#define _CRT_SECURE_NO_DEPRECATE // Disables ridiculous "unsafe" warnings on Windows #define _USE_MATH_DEFINES // For M_PI on MSVC #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_METAL #include #endif #ifdef __ARM_FEATURE_MATMUL_INT8 #undef GGML_USE_LLAMAFILE #endif #ifdef GGML_USE_LLAMAFILE #include "sgemm.h" #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) #endif #if defined(_WIN32) #define WIN32_LEAN_AND_MEAN #ifndef NOMINMAX #define NOMINMAX #endif #include typedef volatile LONG atomic_int; typedef atomic_int atomic_bool; static void atomic_store(atomic_int * ptr, LONG val) { InterlockedExchange(ptr, val); } static LONG atomic_load(atomic_int * ptr) { return InterlockedCompareExchange(ptr, 0, 0); } static LONG atomic_fetch_add(atomic_int * ptr, LONG inc) { return InterlockedExchangeAdd(ptr, inc); } static LONG atomic_fetch_sub(atomic_int * ptr, LONG dec) { return atomic_fetch_add(ptr, -(dec)); } typedef HANDLE pthread_t; typedef DWORD thread_ret_t; static int pthread_create(pthread_t * out, void * unused, thread_ret_t(*func)(void *), void * arg) { (void) unused; HANDLE handle = CreateThread(NULL, 0, (LPTHREAD_START_ROUTINE) func, arg, 0, NULL); if (handle == NULL) { return EAGAIN; } *out = handle; return 0; } static int pthread_join(pthread_t thread, void * unused) { (void) unused; int ret = (int) WaitForSingleObject(thread, INFINITE); CloseHandle(thread); return ret; } static int sched_yield (void) { Sleep (0); return 0; } #else #include #include 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 #endif #if (defined(__linux__) || defined(__APPLE__) || defined(__FreeBSD__) || defined(__NetBSD__) || defined(__OpenBSD__)) && \ (!defined(TARGET_OS_TV) && !defined(TARGET_OS_WATCH)) #include void ggml_print_backtrace(void) { /* #include #include void * trace[100]; int nptrs = backtrace(trace, sizeof(trace)/sizeof(trace[0])); backtrace_symbols_fd(trace, nptrs, STDERR_FILENO); */ // backtrack_symbols does not show line numbers, use gdb instead char attach[32]; snprintf(attach, sizeof(attach), "attach %d", getpid()); int pid = fork(); if (pid == 0) { execlp("gdb", "gdb", "--batch", "-ex", "set style enabled on", "-ex", attach, "-ex", "bt -frame-info source-and-location", "-ex", "detach", "-ex", "quit", (char *) NULL); } else { waitpid(pid, NULL, 0); } } #else void ggml_print_backtrace(void) { // platform not supported } #endif /*#define GGML_PERF*/ #define GGML_DEBUG 0 #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 // // logging // #if (GGML_DEBUG >= 1) #define GGML_PRINT_DEBUG(...) printf(__VA_ARGS__) #else #define GGML_PRINT_DEBUG(...) #endif #if (GGML_DEBUG >= 5) #define GGML_PRINT_DEBUG_5(...) printf(__VA_ARGS__) #else #define GGML_PRINT_DEBUG_5(...) #endif #if (GGML_DEBUG >= 10) #define GGML_PRINT_DEBUG_10(...) printf(__VA_ARGS__) #else #define GGML_PRINT_DEBUG_10(...) #endif #define GGML_PRINT(...) printf(__VA_ARGS__) // // end of logging block // #ifdef GGML_USE_ACCELERATE // uncomment to use vDSP for soft max computation // note: not sure if it is actually faster //#define GGML_SOFT_MAX_ACCELERATE #endif #if defined(_MSC_VER) || defined(__MINGW32__) #define GGML_ALIGNED_MALLOC(size) _aligned_malloc(size, GGML_MEM_ALIGN) #define GGML_ALIGNED_FREE(ptr) _aligned_free(ptr) #else inline static void * ggml_aligned_malloc(size_t size) { if (size == 0) { GGML_PRINT("WARNING: Behavior may be unexpected when allocating 0 bytes for ggml_aligned_malloc!\n"); return NULL; } void * aligned_memory = NULL; #ifdef GGML_USE_CPU_HBM int result = hbw_posix_memalign(&aligned_memory, 16, size); #elif GGML_USE_METAL int result = posix_memalign(&aligned_memory, sysconf(_SC_PAGESIZE), size); #else int result = posix_memalign(&aligned_memory, GGML_MEM_ALIGN, size); #endif if (result != 0) { // Handle allocation failure const char *error_desc = "unknown allocation error"; switch (result) { case EINVAL: error_desc = "invalid alignment value"; break; case ENOMEM: error_desc = "insufficient memory"; break; } GGML_PRINT("%s: %s (attempted to allocate %6.2f MB)\n", __func__, error_desc, size/(1024.0*1024.0)); GGML_ASSERT(false); return NULL; } return aligned_memory; } #define GGML_ALIGNED_MALLOC(size) ggml_aligned_malloc(size) #ifdef GGML_USE_CPU_HBM #define GGML_ALIGNED_FREE(ptr) if(NULL != ptr) hbw_free(ptr) #else #define GGML_ALIGNED_FREE(ptr) free(ptr) #endif #endif inline static void * ggml_malloc(size_t size) { if (size == 0) { GGML_PRINT("WARNING: Behavior may be unexpected when allocating 0 bytes for ggml_malloc!\n"); return NULL; } void * result = malloc(size); if (result == NULL) { GGML_PRINT("%s: failed to allocate %6.2f MB\n", __func__, size/(1024.0*1024.0)); GGML_ASSERT(false); } return result; } // calloc inline static void * ggml_calloc(size_t num, size_t size) { if (num == 0 || size == 0) { GGML_PRINT("WARNING: Behavior may be unexpected when allocating 0 bytes for ggml_calloc!\n"); return NULL; } void * result = calloc(num, size); if (result == NULL) { GGML_PRINT("%s: failed to allocate %6.2f MB\n", __func__, size/(1024.0*1024.0)); GGML_ASSERT(false); } return result; } #define GGML_MALLOC(size) ggml_malloc(size) #define GGML_CALLOC(num, size) ggml_calloc(num, size) #define GGML_FREE(ptr) free(ptr) #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 #if defined(GGML_USE_CLBLAST) // allow usage of CLBlast alongside Accelerate functions #include "ggml-opencl.h" #endif #elif defined(GGML_USE_OPENBLAS) #if defined(GGML_BLAS_USE_MKL) #include #else #include #endif #elif defined(GGML_USE_CLBLAST) #include "ggml-opencl.h" #endif // floating point type used to accumulate sums typedef double ggml_float; #undef MIN #undef MAX #define MIN(a, b) ((a) < (b) ? (a) : (b)) #define MAX(a, b) ((a) > (b) ? (a) : (b)) // // 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]; GGML_CALL const char * ggml_status_to_string(enum ggml_status status) { switch (status) { case GGML_STATUS_ALLOC_FAILED: return "GGML status: error (failed to allocate memory)"; case GGML_STATUS_FAILED: return "GGML status: error (operation failed)"; case GGML_STATUS_SUCCESS: return "GGML status: success"; case GGML_STATUS_ABORTED: return "GGML status: warning (operation aborted)"; } return "GGML status: unknown"; } float ggml_fp16_to_fp32(ggml_fp16_t x) { #define ggml_fp16_to_fp32 do_not_use__ggml_fp16_to_fp32__in_ggml return GGML_FP16_TO_FP32(x); } ggml_fp16_t ggml_fp32_to_fp16(float x) { #define ggml_fp32_to_fp16 do_not_use__ggml_fp32_to_fp16__in_ggml return GGML_FP32_TO_FP16(x); } float ggml_bf16_to_fp32(ggml_bf16_t x) { #define ggml_bf16_to_fp32 do_not_use__ggml_bf16_to_fp32__in_ggml return GGML_BF16_TO_FP32(x); // it just left shifts } ggml_bf16_t ggml_fp32_to_bf16(float x) { #define ggml_fp32_to_bf16 do_not_use__ggml_fp32_to_bf16__in_ggml return GGML_FP32_TO_BF16(x); } void ggml_fp16_to_fp32_row(const ggml_fp16_t * x, float * y, int64_t n) { for (int64_t i = 0; i < n; i++) { y[i] = GGML_FP16_TO_FP32(x[i]); } } void ggml_fp32_to_fp16_row(const float * x, ggml_fp16_t * y, int64_t n) { int64_t i = 0; #if defined(__F16C__) for (; i + 7 < n; i += 8) { __m256 x_vec = _mm256_loadu_ps(x + i); __m128i y_vec = _mm256_cvtps_ph(x_vec, _MM_FROUND_TO_NEAREST_INT); _mm_storeu_si128((__m128i *)(y + i), y_vec); } for(; i + 3 < n; i += 4) { __m128 x_vec = _mm_loadu_ps(x + i); __m128i y_vec = _mm_cvtps_ph(x_vec, _MM_FROUND_TO_NEAREST_INT); _mm_storel_epi64((__m128i *)(y + i), y_vec); } #endif for (; i < n; i++) { y[i] = GGML_FP32_TO_FP16(x[i]); } } void ggml_bf16_to_fp32_row(const ggml_bf16_t * x, float * y, int64_t n) { int64_t i = 0; #if defined(__AVX512F__) for (; i + 16 <= n; i += 16) { _mm512_storeu_ps(y + i, _mm512_castsi512_ps( _mm512_slli_epi32( _mm512_cvtepu16_epi32( _mm256_loadu_si256( (const __m256i *)(x + i))), 16))); } #elif defined(__AVX2__) for (; i + 8 <= n; i += 8) { _mm256_storeu_ps(y + i, _mm256_castsi256_ps( _mm256_slli_epi32( _mm256_cvtepu16_epi32( _mm_loadu_si128( (const __m128i *)(x + i))), 16))); } #endif for (; i < n; i++) { y[i] = GGML_BF16_TO_FP32(x[i]); } } void ggml_fp32_to_bf16_row(const float * x, ggml_bf16_t * y, int64_t n) { int i = 0; #if defined(__AVX512BF16__) for (; i + 32 <= n; i += 32) { _mm512_storeu_si512( (__m512i *)(y + i), m512i(_mm512_cvtne2ps_pbh(_mm512_loadu_ps(x + i + 16), _mm512_loadu_ps(x + i)))); } #endif for (; i < n; i++) { y[i] = GGML_FP32_TO_BF16(x[i]); } } bool ggml_guid_matches(ggml_guid_t guid_a, ggml_guid_t guid_b) { return memcmp(guid_a, guid_b, sizeof(ggml_guid)) == 0; } // // timing // #if defined(_MSC_VER) || defined(__MINGW32__) static int64_t timer_freq, timer_start; void ggml_time_init(void) { LARGE_INTEGER t; QueryPerformanceFrequency(&t); timer_freq = t.QuadPart; // The multiplication by 1000 or 1000000 below can cause an overflow if timer_freq // and the uptime is high enough. // We subtract the program start time to reduce the likelihood of that happening. QueryPerformanceCounter(&t); timer_start = t.QuadPart; } int64_t ggml_time_ms(void) { LARGE_INTEGER t; QueryPerformanceCounter(&t); return ((t.QuadPart-timer_start) * 1000) / timer_freq; } int64_t ggml_time_us(void) { LARGE_INTEGER t; QueryPerformanceCounter(&t); return ((t.QuadPart-timer_start) * 1000000) / timer_freq; } #else void ggml_time_init(void) {} int64_t ggml_time_ms(void) { struct timespec ts; clock_gettime(CLOCK_MONOTONIC, &ts); return (int64_t)ts.tv_sec*1000 + (int64_t)ts.tv_nsec/1000000; } int64_t ggml_time_us(void) { struct timespec ts; clock_gettime(CLOCK_MONOTONIC, &ts); return (int64_t)ts.tv_sec*1000000 + (int64_t)ts.tv_nsec/1000; } #endif int64_t ggml_cycles(void) { return clock(); } int64_t ggml_cycles_per_ms(void) { return CLOCKS_PER_SEC/1000; } #ifdef GGML_PERF #define ggml_perf_time_ms() ggml_time_ms() #define ggml_perf_time_us() ggml_time_us() #define ggml_perf_cycles() ggml_cycles() #define ggml_perf_cycles_per_ms() ggml_cycles_per_ms() #else #define ggml_perf_time_ms() 0 #define ggml_perf_time_us() 0 #define ggml_perf_cycles() 0 #define ggml_perf_cycles_per_ms() 0 #endif // // cross-platform UTF-8 file paths // #ifdef _WIN32 static wchar_t * ggml_mbstowcs(const char * mbs) { int wlen = MultiByteToWideChar(CP_UTF8, 0, mbs, -1, NULL, 0); if (!wlen) { errno = EINVAL; return NULL; } wchar_t * wbuf = GGML_MALLOC(wlen * sizeof(wchar_t)); wlen = MultiByteToWideChar(CP_UTF8, 0, mbs, -1, wbuf, wlen); if (!wlen) { GGML_FREE(wbuf); errno = EINVAL; return NULL; } return wbuf; } #endif FILE * ggml_fopen(const char * fname, const char * mode) { #ifdef _WIN32 FILE * file = NULL; // convert fname (UTF-8) wchar_t * wfname = ggml_mbstowcs(fname); if (wfname) { // convert mode (ANSI) wchar_t * wmode = GGML_MALLOC((strlen(mode) + 1) * sizeof(wchar_t)); wchar_t * wmode_p = wmode; do { *wmode_p++ = (wchar_t)*mode; } while (*mode++); // open file file = _wfopen(wfname, wmode); GGML_FREE(wfname); GGML_FREE(wmode); } return file; #else return fopen(fname, mode); #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 ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = { [GGML_TYPE_I8] = { .type_name = "i8", .blck_size = 1, .type_size = sizeof(int8_t), .is_quantized = false, }, [GGML_TYPE_I16] = { .type_name = "i16", .blck_size = 1, .type_size = sizeof(int16_t), .is_quantized = false, }, [GGML_TYPE_I32] = { .type_name = "i32", .blck_size = 1, .type_size = sizeof(int32_t), .is_quantized = false, }, [GGML_TYPE_I64] = { .type_name = "i64", .blck_size = 1, .type_size = sizeof(int64_t), .is_quantized = false, }, [GGML_TYPE_F64] = { .type_name = "f64", .blck_size = 1, .type_size = sizeof(double), .is_quantized = false, .nrows = 1, }, [GGML_TYPE_F32] = { .type_name = "f32", .blck_size = 1, .type_size = sizeof(float), .is_quantized = false, .vec_dot = (ggml_vec_dot_t) ggml_vec_dot_f32, .vec_dot_type = GGML_TYPE_F32, .nrows = 1, }, [GGML_TYPE_F16] = { .type_name = "f16", .blck_size = 1, .type_size = sizeof(ggml_fp16_t), .is_quantized = false, .to_float = (ggml_to_float_t) ggml_fp16_to_fp32_row, .from_float = (ggml_from_float_t) ggml_fp32_to_fp16_row, .from_float_reference = (ggml_from_float_t) ggml_fp32_to_fp16_row, .vec_dot = (ggml_vec_dot_t) ggml_vec_dot_f16, .vec_dot_type = GGML_TYPE_F16, .nrows = 1, }, [GGML_TYPE_Q4_0] = { .type_name = "q4_0", .blck_size = QK4_0, .type_size = sizeof(block_q4_0), .is_quantized = true, .to_float = (ggml_to_float_t) dequantize_row_q4_0, .from_float = quantize_row_q4_0, .from_float_reference = (ggml_from_float_t) quantize_row_q4_0_reference, .vec_dot = ggml_vec_dot_q4_0_q8_0, .vec_dot_type = GGML_TYPE_Q8_0, #if defined (__ARM_FEATURE_MATMUL_INT8) .nrows = 2, #else .nrows = 1, #endif }, [GGML_TYPE_Q4_1] = { .type_name = "q4_1", .blck_size = QK4_1, .type_size = sizeof(block_q4_1), .is_quantized = true, .to_float = (ggml_to_float_t) dequantize_row_q4_1, .from_float = quantize_row_q4_1, .from_float_reference = (ggml_from_float_t) quantize_row_q4_1_reference, .vec_dot = ggml_vec_dot_q4_1_q8_1, .vec_dot_type = GGML_TYPE_Q8_1, #if defined (__ARM_FEATURE_MATMUL_INT8) .nrows = 2, #else .nrows = 1, #endif }, [4] = { // GGML_TYPE_Q4_2 .type_name = "DEPRECATED", .blck_size = 0, .type_size = 0, .is_quantized = false, .to_float = NULL, .from_float = NULL, .from_float_reference = NULL, .vec_dot = NULL, .vec_dot_type = GGML_TYPE_COUNT, .nrows = 1, }, [5] = { // GGML_TYPE_Q4_3 .type_name = "DEPRECATED", .blck_size = 0, .type_size = 0, .is_quantized = false, .to_float = NULL, .from_float = NULL, .from_float_reference = NULL, .vec_dot = NULL, .vec_dot_type = GGML_TYPE_COUNT, .nrows = 1, }, [GGML_TYPE_Q5_0] = { .type_name = "q5_0", .blck_size = QK5_0, .type_size = sizeof(block_q5_0), .is_quantized = true, .to_float = (ggml_to_float_t) dequantize_row_q5_0, .from_float = quantize_row_q5_0, .from_float_reference = (ggml_from_float_t) quantize_row_q5_0_reference, .vec_dot = ggml_vec_dot_q5_0_q8_0, .vec_dot_type = GGML_TYPE_Q8_0, .nrows = 1, }, [GGML_TYPE_Q5_1] = { .type_name = "q5_1", .blck_size = QK5_1, .type_size = sizeof(block_q5_1), .is_quantized = true, .to_float = (ggml_to_float_t) dequantize_row_q5_1, .from_float = quantize_row_q5_1, .from_float_reference = (ggml_from_float_t) quantize_row_q5_1_reference, .vec_dot = ggml_vec_dot_q5_1_q8_1, .vec_dot_type = GGML_TYPE_Q8_1, .nrows = 1, }, [GGML_TYPE_Q8_0] = { .type_name = "q8_0", .blck_size = QK8_0, .type_size = sizeof(block_q8_0), .is_quantized = true, .to_float = (ggml_to_float_t) dequantize_row_q8_0, .from_float = quantize_row_q8_0, .from_float_reference = (ggml_from_float_t) quantize_row_q8_0_reference, .vec_dot = ggml_vec_dot_q8_0_q8_0, .vec_dot_type = GGML_TYPE_Q8_0, #if defined (__ARM_FEATURE_MATMUL_INT8) .nrows = 2, #else .nrows = 1, #endif }, [GGML_TYPE_Q8_1] = { .type_name = "q8_1", .blck_size = QK8_1, .type_size = sizeof(block_q8_1), .is_quantized = true, .from_float = quantize_row_q8_1, .from_float_reference = (ggml_from_float_t) quantize_row_q8_1_reference, .vec_dot_type = GGML_TYPE_Q8_1, .nrows = 1, }, [GGML_TYPE_Q2_K] = { .type_name = "q2_K", .blck_size = QK_K, .type_size = sizeof(block_q2_K), .is_quantized = true, .to_float = (ggml_to_float_t) dequantize_row_q2_K, .from_float = quantize_row_q2_K, .from_float_reference = (ggml_from_float_t) quantize_row_q2_K_reference, .vec_dot = ggml_vec_dot_q2_K_q8_K, .vec_dot_type = GGML_TYPE_Q8_K, .nrows = 1, }, [GGML_TYPE_Q3_K] = { .type_name = "q3_K", .blck_size = QK_K, .type_size = sizeof(block_q3_K), .is_quantized = true, .to_float = (ggml_to_float_t) dequantize_row_q3_K, .from_float = quantize_row_q3_K, .from_float_reference = (ggml_from_float_t) quantize_row_q3_K_reference, .vec_dot = ggml_vec_dot_q3_K_q8_K, .vec_dot_type = GGML_TYPE_Q8_K, .nrows = 1, }, [GGML_TYPE_Q4_K] = { .type_name = "q4_K", .blck_size = QK_K, .type_size = sizeof(block_q4_K), .is_quantized = true, .to_float = (ggml_to_float_t) dequantize_row_q4_K, .from_float = quantize_row_q4_K, .from_float_reference = (ggml_from_float_t) quantize_row_q4_K_reference, .vec_dot = ggml_vec_dot_q4_K_q8_K, .vec_dot_type = GGML_TYPE_Q8_K, .nrows = 1, }, [GGML_TYPE_Q5_K] = { .type_name = "q5_K", .blck_size = QK_K, .type_size = sizeof(block_q5_K), .is_quantized = true, .to_float = (ggml_to_float_t) dequantize_row_q5_K, .from_float = quantize_row_q5_K, .from_float_reference = (ggml_from_float_t) quantize_row_q5_K_reference, .vec_dot = ggml_vec_dot_q5_K_q8_K, .vec_dot_type = GGML_TYPE_Q8_K, .nrows = 1, }, [GGML_TYPE_Q6_K] = { .type_name = "q6_K", .blck_size = QK_K, .type_size = sizeof(block_q6_K), .is_quantized = true, .to_float = (ggml_to_float_t) dequantize_row_q6_K, .from_float = quantize_row_q6_K, .from_float_reference = (ggml_from_float_t) quantize_row_q6_K_reference, .vec_dot = ggml_vec_dot_q6_K_q8_K, .vec_dot_type = GGML_TYPE_Q8_K, .nrows = 1, }, [GGML_TYPE_IQ2_XXS] = { .type_name = "iq2_xxs", .blck_size = QK_K, .type_size = sizeof(block_iq2_xxs), .is_quantized = true, .to_float = (ggml_to_float_t) dequantize_row_iq2_xxs, .from_float = NULL, .from_float_reference = NULL, .vec_dot = ggml_vec_dot_iq2_xxs_q8_K, .vec_dot_type = GGML_TYPE_Q8_K, .nrows = 1, }, [GGML_TYPE_IQ2_XS] = { .type_name = "iq2_xs", .blck_size = QK_K, .type_size = sizeof(block_iq2_xs), .is_quantized = true, .to_float = (ggml_to_float_t) dequantize_row_iq2_xs, .from_float = NULL, .from_float_reference = NULL, .vec_dot = ggml_vec_dot_iq2_xs_q8_K, .vec_dot_type = GGML_TYPE_Q8_K, .nrows = 1, }, [GGML_TYPE_IQ3_XXS] = { .type_name = "iq3_xxs", .blck_size = QK_K, .type_size = sizeof(block_iq3_xxs), .is_quantized = true, .to_float = (ggml_to_float_t) dequantize_row_iq3_xxs, .from_float = quantize_row_iq3_xxs, .from_float_reference = (ggml_from_float_t)quantize_row_iq3_xxs_reference, .vec_dot = ggml_vec_dot_iq3_xxs_q8_K, .vec_dot_type = GGML_TYPE_Q8_K, .nrows = 1, }, [GGML_TYPE_IQ3_S] = { .type_name = "iq3_s", .blck_size = QK_K, .type_size = sizeof(block_iq3_s), .is_quantized = true, .to_float = (ggml_to_float_t) dequantize_row_iq3_s, .from_float = quantize_row_iq3_s, .from_float_reference = (ggml_from_float_t)quantize_row_iq3_s_reference, .vec_dot = ggml_vec_dot_iq3_s_q8_K, .vec_dot_type = GGML_TYPE_Q8_K, .nrows = 1, }, [GGML_TYPE_IQ2_S] = { .type_name = "iq2_s", .blck_size = QK_K, .type_size = sizeof(block_iq2_s), .is_quantized = true, .to_float = (ggml_to_float_t) dequantize_row_iq2_s, .from_float = quantize_row_iq2_s, .from_float_reference = (ggml_from_float_t)quantize_row_iq2_s_reference, .vec_dot = ggml_vec_dot_iq2_s_q8_K, .vec_dot_type = GGML_TYPE_Q8_K, .nrows = 1, }, [GGML_TYPE_IQ1_S] = { .type_name = "iq1_s", .blck_size = QK_K, .type_size = sizeof(block_iq1_s), .is_quantized = true, .to_float = (ggml_to_float_t) dequantize_row_iq1_s, .from_float = NULL, .from_float_reference = NULL, .vec_dot = ggml_vec_dot_iq1_s_q8_K, .vec_dot_type = GGML_TYPE_Q8_K, .nrows = 1, }, [GGML_TYPE_IQ1_M] = { .type_name = "iq1_m", .blck_size = QK_K, .type_size = sizeof(block_iq1_m), .is_quantized = true, .to_float = (ggml_to_float_t) dequantize_row_iq1_m, .from_float = NULL, .from_float_reference = NULL, .vec_dot = ggml_vec_dot_iq1_m_q8_K, .vec_dot_type = GGML_TYPE_Q8_K, .nrows = 1, }, [GGML_TYPE_IQ4_NL] = { .type_name = "iq4_nl", .blck_size = QK4_NL, .type_size = sizeof(block_iq4_nl), .is_quantized = true, .to_float = (ggml_to_float_t) dequantize_row_iq4_nl, .from_float = quantize_row_iq4_nl, .from_float_reference = (ggml_from_float_t)quantize_row_iq4_nl_reference, .vec_dot = ggml_vec_dot_iq4_nl_q8_0, .vec_dot_type = GGML_TYPE_Q8_0, .nrows = 1, }, [GGML_TYPE_IQ4_XS] = { .type_name = "iq4_xs", .blck_size = QK_K, .type_size = sizeof(block_iq4_xs), .is_quantized = true, .to_float = (ggml_to_float_t) dequantize_row_iq4_xs, .from_float = quantize_row_iq4_xs, .from_float_reference = (ggml_from_float_t)quantize_row_iq4_xs_reference, .vec_dot = ggml_vec_dot_iq4_xs_q8_K, .vec_dot_type = GGML_TYPE_Q8_K, .nrows = 1, }, [GGML_TYPE_Q8_K] = { .type_name = "q8_K", .blck_size = QK_K, .type_size = sizeof(block_q8_K), .is_quantized = true, .from_float = quantize_row_q8_K, }, [GGML_TYPE_BF16] = { .type_name = "bf16", .blck_size = 1, .type_size = sizeof(ggml_bf16_t), .is_quantized = false, .to_float = (ggml_to_float_t) ggml_bf16_to_fp32_row, .from_float = (ggml_from_float_t) ggml_fp32_to_bf16_row, .from_float_reference = (ggml_from_float_t) ggml_fp32_to_bf16_row, .vec_dot = (ggml_vec_dot_t) ggml_vec_dot_bf16, .vec_dot_type = GGML_TYPE_BF16, .nrows = 1, } }; // For internal test use ggml_type_traits_t ggml_internal_get_type_traits(enum ggml_type type) { GGML_ASSERT(type < GGML_TYPE_COUNT); return type_traits[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(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(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 // // ggml context // struct ggml_context { size_t mem_size; void* mem_buffer; bool mem_buffer_owned; bool no_alloc; bool no_alloc_save; // this is used to save the no_alloc state when using scratch buffers int n_objects; struct ggml_object* objects_begin; struct ggml_object* objects_end; struct ggml_scratch scratch; struct ggml_scratch scratch_save; }; struct ggml_context_container { bool used; struct ggml_context context; }; struct ggml_compute_state_shared { const struct ggml_cgraph* cgraph; const struct ggml_cplan* cplan; int64_t perf_node_start_cycles; int64_t perf_node_start_time_us; const int n_threads; // synchronization primitives atomic_int n_active; // num active threads atomic_int node_n; // active graph node atomic_int node_task; // active graph node task phase ggml_abort_callback abort_callback; // abort ggml_graph_compute when true void* abort_callback_data; atomic_int current_chunk; // currently processing chunk during Mat_Mul, shared between all the threads. }; struct ggml_compute_state { ggml_thread_t thrd; int ith; struct ggml_compute_state_shared* shared; enum ggml_status ec; }; // // 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_abs_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = fabsf(x[i]); } inline static void ggml_vec_sgn_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? 1.f : ((x[i] < 0.f) ? -1.f : 0.f); } inline static void ggml_vec_step_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? 1.f : 0.f; } inline static void ggml_vec_tanh_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = tanhf(x[i]); } inline static void ggml_vec_elu_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? x[i] : expf(x[i])-1; } inline static void ggml_vec_relu_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? x[i] : 0.f; } 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)); } 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 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 __m512i e = _mm512_slli_epi32(_mm512_castps_si512(z), 23); const __m512 k = _mm512_castsi512_ps(_mm512_add_epi32(e, _mm512_castps_si512(_mm512_set1_ps(1)))); const __mmask16 c = _mm512_cmp_ps_mask(_mm512_abs_ps(n), _mm512_set1_ps(126), _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_mul_ps(_mm512_set1_ps(0x1.ffffecp-1f), b)); if (_mm512_kortestz(c, c)) return _mm512_fmadd_ps(j, k, k); const __m512i g = _mm512_and_si512( _mm512_movm_epi32(_mm512_cmp_ps_mask(n, _mm512_setzero_ps(), _CMP_LE_OQ)), _mm512_set1_epi32(0x82000000u)); const __m512 s1 = _mm512_castsi512_ps(_mm512_add_epi32(g, _mm512_set1_epi32(0x7f000000u))); const __m512 s2 = _mm512_castsi512_ps(_mm512_sub_epi32(e, g)); const __mmask16 d = _mm512_cmp_ps_mask(_mm512_abs_ps(n), _mm512_set1_ps(192), _CMP_GT_OQ); return _mm512_mask_blend_ps( d, _mm512_mask_blend_ps( c, _mm512_fmadd_ps(k, j, k), _mm512_mul_ps(_mm512_fmadd_ps(s2, j, s2), s1)), _mm512_mul_ps(s1, s1)); } // 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; } 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; } // // data types // static const char * GGML_OP_NAME[GGML_OP_COUNT] = { "NONE", "DUP", "ADD", "ADD1", "ACC", "SUB", "MUL", "DIV", "SQR", "SQRT", "LOG", "SUM", "SUM_ROWS", "MEAN", "ARGMAX", "REPEAT", "REPEAT_BACK", "CONCAT", "SILU_BACK", "NORM", "RMS_NORM", "RMS_NORM_BACK", "GROUP_NORM", "MUL_MAT", "MUL_MAT_ID", "OUT_PROD", "SCALE", "SET", "CPY", "CONT", "RESHAPE", "VIEW", "PERMUTE", "TRANSPOSE", "GET_ROWS", "GET_ROWS_BACK", "DIAG", "DIAG_MASK_INF", "DIAG_MASK_ZERO", "SOFT_MAX", "SOFT_MAX_BACK", "ROPE", "ROPE_BACK", "CLAMP", "CONV_TRANSPOSE_1D", "IM2COL", "CONV_TRANSPOSE_2D", "POOL_1D", "POOL_2D", "UPSCALE", "PAD", "ARANGE", "TIMESTEP_EMBEDDING", "ARGSORT", "LEAKY_RELU", "FLASH_ATTN_EXT", "FLASH_ATTN_BACK", "SSM_CONV", "SSM_SCAN", "WIN_PART", "WIN_UNPART", "GET_REL_POS", "ADD_REL_POS", "UNARY", "MAP_UNARY", "MAP_BINARY", "MAP_CUSTOM1_F32", "MAP_CUSTOM2_F32", "MAP_CUSTOM3_F32", "MAP_CUSTOM1", "MAP_CUSTOM2", "MAP_CUSTOM3", "CROSS_ENTROPY_LOSS", "CROSS_ENTROPY_LOSS_BACK", }; static_assert(GGML_OP_COUNT == 74, "GGML_OP_COUNT != 74"); static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = { "none", "x", "x+y", "x+y", "view(x,nb,offset)+=y->x", "x-y", "x*y", "x/y", "x^2", "√x", "log(x)", "Σx", "Σx_k", "Σx/n", "argmax(x)", "repeat(x)", "repeat_back(x)", "concat(x, y)", "silu_back(x)", "norm(x)", "rms_norm(x)", "rms_norm_back(x)", "group_norm(x)", "X*Y", "X[i]*Y", "X*Y", "x*v", "y-\\>view(x)", "x-\\>y", "cont(x)", "reshape(x)", "view(x)", "permute(x)", "transpose(x)", "get_rows(x)", "get_rows_back(x)", "diag(x)", "diag_mask_inf(x)", "diag_mask_zero(x)", "soft_max(x)", "soft_max_back(x)", "rope(x)", "rope_back(x)", "clamp(x)", "conv_transpose_1d(x)", "im2col(x)", "conv_transpose_2d(x)", "pool_1d(x)", "pool_2d(x)", "upscale(x)", "pad(x)", "arange(start, stop, step)", "timestep_embedding(timesteps, dim, max_period)", "argsort(x)", "leaky_relu(x)", "flash_attn_ext(x)", "flash_attn_back(x)", "ssm_conv(x)", "ssm_scan(x)", "win_part(x)", "win_unpart(x)", "get_rel_pos(x)", "add_rel_pos(x)", "unary(x)", "f(x)", "f(x,y)", "custom_f32(x)", "custom_f32(x,y)", "custom_f32(x,y,z)", "custom(x)", "custom(x,y)", "custom(x,y,z)", "cross_entropy_loss(x,y)", "cross_entropy_loss_back(x,y)", }; static_assert(GGML_OP_COUNT == 74, "GGML_OP_COUNT != 74"); static_assert(GGML_OP_POOL_COUNT == 2, "GGML_OP_POOL_COUNT != 2"); static const char * GGML_UNARY_OP_NAME[GGML_UNARY_OP_COUNT] = { "ABS", "SGN", "NEG", "STEP", "TANH", "ELU", "RELU", "SIGMOID", "GELU", "GELU_QUICK", "SILU", "HARDSWISH", "HARDSIGMOID", }; static_assert(GGML_UNARY_OP_COUNT == 13, "GGML_UNARY_OP_COUNT != 13"); static_assert(sizeof(struct ggml_object)%GGML_MEM_ALIGN == 0, "ggml_object size must be a multiple of GGML_MEM_ALIGN"); static_assert(sizeof(struct ggml_tensor)%GGML_MEM_ALIGN == 0, "ggml_tensor size must be a multiple of GGML_MEM_ALIGN"); // WARN: // Mis-configuration can lead to problem that's hard to reason about: // * At best it crash or talks nosense. // * At worst it talks slightly difference but hard to perceive. // // An op has to enable INIT or FINALIZE when any of it's branch needs that pass. // Take care about compile options (e.g., GGML_USE_xxx). static bool GGML_OP_HAS_INIT [GGML_OP_COUNT] = { 0 }; static bool GGML_OP_HAS_FINALIZE[GGML_OP_COUNT] = { 0 }; static void ggml_setup_op_has_task_pass(void) { { // INIT bool * p = GGML_OP_HAS_INIT; p[GGML_OP_ACC ] = true; p[GGML_OP_MUL_MAT ] = true; p[GGML_OP_MUL_MAT_ID ] = true; p[GGML_OP_OUT_PROD ] = true; p[GGML_OP_SET ] = true; p[GGML_OP_GET_ROWS_BACK ] = true; p[GGML_OP_DIAG_MASK_INF ] = true; p[GGML_OP_DIAG_MASK_ZERO ] = true; p[GGML_OP_CONV_TRANSPOSE_1D ] = true; p[GGML_OP_CONV_TRANSPOSE_2D ] = true; p[GGML_OP_FLASH_ATTN_BACK ] = true; p[GGML_OP_CROSS_ENTROPY_LOSS ] = true; p[GGML_OP_ADD_REL_POS ] = true; } { // FINALIZE bool * p = GGML_OP_HAS_FINALIZE; p[GGML_OP_CROSS_ENTROPY_LOSS ] = true; } } // // 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_context_container contexts[GGML_MAX_CONTEXTS]; struct ggml_numa_nodes numa; }; // global state static struct ggml_state g_state; static atomic_int g_state_barrier = 0; // barrier via spin lock inline static void ggml_critical_section_start(void) { int processing = atomic_fetch_add(&g_state_barrier, 1); while (processing > 0) { // wait for other threads to finish atomic_fetch_sub(&g_state_barrier, 1); sched_yield(); // TODO: reconsider this processing = atomic_fetch_add(&g_state_barrier, 1); } } // TODO: make this somehow automatically executed // some sort of "sentry" mechanism inline static void ggml_critical_section_end(void) { atomic_fetch_sub(&g_state_barrier, 1); } #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_PRINT("WARNING: /proc/sys/kernel/numa_balancing is enabled, this has been observed to impair performance\n"); } fclose(fptr); } } #else GGML_UNUSED(numa_flag); // TODO #endif } bool ggml_is_numa(void) { return g_state.numa.n_nodes > 1; } //////////////////////////////////////////////////////////////////////////////// void ggml_print_object(const struct ggml_object * obj) { GGML_PRINT(" - ggml_object: type = %d, offset = %zu, size = %zu, next = %p\n", obj->type, obj->offs, obj->size, (const void *) obj->next); } void ggml_print_objects(const struct ggml_context * ctx) { struct ggml_object * obj = ctx->objects_begin; GGML_PRINT("%s: objects in context %p:\n", __func__, (const void *) ctx); while (obj != NULL) { ggml_print_object(obj); obj = obj->next; } GGML_PRINT("%s: --- end ---\n", __func__); } GGML_CALL int64_t ggml_nelements(const struct ggml_tensor * tensor) { static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); return tensor->ne[0]*tensor->ne[1]*tensor->ne[2]*tensor->ne[3]; } GGML_CALL int64_t ggml_nrows(const struct ggml_tensor * tensor) { static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); return tensor->ne[1]*tensor->ne[2]*tensor->ne[3]; } GGML_CALL size_t ggml_nbytes(const struct ggml_tensor * tensor) { size_t nbytes; size_t blck_size = ggml_blck_size(tensor->type); if (blck_size == 1) { nbytes = ggml_type_size(tensor->type); for (int i = 0; i < GGML_MAX_DIMS; ++i) { nbytes += (tensor->ne[i] - 1)*tensor->nb[i]; } } else { nbytes = tensor->ne[0]*tensor->nb[0]/blck_size; for (int i = 1; i < GGML_MAX_DIMS; ++i) { nbytes += (tensor->ne[i] - 1)*tensor->nb[i]; } } return nbytes; } size_t ggml_nbytes_pad(const struct ggml_tensor * tensor) { return GGML_PAD(ggml_nbytes(tensor), GGML_MEM_ALIGN); } GGML_CALL int ggml_blck_size(enum ggml_type type) { return type_traits[type].blck_size; } GGML_CALL size_t ggml_type_size(enum ggml_type type) { return type_traits[type].type_size; } GGML_CALL size_t ggml_row_size(enum ggml_type type, int64_t ne) { assert(ne % ggml_blck_size(type) == 0); return ggml_type_size(type)*ne/ggml_blck_size(type); } double ggml_type_sizef(enum ggml_type type) { return ((double)(type_traits[type].type_size))/type_traits[type].blck_size; } GGML_CALL const char * ggml_type_name(enum ggml_type type) { return type_traits[type].type_name; } GGML_CALL bool ggml_is_quantized(enum ggml_type type) { return type_traits[type].is_quantized; } GGML_CALL const char * ggml_op_name(enum ggml_op op) { return GGML_OP_NAME[op]; } const char * ggml_op_symbol(enum ggml_op op) { return GGML_OP_SYMBOL[op]; } const char * ggml_unary_op_name(enum ggml_unary_op op) { return GGML_UNARY_OP_NAME[op]; } GGML_CALL const char * ggml_op_desc(const struct ggml_tensor * t) { if (t->op == GGML_OP_UNARY) { enum ggml_unary_op uop = ggml_get_unary_op(t); return ggml_unary_op_name(uop); } else { return ggml_op_name(t->op); } } GGML_CALL size_t ggml_element_size(const struct ggml_tensor * tensor) { return ggml_type_size(tensor->type); } bool ggml_is_scalar(const struct ggml_tensor * tensor) { static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); return tensor->ne[0] == 1 && tensor->ne[1] == 1 && tensor->ne[2] == 1 && tensor->ne[3] == 1; } bool ggml_is_vector(const struct ggml_tensor * tensor) { static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); return tensor->ne[1] == 1 && tensor->ne[2] == 1 && tensor->ne[3] == 1; } bool ggml_is_matrix(const struct ggml_tensor * tensor) { static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); return tensor->ne[2] == 1 && tensor->ne[3] == 1; } bool ggml_is_3d(const struct ggml_tensor * tensor) { return tensor->ne[3] == 1; } int ggml_n_dims(const struct ggml_tensor * tensor) { for (int i = GGML_MAX_DIMS - 1; i >= 1; --i) { if (tensor->ne[i] > 1) { return i + 1; } } return 1; } static inline bool ggml_can_mul_mat(const struct ggml_tensor * t0, const struct ggml_tensor * t1) { static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); return (t0->ne[0] == t1->ne[0]) && (t1->ne[2]%t0->ne[2] == 0) && // verify t0 is broadcastable (t1->ne[3]%t0->ne[3] == 0); } static inline bool ggml_can_out_prod(const struct ggml_tensor * t0, const struct ggml_tensor * t1) { static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); return (t0->ne[1] == t1->ne[1]) && (t1->ne[2]%t0->ne[2] == 0) && // verify t0 is broadcastable (t1->ne[3]%t0->ne[3] == 0); } enum ggml_type ggml_ftype_to_ggml_type(enum ggml_ftype ftype) { enum ggml_type wtype = GGML_TYPE_COUNT; switch (ftype) { case GGML_FTYPE_ALL_F32: wtype = GGML_TYPE_F32; break; case GGML_FTYPE_MOSTLY_F16: wtype = GGML_TYPE_F16; break; case GGML_FTYPE_MOSTLY_BF16: wtype = GGML_TYPE_BF16; break; case GGML_FTYPE_MOSTLY_Q4_0: wtype = GGML_TYPE_Q4_0; break; case GGML_FTYPE_MOSTLY_Q4_1: wtype = GGML_TYPE_Q4_1; break; case GGML_FTYPE_MOSTLY_Q5_0: wtype = GGML_TYPE_Q5_0; break; case GGML_FTYPE_MOSTLY_Q5_1: wtype = GGML_TYPE_Q5_1; break; case GGML_FTYPE_MOSTLY_Q8_0: wtype = GGML_TYPE_Q8_0; break; case GGML_FTYPE_MOSTLY_Q2_K: wtype = GGML_TYPE_Q2_K; break; case GGML_FTYPE_MOSTLY_Q3_K: wtype = GGML_TYPE_Q3_K; break; case GGML_FTYPE_MOSTLY_Q4_K: wtype = GGML_TYPE_Q4_K; break; case GGML_FTYPE_MOSTLY_Q5_K: wtype = GGML_TYPE_Q5_K; break; case GGML_FTYPE_MOSTLY_Q6_K: wtype = GGML_TYPE_Q6_K; break; case GGML_FTYPE_MOSTLY_IQ2_XXS: wtype = GGML_TYPE_IQ2_XXS; break; case GGML_FTYPE_MOSTLY_IQ2_XS: wtype = GGML_TYPE_IQ2_XS; break; case GGML_FTYPE_MOSTLY_IQ3_XXS: wtype = GGML_TYPE_IQ3_XXS; break; case GGML_FTYPE_MOSTLY_IQ1_S: wtype = GGML_TYPE_IQ1_S; break; case GGML_FTYPE_MOSTLY_IQ1_M: wtype = GGML_TYPE_IQ1_M; break; case GGML_FTYPE_MOSTLY_IQ4_NL: wtype = GGML_TYPE_IQ4_NL; break; case GGML_FTYPE_MOSTLY_IQ4_XS: wtype = GGML_TYPE_IQ4_XS; break; case GGML_FTYPE_MOSTLY_IQ3_S: wtype = GGML_TYPE_IQ3_S; break; case GGML_FTYPE_MOSTLY_IQ2_S: wtype = GGML_TYPE_IQ2_S; break; case GGML_FTYPE_UNKNOWN: wtype = GGML_TYPE_COUNT; break; case GGML_FTYPE_MOSTLY_Q4_1_SOME_F16: wtype = GGML_TYPE_COUNT; break; } GGML_ASSERT(wtype != GGML_TYPE_COUNT); return wtype; } size_t ggml_tensor_overhead(void) { return GGML_OBJECT_SIZE + GGML_TENSOR_SIZE; } GGML_CALL bool ggml_is_transposed(const struct ggml_tensor * tensor) { return tensor->nb[0] > tensor->nb[1]; } GGML_CALL bool ggml_is_contiguous(const struct ggml_tensor * tensor) { static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); return tensor->nb[0] == ggml_type_size(tensor->type) && tensor->nb[1] == (tensor->nb[0]*tensor->ne[0])/ggml_blck_size(tensor->type) && tensor->nb[2] == tensor->nb[1]*tensor->ne[1] && tensor->nb[3] == tensor->nb[2]*tensor->ne[2]; } static inline bool ggml_is_contiguous_except_dim_1(const struct ggml_tensor * tensor) { static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); return tensor->nb[0] == ggml_type_size(tensor->type) && tensor->nb[2] == tensor->nb[1]*tensor->ne[1] && tensor->nb[3] == tensor->nb[2]*tensor->ne[2]; } GGML_CALL bool ggml_is_permuted(const struct ggml_tensor * tensor) { static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); return tensor->nb[0] > tensor->nb[1] || tensor->nb[1] > tensor->nb[2] || tensor->nb[2] > tensor->nb[3]; } static inline bool ggml_is_padded_1d(const struct ggml_tensor * tensor) { static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); return tensor->nb[0] == ggml_type_size(tensor->type) && tensor->nb[2] == tensor->nb[1]*tensor->ne[1] && tensor->nb[3] == tensor->nb[2]*tensor->ne[2]; } GGML_CALL bool ggml_is_empty(const struct ggml_tensor * tensor) { for (int i = 0; i < GGML_MAX_DIMS; ++i) { if (tensor->ne[i] == 0) { // empty if any dimension has no elements return true; } } return false; } bool ggml_are_same_shape(const struct ggml_tensor * t0, const struct ggml_tensor * t1) { static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); return (t0->ne[0] == t1->ne[0] ) && (t0->ne[1] == t1->ne[1] ) && (t0->ne[2] == t1->ne[2] ) && (t0->ne[3] == t1->ne[3] ); } bool ggml_are_same_stride(const struct ggml_tensor * t0, const struct ggml_tensor * t1) { static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); return (t0->nb[0] == t1->nb[0] ) && (t0->nb[1] == t1->nb[1] ) && (t0->nb[2] == t1->nb[2] ) && (t0->nb[3] == t1->nb[3] ); } // check if t1 can be represented as a repeatition of t0 static inline bool ggml_can_repeat(const struct ggml_tensor * t0, const struct ggml_tensor * t1) { static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); return ggml_is_empty(t0) ? ggml_is_empty(t1) : (t1->ne[0]%t0->ne[0] == 0) && (t1->ne[1]%t0->ne[1] == 0) && (t1->ne[2]%t0->ne[2] == 0) && (t1->ne[3]%t0->ne[3] == 0); } static inline bool ggml_can_repeat_rows(const struct ggml_tensor * t0, const struct ggml_tensor * t1) { static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); return (t0->ne[0] == t1->ne[0]) && ggml_can_repeat(t0, t1); } static inline int ggml_up32(int n) { return (n + 31) & ~31; } //static inline int ggml_up64(int n) { // return (n + 63) & ~63; //} static inline int ggml_up(int n, int m) { // assert m is a power of 2 GGML_ASSERT((m & (m - 1)) == 0); return (n + m - 1) & ~(m - 1); } // assert that pointer is aligned to GGML_MEM_ALIGN #define ggml_assert_aligned(ptr) \ GGML_ASSERT(((uintptr_t) (ptr))%GGML_MEM_ALIGN == 0) //////////////////////////////////////////////////////////////////////////////// struct ggml_context * ggml_init(struct ggml_init_params params) { // make this function thread safe ggml_critical_section_start(); static bool is_first_call = true; if (is_first_call) { // initialize time system (required on Windows) ggml_time_init(); // initialize GELU, Quick GELU, SILU and EXP F32 tables { const uint64_t t_start = ggml_time_us(); UNUSED(t_start); for (int i = 0; i < (1 << 16); ++i) { union { uint16_t u16; ggml_fp16_t fp16; } u = {i}; float f = ggml_table_f32_f16[i] = GGML_COMPUTE_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)/1000.0f); } // initialize g_state { const uint64_t t_start = ggml_time_us(); UNUSED(t_start); g_state = (struct ggml_state) { /*.contexts =*/ { { 0 } }, /*.numa =*/ { .n_nodes = 0, .total_cpus = 0, }, }; for (int i = 0; i < GGML_MAX_CONTEXTS; ++i) { g_state.contexts[i].used = false; } const uint64_t t_end = ggml_time_us(); UNUSED(t_end); GGML_PRINT_DEBUG("%s: g_state initialized in %f ms\n", __func__, (t_end - t_start)/1000.0f); } #if defined(GGML_USE_CLBLAST) ggml_cl_init(); #endif ggml_setup_op_has_task_pass(); is_first_call = false; } // find non-used context in g_state struct ggml_context * ctx = NULL; for (int i = 0; i < GGML_MAX_CONTEXTS; i++) { if (!g_state.contexts[i].used) { g_state.contexts[i].used = true; ctx = &g_state.contexts[i].context; GGML_PRINT_DEBUG("%s: found unused context %d\n", __func__, i); break; } } if (ctx == NULL) { GGML_PRINT_DEBUG("%s: no unused context found\n", __func__); ggml_critical_section_end(); return NULL; } // allow to call ggml_init with 0 size if (params.mem_size == 0) { params.mem_size = GGML_MEM_ALIGN; } const size_t mem_size = params.mem_buffer ? params.mem_size : GGML_PAD(params.mem_size, GGML_MEM_ALIGN); *ctx = (struct ggml_context) { /*.mem_size =*/ mem_size, /*.mem_buffer =*/ params.mem_buffer ? params.mem_buffer : GGML_ALIGNED_MALLOC(mem_size), /*.mem_buffer_owned =*/ params.mem_buffer ? false : true, /*.no_alloc =*/ params.no_alloc, /*.no_alloc_save =*/ params.no_alloc, /*.n_objects =*/ 0, /*.objects_begin =*/ NULL, /*.objects_end =*/ NULL, /*.scratch =*/ { 0, 0, NULL, }, /*.scratch_save =*/ { 0, 0, NULL, }, }; GGML_ASSERT(ctx->mem_buffer != NULL); ggml_assert_aligned(ctx->mem_buffer); GGML_PRINT_DEBUG("%s: context initialized\n", __func__); ggml_critical_section_end(); return ctx; } void ggml_free(struct ggml_context * ctx) { if (ctx == NULL) { return; } // make this function thread safe ggml_critical_section_start(); bool found = false; for (int i = 0; i < GGML_MAX_CONTEXTS; i++) { if (&g_state.contexts[i].context == ctx) { g_state.contexts[i].used = false; GGML_PRINT_DEBUG("%s: context %d has been freed. memory used = %zu\n", __func__, i, ggml_used_mem(ctx)); if (ctx->mem_buffer_owned) { GGML_ALIGNED_FREE(ctx->mem_buffer); } found = true; break; } } if (!found) { GGML_PRINT_DEBUG("%s: context not found\n", __func__); } ggml_critical_section_end(); } size_t ggml_used_mem(const struct ggml_context * ctx) { return ctx->objects_end == NULL ? 0 : ctx->objects_end->offs + ctx->objects_end->size; } size_t ggml_set_scratch(struct ggml_context * ctx, struct ggml_scratch scratch) { const size_t result = ctx->scratch.data ? ctx->scratch.offs : 0; ctx->scratch = scratch; return result; } bool ggml_get_no_alloc(struct ggml_context * ctx) { return ctx->no_alloc; } void ggml_set_no_alloc(struct ggml_context * ctx, bool no_alloc) { ctx->no_alloc = no_alloc; } void * ggml_get_mem_buffer(const struct ggml_context * ctx) { return ctx->mem_buffer; } size_t ggml_get_mem_size(const struct ggml_context * ctx) { return ctx->mem_size; } size_t ggml_get_max_tensor_size(const struct ggml_context * ctx) { size_t max_size = 0; for (struct ggml_tensor * tensor = ggml_get_first_tensor(ctx); tensor != NULL; tensor = ggml_get_next_tensor(ctx, tensor)) { size_t bytes = ggml_nbytes(tensor); max_size = MAX(max_size, bytes); } return max_size; } // IMPORTANT: // when creating "opt" tensors, always save and load the scratch buffer // this is an error prone process, but it is necessary to support inplace // operators when using scratch buffers // TODO: implement a better way static void ggml_scratch_save(struct ggml_context * ctx) { // this is needed to allow opt tensors to store their data // TODO: again, need to find a better way ctx->no_alloc_save = ctx->no_alloc; ctx->no_alloc = false; ctx->scratch_save = ctx->scratch; ctx->scratch.data = NULL; } static void ggml_scratch_load(struct ggml_context * ctx) { ctx->no_alloc = ctx->no_alloc_save; ctx->scratch = ctx->scratch_save; } //////////////////////////////////////////////////////////////////////////////// static struct ggml_object * ggml_new_object(struct ggml_context * ctx, enum ggml_object_type type, size_t size) { // always insert objects at the end of the context's memory pool struct ggml_object * obj_cur = ctx->objects_end; const size_t cur_offs = obj_cur == NULL ? 0 : obj_cur->offs; const size_t cur_size = obj_cur == NULL ? 0 : obj_cur->size; const size_t cur_end = cur_offs + cur_size; // align to GGML_MEM_ALIGN size_t size_needed = GGML_PAD(size, GGML_MEM_ALIGN); char * const mem_buffer = ctx->mem_buffer; struct ggml_object * const obj_new = (struct ggml_object *)(mem_buffer + cur_end); if (cur_end + size_needed + GGML_OBJECT_SIZE > ctx->mem_size) { GGML_PRINT("%s: not enough space in the context's memory pool (needed %zu, available %zu)\n", __func__, cur_end + size_needed, ctx->mem_size); assert(false); return NULL; } *obj_new = (struct ggml_object) { .offs = cur_end + GGML_OBJECT_SIZE, .size = size_needed, .next = NULL, .type = type, }; ggml_assert_aligned(mem_buffer + obj_new->offs); if (obj_cur != NULL) { obj_cur->next = obj_new; } else { // this is the first object in this context ctx->objects_begin = obj_new; } ctx->objects_end = obj_new; //printf("%s: inserted new object at %zu, size = %zu\n", __func__, cur_end, obj_new->size); return obj_new; } static struct ggml_tensor * ggml_new_tensor_impl( struct ggml_context * ctx, enum ggml_type type, int n_dims, const int64_t * ne, struct ggml_tensor * view_src, size_t view_offs) { assert(n_dims >= 1 && n_dims <= GGML_MAX_DIMS); // find the base tensor and absolute offset if (view_src != NULL && view_src->view_src != NULL) { view_offs += view_src->view_offs; view_src = view_src->view_src; } size_t data_size = ggml_row_size(type, ne[0]); for (int i = 1; i < n_dims; i++) { data_size *= ne[i]; } GGML_ASSERT(view_src == NULL || data_size == 0 || data_size + view_offs <= ggml_nbytes(view_src)); void * data = view_src != NULL ? view_src->data : NULL; if (data != NULL) { data = (char *) data + view_offs; } size_t obj_alloc_size = 0; if (view_src == NULL && !ctx->no_alloc) { if (ctx->scratch.data != NULL) { // allocate tensor data in the scratch buffer if (ctx->scratch.offs + data_size > ctx->scratch.size) { GGML_PRINT("%s: not enough space in the scratch memory pool (needed %zu, available %zu)\n", __func__, ctx->scratch.offs + data_size, ctx->scratch.size); assert(false); return NULL; } data = (char * const) ctx->scratch.data + ctx->scratch.offs; ctx->scratch.offs += data_size; } else { // allocate tensor data in the context's memory pool obj_alloc_size = data_size; } } struct ggml_object * const obj_new = ggml_new_object(ctx, GGML_OBJECT_TYPE_TENSOR, GGML_TENSOR_SIZE + obj_alloc_size); // TODO: for recoverable errors, we would need to free the data allocated from the scratch buffer here struct ggml_tensor * const result = (struct ggml_tensor *)((char *)ctx->mem_buffer + obj_new->offs); #ifdef __clang__ // temporary until ggml_tensor::backend is removed #pragma clang diagnostic push #pragma clang diagnostic ignored "-Wdeprecated-declarations" #endif *result = (struct ggml_tensor) { /*.type =*/ type, /*.backend =*/ GGML_BACKEND_TYPE_CPU, /*.buffer =*/ NULL, /*.ne =*/ { 1, 1, 1, 1 }, /*.nb =*/ { 0, 0, 0, 0 }, /*.op =*/ GGML_OP_NONE, /*.op_params =*/ { 0 }, /*.flags =*/ 0, /*.grad =*/ NULL, /*.src =*/ { NULL }, /*.perf_runs =*/ 0, /*.perf_cycles =*/ 0, /*.perf_time_us =*/ 0, /*.view_src =*/ view_src, /*.view_offs =*/ view_offs, /*.data =*/ obj_alloc_size > 0 ? (void *)(result + 1) : data, /*.name =*/ { 0 }, /*.extra =*/ NULL, /*.padding =*/ { 0 }, }; #ifdef __clang__ #pragma clang diagnostic pop #endif // TODO: this should not be needed as long as we don't rely on aligned SIMD loads //ggml_assert_aligned(result->data); for (int i = 0; i < n_dims; i++) { result->ne[i] = ne[i]; } result->nb[0] = ggml_type_size(type); result->nb[1] = result->nb[0]*(result->ne[0]/ggml_blck_size(type)); for (int i = 2; i < GGML_MAX_DIMS; i++) { result->nb[i] = result->nb[i - 1]*result->ne[i - 1]; } ctx->n_objects++; return result; } struct ggml_tensor * ggml_new_tensor( struct ggml_context * ctx, enum ggml_type type, int n_dims, const int64_t * ne) { return ggml_new_tensor_impl(ctx, type, n_dims, ne, NULL, 0); } struct ggml_tensor * ggml_new_tensor_1d( struct ggml_context * ctx, enum ggml_type type, int64_t ne0) { return ggml_new_tensor(ctx, type, 1, &ne0); } struct ggml_tensor * ggml_new_tensor_2d( struct ggml_context * ctx, enum ggml_type type, int64_t ne0, int64_t ne1) { const int64_t ne[2] = { ne0, ne1 }; return ggml_new_tensor(ctx, type, 2, ne); } struct ggml_tensor * ggml_new_tensor_3d( struct ggml_context * ctx, enum ggml_type type, int64_t ne0, int64_t ne1, int64_t ne2) { const int64_t ne[3] = { ne0, ne1, ne2 }; return ggml_new_tensor(ctx, type, 3, ne); } struct ggml_tensor * ggml_new_tensor_4d( struct ggml_context * ctx, enum ggml_type type, int64_t ne0, int64_t ne1, int64_t ne2, int64_t ne3) { const int64_t ne[4] = { ne0, ne1, ne2, ne3 }; return ggml_new_tensor(ctx, type, 4, ne); } struct ggml_tensor * ggml_new_i32(struct ggml_context * ctx, int32_t value) { ggml_scratch_save(ctx); struct ggml_tensor * result = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 1); ggml_scratch_load(ctx); ggml_set_i32(result, value); return result; } struct ggml_tensor * ggml_new_f32(struct ggml_context * ctx, float value) { ggml_scratch_save(ctx); struct ggml_tensor * result = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 1); ggml_scratch_load(ctx); ggml_set_f32(result, value); return result; } struct ggml_tensor * ggml_dup_tensor(struct ggml_context * ctx, const struct ggml_tensor * src) { return ggml_new_tensor(ctx, src->type, GGML_MAX_DIMS, src->ne); } static void ggml_set_op_params(struct ggml_tensor * tensor, const void * params, size_t params_size) { GGML_ASSERT(tensor != NULL); // silence -Warray-bounds warnings assert(params_size <= GGML_MAX_OP_PARAMS); memcpy(tensor->op_params, params, params_size); } static int32_t ggml_get_op_params_i32(const struct ggml_tensor * tensor, uint32_t i) { assert(i < GGML_MAX_OP_PARAMS / sizeof(int32_t)); return ((const int32_t *)(tensor->op_params))[i]; } static float ggml_get_op_params_f32(const struct ggml_tensor * tensor, uint32_t i) { assert(i < GGML_MAX_OP_PARAMS / sizeof(float)); return ((const float *)(tensor->op_params))[i]; } static void ggml_set_op_params_i32(struct ggml_tensor * tensor, uint32_t i, int32_t value) { assert(i < GGML_MAX_OP_PARAMS / sizeof(int32_t)); ((int32_t *)(tensor->op_params))[i] = value; } static void ggml_set_op_params_f32(struct ggml_tensor * tensor, uint32_t i, float value) { assert(i < GGML_MAX_OP_PARAMS / sizeof(float)); ((float *)(tensor->op_params))[i] = value; } struct ggml_tensor * ggml_set_zero(struct ggml_tensor * tensor) { memset(tensor->data, 0, ggml_nbytes(tensor)); return tensor; } struct ggml_tensor * ggml_set_i32 (struct ggml_tensor * tensor, int32_t value) { const int n = ggml_nrows(tensor); const int nc = tensor->ne[0]; const size_t n1 = tensor->nb[1]; char * const data = tensor->data; switch (tensor->type) { case GGML_TYPE_I8: { assert(tensor->nb[0] == sizeof(int8_t)); for (int i = 0; i < n; i++) { ggml_vec_set_i8(nc, (int8_t *)(data + i*n1), value); } } break; case GGML_TYPE_I16: { assert(tensor->nb[0] == sizeof(int16_t)); for (int i = 0; i < n; i++) { ggml_vec_set_i16(nc, (int16_t *)(data + i*n1), value); } } break; case GGML_TYPE_I32: { assert(tensor->nb[0] == sizeof(int32_t)); for (int i = 0; i < n; i++) { ggml_vec_set_i32(nc, (int32_t *)(data + i*n1), value); } } break; case GGML_TYPE_F16: { assert(tensor->nb[0] == sizeof(ggml_fp16_t)); for (int i = 0; i < n; i++) { ggml_vec_set_f16(nc, (ggml_fp16_t *)(data + i*n1), GGML_FP32_TO_FP16(value)); } } break; case GGML_TYPE_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_ASSERT(false); } break; } return tensor; } struct ggml_tensor * ggml_set_f32(struct ggml_tensor * tensor, float value) { const int n = ggml_nrows(tensor); const int nc = tensor->ne[0]; const size_t n1 = tensor->nb[1]; char * const data = tensor->data; switch (tensor->type) { case GGML_TYPE_I8: { assert(tensor->nb[0] == sizeof(int8_t)); for (int i = 0; i < n; i++) { ggml_vec_set_i8(nc, (int8_t *)(data + i*n1), value); } } break; case GGML_TYPE_I16: { assert(tensor->nb[0] == sizeof(int16_t)); for (int i = 0; i < n; i++) { ggml_vec_set_i16(nc, (int16_t *)(data + i*n1), value); } } break; case GGML_TYPE_I32: { assert(tensor->nb[0] == sizeof(int32_t)); for (int i = 0; i < n; i++) { ggml_vec_set_i32(nc, (int32_t *)(data + i*n1), value); } } break; case GGML_TYPE_F16: { assert(tensor->nb[0] == sizeof(ggml_fp16_t)); for (int i = 0; i < n; i++) { ggml_vec_set_f16(nc, (ggml_fp16_t *)(data + i*n1), GGML_FP32_TO_FP16(value)); } } break; case GGML_TYPE_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_ASSERT(false); } break; } return tensor; } void ggml_unravel_index(const struct ggml_tensor * tensor, int64_t i, int64_t * i0, int64_t * i1, int64_t * i2, int64_t * i3) { const int64_t ne2 = tensor->ne[2]; const int64_t ne1 = tensor->ne[1]; const int64_t ne0 = tensor->ne[0]; const int64_t i3_ = (i/(ne2*ne1*ne0)); const int64_t i2_ = (i - i3_*ne2*ne1*ne0)/(ne1*ne0); const int64_t i1_ = (i - i3_*ne2*ne1*ne0 - i2_*ne1*ne0)/ne0; const int64_t i0_ = (i - i3_*ne2*ne1*ne0 - i2_*ne1*ne0 - i1_*ne0); if (i0) { * i0 = i0_; } if (i1) { * i1 = i1_; } if (i2) { * i2 = i2_; } if (i3) { * i3 = i3_; } } 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_ASSERT(false); } } return 0.0f; } 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_ASSERT(false); } break; } } 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_ASSERT(false); } return 0.0f; } 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_ASSERT(false); } break; } } 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: { 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_ASSERT(false); } } return 0.0f; } 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: { 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_ASSERT(false); } break; } } 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_ASSERT(false); } return 0.0f; } 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_ASSERT(false); } break; } } void * ggml_get_data(const struct ggml_tensor * tensor) { return tensor->data; } float * ggml_get_data_f32(const struct ggml_tensor * tensor) { assert(tensor->type == GGML_TYPE_F32); return (float *)(tensor->data); } GGML_CALL enum ggml_unary_op ggml_get_unary_op(const struct ggml_tensor * tensor) { GGML_ASSERT(tensor->op == GGML_OP_UNARY); return (enum ggml_unary_op) ggml_get_op_params_i32(tensor, 0); } const char * ggml_get_name(const struct ggml_tensor * tensor) { return tensor->name; } struct ggml_tensor * ggml_set_name(struct ggml_tensor * tensor, const char * name) { strncpy(tensor->name, name, sizeof(tensor->name) - 1); tensor->name[sizeof(tensor->name) - 1] = '\0'; return tensor; } struct ggml_tensor * ggml_format_name(struct ggml_tensor * tensor, const char * fmt, ...) { va_list args; va_start(args, fmt); vsnprintf(tensor->name, sizeof(tensor->name), fmt, args); va_end(args); return tensor; } struct ggml_tensor * ggml_view_tensor( struct ggml_context * ctx, struct ggml_tensor * src) { struct ggml_tensor * result = ggml_new_tensor_impl(ctx, src->type, GGML_MAX_DIMS, src->ne, src, 0); ggml_format_name(result, "%s (view)", src->name); for (int i = 0; i < GGML_MAX_DIMS; i++) { result->nb[i] = src->nb[i]; } return result; } struct ggml_tensor * ggml_get_first_tensor(const struct ggml_context * ctx) { struct ggml_object * obj = ctx->objects_begin; char * const mem_buffer = ctx->mem_buffer; while (obj != NULL) { if (obj->type == GGML_OBJECT_TYPE_TENSOR) { return (struct ggml_tensor *)(mem_buffer + obj->offs); } obj = obj->next; } return NULL; } struct ggml_tensor * ggml_get_next_tensor(const struct ggml_context * ctx, struct ggml_tensor * tensor) { struct ggml_object * obj = (struct ggml_object *) ((char *)tensor - GGML_OBJECT_SIZE); obj = obj->next; char * const mem_buffer = ctx->mem_buffer; while (obj != NULL) { if (obj->type == GGML_OBJECT_TYPE_TENSOR) { return (struct ggml_tensor *)(mem_buffer + obj->offs); } obj = obj->next; } return NULL; } struct ggml_tensor * ggml_get_tensor(struct ggml_context * ctx, const char * name) { struct ggml_object * obj = ctx->objects_begin; char * const mem_buffer = ctx->mem_buffer; while (obj != NULL) { if (obj->type == GGML_OBJECT_TYPE_TENSOR) { struct ggml_tensor * cur = (struct ggml_tensor *)(mem_buffer + obj->offs); if (strcmp(cur->name, name) == 0) { return cur; } } obj = obj->next; } return NULL; } //////////////////////////////////////////////////////////////////////////////// // ggml_dup static struct ggml_tensor * ggml_dup_impl( struct ggml_context * ctx, struct ggml_tensor * a, bool inplace) { bool is_node = false; if (!inplace && (a->grad)) { is_node = true; } struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); result->op = GGML_OP_DUP; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; return result; } struct ggml_tensor * ggml_dup( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_dup_impl(ctx, a, false); } struct ggml_tensor * ggml_dup_inplace( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_dup_impl(ctx, a, true); } // ggml_add static struct ggml_tensor * ggml_add_impl( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, bool inplace) { GGML_ASSERT(ggml_can_repeat(b, a)); bool is_node = false; if (!inplace && (a->grad || b->grad)) { // TODO: support backward pass for broadcasting GGML_ASSERT(ggml_are_same_shape(a, b)); is_node = true; } struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); result->op = GGML_OP_ADD; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; result->src[1] = b; return result; } struct ggml_tensor * ggml_add( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) { return ggml_add_impl(ctx, a, b, false); } struct ggml_tensor * ggml_add_inplace( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) { return ggml_add_impl(ctx, a, b, true); } // ggml_add_cast static struct ggml_tensor * ggml_add_cast_impl( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, enum ggml_type type) { // TODO: support less-strict constraint // GGML_ASSERT(ggml_can_repeat(b, a)); GGML_ASSERT(ggml_can_repeat_rows(b, a)); // currently only supported for quantized input and f16 GGML_ASSERT(ggml_is_quantized(a->type) || a->type == GGML_TYPE_F16 || a->type == GGML_TYPE_BF16); bool is_node = false; if (a->grad || b->grad) { // TODO: support backward pass for broadcasting GGML_ASSERT(ggml_are_same_shape(a, b)); is_node = true; } struct ggml_tensor * result = ggml_new_tensor(ctx, type, GGML_MAX_DIMS, a->ne); result->op = GGML_OP_ADD; result->grad = is_node ? ggml_new_tensor(ctx, GGML_TYPE_F32, GGML_MAX_DIMS, a->ne) : NULL; result->src[0] = a; result->src[1] = b; return result; } struct ggml_tensor * ggml_add_cast( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, enum ggml_type type) { return ggml_add_cast_impl(ctx, a, b, type); } // ggml_add1 static struct ggml_tensor * ggml_add1_impl( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, bool inplace) { GGML_ASSERT(ggml_is_scalar(b)); GGML_ASSERT(ggml_is_padded_1d(a)); bool is_node = false; if (a->grad || b->grad) { is_node = true; } struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); result->op = GGML_OP_ADD1; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; result->src[1] = b; return result; } struct ggml_tensor * ggml_add1( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) { return ggml_add1_impl(ctx, a, b, false); } struct ggml_tensor * ggml_add1_inplace( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) { return ggml_add1_impl(ctx, a, b, true); } // ggml_acc static struct ggml_tensor * ggml_acc_impl( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, size_t nb1, size_t nb2, size_t nb3, size_t offset, bool inplace) { GGML_ASSERT(ggml_nelements(b) <= ggml_nelements(a)); GGML_ASSERT(ggml_is_contiguous(a)); GGML_ASSERT(a->type == GGML_TYPE_F32); GGML_ASSERT(b->type == GGML_TYPE_F32); bool is_node = false; if (!inplace && (a->grad || b->grad)) { is_node = true; } struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); int32_t params[] = { nb1, nb2, nb3, offset, inplace ? 1 : 0 }; ggml_set_op_params(result, params, sizeof(params)); result->op = GGML_OP_ACC; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; result->src[1] = b; return result; } struct ggml_tensor * ggml_acc( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, size_t nb1, size_t nb2, size_t nb3, size_t offset) { return ggml_acc_impl(ctx, a, b, nb1, nb2, nb3, offset, false); } struct ggml_tensor * ggml_acc_inplace( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, size_t nb1, size_t nb2, size_t nb3, size_t offset) { return ggml_acc_impl(ctx, a, b, nb1, nb2, nb3, offset, true); } // ggml_sub static struct ggml_tensor * ggml_sub_impl( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, bool inplace) { GGML_ASSERT(ggml_are_same_shape(a, b)); bool is_node = false; if (!inplace && (a->grad || b->grad)) { is_node = true; } struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); result->op = GGML_OP_SUB; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; result->src[1] = b; return result; } struct ggml_tensor * ggml_sub( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) { return ggml_sub_impl(ctx, a, b, false); } struct ggml_tensor * ggml_sub_inplace( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) { return ggml_sub_impl(ctx, a, b, true); } // ggml_mul static struct ggml_tensor * ggml_mul_impl( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, bool inplace) { GGML_ASSERT(ggml_can_repeat(b, a)); bool is_node = false; if (!inplace && (a->grad || b->grad)) { // TODO: support backward pass for broadcasting GGML_ASSERT(ggml_are_same_shape(a, b)); is_node = true; } if (inplace) { GGML_ASSERT(!is_node); } struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); result->op = GGML_OP_MUL; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; result->src[1] = b; return result; } struct ggml_tensor * ggml_mul( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) { return ggml_mul_impl(ctx, a, b, false); } struct ggml_tensor * ggml_mul_inplace( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) { return ggml_mul_impl(ctx, a, b, true); } // ggml_div static struct ggml_tensor * ggml_div_impl( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, bool inplace) { GGML_ASSERT(ggml_can_repeat(b, a)); bool is_node = false; if (!inplace && (a->grad || b->grad)) { is_node = true; } if (inplace) { GGML_ASSERT(!is_node); } struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); result->op = GGML_OP_DIV; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; result->src[1] = b; return result; } struct ggml_tensor * ggml_div( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) { return ggml_div_impl(ctx, a, b, false); } struct ggml_tensor * ggml_div_inplace( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) { return ggml_div_impl(ctx, a, b, true); } // ggml_sqr static struct ggml_tensor * ggml_sqr_impl( struct ggml_context * ctx, struct ggml_tensor * a, bool inplace) { bool is_node = false; if (!inplace && (a->grad)) { is_node = true; } struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); result->op = GGML_OP_SQR; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; return result; } struct ggml_tensor * ggml_sqr( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_sqr_impl(ctx, a, false); } struct ggml_tensor * ggml_sqr_inplace( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_sqr_impl(ctx, a, true); } // ggml_sqrt static struct ggml_tensor * ggml_sqrt_impl( struct ggml_context * ctx, struct ggml_tensor * a, bool inplace) { bool is_node = false; if (!inplace && (a->grad)) { is_node = true; } struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); result->op = GGML_OP_SQRT; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; return result; } struct ggml_tensor * ggml_sqrt( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_sqrt_impl(ctx, a, false); } struct ggml_tensor * ggml_sqrt_inplace( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_sqrt_impl(ctx, a, true); } // ggml_log static struct ggml_tensor * ggml_log_impl( struct ggml_context * ctx, struct ggml_tensor * a, bool inplace) { bool is_node = false; if (!inplace && (a->grad)) { is_node = true; } struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); result->op = GGML_OP_LOG; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; return result; } struct ggml_tensor * ggml_log( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_log_impl(ctx, a, false); } struct ggml_tensor * ggml_log_inplace( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_log_impl(ctx, a, true); } // ggml_sum struct ggml_tensor * ggml_sum( struct ggml_context * ctx, struct ggml_tensor * a) { bool is_node = false; if (a->grad) { is_node = true; } struct ggml_tensor * result = ggml_new_tensor_1d(ctx, a->type, 1); result->op = GGML_OP_SUM; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; return result; } // ggml_sum_rows struct ggml_tensor * ggml_sum_rows( struct ggml_context * ctx, struct ggml_tensor * a) { bool is_node = false; if (a->grad) { is_node = true; } int64_t ne[GGML_MAX_DIMS] = { 1 }; for (int i = 1; i < GGML_MAX_DIMS; ++i) { ne[i] = a->ne[i]; } struct ggml_tensor * result = ggml_new_tensor(ctx, a->type, GGML_MAX_DIMS, ne); result->op = GGML_OP_SUM_ROWS; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; return result; } // ggml_mean struct ggml_tensor * ggml_mean( struct ggml_context * ctx, struct ggml_tensor * a) { bool is_node = false; if (a->grad) { GGML_ASSERT(false); // TODO: implement is_node = true; } int64_t ne[4] = { 1, a->ne[1], a->ne[2], a->ne[3] }; struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne); result->op = GGML_OP_MEAN; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; return result; } // ggml_argmax struct ggml_tensor * ggml_argmax( struct ggml_context * ctx, struct ggml_tensor * a) { GGML_ASSERT(ggml_is_matrix(a)); bool is_node = false; if (a->grad) { GGML_ASSERT(false); is_node = true; } struct ggml_tensor * result = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, a->ne[1]); result->op = GGML_OP_ARGMAX; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; return result; } // ggml_repeat struct ggml_tensor * ggml_repeat( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) { GGML_ASSERT(ggml_can_repeat(a, b)); bool is_node = false; if (a->grad) { is_node = true; } struct ggml_tensor * result = ggml_new_tensor(ctx, a->type, GGML_MAX_DIMS, b->ne); result->op = GGML_OP_REPEAT; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; return result; } // ggml_repeat_back struct ggml_tensor * ggml_repeat_back( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) { GGML_ASSERT(ggml_can_repeat(b, a)); bool is_node = false; if (a->grad) { is_node = true; } if (ggml_are_same_shape(a, b) && !is_node) { return a; } struct ggml_tensor * result = ggml_new_tensor(ctx, a->type, GGML_MAX_DIMS, b->ne); result->op = GGML_OP_REPEAT_BACK; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; return result; } // ggml_concat struct ggml_tensor * ggml_concat( struct ggml_context* ctx, struct ggml_tensor* a, struct ggml_tensor* b) { GGML_ASSERT(a->ne[0] == b->ne[0] && a->ne[1] == b->ne[1] && a->ne[3] == b->ne[3]); bool is_node = false; if (a->grad || b->grad) { is_node = true; } struct ggml_tensor * result = ggml_new_tensor_4d(ctx, a->type, a->ne[0], a->ne[1], a->ne[2] + b->ne[2], a->ne[3]); result->op = GGML_OP_CONCAT; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; result->src[1] = b; return result; } // ggml_abs struct ggml_tensor * ggml_abs( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_unary(ctx, a, GGML_UNARY_OP_ABS); } struct ggml_tensor * ggml_abs_inplace( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_unary_inplace(ctx, a, GGML_UNARY_OP_ABS); } // ggml_sgn struct ggml_tensor * ggml_sgn( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_unary(ctx, a, GGML_UNARY_OP_SGN); } struct ggml_tensor * ggml_sgn_inplace( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_unary_inplace(ctx, a, GGML_UNARY_OP_SGN); } // ggml_neg struct ggml_tensor * ggml_neg( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_unary(ctx, a, GGML_UNARY_OP_NEG); } struct ggml_tensor * ggml_neg_inplace( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_unary_inplace(ctx, a, GGML_UNARY_OP_NEG); } // ggml_step struct ggml_tensor * ggml_step( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_unary(ctx, a, GGML_UNARY_OP_STEP); } struct ggml_tensor * ggml_step_inplace( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_unary_inplace(ctx, a, GGML_UNARY_OP_STEP); } // ggml_tanh struct ggml_tensor * ggml_tanh( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_unary(ctx, a, GGML_UNARY_OP_TANH); } struct ggml_tensor * ggml_tanh_inplace( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_unary_inplace(ctx, a, GGML_UNARY_OP_TANH); } // ggml_elu struct ggml_tensor * ggml_elu( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_unary(ctx, a, GGML_UNARY_OP_ELU); } struct ggml_tensor * ggml_elu_inplace( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_unary_inplace(ctx, a, GGML_UNARY_OP_ELU); } // ggml_relu struct ggml_tensor * ggml_relu( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_unary(ctx, a, GGML_UNARY_OP_RELU); } struct ggml_tensor * ggml_relu_inplace( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_unary_inplace(ctx, a, GGML_UNARY_OP_RELU); } // ggml_leaky_relu struct ggml_tensor * ggml_leaky_relu( struct ggml_context * ctx, struct ggml_tensor * a, float negative_slope, bool inplace) { bool is_node = false; if (!inplace && (a->grad)) { is_node = true; } struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); ggml_set_op_params(result, &negative_slope, sizeof(negative_slope)); result->op = GGML_OP_LEAKY_RELU; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; return result; } // ggml_sigmoid struct ggml_tensor * ggml_sigmoid( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_unary(ctx, a, GGML_UNARY_OP_SIGMOID); } struct ggml_tensor * ggml_sigmoid_inplace( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_unary_inplace(ctx, a, GGML_UNARY_OP_SIGMOID); } // ggml_gelu struct ggml_tensor * ggml_gelu( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_unary(ctx, a, GGML_UNARY_OP_GELU); } struct ggml_tensor * ggml_gelu_inplace( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_unary_inplace(ctx, a, GGML_UNARY_OP_GELU); } // ggml_gelu_quick struct ggml_tensor * ggml_gelu_quick( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_unary(ctx, a, GGML_UNARY_OP_GELU_QUICK); } struct ggml_tensor * ggml_gelu_quick_inplace( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_unary_inplace(ctx, a, GGML_UNARY_OP_GELU_QUICK); } // ggml_silu struct ggml_tensor * ggml_silu( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_unary(ctx, a, GGML_UNARY_OP_SILU); } struct ggml_tensor * ggml_silu_inplace( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_unary_inplace(ctx, a, GGML_UNARY_OP_SILU); } // ggml_silu_back struct ggml_tensor * ggml_silu_back( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) { bool is_node = false; if (a->grad || b->grad) { // TODO: implement backward is_node = true; } struct ggml_tensor * result = ggml_dup_tensor(ctx, a); result->op = GGML_OP_SILU_BACK; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; result->src[1] = b; return result; } // ggml hardswish struct ggml_tensor * ggml_hardswish( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_unary(ctx, a, GGML_UNARY_OP_HARDSWISH); } // ggml hardsigmoid struct ggml_tensor * ggml_hardsigmoid( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_unary(ctx, a, GGML_UNARY_OP_HARDSIGMOID); } // ggml_norm static struct ggml_tensor * ggml_norm_impl( struct ggml_context * ctx, struct ggml_tensor * a, float eps, bool inplace) { bool is_node = false; if (!inplace && (a->grad)) { GGML_ASSERT(false); // TODO: implement backward is_node = true; } struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); ggml_set_op_params(result, &eps, sizeof(eps)); result->op = GGML_OP_NORM; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; return result; } struct ggml_tensor * ggml_norm( struct ggml_context * ctx, struct ggml_tensor * a, float eps) { return ggml_norm_impl(ctx, a, eps, false); } struct ggml_tensor * ggml_norm_inplace( struct ggml_context * ctx, struct ggml_tensor * a, float eps) { return ggml_norm_impl(ctx, a, eps, true); } // ggml_rms_norm static struct ggml_tensor * ggml_rms_norm_impl( struct ggml_context * ctx, struct ggml_tensor * a, float eps, bool inplace) { bool is_node = false; if (!inplace && (a->grad)) { is_node = true; } struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); ggml_set_op_params(result, &eps, sizeof(eps)); result->op = GGML_OP_RMS_NORM; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; return result; } struct ggml_tensor * ggml_rms_norm( struct ggml_context * ctx, struct ggml_tensor * a, float eps) { return ggml_rms_norm_impl(ctx, a, eps, false); } struct ggml_tensor * ggml_rms_norm_inplace( struct ggml_context * ctx, struct ggml_tensor * a, float eps) { return ggml_rms_norm_impl(ctx, a, eps, true); } // ggml_rms_norm_back struct ggml_tensor * ggml_rms_norm_back( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, float eps) { bool is_node = false; if (a->grad) { // TODO: implement backward is_node = true; } struct ggml_tensor * result = ggml_dup_tensor(ctx, a); ggml_set_op_params(result, &eps, sizeof(eps)); result->op = GGML_OP_RMS_NORM_BACK; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; result->src[1] = b; return result; } // ggml_group_norm static struct ggml_tensor * ggml_group_norm_impl( struct ggml_context * ctx, struct ggml_tensor * a, int n_groups, bool inplace) { bool is_node = false; if (!inplace && (a->grad)) { GGML_ASSERT(false); // TODO: implement backward is_node = true; } struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); result->op_params[0] = n_groups; result->op = GGML_OP_GROUP_NORM; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; return result; } struct ggml_tensor * ggml_group_norm( struct ggml_context * ctx, struct ggml_tensor * a, int n_groups) { return ggml_group_norm_impl(ctx, a, n_groups, false); } struct ggml_tensor * ggml_group_norm_inplace( struct ggml_context * ctx, struct ggml_tensor * a, int n_groups) { return ggml_group_norm_impl(ctx, a, n_groups, true); } // ggml_mul_mat struct ggml_tensor * ggml_mul_mat( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) { GGML_ASSERT(ggml_can_mul_mat(a, b)); GGML_ASSERT(!ggml_is_transposed(a)); bool is_node = false; if (a->grad || b->grad) { is_node = true; } const int64_t ne[4] = { a->ne[1], b->ne[1], b->ne[2], b->ne[3] }; struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne); result->op = GGML_OP_MUL_MAT; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; result->src[1] = b; return result; } void ggml_mul_mat_set_prec( struct ggml_tensor * a, enum ggml_prec prec) { GGML_ASSERT(a->op == GGML_OP_MUL_MAT); const int32_t prec_i32 = (int32_t) prec; ggml_set_op_params_i32(a, 0, prec_i32); } // ggml_mul_mat_id /* c = ggml_mul_mat_id(ctx, as, b, ids); as -> [cols, rows, n_expert] ids -> [n_experts_used, n_tokens] (i32) b -> [cols, n_expert_used, n_tokens] c -> [cols, n_expert_used, n_tokens] in b, n_experts_used can be broadcasted to match the n_expert_used of ids c ~= as[:,:,i] @ b[:,i%r,t], i = ids[e,t] for all e,t in ids */ struct ggml_tensor * ggml_mul_mat_id( struct ggml_context * ctx, struct ggml_tensor * as, struct ggml_tensor * b, struct ggml_tensor * ids) { GGML_ASSERT(!ggml_is_transposed(as)); GGML_ASSERT(ids->type == GGML_TYPE_I32); GGML_ASSERT(as->ne[3] == 1); // as is 3d (one matrix per expert) GGML_ASSERT(b->ne[3] == 1); // b is 3d GGML_ASSERT(ids->ne[2] == 1 && ids->ne[3] == 1); // ids is 2d GGML_ASSERT(ids->ne[1] == b->ne[2]); // must have an expert list per b row GGML_ASSERT(as->ne[0] == b->ne[0]); // can_mul_mat GGML_ASSERT(ids->ne[0] % b->ne[1] == 0); // can broadcast bool is_node = false; if (as->grad || b->grad) { is_node = true; } const int64_t ne[4] = { as->ne[1], ids->ne[0], b->ne[2], 1 }; struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne); result->op = GGML_OP_MUL_MAT_ID; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = as; result->src[1] = b; result->src[2] = ids; return result; } // ggml_out_prod struct ggml_tensor * ggml_out_prod( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) { GGML_ASSERT(ggml_can_out_prod(a, b)); GGML_ASSERT(!ggml_is_transposed(a)); bool is_node = false; if (a->grad || b->grad) { is_node = true; } // a is broadcastable to b for ne[2] and ne[3] -> use b->ne[2] and b->ne[3] const int64_t ne[4] = { a->ne[0], b->ne[0], b->ne[2], b->ne[3] }; struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne); result->op = GGML_OP_OUT_PROD; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; result->src[1] = b; return result; } // ggml_scale static struct ggml_tensor * ggml_scale_impl( struct ggml_context * ctx, struct ggml_tensor * a, float s, bool inplace) { GGML_ASSERT(ggml_is_padded_1d(a)); bool is_node = false; if (a->grad) { is_node = true; } struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); ggml_set_op_params(result, &s, sizeof(s)); result->op = GGML_OP_SCALE; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; return result; } struct ggml_tensor * ggml_scale( struct ggml_context * ctx, struct ggml_tensor * a, float s) { return ggml_scale_impl(ctx, a, s, false); } struct ggml_tensor * ggml_scale_inplace( struct ggml_context * ctx, struct ggml_tensor * a, float s) { return ggml_scale_impl(ctx, a, s, true); } // ggml_set static struct ggml_tensor * ggml_set_impl( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, size_t nb1, size_t nb2, size_t nb3, size_t offset, bool inplace) { GGML_ASSERT(ggml_nelements(a) >= ggml_nelements(b)); bool is_node = false; if (a->grad || b->grad) { is_node = true; } // make a view of the destination struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); int32_t params[] = { nb1, nb2, nb3, offset, inplace ? 1 : 0 }; ggml_set_op_params(result, params, sizeof(params)); result->op = GGML_OP_SET; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; result->src[1] = b; return result; } struct ggml_tensor * ggml_set( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, size_t nb1, size_t nb2, size_t nb3, size_t offset) { return ggml_set_impl(ctx, a, b, nb1, nb2, nb3, offset, false); } struct ggml_tensor * ggml_set_inplace( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, size_t nb1, size_t nb2, size_t nb3, size_t offset) { return ggml_set_impl(ctx, a, b, nb1, nb2, nb3, offset, true); } struct ggml_tensor * ggml_set_1d( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, size_t offset) { return ggml_set_impl(ctx, a, b, a->nb[1], a->nb[2], a->nb[3], offset, false); } struct ggml_tensor * ggml_set_1d_inplace( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, size_t offset) { return ggml_set_impl(ctx, a, b, a->nb[1], a->nb[2], a->nb[3], offset, true); } struct ggml_tensor * ggml_set_2d( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, size_t nb1, size_t offset) { return ggml_set_impl(ctx, a, b, nb1, a->nb[2], a->nb[3], offset, false); } struct ggml_tensor * ggml_set_2d_inplace( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, size_t nb1, size_t offset) { return ggml_set_impl(ctx, a, b, nb1, a->nb[2], a->nb[3], offset, true); } // ggml_cpy static struct ggml_tensor * ggml_cpy_impl( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) { GGML_ASSERT(ggml_nelements(a) == ggml_nelements(b)); bool is_node = false; if (a->grad || b->grad) { // inplace is false and either one have a grad is_node = true; } // make a view of the destination struct ggml_tensor * result = ggml_view_tensor(ctx, b); if (strlen(b->name) > 0) { ggml_format_name(result, "%s (copy of %s)", b->name, a->name); } else { ggml_format_name(result, "%s (copy)", a->name); } result->op = GGML_OP_CPY; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; result->src[1] = b; return result; } struct ggml_tensor * ggml_cpy( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) { return ggml_cpy_impl(ctx, a, b); } struct ggml_tensor * ggml_cast( struct ggml_context * ctx, struct ggml_tensor * a, enum ggml_type type) { bool is_node = false; struct ggml_tensor * result = ggml_new_tensor(ctx, type, GGML_MAX_DIMS, a->ne); ggml_format_name(result, "%s (copy)", a->name); result->op = GGML_OP_CPY; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; result->src[1] = result; return result; } // ggml_cont static struct ggml_tensor * ggml_cont_impl( struct ggml_context * ctx, struct ggml_tensor * a) { bool is_node = false; if (a->grad) { is_node = true; } struct ggml_tensor * result = ggml_dup_tensor(ctx, a); ggml_format_name(result, "%s (cont)", a->name); result->op = GGML_OP_CONT; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; return result; } struct ggml_tensor * ggml_cont( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_cont_impl(ctx, a); } // make contiguous, with new shape GGML_API struct ggml_tensor * ggml_cont_1d( struct ggml_context * ctx, struct ggml_tensor * a, int64_t ne0) { return ggml_cont_4d(ctx, a, ne0, 1, 1, 1); } GGML_API struct ggml_tensor * ggml_cont_2d( struct ggml_context * ctx, struct ggml_tensor * a, int64_t ne0, int64_t ne1) { return ggml_cont_4d(ctx, a, ne0, ne1, 1, 1); } GGML_API struct ggml_tensor * ggml_cont_3d( struct ggml_context * ctx, struct ggml_tensor * a, int64_t ne0, int64_t ne1, int64_t ne2) { return ggml_cont_4d(ctx, a, ne0, ne1, ne2, 1); } struct ggml_tensor * ggml_cont_4d( struct ggml_context * ctx, struct ggml_tensor * a, int64_t ne0, int64_t ne1, int64_t ne2, int64_t ne3) { GGML_ASSERT(ggml_nelements(a) == (ne0*ne1*ne2*ne3)); bool is_node = false; struct ggml_tensor * result = ggml_new_tensor_4d(ctx, a->type, ne0, ne1, ne2, ne3); ggml_format_name(result, "%s (cont)", a->name); result->op = GGML_OP_CONT; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; return result; } // ggml_reshape struct ggml_tensor * ggml_reshape( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) { GGML_ASSERT(ggml_is_contiguous(a)); // as only the shape of b is relevant, and not its memory layout, b is allowed to be non contiguous. GGML_ASSERT(ggml_nelements(a) == ggml_nelements(b)); bool is_node = false; if (a->grad) { is_node = true; } if (b->grad) { // gradient propagation is not supported //GGML_ASSERT(false); } struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, GGML_MAX_DIMS, b->ne, a, 0); ggml_format_name(result, "%s (reshaped)", a->name); result->op = GGML_OP_RESHAPE; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; return result; } struct ggml_tensor * ggml_reshape_1d( struct ggml_context * ctx, struct ggml_tensor * a, int64_t ne0) { GGML_ASSERT(ggml_is_contiguous(a)); GGML_ASSERT(ggml_nelements(a) == ne0); bool is_node = false; if (a->grad) { is_node = true; } const int64_t ne[1] = { ne0 }; struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, 1, ne, a, 0); ggml_format_name(result, "%s (reshaped)", a->name); result->op = GGML_OP_RESHAPE; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; return result; } struct ggml_tensor * ggml_reshape_2d( struct ggml_context * ctx, struct ggml_tensor * a, int64_t ne0, int64_t ne1) { GGML_ASSERT(ggml_is_contiguous(a)); GGML_ASSERT(ggml_nelements(a) == ne0*ne1); bool is_node = false; if (a->grad) { is_node = true; } const int64_t ne[2] = { ne0, ne1 }; struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, 2, ne, a, 0); ggml_format_name(result, "%s (reshaped)", a->name); result->op = GGML_OP_RESHAPE; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; return result; } struct ggml_tensor * ggml_reshape_3d( struct ggml_context * ctx, struct ggml_tensor * a, int64_t ne0, int64_t ne1, int64_t ne2) { GGML_ASSERT(ggml_is_contiguous(a)); GGML_ASSERT(ggml_nelements(a) == ne0*ne1*ne2); bool is_node = false; if (a->grad) { is_node = true; } const int64_t ne[3] = { ne0, ne1, ne2 }; struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, 3, ne, a, 0); ggml_format_name(result, "%s (reshaped)", a->name); result->op = GGML_OP_RESHAPE; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; return result; } struct ggml_tensor * ggml_reshape_4d( struct ggml_context * ctx, struct ggml_tensor * a, int64_t ne0, int64_t ne1, int64_t ne2, int64_t ne3) { GGML_ASSERT(ggml_is_contiguous(a)); GGML_ASSERT(ggml_nelements(a) == ne0*ne1*ne2*ne3); bool is_node = false; if (a->grad) { is_node = true; } const int64_t ne[4] = { ne0, ne1, ne2, ne3 }; struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, 4, ne, a, 0); ggml_format_name(result, "%s (reshaped)", a->name); result->op = GGML_OP_RESHAPE; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; return result; } static struct ggml_tensor * ggml_view_impl( struct ggml_context * ctx, struct ggml_tensor * a, int n_dims, const int64_t * ne, size_t offset) { bool is_node = false; if (a->grad) { is_node = true; } struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, n_dims, ne, a, offset); ggml_format_name(result, "%s (view)", a->name); ggml_set_op_params(result, &offset, sizeof(offset)); result->op = GGML_OP_VIEW; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; return result; } // ggml_view_1d struct ggml_tensor * ggml_view_1d( struct ggml_context * ctx, struct ggml_tensor * a, int64_t ne0, size_t offset) { struct ggml_tensor * result = ggml_view_impl(ctx, a, 1, &ne0, offset); return result; } // ggml_view_2d struct ggml_tensor * ggml_view_2d( struct ggml_context * ctx, struct ggml_tensor * a, int64_t ne0, int64_t ne1, size_t nb1, size_t offset) { const int64_t ne[2] = { ne0, ne1 }; struct ggml_tensor * result = ggml_view_impl(ctx, a, 2, ne, offset); result->nb[1] = nb1; result->nb[2] = result->nb[1]*ne1; result->nb[3] = result->nb[2]; return result; } // ggml_view_3d struct ggml_tensor * ggml_view_3d( struct ggml_context * ctx, struct ggml_tensor * a, int64_t ne0, int64_t ne1, int64_t ne2, size_t nb1, size_t nb2, size_t offset) { const int64_t ne[3] = { ne0, ne1, ne2 }; struct ggml_tensor * result = ggml_view_impl(ctx, a, 3, ne, offset); result->nb[1] = nb1; result->nb[2] = nb2; result->nb[3] = result->nb[2]*ne2; return result; } // ggml_view_4d struct ggml_tensor * ggml_view_4d( struct ggml_context * ctx, struct ggml_tensor * a, int64_t ne0, int64_t ne1, int64_t ne2, int64_t ne3, size_t nb1, size_t nb2, size_t nb3, size_t offset) { const int64_t ne[4] = { ne0, ne1, ne2, ne3 }; struct ggml_tensor * result = ggml_view_impl(ctx, a, 4, ne, offset); result->nb[1] = nb1; result->nb[2] = nb2; result->nb[3] = nb3; return result; } // ggml_permute struct ggml_tensor * ggml_permute( struct ggml_context * ctx, struct ggml_tensor * a, int axis0, int axis1, int axis2, int axis3) { GGML_ASSERT(axis0 >= 0 && axis0 < GGML_MAX_DIMS); GGML_ASSERT(axis1 >= 0 && axis1 < GGML_MAX_DIMS); GGML_ASSERT(axis2 >= 0 && axis2 < GGML_MAX_DIMS); GGML_ASSERT(axis3 >= 0 && axis3 < GGML_MAX_DIMS); GGML_ASSERT(axis0 != axis1); GGML_ASSERT(axis0 != axis2); GGML_ASSERT(axis0 != axis3); GGML_ASSERT(axis1 != axis2); GGML_ASSERT(axis1 != axis3); GGML_ASSERT(axis2 != axis3); bool is_node = false; if (a->grad) { is_node = true; } struct ggml_tensor * result = ggml_view_tensor(ctx, a); ggml_format_name(result, "%s (permuted)", a->name); int ne[GGML_MAX_DIMS]; int nb[GGML_MAX_DIMS]; ne[axis0] = a->ne[0]; ne[axis1] = a->ne[1]; ne[axis2] = a->ne[2]; ne[axis3] = a->ne[3]; nb[axis0] = a->nb[0]; nb[axis1] = a->nb[1]; nb[axis2] = a->nb[2]; nb[axis3] = a->nb[3]; result->ne[0] = ne[0]; result->ne[1] = ne[1]; result->ne[2] = ne[2]; result->ne[3] = ne[3]; result->nb[0] = nb[0]; result->nb[1] = nb[1]; result->nb[2] = nb[2]; result->nb[3] = nb[3]; result->op = GGML_OP_PERMUTE; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; int32_t params[] = { axis0, axis1, axis2, axis3 }; ggml_set_op_params(result, params, sizeof(params)); return result; } // ggml_transpose struct ggml_tensor * ggml_transpose( struct ggml_context * ctx, struct ggml_tensor * a) { bool is_node = false; if (a->grad) { is_node = true; } struct ggml_tensor * result = ggml_view_tensor(ctx, a); ggml_format_name(result, "%s (transposed)", a->name); result->ne[0] = a->ne[1]; result->ne[1] = a->ne[0]; result->nb[0] = a->nb[1]; result->nb[1] = a->nb[0]; result->op = GGML_OP_TRANSPOSE; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; return result; } // ggml_get_rows struct ggml_tensor * ggml_get_rows( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) { GGML_ASSERT(a->ne[2] == b->ne[1]); GGML_ASSERT(b->ne[3] == 1); GGML_ASSERT(b->type == GGML_TYPE_I32); bool is_node = false; if (a->grad || b->grad) { is_node = true; } // TODO: implement non F32 return enum ggml_type type = GGML_TYPE_F32; if (a->type == GGML_TYPE_I32) { type = a->type; } struct ggml_tensor * result = ggml_new_tensor_4d(ctx, type, a->ne[0], b->ne[0], b->ne[1], b->ne[2]); result->op = GGML_OP_GET_ROWS; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; result->src[1] = b; return result; } // ggml_get_rows_back struct ggml_tensor * ggml_get_rows_back( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, struct ggml_tensor * c) { GGML_ASSERT(ggml_is_matrix(a) && ggml_is_vector(b) && b->type == GGML_TYPE_I32); GGML_ASSERT(ggml_is_matrix(c) && (a->ne[0] == c->ne[0])); bool is_node = false; if (a->grad || b->grad) { is_node = true; } // TODO: implement non F32 return //struct ggml_tensor * result = ggml_new_tensor_2d(ctx, a->type, a->ne[0], b->ne[0]); struct ggml_tensor * result = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, c->ne[0], c->ne[1]); result->op = GGML_OP_GET_ROWS_BACK; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; result->src[1] = b; return result; } // ggml_diag struct ggml_tensor * ggml_diag( struct ggml_context * ctx, struct ggml_tensor * a) { GGML_ASSERT(a->ne[1] == 1); bool is_node = false; if (a->grad) { is_node = true; } const int64_t ne[4] = { a->ne[0], a->ne[0], a->ne[2], a->ne[3] }; struct ggml_tensor * result = ggml_new_tensor(ctx, a->type, 4, ne); result->op = GGML_OP_DIAG; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; return result; } // ggml_diag_mask_inf static struct ggml_tensor * ggml_diag_mask_inf_impl( struct ggml_context * ctx, struct ggml_tensor * a, int n_past, bool inplace) { bool is_node = false; if (a->grad) { is_node = true; } struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); int32_t params[] = { n_past }; ggml_set_op_params(result, params, sizeof(params)); result->op = GGML_OP_DIAG_MASK_INF; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; return result; } struct ggml_tensor * ggml_diag_mask_inf( struct ggml_context * ctx, struct ggml_tensor * a, int n_past) { return ggml_diag_mask_inf_impl(ctx, a, n_past, false); } struct ggml_tensor * ggml_diag_mask_inf_inplace( struct ggml_context * ctx, struct ggml_tensor * a, int n_past) { return ggml_diag_mask_inf_impl(ctx, a, n_past, true); } // ggml_diag_mask_zero static struct ggml_tensor * ggml_diag_mask_zero_impl( struct ggml_context * ctx, struct ggml_tensor * a, int n_past, bool inplace) { bool is_node = false; if (a->grad) { is_node = true; } struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); int32_t params[] = { n_past }; ggml_set_op_params(result, params, sizeof(params)); result->op = GGML_OP_DIAG_MASK_ZERO; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; return result; } struct ggml_tensor * ggml_diag_mask_zero( struct ggml_context * ctx, struct ggml_tensor * a, int n_past) { return ggml_diag_mask_zero_impl(ctx, a, n_past, false); } struct ggml_tensor * ggml_diag_mask_zero_inplace( struct ggml_context * ctx, struct ggml_tensor * a, int n_past) { return ggml_diag_mask_zero_impl(ctx, a, n_past, true); } // ggml_soft_max static struct ggml_tensor * ggml_soft_max_impl( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * mask, float scale, float max_bias, bool inplace) { GGML_ASSERT(ggml_is_contiguous(a)); if (mask) { GGML_ASSERT(mask->type == GGML_TYPE_F16 || mask->type == GGML_TYPE_F32); GGML_ASSERT(ggml_is_contiguous(mask)); GGML_ASSERT(ggml_is_matrix(mask)); GGML_ASSERT(mask->ne[0] == a->ne[0]); GGML_ASSERT(mask->ne[1] >= a->ne[1]); } if (max_bias > 0.0f) { GGML_ASSERT(mask); } bool is_node = false; if (a->grad) { is_node = true; } struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); float params[] = { scale, max_bias }; ggml_set_op_params(result, params, sizeof(params)); result->op = GGML_OP_SOFT_MAX; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; result->src[1] = mask; return result; } struct ggml_tensor * ggml_soft_max( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_soft_max_impl(ctx, a, NULL, 1.0f, 0.0f, false); } struct ggml_tensor * ggml_soft_max_inplace( struct ggml_context * ctx, struct ggml_tensor * a) { return ggml_soft_max_impl(ctx, a, NULL, 1.0f, 0.0f, true); } struct ggml_tensor * ggml_soft_max_ext( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * mask, float scale, float max_bias) { return ggml_soft_max_impl(ctx, a, mask, scale, max_bias, false); } // ggml_soft_max_back static struct ggml_tensor * ggml_soft_max_back_impl( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, bool inplace) { bool is_node = false; if (a->grad || b->grad) { is_node = true; // TODO : implement backward pass } struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); result->op = GGML_OP_SOFT_MAX_BACK; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; result->src[1] = b; return result; } struct ggml_tensor * ggml_soft_max_back( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) { return ggml_soft_max_back_impl(ctx, a, b, false); } struct ggml_tensor * ggml_soft_max_back_inplace( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) { return ggml_soft_max_back_impl(ctx, a, b, true); } // ggml_rope static struct ggml_tensor * ggml_rope_impl( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, struct ggml_tensor * c, int n_dims, int mode, int n_ctx, int n_orig_ctx, float freq_base, float freq_scale, float ext_factor, float attn_factor, float beta_fast, float beta_slow, float xpos_base, bool xpos_down, bool inplace) { GGML_ASSERT((mode & 1) == 0 && "mode & 1 == 1 is no longer supported"); GGML_ASSERT(ggml_is_vector(b)); GGML_ASSERT(b->type == GGML_TYPE_I32); GGML_ASSERT(a->ne[2] == b->ne[0]); if (c) { GGML_ASSERT(c->type == GGML_TYPE_F32); GGML_ASSERT(c->ne[0] >= n_dims / 2); } bool is_node = false; if (a->grad) { is_node = true; } struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); int32_t params[13] = { /*n_past*/ 0, n_dims, mode, n_ctx, n_orig_ctx }; memcpy(params + 5, &freq_base, sizeof(float)); memcpy(params + 6, &freq_scale, sizeof(float)); memcpy(params + 7, &ext_factor, sizeof(float)); memcpy(params + 8, &attn_factor, sizeof(float)); memcpy(params + 9, &beta_fast, sizeof(float)); memcpy(params + 10, &beta_slow, sizeof(float)); memcpy(params + 11, &xpos_base, sizeof(float)); memcpy(params + 12, &xpos_down, sizeof(bool)); ggml_set_op_params(result, params, sizeof(params)); result->op = GGML_OP_ROPE; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; result->src[1] = b; result->src[2] = c; return result; } struct ggml_tensor * ggml_rope( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, int n_dims, int mode, int n_ctx) { return ggml_rope_impl( ctx, a, b, NULL, n_dims, mode, n_ctx, 0, 10000.0f, 1.0f, 0.0f, 1.0f, 0.0f, 0.0f, 0.0f, false, false ); } struct ggml_tensor * ggml_rope_inplace( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, int n_dims, int mode, int n_ctx) { return ggml_rope_impl( ctx, a, b, NULL, n_dims, mode, n_ctx, 0, 10000.0f, 1.0f, 0.0f, 1.0f, 0.0f, 0.0f, 0.0f, false, true ); } struct ggml_tensor * ggml_rope_ext( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, struct ggml_tensor * c, int n_dims, int mode, int n_ctx, int n_orig_ctx, float freq_base, float freq_scale, float ext_factor, float attn_factor, float beta_fast, float beta_slow) { return ggml_rope_impl( ctx, a, b, c, n_dims, mode, n_ctx, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow, 0.0f, false, false ); } struct ggml_tensor * ggml_rope_ext_inplace( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, struct ggml_tensor * c, int n_dims, int mode, int n_ctx, int n_orig_ctx, float freq_base, float freq_scale, float ext_factor, float attn_factor, float beta_fast, float beta_slow) { return ggml_rope_impl( ctx, a, b, c, n_dims, mode, n_ctx, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow, 0.0f, false, true ); } struct ggml_tensor * ggml_rope_custom( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, int n_dims, int mode, int n_ctx, int n_orig_ctx, float freq_base, float freq_scale, float ext_factor, float attn_factor, float beta_fast, float beta_slow) { return ggml_rope_impl( ctx, a, b, NULL, n_dims, mode, n_ctx, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow, 0.0f, false, false ); } struct ggml_tensor * ggml_rope_custom_inplace( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, int n_dims, int mode, int n_ctx, int n_orig_ctx, float freq_base, float freq_scale, float ext_factor, float attn_factor, float beta_fast, float beta_slow) { return ggml_rope_impl( ctx, a, b, NULL, n_dims, mode, n_ctx, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow, 0.0f, false, true ); } // ggml_rope_back struct ggml_tensor * ggml_rope_back( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, struct ggml_tensor * c, int n_dims, int mode, int n_ctx, int n_orig_ctx, float freq_base, float freq_scale, float ext_factor, float attn_factor, float beta_fast, float beta_slow, float xpos_base, bool xpos_down) { GGML_ASSERT(ggml_is_vector(b)); GGML_ASSERT(b->type == GGML_TYPE_I32); GGML_ASSERT(a->ne[2] == b->ne[0]); GGML_ASSERT(c == NULL && "freq factors not implemented yet"); GGML_ASSERT((mode & 4) == 0 && "ggml_rope_back() for ChatGLM not implemented yet"); bool is_node = false; if (a->grad) { is_node = false; // TODO: implement backward } struct ggml_tensor * result = ggml_dup_tensor(ctx, a); int32_t params[13] = { /*n_past*/ 0, n_dims, mode, n_ctx, n_orig_ctx }; memcpy(params + 5, &freq_base, sizeof(float)); memcpy(params + 6, &freq_scale, sizeof(float)); memcpy(params + 7, &ext_factor, sizeof(float)); memcpy(params + 8, &attn_factor, sizeof(float)); memcpy(params + 9, &beta_fast, sizeof(float)); memcpy(params + 10, &beta_slow, sizeof(float)); memcpy(params + 11, &xpos_base, sizeof(float)); memcpy(params + 12, &xpos_down, sizeof(bool)); ggml_set_op_params(result, params, sizeof(params)); result->op = GGML_OP_ROPE_BACK; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; result->src[1] = b; return result; } // ggml_clamp struct ggml_tensor * ggml_clamp( struct ggml_context * ctx, struct ggml_tensor * a, float min, float max) { bool is_node = false; if (a->grad) { GGML_ASSERT(false); // TODO: implement backward is_node = true; } // TODO: when implement backward, fix this: struct ggml_tensor * result = ggml_view_tensor(ctx, a); float params[] = { min, max }; ggml_set_op_params(result, params, sizeof(params)); result->op = GGML_OP_CLAMP; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; return result; } // ggml_conv_1d static int64_t ggml_calc_conv_output_size(int64_t ins, int64_t ks, int s, int p, int d) { return (ins + 2 * p - d * (ks - 1) - 1) / s + 1; } GGML_API struct ggml_tensor * ggml_conv_1d( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, int s0, int p0, int d0) { struct ggml_tensor * im2col = ggml_im2col(ctx, a, b, s0, 0, p0, 0, d0, 0, false, GGML_TYPE_F16); // [N, OL, IC * K] struct ggml_tensor * result = ggml_mul_mat(ctx, ggml_reshape_2d(ctx, im2col, im2col->ne[0], (im2col->ne[2] * im2col->ne[1])), // [N, OL, IC * K] => [N*OL, IC * K] ggml_reshape_2d(ctx, a, (a->ne[0] * a->ne[1]), a->ne[2])); // [OC，IC, K] => [OC, IC * K] result = ggml_reshape_3d(ctx, result, im2col->ne[1], a->ne[2], im2col->ne[2]); // [N, OC, OL] return result; } // ggml_conv_1d_ph struct ggml_tensor* ggml_conv_1d_ph( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, int s, int d) { return ggml_conv_1d(ctx, a, b, s, a->ne[0] / 2, d); } // ggml_conv_transpose_1d static int64_t ggml_calc_conv_transpose_1d_output_size(int64_t ins, int64_t ks, int s, int p, int d) { return (ins - 1) * s - 2 * p + d * (ks - 1) + 1; } GGML_API struct ggml_tensor * ggml_conv_transpose_1d( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, int s0, int p0, int d0) { GGML_ASSERT(ggml_is_matrix(b)); GGML_ASSERT(a->ne[2] == b->ne[1]); GGML_ASSERT(a->ne[3] == 1); GGML_ASSERT(p0 == 0); GGML_ASSERT(d0 == 1); bool is_node = false; if (a->grad || b->grad) { GGML_ASSERT(false); // TODO: implement backward is_node = true; } const int64_t ne[4] = { ggml_calc_conv_transpose_1d_output_size(b->ne[0], a->ne[0], s0, 0 /*p0*/, 1 /*d0*/), a->ne[1], b->ne[2], 1, }; struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne); int32_t params[] = { s0, p0, d0 }; ggml_set_op_params(result, params, sizeof(params)); result->op = GGML_OP_CONV_TRANSPOSE_1D; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; result->src[1] = b; return result; } // ggml_conv_depthwise struct ggml_tensor * ggml_conv_depthwise_2d( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, int s0, int s1, int p0, int p1, int d0, int d1) { struct ggml_tensor * new_a = ggml_reshape_4d(ctx, a, a->ne[0], a->ne[1], 1, a->ne[2] * a->ne[3]); struct ggml_tensor * im2col = ggml_im2col(ctx, new_a, ggml_reshape_4d(ctx, b, b->ne[0], b->ne[1], 1, b->ne[2] * b->ne[3]), s0, s1, p0, p1, d0, d1, true, GGML_TYPE_F16); // [N * IC, OH, OW, KH * KW] struct ggml_tensor * new_b = ggml_reshape_4d(ctx, im2col, im2col->ne[0], im2col->ne[2] * im2col->ne[1], b->ne[2], b->ne[3]); // [N * IC, OH, OW, KH * KW] => [N, IC, OH * OW, KH * KW] new_a = ggml_reshape_4d(ctx, new_a, (new_a->ne[0] * new_a->ne[1]), new_a->ne[2], new_a->ne[3], 1); // [OC，1, KH, KW] => [1, OC, 1, KH * KW] struct ggml_tensor * result = ggml_mul_mat(ctx, new_a, new_b); result = ggml_reshape_4d(ctx, result, im2col->ne[1], im2col->ne[2], b->ne[2], b->ne[3]); // [N, OC, OH, OW] return result; } // ggml_conv_2d // im2col: [N, IC, IH, IW] => [N, OH, OW, IC*KH*KW] // a: [OC，IC, KH, KW] // b: [N, IC, IH, IW] // result: [N, OH, OW, IC*KH*KW] struct ggml_tensor * ggml_im2col( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, int s0, int s1, int p0, int p1, int d0, int d1, bool is_2D, enum ggml_type dst_type) { if(is_2D) { GGML_ASSERT(a->ne[2] == b->ne[2]); } else { GGML_ASSERT(a->ne[1] == b->ne[1]); } bool is_node = false; if (a->grad || b->grad) { GGML_ASSERT(false); // TODO: implement backward is_node = true; } const int64_t OH = is_2D ? ggml_calc_conv_output_size(b->ne[1], a->ne[1], s1, p1, d1) : 0; const int64_t OW = ggml_calc_conv_output_size(b->ne[0], a->ne[0], s0, p0, d0); const int64_t ne[4] = { is_2D ? (a->ne[2] * a->ne[1] * a->ne[0]) : a->ne[1] * a->ne[0], OW, is_2D ? OH : b->ne[2], is_2D ? b->ne[3] : 1, }; struct ggml_tensor * result = ggml_new_tensor(ctx, dst_type, 4, ne); int32_t params[] = { s0, s1, p0, p1, d0, d1, (is_2D ? 1 : 0) }; ggml_set_op_params(result, params, sizeof(params)); result->op = GGML_OP_IM2COL; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; result->src[1] = b; return result; } // a: [OC，IC, KH, KW] // b: [N, IC, IH, IW] // result: [N, OC, OH, OW] struct ggml_tensor * ggml_conv_2d( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, int s0, int s1, int p0, int p1, int d0, int d1) { struct ggml_tensor * im2col = ggml_im2col(ctx, a, b, s0, s1, p0, p1, d0, d1, true, GGML_TYPE_F16); // [N, OH, OW, IC * KH * KW] struct ggml_tensor * result = ggml_mul_mat(ctx, ggml_reshape_2d(ctx, im2col, im2col->ne[0], im2col->ne[3] * im2col->ne[2] * im2col->ne[1]), // [N, OH, OW, IC * KH * KW] => [N*OH*OW, IC * KH * KW] ggml_reshape_2d(ctx, a, (a->ne[0] * a->ne[1] * a->ne[2]), a->ne[3])); // [OC，IC, KH, KW] => [OC, IC * KH * KW] result = ggml_reshape_4d(ctx, result, im2col->ne[1], im2col->ne[2], im2col->ne[3], a->ne[3]); // [OC, N, OH, OW] result = ggml_cont(ctx, ggml_permute(ctx, result, 0, 1, 3, 2)); // [N, OC, OH, OW] return result; } // ggml_conv_2d_sk_p0 struct ggml_tensor * ggml_conv_2d_sk_p0( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) { return ggml_conv_2d(ctx, a, b, a->ne[0], a->ne[1], 0, 0, 1, 1); } // ggml_conv_2d_s1_ph struct ggml_tensor * ggml_conv_2d_s1_ph( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) { return ggml_conv_2d(ctx, a, b, 1, 1, a->ne[0] / 2, a->ne[1] / 2, 1, 1); } // ggml_conv_transpose_2d_p0 static int64_t ggml_calc_conv_transpose_output_size(int64_t ins, int64_t ks, int s, int p) { return (ins - 1) * s - 2 * p + ks; } struct ggml_tensor * ggml_conv_transpose_2d_p0( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, int stride) { GGML_ASSERT(a->ne[3] == b->ne[2]); bool is_node = false; if (a->grad || b->grad) { GGML_ASSERT(false); // TODO: implement backward is_node = true; } const int64_t ne[4] = { ggml_calc_conv_transpose_output_size(b->ne[0], a->ne[0], stride, 0 /*p0*/), ggml_calc_conv_transpose_output_size(b->ne[1], a->ne[1], stride, 0 /*p1*/), a->ne[2], b->ne[3], }; struct ggml_tensor* result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne); ggml_set_op_params_i32(result, 0, stride); result->op = GGML_OP_CONV_TRANSPOSE_2D; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; result->src[1] = b; return result; } // ggml_pool_* static int64_t ggml_calc_pool_output_size(int64_t ins, int ks, int s, float p) { return (ins + 2 * p - ks) / s + 1; } // ggml_pool_1d struct ggml_tensor * ggml_pool_1d( struct ggml_context * ctx, struct ggml_tensor * a, enum ggml_op_pool op, int k0, int s0, int p0) { bool is_node = false; if (a->grad) { GGML_ASSERT(false); // TODO: implement backward is_node = true; } const int64_t ne[4] = { ggml_calc_pool_output_size(a->ne[0], k0, s0, p0), a->ne[1], a->ne[2], a->ne[3], }; struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne); int32_t params[] = { op, k0, s0, p0 }; ggml_set_op_params(result, params, sizeof(params)); result->op = GGML_OP_POOL_1D; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; return result; } // ggml_pool_2d struct ggml_tensor * ggml_pool_2d( struct ggml_context * ctx, struct ggml_tensor * a, enum ggml_op_pool op, int k0, int k1, int s0, int s1, float p0, float p1) { bool is_node = false; if (a->grad) { GGML_ASSERT(false); // TODO: implement backward is_node = true; } struct ggml_tensor * result; const int64_t ne[3] = { ggml_calc_pool_output_size(a->ne[0], k0, s0, p0), ggml_calc_pool_output_size(a->ne[1], k1, s1, p1), a->ne[2], }; result = ggml_new_tensor(ctx, GGML_TYPE_F32, 3, ne); int32_t params[] = { op, k0, k1, s0, s1, p0, p1 }; ggml_set_op_params(result, params, sizeof(params)); result->op = GGML_OP_POOL_2D; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; return result; } // ggml_upscale static struct ggml_tensor * ggml_upscale_impl( struct ggml_context * ctx, struct ggml_tensor * a, int ne0, int ne1, int ne2, int ne3) { bool is_node = false; if (a->grad) { GGML_ASSERT(false); // TODO: implement backward is_node = true; } GGML_ASSERT(a->ne[0] <= ne0); GGML_ASSERT(a->ne[1] <= ne1); GGML_ASSERT(a->ne[2] <= ne2); GGML_ASSERT(a->ne[3] <= ne3); struct ggml_tensor * result = ggml_new_tensor_4d(ctx, a->type, ne0, ne1, ne2, ne3 ); result->op = GGML_OP_UPSCALE; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; return result; } struct ggml_tensor * ggml_upscale( struct ggml_context * ctx, struct ggml_tensor * a, int scale_factor) { return ggml_upscale_impl(ctx, a, a->ne[0] * scale_factor, a->ne[1] * scale_factor, a->ne[2], a->ne[3]); } struct ggml_tensor * ggml_upscale_ext( struct ggml_context * ctx, struct ggml_tensor * a, int ne0, int ne1, int ne2, int ne3) { return ggml_upscale_impl(ctx, a, ne0, ne1, ne2, ne3); } // ggml_pad struct ggml_tensor * ggml_pad( struct ggml_context * ctx, struct ggml_tensor * a, int p0, int p1, int p2, int p3) { bool is_node = false; if (a->grad) { GGML_ASSERT(false); // TODO: implement backward is_node = true; } struct ggml_tensor * result = ggml_new_tensor_4d(ctx, a->type, a->ne[0] + p0, a->ne[1] + p1, a->ne[2] + p2, a->ne[3] + p3); result->op = GGML_OP_PAD; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; return result; } // ggml_arange struct ggml_tensor * ggml_arange( struct ggml_context * ctx, float start, float stop, float step) { GGML_ASSERT(stop > start); const int64_t steps = (int64_t) ceilf((stop - start) / step); struct ggml_tensor * result = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, steps); result->op = GGML_OP_ARANGE; ggml_set_op_params_f32(result, 0, start); ggml_set_op_params_f32(result, 1, stop); ggml_set_op_params_f32(result, 2, step); return result; } // ggml_timestep_embedding struct ggml_tensor * ggml_timestep_embedding( struct ggml_context * ctx, struct ggml_tensor * timesteps, int dim, int max_period) { bool is_node = false; if (timesteps->grad) { GGML_ASSERT(false); // TODO: implement backward is_node = true; } int actual_dim = dim; if (dim % 2 != 0) { actual_dim = dim + 1; } struct ggml_tensor * result = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, actual_dim, timesteps->ne[0]); result->op = GGML_OP_TIMESTEP_EMBEDDING; ggml_set_op_params_i32(result, 0, dim); ggml_set_op_params_i32(result, 1, max_period); result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = timesteps; return result; } // ggml_argsort struct ggml_tensor * ggml_argsort( struct ggml_context * ctx, struct ggml_tensor * a, enum ggml_sort_order order) { bool is_node = false; struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_I32, GGML_MAX_DIMS, a->ne); ggml_set_op_params_i32(result, 0, (int32_t) order); result->op = GGML_OP_ARGSORT; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; return result; } // ggml_top_k struct ggml_tensor * ggml_top_k( struct ggml_context * ctx, struct ggml_tensor * a, int k) { GGML_ASSERT(a->ne[0] >= k); struct ggml_tensor * result = ggml_argsort(ctx, a, GGML_SORT_ORDER_DESC); result = ggml_view_4d(ctx, result, k, result->ne[1], result->ne[2], result->ne[3], result->nb[1], result->nb[2], result->nb[3], 0); return result; } // ggml_flash_attn_ext struct ggml_tensor * ggml_flash_attn_ext( struct ggml_context * ctx, struct ggml_tensor * q, struct ggml_tensor * k, struct ggml_tensor * v, struct ggml_tensor * mask, float scale, float max_bias) { GGML_ASSERT(ggml_can_mul_mat(k, q)); // TODO: check if vT can be multiplied by (k*qT) if (mask) { GGML_ASSERT(ggml_is_contiguous(mask)); GGML_ASSERT(mask->ne[2] == 1); GGML_ASSERT(mask->ne[3] == 1); GGML_ASSERT(mask->ne[1] >= GGML_PAD(q->ne[1], GGML_KQ_MASK_PAD) && "the Flash-Attention kernel requires the mask to be padded to GGML_KQ_MASK_PAD and at least n_queries big"); //GGML_ASSERT(ggml_can_repeat_rows(mask, qk)); } if (max_bias > 0.0f) { GGML_ASSERT(mask); } bool is_node = false; if (q->grad || k->grad || v->grad) { is_node = true; } // permute(0, 2, 1, 3) int64_t ne[4] = { q->ne[0], q->ne[2], q->ne[1], q->ne[3] }; struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne); float params[] = { scale, max_bias }; ggml_set_op_params(result, params, sizeof(params)); result->op = GGML_OP_FLASH_ATTN_EXT; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = q; result->src[1] = k; result->src[2] = v; result->src[3] = mask; return result; } void ggml_flash_attn_ext_set_prec( struct ggml_tensor * a, enum ggml_prec prec) { GGML_ASSERT(a->op == GGML_OP_FLASH_ATTN_EXT); const int32_t prec_i32 = (int32_t) prec; ggml_set_op_params_i32(a, 2, prec_i32); // scale is on first pos, max_bias on second } // ggml_flash_attn_back struct ggml_tensor * ggml_flash_attn_back( struct ggml_context * ctx, struct ggml_tensor * q, struct ggml_tensor * k, struct ggml_tensor * v, struct ggml_tensor * d, bool masked) { GGML_ASSERT(false && "TODO: adapt to ggml_flash_attn_ext() changes"); GGML_ASSERT(ggml_can_mul_mat(k, q)); // TODO: check if vT can be multiplied by (k*qT) // d shape [D,N,ne2,ne3] // q shape [D,N,ne2,ne3] // k shape [D,M,kvne2,ne3] // v shape [M,D,kvne2,ne3] const int64_t D = q->ne[0]; const int64_t N = q->ne[1]; const int64_t M = k->ne[1]; const int64_t ne2 = q->ne[2]; const int64_t ne3 = q->ne[3]; const int64_t kvne2 = k->ne[2]; GGML_ASSERT(k->ne[0] == D); GGML_ASSERT(v->ne[0] == M); GGML_ASSERT(v->ne[1] == D); GGML_ASSERT(d->ne[0] == D); GGML_ASSERT(d->ne[1] == N); GGML_ASSERT(k->ne[2] == kvne2); GGML_ASSERT(k->ne[3] == ne3); GGML_ASSERT(v->ne[2] == kvne2); GGML_ASSERT(v->ne[3] == ne3); GGML_ASSERT(d->ne[2] == ne2); GGML_ASSERT(d->ne[3] == ne3); GGML_ASSERT(ne2 % kvne2 == 0); bool is_node = false; if (q->grad || k->grad || v->grad) { // when using this operation (in backwards pass) these grads are set. // we don't want to create (big) grad of our result, so is_node is false. is_node = false; } // store gradients of q, k and v as continuous tensors concatenated in result. // note: v and gradv are actually transposed, i.e. v->ne[0] != D. const int64_t elem_q = ggml_nelements(q); const int64_t elem_k = ggml_nelements(k); const int64_t elem_v = ggml_nelements(v); enum ggml_type result_type = GGML_TYPE_F32; 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); const size_t end = offs_v + GGML_PAD(elem_v * tsize, GGML_MEM_ALIGN); const size_t nelements = (end + tsize - 1)/tsize; struct ggml_tensor * result = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nelements); int32_t masked_i = masked ? 1 : 0; ggml_set_op_params(result, &masked_i, sizeof(masked_i)); result->op = GGML_OP_FLASH_ATTN_BACK; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = q; result->src[1] = k; result->src[2] = v; result->src[3] = d; return result; } // ggml_ssm_conv struct ggml_tensor * ggml_ssm_conv( struct ggml_context * ctx, struct ggml_tensor * s, struct ggml_tensor * x, struct ggml_tensor * c, struct ggml_tensor * sq) { GGML_ASSERT(ggml_is_3d(s)); GGML_ASSERT(ggml_is_matrix(x)); GGML_ASSERT(ggml_is_matrix(c)); GGML_ASSERT(ggml_is_matrix(sq)); GGML_ASSERT(sq->type == GGML_TYPE_I32); const int64_t d_conv = c->ne[0]; const int64_t d_inner = c->ne[1]; const int64_t n_tokens = x->ne[1]; const int64_t n_kv = s->ne[2]; GGML_ASSERT( s->ne[0] == d_conv - 1); GGML_ASSERT( s->ne[1] == d_inner); GGML_ASSERT( x->ne[0] == d_inner); GGML_ASSERT(sq->ne[0] == n_kv); GGML_ASSERT(sq->ne[1] == n_tokens); bool is_node = false; if (s->grad || x->grad || c->grad || sq->grad) { GGML_ASSERT(false); // TODO: implement is_node = true; } // 2-in-1 concatenated x and conv_states, {d_inner, n_tokens} with {d_conv, d_inner, n_kv} struct ggml_tensor * result = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, (d_inner*n_tokens) + (d_conv*d_inner*n_kv)); result->op = GGML_OP_SSM_CONV; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = s; result->src[1] = x; result->src[2] = c; result->src[3] = sq; return result; } // ggml_ssm_scan struct ggml_tensor * ggml_ssm_scan( struct ggml_context * ctx, struct ggml_tensor * s, struct ggml_tensor * x, struct ggml_tensor * dt, struct ggml_tensor * A, struct ggml_tensor * B, struct ggml_tensor * C, struct ggml_tensor * sq) { GGML_ASSERT(ggml_is_contiguous(s)); GGML_ASSERT(ggml_is_contiguous(x)); GGML_ASSERT(ggml_is_contiguous(dt)); GGML_ASSERT(ggml_is_contiguous(A)); GGML_ASSERT(sq->type == GGML_TYPE_I32); GGML_ASSERT(B->nb[0] == ggml_type_size(B->type)); GGML_ASSERT(C->nb[0] == ggml_type_size(C->type)); GGML_ASSERT(ggml_are_same_shape(x, dt)); { const int64_t d_state = s->ne[0]; const int64_t d_inner = s->ne[1]; const int64_t n_tokens = x->ne[1]; GGML_ASSERT(x->ne[0] == d_inner); GGML_ASSERT(A->ne[0] == d_state); GGML_ASSERT(A->ne[1] == d_inner); GGML_ASSERT(B->ne[0] == d_state); GGML_ASSERT(B->ne[1] == n_tokens); GGML_ASSERT(C->ne[0] == d_state); GGML_ASSERT(C->ne[1] == n_tokens); } bool is_node = false; if (s->grad || x->grad || dt->grad || A->grad || B->grad || C->grad || sq->grad) { GGML_ASSERT(false); // TODO: implement is_node = true; } // 2-in-1 concatenated y and ssm_states, {d_inner, n_tokens} with {d_state, d_inner, n_kv} struct ggml_tensor * result = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, ggml_nelements(x) + ggml_nelements(s)); result->op = GGML_OP_SSM_SCAN; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = s; result->src[1] = x; result->src[2] = dt; result->src[3] = A; result->src[4] = B; result->src[5] = C; result->src[6] = sq; return result; } // ggml_win_part struct ggml_tensor * ggml_win_part( struct ggml_context * ctx, struct ggml_tensor * a, int w) { GGML_ASSERT(a->ne[3] == 1); GGML_ASSERT(a->type == GGML_TYPE_F32); bool is_node = false; if (a->grad) { GGML_ASSERT(false); // TODO: implement backward is_node = true; } // padding const int px = (w - a->ne[1]%w)%w; const int py = (w - a->ne[2]%w)%w; const int npx = (px + a->ne[1])/w; const int npy = (py + a->ne[2])/w; const int np = npx*npy; const int64_t ne[4] = { a->ne[0], w, w, np, }; struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne); int32_t params[] = { npx, npy, w }; ggml_set_op_params(result, params, sizeof(params)); result->op = GGML_OP_WIN_PART; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; return result; } // ggml_win_unpart struct ggml_tensor * ggml_win_unpart( struct ggml_context * ctx, struct ggml_tensor * a, int w0, int h0, int w) { GGML_ASSERT(a->type == GGML_TYPE_F32); bool is_node = false; if (a->grad) { GGML_ASSERT(false); // TODO: implement backward is_node = true; } const int64_t ne[4] = { a->ne[0], w0, h0, 1, }; struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 3, ne); int32_t params[] = { w }; ggml_set_op_params(result, params, sizeof(params)); result->op = GGML_OP_WIN_UNPART; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; return result; } // ggml_get_rel_pos struct ggml_tensor * ggml_get_rel_pos( struct ggml_context * ctx, struct ggml_tensor * a, int qh, int kh) { GGML_ASSERT(qh == kh); GGML_ASSERT(2*MAX(qh, kh) - 1 == a->ne[1]); bool is_node = false; if (a->grad) { GGML_ASSERT(false); // TODO: implement backward is_node = true; } const int64_t ne[4] = { a->ne[0], kh, qh, 1, }; struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F16, 3, ne); result->op = GGML_OP_GET_REL_POS; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; return result; } // ggml_add_rel_pos static struct ggml_tensor * ggml_add_rel_pos_impl( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * pw, struct ggml_tensor * ph, bool inplace) { GGML_ASSERT(ggml_are_same_shape(pw, ph)); GGML_ASSERT(ggml_is_contiguous(a)); GGML_ASSERT(ggml_is_contiguous(pw)); GGML_ASSERT(ggml_is_contiguous(ph)); GGML_ASSERT(ph->type == GGML_TYPE_F32); GGML_ASSERT(pw->type == GGML_TYPE_F32); GGML_ASSERT(pw->ne[3] == a->ne[2]); GGML_ASSERT(pw->ne[0]*pw->ne[0] == a->ne[0]); GGML_ASSERT(pw->ne[1]*pw->ne[2] == a->ne[1]); bool is_node = false; if (!inplace && (a->grad || pw->grad || ph->grad)) { is_node = true; } struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); ggml_set_op_params_i32(result, 0, inplace ? 1 : 0); result->op = GGML_OP_ADD_REL_POS; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; result->src[1] = pw; result->src[2] = ph; return result; } struct ggml_tensor * ggml_add_rel_pos( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * pw, struct ggml_tensor * ph) { return ggml_add_rel_pos_impl(ctx, a, pw, ph, false); } struct ggml_tensor * ggml_add_rel_pos_inplace( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * pw, struct ggml_tensor * ph) { return ggml_add_rel_pos_impl(ctx, a, pw, ph, true); } // gmml_unary static struct ggml_tensor * ggml_unary_impl( struct ggml_context * ctx, struct ggml_tensor * a, enum ggml_unary_op op, bool inplace) { bool is_node = false; if (!inplace && (a->grad)) { is_node = true; } struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); ggml_set_op_params_i32(result, 0, (int32_t) op); result->op = GGML_OP_UNARY; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; return result; } struct ggml_tensor * ggml_unary( struct ggml_context * ctx, struct ggml_tensor * a, enum ggml_unary_op op) { return ggml_unary_impl(ctx, a, op, false); } struct ggml_tensor * ggml_unary_inplace( struct ggml_context * ctx, struct ggml_tensor * a, enum ggml_unary_op op) { return ggml_unary_impl(ctx, a, op, true); } // ggml_map_unary static struct ggml_tensor * ggml_map_unary_impl_f32( struct ggml_context * ctx, struct ggml_tensor * a, const ggml_unary_op_f32_t fun, bool inplace) { bool is_node = false; if (!inplace && a->grad) { is_node = true; } struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); ggml_set_op_params(result, (const void *) &fun, sizeof(fun)); result->op = GGML_OP_MAP_UNARY; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; return result; } struct ggml_tensor * ggml_map_unary_f32( struct ggml_context * ctx, struct ggml_tensor * a, const ggml_unary_op_f32_t fun) { return ggml_map_unary_impl_f32(ctx, a, fun, false); } struct ggml_tensor * ggml_map_unary_inplace_f32( struct ggml_context * ctx, struct ggml_tensor * a, const ggml_unary_op_f32_t fun) { return ggml_map_unary_impl_f32(ctx, a, fun, true); } // ggml_map_binary static struct ggml_tensor * ggml_map_binary_impl_f32( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, const ggml_binary_op_f32_t fun, bool inplace) { GGML_ASSERT(ggml_are_same_shape(a, b)); bool is_node = false; if (!inplace && (a->grad || b->grad)) { is_node = true; } struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); ggml_set_op_params(result, (const void *) &fun, sizeof(fun)); result->op = GGML_OP_MAP_BINARY; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; result->src[1] = b; return result; } struct ggml_tensor * ggml_map_binary_f32( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, const ggml_binary_op_f32_t fun) { return ggml_map_binary_impl_f32(ctx, a, b, fun, false); } struct ggml_tensor * ggml_map_binary_inplace_f32( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, const ggml_binary_op_f32_t fun) { return ggml_map_binary_impl_f32(ctx, a, b, fun, true); } // ggml_map_custom1_f32 static struct ggml_tensor * ggml_map_custom1_impl_f32( struct ggml_context * ctx, struct ggml_tensor * a, const ggml_custom1_op_f32_t fun, bool inplace) { bool is_node = false; if (!inplace && a->grad) { is_node = true; } struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); ggml_set_op_params(result, (const void *) &fun, sizeof(fun)); result->op = GGML_OP_MAP_CUSTOM1_F32; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; return result; } struct ggml_tensor * ggml_map_custom1_f32( struct ggml_context * ctx, struct ggml_tensor * a, const ggml_custom1_op_f32_t fun) { return ggml_map_custom1_impl_f32(ctx, a, fun, false); } struct ggml_tensor * ggml_map_custom1_inplace_f32( struct ggml_context * ctx, struct ggml_tensor * a, const ggml_custom1_op_f32_t fun) { return ggml_map_custom1_impl_f32(ctx, a, fun, true); } // ggml_map_custom2_f32 static struct ggml_tensor * ggml_map_custom2_impl_f32( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, const ggml_custom2_op_f32_t fun, bool inplace) { bool is_node = false; if (!inplace && (a->grad || b->grad)) { is_node = true; } struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); ggml_set_op_params(result, (const void *) &fun, sizeof(fun)); result->op = GGML_OP_MAP_CUSTOM2_F32; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; result->src[1] = b; return result; } struct ggml_tensor * ggml_map_custom2_f32( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, const ggml_custom2_op_f32_t fun) { return ggml_map_custom2_impl_f32(ctx, a, b, fun, false); } struct ggml_tensor * ggml_map_custom2_inplace_f32( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, const ggml_custom2_op_f32_t fun) { return ggml_map_custom2_impl_f32(ctx, a, b, fun, true); } // ggml_map_custom3_f32 static struct ggml_tensor * ggml_map_custom3_impl_f32( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, struct ggml_tensor * c, const ggml_custom3_op_f32_t fun, bool inplace) { bool is_node = false; if (!inplace && (a->grad || b->grad || c->grad)) { is_node = true; } struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); ggml_set_op_params(result, (const void *) &fun, sizeof(fun)); result->op = GGML_OP_MAP_CUSTOM3_F32; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; result->src[1] = b; result->src[2] = c; return result; } struct ggml_tensor * ggml_map_custom3_f32( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, struct ggml_tensor * c, const ggml_custom3_op_f32_t fun) { return ggml_map_custom3_impl_f32(ctx, a, b, c, fun, false); } struct ggml_tensor * ggml_map_custom3_inplace_f32( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, struct ggml_tensor * c, const ggml_custom3_op_f32_t fun) { return ggml_map_custom3_impl_f32(ctx, a, b, c, fun, true); } // ggml_map_custom1 struct ggml_map_custom1_op_params { ggml_custom1_op_t fun; int n_tasks; void * userdata; }; static struct ggml_tensor * ggml_map_custom1_impl( struct ggml_context * ctx, struct ggml_tensor * a, const ggml_custom1_op_t fun, int n_tasks, void * userdata, bool inplace) { GGML_ASSERT(n_tasks == GGML_N_TASKS_MAX || n_tasks > 0); bool is_node = false; if (!inplace && a->grad) { is_node = true; } struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); struct ggml_map_custom1_op_params params = { /*.fun =*/ fun, /*.n_tasks =*/ n_tasks, /*.userdata =*/ userdata }; ggml_set_op_params(result, (const void *) ¶ms, sizeof(params)); result->op = GGML_OP_MAP_CUSTOM1; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; return result; } struct ggml_tensor * ggml_map_custom1( struct ggml_context * ctx, struct ggml_tensor * a, const ggml_custom1_op_t fun, int n_tasks, void * userdata) { return ggml_map_custom1_impl(ctx, a, fun, n_tasks, userdata, false); } struct ggml_tensor * ggml_map_custom1_inplace( struct ggml_context * ctx, struct ggml_tensor * a, const ggml_custom1_op_t fun, int n_tasks, void * userdata) { return ggml_map_custom1_impl(ctx, a, fun, n_tasks, userdata, true); } // ggml_map_custom2 struct ggml_map_custom2_op_params { ggml_custom2_op_t fun; int n_tasks; void * userdata; }; static struct ggml_tensor * ggml_map_custom2_impl( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, const ggml_custom2_op_t fun, int n_tasks, void * userdata, bool inplace) { GGML_ASSERT(n_tasks == GGML_N_TASKS_MAX || n_tasks > 0); bool is_node = false; if (!inplace && (a->grad || b->grad)) { is_node = true; } struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); struct ggml_map_custom2_op_params params = { /*.fun =*/ fun, /*.n_tasks =*/ n_tasks, /*.userdata =*/ userdata }; ggml_set_op_params(result, (const void *) ¶ms, sizeof(params)); result->op = GGML_OP_MAP_CUSTOM2; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; result->src[1] = b; return result; } struct ggml_tensor * ggml_map_custom2( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, const ggml_custom2_op_t fun, int n_tasks, void * userdata) { return ggml_map_custom2_impl(ctx, a, b, fun, n_tasks, userdata, false); } struct ggml_tensor * ggml_map_custom2_inplace( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, const ggml_custom2_op_t fun, int n_tasks, void * userdata) { return ggml_map_custom2_impl(ctx, a, b, fun, n_tasks, userdata, true); } // ggml_map_custom3 struct ggml_map_custom3_op_params { ggml_custom3_op_t fun; int n_tasks; void * userdata; }; static struct ggml_tensor * ggml_map_custom3_impl( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, struct ggml_tensor * c, const ggml_custom3_op_t fun, int n_tasks, void * userdata, bool inplace) { GGML_ASSERT(n_tasks == GGML_N_TASKS_MAX || n_tasks > 0); bool is_node = false; if (!inplace && (a->grad || b->grad || c->grad)) { is_node = true; } struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); struct ggml_map_custom3_op_params params = { /*.fun =*/ fun, /*.n_tasks =*/ n_tasks, /*.userdata =*/ userdata }; ggml_set_op_params(result, (const void *) ¶ms, sizeof(params)); result->op = GGML_OP_MAP_CUSTOM3; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; result->src[1] = b; result->src[2] = c; return result; } struct ggml_tensor * ggml_map_custom3( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, struct ggml_tensor * c, const ggml_custom3_op_t fun, int n_tasks, void * userdata) { return ggml_map_custom3_impl(ctx, a, b, c, fun, n_tasks, userdata, false); } struct ggml_tensor * ggml_map_custom3_inplace( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, struct ggml_tensor * c, const ggml_custom3_op_t fun, int n_tasks, void * userdata) { return ggml_map_custom3_impl(ctx, a, b, c, fun, n_tasks, userdata, true); } // ggml_cross_entropy_loss struct ggml_tensor * ggml_cross_entropy_loss( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) { GGML_ASSERT(ggml_are_same_shape(a, b)); bool is_node = false; if (a->grad || b->grad) { is_node = true; } struct ggml_tensor * result = ggml_new_tensor_1d(ctx, a->type, 1); result->op = GGML_OP_CROSS_ENTROPY_LOSS; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; result->src[1] = b; return result; } // ggml_cross_entropy_loss_back struct ggml_tensor * ggml_cross_entropy_loss_back( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, struct ggml_tensor * c) { GGML_ASSERT(ggml_are_same_shape(a, b)); GGML_ASSERT(ggml_is_scalar(c)); struct ggml_tensor * result = ggml_dup_tensor(ctx, a); result->op = GGML_OP_CROSS_ENTROPY_LOSS_BACK; result->grad = NULL; result->src[0] = a; result->src[1] = b; result->src[2] = c; return result; } //////////////////////////////////////////////////////////////////////////////// void ggml_set_param( struct ggml_context * ctx, struct ggml_tensor * tensor) { tensor->flags |= GGML_TENSOR_FLAG_PARAM; GGML_ASSERT(tensor->grad == NULL); tensor->grad = ggml_dup_tensor(ctx, tensor); ggml_format_name(tensor->grad, "%s (grad)", tensor->name); } // 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); if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } const size_t nb00 = src0->nb[0]; const size_t nb0 = dst->nb[0]; const int ith = params->ith; // thread index const int nth = params->nth; // number of threads // parallelize by elements const int ne = ggml_nelements(dst); const int dr = (ne + nth - 1) / nth; const int ie0 = dr * ith; const int ie1 = MIN(ie0 + dr, ne); if (ie0 < ie1) { memcpy( ((char *) dst->data + ie0*nb0), ((char *) src0->data + ie0*nb00), (ie1 - ie0) * ggml_type_size(src0->type)); } } static void ggml_compute_forward_dup_f16( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0)); if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } GGML_TENSOR_UNARY_OP_LOCALS const int ith = params->ith; // thread index const int nth = params->nth; // number of threads if (ggml_is_contiguous(src0) && ggml_is_contiguous(dst) && src0->type == dst->type) { ggml_compute_forward_dup_same_cont(params, dst); return; } // parallelize by rows const int nr = ne01; // number of rows per thread const int dr = (nr + nth - 1) / nth; // row range for this thread const int ir0 = dr * ith; const int ir1 = MIN(ir0 + dr, nr); if (src0->type == dst->type && ne00 == ne0 && nb00 == ggml_type_size(src0->type) && nb0 == ggml_type_size(dst->type)) { // copy by rows const size_t rs = ne00*nb00; for (int64_t i03 = 0; i03 < ne03; i03++) { for (int64_t i02 = 0; i02 < ne02; i02++) { for (int64_t i01 = ir0; i01 < ir1; i01++) { memcpy( ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3), ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03), rs); } } } return; } // TODO: add more special-case implementations for tensor shapes/strides that can benefit from memcpy if (ggml_is_contiguous(dst)) { if (nb00 == sizeof(ggml_fp16_t)) { if (dst->type == GGML_TYPE_F16) { size_t id = 0; const size_t rs = ne00 * nb00; char * dst_ptr = (char *) dst->data; for (int i03 = 0; i03 < ne03; i03++) { for (int i02 = 0; i02 < ne02; i02++) { id += rs * ir0; for (int i01 = ir0; i01 < ir1; i01++) { const char * src0_ptr = (char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03; memcpy(dst_ptr + id, src0_ptr, rs); id += rs; } id += rs * (ne01 - ir1); } } } else if (dst->type == GGML_TYPE_F32) { size_t id = 0; float * dst_ptr = (float *) dst->data; for (int i03 = 0; i03 < ne03; i03++) { for (int i02 = 0; i02 < ne02; i02++) { id += ne00 * ir0; for (int i01 = ir0; i01 < ir1; i01++) { const ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); for (int i00 = 0; i00 < ne00; i00++) { dst_ptr[id] = GGML_FP16_TO_FP32(src0_ptr[i00]); id++; } } id += ne00 * (ne01 - ir1); } } } else if (type_traits[dst->type].from_float) { ggml_from_float_t const quantize_row_q = type_traits[dst->type].from_float; float * src0_f32 = (float *) params->wdata + (ne00 + CACHE_LINE_SIZE_F32) * ith; size_t id = 0; size_t rs = nb0 * (ne00 / ggml_blck_size(dst->type)); char * dst_ptr = (char *) dst->data; for (int i03 = 0; i03 < ne03; i03++) { for (int i02 = 0; i02 < ne02; i02++) { id += rs * ir0; for (int i01 = ir0; i01 < ir1; i01++) { const ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); for (int i00 = 0; i00 < ne00; i00++) { src0_f32[i00] = GGML_FP16_TO_FP32(src0_ptr[i00]); } quantize_row_q(src0_f32, dst_ptr + id, ne00); id += rs; } id += rs * (ne01 - ir1); } } } else { GGML_ASSERT(false); // TODO: implement } } else { //printf("%s: this is not optimal - fix me\n", __func__); if (dst->type == GGML_TYPE_F32) { size_t id = 0; float * dst_ptr = (float *) dst->data; for (int i03 = 0; i03 < ne03; i03++) { for (int i02 = 0; i02 < ne02; i02++) { id += ne00 * ir0; for (int i01 = ir0; i01 < ir1; i01++) { for (int i00 = 0; i00 < ne00; i00++) { const ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); dst_ptr[id] = GGML_FP16_TO_FP32(*src0_ptr); id++; } } id += ne00 * (ne01 - ir1); } } } else if (dst->type == GGML_TYPE_F16) { size_t id = 0; ggml_fp16_t * dst_ptr = (ggml_fp16_t *) dst->data; for (int i03 = 0; i03 < ne03; i03++) { for (int i02 = 0; i02 < ne02; i02++) { id += ne00 * ir0; for (int i01 = ir0; i01 < ir1; i01++) { for (int i00 = 0; i00 < ne00; i00++) { const ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); dst_ptr[id] = *src0_ptr; id++; } } id += ne00 * (ne01 - ir1); } } } else { GGML_ASSERT(false); // TODO: implement } } return; } // dst counters int64_t i10 = 0; int64_t i11 = 0; int64_t i12 = 0; int64_t i13 = 0; if (dst->type == GGML_TYPE_F16) { for (int64_t i03 = 0; i03 < ne03; i03++) { for (int64_t i02 = 0; i02 < ne02; i02++) { i10 += ne00 * ir0; while (i10 >= ne0) { i10 -= ne0; if (++i11 == ne1) { i11 = 0; if (++i12 == ne2) { i12 = 0; if (++i13 == ne3) { i13 = 0; } } } } for (int64_t i01 = ir0; i01 < ir1; i01++) { for (int64_t i00 = 0; i00 < ne00; i00++) { const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); char * dst_ptr = ((char *) dst->data + i10*nb0 + i11*nb1 + i12*nb2 + i13*nb3); memcpy(dst_ptr, src0_ptr, sizeof(ggml_fp16_t)); if (++i10 == ne00) { i10 = 0; if (++i11 == ne01) { i11 = 0; if (++i12 == ne02) { i12 = 0; if (++i13 == ne03) { i13 = 0; } } } } } } i10 += ne00 * (ne01 - ir1); while (i10 >= ne0) { i10 -= ne0; if (++i11 == ne1) { i11 = 0; if (++i12 == ne2) { i12 = 0; if (++i13 == ne3) { i13 = 0; } } } } } } } else if (dst->type == GGML_TYPE_F32) { for (int64_t i03 = 0; i03 < ne03; i03++) { for (int64_t i02 = 0; i02 < ne02; i02++) { i10 += ne00 * ir0; while (i10 >= ne0) { i10 -= ne0; if (++i11 == ne1) { i11 = 0; if (++i12 == ne2) { i12 = 0; if (++i13 == ne3) { i13 = 0; } } } } for (int64_t i01 = ir0; i01 < ir1; i01++) { for (int64_t i00 = 0; i00 < ne00; i00++) { const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); char * dst_ptr = ((char *) dst->data + i10*nb0 + i11*nb1 + i12*nb2 + i13*nb3); *(float *) dst_ptr = GGML_FP16_TO_FP32(*(const ggml_fp16_t *) src0_ptr); if (++i10 == ne0) { i10 = 0; if (++i11 == ne1) { i11 = 0; if (++i12 == ne2) { i12 = 0; if (++i13 == ne3) { i13 = 0; } } } } } } i10 += ne00 * (ne01 - ir1); while (i10 >= ne0) { i10 -= ne0; if (++i11 == ne1) { i11 = 0; if (++i12 == ne2) { i12 = 0; if (++i13 == ne3) { i13 = 0; } } } } } } } else { GGML_ASSERT(false); // TODO: implement } } static void ggml_compute_forward_dup_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)); if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } GGML_TENSOR_UNARY_OP_LOCALS const int ith = params->ith; // thread index const int nth = params->nth; // number of threads if (ggml_is_contiguous(src0) && ggml_is_contiguous(dst) && src0->type == dst->type) { ggml_compute_forward_dup_same_cont(params, dst); return; } // parallelize by rows const int nr = ne01; // number of rows per thread const int dr = (nr + nth - 1) / nth; // row range for this thread const int ir0 = dr * ith; const int ir1 = MIN(ir0 + dr, nr); if (src0->type == dst->type && ne00 == ne0 && nb00 == ggml_type_size(src0->type) && nb0 == ggml_type_size(dst->type)) { // copy by rows const size_t rs = ne00*nb00; for (int64_t i03 = 0; i03 < ne03; i03++) { for (int64_t i02 = 0; i02 < ne02; i02++) { for (int64_t i01 = ir0; i01 < ir1; i01++) { memcpy( ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3), ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03), rs); } } } return; } // TODO: add more special-case implementations for tensor shapes/strides that can benefit from memcpy if (ggml_is_contiguous(dst)) { if (nb00 == sizeof(ggml_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 (type_traits[dst->type].from_float) { ggml_from_float_t const quantize_row_q = type_traits[dst->type].from_float; float * src0_f32 = (float *) params->wdata + (ne00 + CACHE_LINE_SIZE_F32) * ith; size_t id = 0; size_t rs = nb0 * (ne00 / ggml_blck_size(dst->type)); char * dst_ptr = (char *) dst->data; for (int i03 = 0; i03 < ne03; i03++) { for (int i02 = 0; i02 < ne02; i02++) { id += rs * ir0; for (int i01 = ir0; i01 < ir1; i01++) { const ggml_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_ASSERT(false); // TODO: implement } } else { //printf("%s: this is not optimal - fix me\n", __func__); if (dst->type == GGML_TYPE_F32) { size_t id = 0; float * dst_ptr = (float *) dst->data; for (int i03 = 0; i03 < ne03; i03++) { for (int i02 = 0; i02 < ne02; i02++) { id += ne00 * ir0; for (int i01 = ir0; i01 < ir1; i01++) { for (int i00 = 0; i00 < ne00; i00++) { const ggml_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_ASSERT(false); // TODO: implement } } return; } // dst counters int64_t i10 = 0; int64_t i11 = 0; int64_t i12 = 0; int64_t i13 = 0; if (dst->type == GGML_TYPE_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_ASSERT(false); // 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)); if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } GGML_TENSOR_UNARY_OP_LOCALS const int ith = params->ith; // thread index const int nth = params->nth; // number of threads if (ggml_is_contiguous(src0) && ggml_is_contiguous(dst) && src0->type == dst->type) { ggml_compute_forward_dup_same_cont(params, dst); return; } // parallelize by rows const int nr = ne01; // number of rows per thread const int dr = (nr + nth - 1) / nth; // row range for this thread const int ir0 = dr * ith; const int ir1 = MIN(ir0 + dr, nr); if (src0->type == dst->type && ne00 == ne0 && nb00 == ggml_type_size(src0->type) && nb0 == ggml_type_size(dst->type)) { // copy by rows const size_t rs = ne00*nb00; for (int64_t i03 = 0; i03 < ne03; i03++) { for (int64_t i02 = 0; i02 < ne02; i02++) { for (int64_t i01 = ir0; i01 < ir1; i01++) { memcpy( ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3), ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03), rs); } } } return; } if (ggml_is_contiguous(dst)) { // TODO: simplify if (nb00 == sizeof(float)) { if (dst->type == GGML_TYPE_F32) { size_t id = 0; const size_t rs = ne00 * nb00; char * dst_ptr = (char *) dst->data; for (int i03 = 0; i03 < ne03; i03++) { for (int i02 = 0; i02 < ne02; i02++) { id += rs * ir0; for (int i01 = ir0; i01 < ir1; i01++) { const char * src0_ptr = (char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03; memcpy(dst_ptr + id, src0_ptr, rs); id += rs; } id += rs * (ne01 - ir1); } } } else if (type_traits[dst->type].from_float) { ggml_from_float_t const quantize_row_q = type_traits[dst->type].from_float; size_t id = 0; size_t rs = nb0 * (ne00 / ggml_blck_size(dst->type)); char * dst_ptr = (char *) dst->data; for (int i03 = 0; i03 < ne03; i03++) { for (int i02 = 0; i02 < ne02; i02++) { id += rs * ir0; for (int i01 = ir0; i01 < ir1; i01++) { const float * src0_ptr = (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); quantize_row_q(src0_ptr, dst_ptr + id, ne00); id += rs; } id += rs * (ne01 - ir1); } } } else { GGML_ASSERT(false); // TODO: implement } } else { //printf("%s: this is not optimal - fix me\n", __func__); if (dst->type == GGML_TYPE_F32) { size_t id = 0; float * dst_ptr = (float *) dst->data; for (int i03 = 0; i03 < ne03; i03++) { for (int i02 = 0; i02 < ne02; i02++) { id += ne00 * ir0; for (int i01 = ir0; i01 < ir1; i01++) { for (int i00 = 0; i00 < ne00; i00++) { const float * src0_ptr = (float *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); dst_ptr[id] = *src0_ptr; id++; } } id += ne00 * (ne01 - ir1); } } } else if (dst->type == GGML_TYPE_F16) { size_t id = 0; ggml_fp16_t * dst_ptr = (ggml_fp16_t *) dst->data; for (int i03 = 0; i03 < ne03; i03++) { for (int i02 = 0; i02 < ne02; i02++) { id += ne00 * ir0; for (int i01 = ir0; i01 < ir1; i01++) { for (int i00 = 0; i00 < ne00; i00++) { const float * src0_ptr = (float *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); dst_ptr[id] = GGML_FP32_TO_FP16(*src0_ptr); id++; } } id += ne00 * (ne01 - ir1); } } } else 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_ASSERT(false); // TODO: implement } } return; } // dst counters int64_t i10 = 0; int64_t i11 = 0; int64_t i12 = 0; int64_t i13 = 0; if (dst->type == GGML_TYPE_F32) { for (int64_t i03 = 0; i03 < ne03; i03++) { for (int64_t i02 = 0; i02 < ne02; i02++) { i10 += ne00 * ir0; while (i10 >= ne0) { i10 -= ne0; if (++i11 == ne1) { i11 = 0; if (++i12 == ne2) { i12 = 0; if (++i13 == ne3) { i13 = 0; } } } } for (int64_t i01 = ir0; i01 < ir1; i01++) { for (int64_t i00 = 0; i00 < ne00; i00++) { const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); char * dst_ptr = ((char *) dst->data + i10*nb0 + i11*nb1 + i12*nb2 + i13*nb3); memcpy(dst_ptr, src0_ptr, sizeof(float)); if (++i10 == ne0) { i10 = 0; if (++i11 == ne1) { i11 = 0; if (++i12 == ne2) { i12 = 0; if (++i13 == ne3) { i13 = 0; } } } } } } i10 += ne00 * (ne01 - ir1); while (i10 >= ne0) { i10 -= ne0; if (++i11 == ne1) { i11 = 0; if (++i12 == ne2) { i12 = 0; if (++i13 == ne3) { i13 = 0; } } } } } } } else if (dst->type == GGML_TYPE_F16) { for (int64_t i03 = 0; i03 < ne03; i03++) { for (int64_t i02 = 0; i02 < ne02; i02++) { i10 += ne00 * ir0; while (i10 >= ne0) { i10 -= ne0; if (++i11 == ne1) { i11 = 0; if (++i12 == ne2) { i12 = 0; if (++i13 == ne3) { i13 = 0; } } } } for (int64_t i01 = ir0; i01 < ir1; i01++) { for (int64_t i00 = 0; i00 < ne00; i00++) { const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); char * dst_ptr = ((char *) dst->data + i10*nb0 + i11*nb1 + i12*nb2 + i13*nb3); *(ggml_fp16_t *) dst_ptr = GGML_FP32_TO_FP16(*(const float *) src0_ptr); if (++i10 == ne0) { i10 = 0; if (++i11 == ne1) { i11 = 0; if (++i12 == ne2) { i12 = 0; if (++i13 == ne3) { i13 = 0; } } } } } } i10 += ne00 * (ne01 - ir1); while (i10 >= ne0) { i10 -= ne0; if (++i11 == ne1) { i11 = 0; if (++i12 == ne2) { i12 = 0; if (++i13 == ne3) { i13 = 0; } } } } } } } else 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_ASSERT(false); // 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); if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } if (ggml_is_contiguous(src0) && ggml_is_contiguous(dst)) { ggml_compute_forward_dup_same_cont(params, dst); return; } GGML_TENSOR_UNARY_OP_LOCALS; 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_ASSERT(false); } break; } } // 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)); if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } const int ith = params->ith; const int nth = params->nth; #ifdef GGML_USE_CLBLAST if (src1->backend == GGML_BACKEND_TYPE_GPU) { // TODO: OpenCL kernel support full broadcast GGML_ASSERT(ggml_can_repeat_rows(src1, src0)); if (ith == 0) { ggml_cl_add(src0, src1, dst); } return; } #endif 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)); if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } const int ith = params->ith; const int nth = params->nth; const int nr = ggml_nrows(src0); GGML_TENSOR_BINARY_OP_LOCALS GGML_ASSERT(src0->type == GGML_TYPE_F16); GGML_ASSERT(src1->type == GGML_TYPE_F32); 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_ASSERT(false); } } 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)); if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } const int ith = params->ith; const int nth = params->nth; const int nr = ggml_nrows(src0); GGML_TENSOR_BINARY_OP_LOCALS GGML_ASSERT(src0->type == GGML_TYPE_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_ASSERT(false); } } 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)); if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } const int ith = params->ith; const int nth = params->nth; const int nr = ggml_nrows(src0); GGML_TENSOR_BINARY_OP_LOCALS GGML_ASSERT(src0->type == GGML_TYPE_F16); GGML_ASSERT(src1->type == GGML_TYPE_F16); GGML_ASSERT(dst->type == GGML_TYPE_F16); GGML_ASSERT( nb0 == sizeof(ggml_fp16_t)); GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); if (nb10 == sizeof(ggml_fp16_t)) { for (int ir = ir0; ir < ir1; ++ir) { // src0, src1 and dst are same shape => same indices const int i3 = ir/(ne2*ne1); const int i2 = (ir - i3*ne2*ne1)/ne1; const int i1 = (ir - i3*ne2*ne1 - i2*ne1); ggml_fp16_t * dst_ptr = (ggml_fp16_t *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1); ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01); ggml_fp16_t * src1_ptr = (ggml_fp16_t *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11); for (int i = 0; i < ne0; i++) { dst_ptr[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(src0_ptr[i]) + GGML_FP16_TO_FP32(src1_ptr[i])); } } } else { // src1 is not contiguous GGML_ASSERT(false); } } static void ggml_compute_forward_add_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)); if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } const int ith = params->ith; const int nth = params->nth; const int nr = ggml_nrows(src0); GGML_TENSOR_BINARY_OP_LOCALS GGML_ASSERT(src0->type == GGML_TYPE_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_ASSERT(false); } } 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)); if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } const int nr = ggml_nrows(src0); GGML_TENSOR_BINARY_OP_LOCALS const int ith = params->ith; const int nth = params->nth; const enum ggml_type type = src0->type; const enum ggml_type dtype = dst->type; ggml_to_float_t const dequantize_row_q = type_traits[type].to_float; ggml_from_float_t const quantize_row_q = 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_ASSERT(false); } } 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_ASSERT(false); } } 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_ASSERT(false); } } break; case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_1: case GGML_TYPE_Q5_0: case GGML_TYPE_Q5_1: case GGML_TYPE_Q8_0: case GGML_TYPE_Q2_K: case GGML_TYPE_Q3_K: case GGML_TYPE_Q4_K: case GGML_TYPE_Q5_K: case GGML_TYPE_Q6_K: 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: { ggml_compute_forward_add_q_f32(params, dst); } break; default: { GGML_ASSERT(false); } break; } } // 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)); if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } const int ith = params->ith; const int nth = params->nth; const int nr = ggml_nrows(src0); GGML_TENSOR_UNARY_OP_LOCALS GGML_ASSERT( nb0 == sizeof(float)); GGML_ASSERT(nb00 == sizeof(float)); // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); for (int ir = ir0; ir < ir1; ++ir) { // src0 and dst are same shape => same indices const int i3 = ir/(ne2*ne1); const int i2 = (ir - i3*ne2*ne1)/ne1; const int i1 = (ir - i3*ne2*ne1 - i2*ne1); #ifdef GGML_USE_ACCELERATE UNUSED(ggml_vec_add1_f32); vDSP_vadd( (float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01), 1, (float *) ((char *) src1->data), 0, (float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 ), 1, ne0); #else ggml_vec_add1_f32(ne0, (float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 ), (float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01), *(float *) src1->data); #endif } } static void ggml_compute_forward_add1_f16_f32( const struct ggml_compute_params * params, 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)); if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } // scalar to add const float v = *(float *) src1->data; const int ith = params->ith; const int nth = params->nth; const int nr = ggml_nrows(src0); GGML_TENSOR_UNARY_OP_LOCALS GGML_ASSERT(src0->type == GGML_TYPE_F16); GGML_ASSERT(src1->type == GGML_TYPE_F32); GGML_ASSERT(dst->type == GGML_TYPE_F16); GGML_ASSERT( nb0 == sizeof(ggml_fp16_t)); GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); for (int ir = ir0; ir < ir1; ++ir) { // src0 and dst are same shape => same indices const int i3 = ir/(ne2*ne1); const int i2 = (ir - i3*ne2*ne1)/ne1; const int i1 = (ir - i3*ne2*ne1 - i2*ne1); ggml_fp16_t * dst_ptr = (ggml_fp16_t *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 ); ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01); for (int i = 0; i < ne0; i++) { dst_ptr[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(src0_ptr[i]) + v); } } } static void ggml_compute_forward_add1_f16_f16( const struct ggml_compute_params * params, 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)); if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } // scalar to add const float v = GGML_FP16_TO_FP32(*(ggml_fp16_t *) src1->data); const int ith = params->ith; const int nth = params->nth; const int nr = ggml_nrows(src0); GGML_TENSOR_UNARY_OP_LOCALS GGML_ASSERT(src0->type == GGML_TYPE_F16); GGML_ASSERT(src1->type == GGML_TYPE_F16); GGML_ASSERT(dst->type == GGML_TYPE_F16); GGML_ASSERT( nb0 == sizeof(ggml_fp16_t)); GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); for (int ir = ir0; ir < ir1; ++ir) { // src0 and dst are same shape => same indices const int i3 = ir/(ne2*ne1); const int i2 = (ir - i3*ne2*ne1)/ne1; const int i1 = (ir - i3*ne2*ne1 - i2*ne1); ggml_fp16_t * dst_ptr = (ggml_fp16_t *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 ); ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01); for (int i = 0; i < ne0; i++) { dst_ptr[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(src0_ptr[i]) + v); } } } static void ggml_compute_forward_add1_q_f32( const struct ggml_compute_params * params, 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)); if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } // scalar to add const float v = *(float *) src1->data; const int ith = params->ith; const int nth = params->nth; const int nr = ggml_nrows(src0); GGML_TENSOR_UNARY_OP_LOCALS const enum ggml_type type = src0->type; ggml_to_float_t const dequantize_row_q = type_traits[type].to_float; ggml_from_float_t const quantize_row_q = type_traits[type].from_float; // we don't support permuted src0 GGML_ASSERT(nb00 == ggml_type_size(type)); // dst cannot be transposed or permuted GGML_ASSERT(nb0 <= nb1); GGML_ASSERT(nb1 <= nb2); GGML_ASSERT(nb2 <= nb3); GGML_ASSERT(ggml_is_quantized(src0->type)); GGML_ASSERT(dst->type == src0->type); GGML_ASSERT(src1->type == GGML_TYPE_F32); // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); float * wdata = (float *) params->wdata + (ne0 + CACHE_LINE_SIZE_F32) * ith; for (int ir = ir0; ir < ir1; ++ir) { // src0 and dst are same shape => same indices const int i3 = ir/(ne2*ne1); const int i2 = (ir - i3*ne2*ne1)/ne1; const int i1 = (ir - i3*ne2*ne1 - i2*ne1); void * src0_row = (void *) ((char *) src0->data + (i1*nb01 + i2*nb02 + i3*nb03)); void * dst_row = (void *) ((char *) dst->data + (i1*nb1 + i2*nb2 + i3*nb0 )); assert(ne0 % 32 == 0); // unquantize row from src0 to temp buffer dequantize_row_q(src0_row, wdata, ne0); // add src1 ggml_vec_acc1_f32(ne0, wdata, v); // quantize row to dst quantize_row_q(wdata, dst_row, ne0); } } static void ggml_compute_forward_add1_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)); if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } // scalar to add const float v = *(float *) src1->data; const int ith = params->ith; const int nth = params->nth; const int nr = ggml_nrows(src0); GGML_TENSOR_UNARY_OP_LOCALS GGML_ASSERT(src0->type == GGML_TYPE_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)); if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } // 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_ASSERT(false); } } 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_ASSERT(false); } } break; case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_1: case GGML_TYPE_Q5_0: case GGML_TYPE_Q5_1: case GGML_TYPE_Q8_0: case GGML_TYPE_Q8_1: case GGML_TYPE_Q2_K: case GGML_TYPE_Q3_K: case GGML_TYPE_Q4_K: case GGML_TYPE_Q5_K: case GGML_TYPE_Q6_K: 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: { ggml_compute_forward_add1_q_f32(params, dst); } break; default: { GGML_ASSERT(false); } break; } } // 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 && (params->type == GGML_TASK_TYPE_INIT)) { if (params->ith != 0) { return; } // memcpy needs to be synchronized across threads to avoid race conditions. // => do it in INIT phase memcpy( ((char *) dst->data), ((char *) src0->data), ggml_nbytes(dst)); } if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } const int ith = params->ith; const int nth = params->nth; const int nr = ggml_nrows(src1); const int nc = src1->ne[0]; GGML_TENSOR_LOCALS(int64_t, ne1, src1, ne) GGML_TENSOR_LOCALS(size_t, nb1, src1, nb) // src0 and dst as viewed during acc const size_t nb0 = ggml_element_size(src0); const size_t nb00 = nb0; const size_t nb01 = nb1; const size_t nb02 = nb2; const size_t nb03 = nb3; GGML_ASSERT(offset + (ne10 == 0 ? 0 : ne10-1)*nb0 + (ne11 == 0 ? 0 : ne11-1)*nb1 + (ne12 == 0 ? 0 : ne12-1)*nb2 + (ne13 == 0 ? 0 : ne13-1)*nb3 < ggml_nbytes(dst)); GGML_ASSERT(offset + (ne10 == 0 ? 0 : ne10-1)*nb00 + (ne11 == 0 ? 0 : ne11-1)*nb01 + (ne12 == 0 ? 0 : ne12-1)*nb02 + (ne13 == 0 ? 0 : ne13-1)*nb03 < ggml_nbytes(src0)); GGML_ASSERT(nb10 == sizeof(float)); // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); for (int ir = ir0; ir < ir1; ++ir) { // src0 and dst are viewed with shape of src1 and offset // => same indices const int i3 = ir/(ne12*ne11); const int i2 = (ir - i3*ne12*ne11)/ne11; const int i1 = (ir - i3*ne12*ne11 - i2*ne11); #ifdef GGML_USE_ACCELERATE vDSP_vadd( (float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + offset), 1, (float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11), 1, (float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + offset), 1, nc); #else ggml_vec_add_f32(nc, (float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + offset), (float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + offset), (float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11)); #endif } } static void ggml_compute_forward_acc( const struct ggml_compute_params * params, 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_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: default: { GGML_ASSERT(false); } break; } } // 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(params->ith == 0); assert(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } const int nr = ggml_nrows(src0); GGML_TENSOR_BINARY_OP_LOCALS GGML_ASSERT( nb0 == sizeof(float)); GGML_ASSERT(nb00 == sizeof(float)); if (nb10 == sizeof(float)) { for (int ir = 0; ir < nr; ++ir) { // src0, src1 and dst are same shape => same indices const int i3 = ir/(ne2*ne1); const int i2 = (ir - i3*ne2*ne1)/ne1; const int i1 = (ir - i3*ne2*ne1 - i2*ne1); #ifdef GGML_USE_ACCELERATE vDSP_vsub( (float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11), 1, (float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01), 1, (float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 ), 1, ne0); #else ggml_vec_sub_f32(ne0, (float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 ), (float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01), (float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11)); #endif // } // } } } else { // src1 is not contiguous for (int ir = 0; ir < nr; ++ir) { // src0, src1 and dst are same shape => same indices const int i3 = ir/(ne2*ne1); const int i2 = (ir - i3*ne2*ne1)/ne1; const int i1 = (ir - i3*ne2*ne1 - i2*ne1); float * dst_ptr = (float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 ); float * src0_ptr = (float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01); for (int i0 = 0; i0 < ne0; i0++) { float * src1_ptr = (float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11 + i0*nb10); dst_ptr[i0] = src0_ptr[i0] - *src1_ptr; } } } } static void ggml_compute_forward_sub( const struct ggml_compute_params * params, 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_ASSERT(false); } break; } } // 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)); if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } const int ith = params->ith; const int nth = params->nth; #if defined(GGML_USE_CLBLAST) if (src1->backend == GGML_BACKEND_TYPE_GPU) { // TODO: OpenCL kernel support full broadcast GGML_ASSERT(ggml_can_repeat_rows(src1, src0)); if (ith == 0) { ggml_cl_mul(src0, src1, dst); } return; } #endif const int64_t nr = ggml_nrows(src0); GGML_TENSOR_BINARY_OP_LOCALS GGML_ASSERT( nb0 == sizeof(float)); GGML_ASSERT(nb00 == sizeof(float)); 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_ASSERT(false); } break; } } // 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)); if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } 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_ASSERT(false); } break; } } // 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]; assert(params->ith == 0); assert(ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } const int n = ggml_nrows(src0); const int nc = src0->ne[0]; assert( dst->nb[0] == sizeof(float)); assert(src0->nb[0] == sizeof(float)); for (int i = 0; i < n; i++) { ggml_vec_sqr_f32(nc, (float *) ((char *) dst->data + i*( dst->nb[1])), (float *) ((char *) src0->data + i*(src0->nb[1]))); } } static void ggml_compute_forward_sqr( const struct ggml_compute_params * params, 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_ASSERT(false); } break; } } // 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]; assert(params->ith == 0); assert(ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } const int n = ggml_nrows(src0); const int nc = src0->ne[0]; assert( dst->nb[0] == sizeof(float)); assert(src0->nb[0] == sizeof(float)); for (int i = 0; i < n; i++) { ggml_vec_sqrt_f32(nc, (float *) ((char *) dst->data + i*( dst->nb[1])), (float *) ((char *) src0->data + i*(src0->nb[1]))); } } static void ggml_compute_forward_sqrt( const struct ggml_compute_params * params, 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_ASSERT(false); } break; } } // 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]; GGML_ASSERT(params->ith == 0); GGML_ASSERT(ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } const int n = ggml_nrows(src0); const int nc = src0->ne[0]; GGML_ASSERT( dst->nb[0] == sizeof(float)); GGML_ASSERT(src0->nb[0] == sizeof(float)); for (int i = 0; i < n; i++) { ggml_vec_log_f32(nc, (float *) ((char *) dst->data + i*( dst->nb[1])), (float *) ((char *) src0->data + i*(src0->nb[1]))); } } static void ggml_compute_forward_log( const struct ggml_compute_params * params, 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_ASSERT(false); } break; } } // 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]; assert(params->ith == 0); assert(ggml_is_scalar(dst)); if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } assert(ggml_is_scalar(dst)); assert(src0->nb[0] == sizeof(float)); GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne) GGML_TENSOR_LOCALS(size_t, nb0, src0, nb) ggml_float sum = 0; ggml_float row_sum = 0; for (int64_t i03 = 0; i03 < ne03; i03++) { for (int64_t i02 = 0; i02 < ne02; i02++) { for (int64_t i01 = 0; i01 < ne01; i01++) { ggml_vec_sum_f32_ggf(ne00, &row_sum, (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03)); sum += row_sum; } } } ((float *) dst->data)[0] = sum; } static void ggml_compute_forward_sum_f16( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; assert(params->ith == 0); assert(ggml_is_scalar(dst)); if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } assert(src0->nb[0] == sizeof(ggml_fp16_t)); GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne) GGML_TENSOR_LOCALS(size_t, nb0, src0, nb) float sum = 0; float row_sum = 0; for (int64_t i03 = 0; i03 < ne03; i03++) { for (int64_t i02 = 0; i02 < ne02; i02++) { for (int64_t i01 = 0; i01 < ne01; i01++) { ggml_vec_sum_f16_ggf(ne00, &row_sum, (ggml_fp16_t *) ((char *) src0->data + i01 * nb01 + i02 * nb02 + i03 * nb03)); sum += row_sum; } } } ((ggml_fp16_t *) dst->data)[0] = GGML_FP32_TO_FP16(sum); } static void ggml_compute_forward_sum_bf16( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; assert(params->ith == 0); assert(ggml_is_scalar(dst)); if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } 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_ASSERT(false); } break; } } // 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]; GGML_ASSERT(params->ith == 0); if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } GGML_ASSERT(src0->nb[0] == sizeof(float)); GGML_ASSERT(dst->nb[0] == sizeof(float)); GGML_TENSOR_UNARY_OP_LOCALS GGML_ASSERT(ne0 == 1); GGML_ASSERT(ne1 == ne01); GGML_ASSERT(ne2 == ne02); GGML_ASSERT(ne3 == ne03); for (int64_t i3 = 0; i3 < ne03; i3++) { for (int64_t i2 = 0; i2 < ne02; i2++) { for (int64_t i1 = 0; i1 < ne01; i1++) { float * src_row = (float *) ((char *) src0->data + i1*nb01 + i2*nb02 + i3*nb03); float * dst_row = (float *) ((char *) dst->data + i1*nb1 + i2*nb2 + i3*nb3); float row_sum = 0; ggml_vec_sum_f32(ne00, &row_sum, src_row); dst_row[0] = row_sum; } } } } static void ggml_compute_forward_sum_rows( const struct ggml_compute_params * params, 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_ASSERT(false); } break; } } // 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]; assert(params->ith == 0); if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } assert(src0->nb[0] == sizeof(float)); GGML_TENSOR_UNARY_OP_LOCALS assert(ne0 == 1); assert(ne1 == ne01); assert(ne2 == ne02); assert(ne3 == ne03); UNUSED(ne0); UNUSED(ne1); UNUSED(ne2); UNUSED(ne3); for (int64_t i03 = 0; i03 < ne03; i03++) { for (int64_t i02 = 0; i02 < ne02; i02++) { for (int64_t i01 = 0; i01 < ne01; i01++) { ggml_vec_sum_f32(ne00, (float *) ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3), (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03)); *(float *) ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3) /= (float) ne00; } } } } static void ggml_compute_forward_mean( const struct ggml_compute_params * params, 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_ASSERT(false); } break; } } // 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]; assert(params->ith == 0); if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } assert(src0->nb[0] == sizeof(float)); assert(dst->nb[0] == sizeof(float)); const int64_t ne00 = src0->ne[0]; const int64_t ne01 = src0->ne[1]; const size_t nb01 = src0->nb[1]; const size_t nb0 = dst->nb[0]; for (int64_t i1 = 0; i1 < ne01; i1++) { float * src = (float *) ((char *) src0->data + i1*nb01); int32_t * dst_ = (int32_t *) ((char *) dst->data + i1*nb0); int v = 0; ggml_vec_argmax_f32(ne00, &v, src); dst_[0] = v; } } static void ggml_compute_forward_argmax( const struct ggml_compute_params * params, 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_ASSERT(false); } break; } } // 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]; GGML_ASSERT(params->ith == 0); GGML_ASSERT(ggml_can_repeat(src0, dst)); if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } GGML_TENSOR_UNARY_OP_LOCALS // guaranteed to be an integer due to the check in ggml_can_repeat const int nr0 = (int)(ne0/ne00); const int nr1 = (int)(ne1/ne01); const int nr2 = (int)(ne2/ne02); const int nr3 = (int)(ne3/ne03); // TODO: support for transposed / permuted tensors GGML_ASSERT(nb0 == sizeof(float)); GGML_ASSERT(nb00 == sizeof(float)); // TODO: maybe this is not optimal? for (int i3 = 0; i3 < nr3; i3++) { for (int k3 = 0; k3 < ne03; k3++) { for (int i2 = 0; i2 < nr2; i2++) { for (int k2 = 0; k2 < ne02; k2++) { for (int i1 = 0; i1 < nr1; i1++) { for (int k1 = 0; k1 < ne01; k1++) { for (int i0 = 0; i0 < nr0; i0++) { ggml_vec_cpy_f32(ne00, (float *) ((char *) dst->data + (i3*ne03 + k3)*nb3 + (i2*ne02 + k2)*nb2 + (i1*ne01 + k1)*nb1 + (i0*ne00)*nb0), (float *) ((char *) src0->data + ( k3)*nb03 + ( k2)*nb02 + ( k1)*nb01)); } } } } } } } } static void ggml_compute_forward_repeat_f16( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; GGML_ASSERT(params->ith == 0); GGML_ASSERT(ggml_can_repeat(src0, dst)); if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } GGML_TENSOR_UNARY_OP_LOCALS // guaranteed to be an integer due to the check in ggml_can_repeat const int nr0 = (int)(ne0/ne00); const int nr1 = (int)(ne1/ne01); const int nr2 = (int)(ne2/ne02); const int nr3 = (int)(ne3/ne03); // TODO: support for transposed / permuted tensors GGML_ASSERT(nb0 == sizeof(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_ASSERT(false); } break; } } // 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]; GGML_ASSERT(params->ith == 0); GGML_ASSERT(ggml_can_repeat(dst, src0)); if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } GGML_TENSOR_UNARY_OP_LOCALS // guaranteed to be an integer due to the check in ggml_can_repeat const int nr0 = (int)(ne00/ne0); const int nr1 = (int)(ne01/ne1); const int nr2 = (int)(ne02/ne2); const int nr3 = (int)(ne03/ne3); // TODO: support for transposed / permuted tensors GGML_ASSERT(nb0 == sizeof(float)); GGML_ASSERT(nb00 == sizeof(float)); if (ggml_is_contiguous(dst)) { ggml_vec_set_f32(ne0*ne1*ne2*ne3, dst->data, 0); } else { for (int k3 = 0; k3 < ne3; k3++) { for (int k2 = 0; k2 < ne2; k2++) { for (int k1 = 0; k1 < ne1; k1++) { ggml_vec_set_f32(ne0, (float *) ((char *) dst->data + k1*nb1 + k2*nb2 + k3*nb3), 0); } } } } // TODO: maybe this is not optimal? for (int i3 = 0; i3 < nr3; i3++) { for (int k3 = 0; k3 < ne3; k3++) { for (int i2 = 0; i2 < nr2; i2++) { for (int k2 = 0; k2 < ne2; k2++) { for (int i1 = 0; i1 < nr1; i1++) { for (int k1 = 0; k1 < ne1; k1++) { for (int i0 = 0; i0 < nr0; i0++) { ggml_vec_acc_f32(ne0, (float *) ((char *) dst->data + ( k3)*nb3 + ( k2)*nb2 + ( k1)*nb1), (float *) ((char *) src0->data + (i3*ne3 + k3)*nb03 + (i2*ne2 + k2)*nb02 + (i1*ne1 + k1)*nb01 + (i0*ne0)*nb00)); } } } } } } } } static void ggml_compute_forward_repeat_back( const struct ggml_compute_params * params, 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_ASSERT(false); } break; } } // 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]; if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } GGML_ASSERT(src0->nb[0] == sizeof(float)); const int ith = params->ith; const int nth = params->nth; GGML_TENSOR_BINARY_OP_LOCALS // TODO: support for transposed / permuted tensors GGML_ASSERT(nb0 == sizeof(float)); GGML_ASSERT(nb00 == sizeof(float)); GGML_ASSERT(nb10 == sizeof(float)); for (int i3 = 0; i3 < ne3; i3++) { for (int i2 = ith; i2 < ne2; i2 += nth) { if (i2 < ne02) { // src0 for (int i1 = 0; i1 < ne1; i1++) { for (int i0 = 0; i0 < ne0; i0++) { const float * x = (float *)((char *) src0->data + i0 * nb00 + i1 * nb01 + i2 * nb02 + i3 * nb03); float * y = (float *)((char *)dst->data + i0 * nb0 + i1 * nb1 + i2 * nb2 + i3 * nb3); *y = *x; } } } // src1 else { for (int i1 = 0; i1 < ne1; i1++) { for (int i0 = 0; i0 < ne0; i0++) { const float * x = (float *)((char *) src1->data + i0 * nb10 + i1 * nb11 + (i2 - ne02) * nb12 + i3 * 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_ASSERT(false); } break; } } // 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]; assert(params->ith == 0); assert(ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } const int n = ggml_nrows(src0); const int nc = src0->ne[0]; assert(dst->nb[0] == sizeof(float)); assert(src0->nb[0] == sizeof(float)); for (int i = 0; i < n; i++) { ggml_vec_abs_f32(nc, (float *) ((char *) dst->data + i*( dst->nb[1])), (float *) ((char *) src0->data + i*(src0->nb[1]))); } } static void ggml_compute_forward_abs( const struct ggml_compute_params * params, 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_ASSERT(false); } break; } } // 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]; assert(params->ith == 0); assert(ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } const int n = ggml_nrows(src0); const int nc = src0->ne[0]; assert(dst->nb[0] == sizeof(float)); assert(src0->nb[0] == sizeof(float)); for (int i = 0; i < n; i++) { ggml_vec_sgn_f32(nc, (float *) ((char *) dst->data + i*( dst->nb[1])), (float *) ((char *) src0->data + i*(src0->nb[1]))); } } static void ggml_compute_forward_sgn( const struct ggml_compute_params * params, 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_ASSERT(false); } break; } } // 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]; assert(params->ith == 0); assert(ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } const int n = ggml_nrows(src0); const int nc = src0->ne[0]; assert(dst->nb[0] == sizeof(float)); assert(src0->nb[0] == sizeof(float)); for (int i = 0; i < n; i++) { ggml_vec_neg_f32(nc, (float *) ((char *) dst->data + i*( dst->nb[1])), (float *) ((char *) src0->data + i*(src0->nb[1]))); } } static void ggml_compute_forward_neg( const struct ggml_compute_params * params, 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_ASSERT(false); } break; } } // 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]; assert(params->ith == 0); assert(ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } const int n = ggml_nrows(src0); const int nc = src0->ne[0]; assert(dst->nb[0] == sizeof(float)); assert(src0->nb[0] == sizeof(float)); for (int i = 0; i < n; i++) { ggml_vec_step_f32(nc, (float *) ((char *) dst->data + i*( dst->nb[1])), (float *) ((char *) src0->data + i*(src0->nb[1]))); } } static void ggml_compute_forward_step( const struct ggml_compute_params * params, 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_ASSERT(false); } break; } } // 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]; assert(params->ith == 0); assert(ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } const int n = ggml_nrows(src0); const int nc = src0->ne[0]; assert(dst->nb[0] == sizeof(float)); assert(src0->nb[0] == sizeof(float)); for (int i = 0; i < n; i++) { ggml_vec_tanh_f32(nc, (float *) ((char *) dst->data + i*( dst->nb[1])), (float *) ((char *) src0->data + i*(src0->nb[1]))); } } static void ggml_compute_forward_tanh( const struct ggml_compute_params * params, 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_ASSERT(false); } break; } } // 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]; assert(params->ith == 0); assert(ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } const int n = ggml_nrows(src0); const int nc = src0->ne[0]; assert(dst->nb[0] == sizeof(float)); assert(src0->nb[0] == sizeof(float)); for (int i = 0; i < n; i++) { ggml_vec_elu_f32(nc, (float *) ((char *) dst->data + i*( dst->nb[1])), (float *) ((char *) src0->data + i*(src0->nb[1]))); } } static void ggml_compute_forward_elu( const struct ggml_compute_params * params, 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_ASSERT(false); } break; } } // 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]; assert(params->ith == 0); assert(ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } const int n = ggml_nrows(src0); const int nc = src0->ne[0]; assert(dst->nb[0] == sizeof(float)); assert(src0->nb[0] == sizeof(float)); for (int i = 0; i < n; i++) { ggml_vec_relu_f32(nc, (float *) ((char *) dst->data + i*( dst->nb[1])), (float *) ((char *) src0->data + i*(src0->nb[1]))); } } static void ggml_compute_forward_relu( const struct ggml_compute_params * params, 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_ASSERT(false); } break; } } // 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]; assert(params->ith == 0); assert(ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } const int n = ggml_nrows(src0); const int nc = src0->ne[0]; assert(dst->nb[0] == sizeof(float)); assert(src0->nb[0] == sizeof(float)); for (int i = 0; i < n; i++) { ggml_vec_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_ASSERT(false); } break; } } // 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]; GGML_ASSERT(ggml_is_contiguous_except_dim_1(src0)); GGML_ASSERT(ggml_is_contiguous_except_dim_1(dst)); GGML_ASSERT(ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } const int ith = params->ith; const int nth = params->nth; const int nc = src0->ne[0]; const int nr = ggml_nrows(src0); // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); for (int i1 = ir0; i1 < ir1; i1++) { ggml_vec_gelu_f32(nc, (float *) ((char *) dst->data + i1*( dst->nb[1])), (float *) ((char *) src0->data + i1*(src0->nb[1]))); #ifndef NDEBUG for (int k = 0; k < nc; k++) { const float x = ((float *) ((char *) dst->data + i1*( dst->nb[1])))[k]; UNUSED(x); assert(!isnan(x)); assert(!isinf(x)); } #endif } } static void ggml_compute_forward_gelu( const struct ggml_compute_params * params, 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_ASSERT(false); } break; } } // 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]; GGML_ASSERT(ggml_is_contiguous_except_dim_1(src0)); GGML_ASSERT(ggml_is_contiguous_except_dim_1(dst)); GGML_ASSERT(ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } const int ith = params->ith; const int nth = params->nth; const int nc = src0->ne[0]; const int nr = ggml_nrows(src0); // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); for (int i1 = ir0; i1 < ir1; i1++) { ggml_vec_gelu_quick_f32(nc, (float *) ((char *) dst->data + i1*( dst->nb[1])), (float *) ((char *) src0->data + i1*(src0->nb[1]))); #ifndef NDEBUG for (int k = 0; k < nc; k++) { const float x = ((float *) ((char *) dst->data + i1*( dst->nb[1])))[k]; UNUSED(x); assert(!isnan(x)); assert(!isinf(x)); } #endif } } static void ggml_compute_forward_gelu_quick( const struct ggml_compute_params * params, 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_ASSERT(false); } break; } } // 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]; GGML_ASSERT(ggml_is_contiguous_except_dim_1(src0)); GGML_ASSERT(ggml_is_contiguous_except_dim_1(dst)); GGML_ASSERT(ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } const int ith = params->ith; const int nth = params->nth; const int nc = src0->ne[0]; const int nr = ggml_nrows(src0); // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); for (int i1 = ir0; i1 < ir1; i1++) { ggml_vec_silu_f32(nc, (float *) ((char *) dst->data + i1*( dst->nb[1])), (float *) ((char *) src0->data + i1*(src0->nb[1]))); #ifndef NDEBUG for (int k = 0; k < nc; k++) { const float x = ((float *) ((char *) dst->data + i1*(dst->nb[1])))[k]; UNUSED(x); assert(!isnan(x)); assert(!isinf(x)); } #endif } } static void ggml_compute_forward_silu( const struct ggml_compute_params * params, 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_ASSERT(false); } break; } } // 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]; assert(params->ith == 0); assert(ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } 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_ASSERT(false); } break; } } // 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]; GGML_ASSERT(ggml_is_contiguous_except_dim_1(grad)); GGML_ASSERT(ggml_is_contiguous_except_dim_1(src0)); GGML_ASSERT(ggml_is_contiguous_except_dim_1(dst)); GGML_ASSERT(ggml_are_same_shape(src0, dst)); GGML_ASSERT(ggml_are_same_shape(src0, grad)); if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } const int ith = params->ith; const int nth = params->nth; const int nc = src0->ne[0]; const int nr = ggml_nrows(src0); // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); for (int i1 = ir0; i1 < ir1; i1++) { ggml_vec_silu_backward_f32(nc, (float *) ((char *) dst->data + i1*( dst->nb[1])), (float *) ((char *) src0->data + i1*(src0->nb[1])), (float *) ((char *) grad->data + i1*(grad->nb[1]))); #ifndef NDEBUG for (int k = 0; k < nc; k++) { const float x = ((float *) ((char *) dst->data + i1*( dst->nb[1])))[k]; UNUSED(x); assert(!isnan(x)); assert(!isinf(x)); } #endif } } static void ggml_compute_forward_silu_back( const struct ggml_compute_params * params, 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_ASSERT(false); } break; } } 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]; assert(params->ith == 0); assert(ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } const int n = ggml_nrows(src0); const int nc = src0->ne[0]; assert(dst->nb[0] == sizeof(float)); assert(src0->nb[0] == sizeof(float)); for (int i = 0; i < n; i++) { ggml_vec_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_ASSERT(false); } break; } } 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]; assert(params->ith == 0); assert(ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } const int n = ggml_nrows(src0); const int nc = src0->ne[0]; assert(dst->nb[0] == sizeof(float)); assert(src0->nb[0] == sizeof(float)); for (int i = 0; i < n; i++) { ggml_vec_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_ASSERT(false); } break; } } // 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)); if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } GGML_ASSERT(src0->nb[0] == sizeof(float)); const int ith = params->ith; const int nth = params->nth; GGML_TENSOR_UNARY_OP_LOCALS float eps; memcpy(&eps, dst->op_params, sizeof(float)); 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_ASSERT(false); } break; } } // 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)); if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } GGML_ASSERT(src0->nb[0] == sizeof(float)); const int ith = params->ith; const int nth = params->nth; GGML_TENSOR_UNARY_OP_LOCALS float eps; memcpy(&eps, dst->op_params, sizeof(float)); 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_ASSERT(false); } break; } } 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)); if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } 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_ASSERT(false); } break; } } // 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)); if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } GGML_ASSERT(src0->nb[0] == sizeof(float)); const int ith = params->ith; const int nth = params->nth; GGML_TENSOR_UNARY_OP_LOCALS const float eps = 1e-6f; // TODO: make this a parameter // TODO: optimize 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_ASSERT(false); } break; } } // ggml_compute_forward_mul_mat #if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) // helper function to determine if it is better to use BLAS or not // for large matrices, BLAS is faster static bool ggml_compute_forward_mul_mat_use_blas(struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; const struct ggml_tensor * src1 = dst->src[1]; //const int64_t ne00 = src0->ne[0]; //const int64_t ne01 = src0->ne[1]; const int64_t ne10 = src1->ne[0]; const int64_t ne0 = dst->ne[0]; const int64_t ne1 = dst->ne[1]; // NOTE: with GGML_OP_MUL_MAT_ID we don't want to go through the BLAS branch because it will dequantize (to_float) // all the experts for each batch element and the processing would become incredibly slow // TODO: find the optimal values for these if (dst->op != GGML_OP_MUL_MAT_ID && ggml_is_contiguous(src0) && ggml_is_contiguous(src1) && //src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && (ne0 >= 32 && ne1 >= 32 && ne10 >= 32)) { /*printf("BLAS: %d %d %d %d %d\n", ne0, ne1, ne10, ne00, ne01);*/ return true; } return false; } #endif static void ggml_compute_forward_mul_mat_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[type].vec_dot; enum ggml_type const vec_dot_type = type_traits[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, struct ggml_compute_state * state) { const struct ggml_tensor * src0 = dst->src[0]; const struct ggml_tensor * src1 = dst->src[1]; int64_t t0 = ggml_perf_time_us(); UNUSED(t0); GGML_TENSOR_BINARY_OP_LOCALS const int ith = params->ith; const int nth = params->nth; const enum ggml_type type = src0->type; enum ggml_type const vec_dot_type = type_traits[type].vec_dot_type; ggml_from_float_t const from_float_to_vec_dot = type_traits[vec_dot_type].from_float; int64_t const vec_dot_num_rows = type_traits[type].nrows; 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); // broadcast factors const int64_t r2 = ne12 / ne02; const int64_t r3 = ne13 / ne03; UNUSED(r2); UNUSED(r3); // nb01 >= nb00 - src0 is not transposed // compute by src0 rows #if defined(GGML_USE_CLBLAST) if (ggml_cl_can_mul_mat(src0, src1, dst)) { if (params->ith == 0 && params->type == GGML_TASK_TYPE_COMPUTE) { ggml_cl_mul_mat(src0, src1, dst, params->wdata, params->wsize); } return; } #endif #if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) if (ggml_compute_forward_mul_mat_use_blas(dst)) { const int64_t ne_plane = ne01*ne00; const size_t desired_wsize = ne13*ne12*ne_plane*sizeof(float); UNUSED(desired_wsize); if (params->type == GGML_TASK_TYPE_INIT) { if (type != GGML_TYPE_F32) { assert(params->wsize >= desired_wsize); // parallelize by src0 rows for (int64_t i13 = 0; i13 < ne13; i13++) { for (int64_t i12 = 0; i12 < ne12; i12++) { // broadcast src0 into src1 across 2nd,3rd dimension const int64_t i03 = i13/r3; const int64_t i02 = i12/r2; const void * x = (char *) src0->data + i02*nb02 + i03*nb03; float * const wdata = (float *) params->wdata + i13*ne12*ne_plane + i12*ne_plane; ggml_to_float_t const to_float = type_traits[type].to_float; for (int64_t i01 = ith; i01 < ne01; i01 += nth) { to_float((const char *) x + i01*nb01, wdata + i01*ne00, ne00); } } } } return; } if (params->type == GGML_TASK_TYPE_FINALIZE) { return; } // perform sgemm, parallelization controlled by blas lib if (ith != 0) { return; } //const int64_t tgemm0 = ggml_perf_time_us(); for (int64_t i13 = 0; i13 < ne13; i13++) { for (int64_t i12 = 0; i12 < ne12; i12++) { const int64_t i03 = i13/r3; const int64_t i02 = i12/r2; const void * x = (char *) src0->data + i02*nb02 + i03*nb03; const float * y = (float *) ((char *) src1->data + i12*nb12 + i13*nb13); float * d = (float *) ((char *) dst->data + i12*nb2 + i13*nb3); if (type != GGML_TYPE_F32) { x = (float *) params->wdata + i13*ne12*ne_plane + i12*ne_plane; } cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasTrans, ne1, ne01, ne10, 1.0f, y, ne10, x, ne00, 0.0f, d, ne01); } } //printf("cblas_sgemm = %.3f ms, %lld flops\n", (ggml_perf_time_us() - tgemm0)/1000.0, ne13*ne12*ne1*ne01*ne10*2); //printf("CBLAS = %f ms, %d x %d x %d x %d\n", (ggml_perf_time_us() - t0)/1000.0, ne0, ne1, ne2, ne3); return; } #endif #if GGML_USE_LLAMAFILE 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, params->type, src0->type, src1->type, dst->type)) goto UseGgmlGemm1; return; } UseGgmlGemm1:; #endif if (params->type == GGML_TASK_TYPE_INIT) { if (ith != 0) { return; } // Every thread starts at ith, so the first unprocessed chunk is nth. This save a bit of coordination right at the start. atomic_store(&state->shared->current_chunk, nth); if (src1->type != vec_dot_type) { char * wdata = params->wdata; const size_t row_size = ggml_row_size(vec_dot_type, ne10); assert(params->wsize >= ne11*ne12*ne13*row_size); 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 = 0; i11 < ne11; ++i11) { from_float_to_vec_dot((float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11), (void *) wdata, ne10); wdata += row_size; } } } } return; } if (params->type == GGML_TASK_TYPE_FINALIZE) { return; } #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, params->type, src0->type, vec_dot_type, dst->type)) goto UseGgmlGemm2; return; } UseGgmlGemm2:; #endif #ifdef GGML_PERF int chunks_executed = 0; UNUSED(chunks_executed); #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 (ith == 0) // printf("MUL_MAT = [%d, %d, %d, %d] x [%d, %d, %d, %d] = %d x %d = %d. Fp Ops/Ch %d\n", ne00, ne01, ne02, ne03, ne10, ne11, ne12, ne13, nchunk0, nchunk1, nchunk0 * nchunk1, ne00 * nr0 * nr1 / nchunk0 / nchunk1); // 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); #ifdef GGML_PERF chunks_executed++; #endif if (nth >= nchunk0 * nchunk1) { break; } current_chunk = atomic_fetch_add(&state->shared->current_chunk, 1); } #ifdef GGML_PERF // These numbers are useful when trying to measure how well the threading scheduling works. //int64_t workSize = (ne01 * ne11 * ne12 * ne13 * ne00) / nchunk0 / nchunk1; //float time = (ggml_perf_time_us() - t0); //printf("MUL_MAT = %f ms, [%d, %d, %d, %d] x [%d, %d, %d, %d] = %I64u, %f ops/usec in %d chunks.\n", time / 1000.0, ne00, ne01, ne02, ne03, ne10, ne11, ne12, ne13, workSize, (float)workSize/time, chunks_executed); #endif } // 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[type].vec_dot; enum ggml_type const vec_dot_type = type_traits[type].vec_dot_type; ggml_from_float_t const from_float_to_vec_dot = type_traits[vec_dot_type].from_float; // 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 (params->type == GGML_TASK_TYPE_INIT) { if (ith != 0) { return; } char * wdata = params->wdata; if (src1->type != vec_dot_type) { const size_t row_size = ggml_row_size(vec_dot_type, ne10); assert(params->wsize >= ne11*ne12*ne13*row_size); 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 = 0; i11 < ne11; ++i11) { from_float_to_vec_dot((float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11), (void *) wdata, ne10); wdata += row_size; } } } } // initialize matrix_row_counts memset(matrix_row_counts, 0, n_as*sizeof(int64_t)); #define MMID_MATRIX_ROW(row_id, i1) matrix_rows[(row_id)*ne12 + (i1)] // 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; } } return; } if (params->type == GGML_TASK_TYPE_FINALIZE) { return; } // 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 // 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]; // int64_t t0 = ggml_perf_time_us(); // UNUSED(t0); GGML_TENSOR_BINARY_OP_LOCALS const int ith = params->ith; const int nth = params->nth; GGML_ASSERT(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 // TODO: #if defined(GGML_USE_CLBLAST) #if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) bool use_blas = ggml_is_matrix(src0) && ggml_is_matrix(src1) && ggml_is_contiguous(src0) && (ggml_is_contiguous(src1) || ggml_is_transposed(src1)); #endif if (params->type == GGML_TASK_TYPE_INIT) { #if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) // gemm beta will zero dst if (use_blas) { return; } #endif if (ith != 0) { return; } ggml_vec_set_f32(ne0*ne1*ne2*ne3, dst->data, 0); return; } if (params->type == GGML_TASK_TYPE_FINALIZE) { return; } #if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) if (use_blas) { if (params->ith != 0) { // All threads other than the first do no work. return; } // Arguments to ggml_compute_forward_out_prod (expressed as major,minor) // src0: (k,n) // src1: (k,m) // dst: (m,n) // // Arguments to sgemm (see https://github.com/Reference-LAPACK/lapack/blob/master/BLAS/SRC/sgemm.f) // Also expressed as (major,minor) // a: (m,k): so src1 transposed // b: (k,n): so src0 // c: (m,n) // // However, if ggml_is_transposed(src1) is true, then // src1->data already contains a transposed version, so sgemm mustn't // transpose it further. int n = src0->ne[0]; int k = src0->ne[1]; int m = src1->ne[0]; int transposeA, lda; if (!ggml_is_transposed(src1)) { transposeA = CblasTrans; lda = m; } else { transposeA = CblasNoTrans; lda = k; } float * a = (float *) ((char *) src1->data); float * b = (float *) ((char *) src0->data); float * c = (float *) ((char *) dst->data); cblas_sgemm(CblasRowMajor, transposeA, CblasNoTrans, m, n, k, 1.0, a, lda, b, n, 0.0, c, n); return; } #endif // 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 } } } //int64_t t1 = ggml_perf_time_us(); //static int64_t acc = 0; //acc += t1 - t0; //if (t1 - t0 > 10) { // printf("\n"); // printf("ne00 = %5d, ne01 = %5d, ne02 = %5d, ne03 = %5d\n", ne00, ne01, ne02, ne03); // printf("nb00 = %5d, nb01 = %5d, nb02 = %5d, nb03 = %5d\n", nb00, nb01, nb02, nb03); // printf("ne10 = %5d, ne11 = %5d, ne12 = %5d, ne13 = %5d\n", ne10, ne11, ne12, ne13); // printf("nb10 = %5d, nb11 = %5d, nb12 = %5d, nb13 = %5d\n", nb10, nb11, nb12, nb13); // printf("XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX task %d/%d: %d us, acc = %d\n", ith, nth, (int) (t1 - t0), (int) acc); //} } static void ggml_compute_forward_out_prod_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]; // int64_t t0 = ggml_perf_time_us(); // UNUSED(t0); GGML_TENSOR_BINARY_OP_LOCALS; const int ith = params->ith; const int nth = params->nth; const enum ggml_type type = src0->type; ggml_to_float_t const dequantize_row_q = 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 // TODO: #if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CLBLAST) if (params->type == GGML_TASK_TYPE_INIT) { if (ith != 0) { return; } ggml_vec_set_f32(ne0*ne1*ne2*ne3, dst->data, 0); return; } if (params->type == GGML_TASK_TYPE_FINALIZE) { return; } // parallelize by last three dimensions // total rows in dst const int64_t nr = ne1*ne2*ne3; // rows per thread const int64_t dr = (nr + nth - 1)/nth; // row range for this thread const int64_t ir0 = dr*ith; const int64_t ir1 = MIN(ir0 + dr, nr); // dst[:,:,:,:] = 0 // for i2,i3: // for i1: // for i01: // for i0: // dst[i0,i1,i2,i3] += src0[i0,i01,i2,i3] * src1[i1,i01,i2,i3] 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); } } //int64_t t1 = ggml_perf_time_us(); //static int64_t acc = 0; //acc += t1 - t0; //if (t1 - t0 > 10) { // printf("\n"); // printf("ne00 = %5d, ne01 = %5d, ne02 = %5d, ne03 = %5d\n", ne00, ne01, ne02, ne03); // printf("nb00 = %5d, nb01 = %5d, nb02 = %5d, nb03 = %5d\n", nb00, nb01, nb02, nb03); // printf("ne10 = %5d, ne11 = %5d, ne12 = %5d, ne13 = %5d\n", ne10, ne11, ne12, ne13); // printf("nb10 = %5d, nb11 = %5d, nb12 = %5d, nb13 = %5d\n", nb10, nb11, nb12, nb13); // printf("XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX task %d/%d: %d us, acc = %d\n", ith, nth, (int) (t1 - t0), (int) acc); //} } static void ggml_compute_forward_out_prod( const struct ggml_compute_params * params, 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_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: { ggml_compute_forward_out_prod_q_f32(params, dst); } break; case GGML_TYPE_F16: { GGML_ASSERT(false); // todo // ggml_compute_forward_out_prod_f16_f32(params, dst); } break; case GGML_TYPE_F32: { ggml_compute_forward_out_prod_f32(params, dst); } break; default: { GGML_ASSERT(false); } break; } } // 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)); if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } // 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_ASSERT(false); } break; } } // 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 && (params->type == GGML_TASK_TYPE_INIT)) { if (params->ith != 0) { return; } // memcpy needs to be synchronized across threads to avoid race conditions. // => do it in INIT phase memcpy( ((char *) dst->data), ((char *) src0->data), ggml_nbytes(dst)); } if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } const int ith = params->ith; const int nth = params->nth; const int nr = ggml_nrows(src1); const int nc = src1->ne[0]; GGML_TENSOR_LOCALS(int64_t, ne1, src1, ne) GGML_TENSOR_LOCALS(size_t, nb1, src1, nb) // src0 and dst as viewed during set const size_t nb0 = ggml_element_size(src0); const int im0 = (ne10 == 0 ? 0 : ne10-1); const int im1 = (ne11 == 0 ? 0 : ne11-1); const int im2 = (ne12 == 0 ? 0 : ne12-1); const int im3 = (ne13 == 0 ? 0 : ne13-1); GGML_ASSERT(offset + im0*nb0 + im1*nb1 + im2*nb2 + im3*nb3 <= ggml_nbytes(dst)); GGML_ASSERT(nb10 == sizeof(float)); // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); for (int ir = ir0; ir < ir1; ++ir) { // src0 and dst are viewed with shape of src1 and offset // => same indices const int i3 = ir/(ne12*ne11); const int i2 = (ir - i3*ne12*ne11)/ne11; const int i1 = (ir - i3*ne12*ne11 - i2*ne11); ggml_vec_cpy_f32(nc, (float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + offset), (float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11)); } } static void ggml_compute_forward_set( const struct ggml_compute_params * params, 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_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: default: { GGML_ASSERT(false); } break; } } // 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]; if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } 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 = 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); 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]; if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } 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_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]; if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } 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_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]; if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } 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_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_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: { 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_ASSERT(false); } break; } //static bool first = true; //printf("ne0 = %d, ne1 = %d, ne2 = %d\n", dst->ne[0], dst->ne[1], dst->ne[2]); //if (first) { // first = false; //} else { // for (int k = 0; k < dst->ne[1]; ++k) { // for (int j = 0; j < dst->ne[0]/16; ++j) { // for (int i = 0; i < 16; ++i) { // printf("%8.4f ", ((float *) dst->data)[k*dst->ne[0] + j*16 + i]); // } // printf("\n"); // } // printf("\n"); // } // printf("\n"); // exit(0); //} } // ggml_compute_forward_get_rows_back static void ggml_compute_forward_get_rows_back_f32_f16( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; const struct ggml_tensor * src1 = dst->src[1]; GGML_ASSERT(params->ith == 0); GGML_ASSERT(ggml_is_contiguous(dst)); // ggml_compute_forward_dup_same_cont(params, opt0, dst); if (params->type == GGML_TASK_TYPE_INIT) { if (params->ith != 0) { return; } memset(dst->data, 0, ggml_nbytes(dst)); } if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } const int nc = src0->ne[0]; const int nr = ggml_nelements(src1); GGML_ASSERT( dst->ne[0] == nc); GGML_ASSERT(src0->nb[0] == sizeof(ggml_fp16_t)); for (int i = 0; i < nr; ++i) { const int r = ((int32_t *) src1->data)[i]; for (int j = 0; j < nc; ++j) { ggml_fp16_t v = ((ggml_fp16_t *) ((char *) src0->data + i*src0->nb[1]))[j]; ((float *) ((char *) dst->data + r*dst->nb[1]))[j] += GGML_FP16_TO_FP32(v); } } } static void ggml_compute_forward_get_rows_back_f32( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; const struct ggml_tensor * src1 = dst->src[1]; GGML_ASSERT(params->ith == 0); GGML_ASSERT(ggml_is_contiguous(dst)); // ggml_compute_forward_dup_same_cont(params, opt0, dst); if (params->type == GGML_TASK_TYPE_INIT) { if (params->ith != 0) { return; } memset(dst->data, 0, ggml_nbytes(dst)); } if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } const int nc = src0->ne[0]; const int nr = ggml_nelements(src1); GGML_ASSERT( dst->ne[0] == nc); GGML_ASSERT(src0->nb[0] == sizeof(float)); for (int i = 0; i < nr; ++i) { const int r = ((int32_t *) src1->data)[i]; ggml_vec_add_f32(nc, (float *) ((char *) dst->data + r*dst->nb[1]), (float *) ((char *) dst->data + r*dst->nb[1]), (float *) ((char *) src0->data + i*src0->nb[1])); } } static void ggml_compute_forward_get_rows_back( const struct ggml_compute_params * params, 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_ASSERT(false); } break; } //static bool first = true; //printf("ne0 = %d, ne1 = %d, ne2 = %d\n", dst->ne[0], dst->ne[1], dst->ne[2]); //if (first) { // first = false; //} else { // for (int k = 0; k < dst->ne[1]; ++k) { // for (int j = 0; j < dst->ne[0]/16; ++j) { // for (int i = 0; i < 16; ++i) { // printf("%8.4f ", ((float *) dst->data)[k*dst->ne[0] + j*16 + i]); // } // printf("\n"); // } // printf("\n"); // } // printf("\n"); // exit(0); //} } // ggml_compute_forward_diag static void ggml_compute_forward_diag_f32( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; GGML_ASSERT(params->ith == 0); if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } // TODO: handle transposed/permuted matrices GGML_TENSOR_UNARY_OP_LOCALS GGML_ASSERT(ne00 == ne0); GGML_ASSERT(ne00 == ne1); GGML_ASSERT(ne01 == 1); GGML_ASSERT(ne02 == ne2); GGML_ASSERT(ne03 == ne3); GGML_ASSERT(nb00 == sizeof(float)); GGML_ASSERT(nb0 == sizeof(float)); for (int i3 = 0; i3 < ne3; i3++) { for (int i2 = 0; i2 < ne2; i2++) { for (int i1 = 0; i1 < ne1; i1++) { float * d = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1); float * s = (float *)((char *) src0->data + i3*nb03 + i2*nb02); for (int i0 = 0; i0 < i1; i0++) { d[i0] = 0; } d[i1] = s[i1]; for (int i0 = i1+1; i0 < ne0; i0++) { d[i0] = 0; } } } } } static void ggml_compute_forward_diag( const struct ggml_compute_params * params, 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_ASSERT(false); } break; } } // 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 && (params->type == GGML_TASK_TYPE_INIT)) { if (ith != 0) { return; } // memcpy needs to be synchronized across threads to avoid race conditions. // => do it in INIT phase GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0)); GGML_ASSERT(ggml_is_contiguous(dst) && ggml_is_contiguous(src0)); memcpy( ((char *) dst->data), ((char *) src0->data), ggml_nbytes(dst)); } if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } // TODO: handle transposed/permuted matrices const int n = ggml_nrows(src0); const int nc = src0->ne[0]; const int nr = src0->ne[1]; const int nz = n/nr; GGML_ASSERT( dst->nb[0] == sizeof(float)); GGML_ASSERT(src0->nb[0] == sizeof(float)); for (int k = 0; k < nz; k++) { for (int j = ith; j < nr; j += nth) { for (int i = n_past; i < nc; i++) { if (i > n_past + j) { *(float *)((char *) dst->data + k*dst->nb[2] + j*dst->nb[1] + i*dst->nb[0]) = value; } } } } } static void ggml_compute_forward_diag_mask_inf( const struct ggml_compute_params * params, 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_ASSERT(false); } break; } } 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_ASSERT(false); } break; } } // 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)); if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } 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_ASSERT(false); } break; } } // 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)); if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } // TODO: handle transposed/permuted matrices const int ith = params->ith; const int nth = params->nth; const int nc = src0->ne[0]; const int nr = ggml_nrows(src0); // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); for (int i1 = ir0; i1 < ir1; i1++) { float *dy = (float *)((char *) src0->data + i1*src0->nb[1]); float *y = (float *)((char *) src1->data + i1*src1->nb[1]); float *dx = (float *)((char *) dst->data + i1*dst->nb[1]); #ifndef NDEBUG for (int i = 0; i < nc; ++i) { //printf("p[%d] = %f\n", i, p[i]); assert(!isnan(dy[i])); assert(!isnan(y[i])); } #endif // Jii = yi - yi*yi // Jij = -yi*yj // J = diag(y)-y.T*y // dx = J * dy // dxk = sum_i(Jki * dyi) // dxk = sum_i(-yk*yi * dyi) - (-yk*yk)*dyk + (yk - yk*yk)*dyk // dxk = sum_i(-yk*yi * dyi) + yk*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_ASSERT(false); } break; } } // 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]; assert(params->ith == 0); if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } float min; float max; memcpy(&min, (float *) dst->op_params + 0, sizeof(float)); memcpy(&max, (float *) dst->op_params + 1, sizeof(float)); const int ith = params->ith; const int nth = params->nth; const int n = ggml_nrows(src0); const int nc = src0->ne[0]; const size_t nb00 = src0->nb[0]; const size_t nb01 = src0->nb[1]; const size_t nb0 = dst->nb[0]; const size_t nb1 = dst->nb[1]; GGML_ASSERT( nb0 == sizeof(float)); GGML_ASSERT(nb00 == sizeof(float)); for (int j = ith; j < n; j += nth) { float * dst_ptr = (float *) ((char *) dst->data + j*nb1); float * src0_ptr = (float *) ((char *) src0->data + j*nb01); for (int i = 0; i < nc; i++) { dst_ptr[i] = MAX(MIN(src0_ptr[i], max), min); } } } static void ggml_compute_forward_clamp( const struct ggml_compute_params * params, 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_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_I8: case GGML_TYPE_I16: case GGML_TYPE_I32: case GGML_TYPE_I64: case GGML_TYPE_F64: case GGML_TYPE_COUNT: { GGML_ASSERT(false); } break; } } // 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_orig_ctx, float n_rot, float base) { return n_dims * logf(n_orig_ctx / (n_rot * 2 * (float)M_PI)) / (2 * logf(base)); } static void ggml_rope_cache_init( float theta_base, float freq_scale, float corr_dims[2], int64_t ne0, float ext_factor, float mscale, float * cache, float sin_sign, float theta_scale ) { float theta = theta_base; for (int64_t i0 = 0; i0 < ne0; i0 += 2) { rope_yarn( theta, freq_scale, corr_dims, i0, ext_factor, mscale, &cache[i0 + 0], &cache[i0 + 1] ); cache[i0 + 1] *= sin_sign; theta *= theta_scale; } } GGML_CALL void ggml_rope_yarn_corr_dims( int n_dims, int n_orig_ctx, 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_orig_ctx, beta_fast, freq_base)); float end = ceilf(ggml_rope_yarn_corr_dim(n_dims, n_orig_ctx, 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]; if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow; // these two only relevant for xPos RoPE: float xpos_base; bool xpos_down; //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_orig_ctx = ((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)); memcpy(&xpos_base, (int32_t *) dst->op_params + 11, sizeof(float)); memcpy(&xpos_down, (int32_t *) dst->op_params + 12, sizeof(bool)); GGML_TENSOR_UNARY_OP_LOCALS //printf("ne0: %d, ne1: %d, ne2: %d, ne3: %d\n", ne0, ne1, ne2, ne3); //printf("n_past = %d, ne2 = %d\n", n_past, ne2); GGML_ASSERT(nb00 == sizeof(float)); const int ith = params->ith; const int nth = params->nth; const int nr = ggml_nrows(dst); GGML_ASSERT(n_dims <= ne0); GGML_ASSERT(n_dims % 2 == 0); // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); // row index used to determine which thread to use int ir = 0; const float theta_scale = powf(freq_base, -2.0f/n_dims); const float inv_ndims = -1.f/n_dims; float corr_dims[2]; ggml_rope_yarn_corr_dims(n_dims, n_orig_ctx, freq_base, beta_fast, beta_slow, corr_dims); const bool is_neox = mode & 2; const bool is_glm = mode & 4; const float * freq_factors = NULL; if (is_neox) { if (src2 != NULL) { GGML_ASSERT(src2->type == GGML_TYPE_F32); GGML_ASSERT(src2->ne[0] >= n_dims / 2); freq_factors = (const float *) src2->data; } } else { GGML_ASSERT(src2 == NULL && "TODO: freq_factors not implemented for !is_neox"); } // 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; if (!is_glm && !is_neox) { // TODO: cache sin/cos for glm, neox ggml_rope_cache_init(p, freq_scale, 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; float theta_base = (float)p; if (is_glm) { theta_base = MIN(p, n_ctx - 2); float block_theta = MAX(p - (n_ctx - 2), 0); for (int64_t i0 = 0; i0 < ne0 / 4; i0++) { const float cos_theta = cosf(theta_base); const float sin_theta = sinf(theta_base) * sin_sign; const float cos_block_theta = cosf(block_theta); const float sin_block_theta = sinf(block_theta) * sin_sign; theta_base *= theta_scale; block_theta *= theta_scale; const float * const src = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); float * dst_data = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); const float x0 = src[0]; const float x1 = src[n_dims/2]; const float x2 = src[n_dims]; const float x3 = src[n_dims/2*3]; dst_data[0] = x0*cos_theta - x1*sin_theta; dst_data[n_dims/2] = x0*sin_theta + x1*cos_theta; dst_data[n_dims] = x2*cos_block_theta - x3*sin_block_theta; dst_data[n_dims/2*3] = x2*sin_block_theta + x3*cos_block_theta; } } else if (!is_neox) { for (int64_t i0 = 0; i0 < ne0; i0 += 2) { const float cos_theta = cache[i0 + 0]; const float sin_theta = cache[i0 + 1]; // zeta scaling for xPos only: float zeta = xpos_base != 0.0f ? powf((i0 + 0.4f * ne0) / (1.4f * ne0), p / xpos_base) : 1.0f; if (xpos_down) zeta = 1.0f / zeta; 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*zeta - x1*sin_theta*zeta; dst_data[1] = x0*sin_theta*zeta + x1*cos_theta*zeta; } } else { // TODO: this might be wrong for ne0 != n_dims - need double check // it seems we have to rope just the first n_dims elements and do nothing with the rest // ref: https://github.com/ml-explore/mlx/blob/dc2edc762c797e3b8de50b1dad4dc0a131691033/benchmarks/python/llama_jax_bench.py#L11-L26 theta_base *= freq_scale; for (int64_t ic = 0; ic < ne0; ic += 2) { if (ic < n_dims) { const int64_t ib = 0; // simplified from `(ib * n_dims + ic) * inv_ndims` float cur_rot = inv_ndims * ic - ib; float freq_factor = freq_factors ? freq_factors[ic/2] : 1.0f; float cos_theta, sin_theta; rope_yarn( theta_base/freq_factor, freq_scale, corr_dims, cur_rot, ext_factor, attn_factor, &cos_theta, &sin_theta ); sin_theta *= sin_sign; theta_base *= theta_scale; const int64_t i0 = ib*n_dims + ic/2; const float * const src = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); float * dst_data = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); const float x0 = src[0]; const float x1 = src[n_dims/2]; dst_data[0] = x0*cos_theta - x1*sin_theta; dst_data[n_dims/2] = x0*sin_theta + x1*cos_theta; } else { const int64_t i0 = ic; 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]; if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } 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_orig_ctx = ((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); const float inv_ndims = -1.f/n_dims; float corr_dims[2]; ggml_rope_yarn_corr_dims(n_dims, n_orig_ctx, freq_base, beta_fast, beta_slow, corr_dims); const bool is_neox = mode & 2; const bool is_glm = mode & 4; const float * freq_factors = NULL; if (is_neox) { if (src2 != NULL) { GGML_ASSERT(src2->type == GGML_TYPE_F32); GGML_ASSERT(src2->ne[0] >= n_dims / 2); freq_factors = (const float *) src2->data; } } else { GGML_ASSERT(src2 == NULL && "TODO: freq_factors not implemented for !is_neox"); } // 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; if (!is_glm && !is_neox) { // TODO: cache sin/cos for glm, neox ggml_rope_cache_init(p, freq_scale, 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; float theta_base = (float)p; if (is_glm) { theta_base = MIN(p, n_ctx - 2); float block_theta = MAX(p - (n_ctx - 2), 0); for (int64_t i0 = 0; i0 < ne0 / 4; i0++) { const float cos_theta = cosf(theta_base); const float sin_theta = sinf(theta_base) * sin_sign; const float cos_block_theta = cosf(block_theta); const float sin_block_theta = sinf(block_theta) * sin_sign; theta_base *= theta_scale; block_theta *= theta_scale; const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); ggml_fp16_t * dst_data = (ggml_fp16_t *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); const float x0 = GGML_FP16_TO_FP32(src[0]); const float x1 = GGML_FP16_TO_FP32(src[n_dims/2]); const float x2 = GGML_FP16_TO_FP32(src[n_dims]); const float x3 = GGML_FP16_TO_FP32(src[n_dims/2*3]); dst_data[0] = GGML_FP32_TO_FP16(x0*cos_theta - x1*sin_theta); dst_data[n_dims/2] = GGML_FP32_TO_FP16(x0*sin_theta + x1*cos_theta); dst_data[n_dims] = GGML_FP32_TO_FP16(x2*cos_block_theta - x3*sin_block_theta); dst_data[n_dims/2*3] = GGML_FP32_TO_FP16(x2*sin_block_theta + x3*cos_block_theta); } } else if (!is_neox) { for (int64_t i0 = 0; i0 < ne0; 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 { // TODO: this might be wrong for ne0 != n_dims - need double check // it seems we have to rope just the first n_dims elements and do nothing with the rest // ref: https://github.com/ml-explore/mlx/blob/dc2edc762c797e3b8de50b1dad4dc0a131691033/benchmarks/python/llama_jax_bench.py#L11-L26 theta_base *= freq_scale; for (int64_t ic = 0; ic < ne0; ic += 2) { if (ic < n_dims) { const int64_t ib = 0; // simplified from `(ib * n_dims + ic) * inv_ndims` float cur_rot = inv_ndims * ic - ib; float freq_factor = freq_factors ? freq_factors[ic/2] : 1.0f; float cos_theta, sin_theta; rope_yarn( theta_base/freq_factor, freq_scale, corr_dims, cur_rot, ext_factor, attn_factor, &cos_theta, &sin_theta ); sin_theta *= sin_sign; theta_base *= theta_scale; const int64_t i0 = ib*n_dims + ic/2; const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); ggml_fp16_t * dst_data = (ggml_fp16_t *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); const float x0 = GGML_FP16_TO_FP32(src[0]); const float x1 = GGML_FP16_TO_FP32(src[n_dims/2]); dst_data[0] = GGML_FP32_TO_FP16(x0*cos_theta - x1*sin_theta); dst_data[n_dims/2] = GGML_FP32_TO_FP16(x0*sin_theta + x1*cos_theta); } else { const int64_t i0 = ic; 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_ASSERT(false); } break; } } // 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_ASSERT(false); } break; } } // 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); int64_t t0 = ggml_perf_time_us(); UNUSED(t0); GGML_TENSOR_BINARY_OP_LOCALS const int ith = params->ith; const int nth = params->nth; const int nk = ne00*ne01*ne02; GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); GGML_ASSERT(nb10 == sizeof(float)); if (params->type == GGML_TASK_TYPE_INIT) { if (ith != 0) { return; } 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)); return; } if (params->type == GGML_TASK_TYPE_FINALIZE) { return; } 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); int64_t t0 = ggml_perf_time_us(); UNUSED(t0); GGML_TENSOR_BINARY_OP_LOCALS const int ith = params->ith; const int nth = params->nth; const int nk = ne00*ne01*ne02; GGML_ASSERT(nb00 == sizeof(float)); GGML_ASSERT(nb10 == sizeof(float)); if (params->type == GGML_TASK_TYPE_INIT) { if (ith != 0) { return; } 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)); return; } if (params->type == GGML_TASK_TYPE_FINALIZE) { return; } 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_ASSERT(false); } break; } } // 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(src0->type == GGML_TYPE_F16); GGML_ASSERT(src1->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); int64_t t0 = ggml_perf_time_us(); UNUSED(t0); GGML_TENSOR_BINARY_OP_LOCALS; const 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)); if (params->type == GGML_TASK_TYPE_INIT) { return; } if (params->type == GGML_TASK_TYPE_FINALIZE) { return; } // 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]); } } } } } } } } } // 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); int64_t t0 = ggml_perf_time_us(); UNUSED(t0); 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)); if (params->type == GGML_TASK_TYPE_INIT) { return; } if (params->type == GGML_TASK_TYPE_FINALIZE) { return; } // 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_ASSERT(false); } break; } } // 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); int64_t t0 = ggml_perf_time_us(); UNUSED(t0); GGML_TENSOR_BINARY_OP_LOCALS const int ith = params->ith; const int nth = params->nth; const int nk = ne00*ne01*ne02*ne03; GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); GGML_ASSERT(nb10 == sizeof(float)); if (params->type == GGML_TASK_TYPE_INIT) { if (ith != 0) { return; } 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)); return; } if (params->type == GGML_TASK_TYPE_FINALIZE) { return; } 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); assert(params->ith == 0); if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } const char * cdata = (const char *)src->data; const char * const data_end = cdata + ggml_nbytes(src); float * drow = (float *)dst->data; const int64_t rs = dst->ne[0]; while (cdata < data_end) { const float * const srow = (const float *)cdata; int j = 0; for (int64_t i = 0; i < rs; ++i) { switch (op) { case GGML_OP_POOL_AVG: drow[i] = 0; break; case GGML_OP_POOL_MAX: drow[i] = -FLT_MAX; break; case GGML_OP_POOL_COUNT: GGML_ASSERT(false); break; } for (int ki = 0; ki < k; ++ki) { switch (op) { case GGML_OP_POOL_AVG: drow[i] += srow[j]; break; case GGML_OP_POOL_MAX: if (srow[j] > drow[i]) drow[i] = srow[j]; break; case GGML_OP_POOL_COUNT: GGML_ASSERT(false); break; } ++j; } switch (op) { case GGML_OP_POOL_AVG: drow[i] /= k; break; case GGML_OP_POOL_MAX: break; case GGML_OP_POOL_COUNT: GGML_ASSERT(false); break; } } cdata += src->nb[1]; drow += rs; } } // ggml_compute_forward_pool_1d static void ggml_compute_forward_pool_1d( const struct ggml_compute_params * params, 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]; GGML_ASSERT(src->type == GGML_TYPE_F32); GGML_ASSERT(params->ith == 0); if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { 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_ASSERT(false); break; } 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 float * const srow = (const float *)(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; switch (op) { case GGML_OP_POOL_AVG: *out += srow[j]; break; case GGML_OP_POOL_MAX: if (srow[j] > *out) *out = srow[j]; break; case GGML_OP_POOL_COUNT: GGML_ASSERT(false); break; } } } switch (op) { case GGML_OP_POOL_AVG: *out /= ka; break; case GGML_OP_POOL_MAX: break; case GGML_OP_POOL_COUNT: GGML_ASSERT(false); break; } } } cdata += src->nb[2]; dplane += pa; } } // ggml_compute_forward_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]; if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } 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_ASSERT(false); } break; } } // 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]; if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } 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_ASSERT(false); } break; } } // ggml_compute_forward_arange static void ggml_compute_forward_arange_f32( const struct ggml_compute_params * params, struct ggml_tensor * dst) { if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } 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_ASSERT(false); } break; } } static void ggml_compute_forward_timestep_embedding_f32( const struct ggml_compute_params * params, struct ggml_tensor * dst) { if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } 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_ASSERT(false); } break; } } // 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]; if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } 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_ASSERT(false); } break; } } // 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) { int64_t t0 = ggml_perf_time_us(); UNUSED(t0); GGML_TENSOR_LOCALS(int64_t, neq, q, ne) GGML_TENSOR_LOCALS(size_t, nbq, q, nb) GGML_TENSOR_LOCALS(int64_t, nek, k, ne) GGML_TENSOR_LOCALS(size_t, nbk, k, nb) GGML_TENSOR_LOCALS(int64_t, nev, v, ne) GGML_TENSOR_LOCALS(size_t, nbv, v, nb) GGML_TENSOR_LOCALS(int64_t, ne, dst, ne) GGML_TENSOR_LOCALS(size_t, nb, dst, nb) const int ith = params->ith; const int nth = params->nth; const int64_t D = neq0; const int64_t N = neq1; 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; if (params->type == GGML_TASK_TYPE_INIT) { return; } if (params->type == GGML_TASK_TYPE_FINALIZE) { return; } // parallelize by q rows using ggml_vec_dot_f32 // total rows in q const int nr = neq1*neq2*neq3; // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); 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)); 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[k->type].vec_dot_type; ggml_from_float_t const q_to_vec_dot = type_traits[k_vec_dot_type].from_float; ggml_vec_dot_t const kq_vec_dot = type_traits[k->type].vec_dot; ggml_to_float_t const v_to_float = type_traits[v->type].to_float; // 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 + mv; // scale KQ value and 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[2]) { 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_ASSERT(false); } break; } } // ggml_compute_forward_flash_attn_back static void ggml_compute_forward_flash_attn_back_f32( const struct ggml_compute_params * params, const 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]; int64_t t0 = ggml_perf_time_us(); UNUSED(t0); GGML_TENSOR_LOCALS(int64_t, neq, q, ne) GGML_TENSOR_LOCALS(size_t, nbq, q, nb) GGML_TENSOR_LOCALS(int64_t, nek, k, ne) GGML_TENSOR_LOCALS(size_t, nbk, k, nb) GGML_TENSOR_LOCALS(int64_t, nev, v, ne) GGML_TENSOR_LOCALS(size_t, nbv, v, nb) GGML_TENSOR_LOCALS(int64_t, ned, d, ne) GGML_TENSOR_LOCALS(size_t, nbd, d, nb) GGML_TENSOR_LOCALS(int64_t, ne, dst, ne) GGML_TENSOR_LOCALS(size_t, nb, dst, nb) const int ith = params->ith; const int nth = params->nth; const int64_t D = neq0; const int64_t N = neq1; const int64_t P = nek1 - N; const int64_t M = P + N; const int Mup = ggml_up(M, GGML_SOFT_MAX_UNROLL); const int mxDM = MAX(D, Mup); // GGML_ASSERT(ne0 == D); // GGML_ASSERT(ne1 == N); GGML_ASSERT(P >= 0); GGML_ASSERT(nbq0 == sizeof(float)); GGML_ASSERT(nbk0 == sizeof(float)); GGML_ASSERT(nbv0 == sizeof(float)); GGML_ASSERT(neq0 == D); GGML_ASSERT(nek0 == D); GGML_ASSERT(nev1 == D); GGML_ASSERT(ned0 == D); GGML_ASSERT(neq1 == N); GGML_ASSERT(nek1 == N + P); GGML_ASSERT(nev1 == D); GGML_ASSERT(ned1 == N); // dst cannot be transposed or permuted GGML_ASSERT(nb0 == sizeof(float)); GGML_ASSERT(nb0 <= nb1); GGML_ASSERT(nb1 <= nb2); GGML_ASSERT(nb2 <= nb3); if (params->type == GGML_TASK_TYPE_INIT) { if (ith == 0) { memset(dst->data, 0, nb0*ne0*ne1*ne2*ne3); } return; } if (params->type == GGML_TASK_TYPE_FINALIZE) { return; } 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_ASSERT(false); } break; } } // ggml_compute_forward_ssm_conv static void ggml_compute_forward_ssm_conv_f32( const struct ggml_compute_params * params, struct ggml_tensor * dst) { if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } const struct ggml_tensor * src0 = dst->src[0]; // conv_state const struct ggml_tensor * src1 = dst->src[1]; // x const struct ggml_tensor * src2 = dst->src[2]; // conv1d.weight const struct ggml_tensor * src3 = dst->src[3]; // state_seq const int ith = params->ith; const int nth = params->nth; const int nc = src2->ne[0]; // d_conv const int nr = src0->ne[1]; // d_inner const int n_t = src1->ne[1]; // n_tokens const int n_kv = src0->ne[2]; // max number of sequences in the batch GGML_ASSERT((nr*n_t) + (nc*nr*n_kv) == 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(int32_t)); GGML_ASSERT(src0->nb[1] == src0->ne[0]*sizeof(float)); // for use with the destination state offset between sequences GGML_ASSERT(src2->nb[2] == src2->ne[1]*src2->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; if (n_kv > 1) { // multiple sequences means it's hard to know when it's the first time a state is read, // so copy them all over to the destination, just to be sure. for (int i3 = 0; i3 < n_kv; ++i3) { float * s0 = (float *) ((char *) src0->data + ir0*(src0->nb[1]) + i3*(src0->nb[2])); float * s = (float *) ((char *) dst->data + ir0*(src2->nb[1]) + i3*(src2->nb[2]) + nr*n_t*sizeof(float)); // can't use memcpy because of d_conv vs d_conv - 1 for (int i1 = 0; i1 < ir; ++i1) { for (int i0 = 0; i0 < nc - 1; ++i0) { // copy s0 to last (d_conv - 1) columns of s s[1 + i0 + i1*nc] = s0[i0 + i1*(nc - 1)]; } } } } for (int i2 = 0; i2 < n_t; ++i2) { int32_t * sq = (int32_t *) ((char *) src3->data + i2*(src3->nb[1])); // {n_kv, n_tokens} float * x = (float *) ((char *) dst->data + ir0*sizeof(float) + i2*(nr*sizeof(float))); // {d_inner, n_tokens} float * s = (float *) ((char *) dst->data + ir0*(src2->nb[1]) + sq[0]*(src2->nb[2]) + nr*n_t*sizeof(float)); // {d_conv, d_inner, n_kv} float * s0; // {d_conv - 1, d_inner, n_kv} float * x0 = (float *) ((char *) src1->data + ir0*(src1->nb[0]) + i2*(src1->nb[1])); // {d_inner, n_tokens} float * c = (float *) ((char *) src2->data + ir0*(src2->nb[1])); // {d_conv, d_inner} int ne0s0; GGML_ASSERT(0 <= sq[0] && sq[0] < n_kv); // avoid needing to copy the state for the first token if (i2 == 0) { s0 = (float *) ((char *) src0->data + ir0*(src0->nb[1]) + sq[0]*(src0->nb[2])); // {d_conv - 1, d_inner, n_kv} ne0s0 = src0->ne[0]; } else { // the source is the last (d_conv - 1) columns of the destination s0 = s + 1; ne0s0 = nc; } // d_inner for (int i1 = 0; i1 < ir; ++i1) { // shift state left for (int i0 = 0; i0 < nc - 1; ++i0) { s[i0 + i1*nc] = s0[i0 + i1*ne0s0]; } // insert x on the last column s[(nc - 1) + i1*nc] = x0[i1]; } // handle copies when there are multiple output states for (int i3 = 1; i3 < n_kv; ++i3) { int32_t seq = sq[i3]; if (0 <= seq && seq < n_kv) { float * s1 = s + (seq - sq[0])*nc*nr; memcpy(s1, s, nc*ir*sizeof(float)); } else { // stop at negative or too big seq_ids break; } } // it seems a little faster when this is separate from the state shift for (int i1 = 0; i1 < ir; ++i1) { // rowwise dot product float sumf = 0.0f; for (int i0 = 0; i0 < nc; ++i0) { int i = i0 + i1*nc; sumf += s[i] * c[i]; } 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_ASSERT(false); } break; } } // ggml_compute_forward_ssm_scan static void ggml_compute_forward_ssm_scan_f32( const struct ggml_compute_params * params, struct ggml_tensor * dst) { if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } 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 struct ggml_tensor * src6 = dst->src[6]; // sq 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 in the batch const int64_t n_kv = src0->ne[2]; // max 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, and when copying the states 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[2]) GGML_ASSERT(src1->nb[2] == src1->ne[0]*src1->ne[1]*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; if (n_kv > 1) { // it's hard to know if the source states have already been copied // when there are multiple, so copy them already. for (int i3 = 0; i3 < n_kv; ++i3) { float * s0 = (float *) ((char *) src0->data + ir0*(src0->nb[1]) + i3*(src0->nb[2])); float * s = (float *) ((char *) dst->data + ir0*(src0->nb[1]) + i3*(src0->nb[2]) + src1->nb[2]); memcpy(s, s0, nc*ir*sizeof(float)); } } for (int i2 = 0; i2 < n_t; ++i2) { int32_t * sq = (int32_t *) ((char *) src6->data + i2*(src6->nb[1])); // {n_kv, n_tokens} float * y = (float *) ((char *) dst->data + ir0*(src1->nb[0]) + i2*(src1->nb[1])); // {d_inner, n_tokens} float * s = (float *) ((char *) dst->data + ir0*(src0->nb[1]) + sq[0]*(src0->nb[2]) + src1->nb[2]); // {d_state, d_inner, n_kv} float * s0; float * x = (float *) ((char *) src1->data + ir0*(src1->nb[0]) + i2*(src1->nb[1])); // {d_inner, n_tokens} float * dt = (float *) ((char *) src2->data + ir0*(src2->nb[0]) + i2*(src2->nb[1])); // {d_inner, n_tokens} float * A = (float *) ((char *) src3->data + ir0*(src3->nb[1])); // {d_state, d_inner} float * B = (float *) ((char *) src4->data + i2*(src4->nb[1])); // {d_state, n_tokens} float * C = (float *) ((char *) src5->data + i2*(src5->nb[1])); // {d_state, n_tokens} GGML_ASSERT(0 <= sq[0] && sq[0] < n_kv); // avoid needing to copy the state for the first token if (i2 == 0) { s0 = (float *) ((char *) src0->data + ir0*(src0->nb[1]) + sq[0]*(src0->nb[2])); // {d_state, d_inner, n_kv} } else { // otherwise the source is the same as the destination 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; } // handle copies when there are multiple output states for (int i3 = 1; i3 < n_kv; ++i3) { int32_t seq = sq[i3]; if (0 <= seq && seq < n_kv) { float * s1 = s + (seq - sq[0])*nc*nr; memcpy(s1, s, nc*ir*sizeof(float)); } else { // stop at negative or too big seq_ids break; } } } } 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_ASSERT(false); } break; } } // ggml_compute_forward_win_part static void ggml_compute_forward_win_part_f32( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne) GGML_TENSOR_LOCALS(int64_t, ne, dst, ne) const int32_t nep0 = ((const int32_t *)(dst->op_params))[0]; const int32_t nep1 = ((const int32_t *)(dst->op_params))[1]; const int32_t w = ((const int32_t *)(dst->op_params))[2]; assert(ne00 == ne0); assert(ne3 == nep0*nep1); // TODO: optimize / multi-thread for (int py = 0; py < nep1; ++py) { for (int px = 0; px < nep0; ++px) { const int64_t i3 = py*nep0 + px; for (int64_t i2 = 0; i2 < ne2; ++i2) { for (int64_t i1 = 0; i1 < ne1; ++i1) { for (int64_t i0 = 0; i0 < ne0; ++i0) { const int64_t i02 = py*w + i2; const int64_t i01 = px*w + i1; const int64_t i00 = i0; const int64_t i = i3*ne2*ne1*ne0 + i2*ne1*ne0 + i1*ne0 + i0; const int64_t j = i02*ne01*ne00 + i01*ne00 + i00; if (py*w + i2 >= ne02 || px*w + i1 >= ne01) { ((float *) dst->data)[i] = 0.0f; } else { ((float *) dst->data)[i] = ((float *) src0->data)[j]; } } } } } } } static void ggml_compute_forward_win_part( const struct ggml_compute_params * params, 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_ASSERT(false); } break; } } // ggml_compute_forward_win_unpart static void ggml_compute_forward_win_unpart_f32( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne) GGML_TENSOR_LOCALS(int64_t, ne, dst, ne) const int32_t w = ((const int32_t *)(dst->op_params))[0]; // padding const int px = (w - ne1%w)%w; //const int py = (w - ne2%w)%w; const int npx = (px + ne1)/w; //const int npy = (py + ne2)/w; assert(ne0 == ne00); // TODO: optimize / multi-thread for (int64_t i2 = 0; i2 < ne2; ++i2) { for (int64_t i1 = 0; i1 < ne1; ++i1) { for (int64_t i0 = 0; i0 < ne0; ++i0) { const int ip2 = i2/w; const int ip1 = i1/w; const int64_t i02 = i2%w; const int64_t i01 = i1%w; const int64_t i00 = i0; const int64_t i = (ip2*npx + ip1)*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00 + i00; const int64_t j = i2*ne1*ne0 + i1*ne0 + i0; ((float *) dst->data)[j] = ((float *) src0->data)[i]; } } } } static void ggml_compute_forward_win_unpart( const struct ggml_compute_params * params, 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_ASSERT(false); } break; } } //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; default: { GGML_ASSERT(false); } break; } } // 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) { const struct ggml_tensor * src0 = dst->src[0]; if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } // 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_ASSERT(false); } break; } } // 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 && params->type == GGML_TASK_TYPE_INIT) { if (params->ith != 0) { return; } memcpy((char *) dst->data, (char *) src0->data, ggml_nbytes(dst)); return; } if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } int64_t t0 = ggml_perf_time_us(); UNUSED(t0); // 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_ASSERT(false); } break; } } // 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]; GGML_ASSERT(ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } const int n = ggml_nrows(src0); const int nc = src0->ne[0]; assert( dst->nb[0] == sizeof(float)); assert(src0->nb[0] == sizeof(float)); for (int i = 0; i < n; i++) { fun(nc, (float *) ((char *) dst->data + i*( dst->nb[1])), (float *) ((char *) src0->data + i*(src0->nb[1]))); } } static void ggml_compute_forward_map_unary( const struct ggml_compute_params * params, 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_ASSERT(false); } break; } } // 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]; assert(params->ith == 0); assert(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } const int n = ggml_nrows(src0); const int nc = src0->ne[0]; assert( dst->nb[0] == sizeof(float)); assert(src0->nb[0] == sizeof(float)); assert(src1->nb[0] == sizeof(float)); for (int i = 0; i < n; i++) { fun(nc, (float *) ((char *) dst->data + i*( dst->nb[1])), (float *) ((char *) src0->data + i*(src0->nb[1])), (float *) ((char *) src1->data + i*(src1->nb[1]))); } } static void ggml_compute_forward_map_binary( const struct ggml_compute_params * params, 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_ASSERT(false); } break; } } // 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]; assert(params->ith == 0); if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { 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]; assert(params->ith == 0); if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } fun(dst, a, b); } // ggml_compute_forward_map_custom3 static void ggml_compute_forward_map_custom3_f32( const struct ggml_compute_params * params, 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]; assert(params->ith == 0); if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } fun(dst, a, b, c); } // ggml_compute_forward_map_custom1 static void ggml_compute_forward_map_custom1( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * a = dst->src[0]; if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } 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]; if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } 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]; if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } 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(ggml_is_contiguous(src0)); GGML_ASSERT(ggml_is_contiguous(src1)); GGML_ASSERT(ggml_is_scalar(dst)); GGML_ASSERT(ggml_are_same_shape(src0, src1)); const int ith = params->ith; const int nth = params->nth; float * sums = (float *) params->wdata; // TODO: handle transposed/permuted matrices const int nc = src0->ne[0]; const int nr = ggml_nrows(src0); GGML_ASSERT(params->wsize >= sizeof(float) * (nth + nth * nc)); if (params->type == GGML_TASK_TYPE_INIT) { if (ith == 0) { memset(sums, 0, sizeof(float) * (nth + nth * nc)); } return; } if (params->type == GGML_TASK_TYPE_FINALIZE) { if (ith == 0) { float * dp = (float *) dst->data; ggml_vec_sum_f32(nth, dp, sums); dp[0] *= -1.0f / (float) nr; } return; } const double eps = 1e-9; // rows per thread const int dr = (nr + nth - 1)/nth; // row range for this thread const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); for (int i1 = ir0; i1 < ir1; i1++) { float * s0 = (float *)((char *) src0->data + i1*src0->nb[1]); float * s1 = (float *)((char *) src1->data + i1*src1->nb[1]); float * st = ((float *) params->wdata) + nth + ith*nc; #ifndef NDEBUG for (int i = 0; i < nc; ++i) { //printf("p[%d] = %f\n", i, p[i]); assert(!isnan(s0[i])); assert(!isnan(s1[i])); } #endif // soft_max float max = -INFINITY; ggml_vec_max_f32(nc, &max, s0); ggml_float sum = ggml_vec_soft_max_f32(nc, st, s0, max); assert(sum > 0.0); sum = (1.0 - eps) / sum; // avoid log(0) by rescaling from [0..1] to [eps..1] ggml_vec_scale_f32(nc, st, sum); ggml_vec_add1_f32(nc, st, st, eps); ggml_vec_log_f32(nc, st, st); ggml_vec_mul_f32(nc, st, st, s1); float st_sum = 0; ggml_vec_sum_f32(nc, &st_sum, st); sums[ith] += st_sum; #ifndef NDEBUG for (int i = 0; i < nc; ++i) { assert(!isnan(st[i])); assert(!isinf(st[i])); } #endif } } static void ggml_compute_forward_cross_entropy_loss( const struct ggml_compute_params * params, 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_ASSERT(false); } break; } } // ggml_compute_forward_cross_entropy_loss_back static void ggml_compute_forward_cross_entropy_loss_back_f32( const struct ggml_compute_params * params, 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; if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; } const double eps = 1e-9; // TODO: handle transposed/permuted matrices const int64_t nc = src0->ne[0]; const int64_t nr = ggml_nrows(src0); // rows per thread const int64_t dr = (nr + nth - 1)/nth; // row range for this thread const int64_t ir0 = dr*ith; const int64_t ir1 = MIN(ir0 + dr, nr); float * d = (float *) opt0->data; for (int64_t i1 = ir0; i1 < ir1; i1++) { float * ds0 = (float *)((char *) dst->data + i1*dst->nb[1]); float * s0 = (float *)((char *) src0->data + i1*src0->nb[1]); float * s1 = (float *)((char *) src1->data + i1*src1->nb[1]); #ifndef NDEBUG for (int i = 0; i < nc; ++i) { //printf("p[%d] = %f\n", i, p[i]); assert(!isnan(s0[i])); assert(!isnan(s1[i])); } #endif // soft_max 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); sum = (1.0 - eps) / sum; // grad(src0) = (softmax(src0) - src1) * grad(cross_entropy_loss(src0, src1)) / nr ggml_vec_scale_f32(nc, ds0, sum); ggml_vec_add1_f32(nc, ds0, ds0, eps); ggml_vec_sub_f32(nc, ds0, ds0, s1); ggml_vec_scale_f32(nc, ds0, d[0] / (float) nr); #ifndef NDEBUG for (int 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_ASSERT(false); } break; } } ///////////////////////////////// static void ggml_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor, struct ggml_compute_state * state) { 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_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_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, state); } 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_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_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_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_NONE: { // nop } break; case GGML_OP_COUNT: { GGML_ASSERT(false); } break; } } //////////////////////////////////////////////////////////////////////////////// static size_t ggml_hash_size(size_t min_sz) { // next primes after powers of two static const size_t primes[] = { 2, 3, 5, 11, 17, 37, 67, 131, 257, 521, 1031, 2053, 4099, 8209, 16411, 32771, 65537, 131101, 262147, 524309, 1048583, 2097169, 4194319, 8388617, 16777259, 33554467, 67108879, 134217757, 268435459, 536870923, 1073741827, 2147483659 }; static const size_t n_primes = sizeof(primes)/sizeof(primes[0]); // find the smallest prime that is larger or equal to min_sz size_t l = 0; size_t r = n_primes; while (l < r) { size_t m = (l + r)/2; if (primes[m] < min_sz) { l = m + 1; } else { r = m; } } size_t sz = l < n_primes ? primes[l] : min_sz | 1; return sz; } static size_t ggml_hash(const void * p) { return (size_t)p; } size_t ggml_hash_find(const struct ggml_hash_set hash_set, struct ggml_tensor * key) { size_t h = ggml_hash(key) % hash_set.size; // linear probing size_t i = h; while (hash_set.keys[i] != NULL && hash_set.keys[i] != key) { i = (i + 1) % hash_set.size; if (i == h) { // visited all hash table entries -> not found return GGML_HASHTABLE_FULL; } } return i; } bool ggml_hash_contains(struct ggml_hash_set hash_set, struct ggml_tensor * key) { size_t i = ggml_hash_find(hash_set, key); return i != GGML_HASHTABLE_FULL && hash_set.keys[i] == key; } size_t ggml_hash_insert(struct ggml_hash_set hash_set, struct ggml_tensor * key) { size_t i = ggml_hash_find(hash_set, key); GGML_ASSERT(i != GGML_HASHTABLE_FULL); if (hash_set.keys[i] == key) { return GGML_HASHTABLE_ALREADY_EXISTS; } // insert GGML_ASSERT(hash_set.keys[i] == NULL); hash_set.keys[i] = key; return i; } size_t ggml_hash_find_or_insert(struct ggml_hash_set hash_set, struct ggml_tensor * key) { size_t i = ggml_hash_find(hash_set, key); GGML_ASSERT(i != GGML_HASHTABLE_FULL); hash_set.keys[i] = key; return i; } struct ggml_hash_set ggml_hash_set_new(size_t size) { size = ggml_hash_size(size); struct ggml_hash_set result; result.size = size; result.keys = GGML_MALLOC(sizeof(struct ggml_tensor *) * size); memset(result.keys, 0, sizeof(struct ggml_tensor *) * size); return result; } static void ggml_hash_set_free(struct ggml_hash_set hash_set) { GGML_FREE(hash_set.keys); } struct hash_map { struct ggml_hash_set set; struct ggml_tensor ** vals; }; static struct hash_map * ggml_new_hash_map(size_t size) { struct hash_map * result = GGML_MALLOC(sizeof(struct hash_map)); result->set = ggml_hash_set_new(size); result->vals = GGML_MALLOC(sizeof(struct ggml_tensor *) * result->set.size); memset(result->vals, 0, sizeof(struct ggml_tensor *) * result->set.size); return result; } static void ggml_hash_map_free(struct hash_map * map) { ggml_hash_set_free(map->set); GGML_FREE(map->vals); GGML_FREE(map); } // gradient checkpointing static struct ggml_tensor * ggml_recompute_graph_node( struct ggml_context * ctx, struct ggml_cgraph * graph, struct hash_map * replacements, struct ggml_tensor * node) { if (node == NULL) { return NULL; } if (node->flags & GGML_TENSOR_FLAG_PARAM) { return node; } if (!ggml_hash_contains(graph->visited_hash_table, node)) { return node; } int count_children = 0; for (int k = 0; k < GGML_MAX_SRC; ++k) { if (node->src[k]) { ++count_children; } } if (count_children == 0) { return node; } size_t i = ggml_hash_find(replacements->set, node); GGML_ASSERT(i != GGML_HASHTABLE_FULL); // assert that not full if (replacements->set.keys[i] == node) { return replacements->vals[i]; } struct ggml_tensor * clone = ggml_new_tensor(ctx, node->type, GGML_MAX_DIMS, node->ne); // insert clone into replacements GGML_ASSERT(replacements->set.keys[i] == NULL); // assert that we don't overwrite replacements->set.keys[i] = node; replacements->vals[i] = clone; clone->op = node->op; clone->grad = node->grad; clone->flags = node->flags; clone->extra = node->extra; for (int k = 0; k < GGML_MAX_DIMS; ++k) { clone->nb[k] = node->nb[k]; } for (int k = 0; k < GGML_MAX_SRC; ++k) { clone->src[k] = ggml_recompute_graph_node(ctx, graph, replacements, node->src[k]); } if (node->view_src != NULL) { clone->data = (node->view_src->data == NULL) ? NULL // view_src not yet allocated : (char *) node->view_src->data // view_src already allocated + node->view_offs; clone->view_src = node->view_src; clone->view_offs = node->view_offs; } GGML_ASSERT(sizeof(node->op_params) == sizeof(int32_t) * (GGML_MAX_OP_PARAMS / sizeof(int32_t))); GGML_ASSERT(sizeof(node->name) == GGML_MAX_NAME); memcpy(clone->op_params, node->op_params, sizeof(node->op_params)); ggml_format_name(clone, "%s (clone)", ggml_get_name(node)); return clone; } void ggml_build_backward_gradient_checkpointing( struct ggml_context * ctx, struct ggml_cgraph * gf, struct ggml_cgraph * gb, struct ggml_cgraph * gb_tmp, struct ggml_tensor * * checkpoints, int n_checkpoints) { ggml_graph_cpy(gf, gb_tmp); ggml_build_backward_expand(ctx, gf, gb_tmp, true); if (n_checkpoints <= 0) { ggml_graph_cpy(gb_tmp, gb); return; } struct hash_map * replacements = ggml_new_hash_map(gf->n_nodes + gf->n_leafs + n_checkpoints); // insert checkpoints in replacements for (int i = 0; i < n_checkpoints; ++i) { size_t k = ggml_hash_find(replacements->set, checkpoints[i]); GGML_ASSERT(k != GGML_HASHTABLE_FULL); // assert that not full GGML_ASSERT(replacements->set.keys[k] == NULL); // assert that we don't overwrite replacements->set.keys[k] = checkpoints[i]; replacements->vals[k] = checkpoints[i]; } ggml_graph_cpy(gf, gb); // rewrite gb_tmp->nodes[gf->n_nodes:gb_tmp->n_nodes], // replacing references to gb_tmp->nodes[0:gf->n_nodes] ( == gf->nodes[0:gf->n_nodes]), // by recomputing them from checkpoints for (int i = gf->n_nodes; in_nodes; ++i) { struct ggml_tensor * node = gb_tmp->nodes[i]; for (int k = 0; k < GGML_MAX_SRC; ++k) { // insert new tensors recomputing src, reusing already made replacements, // remember replacements: remember new tensors with mapping from corresponding gf nodes // recurse for input tensors, // unless (i.e. terminating when) input tensors are replacements (like checkpoints) node->src[k] = ggml_recompute_graph_node(ctx, gf, replacements, node->src[k]); } // insert rewritten backward node with replacements made into resulting backward graph gb ggml_build_forward_expand(gb, node); } ggml_hash_map_free(replacements); } // functions to change gradients considering the case that input a might be initial gradient with zero value static struct ggml_tensor * ggml_add_or_set(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, struct ggml_hash_set zero_table) { if (ggml_hash_contains(zero_table, a)) { return b; } else { return ggml_add_impl(ctx, a, b, false); } } static struct ggml_tensor * ggml_acc_or_set(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, size_t nb1, size_t nb2, size_t nb3, size_t offset, struct ggml_hash_set zero_table) { if (ggml_hash_contains(zero_table, a)) { struct ggml_tensor * a_zero = ggml_scale(ctx, a, 0.0f); return ggml_acc_impl(ctx, a_zero, b, nb1, nb2, nb3, offset, false); } else { return ggml_acc_impl(ctx, a, b, nb1, nb2, nb3, offset, false); } } static struct ggml_tensor * ggml_add1_or_set(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, struct ggml_hash_set zero_table) { if (ggml_hash_contains(zero_table, a)) { return ggml_repeat(ctx, b, a); } else { return ggml_add1_impl(ctx, a, b, false); } } static struct ggml_tensor * ggml_sub_or_set(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, struct ggml_hash_set zero_table) { if (ggml_hash_contains(zero_table, a)) { return ggml_neg(ctx, b); } else { return ggml_sub_impl(ctx, a, b, false); } } static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor * tensor, struct ggml_hash_set zero_table) { struct ggml_tensor * src0 = tensor->src[0]; struct ggml_tensor * src1 = tensor->src[1]; struct ggml_tensor * src2 = tensor->src[2]; switch (tensor->op) { case GGML_OP_DUP: { if (src0->grad) { src0->grad = ggml_add_or_set(ctx, src0->grad, tensor->grad, zero_table); } } break; case GGML_OP_ADD: { if (src0->grad) { src0->grad = ggml_add_or_set(ctx, src0->grad, tensor->grad, zero_table); } if (src1->grad) { src1->grad = ggml_add_or_set(ctx, src1->grad, tensor->grad, zero_table); } } break; case GGML_OP_ADD1: { if (src0->grad) { src0->grad = ggml_add_or_set(ctx, src0->grad, tensor->grad, zero_table); } if (src1->grad) { src1->grad = ggml_add_or_set(ctx, src1->grad, ggml_mean(ctx, tensor->grad), // TODO: should probably be sum instead of mean zero_table); } } break; case GGML_OP_ACC: { if (src0->grad) { src0->grad = ggml_add_or_set(ctx, src0->grad, tensor->grad, zero_table); } if (src1->grad) { const size_t nb1 = ((int32_t *) tensor->op_params)[0]; const size_t nb2 = ((int32_t *) tensor->op_params)[1]; const size_t nb3 = ((int32_t *) tensor->op_params)[2]; const size_t offset = ((int32_t *) tensor->op_params)[3]; struct ggml_tensor * tensor_grad_view = ggml_view_4d(ctx, tensor->grad, src1->grad->ne[0], src1->grad->ne[1], src1->grad->ne[2], src1->grad->ne[3], nb1, nb2, nb3, offset); src1->grad = ggml_add_or_set(ctx, src1->grad, ggml_reshape(ctx, ggml_cont(ctx, tensor_grad_view), src1->grad), zero_table); } } break; case GGML_OP_SUB: { if (src0->grad) { src0->grad = ggml_add_or_set(ctx, src0->grad, tensor->grad, zero_table); } if (src1->grad) { src1->grad = ggml_sub_or_set(ctx, src1->grad, tensor->grad, zero_table); } } break; case GGML_OP_MUL: { if (src0->grad) { src0->grad = ggml_add_or_set(ctx, src0->grad, ggml_mul(ctx, src1, tensor->grad), zero_table); } if (src1->grad) { src1->grad = ggml_add_or_set(ctx, src1->grad, ggml_mul(ctx, src0, tensor->grad), zero_table); } } break; case GGML_OP_DIV: { if (src0->grad) { src0->grad = ggml_add_or_set(ctx, src0->grad, ggml_div(ctx, tensor->grad, src1), zero_table); } if (src1->grad) { src1->grad = ggml_sub_or_set(ctx, src1->grad, ggml_mul(ctx, tensor->grad, ggml_div(ctx, tensor, src1)), zero_table); } } break; case GGML_OP_SQR: { if (src0->grad) { src0->grad = ggml_add_or_set(ctx, src0->grad, ggml_scale(ctx, ggml_mul(ctx, src0, tensor->grad), 2.0f), zero_table); } } break; case GGML_OP_SQRT: { if (src0->grad) { src0->grad = ggml_add_or_set(ctx, src0->grad, ggml_scale(ctx, ggml_div(ctx, tensor->grad, tensor), 0.5f), zero_table); } } break; case GGML_OP_LOG: { if (src0->grad) { src0->grad = ggml_add_or_set(ctx, src0->grad, ggml_div(ctx, tensor->grad, src0), zero_table); } } break; case GGML_OP_SUM: { if (src0->grad) { src0->grad = ggml_add1_or_set(ctx, src0->grad, tensor->grad, zero_table); } } break; case GGML_OP_SUM_ROWS: { if (src0->grad) { src0->grad = ggml_add_or_set(ctx, src0->grad, ggml_repeat(ctx, tensor->grad, src0->grad), zero_table); } } break; case GGML_OP_MEAN: case GGML_OP_ARGMAX: { GGML_ASSERT(false); // TODO: implement } break; case GGML_OP_REPEAT: { // necessary for llama if (src0->grad) { src0->grad = ggml_add_or_set(ctx, src0->grad, ggml_repeat_back(ctx, tensor->grad, src0->grad), zero_table); } } break; case GGML_OP_REPEAT_BACK: { if (src0->grad) { // TODO: test this src0->grad = ggml_add_or_set(ctx, src0->grad, ggml_repeat(ctx, tensor->grad, src0->grad), zero_table); } } break; case GGML_OP_CONCAT: { GGML_ASSERT(false); // TODO: implement } break; case GGML_OP_SILU_BACK: { GGML_ASSERT(false); // TODO: not implemented } break; case GGML_OP_NORM: { GGML_ASSERT(false); // TODO: not implemented } break; case GGML_OP_RMS_NORM: { // necessary for llama if (src0->grad) { float eps; memcpy(&eps, tensor->op_params, sizeof(float)); src0->grad = ggml_add_or_set(ctx, src0->grad, ggml_rms_norm_back(ctx, src0, tensor->grad, eps), zero_table); } } break; case GGML_OP_RMS_NORM_BACK: { GGML_ASSERT(false); // TODO: not implemented } break; case GGML_OP_GROUP_NORM: { GGML_ASSERT(false); // TODO: not implemented } break; case GGML_OP_MUL_MAT: { // https://cs231n.github.io/optimization-2/#staged // # forward pass // s0 = np.random.randn(5, 10) // s1 = np.random.randn(10, 3) // t = s0.dot(s1) // # now suppose we had the gradient on t from above in the circuit // dt = np.random.randn(*t.shape) # same shape as t // ds0 = dt.dot(s1.T) #.T gives the transpose of the matrix // ds1 = t.T.dot(dt) // tensor.shape [m,p,qq,rr] // src0.shape [n,m,q1,r1] // src1.shape [n,p,qq,rr] // necessary for llama if (src0->grad) { struct ggml_tensor * s1_tg = ggml_out_prod(ctx, // [n,m,qq,rr] src1, // [n,p,qq,rr] tensor->grad); // [m,p,qq,rr] const int64_t qq = s1_tg->ne[2]; const int64_t rr = s1_tg->ne[3]; const int64_t q1 = src0->ne[2]; const int64_t r1 = src0->ne[3]; const bool ne2_broadcasted = qq > q1; const bool ne3_broadcasted = rr > r1; if (ne2_broadcasted || ne3_broadcasted) { // sum broadcast repetitions of s1_tg into shape of src0 s1_tg = ggml_repeat_back(ctx, s1_tg, src0); } src0->grad = ggml_add_or_set(ctx, src0->grad, // [n,m,q1,r1] s1_tg, // [n,m,q1,r1] zero_table); } if (src1->grad) { src1->grad = ggml_add_or_set(ctx, src1->grad, // [n,p,qq,rr] // ggml_mul_mat(ctx, // [n,p,qq,rr] // ggml_cont(ctx, // [m,n,q1,r1] // ggml_transpose(ctx, src0)), // [m,n,q1,r1] // tensor->grad), // [m,p,qq,rr] // // when src0 is bigger than tensor->grad (this is mostly the case in llama), // // avoid transpose of src0, rather transpose smaller tensor->grad // // and then use ggml_out_prod ggml_out_prod(ctx, // [n,p,qq,rr] src0, // [n,m,q1,r1] ggml_transpose(ctx, // [p,m,qq,rr] tensor->grad)), // [m,p,qq,rr] zero_table); } } break; case GGML_OP_MUL_MAT_ID: { GGML_ASSERT(false); // TODO: not implemented } break; case GGML_OP_OUT_PROD: { GGML_ASSERT(false); // TODO: not implemented } break; case GGML_OP_SCALE: { // necessary for llama if (src0->grad) { float s; memcpy(&s, tensor->op_params, sizeof(float)); src0->grad = ggml_add_or_set(ctx, src0->grad, ggml_scale_impl(ctx, tensor->grad, s, false), zero_table); } } break; case GGML_OP_SET: { const size_t nb1 = ((int32_t *) tensor->op_params)[0]; const size_t nb2 = ((int32_t *) tensor->op_params)[1]; const size_t nb3 = ((int32_t *) tensor->op_params)[2]; const size_t offset = ((int32_t *) tensor->op_params)[3]; struct ggml_tensor * tensor_grad_view = NULL; if (src0->grad || src1->grad) { GGML_ASSERT(src0->type == tensor->type); GGML_ASSERT(tensor->grad->type == tensor->type); GGML_ASSERT(tensor->grad->type == src1->grad->type); tensor_grad_view = ggml_view_4d(ctx, tensor->grad, src1->grad->ne[0], src1->grad->ne[1], src1->grad->ne[2], src1->grad->ne[3], nb1, nb2, nb3, offset); } if (src0->grad) { src0->grad = ggml_add_or_set(ctx, src0->grad, ggml_acc_impl(ctx, tensor->grad, ggml_neg(ctx, tensor_grad_view), nb1, nb2, nb3, offset, false), zero_table); } if (src1->grad) { src1->grad = ggml_add_or_set(ctx, src1->grad, ggml_reshape(ctx, ggml_cont(ctx, tensor_grad_view), src1->grad), zero_table); } } break; case GGML_OP_CPY: { // necessary for llama // cpy overwrites value of src1 by src0 and returns view(src1) // the overwriting is mathematically equivalent to: // tensor = src0 * 1 + src1 * 0 if (src0->grad) { // dsrc0 = dtensor * 1 src0->grad = ggml_add_or_set(ctx, src0->grad, tensor->grad, zero_table); } if (src1->grad) { // dsrc1 = dtensor * 0 -> noop } } break; case GGML_OP_CONT: { // same as cpy if (src0->grad) { GGML_ASSERT(ggml_is_contiguous(src0->grad)); GGML_ASSERT(ggml_is_contiguous(tensor->grad)); src0->grad = ggml_add_or_set(ctx, src0->grad, tensor->grad, zero_table); } } break; case GGML_OP_RESHAPE: { // necessary for llama if (src0->grad) { src0->grad = ggml_add_or_set(ctx, src0->grad, ggml_reshape(ctx, ggml_is_contiguous(tensor->grad) ? tensor->grad : ggml_cont(ctx, tensor->grad), src0->grad), zero_table); } } break; case GGML_OP_VIEW: { // necessary for llama if (src0->grad) { size_t offset; memcpy(&offset, tensor->op_params, sizeof(offset)); size_t nb1 = tensor->nb[1]; size_t nb2 = tensor->nb[2]; size_t nb3 = tensor->nb[3]; if (src0->type != src0->grad->type) { // gradient is typically F32, but src0 could be other type size_t ng = ggml_element_size(src0->grad); size_t n0 = ggml_element_size(src0); GGML_ASSERT(offset % n0 == 0); GGML_ASSERT(nb1 % n0 == 0); GGML_ASSERT(nb2 % n0 == 0); GGML_ASSERT(nb3 % n0 == 0); offset = (offset / n0) * ng; nb1 = (nb1 / n0) * ng; nb2 = (nb2 / n0) * ng; nb3 = (nb3 / n0) * ng; } src0->grad = ggml_acc_or_set(ctx, src0->grad, tensor->grad, nb1, nb2, nb3, offset, zero_table); } } break; case GGML_OP_PERMUTE: { // necessary for llama if (src0->grad) { int32_t * axes = (int32_t *) tensor->op_params; int axis0 = axes[0] & 0x3; int axis1 = axes[1] & 0x3; int axis2 = axes[2] & 0x3; int axis3 = axes[3] & 0x3; int axes_backward[4] = {0,0,0,0}; axes_backward[axis0] = 0; axes_backward[axis1] = 1; axes_backward[axis2] = 2; axes_backward[axis3] = 3; src0->grad = ggml_add_or_set(ctx, src0->grad, ggml_permute(ctx, tensor->grad, axes_backward[0], axes_backward[1], axes_backward[2], axes_backward[3]), zero_table); } } break; case GGML_OP_TRANSPOSE: { // necessary for llama if (src0->grad) { src0->grad = ggml_add_or_set(ctx, src0->grad, ggml_transpose(ctx, tensor->grad), zero_table); } } break; case GGML_OP_GET_ROWS: { // necessary for llama (only for tokenizer) if (src0->grad) { src0->grad = ggml_add_or_set(ctx, src0->grad, // last ggml_get_rows_back argument src0->grad is only // necessary to setup correct output shape ggml_get_rows_back(ctx, tensor->grad, src1, src0->grad), zero_table); } if (src1->grad) { // noop } } break; case GGML_OP_GET_ROWS_BACK: { GGML_ASSERT(false); // TODO: not implemented } break; case GGML_OP_DIAG: { GGML_ASSERT(false); // TODO: not implemented } break; case GGML_OP_DIAG_MASK_INF: { // necessary for llama if (src0->grad) { const int n_past = ((int32_t *) tensor->op_params)[0]; src0->grad = ggml_add_or_set(ctx, src0->grad, /* ggml_diag_mask_inf_impl() shouldn't be here */ /* ref: https://github.com/ggerganov/llama.cpp/pull/4203#discussion_r1412377992 */ ggml_diag_mask_zero_impl(ctx, tensor->grad, n_past, false), zero_table); } } break; case GGML_OP_DIAG_MASK_ZERO: { // necessary for llama if (src0->grad) { const int n_past = ((int32_t *) tensor->op_params)[0]; src0->grad = ggml_add_or_set(ctx, src0->grad, ggml_diag_mask_zero_impl(ctx, tensor->grad, n_past, false), zero_table); } } break; case GGML_OP_SOFT_MAX: { // necessary for llama if (src0->grad) { src0->grad = ggml_add_or_set(ctx, src0->grad, ggml_soft_max_back(ctx, tensor->grad, tensor), zero_table); } } break; case GGML_OP_SOFT_MAX_BACK: { GGML_ASSERT(false); // TODO: not implemented } break; case GGML_OP_ROPE: { // necessary for llama if (src0->grad) { //const int n_past = ((int32_t *) tensor->op_params)[0]; const int n_dims = ((int32_t *) tensor->op_params)[1]; const int mode = ((int32_t *) tensor->op_params)[2]; const int n_ctx = ((int32_t *) tensor->op_params)[3]; const int n_orig_ctx = ((int32_t *) tensor->op_params)[4]; float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow, xpos_base, xpos_down; memcpy(&freq_base, (int32_t *) tensor->op_params + 5, sizeof(float)); memcpy(&freq_scale, (int32_t *) tensor->op_params + 6, sizeof(float)); memcpy(&ext_factor, (int32_t *) tensor->op_params + 7, sizeof(float)); memcpy(&attn_factor, (int32_t *) tensor->op_params + 8, sizeof(float)); memcpy(&beta_fast, (int32_t *) tensor->op_params + 9, sizeof(float)); memcpy(&beta_slow, (int32_t *) tensor->op_params + 10, sizeof(float)); memcpy(&xpos_base, (int32_t *) tensor->op_params + 11, sizeof(float)); memcpy(&xpos_down, (int32_t *) tensor->op_params + 12, sizeof(bool)); src0->grad = ggml_add_or_set(ctx, src0->grad, ggml_rope_back(ctx, tensor->grad, src1, src2, n_dims, mode, n_ctx, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow, xpos_base, xpos_down), zero_table); } } break; case GGML_OP_ROPE_BACK: { if (src0->grad) { //const int n_past = ((int32_t *) tensor->op_params)[0]; const int n_dims = ((int32_t *) tensor->op_params)[1]; const int mode = ((int32_t *) tensor->op_params)[2]; const int n_ctx = ((int32_t *) tensor->op_params)[3]; const int n_orig_ctx = ((int32_t *) tensor->op_params)[4]; float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow, xpos_base, xpos_down; memcpy(&freq_base, (int32_t *) tensor->op_params + 5, sizeof(float)); memcpy(&freq_scale, (int32_t *) tensor->op_params + 6, sizeof(float)); memcpy(&ext_factor, (int32_t *) tensor->op_params + 7, sizeof(float)); memcpy(&attn_factor, (int32_t *) tensor->op_params + 8, sizeof(float)); memcpy(&beta_fast, (int32_t *) tensor->op_params + 9, sizeof(float)); memcpy(&beta_slow, (int32_t *) tensor->op_params + 10, sizeof(float)); memcpy(&xpos_base, (int32_t *) tensor->op_params + 11, sizeof(float)); memcpy(&xpos_down, (int32_t *) tensor->op_params + 12, sizeof(bool)); src0->grad = ggml_add_or_set(ctx, src0->grad, ggml_rope_impl(ctx, tensor->grad, src1, src2, n_dims, mode, n_ctx, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow, xpos_base, xpos_down, false), zero_table); } } break; case GGML_OP_CLAMP: { GGML_ASSERT(false); // TODO: not implemented } break; case GGML_OP_CONV_TRANSPOSE_1D: { GGML_ASSERT(false); // TODO: not implemented } break; case GGML_OP_IM2COL: { GGML_ASSERT(false); // TODO: not implemented } break; case GGML_OP_CONV_TRANSPOSE_2D: { GGML_ASSERT(false); // TODO: not implemented } break; case GGML_OP_POOL_1D: { GGML_ASSERT(false); // TODO: not implemented } break; case GGML_OP_POOL_2D: { GGML_ASSERT(false); // TODO: not implemented } break; case GGML_OP_UPSCALE: { GGML_ASSERT(false); // TODO: not implemented } break; case GGML_OP_PAD: { GGML_ASSERT(false); // TODO: not implemented } break; case GGML_OP_ARANGE: { GGML_ASSERT(false); // TODO: not implemented } break; case GGML_OP_TIMESTEP_EMBEDDING: { GGML_ASSERT(false); // TODO: not implemented } break; case GGML_OP_ARGSORT: { GGML_ASSERT(false); // TODO: not implemented } break; case GGML_OP_LEAKY_RELU: { GGML_ASSERT(false); // TODO: not implemented } break; case GGML_OP_FLASH_ATTN_EXT: { struct ggml_tensor * flash_grad = NULL; if (src0->grad || src1->grad || tensor->src[2]->grad) { int32_t t = ggml_get_op_params_i32(tensor, 0); GGML_ASSERT(t == 0 || t == 1); bool masked = t != 0; flash_grad = ggml_flash_attn_back(ctx, src0, src1, tensor->src[2], tensor->grad, masked); } const int64_t elem_q = ggml_nelements(src0); const int64_t elem_k = ggml_nelements(src1); const int64_t elem_v = ggml_nelements(src2); enum ggml_type result_type = flash_grad->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); if (src0->grad) { struct ggml_tensor * view_q = ggml_view_1d(ctx, flash_grad, elem_q, offs_q); struct ggml_tensor * grad_q = ggml_reshape(ctx, view_q, src0); src0->grad = ggml_add_or_set(ctx, src0->grad, grad_q, zero_table); } if (src1->grad) { struct ggml_tensor * view_k = ggml_view_1d(ctx, flash_grad, elem_k, offs_k); struct ggml_tensor * grad_k = ggml_reshape(ctx, view_k, src1); src1->grad = ggml_add_or_set(ctx, src1->grad, grad_k, zero_table); } if (src2->grad) { struct ggml_tensor * view_v = ggml_view_1d(ctx, flash_grad, elem_v, offs_v); struct ggml_tensor * grad_v = ggml_reshape(ctx, view_v, src2); src2->grad = ggml_add_or_set(ctx, src2->grad, grad_v, zero_table); } } break; case GGML_OP_FLASH_ATTN_BACK: { GGML_ASSERT(false); // not supported } break; case GGML_OP_SSM_CONV: case GGML_OP_SSM_SCAN: { GGML_ASSERT(false); // TODO: not implemented } break; case GGML_OP_WIN_PART: case GGML_OP_WIN_UNPART: case GGML_OP_UNARY: { switch (ggml_get_unary_op(tensor)) { case GGML_UNARY_OP_ABS: { if (src0->grad) { src0->grad = ggml_add_or_set(ctx, src0->grad, ggml_mul(ctx, ggml_sgn(ctx, src0), tensor->grad), zero_table); } } break; case GGML_UNARY_OP_SGN: { if (src0->grad) { // noop } } break; case GGML_UNARY_OP_NEG: { if (src0->grad) { src0->grad = ggml_sub_or_set(ctx, src0->grad, tensor->grad, zero_table); } } break; case GGML_UNARY_OP_STEP: { if (src0->grad) { // noop } } break; case GGML_UNARY_OP_TANH: { GGML_ASSERT(false); // TODO: not implemented } break; case GGML_UNARY_OP_ELU: { GGML_ASSERT(false); // TODO: not implemented } break; case GGML_UNARY_OP_RELU: { if (src0->grad) { src0->grad = ggml_add_or_set(ctx, src0->grad, ggml_mul(ctx, ggml_step(ctx, src0), tensor->grad), zero_table); } } break; case GGML_UNARY_OP_SIGMOID: { GGML_ASSERT(false); // TODO: not implemented } break; case GGML_UNARY_OP_GELU: { GGML_ASSERT(false); // TODO: not implemented } break; case GGML_UNARY_OP_GELU_QUICK: { GGML_ASSERT(false); // TODO: not implemented } break; case GGML_UNARY_OP_SILU: { // necessary for llama if (src0->grad) { src0->grad = ggml_add_or_set(ctx, src0->grad, ggml_silu_back(ctx, src0, tensor->grad), zero_table); } } break; default: GGML_ASSERT(false); } } break; case GGML_OP_GET_REL_POS: case GGML_OP_ADD_REL_POS: case GGML_OP_MAP_UNARY: case GGML_OP_MAP_BINARY: case GGML_OP_MAP_CUSTOM1_F32: case GGML_OP_MAP_CUSTOM2_F32: case GGML_OP_MAP_CUSTOM3_F32: case GGML_OP_MAP_CUSTOM1: case GGML_OP_MAP_CUSTOM2: case GGML_OP_MAP_CUSTOM3: { GGML_ASSERT(false); // not supported } break; case GGML_OP_CROSS_ENTROPY_LOSS: { if (src0->grad) { src0->grad = ggml_add_or_set(ctx, src0->grad, ggml_cross_entropy_loss_back(ctx, src0, src1, tensor->grad), zero_table); } } break; case GGML_OP_CROSS_ENTROPY_LOSS_BACK: { GGML_ASSERT(false); // not supported } break; case GGML_OP_NONE: { // nop } break; case GGML_OP_COUNT: { GGML_ASSERT(false); } break; } for (int i = 0; i < GGML_MAX_SRC; ++i) { if (tensor->src[i] && tensor->src[i]->grad) { GGML_ASSERT(ggml_are_same_shape(tensor->src[i], tensor->src[i]->grad)); } } } static void ggml_visit_parents(struct ggml_cgraph * cgraph, struct ggml_tensor * node) { if (node->grad == NULL) { // this usually happens when we generate intermediate nodes from constants in the backward pass // it can also happen during forward pass, if the user performs computations with constants if (node->op != GGML_OP_NONE) { //GGML_PRINT_DEBUG("%s: warning: node %p has no grad, but op %d\n", __func__, (void *) node, node->op); } } // check if already visited if (ggml_hash_insert(cgraph->visited_hash_table, node) == GGML_HASHTABLE_ALREADY_EXISTS) { return; } for (int i = 0; i < GGML_MAX_SRC; ++i) { const int k = (cgraph->order == GGML_CGRAPH_EVAL_ORDER_LEFT_TO_RIGHT) ? i : (cgraph->order == GGML_CGRAPH_EVAL_ORDER_RIGHT_TO_LEFT) ? (GGML_MAX_SRC-1-i) : /* unknown order, just fall back to using i*/ i; if (node->src[k]) { ggml_visit_parents(cgraph, node->src[k]); } } if (node->op == GGML_OP_NONE && node->grad == NULL) { // reached a leaf node, not part of the gradient graph (e.g. a constant) GGML_ASSERT(cgraph->n_leafs < cgraph->size); if (strlen(node->name) == 0) { ggml_format_name(node, "leaf_%d", cgraph->n_leafs); } cgraph->leafs[cgraph->n_leafs] = node; cgraph->n_leafs++; } else { GGML_ASSERT(cgraph->n_nodes < cgraph->size); if (strlen(node->name) == 0) { ggml_format_name(node, "node_%d", cgraph->n_nodes); } cgraph->nodes[cgraph->n_nodes] = node; if (cgraph->grads) { cgraph->grads[cgraph->n_nodes] = node->grad; } cgraph->n_nodes++; } } static void ggml_build_forward_impl(struct ggml_cgraph * cgraph, struct ggml_tensor * tensor, bool expand) { if (!expand) { // TODO: this branch isn't accessible anymore, maybe move this to ggml_build_forward_expand ggml_graph_clear(cgraph); } const int n0 = cgraph->n_nodes; UNUSED(n0); ggml_visit_parents(cgraph, tensor); const int n_new = cgraph->n_nodes - n0; GGML_PRINT_DEBUG("%s: visited %d new nodes\n", __func__, n_new); if (n_new > 0) { // the last added node should always be starting point GGML_ASSERT(cgraph->nodes[cgraph->n_nodes - 1] == tensor); } } void ggml_build_forward_expand(struct ggml_cgraph * cgraph, struct ggml_tensor * tensor) { ggml_build_forward_impl(cgraph, tensor, true); } void ggml_build_backward_expand(struct ggml_context * ctx, struct ggml_cgraph * gf, struct ggml_cgraph * gb, bool keep) { GGML_ASSERT(gf->n_nodes > 0); // if we are keeping the gradient graph, we have to detach the gradient nodes from the original graph if (keep) { for (int i = 0; i < gf->n_nodes; i++) { struct ggml_tensor * node = gf->nodes[i]; if (node->grad) { node->grad = ggml_dup_tensor(ctx, node); gf->grads[i] = node->grad; } } } // remember original gradients which start with zero values struct ggml_hash_set zero_table = ggml_hash_set_new(gf->size); for (int i = 0; i < gf->n_nodes; i++) { if (gf->grads[i]) { ggml_hash_insert(zero_table, gf->grads[i]); } } for (int i = gf->n_nodes - 1; i >= 0; i--) { struct ggml_tensor * node = gf->nodes[i]; // inplace operations to add gradients are not created by ggml_compute_backward // use allocator to automatically make inplace operations if (node->grad) { ggml_compute_backward(ctx, node, zero_table); } } for (int i = 0; i < gf->n_nodes; i++) { struct ggml_tensor * node = gf->nodes[i]; if (node->flags & GGML_TENSOR_FLAG_PARAM) { GGML_PRINT_DEBUG("%s: found root node %p\n", __func__, (void *) node); ggml_build_forward_expand(gb, node->grad); } } ggml_hash_set_free(zero_table); } static size_t ggml_graph_nbytes(size_t size, bool grads) { size_t nbytes = sizeof(struct ggml_cgraph); nbytes += size * sizeof(struct ggml_tensor *) * 2; // leafs + nodes if (grads) { nbytes += size * sizeof(struct ggml_tensor *); // grads } nbytes += ggml_hash_size(size * 2) * sizeof(struct ggml_tensor *); // hash set return nbytes; } size_t ggml_graph_overhead_custom(size_t size, bool grads) { return GGML_OBJECT_SIZE + GGML_PAD(ggml_graph_nbytes(size, grads), GGML_MEM_ALIGN); } size_t ggml_graph_overhead(void) { return ggml_graph_overhead_custom(GGML_DEFAULT_GRAPH_SIZE, false); } struct ggml_cgraph * ggml_new_graph_custom(struct ggml_context * ctx, size_t size, bool grads) { const size_t obj_size = ggml_graph_nbytes(size, grads); struct ggml_object * obj = ggml_new_object(ctx, GGML_OBJECT_TYPE_GRAPH, obj_size); struct ggml_cgraph * cgraph = (struct ggml_cgraph *) ((char *) ctx->mem_buffer + obj->offs); struct ggml_tensor ** data_start = (struct ggml_tensor **) (cgraph + 1); size_t hash_size = ggml_hash_size(size * 2); struct ggml_tensor ** nodes_ptr = data_start; struct ggml_tensor ** leafs_ptr = nodes_ptr + size; struct ggml_tensor ** hash_keys_ptr = leafs_ptr + size; struct ggml_tensor ** grads_ptr = grads ? hash_keys_ptr + hash_size : NULL; // check that we allocated the correct amount of memory assert(obj_size == (size_t) ( (grads ? (char *)(grads_ptr + size) : (char *)(hash_keys_ptr + hash_size)) - (char *)cgraph)); memset(hash_keys_ptr, 0, hash_size * sizeof(struct ggml_tensor *)); *cgraph = (struct ggml_cgraph) { /*.size =*/ size, /*.n_nodes =*/ 0, /*.n_leafs =*/ 0, /*.nodes =*/ nodes_ptr, /*.grads =*/ grads_ptr, /*.leafs =*/ leafs_ptr, /*.hash_table =*/ { hash_size, hash_keys_ptr }, /*.order =*/ GGML_CGRAPH_EVAL_ORDER_LEFT_TO_RIGHT, /*.perf_runs =*/ 0, /*.perf_cycles =*/ 0, /*.perf_time_us =*/ 0, }; return cgraph; } struct ggml_cgraph * ggml_new_graph(struct ggml_context * ctx) { return ggml_new_graph_custom(ctx, GGML_DEFAULT_GRAPH_SIZE, false); } struct ggml_cgraph ggml_graph_view(struct ggml_cgraph * cgraph0, int i0, int i1) { struct ggml_cgraph cgraph = { /*.size =*/ 0, /*.n_nodes =*/ i1 - i0, /*.n_leafs =*/ 0, /*.nodes =*/ cgraph0->nodes + i0, /*.grads =*/ cgraph0->grads ? cgraph0->grads + i0 : NULL, /*.leafs =*/ NULL, /*.hash_table =*/ { 0, NULL }, /*.order =*/ cgraph0->order, /*.perf_runs =*/ 0, /*.perf_cycles =*/ 0, /*.perf_time_us =*/ 0, }; return cgraph; } void ggml_graph_cpy(struct ggml_cgraph * src, struct ggml_cgraph * dst) { GGML_ASSERT(dst->size >= src->n_leafs); GGML_ASSERT(dst->size >= src->n_nodes); GGML_ASSERT(dst->visited_hash_table.size >= src->visited_hash_table.size); dst->n_leafs = src->n_leafs; dst->n_nodes = src->n_nodes; dst->order = src->order; for (int i = 0; i < src->n_leafs; ++i) { dst->leafs[i] = src->leafs[i]; } for (int i = 0; i < src->n_nodes; ++i) { dst->nodes[i] = src->nodes[i]; } if (src->grads) { GGML_ASSERT(dst->grads != NULL); for (int i = 0; i < src->n_nodes; ++i) { dst->grads[i] = src->grads[i]; } } for (size_t i = 0; i < src->visited_hash_table.size; ++i) { if (src->visited_hash_table.keys[i]) { ggml_hash_insert(dst->visited_hash_table, src->visited_hash_table.keys[i]); } } } struct ggml_cgraph * ggml_graph_dup(struct ggml_context * ctx, struct ggml_cgraph * cgraph) { struct ggml_cgraph * result = ggml_new_graph_custom(ctx, cgraph->size, cgraph->grads != NULL); ggml_graph_cpy(cgraph, result); return result; } void ggml_graph_reset(struct ggml_cgraph * cgraph) { GGML_ASSERT(cgraph->grads != NULL); for (int i = 0; i < cgraph->n_nodes; i++) { struct ggml_tensor * grad = cgraph->grads[i]; if (grad) { ggml_set_zero(grad); } } } void ggml_graph_clear(struct ggml_cgraph * cgraph) { cgraph->n_leafs = 0; cgraph->n_nodes = 0; memset(cgraph->visited_hash_table.keys, 0, cgraph->visited_hash_table.size * sizeof(struct ggml_tensor *)); } // // thread data // // synchronization is done via busy loops // I tried using spin locks, but not sure how to use them correctly - the things I tried were slower than busy loops // #ifdef __APPLE__ //#include // //typedef os_unfair_lock ggml_lock_t; // //#define ggml_lock_init(x) UNUSED(x) //#define ggml_lock_destroy(x) UNUSED(x) //#define ggml_lock_lock os_unfair_lock_lock //#define ggml_lock_unlock os_unfair_lock_unlock // //#define GGML_LOCK_INITIALIZER OS_UNFAIR_LOCK_INIT typedef int ggml_lock_t; #define ggml_lock_init(x) UNUSED(x) #define ggml_lock_destroy(x) UNUSED(x) #define ggml_lock_lock(x) UNUSED(x) #define ggml_lock_unlock(x) UNUSED(x) #define GGML_LOCK_INITIALIZER 0 #define ggml_thread_create pthread_create #define ggml_thread_join pthread_join #else //typedef pthread_spinlock_t ggml_lock_t; //#define ggml_lock_init(x) pthread_spin_init(x, PTHREAD_PROCESS_PRIVATE) //#define ggml_lock_destroy pthread_spin_destroy //#define ggml_lock_lock pthread_spin_lock //#define ggml_lock_unlock pthread_spin_unlock typedef int ggml_lock_t; #define ggml_lock_init(x) UNUSED(x) #define ggml_lock_destroy(x) UNUSED(x) #if defined(__x86_64__) || (defined(_MSC_VER) && defined(_M_AMD64)) #define ggml_lock_lock(x) _mm_pause() #else #define ggml_lock_lock(x) UNUSED(x) #endif #define ggml_lock_unlock(x) UNUSED(x) #define GGML_LOCK_INITIALIZER 0 #define ggml_thread_create pthread_create #define ggml_thread_join pthread_join #endif // Android's libc implementation "bionic" does not support setting affinity #if defined(__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 void ggml_graph_compute_perf_stats_node(struct ggml_tensor * node, const struct ggml_compute_state_shared * st) { int64_t cycles_cur = ggml_perf_cycles() - st->perf_node_start_cycles; int64_t time_us_cur = ggml_perf_time_us() - st->perf_node_start_time_us; node->perf_runs++; node->perf_cycles += cycles_cur; node->perf_time_us += time_us_cur; } static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads, int n_cur_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_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_SUM: case GGML_OP_SUM_ROWS: case GGML_OP_MEAN: case GGML_OP_ARGMAX: 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: // to opt for multiple threads case GGML_UNARY_OP_HARDSIGMOID: // to opt for multiple threads { 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_ASSERT(false); } 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: { n_tasks = n_threads; } break; case GGML_OP_MUL_MAT: { n_tasks = n_threads; // TODO: use different scheduling for different matrix sizes //const int nr0 = ggml_nrows(node->src[0]); //const int nr1 = ggml_nrows(node->src[1]); //n_tasks = MIN(n_threads, MAX(1, nr0/128)); //printf("nr0 = %8d, nr1 = %8d, nr0*nr1 = %8d, n_tasks%d\n", nr0, nr1, nr0*nr1, n_tasks); } break; case GGML_OP_MUL_MAT_ID: { n_tasks = n_threads; } break; case GGML_OP_OUT_PROD: { n_tasks = n_threads; } break; case GGML_OP_GET_ROWS: { // FIXME: the cost of launching additional threads decreases performance with GPU offloading //n_tasks = MIN(n_threads, ggml_nelements(node->src[1])); n_tasks = MIN(n_cur_threads, ggml_nelements(node->src[1])); } break; case GGML_OP_SCALE: case GGML_OP_SET: case GGML_OP_CONT: case GGML_OP_RESHAPE: case GGML_OP_VIEW: case GGML_OP_PERMUTE: case GGML_OP_TRANSPOSE: case GGML_OP_GET_ROWS_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_CONV_TRANSPOSE_1D: { n_tasks = n_threads; } break; case GGML_OP_IM2COL: { n_tasks = n_threads; } break; case GGML_OP_CONV_TRANSPOSE_2D: { n_tasks = n_threads; } break; case GGML_OP_POOL_1D: case GGML_OP_POOL_2D: { n_tasks = 1; } break; case GGML_OP_UPSCALE: { n_tasks = n_threads; } break; case GGML_OP_PAD: { n_tasks = n_threads; } break; case GGML_OP_ARANGE: { n_tasks = n_threads; } break; case GGML_OP_TIMESTEP_EMBEDDING: { n_tasks = n_threads; } break; case GGML_OP_ARGSORT: { n_tasks = n_threads; } break; case GGML_OP_FLASH_ATTN_EXT: { n_tasks = n_threads; } break; case GGML_OP_FLASH_ATTN_BACK: { n_tasks = n_threads; } break; 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_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: { n_tasks = n_threads; } break; case GGML_OP_CROSS_ENTROPY_LOSS_BACK: { n_tasks = n_threads; } break; case GGML_OP_NONE: { n_tasks = 1; } break; case GGML_OP_COUNT: { GGML_ASSERT(false); } break; 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_ASSERT(false); } break; } assert(n_tasks > 0); return n_tasks; } static void ggml_graph_compute_thread_sync_node(int * node_n, struct ggml_compute_state * state, const bool do_yield) { // wait for other threads to finish const int last_node_n = * node_n; while (true) { if (do_yield) { sched_yield(); } * node_n = atomic_load(&state->shared->node_n); if (* node_n != last_node_n) break; #if defined(__SSE3__) // Tell the processor we're spinning. It's a processor hint for spinlocks. _mm_pause(); #endif } } static void ggml_graph_compute_thread_sync_task(int * task_phase, struct ggml_compute_state * state, const bool do_yield) { // wait for other threads to finish const int last_task_phase = * task_phase; while (true) { if (do_yield) { sched_yield(); } * task_phase = atomic_load(&state->shared->node_task); if (* task_phase != last_task_phase) break; #if defined(__SSE3__) // Tell the processor we're spinning. It's a processor hint for spinlocks. _mm_pause(); #endif } } static thread_ret_t ggml_graph_compute_thread(void * data) { struct ggml_compute_state * state = (struct ggml_compute_state *) data; const struct ggml_cgraph * cgraph = state->shared->cgraph; const struct ggml_cplan * cplan = state->shared->cplan; const int n_threads = state->shared->n_threads; set_numa_thread_affinity(state->ith); int node_n = -1; int task_phase = GGML_TASK_TYPE_FINALIZE; while (true) { if (cplan->abort_callback && cplan->abort_callback(cplan->abort_callback_data)) { state->shared->node_n += 1; state->ec = GGML_STATUS_ABORTED; return 0; } if (atomic_fetch_sub(&state->shared->n_active, 1) == 1) { // all other threads are finished and spinning // do finalize and init here so we don't have synchronize again struct ggml_compute_params params = { /*.type =*/ GGML_TASK_TYPE_FINALIZE, /*.ith =*/ 0, /*.nth =*/ 0, /*.wsize =*/ cplan->work_size, /*.wdata =*/ cplan->work_data, }; if (node_n != -1) { /* FINALIZE */ struct ggml_tensor * node = cgraph->nodes[node_n]; if (GGML_OP_HAS_FINALIZE[node->op]) { params.nth = ggml_get_n_tasks(node, n_threads, state->shared->n_threads); ggml_compute_forward(¶ms, node, state); } ggml_graph_compute_perf_stats_node(node, state->shared); } // distribute new work or execute it direct if 1T while (++node_n < cgraph->n_nodes) { GGML_PRINT_DEBUG_5("%s: %d/%d\n", __func__, node_n, cgraph->n_nodes); struct ggml_tensor * node = cgraph->nodes[node_n]; const int n_tasks = ggml_get_n_tasks(node, n_threads, state->shared->n_threads); state->shared->perf_node_start_cycles = ggml_perf_cycles(); state->shared->perf_node_start_time_us = ggml_perf_time_us(); params.nth = n_tasks; if (n_tasks == 1) { /* INIT */ if (GGML_OP_HAS_INIT[node->op]) { params.type = GGML_TASK_TYPE_INIT; ggml_compute_forward(¶ms, node, state); } // TODO: maybe push node_n to the atomic but if other threads see n_tasks is 1, // they do something more efficient than spinning (?) params.type = GGML_TASK_TYPE_COMPUTE; ggml_compute_forward(¶ms, node, state); if (GGML_OP_HAS_FINALIZE[node->op]) { params.type = GGML_TASK_TYPE_FINALIZE; ggml_compute_forward(¶ms, node, state); } ggml_graph_compute_perf_stats_node(node, state->shared); } else { break; } if (cplan->abort_callback && cplan->abort_callback(cplan->abort_callback_data)) { break; } } task_phase = GGML_TASK_TYPE_INIT; atomic_store(&state->shared->n_active, n_threads); atomic_store(&state->shared->node_n, node_n); atomic_store(&state->shared->node_task, task_phase); } else { ggml_graph_compute_thread_sync_node(&node_n, state, false); ggml_graph_compute_thread_sync_task(&task_phase, state, false); } // check if we should stop if (node_n >= cgraph->n_nodes) break; /* INIT & COMPUTE */ struct ggml_tensor * node = cgraph->nodes[node_n]; const int n_tasks = ggml_get_n_tasks(node, n_threads, state->shared->n_threads); struct ggml_compute_params params = { /*.type =*/ GGML_TASK_TYPE_INIT, /*.ith =*/ state->ith, /*.nth =*/ n_tasks, /*.wsize =*/ cplan->work_size, /*.wdata =*/ cplan->work_data, }; if (state->ith < n_tasks) { if (GGML_OP_HAS_INIT[node->op]) { ggml_compute_forward(¶ms, node, state); } } if (atomic_fetch_sub(&state->shared->n_active, 1) == 1) { task_phase = GGML_TASK_TYPE_COMPUTE; atomic_store(&state->shared->n_active, n_threads); atomic_store(&state->shared->node_task, task_phase); } else { // TODO: this sched_yield can have significant impact on the performance - either positive or negative // depending on the workload and the operating system. // since it is not clear what is the best approach, it should potentially become user-configurable // ref: https://github.com/ggerganov/ggml/issues/291 // UPD: adding the do_yield flag seems to resolve the issue universally const bool do_yield = node_n < 0 || cgraph->nodes[node_n]->op == GGML_OP_MUL_MAT; ggml_graph_compute_thread_sync_task(&task_phase, state, do_yield); } if (state->ith < n_tasks) { params.type = GGML_TASK_TYPE_COMPUTE; ggml_compute_forward(¶ms, node, state); } if (atomic_fetch_sub(&state->shared->n_active, 1) == 1) { task_phase = GGML_TASK_TYPE_FINALIZE; atomic_store(&state->shared->n_active, n_threads); atomic_store(&state->shared->node_task, task_phase); } else { ggml_graph_compute_thread_sync_task(&task_phase, state, false); } } return 0; } struct ggml_cplan ggml_graph_plan(const struct ggml_cgraph * cgraph, int n_threads) { if (n_threads <= 0) { n_threads = GGML_DEFAULT_N_THREADS; } size_t work_size = 0; struct ggml_cplan cplan; memset(&cplan, 0, sizeof(struct ggml_cplan)); 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, 1); 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_MUL_MAT: { const enum ggml_type vec_dot_type = type_traits[node->src[0]->type].vec_dot_type; #if defined(GGML_USE_CLBLAST) if (ggml_cl_can_mul_mat(node->src[0], node->src[1], node)) { cur = ggml_cl_mul_mat_get_wsize(node->src[0], node->src[1], node); } else #endif #if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) if (ggml_compute_forward_mul_mat_use_blas(node)) { if (node->src[0]->type != GGML_TYPE_F32) { // here we need memory for fully dequantized matrix from src0 // take into account that src0 can be broadcasted into src1[2,3] cur = ggml_type_size(GGML_TYPE_F32) * node->src[0]->ne[0]*node->src[0]->ne[1] * node->src[1]->ne[2]*node->src[1]->ne[3]; } } else #endif 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[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_ASSERT(false); } } 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_ASSERT(false); } break; default: break; } work_size = MAX(work_size, cur); } if (work_size > 0) { work_size += CACHE_LINE_SIZE*(n_threads - 1); } cplan.n_threads = MIN(max_tasks, n_threads); cplan.work_size = work_size; cplan.work_data = NULL; return cplan; } enum ggml_status ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan) { { GGML_ASSERT(cplan); GGML_ASSERT(cplan->n_threads > 0); if (cplan->work_size > 0) { GGML_ASSERT(cplan->work_data); } } const int n_threads = cplan->n_threads; struct ggml_compute_state_shared state_shared = { /*.cgraph =*/ cgraph, /*.cgraph_plan =*/ cplan, /*.perf_node_start_cycles =*/ 0, /*.perf_node_start_time_us =*/ 0, /*.n_threads =*/ n_threads, /*.n_active =*/ n_threads, /*.node_n =*/ -1, /*.node_task =*/ GGML_TASK_TYPE_FINALIZE, /*.abort_callback =*/ NULL, /*.abort_callback_data =*/ NULL, /*.current_chunk; =*/ 0, }; struct ggml_compute_state * workers = alloca(sizeof(struct ggml_compute_state)*n_threads); // create thread pool if (n_threads > 1) { for (int j = 1; j < n_threads; ++j) { workers[j] = (struct ggml_compute_state) { .thrd = 0, .ith = j, .shared = &state_shared, .ec = GGML_STATUS_SUCCESS, }; const int rc = ggml_thread_create(&workers[j].thrd, NULL, ggml_graph_compute_thread, &workers[j]); GGML_ASSERT(rc == 0); UNUSED(rc); } } workers[0].ith = 0; workers[0].shared = &state_shared; workers[0].ec = GGML_STATUS_SUCCESS; const int64_t perf_start_cycles = ggml_perf_cycles(); const int64_t perf_start_time_us = ggml_perf_time_us(); // this is a work thread too ggml_graph_compute_thread(&workers[0]); enum ggml_status compute_status = workers[0].ec; // don't leave affinity set on the main thread clear_numa_thread_affinity(); // join or kill thread pool if (n_threads > 1) { for (int j = 1; j < n_threads; j++) { const int rc = ggml_thread_join(workers[j].thrd, NULL); GGML_ASSERT(rc == 0); if (workers[j].ec != GGML_STATUS_SUCCESS) compute_status = workers[j].ec; } } // performance stats (graph) { int64_t perf_cycles_cur = ggml_perf_cycles() - perf_start_cycles; int64_t perf_time_us_cur = ggml_perf_time_us() - perf_start_time_us; cgraph->perf_runs++; cgraph->perf_cycles += perf_cycles_cur; cgraph->perf_time_us += perf_time_us_cur; GGML_PRINT_DEBUG("%s: perf (%d) - cpu = %.3f / %.3f ms, wall = %.3f / %.3f ms\n", __func__, cgraph->perf_runs, (double) perf_cycles_cur / (double) ggml_cycles_per_ms(), (double) cgraph->perf_cycles / (double) ggml_cycles_per_ms() / (double) cgraph->perf_runs, (double) perf_time_us_cur / 1000.0, (double) cgraph->perf_time_us / 1000.0 / cgraph->perf_runs); } return compute_status; } 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); struct ggml_object * obj = ggml_new_object(ctx, GGML_OBJECT_TYPE_WORK_BUFFER, cplan.work_size); cplan.work_data = (uint8_t *)ctx->mem_buffer + obj->offs; return ggml_graph_compute(cgraph, &cplan); } struct ggml_tensor * ggml_graph_get_tensor(struct ggml_cgraph * cgraph, const char * name) { for (int i = 0; i < cgraph->n_leafs; i++) { struct ggml_tensor * leaf = cgraph->leafs[i]; if (strcmp(leaf->name, name) == 0) { return leaf; } } for (int i = 0; i < cgraph->n_nodes; i++) { struct ggml_tensor * node = cgraph->nodes[i]; if (strcmp(node->name, name) == 0) { return node; } } return NULL; } static void ggml_graph_export_leaf(const struct ggml_tensor * tensor, FILE * fout) { const int64_t * ne = tensor->ne; const size_t * nb = tensor->nb; fprintf(fout, "%-6s %-12s %8d %" PRId64 " %" PRId64 " %" PRId64 " %" PRId64 " %16zu %16zu %16zu %16zu %16p %32s\n", ggml_type_name(tensor->type), ggml_op_name (tensor->op), ggml_n_dims(tensor), ne[0], ne[1], ne[2], ne[3], nb[0], nb[1], nb[2], nb[3], tensor->data, tensor->name); } static void ggml_graph_export_node(const struct ggml_tensor * tensor, const char * arg, FILE * fout) { const int64_t * ne = tensor->ne; const size_t * nb = tensor->nb; fprintf(fout, "%-6s %-6s %-12s %8d %" PRId64 " %" PRId64 " %" PRId64 " %" PRId64 " %16zu %16zu %16zu %16zu %16p %32s\n", arg, ggml_type_name(tensor->type), ggml_op_name (tensor->op), ggml_n_dims(tensor), ne[0], ne[1], ne[2], ne[3], nb[0], nb[1], nb[2], nb[3], tensor->data, tensor->name); } void ggml_graph_export(const struct ggml_cgraph * cgraph, const char * fname) { uint64_t size_eval = 0; // compute size of intermediate results // TODO: does not take into account scratch buffers !!!! for (int i = 0; i < cgraph->n_nodes; ++i) { size_eval += ggml_nbytes_pad(cgraph->nodes[i]); } // print { FILE * fout = stdout; fprintf(fout, "\n"); fprintf(fout, "%-16s %8x\n", "magic", GGML_FILE_MAGIC); fprintf(fout, "%-16s %8d\n", "version", GGML_FILE_VERSION); fprintf(fout, "%-16s %8d\n", "leafs", cgraph->n_leafs); fprintf(fout, "%-16s %8d\n", "nodes", cgraph->n_nodes); fprintf(fout, "%-16s %" PRIu64 "\n", "eval", size_eval); // header fprintf(fout, "\n"); fprintf(fout, "%-6s %-12s %8s %8s %8s %8s %8s %16s %16s %16s %16s %16s %16s\n", "TYPE", "OP", "NDIMS", "NE0", "NE1", "NE2", "NE3", "NB0", "NB1", "NB2", "NB3", "DATA", "NAME"); for (int i = 0; i < cgraph->n_leafs; ++i) { ggml_graph_export_leaf(cgraph->leafs[i], fout); GGML_ASSERT(cgraph->leafs[i]->op == GGML_OP_NONE); GGML_ASSERT(cgraph->leafs[i]->src[0] == NULL); GGML_ASSERT(cgraph->leafs[i]->src[1] == NULL); } // header fprintf(fout, "\n"); fprintf(fout, "%-6s %-6s %-12s %8s %8s %8s %8s %8s %16s %16s %16s %16s %8s %16s %16s\n", "ARG", "TYPE", "OP", "NDIMS", "NE0", "NE1", "NE2", "NE3", "NB0", "NB1", "NB2", "NB3", "NTASKS", "DATA", "NAME"); for (int i = 0; i < cgraph->n_nodes; ++i) { ggml_graph_export_node(cgraph->nodes[i], "DST", fout); for (int j = 0; j < GGML_MAX_SRC; ++j) { if (cgraph->nodes[i]->src[j]) { ggml_graph_export_node(cgraph->nodes[i]->src[j], "SRC", fout); } } fprintf(fout, "\n"); } fprintf(fout, "\n"); } // write binary data { FILE * fout = ggml_fopen(fname, "wb"); if (!fout) { fprintf(stderr, "%s: failed to open %s\n", __func__, fname); return; } // header { const uint32_t magic = GGML_FILE_MAGIC; const uint32_t version = GGML_FILE_VERSION; const uint32_t n_leafs = cgraph->n_leafs; const uint32_t n_nodes = cgraph->n_nodes; fwrite(&magic, sizeof(uint32_t), 1, fout); fwrite(&version, sizeof(uint32_t), 1, fout); fwrite(&n_leafs, sizeof(uint32_t), 1, fout); fwrite(&n_nodes, sizeof(uint32_t), 1, fout); fwrite(&size_eval, sizeof(uint64_t), 1, fout); } // leafs { for (int i = 0; i < cgraph->n_leafs; ++i) { const struct ggml_tensor * tensor = cgraph->leafs[i]; const uint32_t type = tensor->type; const uint32_t op = tensor->op; fwrite(&type, sizeof(uint32_t), 1, fout); fwrite(&op, sizeof(uint32_t), 1, fout); for (int j = 0; j < GGML_MAX_DIMS; ++j) { const uint64_t ne = tensor->ne[j]; const uint64_t nb = tensor->nb[j]; fwrite(&ne, sizeof(uint64_t), 1, fout); fwrite(&nb, sizeof(uint64_t), 1, fout); } fwrite(tensor->name, sizeof(char), GGML_MAX_NAME, fout); fwrite(tensor->op_params, sizeof(char), GGML_MAX_OP_PARAMS, fout); // dump the data // TODO: pad this to 32 byte boundary { const size_t size = ggml_nbytes(tensor); fwrite(tensor->data, sizeof(char), size, fout); } } } // nodes { for (int i = 0; i < cgraph->n_nodes; ++i) { const struct ggml_tensor * tensor = cgraph->nodes[i]; const uint32_t type = tensor->type; const uint32_t op = tensor->op; fwrite(&type, sizeof(uint32_t), 1, fout); fwrite(&op, sizeof(uint32_t), 1, fout); for (int j = 0; j < GGML_MAX_DIMS; ++j) { const uint64_t ne = tensor->ne[j]; const uint64_t nb = tensor->nb[j]; fwrite(&ne, sizeof(uint64_t), 1, fout); fwrite(&nb, sizeof(uint64_t), 1, fout); } fwrite(tensor->name, sizeof(char), GGML_MAX_NAME, fout); fwrite(tensor->op_params, sizeof(char), GGML_MAX_OP_PARAMS, fout); // output the op arguments { struct ggml_tensor * args[GGML_MAX_SRC] = { NULL }; for (int j = 0; j < GGML_MAX_SRC; ++j) { args[j] = tensor->src[j]; } for (int j = 0; j < GGML_MAX_SRC; ++j) { if (args[j]) { int32_t idx = -1; // check if leaf { for (int k = 0; k < cgraph->n_leafs; ++k) { if (args[j] == cgraph->leafs[k]) { idx = k; break; } } } // check if node if (idx == -1) { for (int k = 0; k < cgraph->n_nodes; ++k) { if (args[j] == cgraph->nodes[k]) { idx = cgraph->n_leafs + k; break; } } } if (idx == -1) { fprintf(stderr, "%s: failed to find tensor, arg = %d, node = %d\n", __func__, j, i); fclose(fout); return; } fwrite(&idx, sizeof(int32_t), 1, fout); } else { const int32_t nul = -1; fwrite(&nul, sizeof(int32_t), 1, fout); } } } } } fclose(fout); } } struct ggml_cgraph * ggml_graph_import(const char * fname, struct ggml_context ** ctx_data, struct ggml_context ** ctx_eval) { assert(*ctx_data == NULL); assert(*ctx_eval == NULL); struct ggml_cgraph * result = NULL; struct ggml_tensor * data = NULL; // read file into data { FILE * fin = ggml_fopen(fname, "rb"); if (!fin) { fprintf(stderr, "%s: failed to open %s\n", __func__, fname); return result; } size_t fsize = 0; fseek(fin, 0, SEEK_END); fsize = ftell(fin); fseek(fin, 0, SEEK_SET); // create the data context { const size_t overhead = 1*ggml_tensor_overhead(); struct ggml_init_params params = { .mem_size = fsize + overhead, .mem_buffer = NULL, .no_alloc = false, }; *ctx_data = ggml_init(params); if (!*ctx_data) { fprintf(stderr, "%s: failed to create ggml context\n", __func__); fclose(fin); return result; } } data = ggml_new_tensor_1d(*ctx_data, GGML_TYPE_I8, fsize); { const size_t ret = fread(data->data, sizeof(char), fsize, fin); if (ret != fsize) { fprintf(stderr, "%s: failed to read %s\n", __func__, fname); fclose(fin); return result; } } fclose(fin); } // populate result { char * ptr = (char *) data->data; const uint32_t magic = *(const uint32_t *) ptr; ptr += sizeof(magic); if (magic != GGML_FILE_MAGIC) { fprintf(stderr, "%s: invalid magic number, got %08x\n", __func__, magic); return result; } const uint32_t version = *(const uint32_t *) ptr; ptr += sizeof(version); if (version != GGML_FILE_VERSION) { fprintf(stderr, "%s: invalid version number\n", __func__); return result; } const uint32_t n_leafs = *(const uint32_t *) ptr; ptr += sizeof(n_leafs); const uint32_t n_nodes = *(const uint32_t *) ptr; ptr += sizeof(n_nodes); const uint64_t size_eval = *(const uint64_t *) ptr; ptr += sizeof(size_eval); const int graph_size = MAX(n_leafs, n_nodes); // create the data context { const size_t overhead = (n_leafs + n_nodes)*ggml_tensor_overhead() + ggml_graph_overhead_custom(graph_size, false); struct ggml_init_params params = { .mem_size = size_eval + overhead, .mem_buffer = NULL, .no_alloc = true, }; *ctx_eval = ggml_init(params); if (!*ctx_eval) { fprintf(stderr, "%s: failed to create ggml context\n", __func__); return result; } } result = ggml_new_graph_custom(*ctx_eval, graph_size, false); result->n_leafs = n_leafs; result->n_nodes = n_nodes; // leafs { uint32_t type; uint32_t op; for (uint32_t i = 0; i < n_leafs; ++i) { type = *(const uint32_t *) ptr; ptr += sizeof(type); op = *(const uint32_t *) ptr; ptr += sizeof(op); int64_t ne[GGML_MAX_DIMS]; size_t nb[GGML_MAX_DIMS]; for (int j = 0; j < GGML_MAX_DIMS; ++j) { uint64_t ne_cur; uint64_t nb_cur; ne_cur = *(const uint64_t *) ptr; ptr += sizeof(ne_cur); nb_cur = *(const uint64_t *) ptr; ptr += sizeof(nb_cur); ne[j] = ne_cur; nb[j] = nb_cur; } struct ggml_tensor * tensor = ggml_new_tensor(*ctx_eval, (enum ggml_type) type, GGML_MAX_DIMS, ne); tensor->op = (enum ggml_op) op; memcpy(tensor->name, ptr, GGML_MAX_NAME); ptr += GGML_MAX_NAME; memcpy(tensor->op_params, ptr, GGML_MAX_OP_PARAMS); ptr += GGML_MAX_OP_PARAMS; tensor->data = (void *) ptr; for (int j = 0; j < GGML_MAX_DIMS; ++j) { tensor->nb[j] = nb[j]; } result->leafs[i] = tensor; ptr += ggml_nbytes(tensor); fprintf(stderr, "%s: loaded leaf %u: '%16s', %9zu bytes\n", __func__, i, tensor->name, ggml_nbytes(tensor)); } } ggml_set_no_alloc(*ctx_eval, false); // nodes { uint32_t type; uint32_t op; for (uint32_t i = 0; i < n_nodes; ++i) { type = *(const uint32_t *) ptr; ptr += sizeof(type); op = *(const uint32_t *) ptr; ptr += sizeof(op); enum ggml_op eop = (enum ggml_op) op; int64_t ne[GGML_MAX_DIMS]; size_t nb[GGML_MAX_DIMS]; for (int j = 0; j < GGML_MAX_DIMS; ++j) { uint64_t ne_cur; uint64_t nb_cur; ne_cur = *(const uint64_t *) ptr; ptr += sizeof(ne_cur); nb_cur = *(const uint64_t *) ptr; ptr += sizeof(nb_cur); ne[j] = ne_cur; nb[j] = nb_cur; } const char * ptr_name = ptr; ptr += GGML_MAX_NAME; const char * ptr_op_params = ptr; ptr += GGML_MAX_OP_PARAMS; const int32_t * ptr_arg_idx = (const int32_t *) ptr; ptr += GGML_MAX_SRC*sizeof(int32_t); struct ggml_tensor * args[GGML_MAX_SRC] = { NULL }; // parse args for (int j = 0; j < GGML_MAX_SRC; ++j) { const int32_t arg_idx = ptr_arg_idx[j]; if (arg_idx == -1) { continue; } if (arg_idx < result->n_leafs) { args[j] = result->leafs[arg_idx]; } else { args[j] = result->nodes[arg_idx - result->n_leafs]; } } // create the tensor // "view" operations are handled differently // TODO: handle inplace ops - currently a copy is always made struct ggml_tensor * tensor = NULL; switch (eop) { // TODO: implement other view ops case GGML_OP_RESHAPE: { tensor = ggml_reshape_4d(*ctx_eval, args[0], ne[0], ne[1], ne[2], ne[3]); } break; case GGML_OP_VIEW: { tensor = ggml_view_4d(*ctx_eval, args[0], ne[0], ne[1], ne[2], ne[3], 0, 0, 0, 0); size_t offs; memcpy(&offs, ptr_op_params, sizeof(offs)); tensor->data = ((char *) tensor->data) + offs; } break; case GGML_OP_TRANSPOSE: { tensor = ggml_transpose(*ctx_eval, args[0]); } break; case GGML_OP_PERMUTE: { tensor = ggml_view_4d(*ctx_eval, args[0], ne[0], ne[1], ne[2], ne[3], 0, 0, 0, 0); } break; default: { tensor = ggml_new_tensor(*ctx_eval, (enum ggml_type) type, GGML_MAX_DIMS, ne); tensor->op = eop; } break; } memcpy(tensor->name, ptr_name, GGML_MAX_NAME); memcpy(tensor->op_params, ptr_op_params, GGML_MAX_OP_PARAMS); for (int j = 0; j < GGML_MAX_DIMS; ++j) { tensor->nb[j] = nb[j]; } for (int j = 0; j < GGML_MAX_SRC; ++j) { tensor->src[j] = args[j]; } result->nodes[i] = tensor; fprintf(stderr, "%s: loaded node %u: '%16s', %9zu bytes\n", __func__, i, tensor->name, ggml_nbytes(tensor)); } } } return result; } void ggml_graph_print(const struct ggml_cgraph * cgraph) { int64_t perf_total_per_op_us[GGML_OP_COUNT] = {0}; GGML_PRINT("=== GRAPH ===\n"); GGML_PRINT("n_nodes = %d\n", cgraph->n_nodes); for (int i = 0; i < cgraph->n_nodes; i++) { struct ggml_tensor * node = cgraph->nodes[i]; perf_total_per_op_us[node->op] += MAX(1, node->perf_time_us); GGML_PRINT(" - %3d: [ %5" PRId64 ", %5" PRId64 ", %5" PRId64 "] %16s %s (%3d) cpu = %7.3f / %7.3f ms, wall = %7.3f / %7.3f ms\n", i, node->ne[0], node->ne[1], node->ne[2], ggml_op_name(node->op), (node->flags & GGML_TENSOR_FLAG_PARAM) ? "x" : node->grad ? "g" : " ", node->perf_runs, (double) node->perf_cycles / (double) ggml_cycles_per_ms(), (double) node->perf_cycles / (double) ggml_cycles_per_ms() / (double) node->perf_runs, (double) node->perf_time_us / 1000.0, (double) node->perf_time_us / 1000.0 / node->perf_runs); } GGML_PRINT("n_leafs = %d\n", cgraph->n_leafs); for (int i = 0; i < cgraph->n_leafs; i++) { struct ggml_tensor * node = cgraph->leafs[i]; GGML_PRINT(" - %3d: [ %5" PRId64 ", %5" PRId64 "] %8s %16s\n", i, node->ne[0], node->ne[1], ggml_op_name(node->op), ggml_get_name(node)); } for (int i = 0; i < GGML_OP_COUNT; i++) { if (perf_total_per_op_us[i] == 0) { continue; } GGML_PRINT("perf_total_per_op_us[%16s] = %7.3f ms\n", ggml_op_name(i), (double) perf_total_per_op_us[i] / 1000.0); } GGML_PRINT("========================================\n"); } // check if node is part of the graph static bool ggml_graph_find(const struct ggml_cgraph * cgraph, const struct ggml_tensor * node) { if (cgraph == NULL) { return true; } for (int i = 0; i < cgraph->n_nodes; i++) { if (cgraph->nodes[i] == node) { return true; } } return false; } static struct ggml_tensor * ggml_graph_get_parent(const struct ggml_cgraph * cgraph, const struct ggml_tensor * node) { for (int i = 0; i < cgraph->n_nodes; i++) { struct ggml_tensor * parent = cgraph->nodes[i]; if (parent->grad == node) { return parent; } } return NULL; } static void ggml_graph_dump_dot_node_edge(FILE * fp, const struct ggml_cgraph * gb, struct ggml_tensor * node, struct ggml_tensor * parent, const char * label) { struct ggml_tensor * gparent = ggml_graph_get_parent(gb, node); struct ggml_tensor * gparent0 = ggml_graph_get_parent(gb, parent); fprintf(fp, " \"%p\":%s -> \"%p\":%s [ arrowhead = %s; style = %s; label = \"%s\"; ]\n", gparent0 ? (void *) gparent0 : (void *) parent, gparent0 ? "g" : "x", gparent ? (void *) gparent : (void *) node, gparent ? "g" : "x", gparent ? "empty" : "vee", gparent ? "dashed" : "solid", label); } static void ggml_graph_dump_dot_leaf_edge(FILE * fp, struct ggml_tensor * node, struct ggml_tensor * parent, const char * label) { fprintf(fp, " \"%p\":%s -> \"%p\":%s [ label = \"%s\"; ]\n", (void *) parent, "x", (void *) node, "x", label); } void ggml_graph_dump_dot(const struct ggml_cgraph * gb, const struct ggml_cgraph * gf, const char * filename) { char color[16]; FILE * fp = ggml_fopen(filename, "w"); GGML_ASSERT(fp); fprintf(fp, "digraph G {\n"); fprintf(fp, " newrank = true;\n"); fprintf(fp, " rankdir = LR;\n"); for (int i = 0; i < gb->n_nodes; i++) { struct ggml_tensor * node = gb->nodes[i]; if (ggml_graph_get_parent(gb, node) != NULL) { continue; } if (node->flags & GGML_TENSOR_FLAG_PARAM) { snprintf(color, sizeof(color), "yellow"); } else if (node->grad) { if (ggml_graph_find(gf, node)) { snprintf(color, sizeof(color), "green"); } else { snprintf(color, sizeof(color), "lightblue"); } } else { snprintf(color, sizeof(color), "white"); } fprintf(fp, " \"%p\" [ " "style = filled; fillcolor = %s; shape = record; " "label=\"", (void *) node, color); if (strlen(node->name) > 0) { fprintf(fp, "%s (%s)|", node->name, ggml_type_name(node->type)); } else { fprintf(fp, "(%s)|", ggml_type_name(node->type)); } if (ggml_is_matrix(node)) { fprintf(fp, "%d [%" PRId64 ", %" PRId64 "] | %s", i, node->ne[0], node->ne[1], ggml_op_symbol(node->op)); } else { fprintf(fp, "%d [%" PRId64 ", %" PRId64 ", %" PRId64 "] | %s", i, node->ne[0], node->ne[1], node->ne[2], ggml_op_symbol(node->op)); } if (node->grad) { fprintf(fp, " | %s\"; ]\n", ggml_op_symbol(node->grad->op)); } else { fprintf(fp, "\"; ]\n"); } } for (int i = 0; i < gb->n_leafs; i++) { struct ggml_tensor * node = gb->leafs[i]; snprintf(color, sizeof(color), "pink"); fprintf(fp, " \"%p\" [ " "style = filled; fillcolor = %s; shape = record; " "label=\"", (void *) node, color); if (strlen(node->name) > 0) { fprintf(fp, "%s (%s)|", node->name, ggml_type_name(node->type)); } else { fprintf(fp, "(%s)|", ggml_type_name(node->type)); } fprintf(fp, "CONST %d [%" PRId64 ", %" PRId64 "]", i, node->ne[0], node->ne[1]); if (ggml_nelements(node) < 5) { fprintf(fp, " | ("); for (int j = 0; j < ggml_nelements(node); j++) { if (node->type == GGML_TYPE_I8 || node->type == GGML_TYPE_I16 || node->type == GGML_TYPE_I32) { fprintf(fp, "%d", ggml_get_i32_1d(node, j)); } else if (node->type == GGML_TYPE_F32 || node->type == GGML_TYPE_F16 || node->type == GGML_TYPE_BF16) { fprintf(fp, "%.1e", (double)ggml_get_f32_1d(node, j)); } else { fprintf(fp, "#"); } if (j < ggml_nelements(node) - 1) { fprintf(fp, ", "); } } fprintf(fp, ")"); } fprintf(fp, "\"; ]\n"); } for (int i = 0; i < gb->n_nodes; i++) { struct ggml_tensor * node = gb->nodes[i]; for (int j = 0; j < GGML_MAX_SRC; j++) { if (node->src[j]) { char label[16]; snprintf(label, sizeof(label), "src %d", j); ggml_graph_dump_dot_node_edge(fp, gb, node, node->src[j], label); } } } for (int i = 0; i < gb->n_leafs; i++) { struct ggml_tensor * node = gb->leafs[i]; for (int j = 0; j < GGML_MAX_SRC; j++) { if (node->src[j]) { char label[16]; snprintf(label, sizeof(label), "src %d", j); ggml_graph_dump_dot_leaf_edge(fp, node, node->src[j], label); } } } fprintf(fp, "}\n"); fclose(fp); GGML_PRINT("%s: dot -Tpng %s -o %s.png && open %s.png\n", __func__, filename, filename, filename); } //////////////////////////////////////////////////////////////////////////////// static void ggml_opt_set_params(int np, struct ggml_tensor * const ps[], const float * x) { int i = 0; for (int p = 0; p < np; ++p) { const int64_t ne = ggml_nelements(ps[p]) ; // TODO: add function to set tensor from array for (int64_t j = 0; j < ne; ++j) { ggml_set_f32_1d(ps[p], j, x[i++]); } } } static void ggml_opt_get_params(int np, struct ggml_tensor * const ps[], float * x) { int i = 0; for (int p = 0; p < np; ++p) { const int64_t ne = ggml_nelements(ps[p]) ; // TODO: add function to get all elements at once for (int64_t j = 0; j < ne; ++j) { x[i++] = ggml_get_f32_1d(ps[p], j); } } } static void ggml_opt_get_grad(int np, struct ggml_tensor * const ps[], float * g) { int64_t i = 0; for (int p = 0; p < np; ++p) { const int64_t ne = ggml_nelements(ps[p]) ; // TODO: add function to get all elements at once for (int64_t j = 0; j < ne; ++j) { g[i++] = ggml_get_f32_1d(ps[p]->grad, j); } } } static void ggml_opt_acc_grad(int np, struct ggml_tensor * const ps[], float * g, float scale) { int64_t i = 0; for (int p = 0; p < np; ++p) { const int64_t ne = ggml_nelements(ps[p]) ; // TODO: add function to get all elements at once for (int64_t j = 0; j < ne; ++j) { g[i++] += ggml_get_f32_1d(ps[p]->grad, j) * scale; } } } // // Using AdamW - ref: https://arxiv.org/pdf/1711.05101v3.pdf // // (Original Adam - ref: https://arxiv.org/pdf/1412.6980.pdf) // static enum ggml_opt_result ggml_opt_adam( struct ggml_context * ctx, struct ggml_opt_context * opt, struct ggml_opt_params params, struct ggml_tensor * f, struct ggml_cgraph * gf, struct ggml_cgraph * gb, ggml_opt_callback callback, void * callback_data) { GGML_ASSERT(ggml_is_scalar(f)); // these will store the parameters we want to optimize struct ggml_tensor * ps[GGML_MAX_PARAMS]; int np = 0; int64_t nx = 0; for (int i = 0; i < gf->n_nodes; ++i) { if (gf->nodes[i]->flags & GGML_TENSOR_FLAG_PARAM) { GGML_PRINT_DEBUG("found param %d: grad->op = %d\n", np, gf->nodes[i]->grad->op); GGML_ASSERT(np < GGML_MAX_PARAMS); ps[np++] = gf->nodes[i]; nx += ggml_nelements(gf->nodes[i]); } } if ((opt->params.type != params.type) || (opt->nx != nx) || (opt->params.past != params.past)) { int iter = opt->iter; ggml_opt_init(opt->ctx, opt, params, nx); opt->iter = iter; } // constants float sched = params.adam.sched; const float alpha = params.adam.alpha; const float decay = params.adam.decay * alpha; const float beta1 = params.adam.beta1; const float beta2 = params.adam.beta2; const float eps = params.adam.eps; const float gclip = params.adam.gclip; const int decay_min_ndim = params.adam.decay_min_ndim; const int n_accum = MAX(1, params.n_gradient_accumulation); const float accum_norm = 1.0f / (float) n_accum; float * g = opt->adam.g->data; // gradients float * m = opt->adam.m->data; // first moment float * v = opt->adam.v->data; // second moment float * pf = params.past > 0 ? opt->adam.pf->data : NULL; // past function values struct ggml_cplan cplan = ggml_graph_plan(gb, params.n_threads); struct ggml_object * obj = ggml_new_object(ctx, GGML_OBJECT_TYPE_WORK_BUFFER, cplan.work_size); cplan.work_data = (uint8_t *)ctx->mem_buffer + obj->offs; bool cancel = false; // compute the function value float fx = 0; ggml_set_zero(opt->adam.g); for (int accum_step = 0; accum_step < n_accum; ++accum_step) { if (callback) { callback(callback_data, accum_step, &sched, &cancel); if (cancel) { return GGML_OPT_RESULT_CANCEL; } } // ggml_graph_reset (gf); ggml_set_f32 (f->grad, 1.0f); ggml_graph_compute(gb, &cplan); ggml_opt_acc_grad(np, ps, g, accum_norm); fx += ggml_get_f32_1d(f, 0); } fx *= accum_norm; opt->adam.fx_prev = fx; opt->adam.fx_best = opt->adam.fx_prev; if (pf) { pf[opt->iter % params.past] = opt->adam.fx_prev; } opt->loss_before = opt->adam.fx_prev; opt->loss_after = opt->adam.fx_prev; // initialize if (opt->just_initialized) { opt->adam.n_no_improvement = 0; opt->just_initialized = false; } float * fx_best = &opt->adam.fx_best; float * fx_prev = &opt->adam.fx_prev; int * n_no_improvement = &opt->adam.n_no_improvement; int iter0 = opt->iter; // run the optimizer for (int t = 0; t < params.adam.n_iter; ++t) { opt->iter = iter0 + t + 1; GGML_PRINT_DEBUG ("=== iter %d ===\n", t); GGML_PRINT_DEBUG ("f = %10.6f\n", ggml_get_f32_1d(f, 0)); GGML_PRINT_DEBUG_5("df/dx0 = %10.6f\n", ggml_get_f32_1d(ps[0]->grad, 0)); GGML_PRINT_DEBUG_5("df/dx1 = %10.6f\n", ggml_get_f32_1d(ps[1]->grad, 0)); for (int i = 0; i < np; ++i) { GGML_PRINT_DEBUG("param %d: %10.6f, g = %10.6f\n", i, ggml_get_f32_1d(ps[i], 0), ggml_get_f32_1d(ps[i]->grad, 0)); } const int64_t t_start_wall = ggml_time_us(); const int64_t t_start_cpu = ggml_cycles(); UNUSED(t_start_wall); UNUSED(t_start_cpu); { float gnorm = 1.0f; if (gclip > 0.0f) { // gradient clipping ggml_float sum = 0.0; for (int64_t i = 0; i < nx; ++i) { sum += (ggml_float)(g[i]*g[i]); } ggml_float norm = sqrt(sum); if (norm > (ggml_float) gclip) { gnorm = (float) ((ggml_float) gclip / norm); } } const float beta1h = alpha*sched/(1.0f - powf(beta1, opt->iter)); const float beta2h = 1.0f/(1.0f - powf(beta2, opt->iter)); int64_t i = 0; for (int p = 0; p < np; ++p) { const int64_t ne = ggml_nelements(ps[p]); const float p_decay = ((ggml_n_dims(ps[p]) >= decay_min_ndim) ? decay : 0.0f) * sched; for (int64_t j = 0; j < ne; ++j) { float x = ggml_get_f32_1d(ps[p], j); float g_ = g[i]*gnorm; m[i] = m[i]*beta1 + g_*(1.0f - beta1); v[i] = v[i]*beta2 + g_*g_*(1.0f - beta2); float mh = m[i]*beta1h; float vh = v[i]*beta2h; vh = sqrtf(vh) + eps; x = x*(1.0f - p_decay) - mh/vh; ggml_set_f32_1d(ps[p], j, x); ++i; } } } fx = 0; ggml_set_zero(opt->adam.g); for (int accum_step = 0; accum_step < n_accum; ++accum_step) { if (callback) { callback(callback_data, accum_step, &sched, &cancel); if (cancel) { return GGML_OPT_RESULT_CANCEL;; } } // ggml_graph_reset (gf); ggml_set_f32 (f->grad, 1.0f); ggml_graph_compute(gb, &cplan); ggml_opt_acc_grad(np, ps, g, accum_norm); fx += ggml_get_f32_1d(f, 0); } fx *= accum_norm; opt->loss_after = fx; // check convergence if (fabsf(fx - fx_prev[0])/fx < params.adam.eps_f) { GGML_PRINT_DEBUG("converged\n"); return GGML_OPT_RESULT_OK; } // delta-based convergence test if (pf != NULL) { // need at least params.past iterations to start checking for convergence if (params.past <= iter0 + t) { const float rate = (pf[(iter0 + t)%params.past] - fx)/fx; if (fabsf(rate) < params.delta) { return GGML_OPT_RESULT_OK; } } pf[(iter0 + t)%params.past] = fx; } // check for improvement if (params.max_no_improvement > 0) { if (fx_best[0] > fx) { fx_best[0] = fx; n_no_improvement[0] = 0; } else { ++n_no_improvement[0]; if (n_no_improvement[0] >= params.max_no_improvement) { return GGML_OPT_RESULT_OK; } } } fx_prev[0] = fx; { const int64_t t_end_cpu = ggml_cycles(); GGML_PRINT_DEBUG("time iter: %5.3f s\n", ((float)(t_end_cpu - t_start_cpu))/CLOCKS_PER_SEC); UNUSED(t_end_cpu); const int64_t t_end_wall = ggml_time_us(); GGML_PRINT_DEBUG("wall time iter: %5.3f s\n", (t_end_wall - t_start_wall)/1e6); UNUSED(t_end_wall); } } return GGML_OPT_RESULT_DID_NOT_CONVERGE; } // // L-BFGS // // the L-BFGS implementation below is based on the following implementation: // // https://github.com/chokkan/liblbfgs // struct ggml_lbfgs_iteration_data { float alpha; float ys; float * s; float * y; }; static enum ggml_opt_result linesearch_backtracking( const struct ggml_opt_params * params, int nx, float * x, float * fx, float * g, float * d, float * step, const float * xp, struct ggml_tensor * f, struct ggml_cgraph * gb, struct ggml_cplan * cplan, const int np, struct ggml_tensor * ps[], bool * cancel, ggml_opt_callback callback, void * callback_data) { int count = 0; float width = 0.0f; float dg = 0.0f; float finit = 0.0f; float dginit = 0.0f; float dgtest = 0.0f; const float dec = 0.5f; const float inc = 2.1f; const int n_accum = MAX(1, params->n_gradient_accumulation); const float accum_norm = 1.0f / (float) n_accum; if (*step <= 0.f) { return GGML_LINESEARCH_INVALID_PARAMETERS; } // compute the initial gradient in the search direction ggml_vec_dot_f32(nx, &dginit, 0, g, 0, d, 0, 1); // make sure that d points to a descent direction if (0 < dginit) { return GGML_LINESEARCH_FAIL; } // initialize local variables finit = *fx; dgtest = params->lbfgs.ftol*dginit; while (true) { ggml_vec_cpy_f32(nx, x, xp); ggml_vec_mad_f32(nx, x, d, *step); // evaluate the function and gradient values { ggml_opt_set_params(np, ps, x); *fx = 0; memset(g, 0, sizeof(float)*nx); for (int accum_step = 0; accum_step < n_accum; ++accum_step) { if (callback) { // LBFG-S does not support learning rate -> ignore learning schedule float sched = 0; callback(callback_data, accum_step, &sched, cancel); if (*cancel) { return GGML_OPT_RESULT_CANCEL; } } // ggml_graph_reset (gf); ggml_set_f32 (f->grad, 1.0f); ggml_graph_compute(gb, cplan); ggml_opt_acc_grad(np, ps, g, accum_norm); *fx += ggml_get_f32_1d(f, 0); } *fx *= accum_norm; } ++count; if (*fx > finit + (*step)*dgtest) { width = dec; } else { // Armijo condition is satisfied if (params->lbfgs.linesearch == GGML_LINESEARCH_BACKTRACKING_ARMIJO) { return count; } ggml_vec_dot_f32(nx, &dg, 0, g, 0, d, 0, 1); // check the Wolfe condition if (dg < params->lbfgs.wolfe * dginit) { width = inc; } else { if(params->lbfgs.linesearch == GGML_LINESEARCH_BACKTRACKING_WOLFE) { // regular Wolfe conditions return count; } if(dg > -params->lbfgs.wolfe*dginit) { width = dec; } else { // strong Wolfe condition (GGML_LINESEARCH_BACKTRACKING_STRONG_WOLFE) return count; } } } if (*step < params->lbfgs.min_step) { return GGML_LINESEARCH_MINIMUM_STEP; } if (*step > params->lbfgs.max_step) { return GGML_LINESEARCH_MAXIMUM_STEP; } if (params->lbfgs.max_linesearch <= count) { return GGML_LINESEARCH_MAXIMUM_ITERATIONS; } (*step) *= width; } GGML_ASSERT(false && "line search failed"); return GGML_LINESEARCH_FAIL; } static enum ggml_opt_result ggml_opt_lbfgs( struct ggml_context * ctx, struct ggml_opt_context * opt, struct ggml_opt_params params, struct ggml_tensor * f, struct ggml_cgraph * gf, struct ggml_cgraph * gb, ggml_opt_callback callback, void * callback_data) { if (params.lbfgs.linesearch == GGML_LINESEARCH_BACKTRACKING_WOLFE || params.lbfgs.linesearch == GGML_LINESEARCH_BACKTRACKING_STRONG_WOLFE) { if (params.lbfgs.wolfe <= params.lbfgs.ftol || 1.f <= params.lbfgs.wolfe) { return GGML_OPT_RESULT_INVALID_WOLFE; } } const int m = params.lbfgs.m; // these will store the parameters we want to optimize struct ggml_tensor * ps[GGML_MAX_PARAMS]; int np = 0; int nx = 0; for (int i = 0; i < gf->n_nodes; ++i) { if (gf->nodes[i]->flags & GGML_TENSOR_FLAG_PARAM) { GGML_PRINT_DEBUG("found param %d: grad->op = %d\n", np, gf->nodes[i]->grad->op); GGML_ASSERT(np < GGML_MAX_PARAMS); ps[np++] = gf->nodes[i]; nx += ggml_nelements(gf->nodes[i]); } } if ((opt->params.type != params.type) || (opt->nx != nx) || (opt->params.past != params.past) || (opt->params.lbfgs.m != params.lbfgs.m)) { int iter = opt->iter; ggml_opt_init(ctx, opt, params, nx); opt->iter = iter; } struct ggml_cplan cplan = ggml_graph_plan(gb, params.n_threads); struct ggml_object * obj = ggml_new_object(ctx, GGML_OBJECT_TYPE_WORK_BUFFER, cplan.work_size); cplan.work_data = (uint8_t *)ctx->mem_buffer + obj->offs; float * x = opt->lbfgs.x->data; // current parameters float * xp = opt->lbfgs.xp->data; // previous parameters float * g = opt->lbfgs.g->data; // current gradient float * gp = opt->lbfgs.gp->data; // previous gradient float * d = opt->lbfgs.d->data; // search direction float * pf = params.past > 0 ? opt->lbfgs.pf->data : NULL; // past function values const int n_accum = MAX(1, params.n_gradient_accumulation); const float accum_norm = 1.0f / (float) n_accum; float fx = 0.0f; // cost function value float xnorm = 0.0f; // ||x|| float gnorm = 0.0f; // ||g|| // initialize x from the graph nodes ggml_opt_get_params(np, ps, x); // the L-BFGS memory float * lm_alpha = opt->lbfgs.lmal->data; float * lm_ys = opt->lbfgs.lmys->data; float * lm_s = opt->lbfgs.lms->data; float * lm_y = opt->lbfgs.lmy->data; bool cancel = false; // evaluate the function value and its gradient { ggml_opt_set_params(np, ps, x); fx = 0; memset(g, 0, sizeof(float)*nx); for (int accum_step = 0; accum_step < n_accum; ++accum_step) { if (callback) { // LBFG-S does not support learning rate -> ignore learning schedule float sched = 0; callback(callback_data, accum_step, &sched, &cancel); if (cancel) { return GGML_OPT_RESULT_CANCEL; } } // ggml_graph_reset (gf); ggml_set_f32 (f->grad, 1.0f); ggml_graph_compute(gb, &cplan); ggml_opt_acc_grad(np, ps, g, accum_norm); fx += ggml_get_f32_1d(f, 0); } fx *= accum_norm; opt->loss_before = fx; opt->loss_after = fx; } // search direction = -gradient ggml_vec_neg_f32(nx, d, g); // ||x||, ||g|| ggml_vec_norm_f32(nx, &xnorm, x); ggml_vec_norm_f32(nx, &gnorm, g); if (xnorm < 1.0f) { xnorm = 1.0f; } // already optimized if (gnorm/xnorm <= params.lbfgs.eps) { return GGML_OPT_RESULT_OK; } if (opt->just_initialized) { if (pf) { pf[0] = fx; } opt->lbfgs.fx_best = fx; // initial step ggml_vec_norm_inv_f32(nx, &opt->lbfgs.step, d); opt->lbfgs.j = 0; opt->lbfgs.k = 1; opt->lbfgs.end = 0; opt->lbfgs.n_no_improvement = 0; opt->just_initialized = false; } float * fx_best = &opt->lbfgs.fx_best; float * step = &opt->lbfgs.step; int * j = &opt->lbfgs.j; int * k = &opt->lbfgs.k; int * end = &opt->lbfgs.end; int * n_no_improvement = &opt->lbfgs.n_no_improvement; int ls = 0; int bound = 0; float ys = 0.0f; float yy = 0.0f; float beta = 0.0f; int it = 0; while (true) { // store the current position and gradient vectors ggml_vec_cpy_f32(nx, xp, x); ggml_vec_cpy_f32(nx, gp, g); // TODO: instead of passing &cancel here, use the return code of the linesearch // to determine if the optimization should be cancelled // this is a simple change, but not doing this atm, since I don't have a nice // way to test and don't want to break something with so many changes lined up ls = linesearch_backtracking(¶ms, nx, x, &fx, g, d, step, xp, f, gb, &cplan, np, ps, &cancel, callback, callback_data); if (cancel) { return GGML_OPT_RESULT_CANCEL; } if (ls < 0) { // linesearch failed - go back to the previous point and return ggml_vec_cpy_f32(nx, x, xp); ggml_vec_cpy_f32(nx, g, gp); return ls; } opt->loss_after = fx; ggml_vec_norm_f32(nx, &xnorm, x); ggml_vec_norm_f32(nx, &gnorm, g); GGML_PRINT_DEBUG("f = %10.6f\n", ggml_get_f32_1d(f, 0)); if (xnorm < 1.0f) { xnorm = 1.0f; } if (gnorm/xnorm <= params.lbfgs.eps) { // converged return GGML_OPT_RESULT_OK; } // delta-based convergence test if (pf != NULL) { // need at least params.past iterations to start checking for convergence if (params.past <= k[0]) { const float rate = (pf[k[0]%params.past] - fx)/fx; if (fabsf(rate) < params.delta) { return GGML_OPT_RESULT_OK; } } pf[k[0]%params.past] = fx; } // check for improvement if (params.max_no_improvement > 0) { if (fx < fx_best[0]) { fx_best[0] = fx; n_no_improvement[0] = 0; } else { n_no_improvement[0]++; if (n_no_improvement[0] >= params.max_no_improvement) { return GGML_OPT_RESULT_OK; } } } if (params.lbfgs.n_iter != 0 && params.lbfgs.n_iter < it + 1) { // reached the maximum number of iterations return GGML_OPT_RESULT_DID_NOT_CONVERGE; } // update vectors s and y: // s_{k+1} = x_{k+1} - x_{k} = \step * d_{k}. // y_{k+1} = g_{k+1} - g_{k}. // ggml_vec_sub_f32(nx, &lm_s[end[0]*nx], x, xp); ggml_vec_sub_f32(nx, &lm_y[end[0]*nx], g, gp); // compute scalars ys and yy: // ys = y^t \cdot s -> 1 / \rho. // yy = y^t \cdot y. // ggml_vec_dot_f32(nx, &ys, 0, &lm_y[end[0]*nx], 0, &lm_s[end[0]*nx], 0, 1); ggml_vec_dot_f32(nx, &yy, 0, &lm_y[end[0]*nx], 0, &lm_y[end[0]*nx], 0, 1); lm_ys[end[0]] = ys; // find new search direction // ref: https://en.wikipedia.org/wiki/Limited-memory_BFGS bound = (m <= k[0]) ? m : k[0]; k[0]++; it++; end[0] = (end[0] + 1)%m; // initialize search direction with -g ggml_vec_neg_f32(nx, d, g); j[0] = end[0]; for (int i = 0; i < bound; ++i) { j[0] = (j[0] + m - 1) % m; // \alpha_{j} = \rho_{j} s^{t}_{j} \cdot q_{k+1} ggml_vec_dot_f32(nx, &lm_alpha[j[0]], 0, &lm_s[j[0]*nx], 0, d, 0, 1); lm_alpha[j[0]] /= lm_ys[j[0]]; // q_{i} = q_{i+1} - \alpha_{i} y_{i} ggml_vec_mad_f32(nx, d, &lm_y[j[0]*nx], -lm_alpha[j[0]]); } ggml_vec_scale_f32(nx, d, ys/yy); for (int i = 0; i < bound; ++i) { // \beta_{j} = \rho_{j} y^t_{j} \cdot \gamma_{i} ggml_vec_dot_f32(nx, &beta, 0, &lm_y[j[0]*nx], 0, d, 0, 1); beta /= lm_ys[j[0]]; // \gamma_{i+1} = \gamma_{i} + (\alpha_{j} - \beta_{j}) s_{j} ggml_vec_mad_f32(nx, d, &lm_s[j[0]*nx], lm_alpha[j[0]] - beta); j[0] = (j[0] + 1)%m; } step[0] = 1.0; } GGML_ASSERT(false && "lbfgs failed"); return GGML_OPT_RESULT_DID_NOT_CONVERGE; } struct ggml_opt_params ggml_opt_default_params(enum ggml_opt_type type) { struct ggml_opt_params result; switch (type) { case GGML_OPT_TYPE_ADAM: { result = (struct ggml_opt_params) { .type = GGML_OPT_TYPE_ADAM, .graph_size = GGML_DEFAULT_GRAPH_SIZE, .n_threads = 1, // FIXME: GGML_DEFAULT_N_THREADS ? .past = 0, .delta = 1e-5f, .max_no_improvement = 100, .print_forward_graph = true, .print_backward_graph = true, .n_gradient_accumulation = 1, .adam = { .n_iter = 10000, .sched = 1.000f, .decay = 0.0f, .decay_min_ndim = 2, .alpha = 0.001f, .beta1 = 0.9f, .beta2 = 0.999f, .eps = 1e-8f, .eps_f = 1e-5f, .eps_g = 1e-3f, .gclip = 0.0f, }, }; } break; case GGML_OPT_TYPE_LBFGS: { result = (struct ggml_opt_params) { .type = GGML_OPT_TYPE_LBFGS, .graph_size = GGML_DEFAULT_GRAPH_SIZE, .n_threads = 1, .past = 0, .delta = 1e-5f, .max_no_improvement = 0, .print_forward_graph = true, .print_backward_graph = true, .n_gradient_accumulation = 1, .lbfgs = { .m = 6, .n_iter = 100, .max_linesearch = 20, .eps = 1e-5f, .ftol = 1e-4f, .wolfe = 0.9f, .min_step = 1e-20f, .max_step = 1e+20f, .linesearch = GGML_LINESEARCH_DEFAULT, }, }; } break; } return result; } GGML_API void ggml_opt_init( struct ggml_context * ctx, struct ggml_opt_context * opt, struct ggml_opt_params params, int64_t nx) { opt->ctx = ctx; opt->params = params; opt->iter = 0; opt->nx = nx; opt->just_initialized = true; if (opt->ctx == NULL) { struct ggml_init_params ctx_opt_params; if (opt->params.type == GGML_OPT_TYPE_ADAM) { ctx_opt_params.mem_size = GGML_MEM_ALIGN*3 + ggml_tensor_overhead()*3 + ggml_type_size(GGML_TYPE_F32)*nx*3; if (opt->params.past > 0) { ctx_opt_params.mem_size += GGML_MEM_ALIGN + ggml_tensor_overhead() + ggml_type_size(GGML_TYPE_F32)*opt->params.past; } } else if (opt->params.type == GGML_OPT_TYPE_LBFGS) { ctx_opt_params.mem_size = GGML_MEM_ALIGN*9 + ggml_tensor_overhead()*9 + ggml_type_size(GGML_TYPE_F32)*(nx*5 + opt->params.lbfgs.m*2 + nx*opt->params.lbfgs.m*2); if (opt->params.past > 0) { ctx_opt_params.mem_size += GGML_MEM_ALIGN + ggml_tensor_overhead() + ggml_type_size(GGML_TYPE_F32)*opt->params.past; } } ctx_opt_params.mem_buffer = NULL; ctx_opt_params.no_alloc = false; opt->ctx = ggml_init(ctx_opt_params); } switch (opt->params.type) { case GGML_OPT_TYPE_ADAM: { opt->adam.g = ggml_new_tensor_1d(opt->ctx, GGML_TYPE_F32, nx); opt->adam.m = ggml_new_tensor_1d(opt->ctx, GGML_TYPE_F32, nx); opt->adam.v = ggml_new_tensor_1d(opt->ctx, GGML_TYPE_F32, nx); opt->adam.pf = params.past > 0 ? ggml_new_tensor_1d(opt->ctx, GGML_TYPE_F32, params.past) : NULL; ggml_set_zero(opt->adam.m); ggml_set_zero(opt->adam.v); if (opt->adam.pf) { ggml_set_zero(opt->adam.pf); } } break; case GGML_OPT_TYPE_LBFGS: { opt->lbfgs.x = ggml_new_tensor_1d(opt->ctx, GGML_TYPE_F32, nx); opt->lbfgs.xp = ggml_new_tensor_1d(opt->ctx, GGML_TYPE_F32, nx); opt->lbfgs.g = ggml_new_tensor_1d(opt->ctx, GGML_TYPE_F32, nx); opt->lbfgs.gp = ggml_new_tensor_1d(opt->ctx, GGML_TYPE_F32, nx); opt->lbfgs.d = ggml_new_tensor_1d(opt->ctx, GGML_TYPE_F32, nx); opt->lbfgs.pf = params.past > 0 ? ggml_new_tensor_1d(opt->ctx, GGML_TYPE_F32, params.past) : NULL; opt->lbfgs.lmal = ggml_new_tensor_1d(opt->ctx, GGML_TYPE_F32, params.lbfgs.m); opt->lbfgs.lmys = ggml_new_tensor_1d(opt->ctx, GGML_TYPE_F32, params.lbfgs.m); opt->lbfgs.lms = ggml_new_tensor_2d(opt->ctx, GGML_TYPE_F32, nx, params.lbfgs.m); opt->lbfgs.lmy = ggml_new_tensor_2d(opt->ctx, GGML_TYPE_F32, nx, params.lbfgs.m); ggml_set_zero(opt->lbfgs.x); ggml_set_zero(opt->lbfgs.xp); ggml_set_zero(opt->lbfgs.g); ggml_set_zero(opt->lbfgs.gp); ggml_set_zero(opt->lbfgs.d); if (opt->lbfgs.pf) { ggml_set_zero(opt->lbfgs.pf); } ggml_set_zero(opt->lbfgs.lmal); ggml_set_zero(opt->lbfgs.lmys); ggml_set_zero(opt->lbfgs.lms); ggml_set_zero(opt->lbfgs.lmy); } break; } } enum ggml_opt_result ggml_opt( struct ggml_context * ctx, struct ggml_opt_params params, struct ggml_tensor * f) { bool free_ctx = false; if (ctx == NULL) { struct ggml_init_params params_ctx = { .mem_size = 16*1024*1024, .mem_buffer = NULL, .no_alloc = false, }; ctx = ggml_init(params_ctx); if (ctx == NULL) { return GGML_OPT_RESULT_NO_CONTEXT; } free_ctx = true; } enum ggml_opt_result result = GGML_OPT_RESULT_OK; struct ggml_opt_context * opt = (struct ggml_opt_context *) alloca(sizeof(struct ggml_opt_context)); ggml_opt_init(ctx, opt, params, 0); result = ggml_opt_resume(ctx, opt, f); if (free_ctx) { ggml_free(ctx); } return result; } enum ggml_opt_result ggml_opt_resume( struct ggml_context * ctx, struct ggml_opt_context * opt, struct ggml_tensor * f) { // build forward + backward compute graphs struct ggml_cgraph * gf = ggml_new_graph_custom(ctx, opt->params.graph_size, true); ggml_build_forward_expand(gf, f); struct ggml_cgraph * gb = ggml_graph_dup(ctx, gf); ggml_build_backward_expand(ctx, gf, gb, true); return ggml_opt_resume_g(ctx, opt, f, gf, gb, NULL, NULL); } enum ggml_opt_result ggml_opt_resume_g( struct ggml_context * ctx, struct ggml_opt_context * opt, struct ggml_tensor * f, struct ggml_cgraph * gf, struct ggml_cgraph * gb, ggml_opt_callback callback, void * callback_data) { // build forward + backward compute graphs enum ggml_opt_result result = GGML_OPT_RESULT_OK; switch (opt->params.type) { case GGML_OPT_TYPE_ADAM: { result = ggml_opt_adam(ctx, opt, opt->params, f, gf, gb, callback, callback_data); } break; case GGML_OPT_TYPE_LBFGS: { result = ggml_opt_lbfgs(ctx, opt, opt->params, f, gf, gb, callback, callback_data); } break; } if (opt->params.print_forward_graph) { ggml_graph_print (gf); ggml_graph_dump_dot(gf, NULL, "opt-forward.dot"); } if (opt->params.print_backward_graph) { ggml_graph_print (gb); ggml_graph_dump_dot(gb, gf, "opt-backward.dot"); } return result; } //////////////////////////////////////////////////////////////////////////////// void ggml_set_input(struct ggml_tensor * tensor) { tensor->flags |= GGML_TENSOR_FLAG_INPUT; } void ggml_set_output(struct ggml_tensor * tensor) { tensor->flags |= GGML_TENSOR_FLAG_OUTPUT; } //////////////////////////////////////////////////////////////////////////////// void ggml_quantize_init(enum ggml_type type) { ggml_critical_section_start(); switch (type) { case GGML_TYPE_IQ2_XXS: case GGML_TYPE_IQ2_XS: case GGML_TYPE_IQ2_S: case GGML_TYPE_IQ1_S: case GGML_TYPE_IQ1_M: iq2xs_init_impl(type); break; case GGML_TYPE_IQ3_XXS: iq3xs_init_impl(256); break; case GGML_TYPE_IQ3_S: iq3xs_init_impl(512); break; default: // nothing break; } ggml_critical_section_end(); } void ggml_quantize_free(void) { ggml_critical_section_start(); iq2xs_free_impl(GGML_TYPE_IQ2_XXS); iq2xs_free_impl(GGML_TYPE_IQ2_XS); iq2xs_free_impl(GGML_TYPE_IQ1_S); iq3xs_free_impl(256); ggml_critical_section_end(); } bool ggml_quantize_requires_imatrix(enum ggml_type type) { return type == GGML_TYPE_IQ2_XXS || type == GGML_TYPE_IQ2_XS || type == GGML_TYPE_IQ1_S;// || //type == GGML_TYPE_IQ1_M; } size_t ggml_quantize_chunk( enum ggml_type type, const float * src, void * dst, int64_t start, int64_t nrows, int64_t n_per_row, const float * imatrix) { const int64_t n = (int64_t) nrows * n_per_row; if (ggml_quantize_requires_imatrix(type)) { GGML_ASSERT(imatrix != NULL); } GGML_ASSERT(start % type_traits[type].blck_size == 0); GGML_ASSERT(start % n_per_row == 0); ggml_quantize_init(type); // this is noop if already initialized const size_t start_row = start / n_per_row; const size_t row_size = ggml_row_size(type, n_per_row); size_t result = 0; switch (type) { case GGML_TYPE_Q4_0: result = quantize_q4_0(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; case GGML_TYPE_Q4_1: result = quantize_q4_1(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; case GGML_TYPE_Q5_0: result = quantize_q5_0(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; case GGML_TYPE_Q5_1: result = quantize_q5_1(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; case GGML_TYPE_Q8_0: result = quantize_q8_0(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; case GGML_TYPE_Q2_K: result = quantize_q2_K(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; case GGML_TYPE_Q3_K: result = quantize_q3_K(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; case GGML_TYPE_Q4_K: result = quantize_q4_K(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; case GGML_TYPE_Q5_K: result = quantize_q5_K(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; case GGML_TYPE_Q6_K: result = quantize_q6_K(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; case GGML_TYPE_IQ2_XXS: result = quantize_iq2_xxs(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; case GGML_TYPE_IQ2_XS: result = quantize_iq2_xs (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; case GGML_TYPE_IQ3_XXS: result = quantize_iq3_xxs(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; case GGML_TYPE_IQ3_S: result = quantize_iq3_s (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; case GGML_TYPE_IQ2_S: result = quantize_iq2_s (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; case GGML_TYPE_IQ1_S: result = quantize_iq1_s (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; case GGML_TYPE_IQ1_M: result = quantize_iq1_m (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; case GGML_TYPE_IQ4_NL: result = quantize_iq4_nl (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; case GGML_TYPE_IQ4_XS: result = quantize_iq4_xs (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; case GGML_TYPE_F16: { size_t elemsize = sizeof(ggml_fp16_t); ggml_fp32_to_fp16_row(src + start, (ggml_fp16_t *)dst + start, n); result = n * elemsize; } break; case GGML_TYPE_BF16: { size_t elemsize = sizeof(ggml_bf16_t); ggml_fp32_to_bf16_row(src + start, (ggml_bf16_t *)dst + start, n); result = n * elemsize; } break; case GGML_TYPE_F32: { size_t elemsize = sizeof(float); result = n * elemsize; memcpy((uint8_t *)dst + start * elemsize, src + start, result); } break; default: assert(false); } GGML_ASSERT(result == nrows * row_size); return result; } //////////////////////////////////////////////////////////////////////////////// struct gguf_str { uint64_t n; // GGUFv2 char * data; }; static const size_t GGUF_TYPE_SIZE[GGUF_TYPE_COUNT] = { [GGUF_TYPE_UINT8] = sizeof(uint8_t), [GGUF_TYPE_INT8] = sizeof(int8_t), [GGUF_TYPE_UINT16] = sizeof(uint16_t), [GGUF_TYPE_INT16] = sizeof(int16_t), [GGUF_TYPE_UINT32] = sizeof(uint32_t), [GGUF_TYPE_INT32] = sizeof(int32_t), [GGUF_TYPE_FLOAT32] = sizeof(float), [GGUF_TYPE_BOOL] = sizeof(bool), [GGUF_TYPE_STRING] = sizeof(struct gguf_str), [GGUF_TYPE_UINT64] = sizeof(uint64_t), [GGUF_TYPE_INT64] = sizeof(int64_t), [GGUF_TYPE_FLOAT64] = sizeof(double), [GGUF_TYPE_ARRAY] = 0, // undefined }; static_assert(GGUF_TYPE_COUNT == 13, "GGUF_TYPE_COUNT != 13"); static const char * GGUF_TYPE_NAME[GGUF_TYPE_COUNT] = { [GGUF_TYPE_UINT8] = "u8", [GGUF_TYPE_INT8] = "i8", [GGUF_TYPE_UINT16] = "u16", [GGUF_TYPE_INT16] = "i16", [GGUF_TYPE_UINT32] = "u32", [GGUF_TYPE_INT32] = "i32", [GGUF_TYPE_FLOAT32] = "f32", [GGUF_TYPE_BOOL] = "bool", [GGUF_TYPE_STRING] = "str", [GGUF_TYPE_ARRAY] = "arr", [GGUF_TYPE_UINT64] = "u64", [GGUF_TYPE_INT64] = "i64", [GGUF_TYPE_FLOAT64] = "f64", }; static_assert(GGUF_TYPE_COUNT == 13, "GGUF_TYPE_COUNT != 13"); union gguf_value { uint8_t uint8; int8_t int8; uint16_t uint16; int16_t int16; uint32_t uint32; int32_t int32; float float32; uint64_t uint64; int64_t int64; double float64; bool bool_; struct gguf_str str; struct { enum gguf_type type; uint64_t n; // GGUFv2 void * data; } arr; }; struct gguf_kv { struct gguf_str key; enum gguf_type type; union gguf_value value; }; struct gguf_header { char magic[4]; uint32_t version; uint64_t n_tensors; // GGUFv2 uint64_t n_kv; // GGUFv2 }; struct gguf_tensor_info { struct gguf_str name; uint32_t n_dims; uint64_t ne[GGML_MAX_DIMS]; enum ggml_type type; uint64_t offset; // offset from start of `data`, must be a multiple of `ALIGNMENT` // for writing API const void * data; size_t size; }; struct gguf_context { struct gguf_header header; struct gguf_kv * kv; struct gguf_tensor_info * infos; size_t alignment; size_t offset; // offset of `data` from beginning of file size_t size; // size of `data` in bytes //uint8_t * padding; void * data; }; static size_t gguf_type_size(enum gguf_type type) { GGML_ASSERT(0 <= type && type < GGUF_TYPE_COUNT); return GGUF_TYPE_SIZE[type]; } static void gguf_tensor_info_sanitize(struct gguf_tensor_info * info) { GGML_ASSERT(info->n_dims <= GGML_MAX_DIMS); GGML_ASSERT(0 <= info->type && info->type < GGML_TYPE_COUNT); for (uint32_t i = 0; i < info->n_dims; ++i) { GGML_ASSERT(info->ne[i] > 0); } // prevent overflow for total number of elements GGML_ASSERT(INT64_MAX/info->ne[1] > info->ne[0]); GGML_ASSERT(INT64_MAX/info->ne[2] > info->ne[0]*info->ne[1]); GGML_ASSERT(INT64_MAX/info->ne[3] > info->ne[0]*info->ne[1]*info->ne[2]); } static bool gguf_fread_el(FILE * file, void * dst, size_t size, size_t * offset) { const size_t n = fread(dst, 1, size, file); *offset += n; return n == size; } static bool gguf_fread_str(FILE * file, struct gguf_str * p, size_t * offset) { p->n = 0; p->data = NULL; bool ok = true; ok = ok && gguf_fread_el(file, &p->n, sizeof(p->n), offset); // early exit if string length is invalid, prevents from integer overflow if (p->n == SIZE_MAX) { fprintf(stderr, "%s: invalid string length (%" PRIu64 ")\n", __func__, p->n); return false; } p->data = GGML_CALLOC(p->n + 1, 1); ok = ok && gguf_fread_el(file, p->data, p->n, offset); return ok; } static void gguf_free_kv(struct gguf_kv * kv) { if (kv->key.data) { GGML_FREE(kv->key.data); } if (kv->type == GGUF_TYPE_STRING) { if (kv->value.str.data) { GGML_FREE(kv->value.str.data); } } if (kv->type == GGUF_TYPE_ARRAY) { if (kv->value.arr.data) { if (kv->value.arr.type == GGUF_TYPE_STRING) { for (uint64_t j = 0; j < kv->value.arr.n; ++j) { struct gguf_str * str = &((struct gguf_str *) kv->value.arr.data)[j]; if (str->data) { GGML_FREE(str->data); } } } GGML_FREE(kv->value.arr.data); } } } struct gguf_context * gguf_init_empty(void) { struct gguf_context * ctx = GGML_CALLOC(1, sizeof(struct gguf_context)); memcpy(ctx->header.magic, GGUF_MAGIC, sizeof(ctx->header.magic)); ctx->header.version = GGUF_VERSION; ctx->header.n_tensors = 0; ctx->header.n_kv = 0; ctx->kv = NULL; ctx->infos = NULL; ctx->alignment = GGUF_DEFAULT_ALIGNMENT; ctx->offset = 0; ctx->size = 0; ctx->data = NULL; return ctx; } struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_params params) { FILE * file = ggml_fopen(fname, "rb"); if (!file) { return NULL; } // offset from start of file size_t offset = 0; char magic[4]; // check the magic before making allocations { gguf_fread_el(file, &magic, sizeof(magic), &offset); for (uint32_t i = 0; i < sizeof(magic); i++) { if (magic[i] != GGUF_MAGIC[i]) { fprintf(stderr, "%s: invalid magic characters '%c%c%c%c'\n", __func__, magic[0], magic[1], magic[2], magic[3]); fclose(file); return NULL; } } } bool ok = true; struct gguf_context * ctx = GGML_CALLOC(1, sizeof(struct gguf_context)); // read the header { strncpy(ctx->header.magic, magic, 4); ctx->kv = NULL; ctx->infos = NULL; ctx->data = NULL; ok = ok && gguf_fread_el(file, &ctx->header.version, sizeof(ctx->header.version), &offset); ok = ok && gguf_fread_el(file, &ctx->header.n_tensors, sizeof(ctx->header.n_tensors), &offset); ok = ok && gguf_fread_el(file, &ctx->header.n_kv, sizeof(ctx->header.n_kv), &offset); if (ctx->header.version == 1) { fprintf(stderr, "%s: GGUFv1 is no longer supported. please use a more up-to-date version\n", __func__); fclose(file); gguf_free(ctx); return NULL; } // sanity-checks to prevent from integer/buffer overflows ok = ok && (ctx->header.n_tensors < (SIZE_MAX/2)/sizeof(struct gguf_tensor_info)); ok = ok && (ctx->header.n_tensors < (SIZE_MAX/2)/ggml_tensor_overhead()); ok = ok && (ctx->header.n_kv < (SIZE_MAX/2)/sizeof(struct gguf_kv)); if (!ok) { fprintf(stderr, "%s: failed to read header\n", __func__); fclose(file); gguf_free(ctx); return NULL; } } // read the kv pairs { const uint64_t n_kv = ctx->header.n_kv; // header.n_kv will hold the actual value of pairs that were successfully read in the loop below ctx->header.n_kv = 0; ctx->kv = GGML_CALLOC(n_kv, sizeof(struct gguf_kv)); for (uint64_t i = 0; i < n_kv; ++i) { struct gguf_kv * kv = &ctx->kv[i]; //fprintf(stderr, "%s: reading kv %d\n", __func__, i); ok = ok && gguf_fread_str(file, &kv->key, &offset); ok = ok && gguf_fread_el (file, &kv->type, sizeof(kv->type), &offset); //fprintf(stderr, "%s: reading kv with key %s\n", __func__, kv->key.data); switch (kv->type) { case GGUF_TYPE_UINT8: ok = ok && gguf_fread_el (file, &kv->value.uint8, sizeof(kv->value.uint8), &offset); break; case GGUF_TYPE_INT8: ok = ok && gguf_fread_el (file, &kv->value.int8, sizeof(kv->value.int8), &offset); break; case GGUF_TYPE_UINT16: ok = ok && gguf_fread_el (file, &kv->value.uint16, sizeof(kv->value.uint16), &offset); break; case GGUF_TYPE_INT16: ok = ok && gguf_fread_el (file, &kv->value.int16, sizeof(kv->value.int16), &offset); break; case GGUF_TYPE_UINT32: ok = ok && gguf_fread_el (file, &kv->value.uint32, sizeof(kv->value.uint32), &offset); break; case GGUF_TYPE_INT32: ok = ok && gguf_fread_el (file, &kv->value.int32, sizeof(kv->value.int32), &offset); break; case GGUF_TYPE_FLOAT32: ok = ok && gguf_fread_el (file, &kv->value.float32, sizeof(kv->value.float32), &offset); break; case GGUF_TYPE_UINT64: ok = ok && gguf_fread_el (file, &kv->value.uint64, sizeof(kv->value.uint64), &offset); break; case GGUF_TYPE_INT64: ok = ok && gguf_fread_el (file, &kv->value.int64, sizeof(kv->value.int64), &offset); break; case GGUF_TYPE_FLOAT64: ok = ok && gguf_fread_el (file, &kv->value.float64, sizeof(kv->value.float64), &offset); break; case GGUF_TYPE_BOOL: ok = ok && gguf_fread_el (file, &kv->value.bool_, sizeof(kv->value.bool_), &offset); break; case GGUF_TYPE_STRING: ok = ok && gguf_fread_str(file, &kv->value.str, &offset); break; case GGUF_TYPE_ARRAY: { ok = ok && gguf_fread_el(file, &kv->value.arr.type, sizeof(kv->value.arr.type), &offset); ok = ok && gguf_fread_el(file, &kv->value.arr.n, sizeof(kv->value.arr.n), &offset); switch (kv->value.arr.type) { case GGUF_TYPE_UINT8: case GGUF_TYPE_INT8: case GGUF_TYPE_UINT16: case GGUF_TYPE_INT16: case GGUF_TYPE_UINT32: case GGUF_TYPE_INT32: case GGUF_TYPE_FLOAT32: case GGUF_TYPE_UINT64: case GGUF_TYPE_INT64: case GGUF_TYPE_FLOAT64: case GGUF_TYPE_BOOL: { // prevent from integer overflow in the malloc below if (kv->value.arr.n >= SIZE_MAX/gguf_type_size(kv->value.arr.type)) { fprintf(stderr, "%s: array size is too large (%" PRIu64 ")\n", __func__, kv->value.arr.n); fclose(file); gguf_free(ctx); return NULL; } kv->value.arr.data = GGML_CALLOC(kv->value.arr.n, gguf_type_size(kv->value.arr.type)); ok = ok && gguf_fread_el(file, kv->value.arr.data, kv->value.arr.n * gguf_type_size(kv->value.arr.type), &offset); } break; case GGUF_TYPE_STRING: { // prevent from integer overflow in the malloc below if (kv->value.arr.n >= SIZE_MAX/sizeof(struct gguf_str)) { fprintf(stderr, "%s: array size is too large (%" PRIu64 ")\n", __func__, kv->value.arr.n); fclose(file); gguf_free(ctx); return NULL; } kv->value.arr.data = GGML_CALLOC(kv->value.arr.n, sizeof(struct gguf_str)); for (uint64_t j = 0; j < kv->value.arr.n; ++j) { ok = ok && gguf_fread_str(file, &((struct gguf_str *) kv->value.arr.data)[j], &offset); } } break; case GGUF_TYPE_ARRAY: default: GGML_ASSERT(false && "invalid type"); break; } } break; default: GGML_ASSERT(false && "invalid type"); } if (!ok) { break; } ctx->header.n_kv++; } if (!ok) { fprintf(stderr, "%s: failed to read key-value pairs\n", __func__); fclose(file); gguf_free(ctx); return NULL; } } // read the tensor infos if (ctx->header.n_tensors > 0) { ctx->infos = GGML_CALLOC(ctx->header.n_tensors, sizeof(struct gguf_tensor_info)); for (uint64_t i = 0; i < ctx->header.n_tensors; ++i) { struct gguf_tensor_info * info = &ctx->infos[i]; for (int j = 0; j < GGML_MAX_DIMS; ++j) { info->ne[j] = 1; } ok = ok && gguf_fread_str(file, &info->name, &offset); ok = ok && gguf_fread_el (file, &info->n_dims, sizeof(info->n_dims), &offset); ok = ok && (info->n_dims <= GGML_MAX_DIMS); for (uint32_t j = 0; j < info->n_dims; ++j) { ok = ok && gguf_fread_el(file, &info->ne[j], sizeof(info->ne[j]), &offset); } ok = ok && gguf_fread_el (file, &info->type, sizeof(info->type), &offset); ok = ok && gguf_fread_el (file, &info->offset, sizeof(info->offset), &offset); // TODO: return an error instead of crashing with GGML_ASSERT gguf_tensor_info_sanitize(info); // make sure there is no duplicated tensor names for (uint64_t j = 0; j < i; ++j) { if (strcmp(info->name.data, ctx->infos[j].name.data) == 0) { fprintf(stderr, "%s: duplicated tensor name %s\n", __func__, info->name.data); ok = false; } } if (!ok) { fprintf(stderr, "%s: failed to read tensor info\n", __func__); fclose(file); gguf_free(ctx); return NULL; } } } ctx->alignment = GGUF_DEFAULT_ALIGNMENT; int alignment_idx = gguf_find_key(ctx, "general.alignment"); if (alignment_idx != -1) { ctx->alignment = gguf_get_val_u32(ctx, alignment_idx); } // we require the data section to be aligned, so take into account any padding { const size_t offset_pad = offset % ctx->alignment; if (offset_pad != 0) { offset += ctx->alignment - offset_pad; fseek(file, offset, SEEK_SET); } } // store the current file offset - this is where the data section starts ctx->offset = offset; // compute the total size of the data section, taking into account the alignment { ctx->size = 0; for (uint64_t i = 0; i < ctx->header.n_tensors; ++i) { struct gguf_tensor_info * info = &ctx->infos[i]; const int64_t ne = (int64_t) info->ne[0] * (int64_t) info->ne[1] * (int64_t) info->ne[2] * (int64_t) info->ne[3]; if (ne % ggml_blck_size(info->type) != 0) { fprintf(stderr, "%s: tensor '%s' of type %d (%s) number of elements (%" PRId64 ") is not a multiple of block size (%d)\n", __func__, info->name.data, (int)info->type, ggml_type_name(info->type), ne, ggml_blck_size(info->type)); fclose(file); gguf_free(ctx); return NULL; } const size_t size_cur = ggml_row_size(info->type, ne); ctx->size += GGML_PAD(size_cur, ctx->alignment); } } // load the tensor data only if requested if (params.ctx != NULL) { // if the provided gguf_context is no_alloc, then we create "empty" tensors and do not read the binary blob // otherwise, we load the binary blob into the created ggml_context as well, and point the "data" members of // the ggml_tensor structs to the appropriate locations in the binary blob // compute the exact size needed for the new ggml_context const size_t mem_size = params.no_alloc ? (ctx->header.n_tensors )*ggml_tensor_overhead() : (ctx->header.n_tensors + 1)*ggml_tensor_overhead() + ctx->size; struct ggml_init_params pdata = { .mem_size = mem_size, .mem_buffer = NULL, .no_alloc = params.no_alloc, }; *params.ctx = ggml_init(pdata); struct ggml_context * ctx_data = *params.ctx; struct ggml_tensor * data = NULL; if (!params.no_alloc) { data = ggml_new_tensor_1d(ctx_data, GGML_TYPE_I8, ctx->size); ok = ok && data != NULL; // read the binary blob with the tensor data ok = ok && gguf_fread_el(file, data->data, ctx->size, &offset); if (!ok) { fprintf(stderr, "%s: failed to read tensor data\n", __func__); fclose(file); ggml_free(ctx_data); gguf_free(ctx); return NULL; } ctx->data = data->data; } ggml_set_no_alloc(ctx_data, true); // create the tensors for (uint64_t i = 0; i < ctx->header.n_tensors; ++i) { const int64_t ne[GGML_MAX_DIMS] = { ctx->infos[i].ne[0], ctx->infos[i].ne[1], ctx->infos[i].ne[2], ctx->infos[i].ne[3], }; struct ggml_tensor * cur = ggml_new_tensor(ctx_data, ctx->infos[i].type, ctx->infos[i].n_dims, ne); ok = ok && cur != NULL; if (!ok) { break; } ggml_set_name(cur, ctx->infos[i].name.data); // point the data member to the appropriate location in the binary blob using the tensor infos if (!params.no_alloc) { //cur->data = (char *) data->data + ctx->infos[i].offset - ctx->offset; // offset from start of file cur->data = (char *) data->data + ctx->infos[i].offset; // offset from data } } if (!ok) { fprintf(stderr, "%s: failed to read the tensor data\n", __func__); fclose(file); ggml_free(ctx_data); gguf_free(ctx); return NULL; } ggml_set_no_alloc(ctx_data, params.no_alloc); } fclose(file); return ctx; } void gguf_free(struct gguf_context * ctx) { if (ctx == NULL) { return; } if (ctx->kv) { // free string memory - not great.. for (uint64_t i = 0; i < ctx->header.n_kv; ++i) { gguf_free_kv(&ctx->kv[i]); } GGML_FREE(ctx->kv); } if (ctx->infos) { for (uint64_t i = 0; i < ctx->header.n_tensors; ++i) { struct gguf_tensor_info * info = &ctx->infos[i]; if (info->name.data) { GGML_FREE(info->name.data); } } GGML_FREE(ctx->infos); } GGML_FREE(ctx); } const char * gguf_type_name(enum gguf_type type) { return GGUF_TYPE_NAME[type]; } int gguf_get_version(const struct gguf_context * ctx) { return ctx->header.version; } size_t gguf_get_alignment(const struct gguf_context * ctx) { return ctx->alignment; } size_t gguf_get_data_offset(const struct gguf_context * ctx) { return ctx->offset; } void * gguf_get_data(const struct gguf_context * ctx) { return ctx->data; } int gguf_get_n_kv(const struct gguf_context * ctx) { return ctx->header.n_kv; } int gguf_find_key(const struct gguf_context * ctx, const char * key) { // return -1 if key not found int keyfound = -1; const int n_kv = gguf_get_n_kv(ctx); for (int i = 0; i < n_kv; ++i) { if (strcmp(key, gguf_get_key(ctx, i)) == 0) { keyfound = i; break; } } return keyfound; } const char * gguf_get_key(const struct gguf_context * ctx, int key_id) { GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx)); return ctx->kv[key_id].key.data; } enum gguf_type gguf_get_kv_type(const struct gguf_context * ctx, int key_id) { GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx)); return ctx->kv[key_id].type; } enum gguf_type gguf_get_arr_type(const struct gguf_context * ctx, int key_id) { GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx)); GGML_ASSERT(ctx->kv[key_id].type == GGUF_TYPE_ARRAY); return ctx->kv[key_id].value.arr.type; } const void * gguf_get_arr_data(const struct gguf_context * ctx, int key_id) { GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx)); GGML_ASSERT(ctx->kv[key_id].type == GGUF_TYPE_ARRAY); return ctx->kv[key_id].value.arr.data; } const char * gguf_get_arr_str(const struct gguf_context * ctx, int key_id, int i) { GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx)); GGML_ASSERT(ctx->kv[key_id].type == GGUF_TYPE_ARRAY); struct gguf_kv * kv = &ctx->kv[key_id]; struct gguf_str * str = &((struct gguf_str *) kv->value.arr.data)[i]; return str->data; } int gguf_get_arr_n(const struct gguf_context * ctx, int key_id) { GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx)); GGML_ASSERT(ctx->kv[key_id].type == GGUF_TYPE_ARRAY); return ctx->kv[key_id].value.arr.n; } uint8_t gguf_get_val_u8(const struct gguf_context * ctx, int key_id) { GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx)); GGML_ASSERT(ctx->kv[key_id].type == GGUF_TYPE_UINT8); return ctx->kv[key_id].value.uint8; } int8_t gguf_get_val_i8(const struct gguf_context * ctx, int key_id) { GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx)); GGML_ASSERT(ctx->kv[key_id].type == GGUF_TYPE_INT8); return ctx->kv[key_id].value.int8; } uint16_t gguf_get_val_u16(const struct gguf_context * ctx, int key_id) { GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx)); GGML_ASSERT(ctx->kv[key_id].type == GGUF_TYPE_UINT16); return ctx->kv[key_id].value.uint16; } int16_t gguf_get_val_i16(const struct gguf_context * ctx, int key_id) { GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx)); GGML_ASSERT(ctx->kv[key_id].type == GGUF_TYPE_INT16); return ctx->kv[key_id].value.int16; } uint32_t gguf_get_val_u32(const struct gguf_context * ctx, int key_id) { GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx)); GGML_ASSERT(ctx->kv[key_id].type == GGUF_TYPE_UINT32); return ctx->kv[key_id].value.uint32; } int32_t gguf_get_val_i32(const struct gguf_context * ctx, int key_id) { GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx)); GGML_ASSERT(ctx->kv[key_id].type == GGUF_TYPE_INT32); return ctx->kv[key_id].value.int32; } float gguf_get_val_f32(const struct gguf_context * ctx, int key_id) { GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx)); GGML_ASSERT(ctx->kv[key_id].type == GGUF_TYPE_FLOAT32); return ctx->kv[key_id].value.float32; } uint64_t gguf_get_val_u64(const struct gguf_context * ctx, int key_id) { GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx)); GGML_ASSERT(ctx->kv[key_id].type == GGUF_TYPE_UINT64); return ctx->kv[key_id].value.uint64; } int64_t gguf_get_val_i64(const struct gguf_context * ctx, int key_id) { GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx)); GGML_ASSERT(ctx->kv[key_id].type == GGUF_TYPE_INT64); return ctx->kv[key_id].value.int64; } double gguf_get_val_f64(const struct gguf_context * ctx, int key_id) { GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx)); GGML_ASSERT(ctx->kv[key_id].type == GGUF_TYPE_FLOAT64); return ctx->kv[key_id].value.float64; } bool gguf_get_val_bool(const struct gguf_context * ctx, int key_id) { GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx)); GGML_ASSERT(ctx->kv[key_id].type == GGUF_TYPE_BOOL); return ctx->kv[key_id].value.bool_; } const char * gguf_get_val_str(const struct gguf_context * ctx, int key_id) { GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx)); GGML_ASSERT(ctx->kv[key_id].type == GGUF_TYPE_STRING); return ctx->kv[key_id].value.str.data; } const void * gguf_get_val_data(const struct gguf_context * ctx, int key_id) { GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx)); GGML_ASSERT(ctx->kv[key_id].type != GGUF_TYPE_ARRAY); GGML_ASSERT(ctx->kv[key_id].type != GGUF_TYPE_STRING); return &ctx->kv[key_id].value; } int gguf_get_n_tensors(const struct gguf_context * ctx) { return ctx->header.n_tensors; } int gguf_find_tensor(const struct gguf_context * ctx, const char * name) { // return -1 if tensor not found int tensorfound = -1; const int n_tensors = gguf_get_n_tensors(ctx); for (int i = 0; i < n_tensors; ++i) { if (strcmp(name, gguf_get_tensor_name(ctx, i)) == 0) { tensorfound = i; break; } } return tensorfound; } size_t gguf_get_tensor_offset(const struct gguf_context * ctx, int i) { return ctx->infos[i].offset; } char * gguf_get_tensor_name(const struct gguf_context * ctx, int i) { return ctx->infos[i].name.data; } enum ggml_type gguf_get_tensor_type(const struct gguf_context * ctx, int i) { return ctx->infos[i].type; } // returns the index static int gguf_get_or_add_key(struct gguf_context * ctx, const char * key) { const int idx = gguf_find_key(ctx, key); if (idx >= 0) { return idx; } const int n_kv = gguf_get_n_kv(ctx); ctx->kv = realloc(ctx->kv, (n_kv + 1) * sizeof(struct gguf_kv)); ctx->kv[n_kv].key.n = strlen(key); ctx->kv[n_kv].key.data = strdup(key); ctx->header.n_kv++; return n_kv; } void gguf_remove_key(struct gguf_context * ctx, const char * key) { const int idx = gguf_find_key(ctx, key); if (idx >= 0) { const int n_kv = gguf_get_n_kv(ctx); gguf_free_kv(&ctx->kv[idx]); for (int i = idx; i < n_kv-1; ++i) { ctx->kv[i] = ctx->kv[i+1]; } ctx->kv = realloc(ctx->kv, (n_kv - 1) * sizeof(struct gguf_kv)); ctx->header.n_kv--; } } void gguf_set_val_u8(struct gguf_context * ctx, const char * key, uint8_t val) { const int idx = gguf_get_or_add_key(ctx, key); ctx->kv[idx].type = GGUF_TYPE_UINT8; ctx->kv[idx].value.uint8 = val; } void gguf_set_val_i8(struct gguf_context * ctx, const char * key, int8_t val) { const int idx = gguf_get_or_add_key(ctx, key); ctx->kv[idx].type = GGUF_TYPE_INT8; ctx->kv[idx].value.int8 = val; } void gguf_set_val_u16(struct gguf_context * ctx, const char * key, uint16_t val) { const int idx = gguf_get_or_add_key(ctx, key); ctx->kv[idx].type = GGUF_TYPE_UINT16; ctx->kv[idx].value.uint16 = val; } void gguf_set_val_i16(struct gguf_context * ctx, const char * key, int16_t val) { const int idx = gguf_get_or_add_key(ctx, key); ctx->kv[idx].type = GGUF_TYPE_INT16; ctx->kv[idx].value.int16 = val; } void gguf_set_val_u32(struct gguf_context * ctx, const char * key, uint32_t val) { const int idx = gguf_get_or_add_key(ctx, key); ctx->kv[idx].type = GGUF_TYPE_UINT32; ctx->kv[idx].value.uint32 = val; } void gguf_set_val_i32(struct gguf_context * ctx, const char * key, int32_t val) { const int idx = gguf_get_or_add_key(ctx, key); ctx->kv[idx].type = GGUF_TYPE_INT32; ctx->kv[idx].value.int32 = val; } void gguf_set_val_f32(struct gguf_context * ctx, const char * key, float val) { const int idx = gguf_get_or_add_key(ctx, key); ctx->kv[idx].type = GGUF_TYPE_FLOAT32; ctx->kv[idx].value.float32 = val; } void gguf_set_val_u64(struct gguf_context * ctx, const char * key, uint64_t val) { const int idx = gguf_get_or_add_key(ctx, key); ctx->kv[idx].type = GGUF_TYPE_UINT64; ctx->kv[idx].value.uint64 = val; } void gguf_set_val_i64(struct gguf_context * ctx, const char * key, int64_t val) { const int idx = gguf_get_or_add_key(ctx, key); ctx->kv[idx].type = GGUF_TYPE_INT64; ctx->kv[idx].value.int64 = val; } void gguf_set_val_f64(struct gguf_context * ctx, const char * key, double val) { const int idx = gguf_get_or_add_key(ctx, key); ctx->kv[idx].type = GGUF_TYPE_FLOAT64; ctx->kv[idx].value.float64 = val; } void gguf_set_val_bool(struct gguf_context * ctx, const char * key, bool val) { const int idx = gguf_get_or_add_key(ctx, key); ctx->kv[idx].type = GGUF_TYPE_BOOL; ctx->kv[idx].value.bool_ = val; } void gguf_set_val_str(struct gguf_context * ctx, const char * key, const char * val) { const int idx = gguf_get_or_add_key(ctx, key); ctx->kv[idx].type = GGUF_TYPE_STRING; ctx->kv[idx].value.str.n = strlen(val); ctx->kv[idx].value.str.data = strdup(val); } void gguf_set_arr_data(struct gguf_context * ctx, const char * key, enum gguf_type type, const void * data, int n) { const int idx = gguf_get_or_add_key(ctx, key); ctx->kv[idx].type = GGUF_TYPE_ARRAY; ctx->kv[idx].value.arr.type = type; ctx->kv[idx].value.arr.n = n; ctx->kv[idx].value.arr.data = GGML_CALLOC(n, gguf_type_size(type)); memcpy(ctx->kv[idx].value.arr.data, data, n*gguf_type_size(type)); } void gguf_set_arr_str(struct gguf_context * ctx, const char * key, const char ** data, int n) { const int idx = gguf_get_or_add_key(ctx, key); ctx->kv[idx].type = GGUF_TYPE_ARRAY; ctx->kv[idx].value.arr.type = GGUF_TYPE_STRING; ctx->kv[idx].value.arr.n = n; ctx->kv[idx].value.arr.data = GGML_CALLOC(n, sizeof(struct gguf_str)); for (int i = 0; i < n; i++) { struct gguf_str * str = &((struct gguf_str *)ctx->kv[idx].value.arr.data)[i]; str->n = strlen(data[i]); str->data = strdup(data[i]); } } // set or add KV pairs from another context void gguf_set_kv(struct gguf_context * ctx, struct gguf_context * src) { for (uint32_t i = 0; i < src->header.n_kv; i++) { switch (src->kv[i].type) { case GGUF_TYPE_UINT8: gguf_set_val_u8 (ctx, src->kv[i].key.data, src->kv[i].value.uint8); break; case GGUF_TYPE_INT8: gguf_set_val_i8 (ctx, src->kv[i].key.data, src->kv[i].value.int8); break; case GGUF_TYPE_UINT16: gguf_set_val_u16 (ctx, src->kv[i].key.data, src->kv[i].value.uint16); break; case GGUF_TYPE_INT16: gguf_set_val_i16 (ctx, src->kv[i].key.data, src->kv[i].value.int16); break; case GGUF_TYPE_UINT32: gguf_set_val_u32 (ctx, src->kv[i].key.data, src->kv[i].value.uint32); break; case GGUF_TYPE_INT32: gguf_set_val_i32 (ctx, src->kv[i].key.data, src->kv[i].value.int32); break; case GGUF_TYPE_FLOAT32: gguf_set_val_f32 (ctx, src->kv[i].key.data, src->kv[i].value.float32); break; case GGUF_TYPE_UINT64: gguf_set_val_u64 (ctx, src->kv[i].key.data, src->kv[i].value.uint64); break; case GGUF_TYPE_INT64: gguf_set_val_i64 (ctx, src->kv[i].key.data, src->kv[i].value.int64); break; case GGUF_TYPE_FLOAT64: gguf_set_val_f64 (ctx, src->kv[i].key.data, src->kv[i].value.float64); break; case GGUF_TYPE_BOOL: gguf_set_val_bool(ctx, src->kv[i].key.data, src->kv[i].value.bool_); break; case GGUF_TYPE_STRING: gguf_set_val_str (ctx, src->kv[i].key.data, src->kv[i].value.str.data); break; case GGUF_TYPE_ARRAY: { if (src->kv[i].value.arr.type == GGUF_TYPE_STRING) { const char ** data = GGML_CALLOC(src->kv[i].value.arr.n, sizeof(char *)); for (uint32_t j = 0; j < src->kv[i].value.arr.n; j++) { data[j] = ((struct gguf_str *)src->kv[i].value.arr.data)[j].data; } gguf_set_arr_str(ctx, src->kv[i].key.data, data, src->kv[i].value.arr.n); GGML_FREE((void *)data); } else if (src->kv[i].value.arr.type == GGUF_TYPE_ARRAY) { GGML_ASSERT(false && "nested arrays not supported"); } else { gguf_set_arr_data(ctx, src->kv[i].key.data, src->kv[i].value.arr.type, src->kv[i].value.arr.data, src->kv[i].value.arr.n); } } break; default: GGML_ASSERT(false && "invalid type"); break; } } } void gguf_add_tensor( struct gguf_context * ctx, const struct ggml_tensor * tensor) { if (gguf_find_tensor(ctx, tensor->name) != -1) { GGML_ASSERT(false && "duplicated tensor name"); } const int idx = ctx->header.n_tensors; ctx->infos = realloc(ctx->infos, (idx + 1)*sizeof(struct gguf_tensor_info)); ctx->infos[idx].name.n = strlen(tensor->name); ctx->infos[idx].name.data = strdup(tensor->name); for (int i = 0; i < GGML_MAX_DIMS; ++i) { ctx->infos[idx].ne[i] = 1; } ctx->infos[idx].n_dims = ggml_n_dims(tensor); for (uint32_t i = 0; i < ctx->infos[idx].n_dims; i++) { ctx->infos[idx].ne[i] = tensor->ne[i]; } ctx->infos[idx].type = tensor->type; ctx->infos[idx].offset = 0; ctx->infos[idx].data = tensor->data; ctx->infos[idx].size = ggml_nbytes(tensor); if (ctx->header.n_tensors > 0) { ctx->infos[idx].offset = ctx->infos[idx - 1].offset + GGML_PAD(ctx->infos[idx - 1].size, ctx->alignment); } ctx->header.n_tensors++; } void gguf_set_tensor_type(struct gguf_context * ctx, const char * name, enum ggml_type type) { const int idx = gguf_find_tensor(ctx, name); if (idx < 0) { GGML_ASSERT(false && "tensor not found"); } ctx->infos[idx].type = type; } void gguf_set_tensor_data(struct gguf_context * ctx, const char * name, const void * data, size_t size) { const int idx = gguf_find_tensor(ctx, name); if (idx < 0) { GGML_ASSERT(false && "tensor not found"); } ctx->infos[idx].data = data; ctx->infos[idx].size = size; // update offsets for (uint32_t i = idx + 1; i < ctx->header.n_tensors; ++i) { ctx->infos[i].offset = ctx->infos[i - 1].offset + GGML_PAD(ctx->infos[i - 1].size, ctx->alignment); } } //static void gguf_fwrite_str(FILE * file, const struct gguf_str * val) { // fwrite(&val->n, sizeof(val->n), 1, file); // fwrite(val->data, sizeof(char), val->n, file); //} // //static void gguf_fwrite_el(FILE * file, const void * val, size_t size) { // fwrite(val, sizeof(char), size, file); //} struct gguf_buf { void * data; size_t size; size_t offset; }; static struct gguf_buf gguf_buf_init(size_t size) { struct gguf_buf buf = { /*buf.data =*/ size == 0 ? NULL : GGML_CALLOC(1, size), /*buf.size =*/ size, /*buf.offset =*/ 0, }; return buf; } static void gguf_buf_free(struct gguf_buf buf) { if (buf.data) { GGML_FREE(buf.data); } } static void gguf_buf_grow(struct gguf_buf * buf, size_t size) { if (buf->offset + size > buf->size) { buf->size = 1.5*(buf->offset + size); if (buf->data) { buf->data = realloc(buf->data, buf->size); } } } static void gguf_bwrite_str(struct gguf_buf * buf, const struct gguf_str * val) { gguf_buf_grow(buf, sizeof(val->n) + val->n); if (buf->data) { memcpy((char *) buf->data + buf->offset, &val->n, sizeof(val->n)); } buf->offset += sizeof(val->n); if (buf->data) { memcpy((char *) buf->data + buf->offset, val->data, val->n); } buf->offset += val->n; } static void gguf_bwrite_el(struct gguf_buf * buf, const void * val, size_t el_size) { gguf_buf_grow(buf, el_size); if (buf->data) { memcpy((char *) buf->data + buf->offset, val, el_size); } buf->offset += el_size; } static void gguf_write_to_buf(const struct gguf_context * ctx, struct gguf_buf * buf, bool only_meta) { // write header gguf_bwrite_el(buf, &ctx->header.magic, sizeof(ctx->header.magic)); gguf_bwrite_el(buf, &ctx->header.version, sizeof(ctx->header.version)); gguf_bwrite_el(buf, &ctx->header.n_tensors, sizeof(ctx->header.n_tensors)); gguf_bwrite_el(buf, &ctx->header.n_kv, sizeof(ctx->header.n_kv)); // write key-value pairs for (uint32_t i = 0; i < ctx->header.n_kv; ++i) { struct gguf_kv * kv = &ctx->kv[i]; gguf_bwrite_str(buf, &kv->key); gguf_bwrite_el (buf, &kv->type, sizeof(kv->type)); switch (kv->type) { case GGUF_TYPE_UINT8: gguf_bwrite_el( buf, &kv->value.uint8, sizeof(kv->value.uint8) ); break; case GGUF_TYPE_INT8: gguf_bwrite_el (buf, &kv->value.int8, sizeof(kv->value.int8) ); break; case GGUF_TYPE_UINT16: gguf_bwrite_el (buf, &kv->value.uint16, sizeof(kv->value.uint16) ); break; case GGUF_TYPE_INT16: gguf_bwrite_el (buf, &kv->value.int16, sizeof(kv->value.int16) ); break; case GGUF_TYPE_UINT32: gguf_bwrite_el (buf, &kv->value.uint32, sizeof(kv->value.uint32) ); break; case GGUF_TYPE_INT32: gguf_bwrite_el (buf, &kv->value.int32, sizeof(kv->value.int32) ); break; case GGUF_TYPE_FLOAT32: gguf_bwrite_el (buf, &kv->value.float32, sizeof(kv->value.float32)); break; case GGUF_TYPE_UINT64: gguf_bwrite_el (buf, &kv->value.uint64, sizeof(kv->value.uint64) ); break; case GGUF_TYPE_INT64: gguf_bwrite_el (buf, &kv->value.int64, sizeof(kv->value.int64) ); break; case GGUF_TYPE_FLOAT64: gguf_bwrite_el (buf, &kv->value.float64, sizeof(kv->value.float64)); break; case GGUF_TYPE_BOOL: gguf_bwrite_el (buf, &kv->value.bool_, sizeof(kv->value.bool_) ); break; case GGUF_TYPE_STRING: gguf_bwrite_str(buf, &kv->value.str ); break; case GGUF_TYPE_ARRAY: { gguf_bwrite_el(buf, &kv->value.arr.type, sizeof(kv->value.arr.type)); gguf_bwrite_el(buf, &kv->value.arr.n, sizeof(kv->value.arr.n) ); switch (kv->value.arr.type) { case GGUF_TYPE_UINT8: case GGUF_TYPE_INT8: case GGUF_TYPE_UINT16: case GGUF_TYPE_INT16: case GGUF_TYPE_UINT32: case GGUF_TYPE_INT32: case GGUF_TYPE_FLOAT32: case GGUF_TYPE_UINT64: case GGUF_TYPE_INT64: case GGUF_TYPE_FLOAT64: case GGUF_TYPE_BOOL: { gguf_bwrite_el(buf, kv->value.arr.data, kv->value.arr.n * gguf_type_size(kv->value.arr.type)); } break; case GGUF_TYPE_STRING: { for (uint32_t j = 0; j < kv->value.arr.n; ++j) { gguf_bwrite_str(buf, &((struct gguf_str *) kv->value.arr.data)[j]); } } break; case GGUF_TYPE_ARRAY: default: GGML_ASSERT(false && "invalid type"); break; } } break; default: GGML_ASSERT(false && "invalid type"); } } // write tensor infos for (uint32_t i = 0; i < ctx->header.n_tensors; ++i) { struct gguf_tensor_info * info = &ctx->infos[i]; gguf_bwrite_str(buf, &info->name); gguf_bwrite_el (buf, &info->n_dims, sizeof(info->n_dims)); for (uint32_t j = 0; j < info->n_dims; ++j) { gguf_bwrite_el(buf, &info->ne[j], sizeof(info->ne[j])); } gguf_bwrite_el(buf, &info->type, sizeof(info->type)); gguf_bwrite_el(buf, &info->offset, sizeof(info->offset)); } // we require the data section to be aligned, so take into account any padding { const size_t offset = buf->offset; const size_t offset_pad = GGML_PAD(offset, ctx->alignment); if (offset_pad != offset) { uint8_t pad = 0; for (size_t i = 0; i < offset_pad - offset; ++i) { gguf_bwrite_el(buf, &pad, sizeof(pad)); } } } if (only_meta) { return; } size_t offset = 0; // write tensor data for (uint32_t i = 0; i < ctx->header.n_tensors; ++i) { struct gguf_tensor_info * info = &ctx->infos[i]; const size_t size = info->size; const size_t size_pad = GGML_PAD(size, ctx->alignment); gguf_bwrite_el(buf, info->data, size); if (size_pad != size) { uint8_t pad = 0; for (size_t j = 0; j < size_pad - size; ++j) { gguf_bwrite_el(buf, &pad, sizeof(pad)); } } GGML_ASSERT(offset == info->offset); offset += size_pad; } } void gguf_write_to_file(const struct gguf_context * ctx, const char * fname, bool only_meta) { FILE * file = ggml_fopen(fname, "wb"); if (!file) { GGML_ASSERT(false && "failed to open file for writing"); } struct gguf_buf buf = gguf_buf_init(16*1024); gguf_write_to_buf(ctx, &buf, only_meta); fwrite(buf.data, 1, buf.offset, file); gguf_buf_free(buf); fclose(file); } size_t gguf_get_meta_size(const struct gguf_context * ctx) { // no allocs - only compute size struct gguf_buf buf = gguf_buf_init(0); gguf_write_to_buf(ctx, &buf, true); return buf.offset; } void gguf_get_meta_data(const struct gguf_context * ctx, void * data) { struct gguf_buf buf = gguf_buf_init(16*1024); gguf_write_to_buf(ctx, &buf, true); memcpy(data, buf.data, buf.offset); gguf_buf_free(buf); } //////////////////////////////////////////////////////////////////////////////// int ggml_cpu_has_avx(void) { #if defined(__AVX__) return 1; #else return 0; #endif } int ggml_cpu_has_avx_vnni(void) { #if defined(__AVXVNNI__) return 1; #else return 0; #endif } int ggml_cpu_has_avx2(void) { #if defined(__AVX2__) return 1; #else return 0; #endif } int ggml_cpu_has_avx512(void) { #if defined(__AVX512F__) return 1; #else return 0; #endif } int ggml_cpu_has_avx512_vbmi(void) { #if defined(__AVX512VBMI__) return 1; #else return 0; #endif } int ggml_cpu_has_avx512_vnni(void) { #if defined(__AVX512VNNI__) return 1; #else return 0; #endif } int ggml_cpu_has_avx512_bf16(void) { #if defined(__AVX512BF16__) return 1; #else return 0; #endif } int ggml_cpu_has_fma(void) { #if defined(__FMA__) return 1; #else return 0; #endif } int ggml_cpu_has_neon(void) { #if defined(__ARM_NEON) return 1; #else return 0; #endif } int ggml_cpu_has_arm_fma(void) { #if defined(__ARM_FEATURE_FMA) return 1; #else return 0; #endif } int ggml_cpu_has_metal(void) { #if defined(GGML_USE_METAL) return 1; #else return 0; #endif } int ggml_cpu_has_f16c(void) { #if defined(__F16C__) return 1; #else return 0; #endif } int ggml_cpu_has_fp16_va(void) { #if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) return 1; #else return 0; #endif } int ggml_cpu_has_wasm_simd(void) { #if defined(__wasm_simd128__) return 1; #else return 0; #endif } int ggml_cpu_has_blas(void) { #if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CUDA) || defined(GGML_USE_VULKAN) || defined(GGML_USE_CLBLAST) || defined(GGML_USE_SYCL) return 1; #else return 0; #endif } int ggml_cpu_has_cuda(void) { #if defined(GGML_USE_CUDA) return 1; #else return 0; #endif } int ggml_cpu_has_clblast(void) { #if defined(GGML_USE_CLBLAST) return 1; #else return 0; #endif } int ggml_cpu_has_vulkan(void) { #if defined(GGML_USE_VULKAN) return 1; #else return 0; #endif } int ggml_cpu_has_kompute(void) { #if defined(GGML_USE_KOMPUTE) return 1; #else return 0; #endif } int ggml_cpu_has_sycl(void) { #if defined(GGML_USE_SYCL) return 1; #else return 0; #endif } int ggml_cpu_has_gpublas(void) { return ggml_cpu_has_cuda() || ggml_cpu_has_clblast() || ggml_cpu_has_vulkan() || ggml_cpu_has_kompute() || ggml_cpu_has_sycl(); } int ggml_cpu_has_sse3(void) { #if defined(__SSE3__) return 1; #else return 0; #endif } int ggml_cpu_has_ssse3(void) { #if defined(__SSSE3__) return 1; #else return 0; #endif } int ggml_cpu_has_vsx(void) { #if defined(__POWER9_VECTOR__) return 1; #else return 0; #endif } int ggml_cpu_has_matmul_int8(void) { #if defined(__ARM_FEATURE_MATMUL_INT8) return 1; #else return 0; #endif } ////////////////////////////////////////////////////////////////////////////////