#define _CRT_SECURE_NO_DEPRECATE // Disables "unsafe" warnings on Windows #define _USE_MATH_DEFINES // For M_PI on MSVC #include "ggml-aarch64.h" #include "ggml-backend-impl.h" #include "ggml-backend.h" #include "ggml-cpu-impl.h" #include "ggml-cpu.h" #include "ggml-impl.h" #include "ggml-quants.h" #include "ggml.h" #if defined(_MSC_VER) || defined(__MINGW32__) #include // using malloc.h with MSC/MINGW #elif !defined(__FreeBSD__) && !defined(__NetBSD__) && !defined(__OpenBSD__) #include #endif #include #include #include #include #include #include #include #include #include #include #include #include #include #if defined(__gnu_linux__) #include #endif #ifdef GGML_USE_OPENMP #include #endif #if defined(__ARM_FEATURE_SVE) || defined(__ARM_FEATURE_MATMUL_INT8) #undef GGML_USE_LLAMAFILE #endif #ifdef GGML_USE_LLAMAFILE #include #endif #if defined(_MSC_VER) // disable "possible loss of data" to avoid hundreds of casts // we should just be careful :) #pragma warning(disable: 4244 4267) // disable POSIX deprecation warnings // these functions are never going away, anyway #pragma warning(disable: 4996) // unreachable code because of multiple instances of code after GGML_ABORT #pragma warning(disable: 4702) #endif // Note: once we move threading into a separate C++ file // will use std::hardware_destructive_interference_size instead of hardcoding it here // and we'll use C++ attribute syntax. #define GGML_CACHE_LINE 64 #if defined(__clang__) || defined(__GNUC__) #define GGML_CACHE_ALIGN __attribute__((aligned(GGML_CACHE_LINE))) #endif #if defined(__has_feature) #if __has_feature(thread_sanitizer) #define GGML_TSAN_ENABLED 1 #endif #else // __has_feature #if defined(__SANITIZE_THREAD__) #define GGML_TSAN_ENABLED 1 #endif #endif // __has_feature #define UNUSED GGML_UNUSED #define SWAP(x, y, T) do { T SWAP = x; (x) = y; (y) = SWAP; } while (0) #if defined(GGML_USE_ACCELERATE) #include #endif // floating point type used to accumulate sums typedef double ggml_float; #define GGML_GELU_FP16 #define GGML_GELU_QUICK_FP16 #define GGML_SOFT_MAX_UNROLL 4 #define GGML_VEC_DOT_UNROLL 2 #define GGML_VEC_MAD_UNROLL 32 // // global data // // precomputed gelu table for f16 (128 KB) static ggml_fp16_t ggml_table_gelu_f16[1 << 16]; // precomputed quick gelu table for f16 (128 KB) static ggml_fp16_t ggml_table_gelu_quick_f16[1 << 16]; // precomputed f32 table for f16 (256 KB) (ggml-impl.h) float ggml_table_f32_f16[1 << 16]; #if defined(__ARM_ARCH) struct ggml_arm_arch_features_type { int has_neon; int has_i8mm; int has_sve; int sve_cnt; } ggml_arm_arch_features = {-1, -1, -1, 0}; #endif #if defined(_WIN32) #define WIN32_LEAN_AND_MEAN #ifndef NOMINMAX #define NOMINMAX #endif #include #if !defined(__clang__) #define GGML_CACHE_ALIGN __declspec(align(GGML_CACHE_LINE)) typedef volatile LONG atomic_int; typedef atomic_int atomic_bool; typedef atomic_int atomic_flag; #define ATOMIC_FLAG_INIT 0 typedef enum { memory_order_relaxed, memory_order_consume, memory_order_acquire, memory_order_release, memory_order_acq_rel, memory_order_seq_cst } memory_order; static void atomic_store(atomic_int * ptr, LONG val) { InterlockedExchange(ptr, val); } static void atomic_store_explicit(atomic_int * ptr, LONG val, memory_order mo) { // TODO: add support for explicit memory order InterlockedExchange(ptr, val); } static LONG atomic_load(atomic_int * ptr) { return InterlockedCompareExchange(ptr, 0, 0); } static LONG atomic_load_explicit(atomic_int * ptr, memory_order mo) { // TODO: add support for explicit memory order return InterlockedCompareExchange(ptr, 0, 0); } static LONG atomic_fetch_add(atomic_int * ptr, LONG inc) { return InterlockedExchangeAdd(ptr, inc); } static LONG atomic_fetch_add_explicit(atomic_int * ptr, LONG inc, memory_order mo) { // TODO: add support for explicit memory order return InterlockedExchangeAdd(ptr, inc); } static atomic_bool atomic_flag_test_and_set(atomic_flag * ptr) { return InterlockedExchange(ptr, 1); } static void atomic_flag_clear(atomic_flag * ptr) { InterlockedExchange(ptr, 0); } static void atomic_thread_fence(memory_order mo) { MemoryBarrier(); } #else // clang #include #endif typedef HANDLE pthread_t; typedef DWORD thread_ret_t; static int pthread_create(pthread_t * out, void * unused, thread_ret_t(*func)(void *), void * arg) { (void) unused; HANDLE handle = CreateThread(NULL, 0, (LPTHREAD_START_ROUTINE) func, arg, 0, NULL); if (handle == NULL) { return EAGAIN; } *out = handle; return 0; } static int pthread_join(pthread_t thread, void * unused) { (void) unused; int ret = (int) WaitForSingleObject(thread, INFINITE); CloseHandle(thread); return ret; } static int sched_yield (void) { Sleep (0); return 0; } #else #include #include #include #if defined(__FreeBSD__) #include #endif typedef void * thread_ret_t; #include #include #include #endif typedef pthread_t ggml_thread_t; #ifdef GGML_USE_CPU_HBM #include #endif #if defined(__APPLE__) #include #include #include #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); static void ggml_vec_dot_f32(int n, float * restrict s, size_t bs, const float * restrict x, size_t bx, const float * restrict y, size_t by, int nrc); static void ggml_vec_dot_f16(int n, float * restrict s, size_t bs, ggml_fp16_t * restrict x, size_t bx, ggml_fp16_t * restrict y, size_t by, int nrc); static void ggml_vec_dot_bf16(int n, float * restrict s, size_t bs, ggml_bf16_t * restrict x, size_t bx, ggml_bf16_t * restrict y, size_t by, int nrc); static const struct ggml_type_traits_cpu type_traits_cpu[GGML_TYPE_COUNT] = { [GGML_TYPE_F32] = { .vec_dot = (ggml_vec_dot_t) ggml_vec_dot_f32, .vec_dot_type = GGML_TYPE_F32, .nrows = 1, }, [GGML_TYPE_F16] = { .vec_dot = (ggml_vec_dot_t) ggml_vec_dot_f16, .vec_dot_type = GGML_TYPE_F16, .nrows = 1, }, [GGML_TYPE_Q4_0] = { .vec_dot = ggml_vec_dot_q4_0_q8_0, .vec_dot_type = GGML_TYPE_Q8_0, #if defined (__ARM_FEATURE_MATMUL_INT8) .nrows = 2, #else .nrows = 1, #endif }, [GGML_TYPE_Q4_1] = { .vec_dot = ggml_vec_dot_q4_1_q8_1, .vec_dot_type = GGML_TYPE_Q8_1, #if defined (__ARM_FEATURE_MATMUL_INT8) .nrows = 2, #else .nrows = 1, #endif }, [4] = { // GGML_TYPE_Q4_2 .vec_dot = NULL, .vec_dot_type = GGML_TYPE_COUNT, .nrows = 1, }, [5] = { // GGML_TYPE_Q4_3 .vec_dot = NULL, .vec_dot_type = GGML_TYPE_COUNT, .nrows = 1, }, [GGML_TYPE_Q5_0] = { .vec_dot = ggml_vec_dot_q5_0_q8_0, .vec_dot_type = GGML_TYPE_Q8_0, .nrows = 1, }, [GGML_TYPE_Q5_1] = { .vec_dot = ggml_vec_dot_q5_1_q8_1, .vec_dot_type = GGML_TYPE_Q8_1, .nrows = 1, }, [GGML_TYPE_Q8_0] = { .from_float_to_mat = quantize_mat_q8_0, .vec_dot = ggml_vec_dot_q8_0_q8_0, .vec_dot_type = GGML_TYPE_Q8_0, #if defined (__ARM_FEATURE_MATMUL_INT8) .nrows = 2, #else .nrows = 1, #endif }, [GGML_TYPE_Q8_1] = { .vec_dot_type = GGML_TYPE_Q8_1, .nrows = 1, }, [GGML_TYPE_Q2_K] = { .vec_dot = ggml_vec_dot_q2_K_q8_K, .vec_dot_type = GGML_TYPE_Q8_K, .nrows = 1, }, [GGML_TYPE_Q3_K] = { .vec_dot = ggml_vec_dot_q3_K_q8_K, .vec_dot_type = GGML_TYPE_Q8_K, .nrows = 1, }, [GGML_TYPE_Q4_K] = { .vec_dot = ggml_vec_dot_q4_K_q8_K, .vec_dot_type = GGML_TYPE_Q8_K, .nrows = 1, }, [GGML_TYPE_Q5_K] = { .vec_dot = ggml_vec_dot_q5_K_q8_K, .vec_dot_type = GGML_TYPE_Q8_K, .nrows = 1, }, [GGML_TYPE_Q6_K] = { .vec_dot = ggml_vec_dot_q6_K_q8_K, .vec_dot_type = GGML_TYPE_Q8_K, .nrows = 1, }, [GGML_TYPE_IQ2_XXS] = { .vec_dot = ggml_vec_dot_iq2_xxs_q8_K, .vec_dot_type = GGML_TYPE_Q8_K, .nrows = 1, }, [GGML_TYPE_IQ2_XS] = { .vec_dot = ggml_vec_dot_iq2_xs_q8_K, .vec_dot_type = GGML_TYPE_Q8_K, .nrows = 1, }, [GGML_TYPE_IQ3_XXS] = { .vec_dot = ggml_vec_dot_iq3_xxs_q8_K, .vec_dot_type = GGML_TYPE_Q8_K, .nrows = 1, }, [GGML_TYPE_IQ3_S] = { .vec_dot = ggml_vec_dot_iq3_s_q8_K, .vec_dot_type = GGML_TYPE_Q8_K, .nrows = 1, }, [GGML_TYPE_IQ2_S] = { .vec_dot = ggml_vec_dot_iq2_s_q8_K, .vec_dot_type = GGML_TYPE_Q8_K, .nrows = 1, }, [GGML_TYPE_IQ1_S] = { .vec_dot = ggml_vec_dot_iq1_s_q8_K, .vec_dot_type = GGML_TYPE_Q8_K, .nrows = 1, }, [GGML_TYPE_IQ1_M] = { .vec_dot = ggml_vec_dot_iq1_m_q8_K, .vec_dot_type = GGML_TYPE_Q8_K, .nrows = 1, }, [GGML_TYPE_IQ4_NL] = { .vec_dot = ggml_vec_dot_iq4_nl_q8_0, .vec_dot_type = GGML_TYPE_Q8_0, .nrows = 1, }, [GGML_TYPE_IQ4_XS] = { .vec_dot = ggml_vec_dot_iq4_xs_q8_K, .vec_dot_type = GGML_TYPE_Q8_K, .nrows = 1, }, [GGML_TYPE_BF16] = { .vec_dot = (ggml_vec_dot_t) ggml_vec_dot_bf16, .vec_dot_type = GGML_TYPE_BF16, .nrows = 1, }, [GGML_TYPE_Q4_0_4_4] = { .vec_dot = NULL, .vec_dot_type = GGML_TYPE_Q8_0, .nrows = 1, .ncols = 4, .gemv = ggml_gemv_q4_0_4x4_q8_0, .gemm = ggml_gemm_q4_0_4x4_q8_0, }, [GGML_TYPE_Q4_0_4_8] = { .vec_dot = NULL, .vec_dot_type = GGML_TYPE_Q8_0, .nrows = 1, .ncols = 4, .gemv = ggml_gemv_q4_0_4x8_q8_0, .gemm = ggml_gemm_q4_0_4x8_q8_0, }, [GGML_TYPE_Q4_0_8_8] = { .vec_dot = NULL, .vec_dot_type = GGML_TYPE_Q8_0, .nrows = 1, .ncols = 8, .gemv = ggml_gemv_q4_0_8x8_q8_0, .gemm = ggml_gemm_q4_0_8x8_q8_0, }, [GGML_TYPE_TQ1_0] = { .vec_dot = ggml_vec_dot_tq1_0_q8_K, .vec_dot_type = GGML_TYPE_Q8_K, .nrows = 1, }, [GGML_TYPE_TQ2_0] = { .vec_dot = ggml_vec_dot_tq2_0_q8_K, .vec_dot_type = GGML_TYPE_Q8_K, .nrows = 1, }, }; const struct ggml_type_traits_cpu * ggml_get_type_traits_cpu(enum ggml_type type) { return &type_traits_cpu[type]; } // // simd mappings // // we define a common set of C macros which map to specific intrinsics based on the current architecture // we then implement the fundamental computation operations below using only these macros // adding support for new architectures requires to define the corresponding SIMD macros // // GGML_F32_STEP / GGML_F16_STEP // number of elements to process in a single step // // GGML_F32_EPR / GGML_F16_EPR // number of elements to fit in a single register // #if defined(__ARM_NEON) && defined(__ARM_FEATURE_FMA) #define GGML_SIMD // F32 NEON #define GGML_F32_STEP 16 #define GGML_F32_EPR 4 #define GGML_F32x4 float32x4_t #define GGML_F32x4_ZERO vdupq_n_f32(0.0f) #define GGML_F32x4_SET1(x) vdupq_n_f32(x) #define GGML_F32x4_LOAD vld1q_f32 #define GGML_F32x4_STORE vst1q_f32 #define GGML_F32x4_FMA(a, b, c) vfmaq_f32(a, b, c) #define GGML_F32x4_ADD vaddq_f32 #define GGML_F32x4_MUL vmulq_f32 #define GGML_F32x4_REDUCE_ONE(x) vaddvq_f32(x) #define GGML_F32x4_REDUCE(res, x) \ { \ int offset = GGML_F32_ARR >> 1; \ for (int i = 0; i < offset; ++i) { \ (x)[i] = vaddq_f32((x)[i], (x)[offset+i]); \ } \ offset >>= 1; \ for (int i = 0; i < offset; ++i) { \ (x)[i] = vaddq_f32((x)[i], (x)[offset+i]); \ } \ offset >>= 1; \ for (int i = 0; i < offset; ++i) { \ (x)[i] = vaddq_f32((x)[i], (x)[offset+i]); \ } \ (res) = GGML_F32x4_REDUCE_ONE((x)[0]); \ } #define GGML_F32_VEC GGML_F32x4 #define GGML_F32_VEC_ZERO GGML_F32x4_ZERO #define GGML_F32_VEC_SET1 GGML_F32x4_SET1 #define GGML_F32_VEC_LOAD GGML_F32x4_LOAD #define GGML_F32_VEC_STORE GGML_F32x4_STORE #define GGML_F32_VEC_FMA GGML_F32x4_FMA #define GGML_F32_VEC_ADD GGML_F32x4_ADD #define GGML_F32_VEC_MUL GGML_F32x4_MUL #define GGML_F32_VEC_REDUCE GGML_F32x4_REDUCE // F16 NEON #if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) #define GGML_F16_STEP 32 #define GGML_F16_EPR 8 #define GGML_F16x8 float16x8_t #define GGML_F16x8_ZERO vdupq_n_f16(0.0f) #define GGML_F16x8_SET1(x) vdupq_n_f16(x) #define GGML_F16x8_LOAD(x) vld1q_f16((const ggml_fp16_internal_t *)(x)) #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) \ do { \ int offset = GGML_F16_ARR >> 1; \ for (int i = 0; i < offset; ++i) { \ (x)[i] = vaddq_f16((x)[i], (x)[offset+i]); \ } \ offset >>= 1; \ for (int i = 0; i < offset; ++i) { \ (x)[i] = vaddq_f16((x)[i], (x)[offset+i]); \ } \ offset >>= 1; \ for (int i = 0; i < offset; ++i) { \ (x)[i] = vaddq_f16((x)[i], (x)[offset+i]); \ } \ const float32x4_t t0 = vcvt_f32_f16(vget_low_f16 ((x)[0])); \ const float32x4_t t1 = vcvt_f32_f16(vget_high_f16((x)[0])); \ (res) = (ggml_float) vaddvq_f32(vaddq_f32(t0, t1)); \ } while (0) #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((ggml_fp16_internal_t *)(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((const ggml_fp16_internal_t *)(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((ggml_fp16_internal_t *)(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(__AVX512F__) #define GGML_SIMD // F32 AVX512 #define GGML_F32_STEP 64 #define GGML_F32_EPR 16 #define GGML_F32x16 __m512 #define GGML_F32x16_ZERO _mm512_setzero_ps() #define GGML_F32x16_SET1(x) _mm512_set1_ps(x) #define GGML_F32x16_LOAD _mm512_loadu_ps #define GGML_F32x16_STORE _mm512_storeu_ps // _mm512_fmadd_ps is defined in AVX512F so no guard is required #define GGML_F32x16_FMA(a, b, c) _mm512_fmadd_ps(b, c, a) #define GGML_F32x16_ADD _mm512_add_ps #define GGML_F32x16_MUL _mm512_mul_ps #define GGML_F32x16_REDUCE(res, x) \ do { \ int offset = GGML_F32_ARR >> 1; \ for (int i = 0; i < offset; ++i) { \ x[i] = _mm512_add_ps(x[i], x[offset+i]); \ } \ offset >>= 1; \ for (int i = 0; i < offset; ++i) { \ x[i] = _mm512_add_ps(x[i], x[offset+i]); \ } \ offset >>= 1; \ for (int i = 0; i < offset; ++i) { \ x[i] = _mm512_add_ps(x[i], x[offset+i]); \ } \ res = _mm512_reduce_add_ps(x[0]); \ } while (0) // TODO: is this optimal ? #define GGML_F32_VEC GGML_F32x16 #define GGML_F32_VEC_ZERO GGML_F32x16_ZERO #define GGML_F32_VEC_SET1 GGML_F32x16_SET1 #define GGML_F32_VEC_LOAD GGML_F32x16_LOAD #define GGML_F32_VEC_STORE GGML_F32x16_STORE #define GGML_F32_VEC_FMA GGML_F32x16_FMA #define GGML_F32_VEC_ADD GGML_F32x16_ADD #define GGML_F32_VEC_MUL GGML_F32x16_MUL #define GGML_F32_VEC_REDUCE GGML_F32x16_REDUCE // F16 AVX512 // F16 AVX #define GGML_F16_STEP 64 #define GGML_F16_EPR 16 // AVX512 has FP16 extension (AVX512_FP16) but I don't have it on my machine so I use FP32 instead #define GGML_F32Cx16 __m512 #define GGML_F32Cx16_ZERO _mm512_setzero_ps() #define GGML_F32Cx16_SET1(x) _mm512_set1_ps(x) // unlike _mm256_cvt intrinsics that require F16C, _mm512_cvt is defined in AVX512F // so F16C guard isn't required #define GGML_F32Cx16_LOAD(x) _mm512_cvtph_ps(_mm256_loadu_si256((const __m256i *)(x))) #define GGML_F32Cx16_STORE(x, y) _mm256_storeu_si256((__m256i *)(x), _mm512_cvtps_ph(y, 0)) #define GGML_F32Cx16_FMA(a, b, c) _mm512_fmadd_ps(b, c, a) #define GGML_F32Cx16_ADD _mm512_add_ps #define GGML_F32Cx16_MUL _mm512_mul_ps #define GGML_F32Cx16_REDUCE(res, x) \ do { \ int offset = GGML_F32_ARR >> 1; \ for (int i = 0; i < offset; ++i) { \ x[i] = _mm512_add_ps(x[i], x[offset+i]); \ } \ offset >>= 1; \ for (int i = 0; i < offset; ++i) { \ x[i] = _mm512_add_ps(x[i], x[offset+i]); \ } \ offset >>= 1; \ for (int i = 0; i < offset; ++i) { \ x[i] = _mm512_add_ps(x[i], x[offset+i]); \ } \ res = _mm512_reduce_add_ps(x[0]); \ } while (0) #define GGML_F16_VEC GGML_F32Cx16 #define GGML_F16_VEC_ZERO GGML_F32Cx16_ZERO #define GGML_F16_VEC_SET1 GGML_F32Cx16_SET1 #define GGML_F16_VEC_LOAD(p, i) GGML_F32Cx16_LOAD(p) #define GGML_F16_VEC_STORE(p, r, i) GGML_F32Cx16_STORE(p, r[i]) #define GGML_F16_VEC_FMA GGML_F32Cx16_FMA #define GGML_F16_VEC_ADD GGML_F32Cx16_ADD #define GGML_F16_VEC_MUL GGML_F32Cx16_MUL #define GGML_F16_VEC_REDUCE GGML_F32Cx16_REDUCE #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) \ do { \ int offset = GGML_F32_ARR >> 1; \ for (int i = 0; i < offset; ++i) { \ x[i] = _mm256_add_ps(x[i], x[offset+i]); \ } \ offset >>= 1; \ for (int i = 0; i < offset; ++i) { \ x[i] = _mm256_add_ps(x[i], x[offset+i]); \ } \ offset >>= 1; \ for (int i = 0; i < offset; ++i) { \ x[i] = _mm256_add_ps(x[i], x[offset+i]); \ } \ const __m128 t0 = _mm_add_ps(_mm256_castps256_ps128(x[0]), \ _mm256_extractf128_ps(x[0], 1)); \ const __m128 t1 = _mm_hadd_ps(t0, t0); \ res = (ggml_float) _mm_cvtss_f32(_mm_hadd_ps(t1, t1)); \ } while (0) // TODO: is this optimal ? #define GGML_F32_VEC GGML_F32x8 #define GGML_F32_VEC_ZERO GGML_F32x8_ZERO #define GGML_F32_VEC_SET1 GGML_F32x8_SET1 #define GGML_F32_VEC_LOAD GGML_F32x8_LOAD #define GGML_F32_VEC_STORE GGML_F32x8_STORE #define GGML_F32_VEC_FMA GGML_F32x8_FMA #define GGML_F32_VEC_ADD GGML_F32x8_ADD #define GGML_F32_VEC_MUL GGML_F32x8_MUL #define GGML_F32_VEC_REDUCE GGML_F32x8_REDUCE // F16 AVX #define GGML_F16_STEP 32 #define GGML_F16_EPR 8 // F16 arithmetic is not supported by AVX, so we use F32 instead #define GGML_F32Cx8 __m256 #define GGML_F32Cx8_ZERO _mm256_setzero_ps() #define GGML_F32Cx8_SET1(x) _mm256_set1_ps(x) #if defined(__F16C__) // the _mm256_cvt intrinsics require F16C #define GGML_F32Cx8_LOAD(x) _mm256_cvtph_ps(_mm_loadu_si128((const __m128i *)(x))) #define GGML_F32Cx8_STORE(x, y) _mm_storeu_si128((__m128i *)(x), _mm256_cvtps_ph(y, 0)) #else static inline __m256 __avx_f32cx8_load(ggml_fp16_t *x) { float tmp[8]; for (int i = 0; i < 8; i++) { tmp[i] = GGML_FP16_TO_FP32(x[i]); } return _mm256_loadu_ps(tmp); } static inline void __avx_f32cx8_store(ggml_fp16_t *x, __m256 y) { float arr[8]; _mm256_storeu_ps(arr, y); for (int i = 0; i < 8; i++) x[i] = GGML_FP32_TO_FP16(arr[i]); } #define GGML_F32Cx8_LOAD(x) __avx_f32cx8_load(x) #define GGML_F32Cx8_STORE(x, y) __avx_f32cx8_store(x, y) #endif #define GGML_F32Cx8_FMA GGML_F32x8_FMA #define GGML_F32Cx8_ADD _mm256_add_ps #define GGML_F32Cx8_MUL _mm256_mul_ps #define GGML_F32Cx8_REDUCE GGML_F32x8_REDUCE #define GGML_F16_VEC GGML_F32Cx8 #define GGML_F16_VEC_ZERO GGML_F32Cx8_ZERO #define GGML_F16_VEC_SET1 GGML_F32Cx8_SET1 #define GGML_F16_VEC_LOAD(p, i) GGML_F32Cx8_LOAD(p) #define GGML_F16_VEC_STORE(p, r, i) GGML_F32Cx8_STORE(p, r[i]) #define GGML_F16_VEC_FMA GGML_F32Cx8_FMA #define GGML_F16_VEC_ADD GGML_F32Cx8_ADD #define GGML_F16_VEC_MUL GGML_F32Cx8_MUL #define GGML_F16_VEC_REDUCE GGML_F32Cx8_REDUCE #elif defined(__POWER9_VECTOR__) #define GGML_SIMD // F32 POWER9 #define GGML_F32_STEP 32 #define GGML_F32_EPR 4 #define GGML_F32x4 vector float #define GGML_F32x4_ZERO 0.0f #define GGML_F32x4_SET1 vec_splats #define GGML_F32x4_LOAD(p) vec_xl(0, p) #define GGML_F32x4_STORE(p, r) vec_xst(r, 0, p) #define GGML_F32x4_FMA(a, b, c) vec_madd(b, c, a) #define GGML_F32x4_ADD vec_add #define GGML_F32x4_MUL vec_mul #define GGML_F32x4_REDUCE(res, x) \ { \ int offset = GGML_F32_ARR >> 1; \ for (int i = 0; i < offset; ++i) { \ x[i] = vec_add(x[i], x[offset+i]); \ } \ offset >>= 1; \ for (int i = 0; i < offset; ++i) { \ x[i] = vec_add(x[i], x[offset+i]); \ } \ offset >>= 1; \ for (int i = 0; i < offset; ++i) { \ x[i] = vec_add(x[i], x[offset+i]); \ } \ res = vec_extract(x[0], 0) + \ vec_extract(x[0], 1) + \ vec_extract(x[0], 2) + \ vec_extract(x[0], 3); \ } #define GGML_F32_VEC GGML_F32x4 #define GGML_F32_VEC_ZERO GGML_F32x4_ZERO #define GGML_F32_VEC_SET1 GGML_F32x4_SET1 #define GGML_F32_VEC_LOAD GGML_F32x4_LOAD #define GGML_F32_VEC_STORE GGML_F32x4_STORE #define GGML_F32_VEC_FMA GGML_F32x4_FMA #define GGML_F32_VEC_ADD GGML_F32x4_ADD #define GGML_F32_VEC_MUL GGML_F32x4_MUL #define GGML_F32_VEC_REDUCE GGML_F32x4_REDUCE // F16 POWER9 #define GGML_F16_STEP GGML_F32_STEP #define GGML_F16_EPR GGML_F32_EPR #define GGML_F16_VEC GGML_F32x4 #define GGML_F16_VEC_ZERO GGML_F32x4_ZERO #define GGML_F16_VEC_SET1 GGML_F32x4_SET1 #define GGML_F16_VEC_FMA GGML_F32x4_FMA #define GGML_F16_VEC_ADD GGML_F32x4_ADD #define GGML_F16_VEC_MUL GGML_F32x4_MUL #define GGML_F16_VEC_REDUCE GGML_F32x4_REDUCE // Use vec_xl, not vec_ld, in case the load address is not aligned. #define GGML_F16_VEC_LOAD(p, i) (i & 0x1) ? \ vec_extract_fp32_from_shorth(vec_xl(0, p - GGML_F16_EPR)) : \ vec_extract_fp32_from_shortl(vec_xl(0, p)) #define GGML_ENDIAN_BYTE(i) ((unsigned char *)&(uint16_t){1})[i] #define GGML_F16_VEC_STORE(p, r, i) \ if (i & 0x1) \ vec_xst(vec_pack_to_short_fp32(r[i - GGML_ENDIAN_BYTE(1)], \ r[i - GGML_ENDIAN_BYTE(0)]), \ 0, p - GGML_F16_EPR) #elif defined(__wasm_simd128__) #define GGML_SIMD // F32 WASM #define GGML_F32_STEP 16 #define GGML_F32_EPR 4 #define GGML_F32x4 v128_t #define GGML_F32x4_ZERO wasm_f32x4_splat(0.0f) #define GGML_F32x4_SET1(x) wasm_f32x4_splat(x) #define GGML_F32x4_LOAD wasm_v128_load #define GGML_F32x4_STORE wasm_v128_store #define GGML_F32x4_FMA(a, b, c) wasm_f32x4_add(wasm_f32x4_mul(b, c), a) #define GGML_F32x4_ADD wasm_f32x4_add #define GGML_F32x4_MUL wasm_f32x4_mul #define GGML_F32x4_REDUCE(res, x) \ { \ int offset = GGML_F32_ARR >> 1; \ for (int i = 0; i < offset; ++i) { \ x[i] = wasm_f32x4_add(x[i], x[offset+i]); \ } \ offset >>= 1; \ for (int i = 0; i < offset; ++i) { \ x[i] = wasm_f32x4_add(x[i], x[offset+i]); \ } \ offset >>= 1; \ for (int i = 0; i < offset; ++i) { \ x[i] = wasm_f32x4_add(x[i], x[offset+i]); \ } \ res = wasm_f32x4_extract_lane(x[0], 0) + \ wasm_f32x4_extract_lane(x[0], 1) + \ wasm_f32x4_extract_lane(x[0], 2) + \ wasm_f32x4_extract_lane(x[0], 3); \ } #define GGML_F32_VEC GGML_F32x4 #define GGML_F32_VEC_ZERO GGML_F32x4_ZERO #define GGML_F32_VEC_SET1 GGML_F32x4_SET1 #define GGML_F32_VEC_LOAD GGML_F32x4_LOAD #define GGML_F32_VEC_STORE GGML_F32x4_STORE #define GGML_F32_VEC_FMA GGML_F32x4_FMA #define GGML_F32_VEC_ADD GGML_F32x4_ADD #define GGML_F32_VEC_MUL GGML_F32x4_MUL #define GGML_F32_VEC_REDUCE GGML_F32x4_REDUCE // F16 WASM #define GGML_F16_STEP 16 #define GGML_F16_EPR 4 inline static v128_t __wasm_f16x4_load(const ggml_fp16_t * p) { float tmp[4]; tmp[0] = GGML_FP16_TO_FP32(p[0]); tmp[1] = GGML_FP16_TO_FP32(p[1]); tmp[2] = GGML_FP16_TO_FP32(p[2]); tmp[3] = GGML_FP16_TO_FP32(p[3]); return wasm_v128_load(tmp); } inline static void __wasm_f16x4_store(ggml_fp16_t * p, v128_t x) { float tmp[4]; wasm_v128_store(tmp, x); p[0] = GGML_FP32_TO_FP16(tmp[0]); p[1] = GGML_FP32_TO_FP16(tmp[1]); p[2] = GGML_FP32_TO_FP16(tmp[2]); p[3] = GGML_FP32_TO_FP16(tmp[3]); } #define GGML_F16x4 v128_t #define GGML_F16x4_ZERO wasm_f32x4_splat(0.0f) #define GGML_F16x4_SET1(x) wasm_f32x4_splat(x) #define GGML_F16x4_LOAD(x) __wasm_f16x4_load(x) #define GGML_F16x4_STORE(x, y) __wasm_f16x4_store(x, y) #define GGML_F16x4_FMA GGML_F32x4_FMA #define GGML_F16x4_ADD wasm_f32x4_add #define GGML_F16x4_MUL wasm_f32x4_mul #define GGML_F16x4_REDUCE(res, x) \ { \ int offset = GGML_F16_ARR >> 1; \ for (int i = 0; i < offset; ++i) { \ x[i] = wasm_f32x4_add(x[i], x[offset+i]); \ } \ offset >>= 1; \ for (int i = 0; i < offset; ++i) { \ x[i] = wasm_f32x4_add(x[i], x[offset+i]); \ } \ offset >>= 1; \ for (int i = 0; i < offset; ++i) { \ x[i] = wasm_f32x4_add(x[i], x[offset+i]); \ } \ res = wasm_f32x4_extract_lane(x[0], 0) + \ wasm_f32x4_extract_lane(x[0], 1) + \ wasm_f32x4_extract_lane(x[0], 2) + \ wasm_f32x4_extract_lane(x[0], 3); \ } #define GGML_F16_VEC GGML_F16x4 #define GGML_F16_VEC_ZERO GGML_F16x4_ZERO #define GGML_F16_VEC_SET1 GGML_F16x4_SET1 #define GGML_F16_VEC_LOAD(p, i) GGML_F16x4_LOAD(p) #define GGML_F16_VEC_STORE(p, r, i) GGML_F16x4_STORE(p, r[i]) #define GGML_F16_VEC_FMA GGML_F16x4_FMA #define GGML_F16_VEC_ADD GGML_F16x4_ADD #define GGML_F16_VEC_MUL GGML_F16x4_MUL #define GGML_F16_VEC_REDUCE GGML_F16x4_REDUCE #elif defined(__SSE3__) #define GGML_SIMD // F32 SSE #define GGML_F32_STEP 32 #define GGML_F32_EPR 4 #define GGML_F32x4 __m128 #define GGML_F32x4_ZERO _mm_setzero_ps() #define GGML_F32x4_SET1(x) _mm_set1_ps(x) #define GGML_F32x4_LOAD _mm_loadu_ps #define GGML_F32x4_STORE _mm_storeu_ps #if defined(__FMA__) // TODO: Does this work? #define GGML_F32x4_FMA(a, b, c) _mm_fmadd_ps(b, c, a) #else #define GGML_F32x4_FMA(a, b, c) _mm_add_ps(_mm_mul_ps(b, c), a) #endif #define GGML_F32x4_ADD _mm_add_ps #define GGML_F32x4_MUL _mm_mul_ps #define GGML_F32x4_REDUCE(res, x) \ { \ int offset = GGML_F32_ARR >> 1; \ for (int i = 0; i < offset; ++i) { \ x[i] = _mm_add_ps(x[i], x[offset+i]); \ } \ offset >>= 1; \ for (int i = 0; i < offset; ++i) { \ x[i] = _mm_add_ps(x[i], x[offset+i]); \ } \ offset >>= 1; \ for (int i = 0; i < offset; ++i) { \ x[i] = _mm_add_ps(x[i], x[offset+i]); \ } \ const __m128 t0 = _mm_hadd_ps(x[0], x[0]); \ res = (ggml_float) _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 #elif defined(__loongarch_asx) #define GGML_SIMD // F32 LASX #define GGML_F32_STEP 32 #define GGML_F32_EPR 8 #define GGML_F32x8 __m256 #define GGML_F32x8_ZERO (__m256)__lasx_xvldi(0) #define GGML_F32x8_SET1(x) (__m256)__lasx_xvreplfr2vr_s((x)) #define GGML_F32x8_LOAD(x) (__m256)__lasx_xvld((x), 0) #define GGML_F32x8_STORE(x,y) __lasx_xvst((y), (x), 0) #define GGML_F32x8_FMA(a, b, c) __lasx_xvfmadd_s(b, c, a) #define GGML_F32x8_ADD __lasx_xvfadd_s #define GGML_F32x8_MUL __lasx_xvfmul_s #define GGML_F32x8_REDUCE(res, x) \ do { \ int offset = GGML_F32_ARR >> 1; \ for (int i = 0; i < offset; ++i) { \ x[i] = __lasx_xvfadd_s(x[i], x[offset+i]); \ } \ offset >>= 1; \ for (int i = 0; i < offset; ++i) { \ x[i] = __lasx_xvfadd_s(x[i], x[offset+i]); \ } \ offset >>= 1; \ for (int i = 0; i < offset; ++i) { \ x[i] = __lasx_xvfadd_s(x[i], x[offset+i]); \ } \ float *tmp_p = (float *)&x[0]; \ res = tmp_p[0] + tmp_p[1] + tmp_p[2] + tmp_p[3] + tmp_p[4] + tmp_p[5] + tmp_p[6] + tmp_p[7]; \ } while (0) // 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 LASX #define GGML_F16_STEP 32 #define GGML_F16_EPR 8 // F16 arithmetic is not supported by AVX, so we use F32 instead #define GGML_F32Cx8 __m256 #define GGML_F32Cx8_ZERO (__m256)__lasx_xvldi(0) #define GGML_F32Cx8_SET1(x) (__m256)__lasx_xvreplgr2vr_w((x)) static inline __m256 __lasx_f32cx8_load(const ggml_fp16_t * x) { float tmp[8]; for (int i = 0; i < 8; i++) { tmp[i] = GGML_FP16_TO_FP32(x[i]); } return (__m256)__lasx_xvld(tmp, 0); } static inline void __lasx_f32cx8_store(ggml_fp16_t * x, __m256 y) { float arr[8]; __lasx_xvst(y, arr, 0); for (int i = 0; i < 8; i++) { x[i] = GGML_FP32_TO_FP16(arr[i]); } } #define GGML_F32Cx8_LOAD(x) __lasx_f32cx8_load(x) #define GGML_F32Cx8_STORE(x, y) __lasx_f32cx8_store(x, y) #define GGML_F32Cx8_FMA GGML_F32x8_FMA #define GGML_F32Cx8_ADD __lasx_xvfadd_s #define GGML_F32Cx8_MUL __lasx_xvfmul_s #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(__loongarch_sx) #define GGML_SIMD // F32 LSX #define GGML_F32_STEP 32 #define GGML_F32_EPR 4 #define GGML_F32x4 __m128 #define GGML_F32x4_ZERO __lsx_vldi(0) #define GGML_F32x4_SET1(x) __lsx_vinsgr2vr_w(__lsx_vldi(0),(x), 0) #define GGML_F32x4_LOAD(x) __lsx_vld((x), 0) #define GGML_F32x4_STORE((x),(y)) __lsx_vst((y), (x), 0) #define GGML_F32x4_FMA(a, b, c) __lsx_vfmadd_s(b, c, a) #define GGML_F32x4_ADD __lsx_vfadd_s #define GGML_F32x4_MUL __lsx_vfmul_s #define GGML_F32x4_REDUCE(res, x) \ { \ int offset = GGML_F32_ARR >> 1; \ for (int i = 0; i < offset; ++i) { \ x[i] = __lsx_vfadd_s(x[i], x[offset+i]); \ } \ offset >>= 1; \ for (int i = 0; i < offset; ++i) { \ x[i] = __lsx_vfadd_s(x[i], x[offset+i]); \ } \ offset >>= 1; \ for (int i = 0; i < offset; ++i) { \ x[i] = __lsx_vfadd_s(x[i], x[offset+i]); \ } \ __m128i tmp = __lsx_vsrli_d((__m128i)x[0], 32); \ tmp = (__m128i)__lsx_vfadd_s((__m128)tmp, x[0]); \ tmp = __lsx_vpickev_w(__lsx_vldi(0), tmp); \ const __m128 t0 = __lsx_vshuf4i_w(tmp, 0x88); \ tmp = __lsx_vsrli_d((__m128i)t0, 32); \ tmp = (__m128i)__lsx_vfadd_s((__m128)tmp, t0); \ tmp = __lsx_vpickev_w(__lsx_vldi(0), tmp); \ res = (ggml_float) __lsx_vpickve2gr_w(__lsx_vshuf4i_w(tmp, 0x88), 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 LSX #define GGML_F16_STEP 32 #define GGML_F16_EPR 4 static inline __m128 __lsx_f16x4_load(const 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 __lsx_vld(tmp, 0); } static inline void __lsx_f16x4_store(ggml_fp16_t * x, __m128 y) { float arr[4]; __lsx_vst(y, arr, 0); 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 __lsx_vldi(0) #define GGML_F32Cx4_SET1(x) __lsx_vinsgr2vr_w(__lsx_vldi(0),(x), 0) #define GGML_F32Cx4_LOAD(x) __lsx_f16x4_load(x) #define GGML_F32Cx4_STORE(x, y) __lsx_f16x4_store(x, y) #define GGML_F32Cx4_FMA GGML_F32x4_FMA #define GGML_F32Cx4_ADD __lsx_vfadd_s #define GGML_F32Cx4_MUL __lsx_vfmul_s #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 // // Threading defs // typedef pthread_t ggml_thread_t; #if defined(_WIN32) typedef CONDITION_VARIABLE ggml_cond_t; typedef SRWLOCK ggml_mutex_t; #define ggml_mutex_init(m) InitializeSRWLock(m) #define ggml_mutex_destroy(m) #define ggml_mutex_lock(m) AcquireSRWLockExclusive(m) #define ggml_mutex_unlock(m) ReleaseSRWLockExclusive(m) #define ggml_mutex_lock_shared(m) AcquireSRWLockShared(m) #define ggml_mutex_unlock_shared(m) ReleaseSRWLockShared(m) #define ggml_cond_init(c) InitializeConditionVariable(c) #define ggml_cond_destroy(c) #define ggml_cond_wait(c, m) SleepConditionVariableSRW(c, m, INFINITE, CONDITION_VARIABLE_LOCKMODE_SHARED) #define ggml_cond_broadcast(c) WakeAllConditionVariable(c) #define ggml_thread_create pthread_create #define ggml_thread_join pthread_join #else typedef pthread_cond_t ggml_cond_t; typedef pthread_mutex_t ggml_mutex_t; #define ggml_mutex_init(m) pthread_mutex_init(m, NULL) #define ggml_mutex_destroy(m) pthread_mutex_destroy(m) #define ggml_mutex_lock(m) pthread_mutex_lock(m) #define ggml_mutex_unlock(m) pthread_mutex_unlock(m) #define ggml_mutex_lock_shared(m) pthread_mutex_lock(m) #define ggml_mutex_unlock_shared(m) pthread_mutex_unlock(m) #define ggml_lock_init(x) UNUSED(x) #define ggml_lock_destroy(x) UNUSED(x) #if defined(__x86_64__) || (defined(_MSC_VER) && defined(_M_AMD64)) #define ggml_lock_lock(x) _mm_pause() #else #define ggml_lock_lock(x) UNUSED(x) #endif #define ggml_lock_unlock(x) UNUSED(x) #define GGML_LOCK_INITIALIZER 0 #define ggml_cond_init(c) pthread_cond_init(c, NULL) #define ggml_cond_destroy(c) pthread_cond_destroy(c) #define ggml_cond_wait(c, m) pthread_cond_wait(c, m) #define ggml_cond_broadcast(c) pthread_cond_broadcast(c) #define ggml_thread_create pthread_create #define ggml_thread_join pthread_join #endif // Threadpool def struct ggml_threadpool { ggml_mutex_t mutex; // mutex for cond.var ggml_cond_t cond; // cond.var for waiting for new work struct ggml_cgraph * cgraph; struct ggml_cplan * cplan; // synchronization primitives atomic_int n_graph; // incremented when there is work to be done (i.e each graph) atomic_int GGML_CACHE_ALIGN n_barrier; atomic_int GGML_CACHE_ALIGN n_barrier_passed; atomic_int current_chunk; // currently processing chunk during Mat_Mul, shared between all the threads. // these are atomic as an annotation for thread-sanitizer atomic_bool stop; // Used for stopping the threadpool altogether atomic_bool pause; // Used for pausing the threadpool or individual threads atomic_bool abort; // Used for aborting processing of a graph struct ggml_compute_state * workers; // per thread state int n_threads_max; // number of threads in the pool atomic_int n_threads_cur; // number of threads used in the current graph int32_t prio; // Scheduling priority uint32_t poll; // Polling level (0 - no polling) enum ggml_status ec; }; // Per-thread state struct ggml_compute_state { #ifndef GGML_USE_OPENMP ggml_thread_t thrd; bool cpumask[GGML_MAX_N_THREADS]; int last_graph; bool pending; #endif struct ggml_threadpool * threadpool; int ith; }; struct ggml_compute_params { // ith = thread index, nth = number of threads int ith, nth; // work buffer for all threads size_t wsize; void * wdata; struct ggml_threadpool * threadpool; }; // // 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_set_bf16(const int n, ggml_bf16_t * x, const ggml_bf16_t v) { for (int i = 0; i < n; ++i) x[i] = v; } inline static void ggml_vec_add_f32 (const int n, float * z, const float * x, const float * y) { for (int i = 0; i < n; ++i) z[i] = x[i] + y[i]; } inline static void ggml_vec_add1_f32(const int n, float * z, const float * x, const float v) { for (int i = 0; i < n; ++i) z[i] = x[i] + v; } inline static void ggml_vec_acc_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] += x[i]; } inline static void ggml_vec_acc1_f32(const int n, float * y, const float v) { for (int i = 0; i < n; ++i) y[i] += v; } inline static void ggml_vec_sub_f32 (const int n, float * z, const float * x, const float * y) { for (int i = 0; i < n; ++i) z[i] = x[i] - y[i]; } inline static void ggml_vec_set_f32 (const int n, float * x, const float v) { for (int i = 0; i < n; ++i) x[i] = v; } inline static void ggml_vec_cpy_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = x[i]; } inline static void ggml_vec_neg_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = -x[i]; } inline static void ggml_vec_mul_f32 (const int n, float * z, const float * x, const float * y) { for (int i = 0; i < n; ++i) z[i] = x[i]*y[i]; } inline static void ggml_vec_div_f32 (const int n, float * z, const float * x, const float * y) { for (int i = 0; i < n; ++i) z[i] = x[i]/y[i]; } static void ggml_vec_dot_f32(int n, float * restrict s, size_t bs, const float * restrict x, size_t bx, const float * restrict y, size_t by, int nrc) { assert(nrc == 1); UNUSED(nrc); UNUSED(bx); UNUSED(by); UNUSED(bs); #if defined(GGML_SIMD) float sumf = 0.0f; const int np = (n & ~(GGML_F32_STEP - 1)); GGML_F32_VEC sum[GGML_F32_ARR] = { GGML_F32_VEC_ZERO }; GGML_F32_VEC ax[GGML_F32_ARR]; GGML_F32_VEC ay[GGML_F32_ARR]; for (int i = 0; i < np; i += GGML_F32_STEP) { for (int j = 0; j < GGML_F32_ARR; j++) { ax[j] = GGML_F32_VEC_LOAD(x + i + j*GGML_F32_EPR); ay[j] = GGML_F32_VEC_LOAD(y + i + j*GGML_F32_EPR); sum[j] = GGML_F32_VEC_FMA(sum[j], ax[j], ay[j]); } } // reduce sum0..sum3 to sum0 GGML_F32_VEC_REDUCE(sumf, sum); // leftovers for (int i = np; i < n; ++i) { sumf += x[i]*y[i]; } #else // scalar ggml_float sumf = 0.0; for (int i = 0; i < n; ++i) { sumf += (ggml_float)(x[i]*y[i]); } #endif *s = sumf; } static void ggml_vec_dot_bf16(int n, float * restrict s, size_t bs, ggml_bf16_t * restrict x, size_t bx, ggml_bf16_t * restrict y, size_t by, int nrc) { assert(nrc == 1); UNUSED(nrc); UNUSED(bx); UNUSED(by); UNUSED(bs); int i = 0; ggml_float sumf = 0; #if defined(__AVX512BF16__) __m512 c1 = _mm512_setzero_ps(); __m512 c2 = _mm512_setzero_ps(); for (; i + 64 <= n; i += 64) { c1 = _mm512_dpbf16_ps(c1, m512bh(_mm512_loadu_si512((x + i))), m512bh(_mm512_loadu_si512((y + i)))); c2 = _mm512_dpbf16_ps(c2, m512bh(_mm512_loadu_si512((x + i + 32))), m512bh(_mm512_loadu_si512((y + i + 32)))); } sumf += (ggml_float)_mm512_reduce_add_ps(c1); sumf += (ggml_float)_mm512_reduce_add_ps(c2); #elif defined(__AVX512F__) #define LOAD(p) _mm512_castsi512_ps(_mm512_slli_epi32(_mm512_cvtepu16_epi32(_mm256_loadu_si256((const __m256i *)(p))), 16)) __m512 c1 = _mm512_setzero_ps(); __m512 c2 = _mm512_setzero_ps(); for (; i + 32 <= n; i += 32) { c1 = _mm512_add_ps(_mm512_mul_ps(LOAD(x + i), LOAD(y + i)), c1); c2 = _mm512_add_ps(_mm512_mul_ps(LOAD(x + i + 16), LOAD(y + i + 16)), c2); } sumf += (ggml_float)_mm512_reduce_add_ps(c1); sumf += (ggml_float)_mm512_reduce_add_ps(c2); #undef LOAD #elif defined(__AVX2__) #define LOAD(p) _mm256_castsi256_ps(_mm256_slli_epi32(_mm256_cvtepu16_epi32(_mm_loadu_si128((const __m128i *)(p))), 16)) __m256 c1 = _mm256_setzero_ps(); __m256 c2 = _mm256_setzero_ps(); __m256 c3 = _mm256_setzero_ps(); __m256 c4 = _mm256_setzero_ps(); for (; i + 32 <= n; i += 32) { c1 = _mm256_add_ps(_mm256_mul_ps(LOAD(x + i), LOAD(y + i)), c1); c2 = _mm256_add_ps(_mm256_mul_ps(LOAD(x + i + 8), LOAD(y + i + 8)), c2); c3 = _mm256_add_ps(_mm256_mul_ps(LOAD(x + i + 16), LOAD(y + i + 16)), c3); c4 = _mm256_add_ps(_mm256_mul_ps(LOAD(x + i + 24), LOAD(y + i + 24)), c4); } __m128 g; c1 = _mm256_add_ps(_mm256_add_ps(c1, c3), _mm256_add_ps(c2, c4)); g = _mm_add_ps(_mm256_extractf128_ps(c1, 1), _mm256_castps256_ps128(c1)); g = _mm_add_ps(g, _mm_movehl_ps(g, g)); g = _mm_add_ss(g, _mm_movehdup_ps(g)); sumf += (ggml_float)_mm_cvtss_f32(g); #undef LOAD #endif for (; i < n; ++i) { sumf += (ggml_float)(GGML_BF16_TO_FP32(x[i]) * GGML_BF16_TO_FP32(y[i])); } *s = sumf; } static void ggml_vec_dot_f16(int n, float * restrict s, size_t bs, ggml_fp16_t * restrict x, size_t bx, ggml_fp16_t * restrict y, size_t by, int nrc) { assert(nrc == 1); UNUSED(nrc); UNUSED(bx); UNUSED(by); UNUSED(bs); ggml_float sumf = 0.0; #if defined(GGML_SIMD) const int np = (n & ~(GGML_F16_STEP - 1)); GGML_F16_VEC sum[GGML_F16_ARR] = { GGML_F16_VEC_ZERO }; GGML_F16_VEC ax[GGML_F16_ARR]; GGML_F16_VEC ay[GGML_F16_ARR]; for (int i = 0; i < np; i += GGML_F16_STEP) { for (int j = 0; j < GGML_F16_ARR; j++) { ax[j] = GGML_F16_VEC_LOAD(x + i + j*GGML_F16_EPR, j); ay[j] = GGML_F16_VEC_LOAD(y + i + j*GGML_F16_EPR, j); sum[j] = GGML_F16_VEC_FMA(sum[j], ax[j], ay[j]); } } // reduce sum0..sum3 to sum0 GGML_F16_VEC_REDUCE(sumf, sum); // leftovers for (int i = np; i < n; ++i) { sumf += (ggml_float)(GGML_FP16_TO_FP32(x[i])*GGML_FP16_TO_FP32(y[i])); } #else for (int i = 0; i < n; ++i) { sumf += (ggml_float)(GGML_FP16_TO_FP32(x[i])*GGML_FP16_TO_FP32(y[i])); } #endif *s = sumf; } // compute GGML_VEC_DOT_UNROLL dot products at once // xs - x row stride in bytes inline static void ggml_vec_dot_f16_unroll(const int n, const int xs, float * restrict s, void * restrict xv, ggml_fp16_t * restrict y) { ggml_float sumf[GGML_VEC_DOT_UNROLL] = { 0.0 }; ggml_fp16_t * restrict x[GGML_VEC_DOT_UNROLL]; for (int i = 0; i < GGML_VEC_DOT_UNROLL; ++i) { x[i] = (ggml_fp16_t *) ((char *) xv + i*xs); } #if defined(GGML_SIMD) const int np = (n & ~(GGML_F16_STEP - 1)); GGML_F16_VEC sum[GGML_VEC_DOT_UNROLL][GGML_F16_ARR] = { { GGML_F16_VEC_ZERO } }; GGML_F16_VEC ax[GGML_F16_ARR]; GGML_F16_VEC ay[GGML_F16_ARR]; for (int i = 0; i < np; i += GGML_F16_STEP) { for (int j = 0; j < GGML_F16_ARR; j++) { ay[j] = GGML_F16_VEC_LOAD(y + i + j*GGML_F16_EPR, j); for (int k = 0; k < GGML_VEC_DOT_UNROLL; ++k) { ax[j] = GGML_F16_VEC_LOAD(x[k] + i + j*GGML_F16_EPR, j); sum[k][j] = GGML_F16_VEC_FMA(sum[k][j], ax[j], ay[j]); } } } // reduce sum0..sum3 to sum0 for (int k = 0; k < GGML_VEC_DOT_UNROLL; ++k) { GGML_F16_VEC_REDUCE(sumf[k], sum[k]); } // leftovers for (int i = np; i < n; ++i) { for (int j = 0; j < GGML_VEC_DOT_UNROLL; ++j) { sumf[j] += (ggml_float)(GGML_FP16_TO_FP32(x[j][i])*GGML_FP16_TO_FP32(y[i])); } } #else for (int i = 0; i < n; ++i) { for (int j = 0; j < GGML_VEC_DOT_UNROLL; ++j) { sumf[j] += (ggml_float)(GGML_FP16_TO_FP32(x[j][i])*GGML_FP16_TO_FP32(y[i])); } } #endif for (int i = 0; i < GGML_VEC_DOT_UNROLL; ++i) { s[i] = sumf[i]; } } inline static void ggml_vec_mad_f32(const int n, float * restrict y, const float * restrict x, const float v) { #if defined(GGML_SIMD) const int np = (n & ~(GGML_F32_STEP - 1)); GGML_F32_VEC vx = GGML_F32_VEC_SET1(v); GGML_F32_VEC ax[GGML_F32_ARR]; GGML_F32_VEC ay[GGML_F32_ARR]; for (int i = 0; i < np; i += GGML_F32_STEP) { for (int j = 0; j < GGML_F32_ARR; j++) { ax[j] = GGML_F32_VEC_LOAD(x + i + j*GGML_F32_EPR); ay[j] = GGML_F32_VEC_LOAD(y + i + j*GGML_F32_EPR); ay[j] = GGML_F32_VEC_FMA(ay[j], ax[j], vx); GGML_F32_VEC_STORE(y + i + j*GGML_F32_EPR, ay[j]); } } // leftovers for (int i = np; i < n; ++i) { y[i] += x[i]*v; } #else // scalar for (int i = 0; i < n; ++i) { y[i] += x[i]*v; } #endif } inline static void ggml_vec_mad_f16(const int n, ggml_fp16_t * restrict y, const 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) { y[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(y[i]) + GGML_FP16_TO_FP32(x[i])*v); } #else // scalar 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 } // xs and vs are byte strides of x and v inline static void ggml_vec_mad_f32_unroll(const int n, const int xs, const int vs, float * restrict y, const float * restrict xv, const float * restrict vv) { const float * restrict x[GGML_VEC_MAD_UNROLL]; const float * restrict v[GGML_VEC_MAD_UNROLL]; for (int i = 0; i < GGML_VEC_MAD_UNROLL; ++i) { x[i] = (const float *) ((const char *) xv + i*xs); v[i] = (const float *) ((const char *) vv + i*vs); } #if defined(GGML_SIMD) const int np = (n & ~(GGML_F32_STEP - 1)); GGML_F32_VEC vx[GGML_VEC_MAD_UNROLL]; for (int k = 0; k < GGML_VEC_MAD_UNROLL; ++k) { vx[k] = GGML_F32_VEC_SET1(v[k][0]); } GGML_F32_VEC ax[GGML_VEC_MAD_UNROLL][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++) { ay[j] = GGML_F32_VEC_LOAD(y + i + j*GGML_F32_EPR); for (int k = 0; k < GGML_VEC_MAD_UNROLL; ++k) { ax[k][j] = GGML_F32_VEC_LOAD(x[k] + i + j*GGML_F32_EPR); ay[j] = GGML_F32_VEC_FMA(ay[j], ax[k][j], vx[k]); } GGML_F32_VEC_STORE(y + i + j*GGML_F32_EPR, ay[j]); } } // leftovers for (int k = 0; k < GGML_VEC_MAD_UNROLL; ++k) { for (int i = np; i < n; ++i) { y[i] += x[k][i]*v[k][0]; } } #else // scalar for (int k = 0; k < GGML_VEC_MAD_UNROLL; ++k) { for (int i = 0; i < n; ++i) { y[i] += x[k][i]*v[k][0]; } } #endif } //inline static void ggml_vec_scale_f32(const int n, float * y, const float v) { for (int i = 0; i < n; ++i) y[i] *= v; } inline static void ggml_vec_scale_f32(const int n, float * y, const float v) { #if defined(GGML_USE_ACCELERATE) vDSP_vsmul(y, 1, &v, y, 1, n); #elif defined(GGML_SIMD) const int np = (n & ~(GGML_F32_STEP - 1)); GGML_F32_VEC vx = GGML_F32_VEC_SET1(v); GGML_F32_VEC ay[GGML_F32_ARR]; for (int i = 0; i < np; i += GGML_F32_STEP) { for (int j = 0; j < GGML_F32_ARR; j++) { ay[j] = GGML_F32_VEC_LOAD(y + i + j*GGML_F32_EPR); ay[j] = GGML_F32_VEC_MUL(ay[j], vx); GGML_F32_VEC_STORE(y + i + j*GGML_F32_EPR, ay[j]); } } // leftovers for (int i = np; i < n; ++i) { y[i] *= v; } #else // scalar for (int i = 0; i < n; ++i) { y[i] *= v; } #endif } inline static void ggml_vec_scale_f16(const int n, ggml_fp16_t * y, 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 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); ay[j] = GGML_F16_VEC_MUL(ay[j], vx); GGML_F16_VEC_STORE(y + i + j*GGML_F16_EPR, ay, j); } } // leftovers for (int i = np; i < n; ++i) { y[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(y[i])*v); } #else // scalar for (int i = 0; i < n; ++i) { y[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(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, 0, x, 0, x, 0, 1); *s = sqrtf(*s); } inline static void ggml_vec_sqr_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = x[i]*x[i]; } inline static void ggml_vec_sqrt_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = sqrtf(x[i]); } inline static void ggml_vec_log_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = logf(x[i]); } inline static void ggml_vec_sin_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = sinf(x[i]); } inline static void ggml_vec_cos_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = cosf(x[i]); } inline static void ggml_vec_abs_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = fabsf(x[i]); } inline static void ggml_vec_sgn_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? 1.f : ((x[i] < 0.f) ? -1.f : 0.f); } inline static void ggml_vec_step_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? 1.f : 0.f; } inline static void ggml_vec_tanh_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = tanhf(x[i]); } inline static void ggml_vec_elu_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? x[i] : expm1f(x[i]); } 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; } inline static void ggml_vec_leaky_relu_f32 (const int n, float * y, const float * x, const float ns) { for (int i = 0; i < n; ++i) y[i] = ((x[i] > 0.f) ? x[i] : 0.f) + ns * ((x[i] < 0.0f) ? x[i] : 0.f); } inline static void ggml_vec_sigmoid_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = 1.f / (1.f + expf(-x[i])); } // TODO: optimize performance inline static void ggml_vec_hardswish_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = x[i] * fminf(1.0f, fmaxf(0.0f, (x[i] + 3.0f) / 6.0f)); } inline static void ggml_vec_hardsigmoid_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = fminf(1.0f, fmaxf(0.0f, (x[i] + 3.0f) / 6.0f)); } inline static void ggml_vec_exp_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = expf(x[i]); } static const float GELU_COEF_A = 0.044715f; static const float GELU_QUICK_COEF = -1.702f; static const float SQRT_2_OVER_PI = 0.79788456080286535587989211986876f; inline static float ggml_gelu_f32(float x) { return 0.5f*x*(1.0f + tanhf(SQRT_2_OVER_PI*x*(1.0f + GELU_COEF_A*x*x))); } inline static void ggml_vec_gelu_f16(const int n, ggml_fp16_t * y, const ggml_fp16_t * x) { const uint16_t * i16 = (const uint16_t *) x; for (int i = 0; i < n; ++i) { y[i] = ggml_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) { if (x[i] <= -10.0f) { y[i] = 0.0f; } else if (x[i] >= 10.0f) { y[i] = x[i]; } else { ggml_fp16_t fp16 = GGML_FP32_TO_FP16(x[i]); memcpy(&t, &fp16, sizeof(uint16_t)); y[i] = GGML_FP16_TO_FP32(ggml_table_gelu_f16[t]); } } } #else inline static void ggml_vec_gelu_f32(const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) { y[i] = ggml_gelu_f32(x[i]); } } #endif inline static float ggml_gelu_quick_f32(float x) { return x*(1.0f/(1.0f+expf(GELU_QUICK_COEF*x))); } //inline static void ggml_vec_gelu_quick_f16(const int n, ggml_fp16_t * y, const ggml_fp16_t * x) { // const uint16_t * i16 = (const uint16_t *) x; // for (int i = 0; i < n; ++i) { // y[i] = ggml_table_gelu_quick_f16[i16[i]]; // } //} #ifdef GGML_GELU_QUICK_FP16 inline static void ggml_vec_gelu_quick_f32(const int n, float * y, const float * x) { uint16_t t; for (int i = 0; i < n; ++i) { ggml_fp16_t fp16 = GGML_FP32_TO_FP16(x[i]); memcpy(&t, &fp16, sizeof(uint16_t)); y[i] = GGML_FP16_TO_FP32(ggml_table_gelu_quick_f16[t]); } } #else inline static void ggml_vec_gelu_quick_f32(const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) { y[i] = ggml_gelu_quick_f32(x[i]); } } #endif // Sigmoid Linear Unit (SiLU) function inline static float ggml_silu_f32(float x) { return x/(1.0f + expf(-x)); } #if __FINITE_MATH_ONLY__ #error "some routines in ggml.c require non-finite math arithmetics -- pass -fno-finite-math-only to the compiler to fix" #error "ref: https://github.com/ggerganov/llama.cpp/pull/7154#issuecomment-2143844461" #endif #if defined(__ARM_NEON) && defined(__aarch64__) // adapted from arm limited optimized routine // the maximum error is 1.45358 plus 0.5 ulps // numbers above 88.38 will flush to infinity // numbers beneath -103.97 will flush to zero inline static float32x4_t ggml_v_expf(float32x4_t x) { const float32x4_t r = vdupq_n_f32(0x1.8p23f); const float32x4_t z = vfmaq_f32(r, x, vdupq_n_f32(0x1.715476p+0f)); const float32x4_t n = vsubq_f32(z, r); const float32x4_t b = vfmsq_f32(vfmsq_f32(x, n, vdupq_n_f32(0x1.62e4p-1f)), n, vdupq_n_f32(0x1.7f7d1cp-20f)); const uint32x4_t e = vshlq_n_u32(vreinterpretq_u32_f32(z), 23); const float32x4_t k = vreinterpretq_f32_u32(vaddq_u32(e, vreinterpretq_u32_f32(vdupq_n_f32(1)))); const uint32x4_t c = vcagtq_f32(n, vdupq_n_f32(126)); const float32x4_t u = vmulq_f32(b, b); const float32x4_t j = vfmaq_f32( vmulq_f32(vdupq_n_f32(0x1.ffffecp-1f), b), vfmaq_f32(vfmaq_f32(vdupq_n_f32(0x1.fffdb6p-2f), vdupq_n_f32(0x1.555e66p-3f), b), vfmaq_f32(vdupq_n_f32(0x1.573e2ep-5f), vdupq_n_f32(0x1.0e4020p-7f), b), u), u); if (!vpaddd_u64(vreinterpretq_u64_u32(c))) return vfmaq_f32(k, j, k); const uint32x4_t d = vandq_u32(vclezq_f32(n), vdupq_n_u32(0x82000000)); const float32x4_t s1 = vreinterpretq_f32_u32(vaddq_u32(d, vdupq_n_u32(0x7f000000))); const float32x4_t s2 = vreinterpretq_f32_u32(vsubq_u32(e, d)); return vbslq_f32(vcagtq_f32(n, vdupq_n_f32(192)), vmulq_f32(s1, s1), vbslq_f32(c, vmulq_f32(vfmaq_f32(s2, s2, j), s1), vfmaq_f32(k, k, j))); } // computes silu x/(1+exp(-x)) in single precision vector inline static float32x4_t ggml_v_silu(float32x4_t x) { const float32x4_t one = vdupq_n_f32(1.0f); const float32x4_t zero = vdupq_n_f32(0.0f); const float32x4_t neg_x = vsubq_f32(zero, x); const float32x4_t exp_neg_x = ggml_v_expf(neg_x); const float32x4_t one_plus_exp_neg_x = vaddq_f32(one, exp_neg_x); return vdivq_f32(x, one_plus_exp_neg_x); } #elif defined(__AVX512F__) && defined(__AVX512DQ__) // adapted from arm limited optimized routine // the maximum error is 1.45358 plus 0.5 ulps // numbers above 88.38 will flush to infinity // numbers beneath -103.97 will flush to zero inline static __m512 ggml_v_expf(__m512 x) { const __m512 r = _mm512_set1_ps(0x1.8p23f); const __m512 z = _mm512_fmadd_ps(x, _mm512_set1_ps(0x1.715476p+0f), r); const __m512 n = _mm512_sub_ps(z, r); const __m512 b = _mm512_fnmadd_ps(n, _mm512_set1_ps(0x1.7f7d1cp-20f), _mm512_fnmadd_ps(n, _mm512_set1_ps(0x1.62e4p-1f), x)); const __mmask16 d = _mm512_cmp_ps_mask(_mm512_abs_ps(n), _mm512_set1_ps(192), _CMP_GT_OQ); const __m512 u = _mm512_mul_ps(b, b); const __m512 j = _mm512_fmadd_ps( _mm512_fmadd_ps(_mm512_fmadd_ps(_mm512_set1_ps(0x1.0e4020p-7f), b, _mm512_set1_ps(0x1.573e2ep-5f)), u, _mm512_fmadd_ps(_mm512_set1_ps(0x1.555e66p-3f), b, _mm512_set1_ps(0x1.fffdb6p-2f))), u, _mm512_fmadd_ps(_mm512_set1_ps(0x1.ffffecp-1f), b, _mm512_set1_ps(1.0F))); const __m512 res = _mm512_scalef_ps(j, n); if (_mm512_kortestz(d, d)) return res; const __m512 zero = _mm512_setzero_ps(); const __m512 alt = _mm512_mask_blend_ps( _mm512_cmp_ps_mask(n, zero, _CMP_LE_OQ), _mm512_set1_ps(INFINITY), zero); return _mm512_mask_blend_ps(d, res, alt); } // computes silu x/(1+exp(-x)) in single precision vector inline static __m512 ggml_v_silu(__m512 x) { const __m512 one = _mm512_set1_ps(1); const __m512 zero = _mm512_setzero_ps(); const __m512 neg_x = _mm512_sub_ps(zero, x); const __m512 exp_neg_x = ggml_v_expf(neg_x); const __m512 one_plus_exp_neg_x = _mm512_add_ps(one, exp_neg_x); return _mm512_div_ps(x, one_plus_exp_neg_x); } #elif defined(__AVX2__) && defined(__FMA__) // adapted from arm limited optimized routine // the maximum error is 1.45358 plus 0.5 ulps // numbers above 88.38 will flush to infinity // numbers beneath -103.97 will flush to zero inline static __m256 ggml_v_expf(__m256 x) { const __m256 r = _mm256_set1_ps(0x1.8p23f); const __m256 z = _mm256_fmadd_ps(x, _mm256_set1_ps(0x1.715476p+0f), r); const __m256 n = _mm256_sub_ps(z, r); const __m256 b = _mm256_fnmadd_ps(n, _mm256_set1_ps(0x1.7f7d1cp-20f), _mm256_fnmadd_ps(n, _mm256_set1_ps(0x1.62e4p-1f), x)); const __m256i e = _mm256_slli_epi32(_mm256_castps_si256(z), 23); const __m256 k = _mm256_castsi256_ps( _mm256_add_epi32(e, _mm256_castps_si256(_mm256_set1_ps(1)))); const __m256i c = _mm256_castps_si256( _mm256_cmp_ps(_mm256_andnot_ps(_mm256_set1_ps(-0.f), n), _mm256_set1_ps(126), _CMP_GT_OQ)); const __m256 u = _mm256_mul_ps(b, b); const __m256 j = _mm256_fmadd_ps(_mm256_fmadd_ps(_mm256_fmadd_ps(_mm256_set1_ps(0x1.0e4020p-7f), b, _mm256_set1_ps(0x1.573e2ep-5f)), u, _mm256_fmadd_ps(_mm256_set1_ps(0x1.555e66p-3f), b, _mm256_set1_ps(0x1.fffdb6p-2f))), u, _mm256_mul_ps(_mm256_set1_ps(0x1.ffffecp-1f), b)); if (!_mm256_movemask_ps(_mm256_castsi256_ps(c))) return _mm256_fmadd_ps(j, k, k); const __m256i g = _mm256_and_si256( _mm256_castps_si256(_mm256_cmp_ps(n, _mm256_setzero_ps(), _CMP_LE_OQ)), _mm256_set1_epi32(0x82000000u)); const __m256 s1 = _mm256_castsi256_ps(_mm256_add_epi32(g, _mm256_set1_epi32(0x7f000000u))); const __m256 s2 = _mm256_castsi256_ps(_mm256_sub_epi32(e, g)); const __m256i d = _mm256_castps_si256( _mm256_cmp_ps(_mm256_andnot_ps(_mm256_set1_ps(-0.f), n), _mm256_set1_ps(192), _CMP_GT_OQ)); return _mm256_or_ps( _mm256_and_ps(_mm256_castsi256_ps(d), _mm256_mul_ps(s1, s1)), _mm256_andnot_ps( _mm256_castsi256_ps(d), _mm256_or_ps( _mm256_and_ps(_mm256_castsi256_ps(c), _mm256_mul_ps(_mm256_fmadd_ps(s2, j, s2), s1)), _mm256_andnot_ps(_mm256_castsi256_ps(c), _mm256_fmadd_ps(k, j, k))))); } // computes silu x/(1+exp(-x)) in single precision vector inline static __m256 ggml_v_silu(__m256 x) { const __m256 one = _mm256_set1_ps(1); const __m256 zero = _mm256_setzero_ps(); const __m256 neg_x = _mm256_sub_ps(zero, x); const __m256 exp_neg_x = ggml_v_expf(neg_x); const __m256 one_plus_exp_neg_x = _mm256_add_ps(one, exp_neg_x); return _mm256_div_ps(x, one_plus_exp_neg_x); } #elif defined(__SSE2__) // __AVX2__ / __ARM_NEON #if defined(__FMA__) #define MADD128(x, y, z) _mm_fmadd_ps(x, y, z) #define NMADD128(x, y, z) _mm_fnmadd_ps(x, y, z) #else #define MADD128(x, y, z) _mm_add_ps(_mm_mul_ps(x, y), z) #define NMADD128(x, y, z) _mm_sub_ps(z, _mm_mul_ps(x, y)) #endif // adapted from arm limited optimized routine // the maximum error is 1.45358 plus 0.5 ulps // numbers above 88.38 will flush to infinity // numbers beneath -103.97 will flush to zero inline static __m128 ggml_v_expf(__m128 x) { const __m128 r = _mm_set1_ps(0x1.8p23f); const __m128 z = MADD128(x, _mm_set1_ps(0x1.715476p+0f), r); const __m128 n = _mm_sub_ps(z, r); const __m128 b = NMADD128(n, _mm_set1_ps(0x1.7f7d1cp-20f), NMADD128(n, _mm_set1_ps(0x1.62e4p-1f), x)); const __m128i e = _mm_slli_epi32(_mm_castps_si128(z), 23); const __m128 k = _mm_castsi128_ps(_mm_add_epi32(e, _mm_castps_si128(_mm_set1_ps(1)))); const __m128i c = _mm_castps_si128(_mm_cmpgt_ps(_mm_andnot_ps(_mm_set1_ps(-0.f), n), _mm_set1_ps(126))); const __m128 u = _mm_mul_ps(b, b); const __m128 j = MADD128(MADD128(MADD128(_mm_set1_ps(0x1.0e4020p-7f), b, _mm_set1_ps(0x1.573e2ep-5f)), u, MADD128(_mm_set1_ps(0x1.555e66p-3f), b, _mm_set1_ps(0x1.fffdb6p-2f))), u, _mm_mul_ps(_mm_set1_ps(0x1.ffffecp-1f), b)); if (!_mm_movemask_epi8(c)) return MADD128(j, k, k); const __m128i g = _mm_and_si128(_mm_castps_si128(_mm_cmple_ps(n, _mm_setzero_ps())), _mm_set1_epi32(0x82000000u)); const __m128 s1 = _mm_castsi128_ps(_mm_add_epi32(g, _mm_set1_epi32(0x7f000000u))); const __m128 s2 = _mm_castsi128_ps(_mm_sub_epi32(e, g)); const __m128i d = _mm_castps_si128(_mm_cmpgt_ps(_mm_andnot_ps(_mm_set1_ps(-0.f), n), _mm_set1_ps(192))); return _mm_or_ps( _mm_and_ps(_mm_castsi128_ps(d), _mm_mul_ps(s1, s1)), _mm_andnot_ps(_mm_castsi128_ps(d), _mm_or_ps(_mm_and_ps(_mm_castsi128_ps(c), _mm_mul_ps(MADD128(s2, j, s2), s1)), _mm_andnot_ps(_mm_castsi128_ps(c), MADD128(k, j, k))))); } // computes silu x/(1+exp(-x)) in single precision vector inline static __m128 ggml_v_silu(__m128 x) { const __m128 one = _mm_set1_ps(1); const __m128 zero = _mm_setzero_ps(); const __m128 neg_x = _mm_sub_ps(zero, x); const __m128 exp_neg_x = ggml_v_expf(neg_x); const __m128 one_plus_exp_neg_x = _mm_add_ps(one, exp_neg_x); return _mm_div_ps(x, one_plus_exp_neg_x); } #endif // __ARM_NEON / __AVX2__ / __SSE2__ static void ggml_vec_silu_f32(const int n, float * y, const float * x) { int i = 0; #if defined(__AVX512F__) && defined(__AVX512DQ__) for (; i + 15 < n; i += 16) { _mm512_storeu_ps(y + i, ggml_v_silu(_mm512_loadu_ps(x + i))); } #elif defined(__AVX2__) && defined(__FMA__) for (; i + 7 < n; i += 8) { _mm256_storeu_ps(y + i, ggml_v_silu(_mm256_loadu_ps(x + i))); } #elif defined(__SSE2__) for (; i + 3 < n; i += 4) { _mm_storeu_ps(y + i, ggml_v_silu(_mm_loadu_ps(x + i))); } #elif defined(__ARM_NEON) && defined(__aarch64__) for (; i + 3 < n; i += 4) { vst1q_f32(y + i, ggml_v_silu(vld1q_f32(x + i))); } #endif for (; i < n; ++i) { y[i] = ggml_silu_f32(x[i]); } } static ggml_float ggml_vec_soft_max_f32(const int n, float * y, const float * x, float max) { int i = 0; ggml_float sum = 0; #if defined(__AVX512F__) && defined(__AVX512DQ__) for (; i + 15 < n; i += 16) { __m512 val = ggml_v_expf(_mm512_sub_ps(_mm512_loadu_ps(x + i), _mm512_set1_ps(max))); _mm512_storeu_ps(y + i, val); sum += (ggml_float)_mm512_reduce_add_ps(val); } #elif defined(__AVX2__) && defined(__FMA__) for (; i + 7 < n; i += 8) { __m256 val = ggml_v_expf(_mm256_sub_ps(_mm256_loadu_ps(x + i), _mm256_set1_ps(max))); _mm256_storeu_ps(y + i, val); __m128 val2 = _mm_add_ps(_mm256_extractf128_ps(val, 1), _mm256_castps256_ps128(val)); val2 = _mm_add_ps(val2, _mm_movehl_ps(val2, val2)); val2 = _mm_add_ss(val2, _mm_movehdup_ps(val2)); sum += (ggml_float)_mm_cvtss_f32(val2); } #elif defined(__SSE2__) for (; i + 3 < n; i += 4) { __m128 val = ggml_v_expf(_mm_sub_ps(_mm_loadu_ps(x + i), _mm_set1_ps(max))); _mm_storeu_ps(y + i, val); #if defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) val = _mm_add_ps(val, _mm_movehl_ps(val, val)); val = _mm_add_ss(val, _mm_movehdup_ps(val)); #else __m128 tmp = _mm_shuffle_ps(val, val, _MM_SHUFFLE(2, 3, 0, 1)); val = _mm_add_ps(val, tmp); tmp = _mm_movehl_ps(tmp, val); val = _mm_add_ss(val, tmp); #endif sum += (ggml_float)_mm_cvtss_f32(val); } #elif defined(__ARM_NEON) && defined(__aarch64__) for (; i + 3 < n; i += 4) { float32x4_t val = ggml_v_expf(vsubq_f32(vld1q_f32(x + i), vdupq_n_f32(max))); vst1q_f32(y + i, val); sum += (ggml_float)vaddvq_f32(val); } #endif for (; i < n; ++i) { float val = expf(x[i] - max); sum += (ggml_float)val; y[i] = val; } return sum; } static ggml_float ggml_vec_log_soft_max_f32(const int n, float * y, const float * x, float max) { // log(soft_max) = log(soft_max_i / soft_max_sum) = log(soft_max_i) - log(soft_max_sum) = (logit_i - max) - log(soft_max_i) int i = 0; ggml_float sum = 0; for (; i < n; ++i) { float val = x[i] - max; y[i] = val; sum += (ggml_float)expf(val); } return sum = (ggml_float)logf(sum); } inline static float ggml_silu_backward_f32(float x, float dy) { const float s = 1.0f/(1.0f + expf(-x)); return dy*s*(1.0f + x*(1.0f - s)); } inline static void ggml_vec_silu_backward_f32(const int n, float * dx, const float * x, const float * dy) { for (int i = 0; i < n; ++i) { dx[i] = ggml_silu_backward_f32(x[i], dy[i]); } } inline static void ggml_vec_sum_f32(const int n, float * s, const float * x) { #ifndef GGML_USE_ACCELERATE ggml_float sum = 0.0; for (int i = 0; i < n; ++i) { sum += (ggml_float)x[i]; } *s = sum; #else vDSP_sve(x, 1, s, n); #endif } inline static void ggml_vec_sum_f32_ggf(const int n, ggml_float * s, const float * x) { ggml_float sum = 0.0; for (int i = 0; i < n; ++i) { sum += (ggml_float)x[i]; } *s = sum; } inline static void ggml_vec_sum_f16_ggf(const int n, float * s, const ggml_fp16_t * x) { float sum = 0.0f; for (int i = 0; i < n; ++i) { sum += GGML_FP16_TO_FP32(x[i]); } *s = sum; } inline static void ggml_vec_sum_bf16_ggf(const int n, float * s, const ggml_bf16_t * x) { float sum = 0.0f; for (int i = 0; i < n; ++i) { sum += GGML_BF16_TO_FP32(x[i]); } *s = sum; } inline static void ggml_vec_max_f32(const int n, float * s, const float * x) { #ifndef GGML_USE_ACCELERATE float max = -INFINITY; for (int i = 0; i < n; ++i) { max = MAX(max, x[i]); } *s = max; #else vDSP_maxv(x, 1, s, n); #endif } inline static void ggml_vec_norm_inv_f32(const int n, float * s, const float * x) { ggml_vec_norm_f32(n, s, x); *s = 1.f/(*s); } inline static void ggml_vec_argmax_f32(const int n, int * s, const float * x) { float max = -INFINITY; int idx = 0; for (int i = 0; i < n; ++i) { max = MAX(max, x[i]); if (max == x[i]) { idx = i; } } *s = idx; } // Helpers for polling loops #if defined(__aarch64__) && ( defined(__clang__) || defined(__GNUC__) ) static inline void ggml_thread_cpu_relax(void) { __asm__ volatile("yield" ::: "memory"); } #elif defined(__x86_64__) static inline void ggml_thread_cpu_relax(void) { _mm_pause(); } #else static inline void ggml_thread_cpu_relax(void) {;} #endif // // NUMA support // #define GGML_NUMA_MAX_NODES 8 #define GGML_NUMA_MAX_CPUS 512 struct ggml_numa_node { uint32_t cpus[GGML_NUMA_MAX_CPUS]; // hardware threads on this node uint32_t n_cpus; }; struct ggml_numa_nodes { enum ggml_numa_strategy numa_strategy; struct ggml_numa_node nodes[GGML_NUMA_MAX_NODES]; uint32_t n_nodes; uint32_t total_cpus; // hardware threads on system uint32_t current_node; // node on which main process is execting #if defined(__gnu_linux__) cpu_set_t cpuset; // cpuset from numactl #else uint32_t cpuset; // no NUMA support outside of Linux at this time. Use a portable datatype #endif }; // // ggml state // struct ggml_state { struct ggml_numa_nodes numa; }; // global state static struct ggml_state g_state = {0}; static atomic_flag g_state_critical = ATOMIC_FLAG_INIT; // TODO: move to threading file // critical section via spin lock void ggml_critical_section_start(void) { while (atomic_flag_test_and_set(&g_state_critical)) { // spin sched_yield(); } } void ggml_critical_section_end(void) { atomic_flag_clear(&g_state_critical); } static void ggml_barrier(struct ggml_threadpool * tp) { int n_threads = atomic_load_explicit(&tp->n_threads_cur, memory_order_relaxed); if (n_threads == 1) { return; } #ifdef GGML_USE_OPENMP #pragma omp barrier #else int n_passed = atomic_load_explicit(&tp->n_barrier_passed, memory_order_relaxed); // enter barrier (full seq-cst fence) int n_barrier = atomic_fetch_add_explicit(&tp->n_barrier, 1, memory_order_seq_cst); if (n_barrier == (n_threads - 1)) { // last thread atomic_store_explicit(&tp->n_barrier, 0, memory_order_relaxed); // exit barrier (fill seq-cst fence) atomic_fetch_add_explicit(&tp->n_barrier_passed, 1, memory_order_seq_cst); return; } // wait for other threads while (atomic_load_explicit(&tp->n_barrier_passed, memory_order_relaxed) == n_passed) { ggml_thread_cpu_relax(); } // exit barrier (full seq-cst fence) // TSAN doesn't support standalone fence yet, we use a dummy read-modify-write instead #ifdef GGML_TSAN_ENABLED atomic_fetch_add_explicit(&tp->n_barrier_passed, 0, memory_order_seq_cst); #else atomic_thread_fence(memory_order_seq_cst); #endif #endif } #if defined(__gnu_linux__) static cpu_set_t ggml_get_numa_affinity(void) { cpu_set_t cpuset; pthread_t thread; thread = pthread_self(); CPU_ZERO(&cpuset); pthread_getaffinity_np(thread, sizeof(cpu_set_t), &cpuset); return cpuset; } #else static uint32_t ggml_get_numa_affinity(void) { return 0; // no NUMA support } #endif void ggml_numa_init(enum ggml_numa_strategy numa_flag) { if (g_state.numa.n_nodes > 0) { fprintf(stderr, "ggml_numa_init: NUMA already initialized\n"); return; } #if defined(__gnu_linux__) struct stat st; char path[256]; int rv; // set numa scheme g_state.numa.numa_strategy = numa_flag; GGML_PRINT_DEBUG("numa strategy %u\n",g_state.numa.numa_strategy); g_state.numa.cpuset = ggml_get_numa_affinity(); // enumerate nodes while (g_state.numa.n_nodes < GGML_NUMA_MAX_NODES) { rv = snprintf(path, sizeof(path), "/sys/devices/system/node/node%u", g_state.numa.n_nodes); GGML_ASSERT(rv > 0 && (unsigned)rv < sizeof(path)); if (stat(path, &st) != 0) { break; } ++g_state.numa.n_nodes; } // enumerate CPUs while (g_state.numa.total_cpus < GGML_NUMA_MAX_CPUS) { rv = snprintf(path, sizeof(path), "/sys/devices/system/cpu/cpu%u", g_state.numa.total_cpus); GGML_ASSERT(rv > 0 && (unsigned)rv < sizeof(path)); if (stat(path, &st) != 0) { break; } ++g_state.numa.total_cpus; } GGML_PRINT_DEBUG("found %u numa nodes, %u CPUs\n", g_state.numa.n_nodes, g_state.numa.total_cpus); // figure out which node we're on uint current_cpu; int getcpu_ret = 0; #if __GLIBC__ > 2 || (__GLIBC__ == 2 && __GLIBC_MINOR__ > 28) || defined(__COSMOPOLITAN__) getcpu_ret = getcpu(¤t_cpu, &g_state.numa.current_node); #else // old glibc doesn't have a wrapper for this call. Fall back on direct syscall # if !defined(SYS_getcpu) && defined(SYS_get_cpu) # define SYS_getcpu SYS_get_cpu // some older glibc versions use this name # endif getcpu_ret = syscall(SYS_getcpu, ¤t_cpu, &g_state.numa.current_node); #endif if (g_state.numa.n_nodes < 1 || g_state.numa.total_cpus < 1 || getcpu_ret != 0) { g_state.numa.n_nodes = 0; return; } GGML_PRINT_DEBUG("found our process on numa node %u, CPU %u\n", g_state.numa.current_node, current_cpu); for (uint32_t n = 0; n < g_state.numa.n_nodes; ++n) { struct ggml_numa_node * node = &g_state.numa.nodes[n]; GGML_PRINT_DEBUG("CPUs on node %u:", n); node->n_cpus = 0; for (uint32_t c = 0; c < g_state.numa.total_cpus; ++c) { rv = snprintf(path, sizeof(path), "/sys/devices/system/node/node%u/cpu%u", n, c); GGML_ASSERT(rv > 0 && (unsigned)rv < sizeof(path)); if (stat(path, &st) == 0) { node->cpus[node->n_cpus++] = c; GGML_PRINT_DEBUG(" %u", c); } } GGML_PRINT_DEBUG("\n"); } if (ggml_is_numa()) { FILE *fptr = fopen("/proc/sys/kernel/numa_balancing", "r"); if (fptr != NULL) { char buf[42]; if (fgets(buf, sizeof(buf), fptr) && strncmp(buf, "0\n", sizeof(buf)) != 0) { GGML_LOG_WARN("/proc/sys/kernel/numa_balancing is enabled, this has been observed to impair performance\n"); } fclose(fptr); } } #else UNUSED(numa_flag); // TODO #endif } bool ggml_is_numa(void) { return g_state.numa.n_nodes > 1; } #if defined(__ARM_ARCH) #if defined(__linux__) && defined(__aarch64__) #include #elif defined(__APPLE__) #include #endif #if !defined(HWCAP2_I8MM) #define HWCAP2_I8MM 0 #endif static void ggml_init_arm_arch_features(void) { #if defined(__linux__) && defined(__aarch64__) uint32_t hwcap = getauxval(AT_HWCAP); uint32_t hwcap2 = getauxval(AT_HWCAP2); ggml_arm_arch_features.has_neon = !!(hwcap & HWCAP_ASIMD); ggml_arm_arch_features.has_i8mm = !!(hwcap2 & HWCAP2_I8MM); ggml_arm_arch_features.has_sve = !!(hwcap & HWCAP_SVE); #if defined(__ARM_FEATURE_SVE) ggml_arm_arch_features.sve_cnt = PR_SVE_VL_LEN_MASK & prctl(PR_SVE_GET_VL); #endif #elif defined(__APPLE__) int oldp = 0; size_t size = sizeof(oldp); if (sysctlbyname("hw.optional.AdvSIMD", &oldp, &size, NULL, 0) != 0) { oldp = 0; } ggml_arm_arch_features.has_neon = oldp; if (sysctlbyname("hw.optional.arm.FEAT_I8MM", &oldp, &size, NULL, 0) != 0) { oldp = 0; } ggml_arm_arch_features.has_i8mm = oldp; ggml_arm_arch_features.has_sve = 0; ggml_arm_arch_features.sve_cnt = 0; #else // Run-time CPU feature detection not implemented for this platform, fallback to compile time #if defined(__ARM_NEON) ggml_arm_arch_features.has_neon = 1; #else ggml_arm_arch_features.has_neon = 0; #endif #if defined(__ARM_FEATURE_MATMUL_INT8) ggml_arm_arch_features.has_i8mm = 1; #else ggml_arm_arch_features.has_i8mm = 0; #endif #if defined(__ARM_FEATURE_SVE) ggml_arm_arch_features.has_sve = 1; ggml_arm_arch_features.sve_cnt = 16; #else ggml_arm_arch_features.has_sve = 0; ggml_arm_arch_features.sve_cnt = 0; #endif #endif } #endif struct ggml_tensor * ggml_new_i32(struct ggml_context * ctx, int32_t value) { GGML_ASSERT(!ggml_get_no_alloc(ctx)); struct ggml_tensor * result = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 1); ggml_set_i32(result, value); return result; } struct ggml_tensor * ggml_new_f32(struct ggml_context * ctx, float value) { GGML_ASSERT(!ggml_get_no_alloc(ctx)); struct ggml_tensor * result = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 1); ggml_set_f32(result, value); return result; } struct ggml_tensor * ggml_set_i32 (struct ggml_tensor * tensor, int32_t value) { const int n = ggml_nrows(tensor); const int nc = tensor->ne[0]; const size_t n1 = tensor->nb[1]; char * const data = tensor->data; switch (tensor->type) { case GGML_TYPE_I8: { assert(tensor->nb[0] == sizeof(int8_t)); for (int i = 0; i < n; i++) { ggml_vec_set_i8(nc, (int8_t *)(data + i*n1), value); } } break; case GGML_TYPE_I16: { assert(tensor->nb[0] == sizeof(int16_t)); for (int i = 0; i < n; i++) { ggml_vec_set_i16(nc, (int16_t *)(data + i*n1), value); } } break; case GGML_TYPE_I32: { assert(tensor->nb[0] == sizeof(int32_t)); for (int i = 0; i < n; i++) { ggml_vec_set_i32(nc, (int32_t *)(data + i*n1), value); } } break; case GGML_TYPE_F16: { assert(tensor->nb[0] == sizeof(ggml_fp16_t)); for (int i = 0; i < n; i++) { ggml_vec_set_f16(nc, (ggml_fp16_t *)(data + i*n1), GGML_FP32_TO_FP16(value)); } } break; case GGML_TYPE_BF16: { assert(tensor->nb[0] == sizeof(ggml_fp16_t)); for (int i = 0; i < n; i++) { ggml_vec_set_bf16(nc, (ggml_bf16_t *)(data + i*n1), GGML_FP32_TO_BF16(value)); } } break; case GGML_TYPE_F32: { assert(tensor->nb[0] == sizeof(float)); for (int i = 0; i < n; i++) { ggml_vec_set_f32(nc, (float *)(data + i*n1), value); } } break; default: { GGML_ABORT("fatal error"); } } return tensor; } struct ggml_tensor * ggml_set_f32(struct ggml_tensor * tensor, float value) { const int n = ggml_nrows(tensor); const int nc = tensor->ne[0]; const size_t n1 = tensor->nb[1]; char * const data = tensor->data; switch (tensor->type) { case GGML_TYPE_I8: { assert(tensor->nb[0] == sizeof(int8_t)); for (int i = 0; i < n; i++) { ggml_vec_set_i8(nc, (int8_t *)(data + i*n1), value); } } break; case GGML_TYPE_I16: { assert(tensor->nb[0] == sizeof(int16_t)); for (int i = 0; i < n; i++) { ggml_vec_set_i16(nc, (int16_t *)(data + i*n1), value); } } break; case GGML_TYPE_I32: { assert(tensor->nb[0] == sizeof(int32_t)); for (int i = 0; i < n; i++) { ggml_vec_set_i32(nc, (int32_t *)(data + i*n1), value); } } break; case GGML_TYPE_F16: { assert(tensor->nb[0] == sizeof(ggml_fp16_t)); for (int i = 0; i < n; i++) { ggml_vec_set_f16(nc, (ggml_fp16_t *)(data + i*n1), GGML_FP32_TO_FP16(value)); } } break; case GGML_TYPE_BF16: { assert(tensor->nb[0] == sizeof(ggml_bf16_t)); for (int i = 0; i < n; i++) { ggml_vec_set_bf16(nc, (ggml_bf16_t *)(data + i*n1), GGML_FP32_TO_BF16(value)); } } break; case GGML_TYPE_F32: { assert(tensor->nb[0] == sizeof(float)); for (int i = 0; i < n; i++) { ggml_vec_set_f32(nc, (float *)(data + i*n1), value); } } break; default: { GGML_ABORT("fatal error"); } } return tensor; } int32_t ggml_get_i32_1d(const struct ggml_tensor * tensor, int i) { if (!ggml_is_contiguous(tensor)) { int64_t id[4] = { 0, 0, 0, 0 }; ggml_unravel_index(tensor, i, &id[0], &id[1], &id[2], &id[3]); return ggml_get_i32_nd(tensor, id[0], id[1], id[2], id[3]); } switch (tensor->type) { case GGML_TYPE_I8: { GGML_ASSERT(tensor->nb[0] == sizeof(int8_t)); return ((int8_t *)(tensor->data))[i]; } case GGML_TYPE_I16: { GGML_ASSERT(tensor->nb[0] == sizeof(int16_t)); return ((int16_t *)(tensor->data))[i]; } case GGML_TYPE_I32: { GGML_ASSERT(tensor->nb[0] == sizeof(int32_t)); return ((int32_t *)(tensor->data))[i]; } case GGML_TYPE_F16: { GGML_ASSERT(tensor->nb[0] == sizeof(ggml_fp16_t)); return GGML_FP16_TO_FP32(((ggml_fp16_t *)(tensor->data))[i]); } case GGML_TYPE_BF16: { GGML_ASSERT(tensor->nb[0] == sizeof(ggml_bf16_t)); return GGML_BF16_TO_FP32(((ggml_bf16_t *)(tensor->data))[i]); } case GGML_TYPE_F32: { GGML_ASSERT(tensor->nb[0] == sizeof(float)); return ((float *)(tensor->data))[i]; } default: { GGML_ABORT("fatal error"); } } } void ggml_set_i32_1d(const struct ggml_tensor * tensor, int i, int32_t value) { if (!ggml_is_contiguous(tensor)) { int64_t id[4] = { 0, 0, 0, 0 }; ggml_unravel_index(tensor, i, &id[0], &id[1], &id[2], &id[3]); ggml_set_i32_nd(tensor, id[0], id[1], id[2], id[3], value); return; } 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_BF16: { GGML_ASSERT(tensor->nb[0] == sizeof(ggml_bf16_t)); ((ggml_bf16_t *)(tensor->data))[i] = GGML_FP32_TO_BF16(value); } break; case GGML_TYPE_F32: { GGML_ASSERT(tensor->nb[0] == sizeof(float)); ((float *)(tensor->data))[i] = value; } break; default: { GGML_ABORT("fatal error"); } } } int32_t ggml_get_i32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2, int i3) { void * data = (char *) tensor->data + i0*tensor->nb[0] + i1*tensor->nb[1] + i2*tensor->nb[2] + i3*tensor->nb[3]; switch (tensor->type) { case GGML_TYPE_I8: return ((int8_t *) data)[0]; case GGML_TYPE_I16: return ((int16_t *) data)[0]; case GGML_TYPE_I32: return ((int32_t *) data)[0]; case GGML_TYPE_F16: return GGML_FP16_TO_FP32(((ggml_fp16_t *) data)[0]); case GGML_TYPE_BF16: return GGML_BF16_TO_FP32(((ggml_bf16_t *) data)[0]); case GGML_TYPE_F32: return ((float *) data)[0]; default: GGML_ABORT("fatal error"); } } void ggml_set_i32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2, int i3, int32_t value) { void * data = (char *) tensor->data + i0*tensor->nb[0] + i1*tensor->nb[1] + i2*tensor->nb[2] + i3*tensor->nb[3]; switch (tensor->type) { case GGML_TYPE_I8: { ((int8_t *)(data))[0] = value; } break; case GGML_TYPE_I16: { ((int16_t *)(data))[0] = value; } break; case GGML_TYPE_I32: { ((int32_t *)(data))[0] = value; } break; case GGML_TYPE_F16: { ((ggml_fp16_t *)(data))[0] = GGML_FP32_TO_FP16(value); } break; case GGML_TYPE_BF16: { ((ggml_bf16_t *)(data))[0] = GGML_FP32_TO_BF16(value); } break; case GGML_TYPE_F32: { ((float *)(data))[0] = value; } break; default: { GGML_ABORT("fatal error"); } } } float ggml_get_f32_1d(const struct ggml_tensor * tensor, int i) { if (!ggml_is_contiguous(tensor)) { int64_t id[4] = { 0, 0, 0, 0 }; ggml_unravel_index(tensor, i, &id[0], &id[1], &id[2], &id[3]); return ggml_get_f32_nd(tensor, id[0], id[1], id[2], id[3]); } switch (tensor->type) { case GGML_TYPE_I8: { return ((int8_t *)(tensor->data))[i]; } case GGML_TYPE_I16: { return ((int16_t *)(tensor->data))[i]; } case GGML_TYPE_I32: { return ((int32_t *)(tensor->data))[i]; } case GGML_TYPE_F16: { return GGML_FP16_TO_FP32(((ggml_fp16_t *)(tensor->data))[i]); } case GGML_TYPE_BF16: { return GGML_BF16_TO_FP32(((ggml_bf16_t *)(tensor->data))[i]); } case GGML_TYPE_F32: { return ((float *)(tensor->data))[i]; } default: { GGML_ABORT("fatal error"); } } } void ggml_set_f32_1d(const struct ggml_tensor * tensor, int i, float value) { if (!ggml_is_contiguous(tensor)) { int64_t id[4] = { 0, 0, 0, 0 }; ggml_unravel_index(tensor, i, &id[0], &id[1], &id[2], &id[3]); ggml_set_f32_nd(tensor, id[0], id[1], id[2], id[3], value); return; } switch (tensor->type) { case GGML_TYPE_I8: { ((int8_t *)(tensor->data))[i] = value; } break; case GGML_TYPE_I16: { ((int16_t *)(tensor->data))[i] = value; } break; case GGML_TYPE_I32: { ((int32_t *)(tensor->data))[i] = value; } break; case GGML_TYPE_F16: { ((ggml_fp16_t *)(tensor->data))[i] = GGML_FP32_TO_FP16(value); } break; case GGML_TYPE_BF16: { ((ggml_bf16_t *)(tensor->data))[i] = GGML_FP32_TO_BF16(value); } break; case GGML_TYPE_F32: { ((float *)(tensor->data))[i] = value; } break; default: { GGML_ABORT("fatal error"); } } } float ggml_get_f32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2, int i3) { void * data = (char *) tensor->data + i0*tensor->nb[0] + i1*tensor->nb[1] + i2*tensor->nb[2] + i3*tensor->nb[3]; switch (tensor->type) { case GGML_TYPE_I8: return ((int8_t *) data)[0]; case GGML_TYPE_I16: return ((int16_t *) data)[0]; case GGML_TYPE_I32: return ((int32_t *) data)[0]; case GGML_TYPE_F16: return GGML_FP16_TO_FP32(((ggml_fp16_t *) data)[0]); case GGML_TYPE_BF16: return GGML_BF16_TO_FP32(((ggml_bf16_t *) data)[0]); case GGML_TYPE_F32: return ((float *) data)[0]; default: GGML_ABORT("fatal error"); } } void ggml_set_f32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2, int i3, float value) { void * data = (char *) tensor->data + i0*tensor->nb[0] + i1*tensor->nb[1] + i2*tensor->nb[2] + i3*tensor->nb[3]; switch (tensor->type) { case GGML_TYPE_I8: { ((int8_t *)(data))[0] = value; } break; case GGML_TYPE_I16: { ((int16_t *)(data))[0] = value; } break; case GGML_TYPE_I32: { ((int32_t *)(data))[0] = value; } break; case GGML_TYPE_F16: { ((ggml_fp16_t *)(data))[0] = GGML_FP32_TO_FP16(value); } break; case GGML_TYPE_BF16: { ((ggml_bf16_t *)(data))[0] = GGML_FP32_TO_BF16(value); } break; case GGML_TYPE_F32: { ((float *)(data))[0] = value; } break; default: { GGML_ABORT("fatal error"); } } } //////////////////////////////////////////////////////////////////////////////// // ggml_compute_forward_dup static void ggml_compute_forward_dup_same_cont( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0)); GGML_ASSERT(ggml_is_contiguous(dst) && ggml_is_contiguous(src0)); GGML_ASSERT(src0->type == dst->type); const size_t nb0 = ggml_type_size(src0->type); const int ith = params->ith; // thread index const int nth = params->nth; // number of threads // parallelize by elements const int ne = ggml_nelements(dst); const int dr = (ne + nth - 1) / nth; const int ie0 = dr * ith; const int ie1 = MIN(ie0 + dr, ne); if (ie0 < ie1) { memcpy( ((char *) dst->data + ie0*nb0), ((char *) src0->data + ie0*nb0), (ie1 - ie0) * nb0); } } static void ggml_compute_forward_dup_f16( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0)); GGML_TENSOR_UNARY_OP_LOCALS const int ith = params->ith; // thread index const int nth = params->nth; // number of threads // parallelize by rows const int nr = ne01; // number of rows per thread const int dr = (nr + nth - 1) / nth; // row range for this thread const int ir0 = dr * ith; const int ir1 = MIN(ir0 + dr, nr); if (src0->type == dst->type && ne00 == ne0 && nb00 == ggml_type_size(src0->type) && nb0 == ggml_type_size(dst->type)) { // copy by rows const size_t rs = ne00*nb00; for (int64_t i03 = 0; i03 < ne03; i03++) { for (int64_t i02 = 0; i02 < ne02; i02++) { for (int64_t i01 = ir0; i01 < ir1; i01++) { memcpy( ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3), ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03), rs); } } } return; } // TODO: add more special-case implementations for tensor shapes/strides that can benefit from memcpy if (ggml_is_contiguous(dst)) { if (nb00 == sizeof(ggml_fp16_t)) { if (dst->type == GGML_TYPE_F16) { size_t id = 0; const size_t rs = ne00 * nb00; char * dst_ptr = (char *) dst->data; for (int i03 = 0; i03 < ne03; i03++) { for (int i02 = 0; i02 < ne02; i02++) { id += rs * ir0; for (int i01 = ir0; i01 < ir1; i01++) { const char * src0_ptr = (char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03; memcpy(dst_ptr + id, src0_ptr, rs); id += rs; } id += rs * (ne01 - ir1); } } } else if (dst->type == GGML_TYPE_F32) { size_t id = 0; float * dst_ptr = (float *) dst->data; for (int i03 = 0; i03 < ne03; i03++) { for (int i02 = 0; i02 < ne02; i02++) { id += ne00 * ir0; for (int i01 = ir0; i01 < ir1; i01++) { const ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); for (int i00 = 0; i00 < ne00; i00++) { dst_ptr[id] = GGML_FP16_TO_FP32(src0_ptr[i00]); id++; } } id += ne00 * (ne01 - ir1); } } } else if (ggml_get_type_traits(dst->type)->from_float) { ggml_from_float_t const quantize_row_q = ggml_get_type_traits(dst->type)->from_float; float * src0_f32 = (float *) params->wdata + (ne00 + CACHE_LINE_SIZE_F32) * ith; size_t id = 0; size_t rs = nb0 * (ne00 / ggml_blck_size(dst->type)); char * dst_ptr = (char *) dst->data; for (int i03 = 0; i03 < ne03; i03++) { for (int i02 = 0; i02 < ne02; i02++) { id += rs * ir0; for (int i01 = ir0; i01 < ir1; i01++) { const ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); for (int i00 = 0; i00 < ne00; i00++) { src0_f32[i00] = GGML_FP16_TO_FP32(src0_ptr[i00]); } quantize_row_q(src0_f32, dst_ptr + id, ne00); id += rs; } id += rs * (ne01 - ir1); } } } else { GGML_ABORT("fatal error"); // TODO: implement } } else { //printf("%s: this is not optimal - fix me\n", __func__); if (dst->type == GGML_TYPE_F32) { size_t id = 0; float * dst_ptr = (float *) dst->data; for (int i03 = 0; i03 < ne03; i03++) { for (int i02 = 0; i02 < ne02; i02++) { id += ne00 * ir0; for (int i01 = ir0; i01 < ir1; i01++) { for (int i00 = 0; i00 < ne00; i00++) { const ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); dst_ptr[id] = GGML_FP16_TO_FP32(*src0_ptr); id++; } } id += ne00 * (ne01 - ir1); } } } else if (dst->type == GGML_TYPE_F16) { size_t id = 0; ggml_fp16_t * dst_ptr = (ggml_fp16_t *) dst->data; for (int i03 = 0; i03 < ne03; i03++) { for (int i02 = 0; i02 < ne02; i02++) { id += ne00 * ir0; for (int i01 = ir0; i01 < ir1; i01++) { for (int i00 = 0; i00 < ne00; i00++) { const ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); dst_ptr[id] = *src0_ptr; id++; } } id += ne00 * (ne01 - ir1); } } } else { GGML_ABORT("fatal error"); // TODO: implement } } return; } // dst counters int64_t i10 = 0; int64_t i11 = 0; int64_t i12 = 0; int64_t i13 = 0; if (dst->type == GGML_TYPE_F16) { for (int64_t i03 = 0; i03 < ne03; i03++) { for (int64_t i02 = 0; i02 < ne02; i02++) { i10 += ne00 * ir0; while (i10 >= ne0) { i10 -= ne0; if (++i11 == ne1) { i11 = 0; if (++i12 == ne2) { i12 = 0; if (++i13 == ne3) { i13 = 0; } } } } for (int64_t i01 = ir0; i01 < ir1; i01++) { for (int64_t i00 = 0; i00 < ne00; i00++) { const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); char * dst_ptr = ((char *) dst->data + i10*nb0 + i11*nb1 + i12*nb2 + i13*nb3); memcpy(dst_ptr, src0_ptr, sizeof(ggml_fp16_t)); if (++i10 == ne00) { i10 = 0; if (++i11 == ne01) { i11 = 0; if (++i12 == ne02) { i12 = 0; if (++i13 == ne03) { i13 = 0; } } } } } } i10 += ne00 * (ne01 - ir1); while (i10 >= ne0) { i10 -= ne0; if (++i11 == ne1) { i11 = 0; if (++i12 == ne2) { i12 = 0; if (++i13 == ne3) { i13 = 0; } } } } } } } else if (dst->type == GGML_TYPE_F32) { for (int64_t i03 = 0; i03 < ne03; i03++) { for (int64_t i02 = 0; i02 < ne02; i02++) { i10 += ne00 * ir0; while (i10 >= ne0) { i10 -= ne0; if (++i11 == ne1) { i11 = 0; if (++i12 == ne2) { i12 = 0; if (++i13 == ne3) { i13 = 0; } } } } for (int64_t i01 = ir0; i01 < ir1; i01++) { for (int64_t i00 = 0; i00 < ne00; i00++) { const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); char * dst_ptr = ((char *) dst->data + i10*nb0 + i11*nb1 + i12*nb2 + i13*nb3); *(float *) dst_ptr = GGML_FP16_TO_FP32(*(const ggml_fp16_t *) src0_ptr); if (++i10 == ne0) { i10 = 0; if (++i11 == ne1) { i11 = 0; if (++i12 == ne2) { i12 = 0; if (++i13 == ne3) { i13 = 0; } } } } } } i10 += ne00 * (ne01 - ir1); while (i10 >= ne0) { i10 -= ne0; if (++i11 == ne1) { i11 = 0; if (++i12 == ne2) { i12 = 0; if (++i13 == ne3) { i13 = 0; } } } } } } } else { GGML_ABORT("fatal error"); // TODO: implement } } static void ggml_compute_forward_dup_bf16( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0)); GGML_TENSOR_UNARY_OP_LOCALS const int ith = params->ith; // thread index const int nth = params->nth; // number of threads // parallelize by rows const int nr = ne01; // number of rows per thread const int dr = (nr + nth - 1) / nth; // row range for this thread const int ir0 = dr * ith; const int ir1 = MIN(ir0 + dr, nr); if (src0->type == dst->type && ne00 == ne0 && nb00 == ggml_type_size(src0->type) && nb0 == ggml_type_size(dst->type)) { // copy by rows const size_t rs = ne00*nb00; for (int64_t i03 = 0; i03 < ne03; i03++) { for (int64_t i02 = 0; i02 < ne02; i02++) { for (int64_t i01 = ir0; i01 < ir1; i01++) { memcpy( ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3), ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03), rs); } } } return; } // TODO: add more special-case implementations for tensor shapes/strides that can benefit from memcpy if (ggml_is_contiguous(dst)) { if (nb00 == sizeof(ggml_bf16_t)) { if (dst->type == GGML_TYPE_BF16) { size_t id = 0; const size_t rs = ne00 * nb00; char * dst_ptr = (char *) dst->data; for (int i03 = 0; i03 < ne03; i03++) { for (int i02 = 0; i02 < ne02; i02++) { id += rs * ir0; for (int i01 = ir0; i01 < ir1; i01++) { const char * src0_ptr = (char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03; memcpy(dst_ptr + id, src0_ptr, rs); id += rs; } id += rs * (ne01 - ir1); } } } else if (dst->type == GGML_TYPE_F16) { size_t id = 0; ggml_fp16_t * dst_ptr = (ggml_fp16_t *) dst->data; for (int i03 = 0; i03 < ne03; i03++) { for (int i02 = 0; i02 < ne02; i02++) { id += ne00 * ir0; for (int i01 = ir0; i01 < ir1; i01++) { const ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); for (int i00 = 0; i00 < ne00; i00++) { dst_ptr[id] = GGML_FP32_TO_FP16(GGML_BF16_TO_FP32(src0_ptr[i00])); id++; } } id += ne00 * (ne01 - ir1); } } } else if (dst->type == GGML_TYPE_F32) { size_t id = 0; float * dst_ptr = (float *) dst->data; for (int i03 = 0; i03 < ne03; i03++) { for (int i02 = 0; i02 < ne02; i02++) { id += ne00 * ir0; for (int i01 = ir0; i01 < ir1; i01++) { const ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); for (int i00 = 0; i00 < ne00; i00++) { dst_ptr[id] = GGML_BF16_TO_FP32(src0_ptr[i00]); id++; } } id += ne00 * (ne01 - ir1); } } } else if (ggml_get_type_traits(dst->type)->from_float) { ggml_from_float_t const quantize_row_q = ggml_get_type_traits(dst->type)->from_float; float * src0_f32 = (float *) params->wdata + (ne00 + CACHE_LINE_SIZE_F32) * ith; size_t id = 0; size_t rs = nb0 * (ne00 / ggml_blck_size(dst->type)); char * dst_ptr = (char *) dst->data; for (int i03 = 0; i03 < ne03; i03++) { for (int i02 = 0; i02 < ne02; i02++) { id += rs * ir0; for (int i01 = ir0; i01 < ir1; i01++) { const ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); for (int i00 = 0; i00 < ne00; i00++) { src0_f32[i00] = GGML_BF16_TO_FP32(src0_ptr[i00]); } quantize_row_q(src0_f32, dst_ptr + id, ne00); id += rs; } id += rs * (ne01 - ir1); } } } else { GGML_ABORT("fatal error"); // TODO: implement } } else { //printf("%s: this is not optimal - fix me\n", __func__); if (dst->type == GGML_TYPE_F32) { size_t id = 0; float * dst_ptr = (float *) dst->data; for (int i03 = 0; i03 < ne03; i03++) { for (int i02 = 0; i02 < ne02; i02++) { id += ne00 * ir0; for (int i01 = ir0; i01 < ir1; i01++) { for (int i00 = 0; i00 < ne00; i00++) { const ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); dst_ptr[id] = GGML_BF16_TO_FP32(*src0_ptr); id++; } } id += ne00 * (ne01 - ir1); } } } else if (dst->type == GGML_TYPE_BF16) { size_t id = 0; ggml_bf16_t * dst_ptr = (ggml_bf16_t *) dst->data; for (int i03 = 0; i03 < ne03; i03++) { for (int i02 = 0; i02 < ne02; i02++) { id += ne00 * ir0; for (int i01 = ir0; i01 < ir1; i01++) { for (int i00 = 0; i00 < ne00; i00++) { const ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); dst_ptr[id] = *src0_ptr; id++; } } id += ne00 * (ne01 - ir1); } } } else if (dst->type == GGML_TYPE_F16) { size_t id = 0; ggml_fp16_t * dst_ptr = (ggml_fp16_t *) dst->data; for (int i03 = 0; i03 < ne03; i03++) { for (int i02 = 0; i02 < ne02; i02++) { id += ne00 * ir0; for (int i01 = ir0; i01 < ir1; i01++) { for (int i00 = 0; i00 < ne00; i00++) { const ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); dst_ptr[id] = GGML_FP32_TO_FP16(GGML_BF16_TO_FP32(*src0_ptr)); id++; } } id += ne00 * (ne01 - ir1); } } } else { GGML_ABORT("fatal error"); // TODO: implement } } return; } // dst counters int64_t i10 = 0; int64_t i11 = 0; int64_t i12 = 0; int64_t i13 = 0; if (dst->type == GGML_TYPE_BF16) { for (int64_t i03 = 0; i03 < ne03; i03++) { for (int64_t i02 = 0; i02 < ne02; i02++) { i10 += ne00 * ir0; while (i10 >= ne0) { i10 -= ne0; if (++i11 == ne1) { i11 = 0; if (++i12 == ne2) { i12 = 0; if (++i13 == ne3) { i13 = 0; } } } } for (int64_t i01 = ir0; i01 < ir1; i01++) { for (int64_t i00 = 0; i00 < ne00; i00++) { const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); char * dst_ptr = ((char *) dst->data + i10*nb0 + i11*nb1 + i12*nb2 + i13*nb3); memcpy(dst_ptr, src0_ptr, sizeof(ggml_bf16_t)); if (++i10 == ne00) { i10 = 0; if (++i11 == ne01) { i11 = 0; if (++i12 == ne02) { i12 = 0; if (++i13 == ne03) { i13 = 0; } } } } } } i10 += ne00 * (ne01 - ir1); while (i10 >= ne0) { i10 -= ne0; if (++i11 == ne1) { i11 = 0; if (++i12 == ne2) { i12 = 0; if (++i13 == ne3) { i13 = 0; } } } } } } } else if (dst->type == GGML_TYPE_F16) { for (int64_t i03 = 0; i03 < ne03; i03++) { for (int64_t i02 = 0; i02 < ne02; i02++) { i10 += ne00 * ir0; while (i10 >= ne0) { i10 -= ne0; if (++i11 == ne1) { i11 = 0; if (++i12 == ne2) { i12 = 0; if (++i13 == ne3) { i13 = 0; } } } } for (int64_t i01 = ir0; i01 < ir1; i01++) { for (int64_t i00 = 0; i00 < ne00; i00++) { const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); char * dst_ptr = ((char *) dst->data + i10*nb0 + i11*nb1 + i12*nb2 + i13*nb3); *(ggml_fp16_t *) dst_ptr = GGML_FP32_TO_FP16(GGML_BF16_TO_FP32(*(const ggml_bf16_t *) src0_ptr)); if (++i10 == ne0) { i10 = 0; if (++i11 == ne1) { i11 = 0; if (++i12 == ne2) { i12 = 0; if (++i13 == ne3) { i13 = 0; } } } } } } i10 += ne00 * (ne01 - ir1); while (i10 >= ne0) { i10 -= ne0; if (++i11 == ne1) { i11 = 0; if (++i12 == ne2) { i12 = 0; if (++i13 == ne3) { i13 = 0; } } } } } } } else if (dst->type == GGML_TYPE_F32) { for (int64_t i03 = 0; i03 < ne03; i03++) { for (int64_t i02 = 0; i02 < ne02; i02++) { i10 += ne00 * ir0; while (i10 >= ne0) { i10 -= ne0; if (++i11 == ne1) { i11 = 0; if (++i12 == ne2) { i12 = 0; if (++i13 == ne3) { i13 = 0; } } } } for (int64_t i01 = ir0; i01 < ir1; i01++) { for (int64_t i00 = 0; i00 < ne00; i00++) { const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); char * dst_ptr = ((char *) dst->data + i10*nb0 + i11*nb1 + i12*nb2 + i13*nb3); *(float *) dst_ptr = GGML_BF16_TO_FP32(*(const ggml_bf16_t *) src0_ptr); if (++i10 == ne0) { i10 = 0; if (++i11 == ne1) { i11 = 0; if (++i12 == ne2) { i12 = 0; if (++i13 == ne3) { i13 = 0; } } } } } } i10 += ne00 * (ne01 - ir1); while (i10 >= ne0) { i10 -= ne0; if (++i11 == ne1) { i11 = 0; if (++i12 == ne2) { i12 = 0; if (++i13 == ne3) { i13 = 0; } } } } } } } else { GGML_ABORT("fatal error"); // TODO: implement } } static void ggml_compute_forward_dup_f32( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0)); GGML_TENSOR_UNARY_OP_LOCALS const int ith = params->ith; // thread index const int nth = params->nth; // number of threads // parallelize by rows const int nr = ne01; // number of rows per thread const int dr = (nr + nth - 1) / nth; // row range for this thread const int ir0 = dr * ith; const int ir1 = MIN(ir0 + dr, nr); if (src0->type == dst->type && ne00 == ne0 && nb00 == ggml_type_size(src0->type) && nb0 == ggml_type_size(dst->type)) { // copy by rows const size_t rs = ne00*nb00; for (int64_t i03 = 0; i03 < ne03; i03++) { for (int64_t i02 = 0; i02 < ne02; i02++) { for (int64_t i01 = ir0; i01 < ir1; i01++) { memcpy( ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3), ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03), rs); } } } return; } if (ggml_is_contiguous(dst)) { // TODO: simplify if (nb00 == sizeof(float)) { if (dst->type == GGML_TYPE_F32) { size_t id = 0; const size_t rs = ne00 * nb00; char * dst_ptr = (char *) dst->data; for (int i03 = 0; i03 < ne03; i03++) { for (int i02 = 0; i02 < ne02; i02++) { id += rs * ir0; for (int i01 = ir0; i01 < ir1; i01++) { const char * src0_ptr = (char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03; memcpy(dst_ptr + id, src0_ptr, rs); id += rs; } id += rs * (ne01 - ir1); } } } else if (ggml_get_type_traits(dst->type)->from_float) { ggml_from_float_t const quantize_row_q = ggml_get_type_traits(dst->type)->from_float; size_t id = 0; size_t rs = nb0 * (ne00 / ggml_blck_size(dst->type)); char * dst_ptr = (char *) dst->data; for (int i03 = 0; i03 < ne03; i03++) { for (int i02 = 0; i02 < ne02; i02++) { id += rs * ir0; for (int i01 = ir0; i01 < ir1; i01++) { const float * src0_ptr = (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); quantize_row_q(src0_ptr, dst_ptr + id, ne00); id += rs; } id += rs * (ne01 - ir1); } } } else { GGML_ABORT("fatal error"); // TODO: implement } } else { //printf("%s: this is not optimal - fix me\n", __func__); if (dst->type == GGML_TYPE_F32) { size_t id = 0; float * dst_ptr = (float *) dst->data; for (int i03 = 0; i03 < ne03; i03++) { for (int i02 = 0; i02 < ne02; i02++) { id += ne00 * ir0; for (int i01 = ir0; i01 < ir1; i01++) { for (int i00 = 0; i00 < ne00; i00++) { const float * src0_ptr = (float *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); dst_ptr[id] = *src0_ptr; id++; } } id += ne00 * (ne01 - ir1); } } } else if (dst->type == GGML_TYPE_F16) { size_t id = 0; ggml_fp16_t * dst_ptr = (ggml_fp16_t *) dst->data; for (int i03 = 0; i03 < ne03; i03++) { for (int i02 = 0; i02 < ne02; i02++) { id += ne00 * ir0; for (int i01 = ir0; i01 < ir1; i01++) { for (int i00 = 0; i00 < ne00; i00++) { const float * src0_ptr = (float *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); dst_ptr[id] = GGML_FP32_TO_FP16(*src0_ptr); id++; } } id += ne00 * (ne01 - ir1); } } } else if (dst->type == GGML_TYPE_BF16) { size_t id = 0; ggml_bf16_t * dst_ptr = (ggml_bf16_t *) dst->data; for (int i03 = 0; i03 < ne03; i03++) { for (int i02 = 0; i02 < ne02; i02++) { id += ne00 * ir0; for (int i01 = ir0; i01 < ir1; i01++) { for (int i00 = 0; i00 < ne00; i00++) { const float * src0_ptr = (float *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); dst_ptr[id] = GGML_FP32_TO_BF16(*src0_ptr); id++; } } id += ne00 * (ne01 - ir1); } } } else { GGML_ABORT("fatal error"); // TODO: implement } } return; } // dst counters int64_t i10 = 0; int64_t i11 = 0; int64_t i12 = 0; int64_t i13 = 0; if (dst->type == GGML_TYPE_F32) { for (int64_t i03 = 0; i03 < ne03; i03++) { for (int64_t i02 = 0; i02 < ne02; i02++) { i10 += ne00 * ir0; while (i10 >= ne0) { i10 -= ne0; if (++i11 == ne1) { i11 = 0; if (++i12 == ne2) { i12 = 0; if (++i13 == ne3) { i13 = 0; } } } } for (int64_t i01 = ir0; i01 < ir1; i01++) { for (int64_t i00 = 0; i00 < ne00; i00++) { const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); char * dst_ptr = ((char *) dst->data + i10*nb0 + i11*nb1 + i12*nb2 + i13*nb3); memcpy(dst_ptr, src0_ptr, sizeof(float)); if (++i10 == ne0) { i10 = 0; if (++i11 == ne1) { i11 = 0; if (++i12 == ne2) { i12 = 0; if (++i13 == ne3) { i13 = 0; } } } } } } i10 += ne00 * (ne01 - ir1); while (i10 >= ne0) { i10 -= ne0; if (++i11 == ne1) { i11 = 0; if (++i12 == ne2) { i12 = 0; if (++i13 == ne3) { i13 = 0; } } } } } } } else if (dst->type == GGML_TYPE_F16) { for (int64_t i03 = 0; i03 < ne03; i03++) { for (int64_t i02 = 0; i02 < ne02; i02++) { i10 += ne00 * ir0; while (i10 >= ne0) { i10 -= ne0; if (++i11 == ne1) { i11 = 0; if (++i12 == ne2) { i12 = 0; if (++i13 == ne3) { i13 = 0; } } } } for (int64_t i01 = ir0; i01 < ir1; i01++) { for (int64_t i00 = 0; i00 < ne00; i00++) { const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); char * dst_ptr = ((char *) dst->data + i10*nb0 + i11*nb1 + i12*nb2 + i13*nb3); *(ggml_fp16_t *) dst_ptr = GGML_FP32_TO_FP16(*(const float *) src0_ptr); if (++i10 == ne0) { i10 = 0; if (++i11 == ne1) { i11 = 0; if (++i12 == ne2) { i12 = 0; if (++i13 == ne3) { i13 = 0; } } } } } } i10 += ne00 * (ne01 - ir1); while (i10 >= ne0) { i10 -= ne0; if (++i11 == ne1) { i11 = 0; if (++i12 == ne2) { i12 = 0; if (++i13 == ne3) { i13 = 0; } } } } } } } else if (dst->type == GGML_TYPE_BF16) { for (int64_t i03 = 0; i03 < ne03; i03++) { for (int64_t i02 = 0; i02 < ne02; i02++) { i10 += ne00 * ir0; while (i10 >= ne0) { i10 -= ne0; if (++i11 == ne1) { i11 = 0; if (++i12 == ne2) { i12 = 0; if (++i13 == ne3) { i13 = 0; } } } } for (int64_t i01 = ir0; i01 < ir1; i01++) { for (int64_t i00 = 0; i00 < ne00; i00++) { const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); char * dst_ptr = ((char *) dst->data + i10*nb0 + i11*nb1 + i12*nb2 + i13*nb3); *(ggml_bf16_t *) dst_ptr = GGML_FP32_TO_BF16(*(const float *) src0_ptr); if (++i10 == ne0) { i10 = 0; if (++i11 == ne1) { i11 = 0; if (++i12 == ne2) { i12 = 0; if (++i13 == ne3) { i13 = 0; } } } } } } i10 += ne00 * (ne01 - ir1); while (i10 >= ne0) { i10 -= ne0; if (++i11 == ne1) { i11 = 0; if (++i12 == ne2) { i12 = 0; if (++i13 == ne3) { i13 = 0; } } } } } } } else { GGML_ABORT("fatal error"); // TODO: implement } } // A simplified version of ggml_compute_forward_dup that doesn't do float upcasting, and just plain old memcpy. static void ggml_compute_forward_dup_bytes( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0)); GGML_ASSERT(src0->type == dst->type); GGML_TENSOR_UNARY_OP_LOCALS; if (ggml_is_contiguous(src0) && ggml_is_contiguous(dst)) { ggml_compute_forward_dup_same_cont(params, dst); return; } const size_t type_size = ggml_type_size(src0->type); const int ith = params->ith; // thread index const int nth = params->nth; // number of threads // parallelize by rows const int nr = ne01; // number of rows per thread const int dr = (nr + nth - 1) / nth; // row range for this thread const int ir0 = dr * ith; const int ir1 = MIN(ir0 + dr, nr); if (src0->type == dst->type && ne00 == ne0 && nb00 == type_size && nb0 == type_size) { // copy by rows const size_t rs = ne00 * type_size; for (int64_t i03 = 0; i03 < ne03; i03++) { for (int64_t i02 = 0; i02 < ne02; i02++) { for (int64_t i01 = ir0; i01 < ir1; i01++) { memcpy( ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3), ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03), rs); } } } return; } if (ggml_is_contiguous(dst)) { size_t id = 0; char * dst_ptr = (char *) dst->data; const size_t rs = ne00 * type_size; if (nb00 == type_size) { // src0 is contigous on first dimension, copy by rows for (int64_t i03 = 0; i03 < ne03; i03++) { for (int64_t i02 = 0; i02 < ne02; i02++) { id += rs * ir0; for (int64_t i01 = ir0; i01 < ir1; i01++) { const char * src0_ptr = (char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03; memcpy(dst_ptr + id, src0_ptr, rs); id += rs; } id += rs * (ne01 - ir1); } } } else { //printf("%s: this is not optimal - fix me\n", __func__); for (int64_t i03 = 0; i03 < ne03; i03++) { for (int64_t i02 = 0; i02 < ne02; i02++) { id += rs * ir0; for (int64_t i01 = ir0; i01 < ir1; i01++) { for (int64_t i00 = 0; i00 < ne00; i00++) { const char * src0_ptr = (char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03; memcpy(dst_ptr + id, src0_ptr, type_size); id += type_size; } } id += rs * (ne01 - ir1); } } } return; } // dst counters int64_t i10 = 0; int64_t i11 = 0; int64_t i12 = 0; int64_t i13 = 0; for (int64_t i03 = 0; i03 < ne03; i03++) { for (int64_t i02 = 0; i02 < ne02; i02++) { i10 += ne00 * ir0; while (i10 >= ne0) { i10 -= ne0; if (++i11 == ne1) { i11 = 0; if (++i12 == ne2) { i12 = 0; if (++i13 == ne3) { i13 = 0; } } } } for (int64_t i01 = ir0; i01 < ir1; i01++) { for (int64_t i00 = 0; i00 < ne00; i00++) { const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); char * dst_ptr = ((char *) dst->data + i10*nb0 + i11*nb1 + i12*nb2 + i13*nb3); memcpy(dst_ptr, src0_ptr, type_size); if (++i10 == ne0) { i10 = 0; if (++i11 == ne1) { i11 = 0; if (++i12 == ne2) { i12 = 0; if (++i13 == ne3) { i13 = 0; } } } } } } i10 += ne00 * (ne01 - ir1); while (i10 >= ne0) { i10 -= ne0; if (++i11 == ne1) { i11 = 0; if (++i12 == ne2) { i12 = 0; if (++i13 == ne3) { i13 = 0; } } } } } } } static void ggml_compute_forward_dup( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; if (src0->type == dst->type) { ggml_compute_forward_dup_bytes(params, dst); return; } switch (src0->type) { case GGML_TYPE_F16: { ggml_compute_forward_dup_f16(params, dst); } break; case GGML_TYPE_BF16: { ggml_compute_forward_dup_bf16(params, dst); } break; case GGML_TYPE_F32: { ggml_compute_forward_dup_f32(params, dst); } break; default: { GGML_ABORT("fatal error"); } } } // ggml_compute_forward_add static void ggml_compute_forward_add_f32( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; const struct ggml_tensor * src1 = dst->src[1]; GGML_ASSERT(ggml_can_repeat(src1, src0) && ggml_are_same_shape(src0, dst)); const int ith = params->ith; const int nth = params->nth; const int nr = ggml_nrows(src0); GGML_TENSOR_BINARY_OP_LOCALS GGML_ASSERT( nb0 == sizeof(float)); GGML_ASSERT(nb00 == sizeof(float)); // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); if (nb10 == sizeof(float)) { for (int ir = ir0; ir < ir1; ++ir) { // src1 is broadcastable across src0 and dst in i1, i2, i3 const int64_t i03 = ir/(ne02*ne01); const int64_t i02 = (ir - i03*ne02*ne01)/ne01; const int64_t i01 = (ir - i03*ne02*ne01 - i02*ne01); const int64_t i13 = i03 % ne13; const int64_t i12 = i02 % ne12; const int64_t i11 = i01 % ne11; const int64_t nr0 = ne00 / ne10; float * dst_ptr = (float *) ((char *) dst->data + i03*nb3 + i02*nb2 + i01*nb1 ); float * src0_ptr = (float *) ((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01); float * src1_ptr = (float *) ((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11); for (int64_t r = 0; r < nr0; ++r) { #ifdef GGML_USE_ACCELERATE vDSP_vadd(src0_ptr + r*ne10, 1, src1_ptr, 1, dst_ptr + r*ne10, 1, ne10); #else ggml_vec_add_f32(ne10, dst_ptr + r*ne10, src0_ptr + r*ne10, src1_ptr); #endif } } } else { // src1 is not contiguous for (int ir = ir0; ir < ir1; ++ir) { // src1 is broadcastable across src0 and dst in i1, i2, i3 const int64_t i03 = ir/(ne02*ne01); const int64_t i02 = (ir - i03*ne02*ne01)/ne01; const int64_t i01 = (ir - i03*ne02*ne01 - i02*ne01); const int64_t i13 = i03 % ne13; const int64_t i12 = i02 % ne12; const int64_t i11 = i01 % ne11; float * dst_ptr = (float *) ((char *) dst->data + i03*nb3 + i02*nb2 + i01*nb1 ); float * src0_ptr = (float *) ((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01); for (int64_t i0 = 0; i0 < ne0; ++i0) { const int64_t i10 = i0 % ne10; float * src1_ptr = (float *) ((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11 + i10*nb10); dst_ptr[i0] = src0_ptr[i0] + *src1_ptr; } } } } static void ggml_compute_forward_add_f16_f32( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; const struct ggml_tensor * src1 = dst->src[1]; GGML_ASSERT(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst)); const int ith = params->ith; const int nth = params->nth; const int nr = ggml_nrows(src0); GGML_TENSOR_BINARY_OP_LOCALS GGML_ASSERT(src0->type == GGML_TYPE_F16); GGML_ASSERT(src1->type == GGML_TYPE_F32); if (dst->type == GGML_TYPE_F32) { GGML_ASSERT( nb0 == sizeof(float)); } else { GGML_ASSERT(dst->type == GGML_TYPE_F16); GGML_ASSERT( nb0 == sizeof(ggml_fp16_t)); } GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); if (nb10 == sizeof(float)) { if (dst->type == GGML_TYPE_F16) { for (int ir = ir0; ir < ir1; ++ir) { // src0, src1 and dst are same shape => same indices const int i3 = ir/(ne2*ne1); const int i2 = (ir - i3*ne2*ne1)/ne1; const int i1 = (ir - i3*ne2*ne1 - i2*ne1); ggml_fp16_t * dst_ptr = (ggml_fp16_t *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1); ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01); float * src1_ptr = (float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11); for (int i = 0; i < ne0; i++) { dst_ptr[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(src0_ptr[i]) + src1_ptr[i]); } } } else { for (int ir = ir0; ir < ir1; ++ir) { // src0, src1 and dst are same shape => same indices const int i3 = ir/(ne2*ne1); const int i2 = (ir - i3*ne2*ne1)/ne1; const int i1 = (ir - i3*ne2*ne1 - i2*ne1); float * dst_ptr = (float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1); ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01); float * src1_ptr = (float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11); for (int i = 0; i < ne0; i++) { dst_ptr[i] = GGML_FP16_TO_FP32(src0_ptr[i]) + src1_ptr[i]; } } } } else { // src1 is not contiguous GGML_ABORT("fatal error"); } } static void ggml_compute_forward_add_bf16_f32( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; const struct ggml_tensor * src1 = dst->src[1]; GGML_ASSERT(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst)); const int ith = params->ith; const int nth = params->nth; const int nr = ggml_nrows(src0); GGML_TENSOR_BINARY_OP_LOCALS GGML_ASSERT(src0->type == GGML_TYPE_BF16); GGML_ASSERT(src1->type == GGML_TYPE_F32); if (dst->type == GGML_TYPE_F32) { GGML_ASSERT( nb0 == sizeof(float)); } else { GGML_ASSERT(dst->type == GGML_TYPE_BF16); GGML_ASSERT( nb0 == sizeof(ggml_bf16_t)); } GGML_ASSERT(nb00 == sizeof(ggml_bf16_t)); // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); if (nb10 == sizeof(float)) { if (dst->type == GGML_TYPE_BF16) { for (int ir = ir0; ir < ir1; ++ir) { // src0, src1 and dst are same shape => same indices const int i3 = ir/(ne2*ne1); const int i2 = (ir - i3*ne2*ne1)/ne1; const int i1 = (ir - i3*ne2*ne1 - i2*ne1); ggml_bf16_t * dst_ptr = (ggml_bf16_t *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1); ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01); float * src1_ptr = (float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11); for (int i = 0; i < ne0; i++) { dst_ptr[i] = GGML_FP32_TO_BF16(GGML_BF16_TO_FP32(src0_ptr[i]) + src1_ptr[i]); } } } else { for (int ir = ir0; ir < ir1; ++ir) { // src0, src1 and dst are same shape => same indices const int i3 = ir/(ne2*ne1); const int i2 = (ir - i3*ne2*ne1)/ne1; const int i1 = (ir - i3*ne2*ne1 - i2*ne1); float * dst_ptr = (float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1); ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01); float * src1_ptr = (float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11); for (int i = 0; i < ne0; i++) { dst_ptr[i] = GGML_BF16_TO_FP32(src0_ptr[i]) + src1_ptr[i]; } } } } else { // src1 is not contiguous GGML_ABORT("fatal error"); } } static void ggml_compute_forward_add_f16_f16( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; const struct ggml_tensor * src1 = dst->src[1]; GGML_ASSERT(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst)); const int ith = params->ith; const int nth = params->nth; const int nr = ggml_nrows(src0); GGML_TENSOR_BINARY_OP_LOCALS GGML_ASSERT(src0->type == GGML_TYPE_F16); GGML_ASSERT(src1->type == GGML_TYPE_F16); GGML_ASSERT(dst->type == GGML_TYPE_F16); GGML_ASSERT( nb0 == sizeof(ggml_fp16_t)); GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); if (nb10 == sizeof(ggml_fp16_t)) { for (int ir = ir0; ir < ir1; ++ir) { // src0, src1 and dst are same shape => same indices const int i3 = ir/(ne2*ne1); const int i2 = (ir - i3*ne2*ne1)/ne1; const int i1 = (ir - i3*ne2*ne1 - i2*ne1); ggml_fp16_t * dst_ptr = (ggml_fp16_t *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1); ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01); ggml_fp16_t * src1_ptr = (ggml_fp16_t *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11); for (int i = 0; i < ne0; i++) { dst_ptr[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(src0_ptr[i]) + GGML_FP16_TO_FP32(src1_ptr[i])); } } } else { // src1 is not contiguous GGML_ABORT("fatal error"); } } static void ggml_compute_forward_add_bf16_bf16( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; const struct ggml_tensor * src1 = dst->src[1]; GGML_ASSERT(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst)); const int ith = params->ith; const int nth = params->nth; const int nr = ggml_nrows(src0); GGML_TENSOR_BINARY_OP_LOCALS GGML_ASSERT(src0->type == GGML_TYPE_BF16); GGML_ASSERT(src1->type == GGML_TYPE_BF16); GGML_ASSERT(dst->type == GGML_TYPE_BF16); GGML_ASSERT( nb0 == sizeof(ggml_bf16_t)); GGML_ASSERT(nb00 == sizeof(ggml_bf16_t)); // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); if (nb10 == sizeof(ggml_bf16_t)) { for (int ir = ir0; ir < ir1; ++ir) { // src0, src1 and dst are same shape => same indices const int i3 = ir/(ne2*ne1); const int i2 = (ir - i3*ne2*ne1)/ne1; const int i1 = (ir - i3*ne2*ne1 - i2*ne1); ggml_bf16_t * dst_ptr = (ggml_bf16_t *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1); ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01); ggml_bf16_t * src1_ptr = (ggml_bf16_t *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11); for (int i = 0; i < ne0; i++) { dst_ptr[i] = GGML_FP32_TO_BF16(GGML_BF16_TO_FP32(src0_ptr[i]) + GGML_BF16_TO_FP32(src1_ptr[i])); } } } else { // src1 is not contiguous GGML_ABORT("fatal error"); } } static void ggml_compute_forward_add_q_f32( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; const struct ggml_tensor * src1 = dst->src[1]; GGML_ASSERT(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst)); const int nr = ggml_nrows(src0); GGML_TENSOR_BINARY_OP_LOCALS const int ith = params->ith; const int nth = params->nth; const enum ggml_type type = src0->type; const enum ggml_type dtype = dst->type; ggml_to_float_t const dequantize_row_q = ggml_get_type_traits(type)->to_float; ggml_from_float_t const quantize_row_q = ggml_get_type_traits(dtype)->from_float; // we don't support permuted src0 or src1 GGML_ASSERT(nb00 == ggml_type_size(type)); GGML_ASSERT(nb10 == sizeof(float)); // dst cannot be transposed or permuted GGML_ASSERT(nb0 <= nb1); GGML_ASSERT(nb1 <= nb2); GGML_ASSERT(nb2 <= nb3); GGML_ASSERT(ggml_is_quantized(src0->type)); GGML_ASSERT(src1->type == GGML_TYPE_F32); // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); float * wdata = (float *) params->wdata + (ne00 + CACHE_LINE_SIZE_F32) * ith; for (int ir = ir0; ir < ir1; ++ir) { // src0 indices const int i03 = ir/(ne02*ne01); const int i02 = (ir - i03*ne02*ne01)/ne01; const int i01 = (ir - i03*ne02*ne01 - i02*ne01); // src1 and dst are same shape as src0 => same indices const int i13 = i03; const int i12 = i02; const int i11 = i01; const int i3 = i03; const int i2 = i02; const int i1 = i01; void * src0_row = (void *) ((char *) src0->data + (i01*nb01 + i02*nb02 + i03*nb03)); float * src1_row = (float *)((char *) src1->data + (i11*nb11 + i12*nb12 + i13*nb13)); void * dst_row = (void *) ((char *) dst->data + ( i1*nb1 + i2*nb2 + i3*nb3)); assert(ne00 % 32 == 0); // unquantize row from src0 to temp buffer dequantize_row_q(src0_row, wdata, ne00); // add src1 ggml_vec_acc_f32(ne00, wdata, src1_row); // quantize row to dst if (quantize_row_q != NULL) { quantize_row_q(wdata, dst_row, ne00); } else { memcpy(dst_row, wdata, ne0*nb0); } } } static void ggml_compute_forward_add( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; const struct ggml_tensor * src1 = dst->src[1]; switch (src0->type) { case GGML_TYPE_F32: { if (src1->type == GGML_TYPE_F32) { ggml_compute_forward_add_f32(params, dst); } else { GGML_ABORT("fatal error"); } } break; case GGML_TYPE_F16: { if (src1->type == GGML_TYPE_F16) { ggml_compute_forward_add_f16_f16(params, dst); } else if (src1->type == GGML_TYPE_F32) { ggml_compute_forward_add_f16_f32(params, dst); } else { GGML_ABORT("fatal error"); } } break; case GGML_TYPE_BF16: { if (src1->type == GGML_TYPE_BF16) { ggml_compute_forward_add_bf16_bf16(params, dst); } else if (src1->type == GGML_TYPE_F32) { ggml_compute_forward_add_bf16_f32(params, dst); } else { GGML_ABORT("fatal error"); } } break; case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_1: case GGML_TYPE_Q5_0: case GGML_TYPE_Q5_1: case GGML_TYPE_Q8_0: case GGML_TYPE_Q2_K: case GGML_TYPE_Q3_K: case GGML_TYPE_Q4_K: case GGML_TYPE_Q5_K: case GGML_TYPE_Q6_K: case GGML_TYPE_TQ1_0: case GGML_TYPE_TQ2_0: case GGML_TYPE_IQ2_XXS: case GGML_TYPE_IQ2_XS: case GGML_TYPE_IQ3_XXS: case GGML_TYPE_IQ1_S: case GGML_TYPE_IQ1_M: case GGML_TYPE_IQ4_NL: case GGML_TYPE_IQ4_XS: case GGML_TYPE_IQ3_S: case GGML_TYPE_IQ2_S: case GGML_TYPE_Q4_0_4_4: case GGML_TYPE_Q4_0_4_8: case GGML_TYPE_Q4_0_8_8: { ggml_compute_forward_add_q_f32(params, dst); } break; default: { GGML_ABORT("fatal error"); } } } // ggml_compute_forward_add1 static void ggml_compute_forward_add1_f32( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; const struct ggml_tensor * src1 = dst->src[1]; GGML_ASSERT(ggml_are_same_shape(src0, dst)); GGML_ASSERT(ggml_is_scalar(src1)); const int ith = params->ith; const int nth = params->nth; const int nr = ggml_nrows(src0); GGML_TENSOR_UNARY_OP_LOCALS GGML_ASSERT( nb0 == sizeof(float)); GGML_ASSERT(nb00 == sizeof(float)); // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); for (int ir = ir0; ir < ir1; ++ir) { // src0 and dst are same shape => same indices const int i3 = ir/(ne2*ne1); const int i2 = (ir - i3*ne2*ne1)/ne1; const int i1 = (ir - i3*ne2*ne1 - i2*ne1); #ifdef GGML_USE_ACCELERATE UNUSED(ggml_vec_add1_f32); vDSP_vadd( (float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01), 1, (float *) ((char *) src1->data), 0, (float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 ), 1, ne0); #else ggml_vec_add1_f32(ne0, (float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 ), (float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01), *(float *) src1->data); #endif } } static void ggml_compute_forward_add1_f16_f32( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; const struct ggml_tensor * src1 = dst->src[1]; GGML_ASSERT(ggml_are_same_shape(src0, dst)); GGML_ASSERT(ggml_is_scalar(src1)); // scalar to add const float v = *(float *) src1->data; const int ith = params->ith; const int nth = params->nth; const int nr = ggml_nrows(src0); GGML_TENSOR_UNARY_OP_LOCALS GGML_ASSERT(src0->type == GGML_TYPE_F16); GGML_ASSERT(src1->type == GGML_TYPE_F32); GGML_ASSERT(dst->type == GGML_TYPE_F16); GGML_ASSERT( nb0 == sizeof(ggml_fp16_t)); GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); for (int ir = ir0; ir < ir1; ++ir) { // src0 and dst are same shape => same indices const int i3 = ir/(ne2*ne1); const int i2 = (ir - i3*ne2*ne1)/ne1; const int i1 = (ir - i3*ne2*ne1 - i2*ne1); ggml_fp16_t * dst_ptr = (ggml_fp16_t *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 ); ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01); for (int i = 0; i < ne0; i++) { dst_ptr[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(src0_ptr[i]) + v); } } } static void ggml_compute_forward_add1_f16_f16( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; const struct ggml_tensor * src1 = dst->src[1]; GGML_ASSERT(ggml_are_same_shape(src0, dst)); GGML_ASSERT(ggml_is_scalar(src1)); // scalar to add const float v = GGML_FP16_TO_FP32(*(ggml_fp16_t *) src1->data); const int ith = params->ith; const int nth = params->nth; const int nr = ggml_nrows(src0); GGML_TENSOR_UNARY_OP_LOCALS GGML_ASSERT(src0->type == GGML_TYPE_F16); GGML_ASSERT(src1->type == GGML_TYPE_F16); GGML_ASSERT(dst->type == GGML_TYPE_F16); GGML_ASSERT( nb0 == sizeof(ggml_fp16_t)); GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); for (int ir = ir0; ir < ir1; ++ir) { // src0 and dst are same shape => same indices const int i3 = ir/(ne2*ne1); const int i2 = (ir - i3*ne2*ne1)/ne1; const int i1 = (ir - i3*ne2*ne1 - i2*ne1); ggml_fp16_t * dst_ptr = (ggml_fp16_t *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 ); ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01); for (int i = 0; i < ne0; i++) { dst_ptr[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(src0_ptr[i]) + v); } } } static void ggml_compute_forward_add1_q_f32( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; const struct ggml_tensor * src1 = dst->src[1]; GGML_ASSERT(ggml_are_same_shape(src0, dst)); GGML_ASSERT(ggml_is_scalar(src1)); // scalar to add const float v = *(float *) src1->data; const int ith = params->ith; const int nth = params->nth; const int nr = ggml_nrows(src0); GGML_TENSOR_UNARY_OP_LOCALS const enum ggml_type type = src0->type; ggml_to_float_t const dequantize_row_q = ggml_get_type_traits(type)->to_float; ggml_from_float_t const quantize_row_q = ggml_get_type_traits(type)->from_float; // we don't support permuted src0 GGML_ASSERT(nb00 == ggml_type_size(type)); // dst cannot be transposed or permuted GGML_ASSERT(nb0 <= nb1); GGML_ASSERT(nb1 <= nb2); GGML_ASSERT(nb2 <= nb3); GGML_ASSERT(ggml_is_quantized(src0->type)); GGML_ASSERT(dst->type == src0->type); GGML_ASSERT(src1->type == GGML_TYPE_F32); // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); float * wdata = (float *) params->wdata + (ne0 + CACHE_LINE_SIZE_F32) * ith; for (int ir = ir0; ir < ir1; ++ir) { // src0 and dst are same shape => same indices const int i3 = ir/(ne2*ne1); const int i2 = (ir - i3*ne2*ne1)/ne1; const int i1 = (ir - i3*ne2*ne1 - i2*ne1); void * src0_row = (void *) ((char *) src0->data + (i1*nb01 + i2*nb02 + i3*nb03)); void * dst_row = (void *) ((char *) dst->data + (i1*nb1 + i2*nb2 + i3*nb0 )); assert(ne0 % 32 == 0); // unquantize row from src0 to temp buffer dequantize_row_q(src0_row, wdata, ne0); // add src1 ggml_vec_acc1_f32(ne0, wdata, v); // quantize row to dst quantize_row_q(wdata, dst_row, ne0); } } static void ggml_compute_forward_add1_bf16_f32( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; const struct ggml_tensor * src1 = dst->src[1]; GGML_ASSERT(ggml_are_same_shape(src0, dst)); GGML_ASSERT(ggml_is_scalar(src1)); // scalar to add const float v = *(float *) src1->data; const int ith = params->ith; const int nth = params->nth; const int nr = ggml_nrows(src0); GGML_TENSOR_UNARY_OP_LOCALS GGML_ASSERT(src0->type == GGML_TYPE_BF16); GGML_ASSERT(src1->type == GGML_TYPE_F32); GGML_ASSERT(dst->type == GGML_TYPE_BF16); GGML_ASSERT( nb0 == sizeof(ggml_bf16_t)); GGML_ASSERT(nb00 == sizeof(ggml_bf16_t)); // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); for (int ir = ir0; ir < ir1; ++ir) { // src0 and dst are same shape => same indices const int i3 = ir/(ne2*ne1); const int i2 = (ir - i3*ne2*ne1)/ne1; const int i1 = (ir - i3*ne2*ne1 - i2*ne1); ggml_bf16_t * dst_ptr = (ggml_bf16_t *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 ); ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01); for (int i = 0; i < ne0; i++) { dst_ptr[i] = GGML_FP32_TO_BF16(GGML_BF16_TO_FP32(src0_ptr[i]) + v); } } } static void ggml_compute_forward_add1_bf16_bf16( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; const struct ggml_tensor * src1 = dst->src[1]; GGML_ASSERT(ggml_are_same_shape(src0, dst)); GGML_ASSERT(ggml_is_scalar(src1)); // scalar to add const float v = GGML_BF16_TO_FP32(*(ggml_bf16_t *) src1->data); const int ith = params->ith; const int nth = params->nth; const int nr = ggml_nrows(src0); GGML_TENSOR_UNARY_OP_LOCALS GGML_ASSERT(src0->type == GGML_TYPE_BF16); GGML_ASSERT(src1->type == GGML_TYPE_BF16); GGML_ASSERT(dst->type == GGML_TYPE_BF16); GGML_ASSERT( nb0 == sizeof(ggml_bf16_t)); GGML_ASSERT(nb00 == sizeof(ggml_bf16_t)); // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); for (int ir = ir0; ir < ir1; ++ir) { // src0 and dst are same shape => same indices const int i3 = ir/(ne2*ne1); const int i2 = (ir - i3*ne2*ne1)/ne1; const int i1 = (ir - i3*ne2*ne1 - i2*ne1); ggml_bf16_t * dst_ptr = (ggml_bf16_t *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 ); ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01); for (int i = 0; i < ne0; i++) { dst_ptr[i] = GGML_FP32_TO_BF16(GGML_BF16_TO_FP32(src0_ptr[i]) + v); } } } static void ggml_compute_forward_add1( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; const struct ggml_tensor * src1 = dst->src[1]; switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_add1_f32(params, dst); } break; case GGML_TYPE_F16: { if (src1->type == GGML_TYPE_F16) { ggml_compute_forward_add1_f16_f16(params, dst); } else if (src1->type == GGML_TYPE_F32) { ggml_compute_forward_add1_f16_f32(params, dst); } else { GGML_ABORT("fatal error"); } } break; case GGML_TYPE_BF16: { if (src1->type == GGML_TYPE_BF16) { ggml_compute_forward_add1_bf16_bf16(params, dst); } else if (src1->type == GGML_TYPE_F32) { ggml_compute_forward_add1_bf16_f32(params, dst); } else { GGML_ABORT("fatal error"); } } break; case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_1: case GGML_TYPE_Q5_0: case GGML_TYPE_Q5_1: case GGML_TYPE_Q8_0: case GGML_TYPE_Q8_1: case GGML_TYPE_Q2_K: case GGML_TYPE_Q3_K: case GGML_TYPE_Q4_K: case GGML_TYPE_Q5_K: case GGML_TYPE_Q6_K: case GGML_TYPE_TQ1_0: case GGML_TYPE_TQ2_0: case GGML_TYPE_IQ2_XXS: case GGML_TYPE_IQ2_XS: case GGML_TYPE_IQ3_XXS: case GGML_TYPE_IQ1_S: case GGML_TYPE_IQ1_M: case GGML_TYPE_IQ4_NL: case GGML_TYPE_IQ4_XS: case GGML_TYPE_IQ3_S: case GGML_TYPE_IQ2_S: case GGML_TYPE_Q4_0_4_4: case GGML_TYPE_Q4_0_4_8: case GGML_TYPE_Q4_0_8_8: { ggml_compute_forward_add1_q_f32(params, dst); } break; default: { GGML_ABORT("fatal error"); } } } // ggml_compute_forward_acc static void ggml_compute_forward_acc_f32( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; const struct ggml_tensor * src1 = dst->src[1]; GGML_ASSERT(ggml_are_same_shape(src0, dst)); GGML_ASSERT(ggml_is_contiguous(dst) && ggml_is_contiguous(src0)); // view src0 and dst with these strides and data offset inbytes during acc // nb0 is implicitly element_size because src0 and dst are contiguous size_t nb1 = ((int32_t *) dst->op_params)[0]; size_t nb2 = ((int32_t *) dst->op_params)[1]; size_t nb3 = ((int32_t *) dst->op_params)[2]; size_t offset = ((int32_t *) dst->op_params)[3]; bool inplace = (bool) ((int32_t *) dst->op_params)[4]; if (!inplace) { if (params->ith == 0) { // memcpy needs to be synchronized across threads to avoid race conditions. // => do it in INIT phase memcpy( ((char *) dst->data), ((char *) src0->data), ggml_nbytes(dst)); } ggml_barrier(params->threadpool); } const int ith = params->ith; const int nth = params->nth; const int nr = ggml_nrows(src1); const int nc = src1->ne[0]; GGML_TENSOR_LOCALS(int64_t, ne1, src1, ne) GGML_TENSOR_LOCALS(size_t, nb1, src1, nb) // src0 and dst as viewed during acc const size_t nb0 = ggml_element_size(src0); const size_t nb00 = nb0; const size_t nb01 = nb1; const size_t nb02 = nb2; const size_t nb03 = nb3; GGML_ASSERT(offset + (ne10 == 0 ? 0 : ne10-1)*nb0 + (ne11 == 0 ? 0 : ne11-1)*nb1 + (ne12 == 0 ? 0 : ne12-1)*nb2 + (ne13 == 0 ? 0 : ne13-1)*nb3 < ggml_nbytes(dst)); GGML_ASSERT(offset + (ne10 == 0 ? 0 : ne10-1)*nb00 + (ne11 == 0 ? 0 : ne11-1)*nb01 + (ne12 == 0 ? 0 : ne12-1)*nb02 + (ne13 == 0 ? 0 : ne13-1)*nb03 < ggml_nbytes(src0)); GGML_ASSERT(nb10 == sizeof(float)); // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); for (int ir = ir0; ir < ir1; ++ir) { // src0 and dst are viewed with shape of src1 and offset // => same indices const int i3 = ir/(ne12*ne11); const int i2 = (ir - i3*ne12*ne11)/ne11; const int i1 = (ir - i3*ne12*ne11 - i2*ne11); #ifdef GGML_USE_ACCELERATE vDSP_vadd( (float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + offset), 1, (float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11), 1, (float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + offset), 1, nc); #else ggml_vec_add_f32(nc, (float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + offset), (float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + offset), (float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11)); #endif } } static void ggml_compute_forward_acc( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_acc_f32(params, dst); } break; case GGML_TYPE_F16: case GGML_TYPE_BF16: case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_1: case GGML_TYPE_Q5_0: case GGML_TYPE_Q5_1: case GGML_TYPE_Q8_0: case GGML_TYPE_Q8_1: case GGML_TYPE_Q2_K: case GGML_TYPE_Q3_K: case GGML_TYPE_Q4_K: case GGML_TYPE_Q5_K: case GGML_TYPE_Q6_K: case GGML_TYPE_TQ1_0: case GGML_TYPE_TQ2_0: case GGML_TYPE_IQ2_XXS: case GGML_TYPE_IQ2_XS: case GGML_TYPE_IQ3_XXS: case GGML_TYPE_IQ1_S: case GGML_TYPE_IQ1_M: case GGML_TYPE_IQ4_NL: case GGML_TYPE_IQ4_XS: case GGML_TYPE_IQ3_S: case GGML_TYPE_IQ2_S: case GGML_TYPE_Q4_0_4_4: case GGML_TYPE_Q4_0_4_8: case GGML_TYPE_Q4_0_8_8: default: { GGML_ABORT("fatal error"); } } } // ggml_compute_forward_sub static void ggml_compute_forward_sub_f32( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; const struct ggml_tensor * src1 = dst->src[1]; assert(ggml_can_repeat(src1, src0) && ggml_are_same_shape(src0, dst)); const int ith = params->ith; const int nth = params->nth; const int nr = ggml_nrows(src0); GGML_TENSOR_BINARY_OP_LOCALS GGML_ASSERT( nb0 == sizeof(float)); GGML_ASSERT(nb00 == sizeof(float)); // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); if (nb10 == sizeof(float)) { for (int ir = ir0; ir < ir1; ++ir) { // src1 is broadcastable across src0 and dst in i1, i2, i3 const int64_t i03 = ir/(ne02*ne01); const int64_t i02 = (ir - i03*ne02*ne01)/ne01; const int64_t i01 = (ir - i03*ne02*ne01 - i02*ne01); const int64_t i13 = i03 % ne13; const int64_t i12 = i02 % ne12; const int64_t i11 = i01 % ne11; const int64_t nr0 = ne00 / ne10; float * dst_ptr = (float *) ((char *) dst->data + i03*nb3 + i02*nb2 + i01*nb1 ); float * src0_ptr = (float *) ((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01); float * src1_ptr = (float *) ((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11); for (int64_t r = 0; r < nr0; ++r) { #ifdef GGML_USE_ACCELERATE vDSP_vsub(src1_ptr, 1, src0_ptr + r*ne10, 1, dst_ptr + r*ne10, 1, ne10); #else ggml_vec_sub_f32(ne10, dst_ptr + r*ne10, src0_ptr + r*ne10, src1_ptr); #endif } } } else { // src1 is not contiguous for (int ir = ir0; ir < ir1; ++ir) { // src1 is broadcastable across src0 and dst in i1, i2, i3 const int64_t i03 = ir/(ne02*ne01); const int64_t i02 = (ir - i03*ne02*ne01)/ne01; const int64_t i01 = (ir - i03*ne02*ne01 - i02*ne01); const int64_t i13 = i03 % ne13; const int64_t i12 = i02 % ne12; const int64_t i11 = i01 % ne11; float * dst_ptr = (float *) ((char *) dst->data + i03*nb3 + i02*nb2 + i01*nb1 ); float * src0_ptr = (float *) ((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01); for (int64_t i0 = 0; i0 < ne0; ++i0) { const int64_t i10 = i0 % ne10; float * src1_ptr = (float *) ((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11 + i10*nb10); dst_ptr[i0] = src0_ptr[i0] - *src1_ptr; } } } } static void ggml_compute_forward_sub( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_sub_f32(params, dst); } break; default: { GGML_ABORT("fatal error"); } } } // ggml_compute_forward_mul static void ggml_compute_forward_mul_f32( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; const struct ggml_tensor * src1 = dst->src[1]; GGML_ASSERT(ggml_can_repeat(src1, src0) && ggml_are_same_shape(src0, dst)); const int ith = params->ith; const int nth = params->nth; const int64_t nr = ggml_nrows(src0); GGML_TENSOR_BINARY_OP_LOCALS GGML_ASSERT( nb0 == sizeof(float)); GGML_ASSERT(nb00 == sizeof(float)); if (nb10 == sizeof(float)) { for (int64_t ir = ith; ir < nr; ir += nth) { // src0 and dst are same shape => same indices const int64_t i03 = ir/(ne02*ne01); const int64_t i02 = (ir - i03*ne02*ne01)/ne01; const int64_t i01 = (ir - i03*ne02*ne01 - i02*ne01); const int64_t i13 = i03 % ne13; const int64_t i12 = i02 % ne12; const int64_t i11 = i01 % ne11; const int64_t nr0 = ne00 / ne10; float * dst_ptr = (float *) ((char *) dst->data + i03*nb3 + i02*nb2 + i01*nb1 ); float * src0_ptr = (float *) ((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01); float * src1_ptr = (float *) ((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11); for (int64_t r = 0 ; r < nr0; ++r) { #ifdef GGML_USE_ACCELERATE UNUSED(ggml_vec_mul_f32); vDSP_vmul(src0_ptr + r*ne10, 1, src1_ptr, 1, dst_ptr + r*ne10, 1, ne10); #else ggml_vec_mul_f32(ne10, dst_ptr + r*ne10, src0_ptr + r*ne10, src1_ptr); #endif } } } else { // src1 is not contiguous for (int64_t ir = ith; ir < nr; ir += nth) { // src0 and dst are same shape => same indices // src1 is broadcastable across src0 and dst in i1, i2, i3 const int64_t i03 = ir/(ne02*ne01); const int64_t i02 = (ir - i03*ne02*ne01)/ne01; const int64_t i01 = (ir - i03*ne02*ne01 - i02*ne01); const int64_t i13 = i03 % ne13; const int64_t i12 = i02 % ne12; const int64_t i11 = i01 % ne11; float * dst_ptr = (float *) ((char *) dst->data + i03*nb3 + i02*nb2 + i01*nb1 ); float * src0_ptr = (float *) ((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01); for (int64_t i0 = 0; i0 < ne00; ++i0) { const int64_t i10 = i0 % ne10; float * src1_ptr = (float *) ((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11 + i10*nb10); dst_ptr[i0] = src0_ptr[i0] * (*src1_ptr); } } } } static void ggml_compute_forward_mul( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; const struct ggml_tensor * src1 = dst->src[1]; GGML_ASSERT(src1->type == GGML_TYPE_F32 && "only f32 src1 supported for now"); switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_mul_f32(params, dst); } break; default: { GGML_ABORT("fatal error"); } } } // ggml_compute_forward_div static void ggml_compute_forward_div_f32( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; const struct ggml_tensor * src1 = dst->src[1]; GGML_ASSERT(ggml_can_repeat(src1, src0) && ggml_are_same_shape(src0, dst)); const int ith = params->ith; const int nth = params->nth; const int64_t nr = ggml_nrows(src0); GGML_TENSOR_BINARY_OP_LOCALS GGML_ASSERT( nb0 == sizeof(float)); GGML_ASSERT(nb00 == sizeof(float)); if (nb10 == sizeof(float)) { for (int64_t ir = ith; ir < nr; ir += nth) { // src0 and dst are same shape => same indices const int64_t i03 = ir/(ne02*ne01); const int64_t i02 = (ir - i03*ne02*ne01)/ne01; const int64_t i01 = (ir - i03*ne02*ne01 - i02*ne01); const int64_t i13 = i03 % ne13; const int64_t i12 = i02 % ne12; const int64_t i11 = i01 % ne11; const int64_t nr0 = ne00 / ne10; float * dst_ptr = (float *) ((char *) dst->data + i03*nb3 + i02*nb2 + i01*nb1 ); float * src0_ptr = (float *) ((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01); float * src1_ptr = (float *) ((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11); for (int64_t r = 0; r < nr0; ++r) { #ifdef GGML_USE_ACCELERATE UNUSED(ggml_vec_div_f32); vDSP_vdiv(src1_ptr, 1, src0_ptr + r*ne10, 1, dst_ptr + r*ne10, 1, ne10); #else ggml_vec_div_f32(ne10, dst_ptr + r*ne10, src0_ptr + r*ne10, src1_ptr); #endif } } } else { // src1 is not contiguous for (int64_t ir = ith; ir < nr; ir += nth) { // src0 and dst are same shape => same indices // src1 is broadcastable across src0 and dst in i1, i2, i3 const int64_t i03 = ir/(ne02*ne01); const int64_t i02 = (ir - i03*ne02*ne01)/ne01; const int64_t i01 = (ir - i03*ne02*ne01 - i02*ne01); const int64_t i13 = i03 % ne13; const int64_t i12 = i02 % ne12; const int64_t i11 = i01 % ne11; float * dst_ptr = (float *) ((char *) dst->data + i03*nb3 + i02*nb2 + i01*nb1 ); float * src0_ptr = (float *) ((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01); for (int64_t i0 = 0; i0 < ne00; ++i0) { const int64_t i10 = i0 % ne10; float * src1_ptr = (float *) ((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11 + i10*nb10); dst_ptr[i0] = src0_ptr[i0] / (*src1_ptr); } } } } static void ggml_compute_forward_div( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_div_f32(params, dst); } break; default: { GGML_ABORT("fatal error"); } } } // ggml_compute_forward_sqr static void ggml_compute_forward_sqr_f32( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; if (params->ith != 0) { return; } assert(ggml_are_same_shape(src0, dst)); 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, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_sqr_f32(params, dst); } break; default: { GGML_ABORT("fatal error"); } } } // ggml_compute_forward_sqrt static void ggml_compute_forward_sqrt_f32( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; if (params->ith != 0) { return; } assert(ggml_are_same_shape(src0, dst)); 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, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_sqrt_f32(params, dst); } break; default: { GGML_ABORT("fatal error"); } } } // ggml_compute_forward_log static void ggml_compute_forward_log_f32( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; if (params->ith != 0) { return; } GGML_ASSERT(ggml_are_same_shape(src0, dst)); const int n = ggml_nrows(src0); const int nc = src0->ne[0]; GGML_ASSERT( dst->nb[0] == sizeof(float)); GGML_ASSERT(src0->nb[0] == sizeof(float)); for (int i = 0; i < n; i++) { ggml_vec_log_f32(nc, (float *) ((char *) dst->data + i*( dst->nb[1])), (float *) ((char *) src0->data + i*(src0->nb[1]))); } } static void ggml_compute_forward_log( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_log_f32(params, dst); } break; default: { GGML_ABORT("fatal error"); } } } // ggml_compute_forward_sin static void ggml_compute_forward_sin_f32( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; if (params->ith != 0) { return; } GGML_ASSERT(ggml_are_same_shape(src0, dst)); const int n = ggml_nrows(src0); const int nc = src0->ne[0]; GGML_ASSERT( dst->nb[0] == sizeof(float)); GGML_ASSERT(src0->nb[0] == sizeof(float)); for (int i = 0; i < n; i++) { ggml_vec_sin_f32(nc, (float *) ((char *) dst->data + i*( dst->nb[1])), (float *) ((char *) src0->data + i*(src0->nb[1]))); } } static void ggml_compute_forward_sin( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_sin_f32(params, dst); } break; default: { GGML_ABORT("fatal error"); } } } // ggml_compute_forward_cos static void ggml_compute_forward_cos_f32( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; if (params->ith != 0) { return; } GGML_ASSERT(ggml_are_same_shape(src0, dst)); const int n = ggml_nrows(src0); const int nc = src0->ne[0]; GGML_ASSERT( dst->nb[0] == sizeof(float)); GGML_ASSERT(src0->nb[0] == sizeof(float)); for (int i = 0; i < n; i++) { ggml_vec_cos_f32(nc, (float *) ((char *) dst->data + i*( dst->nb[1])), (float *) ((char *) src0->data + i*(src0->nb[1]))); } } static void ggml_compute_forward_cos( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_cos_f32(params, dst); } break; default: { GGML_ABORT("fatal error"); } } } // ggml_compute_forward_sum static void ggml_compute_forward_sum_f32( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; if (params->ith != 0) { return; } assert(ggml_is_scalar(dst)); assert(src0->nb[0] == sizeof(float)); GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne) GGML_TENSOR_LOCALS(size_t, nb0, src0, nb) ggml_float sum = 0; ggml_float row_sum = 0; for (int64_t i03 = 0; i03 < ne03; i03++) { for (int64_t i02 = 0; i02 < ne02; i02++) { for (int64_t i01 = 0; i01 < ne01; i01++) { ggml_vec_sum_f32_ggf(ne00, &row_sum, (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03)); sum += row_sum; } } } ((float *) dst->data)[0] = sum; } static void ggml_compute_forward_sum_f16( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; if (params->ith != 0) { return; } assert(ggml_is_scalar(dst)); assert(src0->nb[0] == sizeof(ggml_fp16_t)); GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne) GGML_TENSOR_LOCALS(size_t, nb0, src0, nb) float sum = 0; float row_sum = 0; for (int64_t i03 = 0; i03 < ne03; i03++) { for (int64_t i02 = 0; i02 < ne02; i02++) { for (int64_t i01 = 0; i01 < ne01; i01++) { ggml_vec_sum_f16_ggf(ne00, &row_sum, (ggml_fp16_t *) ((char *) src0->data + i01 * nb01 + i02 * nb02 + i03 * nb03)); sum += row_sum; } } } ((ggml_fp16_t *) dst->data)[0] = GGML_FP32_TO_FP16(sum); } static void ggml_compute_forward_sum_bf16( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; if (params->ith != 0) { return; } assert(ggml_is_scalar(dst)); assert(src0->nb[0] == sizeof(ggml_bf16_t)); GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne) GGML_TENSOR_LOCALS(size_t, nb0, src0, nb) float sum = 0; float row_sum = 0; for (int64_t i03 = 0; i03 < ne03; i03++) { for (int64_t i02 = 0; i02 < ne02; i02++) { for (int64_t i01 = 0; i01 < ne01; i01++) { ggml_vec_sum_bf16_ggf(ne00, &row_sum, (ggml_bf16_t *) ((char *) src0->data + i01 * nb01 + i02 * nb02 + i03 * nb03)); sum += row_sum; } } } ((ggml_bf16_t *) dst->data)[0] = GGML_FP32_TO_BF16(sum); } static void ggml_compute_forward_sum( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_sum_f32(params, dst); } break; case GGML_TYPE_F16: { ggml_compute_forward_sum_f16(params, dst); } break; case GGML_TYPE_BF16: { ggml_compute_forward_sum_bf16(params, dst); } break; default: { GGML_ABORT("fatal error"); } } } // ggml_compute_forward_sum_rows static void ggml_compute_forward_sum_rows_f32( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; if (params->ith != 0) { return; } GGML_ASSERT(src0->nb[0] == sizeof(float)); GGML_ASSERT(dst->nb[0] == sizeof(float)); GGML_TENSOR_UNARY_OP_LOCALS GGML_ASSERT(ne0 == 1); GGML_ASSERT(ne1 == ne01); GGML_ASSERT(ne2 == ne02); GGML_ASSERT(ne3 == ne03); for (int64_t i3 = 0; i3 < ne03; i3++) { for (int64_t i2 = 0; i2 < ne02; i2++) { for (int64_t i1 = 0; i1 < ne01; i1++) { float * src_row = (float *) ((char *) src0->data + i1*nb01 + i2*nb02 + i3*nb03); float * dst_row = (float *) ((char *) dst->data + i1*nb1 + i2*nb2 + i3*nb3); float row_sum = 0; ggml_vec_sum_f32(ne00, &row_sum, src_row); dst_row[0] = row_sum; } } } } static void ggml_compute_forward_sum_rows( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_sum_rows_f32(params, dst); } break; default: { GGML_ABORT("fatal error"); } } } // ggml_compute_forward_mean static void ggml_compute_forward_mean_f32( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; if (params->ith != 0) { return; } assert(src0->nb[0] == sizeof(float)); GGML_TENSOR_UNARY_OP_LOCALS assert(ne0 == 1); assert(ne1 == ne01); assert(ne2 == ne02); assert(ne3 == ne03); UNUSED(ne0); UNUSED(ne1); UNUSED(ne2); UNUSED(ne3); for (int64_t i03 = 0; i03 < ne03; i03++) { for (int64_t i02 = 0; i02 < ne02; i02++) { for (int64_t i01 = 0; i01 < ne01; i01++) { ggml_vec_sum_f32(ne00, (float *) ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3), (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03)); *(float *) ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3) /= (float) ne00; } } } } static void ggml_compute_forward_mean( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_mean_f32(params, dst); } break; default: { GGML_ABORT("fatal error"); } } } // ggml_compute_forward_argmax static void ggml_compute_forward_argmax_f32( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; if (params->ith != 0) { return; } assert(src0->nb[0] == sizeof(float)); assert(dst->nb[0] == sizeof(float)); const int64_t ne00 = src0->ne[0]; const int64_t ne01 = src0->ne[1]; const size_t nb01 = src0->nb[1]; const size_t nb0 = dst->nb[0]; for (int64_t i1 = 0; i1 < ne01; i1++) { float * src = (float *) ((char *) src0->data + i1*nb01); int32_t * dst_ = (int32_t *) ((char *) dst->data + i1*nb0); int v = 0; ggml_vec_argmax_f32(ne00, &v, src); dst_[0] = v; } } static void ggml_compute_forward_argmax( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_argmax_f32(params, dst); } break; default: { GGML_ABORT("fatal error"); } } } // ggml_compute_forward_count_equal static void ggml_compute_forward_count_equal_i32( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; const struct ggml_tensor * src1 = dst->src[1]; GGML_TENSOR_BINARY_OP_LOCALS; GGML_ASSERT(src0->type == GGML_TYPE_I32); GGML_ASSERT(src1->type == GGML_TYPE_I32); GGML_ASSERT(ggml_are_same_shape(src0, src1)); GGML_ASSERT(ggml_is_scalar(dst)); GGML_ASSERT(dst->type == GGML_TYPE_I64); const int64_t nr = ggml_nrows(src0); const int ith = params->ith; const int nth = params->nth; int64_t * sums = (int64_t *) params->wdata; int64_t sum_thread = 0; // rows per thread const int64_t dr = (nr + nth - 1)/nth; // row range for this thread const int64_t ir0 = dr*ith; const int64_t ir1 = MIN(ir0 + dr, nr); for (int64_t ir = ir0; ir < ir1; ++ir) { const int64_t i03 = ir / (ne02*ne01); const int64_t i02 = (ir - i03*ne03) / ne01; const int64_t i01 = ir - i03*ne03 - i02*ne02; const char * data0 = (const char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01; const char * data1 = (const char *) src1->data + i03*nb13 + i02*nb12 + i01*nb11; for (int64_t i00 = 0; i00 < ne00; ++i00) { const int32_t val0 = *((const int32_t *) (data0 + i00*nb00)); const int32_t val1 = *((const int32_t *) (data1 + i00*nb10)); sum_thread += val0 == val1; } } if (ith != 0) { sums[ith] = sum_thread; } ggml_barrier(params->threadpool); if (ith != 0) { return; } for (int ith_other = 1; ith_other < nth; ++ith_other) { sum_thread += sums[ith_other]; } *((int64_t *) dst->data) = sum_thread; } static void ggml_compute_forward_count_equal( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; switch (src0->type) { case GGML_TYPE_I32: { ggml_compute_forward_count_equal_i32(params, dst); } break; default: { GGML_ABORT("fatal error"); } } } // ggml_compute_forward_repeat static void ggml_compute_forward_repeat_f32( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; if (params->ith != 0) { return; } GGML_ASSERT(ggml_can_repeat(src0, dst)); GGML_TENSOR_UNARY_OP_LOCALS // guaranteed to be an integer due to the check in ggml_can_repeat const int nr0 = (int)(ne0/ne00); const int nr1 = (int)(ne1/ne01); const int nr2 = (int)(ne2/ne02); const int nr3 = (int)(ne3/ne03); // TODO: support for transposed / permuted tensors GGML_ASSERT(nb0 == sizeof(float)); GGML_ASSERT(nb00 == sizeof(float)); // TODO: maybe this is not optimal? for (int i3 = 0; i3 < nr3; i3++) { for (int k3 = 0; k3 < ne03; k3++) { for (int i2 = 0; i2 < nr2; i2++) { for (int k2 = 0; k2 < ne02; k2++) { for (int i1 = 0; i1 < nr1; i1++) { for (int k1 = 0; k1 < ne01; k1++) { for (int i0 = 0; i0 < nr0; i0++) { ggml_vec_cpy_f32(ne00, (float *) ((char *) dst->data + (i3*ne03 + k3)*nb3 + (i2*ne02 + k2)*nb2 + (i1*ne01 + k1)*nb1 + (i0*ne00)*nb0), (float *) ((char *) src0->data + ( k3)*nb03 + ( k2)*nb02 + ( k1)*nb01)); } } } } } } } } static void ggml_compute_forward_repeat_f16( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; if (params->ith != 0) { return; } GGML_ASSERT(ggml_can_repeat(src0, dst)); GGML_TENSOR_UNARY_OP_LOCALS // guaranteed to be an integer due to the check in ggml_can_repeat const int nr0 = (int)(ne0/ne00); const int nr1 = (int)(ne1/ne01); const int nr2 = (int)(ne2/ne02); const int nr3 = (int)(ne3/ne03); // TODO: support for transposed / permuted tensors GGML_ASSERT(nb0 == sizeof(ggml_fp16_t)); GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); // TODO: maybe this is not optimal? for (int i3 = 0; i3 < nr3; i3++) { for (int k3 = 0; k3 < ne03; k3++) { for (int i2 = 0; i2 < nr2; i2++) { for (int k2 = 0; k2 < ne02; k2++) { for (int i1 = 0; i1 < nr1; i1++) { for (int k1 = 0; k1 < ne01; k1++) { for (int i0 = 0; i0 < nr0; i0++) { ggml_fp16_t * y = (ggml_fp16_t *) ((char *) dst->data + (i3*ne03 + k3)*nb3 + (i2*ne02 + k2)*nb2 + (i1*ne01 + k1)*nb1 + (i0*ne00)*nb0); ggml_fp16_t * x = (ggml_fp16_t *) ((char *) src0->data + ( k3)*nb03 + ( k2)*nb02 + ( k1)*nb01); // ggml_vec_cpy_f16(ne00, y, x) for (int i = 0; i < ne00; ++i) { y[i] = x[i]; } } } } } } } } } static void ggml_compute_forward_repeat( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; switch (src0->type) { case GGML_TYPE_F16: case GGML_TYPE_BF16: case GGML_TYPE_I16: { ggml_compute_forward_repeat_f16(params, dst); } break; case GGML_TYPE_F32: case GGML_TYPE_I32: { ggml_compute_forward_repeat_f32(params, dst); } break; default: { GGML_ABORT("fatal error"); } } } // ggml_compute_forward_repeat_back static void ggml_compute_forward_repeat_back_f32( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; if (params->ith != 0) { return; } GGML_ASSERT(ggml_can_repeat(dst, src0)); GGML_TENSOR_UNARY_OP_LOCALS // guaranteed to be an integer due to the check in ggml_can_repeat const int nr0 = (int)(ne00/ne0); const int nr1 = (int)(ne01/ne1); const int nr2 = (int)(ne02/ne2); const int nr3 = (int)(ne03/ne3); // TODO: support for transposed / permuted tensors GGML_ASSERT(nb0 == sizeof(float)); GGML_ASSERT(nb00 == sizeof(float)); if (ggml_is_contiguous(dst)) { ggml_vec_set_f32(ne0*ne1*ne2*ne3, dst->data, 0); } else { for (int k3 = 0; k3 < ne3; k3++) { for (int k2 = 0; k2 < ne2; k2++) { for (int k1 = 0; k1 < ne1; k1++) { ggml_vec_set_f32(ne0, (float *) ((char *) dst->data + k1*nb1 + k2*nb2 + k3*nb3), 0); } } } } // TODO: maybe this is not optimal? for (int i3 = 0; i3 < nr3; i3++) { for (int k3 = 0; k3 < ne3; k3++) { for (int i2 = 0; i2 < nr2; i2++) { for (int k2 = 0; k2 < ne2; k2++) { for (int i1 = 0; i1 < nr1; i1++) { for (int k1 = 0; k1 < ne1; k1++) { for (int i0 = 0; i0 < nr0; i0++) { ggml_vec_acc_f32(ne0, (float *) ((char *) dst->data + ( k3)*nb3 + ( k2)*nb2 + ( k1)*nb1), (float *) ((char *) src0->data + (i3*ne3 + k3)*nb03 + (i2*ne2 + k2)*nb02 + (i1*ne1 + k1)*nb01 + (i0*ne0)*nb00)); } } } } } } } } static void ggml_compute_forward_repeat_back( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_repeat_back_f32(params, dst); } break; default: { GGML_ABORT("fatal error"); } } } // ggml_compute_forward_concat static void ggml_compute_forward_concat_f32( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; const struct ggml_tensor * src1 = dst->src[1]; GGML_ASSERT(src0->nb[0] == sizeof(float)); const int ith = params->ith; const int nth = params->nth; GGML_TENSOR_BINARY_OP_LOCALS const int32_t dim = ggml_get_op_params_i32(dst, 0); GGML_ASSERT(dim >= 0 && dim < 4); int64_t o[4] = {0, 0, 0, 0}; o[dim] = src0->ne[dim]; const float * x; // TODO: smarter multi-theading for (int i3 = 0; i3 < ne3; i3++) { for (int i2 = ith; i2 < ne2; i2 += nth) { for (int i1 = 0; i1 < ne1; i1++) { for (int i0 = 0; i0 < ne0; i0++) { if (i0 < ne00 && i1 < ne01 && i2 < ne02 && i3 < ne03) { x = (const float *) ((const char *)src0->data + (i0 )*nb00 + (i1 )*nb01 + (i2 )*nb02 + (i3 )*nb03); } else { x = (const float *) ((const char *)src1->data + (i0 - o[0])*nb10 + (i1 - o[1])*nb11 + (i2 - o[2])*nb12 + (i3 - o[3])*nb13); } float * y = (float *)((char *)dst->data + i0*nb0 + i1*nb1 + i2*nb2 + i3*nb3); *y = *x; } } } } } static void ggml_compute_forward_concat( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; switch (src0->type) { case GGML_TYPE_F32: case GGML_TYPE_I32: { ggml_compute_forward_concat_f32(params, dst); } break; default: { GGML_ABORT("fatal error"); } } } // ggml_compute_forward_abs static void ggml_compute_forward_abs_f32( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; if (params->ith != 0) { return; } assert(ggml_is_contiguous_1(src0)); assert(ggml_is_contiguous_1(dst)); assert(ggml_are_same_shape(src0, dst)); const int n = ggml_nrows(src0); const int nc = src0->ne[0]; 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, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_abs_f32(params, dst); } break; default: { GGML_ABORT("fatal error"); } } } // ggml_compute_forward_sgn static void ggml_compute_forward_sgn_f32( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; if (params->ith != 0) { return; } assert(ggml_is_contiguous_1(src0)); assert(ggml_is_contiguous_1(dst)); assert(ggml_are_same_shape(src0, dst)); const int n = ggml_nrows(src0); const int nc = src0->ne[0]; 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, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_sgn_f32(params, dst); } break; default: { GGML_ABORT("fatal error"); } } } // ggml_compute_forward_neg static void ggml_compute_forward_neg_f32( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; if (params->ith != 0) { return; } assert(ggml_is_contiguous_1(src0)); assert(ggml_is_contiguous_1(dst)); assert(ggml_are_same_shape(src0, dst)); const int n = ggml_nrows(src0); const int nc = src0->ne[0]; 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, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_neg_f32(params, dst); } break; default: { GGML_ABORT("fatal error"); } } } // ggml_compute_forward_step static void ggml_compute_forward_step_f32( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; if (params->ith != 0) { return; } assert(ggml_is_contiguous_1(src0)); assert(ggml_is_contiguous_1(dst)); assert(ggml_are_same_shape(src0, dst)); const int n = ggml_nrows(src0); const int nc = src0->ne[0]; 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, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_step_f32(params, dst); } break; default: { GGML_ABORT("fatal error"); } } } // ggml_compute_forward_tanh static void ggml_compute_forward_tanh_f32( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; if (params->ith != 0) { return; } assert(ggml_is_contiguous_1(src0)); assert(ggml_is_contiguous_1(dst)); assert(ggml_are_same_shape(src0, dst)); const int n = ggml_nrows(src0); const int nc = src0->ne[0]; for (int i = 0; i < n; i++) { ggml_vec_tanh_f32(nc, (float *) ((char *) dst->data + i*( dst->nb[1])), (float *) ((char *) src0->data + i*(src0->nb[1]))); } } static void ggml_compute_forward_tanh( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_tanh_f32(params, dst); } break; default: { GGML_ABORT("fatal error"); } } } // ggml_compute_forward_elu static void ggml_compute_forward_elu_f32( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; if (params->ith != 0) { return; } assert(ggml_is_contiguous_1(src0)); assert(ggml_is_contiguous_1(dst)); assert(ggml_are_same_shape(src0, dst)); const int n = ggml_nrows(src0); const int nc = src0->ne[0]; for (int i = 0; i < n; i++) { ggml_vec_elu_f32(nc, (float *) ((char *) dst->data + i*( dst->nb[1])), (float *) ((char *) src0->data + i*(src0->nb[1]))); } } static void ggml_compute_forward_elu( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_elu_f32(params, dst); } break; default: { GGML_ABORT("fatal error"); } } } // ggml_compute_forward_relu static void ggml_compute_forward_relu_f32( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; if (params->ith != 0) { return; } assert(ggml_is_contiguous_1(src0)); assert(ggml_is_contiguous_1(dst)); assert(ggml_are_same_shape(src0, dst)); const int n = ggml_nrows(src0); const int nc = src0->ne[0]; 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, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_relu_f32(params, dst); } break; default: { GGML_ABORT("fatal error"); } } } // ggml_compute_forward_sigmoid static void ggml_compute_forward_sigmoid_f32( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; if (params->ith != 0) { return; } assert(ggml_is_contiguous_1(src0)); assert(ggml_is_contiguous_1(dst)); assert(ggml_are_same_shape(src0, dst)); const int n = ggml_nrows(src0); const int nc = src0->ne[0]; for (int i = 0; i < n; i++) { ggml_vec_sigmoid_f32(nc, (float *) ((char *) dst->data + i*( dst->nb[1])), (float *) ((char *) src0->data + i*(src0->nb[1]))); } } static void ggml_compute_forward_sigmoid( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_sigmoid_f32(params, dst); } break; default: { GGML_ABORT("fatal error"); } } } // ggml_compute_forward_gelu static void ggml_compute_forward_gelu_f32( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; assert(ggml_is_contiguous_1(src0)); assert(ggml_is_contiguous_1(dst)); assert(ggml_are_same_shape(src0, dst)); 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, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_gelu_f32(params, dst); } break; default: { GGML_ABORT("fatal error"); } } } // ggml_compute_forward_gelu_quick static void ggml_compute_forward_gelu_quick_f32( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; assert(ggml_is_contiguous_1(src0)); assert(ggml_is_contiguous_1(dst)); assert(ggml_are_same_shape(src0, dst)); const int ith = params->ith; const int nth = params->nth; const int nc = src0->ne[0]; const int nr = ggml_nrows(src0); // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); for (int i1 = ir0; i1 < ir1; i1++) { ggml_vec_gelu_quick_f32(nc, (float *) ((char *) dst->data + i1*( dst->nb[1])), (float *) ((char *) src0->data + i1*(src0->nb[1]))); #ifndef NDEBUG for (int k = 0; k < nc; k++) { const float x = ((float *) ((char *) dst->data + i1*( dst->nb[1])))[k]; UNUSED(x); assert(!isnan(x)); assert(!isinf(x)); } #endif } } static void ggml_compute_forward_gelu_quick( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_gelu_quick_f32(params, dst); } break; default: { GGML_ABORT("fatal error"); } } } // ggml_compute_forward_silu static void ggml_compute_forward_silu_f32( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; assert(ggml_is_contiguous_1(src0)); assert(ggml_is_contiguous_1(dst)); assert(ggml_are_same_shape(src0, dst)); const int ith = params->ith; const int nth = params->nth; const int nc = src0->ne[0]; const int nr = ggml_nrows(src0); // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); for (int i1 = ir0; i1 < ir1; i1++) { ggml_vec_silu_f32(nc, (float *) ((char *) dst->data + i1*( dst->nb[1])), (float *) ((char *) src0->data + i1*(src0->nb[1]))); #ifndef NDEBUG for (int k = 0; k < nc; k++) { const float x = ((float *) ((char *) dst->data + i1*(dst->nb[1])))[k]; UNUSED(x); assert(!isnan(x)); assert(!isinf(x)); } #endif } } static void ggml_compute_forward_silu( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_silu_f32(params, dst); } break; default: { GGML_ABORT("fatal error"); } } } // ggml_compute_forward_leaky_relu static void ggml_compute_forward_leaky_relu_f32( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; if (params->ith != 0) { return; } assert(ggml_is_contiguous_1(src0)); assert(ggml_is_contiguous_1(dst)); assert(ggml_are_same_shape(src0, dst)); const int n = ggml_nrows(src0); const int nc = src0->ne[0]; float negative_slope; memcpy(&negative_slope, dst->op_params, sizeof(float)); assert(dst->nb[0] == sizeof(float)); assert(src0->nb[0] == sizeof(float)); for (int i = 0; i < n; i++) { ggml_vec_leaky_relu_f32(nc, (float *) ((char *) dst->data + i*( dst->nb[1])), (float *) ((char *) src0->data + i*(src0->nb[1])), negative_slope); } } static void ggml_compute_forward_leaky_relu( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_leaky_relu_f32(params, dst); } break; default: { GGML_ABORT("fatal error"); } } } // ggml_compute_forward_silu_back static void ggml_compute_forward_silu_back_f32( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; const struct ggml_tensor * grad = dst->src[1]; assert(ggml_is_contiguous_1(grad)); assert(ggml_is_contiguous_1(src0)); assert(ggml_is_contiguous_1(dst)); assert(ggml_are_same_shape(src0, dst)); assert(ggml_are_same_shape(src0, grad)); const int ith = params->ith; const int nth = params->nth; const int nc = src0->ne[0]; const int nr = ggml_nrows(src0); // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); for (int i1 = ir0; i1 < ir1; i1++) { ggml_vec_silu_backward_f32(nc, (float *) ((char *) dst->data + i1*( dst->nb[1])), (float *) ((char *) src0->data + i1*(src0->nb[1])), (float *) ((char *) grad->data + i1*(grad->nb[1]))); #ifndef NDEBUG for (int k = 0; k < nc; k++) { const float x = ((float *) ((char *) dst->data + i1*( dst->nb[1])))[k]; UNUSED(x); assert(!isnan(x)); assert(!isinf(x)); } #endif } } static void ggml_compute_forward_silu_back( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_silu_back_f32(params, dst); } break; default: { GGML_ABORT("fatal error"); } } } static void ggml_compute_forward_hardswish_f32( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; if (params->ith != 0) { return; } assert(ggml_is_contiguous_1(src0)); assert(ggml_is_contiguous_1(dst)); assert(ggml_are_same_shape(src0, dst)); const int n = ggml_nrows(src0); const int nc = src0->ne[0]; for (int i = 0; i < n; i++) { ggml_vec_hardswish_f32(nc, (float *) ((char *) dst->data + i*( dst->nb[1])), (float *) ((char *) src0->data + i*(src0->nb[1]))); } } static void ggml_compute_forward_hardswish( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_hardswish_f32(params, dst); } break; default: { GGML_ABORT("fatal error"); } } } static void ggml_compute_forward_hardsigmoid_f32( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; if (params->ith != 0) { return; } assert(ggml_is_contiguous_1(src0)); assert(ggml_is_contiguous_1(dst)); assert(ggml_are_same_shape(src0, dst)); const int n = ggml_nrows(src0); const int nc = src0->ne[0]; for (int i = 0; i < n; i++) { ggml_vec_hardsigmoid_f32(nc, (float *) ((char *) dst->data + i*( dst->nb[1])), (float *) ((char *) src0->data + i*(src0->nb[1]))); } } static void ggml_compute_forward_hardsigmoid( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_hardsigmoid_f32(params, dst); } break; default: { GGML_ABORT("fatal error"); } } } static void ggml_compute_forward_exp_f32( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; if (params->ith != 0) { return; } assert(ggml_is_contiguous_1(src0)); assert(ggml_is_contiguous_1(dst)); assert(ggml_are_same_shape(src0, dst)); const int n = ggml_nrows(src0); const int nc = src0->ne[0]; for (int i = 0; i < n; i++) { ggml_vec_exp_f32(nc, (float *) ((char *) dst->data + i*( dst->nb[1])), (float *) ((char *) src0->data + i*(src0->nb[1]))); } } static void ggml_compute_forward_exp( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_exp_f32(params, dst); } break; default: { GGML_ABORT("fatal error"); } } } // ggml_compute_forward_norm static void ggml_compute_forward_norm_f32( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; GGML_ASSERT(ggml_are_same_shape(src0, dst)); GGML_ASSERT(src0->nb[0] == sizeof(float)); const int ith = params->ith; const int nth = params->nth; GGML_TENSOR_UNARY_OP_LOCALS float eps; memcpy(&eps, dst->op_params, sizeof(float)); GGML_ASSERT(eps > 0.0f); // TODO: optimize for (int64_t i03 = 0; i03 < ne03; i03++) { for (int64_t i02 = 0; i02 < ne02; i02++) { for (int64_t i01 = ith; i01 < ne01; i01 += nth) { const float * x = (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); ggml_float sum = 0.0; for (int64_t i00 = 0; i00 < ne00; i00++) { sum += (ggml_float)x[i00]; } float mean = sum/ne00; float * y = (float *) ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3); ggml_float sum2 = 0.0; for (int64_t i00 = 0; i00 < ne00; i00++) { float v = x[i00] - mean; y[i00] = v; sum2 += (ggml_float)(v*v); } float variance = sum2/ne00; const float scale = 1.0f/sqrtf(variance + eps); ggml_vec_scale_f32(ne00, y, scale); } } } } static void ggml_compute_forward_norm( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_norm_f32(params, dst); } break; default: { GGML_ABORT("fatal error"); } } } // ggml_compute_forward_group_rms_norm static void ggml_compute_forward_rms_norm_f32( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; GGML_ASSERT(ggml_are_same_shape(src0, dst)); GGML_ASSERT(src0->nb[0] == sizeof(float)); const int ith = params->ith; const int nth = params->nth; GGML_TENSOR_UNARY_OP_LOCALS float eps; memcpy(&eps, dst->op_params, sizeof(float)); GGML_ASSERT(eps > 0.0f); // TODO: optimize for (int64_t i03 = 0; i03 < ne03; i03++) { for (int64_t i02 = 0; i02 < ne02; i02++) { for (int64_t i01 = ith; i01 < ne01; i01 += nth) { const float * x = (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); ggml_float sum = 0.0; for (int64_t i00 = 0; i00 < ne00; i00++) { sum += (ggml_float)(x[i00] * x[i00]); } const float mean = sum/ne00; float * y = (float *) ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3); memcpy(y, x, ne00 * sizeof(float)); // for (int i00 = 0; i00 < ne00; i00++) { // y[i00] = x[i00]; // } const float scale = 1.0f/sqrtf(mean + eps); ggml_vec_scale_f32(ne00, y, scale); } } } } static void ggml_compute_forward_rms_norm( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_rms_norm_f32(params, dst); } break; default: { GGML_ABORT("fatal error"); } } } static void ggml_compute_forward_rms_norm_back_f32( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; const struct ggml_tensor * src1 = dst->src[1]; GGML_ASSERT(ggml_are_same_shape(src0, dst) && ggml_are_same_shape(src0, src1)); GGML_ASSERT(src0->nb[0] == sizeof(float)); const int ith = params->ith; const int nth = params->nth; GGML_TENSOR_BINARY_OP_LOCALS float eps; memcpy(&eps, dst->op_params, sizeof(float)); // TODO: optimize for (int64_t i03 = 0; i03 < ne03; i03++) { for (int64_t i02 = 0; i02 < ne02; i02++) { for (int64_t i01 = ith; i01 < ne01; i01 += nth) { // src1 is same shape as src0 => same indices const int64_t i11 = i01; const int64_t i12 = i02; const int64_t i13 = i03; const float * x = (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); const float * dz = (float *) ((char *) src1->data + i11*nb11 + i12*nb12 + i13*nb13); ggml_float sum_xx = 0.0; ggml_float sum_xdz = 0.0; for (int64_t i00 = 0; i00 < ne00; i00++) { sum_xx += (ggml_float)(x[i00] * x[i00]); sum_xdz += (ggml_float)(x[i00] * dz[i00]); } //const float mean = (float)(sum_xx)/ne00; const float mean_eps = (float)(sum_xx)/ne00 + eps; const float sum_eps = (float)(sum_xx) + eps*ne00; //const float mean_xdz = (float)(sum_xdz)/ne00; // we could cache rms from forward pass to improve performance. // to do this implement ggml_rms and compose ggml_rms_norm using ggml_rms. //const float rms = sqrtf(mean_eps); const float rrms = 1.0f / sqrtf(mean_eps); //const float scale = -rrms/(ne00 * mean_eps); // -1/(n*rms**3) { // z = rms_norm(x) // // rms_norm(src0) = // scale( // src0, // div( // 1, // sqrt( // add( // scale( // sum( // sqr( // src0)), // (1.0/N)), // eps)))); // postorder: // ## op args grad // 00 param src0 grad[#00] // 01 const 1 // 02 sqr (#00) grad[#02] // 03 sum (#02) grad[#03] // 04 const 1/N // 05 scale (#03, #04) grad[#05] // 06 const eps // 07 add (#05, #06) grad[#07] // 08 sqrt (#07) grad[#08] // 09 div (#01,#08) grad[#09] // 10 scale (#00,#09) grad[#10] // // backward pass, given grad[#10] // #10: scale // grad[#00] += scale(grad[#10],#09) // grad[#09] += sum(mul(grad[#10],#00)) // #09: div // grad[#08] += neg(mul(grad[#09], div(#09,#08))) // #08: sqrt // grad[#07] += mul(grad[#08], div(0.5, #08)) // #07: add // grad[#05] += grad[#07] // #05: scale // grad[#03] += scale(grad[#05],#04) // #03: sum // grad[#02] += repeat(grad[#03], #02) // #02: // grad[#00] += scale(mul(#00, grad[#02]), 2.0) // // substitute and simplify: // grad[#00] = scale(grad(#10), #09) + scale(mul(#00, grad[#02]), 2.0) // grad[#02] = repeat(grad[#03], #02) // grad[#02] = repeat(scale(grad[#05],#04), #02) // grad[#02] = repeat(scale(grad[#07],#04), #02) // grad[#02] = repeat(scale(mul(grad[#08], div(0.5, #08)),#04), #02) // grad[#02] = repeat(scale(mul(neg(mul(grad[#09], div(#09,#08))), div(0.5, #08)),#04), #02) // grad[#02] = repeat(scale(mul(neg(mul(sum(mul(grad[#10],#00)), div(#09,#08))), div(0.5, #08)),#04), #02) // grad[#02] = repeat(-(sum(mul(grad[#10],#00)) * div(#09,#08) * div(0.5, #08) * (1/N)), #02) // grad[#02] = repeat(-(sum(mul(grad[#10],#00)) * div(div(#01,#08),#08) * div(0.5, #08) * (1/N)), #02) // grad[#02] = repeat(-(sum(mul(grad[#10],#00)) * div(1,#08*#08) * div(0.5, #08) * (1/N)), #02) // grad[#02] = repeat(-(sum(mul(grad[#10],#00)) * div(1,#07) * div(0.5, #08) * (1/N)), #02) // grad[#00] = scale(grad(#10), #09) + scale(mul(#00, grad[#02]), 2.0) // grad[#00] = scale(grad(#10), #09) + scale(mul(#00, repeat(-(sum(mul(grad[#10],#00)) * div(1,#07) * div(0.5, #08) * (1/N)), #02)), 2.0) // grad[#00] = scale(grad(#10), #09) + scale(scale(#00, -(sum(mul(grad[#10],#00)) * div(1,#07) * div(0.5, #08) * (1/N))), 2.0) // grad[#00] = scale(grad(#10), #09) + scale(#00, -(sum(mul(grad[#10],#00)) * div(1,#07) * div(1,#08) * (1/N))) // grad[#00] = scale(grad(#10), #09) + scale(#00, sum(mul(grad[#10],#00)) * div(1,#07*#08) * (-1/N)) // grad[#00] = scale(grad(#10), #09) + scale(#00, sum(mul(grad[#10],#00)) * div(1,#07*#08) * (-1/N)) // grad[#00] = scale(grad(#10), #09) + scale(#00, sum(mul(grad[#10],#00)) * div(1,mean_eps*rms) * (-1/N)) // grad[#00] = scale(grad(#10), #09) + scale(#00, sum(mul(grad[#10],#00)) * div(-1,rms*N*mean_eps)) // grad[#00] = scale(grad(#10), #09) + scale(#00, sum(mul(grad[#10],#00)) * div(-1,rms*N*(sum_xx/N+eps))) // grad[#00] = scale(grad(#10), #09) + scale(#00, sum(mul(grad[#10],#00)) * div(-1,rms*N*sum_xx+rms*N*eps)) // grad[#00] = scale(dz, rrms) + scale(x, sum(mul(dz,x)) * div(-1,rms*N*mean_eps)) // grad[#00] = scale(dz, rrms) + scale(x, sum_xdz * div(-1,rms*N*mean_eps)) // a = b*c + d*e // a = b*c*f/f + d*e*f/f // a = (b*c*f + d*e*f)*(1/f) // a = (b*c*(1/c) + d*e*(1/c))*(1/(1/c)) // a = (b + d*e/c)*c // b = dz, c = rrms, d = x, e = sum_xdz * div(-1,rms*N*mean_eps) // a = (dz + x*sum_xdz * div(-1,rms*N*mean_eps)/rrms)*rrms // a = (dz + x*sum_xdz * div(-1,rms*N*mean_eps)*rms)*rrms // a = (dz + x*sum_xdz * div(-rms,rms*N*mean_eps))*rrms // a = (dz + x*sum_xdz * div(-1,N*mean_eps))*rrms // a = (dz + x*div(-sum_xdz,N*mean_eps))*rrms // a = (dz + x*div(-mean_xdz,mean_eps))*rrms // grad[#00] = scale(dz + scale(x, div(-mean_xdz,mean_eps)),rrms) // grad[#00] = scale(dz + scale(x, -mean_xdz/mean_eps),rrms) // dx = scale(dz + scale(x, -mean_xdz/mean_eps),rrms) } // dx = scale(dz + scale(x, -mean_xdz/mean_eps),rrms) // post-order: // dx := x // dx := scale(dx,-mean_xdz/mean_eps) // dx := add(dx, dz) // dx := scale(dx, rrms) float * dx = (float *) ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3); ggml_vec_cpy_f32 (ne00, dx, x); // ggml_vec_scale_f32(ne00, dx, -mean_xdz/mean_eps); ggml_vec_scale_f32(ne00, dx, (float)(-sum_xdz)/sum_eps); ggml_vec_acc_f32 (ne00, dx, dz); ggml_vec_scale_f32(ne00, dx, rrms); } } } } static void ggml_compute_forward_rms_norm_back( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_rms_norm_back_f32(params, dst); } break; default: { GGML_ABORT("fatal error"); } } } // ggml_compute_forward_group_norm static void ggml_compute_forward_group_norm_f32( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; GGML_ASSERT(ggml_are_same_shape(src0, dst)); GGML_ASSERT(src0->nb[0] == sizeof(float)); const int ith = params->ith; const int nth = params->nth; GGML_TENSOR_UNARY_OP_LOCALS // TODO: optimize float eps; memcpy(&eps, dst->op_params + 1, sizeof(float)); int n_channels = src0->ne[2]; int n_groups = dst->op_params[0]; int n_channels_per_group = (n_channels + n_groups - 1) / n_groups; for (int i = ith; i < n_groups; i += nth) { int start = i * n_channels_per_group; int end = start + n_channels_per_group; if (end > n_channels) { end = n_channels; } int step = end - start; for (int64_t i03 = 0; i03 < ne03; i03++) { ggml_float sum = 0.0; for (int64_t i02 = start; i02 < end; i02++) { for (int64_t i01 = 0; i01 < ne01; i01++) { const float * x = (float *)((char *) src0->data + i01 * nb01 + i02 * nb02 + i03 * nb03); ggml_float sumr = 0.0; for (int64_t i00 = 0; i00 < ne00; i00++) { sumr += (ggml_float)x[i00]; } sum += sumr; } } const float mean = sum / (ne00 * ne01 * step); ggml_float sum2 = 0.0; for (int64_t i02 = start; i02 < end; i02++) { for (int64_t i01 = 0; i01 < ne01; i01++) { const float * x = (float *)((char *) src0->data + i01 * nb01 + i02 * nb02 + i03 * nb03); float * y = (float *)((char *) dst->data + i01 * nb1 + i02 * nb2 + i03 * nb3); ggml_float sumr = 0.0; for (int64_t i00 = 0; i00 < ne00; i00++) { float v = x[i00] - mean; y[i00] = v; sumr += (ggml_float)(v * v); } sum2 += sumr; } } const float variance = sum2 / (ne00 * ne01 * step); const float scale = 1.0f / sqrtf(variance + eps); for (int64_t i02 = start; i02 < end; i02++) { for (int64_t i01 = 0; i01 < ne01; i01++) { float * y = (float *)((char *) dst->data + i01 * nb1 + i02 * nb2 + i03 * nb3); ggml_vec_scale_f32(ne00, y, scale); } } } } } static void ggml_compute_forward_group_norm( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_group_norm_f32(params, dst); } break; default: { GGML_ABORT("fatal error"); } } } // ggml_compute_forward_mul_mat static void ggml_compute_forward_mul_mat_one_chunk( const struct ggml_compute_params * params, struct ggml_tensor * dst, const int64_t num_rows_per_vec_dot, const int64_t ir0_start, const int64_t ir0_end, const int64_t ir1_start, const int64_t ir1_end) { const struct ggml_tensor * src0 = dst->src[0]; const struct ggml_tensor * src1 = dst->src[1]; GGML_TENSOR_BINARY_OP_LOCALS const enum ggml_type type = src0->type; const bool src1_cont = ggml_is_contiguous(src1); ggml_vec_dot_t const vec_dot = type_traits_cpu[type].vec_dot; enum ggml_type const vec_dot_type = type_traits_cpu[type].vec_dot_type; // broadcast factors const int64_t r2 = ne12 / ne02; const int64_t r3 = ne13 / ne03; //printf("ir0_start = %6lld, ir0_end = %6lld, ir1_start = %6lld, ir1_end = %6lld\n", ir0_start, ir0_end, ir1_start, ir1_end); // threads with no work simply yield (not sure if it helps) if (ir0_start >= ir0_end || ir1_start >= ir1_end) { return; } const void * wdata = (src1->type == vec_dot_type) ? src1->data : params->wdata; const size_t row_size = ggml_row_size(vec_dot_type, ne10); assert(ne12 % ne02 == 0); assert(ne13 % ne03 == 0); // block-tiling attempt const int64_t blck_0 = 16; const int64_t blck_1 = 16; const size_t src1_col_stride = src1_cont || src1->type != vec_dot_type ? row_size : nb11; // attempt to reduce false-sharing (does not seem to make a difference) // 16 * 2, accounting for mmla kernels float tmp[32]; for (int64_t iir1 = ir1_start; iir1 < ir1_end; iir1 += blck_1) { for (int64_t iir0 = ir0_start; iir0 < ir0_end; iir0 += blck_0) { for (int64_t ir1 = iir1; ir1 < iir1 + blck_1 && ir1 < ir1_end; ir1 += num_rows_per_vec_dot) { const int64_t i13 = (ir1 / (ne12 * ne1)); const int64_t i12 = (ir1 - i13 * ne12 * ne1) / ne1; const int64_t i11 = (ir1 - i13 * ne12 * ne1 - i12 * ne1); // broadcast src0 into src1 const int64_t i03 = i13 / r3; const int64_t i02 = i12 / r2; const int64_t i1 = i11; const int64_t i2 = i12; const int64_t i3 = i13; const char * src0_row = (const char*)src0->data + (0 + i02 * nb02 + i03 * nb03); // desc: when src1 is not a contiguous memory block we have to calculate the offset using the strides // if it is, then we have either copied the data to params->wdata and made it contiguous or we are using // the original src1 data pointer, so we should index using the indices directly // TODO: this is a bit of a hack, we should probably have a better way to handle this const char * src1_col = (const char*)wdata + (src1_cont || src1->type != vec_dot_type ? (i11 + i12 * ne11 + i13 * ne12 * ne11) * row_size : (i11 * nb11 + i12 * nb12 + i13 * nb13)); float * dst_col = (float*)((char*)dst->data + (i1 * nb1 + i2 * nb2 + i3 * nb3)); //for (int64_t ir0 = iir0; ir0 < iir0 + blck_0 && ir0 < ir0_end; ++ir0) { // vec_dot(ne00, &dst_col[ir0], src0_row + ir0*nb01, src1_col); //} for (int64_t ir0 = iir0; ir0 < iir0 + blck_0 && ir0 < ir0_end; ir0 += num_rows_per_vec_dot) { vec_dot(ne00, &tmp[ir0 - iir0], (num_rows_per_vec_dot > 1 ? 16 : 0), src0_row + ir0 * nb01, (num_rows_per_vec_dot > 1 ? nb01 : 0), src1_col, (num_rows_per_vec_dot > 1 ? src1_col_stride : 0), num_rows_per_vec_dot); } for (int cn = 0; cn < num_rows_per_vec_dot; ++cn) { memcpy(&dst_col[iir0 + cn * nb1 / nb0], tmp + (cn * 16), (MIN(iir0 + blck_0, ir0_end) - iir0) * sizeof(float)); } } } } } static void ggml_compute_forward_mul_mat( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; const struct ggml_tensor * src1 = dst->src[1]; GGML_TENSOR_BINARY_OP_LOCALS const int ith = params->ith; const int nth = params->nth; const enum ggml_type type = src0->type; enum ggml_type const vec_dot_type = type_traits_cpu[type].vec_dot_type; ggml_from_float_t const from_float = ggml_get_type_traits(vec_dot_type)->from_float; ggml_from_float_to_mat_t const from_float_to_mat = type_traits_cpu[vec_dot_type].from_float_to_mat; int64_t const vec_dot_num_rows = type_traits_cpu[type].nrows; int64_t const matmul_num_cols = type_traits_cpu[type].ncols; int64_t const blck_size_interleave = ggml_get_type_traits(type)->blck_size_interleave; ggml_gemv_t const gemv = type_traits_cpu[type].gemv; ggml_gemm_t const gemm = type_traits_cpu[type].gemm; GGML_ASSERT(ne0 == ne01); GGML_ASSERT(ne1 == ne11); GGML_ASSERT(ne2 == ne12); GGML_ASSERT(ne3 == ne13); // we don't support permuted src0 or src1 GGML_ASSERT(nb00 == ggml_type_size(type)); GGML_ASSERT(nb10 == ggml_type_size(src1->type)); // dst cannot be transposed or permuted GGML_ASSERT(nb0 == sizeof(float)); GGML_ASSERT(nb0 <= nb1); GGML_ASSERT(nb1 <= nb2); GGML_ASSERT(nb2 <= nb3); // nb01 >= nb00 - src0 is not transposed // compute by src0 rows #if GGML_USE_LLAMAFILE // broadcast factors const int64_t r2 = ne12 / ne02; const int64_t r3 = ne13 / ne03; const bool src1_cont = ggml_is_contiguous(src1); if (src1_cont) { for (int64_t i13 = 0; i13 < ne13; i13++) for (int64_t i12 = 0; i12 < ne12; i12++) if (!llamafile_sgemm(ne01, ne11, ne00/ggml_blck_size(src0->type), (const char *)src0->data + i12/r2*nb02 + i13/r3*nb03, nb01/ggml_type_size(src0->type), (const char *)src1->data + i12*nb12 + i13*nb13, nb11/ggml_type_size(src1->type), (char *)dst->data + i12*nb2 + i13*nb3, nb1/ggml_type_size(dst->type), ith, nth, src0->type, src1->type, dst->type)) goto UseGgmlGemm1; return; } UseGgmlGemm1:; #endif if (src1->type != vec_dot_type) { char * wdata = params->wdata; const size_t nbw1 = ggml_row_size(vec_dot_type, ne10); const size_t nbw2 = nbw1*ne11; const size_t nbw3 = nbw2*ne12; assert(params->wsize >= ne13*nbw3); GGML_ASSERT(src1->type == GGML_TYPE_F32); for (int64_t i13 = 0; i13 < ne13; ++i13) { for (int64_t i12 = 0; i12 < ne12; ++i12) { int64_t i11_processed = 0; if ((ggml_n_dims(src1) == 2) && from_float_to_mat && gemm) { for (int64_t i11 = ith * 4; i11 < ne11 - ne11 % 4; i11 += nth * 4) { from_float_to_mat((float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11), (void *) (wdata + i13*nbw3 + i12*nbw2 + i11*nbw1), 4, ne10, blck_size_interleave); } i11_processed = ne11 - ne11 % 4; } for (int64_t i11 = i11_processed + ith; i11 < ne11; i11 += nth) { from_float((float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11), (void *) (wdata + i13*nbw3 + i12*nbw2 + i11*nbw1), ne10); } } } } if (ith == 0) { // Every thread starts at ith, so the first unprocessed chunk is nth. This save a bit of coordination right at the start. atomic_store_explicit(¶ms->threadpool->current_chunk, nth, memory_order_relaxed); } ggml_barrier(params->threadpool); #if GGML_USE_LLAMAFILE if (src1->type != vec_dot_type) { const void* wdata = (src1->type == vec_dot_type) ? src1->data : params->wdata; const size_t row_size = ggml_row_size(vec_dot_type, ne10); for (int64_t i13 = 0; i13 < ne13; i13++) for (int64_t i12 = 0; i12 < ne12; i12++) if (!llamafile_sgemm(ne01, ne11, ne00/ggml_blck_size(src0->type), (const char *)src0->data + i12/r2*nb02 + i13/r3*nb03, nb01/ggml_type_size(src0->type), (const char *)wdata + (i12*ne11 + i13*ne12*ne11)*row_size, row_size/ggml_type_size(vec_dot_type), (char *)dst->data + i12*nb2 + i13*nb3, nb1/ggml_type_size(dst->type), ith, nth, src0->type, vec_dot_type, dst->type)) goto UseGgmlGemm2; return; } UseGgmlGemm2:; #endif // This is the size of the first dimension of the result, so we can iterate that way. (see the ASSERT above, these are the same numbers) const int64_t nr0 = ne0; // This is the size of the rest of the dimensions of the result const int64_t nr1 = ne1 * ne2 * ne3; // dot kernels can handle 1 row and col at a time, but mmla kernels can process 2 rows and cols int64_t num_rows_per_vec_dot = vec_dot_num_rows; // TODO: currently the mmla kernels support only even numbered rows/cols. // this check can be removed once they are extended to support odd numbered rows/cols too if ((nr0 % 2 != 0) || (ne11 % 2 != 0)) { num_rows_per_vec_dot = 1; } // Now select a reasonable chunk size. int chunk_size = 16; // We need to step up the size if it's small if (nr0 == 1 || nr1 == 1) { chunk_size = 64; } // distribute the work across the inner or outer loop based on which one is larger // The number of chunks in the 0/1 dim. // CEIL(nr0/chunk_size) int64_t nchunk0 = (nr0 + chunk_size - 1) / chunk_size; int64_t nchunk1 = (nr1 + chunk_size - 1) / chunk_size; // If the chunking is poor for the number of threads on this setup, scrap the whole plan. Re-chunk it by thread. // Also, chunking by thread was measured to have perform better on NUMA systems. See https://github.com/ggerganov/llama.cpp/pull/6915 // In theory, chunking should be just as useful on NUMA and non NUMA systems, but testing disagreed with that. if (nchunk0 * nchunk1 < nth * 4 || ggml_is_numa()) { // distribute the thread work across the inner or outer loop based on which one is larger nchunk0 = nr0 > nr1 ? nth : 1; // parallelize by src0 rows nchunk1 = nr0 > nr1 ? 1 : nth; // parallelize by src1 rows } // The number of elements in each chunk const int64_t dr0 = (nr0 + nchunk0 - 1) / nchunk0; const int64_t dr1 = (nr1 + nchunk1 - 1) / nchunk1; if ((ggml_n_dims(src0) == 2) && gemv) { const void * src1_wdata = (src1->type == vec_dot_type) ? src1->data : params->wdata; const size_t src1_col_stride = ggml_is_contiguous(src1) || src1->type != vec_dot_type ? ggml_row_size(vec_dot_type, ne10) : nb11; int64_t src0_start = (ith * ne01) / nth; int64_t src0_end = ((ith + 1) * ne01) / nth; src0_start = (src0_start % matmul_num_cols) ? src0_start + matmul_num_cols - (src0_start % matmul_num_cols): src0_start; src0_end = (src0_end % matmul_num_cols) ? src0_end + matmul_num_cols - (src0_end % matmul_num_cols): src0_end; if (src0_start >= src0_end) return; // If there are more than three rows in src1, use gemm; otherwise, use gemv. if (gemm && (ne11 > 3)) { gemm(ne00, (float *)((char *) dst->data) + src0_start, ne01, (const char *) src0->data + src0_start * nb01, (const char *) src1_wdata, ne11 - ne11 % 4, src0_end - src0_start); } for (int iter = gemm ? ne11 - ne11 % 4 : 0; iter < ne11; iter++) { gemv(ne00, (float *)((char *) dst->data + (iter * nb1)) + src0_start, ne01, (const char *) src0->data + src0_start * nb01, (const char *) src1_wdata + (src1_col_stride * iter), 1, src0_end - src0_start); } return; } // The first chunk comes from our thread_id, the rest will get auto-assigned. int current_chunk = ith; while (current_chunk < nchunk0 * nchunk1) { const int64_t ith0 = current_chunk % nchunk0; const int64_t ith1 = current_chunk / nchunk0; const int64_t ir0_start = dr0 * ith0; const int64_t ir0_end = MIN(ir0_start + dr0, nr0); const int64_t ir1_start = dr1 * ith1; const int64_t ir1_end = MIN(ir1_start + dr1, nr1); ggml_compute_forward_mul_mat_one_chunk(params, dst, num_rows_per_vec_dot, ir0_start, ir0_end, ir1_start, ir1_end); if (nth >= nchunk0 * nchunk1) { break; } current_chunk = atomic_fetch_add_explicit(¶ms->threadpool->current_chunk, 1, memory_order_relaxed); } } // ggml_compute_forward_mul_mat_id static void ggml_compute_forward_mul_mat_id( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; const struct ggml_tensor * src1 = dst->src[1]; const struct ggml_tensor * ids = dst->src[2]; GGML_TENSOR_BINARY_OP_LOCALS const int ith = params->ith; const int nth = params->nth; const enum ggml_type type = src0->type; const bool src1_cont = ggml_is_contiguous(src1); ggml_vec_dot_t const vec_dot = type_traits_cpu[type].vec_dot; enum ggml_type const vec_dot_type = type_traits_cpu[type].vec_dot_type; ggml_from_float_t const from_float = ggml_get_type_traits(vec_dot_type)->from_float; int64_t const matmul_num_cols = type_traits_cpu[type].ncols; ggml_gemv_t const gemv = type_traits_cpu[type].gemv; // we don't support permuted src0 or src1 GGML_ASSERT(nb00 == ggml_type_size(type)); GGML_ASSERT(nb10 == ggml_type_size(src1->type)); // dst cannot be transposed or permuted GGML_ASSERT(nb0 == sizeof(float)); GGML_ASSERT(nb0 <= nb1); GGML_ASSERT(nb1 <= nb2); GGML_ASSERT(nb2 <= nb3); // row groups const int n_ids = ids->ne[0]; // n_expert_used const int n_as = ne02; // n_expert char * wdata_src1_end = (src1->type == vec_dot_type) ? (char *) params->wdata : (char *) params->wdata + GGML_PAD(ggml_row_size(vec_dot_type, ggml_nelements(src1)), sizeof(int64_t)); struct mmid_row_mapping { int32_t i1; int32_t i2; }; int64_t * matrix_row_counts = (int64_t *) (wdata_src1_end); // [n_as] struct mmid_row_mapping * matrix_rows = (struct mmid_row_mapping *)(matrix_row_counts + n_as); // [n_as][ne11] if (src1->type != vec_dot_type) { char * wdata = params->wdata; const size_t nbw1 = ggml_row_size(vec_dot_type, ne10); const size_t nbw2 = nbw1*ne11; const size_t nbw3 = nbw2*ne12; assert(params->wsize >= ne13*nbw3); GGML_ASSERT(src1->type == GGML_TYPE_F32); for (int64_t i13 = 0; i13 < ne13; ++i13) { for (int64_t i12 = 0; i12 < ne12; ++i12) { for (int64_t i11 = ith; i11 < ne11; i11 += nth) { from_float((float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11), (void *) (wdata + i13*nbw3 + i12*nbw2 + i11*nbw1), ne10); } } } } #define MMID_MATRIX_ROW(row_id, i1) matrix_rows[(row_id)*ne12 + (i1)] if (ith == 0) { // initialize matrix_row_counts memset(matrix_row_counts, 0, n_as*sizeof(int64_t)); // group rows by src0 matrix for (int64_t iid1 = 0; iid1 < ids->ne[1]; ++iid1) { for (int id = 0; id < n_ids; ++id) { const int32_t i02 = *(const int32_t *) ((const char *) ids->data + iid1*ids->nb[1] + id*ids->nb[0]); assert(i02 >= 0 && i02 < n_as); MMID_MATRIX_ROW(i02, matrix_row_counts[i02]) = (struct mmid_row_mapping) {id, iid1}; matrix_row_counts[i02] += 1; } } } ggml_barrier(params->threadpool); // compute each matrix multiplication in sequence for (int cur_a = 0; cur_a < n_as; ++cur_a) { const int64_t cne1 = matrix_row_counts[cur_a]; if (cne1 == 0) { continue; } const char * src0_cur = (const char *) src0->data + cur_a*nb02; const void * wdata = (src1->type == vec_dot_type) ? src1->data : params->wdata; const size_t row_size = ggml_row_size(vec_dot_type, ne10); const int64_t nr0 = ne01; // src0 rows const int64_t nr1 = cne1; // src1 rows if (((ggml_n_dims(src0) - 1) == 2) && gemv) { int64_t src0_cur_start = (ith * ne01) / nth; int64_t src0_cur_end = ((ith + 1) * ne01) / nth; src0_cur_start = (src0_cur_start % matmul_num_cols) ? src0_cur_start + matmul_num_cols - (src0_cur_start % matmul_num_cols): src0_cur_start; src0_cur_end = (src0_cur_end % matmul_num_cols) ? src0_cur_end + matmul_num_cols - (src0_cur_end % matmul_num_cols): src0_cur_end; if (src0_cur_start >= src0_cur_end) return; for (int ir1 = 0; ir1 < nr1; ir1++) { struct mmid_row_mapping row_mapping = MMID_MATRIX_ROW(cur_a, ir1); const int id = row_mapping.i1; // selected expert index const int64_t i11 = id % ne11; const int64_t i12 = row_mapping.i2; // row index in src1 const int64_t i1 = id; // selected expert index const int64_t i2 = i12; // row const char * src1_col = (const char *) wdata + (src1_cont || src1->type != vec_dot_type ? (i11 + i12 * ne11) * row_size : (i11 * nb11 + i12 * nb12)); gemv(ne00, (float *)((char *) dst->data + (i1 * nb1 + i2 * nb2)) + src0_cur_start, ne01, (const char *) src0_cur + src0_cur_start * nb01, src1_col, 1, src0_cur_end - src0_cur_start); } continue; } // distribute the thread work across the inner or outer loop based on which one is larger const int64_t nth0 = nr0 > nr1 ? nth : 1; // parallelize by src0 rows const int64_t nth1 = nr0 > nr1 ? 1 : nth; // parallelize by src1 rows const int64_t ith0 = ith % nth0; const int64_t ith1 = ith / nth0; const int64_t dr0 = (nr0 + nth0 - 1)/nth0; const int64_t dr1 = (nr1 + nth1 - 1)/nth1; const int64_t ir010 = dr0*ith0; const int64_t ir011 = MIN(ir010 + dr0, nr0); const int64_t ir110 = dr1*ith1; const int64_t ir111 = MIN(ir110 + dr1, nr1); // threads with no work simply yield (not sure if it helps) //if (ir010 >= ir011 || ir110 >= ir111) { // sched_yield(); // continue; //} // block-tiling attempt const int64_t blck_0 = 16; const int64_t blck_1 = 16; // attempt to reduce false-sharing (does not seem to make a difference) float tmp[16]; for (int64_t iir1 = ir110; iir1 < ir111; iir1 += blck_1) { for (int64_t iir0 = ir010; iir0 < ir011; iir0 += blck_0) { for (int64_t ir1 = iir1; ir1 < iir1 + blck_1 && ir1 < ir111; ++ir1) { const int64_t _i12 = ir1; // logical row index for this expert struct mmid_row_mapping row_mapping = MMID_MATRIX_ROW(cur_a, _i12); const int id = row_mapping.i1; // selected expert index const int64_t i11 = id % ne11; const int64_t i12 = row_mapping.i2; // row index in src1 const int64_t i1 = id; // selected expert index const int64_t i2 = i12; // row // desc: when src1 is not a contiguous memory block we have to calculate the offset using the strides // if it is, then we have either copied the data to params->wdata and made it contiguous or we are using // the original src1 data pointer, so we should index using the indices directly // TODO: this is a bit of a hack, we should probably have a better way to handle this const char * src1_col = (const char *) wdata + (src1_cont || src1->type != vec_dot_type ? (i11 + i12*ne11)*row_size : (i11*nb11 + i12*nb12)); float * dst_col = (float *) ((char *) dst->data + (i1*nb1 + i2*nb2)); //for (int64_t ir0 = iir0; ir0 < iir0 + blck_0 && ir0 < ir011; ++ir0) { // vec_dot(ne00, &dst_col[ir0], src0_row + ir0*nb01, src1_col); //} for (int64_t ir0 = iir0; ir0 < iir0 + blck_0 && ir0 < ir011; ++ir0) { vec_dot(ne00, &tmp[ir0 - iir0], 0, src0_cur + ir0*nb01, 0, src1_col, 0, 1); } memcpy(&dst_col[iir0], tmp, (MIN(iir0 + blck_0, ir011) - iir0)*sizeof(float)); } } } } #undef MMID_MATRIX_ROW } // ggml_compute_forward_out_prod static void ggml_compute_forward_out_prod_f32( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; const struct ggml_tensor * src1 = dst->src[1]; GGML_TENSOR_BINARY_OP_LOCALS GGML_ASSERT(dst->type == GGML_TYPE_F32); GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT(src1->type == GGML_TYPE_F32); const int ith = params->ith; const int nth = params->nth; GGML_ASSERT(ne0 == ne00); GGML_ASSERT(ne1 == ne10); GGML_ASSERT(ne2 == ne02); GGML_ASSERT(ne02 == ne12); GGML_ASSERT(ne3 == ne13); GGML_ASSERT(ne03 == ne13); // we don't support permuted src0 or src1 GGML_ASSERT(nb00 == sizeof(float)); // dst cannot be transposed or permuted GGML_ASSERT(nb0 == sizeof(float)); // GGML_ASSERT(nb0 <= nb1); // GGML_ASSERT(nb1 <= nb2); // GGML_ASSERT(nb2 <= nb3); // nb01 >= nb00 - src0 is not transposed // compute by src0 rows if (ith == 0) { ggml_vec_set_f32(ne0*ne1*ne2*ne3, dst->data, 0); } ggml_barrier(params->threadpool); // dst[:,:,:,:] = 0 // for i2,i3: // for i1: // for i01: // for i0: // dst[i0,i1,i2,i3] += src0[i0,i01,i2,i3] * src1[i1,i01,i2,i3] // parallelize by last three dimensions // total rows in dst const int64_t nr = ne1*ne2*ne3; // rows per thread const int64_t dr = (nr + nth - 1)/nth; // row range for this thread const int64_t ir0 = dr*ith; const int64_t ir1 = MIN(ir0 + dr, nr); // block-tiling attempt const int64_t blck_0 = MAX(GGML_VEC_MAD_UNROLL, 32); const int64_t blck_1 = 16; for (int64_t bir = ir0; bir < ir1; bir += blck_1) { const int64_t bir1 = MIN(bir + blck_1, ir1); for (int64_t bi01 = 0; bi01 < ne01; bi01 += blck_0) { const int64_t bne01 = MIN(bi01 + blck_0, ne01); for (int64_t ir = bir; ir < bir1; ++ir) { // dst indices const int64_t i3 = ir/(ne2*ne1); const int64_t i2 = (ir - i3*ne2*ne1)/ne1; const int64_t i1 = (ir - i3*ne2*ne1 - i2*ne1); const int64_t i02 = i2; const int64_t i03 = i3; //const int64_t i10 = i1; const int64_t i12 = i2; const int64_t i13 = i3; #if GGML_VEC_MAD_UNROLL > 2 const int64_t bne01_unroll = bne01 - (bne01 % GGML_VEC_MAD_UNROLL); for (int64_t i01 = bi01; i01 < bne01_unroll; i01 += GGML_VEC_MAD_UNROLL) { const int64_t i11 = i01; float * s0 = (float *) ((char *) src0->data + ( i01*nb01 + i02*nb02 + i03*nb03)); float * s1 = (float *) ((char *) src1->data + (i1*nb10 + i11*nb11 + i12*nb12 + i13*nb13)); float * d = (float *) ((char *) dst->data + ( i1*nb1 + i2*nb2 + i3*nb3)); ggml_vec_mad_f32_unroll(ne0, nb01, nb11, d, s0, s1); } for (int64_t i01 = bne01_unroll; i01 < bne01; ++i01) { const int64_t i11 = i01; float * s0 = (float *) ((char *) src0->data + ( i01*nb01 + i02*nb02 + i03*nb03)); float * s1 = (float *) ((char *) src1->data + (i1*nb10 + i11*nb11 + i12*nb12 + i13*nb13)); float * d = (float *) ((char *) dst->data + ( i1*nb1 + i2*nb2 + i3*nb3)); ggml_vec_mad_f32(ne0, d, s0, *s1); } #else for (int64_t i01 = bi01; i01 < bne01; ++i01) { const int64_t i11 = i01; float * s0 = (float *) ((char *) src0->data + ( i01*nb01 + i02*nb02 + i03*nb03)); float * s1 = (float *) ((char *) src1->data + (i1*nb10 + i11*nb11 + i12*nb12 + i13*nb13)); float * d = (float *) ((char *) dst->data + ( i1*nb1 + i2*nb2 + i3*nb3)); ggml_vec_mad_f32(ne0, d, s0, *s1); } #endif } } } } static void ggml_compute_forward_out_prod_q_f32( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; const struct ggml_tensor * src1 = dst->src[1]; GGML_TENSOR_BINARY_OP_LOCALS; const int ith = params->ith; const int nth = params->nth; const enum ggml_type type = src0->type; ggml_to_float_t const dequantize_row_q = ggml_get_type_traits(type)->to_float; GGML_ASSERT(ne02 == ne12); GGML_ASSERT(ne03 == ne13); GGML_ASSERT(ne2 == ne12); GGML_ASSERT(ne3 == ne13); // we don't support permuted src0 dim0 GGML_ASSERT(nb00 == ggml_type_size(type)); // dst dim0 cannot be transposed or permuted GGML_ASSERT(nb0 == sizeof(float)); // GGML_ASSERT(nb0 <= nb1); // GGML_ASSERT(nb1 <= nb2); // GGML_ASSERT(nb2 <= nb3); GGML_ASSERT(ne0 == ne00); GGML_ASSERT(ne1 == ne10); GGML_ASSERT(ne2 == ne02); GGML_ASSERT(ne3 == ne03); // nb01 >= nb00 - src0 is not transposed // compute by src0 rows if (ith == 0) { ggml_vec_set_f32(ne0*ne1*ne2*ne3, dst->data, 0); } ggml_barrier(params->threadpool); // parallelize by last three dimensions // total rows in dst const int64_t nr = ne1*ne2*ne3; // rows per thread const int64_t dr = (nr + nth - 1)/nth; // row range for this thread const int64_t ir0 = dr*ith; const int64_t ir1 = MIN(ir0 + dr, nr); // dst[:,:,:,:] = 0 // for i2,i3: // for i1: // for i01: // for i0: // dst[i0,i1,i2,i3] += src0[i0,i01,i2,i3] * src1[i1,i01,i2,i3] float * wdata = (float *) params->wdata + (ne0 + CACHE_LINE_SIZE_F32) * ith; for (int64_t ir = ir0; ir < ir1; ++ir) { // dst indices const int64_t i3 = ir/(ne2*ne1); const int64_t i2 = (ir - i3*ne2*ne1)/ne1; const int64_t i1 = (ir - i3*ne2*ne1 - i2*ne1); const int64_t i02 = i2; const int64_t i03 = i3; //const int64_t i10 = i1; const int64_t i12 = i2; const int64_t i13 = i3; for (int64_t i01 = 0; i01 < ne01; ++i01) { const int64_t i11 = i01; float * s0 = (float *) ((char *) src0->data + ( i01*nb01 + i02*nb02 + i03*nb03)); float * s1 = (float *) ((char *) src1->data + (i1*nb10 + i11*nb11 + i12*nb12 + i13*nb13)); float * d = (float *) ((char *) dst->data + ( i1*nb1 + i2*nb2 + i3*nb3)); dequantize_row_q(s0, wdata, ne0); ggml_vec_mad_f32(ne0, d, wdata, *s1); } } } static void ggml_compute_forward_out_prod( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; switch (src0->type) { case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_1: case GGML_TYPE_Q5_0: case GGML_TYPE_Q5_1: case GGML_TYPE_Q8_0: case GGML_TYPE_Q2_K: case GGML_TYPE_Q3_K: case GGML_TYPE_Q4_K: case GGML_TYPE_Q5_K: case GGML_TYPE_Q6_K: case GGML_TYPE_TQ1_0: case GGML_TYPE_TQ2_0: case GGML_TYPE_IQ2_XXS: case GGML_TYPE_IQ2_XS: case GGML_TYPE_IQ3_XXS: case GGML_TYPE_IQ1_S: case GGML_TYPE_IQ1_M: case GGML_TYPE_IQ4_NL: case GGML_TYPE_IQ4_XS: case GGML_TYPE_IQ3_S: case GGML_TYPE_IQ2_S: case GGML_TYPE_Q4_0_4_4: case GGML_TYPE_Q4_0_4_8: case GGML_TYPE_Q4_0_8_8: { ggml_compute_forward_out_prod_q_f32(params, dst); } break; case GGML_TYPE_F16: { GGML_ABORT("fatal error"); // todo // ggml_compute_forward_out_prod_f16_f32(params, dst); } case GGML_TYPE_F32: { ggml_compute_forward_out_prod_f32(params, dst); } break; default: { GGML_ABORT("fatal error"); } } } // ggml_compute_forward_scale static void ggml_compute_forward_scale_f32( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; GGML_ASSERT(ggml_is_contiguous(src0)); GGML_ASSERT(ggml_is_contiguous(dst)); GGML_ASSERT(ggml_are_same_shape(src0, dst)); // scale factor float v; memcpy(&v, dst->op_params, sizeof(float)); const int ith = params->ith; const int nth = params->nth; const int nc = src0->ne[0]; const int nr = ggml_nrows(src0); // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); const size_t nb01 = src0->nb[1]; const size_t nb1 = dst->nb[1]; for (int i1 = ir0; i1 < ir1; i1++) { if (dst->data != src0->data) { // src0 is same shape as dst => same indices memcpy((char *)dst->data + i1*nb1, (char *)src0->data + i1*nb01, nc * sizeof(float)); } ggml_vec_scale_f32(nc, (float *) ((char *) dst->data + i1*nb1), v); } } static void ggml_compute_forward_scale( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_scale_f32(params, dst); } break; default: { GGML_ABORT("fatal error"); } } } // ggml_compute_forward_set static void ggml_compute_forward_set_f32( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; const struct ggml_tensor * src1 = dst->src[1]; GGML_ASSERT(ggml_are_same_shape(src0, dst)); GGML_ASSERT(ggml_is_contiguous(dst) && ggml_is_contiguous(src0)); // view src0 and dst with these strides and data offset inbytes during set // nb0 is implicitly element_size because src0 and dst are contiguous size_t nb1 = ((int32_t *) dst->op_params)[0]; size_t nb2 = ((int32_t *) dst->op_params)[1]; size_t nb3 = ((int32_t *) dst->op_params)[2]; size_t offset = ((int32_t *) dst->op_params)[3]; bool inplace = (bool) ((int32_t *) dst->op_params)[4]; if (!inplace) { if (params->ith == 0) { // memcpy needs to be synchronized across threads to avoid race conditions. // => do it in INIT phase memcpy( ((char *) dst->data), ((char *) src0->data), ggml_nbytes(dst)); } ggml_barrier(params->threadpool); } const int ith = params->ith; const int nth = params->nth; const int nr = ggml_nrows(src1); const int nc = src1->ne[0]; GGML_TENSOR_LOCALS(int64_t, ne1, src1, ne) GGML_TENSOR_LOCALS(size_t, nb1, src1, nb) // src0 and dst as viewed during set const size_t nb0 = ggml_element_size(src0); const int im0 = (ne10 == 0 ? 0 : ne10-1); const int im1 = (ne11 == 0 ? 0 : ne11-1); const int im2 = (ne12 == 0 ? 0 : ne12-1); const int im3 = (ne13 == 0 ? 0 : ne13-1); GGML_ASSERT(offset + im0*nb0 + im1*nb1 + im2*nb2 + im3*nb3 <= ggml_nbytes(dst)); GGML_ASSERT(nb10 == sizeof(float)); // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); for (int ir = ir0; ir < ir1; ++ir) { // src0 and dst are viewed with shape of src1 and offset // => same indices const int i3 = ir/(ne12*ne11); const int i2 = (ir - i3*ne12*ne11)/ne11; const int i1 = (ir - i3*ne12*ne11 - i2*ne11); ggml_vec_cpy_f32(nc, (float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + offset), (float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11)); } } static void ggml_compute_forward_set( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_set_f32(params, dst); } break; case GGML_TYPE_F16: case GGML_TYPE_BF16: case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_1: case GGML_TYPE_Q5_0: case GGML_TYPE_Q5_1: case GGML_TYPE_Q8_0: case GGML_TYPE_Q8_1: case GGML_TYPE_Q2_K: case GGML_TYPE_Q3_K: case GGML_TYPE_Q4_K: case GGML_TYPE_Q5_K: case GGML_TYPE_Q6_K: case GGML_TYPE_TQ1_0: case GGML_TYPE_TQ2_0: case GGML_TYPE_IQ2_XXS: case GGML_TYPE_IQ2_XS: case GGML_TYPE_IQ3_XXS: case GGML_TYPE_IQ1_S: case GGML_TYPE_IQ1_M: case GGML_TYPE_IQ4_NL: case GGML_TYPE_IQ4_XS: case GGML_TYPE_IQ3_S: case GGML_TYPE_IQ2_S: case GGML_TYPE_Q4_0_4_4: case GGML_TYPE_Q4_0_4_8: case GGML_TYPE_Q4_0_8_8: default: { GGML_ABORT("fatal error"); } } } // ggml_compute_forward_cpy static void ggml_compute_forward_cpy( const struct ggml_compute_params * params, struct ggml_tensor * dst) { ggml_compute_forward_dup(params, dst); } // ggml_compute_forward_cont static void ggml_compute_forward_cont( const struct ggml_compute_params * params, struct ggml_tensor * dst) { ggml_compute_forward_dup(params, dst); } // ggml_compute_forward_reshape static void ggml_compute_forward_reshape( const struct ggml_compute_params * params, struct ggml_tensor * dst) { // NOP UNUSED(params); UNUSED(dst); } // ggml_compute_forward_view static void ggml_compute_forward_view( const struct ggml_compute_params * params, const struct ggml_tensor * dst) { // NOP UNUSED(params); UNUSED(dst); } // ggml_compute_forward_permute static void ggml_compute_forward_permute( const struct ggml_compute_params * params, const struct ggml_tensor * dst) { // NOP UNUSED(params); UNUSED(dst); } // ggml_compute_forward_transpose static void ggml_compute_forward_transpose( const struct ggml_compute_params * params, const struct ggml_tensor * dst) { // NOP UNUSED(params); UNUSED(dst); } // ggml_compute_forward_get_rows static void ggml_compute_forward_get_rows_q( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; const struct ggml_tensor * src1 = dst->src[1]; GGML_TENSOR_BINARY_OP_LOCALS const int64_t nc = ne00; const int64_t nr = ggml_nelements(src1); const enum ggml_type type = src0->type; ggml_to_float_t const dequantize_row_q = ggml_get_type_traits(type)->to_float; assert(ne0 == nc); assert(ne02 == ne11); assert(nb00 == ggml_type_size(type)); assert(ggml_nrows(dst) == nr); const int ith = params->ith; const int nth = params->nth; // 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 (int64_t i = ir0; i < ir1; ++i) { const int64_t i12 = i/(ne11*ne10); const int64_t i11 = (i - i12*ne11*ne10)/ne10; const int64_t i10 = (i - i12*ne11*ne10 - i11*ne10); const int64_t i01 = *(int32_t *) ((char *) src1->data + i10*nb10 + i11*nb11 + i12*nb12); GGML_ASSERT(i01 >= 0 && i01 < ne01); dequantize_row_q( (const void *) ((char *) src0->data + i01*nb01 + i11*nb02 + i12*nb03), (float *) ((char *) dst->data + i10*nb1 + i11*nb2 + i12*nb3), nc); } } static void ggml_compute_forward_get_rows_f16( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; const struct ggml_tensor * src1 = dst->src[1]; GGML_TENSOR_BINARY_OP_LOCALS const int64_t nc = ne00; const int64_t nr = ggml_nelements(src1); assert(ne0 == nc); assert(ne02 == ne11); assert(nb00 == sizeof(ggml_fp16_t)); assert(ggml_nrows(dst) == nr); const int ith = params->ith; const int nth = params->nth; // 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 (int64_t i = ir0; i < ir1; ++i) { const int64_t i12 = i/(ne11*ne10); const int64_t i11 = (i - i12*ne11*ne10)/ne10; const int64_t i10 = (i - i12*ne11*ne10 - i11*ne10); const int64_t i01 = *(int32_t *) ((char *) src1->data + i10*nb10 + i11*nb11 + i12*nb12); GGML_ASSERT(i01 >= 0 && i01 < ne01); ggml_fp16_to_fp32_row( (const void *) ((char *) src0->data + i01*nb01 + i11*nb02 + i12*nb03), (float *) ((char *) dst->data + i10*nb1 + i11*nb2 + i12*nb3), nc); } } static void ggml_compute_forward_get_rows_bf16( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; const struct ggml_tensor * src1 = dst->src[1]; GGML_TENSOR_BINARY_OP_LOCALS const int64_t nc = ne00; const int64_t nr = ggml_nelements(src1); assert(ne0 == nc); assert(ne02 == ne11); assert(nb00 == sizeof(ggml_bf16_t)); assert(ggml_nrows(dst) == nr); const int ith = params->ith; const int nth = params->nth; // 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 (int64_t i = ir0; i < ir1; ++i) { const int64_t i12 = i/(ne11*ne10); const int64_t i11 = (i - i12*ne11*ne10)/ne10; const int64_t i10 = (i - i12*ne11*ne10 - i11*ne10); const int64_t i01 = *(int32_t *) ((char *) src1->data + i10*nb10 + i11*nb11 + i12*nb12); GGML_ASSERT(i01 >= 0 && i01 < ne01); ggml_bf16_to_fp32_row( (const void *) ((char *) src0->data + i01*nb01 + i11*nb02 + i12*nb03), (float *) ((char *) dst->data + i10*nb1 + i11*nb2 + i12*nb3), nc); } } static void ggml_compute_forward_get_rows_f32( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; const struct ggml_tensor * src1 = dst->src[1]; GGML_TENSOR_BINARY_OP_LOCALS const int64_t nc = ne00; const int64_t nr = ggml_nelements(src1); assert(ne0 == nc); assert(ne02 == ne11); assert(nb00 == sizeof(float)); assert(ggml_nrows(dst) == nr); const int ith = params->ith; const int nth = params->nth; // 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 (int64_t i = ir0; i < ir1; ++i) { const int64_t i12 = i/(ne11*ne10); const int64_t i11 = (i - i12*ne11*ne10)/ne10; const int64_t i10 = (i - i12*ne11*ne10 - i11*ne10); const int64_t i01 = *(int32_t *) ((char *) src1->data + i10*nb10 + i11*nb11 + i12*nb12); GGML_ASSERT(i01 >= 0 && i01 < ne01); ggml_vec_cpy_f32(nc, (float *) ((char *) dst->data + i10*nb1 + i11*nb2 + i12*nb3), (float *) ((char *) src0->data + i01*nb01 + i11*nb02 + i12*nb03)); } } static void ggml_compute_forward_get_rows( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; switch (src0->type) { case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_1: case GGML_TYPE_Q5_0: case GGML_TYPE_Q5_1: case GGML_TYPE_Q8_0: case GGML_TYPE_Q8_1: case GGML_TYPE_Q2_K: case GGML_TYPE_Q3_K: case GGML_TYPE_Q4_K: case GGML_TYPE_Q5_K: case GGML_TYPE_Q6_K: case GGML_TYPE_TQ1_0: case GGML_TYPE_TQ2_0: case GGML_TYPE_IQ2_XXS: case GGML_TYPE_IQ2_XS: case GGML_TYPE_IQ3_XXS: case GGML_TYPE_IQ1_S: case GGML_TYPE_IQ1_M: case GGML_TYPE_IQ4_NL: case GGML_TYPE_IQ4_XS: case GGML_TYPE_IQ3_S: case GGML_TYPE_IQ2_S: case GGML_TYPE_Q4_0_4_4: case GGML_TYPE_Q4_0_4_8: case GGML_TYPE_Q4_0_8_8: { ggml_compute_forward_get_rows_q(params, dst); } break; case GGML_TYPE_F16: { ggml_compute_forward_get_rows_f16(params, dst); } break; case GGML_TYPE_BF16: { ggml_compute_forward_get_rows_bf16(params, dst); } break; case GGML_TYPE_F32: case GGML_TYPE_I32: { ggml_compute_forward_get_rows_f32(params, dst); } break; default: { GGML_ABORT("fatal error"); } } //static bool first = true; //printf("ne0 = %d, ne1 = %d, ne2 = %d\n", dst->ne[0], dst->ne[1], dst->ne[2]); //if (first) { // first = false; //} else { // for (int k = 0; k < dst->ne[1]; ++k) { // for (int j = 0; j < dst->ne[0]/16; ++j) { // for (int i = 0; i < 16; ++i) { // printf("%8.4f ", ((float *) dst->data)[k*dst->ne[0] + j*16 + i]); // } // printf("\n"); // } // printf("\n"); // } // printf("\n"); // exit(0); //} } // ggml_compute_forward_get_rows_back static void ggml_compute_forward_get_rows_back_f32_f16( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; const struct ggml_tensor * src1 = dst->src[1]; if (params->ith != 0) { return; } GGML_ASSERT(ggml_is_contiguous(dst)); // ggml_compute_forward_dup_same_cont(params, opt0, dst); memset(dst->data, 0, ggml_nbytes(dst)); const int nc = src0->ne[0]; const int nr = ggml_nelements(src1); GGML_ASSERT( dst->ne[0] == nc); GGML_ASSERT(src0->nb[0] == sizeof(ggml_fp16_t)); for (int i = 0; i < nr; ++i) { const int r = ((int32_t *) src1->data)[i]; for (int j = 0; j < nc; ++j) { ggml_fp16_t v = ((ggml_fp16_t *) ((char *) src0->data + i*src0->nb[1]))[j]; ((float *) ((char *) dst->data + r*dst->nb[1]))[j] += GGML_FP16_TO_FP32(v); } } } static void ggml_compute_forward_get_rows_back_f32( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; const struct ggml_tensor * src1 = dst->src[1]; if (params->ith != 0) { return; } GGML_ASSERT(ggml_is_contiguous(dst)); // ggml_compute_forward_dup_same_cont(params, opt0, dst); memset(dst->data, 0, ggml_nbytes(dst)); const int nc = src0->ne[0]; const int nr = ggml_nelements(src1); GGML_ASSERT( dst->ne[0] == nc); GGML_ASSERT(src0->nb[0] == sizeof(float)); for (int i = 0; i < nr; ++i) { const int r = ((int32_t *) src1->data)[i]; ggml_vec_add_f32(nc, (float *) ((char *) dst->data + r*dst->nb[1]), (float *) ((char *) dst->data + r*dst->nb[1]), (float *) ((char *) src0->data + i*src0->nb[1])); } } static void ggml_compute_forward_get_rows_back( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; switch (src0->type) { case GGML_TYPE_F16: { ggml_compute_forward_get_rows_back_f32_f16(params, dst); } break; case GGML_TYPE_F32: { ggml_compute_forward_get_rows_back_f32(params, dst); } break; default: { GGML_ABORT("fatal error"); } } //static bool first = true; //printf("ne0 = %d, ne1 = %d, ne2 = %d\n", dst->ne[0], dst->ne[1], dst->ne[2]); //if (first) { // first = false; //} else { // for (int k = 0; k < dst->ne[1]; ++k) { // for (int j = 0; j < dst->ne[0]/16; ++j) { // for (int i = 0; i < 16; ++i) { // printf("%8.4f ", ((float *) dst->data)[k*dst->ne[0] + j*16 + i]); // } // printf("\n"); // } // printf("\n"); // } // printf("\n"); // exit(0); //} } // ggml_compute_forward_diag static void ggml_compute_forward_diag_f32( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; if (params->ith != 0) { return; } // TODO: handle transposed/permuted matrices GGML_TENSOR_UNARY_OP_LOCALS GGML_ASSERT(ne00 == ne0); GGML_ASSERT(ne00 == ne1); GGML_ASSERT(ne01 == 1); GGML_ASSERT(ne02 == ne2); GGML_ASSERT(ne03 == ne3); GGML_ASSERT(nb00 == sizeof(float)); GGML_ASSERT(nb0 == sizeof(float)); for (int i3 = 0; i3 < ne3; i3++) { for (int i2 = 0; i2 < ne2; i2++) { for (int i1 = 0; i1 < ne1; i1++) { float * d = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1); float * s = (float *)((char *) src0->data + i3*nb03 + i2*nb02); for (int i0 = 0; i0 < i1; i0++) { d[i0] = 0; } d[i1] = s[i1]; for (int i0 = i1+1; i0 < ne0; i0++) { d[i0] = 0; } } } } } static void ggml_compute_forward_diag( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_diag_f32(params, dst); } break; default: { GGML_ABORT("fatal error"); } } } // ggml_compute_forward_diag_mask_inf static void ggml_compute_forward_diag_mask_f32( const struct ggml_compute_params * params, struct ggml_tensor * dst, const float value) { const struct ggml_tensor * src0 = dst->src[0]; const int ith = params->ith; const int nth = params->nth; const int n_past = ((int32_t *) dst->op_params)[0]; const bool inplace = src0->data == dst->data; GGML_ASSERT(n_past >= 0); if (!inplace) { if (ith == 0) { // memcpy needs to be synchronized across threads to avoid race conditions. // => do it in INIT phase GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0)); GGML_ASSERT(ggml_is_contiguous(dst) && ggml_is_contiguous(src0)); memcpy( ((char *) dst->data), ((char *) src0->data), ggml_nbytes(dst)); } ggml_barrier(params->threadpool); } // TODO: handle transposed/permuted matrices const int n = ggml_nrows(src0); const int nc = src0->ne[0]; const int nr = src0->ne[1]; const int nz = n/nr; GGML_ASSERT( dst->nb[0] == sizeof(float)); GGML_ASSERT(src0->nb[0] == sizeof(float)); for (int k = 0; k < nz; k++) { for (int j = ith; j < nr; j += nth) { for (int i = n_past; i < nc; i++) { if (i > n_past + j) { *(float *)((char *) dst->data + k*dst->nb[2] + j*dst->nb[1] + i*dst->nb[0]) = value; } } } } } static void ggml_compute_forward_diag_mask_inf( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_diag_mask_f32(params, dst, -INFINITY); } break; default: { GGML_ABORT("fatal error"); } } } static void ggml_compute_forward_diag_mask_zero( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_diag_mask_f32(params, dst, 0); } break; default: { GGML_ABORT("fatal error"); } } } // ggml_compute_forward_soft_max static void ggml_compute_forward_soft_max_f32( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; const struct ggml_tensor * src1 = dst->src[1]; assert(ggml_is_contiguous(dst)); assert(ggml_are_same_shape(src0, dst)); float scale = 1.0f; float max_bias = 0.0f; memcpy(&scale, (float *) dst->op_params + 0, sizeof(float)); memcpy(&max_bias, (float *) dst->op_params + 1, sizeof(float)); // TODO: handle transposed/permuted matrices const int ith = params->ith; const int nth = params->nth; GGML_TENSOR_UNARY_OP_LOCALS //const int64_t ne11 = src1 ? src1->ne[1] : 1; // TODO: is this supposed to be ceil instead of floor? // https://huggingface.co/mosaicml/mpt-7b/blob/main/attention.py#L370 const uint32_t n_head = ne02; const uint32_t n_head_log2 = 1u << (uint32_t) floor(log2(n_head)); const float m0 = powf(2.0f, -(max_bias ) / n_head_log2); const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2); 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); float * wp = (float *) params->wdata + (nc + CACHE_LINE_SIZE_F32) * ith; const bool use_f16 = (src1 && src1->type == GGML_TYPE_F16); for (int i1 = ir0; i1 < ir1; i1++) { // ALiBi const uint32_t h = (i1/ne01)%ne02; // head const float slope = (max_bias > 0.0f) ? h < n_head_log2 ? powf(m0, h + 1) : powf(m1, 2*(h - n_head_log2) + 1) : 1.0f; float * sp = (float *)((char *) src0->data + i1*src0->nb[1]); float * dp = (float *)((char *) dst->data + i1*dst->nb[1]); // broadcast the mask across rows ggml_fp16_t * mp_f16 = src1 ? (ggml_fp16_t *)((char *) src1->data) + (i1%ne01)*ne00 : NULL; float * mp_f32 = src1 ? (float *)((char *) src1->data) + (i1%ne01)*ne00 : NULL; ggml_vec_cpy_f32 (nc, wp, sp); ggml_vec_scale_f32(nc, wp, scale); if (mp_f32) { if (use_f16) { for (int i = 0; i < nc; ++i) { wp[i] += slope*GGML_FP16_TO_FP32(mp_f16[i]); } } else { for (int i = 0; i < nc; ++i) { wp[i] += slope*mp_f32[i]; } } } #ifndef NDEBUG for (int i = 0; i < nc; ++i) { //printf("p[%d] = %f\n", i, p[i]); assert(!isnan(wp[i])); } #endif float max = -INFINITY; ggml_vec_max_f32(nc, &max, wp); ggml_float sum = ggml_vec_soft_max_f32(nc, dp, wp, max); assert(sum > 0.0); sum = 1.0/sum; ggml_vec_scale_f32(nc, dp, sum); #ifndef NDEBUG for (int i = 0; i < nc; ++i) { assert(!isnan(dp[i])); assert(!isinf(dp[i])); } #endif } } static void ggml_compute_forward_soft_max( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_soft_max_f32(params, dst); } break; default: { GGML_ABORT("fatal error"); } } } // ggml_compute_forward_soft_max_back static void ggml_compute_forward_soft_max_back_f32( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; const struct ggml_tensor * src1 = dst->src[1]; GGML_ASSERT(ggml_is_contiguous(src0)); GGML_ASSERT(ggml_is_contiguous(src1)); GGML_ASSERT(ggml_is_contiguous(dst)); GGML_ASSERT(ggml_are_same_shape(src0, dst)); GGML_ASSERT(ggml_are_same_shape(src1, dst)); // TODO: handle transposed/permuted matrices const int ith = params->ith; const int nth = params->nth; const int nc = src0->ne[0]; const int nr = ggml_nrows(src0); // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); for (int i1 = ir0; i1 < ir1; i1++) { float *dy = (float *)((char *) src0->data + i1*src0->nb[1]); float *y = (float *)((char *) src1->data + i1*src1->nb[1]); float *dx = (float *)((char *) dst->data + i1*dst->nb[1]); #ifndef NDEBUG for (int i = 0; i < nc; ++i) { //printf("p[%d] = %f\n", i, p[i]); assert(!isnan(dy[i])); assert(!isnan(y[i])); } #endif // Jii = yi - yi*yi // Jij = -yi*yj // J = diag(y)-y.T*y // dx = J * dy // dxk = sum_i(Jki * dyi) // dxk = sum_i(-yk*yi * dyi) - (-yk*yk)*dyk + (yk - yk*yk)*dyk // dxk = sum_i(-yk*yi * dyi) + yk*yk*dyk + yk*dyk - yk*yk*dyk // dxk = sum_i(-yk*yi * dyi) + yk*dyk // dxk = -yk * sum_i(yi * dyi) + yk*dyk // dxk = -yk * dot(y, dy) + yk*dyk // dxk = yk * (- dot(y, dy) + dyk) // dxk = yk * (dyk - dot(y, dy)) // // post-order: // dot_y_dy := dot(y, dy) // dx := dy // dx := dx - dot_y_dy // dx := dx * y // linear runtime, no additional memory float dot_y_dy = 0; ggml_vec_dot_f32 (nc, &dot_y_dy, 0, y, 0, dy, 0, 1); ggml_vec_cpy_f32 (nc, dx, dy); ggml_vec_acc1_f32(nc, dx, -dot_y_dy); ggml_vec_mul_f32 (nc, dx, dx, y); #ifndef NDEBUG for (int i = 0; i < nc; ++i) { assert(!isnan(dx[i])); assert(!isinf(dx[i])); } #endif } } static void ggml_compute_forward_soft_max_back( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_soft_max_back_f32(params, dst); } break; default: { GGML_ABORT("fatal error"); } } } // ggml_compute_forward_clamp static void ggml_compute_forward_clamp_f32( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; if (params->ith != 0) { return; } float min; float max; memcpy(&min, (float *) dst->op_params + 0, sizeof(float)); memcpy(&max, (float *) dst->op_params + 1, sizeof(float)); const int ith = params->ith; const int nth = params->nth; const int n = ggml_nrows(src0); const int nc = src0->ne[0]; const size_t nb00 = src0->nb[0]; const size_t nb01 = src0->nb[1]; const size_t nb0 = dst->nb[0]; const size_t nb1 = dst->nb[1]; GGML_ASSERT( nb0 == sizeof(float)); GGML_ASSERT(nb00 == sizeof(float)); for (int j = ith; j < n; j += nth) { float * dst_ptr = (float *) ((char *) dst->data + j*nb1); float * src0_ptr = (float *) ((char *) src0->data + j*nb01); for (int i = 0; i < nc; i++) { dst_ptr[i] = MAX(MIN(src0_ptr[i], max), min); } } } static void ggml_compute_forward_clamp( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_clamp_f32(params, dst); } break; case GGML_TYPE_F16: case GGML_TYPE_BF16: case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_1: case GGML_TYPE_Q5_0: case GGML_TYPE_Q5_1: case GGML_TYPE_Q8_0: case GGML_TYPE_Q8_1: case GGML_TYPE_Q2_K: case GGML_TYPE_Q3_K: case GGML_TYPE_Q4_K: case GGML_TYPE_Q5_K: case GGML_TYPE_Q6_K: case GGML_TYPE_TQ1_0: case GGML_TYPE_TQ2_0: case GGML_TYPE_IQ2_XXS: case GGML_TYPE_IQ2_XS: case GGML_TYPE_IQ3_XXS: case GGML_TYPE_IQ1_S: case GGML_TYPE_IQ1_M: case GGML_TYPE_IQ4_NL: case GGML_TYPE_IQ4_XS: case GGML_TYPE_IQ3_S: case GGML_TYPE_IQ2_S: case GGML_TYPE_Q8_K: case GGML_TYPE_Q4_0_4_4: case GGML_TYPE_Q4_0_4_8: case GGML_TYPE_Q4_0_8_8: case GGML_TYPE_I8: case GGML_TYPE_I16: case GGML_TYPE_I32: case GGML_TYPE_I64: case GGML_TYPE_F64: case GGML_TYPE_COUNT: { GGML_ABORT("fatal error"); } } } // ggml_compute_forward_rope static float rope_yarn_ramp(const float low, const float high, const int i0) { const float y = (i0 / 2 - low) / MAX(0.001f, high - low); return 1 - MIN(1, MAX(0, y)); } // YaRN algorithm based on LlamaYaRNScaledRotaryEmbedding.py from https://github.com/jquesnelle/yarn // MIT licensed. Copyright (c) 2023 Jeffrey Quesnelle and Bowen Peng. static void rope_yarn( float theta_extrap, float freq_scale, float corr_dims[2], int64_t i0, float ext_factor, float mscale, float * cos_theta, float * sin_theta) { // Get n-d rotational scaling corrected for extrapolation float theta_interp = freq_scale * theta_extrap; float theta = theta_interp; if (ext_factor != 0.0f) { float ramp_mix = rope_yarn_ramp(corr_dims[0], corr_dims[1], i0) * ext_factor; theta = theta_interp * (1 - ramp_mix) + theta_extrap * ramp_mix; // Get n-d magnitude scaling corrected for interpolation mscale *= 1.0f + 0.1f * logf(1.0f / freq_scale); } *cos_theta = cosf(theta) * mscale; *sin_theta = sinf(theta) * mscale; } // Apparently solving `n_rot = 2pi * x * base^((2 * max_pos_emb) / n_dims)` for x, we get // `corr_dim(n_rot) = n_dims * log(max_pos_emb / (n_rot * 2pi)) / (2 * log(base))` static float ggml_rope_yarn_corr_dim(int n_dims, int n_ctx_orig, float n_rot, float base) { return n_dims * logf(n_ctx_orig / (n_rot * 2 * (float)M_PI)) / (2 * logf(base)); } static void ggml_rope_cache_init( float theta_base, float freq_scale, const float * freq_factors, float corr_dims[2], int64_t ne0, float ext_factor, float mscale, float * cache, float sin_sign, float theta_scale) { // ref: https://github.com/jquesnelle/yarn/blob/master/scaled_rope/LlamaYaRNScaledRotaryEmbedding.py float theta = theta_base; for (int64_t i0 = 0; i0 < ne0; i0 += 2) { const float ff = freq_factors ? freq_factors[i0/2] : 1.0f; rope_yarn( theta/ff, freq_scale, corr_dims, i0, ext_factor, mscale, &cache[i0 + 0], &cache[i0 + 1] ); cache[i0 + 1] *= sin_sign; theta *= theta_scale; } } void ggml_rope_yarn_corr_dims( int n_dims, int n_ctx_orig, float freq_base, float beta_fast, float beta_slow, float dims[2] ) { // start and end correction dims float start = floorf(ggml_rope_yarn_corr_dim(n_dims, n_ctx_orig, beta_fast, freq_base)); float end = ceilf(ggml_rope_yarn_corr_dim(n_dims, n_ctx_orig, beta_slow, freq_base)); dims[0] = MAX(0, start); dims[1] = MIN(n_dims - 1, end); } static void ggml_compute_forward_rope_f32( const struct ggml_compute_params * params, struct ggml_tensor * dst, const bool forward) { const struct ggml_tensor * src0 = dst->src[0]; const struct ggml_tensor * src1 = dst->src[1]; const struct ggml_tensor * src2 = dst->src[2]; float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow; //const int n_past = ((int32_t *) dst->op_params)[0]; const int n_dims = ((int32_t *) dst->op_params)[1]; const int mode = ((int32_t *) dst->op_params)[2]; //const int n_ctx = ((int32_t *) dst->op_params)[3]; const int n_ctx_orig = ((int32_t *) dst->op_params)[4]; memcpy(&freq_base, (int32_t *) dst->op_params + 5, sizeof(float)); memcpy(&freq_scale, (int32_t *) dst->op_params + 6, sizeof(float)); memcpy(&ext_factor, (int32_t *) dst->op_params + 7, sizeof(float)); memcpy(&attn_factor, (int32_t *) dst->op_params + 8, sizeof(float)); memcpy(&beta_fast, (int32_t *) dst->op_params + 9, sizeof(float)); memcpy(&beta_slow, (int32_t *) dst->op_params + 10, sizeof(float)); GGML_TENSOR_UNARY_OP_LOCALS //printf("ne0: %d, ne1: %d, ne2: %d, ne3: %d\n", ne0, ne1, ne2, ne3); //printf("n_past = %d, ne2 = %d\n", n_past, ne2); GGML_ASSERT(nb00 == sizeof(float)); const int ith = params->ith; const int nth = params->nth; const int nr = ggml_nrows(dst); GGML_ASSERT(n_dims <= ne0); GGML_ASSERT(n_dims % 2 == 0); // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); // row index used to determine which thread to use int ir = 0; const float theta_scale = powf(freq_base, -2.0f/n_dims); float corr_dims[2]; ggml_rope_yarn_corr_dims(n_dims, n_ctx_orig, freq_base, beta_fast, beta_slow, corr_dims); const bool is_neox = mode & GGML_ROPE_TYPE_NEOX; const float * freq_factors = NULL; if (src2 != NULL) { GGML_ASSERT(src2->type == GGML_TYPE_F32); GGML_ASSERT(src2->ne[0] >= n_dims / 2); freq_factors = (const float *) src2->data; } // backward process uses inverse rotation by cos and sin. // cos and sin build a rotation matrix, where the inverse is the transpose. // this essentially just switches the sign of sin. const float sin_sign = forward ? 1.0f : -1.0f; const int32_t * pos = (const int32_t *) src1->data; for (int64_t i3 = 0; i3 < ne3; i3++) { for (int64_t i2 = 0; i2 < ne2; i2++) { const int64_t p = pos[i2]; float * cache = (float *) params->wdata + (ne0 + CACHE_LINE_SIZE_F32)*ith; ggml_rope_cache_init(p, freq_scale, freq_factors, corr_dims, ne0, ext_factor, attn_factor, cache, sin_sign, theta_scale); for (int64_t i1 = 0; i1 < ne1; i1++) { if (ir++ < ir0) continue; if (ir > ir1) break; if (!is_neox) { for (int64_t i0 = 0; i0 < n_dims; i0 += 2) { const float cos_theta = cache[i0 + 0]; const float sin_theta = cache[i0 + 1]; const float * const src = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); float * dst_data = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); const float x0 = src[0]; const float x1 = src[1]; dst_data[0] = x0*cos_theta - x1*sin_theta; dst_data[1] = x0*sin_theta + x1*cos_theta; } } else { for (int64_t i0 = 0; i0 < n_dims; i0 += 2) { const int64_t ic = i0/2; const float cos_theta = cache[i0 + 0]; const float sin_theta = cache[i0 + 1]; const float * const src = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + ic*nb00); float * dst_data = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + ic*nb0); const float x0 = src[0]; const float x1 = src[n_dims/2]; dst_data[0] = x0*cos_theta - x1*sin_theta; dst_data[n_dims/2] = x0*sin_theta + x1*cos_theta; } } for (int64_t i0 = n_dims; i0 < ne0; i0 += 2) { const float * const src = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); float * dst_data = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); dst_data[0] = src[0]; dst_data[1] = src[1]; } } } } } // TODO: deduplicate f16/f32 code static void ggml_compute_forward_rope_f16( const struct ggml_compute_params * params, struct ggml_tensor * dst, const bool forward) { const struct ggml_tensor * src0 = dst->src[0]; const struct ggml_tensor * src1 = dst->src[1]; const struct ggml_tensor * src2 = dst->src[2]; float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow; //const int n_past = ((int32_t *) dst->op_params)[0]; const int n_dims = ((int32_t *) dst->op_params)[1]; const int mode = ((int32_t *) dst->op_params)[2]; //const int n_ctx = ((int32_t *) dst->op_params)[3]; const int n_ctx_orig = ((int32_t *) dst->op_params)[4]; memcpy(&freq_base, (int32_t *) dst->op_params + 5, sizeof(float)); memcpy(&freq_scale, (int32_t *) dst->op_params + 6, sizeof(float)); memcpy(&ext_factor, (int32_t *) dst->op_params + 7, sizeof(float)); memcpy(&attn_factor, (int32_t *) dst->op_params + 8, sizeof(float)); memcpy(&beta_fast, (int32_t *) dst->op_params + 9, sizeof(float)); memcpy(&beta_slow, (int32_t *) dst->op_params + 10, sizeof(float)); GGML_TENSOR_UNARY_OP_LOCALS //printf("ne0: %d, ne1: %d, ne2: %d, ne3: %d\n", ne0, ne1, ne2, ne3); //printf("n_past = %d, ne2 = %d\n", n_past, ne2); GGML_ASSERT(nb0 == sizeof(ggml_fp16_t)); const int ith = params->ith; const int nth = params->nth; const int nr = ggml_nrows(dst); GGML_ASSERT(n_dims <= ne0); GGML_ASSERT(n_dims % 2 == 0); // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); // row index used to determine which thread to use int ir = 0; const float theta_scale = powf(freq_base, -2.0f/n_dims); float corr_dims[2]; ggml_rope_yarn_corr_dims(n_dims, n_ctx_orig, freq_base, beta_fast, beta_slow, corr_dims); const bool is_neox = mode & GGML_ROPE_TYPE_NEOX; const float * freq_factors = NULL; if (src2 != NULL) { GGML_ASSERT(src2->type == GGML_TYPE_F32); GGML_ASSERT(src2->ne[0] >= n_dims / 2); freq_factors = (const float *) src2->data; } // backward process uses inverse rotation by cos and sin. // cos and sin build a rotation matrix, where the inverse is the transpose. // this essentially just switches the sign of sin. const float sin_sign = forward ? 1.0f : -1.0f; const int32_t * pos = (const int32_t *) src1->data; for (int64_t i3 = 0; i3 < ne3; i3++) { for (int64_t i2 = 0; i2 < ne2; i2++) { const int64_t p = pos[i2]; float * cache = (float *) params->wdata + (ne0 + CACHE_LINE_SIZE_F32)*ith; ggml_rope_cache_init(p, freq_scale, freq_factors, corr_dims, ne0, ext_factor, attn_factor, cache, sin_sign, theta_scale); for (int64_t i1 = 0; i1 < ne1; i1++) { if (ir++ < ir0) continue; if (ir > ir1) break; if (!is_neox) { for (int64_t i0 = 0; i0 < n_dims; i0 += 2) { const float cos_theta = cache[i0 + 0]; const float sin_theta = cache[i0 + 1]; const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); ggml_fp16_t * dst_data = (ggml_fp16_t *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); const float x0 = GGML_FP16_TO_FP32(src[0]); const float x1 = GGML_FP16_TO_FP32(src[1]); dst_data[0] = GGML_FP32_TO_FP16(x0*cos_theta - x1*sin_theta); dst_data[1] = GGML_FP32_TO_FP16(x0*sin_theta + x1*cos_theta); } } else { for (int64_t i0 = 0; i0 < n_dims; i0 += 2) { const int64_t ic = i0/2; const float cos_theta = cache[i0 + 0]; const float sin_theta = cache[i0 + 1]; const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + ic*nb00); ggml_fp16_t * dst_data = (ggml_fp16_t *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + ic*nb0); const float x0 = GGML_FP16_TO_FP32(src[0]); const float x1 = GGML_FP16_TO_FP32(src[n_dims/2]); dst_data[0] = GGML_FP32_TO_FP16(x0*cos_theta - x1*sin_theta); dst_data[n_dims/2] = GGML_FP32_TO_FP16(x0*sin_theta + x1*cos_theta); } } for (int64_t i0 = n_dims; i0 < ne0; i0 += 2) { const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); ggml_fp16_t * dst_data = (ggml_fp16_t *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); dst_data[0] = src[0]; dst_data[1] = src[1]; } } } } } static void ggml_compute_forward_rope( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; switch (src0->type) { case GGML_TYPE_F16: { ggml_compute_forward_rope_f16(params, dst, true); } break; case GGML_TYPE_F32: { ggml_compute_forward_rope_f32(params, dst, true); } break; default: { GGML_ABORT("fatal error"); } } } // ggml_compute_forward_rope_back static void ggml_compute_forward_rope_back( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; switch (src0->type) { case GGML_TYPE_F16: { ggml_compute_forward_rope_f16(params, dst, false); } break; case GGML_TYPE_F32: { ggml_compute_forward_rope_f32(params, dst, false); } break; default: { GGML_ABORT("fatal error"); } } } // ggml_compute_forward_conv_transpose_1d static void ggml_compute_forward_conv_transpose_1d_f16_f32( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; const struct ggml_tensor * src1 = dst->src[1]; GGML_ASSERT(src0->type == GGML_TYPE_F16); GGML_ASSERT(src1->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); GGML_TENSOR_BINARY_OP_LOCALS const int ith = params->ith; const int nth = params->nth; const int nk = ne00*ne01*ne02; GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); GGML_ASSERT(nb10 == sizeof(float)); if (ith == 0) { memset(params->wdata, 0, params->wsize); // permute kernel data (src0) from (K x Cout x Cin) to (Cin x K x Cout) { ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + 0; for (int64_t i02 = 0; i02 < ne02; i02++) { for (int64_t i01 = 0; i01 < ne01; i01++) { const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i02*nb02 + i01*nb01); ggml_fp16_t * dst_data = wdata + i01*ne00*ne02; for (int64_t i00 = 0; i00 < ne00; i00++) { dst_data[i00*ne02 + i02] = src[i00]; } } } } // permute source data (src1) from (L x Cin) to (Cin x L) { ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + nk; ggml_fp16_t * dst_data = wdata; for (int64_t i11 = 0; i11 < ne11; i11++) { const float * const src = (float *)((char *) src1->data + i11*nb11); for (int64_t i10 = 0; i10 < ne10; i10++) { dst_data[i10*ne11 + i11] = GGML_FP32_TO_FP16(src[i10]); } } } // need to zero dst since we are accumulating into it memset(dst->data, 0, ggml_nbytes(dst)); } ggml_barrier(params->threadpool); const int32_t s0 = ((const int32_t*)(dst->op_params))[0]; // total rows in dst const int nr = ne1; // 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 * const wdata = (ggml_fp16_t *) params->wdata + 0; ggml_fp16_t * const wdata_src = wdata + nk; for (int i1 = ir0; i1 < ir1; i1++) { float * dst_data = (float *)((char *) dst->data + i1*nb1); ggml_fp16_t * wdata_kernel = wdata + i1*ne02*ne00; for (int i10 = 0; i10 < ne10; i10++) { const int i1n = i10*ne11; for (int i00 = 0; i00 < ne00; i00++) { float v = 0; ggml_vec_dot_f16(ne02, &v, 0, (ggml_fp16_t *) wdata_src + i1n, 0, (ggml_fp16_t *) wdata_kernel + i00*ne02, 0, 1); dst_data[i10*s0 + i00] += v; } } } } static void ggml_compute_forward_conv_transpose_1d_f32( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; const struct ggml_tensor * src1 = dst->src[1]; GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT(src1->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); GGML_TENSOR_BINARY_OP_LOCALS const int ith = params->ith; const int nth = params->nth; const int nk = ne00*ne01*ne02; GGML_ASSERT(nb00 == sizeof(float)); GGML_ASSERT(nb10 == sizeof(float)); if (ith == 0) { memset(params->wdata, 0, params->wsize); // prepare kernel data (src0) from (K x Cout x Cin) to (Cin x K x Cout) { float * const wdata = (float *) params->wdata + 0; for (int64_t i02 = 0; i02 < ne02; i02++) { for (int64_t i01 = 0; i01 < ne01; i01++) { const float * const src = (float *)((char *) src0->data + i02*nb02 + i01*nb01); float * dst_data = wdata + i01*ne00*ne02; for (int64_t i00 = 0; i00 < ne00; i00++) { dst_data[i00*ne02 + i02] = src[i00]; } } } } // prepare source data (src1) { float * const wdata = (float *) params->wdata + nk; float * dst_data = wdata; for (int64_t i11 = 0; i11 < ne11; i11++) { const float * const src = (float *)((char *) src1->data + i11*nb11); for (int64_t i10 = 0; i10 < ne10; i10++) { dst_data[i10*ne11 + i11] = src[i10]; } } } // need to zero dst since we are accumulating into it memset(dst->data, 0, ggml_nbytes(dst)); } ggml_barrier(params->threadpool); const int32_t s0 = ((const int32_t*)(dst->op_params))[0]; // total rows in dst const int nr = ne1; // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); float * const wdata = (float *) params->wdata + 0; float * const wdata_src = wdata + nk; for (int i1 = ir0; i1 < ir1; i1++) { float * dst_data = (float *)((char *) dst->data + i1*nb1); float * wdata_kernel = wdata + i1*ne02*ne00; for (int i10 = 0; i10 < ne10; i10++) { const int i1n = i10*ne11; for (int i00 = 0; i00 < ne00; i00++) { float v = 0; ggml_vec_dot_f32(ne02, &v, 0, wdata_src + i1n, 0, wdata_kernel + i00*ne02, 0, 1); dst_data[i10*s0 + i00] += v; } } } } static void ggml_compute_forward_conv_transpose_1d( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; switch (src0->type) { case GGML_TYPE_F16: { ggml_compute_forward_conv_transpose_1d_f16_f32(params, dst); } break; case GGML_TYPE_F32: { ggml_compute_forward_conv_transpose_1d_f32(params, dst); } break; default: { GGML_ABORT("fatal error"); } } } // ggml_compute_forward_im2col_f32 // src0: kernel [OC, IC, KH, KW] // src1: image [N, IC, IH, IW] // dst: result [N, OH, OW, IC*KH*KW] static void ggml_compute_forward_im2col_f32( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; const struct ggml_tensor * src1 = dst->src[1]; GGML_ASSERT(src1->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); GGML_TENSOR_BINARY_OP_LOCALS; const int32_t s0 = ((const int32_t *)(dst->op_params))[0]; const int32_t s1 = ((const int32_t *)(dst->op_params))[1]; const int32_t p0 = ((const int32_t *)(dst->op_params))[2]; const int32_t p1 = ((const int32_t *)(dst->op_params))[3]; const int32_t d0 = ((const int32_t *)(dst->op_params))[4]; const int32_t d1 = ((const int32_t *)(dst->op_params))[5]; const bool is_2D = ((const int32_t *)(dst->op_params))[6] == 1; const int ith = params->ith; const int nth = params->nth; const int64_t N = is_2D ? ne13 : ne12; const int64_t IC = is_2D ? ne12 : ne11; const int64_t IH = is_2D ? ne11 : 1; const int64_t IW = ne10; const int64_t KH = is_2D ? ne01 : 1; const int64_t KW = ne00; const int64_t OH = is_2D ? ne2 : 1; const int64_t OW = ne1; int ofs0 = is_2D ? nb13 : nb12; int ofs1 = is_2D ? nb12 : nb11; GGML_ASSERT(nb10 == sizeof(float)); // im2col: [N, IC, IH, IW] => [N, OH, OW, IC*KH*KW] { float * const wdata = (float *) dst->data; for (int64_t in = 0; in < N; in++) { for (int64_t ioh = 0; ioh < OH; ioh++) { // 1 for (int64_t iow = 0; iow < OW; iow++) { for (int64_t iic = ith; iic < IC; iic += nth) { // micro kernel float * dst_data = wdata + (in*OH*OW + ioh*OW + iow)*(IC*KH*KW); // [IC, KH, KW] const float * const src_data = (float *)((char *) src1->data + in*ofs0 + iic*ofs1); // [IH, IW] for (int64_t ikh = 0; ikh < KH; ikh++) { // 1 for (int64_t ikw = 0; ikw < KW; ikw++) { const int64_t iiw = iow*s0 + ikw*d0 - p0; const int64_t iih = ioh*s1 + ikh*d1 - p1; if (iih < 0 || iih >= IH || iiw < 0 || iiw >= IW) { dst_data[iic*(KH*KW) + ikh*KW + ikw] = 0; } else { dst_data[iic*(KH*KW) + ikh*KW + ikw] = (src_data[iih*IW + iiw]); } } } } } } } } } // ggml_compute_forward_im2col_f16 // src0: kernel [OC, IC, KH, KW] // src1: image [N, IC, IH, IW] // dst: result [N, OH, OW, IC*KH*KW] static void ggml_compute_forward_im2col_f16( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; const struct ggml_tensor * src1 = dst->src[1]; GGML_ASSERT(src0->type == GGML_TYPE_F16); GGML_ASSERT(src1->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F16); GGML_TENSOR_BINARY_OP_LOCALS; const int32_t s0 = ((const int32_t *)(dst->op_params))[0]; const int32_t s1 = ((const int32_t *)(dst->op_params))[1]; const int32_t p0 = ((const int32_t *)(dst->op_params))[2]; const int32_t p1 = ((const int32_t *)(dst->op_params))[3]; const int32_t d0 = ((const int32_t *)(dst->op_params))[4]; const int32_t d1 = ((const int32_t *)(dst->op_params))[5]; const bool is_2D = ((const int32_t *)(dst->op_params))[6] == 1; const int ith = params->ith; const int nth = params->nth; const int64_t N = is_2D ? ne13 : ne12; const int64_t IC = is_2D ? ne12 : ne11; const int64_t IH = is_2D ? ne11 : 1; const int64_t IW = ne10; const int64_t KH = is_2D ? ne01 : 1; const int64_t KW = ne00; const int64_t OH = is_2D ? ne2 : 1; const int64_t OW = ne1; int ofs0 = is_2D ? nb13 : nb12; int ofs1 = is_2D ? nb12 : nb11; GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); GGML_ASSERT(nb10 == sizeof(float)); // im2col: [N, IC, IH, IW] => [N, OH, OW, IC*KH*KW] { ggml_fp16_t * const wdata = (ggml_fp16_t *) dst->data; for (int64_t in = 0; in < N; in++) { for (int64_t ioh = 0; ioh < OH; ioh++) { // 1 for (int64_t iow = 0; iow < OW; iow++) { for (int64_t iic = ith; iic < IC; iic += nth) { // micro kernel ggml_fp16_t * dst_data = wdata + (in*OH*OW + ioh*OW + iow)*(IC*KH*KW); // [IC, KH, KW] const float * const src_data = (float *)((char *) src1->data + in*ofs0 + iic*ofs1); // [IH, IW] for (int64_t ikh = 0; ikh < KH; ikh++) { // 1 for (int64_t ikw = 0; ikw < KW; ikw++) { const int64_t iiw = iow*s0 + ikw*d0 - p0; const int64_t iih = ioh*s1 + ikh*d1 - p1; if (iih < 0 || iih >= IH || iiw < 0 || iiw >= IW) { dst_data[iic*(KH*KW) + ikh*KW + ikw] = 0; } else { dst_data[iic*(KH*KW) + ikh*KW + ikw] = GGML_FP32_TO_FP16(src_data[iih*IW + iiw]); } } } } } } } } } static void ggml_compute_forward_im2col( const struct ggml_compute_params * params, struct ggml_tensor * dst) { switch (dst->type) { case GGML_TYPE_F16: { ggml_compute_forward_im2col_f16(params, dst); } break; case GGML_TYPE_F32: { ggml_compute_forward_im2col_f32(params, dst); } break; default: { GGML_ABORT("fatal error"); } } } // ggml_compute_forward_im2col_back_f32 static void ggml_compute_forward_im2col_back_f32( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; const struct ggml_tensor * src1 = dst->src[1]; GGML_ASSERT(src1->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); GGML_TENSOR_BINARY_OP_LOCALS; const int32_t s0 = ((const int32_t *)(dst->op_params))[0]; const int32_t s1 = ((const int32_t *)(dst->op_params))[1]; const int32_t p0 = ((const int32_t *)(dst->op_params))[2]; const int32_t p1 = ((const int32_t *)(dst->op_params))[3]; const int32_t d0 = ((const int32_t *)(dst->op_params))[4]; const int32_t d1 = ((const int32_t *)(dst->op_params))[5]; const bool is_2D = ((const int32_t *)(dst->op_params))[6] == 1; const int ith = params->ith; const int nth = params->nth; const int64_t N = is_2D ? ne3 : ne2; const int64_t IC = is_2D ? ne2 : ne1; const int64_t IH = is_2D ? ne1 : 1; const int64_t IW = ne0; const int64_t KH = is_2D ? ne01 : 1; const int64_t KW = ne00; const int64_t OH = is_2D ? ne12 : 1; const int64_t OW = ne11; int ofs0 = is_2D ? nb3 : nb2; int ofs1 = is_2D ? nb2 : nb1; GGML_ASSERT(nb0 == sizeof(float)); // im2col: [N, IC, IH, IW] => [N, OH, OW, IC*KH*KW] { float * const wdata = (float *) dst->data; for (int64_t in = 0; in < N; in++) { for (int64_t iic = ith; iic < IC; iic += nth) { for (int64_t iih = 0; iih < IH; iih++) { for (int64_t iiw = 0; iiw < IW; iiw++) { // micro kernel float grad = 0.0f; for (int64_t ikh = 0; ikh < KH; ikh++) { for (int64_t ikw = 0; ikw < KW; ikw++) { // For s0 > 1 some values were skipped over in the forward pass. // These values have tmpw % s0 != 0 and need to be skipped in the backwards pass as well. const int64_t tmpw = (iiw + p0 - ikw*d0); if (tmpw % s0 != 0) { continue; } const int64_t iow = tmpw / s0; // Equivalent logic as above except for s1. int64_t ioh; if (is_2D) { const int64_t tmph = iih + p1 - ikh*d1; if (tmph % s1 != 0) { continue; } ioh = tmph / s1; } else { ioh = 0; } if (iow < 0 || iow >= OW || ioh < 0 || ioh >= OH) { continue; } const float * const src_data = (const float *) src1->data + (in*OH*OW + ioh*OW + iow)*(IC*KH*KW); // [IC, KH, KW] grad += src_data[iic*(KH*KW) + ikh*KW + ikw]; } } float * dst_data = (float *)((char *) wdata + (in*ofs0 + iic*ofs1)); // [IH, IW] dst_data[iih*IW + iiw] = grad; } } } } } } // ggml_compute_forward_conv_transpose_2d static void ggml_compute_forward_conv_transpose_2d( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; const struct ggml_tensor * src1 = dst->src[1]; GGML_ASSERT(src0->type == GGML_TYPE_F16); GGML_ASSERT(src1->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); GGML_TENSOR_BINARY_OP_LOCALS const int ith = params->ith; const int nth = params->nth; const int nk = ne00*ne01*ne02*ne03; GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); GGML_ASSERT(nb10 == sizeof(float)); if (ith == 0) { memset(params->wdata, 0, params->wsize); // permute kernel data (src0) from (Kw x Kh x Cout x Cin) to (Cin x Kw x Kh x Cout) { ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + 0; for (int64_t i03 = 0; i03 < ne03; i03++) { for (int64_t i02 = 0; i02 < ne02; i02++) { const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i03*nb03 + i02*nb02); ggml_fp16_t * dst_data = wdata + i02*ne01*ne00*ne03; for (int64_t i01 = 0; i01 < ne01; i01++) { for (int64_t i00 = 0; i00 < ne00; i00++) { dst_data[i01*ne00*ne03 + i00*ne03 + i03] = src[i01 * ne00 + i00]; } } } } } // permute source data (src1) from (Sw x Sh x Cin) to (Cin x Sw x Sh) { ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + nk; for (int i12 = 0; i12 < ne12; i12++) { for (int i11 = 0; i11 < ne11; i11++) { const float * const src = (float *)((char *) src1->data + i12*nb12 + i11*nb11); ggml_fp16_t * dst_data = wdata + i11*ne10*ne12; for (int i10 = 0; i10 < ne10; i10++) { dst_data[i10*ne12 + i12] = GGML_FP32_TO_FP16(src[i10]); } } } } memset(dst->data, 0, ggml_nbytes(dst)); } ggml_barrier(params->threadpool); const int32_t stride = ggml_get_op_params_i32(dst, 0); // total patches in dst const int np = ne2; // patches per thread const int dp = (np + nth - 1)/nth; // patch range for this thread const int ip0 = dp*ith; const int ip1 = MIN(ip0 + dp, np); ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + 0; ggml_fp16_t * const wdata_src = wdata + nk; for (int i2 = ip0; i2 < ip1; i2++) { // Cout float * dst_data = (float *)((char *) dst->data + i2*nb2); ggml_fp16_t * wdata_kernel = wdata + i2*ne01*ne00*ne03; for (int i11 = 0; i11 < ne11; i11++) { for (int i10 = 0; i10 < ne10; i10++) { const int i1n = i11*ne10*ne12 + i10*ne12; for (int i01 = 0; i01 < ne01; i01++) { for (int i00 = 0; i00 < ne00; i00++) { float v = 0; ggml_vec_dot_f16(ne03, &v, 0, wdata_src + i1n, 0, wdata_kernel + i01*ne00*ne03 + i00*ne03, 0, 1); dst_data[(i11*stride + i01)*ne0 + i10*stride + i00] += v; } } } } } } // ggml_compute_forward_pool_1d_sk_p0 static void ggml_compute_forward_pool_1d_sk_p0( const struct ggml_compute_params * params, const enum ggml_op_pool op, const int k, struct ggml_tensor * dst) { const struct ggml_tensor * src = dst->src[0]; assert(src->type == GGML_TYPE_F32 || src->type == GGML_TYPE_F16); if (params->ith != 0) { return; } const char * cdata = (const char *)src->data; const char * const data_end = cdata + ggml_nbytes(src); float * drow = (float *)dst->data; const int64_t rs = dst->ne[0]; while (cdata < data_end) { const void * srow = (const void *)cdata; int j = 0; for (int64_t i = 0; i < rs; ++i) { switch (op) { case GGML_OP_POOL_AVG: drow[i] = 0; break; case GGML_OP_POOL_MAX: drow[i] = -FLT_MAX; break; case GGML_OP_POOL_COUNT: GGML_ABORT("fatal error"); } for (int ki = 0; ki < k; ++ki) { const float srow_j = (src->type == GGML_TYPE_F32) ? ((const float*)srow)[j] : GGML_FP16_TO_FP32(((const ggml_fp16_t*)srow)[j]); switch (op) { case GGML_OP_POOL_AVG: drow[i] += srow_j; break; case GGML_OP_POOL_MAX: if (srow_j > drow[i]) drow[i] = srow_j; break; case GGML_OP_POOL_COUNT: GGML_ABORT("fatal error"); } ++j; } switch (op) { case GGML_OP_POOL_AVG: drow[i] /= k; break; case GGML_OP_POOL_MAX: break; case GGML_OP_POOL_COUNT: GGML_ABORT("fatal error"); } } cdata += src->nb[1]; drow += rs; } } // ggml_compute_forward_pool_1d static void ggml_compute_forward_pool_1d( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const int32_t * opts = (const int32_t *)dst->op_params; enum ggml_op_pool op = opts[0]; const int k0 = opts[1]; const int s0 = opts[2]; const int p0 = opts[3]; GGML_ASSERT(p0 == 0); // padding not supported GGML_ASSERT(k0 == s0); // only s = k supported ggml_compute_forward_pool_1d_sk_p0(params, op, k0, dst); } // ggml_compute_forward_pool_2d static void ggml_compute_forward_pool_2d( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src = dst->src[0]; assert(src->type == GGML_TYPE_F32 || src->type == GGML_TYPE_F16); if (params->ith != 0) { return; } const int32_t * opts = (const int32_t *)dst->op_params; enum ggml_op_pool op = opts[0]; const int k0 = opts[1]; const int k1 = opts[2]; const int s0 = opts[3]; const int s1 = opts[4]; const int p0 = opts[5]; const int p1 = opts[6]; const char * cdata = (const char*)src->data; const char * const data_end = cdata + ggml_nbytes(src); const int64_t px = dst->ne[0]; const int64_t py = dst->ne[1]; const int64_t pa = px * py; float * dplane = (float *)dst->data; const int ka = k0 * k1; const int offset0 = -p0; const int offset1 = -p1; while (cdata < data_end) { for (int oy = 0; oy < py; ++oy) { float * const drow = dplane + oy * px; for (int ox = 0; ox < px; ++ox) { float * const out = drow + ox; switch (op) { case GGML_OP_POOL_AVG: *out = 0; break; case GGML_OP_POOL_MAX: *out = -FLT_MAX; break; case GGML_OP_POOL_COUNT: GGML_ABORT("fatal error"); } const int ix = offset0 + ox * s0; const int iy = offset1 + oy * s1; for (int ky = 0; ky < k1; ++ky) { if (iy + ky < 0 || iy + ky >= src->ne[1]) continue; const void * srow = (const void *)(cdata + src->nb[1] * (iy + ky)); for (int kx = 0; kx < k0; ++kx) { int j = ix + kx; if (j < 0 || j >= src->ne[0]) continue; const float srow_j = (src->type == GGML_TYPE_F32) ? ((const float*)srow)[j] : GGML_FP16_TO_FP32(((const ggml_fp16_t*)srow)[j]); switch (op) { case GGML_OP_POOL_AVG: *out += srow_j; break; case GGML_OP_POOL_MAX: if (srow_j > *out) *out = srow_j; break; case GGML_OP_POOL_COUNT: GGML_ABORT("fatal error"); } } } switch (op) { case GGML_OP_POOL_AVG: *out /= ka; break; case GGML_OP_POOL_MAX: break; case GGML_OP_POOL_COUNT: GGML_ABORT("fatal error"); } } } cdata += src->nb[2]; dplane += pa; } } // ggml_compute_forward_pool_2d_back static void ggml_compute_forward_pool_2d_back( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src = dst->src[0]; const struct ggml_tensor * dstf = dst->src[1]; // forward tensor of dst assert(dst->type == GGML_TYPE_F32 || dst->type == GGML_TYPE_F16); if (params->ith != 0) { return; } const int32_t * opts = (const int32_t *)dst->op_params; enum ggml_op_pool op = opts[0]; const int k0 = opts[1]; const int k1 = opts[2]; const int s0 = opts[3]; const int s1 = opts[4]; const int p0 = opts[5]; const int p1 = opts[6]; char * cdata = (char *) dst->data; const char * cdataf = (const char *) dstf->data; const char * const data_end = cdata + ggml_nbytes(dst); GGML_ASSERT(params->ith == 0); memset(cdata, 0, ggml_nbytes(dst)); const int64_t px = src->ne[0]; const int64_t py = src->ne[1]; const int64_t pa = px * py; const float * splane = (const float *) src->data; const int ka = k0 * k1; const int offset0 = -p0; const int offset1 = -p1; while (cdata < data_end) { for (int oy = 0; oy < py; ++oy) { const float * const srow = splane + oy * px; for (int ox = 0; ox < px; ++ox) { const float grad0 = srow[ox]; const int ix = offset0 + ox * s0; const int iy = offset1 + oy * s1; if (op == GGML_OP_POOL_MAX) { float maxval = -FLT_MAX; int kxmax = -1; int kymax = -1; for (int ky = 0; ky < k1; ++ky) { if (iy + ky < 0 || iy + ky >= dst->ne[1]) { continue; } const void * drowf = (const void *)(cdataf + dst->nb[1] * (iy + ky)); for (int kx = 0; kx < k0; ++kx) { int j = ix + kx; if (j < 0 || j >= dst->ne[0]) { continue; } const float val = dst->type == GGML_TYPE_F32 ? ((const float *) drowf)[j] : GGML_FP16_TO_FP32(((const ggml_fp16_t *) drowf)[j]); if (val <= maxval) { continue; } maxval = val; kxmax = kx; kymax = ky; } } if (kxmax == -1 || kymax == -1) { continue; } void * drow = (void *)(cdata + dst->nb[1] * (iy + kymax)); const int j = ix + kxmax; if (dst->type == GGML_TYPE_F32) { ((float *) drow)[j] += grad0; } else { ((ggml_fp16_t *) drow)[j] = GGML_FP32_TO_FP16(grad0 + GGML_FP16_TO_FP32(((const ggml_fp16_t *) drow)[j])); } } else if (op == GGML_OP_POOL_AVG) { const float grad = grad0 / ka; for (int ky = 0; ky < k1; ++ky) { if (iy + ky < 0 || iy + ky >= dst->ne[1]) { continue; } void * drow = (void *)(cdata + dst->nb[1] * (iy + ky)); for (int kx = 0; kx < k0; ++kx) { int j = ix + kx; if (j < 0 || j >= dst->ne[0]) { continue; } if (dst->type == GGML_TYPE_F32) { ((float *) drow)[j] += grad; } else { ((ggml_fp16_t *) drow)[j] += GGML_FP32_TO_FP16(grad); } } } } else { GGML_ASSERT(false); } } } cdata += dst->nb[2]; cdataf += dst->nb[2]; splane += pa; } } // ggml_compute_forward_upscale static void ggml_compute_forward_upscale_f32( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; GGML_ASSERT(src0->type == GGML_TYPE_F32); const int ith = params->ith; const int nth = params->nth; GGML_TENSOR_UNARY_OP_LOCALS const float sf0 = (float)ne0/src0->ne[0]; const float sf1 = (float)ne1/src0->ne[1]; const float sf2 = (float)ne2/src0->ne[2]; const float sf3 = (float)ne3/src0->ne[3]; // TODO: optimize for (int64_t i3 = 0; i3 < ne3; i3++) { const int64_t i03 = i3 / sf3; for (int64_t i2 = ith; i2 < ne2; i2 += nth) { const int64_t i02 = i2 / sf2; for (int64_t i1 = 0; i1 < ne1; i1++) { const int64_t i01 = i1 / sf1; for (int64_t i0 = 0; i0 < ne0; i0++) { const int64_t i00 = i0 / sf0; const float * x = (float *)((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); float * y = (float *)((char *) dst->data + i0*nb0 + i1*nb1 + i2*nb2 + i3*nb3); *y = *x; } } } } } static void ggml_compute_forward_upscale( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_upscale_f32(params, dst); } break; default: { GGML_ABORT("fatal error"); } } } // ggml_compute_forward_pad static void ggml_compute_forward_pad_f32( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; GGML_ASSERT(src0->nb[0] == sizeof(float)); GGML_ASSERT( dst->nb[0] == sizeof(float)); const int ith = params->ith; const int nth = params->nth; GGML_TENSOR_UNARY_OP_LOCALS float * dst_ptr = (float *) dst->data; // TODO: optimize for (int64_t i2 = 0; i2 < ne2; ++i2) { for (int64_t i1 = ith; i1 < ne1; i1 += nth) { for (int64_t i0 = 0; i0 < ne0; ++i0) { for (int64_t i3 = 0; i3 < ne3; ++i3) { const int64_t dst_idx = i3*(ne0*ne1*ne2) + i2*(ne0*ne1) + i1*ne0 + i0; const float * src_ptr = (const float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); if (i0 < ne00 && i1 < ne01 && i2 < ne02 && i3 < ne03) { dst_ptr[dst_idx] = *src_ptr; } else { dst_ptr[dst_idx] = 0; } } } } } } static void ggml_compute_forward_pad( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_pad_f32(params, dst); } break; default: { GGML_ABORT("fatal error"); } } } // ggml_compute_forward_arange static void ggml_compute_forward_arange_f32( const struct ggml_compute_params * params, struct ggml_tensor * dst) { GGML_ASSERT(dst->nb[0] == sizeof(float)); const int ith = params->ith; const int nth = params->nth; const float start = ggml_get_op_params_f32(dst, 0); const float stop = ggml_get_op_params_f32(dst, 1); const float step = ggml_get_op_params_f32(dst, 2); const int64_t steps = (int64_t) ceilf((stop - start) / step); GGML_ASSERT(ggml_nelements(dst) == steps); for (int64_t i = ith; i < steps; i+= nth) { float value = start + step * i; ((float *)dst->data)[i] = value; } } static void ggml_compute_forward_arange( const struct ggml_compute_params * params, struct ggml_tensor * dst) { switch (dst->type) { case GGML_TYPE_F32: { ggml_compute_forward_arange_f32(params, dst); } break; default: { GGML_ABORT("fatal error"); } } } static void ggml_compute_forward_timestep_embedding_f32( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; GGML_ASSERT(src0->nb[0] == sizeof(float)); const int ith = params->ith; const int nth = params->nth; GGML_TENSOR_UNARY_OP_LOCALS const int dim = ggml_get_op_params_i32(dst, 0); const int max_period = ggml_get_op_params_i32(dst, 1); int half = dim / 2; for (int64_t i = 0; i < ne00; i++) { float * embed_data = (float *)((char *) dst->data + i*nb1); for (int64_t j = ith; j < half; j += nth) { float timestep = ((float *)src0->data)[i]; float freq = (float)expf(-logf(max_period) * j / half); float arg = timestep * freq; embed_data[j] = cosf(arg); embed_data[j + half] = sinf(arg); } if (dim % 2 != 0 && ith == 0) { embed_data[dim] = 0.f; } } } static void ggml_compute_forward_timestep_embedding( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_timestep_embedding_f32(params, dst); } break; default: { GGML_ABORT("fatal error"); } } } // ggml_compute_forward_argsort static void ggml_compute_forward_argsort_f32( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; GGML_TENSOR_UNARY_OP_LOCALS GGML_ASSERT(nb0 == sizeof(float)); const int ith = params->ith; const int nth = params->nth; const int64_t nr = ggml_nrows(src0); enum ggml_sort_order order = (enum ggml_sort_order) ggml_get_op_params_i32(dst, 0); for (int64_t i = ith; i < nr; i += nth) { int32_t * dst_data = (int32_t *)((char *) dst->data + i*nb1); const float * src_data = (float *)((char *) src0->data + i*nb01); for (int64_t j = 0; j < ne0; j++) { dst_data[j] = j; } // C doesn't have a functional sort, so we do a bubble sort instead for (int64_t j = 0; j < ne0; j++) { for (int64_t k = j + 1; k < ne0; k++) { if ((order == GGML_SORT_ORDER_ASC && src_data[dst_data[j]] > src_data[dst_data[k]]) || (order == GGML_SORT_ORDER_DESC && src_data[dst_data[j]] < src_data[dst_data[k]])) { int32_t tmp = dst_data[j]; dst_data[j] = dst_data[k]; dst_data[k] = tmp; } } } } } static void ggml_compute_forward_argsort( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_argsort_f32(params, dst); } break; default: { GGML_ABORT("fatal error"); } } } // ggml_compute_forward_flash_attn_ext static void ggml_compute_forward_flash_attn_ext_f16( const struct ggml_compute_params * params, const struct ggml_tensor * q, const struct ggml_tensor * k, const struct ggml_tensor * v, const struct ggml_tensor * mask, struct ggml_tensor * dst) { GGML_TENSOR_LOCALS(int64_t, neq, q, ne) GGML_TENSOR_LOCALS(size_t, nbq, q, nb) GGML_TENSOR_LOCALS(int64_t, nek, k, ne) GGML_TENSOR_LOCALS(size_t, nbk, k, nb) GGML_TENSOR_LOCALS(int64_t, nev, v, ne) GGML_TENSOR_LOCALS(size_t, nbv, v, nb) GGML_TENSOR_LOCALS(int64_t, ne, dst, ne) GGML_TENSOR_LOCALS(size_t, nb, dst, nb) const int ith = params->ith; const int nth = params->nth; const int64_t D = neq0; const int64_t N = neq1; GGML_ASSERT(ne0 == D); GGML_ASSERT(ne2 == N); // input tensor rows must be contiguous GGML_ASSERT(nbq0 == ggml_type_size(q->type)); GGML_ASSERT(nbk0 == ggml_type_size(k->type)); GGML_ASSERT(nbv0 == ggml_type_size(v->type)); GGML_ASSERT(neq0 == D); GGML_ASSERT(nek0 == D); GGML_ASSERT(nev0 == D); GGML_ASSERT(neq1 == N); GGML_ASSERT(nev0 == D); // dst cannot be transposed or permuted GGML_ASSERT(nb0 == sizeof(float)); GGML_ASSERT(nb0 <= nb1); GGML_ASSERT(nb1 <= nb2); GGML_ASSERT(nb2 <= nb3); // broadcast factors const int64_t rk2 = neq2/nek2; const int64_t rk3 = neq3/nek3; const int64_t rv2 = neq2/nev2; const int64_t rv3 = neq3/nev3; // 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); float scale = 1.0f; float max_bias = 0.0f; float logit_softcap = 0.0f; memcpy(&scale, (float *) dst->op_params + 0, sizeof(float)); memcpy(&max_bias, (float *) dst->op_params + 1, sizeof(float)); memcpy(&logit_softcap, (float *) dst->op_params + 2, sizeof(float)); if (logit_softcap != 0) { scale /= logit_softcap; } const uint32_t n_head = neq2; const uint32_t n_head_log2 = 1u << (uint32_t) floor(log2(n_head)); const float m0 = powf(2.0f, -(max_bias ) / n_head_log2); const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2); enum ggml_type const k_vec_dot_type = type_traits_cpu[k->type].vec_dot_type; ggml_from_float_t const q_to_vec_dot = ggml_get_type_traits(k_vec_dot_type)->from_float; ggml_vec_dot_t const kq_vec_dot = type_traits_cpu[k->type].vec_dot; ggml_to_float_t const v_to_float = ggml_get_type_traits(v->type)->to_float; GGML_ASSERT(q_to_vec_dot && "fattn: unsupported K-type"); GGML_ASSERT(v_to_float && "fattn: unsupported V-type"); // loop over n_batch and n_head 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); const uint32_t h = iq2; // head index const float slope = (max_bias > 0.0f) ? h < n_head_log2 ? powf(m0, h + 1) : powf(m1, 2*(h - n_head_log2) + 1) : 1.0f; float S = 0.0f; // sum float M = -INFINITY; // maximum KQ value float * VKQ32 = (float *) params->wdata + ith*(3*D + CACHE_LINE_SIZE_F32); // FP32 VKQ accumulator float * V32 = (VKQ32 + 1*D); // (temporary) FP32 V buffer ggml_fp16_t * VKQ16 = (ggml_fp16_t *) (VKQ32 + 1*D); // (temporary) FP16 VKQ accumulator ggml_fp16_t * Q_q = (ggml_fp16_t *) (VKQ32 + 2*D); // (temporary) buffer for Q converted to quantized/FP16 if (v->type == GGML_TYPE_F16) { memset(VKQ16, 0, D*sizeof(ggml_fp16_t)); } else { memset(VKQ32, 0, D*sizeof(float)); } const ggml_fp16_t * mp = mask ? (ggml_fp16_t *)((char *) mask->data + iq1*mask->nb[1]) : NULL; // k indices const int ik3 = iq3 / rk3; const int ik2 = iq2 / rk2; // v indices const int iv3 = iq3 / rv3; const int iv2 = iq2 / rv2; const float * pq = (const float *) ((char *) q->data + (iq1*nbq1 + iq2*nbq2 + iq3*nbq3)); q_to_vec_dot(pq, Q_q, D); // online softmax / attention // loop over n_kv and n_head_kv // ref: https://arxiv.org/pdf/2112.05682.pdf for (int64_t ic = 0; ic < nek1; ++ic) { const float mv = mp ? slope*GGML_FP16_TO_FP32(mp[ic]) : 0.0f; if (mv == -INFINITY) { continue; } float s; // KQ value const char * k_data = (const char *) k->data + ( ic*nbk1 + ik2*nbk2 + ik3*nbk3); kq_vec_dot(D, &s, 0, k_data, 0, Q_q, 0, 1); s = s*scale; // scale KQ value if (logit_softcap != 0.0f) { s = logit_softcap*tanhf(s); } s += mv; // apply mask const float Mold = M; float ms = 1.0f; // upon new higher max val, scale VKQ and KQ sum with this value float vs = 1.0f; // post-softmax KQ value, expf(s - M) const char * v_data = ((const char *) v->data + (ic*nbv1 + iv2*nbv2 + iv3*nbv3)); if (v->type == GGML_TYPE_F16) { if (s > M) { // s is new maximum, ms < 1.0f, vs == expf(s - s) == 1.0f M = s; ms = expf(Mold - M); // V = V*expf(Mold - M) ggml_vec_scale_f16(D, VKQ16, ms); } else { // no new maximum, ms == 1.0f, vs != 1.0f vs = expf(s - M); } // V += v*expf(s - M) ggml_vec_mad_f16(D, VKQ16, (const ggml_fp16_t *) v_data, vs); } else { if (s > M) { // s is new maximum, ms < 1.0f, vs == expf(s - s) == 1.0f M = s; ms = expf(Mold - M); // V = V*expf(Mold - M) ggml_vec_scale_f32(D, VKQ32, ms); } else { // no new maximum, ms == 1.0f, vs != 1.0f vs = expf(s - M); } v_to_float(v_data, V32, D); // V += v*expf(s - M) ggml_vec_mad_f32(D, VKQ32, V32, vs); } S = S*ms + vs; // scale and increment sum with partial sum } if (v->type == GGML_TYPE_F16) { for (int64_t d = 0; d < D; ++d) { VKQ32[d] = GGML_FP16_TO_FP32(VKQ16[d]); } } // V /= S const float S_inv = 1.0f/S; ggml_vec_scale_f32(D, VKQ32, S_inv); // dst indices const int i1 = iq1; const int i2 = iq2; const int i3 = iq3; // original //memcpy((char *) dst->data + (i1*nb1 + i2*nb2 + i3*nb3), V, nev0*sizeof(float)); // permute(0, 2, 1, 3) memcpy((char *) dst->data + (i3*ne2*ne1 + i2 + i1*ne1)*nb1, VKQ32, nb1); } } static void ggml_compute_forward_flash_attn_ext( const struct ggml_compute_params * params, const struct ggml_tensor * q, const struct ggml_tensor * k, const struct ggml_tensor * v, const struct ggml_tensor * mask, struct ggml_tensor * dst) { switch (dst->op_params[3]) { case GGML_PREC_DEFAULT: case GGML_PREC_F32: { // uses F32 accumulators ggml_compute_forward_flash_attn_ext_f16(params, q, k, v, mask, dst); } break; default: { GGML_ABORT("fatal error"); } } } // ggml_compute_forward_flash_attn_back static void ggml_compute_forward_flash_attn_back_f32( const struct ggml_compute_params * params, const bool masked, struct ggml_tensor * dst) { const struct ggml_tensor * q = dst->src[0]; const struct ggml_tensor * k = dst->src[1]; const struct ggml_tensor * v = dst->src[2]; const struct ggml_tensor * d = dst->src[3]; GGML_TENSOR_LOCALS(int64_t, neq, q, ne) GGML_TENSOR_LOCALS(size_t, nbq, q, nb) GGML_TENSOR_LOCALS(int64_t, nek, k, ne) GGML_TENSOR_LOCALS(size_t, nbk, k, nb) GGML_TENSOR_LOCALS(int64_t, nev, v, ne) GGML_TENSOR_LOCALS(size_t, nbv, v, nb) GGML_TENSOR_LOCALS(int64_t, ned, d, ne) GGML_TENSOR_LOCALS(size_t, nbd, d, nb) GGML_TENSOR_LOCALS(int64_t, ne, dst, ne) GGML_TENSOR_LOCALS(size_t, nb, dst, nb) const int ith = params->ith; const int nth = params->nth; const int64_t D = neq0; const int64_t N = neq1; const int64_t P = nek1 - N; const int64_t M = P + N; const int Mup = ggml_up(M, GGML_SOFT_MAX_UNROLL); const int mxDM = MAX(D, Mup); // GGML_ASSERT(ne0 == D); // GGML_ASSERT(ne1 == N); GGML_ASSERT(P >= 0); GGML_ASSERT(nbq0 == sizeof(float)); GGML_ASSERT(nbk0 == sizeof(float)); GGML_ASSERT(nbv0 == sizeof(float)); GGML_ASSERT(neq0 == D); GGML_ASSERT(nek0 == D); GGML_ASSERT(nev1 == D); GGML_ASSERT(ned0 == D); GGML_ASSERT(neq1 == N); GGML_ASSERT(nek1 == N + P); GGML_ASSERT(nev1 == D); GGML_ASSERT(ned1 == N); // dst cannot be transposed or permuted GGML_ASSERT(nb0 == sizeof(float)); GGML_ASSERT(nb0 <= nb1); GGML_ASSERT(nb1 <= nb2); GGML_ASSERT(nb2 <= nb3); if (ith == 0) { memset(dst->data, 0, nb0*ne0*ne1*ne2*ne3); } ggml_barrier(params->threadpool); const int64_t elem_q = ggml_nelements(q); const int64_t elem_k = ggml_nelements(k); enum ggml_type result_type = dst->type; GGML_ASSERT(ggml_blck_size(result_type) == 1); const size_t tsize = ggml_type_size(result_type); const size_t offs_q = 0; const size_t offs_k = offs_q + GGML_PAD(elem_q * tsize, GGML_MEM_ALIGN); const size_t offs_v = offs_k + GGML_PAD(elem_k * tsize, GGML_MEM_ALIGN); void * grad_q = (char *) dst->data; void * grad_k = (char *) dst->data + offs_k; void * grad_v = (char *) dst->data + offs_v; const size_t nbgq1 = nb0*neq0; const size_t nbgq2 = nb0*neq0*neq1; const size_t nbgq3 = nb0*neq0*neq1*neq2; const size_t nbgk1 = nb0*nek0; const size_t nbgk2 = nb0*nek0*nek1; const size_t nbgk3 = nb0*nek0*nek1*neq2; const size_t nbgv1 = nb0*nev0; const size_t nbgv2 = nb0*nev0*nev1; const size_t nbgv3 = nb0*nev0*nev1*neq2; // parallelize by k rows using ggml_vec_dot_f32 // total rows in k const int nr = nek2*nek3; // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); const float scale = 1.0f/sqrtf(D); //printf("P=%d N=%d D=%d ir0=%d ir1=%d scale = %f\n", P, N, D, ir0, ir1, scale); // how often k2 (and v2) is repeated in q2 int nrep = neq2/nek2; for (int ir = ir0; ir < ir1; ++ir) { // q indices const int ik3 = ir/(nek2); const int ik2 = ir - ik3*nek2; const int iq3 = ik3; const int id3 = ik3; const int iv3 = ik3; const int iv2 = ik2; for (int irep = 0; irep < nrep; ++irep) { const int iq2 = ik2 + irep*nek2; const int id2 = iq2; // (ik2 + irep*nek2) % nek2 == ik2 for (int iq1 = 0; iq1 < neq1; ++iq1) { const int id1 = iq1; // not sure about CACHE_LINE_SIZE_F32.. // - maybe it must not be multiplied by 2 and excluded from .. in SM 1*(..) offset? float * S = (float *) params->wdata + ith*2*(mxDM + CACHE_LINE_SIZE_F32) + 0*(mxDM+CACHE_LINE_SIZE_F32); float * SM = (float *) params->wdata + ith*2*(mxDM + CACHE_LINE_SIZE_F32) + 1*(mxDM+CACHE_LINE_SIZE_F32); for (int i = M; i < Mup; ++i) { S[i] = -INFINITY; } const int64_t masked_begin = masked ? (P + iq1 + 1) : M; for (int64_t ic = 0; ic < masked_begin; ++ic) { // k indices const int ik1 = ic; // S indices const int i1 = ik1; ggml_vec_dot_f32(neq0, S + i1, 0, (float *) ((char *) k->data + (ik1*nbk1 + ik2*nbk2 + ik3*nbk3)), 0, (float *) ((char *) q->data + (iq1*nbq1 + iq2*nbq2 + iq3*nbq3)), 0, 1); } // scale ggml_vec_scale_f32(masked_begin, S, scale); for (int64_t i = masked_begin; i < M; i++) { S[i] = -INFINITY; } // softmax // exclude known -INF S[..] values from max and loop // dont forget to set their SM values to zero { float max = -INFINITY; ggml_vec_max_f32(masked_begin, &max, S); ggml_float sum = 0.0; { #ifdef GGML_SOFT_MAX_ACCELERATE max = -max; vDSP_vsadd(SM, 1, &max, SM, 1, Mup); vvexpf(SM, SM, &Mup); ggml_vec_sum_f32(Mup, &sum, SM); #else sum = ggml_vec_soft_max_f32(Mup, SM, S, max); #endif } assert(sum > 0.0); sum = 1.0/sum; ggml_vec_scale_f32(masked_begin, SM, sum); } // step-by-step explanation { // forward-process shape grads from backward process // parallel_for ik2,ik3: // for irep: // iq2 = ik2 + irep*nek2 // k[:D,:M,:,:] [D,M,:,:] grad[k][:D,:M,ik2,ik3] += grad[kcur] // q[:D,:N,:,:] [D,N,:,:] grad[q][:D,iq1,iq2,iq3] += grad[qcur] // v[:M,:D,:,:] [M,D,:,:] grad[v][:M,:D,iv2,iv3] += grad[vcur] // for iq1: // kcur = k[:D,:M,ik2,ik3] [D,M,1,1] grad[kcur] = grad[S1].T @ qcur // qcur = q[:D,iq1,iq2,iq3] [D,1,1,1] grad[qcur] = grad[S1] @ kcur // vcur = v[:M,:D,iv2,iv3] [M,D,1,1] grad[vcur] = grad[S5].T @ S4 // S0 = -Inf [D,1,1,1] // ~S1[i] = dot(kcur[:D,i], qcur) // S1 = qcur @ kcur.T [M,1,1,1] grad[S1] = grad[S2] * scale // S2 = S1 * scale [M,1,1,1] grad[S2] = diag_mask_zero(grad[S3], P) // S3 = diag_mask_inf(S2, P) [M,1,1,1] grad[S3] = S4 * (grad[S4] - dot(S4, grad[S4])) // S4 = softmax(S3) [M,1,1,1] grad[S4] = grad[S5] @ vcur // ~S5[i] = dot(vcur[:,i], S4) // S5 = S4 @ vcur.T [D,1,1,1] grad[S5] = d[:D,id1,id2,id3] // ~dst[i,iq1,iq2,iq3] = S5[i] ^ // dst[:D,iq1,iq2,iq3] = S5 | grad[dst[:D,iq1,iq2,iq3]] = d[:D,id1,id2,id3] // dst backward-/ grad[dst] = d // // output gradients with their dependencies: // // grad[kcur] = grad[S1].T @ qcur // grad[S1] = diag_mask_zero(grad[S3], P) * scale // grad[S3] = S4 * (grad[S4] - dot(S4, grad[S4])) // grad[S4] = grad[S5] @ vcur // grad[S4] = d[:D,id1,id2,id3] @ vcur // grad[qcur] = grad[S1] @ kcur // grad[vcur] = grad[S5].T @ S4 // grad[vcur] = d[:D,id1,id2,id3].T @ S4 // // in post-order: // // S1 = qcur @ kcur.T // S2 = S1 * scale // S3 = diag_mask_inf(S2, P) // S4 = softmax(S3) // grad[S4] = d[:D,id1,id2,id3] @ vcur // grad[S3] = S4 * (grad[S4] - dot(S4, grad[S4])) // grad[S1] = diag_mask_zero(grad[S3], P) * scale // grad[qcur] = grad[S1] @ kcur // grad[kcur] = grad[S1].T @ qcur // grad[vcur] = d[:D,id1,id2,id3].T @ S4 // // using less variables (SM=S4): // // S = diag_mask_inf(qcur @ kcur.T * scale, P) // SM = softmax(S) // S = d[:D,iq1,iq2,iq3] @ vcur // dot_SM_gradSM = dot(SM, S) // S = SM * (S - dot(SM, S)) // S = diag_mask_zero(S, P) * scale // // grad[q][:D,iq1,iq2,iq3] += S @ kcur // grad[k][:D,:M,ik2,ik3] += S.T @ qcur // grad[v][:M,:D,iv2,iv3] += d[:D,id1,id2,id3].T @ SM } // S = gradSM = d[:D,id1,id2,id3] @ vcur[:,:,iv2,iv3] // S = d[:D,id1,id2,id3] @ vcur[:,:,iv2,iv3] // for ic: // S[:M] += vcur[:M,ic,iv2,iv3] * d[ic,id1,id2,id3] // exclude known future zero S[..] values from operation ggml_vec_set_f32(masked_begin, S, 0); for (int64_t ic = 0; ic < D; ++ic) { ggml_vec_mad_f32(masked_begin, S, (float *) ((char *) v->data + ( ic*nbv1 + iv2*nbv2 + iv3*nbv3)), *(float *) ((char *) d->data + (ic*nbd0 + id1*nbd1 + id2*nbd2 + id3*nbd3))); } // S = SM * (S - dot(SM, S)) float dot_SM_gradSM = 0; ggml_vec_dot_f32 (masked_begin, &dot_SM_gradSM, 0, SM, 0, S, 0, 1); ggml_vec_acc1_f32(M, S, -dot_SM_gradSM); ggml_vec_mul_f32 (masked_begin, S, S, SM); // S = diag_mask_zero(S, P) * scale // already done by above ggml_vec_set_f32 // exclude known zero S[..] values from operation ggml_vec_scale_f32(masked_begin, S, scale); // S shape [M,1] // SM shape [M,1] // kcur shape [D,M] // qcur shape [D,1] // vcur shape [M,D] // grad[q][:D,iq1,iq2,iq3] += S @ kcur // grad[q][:D,iq1,iq2,iq3] += shape[M,1] @ shape[D,M] // for ic: // grad[q][:D,iq1,iq2,iq3] += S[ic] * kcur[:D,ic,ik2,ik3] // exclude known zero S[..] values from loop for (int64_t ic = 0; ic < masked_begin; ++ic) { ggml_vec_mad_f32(D, (float *) ((char *) grad_q + (iq1*nbgq1 + iq2*nbgq2 + iq3*nbgq3)), (float *) ((char *) k->data + (ic*nbk1 + ik2*nbk2 + ik3*nbk3)), S[ic]); } // grad[k][:D,:M,iq2,iq3] += S.T @ qcur // for ic: // grad[k][:D,ic,iq2,iq3] += S.T[0,ic] * qcur[:D,0] // grad[k][:D,ic,iq2,iq3] += S[ic] * qcur[:D,0] // exclude known zero S[..] values from loop for (int64_t ic = 0; ic < masked_begin; ++ic) { ggml_vec_mad_f32(D, (float *) ((char *) grad_k + (ic*nbgk1 + ik2*nbgk2 + ik3*nbgk3)), (float *) ((char *) q->data + (iq1*nbq1 + iq2*nbq2 + iq3*nbq3)), S[ic]); } // grad[v][:M,:D,iv2,iv3] += d[:D,id1,id2,id3].T @ SM // for ic: // grad[v][:M,ic,iv2,iv3] += d[:D,id1,id2,id3].T[0,ic] * SM[:M] // grad[v][:M,ic,iv2,iv3] += d[ic,id1,id2,id3] * SM[:M] // exclude known zero SM[..] values from mad for (int64_t ic = 0; ic < D; ++ic) { ggml_vec_mad_f32(masked_begin, (float *) ((char *) grad_v + ( ic*nbgv1 + iv2*nbgv2 + iv3*nbgv3)), SM, *(float *) ((char *) d->data + (ic*nbd0 + id1*nbd1 + id2*nbd2 + id3*nbd3))); } } } } } static void ggml_compute_forward_flash_attn_back( const struct ggml_compute_params * params, const bool masked, struct ggml_tensor * dst) { const struct ggml_tensor * q = dst->src[0]; switch (q->type) { case GGML_TYPE_F32: { ggml_compute_forward_flash_attn_back_f32(params, masked, dst); } break; default: { GGML_ABORT("fatal error"); } } } // ggml_compute_forward_ssm_conv static void ggml_compute_forward_ssm_conv_f32( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; // conv_x const struct ggml_tensor * src1 = dst->src[1]; // conv1d.weight const int ith = params->ith; const int nth = params->nth; const int nc = src1->ne[0]; // d_conv const int ncs = src0->ne[0]; // d_conv - 1 + n_t const int nr = src0->ne[1]; // d_inner const int n_t = dst->ne[1]; // tokens per sequence const int n_s = dst->ne[2]; // number of sequences in the batch GGML_ASSERT( dst->ne[0] == nr); GGML_ASSERT(src0->nb[0] == sizeof(float)); GGML_ASSERT(src1->nb[0] == sizeof(float)); GGML_ASSERT(src0->nb[1] == src0->ne[0]*sizeof(float)); // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); const int ir = ir1 - ir0; for (int i3 = 0; i3 < n_s; ++i3) { for (int i2 = 0; i2 < n_t; ++i2) { // {d_conv - 1 + n_t, d_inner, n_seqs} // sliding window const float * s = (const float *) ((const char *) src0->data + ir0*(src0->nb[1]) + i2*(src0->nb[0]) + i3*(src0->nb[2])); // {d_conv, d_inner, n_s} const float * c = (const float *) ((const char *) src1->data + ir0*(src1->nb[1])); // {d_conv, d_inner} float * x = (float *) ((char *) dst->data + ir0*(dst->nb[0]) + i2*(dst->nb[1]) + i3*(dst->nb[2])); // {d_inner, n_t, n_s} // TODO: transpose the output for smaller strides for big batches? // d_inner for (int i1 = 0; i1 < ir; ++i1) { // rowwise dot product // NOTE: not using ggml_vec_dot_f32, because its sum is in double precision float sumf = 0.0f; // d_conv for (int i0 = 0; i0 < nc; ++i0) { sumf += s[i0 + i1*ncs] * c[i0 + i1*nc]; } x[i1] = sumf; } } } } static void ggml_compute_forward_ssm_conv( const struct ggml_compute_params * params, struct ggml_tensor * dst) { switch (dst->src[0]->type) { case GGML_TYPE_F32: { ggml_compute_forward_ssm_conv_f32(params, dst); } break; default: { GGML_ABORT("fatal error"); } } } // ggml_compute_forward_ssm_scan static void ggml_compute_forward_ssm_scan_f32( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; // s const struct ggml_tensor * src1 = dst->src[1]; // x const struct ggml_tensor * src2 = dst->src[2]; // dt const struct ggml_tensor * src3 = dst->src[3]; // A const struct ggml_tensor * src4 = dst->src[4]; // B const struct ggml_tensor * src5 = dst->src[5]; // C const int ith = params->ith; const int nth = params->nth; const int64_t nc = src0->ne[0]; // d_state const int64_t nr = src0->ne[1]; // d_inner const int64_t n_t = src1->ne[1]; // number of tokens per sequence const int64_t n_s = src0->ne[2]; // number of sequences in the batch GGML_ASSERT(ggml_nelements(src1) + ggml_nelements(src0) == ggml_nelements(dst)); GGML_ASSERT(src0->nb[0] == sizeof(float)); GGML_ASSERT(src1->nb[0] == sizeof(float)); GGML_ASSERT(src2->nb[0] == sizeof(float)); GGML_ASSERT(src3->nb[0] == sizeof(float)); GGML_ASSERT(src4->nb[0] == sizeof(float)); GGML_ASSERT(src5->nb[0] == sizeof(float)); // required for the dot product between s and C GGML_ASSERT(src0->nb[1] == src0->ne[0]*sizeof(float)); // required for per-sequence offsets for states GGML_ASSERT(src0->nb[2] == src0->ne[0]*src0->ne[1]*sizeof(float)); // required to get correct offset for state destination (i.e. src1->nb[3]) GGML_ASSERT(src1->nb[3] == src1->ne[0]*src1->ne[1]*src1->ne[2]*sizeof(float)); // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); const int ir = ir1 - ir0; for (int i3 = 0; i3 < n_s; ++i3) { for (int i2 = 0; i2 < n_t; ++i2) { const float * s0 = (const float *) ((const char *) src0->data + ir0*(src0->nb[1]) + i3*(src0->nb[2])); // {d_state, d_inner, n_s} const float * x = (const float *) ((const char *) src1->data + ir0*(src1->nb[0]) + i2*(src1->nb[1]) + i3*(src1->nb[2])); // {d_inner, n_t, n_s} const float * dt = (const float *) ((const char *) src2->data + ir0*(src2->nb[0]) + i2*(src2->nb[1]) + i3*(src2->nb[2])); // {d_inner, n_t, n_s} const float * A = (const float *) ((const char *) src3->data + ir0*(src3->nb[1])); // {d_state, d_inner} const float * B = (const float *) ((const char *) src4->data + i2*(src4->nb[1]) + i3*(src4->nb[2])); // {d_state, n_t, n_s} const float * C = (const float *) ((const char *) src5->data + i2*(src5->nb[1]) + i3*(src5->nb[2])); // {d_state, n_t, n_s} float * y = ( float *) (( char *) dst->data + ir0*(src1->nb[0]) + i2*(src1->nb[1]) + i3*(src1->nb[2])); // {d_inner, n_t, n_s} float * s = ( float *) (( char *) dst->data + ir0*(src0->nb[1]) + i3*(src0->nb[2]) + src1->nb[3]); // {d_state, d_inner, n_s} // use the output as the source for the next token-wise iterations if (i2 > 0) { s0 = s; } // d_inner for (int i1 = 0; i1 < ir; ++i1) { // ref: https://github.com/state-spaces/mamba/blob/34076d664838588a3c97727b263478ab9f621a07/mamba_ssm/ops/triton/selective_state_update.py#L78 float dt_soft_plus = dt[i1] <= 20.0f ? log1pf(expf(dt[i1])) : dt[i1]; float x_dt = x[i1] * dt_soft_plus; float sumf = 0.0f; // d_state for (int i0 = 0; i0 < nc; ++i0) { int i = i0 + i1*nc; // state = prev_state * dA + dB * x float state = (s0[i] * expf(dt_soft_plus * A[i])) + (B[i0] * x_dt); // y = rowwise_dotprod(state, C) sumf += state * C[i0]; s[i] = state; } y[i1] = sumf; } } } } static void ggml_compute_forward_ssm_scan( const struct ggml_compute_params * params, struct ggml_tensor * dst) { switch (dst->src[0]->type) { case GGML_TYPE_F32: { ggml_compute_forward_ssm_scan_f32(params, dst); } break; default: { GGML_ABORT("fatal error"); } } } // ggml_compute_forward_win_part static void ggml_compute_forward_win_part_f32( const struct ggml_compute_params * params, struct ggml_tensor * dst) { UNUSED(params); const struct ggml_tensor * src0 = dst->src[0]; GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne) GGML_TENSOR_LOCALS(int64_t, ne, dst, ne) const int32_t nep0 = ((const int32_t *)(dst->op_params))[0]; const int32_t nep1 = ((const int32_t *)(dst->op_params))[1]; const int32_t w = ((const int32_t *)(dst->op_params))[2]; assert(ne00 == ne0); assert(ne3 == nep0*nep1); // TODO: optimize / multi-thread for (int py = 0; py < nep1; ++py) { for (int px = 0; px < nep0; ++px) { const int64_t i3 = py*nep0 + px; for (int64_t i2 = 0; i2 < ne2; ++i2) { for (int64_t i1 = 0; i1 < ne1; ++i1) { for (int64_t i0 = 0; i0 < ne0; ++i0) { const int64_t i02 = py*w + i2; const int64_t i01 = px*w + i1; const int64_t i00 = i0; const int64_t i = i3*ne2*ne1*ne0 + i2*ne1*ne0 + i1*ne0 + i0; const int64_t j = i02*ne01*ne00 + i01*ne00 + i00; if (py*w + i2 >= ne02 || px*w + i1 >= ne01) { ((float *) dst->data)[i] = 0.0f; } else { ((float *) dst->data)[i] = ((float *) src0->data)[j]; } } } } } } } static void ggml_compute_forward_win_part( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_win_part_f32(params, dst); } break; default: { GGML_ABORT("fatal error"); } } } // ggml_compute_forward_win_unpart static void ggml_compute_forward_win_unpart_f32( const struct ggml_compute_params * params, struct ggml_tensor * dst) { UNUSED(params); const struct ggml_tensor * src0 = dst->src[0]; GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne) GGML_TENSOR_LOCALS(int64_t, ne, dst, ne) const int32_t w = ((const int32_t *)(dst->op_params))[0]; // padding const int px = (w - ne1%w)%w; //const int py = (w - ne2%w)%w; const int npx = (px + ne1)/w; //const int npy = (py + ne2)/w; assert(ne0 == ne00); // TODO: optimize / multi-thread for (int64_t i2 = 0; i2 < ne2; ++i2) { for (int64_t i1 = 0; i1 < ne1; ++i1) { for (int64_t i0 = 0; i0 < ne0; ++i0) { const int ip2 = i2/w; const int ip1 = i1/w; const int64_t i02 = i2%w; const int64_t i01 = i1%w; const int64_t i00 = i0; const int64_t i = (ip2*npx + ip1)*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00 + i00; const int64_t j = i2*ne1*ne0 + i1*ne0 + i0; ((float *) dst->data)[j] = ((float *) src0->data)[i]; } } } } static void ggml_compute_forward_win_unpart( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_win_unpart_f32(params, dst); } break; default: { GGML_ABORT("fatal error"); } } } //gmml_compute_forward_unary static void ggml_compute_forward_unary( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const enum ggml_unary_op op = ggml_get_unary_op(dst); switch (op) { case GGML_UNARY_OP_ABS: { ggml_compute_forward_abs(params, dst); } break; case GGML_UNARY_OP_SGN: { ggml_compute_forward_sgn(params, dst); } break; case GGML_UNARY_OP_NEG: { ggml_compute_forward_neg(params, dst); } break; case GGML_UNARY_OP_STEP: { ggml_compute_forward_step(params, dst); } break; case GGML_UNARY_OP_TANH: { ggml_compute_forward_tanh(params, dst); } break; case GGML_UNARY_OP_ELU: { ggml_compute_forward_elu(params, dst); } break; case GGML_UNARY_OP_RELU: { ggml_compute_forward_relu(params, dst); } break; case GGML_UNARY_OP_SIGMOID: { ggml_compute_forward_sigmoid(params, dst); } break; case GGML_UNARY_OP_GELU: { ggml_compute_forward_gelu(params, dst); } break; case GGML_UNARY_OP_GELU_QUICK: { ggml_compute_forward_gelu_quick(params, dst); } break; case GGML_UNARY_OP_SILU: { ggml_compute_forward_silu(params, dst); } break; case GGML_UNARY_OP_HARDSWISH: { ggml_compute_forward_hardswish(params, dst); } break; case GGML_UNARY_OP_HARDSIGMOID: { ggml_compute_forward_hardsigmoid(params, dst); } break; case GGML_UNARY_OP_EXP: { ggml_compute_forward_exp(params, dst); } break; default: { GGML_ABORT("fatal error"); } } } // ggml_compute_forward_get_rel_pos static void ggml_compute_forward_get_rel_pos_f16( const struct ggml_compute_params * params, struct ggml_tensor * dst) { UNUSED(params); const struct ggml_tensor * src0 = dst->src[0]; // ref: https://github.com/facebookresearch/segment-anything/blob/main/segment_anything/modeling/image_encoder.py#L292-L322 GGML_TENSOR_UNARY_OP_LOCALS const int64_t w = ne1; ggml_fp16_t * src0_data = (ggml_fp16_t *) src0->data; ggml_fp16_t * dst_data = (ggml_fp16_t *) dst->data; for (int64_t i2 = 0; i2 < ne2; ++i2) { for (int64_t i1 = 0; i1 < ne1; ++i1) { const int64_t pos = (w - i1 - 1) + i2; for (int64_t i0 = 0; i0 < ne0; ++i0) { dst_data[i2*ne1*ne0 + i1*ne0 + i0] = src0_data[pos*ne00 + i0]; } } } } static void ggml_compute_forward_get_rel_pos( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; switch (src0->type) { case GGML_TYPE_F16: case GGML_TYPE_BF16: { ggml_compute_forward_get_rel_pos_f16(params, dst); } break; default: { GGML_ABORT("fatal error"); } } } // ggml_compute_forward_add_rel_pos static void ggml_compute_forward_add_rel_pos_f32( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; const struct ggml_tensor * src1 = dst->src[1]; const struct ggml_tensor * src2 = dst->src[2]; const bool inplace = (bool) ((int32_t *) dst->op_params)[0]; if (!inplace) { if (params->ith == 0) { memcpy((char *) dst->data, (char *) src0->data, ggml_nbytes(dst)); } ggml_barrier(params->threadpool); } // ref: https://github.com/facebookresearch/segment-anything/blob/main/segment_anything/modeling/image_encoder.py#L357-L359 float * src1_data = (float *) src1->data; float * src2_data = (float *) src2->data; float * dst_data = (float *) dst->data; const int64_t ne10 = src1->ne[0]; const int64_t ne11 = src1->ne[1]; const int64_t ne12 = src1->ne[2]; const int64_t ne13 = src1->ne[3]; const int ith = params->ith; const int nth = params->nth; // total patches in dst const int np = ne13; // patches per thread const int dp = (np + nth - 1)/nth; // patch range for this thread const int ip0 = dp*ith; const int ip1 = MIN(ip0 + dp, np); for (int64_t i13 = ip0; i13 < ip1; ++i13) { for (int64_t i12 = 0; i12 < ne12; ++i12) { for (int64_t i11 = 0; i11 < ne11; ++i11) { const int64_t jp1 = i13*ne12*ne11*ne10 + i12*ne11*ne10 + i11*ne10; for (int64_t i10 = 0; i10 < ne10; ++i10) { const int64_t jp0 = jp1 + i10; const float src1_e = src1_data[jp0]; const float src2_e = src2_data[jp0]; const int64_t jdh = jp0 * ne10; const int64_t jdw = jdh - (ne10 - 1) * i10; for (int64_t j = 0; j < ne10; ++j) { dst_data[jdh + j ] += src2_e; dst_data[jdw + j*ne10] += src1_e; } } } } } } static void ggml_compute_forward_add_rel_pos( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_add_rel_pos_f32(params, dst); } break; default: { GGML_ABORT("fatal error"); } } } // ggml_compute_forward_rwkv_wkv6 static void ggml_compute_forward_rwkv_wkv6_f32( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const int64_t T = dst->src[1]->ne[3]; const int64_t C = dst->ne[0]; const int64_t HEADS = dst->src[1]->ne[2]; const int64_t n_seqs = dst->src[5]->ne[1]; const int64_t head_size = C / HEADS; float * dst_data = (float *) dst->data; float * state = ((float *) dst->data) + C * T; const int ith = params->ith; const int nth = params->nth; if (ith >= HEADS) { return; } const int h_start = (HEADS * ith) / nth; const int h_end = ((HEADS * (ith + 1)) / nth < HEADS) ? (HEADS * (ith + 1)) / nth : HEADS; float * k = (float *) dst->src[0]->data; float * v = (float *) dst->src[1]->data; float * r = (float *) dst->src[2]->data; float * time_faaaa = (float *) dst->src[3]->data; float * time_decay = (float *) dst->src[4]->data; size_t t_stride = HEADS * head_size; // Same to C size_t h_stride = C / HEADS; GGML_ASSERT(C % HEADS == 0); // C must be divisible by HEADS size_t h_stride_2d = head_size * head_size; if (ith == 0) { memset(dst_data, 0, T * C * sizeof(float)); } ggml_barrier(params->threadpool); #if defined(__AVX__) && !defined(__AVX512F__) #define GGML_F32X GGML_F32x8 #define GGML_F32X_SET1 GGML_F32x8_SET1 #define GGML_F32X_LOAD GGML_F32x8_LOAD #define GGML_F32X_STORE GGML_F32x8_STORE #define GGML_F32X_MUL GGML_F32x8_MUL #define GGML_F32X_FMA GGML_F32x8_FMA #define WKV_VECTOR_SIZE 8 #elif defined(__AVX512F__) #define GGML_F32X GGML_F32x16 #define GGML_F32X_SET1 GGML_F32x16_SET1 #define GGML_F32X_LOAD GGML_F32x16_LOAD #define GGML_F32X_STORE GGML_F32x16_STORE #define GGML_F32X_MUL GGML_F32x16_MUL #define GGML_F32X_FMA GGML_F32x16_FMA #define WKV_VECTOR_SIZE 16 #elif defined(__ARM_NEON) && defined(__aarch64__) #define GGML_F32X GGML_F32x4 #define GGML_F32X_SET1 GGML_F32x4_SET1 #define GGML_F32X_LOAD GGML_F32x4_LOAD #define GGML_F32X_STORE GGML_F32x4_STORE #define GGML_F32X_MUL GGML_F32x4_MUL #define GGML_F32X_FMA GGML_F32x4_FMA #define WKV_VECTOR_SIZE 4 #endif #ifdef WKV_VECTOR_SIZE const int64_t vec_count = head_size / WKV_VECTOR_SIZE; for (int64_t t = 0; t < T; t++) { size_t t_offset = t * t_stride; size_t state_offset = head_size * C * (t / (T / n_seqs)); float * state_cur = state + state_offset; float * state_prev = t % (T / n_seqs) ? state_cur : (float*)dst->src[5]->data + state_offset; for (int64_t h = h_start; h < h_end; h++) { size_t h_offset = h * h_stride; size_t t_h_offset = t_offset + h_offset; size_t h_2d_offset = h * h_stride_2d; for (int64_t i = 0; i < head_size; i++) { size_t t_h_i_offset = t_h_offset + i; size_t h_i_offset = h_offset + i; size_t h_2d_i_offset = h_2d_offset + i * h_stride; float k_val = k[t_h_i_offset]; float r_val = r[t_h_i_offset]; float time_faaaa_val = time_faaaa[h_i_offset]; float time_decay_val = time_decay[t_h_i_offset]; // Broadcast scalar values to vectors GGML_F32X k_vec = GGML_F32X_SET1(k_val); GGML_F32X r_vec = GGML_F32X_SET1(r_val); GGML_F32X time_faaaa_vec = GGML_F32X_SET1(time_faaaa_val); GGML_F32X time_decay_vec = GGML_F32X_SET1(time_decay_val); for (int64_t j = 0; j < vec_count; j++) { size_t base_j = j * WKV_VECTOR_SIZE; size_t t_h_j_offset = t_h_offset + base_j; size_t h_2d_i_j_offset = h_2d_i_offset + base_j; // Load x elements at once GGML_F32X v_vec = GGML_F32X_LOAD(&v[t_h_j_offset]); GGML_F32X prev_state_vec = GGML_F32X_LOAD(&state_prev[h_2d_i_j_offset]); GGML_F32X dst_vec = GGML_F32X_LOAD(&dst_data[t_h_j_offset]); // Compute kv = v * k GGML_F32X kv_vec = GGML_F32X_MUL(v_vec, k_vec); // Compute temp = kv * time_faaaa + prev_state GGML_F32X temp_vec = GGML_F32X_FMA(prev_state_vec, kv_vec, time_faaaa_vec); // Update dst: dst += temp * r dst_vec = GGML_F32X_FMA(dst_vec, temp_vec, r_vec); GGML_F32X_STORE(&dst_data[t_h_j_offset], dst_vec); // Update state: state = prev_state * time_decay + kv GGML_F32X new_state_vec = GGML_F32X_FMA(kv_vec, prev_state_vec, time_decay_vec); GGML_F32X_STORE(&state_cur[h_2d_i_j_offset], new_state_vec); } // Handle remaining elements, this will not be used. for (int64_t j = vec_count * WKV_VECTOR_SIZE; j < head_size; j++) { size_t t_h_j_offset = t_h_offset + j; size_t h_2d_i_j_offset = h_2d_i_offset + j; float v_val = v[t_h_j_offset]; float kv_val = v_val * k_val; float prev_state_val = state_prev[h_2d_i_j_offset]; float temp_val = kv_val * time_faaaa_val + prev_state_val; dst_data[t_h_j_offset] += temp_val * r_val; state_cur[h_2d_i_j_offset] = prev_state_val * time_decay_val + kv_val; } } } } #else // basically fused operations: // dst = r @ (time_faaaa * (k @ v) + state), // state = time_decay * state + (k @ v), // recursive through each token for (int64_t t = 0; t < T; t++) { size_t t_offset = t * t_stride; size_t state_offset = head_size * C * (t / (T / n_seqs)); float * state_cur = state + state_offset; float * state_prev = t % (T / n_seqs) ? state_cur : (float*)dst->src[5]->data + state_offset; for (int64_t h = h_start; h < h_end; h++) { size_t h_offset = h * h_stride; size_t t_h_offset = t_offset + h_offset; size_t h_2d_offset = h * h_stride_2d; for (int64_t i = 0; i < head_size; i++) { size_t t_h_i_offset = t_h_offset + i; size_t h_i_offset = h_offset + i; size_t h_2d_i_offset = h_2d_offset + i * h_stride; float k_val = k[t_h_i_offset]; float r_val = r[t_h_i_offset]; float time_faaaa_val = time_faaaa[h_i_offset]; // RWKV v6: different time_decay for each token. float time_decay_val = time_decay[t_h_i_offset]; for (int64_t j = 0; j < head_size; j++) { size_t t_h_j_offset = t_h_offset + j; size_t h_2d_i_j_offset = h_2d_i_offset + j; float v_val = v[t_h_j_offset]; float kv_val = v_val * k_val; float prev_state_val = state_prev[h_2d_i_j_offset]; float temp_val = kv_val * time_faaaa_val + prev_state_val; dst_data[t_h_j_offset] += temp_val * r_val; state_cur[h_2d_i_j_offset] = prev_state_val * time_decay_val + kv_val; } } } } #endif } static void ggml_compute_forward_rwkv_wkv6( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_rwkv_wkv6_f32(params, dst); } break; default: { GGML_ABORT("fatal error"); } } } // ggml_compute_forward_map_unary static void ggml_compute_forward_map_unary_f32( const struct ggml_compute_params * params, struct ggml_tensor * dst, const ggml_unary_op_f32_t fun) { const struct ggml_tensor * src0 = dst->src[0]; if (params->ith != 0) { return; } assert(ggml_is_contiguous_1(src0)); assert(ggml_is_contiguous_1(dst)); assert(ggml_are_same_shape(src0, dst)); const int n = ggml_nrows(src0); const int nc = src0->ne[0]; for (int i = 0; i < n; i++) { fun(nc, (float *) ((char *) dst->data + i*( dst->nb[1])), (float *) ((char *) src0->data + i*(src0->nb[1]))); } } static void ggml_compute_forward_map_unary( const struct ggml_compute_params * params, struct ggml_tensor * dst, const ggml_unary_op_f32_t fun) { const struct ggml_tensor * src0 = dst->src[0]; switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_map_unary_f32(params, dst, fun); } break; default: { GGML_ABORT("fatal error"); } } } // ggml_compute_forward_map_binary static void ggml_compute_forward_map_binary_f32( const struct ggml_compute_params * params, struct ggml_tensor * dst, const ggml_binary_op_f32_t fun) { const struct ggml_tensor * src0 = dst->src[0]; const struct ggml_tensor * src1 = dst->src[1]; if (params->ith != 0) { return; } assert(ggml_is_contiguous_1(src0)); assert(ggml_is_contiguous_1(src1)); assert(ggml_is_contiguous_1(dst)); assert(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst)); const int n = ggml_nrows(src0); const int nc = src0->ne[0]; for (int i = 0; i < n; i++) { fun(nc, (float *) ((char *) dst->data + i*( dst->nb[1])), (float *) ((char *) src0->data + i*(src0->nb[1])), (float *) ((char *) src1->data + i*(src1->nb[1]))); } } static void ggml_compute_forward_map_binary( const struct ggml_compute_params * params, struct ggml_tensor * dst, const ggml_binary_op_f32_t fun) { const struct ggml_tensor * src0 = dst->src[0]; switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_map_binary_f32(params, dst, fun); } break; default: { GGML_ABORT("fatal error"); } } } // ggml_compute_forward_map_custom1 static void ggml_compute_forward_map_custom1_f32( const struct ggml_compute_params * params, struct ggml_tensor * dst, const ggml_custom1_op_f32_t fun) { const struct ggml_tensor * a = dst->src[0]; if (params->ith != 0) { return; } fun(dst, a); } // ggml_compute_forward_map_custom2 static void ggml_compute_forward_map_custom2_f32( const struct ggml_compute_params * params, struct ggml_tensor * dst, const ggml_custom2_op_f32_t fun) { const struct ggml_tensor * a = dst->src[0]; const struct ggml_tensor * b = dst->src[1]; if (params->ith != 0) { return; } fun(dst, a, b); } // ggml_compute_forward_map_custom3 static void ggml_compute_forward_map_custom3_f32( const struct ggml_compute_params * params, struct ggml_tensor * dst, const ggml_custom3_op_f32_t fun) { const struct ggml_tensor * a = dst->src[0]; const struct ggml_tensor * b = dst->src[1]; const struct ggml_tensor * c = dst->src[1]; if (params->ith != 0) { return; } fun(dst, a, b, c); } // ggml_compute_forward_map_custom1 static void ggml_compute_forward_map_custom1( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * a = dst->src[0]; struct ggml_map_custom1_op_params p; memcpy(&p, dst->op_params, sizeof(p)); p.fun(dst, a, params->ith, params->nth, p.userdata); } // ggml_compute_forward_map_custom2 static void ggml_compute_forward_map_custom2( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * a = dst->src[0]; const struct ggml_tensor * b = dst->src[1]; struct ggml_map_custom2_op_params p; memcpy(&p, dst->op_params, sizeof(p)); p.fun(dst, a, b, params->ith, params->nth, p.userdata); } // ggml_compute_forward_map_custom3 static void ggml_compute_forward_map_custom3( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * a = dst->src[0]; const struct ggml_tensor * b = dst->src[1]; const struct ggml_tensor * c = dst->src[2]; struct ggml_map_custom3_op_params p; memcpy(&p, dst->op_params, sizeof(p)); p.fun(dst, a, b, c, params->ith, params->nth, p.userdata); } // ggml_compute_forward_cross_entropy_loss static void ggml_compute_forward_cross_entropy_loss_f32( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; const struct ggml_tensor * src1 = dst->src[1]; GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT(src1->type == GGML_TYPE_F32); GGML_ASSERT(src0->nb[0] == ggml_type_size(src0->type)); GGML_ASSERT(src1->nb[0] == ggml_type_size(src1->type)); GGML_ASSERT(ggml_are_same_shape(src0, src1)); GGML_ASSERT(ggml_is_scalar(dst)); GGML_ASSERT(dst->type == GGML_TYPE_F32); // TODO: handle transposed/permuted matrices const int64_t nc = src0->ne[0]; const int64_t nr = ggml_nrows(src0); const int ith = params->ith; const int nth = params->nth; float * sums = (float *) params->wdata; float * st = ((float *) params->wdata) + nth + ith*nc; float sum_thread = 0.0f; GGML_ASSERT(params->wsize >= sizeof(float) * (nth + nth * nc)); // rows per thread const int64_t dr = (nr + nth - 1)/nth; // row range for this thread const int64_t ir0 = dr*ith; const int64_t ir1 = MIN(ir0 + dr, nr); for (int64_t i1 = ir0; i1 < ir1; ++i1) { const float * s0 = (const float *)((const char *) src0->data + i1*src0->nb[1]); const float * s1 = (const float *)((const char *) src1->data + i1*src1->nb[1]); #ifndef NDEBUG for (int64_t i = 0; i < nc; ++i) { //printf("p[%d] = %f\n", i, p[i]); assert(!isnan(s0[i])); assert(!isnan(s1[i])); } #endif float max = -INFINITY; ggml_vec_max_f32(nc, &max, s0); const ggml_float sum_softmax = ggml_vec_log_soft_max_f32(nc, st, s0, max); assert(sum_softmax >= 0.0); ggml_vec_add1_f32(nc, st, st, -sum_softmax); ggml_vec_mul_f32(nc, st, st, s1); float sum_st = 0.0f; ggml_vec_sum_f32(nc, &sum_st, st); sum_thread += sum_st; #ifndef NDEBUG for (int64_t i = 0; i < nc; ++i) { assert(!isnan(st[i])); assert(!isinf(st[i])); } #endif } sums[ith] = sum_thread; ggml_barrier(params->threadpool); if (ith == 0) { float * dp = (float *) dst->data; ggml_vec_sum_f32(nth, dp, sums); dp[0] *= -1.0f / (float) nr; } } static void ggml_compute_forward_cross_entropy_loss( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_cross_entropy_loss_f32(params, dst); } break; default: { GGML_ABORT("fatal error"); } } } // ggml_compute_forward_cross_entropy_loss_back static void ggml_compute_forward_cross_entropy_loss_back_f32( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; const struct ggml_tensor * src1 = dst->src[1]; const struct ggml_tensor * opt0 = dst->src[2]; GGML_ASSERT(ggml_is_contiguous(dst)); GGML_ASSERT(ggml_is_contiguous(src0)); GGML_ASSERT(ggml_is_contiguous(src1)); GGML_ASSERT(ggml_is_contiguous(opt0)); GGML_ASSERT(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst)); const int64_t ith = params->ith; const int64_t nth = params->nth; // TODO: handle transposed/permuted matrices const int64_t nc = src0->ne[0]; const int64_t nr = ggml_nrows(src0); // rows per thread const int64_t dr = (nr + nth - 1)/nth; // row range for this thread const int64_t ir0 = dr*ith; const int64_t ir1 = MIN(ir0 + dr, nr); const float d_by_nr = ((const float *) opt0->data)[0] / (float) nr; for (int64_t i1 = ir0; i1 < ir1; i1++) { float * ds0 = (float *)((char *) dst->data + i1*dst->nb[1]); float * s0 = (float *)((char *) src0->data + i1*src0->nb[1]); float * s1 = (float *)((char *) src1->data + i1*src1->nb[1]); #ifndef NDEBUG for (int64_t i = 0; i < nc; ++i) { //printf("p[%d] = %f\n", i, p[i]); assert(!isnan(s0[i])); assert(!isnan(s1[i])); } #endif // soft_max float max = -INFINITY; ggml_vec_max_f32(nc, &max, s0); ggml_float sum = ggml_vec_soft_max_f32(nc, ds0, s0, max); assert(sum > 0.0); ggml_vec_scale_f32(nc, ds0, 1.0/sum); // grad(src0) = (softmax(src0) - src1) * grad(cross_entropy_loss(src0, src1)) / nr ggml_vec_sub_f32(nc, ds0, ds0, s1); ggml_vec_scale_f32(nc, ds0, d_by_nr); #ifndef NDEBUG for (int64_t i = 0; i < nc; ++i) { assert(!isnan(ds0[i])); assert(!isinf(ds0[i])); } #endif } } static void ggml_compute_forward_cross_entropy_loss_back( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_cross_entropy_loss_back_f32(params, dst); } break; default: { GGML_ABORT("fatal error"); } } } static void ggml_compute_forward_opt_step_adamw_f32( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; const struct ggml_tensor * src0_grad = dst->src[1]; const struct ggml_tensor * src0_grad_m = dst->src[2]; const struct ggml_tensor * src0_grad_v = dst->src[3]; GGML_ASSERT(ggml_are_same_shape(src0, src0_grad)); const int ith = params->ith; const int nth = params->nth; const int nr = ggml_nrows(src0); GGML_TENSOR_UNARY_OP_LOCALS GGML_ASSERT(nb00 == sizeof(float)); // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); /* const float gnorm = 1.0f; */ int64_t iter; memcpy(&iter, &dst->op_params[0], sizeof(int64_t)); const float alpha = ggml_get_op_params_f32(dst, 2); const float beta1 = ggml_get_op_params_f32(dst, 3); const float beta2 = ggml_get_op_params_f32(dst, 4); const float eps = ggml_get_op_params_f32(dst, 5); const float wd = ggml_get_op_params_f32(dst, 6); const float beta1h = alpha/(1.0f - powf(beta1, iter)); const float beta2h = 1.0f/(1.0f - powf(beta2, iter)); for (int ir = ir0; ir < ir1; ++ir) { const int64_t i03 = ir/(ne02*ne01); const int64_t i02 = (ir - i03*ne02*ne01)/ne01; const int64_t i01 = (ir - i03*ne02*ne01 - i02*ne01); const size_t offset = i03*nb03 + i02*nb02 + i01*nb01; float * w = (float *) ((char *) src0->data + offset); // weight const float * g = (const float *) ((const char *) src0_grad->data + offset); // grad float * m = (float *) ((char *) src0_grad_m->data + offset); float * v = (float *) ((char *) src0_grad_v->data + offset); for (int i00 = 0; i00 < ne00; ++i00) { m[i00] = m[i00]*beta1 + g[i00]*(1.0f - beta1); v[i00] = v[i00]*beta2 + g[i00]*g[i00]*(1.0f - beta2); const float mh = m[i00]*beta1h; const float vh = sqrtf(v[i00]*beta2h) + eps; // The weight decay is applied independently of the Adam momenta m and v. // This is NOT equivalent to l2 regularization that adds w[i00]*w[i00] to the loss. // See: https://arxiv.org/pdf/1711.05101v3.pdf w[i00] = w[i00]*(1.0f - alpha*wd) - mh/vh; } } ggml_barrier(params->threadpool); if (ith != 0) { return; } iter++; memcpy(&dst->op_params[0], &iter, sizeof(int64_t)); } static void ggml_compute_forward_opt_step_adamw( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; switch (src0->type) { case GGML_TYPE_F32: { ggml_compute_forward_opt_step_adamw_f32(params, dst); } break; default: { GGML_ABORT("fatal error"); } } } ///////////////////////////////// static void ggml_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor) { GGML_ASSERT(params); if (tensor->op == GGML_OP_NONE || ggml_is_empty(tensor)) { return; } switch (tensor->op) { case GGML_OP_DUP: { ggml_compute_forward_dup(params, tensor); } break; case GGML_OP_ADD: { ggml_compute_forward_add(params, tensor); } break; case GGML_OP_ADD1: { ggml_compute_forward_add1(params, tensor); } break; case GGML_OP_ACC: { ggml_compute_forward_acc(params, tensor); } break; case GGML_OP_SUB: { ggml_compute_forward_sub(params, tensor); } break; case GGML_OP_MUL: { ggml_compute_forward_mul(params, tensor); } break; case GGML_OP_DIV: { ggml_compute_forward_div(params, tensor); } break; case GGML_OP_SQR: { ggml_compute_forward_sqr(params, tensor); } break; case GGML_OP_SQRT: { ggml_compute_forward_sqrt(params, tensor); } break; case GGML_OP_LOG: { ggml_compute_forward_log(params, tensor); } break; case GGML_OP_SIN: { ggml_compute_forward_sin(params, tensor); } break; case GGML_OP_COS: { ggml_compute_forward_cos(params, tensor); } break; case GGML_OP_SUM: { ggml_compute_forward_sum(params, tensor); } break; case GGML_OP_SUM_ROWS: { ggml_compute_forward_sum_rows(params, tensor); } break; case GGML_OP_MEAN: { ggml_compute_forward_mean(params, tensor); } break; case GGML_OP_ARGMAX: { ggml_compute_forward_argmax(params, tensor); } break; case GGML_OP_COUNT_EQUAL: { ggml_compute_forward_count_equal(params, tensor); } break; case GGML_OP_REPEAT: { ggml_compute_forward_repeat(params, tensor); } break; case GGML_OP_REPEAT_BACK: { ggml_compute_forward_repeat_back(params, tensor); } break; case GGML_OP_CONCAT: { ggml_compute_forward_concat(params, tensor); } break; case GGML_OP_SILU_BACK: { ggml_compute_forward_silu_back(params, tensor); } break; case GGML_OP_NORM: { ggml_compute_forward_norm(params, tensor); } break; case GGML_OP_RMS_NORM: { ggml_compute_forward_rms_norm(params, tensor); } break; case GGML_OP_RMS_NORM_BACK: { ggml_compute_forward_rms_norm_back(params, tensor); } break; case GGML_OP_GROUP_NORM: { ggml_compute_forward_group_norm(params, tensor); } break; case GGML_OP_MUL_MAT: { ggml_compute_forward_mul_mat(params, tensor); } break; case GGML_OP_MUL_MAT_ID: { ggml_compute_forward_mul_mat_id(params, tensor); } break; case GGML_OP_OUT_PROD: { ggml_compute_forward_out_prod(params, tensor); } break; case GGML_OP_SCALE: { ggml_compute_forward_scale(params, tensor); } break; case GGML_OP_SET: { ggml_compute_forward_set(params, tensor); } break; case GGML_OP_CPY: { ggml_compute_forward_cpy(params, tensor); } break; case GGML_OP_CONT: { ggml_compute_forward_cont(params, tensor); } break; case GGML_OP_RESHAPE: { ggml_compute_forward_reshape(params, tensor); } break; case GGML_OP_VIEW: { ggml_compute_forward_view(params, tensor); } break; case GGML_OP_PERMUTE: { ggml_compute_forward_permute(params, tensor); } break; case GGML_OP_TRANSPOSE: { ggml_compute_forward_transpose(params, tensor); } break; case GGML_OP_GET_ROWS: { ggml_compute_forward_get_rows(params, tensor); } break; case GGML_OP_GET_ROWS_BACK: { ggml_compute_forward_get_rows_back(params, tensor); } break; case GGML_OP_DIAG: { ggml_compute_forward_diag(params, tensor); } break; case GGML_OP_DIAG_MASK_INF: { ggml_compute_forward_diag_mask_inf(params, tensor); } break; case GGML_OP_DIAG_MASK_ZERO: { ggml_compute_forward_diag_mask_zero(params, tensor); } break; case GGML_OP_SOFT_MAX: { ggml_compute_forward_soft_max(params, tensor); } break; case GGML_OP_SOFT_MAX_BACK: { ggml_compute_forward_soft_max_back(params, tensor); } break; case GGML_OP_ROPE: { ggml_compute_forward_rope(params, tensor); } break; case GGML_OP_ROPE_BACK: { ggml_compute_forward_rope_back(params, tensor); } break; case GGML_OP_CLAMP: { ggml_compute_forward_clamp(params, tensor); } break; case GGML_OP_CONV_TRANSPOSE_1D: { ggml_compute_forward_conv_transpose_1d(params, tensor); } break; case GGML_OP_IM2COL: { ggml_compute_forward_im2col(params, tensor); } break; case GGML_OP_IM2COL_BACK: { ggml_compute_forward_im2col_back_f32(params, tensor); } break; case GGML_OP_CONV_TRANSPOSE_2D: { ggml_compute_forward_conv_transpose_2d(params, tensor); } break; case GGML_OP_POOL_1D: { ggml_compute_forward_pool_1d(params, tensor); } break; case GGML_OP_POOL_2D: { ggml_compute_forward_pool_2d(params, tensor); } break; case GGML_OP_POOL_2D_BACK: { ggml_compute_forward_pool_2d_back(params, tensor); } break; case GGML_OP_UPSCALE: { ggml_compute_forward_upscale(params, tensor); } break; case GGML_OP_PAD: { ggml_compute_forward_pad(params, tensor); } break; case GGML_OP_ARANGE: { ggml_compute_forward_arange(params, tensor); } break; case GGML_OP_TIMESTEP_EMBEDDING: { ggml_compute_forward_timestep_embedding(params, tensor); } break; case GGML_OP_ARGSORT: { ggml_compute_forward_argsort(params, tensor); } break; case GGML_OP_LEAKY_RELU: { ggml_compute_forward_leaky_relu(params, tensor); } break; case GGML_OP_FLASH_ATTN_EXT: { ggml_compute_forward_flash_attn_ext(params, tensor->src[0], tensor->src[1], tensor->src[2], tensor->src[3], tensor); } break; case GGML_OP_FLASH_ATTN_BACK: { int32_t t = ggml_get_op_params_i32(tensor, 0); GGML_ASSERT(t == 0 || t == 1); bool masked = t != 0; ggml_compute_forward_flash_attn_back(params, masked, tensor); } break; case GGML_OP_SSM_CONV: { ggml_compute_forward_ssm_conv(params, tensor); } break; case GGML_OP_SSM_SCAN: { ggml_compute_forward_ssm_scan(params, tensor); } break; case GGML_OP_WIN_PART: { ggml_compute_forward_win_part(params, tensor); } break; case GGML_OP_WIN_UNPART: { ggml_compute_forward_win_unpart(params, tensor); } break; case GGML_OP_UNARY: { ggml_compute_forward_unary(params, tensor); } break; case GGML_OP_GET_REL_POS: { ggml_compute_forward_get_rel_pos(params, tensor); } break; case GGML_OP_ADD_REL_POS: { ggml_compute_forward_add_rel_pos(params, tensor); } break; case GGML_OP_RWKV_WKV6: { ggml_compute_forward_rwkv_wkv6(params, tensor); } break; case GGML_OP_MAP_UNARY: { ggml_unary_op_f32_t fun; memcpy(&fun, tensor->op_params, sizeof(fun)); ggml_compute_forward_map_unary(params, tensor, fun); } break; case GGML_OP_MAP_BINARY: { ggml_binary_op_f32_t fun; memcpy(&fun, tensor->op_params, sizeof(fun)); ggml_compute_forward_map_binary(params, tensor, fun); } break; case GGML_OP_MAP_CUSTOM1_F32: { ggml_custom1_op_f32_t fun; memcpy(&fun, tensor->op_params, sizeof(fun)); ggml_compute_forward_map_custom1_f32(params, tensor, fun); } break; case GGML_OP_MAP_CUSTOM2_F32: { ggml_custom2_op_f32_t fun; memcpy(&fun, tensor->op_params, sizeof(fun)); ggml_compute_forward_map_custom2_f32(params, tensor, fun); } break; case GGML_OP_MAP_CUSTOM3_F32: { ggml_custom3_op_f32_t fun; memcpy(&fun, tensor->op_params, sizeof(fun)); ggml_compute_forward_map_custom3_f32(params, tensor, fun); } break; case GGML_OP_MAP_CUSTOM1: { ggml_compute_forward_map_custom1(params, tensor); } break; case GGML_OP_MAP_CUSTOM2: { ggml_compute_forward_map_custom2(params, tensor); } break; case GGML_OP_MAP_CUSTOM3: { ggml_compute_forward_map_custom3(params, tensor); } break; case GGML_OP_CROSS_ENTROPY_LOSS: { ggml_compute_forward_cross_entropy_loss(params, tensor); } break; case GGML_OP_CROSS_ENTROPY_LOSS_BACK: { ggml_compute_forward_cross_entropy_loss_back(params, tensor); } break; case GGML_OP_OPT_STEP_ADAMW: { ggml_compute_forward_opt_step_adamw(params, tensor); } break; case GGML_OP_NONE: { // nop } break; case GGML_OP_COUNT: { GGML_ABORT("fatal error"); } } } // Android's libc implementation "bionic" does not support setting affinity #if defined(__gnu_linux__) static void set_numa_thread_affinity(int thread_n) { if (!ggml_is_numa()) { return; } int node_num; int rv; size_t setsize = CPU_ALLOC_SIZE(g_state.numa.total_cpus); switch(g_state.numa.numa_strategy) { case GGML_NUMA_STRATEGY_DISTRIBUTE: // run thread on node_num thread_n / (threads per node) node_num = thread_n % g_state.numa.n_nodes; break; case GGML_NUMA_STRATEGY_ISOLATE: // run thread on current_node node_num = g_state.numa.current_node; break; case GGML_NUMA_STRATEGY_NUMACTL: // use the cpuset that numactl gave us rv = pthread_setaffinity_np(pthread_self(), setsize, &g_state.numa.cpuset); if (rv) { fprintf(stderr, "warning: pthread_setaffinity_np() failed: %s\n",strerror(rv)); } return; default: return; } struct ggml_numa_node * node = &g_state.numa.nodes[node_num]; cpu_set_t * cpus = CPU_ALLOC(g_state.numa.total_cpus); CPU_ZERO_S(setsize, cpus); for (size_t i = 0; i < node->n_cpus; ++i) { CPU_SET_S(node->cpus[i], setsize, cpus); } rv = pthread_setaffinity_np(pthread_self(), setsize, cpus); if (rv) { fprintf(stderr, "warning: pthread_setaffinity_np() failed: %s\n", strerror(rv)); } CPU_FREE(cpus); } static void clear_numa_thread_affinity(void) { if (!ggml_is_numa()) { return; } size_t setsize = CPU_ALLOC_SIZE(g_state.numa.total_cpus); cpu_set_t * cpus = CPU_ALLOC(g_state.numa.total_cpus); CPU_ZERO_S(setsize, cpus); for (unsigned i = 0; i < g_state.numa.total_cpus; ++i) { CPU_SET_S(i, setsize, cpus); } int rv = pthread_setaffinity_np(pthread_self(), setsize, cpus); if (rv) { fprintf(stderr, "warning: pthread_setaffinity_np() failed: %s\n", strerror(rv)); } CPU_FREE(cpus); } #else // TODO: Windows etc. // (the linux implementation may also work on BSD, someone should test) static void set_numa_thread_affinity(int thread_n) { UNUSED(thread_n); } static void clear_numa_thread_affinity(void) {} #endif static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) { int n_tasks = 0; if (ggml_is_empty(node)) { // no need to multi-thread a no-op n_tasks = 1; return n_tasks; } switch (node->op) { case GGML_OP_CPY: case GGML_OP_DUP: case GGML_OP_CONT: case GGML_OP_ADD: case GGML_OP_ADD1: case GGML_OP_ACC: { n_tasks = n_threads; } break; case GGML_OP_SUB: case GGML_OP_SQR: case GGML_OP_SQRT: case GGML_OP_LOG: case GGML_OP_SIN: case GGML_OP_COS: case GGML_OP_SUM: case GGML_OP_SUM_ROWS: case GGML_OP_MEAN: case GGML_OP_ARGMAX: { n_tasks = 1; } break; case GGML_OP_COUNT_EQUAL: { n_tasks = n_threads; } break; case GGML_OP_REPEAT: case GGML_OP_REPEAT_BACK: case GGML_OP_LEAKY_RELU: { n_tasks = 1; } break; case GGML_OP_UNARY: switch (ggml_get_unary_op(node)) { case GGML_UNARY_OP_ABS: case GGML_UNARY_OP_SGN: case GGML_UNARY_OP_NEG: case GGML_UNARY_OP_STEP: case GGML_UNARY_OP_TANH: case GGML_UNARY_OP_ELU: case GGML_UNARY_OP_RELU: case GGML_UNARY_OP_SIGMOID: case GGML_UNARY_OP_HARDSWISH: case GGML_UNARY_OP_HARDSIGMOID: case GGML_UNARY_OP_EXP: { n_tasks = 1; } break; case GGML_UNARY_OP_GELU: case GGML_UNARY_OP_GELU_QUICK: case GGML_UNARY_OP_SILU: { n_tasks = n_threads; } break; default: GGML_ABORT("fatal error"); } break; case GGML_OP_SILU_BACK: case GGML_OP_MUL: case GGML_OP_DIV: case GGML_OP_NORM: case GGML_OP_RMS_NORM: case GGML_OP_RMS_NORM_BACK: case GGML_OP_GROUP_NORM: case GGML_OP_CONCAT: case GGML_OP_MUL_MAT: case GGML_OP_MUL_MAT_ID: case GGML_OP_OUT_PROD: { n_tasks = n_threads; } break; case GGML_OP_GET_ROWS: { // FIXME: get_rows can use additional threads, but the cost of launching additional threads // decreases performance with GPU offloading //n_tasks = n_threads; n_tasks = 1; } break; case GGML_OP_SCALE: case GGML_OP_SET: case GGML_OP_RESHAPE: case GGML_OP_VIEW: case GGML_OP_PERMUTE: case GGML_OP_TRANSPOSE: case GGML_OP_GET_ROWS_BACK: case GGML_OP_DIAG: { n_tasks = 1; } break; case GGML_OP_DIAG_MASK_ZERO: case GGML_OP_DIAG_MASK_INF: case GGML_OP_SOFT_MAX_BACK: case GGML_OP_ROPE: case GGML_OP_ROPE_BACK: case GGML_OP_ADD_REL_POS: { n_tasks = n_threads; } break; case GGML_OP_CLAMP: { n_tasks = 1; //TODO } break; case GGML_OP_SOFT_MAX: { n_tasks = MIN(n_threads, ggml_nrows(node->src[0])); } break; case GGML_OP_IM2COL: case GGML_OP_IM2COL_BACK: case GGML_OP_CONV_TRANSPOSE_1D: case GGML_OP_CONV_TRANSPOSE_2D: { n_tasks = n_threads; } break; case GGML_OP_POOL_1D: case GGML_OP_POOL_2D: case GGML_OP_POOL_2D_BACK: { n_tasks = 1; } break; case GGML_OP_UPSCALE: case GGML_OP_PAD: case GGML_OP_ARANGE: case GGML_OP_TIMESTEP_EMBEDDING: case GGML_OP_ARGSORT: case GGML_OP_FLASH_ATTN_EXT: case GGML_OP_FLASH_ATTN_BACK: case GGML_OP_SSM_CONV: case GGML_OP_SSM_SCAN: { n_tasks = n_threads; } break; case GGML_OP_WIN_PART: case GGML_OP_WIN_UNPART: case GGML_OP_GET_REL_POS: case GGML_OP_RWKV_WKV6: case GGML_OP_MAP_UNARY: case GGML_OP_MAP_BINARY: case GGML_OP_MAP_CUSTOM1_F32: case GGML_OP_MAP_CUSTOM2_F32: case GGML_OP_MAP_CUSTOM3_F32: { n_tasks = 1; } break; case GGML_OP_MAP_CUSTOM1: { struct ggml_map_custom1_op_params p; memcpy(&p, node->op_params, sizeof(p)); if (p.n_tasks == GGML_N_TASKS_MAX) { n_tasks = n_threads; } else { n_tasks = MIN(p.n_tasks, n_threads); } } break; case GGML_OP_MAP_CUSTOM2: { struct ggml_map_custom2_op_params p; memcpy(&p, node->op_params, sizeof(p)); if (p.n_tasks == GGML_N_TASKS_MAX) { n_tasks = n_threads; } else { n_tasks = MIN(p.n_tasks, n_threads); } } break; case GGML_OP_MAP_CUSTOM3: { struct ggml_map_custom3_op_params p; memcpy(&p, node->op_params, sizeof(p)); if (p.n_tasks == GGML_N_TASKS_MAX) { n_tasks = n_threads; } else { n_tasks = MIN(p.n_tasks, n_threads); } } break; case GGML_OP_CROSS_ENTROPY_LOSS: case GGML_OP_CROSS_ENTROPY_LOSS_BACK: case GGML_OP_OPT_STEP_ADAMW: { n_tasks = n_threads; } break; case GGML_OP_NONE: { n_tasks = 1; } break; case GGML_OP_COUNT: { GGML_ABORT("fatal error"); } default: { fprintf(stderr, "%s: op not implemented: ", __func__); if (node->op < GGML_OP_COUNT) { fprintf(stderr, "%s\n", ggml_op_name(node->op)); } else { fprintf(stderr, "%d\n", node->op); } GGML_ABORT("fatal error"); } } assert(n_tasks > 0); return n_tasks; } static thread_ret_t ggml_graph_compute_secondary_thread(void* data); #if defined(_WIN32) #include "windows.h" // TODO: support > 64 CPUs bool ggml_thread_apply_affinity(bool * mask) { HANDLE h = GetCurrentThread(); uint64_t bitmask = 0ULL; assert(GGML_MAX_N_THREADS >= 64); for (int32_t i = 0; i < 8; i++) { int32_t idx = i * 8; uint8_t val = 0; val |= mask[idx + 0] << 0; val |= mask[idx + 1] << 1; val |= mask[idx + 2] << 2; val |= mask[idx + 3] << 3; val |= mask[idx + 4] << 4; val |= mask[idx + 5] << 5; val |= mask[idx + 6] << 6; val |= mask[idx + 7] << 7; bitmask |= (uint64_t)val << idx; } for (int32_t i = 64; i < GGML_MAX_N_THREADS; i++) { if (mask[i]) { fprintf(stderr, "warn: setting thread-affinity for > 64 CPUs isn't supported on windows!\n"); break; } } DWORD_PTR m = (DWORD_PTR)bitmask; m = SetThreadAffinityMask(h, m); return m != 0; } static bool ggml_thread_apply_priority(int32_t prio) { // Note that on Windows the Process Priority Class must be updated in order to set Thread priority. // This is up to the applications. DWORD p = THREAD_PRIORITY_NORMAL; switch (prio) { case GGML_SCHED_PRIO_NORMAL: p = THREAD_PRIORITY_NORMAL; break; case GGML_SCHED_PRIO_MEDIUM: p = THREAD_PRIORITY_ABOVE_NORMAL; break; case GGML_SCHED_PRIO_HIGH: p = THREAD_PRIORITY_HIGHEST; break; case GGML_SCHED_PRIO_REALTIME: p = THREAD_PRIORITY_TIME_CRITICAL; break; } if (prio == GGML_SCHED_PRIO_NORMAL) { // Keep inherited policy/priority return true; } if (!SetThreadPriority(GetCurrentThread(), p)) { fprintf(stderr, "warn: failed to set thread priority %d : (%d)\n", prio, (int) GetLastError()); return false; } return true; } #elif defined(__APPLE__) #include #include static bool ggml_thread_apply_affinity(const bool * mask) { // Not supported on Apple platforms UNUSED(mask); return true; } static bool ggml_thread_apply_priority(int32_t prio) { struct sched_param p; int32_t policy = SCHED_OTHER; switch (prio) { case GGML_SCHED_PRIO_NORMAL: policy = SCHED_OTHER; p.sched_priority = 0; break; case GGML_SCHED_PRIO_MEDIUM: policy = SCHED_FIFO; p.sched_priority = 40; break; case GGML_SCHED_PRIO_HIGH: policy = SCHED_FIFO; p.sched_priority = 80; break; case GGML_SCHED_PRIO_REALTIME: policy = SCHED_FIFO; p.sched_priority = 90; break; } if (prio == GGML_SCHED_PRIO_NORMAL) { // Keep inherited policy/priority return true; } int32_t err = pthread_setschedparam(pthread_self(), policy, &p); if (err != 0) { fprintf(stderr, "warn: failed to set thread priority %d : %s (%d)\n", prio, strerror(err), err); return false; } return true; } #elif defined(__gnu_linux__) // TODO: this may not work on BSD, to be verified static bool ggml_thread_apply_affinity(const bool * mask) { cpu_set_t cpuset; int err; CPU_ZERO(&cpuset); for (uint32_t i = 0; i < GGML_MAX_N_THREADS; i++) { if (mask[i]) { GGML_PRINT_DEBUG("Thread %lx: adding %d to cpuset\n", pthread_self(), i); CPU_SET(i, &cpuset); } } #ifdef __ANDROID__ err = sched_setaffinity(0, sizeof(cpuset), &cpuset); if (err < 0) { err = errno; } #else err = pthread_setaffinity_np(pthread_self(), sizeof(cpuset), &cpuset); #endif if (err != 0) { fprintf(stderr, "warn: failed to set affinity mask 0x%llx : %s (%d)\n", (unsigned long long)mask, strerror(err), err); return false; } return true; } static bool ggml_thread_apply_priority(int32_t prio) { struct sched_param p; int32_t policy = SCHED_OTHER; switch (prio) { case GGML_SCHED_PRIO_NORMAL: policy = SCHED_OTHER; p.sched_priority = 0; break; case GGML_SCHED_PRIO_MEDIUM: policy = SCHED_FIFO; p.sched_priority = 40; break; case GGML_SCHED_PRIO_HIGH: policy = SCHED_FIFO; p.sched_priority = 80; break; case GGML_SCHED_PRIO_REALTIME: policy = SCHED_FIFO; p.sched_priority = 90; break; } if (prio == GGML_SCHED_PRIO_NORMAL) { // Keep inherited policy/priority return true; } int32_t err = pthread_setschedparam(pthread_self(), policy, &p); if (err != 0) { fprintf(stderr, "warn: failed to set thread priority %d : %s (%d)\n", prio, strerror(err), err); return false; } return true; } #else // unsupported platforms static bool ggml_thread_apply_affinity(const bool * mask) { UNUSED(mask); return true; } static bool ggml_thread_apply_priority(int32_t prio) { UNUSED(prio); return true; } #endif static bool ggml_thread_cpumask_is_valid(const bool * mask) { for (int i = 0; i < GGML_MAX_N_THREADS; i++) { if (mask[i]) { return true; } } return false; } static void ggml_thread_cpumask_next(const bool * global_mask, bool * local_mask, bool strict, int32_t* iter) { if (!strict) { memcpy(local_mask, global_mask, GGML_MAX_N_THREADS); return; } else { memset(local_mask, 0, GGML_MAX_N_THREADS); int32_t base_idx = *iter; for (int32_t i = 0; i < GGML_MAX_N_THREADS; i++) { int32_t idx = base_idx + i; if (idx >= GGML_MAX_N_THREADS) { // Just a cheaper modulo idx -= GGML_MAX_N_THREADS; } if (global_mask[idx]) { local_mask[idx] = 1; *iter = idx + 1; return; } } } } void ggml_threadpool_free(struct ggml_threadpool* threadpool) { if (!threadpool) return; const int n_threads = threadpool->n_threads_max; #ifndef GGML_USE_OPENMP struct ggml_compute_state* workers = threadpool->workers; ggml_mutex_lock(&threadpool->mutex); threadpool->stop = true; threadpool->pause = false; ggml_cond_broadcast(&threadpool->cond); ggml_mutex_unlock(&threadpool->mutex); for (int j = 1; j < n_threads; j++) { int32_t rc = ggml_thread_join(workers[j].thrd, NULL); GGML_ASSERT(rc == GGML_EXIT_SUCCESS || rc == GGML_EXIT_ABORTED); UNUSED(rc); } ggml_mutex_destroy(&threadpool->mutex); ggml_cond_destroy(&threadpool->cond); #endif // GGML_USE_OPENMP const size_t workers_size = sizeof(struct ggml_compute_state) * n_threads; ggml_aligned_free(threadpool->workers, workers_size); ggml_aligned_free(threadpool, sizeof(struct ggml_threadpool)); } #ifndef GGML_USE_OPENMP // pause/resume must be called under mutex static void ggml_threadpool_pause_locked(struct ggml_threadpool * threadpool) { GGML_PRINT_DEBUG("Pausing threadpool\n"); threadpool->pause = true; ggml_cond_broadcast(&threadpool->cond); } static void ggml_threadpool_resume_locked(struct ggml_threadpool * threadpool) { GGML_PRINT_DEBUG("Resuming threadpool\n"); threadpool->pause = false; ggml_cond_broadcast(&threadpool->cond); } #endif void ggml_threadpool_pause(struct ggml_threadpool * threadpool) { #ifndef GGML_USE_OPENMP ggml_mutex_lock(&threadpool->mutex); if (!threadpool->pause) { ggml_threadpool_pause_locked(threadpool); } ggml_mutex_unlock(&threadpool->mutex); #else UNUSED(threadpool); #endif } void ggml_threadpool_resume(struct ggml_threadpool * threadpool) { #ifndef GGML_USE_OPENMP ggml_mutex_lock(&threadpool->mutex); if (threadpool->pause) { ggml_threadpool_resume_locked(threadpool); } ggml_mutex_unlock(&threadpool->mutex); #else UNUSED(threadpool); #endif } struct ggml_cplan ggml_graph_plan( const struct ggml_cgraph * cgraph, int n_threads, struct ggml_threadpool * threadpool) { if (threadpool == NULL) { //GGML_PRINT_DEBUG("Threadpool is not specified. Will create a disposable threadpool : n_threads %d\n", n_threads); } if (n_threads <= 0) { n_threads = threadpool ? threadpool->n_threads_max : GGML_DEFAULT_N_THREADS; } size_t work_size = 0; struct ggml_cplan cplan; memset(&cplan, 0, sizeof(struct ggml_cplan)); int max_tasks = 1; // thread scheduling for the different operations + work buffer size estimation for (int i = 0; i < cgraph->n_nodes; i++) { struct ggml_tensor * node = cgraph->nodes[i]; const int n_tasks = ggml_get_n_tasks(node, n_threads); max_tasks = MAX(max_tasks, n_tasks); size_t cur = 0; switch (node->op) { case GGML_OP_CPY: case GGML_OP_DUP: { if (ggml_is_quantized(node->type) || // F16 -> BF16 and BF16 -> F16 copies go through intermediate F32 (node->src[0]->type == GGML_TYPE_F16 && node->src[1] && node->src[1]->type == GGML_TYPE_BF16) || (node->src[0]->type == GGML_TYPE_BF16 && node->src[1] && node->src[1]->type == GGML_TYPE_F16)) { cur = ggml_type_size(GGML_TYPE_F32) * node->ne[0] * n_tasks; } } break; case GGML_OP_ADD: case GGML_OP_ADD1: { if (ggml_is_quantized(node->src[0]->type)) { cur = ggml_type_size(GGML_TYPE_F32) * node->src[0]->ne[0] * n_tasks; } } break; case GGML_OP_ACC: { if (ggml_is_quantized(node->src[0]->type)) { cur = ggml_type_size(GGML_TYPE_F32) * node->src[1]->ne[0] * n_tasks; } } break; case GGML_OP_COUNT_EQUAL: { cur = ggml_type_size(node->type)*n_tasks; } break; case GGML_OP_MUL_MAT: { const enum ggml_type vec_dot_type = type_traits_cpu[node->src[0]->type].vec_dot_type; if (node->src[1]->type != vec_dot_type) { cur = ggml_row_size(vec_dot_type, ggml_nelements(node->src[1])); } } break; case GGML_OP_MUL_MAT_ID: { cur = 0; const struct ggml_tensor * src0 = node->src[0]; const struct ggml_tensor * src1 = node->src[1]; const enum ggml_type vec_dot_type = type_traits_cpu[src0->type].vec_dot_type; if (src1->type != vec_dot_type) { cur += ggml_row_size(vec_dot_type, ggml_nelements(src1)); } const int n_as = src0->ne[2]; cur += GGML_PAD(cur, sizeof(int64_t)); // align cur += n_as * sizeof(int64_t); // matrix_row_counts cur += n_as * src1->ne[2] * sizeof(int64_t); // matrix_rows } break; case GGML_OP_OUT_PROD: { if (ggml_is_quantized(node->src[0]->type)) { cur = ggml_type_size(GGML_TYPE_F32) * node->src[0]->ne[0] * n_tasks; } } break; case GGML_OP_SOFT_MAX: case GGML_OP_ROPE: { cur = ggml_type_size(GGML_TYPE_F32) * node->ne[0] * n_tasks; } break; case GGML_OP_CONV_TRANSPOSE_1D: { GGML_ASSERT(node->src[0]->ne[3] == 1); GGML_ASSERT(node->src[1]->ne[2] == 1); GGML_ASSERT(node->src[1]->ne[3] == 1); const int64_t ne00 = node->src[0]->ne[0]; // K const int64_t ne01 = node->src[0]->ne[1]; // Cout const int64_t ne02 = node->src[0]->ne[2]; // Cin const int64_t ne10 = node->src[1]->ne[0]; // L const int64_t ne11 = node->src[1]->ne[1]; // Cin if ((node->src[0]->type == GGML_TYPE_F16 || node->src[0]->type == GGML_TYPE_BF16) && node->src[1]->type == GGML_TYPE_F32) { cur += sizeof(ggml_fp16_t)*ne00*ne01*ne02; cur += sizeof(ggml_fp16_t)*ne10*ne11; } else if (node->src[0]->type == GGML_TYPE_F32 && node->src[1]->type == GGML_TYPE_F32) { cur += sizeof(float)*ne00*ne01*ne02; cur += sizeof(float)*ne10*ne11; } else { GGML_ABORT("fatal error"); } } break; case GGML_OP_CONV_TRANSPOSE_2D: { const int64_t ne00 = node->src[0]->ne[0]; // W const int64_t ne01 = node->src[0]->ne[1]; // H const int64_t ne02 = node->src[0]->ne[2]; // Channels Out const int64_t ne03 = node->src[0]->ne[3]; // Channels In const int64_t ne10 = node->src[1]->ne[0]; // W const int64_t ne11 = node->src[1]->ne[1]; // H const int64_t ne12 = node->src[1]->ne[2]; // Channels In cur += sizeof(ggml_fp16_t)*ne00*ne01*ne02*ne03; cur += sizeof(ggml_fp16_t)*ne10*ne11*ne12; } break; case GGML_OP_FLASH_ATTN_EXT: { const int64_t ne00 = node->src[0]->ne[0]; // D cur = 3*sizeof(float)*ne00*n_tasks; // 3x head size/thread } break; case GGML_OP_FLASH_ATTN_BACK: { const int64_t D = node->src[0]->ne[0]; const int64_t ne11 = ggml_up(node->src[1]->ne[1], GGML_SOFT_MAX_UNROLL); const int64_t mxDn = MAX(D, ne11) * 2; // *2 because of S and SM in ggml_compute_forward_flash_attn_back if (node->src[1]->type == GGML_TYPE_F32) { cur = sizeof(float)*mxDn*n_tasks; // TODO: this can become (n_tasks-1) cur += sizeof(float)*mxDn*n_tasks; // this is overestimated by x2 } else if (node->src[1]->type == GGML_TYPE_F16) { cur = sizeof(float)*mxDn*n_tasks; // TODO: this can become (n_tasks-1) cur += sizeof(float)*mxDn*n_tasks; // this is overestimated by x2 } else if (node->src[1]->type == GGML_TYPE_BF16) { cur = sizeof(float)*mxDn*n_tasks; // TODO: this can become (n_tasks-1) cur += sizeof(float)*mxDn*n_tasks; // this is overestimated by x2 } } break; case GGML_OP_CROSS_ENTROPY_LOSS: { cur = ggml_type_size(node->type)*(n_tasks + node->src[0]->ne[0]*n_tasks); } break; case GGML_OP_COUNT: { GGML_ABORT("fatal error"); } default: break; } work_size = MAX(work_size, cur); } if (work_size > 0) { work_size += CACHE_LINE_SIZE*(n_threads); } cplan.threadpool = threadpool; cplan.n_threads = MIN(max_tasks, n_threads); cplan.work_size = work_size; cplan.work_data = NULL; return cplan; } static thread_ret_t ggml_graph_compute_thread(void * data) { struct ggml_compute_state * state = (struct ggml_compute_state *) data; struct ggml_threadpool * tp = state->threadpool; const struct ggml_cgraph * cgraph = tp->cgraph; const struct ggml_cplan * cplan = tp->cplan; set_numa_thread_affinity(state->ith); struct ggml_compute_params params = { /*.ith =*/ state->ith, /*.nth =*/ atomic_load_explicit(&tp->n_threads_cur, memory_order_relaxed), /*.wsize =*/ cplan->work_size, /*.wdata =*/ cplan->work_data, /*.threadpool=*/ tp, }; for (int node_n = 0; node_n < cgraph->n_nodes && !tp->abort; node_n++) { struct ggml_tensor * node = cgraph->nodes[node_n]; ggml_compute_forward(¶ms, node); if (state->ith == 0 && cplan->abort_callback && cplan->abort_callback(cplan->abort_callback_data)) { tp->abort = true; tp->ec = GGML_STATUS_ABORTED; } ggml_barrier(state->threadpool); } return 0; } #ifndef GGML_USE_OPENMP // check if thread is active static inline bool ggml_graph_compute_thread_active(struct ggml_compute_state * state) { struct ggml_threadpool * threadpool = state->threadpool; int n_threads = atomic_load_explicit(&threadpool->n_threads_cur, memory_order_relaxed); return (state->ith < n_threads); } // check if thread is ready to proceed (exit from polling or sleeping) static inline bool ggml_graph_compute_thread_ready(struct ggml_compute_state * state) { struct ggml_threadpool * threadpool = state->threadpool; if (state->pending || threadpool->stop || threadpool->pause) { return true; } // check for new graph/work int new_graph = atomic_load_explicit(&threadpool->n_graph, memory_order_relaxed); if (new_graph != state->last_graph) { state->pending = ggml_graph_compute_thread_active(state); state->last_graph = new_graph; } return state->pending; } // sync thread state after polling static inline void ggml_graph_compute_thread_sync(struct ggml_compute_state * state) { // TSAN doesn't support standalone fence yet, we use a dummy read-modify-write instead #ifdef GGML_TSAN_ENABLED atomic_fetch_add_explicit(&state->threadpool->n_graph, 0, memory_order_seq_cst); #else atomic_thread_fence(memory_order_seq_cst); #endif UNUSED(state); } static inline bool ggml_graph_compute_poll_for_work(struct ggml_compute_state * state) { struct ggml_threadpool * threadpool = state->threadpool; // Skip polling for unused threads if (!ggml_graph_compute_thread_active(state)) { return state->pending; } // This seems to make 0 ... 100 a decent range for polling level across modern processors. // Perhaps, we can adjust it dynamically based on load and things. const uint64_t n_rounds = 1024UL * 128 * threadpool->poll; for (uint64_t i=0; !ggml_graph_compute_thread_ready(state) && i < n_rounds; i++) { // No new work. Keep polling. ggml_thread_cpu_relax(); } return state->pending; } static inline bool ggml_graph_compute_check_for_work(struct ggml_compute_state * state) { struct ggml_threadpool * threadpool = state->threadpool; if (ggml_graph_compute_poll_for_work(state)) { ggml_graph_compute_thread_sync(state); return state->pending; } ggml_mutex_lock_shared(&threadpool->mutex); while (!ggml_graph_compute_thread_ready(state)) { // No new work. Wait for the signal. GGML_PRINT_DEBUG("thread #%d waiting for work (sleeping)\n", state->ith); ggml_cond_wait(&threadpool->cond, &threadpool->mutex); } ggml_mutex_unlock_shared(&threadpool->mutex); return state->pending; } static thread_ret_t ggml_graph_compute_secondary_thread(void* data) { struct ggml_compute_state * state = (struct ggml_compute_state *) data; struct ggml_threadpool * threadpool = state->threadpool; ggml_thread_apply_priority(threadpool->prio); if (ggml_thread_cpumask_is_valid(state->cpumask)) { ggml_thread_apply_affinity(state->cpumask); } while (true) { // Check if we need to sleep while (threadpool->pause) { GGML_PRINT_DEBUG("thread #%d inside pause loop\n", state->ith); ggml_mutex_lock_shared(&threadpool->mutex); if (threadpool->pause) { ggml_cond_wait(&threadpool->cond, &threadpool->mutex); } GGML_PRINT_DEBUG("thread #%d resuming after wait\n", state->ith); ggml_mutex_unlock_shared(&threadpool->mutex); } // This needs to be checked for after the cond_wait if (threadpool->stop) break; // Check if there is new work // The main thread is the only one that can dispatch new work ggml_graph_compute_check_for_work(state); if (state->pending) { state->pending = false; ggml_graph_compute_thread(state); } } return (thread_ret_t) 0; } // Start processing new graph static void ggml_graph_compute_kickoff(struct ggml_threadpool * threadpool, int n_threads) { // Always take the mutex here because the worker threads are doing hybrid poll/wait ggml_mutex_lock(&threadpool->mutex); GGML_PRINT_DEBUG("threadpool: n_threads_cur %d n_threads %d\n", threadpool->n_threads_cur, n_threads); // Update the number of active threads atomic_store_explicit(&threadpool->n_threads_cur, n_threads, memory_order_relaxed); // Indicate the graph is ready to be processed // We need the full seq-cst fence here because of the polling threads (used in thread_sync) atomic_fetch_add_explicit(&threadpool->n_graph, 1, memory_order_seq_cst); if (threadpool->pause) { // Update main thread prio and affinity to match the threadpool settings ggml_thread_apply_priority(threadpool->prio); if (ggml_thread_cpumask_is_valid(threadpool->workers[0].cpumask)) { ggml_thread_apply_affinity(threadpool->workers[0].cpumask); } // resume does cond broadcast ggml_threadpool_resume_locked(threadpool); } else { ggml_cond_broadcast(&threadpool->cond); } ggml_mutex_unlock(&threadpool->mutex); } #endif // GGML_USE_OPENMP void ggml_threadpool_params_init(struct ggml_threadpool_params * p, int n_threads) { p->n_threads = n_threads; p->prio = 0; // default priority (usually means normal or inherited) p->poll = 50; // hybrid-polling enabled p->strict_cpu = false; // no strict placement (all threads share same cpumask) p->paused = false; // threads are ready to go memset(p->cpumask, 0, GGML_MAX_N_THREADS); // all-zero means use the default affinity (usually inherited) } struct ggml_threadpool_params ggml_threadpool_params_default(int n_threads) { struct ggml_threadpool_params p; ggml_threadpool_params_init(&p, n_threads); return p; } bool ggml_threadpool_params_match(const struct ggml_threadpool_params * p0, const struct ggml_threadpool_params * p1) { if (p0->n_threads != p1->n_threads ) return false; if (p0->prio != p1->prio ) return false; if (p0->poll != p1->poll ) return false; if (p0->strict_cpu != p1->strict_cpu ) return false; return memcmp(p0->cpumask, p1->cpumask, GGML_MAX_N_THREADS) == 0; } static struct ggml_threadpool * ggml_threadpool_new_impl( struct ggml_threadpool_params * tpp, struct ggml_cgraph * cgraph, struct ggml_cplan * cplan) { struct ggml_threadpool * threadpool = ggml_aligned_malloc(sizeof(struct ggml_threadpool)); { threadpool->cgraph = cgraph; threadpool->cplan = cplan; threadpool->n_graph = 0; threadpool->n_barrier = 0; threadpool->n_barrier_passed = 0; threadpool->current_chunk = 0; threadpool->stop = false; threadpool->pause = tpp->paused; threadpool->abort = false; threadpool->workers = NULL; threadpool->n_threads_max = tpp->n_threads; threadpool->n_threads_cur = tpp->n_threads; threadpool->poll = tpp->poll; threadpool->prio = tpp->prio; threadpool->ec = GGML_STATUS_SUCCESS; } // Allocate and init workers state const size_t workers_size = sizeof(struct ggml_compute_state) * tpp->n_threads; struct ggml_compute_state * workers = ggml_aligned_malloc(workers_size); memset(workers, 0, workers_size); for (int j = 0; j < tpp->n_threads; j++) { workers[j].threadpool = threadpool; workers[j].ith = j; } threadpool->workers = workers; #ifndef GGML_USE_OPENMP ggml_mutex_init(&threadpool->mutex); ggml_cond_init(&threadpool->cond); // Spin the threads for all workers, and update CPU placements. // Place the main thread last (towards the higher numbered CPU cores). int32_t cpumask_iter = 0; for (int j = 1; j < tpp->n_threads; j++) { ggml_thread_cpumask_next(tpp->cpumask, workers[j].cpumask, tpp->strict_cpu, &cpumask_iter); int32_t rc = ggml_thread_create(&workers[j].thrd, NULL, ggml_graph_compute_secondary_thread, &workers[j]); GGML_ASSERT(rc == 0); } ggml_thread_cpumask_next(tpp->cpumask, workers[0].cpumask, tpp->strict_cpu, &cpumask_iter); if (!threadpool->pause) { // Update main thread prio and affinity at the start, otherwise we'll do it in resume ggml_thread_apply_priority(threadpool->prio); if (ggml_thread_cpumask_is_valid(threadpool->workers[0].cpumask)) { ggml_thread_apply_affinity(threadpool->workers[0].cpumask); } } #endif // GGML_USE_OPENMP return threadpool; } struct ggml_threadpool * ggml_threadpool_new(struct ggml_threadpool_params * tpp) { return ggml_threadpool_new_impl(tpp, NULL, NULL); } enum ggml_status ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan) { ggml_cpu_init(); GGML_ASSERT(cplan); GGML_ASSERT(cplan->n_threads > 0); GGML_ASSERT(cplan->work_size == 0 || cplan->work_data != NULL); int n_threads = cplan->n_threads; struct ggml_threadpool * threadpool = cplan->threadpool; bool disposable_threadpool = false; if (threadpool == NULL) { //GGML_PRINT_DEBUG("Threadpool is not specified. Will create a disposable threadpool : n_threads %d\n", n_threads); disposable_threadpool = true; struct ggml_threadpool_params ttp = ggml_threadpool_params_default(n_threads); threadpool = ggml_threadpool_new_impl(&ttp, cgraph, cplan); } else { // Reset some of the parameters that need resetting // No worker threads should be accessing the parameters below at this stage threadpool->cgraph = cgraph; threadpool->cplan = cplan; threadpool->current_chunk = 0; threadpool->abort = false; threadpool->ec = GGML_STATUS_SUCCESS; } #ifdef GGML_USE_OPENMP if (n_threads > 1) { #pragma omp parallel num_threads(n_threads) { #pragma omp single { // update the number of threads from the actual number of threads that we got from OpenMP n_threads = omp_get_num_threads(); atomic_store_explicit(&threadpool->n_threads_cur, n_threads, memory_order_relaxed); } ggml_graph_compute_thread(&threadpool->workers[omp_get_thread_num()]); } } else { atomic_store_explicit(&threadpool->n_threads_cur, 1, memory_order_relaxed); ggml_graph_compute_thread(&threadpool->workers[0]); } #else if (n_threads > threadpool->n_threads_max) { GGML_LOG_WARN("cplan requested more threads (%d) than available (%d)\n", n_threads, threadpool->n_threads_max); n_threads = threadpool->n_threads_max; } // Kick all threads to start the new graph ggml_graph_compute_kickoff(threadpool, n_threads); // This is a work thread too ggml_graph_compute_thread(&threadpool->workers[0]); #endif // don't leave affinity set on the main thread clear_numa_thread_affinity(); enum ggml_status ret = threadpool->ec; if (disposable_threadpool) { ggml_threadpool_free(threadpool); } return ret; } enum ggml_status ggml_graph_compute_with_ctx(struct ggml_context * ctx, struct ggml_cgraph * cgraph, int n_threads) { struct ggml_cplan cplan = ggml_graph_plan(cgraph, n_threads, NULL); cplan.work_data = (uint8_t *)ggml_new_buffer(ctx, cplan.work_size); return ggml_graph_compute(cgraph, &cplan); } int ggml_cpu_has_neon(void) { #if defined(__ARM_ARCH) return ggml_arm_arch_features.has_neon; #else return 0; #endif } int ggml_cpu_has_sve(void) { #if defined(__ARM_ARCH) return ggml_arm_arch_features.has_sve; #else return 0; #endif } int ggml_cpu_has_matmul_int8(void) { #if defined(__ARM_ARCH) return ggml_arm_arch_features.has_i8mm; #else return 0; #endif } int ggml_cpu_get_sve_cnt(void) { #if defined(__ARM_ARCH) return ggml_arm_arch_features.sve_cnt; #else return 0; #endif } void ggml_cpu_init(void) { // needed to initialize f16 tables { struct ggml_init_params params = { 0, NULL, false }; struct ggml_context * ctx = ggml_init(params); ggml_free(ctx); } ggml_critical_section_start(); static bool is_first_call = true; if (is_first_call) { // initialize GELU, Quick GELU, SILU and EXP F32 tables { const uint64_t t_start = ggml_time_us(); UNUSED(t_start); for (int i = 0; i < (1 << 16); ++i) { union { uint16_t u16; ggml_fp16_t fp16; } u = {i}; float f = GGML_FP16_TO_FP32(u.fp16); ggml_table_gelu_f16[i] = GGML_FP32_TO_FP16(ggml_gelu_f32(f)); ggml_table_gelu_quick_f16[i] = GGML_FP32_TO_FP16(ggml_gelu_quick_f32(f)); } const uint64_t t_end = ggml_time_us(); UNUSED(t_end); GGML_PRINT_DEBUG("%s: GELU, Quick GELU, SILU and EXP tables initialized in %f ms\n", __func__, (t_end - t_start)/1000.0); } #if defined(__ARM_ARCH) ggml_init_arm_arch_features(); #endif is_first_call = false; } ggml_critical_section_end(); }