*nb1); for (int64_t i0 = 0; i0 < ne10; ++i0) { dst_data[i0] = 0; for (int k = -nh; k <= nh; k++) { float v = 0.0f; ggml_vec_dot_f16(ew0, &v, (ggml_fp16_t *) params->wdata + i1*ew0*ne00 + (nh + k)*ew0, (ggml_fp16_t *) params->wdata + ne02*ew0*ne00 + (i0 + nh + k)*ew0); dst_data[i0] += v; } } } } static void ggml_compute_forward_conv_1d_s1_ph_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT(src1->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); int64_t t0 = ggml_perf_time_us(); UNUSED(t0); GGML_TENSOR_BINARY_OP_LOCALS; const int ith = params->ith; const int nth = params->nth; const int nk = ne00; const int nh = nk/2; const int ew0 = ggml_up32(ne01); GGML_ASSERT(ne00 % 2 == 1); // TODO: support even kernel sizes GGML_ASSERT(nb00 == sizeof(float)); GGML_ASSERT(nb10 == sizeof(float)); if (params->type == GGML_TASK_INIT) { // TODO: fix this memset (wsize is overestimated) memset(params->wdata, 0, params->wsize); // prepare kernel data (src0) { float * const wdata = (float *) params->wdata + 0; for (int64_t i02 = 0; i02 < ne02; i02++) { for (int64_t i01 = 0; i01 < ne01; i01++) { const float * const src = (float *)((char *) src0->data + i02*nb02 + i01*nb01); float * dst_data = wdata + i02*ew0*ne00; for (int64_t i00 = 0; i00 < ne00; i00++) { dst_data[i00*ew0 + i01] = src[i00]; } } } } // prepare source data (src1) { float * const wdata = (float *) params->wdata + ne02*ew0*ne00; for (int64_t i11 = 0; i11 < ne11; i11++) { const float * const src = (float *)((char *) src1->data + i11*nb11); float * dst_data = wdata; for (int64_t i10 = 0; i10 < ne10; i10++) { dst_data[(i10 + nh)*ew0 + i11] = src[i10]; } } } return; } if (params->type == GGML_TASK_FINALIZE) { return; } // total rows in dst const int nr = ne02; // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); for (int i1 = ir0; i1 < ir1; i1++) { float * dst_data = (float *)((char *) dst->data + i1*nb1); for (int64_t i0 = 0; i0 < ne10; ++i0) { dst_data[i0] = 0; for (int k = -nh; k <= nh; k++) { float v = 0.0f; ggml_vec_dot_f32(ew0, &v, (float *) params->wdata + i1*ew0*ne00 + (nh + k)*ew0, (float *) params->wdata + ne02*ew0*ne00 + (i0 + nh + k)*ew0); dst_data[i0] += v; } } } } static void ggml_compute_forward_conv_1d_s1_ph( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F16: { ggml_compute_forward_conv_1d_s1_ph_f16_f32(params, src0, src1, dst); } break; case GGML_TYPE_F32: { ggml_compute_forward_conv_1d_s1_ph_f32(params, src0, src1, dst); } break; default: { GGML_ASSERT(false); } break; } } static void ggml_compute_forward_conv_1d_s2_ph_f16_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { GGML_ASSERT(src0->type == GGML_TYPE_F16); GGML_ASSERT(src1->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); int64_t t0 = ggml_perf_time_us(); UNUSED(t0); GGML_TENSOR_BINARY_OP_LOCALS; const int ith = params->ith; const int nth = params->nth; const int nk = ne00; const int nh = nk/2; const int ew0 = ggml_up32(ne01); GGML_ASSERT(ne00 % 2 == 1); // TODO: support even kernel sizes GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); GGML_ASSERT(nb10 == sizeof(float)); if (params->type == GGML_TASK_INIT) { // TODO: fix this memset (wsize is overestimated) memset(params->wdata, 0, params->wsize); // prepare kernel data (src0) { ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + 0; for (int64_t i02 = 0; i02 < ne02; i02++) { for (int64_t i01 = 0; i01 < ne01; i01++) { const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i02*nb02 + i01*nb01); ggml_fp16_t * dst_data = wdata + i02*ew0*ne00; for (int64_t i00 = 0; i00 < ne00; i00++) { dst_data[i00*ew0 + i01] = src[i00]; } } } } // prepare source data (src1) { ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + ne02*ew0*ne00; for (int64_t i11 = 0; i11 < ne11; i11++) { const float * const src = (float *)((char *) src1->data + i11*nb11); ggml_fp16_t * dst_data = wdata; for (int64_t i10 = 0; i10 < ne10; i10++) { dst_data[(i10 + nh)*ew0 + i11] = GGML_FP32_TO_FP16(src[i10]); } } } return; } if (params->type == GGML_TASK_FINALIZE) { return; } // total rows in dst const int nr = ne02; // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); for (int i1 = ir0; i1 < ir1; i1++) { float * dst_data = (float *)((char *) dst->data + i1*nb1); for (int64_t i0 = 0; i0 < ne10; i0 += 2) { dst_data[i0/2] = 0; for (int k = -nh; k <= nh; k++) { float v = 0.0f; ggml_vec_dot_f16(ew0, &v, (ggml_fp16_t *) params->wdata + i1*ew0*ne00 + (nh + k)*ew0, (ggml_fp16_t *) params->wdata + ne02*ew0*ne00 + (i0 + nh + k)*ew0); dst_data[i0/2] += v; } } } } static void ggml_compute_forward_conv_1d_s2_ph_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT(src1->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); int64_t t0 = ggml_perf_time_us(); UNUSED(t0); GGML_TENSOR_BINARY_OP_LOCALS; const int ith = params->ith; const int nth = params->nth; const int nk = ne00; const int nh = nk/2; const int ew0 = ggml_up32(ne01); GGML_ASSERT(ne00 % 2 == 1); // TODO: support even kernel sizes GGML_ASSERT(nb00 == sizeof(float)); GGML_ASSERT(nb10 == sizeof(float)); if (params->type == GGML_TASK_INIT) { // TODO: fix this memset (wsize is overestimated) memset(params->wdata, 0, params->wsize); // prepare kernel data (src0) { float * const wdata = (float *) params->wdata + 0; for (int64_t i02 = 0; i02 < ne02; i02++) { for (int64_t i01 = 0; i01 < ne01; i01++) { const float * const src = (float *)((char *) src0->data + i02*nb02 + i01*nb01); float * dst_data = wdata + i02*ew0*ne00; for (int64_t i00 = 0; i00 < ne00; i00++) { dst_data[i00*ew0 + i01] = src[i00]; } } } } // prepare source data (src1) { float * const wdata = (float *) params->wdata + ne02*ew0*ne00; for (int64_t i11 = 0; i11 < ne11; i11++) { const float * const src = (float *)((char *) src1->data + i11*nb11); float * dst_data = wdata; for (int64_t i10 = 0; i10 < ne10; i10++) { dst_data[(i10 + nh)*ew0 + i11] = src[i10]; } } } return; } if (params->type == GGML_TASK_FINALIZE) { return; } // total rows in dst const int nr = ne02; // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); for (int i1 = ir0; i1 < ir1; i1++) { float * dst_data = (float *)((char *) dst->data + i1*nb1); for (int64_t i0 = 0; i0 < ne10; i0 += 2) { dst_data[i0/2] = 0; for (int k = -nh; k <= nh; k++) { float v = 0.0f; ggml_vec_dot_f32(ew0, &v, (float *) params->wdata + i1*ew0*ne00 + (nh + k)*ew0, (float *) params->wdata + ne02*ew0*ne00 + (i0 + nh + k)*ew0); dst_data[i0/2] += v; } } } } static void ggml_compute_forward_conv_1d_s2_ph( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F16: { ggml_compute_forward_conv_1d_s2_ph_f16_f32(params, src0, src1, dst); } break; case GGML_TYPE_F32: { ggml_compute_forward_conv_1d_s2_ph_f32(params, src0, src1, dst); } break; default: { GGML_ASSERT(false); } break; } } // ggml_compute_forward_conv_1d static void ggml_compute_forward_conv_1d( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { const int32_t s0 = ((const int32_t*)(dst->op_params))[0]; const int32_t p0 = ((const int32_t*)(dst->op_params))[1]; const int32_t d0 = ((const int32_t*)(dst->op_params))[2]; GGML_ASSERT(d0 == 1); // dilation not supported GGML_ASSERT(p0 == src0->ne[0]/2); // only half padding supported if (s0 == 1) { ggml_compute_forward_conv_1d_s1_ph(params, src0, src1, dst); } else if (s0 == 2) { ggml_compute_forward_conv_1d_s2_ph(params, src0, src1, dst); } else { GGML_ASSERT(false); // only stride 1 and 2 supported }; } // ggml_compute_forward_conv_2d static void ggml_compute_forward_conv_2d_f16_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { GGML_ASSERT(src0->type == GGML_TYPE_F16); GGML_ASSERT(src1->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); int64_t t0 = ggml_perf_time_us(); UNUSED(t0); GGML_TENSOR_BINARY_OP_LOCALS; const int ith = params->ith; const int nth = params->nth; const int nk0 = ne00; const int nk1 = ne01; // size of the convolution row - the kernel size unrolled across all channels const int ew0 = nk0*nk1*ne02; const int32_t s0 = ((const int32_t*)(dst->op_params))[0]; const int32_t s1 = ((const int32_t*)(dst->op_params))[1]; const int32_t p0 = ((const int32_t*)(dst->op_params))[2]; const int32_t p1 = ((const int32_t*)(dst->op_params))[3]; const int32_t d0 = ((const int32_t*)(dst->op_params))[4]; const int32_t d1 = ((const int32_t*)(dst->op_params))[5]; GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); GGML_ASSERT(nb10 == sizeof(float)); if (params->type == GGML_TASK_INIT) { memset(params->wdata, 0, params->wsize); // prepare source data (src1) { ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + 0; for (int i12 = 0; i12 < ne12; i12++) { const float * const src = (float *)((char *) src1->data + i12*nb12); ggml_fp16_t * dst_data = wdata; for (int i1 = 0; i1 < ne1; i1++) { for (int i0 = 0; i0 < ne0; i0++) { for (int ik1 = 0; ik1 < nk1; ik1++) { for (int ik0 = 0; ik0 < nk0; ik0++) { const int idx0 = i0*s0 + ik0*d0 - p0; const int idx1 = i1*s1 + ik1*d1 - p1; if (!(idx1 < 0 || idx1 >= ne11 || idx0 < 0 || idx0 >= ne10)) { dst_data[(i1*ne0 + i0)*ew0 + i12*(nk0*nk1) + ik1*nk0 + ik0] = GGML_FP32_TO_FP16(src[idx1*ne10 + idx0]); } } } } } } } return; } if (params->type == GGML_TASK_FINALIZE) { return; } // total patches in dst const int np = ne2; // patches per thread const int dp = (np + nth - 1)/nth; // patch range for this thread const int ip0 = dp*ith; const int ip1 = MIN(ip0 + dp, np); ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + 0; for (int i3 = 0; i3 < ne3; i3++) { for (int i2 = ip0; i2 < ip1; i2++) { float * dst_data = (float *)((char *) dst->data + i3*nb3 + i2*nb2); for (int i1 = 0; i1 < ne1; ++i1) { for (int i0 = 0; i0 < ne0; ++i0) { ggml_vec_dot_f16(ew0, dst_data + i1*ne0 + i0, (ggml_fp16_t *) ((char *) src0->data + i2*nb03), (ggml_fp16_t *) wdata + i3*nb3 + (i1*ne0 + i0)*ew0); } } } } } static void ggml_compute_forward_conv_2d( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F16: { ggml_compute_forward_conv_2d_f16_f32(params, src0, src1, dst); } break; case GGML_TYPE_F32: { //ggml_compute_forward_conv_2d_f32(params, src0, src1, dst); GGML_ASSERT(false); } break; default: { GGML_ASSERT(false); } break; } } // ggml_compute_forward_pool_1d_sk_p0 static void ggml_compute_forward_pool_1d_sk_p0( const struct ggml_compute_params * params, const enum ggml_op_pool op, const struct ggml_tensor * src, const int k, struct ggml_tensor * dst) { assert(src->type == GGML_TYPE_F32); assert(params->ith == 0); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } const char * cdata = (const char *)src->data; const char * const data_end = cdata + ggml_nbytes(src); float * drow = (float *)dst->data; const int64_t rs = dst->ne[0]; while (cdata < data_end) { const float * const srow = (const float *)cdata; int j = 0; for (int64_t i = 0; i < rs; ++i) { switch (op) { case GGML_OP_POOL_AVG: drow[i] = 0; break; case GGML_OP_POOL_MAX: drow[i] = -FLT_MAX; break; case GGML_OP_POOL_COUNT: GGML_ASSERT(false); break; } for (int ki = 0; ki < k; ++ki) { switch (op) { case GGML_OP_POOL_AVG: drow[i] += srow[j]; break; case GGML_OP_POOL_MAX: if (srow[j] > drow[i]) drow[i] = srow[j]; break; case GGML_OP_POOL_COUNT: GGML_ASSERT(false); break; } ++j; } switch (op) { case GGML_OP_POOL_AVG: drow[i] /= k; break; case GGML_OP_POOL_MAX: break; case GGML_OP_POOL_COUNT: GGML_ASSERT(false); break; } } cdata += src->nb[1]; drow += rs; } } // ggml_compute_forward_pool_1d static void ggml_compute_forward_pool_1d( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { const int32_t * opts = (const int32_t *)dst->op_params; enum ggml_op_pool op = opts[0]; const int k0 = opts[1]; const int s0 = opts[2]; const int p0 = opts[3]; GGML_ASSERT(p0 == 0); // padding not supported GGML_ASSERT(k0 == s0); // only s = k supported ggml_compute_forward_pool_1d_sk_p0(params, op, src0, k0, dst); } // ggml_compute_forward_pool_2d_sk_p0 static void ggml_compute_forward_pool_2d_sk_p0( const struct ggml_compute_params * params, const enum ggml_op_pool op, const struct ggml_tensor * src, const int k0, const int k1, struct ggml_tensor * dst) { assert(src->type == GGML_TYPE_F32); assert(params->ith == 0); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } const char * cdata = (const char*)src->data; const char * const data_end = cdata + ggml_nbytes(src); const int64_t px = dst->ne[0]; const int64_t py = dst->ne[1]; const int64_t pa = px * py; float * dplane = (float *)dst->data; const int ka = k0 * k1; while (cdata < data_end) { for (int oy = 0; oy < py; ++oy) { float * const drow = dplane + oy * px; for (int ox = 0; ox < px; ++ox) { float * const out = drow + ox; switch (op) { case GGML_OP_POOL_AVG: *out = 0; break; case GGML_OP_POOL_MAX: *out = -FLT_MAX; break; case GGML_OP_POOL_COUNT: GGML_ASSERT(false); break; } const int ix = ox * k0; const int iy = oy * k1; for (int ky = 0; ky < k1; ++ky) { const float * const srow = (const float *)(cdata + src->nb[1] * (iy + ky)); for (int kx = 0; kx < k0; ++kx) { int j = ix + kx; switch (op) { case GGML_OP_POOL_AVG: *out += srow[j]; break; case GGML_OP_POOL_MAX: if (srow[j] > *out) *out = srow[j]; break; case GGML_OP_POOL_COUNT: GGML_ASSERT(false); break; } } } switch (op) { case GGML_OP_POOL_AVG: *out /= ka; break; case GGML_OP_POOL_MAX: break; case GGML_OP_POOL_COUNT: GGML_ASSERT(false); break; } } } cdata += src->nb[2]; dplane += pa; } } // ggml_compute_forward_pool_2d static void ggml_compute_forward_pool_2d( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { const int32_t * opts = (const int32_t *)dst->op_params; enum ggml_op_pool op = opts[0]; const int k0 = opts[1]; const int k1 = opts[2]; const int s0 = opts[3]; const int s1 = opts[4]; const int p0 = opts[5]; const int p1 = opts[6]; GGML_ASSERT(p0 == 0); GGML_ASSERT(p1 == 0); // padding not supported GGML_ASSERT(k0 == s0); GGML_ASSERT(k1 == s1); // only s = k supported ggml_compute_forward_pool_2d_sk_p0(params, op, src0, k0, k1, dst); } // ggml_compute_forward_flash_attn static void ggml_compute_forward_flash_attn_f32( const struct ggml_compute_params * params, const struct ggml_tensor * q, const struct ggml_tensor * k, const struct ggml_tensor * v, const bool masked, struct ggml_tensor * dst) { int64_t t0 = ggml_perf_time_us(); UNUSED(t0); GGML_TENSOR_LOCALS(int64_t, neq, q, ne); GGML_TENSOR_LOCALS(size_t, nbq, q, nb); GGML_TENSOR_LOCALS(int64_t, nek, k, ne); GGML_TENSOR_LOCALS(size_t, nbk, k, nb); GGML_TENSOR_LOCALS(int64_t, nev, v, ne); GGML_TENSOR_LOCALS(size_t, nbv, v, nb); GGML_TENSOR_LOCALS(int64_t, ne, dst, ne); GGML_TENSOR_LOCALS(size_t, nb, dst, nb); const int ith = params->ith; const int nth = params->nth; const int64_t D = neq0; const int64_t N = neq1; const int64_t P = nek1 - N; const int64_t M = P + N; const int Mup = ggml_up(M, GGML_SOFT_MAX_UNROLL); GGML_ASSERT(ne0 == D); GGML_ASSERT(ne1 == N); GGML_ASSERT(P >= 0); GGML_ASSERT(nbq0 == sizeof(float)); GGML_ASSERT(nbk0 == sizeof(float)); GGML_ASSERT(nbv0 == sizeof(float)); GGML_ASSERT(neq0 == D); GGML_ASSERT(nek0 == D); GGML_ASSERT(nev1 == D); GGML_ASSERT(neq1 == N); GGML_ASSERT(nek1 == N + P); GGML_ASSERT(nev1 == D); // dst cannot be transposed or permuted GGML_ASSERT(nb0 == sizeof(float)); GGML_ASSERT(nb0 <= nb1); GGML_ASSERT(nb1 <= nb2); GGML_ASSERT(nb2 <= nb3); if (params->type == GGML_TASK_INIT) { return; } if (params->type == GGML_TASK_FINALIZE) { return; } // parallelize by q rows using ggml_vec_dot_f32 // total rows in q const int nr = neq1*neq2*neq3; // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); const float scale = 1.0f/sqrtf(D); //printf("P=%d N=%d D=%d ir0=%d ir1=%d scale = %f\n", P, N, D, ir0, ir1, scale); for (int ir = ir0; ir < ir1; ++ir) { // q indices const int iq3 = ir/(neq2*neq1); const int iq2 = (ir - iq3*neq2*neq1)/neq1; const int iq1 = (ir - iq3*neq2*neq1 - iq2*neq1); float * S = (float *) params->wdata + ith*(Mup + CACHE_LINE_SIZE_F32); for (int i = M; i < Mup; ++i) { S[i] = -INFINITY; } for (int64_t ic = 0; ic < nek1; ++ic) { // k indices const int ik3 = iq3; const int ik2 = iq2; const int ik1 = ic; // S indices const int i1 = ik1; ggml_vec_dot_f32(neq0, S + i1, (float *) ((char *) k->data + (ik1*nbk1 + ik2*nbk2 + ik3*nbk3)), (float *) ((char *) q->data + (iq1*nbq1 + iq2*nbq2 + iq3*nbq3))); } // scale ggml_vec_scale_f32(nek1, S, scale); if (masked) { for (int64_t i = P; i < M; i++) { if (i > P + iq1) { S[i] = -INFINITY; } } } // softmax { float max = -INFINITY; ggml_vec_max_f32(M, &max, S); ggml_float sum = 0.0; { #ifdef GGML_SOFT_MAX_ACCELERATE max = -max; vDSP_vsadd(S, 1, &max, S, 1, Mup); vvexpf(S, S, &Mup); ggml_vec_sum_f32(Mup, &sum, S); #else uint16_t scvt[GGML_SOFT_MAX_UNROLL]; 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); GGML_TENSOR_LOCALS(int64_t, neq, q, ne); GGML_TENSOR_LOCALS(size_t, nbq, q, nb); GGML_TENSOR_LOCALS(int64_t, nek, k, ne); GGML_TENSOR_LOCALS(size_t, nbk, k, nb); GGML_TENSOR_LOCALS(int64_t, nev, v, ne); GGML_TENSOR_LOCALS(size_t, nbv, v, nb); GGML_TENSOR_LOCALS(int64_t, ne, dst, ne); GGML_TENSOR_LOCALS(size_t, nb, dst, nb); const int ith = params->ith; const int nth = params->nth; const int64_t D = neq0; const int64_t N = neq1; const int64_t P = nek1 - N; const int64_t M = P + N; const int Mup = ggml_up(M, GGML_SOFT_MAX_UNROLL); GGML_ASSERT(ne0 == D); GGML_ASSERT(ne1 == N); GGML_ASSERT(P >= 0); GGML_ASSERT(nbq0 == sizeof(ggml_fp16_t)); GGML_ASSERT(nbk0 == sizeof(ggml_fp16_t)); GGML_ASSERT(nbv0 == sizeof(ggml_fp16_t)); GGML_ASSERT(neq0 == D); GGML_ASSERT(nek0 == D); GGML_ASSERT(nev1 == D); GGML_ASSERT(neq1 == N); GGML_ASSERT(nek1 == N + P); GGML_ASSERT(nev1 == D); // dst cannot be transposed or permuted GGML_ASSERT(nb0 == sizeof(float)); GGML_ASSERT(nb0 <= nb1); GGML_ASSERT(nb1 <= nb2); GGML_ASSERT(nb2 <= nb3); if (params->type == GGML_TASK_INIT) { return; } if (params->type == GGML_TASK_FINALIZE) { return; } // parallelize by q rows using ggml_vec_dot_f32 // total rows in q const int nr = neq1*neq2*neq3; // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); const float scale = 1.0f/sqrtf(D); //printf("P=%d N=%d D=%d ir0=%d ir1=%d scale = %f\n", P, N, D, ir0, ir1, scale); for (int ir = ir0; ir < ir1; ++ir) { // q indices const int iq3 = ir/(neq2*neq1); const int iq2 = (ir - iq3*neq2*neq1)/neq1; const int iq1 = (ir - iq3*neq2*neq1 - iq2*neq1); float * S = (float *) params->wdata + ith*(2*Mup + CACHE_LINE_SIZE_F32); for (int i = M; i < Mup; ++i) { S[i] = -INFINITY; } if (GGML_VEC_DOT_UNROLL > 2 || nek1 % GGML_VEC_DOT_UNROLL != 0) { for (int64_t ic = 0; ic < nek1; ++ic) { // k indices const int ik3 = iq3; const int ik2 = iq2; const int ik1 = ic; // S indices const int i1 = ik1; ggml_vec_dot_f16(neq0, S + i1, (ggml_fp16_t *) ((char *) k->data + (ik1*nbk1 + ik2*nbk2 + ik3*nbk3)), (ggml_fp16_t *) ((char *) q->data + (iq1*nbq1 + iq2*nbq2 + iq3*nbq3))); } } else { for (int64_t ic = 0; ic < nek1; ic += GGML_VEC_DOT_UNROLL) { // k indices const int ik3 = iq3; const int ik2 = iq2; const int ik1 = ic; // S indices const int i1 = ik1; ggml_vec_dot_f16_unroll(neq0, nbk1, S + i1, ((char *) k->data + (ik1*nbk1 + ik2*nbk2 + ik3*nbk3)), (ggml_fp16_t *) ((char *) q->data + (iq1*nbq1 + iq2*nbq2 + iq3*nbq3))); } } // scale ggml_vec_scale_f32(nek1, S, scale); if (masked) { for (int64_t i = P; i < M; i++) { if (i > P + iq1) { S[i] = -INFINITY; } } } // softmax { float max = -INFINITY; ggml_vec_max_f32(M, &max, S); ggml_float sum = 0.0; { #ifdef GGML_SOFT_MAX_ACCELERATE max = -max; vDSP_vsadd(S, 1, &max, S, 1, Mup); vvexpf(S, S, &Mup); ggml_vec_sum_f32(Mup, &sum, S); #else uint16_t scvt[GGML_SOFT_MAX_UNROLL]; ggml_float sump[GGML_SOFT_MAX_UNROLL] = { 0.0 }; for (int i = 0; i < Mup; i += GGML_SOFT_MAX_UNROLL) { float * SS = S + i; for (int j = 0; j < GGML_SOFT_MAX_UNROLL; ++j) { if (SS[j] == -INFINITY) { SS[j] = 0.0f; } else { ggml_fp16_t s = GGML_FP32_TO_FP16(SS[j] - max); memcpy(&scvt[j], &s, sizeof(uint16_t)); const float val = GGML_FP16_TO_FP32(table_exp_f16[scvt[j]]); sump[j] += (ggml_float)val; SS[j] = val; } } } for (int i = 0; i < GGML_SOFT_MAX_UNROLL; i++) { sum += sump[i]; } #endif } assert(sum > 0.0); sum = 1.0/sum; ggml_vec_scale_f32(M, S, sum); #ifndef NDEBUG for (int i = 0; i < M; ++i) { assert(!isnan(S[i])); assert(!isinf(S[i])); } #endif } ggml_fp16_t * S16 = (ggml_fp16_t *) ((float *) params->wdata + ith*(2*Mup + CACHE_LINE_SIZE_F32) + Mup); for (int64_t i = 0; i < M; i++) { S16[i] = GGML_FP32_TO_FP16(S[i]); } if (GGML_VEC_DOT_UNROLL == 1 || (nev1 % GGML_VEC_DOT_UNROLL != 0)) { for (int64_t ic = 0; ic < nev1; ++ic) { // dst indices const int i1 = iq1; const int i2 = iq2; const int i3 = iq3; ggml_vec_dot_f16(nek1, (float *) ((char *) dst->data + (ic*nb0 + i1*nb1 + i2*nb2 + i3*nb3)), (ggml_fp16_t *) ((char *) v->data + ( ic*nbv1 + i2*nbv2 + i3*nbv3)), S16); } } else { for (int64_t ic = 0; ic < nev1; ic += GGML_VEC_DOT_UNROLL) { // dst indices const int i1 = iq1; const int i2 = iq2; const int i3 = iq3; ggml_vec_dot_f16_unroll(nek1, nbv1, (float *) ((char *) dst->data + (ic*nb0 + i1*nb1 + i2*nb2 + i3*nb3)), ((char *) v->data + ( ic*nbv1 + i2*nbv2 + i3*nbv3)), S16); } } } } static void ggml_compute_forward_flash_attn( const struct ggml_compute_params * params, const struct ggml_tensor * q, const struct ggml_tensor * k, const struct ggml_tensor * v, const bool masked, struct ggml_tensor * dst) { switch (q->type) { case GGML_TYPE_F16: { ggml_compute_forward_flash_attn_f16(params, q, k, v, masked, dst); } break; case GGML_TYPE_F32: { ggml_compute_forward_flash_attn_f32(params, q, k, v, masked, dst); } break; default: { GGML_ASSERT(false); } break; } } // ggml_compute_forward_flash_ff static void ggml_compute_forward_flash_ff_f16( const struct ggml_compute_params * params, const struct ggml_tensor * a, // F16 const struct ggml_tensor * b0, // F16 fc_w const struct ggml_tensor * b1, // F32 fc_b const struct ggml_tensor * c0, // F16 proj_w const struct ggml_tensor * c1, // F32 proj_b struct ggml_tensor * dst) { int64_t t0 = ggml_perf_time_us(); UNUSED(t0); GGML_TENSOR_LOCALS(int64_t, nea, a, ne); GGML_TENSOR_LOCALS(size_t, nba, a, nb); GGML_TENSOR_LOCALS(int64_t, neb0, b0, ne); GGML_TENSOR_LOCALS(size_t, nbb0, b0, nb); GGML_TENSOR_LOCALS(int64_t, neb1, b1, ne); GGML_TENSOR_LOCALS(size_t, nbb1, b1, nb); GGML_TENSOR_LOCALS(int64_t, nec0, c0, ne); GGML_TENSOR_LOCALS(size_t, nbc0, c0, nb); GGML_TENSOR_LOCALS(int64_t, nec1, c1, ne); GGML_TENSOR_LOCALS(size_t, nbc1, c1, nb); GGML_TENSOR_LOCALS(int64_t, ne, dst, ne); GGML_TENSOR_LOCALS(size_t, nb, dst, nb); const int ith = params->ith; const int nth = params->nth; const int64_t D = nea0; //const int64_t N = nea1; const int64_t M = neb01; GGML_ASSERT(ne0 == nea0); GGML_ASSERT(ne1 == nea1); GGML_ASSERT(ne2 == nea2); GGML_ASSERT(nba0 == sizeof(ggml_fp16_t)); GGML_ASSERT(nbb00 == sizeof(ggml_fp16_t)); GGML_ASSERT(nbb10 == sizeof(float)); GGML_ASSERT(nbc00 == sizeof(ggml_fp16_t)); GGML_ASSERT(nbc10 == sizeof(float)); GGML_ASSERT(neb00 == D); GGML_ASSERT(neb01 == M); GGML_ASSERT(neb10 == M); GGML_ASSERT(neb11 == 1); GGML_ASSERT(nec00 == M); GGML_ASSERT(nec01 == D); GGML_ASSERT(nec10 == D); GGML_ASSERT(nec11 == 1); // dst cannot be transposed or permuted GGML_ASSERT(nb0 == sizeof(float)); GGML_ASSERT(nb0 <= nb1); GGML_ASSERT(nb1 <= nb2); GGML_ASSERT(nb2 <= nb3); if (params->type == GGML_TASK_INIT) { return; } if (params->type == GGML_TASK_FINALIZE) { return; } // parallelize by a rows using ggml_vec_dot_f32 // total rows in a const int nr = nea1*nea2*nea3; // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); for (int ir = ir0; ir < ir1; ++ir) { // a indices const int ia3 = ir/(nea2*nea1); const int ia2 = (ir - ia3*nea2*nea1)/nea1; const int ia1 = (ir - ia3*nea2*nea1 - ia2*nea1); float * S = (float *) params->wdata + ith*(2*M + CACHE_LINE_SIZE_F32); for (int64_t ic = 0; ic < neb01; ++ic) { // b0 indices const int ib03 = ia3; const int ib02 = ia2; const int ib01 = ic; // S indices const int i1 = ib01; ggml_vec_dot_f16(nea0, S + i1, (ggml_fp16_t *) ((char *) b0->data + (ib01*nbb01 + ib02*nbb02 + ib03*nbb03)), (ggml_fp16_t *) ((char *) a->data + ( ia1*nba1 + ia2*nba2 + ia3*nba3))); } ggml_vec_add_f32(neb01, S, S, (float *) b1->data); //ggml_vec_gelu_f32(neb01, S, S); ggml_fp16_t * S16 = (ggml_fp16_t *) ((float *) params->wdata + ith*(2*M + CACHE_LINE_SIZE_F32) + M); for (int64_t i = 0; i < M; i++) { S16[i] = GGML_FP32_TO_FP16(S[i]); } ggml_vec_gelu_f16(neb01, S16, S16); { // dst indices const int i1 = ia1; const int i2 = ia2; const int i3 = ia3; for (int64_t ic = 0; ic < nec01; ++ic) { ggml_vec_dot_f16(neb01, (float *) ((char *) dst->data + (ic*nb0 + i1*nb1 + i2*nb2 + i3*nb3)), (ggml_fp16_t *) ((char *) c0->data + ( ic*nbc01 + i2*nbc02 + i3*nbc03)), S16); } ggml_vec_add_f32(nec01, (float *) ((char *) dst->data + (i1*nb1 + i2*nb2 + i3*nb3)), (float *) ((char *) dst->data + (i1*nb1 + i2*nb2 + i3*nb3)), (float *) c1->data); } } } static void ggml_compute_forward_flash_ff( const struct ggml_compute_params * params, const struct ggml_tensor * a, const struct ggml_tensor * b0, const struct ggml_tensor * b1, const struct ggml_tensor * c0, const struct ggml_tensor * c1, struct ggml_tensor * dst) { switch (b0->type) { case GGML_TYPE_F16: { ggml_compute_forward_flash_ff_f16(params, a, b0, b1, c0, c1, dst); } break; case GGML_TYPE_F32: { GGML_ASSERT(false); // TODO } break; default: { GGML_ASSERT(false); } break; } } // ggml_compute_forward_flash_attn_back static void ggml_compute_forward_flash_attn_back_f32( const struct ggml_compute_params * params, const struct ggml_tensor * q, const struct ggml_tensor * k, const struct ggml_tensor * v, const struct ggml_tensor * d, const bool masked, struct ggml_tensor * dst) { int64_t t0 = ggml_perf_time_us(); UNUSED(t0); GGML_TENSOR_LOCALS(int64_t, neq, q, ne); GGML_TENSOR_LOCALS(size_t, nbq, q, nb); GGML_TENSOR_LOCALS(int64_t, nek, k, ne); GGML_TENSOR_LOCALS(size_t, nbk, k, nb); GGML_TENSOR_LOCALS(int64_t, nev, v, ne); GGML_TENSOR_LOCALS(size_t, nbv, v, nb); GGML_TENSOR_LOCALS(int64_t, ned, d, ne); GGML_TENSOR_LOCALS(size_t, nbd, d, nb); GGML_TENSOR_LOCALS(int64_t, ne, dst, ne); GGML_TENSOR_LOCALS(size_t, nb, dst, nb); const int ith = params->ith; const int nth = params->nth; const int64_t D = neq0; const int64_t N = neq1; const int64_t P = nek1 - N; const int64_t M = P + N; const int Mup = ggml_up(M, GGML_SOFT_MAX_UNROLL); const int mxDM = MAX(D, Mup); // GGML_ASSERT(ne0 == D); // GGML_ASSERT(ne1 == N); GGML_ASSERT(P >= 0); GGML_ASSERT(nbq0 == sizeof(float)); GGML_ASSERT(nbk0 == sizeof(float)); GGML_ASSERT(nbv0 == sizeof(float)); GGML_ASSERT(neq0 == D); GGML_ASSERT(nek0 == D); GGML_ASSERT(nev1 == D); GGML_ASSERT(ned0 == D); GGML_ASSERT(neq1 == N); GGML_ASSERT(nek1 == N + P); GGML_ASSERT(nev1 == D); GGML_ASSERT(ned1 == N); // dst cannot be transposed or permuted GGML_ASSERT(nb0 == sizeof(float)); GGML_ASSERT(nb0 <= nb1); GGML_ASSERT(nb1 <= nb2); GGML_ASSERT(nb2 <= nb3); if (params->type == GGML_TASK_INIT) { if (ith == 0) { memset(dst->data, 0, nb0*ne0*ne1*ne2*ne3); } return; } if (params->type == GGML_TASK_FINALIZE) { return; } // parallelize by q rows using ggml_vec_dot_f32 // total rows in q const int nr = neq2*neq3; // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); const float scale = 1.0f/sqrtf(D); //printf("P=%d N=%d D=%d ir0=%d ir1=%d scale = %f\n", P, N, D, ir0, ir1, scale); for (int ir = ir0; ir < ir1; ++ir) { // q indices const int iq3 = ir/(neq2); const int iq2 = ir - iq3*neq2; for ( int iq1 = 0; iq1 < neq1; ++iq1) { // not sure about CACHE_LINE_SIZE_F32.. // - maybe it must not be multiplied by 2 and excluded from .. in SM 1*(..) offset? float * S = (float *) params->wdata + ith*2*(mxDM + CACHE_LINE_SIZE_F32) + 0*(mxDM+CACHE_LINE_SIZE_F32); float * SM = (float *) params->wdata + ith*2*(mxDM + CACHE_LINE_SIZE_F32) + 1*(mxDM+CACHE_LINE_SIZE_F32); for (int i = M; i < Mup; ++i) { S[i] = -INFINITY; } for (int64_t ic = 0; ic < nek1; ++ic) { // k indices const int ik3 = iq3; const int ik2 = iq2; const int ik1 = ic; // S indices const int i1 = ik1; ggml_vec_dot_f32(neq0, S + i1, (float *) ((char *) k->data + (ik1*nbk1 + ik2*nbk2 + ik3*nbk3)), (float *) ((char *) q->data + (iq1*nbq1 + iq2*nbq2 + iq3*nbq3))); } // scale ggml_vec_scale_f32(nek1, S, scale); if (masked) { for (int64_t i = P; i < M; i++) { if (i > P + iq1) { S[i] = -INFINITY; } } } // softmax { float max = -INFINITY; ggml_vec_max_f32(M, &max, S); ggml_float sum = 0.0; { #ifdef GGML_SOFT_MAX_ACCELERATE max = -max; vDSP_vsadd(SM, 1, &max, SM, 1, Mup); vvexpf(SM, SM, &Mup); ggml_vec_sum_f32(Mup, &sum, SM); #else uint16_t scvt[GGML_SOFT_MAX_UNROLL]; ggml_float sump[GGML_SOFT_MAX_UNROLL] = { 0.0 }; for (int i = 0; i < Mup; i += GGML_SOFT_MAX_UNROLL) { float * SR = S + i; float * SW = SM + i; for (int j = 0; j < GGML_SOFT_MAX_UNROLL; ++j) { if (SR[j] == -INFINITY) { SW[j] = 0.0f; } else { ggml_fp16_t s = GGML_FP32_TO_FP16(SR[j] - max); memcpy(&scvt[j], &s, sizeof(uint16_t)); const float val = GGML_FP16_TO_FP32(table_exp_f16[scvt[j]]); sump[j] += (ggml_float)val; SW[j] = val; } } } for (int i = 0; i < GGML_SOFT_MAX_UNROLL; i++) { sum += sump[i]; } #endif } assert(sum > 0.0); sum = 1.0/sum; ggml_vec_scale_f32(M, SM, sum); } // step-by-step explanation { // forward-process shape grads from backward process // parallel_for iq2,iq3: // k[:D,:M,:,:] [D,M,:,:] grad[k][:D,:M,iq2,iq3] += grad[kcur] // q[:D,:N,:,:] [D,N,:,:] grad[q][:D,iq1,iq2,iq3] += grad[qcur] // v[:M,:D,:,:] [M,D,:,:] grad[v][:M,:D,iq2,iq3] += grad[vcur] // for iq1: // kcur = k[:D,:M,iq2,iq3] [D,M,1,1] grad[kcur] = grad[S1].T @ qcur // qcur = q[:D,iq1,iq2,iq3] [D,1,1,1] grad[qcur] = grad[S1] @ kcur // vcur = v[:M,:D,iq2,iq3] [M,D,1,1] grad[vcur] = grad[S5].T @ S4 // S0 = -Inf [D,1,1,1] // ~S1[i] = dot(kcur[:D,i], qcur) // S1 = qcur @ kcur.T [M,1,1,1] grad[S1] = grad[S2] * scale // S2 = S1 * scale [M,1,1,1] grad[S2] = diag_mask_zero(grad[S3], P) // S3 = diag_mask_inf(S2, P) [M,1,1,1] grad[S3] = S4 * (grad[S4] - dot(S4, grad[S4])) // S4 = softmax(S3) [M,1,1,1] grad[S4] = grad[S5] @ vcur // ~S5[i] = dot(vcur[:,i], S4) // S5 = S4 @ vcur.T [D,1,1,1] grad[S5] = d[:D,iq1,iq2,iq3] // ~dst[i,iq1,iq2,iq3] = S5[i] ^ // dst[:D,iq1,iq2,iq3] = S5 | grad[dst[:D,iq1,iq2,iq3]] = d[:D,iq1,iq2,iq3] // dst backward-/ grad[dst] = d // // output gradients with their dependencies: // // grad[kcur] = grad[S1].T @ qcur // grad[S1] = diag_mask_zero(grad[S3], P) * scale // grad[S3] = S4 * (grad[S4] - dot(S4, grad[S4])) // grad[S4] = grad[S5] @ vcur // grad[S4] = d[:D,iq1,iq2,iq3] @ vcur // grad[qcur] = grad[S1] @ kcur // grad[vcur] = grad[S5].T @ S4 // grad[vcur] = d[:D,iq1,iq2,iq3].T @ S4 // // in post-order: // // S1 = qcur @ kcur.T // S2 = S1 * scale // S3 = diag_mask_inf(S2, P) // S4 = softmax(S3) // grad[S4] = d[:D,iq1,iq2,iq3] @ vcur // grad[S3] = S4 * (grad[S4] - dot(S4, grad[S4])) // grad[S1] = diag_mask_zero(grad[S3], P) * scale // grad[qcur] = grad[S1] @ kcur // grad[kcur] = grad[S1].T @ qcur // grad[vcur] = d[:D,iq1,iq2,iq3].T @ S4 // // using less variables (SM=S4): // // S = diag_mask_inf(qcur @ kcur.T * scale, P) // SM = softmax(S) // S = d[:D,iq1,iq2,iq3] @ vcur // dot_SM_gradSM = dot(SM, S) // S = SM * (S - dot(SM, S)) // S = diag_mask_zero(S, P) * scale // // grad[q][:D,iq1,iq2,iq3] += S @ kcur // grad[k][:D,:M,iq2,iq3] += S.T @ qcur // grad[v][:M,:D,iq2,iq3] += d[:D,iq1,iq2,iq3].T @ SM } // S = gradSM = d[:D,iq1,iq2,iq3] @ vcur // S = d[:D,iq1,iq2,iq3] @ vcur // S[:M] += vcur[:M,ic] * d[ic,iq1,iq2,iq3] ggml_vec_set_f32(M, S, 0); for (int64_t ic = 0; ic < D; ++ic) { // dst indices const int i1 = iq1; const int i2 = iq2; const int i3 = iq3; ggml_vec_mad_f32(M, S, (float *) ((char *) v->data + ( ic*nbv1 + i2*nbv2 + i3*nbv3)), *(float *) ((char *) d->data + (ic*nbd0 + i1*nbd1 + i2*nbd2 + i3*nbd3))); } // S = SM * (S - dot(SM, S)) float dot_SM_gradSM = 0; ggml_vec_dot_f32 (M, &dot_SM_gradSM, SM, S); ggml_vec_acc1_f32(M, S, -dot_SM_gradSM); ggml_vec_mul_f32 (M, S, S, SM); // S = diag_mask_zero(S, P) * scale if (masked) { // for (int64_t i = P + iq1 + 1; i < M; i++) { // S[i] = 0; // } for (int64_t i = P; i < M; i++) { if (i > P + iq1) { S[i] = 0; } } } ggml_vec_scale_f32(M, S, scale); void * grad_q = (char *) dst->data; void * grad_k = (char *) dst->data + nb0*D*N*neq2*neq3; void * grad_v = (char *) dst->data + nb0*D*N*neq2*neq3 + nb0*D*M*neq2*neq3; const size_t nbgq1 = nb0*neq0; const size_t nbgq2 = nb0*neq0*neq1; const size_t nbgq3 = nb0*neq0*neq1*neq2; const size_t nbgk1 = nb0*nek0; const size_t nbgk2 = nb0*nek0*nek1; const size_t nbgk3 = nb0*nek0*nek1*neq2; const size_t nbgv1 = nb0*nev0; const size_t nbgv2 = nb0*nev0*nev1; const size_t nbgv3 = nb0*nev0*nev1*neq2; // S shape [M,1] // SM shape [M,1] // kcur shape [D,M] // qcur shape [D,1] // vcur shape [M,D] // // grad[q][:D,iq1,iq2,iq3] += S @ kcur // grad[q][:D,iq1,iq2,iq3] += shape[M,1] @ shape[D,M] // grad[q][:D,iq1,iq2,iq3] += S[ic] * kcur[:D,ic] // //// grad[q][ic,iq1,iq2,iq3] += dot(kcur[:,ic],S.T) //// grad[q][ic,iq1,iq2,iq3] += dot(k[:D,ic,iq2,iq3],S.T) for (int64_t ic = 0; ic < M; ++ic) { // dst indices const int i1 = iq1; const int i2 = iq2; const int i3 = iq3; ggml_vec_mad_f32(D, (float *) ((char *) grad_q + (i1*nbgq1 + i2*nbgq2 + i3*nbgq3)), (float *) ((char *) k->data + (ic*nbk1 + i2*nbk2 + i3*nbk3)), S[ic]); } // grad[k][:D,:M,iq2,iq3] += S.T @ qcur // grad[k][:D,ic,iq2,iq3] += S.T[0,ic] * qcur[:D,0] // grad[k][:D,ic,iq2,iq3] += S[ic] * qcur[:D,0] for (int64_t ic = 0; ic < M; ++ic) { // dst indices const int i1 = iq1; const int i2 = iq2; const int i3 = iq3; // ggml_vec_set_f32(D, // (float *) ((char *) grad_k + (ic*nbgk1 + i2*nbgk2 + i3*nbgk3)), // 0); ggml_vec_mad_f32(D, (float *) ((char *) grad_k + (ic*nbgk1 + i2*nbgk2 + i3*nbgk3)), (float *) ((char *) q->data + (i1*nbq1 + i2*nbq2 + i3*nbq3)), S[ic]); } // grad[v][:M,:D,iq2,iq3] += d[:D,iq1,iq2,iq3].T @ SM // grad[v][:M,ic,iq2,iq3] += d[:D,iq1,iq2,iq3].T[0,ic] * SM[:M] // grad[v][:M,ic,iq2,iq3] += d[ic,iq1,iq2,iq3] * SM[:M] for (int64_t ic = 0; ic < D; ++ic) { // dst indices const int i1 = iq1; const int i2 = iq2; const int i3 = iq3; // ggml_vec_set_f32(M, // (float *) ((char *) grad_v + ( ic*nbgv1 + i2*nbgv2 + i3*nbgv3)), // 0); ggml_vec_mad_f32(M, (float *) ((char *) grad_v + ( ic*nbgv1 + i2*nbgv2 + i3*nbgv3)), SM, *(float *) ((char *) d->data + (ic*nbd0 + i1*nbd1 + i2*nbd2 + i3*nbd3))); } } } } static void ggml_compute_forward_flash_attn_back( const struct ggml_compute_params * params, const struct ggml_tensor * q, const struct ggml_tensor * k, const struct ggml_tensor * v, const struct ggml_tensor * d, const bool masked, struct ggml_tensor * dst) { switch (q->type) { case GGML_TYPE_F32: { ggml_compute_forward_flash_attn_back_f32(params, q, k, v, d, masked, dst); } break; default: { GGML_ASSERT(false); } break; } } // ggml_compute_forward_win_part static void ggml_compute_forward_win_part_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne); GGML_TENSOR_LOCALS(int64_t, ne, dst, ne); const int32_t nep0 = ((const int32_t *)(dst->op_params))[0]; const int32_t nep1 = ((const int32_t *)(dst->op_params))[1]; const int32_t w = ((const int32_t *)(dst->op_params))[2]; assert(ne00 == ne0); assert(ne3 == nep0*nep1); // TODO: optimize / multi-thread for (int py = 0; py < nep1; ++py) { for (int px = 0; px < nep0; ++px) { const int64_t i3 = py*nep0 + px; for (int64_t i2 = 0; i2 < ne2; ++i2) { for (int64_t i1 = 0; i1 < ne1; ++i1) { for (int64_t i0 = 0; i0 < ne0; ++i0) { const int64_t i02 = py*w + i2; const int64_t i01 = px*w + i1; const int64_t i00 = i0; const int64_t i = i3*ne2*ne1*ne0 + i2*ne1*ne0 + i1*ne0 + i0; const int64_t j = i02*ne01*ne00 + i01*ne00 + i00; if (py*w + i2 >= ne02 || px*w + i1 >= ne01) { ((float *) dst->data)[i] = 0.0f; } else { ((float *) dst->data)[i] = ((float *) src0->data)[j]; } } } } } } } static void ggml_compute_forward_win_part( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_win_part_f32(params, src0, dst); } break; default: { GGML_ASSERT(false); } break; } } // ggml_compute_forward_win_unpart static void ggml_compute_forward_win_unpart_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne); GGML_TENSOR_LOCALS(int64_t, ne, dst, ne); const int32_t w = ((const int32_t *)(dst->op_params))[0]; // padding const int px = (w - ne1%w)%w; //const int py = (w - ne2%w)%w; const int npx = (px + ne1)/w; //const int npy = (py + ne2)/w; assert(ne0 == ne00); // TODO: optimize / multi-thread for (int64_t i2 = 0; i2 < ne2; ++i2) { for (int64_t i1 = 0; i1 < ne1; ++i1) { for (int64_t i0 = 0; i0 < ne0; ++i0) { const int ip2 = i2/w; const int ip1 = i1/w; const int64_t i02 = i2%w; const int64_t i01 = i1%w; const int64_t i00 = i0; const int64_t i = (ip2*npx + ip1)*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00 + i00; const int64_t j = i2*ne1*ne0 + i1*ne0 + i0; ((float *) dst->data)[j] = ((float *) src0->data)[i]; } } } } static void ggml_compute_forward_win_unpart( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_win_unpart_f32(params, src0, dst); } break; default: { GGML_ASSERT(false); } break; } } //gmml_compute_forward_unary static void ggml_compute_forward_unary( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { const enum ggml_unary_op op = ggml_get_unary_op(dst); switch (op) { case GGML_UNARY_OP_ABS: { ggml_compute_forward_abs(params, src0, dst); } break; case GGML_UNARY_OP_SGN: { ggml_compute_forward_sgn(params, src0, dst); } break; case GGML_UNARY_OP_NEG: { ggml_compute_forward_neg(params, src0, dst); } break; case GGML_UNARY_OP_STEP: { ggml_compute_forward_step(params, src0, dst); } break; case GGML_UNARY_OP_TANH: { ggml_compute_forward_tanh(params, src0, dst); } break; case GGML_UNARY_OP_ELU: { ggml_compute_forward_elu(params, src0, dst); } break; case GGML_UNARY_OP_RELU: { ggml_compute_forward_relu(params, src0, dst); } break; case GGML_UNARY_OP_GELU: { ggml_compute_forward_gelu(params, src0, dst); } break; case GGML_UNARY_OP_GELU_QUICK: { ggml_compute_forward_gelu_quick(params, src0, dst); } break; case GGML_UNARY_OP_SILU: { ggml_compute_forward_silu(params, src0, dst); } break; default: { GGML_ASSERT(false); } break; } } // ggml_compute_forward_map_unary static void ggml_compute_forward_map_unary_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst, const ggml_unary_op_f32_t fun) { GGML_ASSERT(ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } const int n = ggml_nrows(src0); const int nc = src0->ne[0]; assert( dst->nb[0] == sizeof(float)); assert(src0->nb[0] == sizeof(float)); for (int i = 0; i < n; i++) { fun(nc, (float *) ((char *) dst->data + i*( dst->nb[1])), (float *) ((char *) src0->data + i*(src0->nb[1]))); } } static void ggml_compute_forward_map_unary( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst, const ggml_unary_op_f32_t fun) { switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_map_unary_f32(params, src0, dst, fun); } break; default: { GGML_ASSERT(false); } break; } } // ggml_compute_forward_map_binary static void ggml_compute_forward_map_binary_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst, const ggml_binary_op_f32_t fun) { assert(params->ith == 0); assert(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } const int n = ggml_nrows(src0); const int nc = src0->ne[0]; assert( dst->nb[0] == sizeof(float)); assert(src0->nb[0] == sizeof(float)); assert(src1->nb[0] == sizeof(float)); for (int i = 0; i < n; i++) { fun(nc, (float *) ((char *) dst->data + i*( dst->nb[1])), (float *) ((char *) src0->data + i*(src0->nb[1])), (float *) ((char *) src1->data + i*(src1->nb[1]))); } } static void ggml_compute_forward_map_binary( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst, const ggml_binary_op_f32_t fun) { switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_map_binary_f32(params, src0, src1, dst, fun); } break; default: { GGML_ASSERT(false); } break; } } // ggml_compute_forward_map_custom1 static void ggml_compute_forward_map_custom1_f32( const struct ggml_compute_params * params, const struct ggml_tensor * a, struct ggml_tensor * dst, const ggml_custom1_op_f32_t fun) { assert(params->ith == 0); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } fun(dst, a); } // ggml_compute_forward_map_custom2 static void ggml_compute_forward_map_custom2_f32( const struct ggml_compute_params * params, const struct ggml_tensor * a, const struct ggml_tensor * b, struct ggml_tensor * dst, const ggml_custom2_op_f32_t fun) { assert(params->ith == 0); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } fun(dst, a, b); } // ggml_compute_forward_map_custom3 static void ggml_compute_forward_map_custom3_f32( const struct ggml_compute_params * params, const struct ggml_tensor * a, const struct ggml_tensor * b, const struct ggml_tensor * c, struct ggml_tensor * dst, const ggml_custom3_op_f32_t fun) { assert(params->ith == 0); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } fun(dst, a, b, c); } // ggml_compute_forward_map_custom1 static void ggml_compute_forward_map_custom1( const struct ggml_compute_params * params, const struct ggml_tensor * a, struct ggml_tensor * dst) { if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } struct ggml_map_custom1_op_params * p = (struct ggml_map_custom1_op_params *) dst->op_params; p->fun(dst, a, params->ith, params->nth, p->userdata); } // ggml_compute_forward_map_custom2 static void ggml_compute_forward_map_custom2( const struct ggml_compute_params * params, const struct ggml_tensor * a, const struct ggml_tensor * b, struct ggml_tensor * dst) { if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } struct ggml_map_custom2_op_params * p = (struct ggml_map_custom2_op_params *) dst->op_params; p->fun(dst, a, b, params->ith, params->nth, p->userdata); } // ggml_compute_forward_map_custom3 static void ggml_compute_forward_map_custom3( const struct ggml_compute_params * params, const struct ggml_tensor * a, const struct ggml_tensor * b, const struct ggml_tensor * c, struct ggml_tensor * dst) { if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } struct ggml_map_custom3_op_params * p = (struct ggml_map_custom3_op_params *) dst->op_params; p->fun(dst, a, b, c, params->ith, params->nth, p->userdata); } // ggml_compute_forward_cross_entropy_loss static void ggml_compute_forward_cross_entropy_loss_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { GGML_ASSERT(ggml_is_contiguous(src0)); GGML_ASSERT(ggml_is_contiguous(src1)); GGML_ASSERT(ggml_is_scalar(dst)); GGML_ASSERT(ggml_are_same_shape(src0, src1)); const int ith = params->ith; const int nth = params->nth; float * sums = (float *) params->wdata; // TODO: handle transposed/permuted matrices const int nc = src0->ne[0]; const int nr = ggml_nrows(src0); if (params->type == GGML_TASK_INIT) { if (ith == 0) { memset(sums, 0, sizeof(float) * (nth + nth * nc)); } return; } if (params->type == GGML_TASK_FINALIZE) { if (ith == 0) { float * dp = (float *) dst->data; ggml_vec_sum_f32(nth, dp, sums); dp[0] *= -1.0f; } return; } const double eps = 1e-9; // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); for (int i1 = ir0; i1 < ir1; i1++) { float * s0 = (float *)((char *) src0->data + i1*src0->nb[1]); float * s1 = (float *)((char *) src1->data + i1*src1->nb[1]); float * st = (float *) params->wdata + nth + ith*nc; #ifndef NDEBUG for (int i = 0; i < nc; ++i) { //printf("p[%d] = %f\n", i, p[i]); assert(!isnan(s0[i])); assert(!isnan(s1[i])); } #endif // soft_max ggml_float sum = 0.0; { float max = -INFINITY; ggml_vec_max_f32(nc, &max, s0); uint16_t scvt; for (int i = 0; i < nc; i++) { if (s0[i] == -INFINITY) { st[i] = 0.0f; } else { // const float val = (s0[i] == -INFINITY) ? 0.0 : exp(s0[i] - max); ggml_fp16_t s = GGML_FP32_TO_FP16(s0[i] - max); memcpy(&scvt, &s, sizeof(scvt)); const float val = GGML_FP16_TO_FP32(table_exp_f16[scvt]); sum += (ggml_float)val; st[i] = val; } } assert(sum > 0.0); // sum = 1.0/sum; } // avoid log(0) by rescaling from [0..1] to [eps..1] sum = (1.0 - eps) / sum; ggml_vec_scale_f32(nc, st, sum); ggml_vec_add1_f32(nc, st, st, eps); ggml_vec_log_f32(nc, st, st); ggml_vec_mul_f32(nc, st, st, s1); ggml_vec_sum_f32(nc, sums + ith, st); #ifndef NDEBUG for (int i = 0; i < nc; ++i) { assert(!isnan(st[i])); assert(!isinf(st[i])); } #endif } } static void ggml_compute_forward_cross_entropy_loss( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_cross_entropy_loss_f32(params, src0, src1, dst); } break; default: { GGML_ASSERT(false); } break; } } // ggml_compute_forward_cross_entropy_loss_back static void ggml_compute_forward_cross_entropy_loss_back_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, const struct ggml_tensor * opt0, struct ggml_tensor * dst) { GGML_ASSERT(ggml_is_contiguous(dst)); GGML_ASSERT(ggml_is_contiguous(src0)); GGML_ASSERT(ggml_is_contiguous(src1)); GGML_ASSERT(ggml_is_contiguous(opt0)); GGML_ASSERT(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst)); const int64_t ith = params->ith; const int64_t nth = params->nth; if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } const float eps = 1e-9f; // TODO: handle transposed/permuted matrices const int64_t nc = src0->ne[0]; const int64_t nr = ggml_nrows(src0); // rows per thread const int64_t dr = (nr + nth - 1)/nth; // row range for this thread const int64_t ir0 = dr*ith; const int64_t ir1 = MIN(ir0 + dr, nr); float * d = (float *) opt0->data; for (int64_t i1 = ir0; i1 < ir1; i1++) { float * ds0 = (float *)((char *) dst->data + i1*dst->nb[1]); float * s0 = (float *)((char *) src0->data + i1*src0->nb[1]); float * s1 = (float *)((char *) src1->data + i1*src1->nb[1]); float * sm = (float *) params->wdata + ith*nc; #ifndef NDEBUG for (int i = 0; i < nc; ++i) { //printf("p[%d] = %f\n", i, p[i]); assert(!isnan(s0[i])); assert(!isnan(s1[i])); } #endif // step by step explanation: { //float * sums = (float *) params->wdata; // forward pass with annotated gradients from backward pass // (built by going in reverse operation order, adding to gradients of current operation args) // st0 = exp(s0-max(s0)) grad[st0] = grad[st1]*(1.0 - eps)/sum // from softmax_back: grad[s0] = st1_k * (grad[st1]_k - dot(st1, grad[st1])) // ggml_vec_scale_f32(nc, st, sum); // st1 = st0*/sum = softmax(s0) grad[st1] = grad[st2]*(1.0 - eps) // ggml_vec_scale_f32(nc, st, (1.0f - eps)); // st2 = st1*(1.0 - eps) grad[st2] = grad[st3] // ggml_vec_add1_f32(nc, st, st, eps); // st3 = st2 + eps grad[st3] = grad[st4]/st3 // ggml_vec_log_f32(nc, st, st); // st4 = log(st3) grad[st4] = grad[st5] * s1 // ggml_vec_mul_f32(nc, st, st, s1); // st5 = st4 * s1 grad[st5] = grad[sums[ith]] // ggml_vec_sum_f32(nc, sums + ith, st); // sums[ith] = st5 grad[sums[ith]] = grad[cross_entropy_loss] = -grad[cel] // substitute into grad[st1], because we can reuse softmax_back from this point on // grad[st1] = -grad[cel]*s1*(1.0 - eps)/(eps + softmax(s0)*(1.0 - eps)) // postorder: // grad[st1] := softmax(s0) // grad[st1] := grad[st1]*(1.0 - eps) // grad[st1] := grad[st1] + eps // grad[st1] := s1 / grad[st1] // grad[st1] := grad[st1]*(1.0-eps)*-grad[cel] // src0 gradients by going through softmax_back // grad[s0] = st1_k * (grad[st1]_k - dot(st1, grad[st1])) // from softmax_back: // dxk = yk * (dyk - dot(y, dy)) // dot_y_dy := dot(y, dy) // dx := dy // dx := dx - dot_y_dy // dx := dx * y // postorder: // dot_st1_dst1 := dot(st1, grad[st1]) // grad[s0] := grad[st1] // grad[s0] := grad[s0] - dot_st1_dst1 // grad[s0] := grad[s0] * st1 // prepend postorder from grad[st1] directly using grad[s0] as memory location, as we will grad[s0] := grad[st1] // sm := softmax(s0) // grad[s0] := sm*(1.0 - eps) // grad[s0] := grad[s0] + eps // grad[s0] := s1 / grad[s0] // grad[s0] := grad[s0]*(1.0-eps)*-grad[cel] // dot_st1_dst1 := dot(sm, grad[s0]) // grad[s0] := grad[s0] - dot_st1_dst1 // grad[s0] := grad[s0] * sm } // soft_max ggml_float sum = 0.0; { float max = -INFINITY; ggml_vec_max_f32(nc, &max, s0); uint16_t scvt; for (int i = 0; i < nc; i++) { if (s0[i] == -INFINITY) { sm[i] = 0.0f; } else { // const float val = (s0[i] == -INFINITY) ? 0.0 : exp(s0[i] - max); ggml_fp16_t s = GGML_FP32_TO_FP16(s0[i] - max); memcpy(&scvt, &s, sizeof(scvt)); const float val = GGML_FP16_TO_FP32(table_exp_f16[scvt]); sum += (ggml_float)val; sm[i] = val; } } assert(sum > 0.0); sum = 1.0/sum; } float dot_st1_dst1 = 0; ggml_vec_scale_f32(nc, sm, sum); ggml_vec_cpy_f32 (nc, ds0, sm); ggml_vec_scale_f32(nc, ds0, (1.0f - eps)); ggml_vec_add1_f32 (nc, ds0, ds0, eps); ggml_vec_div_f32 (nc, ds0, s1, ds0); ggml_vec_scale_f32(nc, ds0, -(1.0f - eps)*d[0]); ggml_vec_dot_f32 (nc, &dot_st1_dst1, sm, ds0); ggml_vec_acc1_f32 (nc, ds0, -dot_st1_dst1); ggml_vec_mul_f32 (nc, ds0, ds0, sm); #ifndef NDEBUG for (int i = 0; i < nc; ++i) { assert(!isnan(sm[i])); assert(!isinf(sm[i])); assert(!isnan(ds0[i])); assert(!isinf(ds0[i])); } #endif } } static void ggml_compute_forward_cross_entropy_loss_back( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, const struct ggml_tensor * opt0, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_cross_entropy_loss_back_f32(params, src0, src1, opt0, dst); } break; default: { GGML_ASSERT(false); } break; } } ///////////////////////////////// static void ggml_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor) { GGML_ASSERT(params); #ifdef GGML_USE_CUBLAS bool skip_cpu = ggml_cuda_compute_forward(params, tensor); if (skip_cpu) { return; } GGML_ASSERT(tensor->src[0] == NULL || tensor->src[0]->backend == GGML_BACKEND_CPU); GGML_ASSERT(tensor->src[1] == NULL || tensor->src[1]->backend == GGML_BACKEND_CPU); #endif // GGML_USE_CUBLAS switch (tensor->op) { case GGML_OP_DUP: { ggml_compute_forward_dup(params, tensor->src[0], tensor); } break; case GGML_OP_ADD: { ggml_compute_forward_add(params, tensor->src[0], tensor->src[1], tensor); } break; case GGML_OP_ADD1: { ggml_compute_forward_add1(params, tensor->src[0], tensor->src[1], tensor); } break; case GGML_OP_ACC: { ggml_compute_forward_acc(params, tensor->src[0], tensor->src[1], tensor); } break; case GGML_OP_SUB: { ggml_compute_forward_sub(params, tensor->src[0], tensor->src[1], tensor); } break; case GGML_OP_MUL: { ggml_compute_forward_mul(params, tensor->src[0], tensor->src[1], tensor); } break; case GGML_OP_DIV: { ggml_compute_forward_div(params, tensor->src[0], tensor->src[1], tensor); } break; case GGML_OP_SQR: { ggml_compute_forward_sqr(params, tensor->src[0], tensor); } break; case GGML_OP_SQRT: { ggml_compute_forward_sqrt(params, tensor->src[0], tensor); } break; case GGML_OP_LOG: { ggml_compute_forward_log(params, tensor->src[0], tensor); } break; case GGML_OP_SUM: { ggml_compute_forward_sum(params, tensor->src[0], tensor); } break; case GGML_OP_SUM_ROWS: { ggml_compute_forward_sum_rows(params, tensor->src[0], tensor); } break; case GGML_OP_MEAN: { ggml_compute_forward_mean(params, tensor->src[0], tensor); } break; case GGML_OP_ARGMAX: { ggml_compute_forward_argmax(params, tensor->src[0], tensor); } break; case GGML_OP_REPEAT: { ggml_compute_forward_repeat(params, tensor->src[0], tensor); } break; case GGML_OP_REPEAT_BACK: { ggml_compute_forward_repeat_back(params, tensor->src[0], tensor); } break; case GGML_OP_SILU_BACK: { ggml_compute_forward_silu_back(params, tensor->src[0], tensor->src[1], tensor); } break; case GGML_OP_NORM: { ggml_compute_forward_norm(params, tensor->src[0], tensor); } break; case GGML_OP_RMS_NORM: { ggml_compute_forward_rms_norm(params, tensor->src[0], tensor); } break; case GGML_OP_RMS_NORM_BACK: { ggml_compute_forward_rms_norm_back(params, tensor->src[0], tensor->src[1], tensor); } break; case GGML_OP_MUL_MAT: { ggml_compute_forward_mul_mat(params, tensor->src[0], tensor->src[1], tensor); } break; case GGML_OP_OUT_PROD: { ggml_compute_forward_out_prod(params, tensor->src[0], tensor->src[1], tensor); } break; case GGML_OP_SCALE: { ggml_compute_forward_scale(params, tensor->src[0], tensor->src[1], tensor); } break; case GGML_OP_SET: { ggml_compute_forward_set(params, tensor->src[0], tensor->src[1], tensor); } break; case GGML_OP_CPY: { ggml_compute_forward_cpy(params, tensor->src[0], tensor); } break; case GGML_OP_CONT: { ggml_compute_forward_cont(params, tensor->src[0], tensor); } break; case GGML_OP_RESHAPE: { ggml_compute_forward_reshape(params, tensor->src[0], tensor); } break; case GGML_OP_VIEW: { ggml_compute_forward_view(params, tensor->src[0]); } break; case GGML_OP_PERMUTE: { ggml_compute_forward_permute(params, tensor->src[0]); } break; case GGML_OP_TRANSPOSE: { ggml_compute_forward_transpose(params, tensor->src[0]); } break; case GGML_OP_GET_ROWS: { ggml_compute_forward_get_rows(params, tensor->src[0], tensor->src[1], tensor); } break; case GGML_OP_GET_ROWS_BACK: { ggml_compute_forward_get_rows_back(params, tensor->src[0], tensor->src[1], tensor->src[2], tensor); } break; case GGML_OP_DIAG: { ggml_compute_forward_diag(params, tensor->src[0], tensor); } break; case GGML_OP_DIAG_MASK_INF: { ggml_compute_forward_diag_mask_inf(params, tensor->src[0], tensor); } break; case GGML_OP_DIAG_MASK_ZERO: { ggml_compute_forward_diag_mask_zero(params, tensor->src[0], tensor); } break; case GGML_OP_SOFT_MAX: { ggml_compute_forward_soft_max(params, tensor->src[0], tensor); } break; case GGML_OP_SOFT_MAX_BACK: { ggml_compute_forward_soft_max_back(params, tensor->src[0], tensor->src[1], tensor); } break; case GGML_OP_ROPE: { ggml_compute_forward_rope(params, tensor->src[0], tensor); } break; case GGML_OP_ROPE_BACK: { ggml_compute_forward_rope_back(params, tensor->src[0], tensor); } break; case GGML_OP_ALIBI: { ggml_compute_forward_alibi(params, tensor->src[0], tensor); } break; case GGML_OP_CLAMP: { ggml_compute_forward_clamp(params, tensor->src[0], tensor); } break; case GGML_OP_CONV_1D: { ggml_compute_forward_conv_1d(params, tensor->src[0], tensor->src[1], tensor); } break; case GGML_OP_CONV_2D: { ggml_compute_forward_conv_2d(params, tensor->src[0], tensor->src[1], tensor); } break; case GGML_OP_POOL_1D: { ggml_compute_forward_pool_1d(params, tensor->src[0], tensor); } break; case GGML_OP_POOL_2D: { ggml_compute_forward_pool_2d(params, tensor->src[0], tensor); } break; case GGML_OP_FLASH_ATTN: { const int32_t t = ggml_get_op_params_i32(tensor, 0); GGML_ASSERT(t == 0 || t == 1); const bool masked = t != 0; ggml_compute_forward_flash_attn(params, tensor->src[0], tensor->src[1], tensor->src[2], masked, tensor); } break; case GGML_OP_FLASH_FF: { ggml_compute_forward_flash_ff(params, tensor->src[0], tensor->src[1], tensor->src[2], tensor->src[3], tensor->src[4], tensor); } break; case GGML_OP_FLASH_ATTN_BACK: { int32_t t = ggml_get_op_params_i32(tensor, 0); GGML_ASSERT(t == 0 || t == 1); bool masked = t != 0; ggml_compute_forward_flash_attn_back(params, tensor->src[0], tensor->src[1], tensor->src[2], tensor->src[3], masked, tensor); } break; case GGML_OP_WIN_PART: { ggml_compute_forward_win_part(params, tensor->src[0], tensor); } break; case GGML_OP_WIN_UNPART: { ggml_compute_forward_win_unpart(params, tensor->src[0], tensor); } break; case GGML_OP_UNARY: { ggml_compute_forward_unary(params, tensor->src[0], tensor); } break; case GGML_OP_MAP_UNARY: { ggml_unary_op_f32_t fun; memcpy(&fun, tensor->op_params, sizeof(fun)); ggml_compute_forward_map_unary(params, tensor->src[0], tensor, fun); } break; case GGML_OP_MAP_BINARY: { ggml_binary_op_f32_t fun; memcpy(&fun, tensor->op_params, sizeof(fun)); ggml_compute_forward_map_binary(params, tensor->src[0], tensor->src[1], tensor, fun); } break; case GGML_OP_MAP_CUSTOM1_F32: { ggml_custom1_op_f32_t fun; memcpy(&fun, tensor->op_params, sizeof(fun)); ggml_compute_forward_map_custom1_f32(params, tensor->src[0], tensor, fun); } break; case GGML_OP_MAP_CUSTOM2_F32: { ggml_custom2_op_f32_t fun; memcpy(&fun, tensor->op_params, sizeof(fun)); ggml_compute_forward_map_custom2_f32(params, tensor->src[0], tensor->src[1], tensor, fun); } break; case GGML_OP_MAP_CUSTOM3_F32: { ggml_custom3_op_f32_t fun; memcpy(&fun, tensor->op_params, sizeof(fun)); ggml_compute_forward_map_custom3_f32(params, tensor->src[0], tensor->src[1], tensor->src[2], tensor, fun); } break; case GGML_OP_MAP_CUSTOM1: { ggml_compute_forward_map_custom1(params, tensor->src[0], tensor); } break; case GGML_OP_MAP_CUSTOM2: { ggml_compute_forward_map_custom2(params, tensor->src[0], tensor->src[1], tensor); } break; case GGML_OP_MAP_CUSTOM3: { ggml_compute_forward_map_custom3(params, tensor->src[0], tensor->src[1], tensor->src[2], tensor); } break; case GGML_OP_CROSS_ENTROPY_LOSS: { ggml_compute_forward_cross_entropy_loss(params, tensor->src[0], tensor->src[1], tensor); } break; case GGML_OP_CROSS_ENTROPY_LOSS_BACK: { ggml_compute_forward_cross_entropy_loss_back(params, tensor->src[0], tensor->src[1], tensor->src[2], tensor); } break; case GGML_OP_NONE: { // nop } break; case GGML_OP_COUNT: { GGML_ASSERT(false); } break; } } //////////////////////////////////////////////////////////////////////////////// static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor * tensor, bool inplace) { struct ggml_tensor * src0 = tensor->src[0]; struct ggml_tensor * src1 = tensor->src[1]; switch (tensor->op) { case GGML_OP_DUP: { if (src0->grad) { src0->grad = ggml_add_impl(ctx, src0->grad, tensor->grad, inplace); } } break; case GGML_OP_ADD: { if (src0->grad) { src0->grad = ggml_add_impl(ctx, src0->grad, tensor->grad, inplace); } if (src1->grad) { src1->grad = ggml_add_impl(ctx, src1->grad, tensor->grad, inplace); } } break; case GGML_OP_ADD1: { if (src0->grad) { src0->grad = ggml_add_impl(ctx, src0->grad, tensor->grad, inplace); } if (src1->grad) { src1->grad = ggml_add_impl(ctx, src1->grad, ggml_mean(ctx, tensor->grad), // TODO: should probably be sum instead of mean inplace); } } break; case GGML_OP_ACC: { if (src0->grad) { src0->grad = ggml_add_impl(ctx, src0->grad, tensor->grad, inplace); } if (src1->grad) { const size_t nb1 = ((int32_t *) tensor->op_params)[0]; const size_t nb2 = ((int32_t *) tensor->op_params)[1]; const size_t nb3 = ((int32_t *) tensor->op_params)[2]; const size_t offset = ((int32_t *) tensor->op_params)[3]; struct ggml_tensor * tensor_grad_view = ggml_view_4d(ctx, tensor->grad, src1->grad->ne[0], src1->grad->ne[1], src1->grad->ne[2], src1->grad->ne[3], nb1, nb2, nb3, offset); src1->grad = ggml_add_impl(ctx, src1->grad, ggml_reshape(ctx, ggml_cont(ctx, tensor_grad_view), src1->grad), inplace); } } break; case GGML_OP_SUB: { if (src0->grad) { src0->grad = ggml_add_impl(ctx, src0->grad, tensor->grad, inplace); } if (src1->grad) { src1->grad = ggml_sub_impl(ctx, src1->grad, tensor->grad, inplace); } } break; case GGML_OP_MUL: { if (src0->grad) { src0->grad = ggml_add_impl(ctx, src0->grad, ggml_mul(ctx, src1, tensor->grad), inplace); } if (src1->grad) { src1->grad = ggml_add_impl(ctx, src1->grad, ggml_mul(ctx, src0, tensor->grad), inplace); } } break; case GGML_OP_DIV: { if (src0->grad) { src0->grad = ggml_add_impl(ctx, src0->grad, ggml_div(ctx, tensor->grad, src1), inplace); } if (src1->grad) { src1->grad = ggml_sub_impl(ctx, src1->grad, ggml_mul(ctx, tensor->grad, ggml_div(ctx, tensor, src1)), inplace); } } break; case GGML_OP_SQR: { if (src0->grad) { src0->grad = ggml_add_impl(ctx, src0->grad, ggml_scale(ctx, ggml_mul(ctx, src0, tensor->grad), ggml_new_f32(ctx, 2.0f)), inplace); } } break; case GGML_OP_SQRT: { if (src0->grad) { src0->grad = ggml_add_impl(ctx, src0->grad, ggml_scale(ctx, ggml_div(ctx, tensor->grad, tensor), ggml_new_f32(ctx, 0.5f)), inplace); } } break; case GGML_OP_LOG: { if (src0->grad) { src0->grad = ggml_add_impl(ctx, src0->grad, ggml_div(ctx, tensor->grad, src0), inplace); } } break; case GGML_OP_SUM: { if (src0->grad) { src0->grad = ggml_add1_impl(ctx, src0->grad, tensor->grad, inplace); } } break; case GGML_OP_SUM_ROWS: { if (src0->grad) { src0->grad = ggml_add_impl(ctx, src0->grad, ggml_repeat(ctx, tensor->grad, src0->grad), inplace); } } break; case GGML_OP_MEAN: case GGML_OP_ARGMAX: { GGML_ASSERT(false); // TODO: implement } break; case GGML_OP_REPEAT: { // necessary for llama if (src0->grad) { src0->grad = ggml_add_impl(ctx, src0->grad, ggml_repeat_back(ctx, tensor->grad, src0->grad), inplace); } } break; case GGML_OP_REPEAT_BACK: { if (src0->grad) { // TODO: test this src0->grad = ggml_add_impl(ctx, src0->grad, ggml_repeat(ctx, tensor->grad, src0->grad), inplace); } } break; case GGML_OP_SILU_BACK: { GGML_ASSERT(false); // TODO: not implemented } break; case GGML_OP_NORM: { GGML_ASSERT(false); // TODO: not implemented } break; case GGML_OP_RMS_NORM: { // necessary for llama if (src0->grad) { src0->grad = ggml_add_impl(ctx, src0->grad, ggml_rms_norm_back(ctx, src0, tensor->grad), inplace); } } break; case GGML_OP_RMS_NORM_BACK: { GGML_ASSERT(false); // TODO: not implemented } break; case GGML_OP_MUL_MAT: { // https://cs231n.github.io/optimization-2/#staged // # forward pass // s0 = np.random.randn(5, 10) // s1 = np.random.randn(10, 3) // t = s0.dot(s1) // # now suppose we had the gradient on t from above in the circuit // dt = np.random.randn(*t.shape) # same shape as t // ds0 = dt.dot(s1.T) #.T gives the transpose of the matrix // ds1 = t.T.dot(dt) // tensor.shape [m,p] // src0.shape [n,m] // src1.shape [n,p] // necessary for llama if (src0->grad) { src0->grad = ggml_add_impl(ctx, src0->grad, ggml_out_prod(ctx, // [n,m] src1, // [n,p] tensor->grad), // [m,p] inplace); } if (src1->grad) { src1->grad = ggml_add_impl(ctx, src1->grad, // ggml_mul_mat(ctx, // [n,p] // ggml_cont(ctx, // [m,n] // ggml_transpose(ctx, src0)), // [m,n] // tensor->grad), // [m,p] // // when src0 is bigger than tensor->grad (this is mostly the case in llama), // // avoid transpose of src0, rather transpose smaller tensor->grad // // and then use ggml_out_prod ggml_out_prod(ctx, // [n,p] src0, // [n,m] ggml_transpose(ctx, // [p,m] tensor->grad)), // [m,p] inplace); } } break; case GGML_OP_OUT_PROD: { GGML_ASSERT(false); // TODO: not implemented } break; case GGML_OP_SCALE: { // necessary for llama if (src0->grad) { src0->grad = ggml_add_impl(ctx, src0->grad, ggml_scale_impl(ctx, tensor->grad, src1, false), inplace); } if (src1->grad) { src1->grad = ggml_add_impl(ctx, src1->grad, ggml_sum(ctx, ggml_mul_impl(ctx, tensor->grad, src0, false)), inplace); } } break; case GGML_OP_SET: { const size_t nb1 = ((int32_t *) tensor->op_params)[0]; const size_t nb2 = ((int32_t *) tensor->op_params)[1]; const size_t nb3 = ((int32_t *) tensor->op_params)[2]; const size_t offset = ((int32_t *) tensor->op_params)[3]; struct ggml_tensor * tensor_grad_view = NULL; if (src0->grad || src1->grad) { GGML_ASSERT(src0->type == tensor->type); GGML_ASSERT(tensor->grad->type == tensor->type); GGML_ASSERT(tensor->grad->type == src1->grad->type); tensor_grad_view = ggml_view_4d(ctx, tensor->grad, src1->grad->ne[0], src1->grad->ne[1], src1->grad->ne[2], src1->grad->ne[3], nb1, nb2, nb3, offset); } if (src0->grad) { src0->grad = ggml_add_impl(ctx, src0->grad, ggml_acc_impl(ctx, tensor->grad, ggml_neg(ctx, tensor_grad_view), nb1, nb2, nb3, offset, false), inplace); } if (src1->grad) { src1->grad = ggml_add_impl(ctx, src1->grad, ggml_reshape(ctx, ggml_cont(ctx, tensor_grad_view), src1->grad), inplace); } } break; case GGML_OP_CPY: { // necessary for llama // cpy overwrites value of src1 by src0 and returns view(src1) // the overwriting is mathematically equivalent to: // tensor = src0 * 1 + src1 * 0 if (src0->grad) { // dsrc0 = dtensor * 1 src0->grad = ggml_add_impl(ctx, src0->grad, tensor->grad, inplace); } if (src1->grad) { // dsrc1 = dtensor * 0 -> noop } } break; case GGML_OP_CONT: { // same as cpy if (src0->grad) { GGML_ASSERT(ggml_is_contiguous(src0->grad)); GGML_ASSERT(ggml_is_contiguous(tensor->grad)); src0->grad = ggml_add_impl(ctx, src0->grad, tensor->grad, inplace); } } break; case GGML_OP_RESHAPE: { // necessary for llama if (src0->grad) { src0->grad = ggml_add_impl(ctx, src0->grad, ggml_reshape(ctx, tensor->grad, src0->grad), inplace); } } break; case GGML_OP_VIEW: { // necessary for llama if (src0->grad) { size_t offset; memcpy(&offset, tensor->op_params, sizeof(offset)); size_t nb1 = tensor->nb[1]; size_t nb2 = tensor->nb[2]; size_t nb3 = tensor->nb[3]; if (src0->type != src0->grad->type) { // gradient is typically F32, but src0 could be other type size_t ng = ggml_element_size(src0->grad); size_t n0 = ggml_element_size(src0); GGML_ASSERT(offset % n0 == 0); GGML_ASSERT(nb1 % n0 == 0); GGML_ASSERT(nb2 % n0 == 0); GGML_ASSERT(nb3 % n0 == 0); offset = (offset / n0) * ng; nb1 = (nb1 / n0) * ng; nb2 = (nb2 / n0) * ng; nb3 = (nb3 / n0) * ng; } src0->grad = ggml_acc_impl(ctx, src0->grad, tensor->grad, nb1, nb2, nb3, offset, inplace); } } break; case GGML_OP_PERMUTE: { // necessary for llama if (src0->grad) { int32_t * axes = (int32_t *) tensor->op_params; int axis0 = axes[0] & 0x3; int axis1 = axes[1] & 0x3; int axis2 = axes[2] & 0x3; int axis3 = axes[3] & 0x3; int axes_backward[4] = {0,0,0,0}; axes_backward[axis0] = 0; axes_backward[axis1] = 1; axes_backward[axis2] = 2; axes_backward[axis3] = 3; src0->grad = ggml_add_impl(ctx, src0->grad, ggml_permute(ctx, tensor->grad, axes_backward[0], axes_backward[1], axes_backward[2], axes_backward[3]), inplace); } } break; case GGML_OP_TRANSPOSE: { // necessary for llama if (src0->grad) { src0->grad = ggml_add_impl(ctx, src0->grad, ggml_transpose(ctx, tensor->grad), inplace); } } break; case GGML_OP_GET_ROWS: { // necessary for llama (only for tokenizer) if (src0->grad) { src0->grad = ggml_add_impl(ctx, src0->grad, ggml_get_rows_back(ctx, tensor->grad, src1, src0->grad), inplace); } if (src1->grad) { // noop } } break; case GGML_OP_GET_ROWS_BACK: { GGML_ASSERT(false); // TODO: not implemented } break; case GGML_OP_DIAG: { GGML_ASSERT(false); // TODO: not implemented } break; case GGML_OP_DIAG_MASK_INF: { // necessary for llama if (src0->grad) { const int n_past = ((int32_t *) tensor->op_params)[0]; src0->grad = ggml_add_impl(ctx, src0->grad, ggml_diag_mask_zero_impl(ctx, tensor->grad, n_past, false), inplace); } } break; case GGML_OP_DIAG_MASK_ZERO: { // necessary for llama if (src0->grad) { const int n_past = ((int32_t *) tensor->op_params)[0]; src0->grad = ggml_add_impl(ctx, src0->grad, ggml_diag_mask_zero_impl(ctx, tensor->grad, n_past, false), inplace); } } break; case GGML_OP_SOFT_MAX: { // necessary for llama if (src0->grad) { src0->grad = ggml_add_impl(ctx, src0->grad, ggml_soft_max_back(ctx, tensor->grad, tensor), inplace); } } break; case GGML_OP_SOFT_MAX_BACK: { GGML_ASSERT(false); // TODO: not implemented } break; case GGML_OP_ROPE: { // necessary for llama if (src0->grad) { const int n_past = ((int32_t *) tensor->op_params)[0]; const int n_dims = ((int32_t *) tensor->op_params)[1]; const int mode = ((int32_t *) tensor->op_params)[2]; const int n_ctx = ((int32_t *) tensor->op_params)[3]; src0->grad = ggml_add_impl(ctx, src0->grad, ggml_rope_back(ctx, tensor->grad, n_past, n_dims, mode, n_ctx), inplace); } } break; case GGML_OP_ROPE_BACK: { if (src0->grad) { const int n_past = ((int32_t *) tensor->op_params)[0]; const int n_dims = ((int32_t *) tensor->op_params)[1]; const int mode = ((int32_t *) tensor->op_params)[2]; const int n_ctx = ((int32_t *) tensor->op_params)[3]; src0->grad = ggml_add_impl(ctx, src0->grad, ggml_rope(ctx, tensor->grad, n_past, n_dims, mode, n_ctx), inplace); } } break; case GGML_OP_ALIBI: { GGML_ASSERT(false); // TODO: not implemented } break; case GGML_OP_CLAMP: { GGML_ASSERT(false); // TODO: not implemented } break; case GGML_OP_CONV_1D: { GGML_ASSERT(false); // TODO: not implemented } break; case GGML_OP_CONV_2D: { GGML_ASSERT(false); // TODO: not implemented } break; case GGML_OP_POOL_1D: { GGML_ASSERT(false); // TODO: not implemented } break; case GGML_OP_POOL_2D: { GGML_ASSERT(false); // TODO: not implemented } break; case GGML_OP_FLASH_ATTN: { struct ggml_tensor * flash_grad = NULL; if (src0->grad || src1->grad || tensor->src[2]->grad) { int32_t t = ggml_get_op_params_i32(tensor, 0); GGML_ASSERT(t == 0 || t == 1); bool masked = t != 0; flash_grad = ggml_flash_attn_back(ctx, src0, src1, tensor->src[2], tensor->grad, masked); } if (src0->grad) { struct ggml_tensor * grad_q = NULL; const size_t nb0 = flash_grad->nb[0]; const size_t offset = 0; switch(src0->n_dims) { case 2: { grad_q = ggml_view_2d(ctx, flash_grad, src0->ne[0], src0->ne[1], nb0*src0->ne[0], offset); } break; case 3: { grad_q = ggml_view_3d(ctx, flash_grad, src0->ne[0], src0->ne[1], src0->ne[2], nb0*src0->ne[0], nb0*src0->ne[0]*src0->ne[1], offset); } break; case 4: { grad_q = ggml_view_4d(ctx, flash_grad, src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3], nb0*src0->ne[0], nb0*src0->ne[0]*src0->ne[1], nb0*src0->ne[0]*src0->ne[1]*src0->ne[2], offset); } break; } src0->grad = ggml_add_impl(ctx, src0->grad, grad_q, inplace); } if (src1->grad) { struct ggml_tensor * grad_k = NULL; const size_t nb0 = flash_grad->nb[0]; const size_t offset = nb0*src0->ne[0]*src0->ne[1]*src0->ne[2]*src0->ne[3]; switch(src1->n_dims) { case 2: { grad_k = ggml_view_2d(ctx, flash_grad, src1->ne[0], src1->ne[1], nb0*src1->ne[0], offset); } break; case 3: { grad_k = ggml_view_3d(ctx, flash_grad, src1->ne[0], src1->ne[1], src1->ne[2], nb0*src1->ne[0], nb0*src1->ne[0]*src1->ne[1], offset); } break; case 4: { grad_k = ggml_view_4d(ctx, flash_grad, src1->ne[0], src1->ne[1], src1->ne[2], src1->ne[3], nb0*src1->ne[0], nb0*src1->ne[0]*src1->ne[1], nb0*src1->ne[0]*src1->ne[1]*src1->ne[2], offset); } break; } src1->grad = ggml_add_impl(ctx, src1->grad, grad_k, inplace); } struct ggml_tensor * opt0 = tensor->src[2]; if (opt0->grad) { struct ggml_tensor * grad_v = NULL; const size_t nb0 = flash_grad->nb[0]; const size_t offset = nb0*src0->ne[0]*src0->ne[1]*src0->ne[2]*src0->ne[3] + nb0*src1->ne[0]*src1->ne[1]*src1->ne[2]*src1->ne[3]; switch(opt0->n_dims) { case 2: { grad_v = ggml_view_2d(ctx, flash_grad, opt0->ne[0], opt0->ne[1], nb0*opt0->ne[0], offset); } break; case 3: { grad_v = ggml_view_3d(ctx, flash_grad, opt0->ne[0], opt0->ne[1], opt0->ne[2], nb0*opt0->ne[0], nb0*opt0->ne[0]*opt0->ne[1], offset); } break; case 4: { grad_v = ggml_view_4d(ctx, flash_grad, opt0->ne[0], opt0->ne[1], opt0->ne[2], opt0->ne[3], nb0*opt0->ne[0], nb0*opt0->ne[0]*opt0->ne[1], nb0*opt0->ne[0]*opt0->ne[1]*opt0->ne[2], offset); } break; } opt0->grad = ggml_add_impl(ctx, opt0->grad, grad_v, inplace); } } break; case GGML_OP_FLASH_FF: { GGML_ASSERT(false); // not supported } break; case GGML_OP_FLASH_ATTN_BACK: { GGML_ASSERT(false); // not supported } break; case GGML_OP_WIN_PART: case GGML_OP_WIN_UNPART: case GGML_OP_UNARY: { switch (ggml_get_unary_op(tensor)) { case GGML_UNARY_OP_ABS: { if (src0->grad) { src0->grad = ggml_add_impl(ctx, src0->grad, ggml_mul(ctx, ggml_sgn(ctx, src0), tensor->grad), inplace); } } break; case GGML_UNARY_OP_SGN: { if (src0->grad) { // noop } } break; case GGML_UNARY_OP_NEG: { if (src0->grad) { src0->grad = ggml_sub_impl(ctx, src0->grad, tensor->grad, inplace); } } break; case GGML_UNARY_OP_STEP: { if (src0->grad) { // noop } } break; case GGML_UNARY_OP_TANH: { GGML_ASSERT(false); // TODO: not implemented } break; case GGML_UNARY_OP_ELU: { GGML_ASSERT(false); // TODO: not implemented } break; case GGML_UNARY_OP_RELU: { if (src0->grad) { src0->grad = ggml_add_impl(ctx, src0->grad, ggml_mul(ctx, ggml_step(ctx, src0), tensor->grad), inplace); } } break; case GGML_UNARY_OP_GELU: { GGML_ASSERT(false); // TODO: not implemented } break; case GGML_UNARY_OP_GELU_QUICK: { GGML_ASSERT(false); // TODO: not implemented } break; case GGML_UNARY_OP_SILU: { // necessary for llama if (src0->grad) { src0->grad = ggml_add_impl(ctx, src0->grad, ggml_silu_back(ctx, src0, tensor->grad), inplace); } } break; default: GGML_ASSERT(false); } } break; case GGML_OP_MAP_UNARY: case GGML_OP_MAP_BINARY: case GGML_OP_MAP_CUSTOM1_F32: case GGML_OP_MAP_CUSTOM2_F32: case GGML_OP_MAP_CUSTOM3_F32: case GGML_OP_MAP_CUSTOM1: case GGML_OP_MAP_CUSTOM2: case GGML_OP_MAP_CUSTOM3: { GGML_ASSERT(false); // not supported } break; case GGML_OP_CROSS_ENTROPY_LOSS: { if (src0->grad) { src0->grad = ggml_add_impl(ctx, src0->grad, ggml_cross_entropy_loss_back(ctx, src0, src1, tensor->grad), inplace); } } break; case GGML_OP_CROSS_ENTROPY_LOSS_BACK: { GGML_ASSERT(false); // not supported } break; case GGML_OP_NONE: { // nop } break; case GGML_OP_COUNT: { GGML_ASSERT(false); } break; } } static_assert(GGML_GRAPH_HASHTABLE_SIZE > GGML_MAX_NODES * 2, "GGML_GRAPH_HT_SIZE is too small"); static size_t hash(void * p) { return (size_t)p % GGML_GRAPH_HASHTABLE_SIZE; } static bool hash_insert(void * hash_table[], void * p) { size_t h = hash(p); // linear probing size_t i = h; while (hash_table[i] != NULL && hash_table[i] != p) { i = (i + 1) % GGML_GRAPH_HASHTABLE_SIZE; if (i == h) { // hash table is full GGML_ASSERT(false); } } if (hash_table[i] == p) { return true; } // insert hash_table[i] = p; return false; } static void ggml_visit_parents(struct ggml_cgraph * cgraph, struct ggml_tensor * node) { if (node->grad == NULL) { // this usually happens when we generate intermediate nodes from constants in the backward pass // it can also happen during forward pass, if the user performs computations with constants if (node->op != GGML_OP_NONE) { //GGML_PRINT_DEBUG("%s: warning: node %p has no grad, but op %d\n", __func__, (void *) node, node->op); } } // check if already visited if (hash_insert(cgraph->visited_hash_table, node)) { return; } for (int i = 0; i < GGML_MAX_SRC; ++i) { if (node->src[i]) { ggml_visit_parents(cgraph, node->src[i]); } } if (node->op == GGML_OP_NONE && node->grad == NULL) { // reached a leaf node, not part of the gradient graph (e.g. a constant) GGML_ASSERT(cgraph->n_leafs < GGML_MAX_NODES); if (strlen(node->name) == 0) { ggml_format_name(node, "leaf_%d", cgraph->n_leafs); } cgraph->leafs[cgraph->n_leafs] = node; cgraph->n_leafs++; } else { GGML_ASSERT(cgraph->n_nodes < GGML_MAX_NODES); if (strlen(node->name) == 0) { ggml_format_name(node, "node_%d", cgraph->n_nodes); } cgraph->nodes[cgraph->n_nodes] = node; cgraph->grads[cgraph->n_nodes] = node->grad; cgraph->n_nodes++; } } static void ggml_build_forward_impl(struct ggml_cgraph * cgraph, struct ggml_tensor * tensor, bool expand) { if (!expand) { cgraph->n_nodes = 0; cgraph->n_leafs = 0; } const int n0 = cgraph->n_nodes; UNUSED(n0); ggml_visit_parents(cgraph, tensor); const int n_new = cgraph->n_nodes - n0; GGML_PRINT_DEBUG("%s: visited %d new nodes\n", __func__, n_new); if (n_new > 0) { // the last added node should always be starting point GGML_ASSERT(cgraph->nodes[cgraph->n_nodes - 1] == tensor); } } void ggml_build_forward_expand(struct ggml_cgraph * cgraph, struct ggml_tensor * tensor) { ggml_build_forward_impl(cgraph, tensor, true); } struct ggml_cgraph ggml_build_forward(struct ggml_tensor * tensor) { struct ggml_cgraph result = { /*.n_nodes =*/ 0, /*.n_leafs =*/ 0, /*.nodes =*/ { NULL }, /*.grads =*/ { NULL }, /*.leafs =*/ { NULL }, /*.hash_table =*/ { NULL }, /*.perf_runs =*/ 0, /*.perf_cycles =*/ 0, /*.perf_time_us =*/ 0, }; ggml_build_forward_impl(&result, tensor, false); return result; } 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_expand(&result, node->grad); } } return result; } struct ggml_cgraph * ggml_new_graph(struct ggml_context * ctx) { struct ggml_object * obj = ggml_new_object(ctx, GGML_OBJECT_GRAPH, GGML_GRAPH_SIZE); struct ggml_cgraph * cgraph = (struct ggml_cgraph *) ((char *) ctx->mem_buffer + obj->offs); *cgraph = (struct ggml_cgraph) { /*.n_nodes =*/ 0, /*.n_leafs =*/ 0, /*.nodes =*/ { NULL }, /*.grads =*/ { NULL }, /*.leafs =*/ { NULL }, /*.hash_table =*/ { NULL }, /*.perf_runs =*/ 0, /*.perf_cycles =*/ 0, /*.perf_time_us =*/ 0, }; return cgraph; } struct ggml_cgraph * ggml_build_forward_ctx(struct ggml_context * ctx, struct ggml_tensor * tensor) { struct ggml_cgraph * cgraph = ggml_new_graph(ctx); ggml_build_forward_impl(cgraph, tensor, false); return cgraph; } size_t ggml_graph_overhead(void) { return GGML_OBJECT_SIZE + GGML_PAD(GGML_GRAPH_SIZE, GGML_MEM_ALIGN); } // // thread data // // synchronization is done via busy loops // I tried using spin locks, but not sure how to use them correctly - the things I tried were slower than busy loops // #ifdef __APPLE__ //#include // //typedef os_unfair_lock ggml_lock_t; // //#define ggml_lock_init(x) UNUSED(x) //#define ggml_lock_destroy(x) UNUSED(x) //#define ggml_lock_lock os_unfair_lock_lock //#define ggml_lock_unlock os_unfair_lock_unlock // //#define GGML_LOCK_INITIALIZER OS_UNFAIR_LOCK_INIT typedef int ggml_lock_t; #define ggml_lock_init(x) UNUSED(x) #define ggml_lock_destroy(x) UNUSED(x) #define ggml_lock_lock(x) UNUSED(x) #define ggml_lock_unlock(x) UNUSED(x) #define GGML_LOCK_INITIALIZER 0 typedef pthread_t ggml_thread_t; #define ggml_thread_create pthread_create #define ggml_thread_join pthread_join #else //typedef pthread_spinlock_t ggml_lock_t; //#define ggml_lock_init(x) pthread_spin_init(x, PTHREAD_PROCESS_PRIVATE) //#define ggml_lock_destroy pthread_spin_destroy //#define ggml_lock_lock pthread_spin_lock //#define ggml_lock_unlock pthread_spin_unlock typedef int ggml_lock_t; #define ggml_lock_init(x) UNUSED(x) #define ggml_lock_destroy(x) UNUSED(x) #if defined(__x86_64__) || (defined(_MSC_VER) && defined(_M_AMD64)) #define ggml_lock_lock(x) _mm_pause() #else #define ggml_lock_lock(x) UNUSED(x) #endif #define ggml_lock_unlock(x) UNUSED(x) #define GGML_LOCK_INITIALIZER 0 typedef pthread_t ggml_thread_t; #define ggml_thread_create pthread_create #define ggml_thread_join pthread_join #endif // Android's libc implementation "bionic" does not support setting affinity #if defined(__linux__) && !defined(__BIONIC__) static void set_numa_thread_affinity(int thread_n, int n_threads) { if (!ggml_is_numa()) { return; } // run thread on node_num thread_n / (threads per node) const int node_num = thread_n / ((n_threads + g_state.numa.n_nodes - 1) / g_state.numa.n_nodes); struct ggml_numa_node * node = &g_state.numa.nodes[node_num]; size_t setsize = CPU_ALLOC_SIZE(g_state.numa.total_cpus); cpu_set_t * cpus = CPU_ALLOC(g_state.numa.total_cpus); CPU_ZERO_S(setsize, cpus); for (size_t i = 0; i < node->n_cpus; ++i) { CPU_SET_S(node->cpus[i], setsize, cpus); } int rv = pthread_setaffinity_np(pthread_self(), setsize, cpus); if (rv) { fprintf(stderr, "warning: pthread_setaffinity_np() failed: %s\n", strerror(rv)); } CPU_FREE(cpus); } static void clear_numa_thread_affinity(void) { if (!ggml_is_numa()) { return; } size_t setsize = CPU_ALLOC_SIZE(g_state.numa.total_cpus); cpu_set_t * cpus = CPU_ALLOC(g_state.numa.total_cpus); CPU_ZERO_S(setsize, cpus); for (unsigned i = 0; i < g_state.numa.total_cpus; ++i) { CPU_SET_S(i, setsize, cpus); } int rv = pthread_setaffinity_np(pthread_self(), setsize, cpus); if (rv) { fprintf(stderr, "warning: pthread_setaffinity_np() failed: %s\n", strerror(rv)); } CPU_FREE(cpus); } #else // TODO: Windows etc. // (the linux implementation may also work on BSD, someone should test) static void set_numa_thread_affinity(int thread_n, int n_threads) { UNUSED(thread_n); UNUSED(n_threads); } static void clear_numa_thread_affinity(void) {} #endif struct ggml_compute_state_shared { const struct ggml_cgraph * cgraph; const struct ggml_cplan * cplan; int64_t perf_node_start_cycles; int64_t perf_node_start_time_us; const int n_threads; // synchronization primitives atomic_int n_active; // num active threads atomic_int node_n; // active graph node bool (*abort_callback)(void * data); // abort ggml_graph_compute when true void * abort_callback_data; }; struct ggml_compute_state { ggml_thread_t thrd; int ith; struct ggml_compute_state_shared * shared; }; static void ggml_graph_compute_perf_stats_node(struct ggml_tensor * node, const struct ggml_compute_state_shared * st) { int64_t cycles_cur = ggml_perf_cycles() - st->perf_node_start_cycles; int64_t time_us_cur = ggml_perf_time_us() - st->perf_node_start_time_us; node->perf_runs++; node->perf_cycles += cycles_cur; node->perf_time_us += time_us_cur; } static thread_ret_t ggml_graph_compute_thread(void * data) { struct ggml_compute_state * state = (struct ggml_compute_state *) data; const struct ggml_cgraph * cgraph = state->shared->cgraph; const struct ggml_cplan * cplan = state->shared->cplan; const int * n_tasks_arr = cplan->n_tasks; const int n_threads = state->shared->n_threads; set_numa_thread_affinity(state->ith, n_threads); int node_n = -1; while (true) { if (cplan->abort_callback && cplan->abort_callback(cplan->abort_callback_data)) { state->shared->node_n += 1; return (thread_ret_t) GGML_EXIT_ABORTED; } if (atomic_fetch_sub(&state->shared->n_active, 1) == 1) { // all other threads are finished and spinning // do finalize and init here so we don't have synchronize again struct ggml_compute_params params = { /*.type =*/ GGML_TASK_FINALIZE, /*.ith =*/ 0, /*.nth =*/ 0, /*.wsize =*/ cplan->work_size, /*.wdata =*/ cplan->work_data, }; if (node_n != -1) { /* FINALIZE */ struct ggml_tensor * node = state->shared->cgraph->nodes[node_n]; if (GGML_OP_HAS_FINALIZE[node->op]) { params.nth = n_tasks_arr[node_n]; ggml_compute_forward(¶ms, node); } ggml_graph_compute_perf_stats_node(node, state->shared); } // distribute new work or execute it direct if 1T while (++node_n < cgraph->n_nodes) { GGML_PRINT_DEBUG_5("%s: %d/%d\n", __func__, node_n, cgraph->n_nodes); struct ggml_tensor * node = cgraph->nodes[node_n]; const int n_tasks = n_tasks_arr[node_n]; state->shared->perf_node_start_cycles = ggml_perf_cycles(); state->shared->perf_node_start_time_us = ggml_perf_time_us(); params.nth = n_tasks; /* INIT */ if (GGML_OP_HAS_INIT[node->op]) { params.type = GGML_TASK_INIT; ggml_compute_forward(¶ms, node); } if (n_tasks == 1) { // TODO: maybe push node_n to the atomic but if other threads see n_tasks is 1, // they do something more efficient than spinning (?) params.type = GGML_TASK_COMPUTE; ggml_compute_forward(¶ms, node); if (GGML_OP_HAS_FINALIZE[node->op]) { params.type = GGML_TASK_FINALIZE; ggml_compute_forward(¶ms, node); } ggml_graph_compute_perf_stats_node(node, state->shared); } else { break; } if (cplan->abort_callback && cplan->abort_callback(cplan->abort_callback_data)) { break; } } atomic_store(&state->shared->n_active, n_threads); atomic_store(&state->shared->node_n, node_n); } else { // wait for other threads to finish const int last = node_n; do { //sched_yield(); node_n = atomic_load(&state->shared->node_n); } while (node_n == last); } // check if we should stop if (node_n >= cgraph->n_nodes) break; /* COMPUTE */ struct ggml_tensor * node = cgraph->nodes[node_n]; const int n_tasks = n_tasks_arr[node_n]; struct ggml_compute_params params = { /*.type =*/ GGML_TASK_COMPUTE, /*.ith =*/ state->ith, /*.nth =*/ n_tasks, /*.wsize =*/ cplan->work_size, /*.wdata =*/ cplan->work_data, }; if (state->ith < n_tasks) { ggml_compute_forward(¶ms, node); } } return GGML_EXIT_SUCCESS; } struct ggml_cplan ggml_graph_plan(struct ggml_cgraph * cgraph, int n_threads) { if (n_threads <= 0) { n_threads = GGML_DEFAULT_N_THREADS; } size_t work_size = 0; struct ggml_cplan cplan; memset(&cplan, 0, sizeof(struct ggml_cplan)); // thread scheduling for the different operations + work buffer size estimation for (int i = 0; i < cgraph->n_nodes; i++) { int n_tasks = 1; struct ggml_tensor * node = cgraph->nodes[i]; switch (node->op) { case GGML_OP_CPY: case GGML_OP_DUP: { n_tasks = n_threads; size_t cur = 0; if (ggml_is_quantized(node->type)) { cur = GGML_TYPE_SIZE[GGML_TYPE_F32] * node->ne[0] * n_tasks; } work_size = MAX(work_size, cur); } break; case GGML_OP_ADD: case GGML_OP_ADD1: { n_tasks = n_threads; size_t cur = 0; if (ggml_is_quantized(node->src[0]->type)) { cur = GGML_TYPE_SIZE[GGML_TYPE_F32] * node->src[0]->ne[0] * n_tasks; } work_size = MAX(work_size, cur); } break; case GGML_OP_ACC: { n_tasks = n_threads; size_t cur = 0; if (ggml_is_quantized(node->src[0]->type)) { cur = GGML_TYPE_SIZE[GGML_TYPE_F32] * node->src[1]->ne[0] * n_tasks; } work_size = MAX(work_size, cur); } break; case GGML_OP_SUB: case GGML_OP_DIV: case GGML_OP_SQR: case GGML_OP_SQRT: case GGML_OP_LOG: case GGML_OP_SUM: case GGML_OP_SUM_ROWS: case GGML_OP_MEAN: case GGML_OP_ARGMAX: case GGML_OP_REPEAT: case GGML_OP_REPEAT_BACK: { n_tasks = 1; } break; case GGML_OP_UNARY: { switch (ggml_get_unary_op(node)) { case GGML_UNARY_OP_ABS: case GGML_UNARY_OP_SGN: case GGML_UNARY_OP_NEG: case GGML_UNARY_OP_STEP: case GGML_UNARY_OP_TANH: case GGML_UNARY_OP_ELU: case GGML_UNARY_OP_RELU: { n_tasks = 1; } break; case GGML_UNARY_OP_GELU: case GGML_UNARY_OP_GELU_QUICK: case GGML_UNARY_OP_SILU: { n_tasks = n_threads; } break; } } break; case GGML_OP_SILU_BACK: case GGML_OP_MUL: case GGML_OP_NORM: case GGML_OP_RMS_NORM: case GGML_OP_RMS_NORM_BACK: { n_tasks = n_threads; } break; case GGML_OP_MUL_MAT: case GGML_OP_OUT_PROD: { n_tasks = n_threads; // TODO: use different scheduling for different matrix sizes //const int nr0 = ggml_nrows(node->src[0]); //const int nr1 = ggml_nrows(node->src[1]); //n_tasks = MIN(n_threads, MAX(1, nr0/128)); //printf("nr0 = %8d, nr1 = %8d, nr0*nr1 = %8d, n_tasks%d\n", nr0, nr1, nr0*nr1, n_tasks); size_t cur = 0; const enum ggml_type vec_dot_type = type_traits[node->src[0]->type].vec_dot_type; #if defined(GGML_USE_CUBLAS) if (ggml_cuda_can_mul_mat(node->src[0], node->src[1], node)) { n_tasks = 1; // TODO: this actually is doing nothing // the threads are still spinning } else #elif defined(GGML_USE_CLBLAST) if (ggml_cl_can_mul_mat(node->src[0], node->src[1], node)) { n_tasks = 1; // TODO: this actually is doing nothing // the threads are still spinning cur = ggml_cl_mul_mat_get_wsize(node->src[0], node->src[1], node); } else #endif #if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) if (ggml_compute_forward_mul_mat_use_blas(node->src[0], node->src[1], node)) { n_tasks = 1; // TODO: this actually is doing nothing // the threads are still spinning if (node->src[0]->type != GGML_TYPE_F32) { // here we need memory just for single 2D matrix from src0 cur = GGML_TYPE_SIZE[GGML_TYPE_F32]*(node->src[0]->ne[0]*node->src[0]->ne[1]); } } else #endif if (node->src[1]->type != vec_dot_type) { cur = GGML_TYPE_SIZE[vec_dot_type]*ggml_nelements(node->src[1])/GGML_BLCK_SIZE[vec_dot_type]; } else { cur = 0; } work_size = MAX(work_size, cur); } break; case GGML_OP_SCALE: { n_tasks = 1; } break; case GGML_OP_SET: case GGML_OP_CONT: case GGML_OP_RESHAPE: case GGML_OP_VIEW: case GGML_OP_PERMUTE: case GGML_OP_TRANSPOSE: case GGML_OP_GET_ROWS: case GGML_OP_GET_ROWS_BACK: case GGML_OP_DIAG: { n_tasks = 1; } break; case GGML_OP_DIAG_MASK_ZERO: case GGML_OP_DIAG_MASK_INF: case GGML_OP_SOFT_MAX: case GGML_OP_SOFT_MAX_BACK: case GGML_OP_ROPE: case GGML_OP_ROPE_BACK: { n_tasks = n_threads; } break; case GGML_OP_ALIBI: { n_tasks = 1; //TODO } break; case GGML_OP_CLAMP: { n_tasks = 1; //TODO } break; case GGML_OP_CONV_1D: { n_tasks = n_threads; GGML_ASSERT(node->src[0]->ne[3] == 1); GGML_ASSERT(node->src[1]->ne[2] == 1); GGML_ASSERT(node->src[1]->ne[3] == 1); size_t cur = 0; const int nk = node->src[0]->ne[0]; if (node->src[0]->type == GGML_TYPE_F16 && node->src[1]->type == GGML_TYPE_F32) { cur = sizeof(ggml_fp16_t)*( nk*ggml_up32(node->src[0]->ne[1])*node->src[0]->ne[2] + ( 2*(nk/2) + node->src[1]->ne[0])*node->src[1]->ne[1] ); } else if (node->src[0]->type == GGML_TYPE_F32 && node->src[1]->type == GGML_TYPE_F32) { cur = sizeof(float)*( nk*ggml_up32(node->src[0]->ne[1])*node->src[0]->ne[2] + ( 2*(nk/2) + node->src[1]->ne[0])*node->src[1]->ne[1] ); } else { GGML_ASSERT(false); } work_size = MAX(work_size, cur); } break; case GGML_OP_CONV_2D: { n_tasks = n_threads; const int64_t ne00 = node->src[0]->ne[0]; // W const int64_t ne01 = node->src[0]->ne[1]; // H const int64_t ne02 = node->src[0]->ne[2]; // C const int64_t ne03 = node->src[0]->ne[3]; // N const int64_t ne10 = node->src[1]->ne[0]; // W const int64_t ne11 = node->src[1]->ne[1]; // H const int64_t ne12 = node->src[1]->ne[2]; // C const int64_t ne0 = node->ne[0]; const int64_t ne1 = node->ne[1]; const int64_t ne2 = node->ne[2]; const int64_t nk = ne00*ne01; const int64_t ew0 = nk * ne02; UNUSED(ne03); UNUSED(ne2); size_t cur = 0; if (node->src[0]->type == GGML_TYPE_F16 && node->src[1]->type == GGML_TYPE_F32) { cur = sizeof(ggml_fp16_t)*(ne0*ne1*ew0); } else if (node->src[0]->type == GGML_TYPE_F32 && node->src[1]->type == GGML_TYPE_F32) { cur = sizeof(float)* (ne10*ne11*ne12); } else { GGML_ASSERT(false); } work_size = MAX(work_size, cur); } break; case GGML_OP_POOL_1D: case GGML_OP_POOL_2D: { n_tasks = 1; } break; case GGML_OP_FLASH_ATTN: { n_tasks = n_threads; size_t cur = 0; const int64_t ne11 = ggml_up(node->src[1]->ne[1], GGML_SOFT_MAX_UNROLL); if (node->src[1]->type == GGML_TYPE_F32) { cur = sizeof(float)*ne11*n_tasks; // TODO: this can become (n_tasks-1) cur += sizeof(float)*ne11*n_tasks; // this is overestimated by x2 } if (node->src[1]->type == GGML_TYPE_F16) { cur = sizeof(float)*ne11*n_tasks; // TODO: this can become (n_tasks-1) cur += sizeof(float)*ne11*n_tasks; // this is overestimated by x2 } work_size = MAX(work_size, cur); } break; case GGML_OP_FLASH_FF: { n_tasks = n_threads; size_t cur = 0; if (node->src[1]->type == GGML_TYPE_F32) { cur = sizeof(float)*node->src[1]->ne[1]*n_tasks; // TODO: this can become (n_tasks-1) cur += sizeof(float)*node->src[1]->ne[1]*n_tasks; // this is overestimated by x2 } if (node->src[1]->type == GGML_TYPE_F16) { cur = sizeof(float)*node->src[1]->ne[1]*n_tasks; // TODO: this can become (n_tasks-1) cur += sizeof(float)*node->src[1]->ne[1]*n_tasks; // this is overestimated by x2 } work_size = MAX(work_size, cur); } break; case GGML_OP_FLASH_ATTN_BACK: { n_tasks = n_threads; size_t cur = 0; const int64_t D = node->src[0]->ne[0]; const int64_t ne11 = ggml_up(node->src[1]->ne[1], GGML_SOFT_MAX_UNROLL); const int64_t mxDn = MAX(D, ne11) * 2; // *2 because of S and SM in ggml_compute_forward_flash_attn_back if (node->src[1]->type == GGML_TYPE_F32) { cur = sizeof(float)*mxDn*n_tasks; // TODO: this can become (n_tasks-1) cur += sizeof(float)*mxDn*n_tasks; // this is overestimated by x2 } if (node->src[1]->type == GGML_TYPE_F16) { cur = sizeof(float)*mxDn*n_tasks; // TODO: this can become (n_tasks-1) cur += sizeof(float)*mxDn*n_tasks; // this is overestimated by x2 } work_size = MAX(work_size, cur); } break; case GGML_OP_WIN_PART: case GGML_OP_WIN_UNPART: case GGML_OP_MAP_UNARY: case GGML_OP_MAP_BINARY: case GGML_OP_MAP_CUSTOM1_F32: case GGML_OP_MAP_CUSTOM2_F32: case GGML_OP_MAP_CUSTOM3_F32: { n_tasks = 1; } break; case GGML_OP_MAP_CUSTOM1: { struct ggml_map_custom1_op_params * p = (struct ggml_map_custom1_op_params *) node->op_params; if (p->n_tasks == GGML_N_TASKS_MAX) { n_tasks = n_threads; } else { n_tasks = MIN(p->n_tasks, n_threads); } } break; case GGML_OP_MAP_CUSTOM2: { struct ggml_map_custom2_op_params * p = (struct ggml_map_custom2_op_params *) node->op_params; if (p->n_tasks == GGML_N_TASKS_MAX) { n_tasks = n_threads; } else { n_tasks = MIN(p->n_tasks, n_threads); } } break; case GGML_OP_MAP_CUSTOM3: { struct ggml_map_custom3_op_params * p = (struct ggml_map_custom3_op_params *) node->op_params; if (p->n_tasks == GGML_N_TASKS_MAX) { n_tasks = n_threads; } else { n_tasks = MIN(p->n_tasks, n_threads); } } break; case GGML_OP_CROSS_ENTROPY_LOSS: { n_tasks = n_threads; size_t cur = ggml_type_size(node->type)*(n_tasks + node->src[0]->ne[0]*n_tasks); work_size = MAX(work_size, cur); } break; case GGML_OP_CROSS_ENTROPY_LOSS_BACK: { n_tasks = n_threads; size_t cur = ggml_type_size(node->type)*node->src[0]->ne[0]*n_tasks; work_size = MAX(work_size, cur); } break; case GGML_OP_NONE: { n_tasks = 1; } break; case GGML_OP_COUNT: { GGML_ASSERT(false); } break; } cplan.n_tasks[i] = n_tasks; } if (work_size > 0) { work_size += CACHE_LINE_SIZE*(n_threads - 1); } cplan.n_threads = n_threads; cplan.work_size = work_size; cplan.work_data = NULL; return cplan; } int ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan) { { GGML_ASSERT(cplan); GGML_ASSERT(cplan->n_threads > 0); if (cplan->work_size > 0) { GGML_ASSERT(cplan->work_data); } for (int i = 0; i < cgraph->n_nodes; ++i) { if (cgraph->nodes[i]->op != GGML_OP_NONE) { GGML_ASSERT(cplan->n_tasks[i] > 0); } } } const int n_threads = cplan->n_threads; struct ggml_compute_state_shared state_shared = { /*.cgraph =*/ cgraph, /*.cgraph_plan =*/ cplan, /*.perf_node_start_cycles =*/ 0, /*.perf_node_start_time_us =*/ 0, /*.n_threads =*/ n_threads, /*.n_active =*/ n_threads, /*.node_n =*/ -1, /*.abort_callback =*/ NULL, /*.abort_callback_data =*/ NULL, }; struct ggml_compute_state * workers = alloca(sizeof(struct ggml_compute_state)*n_threads); // create thread pool if (n_threads > 1) { for (int j = 1; j < n_threads; ++j) { workers[j] = (struct ggml_compute_state) { .thrd = 0, .ith = j, .shared = &state_shared, }; const int rc = ggml_thread_create(&workers[j].thrd, NULL, ggml_graph_compute_thread, &workers[j]); GGML_ASSERT(rc == 0); } } workers[0].ith = 0; workers[0].shared = &state_shared; const int64_t perf_start_cycles = ggml_perf_cycles(); const int64_t perf_start_time_us = ggml_perf_time_us(); // this is a work thread too int compute_status = (size_t) ggml_graph_compute_thread(&workers[0]); // don't leave affinity set on the main thread clear_numa_thread_affinity(); // join or kill thread pool if (n_threads > 1) { for (int j = 1; j < n_threads; j++) { const int rc = ggml_thread_join(workers[j].thrd, NULL); GGML_ASSERT(rc == 0); } } // performance stats (graph) { int64_t perf_cycles_cur = ggml_perf_cycles() - perf_start_cycles; int64_t perf_time_us_cur = ggml_perf_time_us() - perf_start_time_us; cgraph->perf_runs++; cgraph->perf_cycles += perf_cycles_cur; cgraph->perf_time_us += perf_time_us_cur; GGML_PRINT_DEBUG("%s: perf (%d) - cpu = %.3f / %.3f ms, wall = %.3f / %.3f ms\n", __func__, cgraph->perf_runs, (double) perf_cycles_cur / (double) ggml_cycles_per_ms(), (double) cgraph->perf_cycles / (double) ggml_cycles_per_ms() / (double) cgraph->perf_runs, (double) perf_time_us_cur / 1000.0, (double) cgraph->perf_time_us / 1000.0 / cgraph->perf_runs); } return compute_status; } void ggml_graph_reset(struct ggml_cgraph * cgraph) { for (int i = 0; i < cgraph->n_nodes; i++) { struct ggml_tensor * grad = cgraph->grads[i]; if (grad) { ggml_set_zero(grad); } } } void ggml_graph_compute_with_ctx(struct ggml_context * ctx, struct ggml_cgraph * cgraph, int n_threads) { struct ggml_cplan cplan = ggml_graph_plan(cgraph, n_threads); struct ggml_object * obj = ggml_new_object(ctx, GGML_OBJECT_WORK_BUFFER, cplan.work_size); cplan.work_data = (uint8_t *)ctx->mem_buffer + obj->offs; ggml_graph_compute(cgraph, &cplan); } struct ggml_tensor * ggml_graph_get_tensor(struct ggml_cgraph * cgraph, const char * name) { for (int i = 0; i < cgraph->n_leafs; i++) { struct ggml_tensor * leaf = cgraph->leafs[i]; if (strcmp(leaf->name, name) == 0) { return leaf; } } for (int i = 0; i < cgraph->n_nodes; i++) { struct ggml_tensor * node = cgraph->nodes[i]; if (strcmp(node->name, name) == 0) { return node; } } return NULL; } static void ggml_graph_export_leaf(const struct ggml_tensor * tensor, FILE * fout) { const int64_t * ne = tensor->ne; const size_t * nb = tensor->nb; fprintf(fout, "%-6s %-12s %8d %" PRId64 " %" PRId64 " %" PRId64 " %" PRId64 " %16zu %16zu %16zu %16zu %16p %32s\n", ggml_type_name(tensor->type), ggml_op_name (tensor->op), tensor->n_dims, ne[0], ne[1], ne[2], ne[3], nb[0], nb[1], nb[2], nb[3], tensor->data, tensor->name); } static void ggml_graph_export_node(const struct ggml_tensor * tensor, const char * arg, FILE * fout) { const int64_t * ne = tensor->ne; const size_t * nb = tensor->nb; fprintf(fout, "%-6s %-6s %-12s %8d %" PRId64 " %" PRId64 " %" PRId64 " %" PRId64 " %16zu %16zu %16zu %16zu %16p %32s\n", arg, ggml_type_name(tensor->type), ggml_op_name (tensor->op), tensor->n_dims, ne[0], ne[1], ne[2], ne[3], nb[0], nb[1], nb[2], nb[3], tensor->data, tensor->name); } void ggml_graph_export(const struct ggml_cgraph * cgraph, const char * fname) { uint64_t size_eval = 0; // compute size of intermediate results // TODO: does not take into account scratch buffers !!!! for (int i = 0; i < cgraph->n_nodes; ++i) { size_eval += ggml_nbytes(cgraph->nodes[i]); } // print { FILE * fout = stdout; fprintf(fout, "\n"); fprintf(fout, "%-16s %8x\n", "magic", GGML_FILE_MAGIC); fprintf(fout, "%-16s %8d\n", "version", GGML_FILE_VERSION); fprintf(fout, "%-16s %8d\n", "leafs", cgraph->n_leafs); fprintf(fout, "%-16s %8d\n", "nodes", cgraph->n_nodes); fprintf(fout, "%-16s %" PRIu64 "\n", "eval", size_eval); // header fprintf(fout, "\n"); fprintf(fout, "%-6s %-12s %8s %8s %8s %8s %8s %16s %16s %16s %16s %16s %16s\n", "TYPE", "OP", "NDIMS", "NE0", "NE1", "NE2", "NE3", "NB0", "NB1", "NB2", "NB3", "DATA", "NAME"); for (int i = 0; i < cgraph->n_leafs; ++i) { ggml_graph_export_leaf(cgraph->leafs[i], fout); GGML_ASSERT(cgraph->leafs[i]->op == GGML_OP_NONE); GGML_ASSERT(cgraph->leafs[i]->src[0] == NULL); GGML_ASSERT(cgraph->leafs[i]->src[1] == NULL); } // header fprintf(fout, "\n"); fprintf(fout, "%-6s %-6s %-12s %8s %8s %8s %8s %8s %16s %16s %16s %16s %8s %16s %16s\n", "ARG", "TYPE", "OP", "NDIMS", "NE0", "NE1", "NE2", "NE3", "NB0", "NB1", "NB2", "NB3", "NTASKS", "DATA", "NAME"); for (int i = 0; i < cgraph->n_nodes; ++i) { ggml_graph_export_node(cgraph->nodes[i], "DST", fout); for (int j = 0; j < GGML_MAX_SRC; ++j) { if (cgraph->nodes[i]->src[j]) { ggml_graph_export_node(cgraph->nodes[i]->src[j], "SRC", fout); } } fprintf(fout, "\n"); } fprintf(fout, "\n"); } // write binary data { FILE * fout = fopen(fname, "wb"); if (!fout) { fprintf(stderr, "%s: failed to open %s\n", __func__, fname); return; } // header { const uint32_t magic = GGML_FILE_MAGIC; const uint32_t version = GGML_FILE_VERSION; const uint32_t n_leafs = cgraph->n_leafs; const uint32_t nodes = cgraph->n_nodes; fwrite(&magic, sizeof(uint32_t), 1, fout); fwrite(&version, sizeof(uint32_t), 1, fout); fwrite(&n_leafs, sizeof(uint32_t), 1, fout); fwrite(&nodes, sizeof(uint32_t), 1, fout); fwrite(&size_eval, sizeof(uint64_t), 1, fout); } // leafs { for (int i = 0; i < cgraph->n_leafs; ++i) { const struct ggml_tensor * tensor = cgraph->leafs[i]; const uint32_t type = tensor->type; const uint32_t op = tensor->op; const uint32_t n_dims = tensor->n_dims; fwrite(&type, sizeof(uint32_t), 1, fout); fwrite(&op, sizeof(uint32_t), 1, fout); fwrite(&n_dims, sizeof(uint32_t), 1, fout); for (int j = 0; j < GGML_MAX_DIMS; ++j) { const uint64_t ne = tensor->ne[j]; const uint64_t nb = tensor->nb[j]; fwrite(&ne, sizeof(uint64_t), 1, fout); fwrite(&nb, sizeof(uint64_t), 1, fout); } fwrite(tensor->name, sizeof(char), GGML_MAX_NAME, fout); fwrite(tensor->op_params, sizeof(char), GGML_MAX_OP_PARAMS, fout); // dump the data // TODO: pad this to 32 byte boundary { const size_t size = ggml_nbytes(tensor); fwrite(tensor->data, sizeof(char), size, fout); } } } // nodes { for (int i = 0; i < cgraph->n_nodes; ++i) { const struct ggml_tensor * tensor = cgraph->nodes[i]; const uint32_t type = tensor->type; const uint32_t op = tensor->op; const uint32_t n_dims = tensor->n_dims; fwrite(&type, sizeof(uint32_t), 1, fout); fwrite(&op, sizeof(uint32_t), 1, fout); fwrite(&n_dims, sizeof(uint32_t), 1, fout); for (int j = 0; j < GGML_MAX_DIMS; ++j) { const uint64_t ne = tensor->ne[j]; const uint64_t nb = tensor->nb[j]; fwrite(&ne, sizeof(uint64_t), 1, fout); fwrite(&nb, sizeof(uint64_t), 1, fout); } fwrite(tensor->name, sizeof(char), GGML_MAX_NAME, fout); fwrite(tensor->op_params, sizeof(char), GGML_MAX_OP_PARAMS, fout); // output the op arguments { struct ggml_tensor * args[GGML_MAX_SRC] = { NULL }; for (int j = 0; j < GGML_MAX_SRC; ++j) { args[j] = tensor->src[j]; } for (int j = 0; j < GGML_MAX_SRC; ++j) { if (args[j]) { int32_t idx = -1; // check if leaf { for (int k = 0; k < cgraph->n_leafs; ++k) { if (args[j] == cgraph->leafs[k]) { idx = k; break; } } } // check if node if (idx == -1) { for (int k = 0; k < cgraph->n_nodes; ++k) { if (args[j] == cgraph->nodes[k]) { idx = GGML_MAX_NODES + k; break; } } } if (idx == -1) { fprintf(stderr, "%s: failed to find tensor, arg = %d, node = %d\n", __func__, j, i); return; } fwrite(&idx, sizeof(int32_t), 1, fout); } else { const int32_t nul = -1; fwrite(&nul, sizeof(int32_t), 1, fout); } } } } } fclose(fout); } } struct ggml_cgraph ggml_graph_import(const char * fname, struct ggml_context ** ctx_data, struct ggml_context ** ctx_eval) { assert(*ctx_data == NULL); assert(*ctx_eval == NULL); struct ggml_cgraph result = { 0 }; struct ggml_tensor * data = NULL; // read file into data { FILE * fin = fopen(fname, "rb"); if (!fin) { fprintf(stderr, "%s: failed to open %s\n", __func__, fname); return result; } size_t fsize = 0; fseek(fin, 0, SEEK_END); fsize = ftell(fin); fseek(fin, 0, SEEK_SET); // create the data context { const size_t overhead = 1*ggml_tensor_overhead(); struct ggml_init_params params = { .mem_size = fsize + overhead, .mem_buffer = NULL, .no_alloc = false, }; *ctx_data = ggml_init(params); if (!*ctx_data) { fprintf(stderr, "%s: failed to create ggml context\n", __func__); fclose(fin); return result; } } data = ggml_new_tensor_1d(*ctx_data, GGML_TYPE_I8, fsize); { const size_t ret = fread(data->data, sizeof(char), fsize, fin); if (ret != fsize) { fprintf(stderr, "%s: failed to read %s\n", __func__, fname); fclose(fin); return result; } } fclose(fin); } // populate result { char * ptr = (char *) data->data; const uint32_t magic = *(const uint32_t *) ptr; ptr += sizeof(magic); if (magic != GGML_FILE_MAGIC) { fprintf(stderr, "%s: invalid magic number, got %08x\n", __func__, magic); return result; } const uint32_t version = *(const uint32_t *) ptr; ptr += sizeof(version); if (version != GGML_FILE_VERSION) { fprintf(stderr, "%s: invalid version number\n", __func__); return result; } const uint32_t n_leafs = *(const uint32_t *) ptr; ptr += sizeof(n_leafs); const uint32_t n_nodes = *(const uint32_t *) ptr; ptr += sizeof(n_nodes); const uint64_t size_eval = *(const uint64_t *) ptr; ptr += sizeof(size_eval); result.n_leafs = n_leafs; result.n_nodes = n_nodes; // create the data context { const size_t overhead = (n_leafs + n_nodes)*ggml_tensor_overhead(); struct ggml_init_params params = { .mem_size = size_eval + overhead, .mem_buffer = NULL, .no_alloc = true, }; *ctx_eval = ggml_init(params); if (!*ctx_eval) { fprintf(stderr, "%s: failed to create ggml context\n", __func__); return result; } } // leafs { uint32_t type; uint32_t op; uint32_t n_dims; for (uint32_t i = 0; i < n_leafs; ++i) { type = *(const uint32_t *) ptr; ptr += sizeof(type); op = *(const uint32_t *) ptr; ptr += sizeof(op); n_dims = *(const uint32_t *) ptr; ptr += sizeof(n_dims); int64_t ne[GGML_MAX_DIMS]; size_t nb[GGML_MAX_DIMS]; for (int j = 0; j < GGML_MAX_DIMS; ++j) { uint64_t ne_cur; uint64_t nb_cur; ne_cur = *(const uint64_t *) ptr; ptr += sizeof(ne_cur); nb_cur = *(const uint64_t *) ptr; ptr += sizeof(nb_cur); ne[j] = ne_cur; nb[j] = nb_cur; } struct ggml_tensor * tensor = ggml_new_tensor(*ctx_eval, (enum ggml_type) type, n_dims, ne); tensor->op = (enum ggml_op) op; memcpy(tensor->name, ptr, GGML_MAX_NAME); ptr += GGML_MAX_NAME; memcpy(tensor->op_params, ptr, GGML_MAX_OP_PARAMS); ptr += GGML_MAX_OP_PARAMS; tensor->data = (void *) ptr; for (int j = 0; j < GGML_MAX_DIMS; ++j) { tensor->nb[j] = nb[j]; } result.leafs[i] = tensor; ptr += ggml_nbytes(tensor); fprintf(stderr, "%s: loaded leaf %d: '%16s', %3d dims, %9zu bytes\n", __func__, i, tensor->name, n_dims, ggml_nbytes(tensor)); } } ggml_set_no_alloc(*ctx_eval, false); // nodes { uint32_t type; uint32_t op; uint32_t n_dims; for (uint32_t i = 0; i < n_nodes; ++i) { type = *(const uint32_t *) ptr; ptr += sizeof(type); op = *(const uint32_t *) ptr; ptr += sizeof(op); n_dims = *(const uint32_t *) ptr; ptr += sizeof(n_dims); enum ggml_op eop = (enum ggml_op) op; int64_t ne[GGML_MAX_DIMS]; size_t nb[GGML_MAX_DIMS]; for (int j = 0; j < GGML_MAX_DIMS; ++j) { uint64_t ne_cur; uint64_t nb_cur; ne_cur = *(const uint64_t *) ptr; ptr += sizeof(ne_cur); nb_cur = *(const uint64_t *) ptr; ptr += sizeof(nb_cur); ne[j] = ne_cur; nb[j] = nb_cur; } const char * ptr_name = ptr; ptr += GGML_MAX_NAME; const char * ptr_op_params = ptr; ptr += GGML_MAX_OP_PARAMS; const int32_t * ptr_arg_idx = (const int32_t *) ptr; ptr += GGML_MAX_SRC*sizeof(int32_t); struct ggml_tensor * args[GGML_MAX_SRC] = { NULL }; // parse args for (int j = 0; j < GGML_MAX_SRC; ++j) { const int32_t arg_idx = ptr_arg_idx[j]; if (arg_idx == -1) { continue; } if (arg_idx < GGML_MAX_NODES) { args[j] = result.leafs[arg_idx]; } else { args[j] = result.nodes[arg_idx - GGML_MAX_NODES]; } } // create the tensor // "view" operations are handled differently // TODO: handle inplace ops - currently a copy is always made struct ggml_tensor * tensor = NULL; switch (eop) { // TODO: implement other view ops case GGML_OP_RESHAPE: { tensor = ggml_reshape_4d(*ctx_eval, args[0], ne[0], ne[1], ne[2], ne[3]); } break; case GGML_OP_VIEW: { tensor = ggml_view_4d(*ctx_eval, args[0], ne[0], ne[1], ne[2], ne[3], 0, 0, 0, 0); size_t offs; memcpy(&offs, ptr_op_params, sizeof(offs)); tensor->data = ((char *) tensor->data) + offs; } break; case GGML_OP_TRANSPOSE: { tensor = ggml_transpose(*ctx_eval, args[0]); } break; case GGML_OP_PERMUTE: { tensor = ggml_view_4d(*ctx_eval, args[0], ne[0], ne[1], ne[2], ne[3], 0, 0, 0, 0); } break; default: { tensor = ggml_new_tensor(*ctx_eval, (enum ggml_type) type, n_dims, ne); tensor->op = eop; } break; } memcpy(tensor->name, ptr_name, GGML_MAX_NAME); memcpy(tensor->op_params, ptr_op_params, GGML_MAX_OP_PARAMS); for (int j = 0; j < GGML_MAX_DIMS; ++j) { tensor->nb[j] = nb[j]; } for (int j = 0; j < GGML_MAX_SRC; ++j) { tensor->src[j] = args[j]; } result.nodes[i] = tensor; fprintf(stderr, "%s: loaded node %d: '%16s', %3d dims, %9zu bytes\n", __func__, i, tensor->name, n_dims, ggml_nbytes(tensor)); } } } return result; } void ggml_graph_print(const struct ggml_cgraph * cgraph) { int64_t perf_total_per_op_us[GGML_OP_COUNT] = {0}; GGML_PRINT("=== GRAPH ===\n"); GGML_PRINT("n_nodes = %d\n", cgraph->n_nodes); for (int i = 0; i < cgraph->n_nodes; i++) { struct ggml_tensor * node = cgraph->nodes[i]; perf_total_per_op_us[node->op] += MAX(1, node->perf_time_us); GGML_PRINT(" - %3d: [ %5" PRId64 ", %5" PRId64 ", %5" PRId64 "] %16s %s (%3d) cpu = %7.3f / %7.3f ms, wall = %7.3f / %7.3f ms\n", i, node->ne[0], node->ne[1], node->ne[2], ggml_op_name(node->op), node->is_param ? "x" : node->grad ? "g" : " ", node->perf_runs, (double) node->perf_cycles / (double) ggml_cycles_per_ms(), (double) node->perf_cycles / (double) ggml_cycles_per_ms() / (double) node->perf_runs, (double) node->perf_time_us / 1000.0, (double) node->perf_time_us / 1000.0 / node->perf_runs); } GGML_PRINT("n_leafs = %d\n", cgraph->n_leafs); for (int i = 0; i < cgraph->n_leafs; i++) { struct ggml_tensor * node = cgraph->leafs[i]; GGML_PRINT(" - %3d: [ %5" PRId64 ", %5" PRId64 "] %8s\n", i, node->ne[0], node->ne[1], ggml_op_name(node->op)); } for (int i = 0; i < GGML_OP_COUNT; i++) { if (perf_total_per_op_us[i] == 0) { continue; } GGML_PRINT("perf_total_per_op_us[%16s] = %7.3f ms\n", ggml_op_name(i), (double) perf_total_per_op_us[i] / 1000.0); } GGML_PRINT("========================================\n"); } // check if node is part of the graph static bool ggml_graph_find(const struct ggml_cgraph * cgraph, const struct ggml_tensor * node) { if (cgraph == NULL) { return true; } for (int i = 0; i < cgraph->n_nodes; i++) { if (cgraph->nodes[i] == node) { return true; } } return false; } static struct ggml_tensor * ggml_graph_get_parent(const struct ggml_cgraph * cgraph, const struct ggml_tensor * node) { for (int i = 0; i < cgraph->n_nodes; i++) { struct ggml_tensor * parent = cgraph->nodes[i]; if (parent->grad == node) { return parent; } } return NULL; } static void ggml_graph_dump_dot_node_edge(FILE * fp, const struct ggml_cgraph * gb, struct ggml_tensor * node, struct ggml_tensor * parent, const char * label) { struct ggml_tensor * gparent = ggml_graph_get_parent(gb, node); struct ggml_tensor * gparent0 = ggml_graph_get_parent(gb, parent); fprintf(fp, " \"%p\":%s -> \"%p\":%s [ arrowhead = %s; style = %s; label = \"%s\"; ]\n", gparent0 ? (void *) gparent0 : (void *) parent, gparent0 ? "g" : "x", gparent ? (void *) gparent : (void *) node, gparent ? "g" : "x", gparent ? "empty" : "vee", gparent ? "dashed" : "solid", label); } static void ggml_graph_dump_dot_leaf_edge(FILE * fp, struct ggml_tensor * node, struct ggml_tensor * parent, const char * label) { fprintf(fp, " \"%p\":%s -> \"%p\":%s [ label = \"%s\"; ]\n", (void *) parent, "x", (void *) node, "x", label); } void ggml_graph_dump_dot(const struct ggml_cgraph * gb, const struct ggml_cgraph * gf, const char * filename) { char color[16]; FILE * fp = fopen(filename, "w"); GGML_ASSERT(fp); fprintf(fp, "digraph G {\n"); fprintf(fp, " newrank = true;\n"); fprintf(fp, " rankdir = LR;\n"); for (int i = 0; i < gb->n_nodes; i++) { struct ggml_tensor * node = gb->nodes[i]; if (ggml_graph_get_parent(gb, node) != NULL) { continue; } if (node->is_param) { snprintf(color, sizeof(color), "yellow"); } else if (node->grad) { if (ggml_graph_find(gf, node)) { snprintf(color, sizeof(color), "green"); } else { snprintf(color, sizeof(color), "lightblue"); } } else { snprintf(color, sizeof(color), "white"); } fprintf(fp, " \"%p\" [ " "style = filled; fillcolor = %s; shape = record; " "label=\"", (void *) node, color); if (strlen(node->name) > 0) { fprintf(fp, "%s (%s)|", node->name, ggml_type_name(node->type)); } else { fprintf(fp, "(%s)|", ggml_type_name(node->type)); } if (node->n_dims == 2) { fprintf(fp, "%d [%" PRId64 ", %" PRId64 "] | %s", i, node->ne[0], node->ne[1], ggml_op_symbol(node->op)); } else { fprintf(fp, "%d [%" PRId64 ", %" PRId64 ", %" PRId64 "] | %s", i, node->ne[0], node->ne[1], node->ne[2], 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"); fprintf(fp, " \"%p\" [ " "style = filled; fillcolor = %s; shape = record; " "label=\"", (void *) node, color); if (strlen(node->name) > 0) { fprintf(fp, "%s (%s)|", node->name, ggml_type_name(node->type)); } else { fprintf(fp, "(%s)|", ggml_type_name(node->type)); } fprintf(fp, "CONST %d [%" PRId64 ", %" PRId64 "]", i, node->ne[0], node->ne[1]); if (ggml_nelements(node) < 5) { fprintf(fp, " | ("); for (int j = 0; j < ggml_nelements(node); j++) { if (node->type == GGML_TYPE_I8 || node->type == GGML_TYPE_I16 || node->type == GGML_TYPE_I32) { fprintf(fp, "%d", ggml_get_i32_1d(node, j)); } else if (node->type == GGML_TYPE_F32 || node->type == GGML_TYPE_F16) { fprintf(fp, "%.1e", (double)ggml_get_f32_1d(node, j)); } else { fprintf(fp, "#"); } if (j < ggml_nelements(node) - 1) { fprintf(fp, ", "); } } fprintf(fp, ")"); } fprintf(fp, "\"; ]\n"); } for (int i = 0; i < gb->n_nodes; i++) { struct ggml_tensor * node = gb->nodes[i]; for (int j = 0; j < GGML_MAX_SRC; j++) { if (node->src[j]) { char label[16]; snprintf(label, sizeof(label), "src %d", j); ggml_graph_dump_dot_node_edge(fp, gb, node, node->src[j], label); } } } for (int i = 0; i < gb->n_leafs; i++) { struct ggml_tensor * node = gb->leafs[i]; for (int j = 0; j < GGML_MAX_SRC; j++) { if (node->src[j]) { char label[16]; snprintf(label, sizeof(label), "src %d", j); ggml_graph_dump_dot_leaf_edge(fp, node, node->src[j], label); } } } fprintf(fp, "}\n"); fclose(fp); GGML_PRINT("%s: dot -Tpng %s -o %s.png && open %s.png\n", __func__, filename, filename, filename); } //////////////////////////////////////////////////////////////////////////////// static void ggml_opt_set_params(int np, struct ggml_tensor * const ps[], const float * x) { int i = 0; for (int p = 0; p < np; ++p) { const int64_t ne = ggml_nelements(ps[p]) ; // TODO: add function to set tensor from array for (int64_t j = 0; j < ne; ++j) { ggml_set_f32_1d(ps[p], j, x[i++]); } } } static void ggml_opt_get_params(int np, struct ggml_tensor * const ps[], float * x) { int i = 0; for (int p = 0; p < np; ++p) { const int64_t ne = ggml_nelements(ps[p]) ; // TODO: add function to get all elements at once for (int64_t j = 0; j < ne; ++j) { x[i++] = ggml_get_f32_1d(ps[p], j); } } } static void ggml_opt_get_grad(int np, struct ggml_tensor * const ps[], float * g) { int i = 0; for (int p = 0; p < np; ++p) { const int64_t ne = ggml_nelements(ps[p]) ; // TODO: add function to get all elements at once for (int64_t j = 0; j < ne; ++j) { g[i++] = ggml_get_f32_1d(ps[p]->grad, j); } } } // // ADAM // // ref: https://arxiv.org/pdf/1412.6980.pdf // static enum ggml_opt_result ggml_opt_adam( struct ggml_context * ctx, struct ggml_opt_context * opt, struct ggml_opt_params params, struct ggml_tensor * f, struct ggml_cgraph * gf, struct ggml_cgraph * gb) { GGML_ASSERT(ggml_is_scalar(f)); // these will store the parameters we want to optimize struct ggml_tensor * ps[GGML_MAX_PARAMS]; int np = 0; int nx = 0; for (int i = 0; i < gf->n_nodes; ++i) { if (gf->nodes[i]->is_param) { GGML_PRINT_DEBUG("found param %d: grad->op = %d\n", np, gf->nodes[i]->grad->op); GGML_ASSERT(np < GGML_MAX_PARAMS); ps[np++] = gf->nodes[i]; nx += ggml_nelements(gf->nodes[i]); } } if ((opt->params.type != params.type) || (opt->nx != nx) || (opt->params.past != params.past)) { int iter = opt->iter; ggml_opt_init(opt->ctx, opt, params, nx); opt->iter = iter; } // constants const float sched = params.adam.sched; const float decay = params.adam.decay * sched; const float alpha = params.adam.alpha * sched; const float beta1 = params.adam.beta1; const float beta2 = params.adam.beta2; const float eps = params.adam.eps; float * x = opt->adam.x->data; // view of the parameters float * g1 = opt->adam.g1->data; // gradient float * g2 = opt->adam.g2->data; // gradient squared float * m = opt->adam.m->data; // first moment float * v = opt->adam.v->data; // second moment float * mh = opt->adam.mh->data; // first moment hat float * vh = opt->adam.vh->data; // second moment hat float * pf = params.past > 0 ? opt->adam.pf->data : NULL; // past function values // 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_with_ctx(ctx, gb, params.n_threads); opt->adam.fx_prev = ggml_get_f32_1d(f, 0); opt->adam.fx_best = opt->adam.fx_prev; if (pf) { pf[opt->iter % params.past] = opt->adam.fx_prev; } // initialize if (opt->just_initialized) { opt->adam.n_no_improvement = 0; opt->just_initialized = false; } float * fx_best = &opt->adam.fx_best; float * fx_prev = &opt->adam.fx_prev; int * n_no_improvement = &opt->adam.n_no_improvement; int iter0 = opt->iter; // run the optimizer for (int t = 0; t < params.adam.n_iter; ++t) { opt->iter = iter0 + t + 1; GGML_PRINT_DEBUG ("=== iter %d ===\n", t); GGML_PRINT_DEBUG ("f = %10.6f\n", ggml_get_f32_1d(f, 0)); GGML_PRINT_DEBUG_5("df/dx0 = %10.6f\n", ggml_get_f32_1d(ps[0]->grad, 0)); GGML_PRINT_DEBUG_5("df/dx1 = %10.6f\n", ggml_get_f32_1d(ps[1]->grad, 0)); for (int i = 0; i < np; ++i) { GGML_PRINT_DEBUG("param %d: %10.6f, g = %10.6f\n", i, ggml_get_f32_1d(ps[i], 0), ggml_get_f32_1d(ps[i]->grad, 0)); } const int64_t t_start_wall = ggml_time_us(); const int64_t t_start_cpu = ggml_cycles(); UNUSED(t_start_wall); UNUSED(t_start_cpu); { // 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 - sched*(alpha*m^hat/(sqrt(v^hat) + eps) + decay*x_t-1) // x_t = x_t-1 - sched*alpha*m^hat/(sqrt(v^hat) + eps) - sched*decay*x_t-1 // x_t = x_t-1*(1-sched*decay) - sched*alpha*m^hat/(sqrt(v^hat) + eps) // x_t = x_t-1*(1-sched*decay) + sched*decay*(-alpha/decay)*m^hat/(sqrt(v^hat) + eps) // x_t = mix(x_t-1, (-alpha/decay)*m^hat/(sqrt(v^hat) + eps), sched*decay) 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, opt->iter))); ggml_vec_scale_f32(nx, vh, 1.0f/(1.0f - powf(beta2, opt->iter))); ggml_vec_sqrt_f32 (nx, vh, vh); ggml_vec_acc1_f32 (nx, vh, eps); ggml_vec_div_f32 (nx, mh, mh, vh); ggml_vec_scale_f32(nx, x, 1.0f - decay); 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_with_ctx(ctx, gb, params.n_threads); const float fx = ggml_get_f32_1d(f, 0); // check convergence if (fabsf(fx - fx_prev[0])/fx < params.adam.eps_f) { GGML_PRINT_DEBUG("converged\n"); return GGML_OPT_OK; } // delta-based convergence test if (pf != NULL) { // need at least params.past iterations to start checking for convergence if (params.past <= iter0 + t) { const float rate = (pf[(iter0 + t)%params.past] - fx)/fx; if (fabsf(rate) < params.delta) { return GGML_OPT_OK; } } pf[(iter0 + t)%params.past] = fx; } // check for improvement if (params.max_no_improvement > 0) { if (fx_best[0] > fx) { fx_best[0] = fx; n_no_improvement[0] = 0; } else { ++n_no_improvement[0]; if (n_no_improvement[0] >= params.max_no_improvement) { return GGML_OPT_OK; } } } fx_prev[0] = fx; { const int64_t t_end_cpu = ggml_cycles(); GGML_PRINT_DEBUG("time iter: %5.3f s\n", ((float)(t_end_cpu - t_start_cpu))/CLOCKS_PER_SEC); UNUSED(t_end_cpu); const int64_t t_end_wall = ggml_time_us(); GGML_PRINT_DEBUG("wall time iter: %5.3f s\n", (t_end_wall - t_start_wall)/1e6); UNUSED(t_end_wall); } } return GGML_OPT_DID_NOT_CONVERGE; } // // L-BFGS // // the L-BFGS implementation below is based on the following implementation: // // https://github.com/chokkan/liblbfgs // struct ggml_lbfgs_iteration_data { float alpha; float ys; float * s; float * y; }; static enum ggml_opt_result linesearch_backtracking( 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_with_ctx(ctx, gb, params->n_threads); 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_context * opt, 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; } } const int m = params.lbfgs.m; // these will store the parameters we want to optimize struct ggml_tensor * ps[GGML_MAX_PARAMS]; int np = 0; int nx = 0; for (int i = 0; i < gf->n_nodes; ++i) { if (gf->nodes[i]->is_param) { GGML_PRINT_DEBUG("found param %d: grad->op = %d\n", np, gf->nodes[i]->grad->op); GGML_ASSERT(np < GGML_MAX_PARAMS); ps[np++] = gf->nodes[i]; nx += ggml_nelements(gf->nodes[i]); } } if ((opt->params.type != params.type) || (opt->nx != nx) || (opt->params.past != params.past) || (opt->params.lbfgs.m != params.lbfgs.m)) { int iter = opt->iter; ggml_opt_init(ctx, opt, params, nx); opt->iter = iter; } float * x = opt->lbfgs.x->data; // current parameters float * xp = opt->lbfgs.xp->data; // previous parameters float * g = opt->lbfgs.g->data; // current gradient float * gp = opt->lbfgs.gp->data; // previous gradient float * d = opt->lbfgs.d->data; // search direction float * pf = params.past > 0 ? opt->lbfgs.pf->data : NULL; // past function values float fx = 0.0f; // cost function value float xnorm = 0.0f; // ||x|| float gnorm = 0.0f; // ||g|| // initialize x from the graph nodes ggml_opt_get_params(np, ps, x); // the L-BFGS memory float * lm_alpha = opt->lbfgs.lmal->data; float * lm_ys = opt->lbfgs.lmys->data; float * lm_s = opt->lbfgs.lms->data; float * lm_y = opt->lbfgs.lmy->data; // 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_with_ctx(ctx, gb, params.n_threads); ggml_opt_get_grad(np, ps, g); fx = ggml_get_f32_1d(f, 0); } // search direction = -gradient ggml_vec_neg_f32(nx, d, g); // ||x||, ||g|| ggml_vec_norm_f32(nx, &xnorm, x); ggml_vec_norm_f32(nx, &gnorm, g); if (xnorm < 1.0f) { xnorm = 1.0f; } // already optimized if (gnorm/xnorm <= params.lbfgs.eps) { return GGML_OPT_OK; } if (opt->just_initialized) { if (pf) { pf[0] = fx; } opt->lbfgs.fx_best = fx; // initial step ggml_vec_norm_inv_f32(nx, &opt->lbfgs.step, d); opt->lbfgs.j = 0; opt->lbfgs.k = 1; opt->lbfgs.end = 0; opt->lbfgs.n_no_improvement = 0; opt->just_initialized = false; } float * fx_best = &opt->lbfgs.fx_best; float * step = &opt->lbfgs.step; int * j = &opt->lbfgs.j; int * k = &opt->lbfgs.k; int * end = &opt->lbfgs.end; int * n_no_improvement = &opt->lbfgs.n_no_improvement; int ls = 0; int bound = 0; float ys = 0.0f; float yy = 0.0f; float beta = 0.0f; int it = 0; while (true) { // store the current position and gradient vectors ggml_vec_cpy_f32(nx, xp, x); ggml_vec_cpy_f32(nx, gp, g); ls = linesearch_backtracking(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[0]) { const float rate = (pf[k[0]%params.past] - fx)/fx; if (fabsf(rate) < params.delta) { return GGML_OPT_OK; } } pf[k[0]%params.past] = fx; } // check for improvement if (params.max_no_improvement > 0) { if (fx < fx_best[0]) { fx_best[0] = fx; n_no_improvement[0] = 0; } else { n_no_improvement[0]++; if (n_no_improvement[0] >= params.max_no_improvement) { return GGML_OPT_OK; } } } if (params.lbfgs.n_iter != 0 && params.lbfgs.n_iter < it + 1) { // reached the maximum number of iterations return GGML_OPT_DID_NOT_CONVERGE; } // update vectors s and y: // s_{k+1} = x_{k+1} - x_{k} = \step * d_{k}. // y_{k+1} = g_{k+1} - g_{k}. // ggml_vec_sub_f32(nx, &lm_s[end[0]*nx], x, xp); ggml_vec_sub_f32(nx, &lm_y[end[0]*nx], g, gp); // compute scalars ys and yy: // ys = y^t \cdot s -> 1 / \rho. // yy = y^t \cdot y. // ggml_vec_dot_f32(nx, &ys, &lm_y[end[0]*nx], &lm_s[end[0] *nx]); ggml_vec_dot_f32(nx, &yy, &lm_y[end[0]*nx], &lm_y[end[0]*nx]); lm_ys[end[0]] = ys; // find new search direction // ref: https://en.wikipedia.org/wiki/Limited-memory_BFGS bound = (m <= k[0]) ? m : k[0]; k[0]++; it++; end[0] = (end[0] + 1)%m; // initialize search direction with -g ggml_vec_neg_f32(nx, d, g); j[0] = end[0]; for (int i = 0; i < bound; ++i) { j[0] = (j[0] + m - 1) % m; // \alpha_{j} = \rho_{j} s^{t}_{j} \cdot q_{k+1} ggml_vec_dot_f32(nx, &lm_alpha[j[0]], &lm_s[j[0]*nx], d); lm_alpha[j[0]] /= lm_ys[j[0]]; // q_{i} = q_{i+1} - \alpha_{i} y_{i} ggml_vec_mad_f32(nx, d, &lm_y[j[0]*nx], -lm_alpha[j[0]]); } ggml_vec_scale_f32(nx, d, ys/yy); for (int i = 0; i < bound; ++i) { // \beta_{j} = \rho_{j} y^t_{j} \cdot \gamma_{i} ggml_vec_dot_f32(nx, &beta, &lm_y[j[0]*nx], d); beta /= lm_ys[j[0]]; // \gamma_{i+1} = \gamma_{i} + (\alpha_{j} - \beta_{j}) s_{j} ggml_vec_mad_f32(nx, d, &lm_s[j[0]*nx], lm_alpha[j[0]] - beta); j[0] = (j[0] + 1)%m; } step[0] = 1.0; } return GGML_OPT_DID_NOT_CONVERGE; } struct ggml_opt_params ggml_opt_default_params(enum ggml_opt_type type) { struct ggml_opt_params result; switch (type) { case GGML_OPT_ADAM: { result = (struct ggml_opt_params) { .type = GGML_OPT_ADAM, .n_threads = 1, .past = 0, .delta = 1e-5f, .max_no_improvement = 100, .print_forward_graph = true, .print_backward_graph = true, .adam = { .n_iter = 10000, .sched = 1.000f, .decay = 0.001f, .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; } GGML_API void ggml_opt_init( struct ggml_context * ctx, struct ggml_opt_context * opt, struct ggml_opt_params params, int64_t nx) { opt->ctx = ctx; opt->params = params; opt->iter = 0; opt->nx = nx; opt->just_initialized = true; switch (opt->params.type) { case GGML_OPT_ADAM: { opt->adam.x = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx); opt->adam.g1 = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx); opt->adam.g2 = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx); opt->adam.m = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx); opt->adam.v = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx); opt->adam.mh = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx); opt->adam.vh = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx); opt->adam.pf = params.past > 0 ? ggml_new_tensor_1d(ctx, GGML_TYPE_F32, params.past) : NULL; ggml_set_zero(opt->adam.x); ggml_set_zero(opt->adam.g1); ggml_set_zero(opt->adam.g2); ggml_set_zero(opt->adam.m); ggml_set_zero(opt->adam.v); ggml_set_zero(opt->adam.mh); ggml_set_zero(opt->adam.vh); if (opt->adam.pf) { ggml_set_zero(opt->adam.pf); } } break; case GGML_OPT_LBFGS: { opt->lbfgs.x = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx); opt->lbfgs.xp = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx); opt->lbfgs.g = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx); opt->lbfgs.gp = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx); opt->lbfgs.d = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx); opt->lbfgs.pf = params.past > 0 ? ggml_new_tensor_1d(ctx, GGML_TYPE_F32, params.past) : NULL; opt->lbfgs.lmal = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, params.lbfgs.m); opt->lbfgs.lmys = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, params.lbfgs.m); opt->lbfgs.lms = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, nx, params.lbfgs.m); opt->lbfgs.lmy = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, nx, params.lbfgs.m); ggml_set_zero(opt->lbfgs.x); ggml_set_zero(opt->lbfgs.xp); ggml_set_zero(opt->lbfgs.g); ggml_set_zero(opt->lbfgs.gp); ggml_set_zero(opt->lbfgs.d); if (opt->lbfgs.pf) { ggml_set_zero(opt->lbfgs.pf); } ggml_set_zero(opt->lbfgs.lmal); ggml_set_zero(opt->lbfgs.lmys); ggml_set_zero(opt->lbfgs.lms); ggml_set_zero(opt->lbfgs.lmy); } break; } } enum ggml_opt_result ggml_opt( struct ggml_context * ctx, struct ggml_opt_params params, struct ggml_tensor * f) { bool free_ctx = false; if (ctx == NULL) { struct ggml_init_params params_ctx = { .mem_size = 16*1024*1024, .mem_buffer = NULL, .no_alloc = false, }; ctx = ggml_init(params_ctx); if (ctx == NULL) { return GGML_OPT_NO_CONTEXT; } free_ctx = true; } enum ggml_opt_result result = GGML_OPT_OK; struct ggml_opt_context * opt = (struct ggml_opt_context *) alloca(sizeof(struct ggml_opt_context)); ggml_opt_init(ctx, opt, params, 0); result = ggml_opt_resume(ctx, opt, f); if (free_ctx) { ggml_free(ctx); } return result; } enum ggml_opt_result ggml_opt_resume( struct ggml_context * ctx, struct ggml_opt_context * opt, struct ggml_tensor * f) { // build forward + backward compute graphs struct ggml_tensor * gfbuf = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, sizeof(struct ggml_cgraph) / GGML_TYPE_SIZE[GGML_TYPE_I32]+ (sizeof(struct ggml_cgraph) % GGML_TYPE_SIZE[GGML_TYPE_I32] ? 1 : 0)); struct ggml_tensor * gbbuf = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, sizeof(struct ggml_cgraph) / GGML_TYPE_SIZE[GGML_TYPE_I32]+ (sizeof(struct ggml_cgraph) % GGML_TYPE_SIZE[GGML_TYPE_I32] ? 1 : 0)); struct ggml_cgraph * gf = (struct ggml_cgraph *) gfbuf->data; struct ggml_cgraph * gb = (struct ggml_cgraph *) gbbuf->data; *gf = ggml_build_forward (f); *gb = ggml_build_backward(ctx, gf, true); return ggml_opt_resume_g(ctx, opt, f, gf, gb); } enum ggml_opt_result ggml_opt_resume_g( struct ggml_context * ctx, struct ggml_opt_context * opt, struct ggml_tensor * f, struct ggml_cgraph * gf, struct ggml_cgraph * gb) { // build forward + backward compute graphs enum ggml_opt_result result = GGML_OPT_OK; switch (opt->params.type) { case GGML_OPT_ADAM: { result = ggml_opt_adam(ctx, opt, opt->params, f, gf, gb); } break; case GGML_OPT_LBFGS: { result = ggml_opt_lbfgs(ctx, opt, opt->params, f, gf, gb); } break; } if (opt->params.print_forward_graph) { ggml_graph_print (gf); ggml_graph_dump_dot(gf, NULL, "opt-forward.dot"); } if (opt->params.print_backward_graph) { ggml_graph_print (gb); ggml_graph_dump_dot(gb, gf, "opt-backward.dot"); } return result; } //////////////////////////////////////////////////////////////////////////////// size_t ggml_quantize_q4_0(const float * src, void * dst, int n, int k, int64_t * hist) { assert(k % QK4_0 == 0); const int nb = k / QK4_0; for (int b = 0; b < n; b += k) { block_q4_0 * restrict y = (block_q4_0 *) dst + b/QK4_0; quantize_row_q4_0_reference(src + b, y, k); for (int i = 0; i < nb; i++) { for (int j = 0; j < QK4_0; j += 2) { const uint8_t vi0 = y[i].qs[j/2] & 0x0F; const uint8_t vi1 = y[i].qs[j/2] >> 4; hist[vi0]++; hist[vi1]++; } } } return (n/QK4_0*sizeof(block_q4_0)); } size_t ggml_quantize_q4_1(const float * src, void * dst, int n, int k, int64_t * hist) { assert(k % QK4_1 == 0); const int nb = k / QK4_1; for (int b = 0; b < n; b += k) { block_q4_1 * restrict y = (block_q4_1 *) dst + b/QK4_1; quantize_row_q4_1_reference(src + b, y, k); for (int i = 0; i < nb; i++) { for (int j = 0; j < QK4_1; j += 2) { const uint8_t vi0 = y[i].qs[j/2] & 0x0F; const uint8_t vi1 = y[i].qs[j/2] >> 4; hist[vi0]++; hist[vi1]++; } } } return (n/QK4_1*sizeof(block_q4_1)); } size_t ggml_quantize_q5_0(const float * src, void * dst, int n, int k, int64_t * hist) { assert(k % QK5_0 == 0); const int nb = k / QK5_0; for (int b = 0; b < n; b += k) { block_q5_0 * restrict y = (block_q5_0 *)dst + b/QK5_0; quantize_row_q5_0_reference(src + b, y, k); for (int i = 0; i < nb; i++) { uint32_t qh; memcpy(&qh, &y[i].qh, sizeof(qh)); for (int j = 0; j < QK5_0; j += 2) { const uint8_t vh0 = ((qh & (1u << (j + 0 ))) >> (j + 0 )) << 4; const uint8_t vh1 = ((qh & (1u << (j + 16))) >> (j + 12)); // cast to 16 bins const uint8_t vi0 = ((y[i].qs[j/2] & 0x0F) | vh0) / 2; const uint8_t vi1 = ((y[i].qs[j/2] >> 4) | vh1) / 2; hist[vi0]++; hist[vi1]++; } } } return (n/QK5_0*sizeof(block_q5_0)); } size_t ggml_quantize_q5_1(const float * src, void * dst, int n, int k, int64_t * hist) { assert(k % QK5_1 == 0); const int nb = k / QK5_1; for (int b = 0; b < n; b += k) { block_q5_1 * restrict y = (block_q5_1 *)dst + b/QK5_1; quantize_row_q5_1_reference(src + b, y, k); for (int i = 0; i < nb; i++) { uint32_t qh; memcpy(&qh, &y[i].qh, sizeof(qh)); for (int j = 0; j < QK5_1; j += 2) { const uint8_t vh0 = ((qh & (1u << (j + 0 ))) >> (j + 0 )) << 4; const uint8_t vh1 = ((qh & (1u << (j + 16))) >> (j + 12)); // cast to 16 bins const uint8_t vi0 = ((y[i].qs[j/2] & 0x0F) | vh0) / 2; const uint8_t vi1 = ((y[i].qs[j/2] >> 4) | vh1) / 2; hist[vi0]++; hist[vi1]++; } } } return (n/QK5_1*sizeof(block_q5_1)); } size_t ggml_quantize_q8_0(const float * src, void * dst, int n, int k, int64_t * hist) { assert(k % QK8_0 == 0); const int nb = k / QK8_0; for (int b = 0; b < n; b += k) { block_q8_0 * restrict y = (block_q8_0 *)dst + b/QK8_0; quantize_row_q8_0_reference(src + b, y, k); for (int i = 0; i < nb; i++) { for (int j = 0; j < QK8_0; ++j) { const int8_t vi = y[i].qs[j]; hist[vi/16 + 8]++; } } } return (n/QK8_0*sizeof(block_q8_0)); } size_t ggml_quantize_chunk(enum ggml_type type, const float * src, void * dst, int start, int n, int64_t * hist) { size_t result = 0; switch (type) { case GGML_TYPE_Q4_0: { GGML_ASSERT(start % QK4_0 == 0); block_q4_0 * block = (block_q4_0*)dst + start / QK4_0; result = ggml_quantize_q4_0(src + start, block, n, n, hist); } break; case GGML_TYPE_Q4_1: { GGML_ASSERT(start % QK4_1 == 0); block_q4_1 * block = (block_q4_1*)dst + start / QK4_1; result = ggml_quantize_q4_1(src + start, block, n, n, hist); } break; case GGML_TYPE_Q5_0: { GGML_ASSERT(start % QK5_0 == 0); block_q5_0 * block = (block_q5_0*)dst + start / QK5_0; result = ggml_quantize_q5_0(src + start, block, n, n, hist); } break; case GGML_TYPE_Q5_1: { GGML_ASSERT(start % QK5_1 == 0); block_q5_1 * block = (block_q5_1*)dst + start / QK5_1; result = ggml_quantize_q5_1(src + start, block, n, n, hist); } break; case GGML_TYPE_Q8_0: { GGML_ASSERT(start % QK8_0 == 0); block_q8_0 * block = (block_q8_0*)dst + start / QK8_0; result = ggml_quantize_q8_0(src + start, block, n, n, hist); } break; #ifdef GGML_USE_K_QUANTS case GGML_TYPE_Q2_K: { GGML_ASSERT(start % QK_K == 0); block_q2_K * block = (block_q2_K*)dst + start / QK_K; result = ggml_quantize_q2_K(src + start, block, n, n, hist); } break; case GGML_TYPE_Q3_K: { GGML_ASSERT(start % QK_K == 0); block_q3_K * block = (block_q3_K*)dst + start / QK_K; result = ggml_quantize_q3_K(src + start, block, n, n, hist); } break; case GGML_TYPE_Q4_K: { GGML_ASSERT(start % QK_K == 0); block_q4_K * block = (block_q4_K*)dst + start / QK_K; result = ggml_quantize_q4_K(src + start, block, n, n, hist); } break; case GGML_TYPE_Q5_K: { GGML_ASSERT(start % QK_K == 0); block_q5_K * block = (block_q5_K*)dst + start / QK_K; result = ggml_quantize_q5_K(src + start, block, n, n, hist); } break; case GGML_TYPE_Q6_K: { GGML_ASSERT(start % QK_K == 0); block_q6_K * block = (block_q6_K*)dst + start / QK_K; result = ggml_quantize_q6_K(src + start, block, n, n, hist); } break; #endif case GGML_TYPE_F16: { int elemsize = sizeof(ggml_fp16_t); ggml_fp32_to_fp16_row(src + start, (ggml_fp16_t *)dst + start, n); result = n * elemsize; } break; case GGML_TYPE_F32: { int elemsize = sizeof(float); result = n * elemsize; memcpy((uint8_t *)dst + start * elemsize, src + start, result); } break; default: assert(false); } return result; } //////////////////////////////////////////////////////////////////////////////// struct gguf_str { uint32_t n; char * data; }; static const size_t GGUF_TYPE_SIZE[GGUF_TYPE_COUNT] = { [GGUF_TYPE_UINT8] = sizeof(uint8_t), [GGUF_TYPE_INT8] = sizeof(int8_t), [GGUF_TYPE_UINT16] = sizeof(uint16_t), [GGUF_TYPE_INT16] = sizeof(int16_t), [GGUF_TYPE_UINT32] = sizeof(uint32_t), [GGUF_TYPE_INT32] = sizeof(int32_t), [GGUF_TYPE_FLOAT32] = sizeof(float), [GGUF_TYPE_BOOL] = sizeof(bool), [GGUF_TYPE_STRING] = sizeof(struct gguf_str), [GGUF_TYPE_ARRAY] = 0, // undefined }; static_assert(GGUF_TYPE_COUNT == 10, "GGUF_TYPE_COUNT != 10"); union gguf_value { uint8_t uint8; int8_t int8; uint16_t uint16; int16_t int16; uint32_t uint32; int32_t int32; float float32; bool bool_; struct gguf_str str; struct { enum gguf_type type; uint32_t n; void * data; } arr; }; struct gguf_kv { struct gguf_str key; uint32_t n_bytes; // TODO: is this actually needed? enum gguf_type type; union gguf_value value; }; struct gguf_header { uint32_t magic; uint32_t version; uint32_t n_tensors; uint32_t n_kv; struct gguf_kv * kv; }; struct gguf_tensor_info { struct gguf_str name; uint32_t n_dims; uint32_t ne[GGML_MAX_DIMS]; uint32_t n_elms; // TODO: is this needed? enum ggml_type type; uint64_t offset; // offset from start of `data`, must be a multiple of `ALIGNMENT` }; struct gguf_context { struct gguf_header header; struct gguf_tensor_info * infos; size_t alignment; size_t offset; // offset of `data` from beginning of file size_t size_data; // size of `data` in bytes //uint8_t * padding; uint8_t * data; }; static bool gguf_fread_el(void * dst, size_t size, FILE * file, size_t * offset) { const size_t n = fread(dst, 1, size, file); *offset += n; return n == size; } static bool gguf_fread_str(struct gguf_str * p, FILE * file, size_t * offset) { p->n = 0; p->data = NULL; bool ok = true; // TODO: how to avoid mallocs for strings? ok = ok && gguf_fread_el(&p->n, sizeof(p->n), file, offset); p->data = calloc(p->n + 1, 1); ok = ok && gguf_fread_el( p->data, p->n, file, offset); return ok; } struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_params params) { FILE * file = fopen(fname, "rb"); if (!file) { return NULL; } // offset from start of file size_t offset = 0; uint32_t magic = 0; // check the magic before making allocations { gguf_fread_el(&magic, sizeof(magic), file, &offset); if (magic != GGUF_MAGIC) { fprintf(stderr, "%s: invalid magic number %08x\n", __func__, magic); fclose(file); return NULL; } } bool ok = true; struct gguf_context * ctx = GGML_ALIGNED_MALLOC(sizeof(struct gguf_context)); // read the header { ctx->header.magic = magic; ctx->header.kv = NULL; ctx->infos = NULL; ctx->data = NULL; ok = ok && gguf_fread_el(&ctx->header.version, sizeof(ctx->header.version), file, &offset); ok = ok && gguf_fread_el(&ctx->header.n_tensors, sizeof(ctx->header.n_tensors), file, &offset); ok = ok && gguf_fread_el(&ctx->header.n_kv, sizeof(ctx->header.n_kv), file, &offset); if (!ok) { fprintf(stderr, "%s: failed to read header\n", __func__); fclose(file); gguf_free(ctx); return NULL; } } // read the kv pairs { ctx->header.kv = GGML_ALIGNED_MALLOC(ctx->header.n_kv * sizeof(struct gguf_kv)); for (uint32_t i = 0; i < ctx->header.n_kv; ++i) { struct gguf_kv * kv = &ctx->header.kv[i]; //fprintf(stderr, "%s: reading kv %d\n", __func__, i); ok = ok && gguf_fread_str(&kv->key, file, &offset); //ok = ok && gguf_fread_el (&kv->n_bytes, sizeof(kv->n_bytes), file, &offset); ok = ok && gguf_fread_el (&kv->type, sizeof(kv->type), file, &offset); //fprintf(stderr, "%s: reading kv with key %s\n", __func__, kv->key.data); switch (kv->type) { case GGUF_TYPE_UINT8: ok = ok && gguf_fread_el (&kv->value.uint8, sizeof(kv->value.uint8), file, &offset); break; case GGUF_TYPE_INT8: ok = ok && gguf_fread_el (&kv->value.int8, sizeof(kv->value.int8), file, &offset); break; case GGUF_TYPE_UINT16: ok = ok && gguf_fread_el (&kv->value.uint16, sizeof(kv->value.uint16), file, &offset); break; case GGUF_TYPE_INT16: ok = ok && gguf_fread_el (&kv->value.int16, sizeof(kv->value.int16), file, &offset); break; case GGUF_TYPE_UINT32: ok = ok && gguf_fread_el (&kv->value.uint32, sizeof(kv->value.uint32), file, &offset); break; case GGUF_TYPE_INT32: ok = ok && gguf_fread_el (&kv->value.int32, sizeof(kv->value.int32), file, &offset); break; case GGUF_TYPE_FLOAT32: ok = ok && gguf_fread_el (&kv->value.float32, sizeof(kv->value.float32), file, &offset); break; case GGUF_TYPE_BOOL: ok = ok && gguf_fread_el (&kv->value.bool_, sizeof(kv->value.bool_), file, &offset); break; case GGUF_TYPE_STRING: ok = ok && gguf_fread_str(&kv->value.str, file, &offset); break; case GGUF_TYPE_ARRAY: { ok = ok && gguf_fread_el(&kv->value.arr.type, sizeof(kv->value.arr.type), file, &offset); ok = ok && gguf_fread_el(&kv->value.arr.n, sizeof(kv->value.arr.n), file, &offset); switch (kv->value.arr.type) { case GGUF_TYPE_UINT8: case GGUF_TYPE_INT8: case GGUF_TYPE_UINT16: case GGUF_TYPE_INT16: case GGUF_TYPE_UINT32: case GGUF_TYPE_INT32: case GGUF_TYPE_FLOAT32: case GGUF_TYPE_BOOL: { kv->value.arr.data = malloc(kv->value.arr.n * GGUF_TYPE_SIZE[kv->value.arr.type]); ok = ok && gguf_fread_el(kv->value.arr.data, kv->value.arr.n * GGUF_TYPE_SIZE[kv->value.arr.type], file, &offset); } break; case GGUF_TYPE_STRING: { kv->value.arr.data = malloc(kv->value.arr.n * sizeof(struct gguf_str)); for (uint32_t j = 0; j < kv->value.arr.n; ++j) { ok = ok && gguf_fread_str(&((struct gguf_str *) kv->value.arr.data)[j], file, &offset); } } break; case GGUF_TYPE_ARRAY: case GGUF_TYPE_COUNT: GGML_ASSERT(false && "invalid type"); }; } break; case GGUF_TYPE_COUNT: GGML_ASSERT(false && "invalid type"); }; if (!ok) { break; } } if (!ok) { fprintf(stderr, "%s: failed to read key-value pairs\n", __func__); fclose(file); gguf_free(ctx); return NULL; } } // read the tensor infos { ctx->infos = GGML_ALIGNED_MALLOC(ctx->header.n_tensors * sizeof(struct gguf_tensor_info)); for (uint32_t i = 0; i < ctx->header.n_tensors; ++i) { struct gguf_tensor_info * info = &ctx->infos[i]; for (int j = 0; j < GGML_MAX_DIMS; ++j) { info->ne[j] = 1; } ok = ok && gguf_fread_str(&info->name, file, &offset); ok = ok && gguf_fread_el (&info->n_dims, sizeof(info->n_dims), file, &offset); for (uint32_t j = 0; j < info->n_dims; ++j) { ok = ok && gguf_fread_el(&info->ne[j], sizeof(info->ne[j]), file, &offset); } //ok = ok && gguf_fread_el (&info->n_elms, sizeof(info->n_elms), file, &offset); ok = ok && gguf_fread_el (&info->type, sizeof(info->type), file, &offset); ok = ok && gguf_fread_el (&info->offset, sizeof(info->offset), file, &offset); if (!ok) { fprintf(stderr, "%s: failed to read tensor info\n", __func__); fclose(file); gguf_free(ctx); return NULL; } } } ctx->alignment = GGUF_DEFAULT_ALIGNMENT; int alignment_idx = gguf_find_key(ctx, "general.alignment"); if (alignment_idx != -1) { ctx->alignment = gguf_get_val_u32(ctx, alignment_idx); } // we require the data section to be aligned, so take into account any padding { const size_t offset_pad = offset % ctx->alignment; if (offset_pad != 0) { offset += ctx->alignment - offset_pad; fseek(file, offset, SEEK_SET); } } // store the current file offset - this is where the data section starts ctx->offset = offset; // compute the total size of the data section, taking into account the alignment { ctx->size_data = 0; for (uint32_t i = 0; i < ctx->header.n_tensors; ++i) { struct gguf_tensor_info * info = &ctx->infos[i]; const int64_t ne = (int64_t) info->ne[0] * (int64_t) info->ne[1] * (int64_t) info->ne[2] * (int64_t) info->ne[3]; if (ne % ggml_blck_size(info->type) != 0) { fprintf(stderr, "%s: tensor '%s' number of elements (%" PRId64 ") is not a multiple of block size (%d)\n", __func__, info->name.data, ne, ggml_blck_size(info->type)); fclose(file); gguf_free(ctx); return NULL; } const size_t size_cur = (ne*ggml_type_size(info->type))/ggml_blck_size(info->type); ctx->size_data += GGML_PAD(size_cur, ctx->alignment); } } // load the tensor data only if requested if (params.ctx != NULL) { // if the provided gguf_context is no_alloc, then we create "empty" tensors and do not read the binary blob // otherwise, we load the binary blob into the created ggml_context as well, and point the "data" members of // the ggml_tensor structs to the appropriate locations in the binary blob // compute the exact size needed for the new ggml_context const size_t mem_size = params.no_alloc ? (ctx->header.n_tensors )*ggml_tensor_overhead() : (ctx->header.n_tensors + 1)*ggml_tensor_overhead() + ctx->size_data; struct ggml_init_params pdata = { .mem_size = mem_size, .mem_buffer = NULL, .no_alloc = params.no_alloc, }; *params.ctx = ggml_init(pdata); struct ggml_context * ctx_data = *params.ctx; struct ggml_tensor * data = NULL; if (params.no_alloc == false) { data = ggml_new_tensor_1d(ctx_data, GGML_TYPE_I8, ctx->size_data); ok = ok && data != NULL; // read the binary blob with the tensor data ok = ok && gguf_fread_el(data->data, ctx->size_data, file, &offset); if (!ok) { fprintf(stderr, "%s: failed to read tensor data\n", __func__); fclose(file); ggml_free(ctx_data); gguf_free(ctx); return NULL; } ctx->data = data->data; } ggml_set_no_alloc(ctx_data, true); // create the tensors for (uint32_t i = 0; i < ctx->header.n_tensors; ++i) { const int64_t ne[GGML_MAX_DIMS] = { ctx->infos[i].ne[0], ctx->infos[i].ne[1], ctx->infos[i].ne[2], ctx->infos[i].ne[3], }; struct ggml_tensor * cur = ggml_new_tensor(ctx_data, ctx->infos[i].type, ctx->infos[i].n_dims, ne); ok = ok && cur != NULL; ggml_set_name(cur, ctx->infos[i].name.data); if (!ok) { break; } // point the data member to the appropriate location in the binary blob using the tensor infos if (params.no_alloc == false) { //cur->data = (char *) data->data + ctx->infos[i].offset - ctx->offset; // offset from start of file cur->data = (char *) data->data + ctx->infos[i].offset; // offset from data } } if (!ok) { fprintf(stderr, "%s: failed to read the tensor data\n", __func__); fclose(file); ggml_free(ctx_data); gguf_free(ctx); return NULL; } ggml_set_no_alloc(ctx_data, params.no_alloc); } fclose(file); return ctx; } void gguf_free(struct gguf_context * ctx) { if (ctx == NULL) { return; } if (ctx->header.kv) { // free string memory - not great.. for (uint32_t i = 0; i < ctx->header.n_kv; ++i) { struct gguf_kv * kv = &ctx->header.kv[i]; if (kv->key.data) { free(kv->key.data); } if (kv->type == GGUF_TYPE_STRING) { if (kv->value.str.data) { free(kv->value.str.data); } } if (kv->type == GGUF_TYPE_ARRAY) { if (kv->value.arr.data) { if (kv->value.arr.type == GGUF_TYPE_STRING) { for (uint32_t j = 0; j < kv->value.arr.n; ++j) { struct gguf_str * str = &((struct gguf_str *) kv->value.arr.data)[j]; if (str->data) { free(str->data); } } } free(kv->value.arr.data); } } } GGML_ALIGNED_FREE(ctx->header.kv); } if (ctx->infos) { for (uint32_t i = 0; i < ctx->header.n_tensors; ++i) { struct gguf_tensor_info * info = &ctx->infos[i]; if (info->name.data) { free(info->name.data); } } GGML_ALIGNED_FREE(ctx->infos); } GGML_ALIGNED_FREE(ctx); } int gguf_get_version(struct gguf_context * ctx) { return ctx->header.version; } size_t gguf_get_alignment(struct gguf_context * ctx) { return ctx->alignment; } size_t gguf_get_data_offset(struct gguf_context * ctx) { return ctx->offset; } void * gguf_get_data(struct gguf_context * ctx) { return ctx->data; } int gguf_get_n_kv(struct gguf_context * ctx) { return ctx->header.n_kv; } int gguf_find_key(struct gguf_context * ctx, const char * key) { // return -1 if key not found const int n_kv = gguf_get_n_kv(ctx); int keyfound = -1; for (int i = 0; i < n_kv; ++i) { if (strcmp(key, gguf_get_key(ctx, i)) == 0) { keyfound = i; break; } } return keyfound; } const char * gguf_get_key(struct gguf_context * ctx, int i) { return ctx->header.kv[i].key.data; } const char * gguf_get_arr_str(struct gguf_context * ctx, int key_id, int i) { struct gguf_kv * kv = &ctx->header.kv[key_id]; struct gguf_str * str = &((struct gguf_str *) kv->value.arr.data)[i]; return str->data; } float gguf_get_arr_f32(struct gguf_context * ctx, int key_id, int i) { return ((float *) ctx->header.kv[key_id].value.arr.data)[i]; } int gguf_get_arr_n(struct gguf_context * ctx, int i) { return ctx->header.kv[i].value.arr.n; } uint8_t gguf_get_val_u8(struct gguf_context * ctx, int i) { return ctx->header.kv[i].value.uint8; } int8_t gguf_get_val_i8(struct gguf_context * ctx, int i) { return ctx->header.kv[i].value.int8; } uint16_t gguf_get_val_u16(struct gguf_context * ctx, int i) { return ctx->header.kv[i].value.uint16; } int16_t gguf_get_val_i16(struct gguf_context * ctx, int i) { return ctx->header.kv[i].value.int16; } uint32_t gguf_get_val_u32(struct gguf_context * ctx, int i) { return ctx->header.kv[i].value.uint32; } int32_t gguf_get_val_i32(struct gguf_context * ctx, int i) { return ctx->header.kv[i].value.int32; } float gguf_get_val_f32(struct gguf_context * ctx, int i) { return ctx->header.kv[i].value.float32; } bool gguf_get_val_bool(struct gguf_context * ctx, int i) { return ctx->header.kv[i].value.bool_; } const char * gguf_get_val_str (struct gguf_context * ctx, int i) { return ctx->header.kv[i].value.str.data; } int gguf_get_n_tensors(struct gguf_context * ctx) { return ctx->header.n_tensors; } size_t gguf_get_tensor_offset(struct gguf_context * ctx, int i) { return ctx->infos[i].offset; } char * gguf_get_tensor_name(struct gguf_context * ctx, int i) { return ctx->infos[i].name.data; } //////////////////////////////////////////////////////////////////////////////// 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) || defined(GGML_USE_CLBLAST) return 1; #else return 0; #endif } int ggml_cpu_has_cublas(void) { #if defined(GGML_USE_CUBLAS) return 1; #else return 0; #endif } int ggml_cpu_has_clblast(void) { #if defined(GGML_USE_CLBLAST) return 1; #else return 0; #endif } int ggml_cpu_has_gpublas(void) { return ggml_cpu_has_cublas() || ggml_cpu_has_clblast(); } int ggml_cpu_has_sse3(void) { #if defined(__SSE3__) return 1; #else return 0; #endif } int ggml_cpu_has_vsx(void) { #if defined(__POWER9_VECTOR__) return 1; #else return 0; #endif } ////////////////////////////////////////////////////////////////////////////////