#include "ggml.h" #if defined(_MSC_VER) || defined(__MINGW32__) #include // using malloc.h with MSC/MINGW #elif !defined(__FreeBSD__) #include #endif #include #include #include #include #include #include #include // if C99 - static_assert is noop // ref: https://stackoverflow.com/a/53923785/4039976 #ifndef static_assert #define static_assert(cond, msg) struct global_scope_noop_trick #endif #if defined _MSC_VER || defined(__MINGW32__) #if !defined(__MINGW32__) #include #else // ref: https://github.com/ggerganov/whisper.cpp/issues/168 #include #include #endif typedef volatile LONG atomic_int; typedef atomic_int atomic_bool; static void atomic_store(atomic_int* ptr, LONG val) { InterlockedExchange(ptr, val); } static LONG atomic_load(atomic_int* ptr) { return InterlockedCompareExchange(ptr, 0, 0); } static LONG atomic_fetch_add(atomic_int* ptr, LONG inc) { return InterlockedExchangeAdd(ptr, inc); } static LONG atomic_fetch_sub(atomic_int* ptr, LONG dec) { return atomic_fetch_add(ptr, -(dec)); } typedef HANDLE pthread_t; typedef DWORD thread_ret_t; static int pthread_create(pthread_t* out, void* unused, thread_ret_t(*func)(void*), void* arg) { HANDLE handle = CreateThread(NULL, 0, (LPTHREAD_START_ROUTINE) func, arg, 0, NULL); if (handle == NULL) { return EAGAIN; } *out = handle; return 0; } static int pthread_join(pthread_t thread, void* unused) { return (int) WaitForSingleObject(thread, INFINITE); } static int sched_yield (void) { Sleep (0); return 0; } #else #include #include typedef void* thread_ret_t; #endif #ifdef __HAIKU__ #define static_assert(cond, msg) _Static_assert(cond, msg) #endif /*#define GGML_PERF*/ #define GGML_DEBUG 0 #define GGML_GELU_FP16 #define GGML_SOFT_MAX_UNROLL 4 #define GGML_VEC_DOT_UNROLL 2 #ifdef GGML_USE_ACCELERATE // uncomment to use vDSP for soft max computation // note: not sure if it is actually faster //#define GGML_SOFT_MAX_ACCELERATE #endif #if UINTPTR_MAX == 0xFFFFFFFF #define GGML_MEM_ALIGN 4 #else #define GGML_MEM_ALIGN 16 #endif #define UNUSED(x) (void)(x) #define SWAP(x, y, T) do { T SWAP = x; x = y; y = SWAP; } while (0) #define GGML_ASSERT(x) \ do { \ if (!(x)) { \ fprintf(stderr, "GGML_ASSERT: %s:%d: %s\n", __FILE__, __LINE__, #x); \ abort(); \ } \ } while (0) #ifdef GGML_USE_ACCELERATE #include #elif GGML_USE_OPENBLAS #include #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) (x) #define GGML_COMPUTE_FP32_TO_FP16(x) (x) #define GGML_FP16_TO_FP32(x) (x) #define GGML_FP32_TO_FP16(x) (x) #else #ifdef __wasm_simd128__ #include #else #ifdef __POWER9_VECTOR__ #include #undef bool #define bool _Bool #else #include #endif #endif #ifdef __F16C__ #define GGML_COMPUTE_FP16_TO_FP32(x) _cvtsh_ss(x) #define GGML_COMPUTE_FP32_TO_FP16(x) _cvtss_sh(x, 0) #else // FP16 <-> FP32 // ref: https://github.com/Maratyszcza/FP16 static inline float fp32_from_bits(uint32_t w) { union { uint32_t as_bits; float as_value; } fp32; fp32.as_bits = w; return fp32.as_value; } static inline uint32_t fp32_to_bits(float f) { union { float as_value; uint32_t as_bits; } fp32; fp32.as_value = f; return fp32.as_bits; } static inline float ggml_compute_fp16_to_fp32(ggml_fp16_t h) { const uint32_t w = (uint32_t) h << 16; const uint32_t sign = w & UINT32_C(0x80000000); const uint32_t two_w = w + w; const uint32_t exp_offset = UINT32_C(0xE0) << 23; #if defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 199901L) || defined(__GNUC__) && !defined(__STRICT_ANSI__) const float exp_scale = 0x1.0p-112f; #else const float exp_scale = fp32_from_bits(UINT32_C(0x7800000)); #endif const float normalized_value = fp32_from_bits((two_w >> 4) + exp_offset) * exp_scale; const uint32_t magic_mask = UINT32_C(126) << 23; const float magic_bias = 0.5f; const float denormalized_value = fp32_from_bits((two_w >> 17) | magic_mask) - magic_bias; const uint32_t denormalized_cutoff = UINT32_C(1) << 27; const uint32_t result = sign | (two_w < denormalized_cutoff ? fp32_to_bits(denormalized_value) : fp32_to_bits(normalized_value)); return fp32_from_bits(result); } static inline ggml_fp16_t ggml_compute_fp32_to_fp16(float f) { #if defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 199901L) || defined(__GNUC__) && !defined(__STRICT_ANSI__) const float scale_to_inf = 0x1.0p+112f; const float scale_to_zero = 0x1.0p-110f; #else const float scale_to_inf = fp32_from_bits(UINT32_C(0x77800000)); const float scale_to_zero = fp32_from_bits(UINT32_C(0x08800000)); #endif float base = (fabsf(f) * scale_to_inf) * scale_to_zero; const uint32_t w = fp32_to_bits(f); const uint32_t shl1_w = w + w; const uint32_t sign = w & UINT32_C(0x80000000); uint32_t bias = shl1_w & UINT32_C(0xFF000000); if (bias < UINT32_C(0x71000000)) { bias = UINT32_C(0x71000000); } base = fp32_from_bits((bias >> 1) + UINT32_C(0x07800000)) + base; const uint32_t bits = fp32_to_bits(base); const uint32_t exp_bits = (bits >> 13) & UINT32_C(0x00007C00); const uint32_t mantissa_bits = bits & UINT32_C(0x00000FFF); const uint32_t nonsign = exp_bits + mantissa_bits; return (sign >> 16) | (shl1_w > UINT32_C(0xFF000000) ? UINT16_C(0x7E00) : nonsign); } #define GGML_COMPUTE_FP16_TO_FP32(x) ggml_compute_fp16_to_fp32(x) #define GGML_COMPUTE_FP32_TO_FP16(x) ggml_compute_fp32_to_fp16(x) #endif // __F16C__ #endif // __ARM_NEON // // global data // // precomputed gelu table for f16 (128 KB) static ggml_fp16_t table_gelu_f16[1 << 16]; // precomputed exp table for f16 (128 KB) static ggml_fp16_t table_exp_f16[1 << 16]; // precomputed f32 table for f16 (256 KB) static float table_f32_f16[1 << 16]; // On ARM NEON, it's quicker to directly convert x -> x instead of calling into ggml_lookup_fp16_to_fp32, // so we define GGML_FP16_TO_FP32 and GGML_FP32_TO_FP16 elsewhere for NEON. #if !defined(GGML_FP16_TO_FP32) || !defined(GGML_FP32_TO_FP16) inline static float ggml_lookup_fp16_to_fp32(ggml_fp16_t f) { uint16_t s; memcpy(&s, &f, sizeof(uint16_t)); return table_f32_f16[s]; } #define GGML_FP16_TO_FP32(x) ggml_lookup_fp16_to_fp32(x) #define GGML_FP32_TO_FP16(x) GGML_COMPUTE_FP32_TO_FP16(x) #endif // note: do not use these inside ggml.c // these are meant to be used via the ggml.h API float ggml_fp16_to_fp32(ggml_fp16_t x) { return GGML_FP16_TO_FP32(x); } ggml_fp16_t ggml_fp32_to_fp16(float x) { return GGML_FP32_TO_FP16(x); } // // timing // #if defined(_MSC_VER) || defined(__MINGW32__) static int64_t timer_freq; void ggml_time_init(void) { LARGE_INTEGER frequency; QueryPerformanceFrequency(&frequency); timer_freq = frequency.QuadPart; } int64_t ggml_time_ms(void) { LARGE_INTEGER t; QueryPerformanceCounter(&t); return (t.QuadPart * 1000) / timer_freq; } int64_t ggml_time_us(void) { LARGE_INTEGER t; QueryPerformanceCounter(&t); return (t.QuadPart * 1000000) / timer_freq; } #else void ggml_time_init(void) {} int64_t ggml_time_ms(void) { struct timespec ts; clock_gettime(CLOCK_MONOTONIC, &ts); return (int64_t)ts.tv_sec*1000 + (int64_t)ts.tv_nsec/1000000; } int64_t ggml_time_us(void) { struct timespec ts; clock_gettime(CLOCK_MONOTONIC, &ts); return (int64_t)ts.tv_sec*1000000 + (int64_t)ts.tv_nsec/1000; } #endif int64_t ggml_cycles(void) { return clock(); } int64_t ggml_cycles_per_ms(void) { return CLOCKS_PER_SEC/1000; } #ifdef GGML_PERF #define ggml_perf_time_ms() ggml_time_ms() #define ggml_perf_time_us() ggml_time_us() #define ggml_perf_cycles() ggml_cycles() #define ggml_perf_cycles_per_ms() ggml_cycles_per_ms() #else #define ggml_perf_time_ms() 0 #define ggml_perf_time_us() 0 #define ggml_perf_cycles() 0 #define ggml_perf_cycles_per_ms() 0 #endif // // cache line // #if defined(__cpp_lib_hardware_interference_size) #define CACHE_LINE_SIZE hardware_destructive_interference_size #else #if defined(__POWER9_VECTOR__) #define CACHE_LINE_SIZE 128 #else #define CACHE_LINE_SIZE 64 #endif #endif static const size_t CACHE_LINE_SIZE_F32 = CACHE_LINE_SIZE/sizeof(float); // // simd mappings // // we define a common set of C macros which map to specific intrinsics based on the current architecture // we then implement the fundamental computation operations below using only these macros // adding support for new architectures requires to define the corresponding SIMD macros // // GGML_F32_STEP / GGML_F16_STEP // number of elements to process in a single step // // GGML_F32_EPR / GGML_F16_EPR // number of elements to fit in a single register // #if defined(__ARM_NEON) && defined(__ARM_FEATURE_FMA) #define GGML_SIMD // F32 NEON #define GGML_F32_STEP 16 #define GGML_F32_EPR 4 #define GGML_F32x4 float32x4_t #define GGML_F32x4_ZERO vdupq_n_f32(0.0f) #define GGML_F32x4_SET1(x) vdupq_n_f32(x) #define GGML_F32x4_LOAD vld1q_f32 #define GGML_F32x4_STORE vst1q_f32 #define GGML_F32x4_FMA(a, b, c) vfmaq_f32(a, b, c) #define GGML_F32x4_ADD vaddq_f32 #define GGML_F32x4_MUL vmulq_f32 #if defined(__ARM_FEATURE_QRDMX) #define GGML_F32x4_REDUCE_ONE(x) vaddvq_f32(x) #else #define GGML_F32x4_REDUCE_ONE(x) \ (vgetq_lane_f32(x, 0) + \ vgetq_lane_f32(x, 1) + \ vgetq_lane_f32(x, 2) + \ vgetq_lane_f32(x, 3)) #endif #define GGML_F32x4_REDUCE(res, x) \ { \ for (int i = 0; i < GGML_F32_ARR/2; ++i) { \ x[2*i] = vaddq_f32(x[2*i], x[2*i+1]); \ } \ for (int i = 0; i < GGML_F32_ARR/4; ++i) { \ x[4*i] = vaddq_f32(x[4*i], x[4*i+2]); \ } \ for (int i = 0; i < GGML_F32_ARR/8; ++i) { \ x[8*i] = vaddq_f32(x[8*i], x[8*i+4]); \ } \ res = GGML_F32x4_REDUCE_ONE(x[0]); \ } #define GGML_F32_VEC GGML_F32x4 #define GGML_F32_VEC_ZERO GGML_F32x4_ZERO #define GGML_F32_VEC_SET1 GGML_F32x4_SET1 #define GGML_F32_VEC_LOAD GGML_F32x4_LOAD #define GGML_F32_VEC_STORE GGML_F32x4_STORE #define GGML_F32_VEC_FMA GGML_F32x4_FMA #define GGML_F32_VEC_ADD GGML_F32x4_ADD #define GGML_F32_VEC_MUL GGML_F32x4_MUL #define GGML_F32_VEC_REDUCE GGML_F32x4_REDUCE // F16 NEON #if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) #define GGML_F16_STEP 32 #define GGML_F16_EPR 8 #define GGML_F16x8 float16x8_t #define GGML_F16x8_ZERO vdupq_n_f16(0.0f) #define GGML_F16x8_SET1(x) vdupq_n_f16(x) #define GGML_F16x8_LOAD vld1q_f16 #define GGML_F16x8_STORE vst1q_f16 #define GGML_F16x8_FMA(a, b, c) vfmaq_f16(a, b, c) #define GGML_F16x8_ADD vaddq_f16 #define GGML_F16x8_MUL vmulq_f16 #define GGML_F16x8_REDUCE(res, x) \ { \ for (int i = 0; i < GGML_F16_ARR/2; ++i) { \ x[2*i] = vaddq_f16(x[2*i], x[2*i+1]); \ } \ for (int i = 0; i < GGML_F16_ARR/4; ++i) { \ x[4*i] = vaddq_f16(x[4*i], x[4*i+2]); \ } \ for (int i = 0; i < GGML_F16_ARR/8; ++i) { \ x[8*i] = vaddq_f16(x[8*i], x[8*i+4]); \ } \ const float32x4_t t0 = vcvt_f32_f16(vget_low_f16 (x[0])); \ const float32x4_t t1 = vcvt_f32_f16(vget_high_f16(x[0])); \ res = vaddvq_f32(vaddq_f32(t0, t1)); \ } #define GGML_F16_VEC GGML_F16x8 #define GGML_F16_VEC_ZERO GGML_F16x8_ZERO #define GGML_F16_VEC_SET1 GGML_F16x8_SET1 #define GGML_F16_VEC_LOAD(p, i) GGML_F16x8_LOAD(p) #define GGML_F16_VEC_STORE(p, r, i) GGML_F16x8_STORE(p, r[i]) #define GGML_F16_VEC_FMA GGML_F16x8_FMA #define GGML_F16_VEC_ADD GGML_F16x8_ADD #define GGML_F16_VEC_MUL GGML_F16x8_MUL #define GGML_F16_VEC_REDUCE GGML_F16x8_REDUCE #else // if FP16 vector arithmetic is not supported, we use FP32 instead // and take advantage of the vcvt_ functions to convert to/from FP16 #define GGML_F16_STEP 16 #define GGML_F16_EPR 4 #define GGML_F32Cx4 float32x4_t #define GGML_F32Cx4_ZERO vdupq_n_f32(0.0f) #define GGML_F32Cx4_SET1(x) vdupq_n_f32(x) #define GGML_F32Cx4_LOAD(x) vcvt_f32_f16(vld1_f16(x)) #define GGML_F32Cx4_STORE(x, y) vst1_f16(x, vcvt_f16_f32(y)) #define GGML_F32Cx4_FMA(a, b, c) vfmaq_f32(a, b, c) #define GGML_F32Cx4_ADD vaddq_f32 #define GGML_F32Cx4_MUL vmulq_f32 #define GGML_F32Cx4_REDUCE GGML_F32x4_REDUCE #define GGML_F16_VEC GGML_F32Cx4 #define GGML_F16_VEC_ZERO GGML_F32Cx4_ZERO #define GGML_F16_VEC_SET1 GGML_F32Cx4_SET1 #define GGML_F16_VEC_LOAD(p, i) GGML_F32Cx4_LOAD(p) #define GGML_F16_VEC_STORE(p, r, i) GGML_F32Cx4_STORE(p, r[i]) #define GGML_F16_VEC_FMA GGML_F32Cx4_FMA #define GGML_F16_VEC_ADD GGML_F32Cx4_ADD #define GGML_F16_VEC_MUL GGML_F32Cx4_MUL #define GGML_F16_VEC_REDUCE GGML_F32Cx4_REDUCE #endif #elif defined(__AVX__) #define GGML_SIMD // F32 AVX #define GGML_F32_STEP 32 #define GGML_F32_EPR 8 #define GGML_F32x8 __m256 #define GGML_F32x8_ZERO _mm256_setzero_ps() #define GGML_F32x8_SET1(x) _mm256_set1_ps(x) #define GGML_F32x8_LOAD _mm256_loadu_ps #define GGML_F32x8_STORE _mm256_storeu_ps #if defined(__FMA__) #define GGML_F32x8_FMA(a, b, c) _mm256_fmadd_ps(b, c, a) #else #define GGML_F32x8_FMA(a, b, c) _mm256_add_ps(_mm256_mul_ps(b, c), a) #endif #define GGML_F32x8_ADD _mm256_add_ps #define GGML_F32x8_MUL _mm256_mul_ps #define GGML_F32x8_REDUCE(res, x) \ { \ for (int i = 0; i < GGML_F32_ARR/2; ++i) { \ x[2*i] = _mm256_add_ps(x[2*i], x[2*i+1]); \ } \ for (int i = 0; i < GGML_F32_ARR/4; ++i) { \ x[4*i] = _mm256_add_ps(x[4*i], x[4*i+2]); \ } \ for (int i = 0; i < GGML_F32_ARR/8; ++i) { \ x[8*i] = _mm256_add_ps(x[8*i], x[8*i+4]); \ } \ const __m128 t0 = _mm_add_ps(_mm256_castps256_ps128(x[0]), \ _mm256_extractf128_ps(x[0], 1)); \ const __m128 t1 = _mm_hadd_ps(t0, t0); \ res = _mm_cvtss_f32(_mm_hadd_ps(t1, t1)); \ } // TODO: is this optimal ? #define GGML_F32_VEC GGML_F32x8 #define GGML_F32_VEC_ZERO GGML_F32x8_ZERO #define GGML_F32_VEC_SET1 GGML_F32x8_SET1 #define GGML_F32_VEC_LOAD GGML_F32x8_LOAD #define GGML_F32_VEC_STORE GGML_F32x8_STORE #define GGML_F32_VEC_FMA GGML_F32x8_FMA #define GGML_F32_VEC_ADD GGML_F32x8_ADD #define GGML_F32_VEC_MUL GGML_F32x8_MUL #define GGML_F32_VEC_REDUCE GGML_F32x8_REDUCE // F16 AVX #define GGML_F16_STEP 32 #define GGML_F16_EPR 8 // F16 arithmetic is not supported by AVX, so we use F32 instead // we take advantage of the _mm256_cvt intrinsics to convert F16 <-> F32 #define GGML_F32Cx8 __m256 #define GGML_F32Cx8_ZERO _mm256_setzero_ps() #define GGML_F32Cx8_SET1(x) _mm256_set1_ps(x) #define GGML_F32Cx8_LOAD(x) _mm256_cvtph_ps(_mm_loadu_si128((__m128i *)(x))) #define GGML_F32Cx8_STORE(x, y) _mm_storeu_si128((__m128i *)(x), _mm256_cvtps_ph(y, 0)) #define GGML_F32Cx8_FMA GGML_F32x8_FMA #define GGML_F32Cx8_ADD _mm256_add_ps #define GGML_F32Cx8_MUL _mm256_mul_ps #define GGML_F32Cx8_REDUCE GGML_F32x8_REDUCE #define GGML_F16_VEC GGML_F32Cx8 #define GGML_F16_VEC_ZERO GGML_F32Cx8_ZERO #define GGML_F16_VEC_SET1 GGML_F32Cx8_SET1 #define GGML_F16_VEC_LOAD(p, i) GGML_F32Cx8_LOAD(p) #define GGML_F16_VEC_STORE(p, r, i) GGML_F32Cx8_STORE(p, r[i]) #define GGML_F16_VEC_FMA GGML_F32Cx8_FMA #define GGML_F16_VEC_ADD GGML_F32Cx8_ADD #define GGML_F16_VEC_MUL GGML_F32Cx8_MUL #define GGML_F16_VEC_REDUCE GGML_F32Cx8_REDUCE #elif defined(__POWER9_VECTOR__) #define GGML_SIMD // F32 POWER9 #define GGML_F32_STEP 32 #define GGML_F32_EPR 4 #define GGML_F32x4 vector float #define GGML_F32x4_ZERO 0.0f #define GGML_F32x4_SET1 vec_splats #define GGML_F32x4_LOAD(p) vec_xl(0, p) #define GGML_F32x4_STORE(p, r) vec_xst(r, 0, p) #define GGML_F32x4_FMA(a, b, c) vec_madd(b, c, a) #define GGML_F32x4_ADD vec_add #define GGML_F32x4_MUL vec_mul #define GGML_F32x4_REDUCE(res, x) \ { \ for (int i = 0; i < GGML_F32_ARR/2; ++i) { \ x[2*i] = vec_add(x[2*i], x[2*i+1]); \ } \ for (int i = 0; i < GGML_F32_ARR/4; ++i) { \ x[4*i] = vec_add(x[4*i], x[4*i+2]); \ } \ for (int i = 0; i < GGML_F32_ARR/8; ++i) { \ x[8*i] = vec_add(x[8*i], x[8*i+4]); \ } \ res = vec_extract(x[0], 0) + \ vec_extract(x[0], 1) + \ vec_extract(x[0], 2) + \ vec_extract(x[0], 3); \ } #define GGML_F32_VEC GGML_F32x4 #define GGML_F32_VEC_ZERO GGML_F32x4_ZERO #define GGML_F32_VEC_SET1 GGML_F32x4_SET1 #define GGML_F32_VEC_LOAD GGML_F32x4_LOAD #define GGML_F32_VEC_STORE GGML_F32x4_STORE #define GGML_F32_VEC_FMA GGML_F32x4_FMA #define GGML_F32_VEC_ADD GGML_F32x4_ADD #define GGML_F32_VEC_MUL GGML_F32x4_MUL #define GGML_F32_VEC_REDUCE GGML_F32x4_REDUCE // F16 POWER9 #define GGML_F16_STEP GGML_F32_STEP #define GGML_F16_EPR GGML_F32_EPR #define GGML_F16_VEC GGML_F32x4 #define GGML_F16_VEC_ZERO GGML_F32x4_ZERO #define GGML_F16_VEC_SET1 GGML_F32x4_SET1 #define GGML_F16_VEC_FMA GGML_F32x4_FMA #define GGML_F16_VEC_REDUCE GGML_F32x4_REDUCE // Use vec_xl, not vec_ld, in case the load address is not aligned. #define GGML_F16_VEC_LOAD(p, i) (i & 0x1) ? \ vec_extract_fp32_from_shorth(vec_xl(0, p - GGML_F16_EPR)) : \ vec_extract_fp32_from_shortl(vec_xl(0, p)) #define GGML_ENDIAN_BYTE(i) ((unsigned char *)&(uint16_t){1})[i] #define GGML_F16_VEC_STORE(p, r, i) \ if (i & 0x1) \ vec_xst(vec_pack_to_short_fp32(r[i - GGML_ENDIAN_BYTE(1)], \ r[i - GGML_ENDIAN_BYTE(0)]), \ 0, p - GGML_F16_EPR) #elif defined(__wasm_simd128__) #define GGML_SIMD // F32 WASM #define GGML_F32_STEP 16 #define GGML_F32_EPR 4 #define GGML_F32x4 v128_t #define GGML_F32x4_ZERO wasm_f32x4_splat(0.0f) #define GGML_F32x4_SET1(x) wasm_f32x4_splat(x) #define GGML_F32x4_LOAD wasm_v128_load #define GGML_F32x4_STORE wasm_v128_store #define GGML_F32x4_FMA(a, b, c) wasm_f32x4_add(wasm_f32x4_mul(b, c), a) #define GGML_F32x4_ADD wasm_f32x4_add #define GGML_F32x4_MUL wasm_f32x4_mul #define GGML_F32x4_REDUCE(res, x) \ { \ for (int i = 0; i < GGML_F32_ARR/2; ++i) { \ x[2*i] = wasm_f32x4_add(x[2*i], x[2*i+1]); \ } \ for (int i = 0; i < GGML_F32_ARR/4; ++i) { \ x[4*i] = wasm_f32x4_add(x[4*i], x[4*i+2]); \ } \ for (int i = 0; i < GGML_F32_ARR/8; ++i) { \ x[8*i] = wasm_f32x4_add(x[8*i], x[8*i+4]); \ } \ res = wasm_f32x4_extract_lane(x[0], 0) + \ wasm_f32x4_extract_lane(x[0], 1) + \ wasm_f32x4_extract_lane(x[0], 2) + \ wasm_f32x4_extract_lane(x[0], 3); \ } #define GGML_F32_VEC GGML_F32x4 #define GGML_F32_VEC_ZERO GGML_F32x4_ZERO #define GGML_F32_VEC_SET1 GGML_F32x4_SET1 #define GGML_F32_VEC_LOAD GGML_F32x4_LOAD #define GGML_F32_VEC_STORE GGML_F32x4_STORE #define GGML_F32_VEC_FMA GGML_F32x4_FMA #define GGML_F32_VEC_ADD GGML_F32x4_ADD #define GGML_F32_VEC_MUL GGML_F32x4_MUL #define GGML_F32_VEC_REDUCE GGML_F32x4_REDUCE // F16 WASM #define GGML_F16_STEP 16 #define GGML_F16_EPR 4 inline static v128_t __wasm_f16x4_load(const ggml_fp16_t * p) { float tmp[4]; tmp[0] = GGML_FP16_TO_FP32(p[0]); tmp[1] = GGML_FP16_TO_FP32(p[1]); tmp[2] = GGML_FP16_TO_FP32(p[2]); tmp[3] = GGML_FP16_TO_FP32(p[3]); return wasm_v128_load(tmp); } inline static void __wasm_f16x4_store(ggml_fp16_t * p, v128_t x) { float tmp[4]; wasm_v128_store(tmp, x); p[0] = GGML_FP32_TO_FP16(tmp[0]); p[1] = GGML_FP32_TO_FP16(tmp[1]); p[2] = GGML_FP32_TO_FP16(tmp[2]); p[3] = GGML_FP32_TO_FP16(tmp[3]); } #define GGML_F16x4 v128_t #define GGML_F16x4_ZERO wasm_f32x4_splat(0.0f) #define GGML_F16x4_SET1(x) wasm_f32x4_splat(x) #define GGML_F16x4_LOAD(x) __wasm_f16x4_load(x) #define GGML_F16x4_STORE(x, y) __wasm_f16x4_store(x, y) #define GGML_F16x4_FMA GGML_F32x4_FMA #define GGML_F16x4_ADD wasm_f32x4_add #define GGML_F16x4_MUL wasm_f32x4_mul #define GGML_F16x4_REDUCE(res, x) \ { \ for (int i = 0; i < GGML_F16_ARR/2; ++i) { \ x[2*i] = wasm_f32x4_add(x[2*i], x[2*i+1]); \ } \ for (int i = 0; i < GGML_F16_ARR/4; ++i) { \ x[4*i] = wasm_f32x4_add(x[4*i], x[4*i+2]); \ } \ for (int i = 0; i < GGML_F16_ARR/8; ++i) { \ x[8*i] = wasm_f32x4_add(x[8*i], x[8*i+4]); \ } \ res = wasm_f32x4_extract_lane(x[0], 0) + \ wasm_f32x4_extract_lane(x[0], 1) + \ wasm_f32x4_extract_lane(x[0], 2) + \ wasm_f32x4_extract_lane(x[0], 3); \ } #define GGML_F16_VEC GGML_F16x4 #define GGML_F16_VEC_ZERO GGML_F16x4_ZERO #define GGML_F16_VEC_SET1 GGML_F16x4_SET1 #define GGML_F16_VEC_LOAD(p, i) GGML_F16x4_LOAD(p) #define GGML_F16_VEC_STORE(p, r, i) GGML_F16x4_STORE(p, r[i]) #define GGML_F16_VEC_FMA GGML_F16x4_FMA #define GGML_F16_VEC_ADD GGML_F16x4_ADD #define GGML_F16_VEC_MUL GGML_F16x4_MUL #define GGML_F16_VEC_REDUCE GGML_F16x4_REDUCE #elif defined(__SSE3__) #define GGML_SIMD // F32 SSE #define GGML_F32_STEP 32 #define GGML_F32_EPR 4 #define GGML_F32x4 __m128 #define GGML_F32x4_ZERO _mm_setzero_ps() #define GGML_F32x4_SET1(x) _mm_set1_ps(x) #define GGML_F32x4_LOAD _mm_loadu_ps #define GGML_F32x4_STORE _mm_storeu_ps #if defined(__FMA__) // TODO: Does this work? #define GGML_F32x4_FMA(a, b, c) _mm_fmadd_ps(b, c, a) #else #define GGML_F32x4_FMA(a, b, c) _mm_add_ps(_mm_mul_ps(b, c), a) #endif #define GGML_F32x4_ADD _mm_add_ps #define GGML_F32x4_MUL _mm_mul_ps #define GGML_F32x4_REDUCE(res, x) \ { \ for (int i = 0; i < GGML_F32_ARR/2; ++i) { \ x[2*i] = _mm_add_ps(x[2*i], x[2*i+1]); \ } \ for (int i = 0; i < GGML_F32_ARR/4; ++i) { \ x[4*i] = _mm_add_ps(x[4*i], x[4*i+2]); \ } \ for (int i = 0; i < GGML_F32_ARR/8; ++i) { \ x[8*i] = _mm_add_ps(x[8*i], x[8*i+4]); \ } \ const __m128 t0 = _mm_hadd_ps(x[0], x[0]); \ res = _mm_cvtss_f32(_mm_hadd_ps(t0, t0)); \ } // TODO: is this optimal ? #define GGML_F32_VEC GGML_F32x4 #define GGML_F32_VEC_ZERO GGML_F32x4_ZERO #define GGML_F32_VEC_SET1 GGML_F32x4_SET1 #define GGML_F32_VEC_LOAD GGML_F32x4_LOAD #define GGML_F32_VEC_STORE GGML_F32x4_STORE #define GGML_F32_VEC_FMA GGML_F32x4_FMA #define GGML_F32_VEC_ADD GGML_F32x4_ADD #define GGML_F32_VEC_MUL GGML_F32x4_MUL #define GGML_F32_VEC_REDUCE GGML_F32x4_REDUCE // F16 SSE #define GGML_F16_STEP 32 #define GGML_F16_EPR 4 static inline __m128 __sse_f16x4_load(ggml_fp16_t *x) { float tmp[4]; tmp[0] = GGML_FP16_TO_FP32(x[0]); tmp[1] = GGML_FP16_TO_FP32(x[1]); tmp[2] = GGML_FP16_TO_FP32(x[2]); tmp[3] = GGML_FP16_TO_FP32(x[3]); return _mm_loadu_ps(tmp); } static inline void __sse_f16x4_store(ggml_fp16_t *x, __m128 y) { float arr[4]; _mm_storeu_ps(arr, y); x[0] = GGML_FP32_TO_FP16(arr[0]); x[1] = GGML_FP32_TO_FP16(arr[1]); x[2] = GGML_FP32_TO_FP16(arr[2]); x[3] = GGML_FP32_TO_FP16(arr[3]); } #define GGML_F32Cx4 __m128 #define GGML_F32Cx4_ZERO _mm_setzero_ps() #define GGML_F32Cx4_SET1(x) _mm_set1_ps(x) #define GGML_F32Cx4_LOAD(x) __sse_f16x4_load(x) #define GGML_F32Cx4_STORE(x, y) __sse_f16x4_store(x, y) #define GGML_F32Cx4_FMA GGML_F32x4_FMA #define GGML_F32Cx4_ADD _mm_add_ps #define GGML_F32Cx4_MUL _mm_mul_ps #define GGML_F32Cx4_REDUCE GGML_F32x4_REDUCE #define GGML_F16_VEC GGML_F32Cx4 #define GGML_F16_VEC_ZERO GGML_F32Cx4_ZERO #define GGML_F16_VEC_SET1 GGML_F32Cx4_SET1 #define GGML_F16_VEC_LOAD(p, i) GGML_F32Cx4_LOAD(p) #define GGML_F16_VEC_STORE(p, r, i) GGML_F32Cx4_STORE(p, r[i]) #define GGML_F16_VEC_FMA GGML_F32Cx4_FMA #define GGML_F16_VEC_ADD GGML_F32Cx4_ADD #define GGML_F16_VEC_MUL GGML_F32Cx4_MUL #define GGML_F16_VEC_REDUCE GGML_F32Cx4_REDUCE #endif // GGML_F32_ARR / GGML_F16_ARR // number of registers to use per step #ifdef GGML_SIMD #define GGML_F32_ARR (GGML_F32_STEP/GGML_F32_EPR) #define GGML_F16_ARR (GGML_F16_STEP/GGML_F16_EPR) #endif // // fundamental operations // inline static void ggml_vec_set_i8(const int n, int8_t * x, const int8_t v) { for (int i = 0; i < n; ++i) x[i] = v; } inline static void ggml_vec_set_i16(const int n, int16_t * x, const int16_t v) { for (int i = 0; i < n; ++i) x[i] = v; } inline static void ggml_vec_set_i32(const int n, int32_t * x, const int32_t v) { for (int i = 0; i < n; ++i) x[i] = v; } inline static void ggml_vec_set_f16(const int n, ggml_fp16_t * x, const int32_t v) { for (int i = 0; i < n; ++i) x[i] = v; } inline static void ggml_vec_add_f32 (const int n, float * z, const float * x, const float * y) { for (int i = 0; i < n; ++i) z[i] = x[i] + y[i]; } inline static void ggml_vec_acc_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] += x[i]; } inline static void ggml_vec_acc1_f32(const int n, float * y, const float v) { for (int i = 0; i < n; ++i) y[i] += v; } inline static void ggml_vec_sub_f32 (const int n, float * z, const float * x, const float * y) { for (int i = 0; i < n; ++i) z[i] = x[i] - y[i]; } inline static void ggml_vec_set_f32 (const int n, float * x, const float v) { for (int i = 0; i < n; ++i) x[i] = v; } inline static void ggml_vec_cpy_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = x[i]; } inline static void ggml_vec_neg_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = -x[i]; } inline static void ggml_vec_mul_f32 (const int n, float * z, const float * x, const float * y) { for (int i = 0; i < n; ++i) z[i] = x[i]*y[i]; } inline static void ggml_vec_div_f32 (const int n, float * z, const float * x, const float * y) { for (int i = 0; i < n; ++i) z[i] = x[i]/y[i]; } inline static void ggml_vec_dot_f32(const int n, float * restrict s, const float * restrict x, const float * restrict y) { ggml_float sumf = 0.0; #ifdef GGML_SIMD const int np = (n & ~(GGML_F32_STEP - 1)); GGML_F32_VEC sum[GGML_F32_ARR] = { GGML_F32_VEC_ZERO }; GGML_F32_VEC ax[GGML_F32_ARR]; GGML_F32_VEC ay[GGML_F32_ARR]; for (int i = 0; i < np; i += GGML_F32_STEP) { for (int j = 0; j < GGML_F32_ARR; j++) { ax[j] = GGML_F32_VEC_LOAD(x + i + j*GGML_F32_EPR); ay[j] = GGML_F32_VEC_LOAD(y + i + j*GGML_F32_EPR); sum[j] = GGML_F32_VEC_FMA(sum[j], ax[j], ay[j]); } } // reduce sum0..sum3 to sum0 GGML_F32_VEC_REDUCE(sumf, sum); // leftovers for (int i = np; i < n; ++i) { sumf += x[i]*y[i]; } #else // scalar for (int i = 0; i < n; ++i) { sumf += x[i]*y[i]; } #endif *s = sumf; } inline static void ggml_vec_dot_f16(const int n, float * restrict s, ggml_fp16_t * restrict x, ggml_fp16_t * restrict y) { ggml_float sumf = 0.0; #if defined(GGML_SIMD) const int np = (n & ~(GGML_F16_STEP - 1)); GGML_F16_VEC sum[GGML_F16_ARR] = { GGML_F16_VEC_ZERO }; GGML_F16_VEC ax[GGML_F16_ARR]; GGML_F16_VEC ay[GGML_F16_ARR]; for (int i = 0; i < np; i += GGML_F16_STEP) { for (int j = 0; j < GGML_F16_ARR; j++) { ax[j] = GGML_F16_VEC_LOAD(x + i + j*GGML_F16_EPR, j); ay[j] = GGML_F16_VEC_LOAD(y + i + j*GGML_F16_EPR, j); sum[j] = GGML_F16_VEC_FMA(sum[j], ax[j], ay[j]); } } // reduce sum0..sum3 to sum0 GGML_F16_VEC_REDUCE(sumf, sum); // leftovers for (int i = np; i < n; ++i) { sumf += GGML_FP16_TO_FP32(x[i])*GGML_FP16_TO_FP32(y[i]); } #else for (int i = 0; i < n; ++i) { sumf += GGML_FP16_TO_FP32(x[i])*GGML_FP16_TO_FP32(y[i]); } #endif *s = sumf; } // compute GGML_VEC_DOT_UNROLL dot products at once // xs - x row stride in bytes inline static void ggml_vec_dot_f16_unroll(const int n, const int xs, float * restrict s, void * restrict xv, ggml_fp16_t * restrict y) { ggml_float sumf[GGML_VEC_DOT_UNROLL] = { 0.0 }; ggml_fp16_t * restrict x[GGML_VEC_DOT_UNROLL]; for (int i = 0; i < GGML_VEC_DOT_UNROLL; ++i) { x[i] = (ggml_fp16_t *) ((char *) xv + i*xs); } #if defined(GGML_SIMD) const int np = (n & ~(GGML_F16_STEP - 1)); GGML_F16_VEC sum[GGML_VEC_DOT_UNROLL][GGML_F16_ARR] = { { GGML_F16_VEC_ZERO } }; GGML_F16_VEC ax[GGML_F16_ARR]; GGML_F16_VEC ay[GGML_F16_ARR]; for (int i = 0; i < np; i += GGML_F16_STEP) { for (int j = 0; j < GGML_F16_ARR; j++) { ay[j] = GGML_F16_VEC_LOAD(y + i + j*GGML_F16_EPR, j); for (int k = 0; k < GGML_VEC_DOT_UNROLL; ++k) { ax[j] = GGML_F16_VEC_LOAD(x[k] + i + j*GGML_F16_EPR, j); sum[k][j] = GGML_F16_VEC_FMA(sum[k][j], ax[j], ay[j]); } } } // reduce sum0..sum3 to sum0 for (int k = 0; k < GGML_VEC_DOT_UNROLL; ++k) { GGML_F16_VEC_REDUCE(sumf[k], sum[k]); } // leftovers for (int i = np; i < n; ++i) { for (int j = 0; j < GGML_VEC_DOT_UNROLL; ++j) { sumf[j] += GGML_FP16_TO_FP32(x[j][i])*GGML_FP16_TO_FP32(y[i]); } } #else for (int i = 0; i < n; ++i) { for (int j = 0; j < GGML_VEC_DOT_UNROLL; ++j) { sumf[j] += GGML_FP16_TO_FP32(x[j][i])*GGML_FP16_TO_FP32(y[i]); } } #endif for (int i = 0; i < GGML_VEC_DOT_UNROLL; ++i) { s[i] = sumf[i]; } } inline static void ggml_vec_mad_f32(const int n, float * restrict y, const float * restrict x, const float v) { #if defined(GGML_SIMD) const int np = (n & ~(GGML_F32_STEP - 1)); GGML_F32_VEC vx = GGML_F32_VEC_SET1(v); GGML_F32_VEC ax[GGML_F32_ARR]; GGML_F32_VEC ay[GGML_F32_ARR]; for (int i = 0; i < np; i += GGML_F32_STEP) { for (int j = 0; j < GGML_F32_ARR; j++) { ax[j] = GGML_F32_VEC_LOAD(x + i + j*GGML_F32_EPR); ay[j] = GGML_F32_VEC_LOAD(y + i + j*GGML_F32_EPR); ay[j] = GGML_F32_VEC_FMA(ay[j], ax[j], vx); GGML_F32_VEC_STORE(y + i + j*GGML_F32_EPR, ay[j]); } } // leftovers for (int i = np; i < n; ++i) { y[i] += x[i]*v; } #else // scalar for (int i = 0; i < n; ++i) { y[i] += x[i]*v; } #endif } inline static void ggml_vec_mad_f16(const int n, ggml_fp16_t * restrict y, ggml_fp16_t * restrict x, const float v) { #if defined(GGML_SIMD) const int np = (n & ~(GGML_F16_STEP - 1)); GGML_F16_VEC vx = GGML_F16_VEC_SET1(v); GGML_F16_VEC ax[GGML_F16_ARR]; GGML_F16_VEC ay[GGML_F16_ARR]; for (int i = 0; i < np; i += GGML_F16_STEP) { for (int j = 0; j < GGML_F16_ARR; j++) { ax[j] = GGML_F16_VEC_LOAD(x + i + j*GGML_F16_EPR, j); ay[j] = GGML_F16_VEC_LOAD(y + i + j*GGML_F16_EPR, j); ay[j] = GGML_F16_VEC_FMA(ay[j], ax[j], vx); GGML_F16_VEC_STORE(y + i + j*GGML_F16_EPR, ay, j); } } // leftovers for (int i = np; i < n; ++i) { GGML_ASSERT(false); y[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(y[i]) + GGML_FP16_TO_FP32(x[i])*v); } #else for (int i = 0; i < n; ++i) { y[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(y[i]) + GGML_FP16_TO_FP32(x[i])*v); } #endif } //inline static void ggml_vec_scale_f32(const int n, float * y, const float v) { for (int i = 0; i < n; ++i) y[i] *= v; } inline static void ggml_vec_scale_f32(const int n, float * y, const float v) { #if defined(GGML_SIMD) const int np = (n & ~(GGML_F32_STEP - 1)); GGML_F32_VEC vx = GGML_F32_VEC_SET1(v); GGML_F32_VEC ay[GGML_F32_ARR]; for (int i = 0; i < np; i += GGML_F32_STEP) { for (int j = 0; j < GGML_F32_ARR; j++) { ay[j] = GGML_F32_VEC_LOAD(y + i + j*GGML_F32_EPR); ay[j] = GGML_F32_VEC_MUL(ay[j], vx); GGML_F32_VEC_STORE(y + i + j*GGML_F32_EPR, ay[j]); } } // leftovers for (int i = np; i < n; ++i) { y[i] *= v; } #else // scalar for (int i = 0; i < n; ++i) { y[i] *= v; } #endif } inline static void ggml_vec_norm_f32 (const int n, float * s, const float * x) { ggml_vec_dot_f32(n, s, x, x); *s = sqrt(*s); } inline static void ggml_vec_sqr_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = x[i]*x[i]; } inline static void ggml_vec_sqrt_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = sqrt(x[i]); } inline static void ggml_vec_abs_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = fabsf(x[i]); } inline static void ggml_vec_sgn_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? 1.f : ((x[i] < 0.f) ? -1.f : 0.f); } inline static void ggml_vec_step_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? 1.f : 0.f; } inline static void ggml_vec_relu_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? x[i] : 0.f; } static const ggml_float GELU_COEF_A = 0.044715; static const ggml_float SQRT_2_OVER_PI = 0.79788456080286535587989211986876; inline static float ggml_gelu_f32(float x) { return 0.5*x*(1.0 + tanh(SQRT_2_OVER_PI*x*(1.0 + GELU_COEF_A*x*x))); } inline static void ggml_vec_gelu_f16(const int n, ggml_fp16_t * y, const ggml_fp16_t * x) { const uint16_t * i16 = (const uint16_t *) x; for (int i = 0; i < n; ++i) { y[i] = table_gelu_f16[i16[i]]; } } #ifdef GGML_GELU_FP16 inline static void ggml_vec_gelu_f32(const int n, float * y, const float * x) { uint16_t t; for (int i = 0; i < n; ++i) { ggml_fp16_t fp16 = GGML_FP32_TO_FP16(x[i]); memcpy(&t, &fp16, sizeof(uint16_t)); y[i] = GGML_FP16_TO_FP32(table_gelu_f16[t]); } } #else inline static void ggml_vec_gelu_f32(const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) { y[i] = ggml_gelu_f32(x[i]); } } #endif inline static void ggml_vec_sum_f32(const int n, float * s, const float * x) { #ifndef GGML_USE_ACCELERATE ggml_float sum = 0.0; for (int i = 0; i < n; ++i) { sum += x[i]; } *s = sum; #else vDSP_sve(x, 1, s, n); #endif } inline static void ggml_vec_max_f32(const int n, float * s, const float * x) { #ifndef GGML_USE_ACCELERATE ggml_float max = -INFINITY; for (int i = 0; i < n; ++i) { max = MAX(max, x[i]); } *s = max; #else vDSP_maxv(x, 1, s, n); #endif } inline static void ggml_vec_norm_inv_f32(const int n, float * s, const float * x) { ggml_vec_norm_f32(n, s, x); *s = 1./(*s); } // // logging // #if (GGML_DEBUG >= 1) #define GGML_PRINT_DEBUG(...) printf(__VA_ARGS__) #else #define GGML_PRINT_DEBUG(...) #endif #if (GGML_DEBUG >= 5) #define GGML_PRINT_DEBUG_5(...) printf(__VA_ARGS__) #else #define GGML_PRINT_DEBUG_5(...) #endif #if (GGML_DEBUG >= 10) #define GGML_PRINT_DEBUG_10(...) printf(__VA_ARGS__) #else #define GGML_PRINT_DEBUG_10(...) #endif #define GGML_PRINT(...) printf(__VA_ARGS__) // // data types // static const size_t GGML_TYPE_SIZE[GGML_TYPE_COUNT] = { sizeof(int8_t ), sizeof(int16_t), sizeof(int32_t), sizeof(ggml_fp16_t), sizeof(float ), }; static const char * GGML_OP_LABEL[GGML_OP_COUNT] = { "NONE", "DUP", "ADD", "SUB", "MUL", "DIV", "SQR", "SQRT", "SUM", "MEAN", "REPEAT", "ABS", "SGN", "NEG", "STEP", "RELU", "GELU", "NORM", "MUL_MAT", "SCALE", "CPY", "RESHAPE", "VIEW", "PERMUTE", "TRANSPOSE", "GET_ROWS", "DIAG_MASK_INF", "SOFT_MAX", "ROPE", "CONV_1D_1S", "CONV_1D_2S", "FLASH_ATTN", "FLASH_FF", }; static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = { "none", "x", "x+y", "x-y", "x*y", "x/y", "x^2", "√x", "Σx", "Σx/n", "repeat(x)", "abs(x)", "sgn(x)", "-x", "step(x)", "relu(x)", "gelu(x)", "norm(x)", "X*Y", "x*v", "x-\\>y", "reshape(x)", "view(x)", "permute(x)", "transpose(x)", "get_rows(x)", "diag_mask_inf(x)", "soft_max(x)", "rope(x)", "conv_1d_1s(x)", "conv_1d_2s(x)", "flash_attn(x)", "flash_ff(x)", }; // // ggml object // struct ggml_object { size_t offs; size_t size; struct ggml_object * next; char padding[8]; }; static const size_t GGML_OBJECT_SIZE = sizeof(struct ggml_object); static_assert(sizeof(struct ggml_object)%GGML_MEM_ALIGN == 0, "ggml_object size must be a multiple of GGML_MEM_ALIGN"); static_assert(sizeof(struct ggml_tensor)%GGML_MEM_ALIGN == 0, "ggml_tensor size must be a multiple of GGML_MEM_ALIGN"); // // ggml context // struct ggml_context { size_t mem_size; void * mem_buffer; bool mem_buffer_owned; int n_objects; struct ggml_object * objects_begin; struct ggml_object * objects_end; struct ggml_scratch scratch; struct ggml_scratch scratch_save; }; struct ggml_context_container { bool used; struct ggml_context context; }; // // compute types // enum ggml_task_type { GGML_TASK_INIT = 0, GGML_TASK_COMPUTE, GGML_TASK_FINALIZE, }; struct ggml_compute_params { enum ggml_task_type type; int ith, nth; // work buffer for all threads size_t wsize; void * wdata; }; // // ggml state // struct ggml_state { struct ggml_context_container contexts[GGML_MAX_CONTEXTS]; }; // global state static struct ggml_state g_state; static atomic_int g_state_barrier = 0; // barrier via spin lock inline static void ggml_critical_section_start(void) { int processing = atomic_fetch_add(&g_state_barrier, 1); while (processing > 0) { // wait for other threads to finish atomic_fetch_sub(&g_state_barrier, 1); sched_yield(); // TODO: reconsider this processing = atomic_fetch_add(&g_state_barrier, 1); } } // TODO: make this somehow automatically executed // some sort of "sentry" mechanism inline static void ggml_critical_section_end(void) { atomic_fetch_sub(&g_state_barrier, 1); } //////////////////////////////////////////////////////////////////////////////// void ggml_print_object(const struct ggml_object * obj) { GGML_PRINT(" - ggml_object: offset = %zu, size = %zu, next = %p\n", obj->offs, obj->size, (const void *) obj->next); } void ggml_print_objects(const struct ggml_context * ctx) { struct ggml_object * obj = ctx->objects_begin; GGML_PRINT("%s: objects in context %p:\n", __func__, (const void *) ctx); while (obj != NULL) { ggml_print_object(obj); obj = obj->next; } GGML_PRINT("%s: --- end ---\n", __func__); } int ggml_nelements(const struct ggml_tensor * tensor) { static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); return tensor->ne[0]*tensor->ne[1]*tensor->ne[2]*tensor->ne[3]; } int ggml_nrows(const struct ggml_tensor * tensor) { static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); return tensor->ne[1]*tensor->ne[2]*tensor->ne[3]; } size_t ggml_nbytes(const struct ggml_tensor * tensor) { static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); return ggml_nelements(tensor)*GGML_TYPE_SIZE[tensor->type]; } size_t ggml_type_size(enum ggml_type type) { return GGML_TYPE_SIZE[type]; } size_t ggml_element_size(const struct ggml_tensor * tensor) { return GGML_TYPE_SIZE[tensor->type]; } static inline bool ggml_is_scalar(const struct ggml_tensor * tensor) { static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); return tensor->ne[0] == 1 && tensor->ne[1] == 1 && tensor->ne[2] == 1 && tensor->ne[3] == 1; } static inline bool ggml_is_vector(const struct ggml_tensor * tensor) { static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); return tensor->ne[1] == 1 && tensor->ne[2] == 1 && tensor->ne[3] == 1; } static inline bool ggml_is_matrix(const struct ggml_tensor * tensor) { static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); return tensor->ne[2] == 1 && tensor->ne[3] == 1; } static inline bool ggml_can_mul_mat(const struct ggml_tensor * t0, const struct ggml_tensor * t1) { static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); return (t0->ne[0] == t1->ne[0]) && (t0->ne[2] == t1->ne[2]) && (t0->ne[3] == t1->ne[3]); } static inline bool ggml_is_contiguous(const struct ggml_tensor * tensor) { static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); return tensor->nb[0] == GGML_TYPE_SIZE[tensor->type] && tensor->nb[1] == tensor->nb[0]*tensor->ne[0] && tensor->nb[2] == tensor->nb[1]*tensor->ne[1] && tensor->nb[3] == tensor->nb[2]*tensor->ne[2]; } static inline bool ggml_is_padded_1d(const struct ggml_tensor * tensor) { static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); return tensor->nb[0] == GGML_TYPE_SIZE[tensor->type] && tensor->nb[2] == tensor->nb[1]*tensor->ne[1] && tensor->nb[3] == tensor->nb[2]*tensor->ne[2]; } static inline bool ggml_are_same_shape(const struct ggml_tensor * t0, const struct ggml_tensor * t1) { static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); return (t0->ne[0] == t1->ne[0] ) && (t0->ne[1] == t1->ne[1] ) && (t0->ne[2] == t1->ne[2] ) && (t0->ne[3] == t1->ne[3] ); } // check if t1 can be represented as a repeatition of t0 static inline bool ggml_can_repeat(const struct ggml_tensor * t0, const struct ggml_tensor * t1) { static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); return (t1->ne[0]%t0->ne[0] == 0) && (t1->ne[1]%t0->ne[1] == 0) && (t1->ne[2]%t0->ne[2] == 0) && (t1->ne[3]%t0->ne[3] == 0); } static inline int ggml_up32(int n) { return (n + 31) & ~31; } static inline int ggml_up64(int n) { return (n + 63) & ~63; } static inline int ggml_up(int n, int m) { // assert m is a power of 2 GGML_ASSERT((m & (m - 1)) == 0); return (n + m - 1) & ~(m - 1); } // assert that pointer is aligned to GGML_MEM_ALIGN #define ggml_assert_aligned(ptr) \ assert(((uintptr_t) (ptr))%GGML_MEM_ALIGN == 0) //////////////////////////////////////////////////////////////////////////////// struct ggml_context * ggml_init(struct ggml_init_params params) { // make this function thread safe ggml_critical_section_start(); static bool is_first_call = true; if (is_first_call) { // initialize GELU, EXP and 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_exp_f16[i] = GGML_FP32_TO_FP16(exp(f)); } const uint64_t t_end = ggml_time_us(); UNUSED(t_end); GGML_PRINT_DEBUG("%s: GELU and EXP tables initialized in %f ms\n", __func__, (t_end - t_start)/1000.0f); } // initialize g_state { const uint64_t t_start = ggml_time_us(); UNUSED(t_start); g_state = (struct ggml_state) { /*.contexts =*/ { { 0 } }, }; for (int i = 0; i < GGML_MAX_CONTEXTS; ++i) { g_state.contexts[i].used = false; } const uint64_t t_end = ggml_time_us(); UNUSED(t_end); GGML_PRINT_DEBUG("%s: g_state initialized in %f ms\n", __func__, (t_end - t_start)/1000.0f); } is_first_call = false; } // find non-used context in g_state struct ggml_context * ctx = NULL; for (int i = 0; i < GGML_MAX_CONTEXTS; i++) { if (!g_state.contexts[i].used) { g_state.contexts[i].used = true; ctx = &g_state.contexts[i].context; GGML_PRINT_DEBUG("%s: found unused context %d\n", __func__, i); break; } } if (ctx == NULL) { GGML_PRINT_DEBUG("%s: no unused context found\n", __func__); ggml_critical_section_end(); return NULL; } *ctx = (struct ggml_context) { /*.mem_size =*/ params.mem_size, /*.mem_buffer =*/ params.mem_buffer ? params.mem_buffer : malloc(params.mem_size), /*.mem_buffer_owned =*/ params.mem_buffer ? false : true, /*.n_objects =*/ 0, /*.objects_begin =*/ NULL, /*.objects_end =*/ NULL, /*.scratch =*/ { 0, 0, NULL, }, /*.scratch_save =*/ { 0, 0, NULL, }, }; ggml_assert_aligned(ctx->mem_buffer); GGML_PRINT_DEBUG("%s: context initialized\n", __func__); ggml_critical_section_end(); return ctx; } void ggml_free(struct ggml_context * ctx) { // make this function thread safe ggml_critical_section_start(); bool found = false; for (int i = 0; i < GGML_MAX_CONTEXTS; i++) { if (&g_state.contexts[i].context == ctx) { g_state.contexts[i].used = false; GGML_PRINT_DEBUG("%s: context %d with %d objects has been freed. memory used = %zu\n", __func__, i, ctx->n_objects, ctx->objects_end->offs + ctx->objects_end->size); if (ctx->mem_buffer_owned) { free(ctx->mem_buffer); } found = true; break; } } if (!found) { GGML_PRINT_DEBUG("%s: context not found\n", __func__); } ggml_critical_section_end(); } size_t ggml_used_mem(const struct ggml_context * ctx) { return ctx->objects_end->offs + ctx->objects_end->size; } size_t ggml_set_scratch(struct ggml_context * ctx, struct ggml_scratch scratch) { const size_t result = ctx->scratch.data ? ctx->scratch.offs : 0; ctx->scratch = scratch; return result; } //////////////////////////////////////////////////////////////////////////////// struct ggml_tensor * ggml_new_tensor_impl( struct ggml_context * ctx, enum ggml_type type, int n_dims, const int* ne, void* data) { // always insert objects at the end of the context's memory pool struct ggml_object * obj_cur = ctx->objects_end; const size_t cur_offs = obj_cur == NULL ? 0 : obj_cur->offs; const size_t cur_size = obj_cur == NULL ? 0 : obj_cur->size; const size_t cur_end = cur_offs + cur_size; size_t size_needed = 0; if (data == NULL) { size_needed += GGML_TYPE_SIZE[type]; for (int i = 0; i < n_dims; i++) { size_needed *= ne[i]; } // align to GGML_MEM_ALIGN size_needed = ((size_needed + GGML_MEM_ALIGN - 1)/GGML_MEM_ALIGN)*GGML_MEM_ALIGN; } char * const mem_buffer = ctx->mem_buffer; struct ggml_object * const obj_new = (struct ggml_object *)(mem_buffer + cur_end); if (ctx->scratch.data == NULL || data != NULL) { size_needed += sizeof(struct ggml_tensor); if (cur_end + size_needed + GGML_OBJECT_SIZE > ctx->mem_size) { GGML_PRINT("%s: not enough space in the context's memory pool (needed %zu, available %zu)\n", __func__, cur_end + size_needed + GGML_OBJECT_SIZE, ctx->mem_size); assert(false); return NULL; } *obj_new = (struct ggml_object) { .offs = cur_end + GGML_OBJECT_SIZE, .size = size_needed, .next = NULL, }; } else { if (ctx->scratch.offs + size_needed > ctx->scratch.size) { GGML_PRINT("%s: not enough space in the scratch memory\n", __func__); assert(false); return NULL; } if (cur_end + sizeof(struct ggml_tensor) + GGML_OBJECT_SIZE > ctx->mem_size) { GGML_PRINT("%s: not enough space in the context's memory pool (needed %zu, available %zu)\n", __func__, cur_end + sizeof(struct ggml_tensor) + GGML_OBJECT_SIZE, ctx->mem_size); assert(false); return NULL; } data = (char * const) ctx->scratch.data + ctx->scratch.offs; *obj_new = (struct ggml_object) { .offs = cur_end + GGML_OBJECT_SIZE, .size = sizeof(struct ggml_tensor), .next = NULL, }; //printf("scratch offs = %zu, size_needed = %zu\n", ctx->scratch.offs, size_needed); ctx->scratch.offs += size_needed; } if (obj_cur != NULL) { obj_cur->next = obj_new; } else { // this is the first object in this context ctx->objects_begin = obj_new; } ctx->objects_end = obj_new; //printf("%s: inserted new object at %zu, size = %zu\n", __func__, cur_end, obj_new->size); struct ggml_tensor * const result = (struct ggml_tensor *)(mem_buffer + obj_new->offs); ggml_assert_aligned(result); *result = (struct ggml_tensor) { /*.type =*/ type, /*.n_dims =*/ n_dims, /*.ne =*/ { 1, 1, 1, 1 }, /*.nb =*/ { 0, 0, 0, 0 }, /*.op =*/ GGML_OP_NONE, /*.is_param =*/ false, /*.grad =*/ NULL, /*.src0 =*/ NULL, /*.src1 =*/ NULL, /*.opt =*/ { NULL }, /*.n_tasks =*/ 0, /*.perf_runs =*/ 0, /*.perf_cycles =*/ 0, /*.perf_time_us =*/ 0, /*.data =*/ data == NULL ? (void *)(result + 1) : data, /*.pad =*/ { 0 }, }; ggml_assert_aligned(result->data); for (int i = 0; i < n_dims; i++) { result->ne[i] = ne[i]; } result->nb[0] = GGML_TYPE_SIZE[type]; for (int i = 1; i < GGML_MAX_DIMS; i++) { result->nb[i] = result->nb[i - 1]*result->ne[i - 1]; } ctx->n_objects++; return result; } struct ggml_tensor * ggml_new_tensor( struct ggml_context * ctx, enum ggml_type type, int n_dims, const int * ne) { return ggml_new_tensor_impl(ctx, type, n_dims, ne, NULL); } struct ggml_tensor * ggml_new_tensor_1d( struct ggml_context * ctx, enum ggml_type type, int ne0) { return ggml_new_tensor(ctx, type, 1, &ne0); } struct ggml_tensor * ggml_new_tensor_2d( struct ggml_context * ctx, enum ggml_type type, int ne0, int ne1) { const int ne[2] = { ne0, ne1 }; return ggml_new_tensor(ctx, type, 2, ne); } struct ggml_tensor * ggml_new_tensor_3d( struct ggml_context * ctx, enum ggml_type type, int ne0, int ne1, int ne2) { const int ne[3] = { ne0, ne1, ne2 }; return ggml_new_tensor(ctx, type, 3, ne); } struct ggml_tensor * ggml_new_tensor_4d( struct ggml_context * ctx, enum ggml_type type, int ne0, int ne1, int ne2, int ne3) { const int ne[4] = { ne0, ne1, ne2, ne3 }; return ggml_new_tensor(ctx, type, 4, ne); } struct ggml_tensor * ggml_new_i32(struct ggml_context * ctx, int32_t value) { ctx->scratch_save = ctx->scratch; ctx->scratch.data = NULL; struct ggml_tensor * result = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 1); ctx->scratch = ctx->scratch_save; ggml_set_i32(result, value); return result; } struct ggml_tensor * ggml_new_f32(struct ggml_context * ctx, float value) { ctx->scratch_save = ctx->scratch; ctx->scratch.data = NULL; struct ggml_tensor * result = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 1); ctx->scratch = ctx->scratch_save; ggml_set_f32(result, value); return result; } struct ggml_tensor * ggml_dup_tensor(struct ggml_context * ctx, const struct ggml_tensor * src) { return ggml_new_tensor_impl(ctx, src->type, src->n_dims, src->ne, NULL); } struct ggml_tensor * ggml_set_zero(struct ggml_tensor * tensor) { memset(tensor->data, 0, ggml_nbytes(tensor)); return tensor; } struct ggml_tensor * ggml_set_i32 (struct ggml_tensor * tensor, int32_t value) { const int n = ggml_nrows(tensor); const int nc = tensor->ne[0]; const size_t n1 = tensor->nb[1]; char * const data = tensor->data; switch (tensor->type) { case GGML_TYPE_I8: { assert(tensor->nb[0] == sizeof(int8_t)); for (int i = 0; i < n; i++) { ggml_vec_set_i8(nc, (int8_t *)(data + i*n1), value); } } break; case GGML_TYPE_I16: { assert(tensor->nb[0] == sizeof(int16_t)); for (int i = 0; i < n; i++) { ggml_vec_set_i16(nc, (int16_t *)(data + i*n1), value); } } break; case GGML_TYPE_I32: { assert(tensor->nb[0] == sizeof(int32_t)); for (int i = 0; i < n; i++) { ggml_vec_set_i32(nc, (int32_t *)(data + i*n1), value); } } break; case GGML_TYPE_F16: { assert(tensor->nb[0] == sizeof(ggml_fp16_t)); for (int i = 0; i < n; i++) { ggml_vec_set_f16(nc, (ggml_fp16_t *)(data + i*n1), value); } } break; case GGML_TYPE_F32: { assert(tensor->nb[0] == sizeof(float)); for (int i = 0; i < n; i++) { ggml_vec_set_f32(nc, (float *)(data + i*n1), value); } } break; case GGML_TYPE_COUNT: { assert(false); } break; } return tensor; } struct ggml_tensor * ggml_set_f32(struct ggml_tensor * tensor, float value) { const int n = ggml_nrows(tensor); const int nc = tensor->ne[0]; const size_t n1 = tensor->nb[1]; char * const data = tensor->data; switch (tensor->type) { case GGML_TYPE_I8: { assert(tensor->nb[0] == sizeof(int8_t)); for (int i = 0; i < n; i++) { ggml_vec_set_i8(nc, (int8_t *)(data + i*n1), value); } } break; case GGML_TYPE_I16: { assert(tensor->nb[0] == sizeof(int16_t)); for (int i = 0; i < n; i++) { ggml_vec_set_i16(nc, (int16_t *)(data + i*n1), value); } } break; case GGML_TYPE_I32: { assert(tensor->nb[0] == sizeof(int32_t)); for (int i = 0; i < n; i++) { ggml_vec_set_i32(nc, (int32_t *)(data + i*n1), value); } } break; case GGML_TYPE_F16: { assert(tensor->nb[0] == sizeof(ggml_fp16_t)); for (int i = 0; i < n; i++) { ggml_vec_set_f16(nc, (ggml_fp16_t *)(data + i*n1), value); } } break; case GGML_TYPE_F32: { assert(tensor->nb[0] == sizeof(float)); for (int i = 0; i < n; i++) { ggml_vec_set_f32(nc, (float *)(data + i*n1), value); } } break; case GGML_TYPE_COUNT: { 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; case GGML_TYPE_COUNT: { GGML_ASSERT(false); } break; } return 0.0f; } void ggml_set_i32_1d(const struct ggml_tensor * tensor, int i, int32_t value) { switch (tensor->type) { case GGML_TYPE_I8: { GGML_ASSERT(tensor->nb[0] == sizeof(int8_t)); ((int8_t *)(tensor->data))[i] = value; } break; case GGML_TYPE_I16: { GGML_ASSERT(tensor->nb[0] == sizeof(int16_t)); ((int16_t *)(tensor->data))[i] = value; } break; case GGML_TYPE_I32: { GGML_ASSERT(tensor->nb[0] == sizeof(int32_t)); ((int32_t *)(tensor->data))[i] = value; } break; case GGML_TYPE_F16: { GGML_ASSERT(tensor->nb[0] == sizeof(ggml_fp16_t)); ((ggml_fp16_t *)(tensor->data))[i] = GGML_FP32_TO_FP16(value); } break; case GGML_TYPE_F32: { GGML_ASSERT(tensor->nb[0] == sizeof(float)); ((float *)(tensor->data))[i] = value; } break; case GGML_TYPE_COUNT: { GGML_ASSERT(false); } break; } } float ggml_get_f32_1d(const struct ggml_tensor * tensor, int i) { switch (tensor->type) { case GGML_TYPE_I8: { GGML_ASSERT(tensor->nb[0] == sizeof(int8_t)); return ((int8_t *)(tensor->data))[i]; } break; case GGML_TYPE_I16: { GGML_ASSERT(tensor->nb[0] == sizeof(int16_t)); return ((int16_t *)(tensor->data))[i]; } break; case GGML_TYPE_I32: { GGML_ASSERT(tensor->nb[0] == sizeof(int32_t)); return ((int32_t *)(tensor->data))[i]; } break; case GGML_TYPE_F16: { GGML_ASSERT(tensor->nb[0] == sizeof(ggml_fp16_t)); return GGML_FP16_TO_FP32(((ggml_fp16_t *)(tensor->data))[i]); } break; case GGML_TYPE_F32: { GGML_ASSERT(tensor->nb[0] == sizeof(float)); return ((float *)(tensor->data))[i]; } break; case GGML_TYPE_COUNT: { GGML_ASSERT(false); } break; } return 0.0f; } void ggml_set_f32_1d(const struct ggml_tensor * tensor, int i, float value) { switch (tensor->type) { case GGML_TYPE_I8: { GGML_ASSERT(tensor->nb[0] == sizeof(int8_t)); ((int8_t *)(tensor->data))[i] = value; } break; case GGML_TYPE_I16: { GGML_ASSERT(tensor->nb[0] == sizeof(int16_t)); ((int16_t *)(tensor->data))[i] = value; } break; case GGML_TYPE_I32: { GGML_ASSERT(tensor->nb[0] == sizeof(int32_t)); ((int32_t *)(tensor->data))[i] = value; } break; case GGML_TYPE_F16: { GGML_ASSERT(tensor->nb[0] == sizeof(ggml_fp16_t)); ((ggml_fp16_t *)(tensor->data))[i] = GGML_FP32_TO_FP16(value); } break; case GGML_TYPE_F32: { GGML_ASSERT(tensor->nb[0] == sizeof(float)); ((float *)(tensor->data))[i] = value; } break; case GGML_TYPE_COUNT: { GGML_ASSERT(false); } break; } } void * ggml_get_data(const struct ggml_tensor * tensor) { return tensor->data; } float * ggml_get_data_f32(const struct ggml_tensor * tensor) { assert(tensor->type == GGML_TYPE_F32); return (float *)(tensor->data); } struct ggml_tensor * ggml_view_tensor( struct ggml_context * ctx, const struct ggml_tensor * src) { return ggml_new_tensor_impl(ctx, src->type, src->n_dims, src->ne, src->data); } //////////////////////////////////////////////////////////////////////////////// // ggml_dup struct ggml_tensor * ggml_dup_impl( struct ggml_context * ctx, struct ggml_tensor * a, bool inplace) { bool is_node = false; if (!inplace && (a->grad)) { is_node = true; } struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); result->op = GGML_OP_DUP; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src0 = a; result->src1 = NULL; return result; } struct ggml_tensor * ggml_dup( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_dup_impl(ctx, a, false); } struct ggml_tensor * ggml_dup_inplace( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_dup_impl(ctx, a, true); } // ggml_add struct ggml_tensor * ggml_add_impl( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, bool inplace) { assert(ggml_are_same_shape(a, b)); bool is_node = false; if (!inplace && (a->grad || b->grad)) { is_node = true; } struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); result->op = GGML_OP_ADD; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src0 = a; result->src1 = b; return result; } struct ggml_tensor * ggml_add( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) { return ggml_add_impl(ctx, a, b, false); } struct ggml_tensor * ggml_add_inplace( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) { return ggml_add_impl(ctx, a, b, true); } // ggml_sub struct ggml_tensor * ggml_sub_impl( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, bool inplace) { assert(ggml_are_same_shape(a, b)); bool is_node = false; if (!inplace && (a->grad || b->grad)) { is_node = true; } struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); result->op = GGML_OP_SUB; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src0 = a; result->src1 = b; return result; } struct ggml_tensor * ggml_sub( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) { return ggml_sub_impl(ctx, a, b, false); } struct ggml_tensor * ggml_sub_inplace( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) { return ggml_sub_impl(ctx, a, b, true); } // ggml_mul struct ggml_tensor * ggml_mul_impl( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, bool inplace) { assert(ggml_are_same_shape(a, b)); bool is_node = false; if (!inplace && (a->grad || b->grad)) { is_node = true; } if (inplace) { assert(is_node == false); } struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); result->op = GGML_OP_MUL; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src0 = a; result->src1 = b; return result; } struct ggml_tensor * ggml_mul( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) { return ggml_mul_impl(ctx, a, b, false); } struct ggml_tensor * ggml_mul_inplace( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) { return ggml_mul_impl(ctx, a, b, true); } // ggml_div struct ggml_tensor * ggml_div_impl( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, bool inplace) { assert(ggml_are_same_shape(a, b)); bool is_node = false; if (!inplace && (a->grad || b->grad)) { is_node = true; } if (inplace) { assert(is_node == false); } struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); result->op = GGML_OP_DIV; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src0 = a; result->src1 = b; return result; } struct ggml_tensor * ggml_div( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) { return ggml_div_impl(ctx, a, b, false); } struct ggml_tensor * ggml_div_inplace( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) { return ggml_div_impl(ctx, a, b, true); } // ggml_sqr struct ggml_tensor * ggml_sqr_impl( struct ggml_context * ctx, struct ggml_tensor * a, bool inplace) { bool is_node = false; if (!inplace && (a->grad)) { is_node = true; } struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); result->op = GGML_OP_SQR; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src0 = a; result->src1 = NULL; return result; } struct ggml_tensor * ggml_sqr( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_sqr_impl(ctx, a, false); } struct ggml_tensor * ggml_sqr_inplace( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_sqr_impl(ctx, a, true); } // ggml_sqrt struct ggml_tensor * ggml_sqrt_impl( struct ggml_context * ctx, struct ggml_tensor * a, bool inplace) { bool is_node = false; if (!inplace && (a->grad)) { is_node = true; } struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); result->op = GGML_OP_SQRT; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src0 = a; result->src1 = NULL; return result; } struct ggml_tensor * ggml_sqrt( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_sqrt_impl(ctx, a, false); } struct ggml_tensor * ggml_sqrt_inplace( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_sqrt_impl(ctx, a, true); } // ggml_sum struct ggml_tensor * ggml_sum( struct ggml_context * ctx, struct ggml_tensor * a) { bool is_node = false; if (a->grad) { is_node = true; } struct ggml_tensor * result = ggml_new_tensor_1d(ctx, a->type, 1); result->op = GGML_OP_SUM; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src0 = a; result->src1 = NULL; return result; } // ggml_mean struct ggml_tensor * ggml_mean( struct ggml_context * ctx, struct ggml_tensor * a) { bool is_node = false; if (a->grad) { assert(false); // TODO: implement is_node = true; } int ne[GGML_MAX_DIMS] = { 1, a->ne[1], a->ne[2], a->ne[3] }; struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, a->n_dims, ne); result->op = GGML_OP_MEAN; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src0 = a; result->src1 = NULL; return result; } // ggml_repeat struct ggml_tensor * ggml_repeat( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) { assert(ggml_can_repeat(a, b)); bool is_node = false; if (a->grad) { is_node = true; } if (ggml_are_same_shape(a, b) && !is_node) { return a; } struct ggml_tensor * result = ggml_new_tensor(ctx, a->type, b->n_dims, b->ne); result->op = GGML_OP_REPEAT; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src0 = a; result->src1 = b; return result; } // ggml_abs struct ggml_tensor * ggml_abs_impl( struct ggml_context * ctx, struct ggml_tensor * a, bool inplace) { bool is_node = false; if (!inplace && (a->grad)) { is_node = true; } struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); result->op = GGML_OP_ABS; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src0 = a; result->src1 = NULL; return result; } struct ggml_tensor * ggml_abs( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_abs_impl(ctx, a, false); } struct ggml_tensor * ggml_abs_inplace( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_abs_impl(ctx, a, true); } // ggml_sgn struct ggml_tensor * ggml_sgn_impl( struct ggml_context * ctx, struct ggml_tensor * a, bool inplace) { bool is_node = false; if (!inplace && (a->grad)) { is_node = true; } struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); result->op = GGML_OP_SGN; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src0 = a; result->src1 = NULL; return result; } struct ggml_tensor * ggml_sgn( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_sgn_impl(ctx, a, false); } struct ggml_tensor * ggml_sgn_inplace( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_sgn_impl(ctx, a, true); } // ggml_neg struct ggml_tensor * ggml_neg_impl( struct ggml_context * ctx, struct ggml_tensor * a, bool inplace) { bool is_node = false; if (!inplace && (a->grad)) { is_node = true; } struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); result->op = GGML_OP_NEG; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src0 = a; result->src1 = NULL; return result; } struct ggml_tensor * ggml_neg( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_neg_impl(ctx, a, false); } struct ggml_tensor * ggml_neg_inplace( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_neg_impl(ctx, a, true); } // ggml_step struct ggml_tensor * ggml_step_impl( struct ggml_context * ctx, struct ggml_tensor * a, bool inplace) { bool is_node = false; if (!inplace && (a->grad)) { is_node = true; } struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); result->op = GGML_OP_STEP; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src0 = a; result->src1 = NULL; return result; } struct ggml_tensor * ggml_step( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_step_impl(ctx, a, false); } struct ggml_tensor * ggml_step_inplace( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_step_impl(ctx, a, true); } // ggml_relu struct ggml_tensor * ggml_relu_impl( struct ggml_context * ctx, struct ggml_tensor * a, bool inplace) { bool is_node = false; if (!inplace && (a->grad)) { is_node = true; } struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); result->op = GGML_OP_RELU; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src0 = a; result->src1 = NULL; return result; } struct ggml_tensor * ggml_relu( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_relu_impl(ctx, a, false); } struct ggml_tensor * ggml_relu_inplace( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_relu_impl(ctx, a, true); } // ggml_gelu struct ggml_tensor * ggml_gelu_impl( struct ggml_context * ctx, struct ggml_tensor * a, bool inplace) { bool is_node = false; if (!inplace && (a->grad)) { is_node = true; } struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); result->op = GGML_OP_GELU; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src0 = a; result->src1 = NULL; return result; } struct ggml_tensor * ggml_gelu( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_gelu_impl(ctx, a, false); } struct ggml_tensor * ggml_gelu_inplace( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_gelu_impl(ctx, a, true); } // ggml_norm struct ggml_tensor * ggml_norm_impl( struct ggml_context * ctx, struct ggml_tensor * a, bool inplace) { bool is_node = false; if (!inplace && (a->grad)) { assert(false); // TODO: implement backward is_node = true; } struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); result->op = GGML_OP_NORM; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src0 = a; result->src1 = NULL; // TODO: maybe store epsilon here? return result; } struct ggml_tensor * ggml_norm( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_norm_impl(ctx, a, false); } struct ggml_tensor * ggml_norm_inplace( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_norm_impl(ctx, a, true); } // ggml_mul_mat struct ggml_tensor * ggml_mul_mat( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) { assert(ggml_can_mul_mat(a, b)); bool is_node = false; if (a->grad || b->grad) { is_node = true; } const int ne[4] = { a->ne[1], b->ne[1], a->ne[2], b->ne[3] }; struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, MIN(a->n_dims, b->n_dims), ne); result->op = GGML_OP_MUL_MAT; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src0 = a; result->src1 = b; return result; } // ggml_scale struct ggml_tensor * ggml_scale_impl( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, bool inplace) { assert(ggml_is_scalar(b)); assert(ggml_is_padded_1d(a)); bool is_node = false; if (!inplace && (a->grad || b->grad)) { assert(false); // TODO: implement backward is_node = true; } // TODO: when implement backward, fix this: //struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); struct ggml_tensor * result = ggml_view_tensor(ctx, a); result->op = GGML_OP_SCALE; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src0 = a; result->src1 = b; return result; } struct ggml_tensor * ggml_scale( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) { return ggml_scale_impl(ctx, a, b, false); } struct ggml_tensor * ggml_scale_inplace( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) { return ggml_scale_impl(ctx, a, b, true); } // ggml_cpy struct ggml_tensor * ggml_cpy_impl( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, bool inplace) { assert(ggml_nelements(a) == ggml_nelements(b)); bool is_node = false; if (!inplace && (a->grad || b->grad)) { assert(false); // TODO: implement backward is_node = true; } // make a view of the destination struct ggml_tensor * result = ggml_view_tensor(ctx, b); result->op = GGML_OP_CPY; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src0 = a; result->src1 = b; return result; } struct ggml_tensor * ggml_cpy( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) { return ggml_cpy_impl(ctx, a, b, false); } struct ggml_tensor * ggml_cpy_inplace( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) { return ggml_cpy_impl(ctx, a, b, true); } // ggml_reshape struct ggml_tensor * ggml_reshape( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) { assert(ggml_is_contiguous(a)); assert(ggml_is_contiguous(b)); assert(ggml_nelements(a) == ggml_nelements(b)); bool is_node = false; if (a->grad || b->grad) { assert(false); // TODO: implement backward is_node = true; } struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, b->n_dims, b->ne, a->data); result->op = GGML_OP_RESHAPE; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src0 = a; result->src1 = NULL; return result; } struct ggml_tensor * ggml_reshape_2d( struct ggml_context * ctx, struct ggml_tensor * a, int ne0, int ne1) { assert(ggml_is_contiguous(a)); assert(ggml_nelements(a) == ne0*ne1); bool is_node = false; if (a->grad) { assert(false); // TODO: implement backward is_node = true; } const int ne[2] = { ne0, ne1 }; struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, 2, ne, a->data); result->op = GGML_OP_RESHAPE; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src0 = a; result->src1 = NULL; return result; } struct ggml_tensor * ggml_reshape_3d( struct ggml_context * ctx, struct ggml_tensor * a, int ne0, int ne1, int ne2) { assert(ggml_is_contiguous(a)); assert(ggml_nelements(a) == ne0*ne1*ne2); bool is_node = false; if (a->grad) { assert(false); // TODO: implement backward is_node = true; } const int ne[3] = { ne0, ne1, ne2 }; struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, 3, ne, a->data); result->op = GGML_OP_RESHAPE; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src0 = a; result->src1 = NULL; return result; } // ggml_view_1d struct ggml_tensor * ggml_view_1d( struct ggml_context * ctx, struct ggml_tensor * a, int ne0, size_t offset) { if (a->grad) { assert(false); // gradient propagation is not supported } struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, 1, &ne0, (char *) a->data + offset); result->op = GGML_OP_VIEW; result->grad = NULL; result->src0 = a; result->src1 = NULL; // TODO: maybe store the offset here? return result; } // ggml_view_2d struct ggml_tensor * ggml_view_2d( struct ggml_context * ctx, struct ggml_tensor * a, int ne0, int ne1, size_t nb1, size_t offset) { if (a->grad) { assert(false); // gradient propagation is not supported } const int ne[GGML_MAX_DIMS] = { ne0, ne1, 1, 1 }; struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, 2, ne, (char *) a->data + offset); result->nb[1] = nb1; result->nb[2] = result->nb[1]*ne1; result->nb[3] = result->nb[2]; result->op = GGML_OP_VIEW; result->grad = NULL; result->src0 = a; result->src1 = NULL; // TODO: maybe store the offset here? return result; } // ggml_permute struct ggml_tensor * ggml_permute( struct ggml_context * ctx, struct ggml_tensor * a, int axis0, int axis1, int axis2, int axis3) { assert(axis0 >= 0 && axis0 < GGML_MAX_DIMS); assert(axis1 >= 0 && axis1 < GGML_MAX_DIMS); assert(axis2 >= 0 && axis2 < GGML_MAX_DIMS); assert(axis3 >= 0 && axis3 < GGML_MAX_DIMS); assert(axis0 != axis1); assert(axis0 != axis2); assert(axis0 != axis3); assert(axis1 != axis2); assert(axis1 != axis3); assert(axis2 != axis3); bool is_node = false; if (a->grad) { assert(false); // TODO: implement backward is_node = true; } struct ggml_tensor * result = ggml_view_tensor(ctx, a); int ne[GGML_MAX_DIMS]; int nb[GGML_MAX_DIMS]; ne[axis0] = a->ne[0]; ne[axis1] = a->ne[1]; ne[axis2] = a->ne[2]; ne[axis3] = a->ne[3]; nb[axis0] = a->nb[0]; nb[axis1] = a->nb[1]; nb[axis2] = a->nb[2]; nb[axis3] = a->nb[3]; result->ne[0] = ne[0]; result->ne[1] = ne[1]; result->ne[2] = ne[2]; result->ne[3] = ne[3]; result->nb[0] = nb[0]; result->nb[1] = nb[1]; result->nb[2] = nb[2]; result->nb[3] = nb[3]; result->op = GGML_OP_PERMUTE; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src0 = a; result->src1 = NULL; // TODO: maybe store the permutation here? return result; } // ggml_transpose struct ggml_tensor * ggml_transpose( struct ggml_context * ctx, struct ggml_tensor * a) { bool is_node = false; if (a->grad) { assert(false); // TODO: implement backward is_node = true; } struct ggml_tensor * result = ggml_view_tensor(ctx, a); result->ne[0] = a->ne[1]; result->ne[1] = a->ne[0]; result->nb[0] = a->nb[1]; result->nb[1] = a->nb[0]; result->op = GGML_OP_TRANSPOSE; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src0 = a; result->src1 = NULL; return result; } // ggml_get_rows struct ggml_tensor * ggml_get_rows( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) { assert(ggml_is_matrix(a) && ggml_is_vector(b) && b->type == GGML_TYPE_I32); bool is_node = false; if (a->grad || b->grad) { assert(false); // TODO: implement backward is_node = true; } // TODO: implement non F32 return //struct ggml_tensor * result = ggml_new_tensor_2d(ctx, a->type, a->ne[0], b->ne[0]); struct ggml_tensor * result = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, a->ne[0], b->ne[0]); result->op = GGML_OP_GET_ROWS; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src0 = a; result->src1 = b; return result; } // ggml_diag_mask_inf struct ggml_tensor * ggml_diag_mask_inf( struct ggml_context * ctx, struct ggml_tensor * a, int n_past) { bool is_node = false; if (a->grad) { assert(false); // TODO: implement backward is_node = true; } // TODO: when implement backward, fix this: //struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); struct ggml_tensor * result = ggml_view_tensor(ctx, a); struct ggml_tensor * b = ggml_new_i32(ctx, n_past); result->op = GGML_OP_DIAG_MASK_INF; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src0 = a; result->src1 = b; return result; } // ggml_soft_max struct ggml_tensor * ggml_soft_max( struct ggml_context * ctx, struct ggml_tensor * a) { bool is_node = false; if (a->grad) { assert(false); // TODO: implement backward is_node = true; } // TODO: when implement backward, fix this: //struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); struct ggml_tensor * result = ggml_view_tensor(ctx, a); result->op = GGML_OP_SOFT_MAX; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src0 = a; result->src1 = NULL; return result; } // ggml_rope struct ggml_tensor * ggml_rope( struct ggml_context * ctx, struct ggml_tensor * a, int n_past, int n_dims, int mode) { assert(n_past >= 0); bool is_node = false; if (a->grad) { assert(false); // TODO: implement backward is_node = true; } // TODO: when implement backward, fix this: //struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); struct ggml_tensor * result = ggml_view_tensor(ctx, a); struct ggml_tensor * b = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 3); ((int32_t *) b->data)[0] = n_past; ((int32_t *) b->data)[1] = n_dims; ((int32_t *) b->data)[2] = mode; result->op = GGML_OP_ROPE; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src0 = a; result->src1 = b; return result; } // ggml_conv_1d_1s struct ggml_tensor * ggml_conv_1d_1s( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) { assert(ggml_is_matrix(b)); assert(a->ne[1] == b->ne[1]); assert(a->ne[3] == 1); bool is_node = false; if (a->grad || b->grad) { assert(false); // TODO: implement backward is_node = true; } const int ne[4] = { b->ne[0], a->ne[2], 1, 1, }; struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 2, ne); result->op = GGML_OP_CONV_1D_1S; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src0 = a; result->src1 = b; return result; } // ggml_conv_1d_2s struct ggml_tensor * ggml_conv_1d_2s( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) { assert(ggml_is_matrix(b)); assert(a->ne[1] == b->ne[1]); assert(a->ne[3] == 1); bool is_node = false; if (a->grad || b->grad) { assert(false); // TODO: implement backward is_node = true; } const int ne[4] = { b->ne[0]/2, a->ne[2], 1, 1, }; struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 2, ne); result->op = GGML_OP_CONV_1D_2S; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src0 = a; result->src1 = b; return result; } // ggml_flash_attn struct ggml_tensor * ggml_flash_attn( struct ggml_context * ctx, struct ggml_tensor * q, struct ggml_tensor * k, struct ggml_tensor * v, bool masked) { assert(ggml_can_mul_mat(k, q)); // TODO: check if vT can be multiplied by (k*qT) bool is_node = false; if (q->grad || k->grad || v->grad) { GGML_ASSERT(false); // TODO: implement backward is_node = true; } //struct ggml_tensor * result = ggml_dup_tensor(ctx, q); struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, q->ne); result->op = GGML_OP_FLASH_ATTN; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src0 = q; result->src1 = k; result->opt[0] = v; result->opt[1] = ggml_new_i32(ctx, masked ? 1 : 0); return result; } // ggml_flash_ff struct ggml_tensor * ggml_flash_ff( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b0, struct ggml_tensor * b1, struct ggml_tensor * c0, struct ggml_tensor * c1) { assert(ggml_can_mul_mat(b0, a)); // TODO: more checks bool is_node = false; if (a->grad || b0->grad || b1->grad || c0->grad || c1->grad) { GGML_ASSERT(false); // TODO: implement backward is_node = true; } //struct ggml_tensor * result = ggml_dup_tensor(ctx, a); struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, a->ne); result->op = GGML_OP_FLASH_FF; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src0 = a; result->src1 = b0; result->opt[0] = b1; result->opt[1] = c0; result->opt[2] = c1; return result; } //////////////////////////////////////////////////////////////////////////////// void ggml_set_param( struct ggml_context * ctx, struct ggml_tensor * tensor) { tensor->is_param = true; assert(tensor->grad == NULL); tensor->grad = ggml_dup_tensor(ctx, tensor); } // ggml_compute_forward_dup static void ggml_compute_forward_dup_f16( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { assert(params->ith == 0); assert(ggml_is_contiguous(dst)); assert(ggml_nelements(dst) == ggml_nelements(src0)); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } const int ne00 = src0->ne[0]; const int ne01 = src0->ne[1]; const int ne02 = src0->ne[2]; const int ne03 = src0->ne[3]; const size_t nb00 = src0->nb[0]; const size_t nb01 = src0->nb[1]; const size_t nb02 = src0->nb[2]; const size_t nb03 = src0->nb[3]; if (ggml_is_contiguous(src0) && src0->type == dst->type) { memcpy(dst->data, src0->data, ggml_nelements(dst) * GGML_TYPE_SIZE[src0->type]); return; } if (src0->nb[0] == sizeof(ggml_fp16_t)) { if (dst->type == GGML_TYPE_F16) { int id = 0; const size_t rs = ne00*nb00; for (int i03 = 0; i03 < ne03; i03++) { for (int i02 = 0; i02 < ne02; i02++) { for (int i01 = 0; i01 < ne01; i01++) { const char * src0_ptr = (char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03; char * dst_ptr = (char *) dst->data + id*rs; memcpy(dst_ptr, src0_ptr, rs); id++; } } } } else if (dst->type == GGML_TYPE_F32) { int id = 0; float * dst_ptr = (float *) dst->data; for (int i03 = 0; i03 < ne03; i03++) { for (int i02 = 0; i02 < ne02; i02++) { for (int i01 = 0; i01 < ne01; i01++) { for (int i00 = 0; i00 < ne00; i00++) { const ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); dst_ptr[id] = GGML_FP16_TO_FP32(*src0_ptr); id++; } } } } } else { GGML_ASSERT(false); // TODO: implement } } else { //printf("%s: this is not optimal - fix me\n", __func__); if (dst->type == GGML_TYPE_F32) { int id = 0; float * dst_ptr = (float *) dst->data; for (int i03 = 0; i03 < ne03; i03++) { for (int i02 = 0; i02 < ne02; i02++) { for (int i01 = 0; i01 < ne01; i01++) { for (int i00 = 0; i00 < ne00; i00++) { const ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); dst_ptr[id] = GGML_FP16_TO_FP32(*src0_ptr); id++; } } } } } else if (dst->type == GGML_TYPE_F16) { int id = 0; ggml_fp16_t * dst_ptr = (ggml_fp16_t *) dst->data; for (int i03 = 0; i03 < ne03; i03++) { for (int i02 = 0; i02 < ne02; i02++) { for (int i01 = 0; i01 < ne01; i01++) { for (int i00 = 0; i00 < ne00; i00++) { const ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); dst_ptr[id] = *src0_ptr; id++; } } } } } else { GGML_ASSERT(false); // TODO: implement } } } static void ggml_compute_forward_dup_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { GGML_ASSERT(params->ith == 0); GGML_ASSERT(ggml_is_contiguous(dst)); GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0)); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } const int ne00 = src0->ne[0]; const int ne01 = src0->ne[1]; const int ne02 = src0->ne[2]; const int ne03 = src0->ne[3]; const size_t nb00 = src0->nb[0]; const size_t nb01 = src0->nb[1]; const size_t nb02 = src0->nb[2]; const size_t nb03 = src0->nb[3]; if (ggml_is_contiguous(src0) && src0->type == dst->type) { memcpy(dst->data, src0->data, ggml_nelements(dst) * GGML_TYPE_SIZE[src0->type]); return; } if (src0->nb[0] == sizeof(float)) { if (dst->type == GGML_TYPE_F32) { int id = 0; const size_t rs = ne00*nb00; for (int i03 = 0; i03 < ne03; i03++) { for (int i02 = 0; i02 < ne02; i02++) { for (int i01 = 0; i01 < ne01; i01++) { const char * src0_ptr = (char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03; char * dst_ptr = (char *) dst->data + id*rs; memcpy(dst_ptr, src0_ptr, rs); id++; } } } } else if (dst->type == GGML_TYPE_F16) { int id = 0; ggml_fp16_t * dst_ptr = (ggml_fp16_t *) dst->data; for (int i03 = 0; i03 < ne03; i03++) { for (int i02 = 0; i02 < ne02; i02++) { for (int i01 = 0; i01 < ne01; i01++) { for (int i00 = 0; i00 < ne00; i00++) { const float * src0_ptr = (float *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); dst_ptr[id] = GGML_FP32_TO_FP16(*src0_ptr); id++; } } } } } else { GGML_ASSERT(false); // TODO: implement } } else { //printf("%s: this is not optimal - fix me\n", __func__); if (dst->type == GGML_TYPE_F32) { int id = 0; float * dst_ptr = (float *) dst->data; for (int i03 = 0; i03 < ne03; i03++) { for (int i02 = 0; i02 < ne02; i02++) { for (int i01 = 0; i01 < ne01; i01++) { for (int i00 = 0; i00 < ne00; i00++) { const float * src0_ptr = (float *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); dst_ptr[id] = *src0_ptr; id++; } } } } } else if (dst->type == GGML_TYPE_F16) { int id = 0; ggml_fp16_t * dst_ptr = (ggml_fp16_t *) dst->data; for (int i03 = 0; i03 < ne03; i03++) { for (int i02 = 0; i02 < ne02; i02++) { for (int i01 = 0; i01 < ne01; i01++) { for (int i00 = 0; i00 < ne00; i00++) { const float * src0_ptr = (float *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); dst_ptr[id] = GGML_FP32_TO_FP16(*src0_ptr); id++; } } } } } else { GGML_ASSERT(false); // TODO: implement } } } static void ggml_compute_forward_dup( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F16: { ggml_compute_forward_dup_f16(params, src0, dst); } break; case GGML_TYPE_F32: { ggml_compute_forward_dup_f32(params, src0, dst); } break; case GGML_TYPE_I8: case GGML_TYPE_I16: case GGML_TYPE_I32: case GGML_TYPE_COUNT: { GGML_ASSERT(false); } break; } } // ggml_compute_forward_add static void ggml_compute_forward_add_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { GGML_ASSERT(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } const int ith = params->ith; const int nth = params->nth; const int n = ggml_nrows(src0); const int nc = src0->ne[0]; const size_t nb00 = src0->nb[0]; const size_t nb01 = src0->nb[1]; const size_t nb10 = src1->nb[0]; const size_t nb11 = src1->nb[1]; const size_t nb0 = dst->nb[0]; const size_t nb1 = dst->nb[1]; GGML_ASSERT( nb0 == sizeof(float)); GGML_ASSERT(nb00 == sizeof(float)); if (nb10 == sizeof(float)) { const int j0 = (n/nth)*ith; const int j1 = ith == nth - 1 ? n : (n/nth)*(ith + 1); for (int j = j0; j < j1; j++) { ggml_vec_add_f32(nc, (float *) ((char *) dst->data + j*nb1), (float *) ((char *) src0->data + j*nb01), (float *) ((char *) src1->data + j*nb11)); } } else { // src1 is not contiguous for (int j = ith; j < n; j += nth) { float * dst_ptr = (float *) ((char *) dst->data + j*nb1); float * src0_ptr = (float *) ((char *) src0->data + j*nb01); for (int i = 0; i < nc; i++) { float * src1_ptr = (float *) ((char *) src1->data + j*nb11 + i*nb10); dst_ptr[i] = src0_ptr[i] + *src1_ptr; } } } } static void ggml_compute_forward_add( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_add_f32(params, src0, src1, dst); } break; case GGML_TYPE_I8: case GGML_TYPE_I16: case GGML_TYPE_I32: case GGML_TYPE_F16: case GGML_TYPE_COUNT: { assert(false); } break; } } // ggml_compute_forward_sub static void ggml_compute_forward_sub_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { assert(params->ith == 0); assert(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } const int n = ggml_nrows(src0); const int nc = src0->ne[0]; assert( dst->nb[0] == sizeof(float)); assert(src0->nb[0] == sizeof(float)); assert(src1->nb[0] == sizeof(float)); for (int i = 0; i < n; i++) { ggml_vec_sub_f32(nc, (float *) ((char *) dst->data + i*( dst->nb[1])), (float *) ((char *) src0->data + i*(src0->nb[1])), (float *) ((char *) src1->data + i*(src1->nb[1]))); } } static void ggml_compute_forward_sub( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_sub_f32(params, src0, src1, dst); } break; case GGML_TYPE_I8: case GGML_TYPE_I16: case GGML_TYPE_I32: case GGML_TYPE_F16: case GGML_TYPE_COUNT: { assert(false); } break; } } // ggml_compute_forward_mul static void ggml_compute_forward_mul_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { assert(params->ith == 0); assert(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } const int n = ggml_nrows(src0); const int nc = src0->ne[0]; assert( dst->nb[0] == sizeof(float)); assert(src0->nb[0] == sizeof(float)); assert(src1->nb[0] == sizeof(float)); for (int i = 0; i < n; i++) { ggml_vec_mul_f32(nc, (float *) ((char *) dst->data + i*( dst->nb[1])), (float *) ((char *) src0->data + i*(src0->nb[1])), (float *) ((char *) src1->data + i*(src1->nb[1]))); } } static void ggml_compute_forward_mul( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_mul_f32(params, src0, src1, dst); } break; case GGML_TYPE_I8: case GGML_TYPE_I16: case GGML_TYPE_I32: case GGML_TYPE_F16: case GGML_TYPE_COUNT: { assert(false); } break; } } // ggml_compute_forward_div static void ggml_compute_forward_div_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { assert(params->ith == 0); assert(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } const int n = ggml_nrows(src0); const int nc = src0->ne[0]; assert( dst->nb[0] == sizeof(float)); assert(src0->nb[0] == sizeof(float)); assert(src1->nb[0] == sizeof(float)); for (int i = 0; i < n; i++) { ggml_vec_div_f32(nc, (float *) ((char *) dst->data + i*( dst->nb[1])), (float *) ((char *) src0->data + i*(src0->nb[1])), (float *) ((char *) src1->data + i*(src1->nb[1]))); } } static void ggml_compute_forward_div( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_div_f32(params, src0, src1, dst); } break; case GGML_TYPE_I8: case GGML_TYPE_I16: case GGML_TYPE_I32: case GGML_TYPE_F16: case GGML_TYPE_COUNT: { assert(false); } break; } } // ggml_compute_forward_sqr static void ggml_compute_forward_sqr_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { assert(params->ith == 0); assert(ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } const int n = ggml_nrows(src0); const int nc = src0->ne[0]; assert( dst->nb[0] == sizeof(float)); assert(src0->nb[0] == sizeof(float)); for (int i = 0; i < n; i++) { ggml_vec_sqr_f32(nc, (float *) ((char *) dst->data + i*( dst->nb[1])), (float *) ((char *) src0->data + i*(src0->nb[1]))); } } static void ggml_compute_forward_sqr( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_sqr_f32(params, src0, dst); } break; case GGML_TYPE_I8: case GGML_TYPE_I16: case GGML_TYPE_I32: case GGML_TYPE_F16: case GGML_TYPE_COUNT: { assert(false); } break; } } // ggml_compute_forward_sqrt static void ggml_compute_forward_sqrt_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { assert(params->ith == 0); assert(ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } const int n = ggml_nrows(src0); const int nc = src0->ne[0]; assert( dst->nb[0] == sizeof(float)); assert(src0->nb[0] == sizeof(float)); for (int i = 0; i < n; i++) { ggml_vec_sqrt_f32(nc, (float *) ((char *) dst->data + i*( dst->nb[1])), (float *) ((char *) src0->data + i*(src0->nb[1]))); } } static void ggml_compute_forward_sqrt( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_sqrt_f32(params, src0, dst); } break; case GGML_TYPE_I8: case GGML_TYPE_I16: case GGML_TYPE_I32: case GGML_TYPE_F16: case GGML_TYPE_COUNT: { assert(false); } break; } } // ggml_compute_forward_sum static void ggml_compute_forward_sum_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { assert(params->ith == 0); assert(ggml_is_scalar(dst)); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } assert(ggml_is_scalar(dst)); assert(src0->nb[0] == sizeof(float)); const int ne00 = src0->ne[0]; const int ne01 = src0->ne[1]; const int ne02 = src0->ne[2]; const int ne03 = src0->ne[3]; const size_t nb01 = src0->nb[1]; const size_t nb02 = src0->nb[2]; const size_t nb03 = src0->nb[3]; for (int i03 = 0; i03 < ne03; i03++) { for (int i02 = 0; i02 < ne02; i02++) { for (int i01 = 0; i01 < ne01; i01++) { ggml_vec_sum_f32(ne00, (float *) (dst->data), (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03)); } } } } static void ggml_compute_forward_sum( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_sum_f32(params, src0, dst); } break; case GGML_TYPE_I8: case GGML_TYPE_I16: case GGML_TYPE_I32: case GGML_TYPE_F16: case GGML_TYPE_COUNT: { assert(false); } break; } } // ggml_compute_forward_mean static void ggml_compute_forward_mean_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { assert(params->ith == 0); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } assert(src0->nb[0] == sizeof(float)); const int ne00 = src0->ne[0]; const int ne01 = src0->ne[1]; const int ne02 = src0->ne[2]; const int ne03 = src0->ne[3]; const size_t nb01 = src0->nb[1]; const size_t nb02 = src0->nb[2]; const size_t nb03 = src0->nb[3]; const int ne0 = dst->ne[0]; const int ne1 = dst->ne[1]; const int ne2 = dst->ne[2]; const int ne3 = dst->ne[3]; assert(ne0 == 1); assert(ne1 == ne01); assert(ne2 == ne02); assert(ne3 == ne03); UNUSED(ne0); UNUSED(ne1); UNUSED(ne2); UNUSED(ne3); const size_t nb1 = dst->nb[1]; const size_t nb2 = dst->nb[2]; const size_t nb3 = dst->nb[3]; for (int i03 = 0; i03 < ne03; i03++) { for (int i02 = 0; i02 < ne02; i02++) { for (int i01 = 0; i01 < ne01; i01++) { ggml_vec_sum_f32(ne00, (float *) ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3), (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03)); *(float *) ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3) /= (float) ne00; } } } } static void ggml_compute_forward_mean( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_mean_f32(params, src0, dst); } break; case GGML_TYPE_I8: case GGML_TYPE_I16: case GGML_TYPE_I32: case GGML_TYPE_F16: case GGML_TYPE_COUNT: { assert(false); } break; } } // ggml_compute_forward_repeat static void ggml_compute_forward_repeat_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { assert(params->ith == 0); assert(ggml_can_repeat(src0, dst)); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } // TODO: implement support for rank > 2 tensors assert(src0->ne[2] == 1); assert(src0->ne[3] == 1); assert( dst->ne[2] == 1); assert( dst->ne[3] == 1); const int nc = dst->ne[0]; const int nr = dst->ne[1]; const int nc0 = src0->ne[0]; const int nr0 = src0->ne[1]; const int ncr = nc/nc0; // guaranteed to be an integer due to the check in ggml_can_repeat const int nrr = nr/nr0; // guaranteed to be an integer due to the check in ggml_can_repeat // TODO: support for transposed / permuted tensors assert( dst->nb[0] == sizeof(float)); assert(src0->nb[0] == sizeof(float)); // TODO: maybe this is not optimal? for (int i = 0; i < nrr; i++) { for (int j = 0; j < ncr; j++) { for (int k = 0; k < nr0; k++) { ggml_vec_cpy_f32(nc0, (float *) ((char *) dst->data + (i*nr0 + k)*( dst->nb[1]) + j*nc0*( dst->nb[0])), (float *) ((char *) src0->data + ( k)*(src0->nb[1]))); } } } } static void ggml_compute_forward_repeat( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_repeat_f32(params, src0, dst); } break; case GGML_TYPE_I8: case GGML_TYPE_I16: case GGML_TYPE_I32: case GGML_TYPE_F16: case GGML_TYPE_COUNT: { assert(false); } break; } } // ggml_compute_forward_abs static void ggml_compute_forward_abs_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { assert(params->ith == 0); assert(ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } const int n = ggml_nrows(src0); const int nc = src0->ne[0]; assert(dst->nb[0] == sizeof(float)); assert(src0->nb[0] == sizeof(float)); for (int i = 0; i < n; i++) { ggml_vec_abs_f32(nc, (float *) ((char *) dst->data + i*( dst->nb[1])), (float *) ((char *) src0->data + i*(src0->nb[1]))); } } static void ggml_compute_forward_abs( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_abs_f32(params, src0, dst); } break; case GGML_TYPE_I8: case GGML_TYPE_I16: case GGML_TYPE_I32: case GGML_TYPE_F16: case GGML_TYPE_COUNT: { assert(false); } break; } } // ggml_compute_forward_sgn static void ggml_compute_forward_sgn_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { assert(params->ith == 0); assert(ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } const int n = ggml_nrows(src0); const int nc = src0->ne[0]; assert(dst->nb[0] == sizeof(float)); assert(src0->nb[0] == sizeof(float)); for (int i = 0; i < n; i++) { ggml_vec_sgn_f32(nc, (float *) ((char *) dst->data + i*( dst->nb[1])), (float *) ((char *) src0->data + i*(src0->nb[1]))); } } static void ggml_compute_forward_sgn( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_sgn_f32(params, src0, dst); } break; case GGML_TYPE_I8: case GGML_TYPE_I16: case GGML_TYPE_I32: case GGML_TYPE_F16: case GGML_TYPE_COUNT: { assert(false); } break; } } // ggml_compute_forward_neg static void ggml_compute_forward_neg_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { assert(params->ith == 0); assert(ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } const int n = ggml_nrows(src0); const int nc = src0->ne[0]; assert(dst->nb[0] == sizeof(float)); assert(src0->nb[0] == sizeof(float)); for (int i = 0; i < n; i++) { ggml_vec_neg_f32(nc, (float *) ((char *) dst->data + i*( dst->nb[1])), (float *) ((char *) src0->data + i*(src0->nb[1]))); } } static void ggml_compute_forward_neg( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_neg_f32(params, src0, dst); } break; case GGML_TYPE_I8: case GGML_TYPE_I16: case GGML_TYPE_I32: case GGML_TYPE_F16: case GGML_TYPE_COUNT: { assert(false); } break; } } // ggml_compute_forward_step static void ggml_compute_forward_step_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { assert(params->ith == 0); assert(ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } const int n = ggml_nrows(src0); const int nc = src0->ne[0]; assert(dst->nb[0] == sizeof(float)); assert(src0->nb[0] == sizeof(float)); for (int i = 0; i < n; i++) { ggml_vec_step_f32(nc, (float *) ((char *) dst->data + i*( dst->nb[1])), (float *) ((char *) src0->data + i*(src0->nb[1]))); } } static void ggml_compute_forward_step( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_step_f32(params, src0, dst); } break; case GGML_TYPE_I8: case GGML_TYPE_I16: case GGML_TYPE_I32: case GGML_TYPE_F16: case GGML_TYPE_COUNT: { assert(false); } break; } } // ggml_compute_forward_relu static void ggml_compute_forward_relu_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { assert(params->ith == 0); assert(ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } const int n = ggml_nrows(src0); const int nc = src0->ne[0]; assert(dst->nb[0] == sizeof(float)); assert(src0->nb[0] == sizeof(float)); for (int i = 0; i < n; i++) { ggml_vec_relu_f32(nc, (float *) ((char *) dst->data + i*( dst->nb[1])), (float *) ((char *) src0->data + i*(src0->nb[1]))); } } static void ggml_compute_forward_relu( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_relu_f32(params, src0, dst); } break; case GGML_TYPE_I8: case GGML_TYPE_I16: case GGML_TYPE_I32: case GGML_TYPE_F16: case GGML_TYPE_COUNT: { assert(false); } break; } } // ggml_compute_forward_gelu static void ggml_compute_forward_gelu_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { GGML_ASSERT(ggml_is_contiguous(src0)); GGML_ASSERT(ggml_is_contiguous(dst)); GGML_ASSERT(ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } const int ith = params->ith; const int nth = params->nth; const int nc = src0->ne[0]; const int nr = ggml_nrows(src0); // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); for (int i1 = ir0; i1 < ir1; i1++) { ggml_vec_gelu_f32(nc, (float *) ((char *) dst->data + i1*( dst->nb[1])), (float *) ((char *) src0->data + i1*(src0->nb[1]))); #ifndef NDEBUG for (int k = 0; k < nc; k++) { const float x = ((float *) ((char *) dst->data + i1*( dst->nb[1])))[k]; UNUSED(x); assert(!isnan(x)); assert(!isinf(x)); } #endif } } static void ggml_compute_forward_gelu( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_gelu_f32(params, src0, dst); } break; case GGML_TYPE_I8: case GGML_TYPE_I16: case GGML_TYPE_I32: case GGML_TYPE_F16: case GGML_TYPE_COUNT: { assert(false); } break; } } // ggml_compute_forward_norm static void ggml_compute_forward_norm_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { GGML_ASSERT(ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } GGML_ASSERT(src0->nb[0] == sizeof(float)); const int ith = params->ith; const int nth = params->nth; const int ne00 = src0->ne[0]; const int ne01 = src0->ne[1]; const int ne02 = src0->ne[2]; const int ne03 = src0->ne[3]; const size_t nb01 = src0->nb[1]; const size_t nb02 = src0->nb[2]; const size_t nb03 = src0->nb[3]; const size_t nb1 = dst->nb[1]; const size_t nb2 = dst->nb[2]; const size_t nb3 = dst->nb[3]; const ggml_float eps = 1e-5f; // TODO: make this a parameter // TODO: optimize for (int i03 = 0; i03 < ne03; i03++) { for (int i02 = 0; i02 < ne02; i02++) { for (int i01 = ith; i01 < ne01; i01 += nth) { const float * x = (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); ggml_float mean = 0.0; for (int i00 = 0; i00 < ne00; i00++) { mean += x[i00]; } mean /= ne00; float * y = (float *) ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3); ggml_float sum2 = 0.0; for (int i00 = 0; i00 < ne00; i00++) { ggml_float v = x[i00] - mean; y[i00] = v; sum2 += v*v; } const float scale = 1.0/sqrt(sum2/ne00 + eps); ggml_vec_scale_f32(ne00, y, scale); } } } } static void ggml_compute_forward_norm( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_norm_f32(params, src0, dst); } break; case GGML_TYPE_I8: case GGML_TYPE_I16: case GGML_TYPE_I32: case GGML_TYPE_F16: case GGML_TYPE_COUNT: { assert(false); } break; } } // ggml_compute_forward_mul_mat #if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) // helper function to determine if it is better to use BLAS or not // for large matrices, BLAS is faster static bool ggml_compute_forward_mul_mat_use_blas( const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { UNUSED(src0); const int ne10 = src1->ne[0]; const int ne0 = dst->ne[0]; const int ne1 = dst->ne[1]; // TODO: find the optimal values for these if (ggml_is_contiguous(src0) && ggml_is_contiguous(src1) && ( (ne0 >= 32 && ne1 >= 32 && ne10 >= 32) )) { //printf("BLAS: %d %d %d\n", ne0, ne1, ne10); return true; } return false; } #endif static void ggml_compute_forward_mul_mat_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { int64_t t0 = ggml_perf_time_us(); UNUSED(t0); const int ne00 = src0->ne[0]; const int ne01 = src0->ne[1]; const int ne02 = src0->ne[2]; const int ne03 = src0->ne[3]; const int ne10 = src1->ne[0]; const int ne11 = src1->ne[1]; const int ne12 = src1->ne[2]; const int ne13 = src1->ne[3]; const int ne0 = dst->ne[0]; const int ne1 = dst->ne[1]; const int ne2 = dst->ne[2]; const int ne3 = dst->ne[3]; const int ne = ne0*ne1*ne2*ne3; const int nb00 = src0->nb[0]; const int nb01 = src0->nb[1]; const int nb02 = src0->nb[2]; const int nb03 = src0->nb[3]; const int nb10 = src1->nb[0]; const int nb11 = src1->nb[1]; const int nb12 = src1->nb[2]; const int nb13 = src1->nb[3]; const int nb0 = dst->nb[0]; const int nb1 = dst->nb[1]; const int nb2 = dst->nb[2]; const int nb3 = dst->nb[3]; const int ith = params->ith; const int nth = params->nth; assert(ne02 == ne12); assert(ne03 == ne13); assert(ne2 == ne12); assert(ne3 == ne13); // TODO: we don't support permuted src0 assert(nb00 == sizeof(float) || nb01 == sizeof(float)); // dst cannot be transposed or permuted assert(nb0 == sizeof(float)); assert(nb0 <= nb1); assert(nb1 <= nb2); assert(nb2 <= nb3); assert(ne0 == ne01); assert(ne1 == ne11); assert(ne2 == ne02); assert(ne3 == ne03); // nb01 >= nb00 - src0 is not transposed // compute by src0 rows // // nb00 < nb01 - src0 is transposed // compute by src0 columns #if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) if (ggml_compute_forward_mul_mat_use_blas(src0, src1, dst)) { GGML_ASSERT(nb10 == sizeof(float)); if (params->ith != 0) { return; } if (params->type == GGML_TASK_INIT) { return; } if (params->type == GGML_TASK_FINALIZE) { return; } for (int i03 = 0; i03 < ne03; i03++) { for (int i02 = 0; i02 < ne02; i02++) { const float * x = (float *) (src0->data); const float * y = (float *) ((char *) src1->data + i02*nb12 + i03*nb13); float * d = (float *) ((char *) dst->data + i02*nb2 + i03*nb3); // zT = y * xT { cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasTrans, ne11, ne01, ne10, 1.0f, y, ne10, x, ne10, 0.0f, d, ne01); } } } //printf("CBLAS F32 = %f ms, %d x %d x %d x %d\n", (ggml_perf_time_us() - t0)/1000.0, ne0, ne1, ne2, ne3); return; } #endif if (params->type == GGML_TASK_INIT) { if (nb01 >= nb00) { return; } // TODO: fix this memset (wsize is overestimated) memset(params->wdata, 0, params->wsize); return; } if (params->type == GGML_TASK_FINALIZE) { if (nb01 >= nb00) { return; } // TODO: fix this memset (wsize is overestimated) //assert(params->wsize == (ggml_nbytes(dst) + CACHE_LINE_SIZE)*nth); float * const wdata = params->wdata; // cols per thread const int dc = (ne + nth - 1)/nth; // col range for this thread const int ic0 = dc*ith; const int ic1 = MIN(ic0 + dc, ne); ggml_vec_cpy_f32(ic1 - ic0, (float *) dst->data + ic0, wdata + ic0); for (int k = 1; k < nth; k++) { ggml_vec_acc_f32(ic1 - ic0, (float *) dst->data + ic0, wdata + (ne + CACHE_LINE_SIZE_F32)*k + ic0); } return; } if (nb01 >= nb00) { // TODO: do not support transposed src1 assert(nb10 == sizeof(float)); // parallelize by src0 rows using ggml_vec_dot_f32 // total rows in src0 const int nr = ne01*ne02*ne03; // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); for (int ir = ir0; ir < ir1; ++ir) { // src0 indices const int i03 = ir/(ne02*ne01); const int i02 = (ir - i03*ne02*ne01)/ne01; const int i01 = (ir - i03*ne02*ne01 - i02*ne01); for (int ic = 0; ic < ne11; ++ic) { // src1 indices const int i13 = i03; const int i12 = i02; const int i11 = ic; // dst indices const int i0 = i01; const int i1 = i11; const int i2 = i02; const int i3 = i03; ggml_vec_dot_f32(ne00, (float *) ((char *) dst->data + (i0*nb0 + i1*nb1 + i2*nb2 + i3*nb3)), (float *) ((char *) src0->data + (i01*nb01 + i02*nb02 + i03*nb03)), (float *) ((char *) src1->data + (i11*nb11 + i12*nb12 + i13*nb13))); } } } else { // parallelize by src1 columns using ggml_vec_mad_f32 // each thread has its own work data // during FINALIZE we accumulate all work data into dst // total columns in src1 const int nc = ne10; // columns per thread const int dc = (nc + nth - 1)/nth; // column range for this thread const int ic0 = dc*ith; const int ic1 = MIN(ic0 + dc, nc); // work data for thread const int wo = (ne + CACHE_LINE_SIZE_F32)*ith; float * const wdata = params->wdata; for (int i13 = 0; i13 < ne13; ++i13) { for (int i12 = 0; i12 < ne12; ++i12) { for (int i11 = 0; i11 < ne11; ++i11) { for (int ic = ic0; ic < ic1; ++ic) { // src1 indices const int i10 = ic; // src0 indices const int i03 = i13; const int i02 = i12; const int i00 = ic; // dst indices const int i1 = i11; const int i2 = i12; const int i3 = i13; assert(sizeof(float)*(wo + i3*ne2*ne1*ne0 + i2*ne1*ne0 + i1*ne0 + ne01) <= params->wsize); ggml_vec_mad_f32(ne01, (float *) (wdata + wo + i3*ne2*ne1*ne0 + i2*ne1*ne0 + i1*ne0), (float *) ((char *) src0->data + (i00*nb00 + i02*nb02 + i03*nb03)), *(float *) ((char *) src1->data + (i10*nb10 + i11*nb11 + i12*nb12 + i13*nb13))); } } } } } //int64_t t1 = ggml_perf_time_us(); //static int64_t acc = 0; //acc += t1 - t0; //if (t1 - t0 > 10) { // printf("\n"); // printf("ne00 = %5d, ne01 = %5d, ne02 = %5d, ne03 = %5d\n", ne00, ne01, ne02, ne03); // printf("nb00 = %5d, nb01 = %5d, nb02 = %5d, nb03 = %5d\n", nb00, nb01, nb02, nb03); // printf("ne10 = %5d, ne11 = %5d, ne12 = %5d, ne13 = %5d\n", ne10, ne11, ne12, ne13); // printf("nb10 = %5d, nb11 = %5d, nb12 = %5d, nb13 = %5d\n", nb10, nb11, nb12, nb13); // printf("XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX task %d/%d: %d us, acc = %d\n", ith, nth, (int) (t1 - t0), (int) acc); //} } static void ggml_compute_forward_mul_mat_f16_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { int64_t t0 = ggml_perf_time_us(); UNUSED(t0); const int ne00 = src0->ne[0]; const int ne01 = src0->ne[1]; const int ne02 = src0->ne[2]; const int ne03 = src0->ne[3]; const int ne10 = src1->ne[0]; const int ne11 = src1->ne[1]; const int ne12 = src1->ne[2]; const int ne13 = src1->ne[3]; const int ne0 = dst->ne[0]; const int ne1 = dst->ne[1]; const int ne2 = dst->ne[2]; const int ne3 = dst->ne[3]; const int ne = ne0*ne1*ne2*ne3; const int nb00 = src0->nb[0]; const int nb01 = src0->nb[1]; const int nb02 = src0->nb[2]; const int nb03 = src0->nb[3]; const int nb10 = src1->nb[0]; const int nb11 = src1->nb[1]; const int nb12 = src1->nb[2]; const int nb13 = src1->nb[3]; const int nb0 = dst->nb[0]; const int nb1 = dst->nb[1]; const int nb2 = dst->nb[2]; const int nb3 = dst->nb[3]; const int ith = params->ith; const int nth = params->nth; GGML_ASSERT(ne02 == ne12); GGML_ASSERT(ne03 == ne13); GGML_ASSERT(ne2 == ne12); GGML_ASSERT(ne3 == ne13); // TODO: we don't support permuted src0 GGML_ASSERT(nb00 == sizeof(ggml_fp16_t) || nb01 == sizeof(ggml_fp16_t)); // dst cannot be transposed or permuted GGML_ASSERT(nb0 == sizeof(float)); GGML_ASSERT(nb0 <= nb1); GGML_ASSERT(nb1 <= nb2); GGML_ASSERT(nb2 <= nb3); GGML_ASSERT(ne0 == ne01); GGML_ASSERT(ne1 == ne11); GGML_ASSERT(ne2 == ne02); GGML_ASSERT(ne3 == ne03); // nb01 >= nb00 - src0 is not transposed // compute by src0 rows // // nb00 < nb01 - src0 is transposed // compute by src0 columns #if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) if (ggml_compute_forward_mul_mat_use_blas(src0, src1, dst)) { GGML_ASSERT(nb10 == sizeof(float)); if (params->ith != 0) { return; } if (params->type == GGML_TASK_INIT) { return; } if (params->type == GGML_TASK_FINALIZE) { return; } float * const wdata = params->wdata; for (int i03 = 0; i03 < ne03; i03++) { for (int i02 = 0; i02 < ne02; i02++) { { int id = 0; for (int i01 = 0; i01 < ne01; ++i01) { for (int i00 = 0; i00 < ne00; ++i00) { wdata[id++] = GGML_FP16_TO_FP32(*(ggml_fp16_t *) ((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01 + i00*nb00)); } } } const float * x = wdata; const float * y = (float *) ((char *) src1->data + i02*nb12 + i03*nb13); // float * z = wdata + ne00*ne01; // z = x * yT //{ // cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasTrans, // ne01, ne11, ne00, // 1.0f, x, ne00, // y, ne00, // 0.0f, z, ne11); //} float * d = (float *) ((char *) dst->data + i02*nb2 + i03*nb3); // transpose z //for (int j = 0; j < ne11; ++j) { // for (int i = 0; i < ne01; ++i) { // d[j*ne01 + i] = z[i*ne11 + j]; // } //} { #if 1 // zT = y * xT cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasTrans, ne11, ne01, ne10, 1.0f, y, ne00, x, ne00, 0.0f, d, ne01); #else // zT = (xT * y)T cblas_sgemm(CblasColMajor, CblasTrans, CblasNoTrans, ne01, ne11, ne10, 1.0f, x, ne00, y, ne00, 0.0f, d, ne01); #endif } } } //printf("CBLAS = %f ms, %d x %d x %d x %d\n", (ggml_perf_time_us() - t0)/1000.0, ne0, ne1, ne2, ne3); return; } #endif if (params->type == GGML_TASK_INIT) { if (nb01 >= nb00) { ggml_fp16_t * const wdata = params->wdata; int id = 0; for (int i13 = 0; i13 < ne13; ++i13) { for (int i12 = 0; i12 < ne12; ++i12) { for (int i11 = 0; i11 < ne11; ++i11) { for (int i10 = 0; i10 < ne10; ++i10) { wdata[id++] = GGML_FP32_TO_FP16(*(float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11 + i10*nb10)); } } } } GGML_ASSERT(id*sizeof(ggml_fp16_t) <= params->wsize); return; } // TODO: fix this memset (wsize is overestimated) memset(params->wdata, 0, params->wsize); return; } if (params->type == GGML_TASK_FINALIZE) { if (nb01 >= nb00) { return; } // TODO: fix this memset (wsize is overestimated) //assert(params->wsize == (ggml_nbytes(dst) + CACHE_LINE_SIZE)*nth); ggml_fp16_t * const wdata = params->wdata; // cols per thread const int dc = (ne + nth - 1)/nth; // col range for this thread const int ic0 = dc*ith; const int ic1 = MIN(ic0 + dc, ne); for (int i = ic0; i < ic1; ++i) { ((float *) dst->data)[i] = GGML_FP16_TO_FP32(wdata[i]); } for (int k = 1; k < nth; k++) { for (int i = ic0; i < ic1; ++i) { ((float *) dst->data)[i] += GGML_FP16_TO_FP32(wdata[(ne + CACHE_LINE_SIZE_F32)*k + i]); } } return; } if (nb01 >= nb00) { // fp16 -> half the size, so divide by 2 // TODO: do not support transposed src1 assert(nb10/2 == sizeof(ggml_fp16_t)); // parallelize by src0 rows using ggml_vec_dot_f16 // total rows in src0 const int nr = ne01*ne02*ne03; // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); ggml_fp16_t * wdata = params->wdata; for (int ir = ir0; ir < ir1; ++ir) { // src0 indices const int i03 = ir/(ne02*ne01); const int i02 = (ir - i03*ne02*ne01)/ne01; const int i01 = (ir - i03*ne02*ne01 - i02*ne01); const int i13 = i03; const int i12 = i02; const int i0 = i01; const int i2 = i02; const int i3 = i03; ggml_fp16_t * src0_row = (ggml_fp16_t *) ((char *) src0->data + (i01*nb01 + i02*nb02 + i03*nb03)); ggml_fp16_t * src1_col = wdata + ( 0 + i12*ne11 + i13*ne12*ne11)*ne00; float * dst_col = (float *) ((char *) dst->data + (i0*nb0 + 0*nb1 + i2*nb2 + i3*nb3)); assert(ne00 % 32 == 0); for (int ic = 0; ic < ne11; ++ic) { ggml_vec_dot_f16(ne00, &dst_col[ic*ne0], src0_row, src1_col + ic*ne00); } } } else { // parallelize by src1 columns using ggml_vec_mad_f16 // each thread has its own work data // during FINALIZE we accumulate all work data into dst // total columns in src1 const int nc = ne10; // columns per thread const int dc = (nc + nth - 1)/nth; // column range for this thread const int ic0 = dc*ith; const int ic1 = MIN(ic0 + dc, nc); // work data for thread const int wo = (ne + CACHE_LINE_SIZE_F32)*ith; ggml_fp16_t * const wdata = params->wdata; for (int i13 = 0; i13 < ne13; ++i13) { for (int i12 = 0; i12 < ne12; ++i12) { for (int i11 = 0; i11 < ne11; ++i11) { // dst indices const int i1 = i11; const int i2 = i12; const int i3 = i13; ggml_fp16_t * dst_row = wdata + wo + i3*ne2*ne1*ne0 + i2*ne1*ne0 + i1*ne0; for (int ic = ic0; ic < ic1; ++ic) { // src1 indices const int i10 = ic; // src0 indices const int i03 = i13; const int i02 = i12; const int i00 = ic; assert(sizeof(ggml_fp16_t)*(wo + i3*ne2*ne1*ne0 + i2*ne1*ne0 + i1*ne0 + ne01) <= params->wsize); ggml_fp16_t * src0_col = (ggml_fp16_t *) ((char *) src0->data + (i00*nb00 + i02*nb02 + i03*nb03)); float src1_val = * (float *) ((char *) src1->data + (i10*nb10 + i11*nb11 + i12*nb12 + i13*nb13)); ggml_vec_mad_f16(ne01, dst_row, src0_col, src1_val); } } } } } //int64_t t1 = ggml_time_us(); //static int64_t acc = 0; //acc += t1 - t0; //if (t1 - t0 > 10) { // printf("\n"); // printf("ne00 = %5d, ne01 = %5d, ne02 = %5d, ne03 = %5d\n", ne00, ne01, ne02, ne03); // printf("nb00 = %5d, nb01 = %5d, nb02 = %5d, nb03 = %5d\n", nb00, nb01, nb02, nb03); // printf("ne10 = %5d, ne11 = %5d, ne12 = %5d, ne13 = %5d\n", ne10, ne11, ne12, ne13); // printf("XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX task %d/%d: %d us, acc = %d\n", ith, nth, (int) (t1 - t0), (int) acc); //} } static void ggml_compute_forward_mul_mat( 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_mul_mat_f16_f32(params, src0, src1, dst); } break; case GGML_TYPE_F32: { ggml_compute_forward_mul_mat_f32(params, src0, src1, dst); } break; case GGML_TYPE_I8: case GGML_TYPE_I16: case GGML_TYPE_I32: case GGML_TYPE_COUNT: { assert(false); } break; } } // ggml_compute_forward_scale static void ggml_compute_forward_scale_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { GGML_ASSERT(ggml_is_contiguous(src0)); GGML_ASSERT(ggml_is_contiguous(dst)); GGML_ASSERT(ggml_are_same_shape(src0, dst)); GGML_ASSERT(ggml_is_scalar(src1)); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } // scale factor const float v = *(float *) src1->data; const int ith = params->ith; const int nth = params->nth; const int nc = src0->ne[0]; const int nr = ggml_nrows(src0); // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); for (int i1 = ir0; i1 < ir1; i1++) { ggml_vec_scale_f32(nc, (float *) ((char *) dst->data + i1*(dst->nb[1])), v); } } static void ggml_compute_forward_scale( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_scale_f32(params, src0, src1, dst); } break; case GGML_TYPE_I8: case GGML_TYPE_I16: case GGML_TYPE_I32: case GGML_TYPE_F16: case GGML_TYPE_COUNT: { assert(false); } break; } } // ggml_compute_forward_cpy static void ggml_compute_forward_cpy( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { ggml_compute_forward_dup(params, src0, dst); } // ggml_compute_forward_reshape static void ggml_compute_forward_reshape( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { // NOP UNUSED(params); UNUSED(src0); UNUSED(dst); } // ggml_compute_forward_view static void ggml_compute_forward_view( const struct ggml_compute_params * params, const struct ggml_tensor * src0) { // NOP UNUSED(params); UNUSED(src0); } // ggml_compute_forward_permute static void ggml_compute_forward_permute( const struct ggml_compute_params * params, const struct ggml_tensor * src0) { // NOP UNUSED(params); UNUSED(src0); } // ggml_compute_forward_transpose static void ggml_compute_forward_transpose( const struct ggml_compute_params * params, const struct ggml_tensor * src0) { // NOP UNUSED(params); UNUSED(src0); } // ggml_compute_forward_get_rows static void ggml_compute_forward_get_rows_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_F16: { ggml_compute_forward_get_rows_f16(params, src0, src1, dst); } break; case GGML_TYPE_F32: { ggml_compute_forward_get_rows_f32(params, src0, src1, dst); } break; case GGML_TYPE_I8: case GGML_TYPE_I16: case GGML_TYPE_I32: case GGML_TYPE_COUNT: { assert(false); } break; } } // ggml_compute_forward_diag_mask_inf static void ggml_compute_forward_diag_mask_inf_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { assert(params->ith == 0); assert(src1->type == GGML_TYPE_I32); assert(ggml_nelements(src1) == 1); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } const int n_past = ((int32_t *) src1->data)[0]; // TODO: handle transposed/permuted matrices const int n = ggml_nrows(src0); const int nc = src0->ne[0]; const int nr = src0->ne[1]; const int nz = n/nr; assert( dst->nb[0] == sizeof(float)); assert(src0->nb[0] == sizeof(float)); for (int k = 0; k < nz; k++) { for (int j = 0; j < nr; j++) { for (int i = n_past; i < nc; i++) { if (i > n_past + j) { *(float *)((char *) dst->data + k*dst->nb[2] + j*dst->nb[1] + i*dst->nb[0]) = -INFINITY; } } } } } static void ggml_compute_forward_diag_mask_inf( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_diag_mask_inf_f32(params, src0, src1, dst); } break; case GGML_TYPE_I8: case GGML_TYPE_I16: case GGML_TYPE_I32: case GGML_TYPE_F16: case GGML_TYPE_COUNT: { assert(false); } break; } } // ggml_compute_forward_soft_max static void ggml_compute_forward_soft_max_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { GGML_ASSERT(ggml_is_contiguous(src0)); GGML_ASSERT(ggml_is_contiguous(dst)); GGML_ASSERT(ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } // TODO: handle transposed/permuted matrices const int ith = params->ith; const int nth = params->nth; const int nc = src0->ne[0]; const int nr = ggml_nrows(src0); // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); for (int i1 = ir0; i1 < ir1; i1++) { float *p = (float *)((char *) dst->data + i1*dst->nb[1]); #ifndef NDEBUG for (int i = 0; i < nc; ++i) { assert(!isnan(p[i])); } #endif float max = -INFINITY; ggml_vec_max_f32(nc, &max, p); ggml_float sum = 0.0; uint16_t scvt; for (int i = 0; i < nc; i++) { if (p[i] == -INFINITY) { p[i] = 0.0f; } else { //const float val = (p[i] == -INFINITY) ? 0.0 : exp(p[i] - max); ggml_fp16_t s = GGML_FP32_TO_FP16(p[i] - max); memcpy(&scvt, &s, sizeof(scvt)); const float val = GGML_FP16_TO_FP32(table_exp_f16[scvt]); sum += val; p[i] = val; } } assert(sum > 0.0f); sum = 1.0/sum; ggml_vec_scale_f32(nc, p, sum); #ifndef NDEBUG for (int i = 0; i < nc; ++i) { assert(!isnan(p[i])); assert(!isinf(p[i])); } #endif } } static void ggml_compute_forward_soft_max( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_soft_max_f32(params, src0, dst); } break; case GGML_TYPE_I8: case GGML_TYPE_I16: case GGML_TYPE_I32: case GGML_TYPE_F16: case GGML_TYPE_COUNT: { assert(false); } break; } } // ggml_compute_forward_rope static void ggml_compute_forward_rope_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { assert(params->ith == 0); assert(src1->type == GGML_TYPE_I32); assert(ggml_nelements(src1) == 3); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } const int n_past = ((int32_t *) src1->data)[0]; const int n_dims = ((int32_t *) src1->data)[1]; const int mode = ((int32_t *) src1->data)[2]; //const int ne0 = src0->ne[0]; const int ne1 = src0->ne[1]; const int ne2 = src0->ne[2]; const int ne3 = src0->ne[3]; const int nb0 = src0->nb[0]; const int nb1 = src0->nb[1]; const int nb2 = src0->nb[2]; const int nb3 = src0->nb[3]; //printf("ne0: %d, ne1: %d, ne2: %d, ne3: %d\n", ne0, ne1, ne2, ne3); //printf("n_past = %d, ne2 = %d\n", n_past, ne2); assert(nb0 == sizeof(float)); // TODO: optimize for (int i3 = 0; i3 < ne3; i3++) { for (int i2 = (mode == 0 ? 0 : n_past); i2 < ne2; i2++) { const int p = (mode == 0 ? n_past + i2 : i2); for (int i1 = 0; i1 < ne1; i1++) { for (int i0 = 0; i0 < n_dims; i0 += 2) { const double theta = pow(10000.0, ((double)-i0)/n_dims); const double cos_theta = cos(p*theta); const double sin_theta = sin(p*theta); const float * const src = (float *)((char *) src0->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); float * dst_data = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); double x0 = src[0]; double x1 = src[1]; dst_data[0] = x0*cos_theta - x1*sin_theta; dst_data[1] = x0*sin_theta + x1*cos_theta; } } } } } static void ggml_compute_forward_rope( 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_rope_f32(params, src0, src1, dst); } break; case GGML_TYPE_I8: case GGML_TYPE_I16: case GGML_TYPE_I32: case GGML_TYPE_F16: case GGML_TYPE_COUNT: { assert(false); } break; } } // ggml_compute_forward_conv_1d_1s static void ggml_compute_forward_conv_1d_1s_f16_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { GGML_ASSERT(src0->type == GGML_TYPE_F16); GGML_ASSERT(src1->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); int64_t t0 = ggml_perf_time_us(); UNUSED(t0); const int ne00 = src0->ne[0]; const int ne01 = src0->ne[1]; const int ne02 = src0->ne[2]; //const int ne03 = src0->ne[3]; const int ne10 = src1->ne[0]; const int ne11 = src1->ne[1]; //const int ne12 = src1->ne[2]; //const int ne13 = src1->ne[3]; //const int ne0 = dst->ne[0]; //const int ne1 = dst->ne[1]; //const int ne2 = dst->ne[2]; //const int ne3 = dst->ne[3]; //const int ne = ne0*ne1*ne2*ne3; const int nb00 = src0->nb[0]; const int nb01 = src0->nb[1]; const int nb02 = src0->nb[2]; //const int nb03 = src0->nb[3]; const int nb10 = src1->nb[0]; const int nb11 = src1->nb[1]; //const int nb12 = src1->nb[2]; //const int nb13 = src1->nb[3]; //const int nb0 = dst->nb[0]; const int nb1 = dst->nb[1]; //const int nb2 = dst->nb[2]; //const int nb3 = dst->nb[3]; const int ith = params->ith; const int nth = params->nth; const int nk = ne00; const int nh = nk/2; const int ew0 = ggml_up32(ne01); GGML_ASSERT(ne00 % 2 == 1); // TODO: support even kernel sizes GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); GGML_ASSERT(nb10 == sizeof(float)); if (params->type == GGML_TASK_INIT) { // TODO: fix this memset (wsize is overestimated) memset(params->wdata, 0, params->wsize); // prepare kernel data (src0) { ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + 0; for (int i02 = 0; i02 < ne02; i02++) { for (int i01 = 0; i01 < ne01; i01++) { const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i02*nb02 + i01*nb01); ggml_fp16_t * dst_data = wdata + i02*ew0*ne00; for (int i00 = 0; i00 < ne00; i00++) { dst_data[i00*ew0 + i01] = src[i00]; } } } } // prepare source data (src1) { ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + ne02*ew0*ne00; for (int i11 = 0; i11 < ne11; i11++) { const float * const src = (float *)((char *) src1->data + i11*nb11); ggml_fp16_t * dst_data = wdata; for (int i10 = 0; i10 < ne10; i10++) { dst_data[(i10 + nh)*ew0 + i11] = GGML_FP32_TO_FP16(src[i10]); } } } return; } if (params->type == GGML_TASK_FINALIZE) { return; } // total rows in dst const int nr = ne02; // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); for (int i1 = ir0; i1 < ir1; i1++) { float * dst_data = (float *)((char *) dst->data + i1*nb1); for (int i0 = 0; i0 < ne10; ++i0) { dst_data[i0] = 0; for (int k = -nh; k <= nh; k++) { float v = 0.0f; ggml_vec_dot_f16(ew0, &v, (ggml_fp16_t *) params->wdata + i1*ew0*ne00 + (nh + k)*ew0, (ggml_fp16_t *) params->wdata + ne02*ew0*ne00 + (i0 + nh + k)*ew0); dst_data[i0] += v; } } } } static void ggml_compute_forward_conv_1d_1s_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT(src1->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); int64_t t0 = ggml_perf_time_us(); UNUSED(t0); const int ne00 = src0->ne[0]; const int ne01 = src0->ne[1]; const int ne02 = src0->ne[2]; //const int ne03 = src0->ne[3]; const int ne10 = src1->ne[0]; const int ne11 = src1->ne[1]; //const int ne12 = src1->ne[2]; //const int ne13 = src1->ne[3]; //const int ne0 = dst->ne[0]; //const int ne1 = dst->ne[1]; //const int ne2 = dst->ne[2]; //const int ne3 = dst->ne[3]; //const int ne = ne0*ne1*ne2*ne3; const int nb00 = src0->nb[0]; const int nb01 = src0->nb[1]; const int nb02 = src0->nb[2]; //const int nb03 = src0->nb[3]; const int nb10 = src1->nb[0]; const int nb11 = src1->nb[1]; //const int nb12 = src1->nb[2]; //const int nb13 = src1->nb[3]; //const int nb0 = dst->nb[0]; const int nb1 = dst->nb[1]; //const int nb2 = dst->nb[2]; //const int nb3 = dst->nb[3]; const int ith = params->ith; const int nth = params->nth; const int nk = ne00; const int nh = nk/2; const int ew0 = ggml_up32(ne01); GGML_ASSERT(ne00 % 2 == 1); // TODO: support even kernel sizes GGML_ASSERT(nb00 == sizeof(float)); GGML_ASSERT(nb10 == sizeof(float)); if (params->type == GGML_TASK_INIT) { // TODO: fix this memset (wsize is overestimated) memset(params->wdata, 0, params->wsize); // prepare kernel data (src0) { float * const wdata = (float *) params->wdata + 0; for (int i02 = 0; i02 < ne02; i02++) { for (int i01 = 0; i01 < ne01; i01++) { const float * const src = (float *)((char *) src0->data + i02*nb02 + i01*nb01); float * dst_data = wdata + i02*ew0*ne00; for (int i00 = 0; i00 < ne00; i00++) { dst_data[i00*ew0 + i01] = src[i00]; } } } } // prepare source data (src1) { float * const wdata = (float *) params->wdata + ne02*ew0*ne00; for (int i11 = 0; i11 < ne11; i11++) { const float * const src = (float *)((char *) src1->data + i11*nb11); float * dst_data = wdata; for (int i10 = 0; i10 < ne10; i10++) { dst_data[(i10 + nh)*ew0 + i11] = src[i10]; } } } return; } if (params->type == GGML_TASK_FINALIZE) { return; } // total rows in dst const int nr = ne02; // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); for (int i1 = ir0; i1 < ir1; i1++) { float * dst_data = (float *)((char *) dst->data + i1*nb1); for (int i0 = 0; i0 < ne10; ++i0) { dst_data[i0] = 0; for (int k = -nh; k <= nh; k++) { float v = 0.0f; ggml_vec_dot_f32(ew0, &v, (float *) params->wdata + i1*ew0*ne00 + (nh + k)*ew0, (float *) params->wdata + ne02*ew0*ne00 + (i0 + nh + k)*ew0); dst_data[i0] += v; } } } } static void ggml_compute_forward_conv_1d_1s( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F16: { ggml_compute_forward_conv_1d_1s_f16_f32(params, src0, src1, dst); } break; case GGML_TYPE_F32: { ggml_compute_forward_conv_1d_1s_f32(params, src0, src1, dst); } break; case GGML_TYPE_I8: case GGML_TYPE_I16: case GGML_TYPE_I32: case GGML_TYPE_COUNT: { GGML_ASSERT(false); } break; } } // ggml_compute_forward_conv_1d_2s static void ggml_compute_forward_conv_1d_2s_f16_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { GGML_ASSERT(src0->type == GGML_TYPE_F16); GGML_ASSERT(src1->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); int64_t t0 = ggml_perf_time_us(); UNUSED(t0); const int ne00 = src0->ne[0]; const int ne01 = src0->ne[1]; const int ne02 = src0->ne[2]; //const int ne03 = src0->ne[3]; const int ne10 = src1->ne[0]; const int ne11 = src1->ne[1]; //const int ne12 = src1->ne[2]; //const int ne13 = src1->ne[3]; //const int ne0 = dst->ne[0]; //const int ne1 = dst->ne[1]; //const int ne2 = dst->ne[2]; //const int ne3 = dst->ne[3]; //const int ne = ne0*ne1*ne2*ne3; const int nb00 = src0->nb[0]; const int nb01 = src0->nb[1]; const int nb02 = src0->nb[2]; //const int nb03 = src0->nb[3]; const int nb10 = src1->nb[0]; const int nb11 = src1->nb[1]; //const int nb12 = src1->nb[2]; //const int nb13 = src1->nb[3]; //const int nb0 = dst->nb[0]; const int nb1 = dst->nb[1]; //const int nb2 = dst->nb[2]; //const int nb3 = dst->nb[3]; const int ith = params->ith; const int nth = params->nth; const int nk = ne00; const int nh = nk/2; const int ew0 = ggml_up32(ne01); GGML_ASSERT(ne00 % 2 == 1); // TODO: support even kernel sizes GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); GGML_ASSERT(nb10 == sizeof(float)); if (params->type == GGML_TASK_INIT) { // TODO: fix this memset (wsize is overestimated) memset(params->wdata, 0, params->wsize); // prepare kernel data (src0) { ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + 0; for (int i02 = 0; i02 < ne02; i02++) { for (int i01 = 0; i01 < ne01; i01++) { const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i02*nb02 + i01*nb01); ggml_fp16_t * dst_data = wdata + i02*ew0*ne00; for (int i00 = 0; i00 < ne00; i00++) { dst_data[i00*ew0 + i01] = src[i00]; } } } } // prepare source data (src1) { ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + ne02*ew0*ne00; for (int i11 = 0; i11 < ne11; i11++) { const float * const src = (float *)((char *) src1->data + i11*nb11); ggml_fp16_t * dst_data = wdata; for (int i10 = 0; i10 < ne10; i10++) { dst_data[(i10 + nh)*ew0 + i11] = GGML_FP32_TO_FP16(src[i10]); } } } return; } if (params->type == GGML_TASK_FINALIZE) { return; } // total rows in dst const int nr = ne02; // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); for (int i1 = ir0; i1 < ir1; i1++) { float * dst_data = (float *)((char *) dst->data + i1*nb1); for (int i0 = 0; i0 < ne10; i0 += 2) { dst_data[i0/2] = 0; for (int k = -nh; k <= nh; k++) { float v = 0.0f; ggml_vec_dot_f16(ew0, &v, (ggml_fp16_t *) params->wdata + i1*ew0*ne00 + (nh + k)*ew0, (ggml_fp16_t *) params->wdata + ne02*ew0*ne00 + (i0 + nh + k)*ew0); dst_data[i0/2] += v; } } } } static void ggml_compute_forward_conv_1d_2s_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT(src1->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); int64_t t0 = ggml_perf_time_us(); UNUSED(t0); const int ne00 = src0->ne[0]; const int ne01 = src0->ne[1]; const int ne02 = src0->ne[2]; //const int ne03 = src0->ne[3]; const int ne10 = src1->ne[0]; const int ne11 = src1->ne[1]; //const int ne12 = src1->ne[2]; //const int ne13 = src1->ne[3]; //const int ne0 = dst->ne[0]; //const int ne1 = dst->ne[1]; //const int ne2 = dst->ne[2]; //const int ne3 = dst->ne[3]; //const int ne = ne0*ne1*ne2*ne3; const int nb00 = src0->nb[0]; const int nb01 = src0->nb[1]; const int nb02 = src0->nb[2]; //const int nb03 = src0->nb[3]; const int nb10 = src1->nb[0]; const int nb11 = src1->nb[1]; //const int nb12 = src1->nb[2]; //const int nb13 = src1->nb[3]; //const int nb0 = dst->nb[0]; const int nb1 = dst->nb[1]; //const int nb2 = dst->nb[2]; //const int nb3 = dst->nb[3]; const int ith = params->ith; const int nth = params->nth; const int nk = ne00; const int nh = nk/2; const int ew0 = ggml_up32(ne01); GGML_ASSERT(ne00 % 2 == 1); // TODO: support even kernel sizes GGML_ASSERT(nb00 == sizeof(float)); GGML_ASSERT(nb10 == sizeof(float)); if (params->type == GGML_TASK_INIT) { // TODO: fix this memset (wsize is overestimated) memset(params->wdata, 0, params->wsize); // prepare kernel data (src0) { float * const wdata = (float *) params->wdata + 0; for (int i02 = 0; i02 < ne02; i02++) { for (int i01 = 0; i01 < ne01; i01++) { const float * const src = (float *)((char *) src0->data + i02*nb02 + i01*nb01); float * dst_data = wdata + i02*ew0*ne00; for (int i00 = 0; i00 < ne00; i00++) { dst_data[i00*ew0 + i01] = src[i00]; } } } } // prepare source data (src1) { float * const wdata = (float *) params->wdata + ne02*ew0*ne00; for (int i11 = 0; i11 < ne11; i11++) { const float * const src = (float *)((char *) src1->data + i11*nb11); float * dst_data = wdata; for (int i10 = 0; i10 < ne10; i10++) { dst_data[(i10 + nh)*ew0 + i11] = src[i10]; } } } return; } if (params->type == GGML_TASK_FINALIZE) { return; } // total rows in dst const int nr = ne02; // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); for (int i1 = ir0; i1 < ir1; i1++) { float * dst_data = (float *)((char *) dst->data + i1*nb1); for (int i0 = 0; i0 < ne10; i0 += 2) { dst_data[i0/2] = 0; for (int k = -nh; k <= nh; k++) { float v = 0.0f; ggml_vec_dot_f32(ew0, &v, (float *) params->wdata + i1*ew0*ne00 + (nh + k)*ew0, (float *) params->wdata + ne02*ew0*ne00 + (i0 + nh + k)*ew0); dst_data[i0/2] += v; } } } } static void ggml_compute_forward_conv_1d_2s( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F16: { ggml_compute_forward_conv_1d_2s_f16_f32(params, src0, src1, dst); } break; case GGML_TYPE_F32: { ggml_compute_forward_conv_1d_2s_f32(params, src0, src1, dst); } break; case GGML_TYPE_I8: case GGML_TYPE_I16: case GGML_TYPE_I32: case GGML_TYPE_COUNT: { GGML_ASSERT(false); } break; } } // ggml_compute_forward_flash_attn static void ggml_compute_forward_flash_attn_f32( const struct ggml_compute_params * params, const struct ggml_tensor * q, const struct ggml_tensor * k, const struct ggml_tensor * v, const bool masked, struct ggml_tensor * dst) { int64_t t0 = ggml_perf_time_us(); UNUSED(t0); const int neq0 = q->ne[0]; const int neq1 = q->ne[1]; const int neq2 = q->ne[2]; const int neq3 = q->ne[3]; const int nek0 = k->ne[0]; const int nek1 = k->ne[1]; //const int nek2 = k->ne[2]; //const int nek3 = k->ne[3]; //const int nev0 = v->ne[0]; const int nev1 = v->ne[1]; //const int nev2 = v->ne[2]; //const int nev3 = v->ne[3]; const int ne0 = dst->ne[0]; const int ne1 = dst->ne[1]; //const int ne2 = dst->ne[2]; //const int ne3 = dst->ne[3]; const int nbk0 = k->nb[0]; const int nbk1 = k->nb[1]; const int nbk2 = k->nb[2]; const int nbk3 = k->nb[3]; const int nbq0 = q->nb[0]; const int nbq1 = q->nb[1]; const int nbq2 = q->nb[2]; const int nbq3 = q->nb[3]; const int nbv0 = v->nb[0]; const int nbv1 = v->nb[1]; const int nbv2 = v->nb[2]; const int nbv3 = v->nb[3]; const int nb0 = dst->nb[0]; const int nb1 = dst->nb[1]; const int nb2 = dst->nb[2]; const int nb3 = dst->nb[3]; const int ith = params->ith; const int nth = params->nth; const int D = neq0; const int N = neq1; const int P = nek1 - N; const int M = P + N; const int Mup = ggml_up(M, GGML_SOFT_MAX_UNROLL); GGML_ASSERT(ne0 == D); GGML_ASSERT(ne1 == N); GGML_ASSERT(P >= 0); GGML_ASSERT(nbq0 == sizeof(float)); GGML_ASSERT(nbk0 == sizeof(float)); GGML_ASSERT(nbv0 == sizeof(float)); GGML_ASSERT(neq0 == D); GGML_ASSERT(nek0 == D); GGML_ASSERT(nev1 == D); GGML_ASSERT(neq1 == N); GGML_ASSERT(nek1 == N + P); GGML_ASSERT(nev1 == D); // dst cannot be transposed or permuted GGML_ASSERT(nb0 == sizeof(float)); GGML_ASSERT(nb0 <= nb1); GGML_ASSERT(nb1 <= nb2); GGML_ASSERT(nb2 <= nb3); if (params->type == GGML_TASK_INIT) { return; } if (params->type == GGML_TASK_FINALIZE) { return; } // parallelize by q rows using ggml_vec_dot_f32 // total rows in q const int nr = neq1*neq2*neq3; // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); const float scale = 1.0/sqrt((double) D); //printf("P=%d N=%d D=%d ir0=%d ir1=%d scale = %f\n", P, N, D, ir0, ir1, scale); for (int ir = ir0; ir < ir1; ++ir) { // q indices const int iq3 = ir/(neq2*neq1); const int iq2 = (ir - iq3*neq2*neq1)/neq1; const int iq1 = (ir - iq3*neq2*neq1 - iq2*neq1); float * S = (float *) params->wdata + ith*(Mup + CACHE_LINE_SIZE_F32); for (int i = M; i < Mup; ++i) { S[i] = -INFINITY; } for (int ic = 0; ic < nek1; ++ic) { // k indices const int ik3 = iq3; const int ik2 = iq2; const int ik1 = ic; // S indices const int i1 = ik1; ggml_vec_dot_f32(neq0, S + i1, (float *) ((char *) k->data + (ik1*nbk1 + ik2*nbk2 + ik3*nbk3)), (float *) ((char *) q->data + (iq1*nbq1 + iq2*nbq2 + iq3*nbq3))); } // scale ggml_vec_scale_f32(nek1, S, scale); if (masked) { for (int i = P; i < M; i++) { if (i > P + iq1) { S[i] = -INFINITY; } } } // softmax { float max = -INFINITY; ggml_vec_max_f32(M, &max, S); float sum = 0.0f; { #ifdef GGML_SOFT_MAX_ACCELERATE max = -max; vDSP_vsadd(S, 1, &max, S, 1, Mup); vvexpf(S, S, &Mup); ggml_vec_sum_f32(Mup, &sum, S); #else uint16_t scvt[GGML_SOFT_MAX_UNROLL]; ggml_float sump[GGML_SOFT_MAX_UNROLL] = { 0.0 }; for (int i = 0; i < Mup; i += GGML_SOFT_MAX_UNROLL) { float * SS = S + i; for (int j = 0; j < GGML_SOFT_MAX_UNROLL; ++j) { if (SS[j] == -INFINITY) { SS[j] = 0.0f; } else { ggml_fp16_t s = GGML_FP32_TO_FP16(SS[j] - max); memcpy(&scvt[j], &s, sizeof(uint16_t)); const float val = GGML_FP16_TO_FP32(table_exp_f16[scvt[j]]); sump[j] += val; SS[j] = val; } } } for (int i = 0; i < GGML_SOFT_MAX_UNROLL; i++) { sum += sump[i]; } #endif } assert(sum > 0.0f); sum = 1.0/sum; ggml_vec_scale_f32(M, S, sum); #ifndef NDEBUG for (int i = 0; i < M; ++i) { assert(!isnan(S[i])); assert(!isinf(S[i])); } #endif } for (int ic = 0; ic < nev1; ++ic) { // dst indices const int i1 = iq1; const int i2 = iq2; const int i3 = iq3; ggml_vec_dot_f32(nek1, (float *) ((char *) dst->data + (ic*nb0 + i1*nb1 + i2*nb2 + i3*nb3)), (float *) ((char *) v->data + ( ic*nbv1 + i2*nbv2 + i3*nbv3)), S); } } } static void ggml_compute_forward_flash_attn_f16( const struct ggml_compute_params * params, const struct ggml_tensor * q, const struct ggml_tensor * k, const struct ggml_tensor * v, const bool masked, struct ggml_tensor * dst) { int64_t t0 = ggml_perf_time_us(); UNUSED(t0); const int neq0 = q->ne[0]; const int neq1 = q->ne[1]; const int neq2 = q->ne[2]; const int neq3 = q->ne[3]; const int nek0 = k->ne[0]; const int nek1 = k->ne[1]; //const int nek2 = k->ne[2]; //const int nek3 = k->ne[3]; //const int nev0 = v->ne[0]; const int nev1 = v->ne[1]; //const int nev2 = v->ne[2]; //const int nev3 = v->ne[3]; const int ne0 = dst->ne[0]; const int ne1 = dst->ne[1]; //const int ne2 = dst->ne[2]; //const int ne3 = dst->ne[3]; const int nbk0 = k->nb[0]; const int nbk1 = k->nb[1]; const int nbk2 = k->nb[2]; const int nbk3 = k->nb[3]; const int nbq0 = q->nb[0]; const int nbq1 = q->nb[1]; const int nbq2 = q->nb[2]; const int nbq3 = q->nb[3]; const int nbv0 = v->nb[0]; const int nbv1 = v->nb[1]; const int nbv2 = v->nb[2]; const int nbv3 = v->nb[3]; const int nb0 = dst->nb[0]; const int nb1 = dst->nb[1]; const int nb2 = dst->nb[2]; const int nb3 = dst->nb[3]; const int ith = params->ith; const int nth = params->nth; const int D = neq0; const int N = neq1; const int P = nek1 - N; const int M = P + N; const int Mup = ggml_up(M, GGML_SOFT_MAX_UNROLL); GGML_ASSERT(ne0 == D); GGML_ASSERT(ne1 == N); GGML_ASSERT(P >= 0); GGML_ASSERT(nbq0 == sizeof(ggml_fp16_t)); GGML_ASSERT(nbk0 == sizeof(ggml_fp16_t)); GGML_ASSERT(nbv0 == sizeof(ggml_fp16_t)); GGML_ASSERT(neq0 == D); GGML_ASSERT(nek0 == D); GGML_ASSERT(nev1 == D); GGML_ASSERT(neq1 == N); GGML_ASSERT(nek1 == N + P); GGML_ASSERT(nev1 == D); // dst cannot be transposed or permuted GGML_ASSERT(nb0 == sizeof(float)); GGML_ASSERT(nb0 <= nb1); GGML_ASSERT(nb1 <= nb2); GGML_ASSERT(nb2 <= nb3); if (params->type == GGML_TASK_INIT) { return; } if (params->type == GGML_TASK_FINALIZE) { return; } // parallelize by q rows using ggml_vec_dot_f32 // total rows in q const int nr = neq1*neq2*neq3; // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); const float scale = 1.0/sqrt((double) D); //printf("P=%d N=%d D=%d ir0=%d ir1=%d scale = %f\n", P, N, D, ir0, ir1, scale); for (int ir = ir0; ir < ir1; ++ir) { // q indices const int iq3 = ir/(neq2*neq1); const int iq2 = (ir - iq3*neq2*neq1)/neq1; const int iq1 = (ir - iq3*neq2*neq1 - iq2*neq1); float * S = (float *) params->wdata + ith*(2*Mup + CACHE_LINE_SIZE_F32); for (int i = M; i < Mup; ++i) { S[i] = -INFINITY; } if (GGML_VEC_DOT_UNROLL > 2 || nek1 % GGML_VEC_DOT_UNROLL != 0) { for (int ic = 0; ic < nek1; ++ic) { // k indices const int ik3 = iq3; const int ik2 = iq2; const int ik1 = ic; // S indices const int i1 = ik1; ggml_vec_dot_f16(neq0, S + i1, (ggml_fp16_t *) ((char *) k->data + (ik1*nbk1 + ik2*nbk2 + ik3*nbk3)), (ggml_fp16_t *) ((char *) q->data + (iq1*nbq1 + iq2*nbq2 + iq3*nbq3))); } } else { for (int ic = 0; ic < nek1; ic += GGML_VEC_DOT_UNROLL) { // k indices const int ik3 = iq3; const int ik2 = iq2; const int ik1 = ic; // S indices const int i1 = ik1; ggml_vec_dot_f16_unroll(neq0, nbk1, S + i1, ((char *) k->data + (ik1*nbk1 + ik2*nbk2 + ik3*nbk3)), (ggml_fp16_t *) ((char *) q->data + (iq1*nbq1 + iq2*nbq2 + iq3*nbq3))); } } // scale ggml_vec_scale_f32(nek1, S, scale); if (masked) { for (int i = P; i < M; i++) { if (i > P + iq1) { S[i] = -INFINITY; } } } // softmax { float max = -INFINITY; ggml_vec_max_f32(M, &max, S); float sum = 0.0f; { #ifdef GGML_SOFT_MAX_ACCELERATE max = -max; vDSP_vsadd(S, 1, &max, S, 1, Mup); vvexpf(S, S, &Mup); ggml_vec_sum_f32(Mup, &sum, S); #else uint16_t scvt[GGML_SOFT_MAX_UNROLL]; ggml_float sump[GGML_SOFT_MAX_UNROLL] = { 0.0 }; for (int i = 0; i < Mup; i += GGML_SOFT_MAX_UNROLL) { float * SS = S + i; for (int j = 0; j < GGML_SOFT_MAX_UNROLL; ++j) { if (SS[j] == -INFINITY) { SS[j] = 0.0f; } else { ggml_fp16_t s = GGML_FP32_TO_FP16(SS[j] - max); memcpy(&scvt[j], &s, sizeof(uint16_t)); const float val = GGML_FP16_TO_FP32(table_exp_f16[scvt[j]]); sump[j] += val; SS[j] = val; } } } for (int i = 0; i < GGML_SOFT_MAX_UNROLL; i++) { sum += sump[i]; } #endif } assert(sum > 0.0f); sum = 1.0/sum; ggml_vec_scale_f32(M, S, sum); #ifndef NDEBUG for (int i = 0; i < M; ++i) { assert(!isnan(S[i])); assert(!isinf(S[i])); } #endif } ggml_fp16_t * S16 = (ggml_fp16_t *) ((float *) params->wdata + ith*(2*Mup + CACHE_LINE_SIZE_F32) + Mup); for (int i = 0; i < M; i++) { S16[i] = GGML_FP32_TO_FP16(S[i]); } if (GGML_VEC_DOT_UNROLL == 1 || (nev1 % GGML_VEC_DOT_UNROLL != 0)) { for (int ic = 0; ic < nev1; ++ic) { // dst indices const int i1 = iq1; const int i2 = iq2; const int i3 = iq3; ggml_vec_dot_f16(nek1, (float *) ((char *) dst->data + (ic*nb0 + i1*nb1 + i2*nb2 + i3*nb3)), (ggml_fp16_t *) ((char *) v->data + ( ic*nbv1 + i2*nbv2 + i3*nbv3)), S16); } } else { for (int ic = 0; ic < nev1; ic += GGML_VEC_DOT_UNROLL) { // dst indices const int i1 = iq1; const int i2 = iq2; const int i3 = iq3; ggml_vec_dot_f16_unroll(nek1, nbv1, (float *) ((char *) dst->data + (ic*nb0 + i1*nb1 + i2*nb2 + i3*nb3)), ((char *) v->data + ( ic*nbv1 + i2*nbv2 + i3*nbv3)), S16); } } } } static void ggml_compute_forward_flash_attn( const struct ggml_compute_params * params, const struct ggml_tensor * q, const struct ggml_tensor * k, const struct ggml_tensor * v, const bool masked, struct ggml_tensor * dst) { switch (q->type) { case GGML_TYPE_F16: { ggml_compute_forward_flash_attn_f16(params, q, k, v, masked, dst); } break; case GGML_TYPE_F32: { ggml_compute_forward_flash_attn_f32(params, q, k, v, masked, dst); } break; case GGML_TYPE_I8: case GGML_TYPE_I16: case GGML_TYPE_I32: case GGML_TYPE_COUNT: { assert(false); } break; } } // ggml_compute_forward_flash_ff static void ggml_compute_forward_flash_ff_f16( const struct ggml_compute_params * params, const struct ggml_tensor * a, // F16 const struct ggml_tensor * b0, // F16 fc_w const struct ggml_tensor * b1, // F32 fc_b const struct ggml_tensor * c0, // F16 proj_w const struct ggml_tensor * c1, // F32 proj_b struct ggml_tensor * dst) { int64_t t0 = ggml_perf_time_us(); UNUSED(t0); const int nea0 = a->ne[0]; const int nea1 = a->ne[1]; const int nea2 = a->ne[2]; const int nea3 = a->ne[3]; const int neb00 = b0->ne[0]; const int neb01 = b0->ne[1]; //const int neb02 = b0->ne[2]; //const int neb03 = b0->ne[3]; const int neb10 = b1->ne[0]; const int neb11 = b1->ne[1]; //const int neb12 = b1->ne[2]; //const int neb13 = b1->ne[3]; const int nec00 = c0->ne[0]; const int nec01 = c0->ne[1]; //const int nec02 = c0->ne[2]; //const int nec03 = c0->ne[3]; const int nec10 = c1->ne[0]; const int nec11 = c1->ne[1]; //const int nec12 = c1->ne[2]; //const int nec13 = c1->ne[3]; const int ne0 = dst->ne[0]; const int ne1 = dst->ne[1]; const int ne2 = dst->ne[2]; //const int ne3 = dst->ne[3]; const int nba0 = a->nb[0]; const int nba1 = a->nb[1]; const int nba2 = a->nb[2]; const int nba3 = a->nb[3]; const int nbb00 = b0->nb[0]; const int nbb01 = b0->nb[1]; const int nbb02 = b0->nb[2]; const int nbb03 = b0->nb[3]; const int nbb10 = b1->nb[0]; //const int nbb11 = b1->nb[1]; //const int nbb12 = b1->nb[2]; //const int nbb13 = b1->nb[3]; const int nbc00 = c0->nb[0]; const int nbc01 = c0->nb[1]; const int nbc02 = c0->nb[2]; const int nbc03 = c0->nb[3]; const int nbc10 = c1->nb[0]; //const int nbc11 = c1->nb[1]; //const int nbc12 = c1->nb[2]; //const int nbc13 = c1->nb[3]; const int nb0 = dst->nb[0]; const int nb1 = dst->nb[1]; const int nb2 = dst->nb[2]; const int nb3 = dst->nb[3]; const int ith = params->ith; const int nth = params->nth; const int D = nea0; //const int N = nea1; const int M = neb01; GGML_ASSERT(ne0 == nea0); GGML_ASSERT(ne1 == nea1); GGML_ASSERT(ne2 == nea2); GGML_ASSERT(nba0 == sizeof(ggml_fp16_t)); GGML_ASSERT(nbb00 == sizeof(ggml_fp16_t)); GGML_ASSERT(nbb10 == sizeof(float)); GGML_ASSERT(nbc00 == sizeof(ggml_fp16_t)); GGML_ASSERT(nbc10 == sizeof(float)); GGML_ASSERT(neb00 == D); GGML_ASSERT(neb01 == M); GGML_ASSERT(neb10 == M); GGML_ASSERT(neb11 == 1); GGML_ASSERT(nec00 == M); GGML_ASSERT(nec01 == D); GGML_ASSERT(nec10 == D); GGML_ASSERT(nec11 == 1); // dst cannot be transposed or permuted GGML_ASSERT(nb0 == sizeof(float)); GGML_ASSERT(nb0 <= nb1); GGML_ASSERT(nb1 <= nb2); GGML_ASSERT(nb2 <= nb3); if (params->type == GGML_TASK_INIT) { return; } if (params->type == GGML_TASK_FINALIZE) { return; } // parallelize by a rows using ggml_vec_dot_f32 // total rows in a const int nr = nea1*nea2*nea3; // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); for (int ir = ir0; ir < ir1; ++ir) { // a indices const int ia3 = ir/(nea2*nea1); const int ia2 = (ir - ia3*nea2*nea1)/nea1; const int ia1 = (ir - ia3*nea2*nea1 - ia2*nea1); float * S = (float *) params->wdata + ith*(2*M + CACHE_LINE_SIZE_F32); for (int ic = 0; ic < neb01; ++ic) { // b0 indices const int ib03 = ia3; const int ib02 = ia2; const int ib01 = ic; // S indices const int i1 = ib01; ggml_vec_dot_f16(nea0, S + i1, (ggml_fp16_t *) ((char *) b0->data + (ib01*nbb01 + ib02*nbb02 + ib03*nbb03)), (ggml_fp16_t *) ((char *) a->data + ( ia1*nba1 + ia2*nba2 + ia3*nba3))); } ggml_vec_add_f32(neb01, S, S, (float *) b1->data); //ggml_vec_gelu_f32(neb01, S, S); ggml_fp16_t * S16 = (ggml_fp16_t *) ((float *) params->wdata + ith*(2*M + CACHE_LINE_SIZE_F32) + M); for (int i = 0; i < M; i++) { S16[i] = GGML_FP32_TO_FP16(S[i]); } ggml_vec_gelu_f16(neb01, S16, S16); { // dst indices const int i1 = ia1; const int i2 = ia2; const int i3 = ia3; for (int ic = 0; ic < nec01; ++ic) { ggml_vec_dot_f16(neb01, (float *) ((char *) dst->data + (ic*nb0 + i1*nb1 + i2*nb2 + i3*nb3)), (ggml_fp16_t *) ((char *) c0->data + ( ic*nbc01 + i2*nbc02 + i3*nbc03)), S16); } ggml_vec_add_f32(nec01, (float *) ((char *) dst->data + (i1*nb1 + i2*nb2 + i3*nb3)), (float *) ((char *) dst->data + (i1*nb1 + i2*nb2 + i3*nb3)), (float *) c1->data); } } } static void ggml_compute_forward_flash_ff( const struct ggml_compute_params * params, const struct ggml_tensor * a, const struct ggml_tensor * b0, const struct ggml_tensor * b1, const struct ggml_tensor * c0, const struct ggml_tensor * c1, struct ggml_tensor * dst) { switch (b0->type) { case GGML_TYPE_F16: { ggml_compute_forward_flash_ff_f16(params, a, b0, b1, c0, c1, dst); } break; case GGML_TYPE_F32: { GGML_ASSERT(false); // TODO } break; case GGML_TYPE_I8: case GGML_TYPE_I16: case GGML_TYPE_I32: case GGML_TYPE_COUNT: { assert(false); } break; } } ///////////////////////////////// static void ggml_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor) { assert(params); switch (tensor->op) { case GGML_OP_DUP: { ggml_compute_forward_dup(params, tensor->src0, tensor); } break; case GGML_OP_ADD: { ggml_compute_forward_add(params, tensor->src0, tensor->src1, tensor); } break; case GGML_OP_SUB: { ggml_compute_forward_sub(params, tensor->src0, tensor->src1, tensor); } break; case GGML_OP_MUL: { ggml_compute_forward_mul(params, tensor->src0, tensor->src1, tensor); } break; case GGML_OP_DIV: { ggml_compute_forward_div(params, tensor->src0, tensor->src1, tensor); } break; case GGML_OP_SQR: { ggml_compute_forward_sqr(params, tensor->src0, tensor); } break; case GGML_OP_SQRT: { ggml_compute_forward_sqrt(params, tensor->src0, tensor); } break; case GGML_OP_SUM: { ggml_compute_forward_sum(params, tensor->src0, tensor); } break; case GGML_OP_MEAN: { ggml_compute_forward_mean(params, tensor->src0, tensor); } break; case GGML_OP_REPEAT: { ggml_compute_forward_repeat(params, tensor->src0, tensor); } break; case GGML_OP_ABS: { ggml_compute_forward_abs(params, tensor->src0, tensor); } break; case GGML_OP_SGN: { ggml_compute_forward_sgn(params, tensor->src0, tensor); } break; case GGML_OP_NEG: { ggml_compute_forward_neg(params, tensor->src0, tensor); } break; case GGML_OP_STEP: { ggml_compute_forward_step(params, tensor->src0, tensor); } break; case GGML_OP_RELU: { ggml_compute_forward_relu(params, tensor->src0, tensor); } break; case GGML_OP_GELU: { ggml_compute_forward_gelu(params, tensor->src0, tensor); } break; case GGML_OP_NORM: { ggml_compute_forward_norm(params, tensor->src0, tensor); } break; case GGML_OP_MUL_MAT: { ggml_compute_forward_mul_mat(params, tensor->src0, tensor->src1, tensor); } break; case GGML_OP_SCALE: { ggml_compute_forward_scale(params, tensor->src0, tensor->src1, tensor); } break; case GGML_OP_CPY: { ggml_compute_forward_cpy(params, tensor->src0, tensor); } break; case GGML_OP_RESHAPE: { ggml_compute_forward_reshape(params, tensor->src0, tensor); } break; case GGML_OP_VIEW: { ggml_compute_forward_view(params, tensor->src0); } break; case GGML_OP_PERMUTE: { ggml_compute_forward_permute(params, tensor->src0); } break; case GGML_OP_TRANSPOSE: { ggml_compute_forward_transpose(params, tensor->src0); } break; case GGML_OP_GET_ROWS: { ggml_compute_forward_get_rows(params, tensor->src0, tensor->src1, tensor); } break; case GGML_OP_DIAG_MASK_INF: { ggml_compute_forward_diag_mask_inf(params, tensor->src0, tensor->src1, tensor); } break; case GGML_OP_SOFT_MAX: { ggml_compute_forward_soft_max(params, tensor->src0, tensor); } break; case GGML_OP_ROPE: { ggml_compute_forward_rope(params, tensor->src0, tensor->src1, tensor); } break; case GGML_OP_CONV_1D_1S: { ggml_compute_forward_conv_1d_1s(params, tensor->src0, tensor->src1, tensor); } break; case GGML_OP_CONV_1D_2S: { ggml_compute_forward_conv_1d_2s(params, tensor->src0, tensor->src1, tensor); } break; case GGML_OP_FLASH_ATTN: { int32_t t = ggml_get_i32_1d(tensor->opt[1], 0); GGML_ASSERT(t == 0 || t == 1); bool masked = t != 0; ggml_compute_forward_flash_attn(params, tensor->src0, tensor->src1, tensor->opt[0], masked, tensor); } break; case GGML_OP_FLASH_FF: { ggml_compute_forward_flash_ff(params, tensor->src0, tensor->src1, tensor->opt[0], tensor->opt[1], tensor->opt[2], tensor); } break; case GGML_OP_NONE: { // nop } break; case GGML_OP_COUNT: { GGML_ASSERT(false); } break; } } //////////////////////////////////////////////////////////////////////////////// static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor * tensor, bool inplace) { struct ggml_tensor * src0 = tensor->src0; struct ggml_tensor * src1 = tensor->src1; switch (tensor->op) { case GGML_OP_DUP: { if (src0->grad) { src0->grad = ggml_add_impl(ctx, src0->grad, tensor->grad, inplace); } } break; case GGML_OP_ADD: { if (src0->grad) { src0->grad = ggml_add_impl(ctx, src0->grad, tensor->grad, inplace); } if (src1->grad) { src1->grad = ggml_add_impl(ctx, src1->grad, tensor->grad, inplace); } } break; case GGML_OP_SUB: { if (src0->grad) { src0->grad = ggml_add_impl(ctx, src0->grad, tensor->grad, inplace); } if (src1->grad) { src1->grad = ggml_sub_impl(ctx, src1->grad, tensor->grad, inplace); } } break; case GGML_OP_MUL: { if (src0->grad) { src0->grad = ggml_add_impl(ctx, src0->grad, ggml_mul(ctx, src1, tensor->grad), inplace); } if (src1->grad) { src1->grad = ggml_add_impl(ctx, src1->grad, ggml_mul(ctx, src0, tensor->grad), inplace); } } break; case GGML_OP_DIV: { if (src0->grad) { src0->grad = ggml_add_impl(ctx, src0->grad, ggml_div(ctx, tensor->grad, src1), inplace); } if (src1->grad) { src1->grad = ggml_sub_impl(ctx, src1->grad, ggml_mul(ctx, tensor->grad, ggml_div(ctx, tensor, src1)), inplace); } } break; case GGML_OP_SQR: { if (src0->grad) { src0->grad = ggml_add_impl(ctx, src0->grad, ggml_mul(ctx, ggml_mul(ctx, src0, tensor->grad), ggml_repeat(ctx, ggml_new_f32(ctx, 2.0f), src0)), inplace); } } break; case GGML_OP_SQRT: { if (src0->grad) { src0->grad = ggml_add_impl(ctx, src0->grad, ggml_div(ctx, ggml_repeat(ctx, ggml_new_f32(ctx, 0.5f), tensor), tensor), inplace); } } break; case GGML_OP_SUM: { if (src0->grad) { src0->grad = ggml_add_impl(ctx, src0->grad, ggml_repeat(ctx, tensor->grad, src0->grad), inplace); } } break; case GGML_OP_MEAN: { assert(false); // TODO: implement } break; case GGML_OP_REPEAT: { if (src0->grad) { src0->grad = ggml_add_impl(ctx, src0->grad, ggml_sum(ctx, tensor->grad), inplace); } } break; case GGML_OP_ABS: { if (src0->grad) { src0->grad = ggml_add_impl(ctx, src0->grad, ggml_mul(ctx, ggml_sgn(ctx, src0), tensor->grad), inplace); } } break; case GGML_OP_SGN: { if (src0->grad) { // noop } } break; case GGML_OP_NEG: { if (src0->grad) { src0->grad = ggml_sub_impl(ctx, src0->grad, tensor->grad, inplace); } } break; case GGML_OP_STEP: { if (src0->grad) { // noop } } break; case GGML_OP_RELU: { if (src0->grad) { src0->grad = ggml_sub_impl(ctx, src0->grad, ggml_mul(ctx, ggml_step(ctx, src0), tensor->grad), inplace); } } break; case GGML_OP_GELU: { assert(false); // TODO: not implemented } break; case GGML_OP_NORM: { assert(false); // TODO: not implemented } break; case GGML_OP_MUL_MAT: { if (src0->grad) { // TODO: this requires outer product - ggml_out_prod(ctx, src1, tensor->grad); assert(false); } if (src1->grad) { src1->grad = ggml_add_impl(ctx, src1->grad, // TODO: fix transpose, the node will break the graph connections ggml_mul_mat(ctx, ggml_transpose(ctx, src0), tensor->grad), inplace); } } break; case GGML_OP_SCALE: { GGML_ASSERT(false); // TODO: not implemented } break; case GGML_OP_CPY: { GGML_ASSERT(false); // TODO: not implemented } break; case GGML_OP_RESHAPE: { GGML_ASSERT(false); // TODO: not implemented } break; case GGML_OP_VIEW: { GGML_ASSERT(false); // not supported } break; case GGML_OP_PERMUTE: { GGML_ASSERT(false); // TODO: not implemented } break; case GGML_OP_TRANSPOSE: { GGML_ASSERT(false); // TODO: not implemented } break; case GGML_OP_GET_ROWS: { GGML_ASSERT(false); // TODO: not implemented } break; case GGML_OP_DIAG_MASK_INF: { GGML_ASSERT(false); // TODO: not implemented } break; case GGML_OP_SOFT_MAX: { GGML_ASSERT(false); // TODO: not implemented } break; case GGML_OP_ROPE: { GGML_ASSERT(false); // TODO: not implemented } break; case GGML_OP_CONV_1D_1S: { GGML_ASSERT(false); // TODO: not implemented } break; case GGML_OP_CONV_1D_2S: { GGML_ASSERT(false); // TODO: not implemented } break; case GGML_OP_FLASH_ATTN: { GGML_ASSERT(false); // not supported } break; case GGML_OP_FLASH_FF: { GGML_ASSERT(false); // not supported } break; case GGML_OP_NONE: { // nop } break; case GGML_OP_COUNT: { GGML_ASSERT(false); } break; } } static void ggml_visit_parents(struct ggml_cgraph * cgraph, struct ggml_tensor * node) { if (node->grad == NULL) { // this usually happens when we generate intermediate nodes from constants in the backward pass // it can also happen during forward pass, if the user performs computations with constants if (node->op != GGML_OP_NONE) { //GGML_PRINT_DEBUG("%s: warning: node %p has no grad, but op %d\n", __func__, (void *) node, node->op); } } // check if already visited for (int i = 0; i < cgraph->n_nodes; i++) { if (cgraph->nodes[i] == node) { return; } } for (int i = 0; i < cgraph->n_leafs; i++) { if (cgraph->leafs[i] == node) { return; } } if (node->src0) { ggml_visit_parents(cgraph, node->src0); } if (node->src1) { ggml_visit_parents(cgraph, node->src1); } for (int i = 0; i < GGML_MAX_OPT; ++i) { if (node->opt[i]) { ggml_visit_parents(cgraph, node->opt[i]); } } if (node->op == GGML_OP_NONE && node->grad == NULL) { // reached a leaf node, not part of the gradient graph (e.g. a constant) assert(cgraph->n_leafs < GGML_MAX_NODES); cgraph->leafs[cgraph->n_leafs] = node; cgraph->n_leafs++; } else { assert(cgraph->n_nodes < GGML_MAX_NODES); cgraph->nodes[cgraph->n_nodes] = node; cgraph->grads[cgraph->n_nodes] = node->grad; cgraph->n_nodes++; } } static void ggml_build_forward_impl(struct ggml_cgraph * cgraph, struct ggml_tensor * tensor, bool expand) { if (!expand) { cgraph->n_nodes = 0; cgraph->n_leafs = 0; } const int n0 = cgraph->n_nodes; UNUSED(n0); ggml_visit_parents(cgraph, tensor); const int n_new = cgraph->n_nodes - n0; GGML_PRINT_DEBUG("%s: visited %d new nodes\n", __func__, n_new); if (n_new > 0) { // the last added node should always be starting point assert(cgraph->nodes[cgraph->n_nodes - 1] == tensor); } } void ggml_build_forward_expand(struct ggml_cgraph * cgraph, struct ggml_tensor * tensor) { ggml_build_forward_impl(cgraph, tensor, true); } struct ggml_cgraph ggml_build_forward(struct ggml_tensor * tensor) { struct ggml_cgraph result = { /*.n_nodes =*/ 0, /*.n_leafs =*/ 0, /*.n_threads =*/ 0, /*.work_size =*/ 0, /*.work =*/ NULL, /*.nodes =*/ { NULL }, /*.grads =*/ { NULL }, /*.leafs =*/ { NULL }, /*.perf_runs =*/ 0, /*.perf_cycles =*/ 0, /*.perf_time_us =*/ 0, }; ggml_build_forward_impl(&result, tensor, false); return result; } struct ggml_cgraph ggml_build_backward(struct ggml_context * ctx, struct ggml_cgraph * gf, bool keep) { struct ggml_cgraph result = *gf; assert(gf->n_nodes > 0); // if we are keeping the gradient graph, we have to detach the gradient nodes from the original graph if (keep) { for (int i = 0; i < gf->n_nodes; i++) { struct ggml_tensor * node = gf->nodes[i]; if (node->grad) { node->grad = ggml_dup_tensor(ctx, node); gf->grads[i] = node->grad; } } } for (int i = gf->n_nodes - 1; i >= 0; i--) { struct ggml_tensor * node = gf->nodes[i]; // because we detached the grad nodes from the original graph, we can afford inplace operations if (node->grad) { ggml_compute_backward(ctx, node, keep); } } for (int i = gf->n_nodes - 1; i >= 0; i--) { struct ggml_tensor * node = gf->nodes[i]; if (node->is_param) { GGML_PRINT_DEBUG("%s: found root node %p\n", __func__, (void *) node); ggml_build_forward_impl(&result, node->grad, true); } } return result; } // // thread data // // synchronization is done via busy loops // I tried using spin locks, but not sure how to use them correctly - the things I tried were slower than busy loops // #ifdef __APPLE__ //#include // //typedef os_unfair_lock ggml_lock_t; // //#define ggml_lock_init(x) UNUSED(x) //#define ggml_lock_destroy(x) UNUSED(x) //#define ggml_lock_lock os_unfair_lock_lock //#define ggml_lock_unlock os_unfair_lock_unlock // //#define GGML_LOCK_INITIALIZER OS_UNFAIR_LOCK_INIT typedef int ggml_lock_t; #define ggml_lock_init(x) UNUSED(x) #define ggml_lock_destroy(x) UNUSED(x) #define ggml_lock_lock(x) UNUSED(x) #define ggml_lock_unlock(x) UNUSED(x) #define GGML_LOCK_INITIALIZER 0 typedef pthread_t ggml_thread_t; #define ggml_thread_create pthread_create #define ggml_thread_join pthread_join #else //typedef pthread_spinlock_t ggml_lock_t; //#define ggml_lock_init(x) pthread_spin_init(x, PTHREAD_PROCESS_PRIVATE) //#define ggml_lock_destroy pthread_spin_destroy //#define ggml_lock_lock pthread_spin_lock //#define ggml_lock_unlock pthread_spin_unlock typedef int ggml_lock_t; #define ggml_lock_init(x) UNUSED(x) #define ggml_lock_destroy(x) UNUSED(x) #define ggml_lock_lock(x) UNUSED(x) #define ggml_lock_unlock(x) UNUSED(x) #define GGML_LOCK_INITIALIZER 0 typedef pthread_t ggml_thread_t; #define ggml_thread_create pthread_create #define ggml_thread_join pthread_join #endif struct ggml_compute_state_shared { ggml_lock_t spin; int n_threads; // synchronization primitives atomic_int n_ready; atomic_bool has_work; atomic_bool stop; // stop all threads }; struct ggml_compute_state { ggml_thread_t thrd; struct ggml_compute_params params; struct ggml_tensor * node; struct ggml_compute_state_shared * shared; }; static thread_ret_t ggml_graph_compute_thread(void * data) { struct ggml_compute_state * state = (struct ggml_compute_state *) data; const int n_threads = state->shared->n_threads; while (true) { if (atomic_fetch_add(&state->shared->n_ready, 1) == n_threads - 1) { atomic_store(&state->shared->has_work, false); } else { while (atomic_load(&state->shared->has_work)) { if (atomic_load(&state->shared->stop)) { return 0; } ggml_lock_lock (&state->shared->spin); ggml_lock_unlock(&state->shared->spin); } } atomic_fetch_sub(&state->shared->n_ready, 1); // wait for work while (!atomic_load(&state->shared->has_work)) { if (atomic_load(&state->shared->stop)) { return 0; } ggml_lock_lock (&state->shared->spin); ggml_lock_unlock(&state->shared->spin); } // check if we should stop if (atomic_load(&state->shared->stop)) { break; } if (state->node) { if (state->params.ith < state->params.nth) { ggml_compute_forward(&state->params, state->node); } state->node = NULL; } else { break; } } return 0; } void ggml_graph_compute(struct ggml_context * ctx, struct ggml_cgraph * cgraph) { if (cgraph->n_threads <= 0) { cgraph->n_threads = 8; } const int n_threads = cgraph->n_threads; struct ggml_compute_state_shared state_shared = { /*.spin =*/ GGML_LOCK_INITIALIZER, /*.n_threads =*/ n_threads, /*.n_ready =*/ 0, /*.has_work =*/ false, /*.stop =*/ false, }; struct ggml_compute_state * workers = n_threads > 1 ? alloca(sizeof(struct ggml_compute_state)*(n_threads - 1)) : NULL; // create thread pool if (n_threads > 1) { ggml_lock_init(&state_shared.spin); atomic_store(&state_shared.has_work, true); for (int j = 0; j < n_threads - 1; j++) { workers[j] = (struct ggml_compute_state) { .thrd = 0, .params = { .type = GGML_TASK_COMPUTE, .ith = j + 1, .nth = n_threads, .wsize = cgraph->work ? ggml_nbytes(cgraph->work) : 0, .wdata = cgraph->work ? cgraph->work->data : NULL, }, .node = NULL, .shared = &state_shared, }; int rc = ggml_thread_create(&workers[j].thrd, NULL, ggml_graph_compute_thread, &workers[j]); assert(rc == 0); UNUSED(rc); } } // initialize tasks + work buffer { size_t work_size = 0; // thread scheduling for the different operations for (int i = 0; i < cgraph->n_nodes; i++) { struct ggml_tensor * node = cgraph->nodes[i]; switch (node->op) { case GGML_OP_DUP: { node->n_tasks = 1; } break; case GGML_OP_ADD: { node->n_tasks = n_threads; } break; case GGML_OP_SUB: case GGML_OP_MUL: case GGML_OP_DIV: case GGML_OP_SQR: case GGML_OP_SQRT: case GGML_OP_SUM: case GGML_OP_MEAN: case GGML_OP_REPEAT: case GGML_OP_ABS: case GGML_OP_SGN: case GGML_OP_NEG: case GGML_OP_STEP: case GGML_OP_RELU: { node->n_tasks = 1; } break; case GGML_OP_GELU: { node->n_tasks = n_threads; } break; case GGML_OP_NORM: { node->n_tasks = n_threads; } break; case GGML_OP_MUL_MAT: { node->n_tasks = n_threads; // TODO: use different scheduling for different matrix sizes //const int nr0 = ggml_nrows(node->src0); //const int nr1 = ggml_nrows(node->src1); //node->n_tasks = MIN(n_threads, MAX(1, nr0/128)); //printf("nr0 = %8d, nr1 = %8d, nr0*nr1 = %8d, n_tasks = %d\n", nr0, nr1, nr0*nr1, node->n_tasks); size_t cur = 0; // TODO: better way to determine if the matrix is transposed if (node->src0->nb[1] < node->src0->nb[0]) { cur = ggml_nbytes(node)*node->n_tasks; // TODO: this can become (n_tasks-1) } else { if (node->src0->type == GGML_TYPE_F16 && node->src1->type == GGML_TYPE_F32) { #if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) if (ggml_compute_forward_mul_mat_use_blas(node->src0, node->src1, node)) { node->n_tasks = 1; // TODO: this actually is doing nothing // the threads are still spinning cur = sizeof(float)*(node->src0->ne[0]*node->src0->ne[1]); //printf("src0: ne0 = %d, ne1 = %d, ne = %d\n", node->src0->ne[0], node->src0->ne[1], node->src0->ne[0]*node->src0->ne[1]); //printf("src1: ne0 = %d, ne1 = %d, ne = %d\n", node->src1->ne[0], node->src1->ne[1], node->src1->ne[0]*node->src1->ne[1]); //printf("cur = %zu\n", cur); } else { cur = sizeof(ggml_fp16_t)*ggml_nelements(node->src1); } #else cur = sizeof(ggml_fp16_t)*ggml_nelements(node->src1); #endif } else if (node->src0->type == GGML_TYPE_F32 && node->src1->type == GGML_TYPE_F32) { cur = 0; } else { GGML_ASSERT(false); } } work_size = MAX(work_size, cur); } break; case GGML_OP_SCALE: { node->n_tasks = n_threads; } break; case GGML_OP_CPY: case GGML_OP_RESHAPE: case GGML_OP_VIEW: case GGML_OP_PERMUTE: case GGML_OP_TRANSPOSE: case GGML_OP_GET_ROWS: case GGML_OP_DIAG_MASK_INF: { node->n_tasks = 1; } break; case GGML_OP_SOFT_MAX: { node->n_tasks = n_threads; } break; case GGML_OP_ROPE: { node->n_tasks = 1; } break; case GGML_OP_CONV_1D_1S: case GGML_OP_CONV_1D_2S: { node->n_tasks = n_threads; GGML_ASSERT(node->src0->ne[3] == 1); GGML_ASSERT(node->src1->ne[2] == 1); GGML_ASSERT(node->src1->ne[3] == 1); size_t cur = 0; const int nk = node->src0->ne[0]; if (node->src0->type == GGML_TYPE_F16 && node->src1->type == GGML_TYPE_F32) { cur = sizeof(ggml_fp16_t)*( nk*ggml_up32(node->src0->ne[1])*node->src0->ne[2] + ( 2*(nk/2) + node->src1->ne[0])*node->src1->ne[1] ); } else if (node->src0->type == GGML_TYPE_F32 && node->src1->type == GGML_TYPE_F32) { cur = sizeof(float)*( nk*ggml_up32(node->src0->ne[1])*node->src0->ne[2] + ( 2*(nk/2) + node->src1->ne[0])*node->src1->ne[1] ); } else { GGML_ASSERT(false); } work_size = MAX(work_size, cur); } break; case GGML_OP_FLASH_ATTN: { node->n_tasks = n_threads; size_t cur = 0; const int ne11 = ggml_up(node->src1->ne[1], GGML_SOFT_MAX_UNROLL); if (node->src1->type == GGML_TYPE_F32) { cur = sizeof(float)*ne11*node->n_tasks; // TODO: this can become (n_tasks-1) cur += sizeof(float)*ne11*node->n_tasks; // this is overestimated by x2 } if (node->src1->type == GGML_TYPE_F16) { cur = sizeof(float)*ne11*node->n_tasks; // TODO: this can become (n_tasks-1) cur += sizeof(float)*ne11*node->n_tasks; // this is overestimated by x2 } work_size = MAX(work_size, cur); } break; case GGML_OP_FLASH_FF: { node->n_tasks = n_threads; size_t cur = 0; if (node->src1->type == GGML_TYPE_F32) { cur = sizeof(float)*node->src1->ne[1]*node->n_tasks; // TODO: this can become (n_tasks-1) cur += sizeof(float)*node->src1->ne[1]*node->n_tasks; // this is overestimated by x2 } if (node->src1->type == GGML_TYPE_F16) { cur = sizeof(float)*node->src1->ne[1]*node->n_tasks; // TODO: this can become (n_tasks-1) cur += sizeof(float)*node->src1->ne[1]*node->n_tasks; // this is overestimated by x2 } work_size = MAX(work_size, cur); } break; case GGML_OP_NONE: { node->n_tasks = 1; } break; case GGML_OP_COUNT: { assert(false); } break; } } if (cgraph->work != NULL && work_size > cgraph->work_size) { assert(false); // TODO: better handling } if (work_size > 0 && cgraph->work == NULL) { cgraph->work_size = work_size + CACHE_LINE_SIZE*(n_threads - 1); GGML_PRINT_DEBUG("%s: allocating work buffer for graph (%zu bytes)\n", __func__, cgraph->work_size); cgraph->work = ggml_new_tensor_1d(ctx, GGML_TYPE_I8, cgraph->work_size); } } const int64_t perf_start_cycles = ggml_perf_cycles(); const int64_t perf_start_time_us = ggml_perf_time_us(); for (int i = 0; i < cgraph->n_nodes; i++) { GGML_PRINT_DEBUG_5("%s: %d/%d\n", __func__, i, cgraph->n_nodes); struct ggml_tensor * node = cgraph->nodes[i]; // TODO: this could be used to avoid unnecessary computations, but it needs to be improved //if (node->grad == NULL && node->perf_runs > 0) { // continue; //} const int64_t perf_node_start_cycles = ggml_perf_cycles(); const int64_t perf_node_start_time_us = ggml_perf_time_us(); // INIT struct ggml_compute_params params = { /*.type =*/ GGML_TASK_INIT, /*.ith =*/ 0, /*.nth =*/ node->n_tasks, /*.wsize =*/ cgraph->work ? ggml_nbytes(cgraph->work) : 0, /*.wdata =*/ cgraph->work ? cgraph->work->data : NULL, }; ggml_compute_forward(¶ms, node); // COMPUTE if (node->n_tasks > 1) { if (atomic_fetch_add(&state_shared.n_ready, 1) == n_threads - 1) { atomic_store(&state_shared.has_work, false); } while (atomic_load(&state_shared.has_work)) { ggml_lock_lock (&state_shared.spin); ggml_lock_unlock(&state_shared.spin); } // launch thread pool for (int j = 0; j < n_threads - 1; j++) { workers[j].params = (struct ggml_compute_params) { .type = GGML_TASK_COMPUTE, .ith = j + 1, .nth = node->n_tasks, .wsize = cgraph->work ? ggml_nbytes(cgraph->work) : 0, .wdata = cgraph->work ? cgraph->work->data : NULL, }; workers[j].node = node; } atomic_fetch_sub(&state_shared.n_ready, 1); while (atomic_load(&state_shared.n_ready) > 0) { ggml_lock_lock (&state_shared.spin); ggml_lock_unlock(&state_shared.spin); } atomic_store(&state_shared.has_work, true); } params.type = GGML_TASK_COMPUTE; ggml_compute_forward(¶ms, node); // wait for thread pool if (node->n_tasks > 1) { if (atomic_fetch_add(&state_shared.n_ready, 1) == n_threads - 1) { atomic_store(&state_shared.has_work, false); } while (atomic_load(&state_shared.has_work)) { ggml_lock_lock (&state_shared.spin); ggml_lock_unlock(&state_shared.spin); } atomic_fetch_sub(&state_shared.n_ready, 1); while (atomic_load(&state_shared.n_ready) != 0) { ggml_lock_lock (&state_shared.spin); ggml_lock_unlock(&state_shared.spin); } } // FINALIZE if (node->n_tasks > 1) { if (atomic_fetch_add(&state_shared.n_ready, 1) == n_threads - 1) { atomic_store(&state_shared.has_work, false); } while (atomic_load(&state_shared.has_work)) { ggml_lock_lock (&state_shared.spin); ggml_lock_unlock(&state_shared.spin); } // launch thread pool for (int j = 0; j < n_threads - 1; j++) { workers[j].params = (struct ggml_compute_params) { .type = GGML_TASK_FINALIZE, .ith = j + 1, .nth = node->n_tasks, .wsize = cgraph->work ? ggml_nbytes(cgraph->work) : 0, .wdata = cgraph->work ? cgraph->work->data : NULL, }; workers[j].node = node; } atomic_fetch_sub(&state_shared.n_ready, 1); while (atomic_load(&state_shared.n_ready) > 0) { ggml_lock_lock (&state_shared.spin); ggml_lock_unlock(&state_shared.spin); } atomic_store(&state_shared.has_work, true); } params.type = GGML_TASK_FINALIZE; ggml_compute_forward(¶ms, node); // wait for thread pool if (node->n_tasks > 1) { if (atomic_fetch_add(&state_shared.n_ready, 1) == n_threads - 1) { atomic_store(&state_shared.has_work, false); } while (atomic_load(&state_shared.has_work)) { ggml_lock_lock (&state_shared.spin); ggml_lock_unlock(&state_shared.spin); } atomic_fetch_sub(&state_shared.n_ready, 1); while (atomic_load(&state_shared.n_ready) != 0) { ggml_lock_lock (&state_shared.spin); ggml_lock_unlock(&state_shared.spin); } } // performance stats (node) { int64_t perf_cycles_cur = ggml_perf_cycles() - perf_node_start_cycles; int64_t perf_time_us_cur = ggml_perf_time_us() - perf_node_start_time_us; node->perf_runs++; node->perf_cycles += perf_cycles_cur; node->perf_time_us += perf_time_us_cur; } } // join thread pool if (n_threads > 1) { atomic_store(&state_shared.stop, true); atomic_store(&state_shared.has_work, true); for (int j = 0; j < n_threads - 1; j++) { int rc = ggml_thread_join(workers[j].thrd, NULL); assert(rc == 0); UNUSED(rc); } ggml_lock_destroy(&state_shared.spin); } // performance stats (graph) { int64_t perf_cycles_cur = ggml_perf_cycles() - perf_start_cycles; int64_t perf_time_us_cur = ggml_perf_time_us() - perf_start_time_us; cgraph->perf_runs++; cgraph->perf_cycles += perf_cycles_cur; cgraph->perf_time_us += perf_time_us_cur; GGML_PRINT_DEBUG("%s: perf (%d) - cpu = %.3f / %.3f ms, wall = %.3f / %.3f ms\n", __func__, cgraph->perf_runs, (double) perf_cycles_cur / (double) ggml_cycles_per_ms(), (double) cgraph->perf_cycles / (double) ggml_cycles_per_ms() / (double) cgraph->perf_runs, (double) perf_time_us_cur / 1000.0, (double) cgraph->perf_time_us / 1000.0 / cgraph->perf_runs); } } void ggml_graph_reset(struct ggml_cgraph * cgraph) { for (int i = 0; i < cgraph->n_nodes; i++) { struct ggml_tensor * grad = cgraph->grads[i]; if (grad) { ggml_set_zero(grad); } } } void ggml_graph_print(const struct ggml_cgraph * cgraph) { int64_t perf_total_per_op_us[GGML_OP_COUNT] = {0}; GGML_PRINT("=== GRAPH ===\n"); GGML_PRINT_DEBUG("n_threads = %d\n", cgraph->n_threads); GGML_PRINT_DEBUG("total work size = %zu bytes\n",cgraph->work_size); GGML_PRINT("n_nodes = %d\n", cgraph->n_nodes); for (int i = 0; i < cgraph->n_nodes; i++) { struct ggml_tensor * node = cgraph->nodes[i]; perf_total_per_op_us[node->op] += node->perf_time_us; GGML_PRINT(" - %3d: [ %6d, %6d, %6d] %16s %s (%3d) cpu = %7.3f / %7.3f ms, wall = %7.3f / %7.3f ms\n", i, node->ne[0], node->ne[1], node->ne[2], GGML_OP_LABEL[node->op], node->is_param ? "x" : node->grad ? "g" : " ", node->perf_runs, (double) node->perf_cycles / (double) ggml_cycles_per_ms(), (double) node->perf_cycles / (double) ggml_cycles_per_ms() / (double) node->perf_runs, (double) node->perf_time_us / 1000.0, (double) node->perf_time_us / 1000.0 / node->perf_runs); } GGML_PRINT("n_leafs = %d\n", cgraph->n_leafs); for (int i = 0; i < cgraph->n_leafs; i++) { struct ggml_tensor * node = cgraph->leafs[i]; GGML_PRINT(" - %3d: [ %6d, %6d] %8s\n", i, node->ne[0], node->ne[1], GGML_OP_LABEL[node->op]); } for (int i = 0; i < GGML_OP_COUNT; i++) { GGML_PRINT("perf_total_per_op_us[%16s] = %7.3f ms\n", GGML_OP_LABEL[i], (double) perf_total_per_op_us[i] / 1000.0); } GGML_PRINT("========================================\n"); } // check if node is part of the graph static bool ggml_graph_find(const struct ggml_cgraph * cgraph, const struct ggml_tensor * node) { if (cgraph == NULL) { return true; } for (int i = 0; i < cgraph->n_nodes; i++) { if (cgraph->nodes[i] == node) { return true; } } return false; } static struct ggml_tensor * ggml_graph_get_parent(const struct ggml_cgraph * cgraph, const struct ggml_tensor * node) { for (int i = 0; i < cgraph->n_nodes; i++) { struct ggml_tensor * parent = cgraph->nodes[i]; if (parent->grad == node) { return parent; } } return NULL; } void ggml_graph_dump_dot(const struct ggml_cgraph * gb, const struct ggml_cgraph * gf, const char * filename) { char color[16]; FILE * fp = fopen(filename, "w"); assert(fp); fprintf(fp, "digraph G {\n"); fprintf(fp, " newrank = true;\n"); fprintf(fp, " rankdir = LR;\n"); for (int i = 0; i < gb->n_nodes; i++) { struct ggml_tensor * node = gb->nodes[i]; if (ggml_graph_get_parent(gb, node) != NULL) { continue; } if (node->is_param) { snprintf(color, sizeof(color), "yellow"); } else if (node->grad) { if (ggml_graph_find(gf, node)) { snprintf(color, sizeof(color), "green"); } else { snprintf(color, sizeof(color), "lightblue"); } } else { snprintf(color, sizeof(color), "white"); } fprintf(fp, " \"%p\" [ \ style = filled; fillcolor = %s; shape = record; \ label=\"%d [%d, %d] | %s", (void *) node, color, i, node->ne[0], node->ne[1], GGML_OP_SYMBOL[node->op]); if (node->grad) { fprintf(fp, " | %s\"; ]\n", GGML_OP_SYMBOL[node->grad->op]); } else { fprintf(fp, "\"; ]\n"); } } for (int i = 0; i < gb->n_leafs; i++) { struct ggml_tensor * node = gb->leafs[i]; snprintf(color, sizeof(color), "pink"); if (ggml_nelements(node) == 1) { fprintf(fp, " \"%p\" [ \ style = filled; fillcolor = %s; shape = record; \ label=\"%.1e\"; ]\n", (void *) node, color, ggml_get_f32_1d(node, 0)); } else { fprintf(fp, " \"%p\" [ \ style = filled; fillcolor = %s; shape = record; \ label=\"CONST %d [%d, %d]\"; ]\n", (void *) node, color, i, node->ne[0], node->ne[1]); } } for (int i = 0; i < gb->n_nodes; i++) { struct ggml_tensor * node = gb->nodes[i]; struct ggml_tensor * parent = ggml_graph_get_parent(gb, node); if (node->src0) { struct ggml_tensor * parent0 = ggml_graph_get_parent(gb, node->src0); fprintf(fp, " \"%p\":%s -> \"%p\":%s [ arrowhead = %s; style = %s; label = \"x\"; ]\n", parent0 ? (void *) parent0 : (void *) node->src0, parent0 ? "g" : "x", parent ? (void *) parent : (void *) node, parent ? "g" : "x", parent ? "empty" : "vee", parent ? "dashed" : "solid"); } if (node->src1) { struct ggml_tensor * parent1 = ggml_graph_get_parent(gb, node->src1); fprintf(fp, " \"%p\":%s -> \"%p\":%s [ arrowhead = %s; style = %s; label = \"y\"; ]\n", parent1 ? (void *) parent1 : (void *) node->src1, parent1 ? "g" : "x", parent ? (void *) parent : (void *) node, parent ? "g" : "x", parent ? "empty" : "vee", parent ? "dashed" : "solid"); } } for (int i = 0; i < gb->n_leafs; i++) { struct ggml_tensor * node = gb->leafs[i]; if (node->src0) { fprintf(fp, " \"%p\":%s -> \"%p\":%s [ label = \"x\"; ]\n", (void *) node->src0, "x", (void *) node, "x"); } if (node->src1) { fprintf(fp, " \"%p\":%s -> \"%p\":%s [ label = \"y\"; ]\n", (void *) node->src1, "x", (void *) node, "x"); } } fprintf(fp, "}\n"); fclose(fp); GGML_PRINT("%s: dot -Tpng %s -o %s.png && open %s.png\n", __func__, filename, filename, filename); } //////////////////////////////////////////////////////////////////////////////// static void ggml_opt_set_params(int np, struct ggml_tensor * const ps[], const float * x) { int i = 0; for (int p = 0; p < np; ++p) { const int ne = ggml_nelements(ps[p]) ; // TODO: add function to set tensor from array for (int j = 0; j < ne; ++j) { ggml_set_f32_1d(ps[p], j, x[i++]); } } } static void ggml_opt_get_params(int np, struct ggml_tensor * const ps[], float * x) { int i = 0; for (int p = 0; p < np; ++p) { const int ne = ggml_nelements(ps[p]) ; // TODO: add function to get all elements at once for (int j = 0; j < ne; ++j) { x[i++] = ggml_get_f32_1d(ps[p], j); } } } static void ggml_opt_get_grad(int np, struct ggml_tensor * const ps[], float * g) { int i = 0; for (int p = 0; p < np; ++p) { const int ne = ggml_nelements(ps[p]) ; // TODO: add function to get all elements at once for (int j = 0; j < ne; ++j) { g[i++] = ggml_get_f32_1d(ps[p]->grad, j); } } } // // ADAM // // ref: https://arxiv.org/pdf/1412.6980.pdf // static enum ggml_opt_result ggml_opt_adam( struct ggml_context * ctx, struct ggml_opt_params params, struct ggml_tensor * f, struct ggml_cgraph * gf, struct ggml_cgraph * gb) { assert(ggml_is_scalar(f)); gf->n_threads = params.n_threads; gb->n_threads = params.n_threads; // these will store the parameters we want to optimize struct ggml_tensor * ps[GGML_MAX_PARAMS]; int np = 0; int nx = 0; for (int i = 0; i < gf->n_nodes; ++i) { if (gf->nodes[i]->is_param) { GGML_PRINT_DEBUG("found param %d: grad->op = %d\n", np, gf->nodes[i]->grad->op); assert(np < GGML_MAX_PARAMS); ps[np++] = gf->nodes[i]; nx += ggml_nelements(gf->nodes[i]); } } // constants const float alpha = params.adam.alpha; const float beta1 = params.adam.beta1; const float beta2 = params.adam.beta2; const float eps = params.adam.eps; float * x = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx)->data; // view of the parameters float * g1 = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx)->data; // gradient float * g2 = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx)->data; // gradient squared float * m = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx)->data; // first moment float * v = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx)->data; // second moment float * mh = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx)->data; // first moment hat float * vh = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx)->data; // second moment hat float * pf = params.past > 0 ? ggml_new_tensor_1d(ctx, GGML_TYPE_F32, params.past)->data : NULL; // past function values // initialize ggml_vec_set_f32(nx, m, 0.0f); ggml_vec_set_f32(nx, v, 0.0f); // update view ggml_opt_get_params(np, ps, x); // compute the function value ggml_graph_reset (gf); ggml_set_f32 (f->grad, 1.0f); ggml_graph_compute(ctx, gb); float fx_prev = ggml_get_f32_1d(f, 0); if (pf) { pf[0] = fx_prev; } int n_no_improvement = 0; float fx_best = fx_prev; // run the optimizer for (int t = 0; t < params.adam.n_iter; ++t) { GGML_PRINT_DEBUG ("=== iter %d ===\n", t); GGML_PRINT_DEBUG ("f = %10.6f\n", ggml_get_f32_1d(f, 0)); GGML_PRINT_DEBUG_5("df/dx0 = %10.6f\n", ggml_get_f32_1d(ps[0]->grad, 0)); GGML_PRINT_DEBUG_5("df/dx1 = %10.6f\n", ggml_get_f32_1d(ps[1]->grad, 0)); for (int i = 0; i < np; ++i) { GGML_PRINT_DEBUG("param %d: %10.6f, g = %10.6f\n", i, ggml_get_f32_1d(ps[i], 0), ggml_get_f32_1d(ps[i]->grad, 0)); } const int64_t t_start_wall = ggml_time_us(); const int64_t t_start_cpu = ggml_cycles(); UNUSED(t_start_wall); UNUSED(t_start_cpu); { // update the gradient ggml_opt_get_grad(np, ps, g1); // m_t = beta1*m_t-1 + (1 - beta1)*g_t ggml_vec_scale_f32(nx, m, beta1); ggml_vec_mad_f32 (nx, m, g1, 1.0f - beta1); // g2 = g1^2 ggml_vec_sqr_f32 (nx, g2, g1); // v_t = beta2*v_t-1 + (1 - beta2)*g_t^2 ggml_vec_scale_f32(nx, v, beta2); ggml_vec_mad_f32 (nx, v, g2, 1.0f - beta2); // m^hat = m_t / (1 - beta1^t) // v^hat = v_t / (1 - beta2^t) // x_t = x_t-1 - alpha*m^hat/(sqrt(v^hat) + eps) ggml_vec_cpy_f32 (nx, mh, m); ggml_vec_cpy_f32 (nx, vh, v); ggml_vec_scale_f32(nx, mh, alpha/(1.0f - powf(beta1, t + 1))); ggml_vec_scale_f32(nx, vh, 1.0f/(1.0f - powf(beta2, t + 1))); ggml_vec_sqrt_f32 (nx, vh, vh); ggml_vec_acc1_f32 (nx, vh, eps); ggml_vec_div_f32 (nx, mh, mh, vh); ggml_vec_sub_f32 (nx, x, x, mh); // update the parameters ggml_opt_set_params(np, ps, x); } ggml_graph_reset (gf); ggml_set_f32 (f->grad, 1.0f); ggml_graph_compute(ctx, gb); const float fx = ggml_get_f32_1d(f, 0); // check convergence if (fabsf(fx - fx_prev)/fx < params.adam.eps_f) { GGML_PRINT_DEBUG("converged\n"); return GGML_OPT_OK; } // delta-based convergence test if (pf != NULL) { // need at least params.past iterations to start checking for convergence if (params.past <= t) { const float rate = (pf[t%params.past] - fx)/fx; if (fabs(rate) < params.delta) { return GGML_OPT_OK; } } pf[t%params.past] = fx; } // check for improvement if (params.max_no_improvement > 0) { if (fx_best > fx) { fx_best = fx; n_no_improvement = 0; } else { ++n_no_improvement; if (n_no_improvement >= params.max_no_improvement) { return GGML_OPT_OK; } } } fx_prev = fx; { const int64_t t_end_cpu = ggml_cycles(); GGML_PRINT_DEBUG("time iter: %5.3f s\n", ((float)(t_end_cpu - t_start_cpu))/CLOCKS_PER_SEC); UNUSED(t_end_cpu); const int64_t t_end_wall = ggml_time_us(); GGML_PRINT_DEBUG("wall time iter: %5.3f s\n", (t_end_wall - t_start_wall)/1e6); UNUSED(t_end_wall); } } return GGML_OPT_DID_NOT_CONVERGE; } // // L-BFGS // // the L-BFGS implementation below is based on the following implementation: // // https://github.com/chokkan/liblbfgs // struct ggml_lbfgs_iteration_data { float alpha; float ys; float * s; float * y; }; static enum ggml_opt_result linesearch_backtracking( struct ggml_context * ctx, const struct ggml_opt_params * params, int nx, float * x, float * fx, float * g, float * d, float * step, const float * xp, struct ggml_tensor * f, struct ggml_cgraph * gf, struct ggml_cgraph * gb, const int np, struct ggml_tensor * ps[]) { int count = 0; float width = 0.0f; float dg = 0.0f; float finit = 0.0f; float dginit = 0.0f; float dgtest = 0.0f; const float dec = 0.5f; const float inc = 2.1f; if (*step <= 0.) { return GGML_LINESEARCH_INVALID_PARAMETERS; } // compute the initial gradient in the search direction ggml_vec_dot_f32(nx, &dginit, g, d); // make sure that d points to a descent direction if (0 < dginit) { return GGML_LINESEARCH_FAIL; } // initialize local variables finit = *fx; dgtest = params->lbfgs.ftol*dginit; while (true) { ggml_vec_cpy_f32(nx, x, xp); ggml_vec_mad_f32(nx, x, d, *step); // evaluate the function and gradient values { ggml_opt_set_params(np, ps, x); ggml_graph_reset (gf); ggml_set_f32 (f->grad, 1.0f); ggml_graph_compute(ctx, gb); ggml_opt_get_grad(np, ps, g); *fx = ggml_get_f32_1d(f, 0); } ++count; if (*fx > finit + (*step)*dgtest) { width = dec; } else { // Armijo condition is satisfied if (params->lbfgs.linesearch == GGML_LINESEARCH_BACKTRACKING_ARMIJO) { return count; } ggml_vec_dot_f32(nx, &dg, g, d); // check the Wolfe condition if (dg < params->lbfgs.wolfe * dginit) { width = inc; } else { if(params->lbfgs.linesearch == GGML_LINESEARCH_BACKTRACKING_WOLFE) { // regular Wolfe conditions return count; } if(dg > -params->lbfgs.wolfe*dginit) { width = dec; } else { // strong Wolfe condition (GGML_LINESEARCH_BACKTRACKING_STRONG_WOLFE) return count; } return count; } } if (*step < params->lbfgs.min_step) { return GGML_LINESEARCH_MINIMUM_STEP; } if (*step > params->lbfgs.max_step) { return GGML_LINESEARCH_MAXIMUM_STEP; } if (params->lbfgs.max_linesearch <= count) { return GGML_LINESEARCH_MAXIMUM_ITERATIONS; } (*step) *= width; } return GGML_LINESEARCH_FAIL; } static enum ggml_opt_result ggml_opt_lbfgs( struct ggml_context * ctx, struct ggml_opt_params params, struct ggml_tensor * f, struct ggml_cgraph * gf, struct ggml_cgraph * gb) { if (params.lbfgs.linesearch == GGML_LINESEARCH_BACKTRACKING_WOLFE || params.lbfgs.linesearch == GGML_LINESEARCH_BACKTRACKING_STRONG_WOLFE) { if (params.lbfgs.wolfe <= params.lbfgs.ftol || 1. <= params.lbfgs.wolfe) { return GGML_OPT_INVALID_WOLFE; } } gf->n_threads = params.n_threads; gb->n_threads = params.n_threads; const int m = params.lbfgs.m; // these will store the parameters we want to optimize struct ggml_tensor * ps[GGML_MAX_PARAMS]; int np = 0; int nx = 0; for (int i = 0; i < gf->n_nodes; ++i) { if (gf->nodes[i]->is_param) { GGML_PRINT_DEBUG("found param %d: grad->op = %d\n", np, gf->nodes[i]->grad->op); assert(np < GGML_MAX_PARAMS); ps[np++] = gf->nodes[i]; nx += ggml_nelements(gf->nodes[i]); } } float * x = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx)->data; // current parameters float * xp = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx)->data; // previous parameters float * g = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx)->data; // current gradient float * gp = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx)->data; // previous gradient float * d = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx)->data; // search direction float * pf = params.past > 0 ? ggml_new_tensor_1d(ctx, GGML_TYPE_F32, params.past)->data : NULL; // past function values float fx = 0.0f; // cost function value float xnorm = 0.0f; // ||x|| float gnorm = 0.0f; // ||g|| float step = 0.0f; // initialize x from the graph nodes ggml_opt_get_params(np, ps, x); // the L-BFGS memory struct ggml_lbfgs_iteration_data * lm = alloca(sizeof(struct ggml_lbfgs_iteration_data)*m); for (int i = 0; i < m; ++i) { lm[i].alpha = 0.0f; lm[i].ys = 0.0f; lm[i].s = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx)->data; lm[i].y = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx)->data; } // evaluate the function value and its gradient { ggml_opt_set_params(np, ps, x); ggml_graph_reset (gf); ggml_set_f32 (f->grad, 1.0f); ggml_graph_compute(ctx, gb); ggml_opt_get_grad(np, ps, g); fx = ggml_get_f32_1d(f, 0); } if (pf) { pf[0] = fx; } float fx_best = fx; // search direction = -gradient ggml_vec_neg_f32(nx, d, g); // ||x||, ||g|| ggml_vec_norm_f32(nx, &xnorm, x); ggml_vec_norm_f32(nx, &gnorm, g); if (xnorm < 1.0f) { xnorm = 1.0f; } // already optimized if (gnorm/xnorm <= params.lbfgs.eps) { return GGML_OPT_OK; } // initial step ggml_vec_norm_inv_f32(nx, &step, d); int j = 0; int k = 1; int ls = 0; int end = 0; int bound = 0; int n_no_improvement = 0; float ys = 0.0f; float yy = 0.0f; float beta = 0.0f; while (true) { // store the current position and gradient vectors ggml_vec_cpy_f32(nx, xp, x); ggml_vec_cpy_f32(nx, gp, g); ls = linesearch_backtracking(ctx, ¶ms, nx, x, &fx, g, d, &step, xp, f, gf, gb, np, ps); if (ls < 0) { // linesearch failed - go back to the previous point and return ggml_vec_cpy_f32(nx, x, xp); ggml_vec_cpy_f32(nx, g, gp); return ls; } ggml_vec_norm_f32(nx, &xnorm, x); ggml_vec_norm_f32(nx, &gnorm, g); GGML_PRINT_DEBUG("f = %10.6f\n", ggml_get_f32_1d(f, 0)); if (xnorm < 1.0) { xnorm = 1.0; } if (gnorm/xnorm <= params.lbfgs.eps) { // converged return GGML_OPT_OK; } // delta-based convergence test if (pf != NULL) { // need at least params.past iterations to start checking for convergence if (params.past <= k) { const float rate = (pf[k%params.past] - fx)/fx; if (fabs(rate) < params.delta) { return GGML_OPT_OK; } } pf[k%params.past] = fx; } // check for improvement if (params.max_no_improvement > 0) { if (fx < fx_best) { fx_best = fx; n_no_improvement = 0; } else { n_no_improvement++; if (n_no_improvement >= params.max_no_improvement) { return GGML_OPT_OK; } } } if (params.lbfgs.n_iter != 0 && params.lbfgs.n_iter < k + 1) { // reached the maximum number of iterations return GGML_OPT_DID_NOT_CONVERGE; } // update vectors s and y: // s_{k+1} = x_{k+1} - x_{k} = \step * d_{k}. // y_{k+1} = g_{k+1} - g_{k}. // ggml_vec_sub_f32(nx, lm[end].s, x, xp); ggml_vec_sub_f32(nx, lm[end].y, g, gp); // compute scalars ys and yy: // ys = y^t \cdot s -> 1 / \rho. // yy = y^t \cdot y. // ggml_vec_dot_f32(nx, &ys, lm[end].y, lm[end].s); ggml_vec_dot_f32(nx, &yy, lm[end].y, lm[end].y); lm[end].ys = ys; // find new search direction // ref: https://en.wikipedia.org/wiki/Limited-memory_BFGS bound = (m <= k) ? m : k; k++; end = (end + 1)%m; // initialize search direction with -g ggml_vec_neg_f32(nx, d, g); j = end; for (int i = 0; i < bound; ++i) { j = (j + m - 1) % m; // \alpha_{j} = \rho_{j} s^{t}_{j} \cdot q_{k+1} ggml_vec_dot_f32(nx, &lm[j].alpha, lm[j].s, d); lm[j].alpha /= lm[j].ys; // q_{i} = q_{i+1} - \alpha_{i} y_{i} ggml_vec_mad_f32(nx, d, lm[j].y, -lm[j].alpha); } ggml_vec_scale_f32(nx, d, ys/yy); for (int i = 0; i < bound; ++i) { // \beta_{j} = \rho_{j} y^t_{j} \cdot \gamma_{i} ggml_vec_dot_f32(nx, &beta, lm[j].y, d); beta /= lm[j].ys; // \gamma_{i+1} = \gamma_{i} + (\alpha_{j} - \beta_{j}) s_{j} ggml_vec_mad_f32(nx, d, lm[j].s, lm[j].alpha - beta); j = (j + 1)%m; } step = 1.0; } return GGML_OPT_DID_NOT_CONVERGE; } struct ggml_opt_params ggml_opt_default_params(enum ggml_opt_type type) { struct ggml_opt_params result; switch (type) { case GGML_OPT_ADAM: { result = (struct ggml_opt_params) { .type = GGML_OPT_ADAM, .n_threads = 1, .past = 0, .delta = 1e-5f, .max_no_improvement = 100, .print_forward_graph = true, .print_backward_graph = true, .adam = { .n_iter = 10000, .alpha = 0.001f, .beta1 = 0.9f, .beta2 = 0.999f, .eps = 1e-8f, .eps_f = 1e-5f, .eps_g = 1e-3f, }, }; } break; case GGML_OPT_LBFGS: { result = (struct ggml_opt_params) { .type = GGML_OPT_LBFGS, .n_threads = 1, .past = 0, .delta = 1e-5f, .max_no_improvement = 0, .print_forward_graph = true, .print_backward_graph = true, .lbfgs = { .m = 6, .n_iter = 100, .max_linesearch = 20, .eps = 1e-5f, .ftol = 1e-4f, .wolfe = 0.9f, .min_step = 1e-20f, .max_step = 1e+20f, .linesearch = GGML_LINESEARCH_DEFAULT, }, }; } break; } return result; } enum ggml_opt_result ggml_opt( struct ggml_context * ctx, struct ggml_opt_params params, struct ggml_tensor * f) { bool free_ctx = false; if (ctx == NULL) { struct ggml_init_params params_ctx = { .mem_size = 16*1024*1024, .mem_buffer = NULL, }; ctx = ggml_init(params_ctx); if (ctx == NULL) { return GGML_OPT_NO_CONTEXT; } free_ctx = true; } enum ggml_opt_result result = GGML_OPT_OK; // build forward + backward compute graphs struct ggml_cgraph gf = ggml_build_forward (f); struct ggml_cgraph gb = ggml_build_backward(ctx, &gf, false); switch (params.type) { case GGML_OPT_ADAM: { result = ggml_opt_adam(ctx, params, f, &gf, &gb); } break; case GGML_OPT_LBFGS: { result = ggml_opt_lbfgs(ctx, params, f, &gf, &gb); } break; } if (params.print_forward_graph) { ggml_graph_print (&gf); ggml_graph_dump_dot(&gf, NULL, "opt-forward.dot"); } if (params.print_backward_graph) { ggml_graph_print (&gb); ggml_graph_dump_dot(&gb, &gf, "opt-backward.dot"); } if (free_ctx) { ggml_free(ctx); } return result; } //////////////////////////////////////////////////////////////////////////////// int ggml_cpu_has_avx(void) { #if defined(__AVX__) return 1; #else return 0; #endif } int ggml_cpu_has_avx2(void) { #if defined(__AVX2__) return 1; #else return 0; #endif } int ggml_cpu_has_avx512(void) { #if defined(__AVX512F__) return 1; #else return 0; #endif } int ggml_cpu_has_fma(void) { #if defined(__FMA__) return 1; #else return 0; #endif } int ggml_cpu_has_neon(void) { #if defined(__ARM_NEON) return 1; #else return 0; #endif } int ggml_cpu_has_arm_fma(void) { #if defined(__ARM_FEATURE_FMA) return 1; #else return 0; #endif } int ggml_cpu_has_f16c(void) { #if defined(__F16C__) return 1; #else return 0; #endif } int ggml_cpu_has_fp16_va(void) { #if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) return 1; #else return 0; #endif } int ggml_cpu_has_wasm_simd(void) { #if defined(__wasm_simd128__) return 1; #else return 0; #endif } int ggml_cpu_has_blas(void) { #if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) return 1; #else return 0; #endif } int ggml_cpu_has_sse3(void) { #if defined(__SSE3__) return 1; #else return 0; #endif } int ggml_cpu_has_vsx(void) { #if defined(__POWER9_VECTOR__) return 1; #else return 0; #endif } ////////////////////////////////////////////////////////////////////////////////