From acec73ab6e6bf8864c7dc4b5a7c8e861767e454e Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sat, 29 Apr 2023 12:31:52 +0300 Subject: [PATCH] ggml : sync latest ggml + llama.cpp updates (quantization) --- ggml-cuda.cu | 365 +++++ ggml-cuda.h | 54 + ggml.c | 3861 +++++++++++++++++++++++++++++++++++++------------- ggml.h | 1309 +++++++++-------- 4 files changed, 3980 insertions(+), 1609 deletions(-) create mode 100644 ggml-cuda.cu create mode 100644 ggml-cuda.h diff --git a/ggml-cuda.cu b/ggml-cuda.cu new file mode 100644 index 0000000..5a2701c --- /dev/null +++ b/ggml-cuda.cu @@ -0,0 +1,365 @@ +#include +#include +#include +#include +#include "ggml-cuda.h" + +typedef uint16_t ggml_fp16_t; +static_assert(sizeof(__half) == sizeof(ggml_fp16_t), "wrong fp16 size"); + +#define QK4_0 32 +typedef struct { + float d; // delta + uint8_t qs[QK4_0 / 2]; // nibbles / quants +} block_q4_0; +static_assert(sizeof(block_q4_0) == sizeof(float) + QK4_0 / 2, "wrong q4_0 block size/padding"); + +#define QK4_1 32 +typedef struct { + float d; // delta + float m; // min + uint8_t qs[QK4_1 / 2]; // nibbles / quants +} block_q4_1; +static_assert(sizeof(block_q4_1) == sizeof(float) * 2 + QK4_1 / 2, "wrong q4_1 block size/padding"); + +#define QK4_2 16 +typedef struct { + __half d; // delta + uint8_t qs[QK4_2 / 2]; // nibbles / quants +} block_q4_2; +static_assert(sizeof(block_q4_2) == sizeof(ggml_fp16_t) + QK4_2 / 2, "wrong q4_2 block size/padding"); + +#define QK5_0 32 +typedef struct { + __half d; // delta + uint8_t qh[4]; // 5-th bit of quants + uint8_t qs[QK5_0 / 2]; // nibbles / quants +} block_q5_0; +static_assert(sizeof(block_q5_0) == sizeof(ggml_fp16_t) + sizeof(uint32_t) + QK5_0 / 2, "wrong q5_0 block size/padding"); + +#define QK5_1 32 +typedef struct { + __half d; // delta + __half m; // min + uint32_t qh; // 5-th bit of quants + uint8_t qs[QK5_1 / 2]; // nibbles / quants +} block_q5_1; +static_assert(sizeof(block_q5_1) == 2 * sizeof(ggml_fp16_t) + sizeof(uint32_t) + QK5_1 / 2, "wrong q5_1 block size/padding"); + +#define QK8_0 32 +typedef struct { + float d; // delta + int8_t qs[QK8_0]; // quants +} block_q8_0; +static_assert(sizeof(block_q8_0) == sizeof(float) + QK8_0, "wrong q8_0 block size/padding"); + +static __global__ void dequantize_block_q4_0(const void * vx, float * y) { + const block_q4_0 * x = (const block_q4_0 *) vx; + + const int i = blockIdx.x; + + const float d = x[i].d; + + const uint8_t * pp = x[i].qs; + + for (int l = 0; l < QK4_0; l += 2) { + const uint8_t vi = pp[l/2]; + + const int8_t vi0 = vi & 0xf; + const int8_t vi1 = vi >> 4; + + const float v0 = (vi0 - 8)*d; + const float v1 = (vi1 - 8)*d; + + y[i*QK4_0 + l + 0] = v0; + y[i*QK4_0 + l + 1] = v1; + } +} + +static __global__ void dequantize_block_q4_1(const void * vx, float * y) { + const block_q4_1 * x = (const block_q4_1 *) vx; + + const int i = blockIdx.x; + + const float d = x[i].d; + const float m = x[i].m; + + const uint8_t * pp = x[i].qs; + + for (int l = 0; l < QK4_1; l += 2) { + const uint8_t vi = pp[l/2]; + + const int8_t vi0 = vi & 0xf; + const int8_t vi1 = vi >> 4; + + const float v0 = vi0*d + m; + const float v1 = vi1*d + m; + + y[i*QK4_1 + l + 0] = v0; + y[i*QK4_1 + l + 1] = v1; + } +} + +static __global__ void dequantize_block_q4_2(const void * vx, float * y) { + const block_q4_2 * x = (const block_q4_2 *) vx; + + const int i = blockIdx.x; + + const float d = x[i].d; + + const uint8_t * pp = x[i].qs; + + for (int l = 0; l < QK4_2; l += 2) { + const uint8_t vi = pp[l/2]; + + const int8_t vi0 = vi & 0xf; + const int8_t vi1 = vi >> 4; + + const float v0 = (vi0 - 8)*d; + const float v1 = (vi1 - 8)*d; + + y[i*QK4_2 + l + 0] = v0; + y[i*QK4_2 + l + 1] = v1; + } +} + +static __global__ void dequantize_block_q5_0(const void * vx, float * y) { + const block_q5_0 * x = (const block_q5_0 *) vx; + + const int i = blockIdx.x; + + const float d = x[i].d; + + const uint8_t * pp = x[i].qs; + + uint32_t qh; + memcpy(&qh, x[i].qh, sizeof(qh)); + + for (int l = 0; l < QK5_0; l += 2) { + const uint8_t vi = pp[l/2]; + + const int8_t vh0 = ((qh & (1 << (l + 0))) >> (l + 0)) << 4; + const int8_t vh1 = ((qh & (1 << (l + 1))) >> (l + 1)) << 4; + + const int8_t vi0 = ((vi & 0xf) | vh0); + const int8_t vi1 = ((vi >> 4) | vh1); + + const float v0 = (vi0 - 16)*d; + const float v1 = (vi1 - 16)*d; + + y[i*QK5_0 + l + 0] = v0; + y[i*QK5_0 + l + 1] = v1; + } +} + +static __global__ void dequantize_block_q5_1(const void * vx, float * y) { + const block_q5_1 * x = (const block_q5_1 *) vx; + + const int i = blockIdx.x; + + const float d = x[i].d; + const float m = x[i].m; + + const uint8_t * pp = x[i].qs; + + const uint32_t qh = x[i].qh; + + for (int l = 0; l < QK5_1; l += 2) { + const uint8_t vi = pp[l/2]; + + const int8_t vh0 = ((qh & (1 << (l + 0))) >> (l + 0)) << 4; + const int8_t vh1 = ((qh & (1 << (l + 1))) >> (l + 1)) << 4; + + const int8_t vi0 = (vi & 0xf) | vh0; + const int8_t vi1 = (vi >> 4) | vh1; + + const float v0 = vi0*d + m; + const float v1 = vi1*d + m; + + y[i*QK5_1 + l + 0] = v0; + y[i*QK5_1 + l + 1] = v1; + } +} + +static __global__ void dequantize_block_q8_0(const void * vx, float * y) { + const block_q8_0 * x = (const block_q8_0 *) vx; + + const int i = blockIdx.x; + + const float d = x[i].d; + + const int8_t * pp = x[i].qs; + + for (int l = 0; l < QK8_0; l++) { + const int8_t vi = pp[l]; + + y[i*QK8_0 + l] = vi*d; + } +} + +void dequantize_row_q4_0_cuda(const void * vx, float * y, int k, cudaStream_t stream) { + const int nb = k / QK4_0; + dequantize_block_q4_0<<>>(vx, y); +} + +void dequantize_row_q4_1_cuda(const void * vx, float * y, int k, cudaStream_t stream) { + const int nb = k / QK4_1; + dequantize_block_q4_1<<>>(vx, y); +} + +void dequantize_row_q4_2_cuda(const void * vx, float * y, int k, cudaStream_t stream) { + const int nb = k / QK4_2; + dequantize_block_q4_2<<>>(vx, y); +} + +void dequantize_row_q5_0_cuda(const void * vx, float * y, int k, cudaStream_t stream) { + const int nb = k / QK5_0; + dequantize_block_q5_0<<>>(vx, y); +} + +void dequantize_row_q5_1_cuda(const void * vx, float * y, int k, cudaStream_t stream) { + const int nb = k / QK5_1; + dequantize_block_q5_1<<>>(vx, y); +} + +void dequantize_row_q8_0_cuda(const void * vx, float * y, int k, cudaStream_t stream) { + const int nb = k / QK8_0; + dequantize_block_q8_0<<>>(vx, y); +} + +dequantize_row_q_cuda_t ggml_get_dequantize_row_q_cuda(ggml_type type) { + switch (type) { + case GGML_TYPE_Q4_0: + return dequantize_row_q4_0_cuda; + case GGML_TYPE_Q4_1: + return dequantize_row_q4_1_cuda; + case GGML_TYPE_Q4_2: + return dequantize_row_q4_2_cuda; + case GGML_TYPE_Q5_0: + return dequantize_row_q5_0_cuda; + case GGML_TYPE_Q5_1: + return dequantize_row_q5_1_cuda; + case GGML_TYPE_Q8_0: + return dequantize_row_q8_0_cuda; + default: + return nullptr; + } +} + +// buffer pool for cuda +#define MAX_CUDA_BUFFERS 16 + +struct scoped_spin_lock { + std::atomic_flag& lock; + scoped_spin_lock(std::atomic_flag& lock) : lock(lock) { + while (lock.test_and_set(std::memory_order_acquire)) { + ; // spin + } + } + ~scoped_spin_lock() { + lock.clear(std::memory_order_release); + } + scoped_spin_lock(const scoped_spin_lock&) = delete; + scoped_spin_lock& operator=(const scoped_spin_lock&) = delete; +}; + +struct cuda_buffer { + void * ptr = nullptr; + size_t size = 0; +}; + +static cuda_buffer g_cuda_buffer_pool[MAX_CUDA_BUFFERS]; +static std::atomic_flag g_cuda_pool_lock = ATOMIC_FLAG_INIT; + +void * ggml_cuda_pool_malloc(size_t size, size_t * actual_size) { + scoped_spin_lock lock(g_cuda_pool_lock); + + for (int i = 0; i < MAX_CUDA_BUFFERS; ++i) { + cuda_buffer& b = g_cuda_buffer_pool[i]; + if (b.size >= size && b.ptr != nullptr) { + void * ptr = b.ptr; + *actual_size = b.size; + b.ptr = nullptr; + b.size = 0; + return ptr; + } + } + void * ptr; + CUDA_CHECK(cudaMalloc((void **) &ptr, size)); + *actual_size = size; + return ptr; +} + +void ggml_cuda_pool_free(void * ptr, size_t size) { + scoped_spin_lock lock(g_cuda_pool_lock); + + for (int i = 0; i < MAX_CUDA_BUFFERS; ++i) { + cuda_buffer& b = g_cuda_buffer_pool[i]; + if (b.ptr == nullptr) { + b.ptr = ptr; + b.size = size; + return; + } + } + fprintf(stderr, "WARNING: cuda buffer pool full, increase MAX_CUDA_BUFFERS\n"); + CUDA_CHECK(cudaFree(ptr)); +} + +cublasHandle_t g_cublasH = nullptr; +cudaStream_t g_cudaStream = nullptr; +cudaStream_t g_cudaStream2 = nullptr; +cudaEvent_t g_cudaEvent = nullptr; + +void ggml_init_cublas() { + if (g_cublasH == nullptr) { + // create cublas handle, bind a stream + CUBLAS_CHECK(cublasCreate(&g_cublasH)); + CUDA_CHECK(cudaStreamCreateWithFlags(&g_cudaStream, cudaStreamNonBlocking)); + CUBLAS_CHECK(cublasSetStream(g_cublasH, g_cudaStream)); + + // create additional stream and event for synchronization + CUDA_CHECK(cudaStreamCreateWithFlags(&g_cudaStream2, cudaStreamNonBlocking)); + CUDA_CHECK(cudaEventCreateWithFlags(&g_cudaEvent, cudaEventDisableTiming)); + + // configure logging to stdout + // CUBLAS_CHECK(cublasLoggerConfigure(1, 1, 0, NULL)); + } +} + +cudaError_t ggml_cuda_h2d_tensor_2d(void * dst, const struct ggml_tensor * src, uint64_t i3, uint64_t i2, cudaStream_t stream) { + const uint64_t ne0 = src->ne[0]; + const uint64_t ne1 = src->ne[1]; + const uint64_t nb0 = src->nb[0]; + const uint64_t nb1 = src->nb[1]; + const uint64_t nb2 = src->nb[2]; + const uint64_t nb3 = src->nb[3]; + const enum ggml_type type = src->type; + const size_t ts = ggml_type_size(type); + const size_t bs = ggml_blck_size(type); + + const void * x = (const void *) ((const char *) src->data + i2*nb2 + i3*nb3); + if (nb0 == ts && nb1 == ts*ne0/bs) { + return cudaMemcpyAsync(dst, x, ne1*nb1, cudaMemcpyHostToDevice, stream); + } else if (nb0 == ts) { + return cudaMemcpy2DAsync(dst, ts*ne0/bs, x, nb1, ts*ne0/bs, ne1, cudaMemcpyHostToDevice, stream); + } else { + for (uint64_t i1 = 0; i1 < ne1; i1++) { + const void * rx = (const void *) ((const char *) x + i1*nb1); + void * rd = (void *) ((char *) dst + i1*ts*ne0/bs); + // pretend the row is a matrix with cols=1 + cudaError_t r = cudaMemcpy2DAsync(rd, ts/bs, rx, nb0, ts/bs, ne0, cudaMemcpyHostToDevice, stream); + if (r != cudaSuccess) return r; + } + return cudaSuccess; + } +} + +void * ggml_cuda_host_malloc(size_t size) { + void * ptr; + CUDA_CHECK(cudaMallocHost((void **) &ptr, size)); + return ptr; +} + +void ggml_cuda_host_free(void * ptr) { + CUDA_CHECK(cudaFreeHost(ptr)); +} diff --git a/ggml-cuda.h b/ggml-cuda.h new file mode 100644 index 0000000..36782d9 --- /dev/null +++ b/ggml-cuda.h @@ -0,0 +1,54 @@ +#include +#include +#include "ggml.h" + +#ifdef __cplusplus +extern "C" { +#endif + +#define CUDA_CHECK(err) \ + do { \ + cudaError_t err_ = (err); \ + if (err_ != cudaSuccess) { \ + fprintf(stderr, "CUDA error %d at %s:%d: %s\n", err_, __FILE__, __LINE__, \ + cudaGetErrorString(err_)); \ + exit(1); \ + } \ + } while (0) + +#define CUBLAS_CHECK(err) \ + do { \ + cublasStatus_t err_ = (err); \ + if (err_ != CUBLAS_STATUS_SUCCESS) { \ + fprintf(stderr, "cuBLAS error %d at %s:%d\n", err_, __FILE__, __LINE__); \ + exit(1); \ + } \ + } while (0) + +extern cublasHandle_t g_cublasH; +extern cudaStream_t g_cudaStream; +extern cudaStream_t g_cudaStream2; +extern cudaEvent_t g_cudaEvent; + +void ggml_init_cublas(void); +void * ggml_cuda_host_malloc(size_t size); +void ggml_cuda_host_free(void * ptr); + +void * ggml_cuda_pool_malloc(size_t size, size_t * actual_size); +void ggml_cuda_pool_free(void * ptr, size_t size); + +void dequantize_row_q4_0_cuda(const void * vx, float * y, int k, cudaStream_t stream); +void dequantize_row_q4_1_cuda(const void * vx, float * y, int k, cudaStream_t stream); +void dequantize_row_q4_2_cuda(const void * vx, float * y, int k, cudaStream_t stream); +void dequantize_row_q5_0_cuda(const void * vx, float * y, int k, cudaStream_t stream); +void dequantize_row_q5_1_cuda(const void * vx, float * y, int k, cudaStream_t stream); +void dequantize_row_q8_0_cuda(const void * vx, float * y, int k, cudaStream_t stream); + +cudaError_t ggml_cuda_h2d_tensor_2d(void * dst, const struct ggml_tensor * src, uint64_t i3, uint64_t i2, cudaStream_t stream); + +typedef void (*dequantize_row_q_cuda_t)(const void * x, float * y, int k, cudaStream_t stream); +dequantize_row_q_cuda_t ggml_get_dequantize_row_q_cuda(enum ggml_type type); + +#ifdef __cplusplus +} +#endif diff --git a/ggml.c b/ggml.c index ca3b7b9..2ec0c0b 100644 --- a/ggml.c +++ b/ggml.c @@ -19,6 +19,7 @@ #include #include #include +#include // if C99 - static_assert is noop // ref: https://stackoverflow.com/a/53923785/4039976 @@ -142,10 +143,14 @@ inline static void* ggml_aligned_malloc(size_t size) { } \ } while (0) -#ifdef GGML_USE_ACCELERATE +#if defined(GGML_USE_ACCELERATE) #include -#elif GGML_USE_OPENBLAS +#elif defined(GGML_USE_OPENBLAS) #include +#elif defined(GGML_USE_CUBLAS) +#include "ggml-cuda.h" +#elif defined(GGML_USE_CLBLAST) +#include "ggml-opencl.h" #endif #undef MIN @@ -325,6 +330,20 @@ static ggml_fp16_t table_exp_f16[1 << 16]; // precomputed f32 table for f16 (256 KB) static float table_f32_f16[1 << 16]; +#if defined(__ARM_NEON) +#define B1(c,s,n) 0x ## n ## c , 0x ## n ## s +#define B2(c,s,n) B1(c,s,n ## c), B1(c,s,n ## s) +#define B3(c,s,n) B2(c,s,n ## c), B2(c,s,n ## s) +#define B4(c,s,n) B3(c,s,n ## c), B3(c,s,n ## s) +#define B5(c,s,n) B4(c,s,n ## c), B4(c,s,n ## s) +#define B6(c,s,n) B5(c,s,n ## c), B5(c,s,n ## s) +#define B7(c,s,n) B6(c,s,n ## c), B6(c,s,n ## s) +#define B8(c,s ) B7(c,s, c), B7(c,s, s) + +// precomputed tables for expanding 8bits to 8 bytes (shl 4) +static const uint64_t table_b2b_u[1 << 8] = { B8(00, 10) }; +#endif + // On ARM NEON, it's quicker to directly convert x -> x instead of calling into ggml_lookup_fp16_to_fp32, // so we define GGML_FP16_TO_FP32 and GGML_FP32_TO_FP16 elsewhere for NEON. // This is also true for POWER9. @@ -427,14 +446,69 @@ static const size_t CACHE_LINE_SIZE_F32 = CACHE_LINE_SIZE/sizeof(float); // quantization // -#define QK 32 +#if __AVX__ || __AVX2__ || __AVX512F__ +// Unpack 16 4-bit fields into 16 bytes +// The output vector contains 16 bytes, each one in [ 0 .. 15 ] interval +static inline __m128i bytes_from_nibbles_16(const uint8_t * rsi) +{ + // Load 8 bytes from memory + __m128i tmp = _mm_loadl_epi64( ( const __m128i* )rsi ); + + // Expand bytes into uint16_t values + __m128i bytes = _mm_cvtepu8_epi16( tmp ); + + // Unpack values into individual bytes + const __m128i lowMask = _mm_set1_epi8( 0xF ); + __m128i high = _mm_andnot_si128( lowMask, bytes ); + __m128i low = _mm_and_si128( lowMask, bytes ); + high = _mm_slli_epi16( high, 4 ); + bytes = _mm_or_si128( low, high ); + return bytes; +} + +// horizontally add 8 floats +static inline float hsum_float_8(const __m256 x) { + __m128 res = _mm256_extractf128_ps(x, 1); + res = _mm_add_ps(res, _mm256_castps256_ps128(x)); + res = _mm_add_ps(res, _mm_movehl_ps(res, res)); + res = _mm_add_ss(res, _mm_movehdup_ps(res)); + return _mm_cvtss_f32(res); +} + +// horizontally add 8 int32_t +static inline int hsum_i32_8(const __m256i a) { + const __m128i sum128 = _mm_add_epi32(_mm256_castsi256_si128(a), _mm256_extractf128_si256(a, 1)); + const __m128i hi64 = _mm_unpackhi_epi64(sum128, sum128); + const __m128i sum64 = _mm_add_epi32(hi64, sum128); + const __m128i hi32 = _mm_shuffle_epi32(sum64, _MM_SHUFFLE(2, 3, 0, 1)); + return _mm_cvtsi128_si32(_mm_add_epi32(sum64, hi32)); +} + +// horizontally add 4 int32_t +static inline int hsum_i32_4(const __m128i a) { + const __m128i hi64 = _mm_unpackhi_epi64(a, a); + const __m128i sum64 = _mm_add_epi32(hi64, a); + const __m128i hi32 = _mm_shuffle_epi32(sum64, _MM_SHUFFLE(2, 3, 0, 1)); + return _mm_cvtsi128_si32(_mm_add_epi32(sum64, hi32)); +} -// AVX routines provided by GH user Const-me -// ref: https://github.com/ggerganov/ggml/pull/27#issuecomment-1464934600 #if __AVX2__ || __AVX512F__ +// spread 32 bits to 32 bytes { 0x00, 0xFF } +static inline __m256i bytes_from_bits_32(const uint8_t * x) { + uint32_t x32; + memcpy(&x32, x, sizeof(uint32_t)); + const __m256i shuf_mask = _mm256_set_epi64x( + 0x0303030303030303, 0x0202020202020202, + 0x0101010101010101, 0x0000000000000000); + __m256i bytes = _mm256_shuffle_epi8(_mm256_set1_epi32(x32), shuf_mask); + const __m256i bit_mask = _mm256_set1_epi64x(0x7fbfdfeff7fbfdfe); + bytes = _mm256_or_si256(bytes, bit_mask); + return _mm256_cmpeq_epi8(bytes, _mm256_set1_epi64x(-1)); +} + // Unpack 32 4-bit fields into 32 bytes // The output vector contains 32 bytes, each one in [ 0 .. 15 ] interval -static inline __m256i bytesFromNibbles( const uint8_t* rsi ) +static inline __m256i bytes_from_nibbles_32(const uint8_t * rsi) { // Load 16 bytes from memory __m128i tmp = _mm_loadu_si128( ( const __m128i* )rsi ); @@ -451,9 +525,38 @@ static inline __m256i bytesFromNibbles( const uint8_t* rsi ) return bytes; } +// add int16_t pairwise and return as float vector +static inline __m256 sum_i16_pairs_float(const __m256i x) { + const __m256i ones = _mm256_set1_epi16(1); + const __m256i summed_pairs = _mm256_madd_epi16(ones, x); + return _mm256_cvtepi32_ps(summed_pairs); +} + +// multiply int8_t, add results pairwise twice and return as float vector +static inline __m256 mul_sum_i8_pairs_float(const __m256i x, const __m256i y) { + // Get absolute values of x vectors + const __m256i ax = _mm256_sign_epi8(x, x); + // Sign the values of the y vectors + const __m256i sy = _mm256_sign_epi8(y, x); +#if __AVXVNNI__ + const __m256i zero = _mm256_setzero_si256(); + const __m256i summed_pairs = _mm256_dpbusd_epi32(zero, ax, sy); + return _mm256_cvtepi32_ps(summed_pairs); +#else + // Perform multiplication and create 16-bit values + const __m256i dot = _mm256_maddubs_epi16(ax, sy); + return sum_i16_pairs_float(dot); +#endif +} + static inline __m128i packNibbles( __m256i bytes ) { // Move bits within 16-bit lanes from 0000_abcd_0000_efgh into 0000_0000_abcd_efgh +#if __AVX512F__ + const __m256i bytes_srli_4 = _mm256_srli_epi16(bytes, 4); // 0000_0000_abcd_0000 + bytes = _mm256_or_si256(bytes, bytes_srli_4); // 0000_abcd_abcd_efgh + return _mm256_cvtepi16_epi8(bytes); // abcd_efgh +#else const __m256i lowByte = _mm256_set1_epi16( 0xFF ); __m256i high = _mm256_andnot_si256( lowByte, bytes ); __m256i low = _mm256_and_si256( lowByte, bytes ); @@ -464,25 +567,9 @@ static inline __m128i packNibbles( __m256i bytes ) __m128i r0 = _mm256_castsi256_si128( bytes ); __m128i r1 = _mm256_extracti128_si256( bytes, 1 ); return _mm_packus_epi16( r0, r1 ); +#endif } -#elif __AVX__ -static inline __m128i bytesFromNibbles( const uint8_t* rsi ) -{ - // Load 8 bytes from memory - __m128i tmp = _mm_loadu_si64( ( const __m128i* )rsi ); - - // Expand bytes into uint16_t values - __m128i bytes = _mm_cvtepu8_epi16( tmp ); - - // Unpack values into individual bytes - const __m128i lowMask = _mm_set1_epi8( 0xF ); - __m128i high = _mm_andnot_si128( lowMask, bytes ); - __m128i low = _mm_and_si128( lowMask, bytes ); - high = _mm_slli_epi16( high, 4 ); - bytes = _mm_or_si128( low, high ); - return bytes; -} - +#else static inline __m128i packNibbles( __m128i bytes1, __m128i bytes2 ) { // Move bits within 16-bit lanes from 0000_abcd_0000_efgh into 0000_0000_abcd_efgh @@ -499,6 +586,7 @@ static inline __m128i packNibbles( __m128i bytes1, __m128i bytes2 ) return _mm_packus_epi16( bytes1, bytes2); } #endif +#endif // __AVX__ || __AVX2__ || __AVX512F__ #if __ARM_NEON @@ -516,6 +604,18 @@ inline static uint16_t vaddvq_u8(uint8x16_t v) { (uint16_t)vgetq_lane_u8(v, 14) + (uint16_t)vgetq_lane_u8(v, 15); } +inline static int16_t vaddvq_s8(int8x16_t v) { + return + (int16_t)vgetq_lane_s8(v, 0) + (int16_t)vgetq_lane_s8(v, 1) + + (int16_t)vgetq_lane_s8(v, 2) + (int16_t)vgetq_lane_s8(v, 3) + + (int16_t)vgetq_lane_s8(v, 4) + (int16_t)vgetq_lane_s8(v, 5) + + (int16_t)vgetq_lane_s8(v, 6) + (int16_t)vgetq_lane_s8(v, 7) + + (int16_t)vgetq_lane_s8(v, 8) + (int16_t)vgetq_lane_s8(v, 9) + + (int16_t)vgetq_lane_s8(v, 10) + (int16_t)vgetq_lane_s8(v, 11) + + (int16_t)vgetq_lane_s8(v, 12) + (int16_t)vgetq_lane_s8(v, 13) + + (int16_t)vgetq_lane_s8(v, 14) + (int16_t)vgetq_lane_s8(v, 15); +} + inline static int32_t vaddvq_s16(int16x8_t v) { return (int32_t)vgetq_lane_s16(v, 0) + (int32_t)vgetq_lane_s16(v, 1) + @@ -571,51 +671,92 @@ uint8x8_t vzip2_u8(uint8x8_t a, uint8x8_t b) { #endif #endif -// method 5 -// blocks of QK elements -// represented with a single float (delta) and QK/2 8-bit ints (i.e QK 4-bit signed integer factors) -typedef struct { - float d; // delta - uint8_t qs[QK / 2]; // nibbles / quants -} block_q4_0; -static_assert(sizeof(block_q4_0) == sizeof(float) + QK / 2, "wrong q4_0 block size/padding"); -// method 4 -// blocks of QK elements -// represented with 2 floats (delta + min) and QK/2 8-bit ints (i.e QK 4-bit unsigned integer factors) +#define QK4_0 32 typedef struct { - float d; - float m; - uint8_t qs[QK / 2]; // nibbles / quants + float d; // delta + uint8_t qs[QK4_0 / 2]; // nibbles / quants +} block_q4_0; +static_assert(sizeof(block_q4_0) == sizeof(float) + QK4_0 / 2, "wrong q4_0 block size/padding"); + +#define QK4_1 32 +typedef struct { + float d; // delta + float m; // min + uint8_t qs[QK4_1 / 2]; // nibbles / quants } block_q4_1; -static_assert(sizeof(block_q4_1) == sizeof(float) * 2 + QK / 2, "wrong q4_1 block size/padding"); +static_assert(sizeof(block_q4_1) == 2 * sizeof(float) + QK4_1 / 2, "wrong q4_1 block size/padding"); + +#define QK4_2 16 +typedef struct { + ggml_fp16_t d; // delta + uint8_t qs[QK4_2 / 2]; // nibbles / quants +} block_q4_2; +static_assert(sizeof(block_q4_2) == sizeof(ggml_fp16_t) + QK4_2 / 2, "wrong q4_2 block size/padding"); + +#define QK5_0 32 +typedef struct { + ggml_fp16_t d; // delta + uint8_t qh[4]; // 5-th bit of quants + uint8_t qs[QK5_0 / 2]; // nibbles / quants +} block_q5_0; +static_assert(sizeof(block_q5_0) == sizeof(ggml_fp16_t) + sizeof(uint32_t) + QK5_0 / 2, "wrong q5_0 block size/padding"); + +#define QK5_1 32 +typedef struct { + ggml_fp16_t d; // delta + ggml_fp16_t m; // min + uint8_t qh[4]; // 5-th bit of quants + uint8_t qs[QK5_1 / 2]; // nibbles / quants +} block_q5_1; +static_assert(sizeof(block_q5_1) == 2 * sizeof(ggml_fp16_t) + sizeof(uint32_t) + QK5_1 / 2, "wrong q5_1 block size/padding"); + +#define QK8_0 32 +typedef struct { + float d; // delta + int8_t qs[QK8_0]; // quants +} block_q8_0; +static_assert(sizeof(block_q8_0) == sizeof(float) + QK8_0, "wrong q8_0 block size/padding"); + +#define QK8_1 32 +typedef struct { + float d; // delta + float s0; // d * sum(qs[i]) low + float s1; // d * sum(qs[i]) high + int8_t qs[QK8_1]; // quants +} block_q8_1; +static_assert(sizeof(block_q8_1) == 3*sizeof(float) + QK8_1, "wrong q8_1 block size/padding"); // reference implementation for deterministic creation of model files static void quantize_row_q4_0_reference(const float * restrict x, block_q4_0 * restrict y, int k) { - assert(k % QK == 0); - const int nb = k / QK; + assert(k % QK4_0 == 0); + const int nb = k / QK4_0; - uint8_t pp[QK/2]; + uint8_t pp[QK4_0/2]; for (int i = 0; i < nb; i++) { float amax = 0.0f; // absolute max + float max = 0.0f; - for (int l = 0; l < QK; l++) { - const float v = x[i*QK + l]; - amax = MAX(amax, fabsf(v)); + for (int l = 0; l < QK4_0; l++) { + const float v = x[i*QK4_0 + l]; + if (amax < fabsf(v)) { + amax = fabsf(v); + max = v; + } } - const float d = amax / ((1 << 3) - 1); + const float d = max / -8; const float id = d ? 1.0f/d : 0.0f; y[i].d = d; - for (int l = 0; l < QK; l += 2) { - const float v0 = x[i*QK + l + 0]*id; - const float v1 = x[i*QK + l + 1]*id; + for (int l = 0; l < QK4_0; l += 2) { + const float v0 = x[i*QK4_0 + l + 0]*id; + const float v1 = x[i*QK4_0 + l + 1]*id; - const uint8_t vi0 = (int8_t)roundf(v0) + 8; - const uint8_t vi1 = (int8_t)roundf(v1) + 8; + const uint8_t vi0 = MIN(15, (int8_t)roundf(v0) + 8); + const uint8_t vi1 = MIN(15, (int8_t)roundf(v1) + 8); assert(vi0 < 16); assert(vi1 < 16); @@ -628,35 +769,49 @@ static void quantize_row_q4_0_reference(const float * restrict x, block_q4_0 * r } static void quantize_row_q4_0(const float * restrict x, void * restrict vy, int k) { - assert(k % QK == 0); - const int nb = k / QK; + assert(k % QK4_0 == 0); + const int nb = k / QK4_0; block_q4_0 * restrict y = vy; #if defined(__POWER9_VECTOR__) const vector float v85 = vec_splats(8.5f); + const vector signed int v15 = vec_splats(15); for (int i = 0; i < nb; i++) { - float amax = 0.0f; // absolute max + float max = 0.0f; + float min = 0.0f; vector float srcv [8]; - vector float asrcv[8]; - vector float amaxv[8]; + vector float maxv[8]; + vector float minv[8]; for (int l = 0; l < 8; l++) srcv[l] = *(vector float *)(x + i*32 + 4*l); - for (int l = 0; l < 8; l++) asrcv[l] = vec_abs(srcv[l]); + //for (int l = 0; l < 8; l++) asrcv[l] = vec_abs(srcv[l]); - for (int l = 0; l < 4; l++) amaxv[2*l] = vec_max(asrcv[2*l], asrcv[2*l+1]); - //for (int l = 0; l < 2; l++) amaxv[4*l] = vec_max(amaxv[4*l], amaxv[4*l+2]); - amaxv[0] = vec_max(amaxv[0], amaxv[2]); - amaxv[4] = vec_max(amaxv[4], amaxv[6]); - //for (int l = 0; l < 1; l++) amaxv[8*l] = vec_max(amaxv[8*l], amaxv[8*l+4]); - amaxv[0] = vec_max(amaxv[0], amaxv[4]); + for (int l = 0; l < 4; l++) maxv[2*l] = vec_max(asrcv[2*l], asrcv[2*l+1]); + //for (int l = 0; l < 2; l++) maxv[4*l] = vec_max(maxv[4*l], maxv[4*l+2]); + maxv[0] = vec_max(maxv[0], maxv[2]); + maxv[4] = vec_max(maxv[4], maxv[6]); + //for (int l = 0; l < 1; l++) maxv[8*l] = vec_max(maxv[8*l], maxv[8*l+4]); + maxv[0] = vec_max(maxv[0], maxv[4]); - amax = MAX( - MAX(vec_extract(amaxv[0], 0), vec_extract(amaxv[0], 1)), - MAX(vec_extract(amaxv[0], 2), vec_extract(amaxv[0], 3))); + for (int l = 0; l < 4; l++) minv[2*l] = vec_min(asrcv[2*l], asrcv[2*l+1]); + //for (int l = 0; l < 2; l++) minv[4*l] = vec_min(minv[4*l], minv[4*l+2]); + minv[0] = vec_min(minv[0], minv[2]); + minv[4] = vec_min(minv[4], minv[6]); + //for (int l = 0; l < 1; l++) minv[8*l] = vec_min(minv[8*l], minv[8*l+4]); + minv[0] = vec_min(minv[0], minv[4]); - const float d = amax / ((1 << 3) - 1); + + max = MAX( + MAX(vec_extract(maxv[0], 0), vec_extract(maxv[0], 1)), + MAX(vec_extract(maxv[0], 2), vec_extract(maxv[0], 3))); + min = MIN( + MIN(vec_extract(minv[0], 0), vec_extract(minv[0], 1)), + MIN(vec_extract(minv[0], 2), vec_extract(minv[0], 3))); + + const float magnitude = max >= fabsf(min) ? max : min; + const float d = magnitude / -8; const float id = d ? 1.0/d : 0.0; y[i].d = d; @@ -666,27 +821,33 @@ static void quantize_row_q4_0(const float * restrict x, void * restrict vy, int for (int l = 0; l < 8; l++) { const vector float vf = vec_madd(srcv[l], vid, v85); const vector signed int vi = vec_signed(vf); + const vector signed int vc = vec_min(vi, v15); - pb[2*l + 0] = vec_extract(vi, 0) | (vec_extract(vi, 1) << 4); - pb[2*l + 1] = vec_extract(vi, 2) | (vec_extract(vi, 3) << 4); + pb[2*l + 0] = vec_extract(vc, 0) | (vec_extract(vc, 1) << 4); + pb[2*l + 1] = vec_extract(vc, 2) | (vec_extract(vc, 3) << 4); } } #elif __ARM_NEON for (int i = 0; i < nb; i++) { float32x4_t srcv [8]; - float32x4_t asrcv[8]; - float32x4_t amaxv[8]; + float32x4_t maxv[8]; + float32x4_t minv[8]; for (int l = 0; l < 8; l++) srcv[l] = vld1q_f32(x + i*32 + 4*l); - for (int l = 0; l < 8; l++) asrcv[l] = vabsq_f32(srcv[l]); - for (int l = 0; l < 4; l++) amaxv[2*l] = vmaxq_f32(asrcv[2*l], asrcv[2*l+1]); - for (int l = 0; l < 2; l++) amaxv[4*l] = vmaxq_f32(amaxv[4*l], amaxv[4*l+2]); - for (int l = 0; l < 1; l++) amaxv[8*l] = vmaxq_f32(amaxv[8*l], amaxv[8*l+4]); + for (int l = 0; l < 4; l++) maxv[2*l] = vmaxq_f32(srcv[2*l], srcv[2*l+1]); + for (int l = 0; l < 2; l++) maxv[4*l] = vmaxq_f32(maxv[4*l], maxv[4*l+2]); + for (int l = 0; l < 1; l++) maxv[8*l] = vmaxq_f32(maxv[8*l], maxv[8*l+4]); - const float amax = vmaxvq_f32(amaxv[0]); + for (int l = 0; l < 4; l++) minv[2*l] = vminq_f32(srcv[2*l], srcv[2*l+1]); + for (int l = 0; l < 2; l++) minv[4*l] = vminq_f32(minv[4*l], minv[4*l+2]); + for (int l = 0; l < 1; l++) minv[8*l] = vminq_f32(minv[8*l], minv[8*l+4]); - const float d = amax / ((1 << 3) - 1); + const float max = vmaxvq_f32(maxv[0]); + const float min = vminvq_f32(minv[0]); + + const float magnitude = max >= fabsf(min) ? max : min; + const float d = magnitude / -8; const float id = d ? 1.0f/d : 0.0f; y[i].d = d; @@ -695,9 +856,10 @@ static void quantize_row_q4_0(const float * restrict x, void * restrict vy, int const float32x4_t v = vmulq_n_f32(srcv[l], id); const float32x4_t vf = vaddq_f32(v, vdupq_n_f32(8.5f)); const int32x4_t vi = vcvtq_s32_f32(vf); + const int32x4_t vc = vminq_s32(vi, vdupq_n_s32(15)); - y[i].qs[2*l + 0] = vgetq_lane_s32(vi, 0) | (vgetq_lane_s32(vi, 1) << 4); - y[i].qs[2*l + 1] = vgetq_lane_s32(vi, 2) | (vgetq_lane_s32(vi, 3) << 4); + y[i].qs[2*l + 0] = vgetq_lane_s32(vc, 0) | (vgetq_lane_s32(vc, 1) << 4); + y[i].qs[2*l + 1] = vgetq_lane_s32(vc, 2) | (vgetq_lane_s32(vc, 3) << 4); } } #elif defined(__AVX2__) @@ -709,22 +871,31 @@ static void quantize_row_q4_0(const float * restrict x, void * restrict vy, int __m256 v3 = _mm256_loadu_ps( x + 24 ); x += 32; - // Compute max(abs(e)) for the block - const __m256 signBit = _mm256_set1_ps( -0.0f ); - __m256 maxAbs = _mm256_andnot_ps( signBit, v0 ); - maxAbs = _mm256_max_ps( maxAbs, _mm256_andnot_ps( signBit, v1 ) ); - maxAbs = _mm256_max_ps( maxAbs, _mm256_andnot_ps( signBit, v2 ) ); - maxAbs = _mm256_max_ps( maxAbs, _mm256_andnot_ps( signBit, v3 ) ); + // Compute max for the block + __m256 max = _mm256_max_ps( v0, v1 ); + __m256 maxTmp = _mm256_max_ps( v2, v3 ); + max = _mm256_max_ps( max, maxTmp ); - __m128 max4 = _mm_max_ps( _mm256_extractf128_ps( maxAbs, 1 ), _mm256_castps256_ps128( maxAbs ) ); + __m128 max4 = _mm_max_ps( _mm256_extractf128_ps( max, 1 ), _mm256_castps256_ps128( max ) ); max4 = _mm_max_ps( max4, _mm_movehl_ps( max4, max4 ) ); max4 = _mm_max_ss( max4, _mm_movehdup_ps( max4 ) ); const float maxScalar = _mm_cvtss_f32( max4 ); + // Compute min for the block + __m256 min = _mm256_min_ps( v0, v1 ); + __m256 minTmp = _mm256_min_ps( v2, v3 ); + min = _mm256_min_ps( min, minTmp ); + + __m128 min4 = _mm_min_ps( _mm256_extractf128_ps( min, 1 ), _mm256_castps256_ps128( min ) ); + min4 = _mm_min_ps( min4, _mm_movehl_ps( min4, min4 ) ); + min4 = _mm_min_ss( min4, _mm_movehdup_ps( min4 ) ); + const float minScalar = _mm_cvtss_f32( min4 ); + // Quantize these floats - const float d = maxScalar / 7.0f; + const float magnitude = maxScalar >= fabsf(minScalar) ? maxScalar : minScalar; + const float d = magnitude / -8.0f; y[i].d = d; - const float id = ( maxScalar != 0.0f ) ? 7.0f / maxScalar : 0.0f; + const float id = ( magnitude != 0.0f ) ? -8.0f / magnitude : 0.0f; const __m256 mul = _mm256_set1_ps( id ); // Apply the multiplier @@ -757,9 +928,11 @@ static void quantize_row_q4_0(const float * restrict x, void * restrict vy, int const __m256i perm = _mm256_setr_epi32( 0, 4, 1, 5, 2, 6, 3, 7 ); i0 = _mm256_permutevar8x32_epi32( i0, perm ); - // Apply offset to translate the range from [ -7 .. +7 ] into [ +1 .. +15 ] + // Apply offset and clamp to translate the range from [ -8 .. +8 ] into [ +0 .. +15 ] const __m256i off = _mm256_set1_epi8( 8 ); i0 = _mm256_add_epi8( i0, off ); + const __m256i maxNibble = _mm256_set1_epi8( 15 ); + i0 = _mm256_min_epi8( i0, maxNibble ); // Compress the vector into 4 bit/value, and store __m128i res = packNibbles( i0 ); @@ -774,22 +947,31 @@ static void quantize_row_q4_0(const float * restrict x, void * restrict vy, int __m256 v3 = _mm256_loadu_ps( x + 24 ); x += 32; - // Compute max(abs(e)) for the block - const __m256 signBit = _mm256_set1_ps( -0.0f ); - __m256 maxAbs = _mm256_andnot_ps( signBit, v0 ); - maxAbs = _mm256_max_ps( maxAbs, _mm256_andnot_ps( signBit, v1 ) ); - maxAbs = _mm256_max_ps( maxAbs, _mm256_andnot_ps( signBit, v2 ) ); - maxAbs = _mm256_max_ps( maxAbs, _mm256_andnot_ps( signBit, v3 ) ); + // Compute max for the block + __m256 max = _mm256_max_ps( v0, v1 ); + __m256 maxTmp = _mm256_max_ps( v2, v3 ); + max = _mm256_max_ps( max, maxTmp ); - __m128 max4 = _mm_max_ps( _mm256_extractf128_ps( maxAbs, 1 ), _mm256_castps256_ps128( maxAbs ) ); + __m128 max4 = _mm_max_ps( _mm256_extractf128_ps( max, 1 ), _mm256_castps256_ps128( max ) ); max4 = _mm_max_ps( max4, _mm_movehl_ps( max4, max4 ) ); max4 = _mm_max_ss( max4, _mm_movehdup_ps( max4 ) ); const float maxScalar = _mm_cvtss_f32( max4 ); + // Compute min for the block + __m256 min = _mm256_min_ps( v0, v1 ); + __m256 minTmp = _mm256_min_ps( v2, v3 ); + min = _mm256_min_ps( min, minTmp ); + + __m128 min4 = _mm_min_ps( _mm256_extractf128_ps( min, 1 ), _mm256_castps256_ps128( min ) ); + min4 = _mm_min_ps( min4, _mm_movehl_ps( min4, min4 ) ); + min4 = _mm_min_ss( min4, _mm_movehdup_ps( min4 ) ); + const float minScalar = _mm_cvtss_f32( min4 ); + // Quantize these floats - const float d = maxScalar / 7.0f; + const float magnitude = maxScalar >= fabsf(minScalar) ? maxScalar : minScalar; + const float d = magnitude / -8.0f; y[i].d = d; - const float id = ( maxScalar != 0.0f ) ? 7.0f / maxScalar : 0.0f; + const float id = ( magnitude != 0.0f ) ? -8.0f / magnitude : 0.0f; const __m256 mul = _mm256_set1_ps( id ); // Apply the multiplier @@ -830,10 +1012,13 @@ static void quantize_row_q4_0(const float * restrict x, void * restrict vy, int ni0 = _mm_packs_epi16( ni0, ni2 ); ni4 = _mm_packs_epi16( ni4, ni6 ); - // Apply offset to translate the range from [ -7 .. +7 ] into [ +1 .. +15 ] - const __m128i off = _mm_set1_epi8( 8); + // Apply offset and clamp to translate the range from [ -8 .. +8 ] into [ +0 .. +15 ] + const __m128i off = _mm_set1_epi8( 8 ); ni0 = _mm_add_epi8( ni0, off ); ni4 = _mm_add_epi8( ni4, off ); + const __m128i maxNibble = _mm_set1_epi8( 15 ); + ni0 = _mm_min_epi8( ni0, maxNibble ); + ni4 = _mm_min_epi8( ni4, maxNibble ); // Compress the vector into 4 bit/value, and store __m128i res = packNibbles( ni0, ni4 ); @@ -841,24 +1026,32 @@ static void quantize_row_q4_0(const float * restrict x, void * restrict vy, int } #elif defined(__wasm_simd128__) for (int i = 0; i < nb; i++) { - float amax = 0.0f; // absolute max + float max = 0.0f; + float min = 0.0f; v128_t srcv [8]; - v128_t asrcv[8]; - v128_t amaxv[8]; + v128_t maxv[8]; + v128_t minv[8]; for (int l = 0; l < 8; l++) srcv[l] = wasm_v128_load(x + i*32 + 4*l); - for (int l = 0; l < 8; l++) asrcv[l] = wasm_f32x4_abs(srcv[l]); - for (int l = 0; l < 4; l++) amaxv[2*l] = wasm_f32x4_max(asrcv[2*l], asrcv[2*l+1]); - for (int l = 0; l < 2; l++) amaxv[4*l] = wasm_f32x4_max(amaxv[4*l], amaxv[4*l+2]); - for (int l = 0; l < 1; l++) amaxv[8*l] = wasm_f32x4_max(amaxv[8*l], amaxv[8*l+4]); + for (int l = 0; l < 4; l++) maxv[2*l] = wasm_f32x4_max(srcv[2*l], srcv[2*l+1]); + for (int l = 0; l < 2; l++) maxv[4*l] = wasm_f32x4_max(maxv[4*l], maxv[4*l+2]); + for (int l = 0; l < 1; l++) maxv[8*l] = wasm_f32x4_max(maxv[8*l], maxv[8*l+4]); - amax = MAX( - MAX(wasm_f32x4_extract_lane(amaxv[0], 0), wasm_f32x4_extract_lane(amaxv[0], 1)), - MAX(wasm_f32x4_extract_lane(amaxv[0], 2), wasm_f32x4_extract_lane(amaxv[0], 3))); + for (int l = 0; l < 4; l++) minv[2*l] = wasm_f32x4_min(srcv[2*l], srcv[2*l+1]); + for (int l = 0; l < 2; l++) minv[4*l] = wasm_f32x4_min(minv[4*l], minv[4*l+2]); + for (int l = 0; l < 1; l++) minv[8*l] = wasm_f32x4_min(minv[8*l], minv[8*l+4]); - const float d = amax / ((1 << 3) - 1); + max = MAX( + MAX(wasm_f32x4_extract_lane(maxv[0], 0), wasm_f32x4_extract_lane(maxv[0], 1)), + MAX(wasm_f32x4_extract_lane(maxv[0], 2), wasm_f32x4_extract_lane(maxv[0], 3))); + min = MIN( + MIN(wasm_f32x4_extract_lane(minv[0], 0), wasm_f32x4_extract_lane(minv[0], 1)), + MIN(wasm_f32x4_extract_lane(minv[0], 2), wasm_f32x4_extract_lane(minv[0], 3))); + + const float magnitude = max >= fabsf(min) ? max : min; + const float d = magnitude / -8; const float id = d ? 1.0/d : 0.0; y[i].d = d; @@ -867,9 +1060,10 @@ static void quantize_row_q4_0(const float * restrict x, void * restrict vy, int const v128_t v = wasm_f32x4_mul(srcv[l], wasm_f32x4_splat(id)); const v128_t vf = wasm_f32x4_add(v, wasm_f32x4_splat(8.5f)); const v128_t vi = wasm_i32x4_trunc_sat_f32x4(vf); + const v128_t vc = wasm_i32x4_min_u(vi, wasm_i32x4_splat(15)); - y[i].qs[2*l + 0] = wasm_i32x4_extract_lane(vi, 0) | (wasm_i32x4_extract_lane(vi, 1) << 4); - y[i].qs[2*l + 1] = wasm_i32x4_extract_lane(vi, 2) | (wasm_i32x4_extract_lane(vi, 3) << 4); + y[i].qs[2*l + 0] = wasm_i32x4_extract_lane(vc, 0) | (wasm_i32x4_extract_lane(vc, 1) << 4); + y[i].qs[2*l + 1] = wasm_i32x4_extract_lane(vc, 2) | (wasm_i32x4_extract_lane(vc, 3) << 4); } } #else @@ -879,19 +1073,19 @@ static void quantize_row_q4_0(const float * restrict x, void * restrict vy, int } static void quantize_row_q4_1_reference(const float * restrict x, void * restrict vy, int k) { - assert(k % QK == 0); - const int nb = k / QK; + assert(k % QK4_1 == 0); + const int nb = k / QK4_1; block_q4_1 * restrict y = vy; - uint8_t pp[QK/2]; + uint8_t pp[QK4_1/2]; for (int i = 0; i < nb; i++) { float min = FLT_MAX; float max = -FLT_MAX; - for (int l = 0; l < QK; l++) { - const float v = x[i*QK + l]; + for (int l = 0; l < QK4_1; l++) { + const float v = x[i*QK4_1 + l]; if (v < min) min = v; if (v > max) max = v; } @@ -902,9 +1096,9 @@ static void quantize_row_q4_1_reference(const float * restrict x, void * restric y[i].d = d; y[i].m = min; - for (int l = 0; l < QK; l += 2) { - const float v0 = (x[i*QK + l + 0] - min)*id; - const float v1 = (x[i*QK + l + 1] - min)*id; + for (int l = 0; l < QK4_1; l += 2) { + const float v0 = (x[i*QK4_1 + l + 0] - min)*id; + const float v1 = (x[i*QK4_1 + l + 1] - min)*id; const uint8_t vi0 = roundf(v0); const uint8_t vi1 = roundf(v1); @@ -920,9 +1114,9 @@ static void quantize_row_q4_1_reference(const float * restrict x, void * restric } static void quantize_row_q4_1(const float * restrict x, void * restrict vy, int k) { - assert(k % QK == 0); + assert(k % QK4_1 == 0); - const int nb = k / QK; + const int nb = k / QK4_1; block_q4_1 * restrict y = vy; @@ -1006,7 +1200,7 @@ static void quantize_row_q4_1(const float * restrict x, void * restrict vy, int float32x4_t minv[8]; float32x4_t maxv[8]; - for (int l = 0; l < 8; l++) srcv[l] = vld1q_f32(x + i*QK + 4*l); + for (int l = 0; l < 8; l++) srcv[l] = vld1q_f32(x + i*QK4_1 + 4*l); for (int l = 0; l < 4; l++) minv[2*l] = vminq_f32(srcv[2*l], srcv[2*l + 1]); for (int l = 0; l < 2; l++) minv[4*l] = vminq_f32(minv[4*l], minv[4*l + 2]); @@ -1042,9 +1236,386 @@ static void quantize_row_q4_1(const float * restrict x, void * restrict vy, int #endif } +// reference implementation for deterministic creation of model files +static void quantize_row_q4_2_reference(const float * restrict x, block_q4_2 * restrict y, int k) { + assert(k % QK4_2 == 0); + + const int nb = k / QK4_2; + + for (int i = 0; i < nb; i++) { + float amax = 0.0f; // absolute max + float max = 0.0f; + + for (int l = 0; l < QK4_2; l++) { + const float v = x[i*QK4_2 + l]; + if (amax < fabsf(v)) { + amax = fabsf(v); + max = v; + } + } + + const float d = max / -8; + + const float id = d ? 1.0f/d : 0.0f; + + y[i].d = GGML_FP32_TO_FP16(d); + + for (int l = 0; l < QK4_2; l += 2) { + const float v0 = x[i*QK4_2 + l + 0]*id; + const float v1 = x[i*QK4_2 + l + 1]*id; + + const uint8_t vi0 = MIN(15, (uint8_t)(v0 + 8.5f)); + const uint8_t vi1 = MIN(15, (uint8_t)(v1 + 8.5f)); + + assert(vi0 < 16); + assert(vi1 < 16); + + y[i].qs[l/2] = vi0 | (vi1 << 4); + } + } +} + +static void quantize_row_q4_2(const float * restrict x, void * restrict vy, int k) { + assert(k % QK4_2 == 0); + + block_q4_2 * restrict y = vy; + + quantize_row_q4_2_reference(x, y, k); +} + +static void quantize_row_q5_0_reference(const float * restrict x, block_q5_0 * restrict y, int k) { + assert(k % QK5_0 == 0); + const int nb = k / QK5_0; + + for (int i = 0; i < nb; i++) { + float amax = 0.0f; // absolute max + float max = 0.0f; + + for (int l = 0; l < QK5_0; l++) { + const float v = x[i*QK5_0 + l]; + if (amax < fabsf(v)) { + amax = fabsf(v); + max = v; + } + } + + const float d = max / -16; + const float id = d ? 1.0f/d : 0.0f; + + y[i].d = GGML_FP32_TO_FP16(d); + + uint32_t qh = 0; + + for (int l = 0; l < QK5_0; l += 2) { + const float v0 = x[i*QK5_0 + l + 0]*id; + const float v1 = x[i*QK5_0 + l + 1]*id; + + const uint32_t vi0 = MIN(31, (int) (v0 + 16.5f)); + const uint32_t vi1 = MIN(31, (int) (v1 + 16.5f)); + + y[i].qs[l/2] = (vi0 & 0x0F) | ((vi1 & 0x0F) << 4); + + // get the 5-th bit and store it in qh at the right position + qh |= ((vi0 & 0x10) >> 4) << (l + 0); + qh |= ((vi1 & 0x10) >> 4) << (l + 1); + } + + memcpy(&y[i].qh, &qh, sizeof(y[i].qh)); + } +} + +static void quantize_row_q5_0(const float * restrict x, void * restrict vy, int k) { + assert(k % QK5_0 == 0); + + block_q5_0 * restrict y = vy; + + quantize_row_q5_0_reference(x, y, k); +} + +static void quantize_row_q5_1_reference(const float * restrict x, block_q5_1 * restrict y, int k) { + assert(k % QK5_1 == 0); + const int nb = k / QK5_1; + + for (int i = 0; i < nb; i++) { + float min = FLT_MAX; + float max = -FLT_MAX; + + for (int l = 0; l < QK5_1; l++) { + const float v = x[i*QK5_1 + l]; + if (v < min) min = v; + if (v > max) max = v; + } + + const float d = (max - min) / ((1 << 5) - 1); + const float id = d ? 1.0f/d : 0.0f; + + y[i].d = GGML_FP32_TO_FP16(d); + y[i].m = GGML_FP32_TO_FP16(min); + + uint32_t qh = 0; + + for (int l = 0; l < QK5_1; l += 2) { + const float v0 = (x[i*QK5_1 + l + 0] - min)*id; + const float v1 = (x[i*QK5_1 + l + 1] - min)*id; + + const uint32_t vi0 = (int) (v0 + 0.5f); + const uint32_t vi1 = (int) (v1 + 0.5f); + + y[i].qs[l/2] = (vi0 & 0x0F) | ((vi1 & 0x0F) << 4); + + // get the 5-th bit and store it in qh at the right position + qh |= ((vi0 & 0x10) >> 4) << (l + 0); + qh |= ((vi1 & 0x10) >> 4) << (l + 1); + } + + memcpy(&y[i].qh, &qh, sizeof(y[i].qh)); + } +} + +static void quantize_row_q5_1(const float * restrict x, void * restrict vy, int k) { + assert(k % QK5_1 == 0); + + block_q5_1 * restrict y = vy; + + quantize_row_q5_1_reference(x, y, k); +} + +// reference implementation for deterministic creation of model files +static void quantize_row_q8_0_reference(const float * restrict x, block_q8_0 * restrict y, int k) { + assert(k % QK8_0 == 0); + const int nb = k / QK8_0; + + for (int i = 0; i < nb; i++) { + float amax = 0.0f; // absolute max + + for (int l = 0; l < QK8_0; l++) { + const float v = x[i*QK8_0 + l]; + amax = MAX(amax, fabsf(v)); + } + + const float d = amax / ((1 << 7) - 1); + const float id = d ? 1.0f/d : 0.0f; + + y[i].d = d; + + for (int l = 0; l < QK8_0; ++l) { + const float v0 = x[i*QK8_0 + l]*id; + + y[i].qs[l] = roundf(v0); + } + } +} + +static void quantize_row_q8_0(const float * restrict x, void * restrict vy, int k) { + assert(k % QK8_0 == 0); + + block_q8_0 * restrict y = vy; + + quantize_row_q8_0_reference(x, y, k); +} + +// reference implementation for deterministic creation of model files +static void quantize_row_q8_1_reference(const float * restrict x, block_q8_1 * restrict y, int k) { + assert(k % QK8_1 == 0); + const int nb = k / QK8_1; + + for (int i = 0; i < nb; i++) { + float amax = 0.0f; // absolute max + + for (int l = 0; l < QK8_1; l++) { + const float v = x[i*QK8_1 + l]; + amax = MAX(amax, fabsf(v)); + } + + const float d = amax / ((1 << 7) - 1); + const float id = d ? 1.0f/d : 0.0f; + + y[i].d = d; + + int sum0 = 0; + int sum1 = 0; + + for (int l = 0; l < QK8_1/2; ++l) { + const float v0 = x[i*QK8_1 + l]*id; + const float v1 = x[i*QK8_1 + QK8_1/2 + l]*id; + + y[i].qs[ l] = roundf(v0); + y[i].qs[QK8_1/2 + l] = roundf(v1); + + sum0 += y[i].qs[ l]; + sum1 += y[i].qs[QK8_1/2 + l]; + } + + y[i].s0 = d * sum0; + y[i].s1 = d * sum1; + } +} + +static void quantize_row_q8_1(const float * restrict x, void * restrict vy, int k) { + assert(k % QK8_1 == 0); + const int nb = k / QK8_1; + + block_q8_1 * restrict y = vy; + +#if defined(__ARM_NEON) + for (int i = 0; i < nb; i++) { + float32x4_t srcv [8]; + float32x4_t asrcv[8]; + float32x4_t amaxv[8]; + + for (int l = 0; l < 8; l++) srcv[l] = vld1q_f32(x + i*32 + 4*l); + for (int l = 0; l < 8; l++) asrcv[l] = vabsq_f32(srcv[l]); + + for (int l = 0; l < 4; l++) amaxv[2*l] = vmaxq_f32(asrcv[2*l], asrcv[2*l+1]); + for (int l = 0; l < 2; l++) amaxv[4*l] = vmaxq_f32(amaxv[4*l], amaxv[4*l+2]); + for (int l = 0; l < 1; l++) amaxv[8*l] = vmaxq_f32(amaxv[8*l], amaxv[8*l+4]); + + const float amax = vmaxvq_f32(amaxv[0]); + + const float d = amax / ((1 << 7) - 1); + const float id = d ? 1.0f/d : 0.0f; + + y[i].d = d; + + int32x4_t accv0 = vdupq_n_s32(0); + int32x4_t accv1 = vdupq_n_s32(0); + + // low half + for (int l = 0; l < 4; l++) { + const float32x4_t v = vmulq_n_f32(srcv[l], id); + const int32x4_t vi = vcvtnq_s32_f32(v); + + y[i].qs[4*l + 0] = vgetq_lane_s32(vi, 0); + y[i].qs[4*l + 1] = vgetq_lane_s32(vi, 1); + y[i].qs[4*l + 2] = vgetq_lane_s32(vi, 2); + y[i].qs[4*l + 3] = vgetq_lane_s32(vi, 3); + + accv0 = vaddq_s32(accv0, vi); + } + + // high half + for (int l = 4; l < 8; l++) { + const float32x4_t v = vmulq_n_f32(srcv[l], id); + const int32x4_t vi = vcvtnq_s32_f32(v); + + y[i].qs[4*l + 0] = vgetq_lane_s32(vi, 0); + y[i].qs[4*l + 1] = vgetq_lane_s32(vi, 1); + y[i].qs[4*l + 2] = vgetq_lane_s32(vi, 2); + y[i].qs[4*l + 3] = vgetq_lane_s32(vi, 3); + + accv1 = vaddq_s32(accv1, vi); + } + + const int32_t sum0 = vaddvq_s32(accv0); + const int32_t sum1 = vaddvq_s32(accv1); + + y[i].s0 = d * sum0; + y[i].s1 = d * sum1; + } +#elif defined(__AVX2__) || defined(__AVX__) + for (int i = 0; i < nb; i++) { + // Load elements into 4 AVX vectors + __m256 v0 = _mm256_loadu_ps( x ); + __m256 v1 = _mm256_loadu_ps( x + 8 ); + __m256 v2 = _mm256_loadu_ps( x + 16 ); + __m256 v3 = _mm256_loadu_ps( x + 24 ); + x += 32; + + // Compute max(abs(e)) for the block + const __m256 signBit = _mm256_set1_ps( -0.0f ); + __m256 maxAbs = _mm256_andnot_ps( signBit, v0 ); + maxAbs = _mm256_max_ps( maxAbs, _mm256_andnot_ps( signBit, v1 ) ); + maxAbs = _mm256_max_ps( maxAbs, _mm256_andnot_ps( signBit, v2 ) ); + maxAbs = _mm256_max_ps( maxAbs, _mm256_andnot_ps( signBit, v3 ) ); + + __m128 max4 = _mm_max_ps( _mm256_extractf128_ps( maxAbs, 1 ), _mm256_castps256_ps128( maxAbs ) ); + max4 = _mm_max_ps( max4, _mm_movehl_ps( max4, max4 ) ); + max4 = _mm_max_ss( max4, _mm_movehdup_ps( max4 ) ); + const float maxScalar = _mm_cvtss_f32( max4 ); + + // Quantize these floats + const float d = maxScalar / 127.f; + y[i].d = d; + const float id = ( maxScalar != 0.0f ) ? 127.f / maxScalar : 0.0f; + const __m256 mul = _mm256_set1_ps( id ); + + // Apply the multiplier + v0 = _mm256_mul_ps( v0, mul ); + v1 = _mm256_mul_ps( v1, mul ); + v2 = _mm256_mul_ps( v2, mul ); + v3 = _mm256_mul_ps( v3, mul ); + + // Round to nearest integer + v0 = _mm256_round_ps( v0, _MM_ROUND_NEAREST ); + v1 = _mm256_round_ps( v1, _MM_ROUND_NEAREST ); + v2 = _mm256_round_ps( v2, _MM_ROUND_NEAREST ); + v3 = _mm256_round_ps( v3, _MM_ROUND_NEAREST ); + + // Convert floats to integers + __m256i i0 = _mm256_cvtps_epi32( v0 ); + __m256i i1 = _mm256_cvtps_epi32( v1 ); + __m256i i2 = _mm256_cvtps_epi32( v2 ); + __m256i i3 = _mm256_cvtps_epi32( v3 ); + +#if defined(__AVX2__) + // Compute the sum of the quants and set y[i].s + //y[i].s = d * hsum_i32_8(_mm256_add_epi32(_mm256_add_epi32(i0, i1), _mm256_add_epi32(i2, i3))); + y[i].s0 = d * hsum_i32_8(_mm256_add_epi32(i0, i1)); + y[i].s1 = d * hsum_i32_8(_mm256_add_epi32(i2, i3)); + + // Convert int32 to int16 + i0 = _mm256_packs_epi32( i0, i1 ); // 0, 1, 2, 3, 8, 9, 10, 11, 4, 5, 6, 7, 12, 13, 14, 15 + i2 = _mm256_packs_epi32( i2, i3 ); // 16, 17, 18, 19, 24, 25, 26, 27, 20, 21, 22, 23, 28, 29, 30, 31 + // Convert int16 to int8 + i0 = _mm256_packs_epi16( i0, i2 ); // 0, 1, 2, 3, 8, 9, 10, 11, 16, 17, 18, 19, 24, 25, 26, 27, 4, 5, 6, 7, 12, 13, 14, 15, 20, 21, 22, 23, 28, 29, 30, 31 + + // We got our precious signed bytes, but the order is now wrong + // These AVX2 pack instructions process 16-byte pieces independently + // The following instruction is fixing the order + const __m256i perm = _mm256_setr_epi32( 0, 4, 1, 5, 2, 6, 3, 7 ); + i0 = _mm256_permutevar8x32_epi32( i0, perm ); + + _mm256_storeu_si256((__m256i *)y[i].qs, i0); +#else + // Since we don't have in AVX some necessary functions, + // we split the registers in half and call AVX2 analogs from SSE + __m128i ni0 = _mm256_castsi256_si128( i0 ); + __m128i ni1 = _mm256_extractf128_si256( i0, 1); + __m128i ni2 = _mm256_castsi256_si128( i1 ); + __m128i ni3 = _mm256_extractf128_si256( i1, 1); + __m128i ni4 = _mm256_castsi256_si128( i2 ); + __m128i ni5 = _mm256_extractf128_si256( i2, 1); + __m128i ni6 = _mm256_castsi256_si128( i3 ); + __m128i ni7 = _mm256_extractf128_si256( i3, 1); + + // Compute the sum of the quants and set y[i].s + const __m128i s0 = _mm_add_epi32(_mm_add_epi32(ni0, ni1), _mm_add_epi32(ni2, ni3)); + const __m128i s1 = _mm_add_epi32(_mm_add_epi32(ni4, ni5), _mm_add_epi32(ni6, ni7)); + y[i].s0 = d * hsum_i32_4(s0); + y[i].s1 = d * hsum_i32_4(s1); + + // Convert int32 to int16 + ni0 = _mm_packs_epi32( ni0, ni1 ); + ni2 = _mm_packs_epi32( ni2, ni3 ); + ni4 = _mm_packs_epi32( ni4, ni5 ); + ni6 = _mm_packs_epi32( ni6, ni7 ); + // Convert int16 to int8 + ni0 = _mm_packs_epi16( ni0, ni2 ); + ni4 = _mm_packs_epi16( ni4, ni6 ); + + _mm_storeu_si128((__m128i *)(y[i].qs + 0), ni0); + _mm_storeu_si128((__m128i *)(y[i].qs + 16), ni4); +#endif + } +#else + // scalar + quantize_row_q8_1_reference(x, y, k); +#endif +} + static void dequantize_row_q4_0(const void * restrict vx, float * restrict y, int k) { - assert(k % QK == 0); - const int nb = k / QK; + assert(k % QK4_0 == 0); + const int nb = k / QK4_0; const block_q4_0 * restrict x = vx; @@ -1055,9 +1626,9 @@ static void dequantize_row_q4_0(const void * restrict vx, float * restrict y, in const uint8_t * restrict pp = x[i].qs; - for (int l = 0; l < QK; l += 32) { + for (int l = 0; l < QK4_0; l += 32) { // Load 32x4-bit integers into 32x8-bit integers - __m256i vx8 = bytesFromNibbles(pp+l/2); + __m256i vx8 = bytes_from_nibbles_32(pp+l/2); // Subtract 8 from the integers vx8 = _mm256_sub_epi8(vx8, _mm256_set1_epi8(8)); @@ -1077,7 +1648,7 @@ static void dequantize_row_q4_0(const void * restrict vx, float * restrict y, in // Scale and store for (int j = 0; j < 4; j++) { const __m256 result = _mm256_mul_ps(vf[j], d_v); - _mm256_storeu_ps(y + i * QK + l + j*8, result); + _mm256_storeu_ps(y + i * QK4_0 + l + j*8, result); } } } @@ -1087,12 +1658,12 @@ static void dequantize_row_q4_0(const void * restrict vx, float * restrict y, in const uint8_t * restrict pp = x[i].qs; - for (int l = 0; l < QK; l += 16) { + for (int l = 0; l < QK4_0; l += 16) { // Load 16x4-bit integers into 8x8-bit integers const uint8x8_t v8 = vld1_u8(pp + l/2); // Expand 4-bit qs to 8-bit bytes - const uint8x8_t v0 = vand_u8(v8, vdup_n_u8(0x0f)); + const uint8x8_t v0 = vand_u8(v8, vdup_n_u8(0x0F)); const uint8x8_t v1 = vshr_n_u8(v8, 4); // Convert to signed 8-bit integers @@ -1126,10 +1697,10 @@ static void dequantize_row_q4_0(const void * restrict vx, float * restrict y, in const float32x4_t r3 = vmulq_f32(vf_3, vd); // Store - vst1q_f32(y + i*QK + l + 0, r0); - vst1q_f32(y + i*QK + l + 4, r1); - vst1q_f32(y + i*QK + l + 8, r2); - vst1q_f32(y + i*QK + l + 12, r3); + vst1q_f32(y + i*QK4_0 + l + 0, r0); + vst1q_f32(y + i*QK4_0 + l + 4, r1); + vst1q_f32(y + i*QK4_0 + l + 8, r2); + vst1q_f32(y + i*QK4_0 + l + 12, r3); } } #else @@ -1139,10 +1710,10 @@ static void dequantize_row_q4_0(const void * restrict vx, float * restrict y, in const uint8_t * restrict pp = x[i].qs; - for (int l = 0; l < QK; l += 2) { + for (int l = 0; l < QK4_0; l += 2) { const uint8_t vi = pp[l/2]; - const int8_t vi0 = vi & 0xf; + const int8_t vi0 = vi & 0x0F; const int8_t vi1 = vi >> 4; const float v0 = (vi0 - 8)*d; @@ -1150,19 +1721,19 @@ static void dequantize_row_q4_0(const void * restrict vx, float * restrict y, in //printf("d = %f, vi = %d, vi0 = %d, vi1 = %d, v0 = %f, v1 = %f\n", d, vi, vi0, vi1, v0, v1); - y[i*QK + l + 0] = v0; - y[i*QK + l + 1] = v1; + y[i*QK4_0 + l + 0] = v0; + y[i*QK4_0 + l + 1] = v1; - assert(!isnan(y[i*QK + l + 0])); - assert(!isnan(y[i*QK + l + 1])); + assert(!isnan(y[i*QK4_0 + l + 0])); + assert(!isnan(y[i*QK4_0 + l + 1])); } } #endif } static void dequantize_row_q4_1(const void * restrict vx, float * restrict y, int k) { - assert(k % QK == 0); - const int nb = k / QK; + assert(k % QK4_1 == 0); + const int nb = k / QK4_1; const block_q4_1 * restrict x = vx; @@ -1173,9 +1744,9 @@ static void dequantize_row_q4_1(const void * restrict vx, float * restrict y, in const uint8_t * restrict pp = x[i].qs; - for (int l = 0; l < QK; l += 32) { + for (int l = 0; l < QK4_1; l += 32) { // Load 32x4-bit integers into 32x8-bit integers - __m256i vx8 = bytesFromNibbles(pp+l/2); + __m256i vx8 = bytes_from_nibbles_32(pp+l/2); // Convert to 16-bit int const __m256i vx16_lo = _mm256_cvtepi8_epi16(_mm256_extracti128_si256(vx8, 0)); @@ -1192,7 +1763,7 @@ static void dequantize_row_q4_1(const void * restrict vx, float * restrict y, in // Scale, add m and store for (int j = 0; j < 4; j++) { const __m256 result = _mm256_add_ps(_mm256_mul_ps(vf[j], d_v), d_m); - _mm256_storeu_ps(y + i * QK + l + j*8, result); + _mm256_storeu_ps(y + i * QK4_1 + l + j*8, result); } } } @@ -1203,12 +1774,12 @@ static void dequantize_row_q4_1(const void * restrict vx, float * restrict y, in const uint8_t * restrict pp = x[i].qs; - for (int l = 0; l < QK; l += 16) { + for (int l = 0; l < QK4_1; l += 16) { // Load 16x4-bit integers into 8x8-bit integers const uint8x8_t v8 = vld1_u8(pp + l/2); // Expand 4-bit qs to 8-bit bytes - const uint8x8_t v0 = vand_u8(v8, vdup_n_u8(0x0f)); + const uint8x8_t v0 = vand_u8(v8, vdup_n_u8(0x0F)); const uint8x8_t v1 = vshr_n_u8(v8, 4); // Interleave and combine @@ -1234,10 +1805,10 @@ static void dequantize_row_q4_1(const void * restrict vx, float * restrict y, in const float32x4_t r3 = vmlaq_f32(vm, vf_3, vd); // Store - vst1q_f32(y + i*QK + l + 0, r0); - vst1q_f32(y + i*QK + l + 4, r1); - vst1q_f32(y + i*QK + l + 8, r2); - vst1q_f32(y + i*QK + l + 12, r3); + vst1q_f32(y + i*QK4_1 + l + 0, r0); + vst1q_f32(y + i*QK4_1 + l + 4, r1); + vst1q_f32(y + i*QK4_1 + l + 8, r2); + vst1q_f32(y + i*QK4_1 + l + 12, r3); } } #else @@ -1247,25 +1818,217 @@ static void dequantize_row_q4_1(const void * restrict vx, float * restrict y, in const uint8_t * restrict pp = x[i].qs; - for (int l = 0; l < QK; l += 2) { + for (int l = 0; l < QK4_1; l += 2) { const uint8_t vi = pp[l/2]; - const int8_t vi0 = vi & 0xf; + const int8_t vi0 = vi & 0x0F; const int8_t vi1 = vi >> 4; const float v0 = vi0*d + m; const float v1 = vi1*d + m; - y[i*QK + l + 0] = v0; - y[i*QK + l + 1] = v1; + y[i*QK4_1 + l + 0] = v0; + y[i*QK4_1 + l + 1] = v1; - assert(!isnan(y[i*QK + l + 0])); - assert(!isnan(y[i*QK + l + 1])); + assert(!isnan(y[i*QK4_1 + l + 0])); + assert(!isnan(y[i*QK4_1 + l + 1])); } } #endif } +static void dequantize_row_q4_2(const void * restrict vx, float * restrict y, int k) { + assert(k % QK4_2 == 0); + const int nb = k / QK4_2; + + const block_q4_2 * restrict x = vx; + + for (int i = 0; i < nb; i++) { + const float d = GGML_FP16_TO_FP32(x[i].d); + + const uint8_t * restrict pp = x[i].qs; + + for (int l = 0; l < QK4_2; l += 2) { + const uint8_t vi = pp[l/2]; + + const int8_t vi0 = vi & 0x0F; + const int8_t vi1 = vi >> 4; + + const float v0 = (vi0 - 8)*d; + const float v1 = (vi1 - 8)*d; + + y[i*QK4_2 + l + 0] = v0; + y[i*QK4_2 + l + 1] = v1; + + assert(!isnan(y[i*QK4_2 + l + 0])); + assert(!isnan(y[i*QK4_2 + l + 1])); + } + } +} + +static void dequantize_row_q5_0(const void * restrict vx, float * restrict y, int k) { + assert(k % QK5_0 == 0); + const int nb = k / QK5_0; + + const block_q5_0 * restrict x = vx; + + for (int i = 0; i < nb; i++) { + const float d = GGML_FP16_TO_FP32(x[i].d); + + const uint8_t * restrict pp = x[i].qs; + + uint32_t qh; + memcpy(&qh, x[i].qh, sizeof(qh)); + + for (int l = 0; l < QK5_0; l += 2) { + const uint8_t vi = pp[l/2]; + + // extract the 5-th bit from qh + const uint8_t vh0 = ((qh & (1 << (l + 0))) >> (l + 0)) << 4; + const uint8_t vh1 = ((qh & (1 << (l + 1))) >> (l + 1)) << 4; + + const int8_t vi0 = (vi & 0x0F) | vh0; + const int8_t vi1 = (vi >> 4) | vh1; + + const float v0 = (vi0 - 16)*d; + const float v1 = (vi1 - 16)*d; + + y[i*QK5_0 + l + 0] = v0; + y[i*QK5_0 + l + 1] = v1; + + assert(!isnan(y[i*QK5_0 + l + 0])); + assert(!isnan(y[i*QK5_0 + l + 1])); + } + } +} + +static void dequantize_row_q5_1(const void * restrict vx, float * restrict y, int k) { + assert(k % QK5_1 == 0); + const int nb = k / QK5_1; + + const block_q5_1 * restrict x = vx; + + for (int i = 0; i < nb; i++) { + const float d = GGML_FP16_TO_FP32(x[i].d); + const float m = GGML_FP16_TO_FP32(x[i].m); + + const uint8_t * restrict pp = x[i].qs; + + uint32_t qh; + memcpy(&qh, x[i].qh, sizeof(qh)); + + for (int l = 0; l < QK5_1; l += 2) { + const uint8_t vi = pp[l/2]; + + // extract the 5-th bit from qh + const uint8_t vh0 = ((qh & (1 << (l + 0))) >> (l + 0)) << 4; + const uint8_t vh1 = ((qh & (1 << (l + 1))) >> (l + 1)) << 4; + + const uint8_t vi0 = (vi & 0x0F) | vh0; + const uint8_t vi1 = (vi >> 4) | vh1; + + const float v0 = vi0*d + m; + const float v1 = vi1*d + m; + + y[i*QK5_1 + l + 0] = v0; + y[i*QK5_1 + l + 1] = v1; + + assert(!isnan(y[i*QK5_1 + l + 0])); + assert(!isnan(y[i*QK5_1 + l + 1])); + } + } +} + +static void dequantize_row_q8_0(const void * restrict vx, float * restrict y, int k) { + assert(k % QK8_0 == 0); + const int nb = k / QK8_0; + + const block_q8_0 * restrict x = vx; + + for (int i = 0; i < nb; i++) { + const float d = x[i].d; + + const int8_t * restrict pp = x[i].qs; + + for (int l = 0; l < QK8_0; ++l) { + y[i*QK8_0 + l] = pp[l]*d; + } + } +} + +static void ggml_vec_dot_q4_0_q8_0(const int n, float * restrict s, const void * restrict vx, const void * restrict vy); +static void ggml_vec_dot_q4_1_q8_1(const int n, float * restrict s, const void * restrict vx, const void * restrict vy); +static void ggml_vec_dot_q4_2_q8_0(const int n, float * restrict s, const void * restrict vx, const void * restrict vy); +static void ggml_vec_dot_q5_0_q8_0(const int n, float * restrict s, const void * restrict vx, const void * restrict vy); +static void ggml_vec_dot_q5_1_q8_1(const int n, float * restrict s, const void * restrict vx, const void * restrict vy); +static void ggml_vec_dot_q8_0_q8_0(const int n, float * restrict s, const void * restrict vx, const void * restrict vy); + +static const quantize_fns_t quantize_fns[GGML_TYPE_COUNT] = { + [GGML_TYPE_Q4_0] = { + .dequantize_row_q = dequantize_row_q4_0, + .quantize_row_q = quantize_row_q4_0, + .quantize_row_q_reference = (quantize_row_q_t) quantize_row_q4_0_reference, + .quantize_row_q_dot = quantize_row_q8_0, + .vec_dot_q = ggml_vec_dot_q4_0_q8_0, + .vec_dot_type = GGML_TYPE_Q8_0, + }, + [GGML_TYPE_Q4_1] = { + .dequantize_row_q = dequantize_row_q4_1, + .quantize_row_q = quantize_row_q4_1, + .quantize_row_q_reference = (quantize_row_q_t) quantize_row_q4_1_reference, + .quantize_row_q_dot = quantize_row_q8_1, + .vec_dot_q = ggml_vec_dot_q4_1_q8_1, + .vec_dot_type = GGML_TYPE_Q8_1, + }, + [GGML_TYPE_Q4_2] = { + .dequantize_row_q = dequantize_row_q4_2, + .quantize_row_q = quantize_row_q4_2, + .quantize_row_q_reference = (quantize_row_q_t) quantize_row_q4_2_reference, + .quantize_row_q_dot = quantize_row_q8_0, + .vec_dot_q = ggml_vec_dot_q4_2_q8_0, + .vec_dot_type = GGML_TYPE_Q8_0, + }, + [GGML_TYPE_Q5_0] = { + .dequantize_row_q = dequantize_row_q5_0, + .quantize_row_q = quantize_row_q5_0, + .quantize_row_q_reference = (quantize_row_q_t) quantize_row_q5_0_reference, + .quantize_row_q_dot = quantize_row_q8_0, + .vec_dot_q = ggml_vec_dot_q5_0_q8_0, + .vec_dot_type = GGML_TYPE_Q8_0, + }, + [GGML_TYPE_Q5_1] = { + .dequantize_row_q = dequantize_row_q5_1, + .quantize_row_q = quantize_row_q5_1, + .quantize_row_q_reference = (quantize_row_q_t) quantize_row_q5_1_reference, + .quantize_row_q_dot = quantize_row_q8_1, + .vec_dot_q = ggml_vec_dot_q5_1_q8_1, + .vec_dot_type = GGML_TYPE_Q8_1, + }, + [GGML_TYPE_Q8_0] = { + .dequantize_row_q = dequantize_row_q8_0, + .quantize_row_q = quantize_row_q8_0, + .quantize_row_q_reference = (quantize_row_q_t) quantize_row_q8_0_reference, + .quantize_row_q_dot = quantize_row_q8_0, + .vec_dot_q = ggml_vec_dot_q8_0_q8_0, + .vec_dot_type = GGML_TYPE_Q8_0, + }, + [GGML_TYPE_Q8_1] = { + .dequantize_row_q = NULL, // TODO + .quantize_row_q = quantize_row_q8_1, + .quantize_row_q_reference = (quantize_row_q_t) quantize_row_q8_1_reference, + .quantize_row_q_dot = quantize_row_q8_1, + .vec_dot_q = NULL, // TODO + .vec_dot_type = GGML_TYPE_Q8_1, + }, +}; + +// For internal test use +quantize_fns_t ggml_internal_get_quantize_fn(size_t i) { + GGML_ASSERT(i < GGML_TYPE_COUNT); + return quantize_fns[i]; +} + + // // simd mappings // @@ -1822,37 +2585,6 @@ inline static void ggml_vec_dot_f32(const int n, float * restrict s, const float *s = sumf; } -#if __AVX512F__ && QK == 32 -static inline __m512 dot_q4_0_oneblock_avx512( - __m512 acc, - const block_q4_0 * restrict x, - const block_q4_0 * restrict y, - int i -) { - // Compute combined scale for the block - __m512 d = _mm512_set1_ps( x[i].d * y[i].d ); - - __m256i bx = bytesFromNibbles( x[i].qs ); - __m256i by = bytesFromNibbles( y[i].qs ); - - // Now we have a vector with bytes in [ 0 .. 15 ] interval. Offset them into [ -8 .. +7 ] interval. - const __m256i off = _mm256_set1_epi8( 8 ); - bx = _mm256_sub_epi8( bx, off ); - by = _mm256_sub_epi8( by, off ); - - // Sign-extend 16 signed bytes into int16_t - __m512i x32 = _mm512_cvtepi8_epi16( bx ); - __m512i y32 = _mm512_cvtepi8_epi16( by ); - // Compute products of int16_t integers, add pairwise - __m512i i64 = _mm512_madd_epi16( x32, y32 ); - - // Convert int32_t to float - __m512 p = _mm512_cvtepi32_ps( i64 ); - // Apply the scale, and accumulate - return _mm512_fmadd_ps( d, p, acc ); -} -#endif - inline static void ggml_vec_dot_f16(const int n, float * restrict s, ggml_fp16_t * restrict x, ggml_fp16_t * restrict y) { ggml_float sumf = 0.0; @@ -1889,67 +2621,62 @@ inline static void ggml_vec_dot_f16(const int n, float * restrict s, ggml_fp16_t *s = sumf; } -static void ggml_vec_dot_q4_0(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) { - const int nb = n / QK; +static void ggml_vec_dot_q4_0_q8_0(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) { + const int nb = n / QK8_0; - assert(n % QK == 0); + assert(n % QK8_0 == 0); assert(nb % 2 == 0); const block_q4_0 * restrict x = vx; - const block_q4_0 * restrict y = vy; - - float sumf = 0.0; + const block_q8_0 * restrict y = vy; #if defined(__ARM_NEON) - float sum0 = 0.0f; - float sum1 = 0.0f; + float32x4_t sumv0 = vdupq_n_f32(0.0f); + float32x4_t sumv1 = vdupq_n_f32(0.0f); for (int i = 0; i < nb; i += 2) { const block_q4_0 * restrict x0 = &x[i + 0]; - const block_q4_0 * restrict y0 = &y[i + 0]; const block_q4_0 * restrict x1 = &x[i + 1]; - const block_q4_0 * restrict y1 = &y[i + 1]; + const block_q8_0 * restrict y0 = &y[i + 0]; + const block_q8_0 * restrict y1 = &y[i + 1]; - const uint8x16_t m4b = vdupq_n_u8(0xf); - const int8x16_t s8b = vdupq_n_s8(0x8); + const uint8x16_t m4b = vdupq_n_u8(0x0F); + const int8x16_t s8b = vdupq_n_s8(0x8); const uint8x16_t v0_0 = vld1q_u8(x0->qs); - const uint8x16_t v1_0 = vld1q_u8(y0->qs); const uint8x16_t v0_1 = vld1q_u8(x1->qs); - const uint8x16_t v1_1 = vld1q_u8(y1->qs); // 4-bit -> 8-bit - const int8x16_t v0_0l = vreinterpretq_s8_u8(vandq_u8(v0_0, m4b)); - const int8x16_t v1_0l = vreinterpretq_s8_u8(vandq_u8(v1_0, m4b)); + const int8x16_t v0_0l = vreinterpretq_s8_u8(vandq_u8 (v0_0, m4b)); const int8x16_t v0_0h = vreinterpretq_s8_u8(vshrq_n_u8(v0_0, 4)); - const int8x16_t v1_0h = vreinterpretq_s8_u8(vshrq_n_u8(v1_0, 4)); - - const int8x16_t v0_1l = vreinterpretq_s8_u8(vandq_u8(v0_1, m4b)); - const int8x16_t v1_1l = vreinterpretq_s8_u8(vandq_u8(v1_1, m4b)); + const int8x16_t v0_1l = vreinterpretq_s8_u8(vandq_u8 (v0_1, m4b)); const int8x16_t v0_1h = vreinterpretq_s8_u8(vshrq_n_u8(v0_1, 4)); - const int8x16_t v1_1h = vreinterpretq_s8_u8(vshrq_n_u8(v1_1, 4)); // sub 8 const int8x16_t v0_0ls = vsubq_s8(v0_0l, s8b); - const int8x16_t v1_0ls = vsubq_s8(v1_0l, s8b); const int8x16_t v0_0hs = vsubq_s8(v0_0h, s8b); - const int8x16_t v1_0hs = vsubq_s8(v1_0h, s8b); - const int8x16_t v0_1ls = vsubq_s8(v0_1l, s8b); - const int8x16_t v1_1ls = vsubq_s8(v1_1l, s8b); const int8x16_t v0_1hs = vsubq_s8(v0_1h, s8b); - const int8x16_t v1_1hs = vsubq_s8(v1_1h, s8b); + + // load y + const int8x16_t v1_0l = vld1q_s8(y0->qs); + const int8x16_t v1_0h = vld1q_s8(y0->qs + 16); + const int8x16_t v1_1l = vld1q_s8(y1->qs); + const int8x16_t v1_1h = vld1q_s8(y1->qs + 16); + + // interleave + const int8x16_t v1_0ls = vuzp1q_s8(v1_0l, v1_0h); + const int8x16_t v1_0hs = vuzp2q_s8(v1_0l, v1_0h); + const int8x16_t v1_1ls = vuzp1q_s8(v1_1l, v1_1h); + const int8x16_t v1_1hs = vuzp2q_s8(v1_1l, v1_1h); #if defined(__ARM_FEATURE_DOTPROD) // dot product into int32x4_t - int32x4_t p_0 = vdotq_s32(vdupq_n_s32(0), v0_0ls, v1_0ls); - int32x4_t p_1 = vdotq_s32(vdupq_n_s32(0), v0_1ls, v1_1ls); + const int32x4_t p_0 = vdotq_s32(vdotq_s32(vdupq_n_s32(0), v0_0ls, v1_0ls), v0_0hs, v1_0hs); + const int32x4_t p_1 = vdotq_s32(vdotq_s32(vdupq_n_s32(0), v0_1ls, v1_1ls), v0_1hs, v1_1hs); - p_0 = vdotq_s32(p_0, v0_0hs, v1_0hs); - p_1 = vdotq_s32(p_1, v0_1hs, v1_1hs); - - sum0 += x0->d*y0->d*vaddvq_s32(p_0); - sum1 += x1->d*y1->d*vaddvq_s32(p_1); + sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(p_0), x0->d*y0->d); + sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(p_1), x1->d*y1->d); #else const int16x8_t pl0l = vmull_s8(vget_low_s8 (v0_0ls), vget_low_s8 (v1_0ls)); const int16x8_t pl0h = vmull_s8(vget_high_s8(v0_0ls), vget_high_s8(v1_0ls)); @@ -1961,124 +2688,41 @@ static void ggml_vec_dot_q4_0(const int n, float * restrict s, const void * rest const int16x8_t ph1l = vmull_s8(vget_low_s8 (v0_1hs), vget_low_s8 (v1_1hs)); const int16x8_t ph1h = vmull_s8(vget_high_s8(v0_1hs), vget_high_s8(v1_1hs)); - const int16x8_t pl_0 = vaddq_s16(pl0l, pl0h); - const int16x8_t ph_0 = vaddq_s16(ph0l, ph0h); + const int32x4_t pl0 = vaddq_s32(vpaddlq_s16(pl0l), vpaddlq_s16(pl0h)); + const int32x4_t ph0 = vaddq_s32(vpaddlq_s16(ph0l), vpaddlq_s16(ph0h)); + const int32x4_t pl1 = vaddq_s32(vpaddlq_s16(pl1l), vpaddlq_s16(pl1h)); + const int32x4_t ph1 = vaddq_s32(vpaddlq_s16(ph1l), vpaddlq_s16(ph1h)); - const int16x8_t pl_1 = vaddq_s16(pl1l, pl1h); - const int16x8_t ph_1 = vaddq_s16(ph1l, ph1h); - - const int16x8_t p_0 = vaddq_s16(pl_0, ph_0); - const int16x8_t p_1 = vaddq_s16(pl_1, ph_1); - - sum0 += x0->d*y0->d*vaddvq_s16(p_0); - sum1 += x1->d*y1->d*vaddvq_s16(p_1); + sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(pl0, ph0)), x0->d*y0->d); + sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(pl1, ph1)), x1->d*y1->d); #endif } - sumf = sum0 + sum1; -#elif defined(__AVX512F__) - // Initialize accumulator with zeros - __m512 acc0 = _mm512_setzero_ps(); - __m512 acc1 = _mm512_setzero_ps(); - - const int superblock_size = 8; - const int superblock_count = nb / superblock_size; - - for (int superblock_ix = 0; superblock_ix < superblock_count; superblock_ix += 1) { - int i = superblock_ix * superblock_size; - - acc0 = dot_q4_0_oneblock_avx512( acc0, x, y, i+0 ); - acc1 = dot_q4_0_oneblock_avx512( acc1, x, y, i+1 ); - acc0 = dot_q4_0_oneblock_avx512( acc0, x, y, i+2 ); - acc1 = dot_q4_0_oneblock_avx512( acc1, x, y, i+3 ); - acc0 = dot_q4_0_oneblock_avx512( acc0, x, y, i+4 ); - acc1 = dot_q4_0_oneblock_avx512( acc1, x, y, i+5 ); - acc0 = dot_q4_0_oneblock_avx512( acc0, x, y, i+6 ); - acc1 = dot_q4_0_oneblock_avx512( acc1, x, y, i+7 ); - } - - // Remainders - for (int i = superblock_count * superblock_size; i < nb; ++i) { - acc0 = dot_q4_0_oneblock_avx512( acc0, x, y, i ); - } - - // Horizontal sum of all lanes of the accumulator - sumf = _mm512_reduce_add_ps( acc0 ) + _mm512_reduce_add_ps( acc1 ); + *s = vaddvq_f32(sumv0) + vaddvq_f32(sumv1); #elif defined(__AVX2__) // Initialize accumulator with zeros __m256 acc = _mm256_setzero_ps(); - /* Prepare the constants we will need during execution */ - const __m256i lowMask = _mm256_set1_epi8( 0xF ); - const __m256i offset_8 = _mm256_set1_epi16( 8 ); - -#define UNROLL_COUNT 8 - // make sure we only unroll multiples of the block count - assert(nb % UNROLL_COUNT == 0); - // Main loop - for (int i = 0; i < nb; i+=UNROLL_COUNT) { - // This loop will be unrolled by the compiler - for (int u=0;u we now have a vector of 8 int_32t */ - __m256i xy_q = _mm256_add_epi32( xy_high_q, xy_low_q ); - - /* Convert to vectore of 8 int32_t to 8 floats */ - __m256 q = _mm256_cvtepi32_ps( xy_q ); - - /* Multiply q with scale and accumulate */ - acc = _mm256_fmadd_ps( scale, q, acc ); - } + /* Multiply q with scale and accumulate */ + acc = _mm256_fmadd_ps( d, q, acc ); } - // Return horizontal sum of the acc vector - __m128 res = _mm256_extractf128_ps( acc, 1 ); - res = _mm_add_ps( res, _mm256_castps256_ps128( acc ) ); - res = _mm_add_ps( res, _mm_movehl_ps( res, res ) ); - res = _mm_add_ss( res, _mm_movehdup_ps( res ) ); - - sumf = _mm_cvtss_f32( res ); + *s = hsum_float_8(acc); #elif defined(__AVX__) // Initialize accumulator with zeros __m256 acc = _mm256_setzero_ps(); @@ -2091,13 +2735,12 @@ static void ggml_vec_dot_q4_0(const int n, float * restrict s, const void * rest __m128i i32[2]; for (int j = 0; j < 2; ++j) { // Load 8 bytes, and unpack 4 bit fields into bytes, making 16 bytes - __m128i bx = bytesFromNibbles( x[i].qs + 8*j ); - __m128i by = bytesFromNibbles( y[i].qs + 8*j ); + __m128i bx = bytes_from_nibbles_16(x[i].qs + 8*j); + __m128i by = _mm_loadu_si128((const __m128i *)(y[i].qs + 16*j)); // Now we have a vector with bytes in [ 0 .. 15 ] interval. Offset them into [ -8 .. +7 ] interval. const __m128i off = _mm_set1_epi8( 8 ); bx = _mm_sub_epi8( bx, off ); - by = _mm_sub_epi8( by, off ); // Get absolute values of x vectors const __m128i ax = _mm_sign_epi8(bx, bx); @@ -2118,303 +2761,693 @@ static void ggml_vec_dot_q4_0(const int n, float * restrict s, const void * rest acc = _mm256_add_ps(_mm256_mul_ps( d, p ), acc); } - // Return horizontal sum of the acc vector - __m128 res = _mm256_extractf128_ps( acc, 1 ); - res = _mm_add_ps( res, _mm256_castps256_ps128( acc ) ); - res = _mm_add_ps( res, _mm_movehl_ps( res, res ) ); - res = _mm_add_ss( res, _mm_movehdup_ps( res ) ); - - sumf = _mm_cvtss_f32( res ); -#elif defined(__wasm_simd128__) - // wasm simd - float sum0 = 0.0f; - float sum1 = 0.0f; - - for (int i = 0; i < nb; i += 2) { - const block_q4_0 * restrict x0 = &x[i + 0]; - const block_q4_0 * restrict y0 = &y[i + 0]; - const block_q4_0 * restrict x1 = &x[i + 1]; - const block_q4_0 * restrict y1 = &y[i + 1]; - - const v128_t m4b = wasm_u8x16_splat(0xf); - const v128_t s8b = wasm_i8x16_splat(0x8); - - const v128_t v0_0 = wasm_v128_load(x0->qs); - const v128_t v0_1 = wasm_v128_load(y0->qs); - const v128_t v1_0 = wasm_v128_load(x1->qs); - const v128_t v1_1 = wasm_v128_load(y1->qs); - - // 4-bit -> 8-bit - const v128_t v0_0l = wasm_v128_and(v0_0, m4b); - const v128_t v1_0l = wasm_v128_and(v1_0, m4b); - - const v128_t v0_0h = wasm_u8x16_shr(v0_0, 4); - const v128_t v1_0h = wasm_u8x16_shr(v1_0, 4); - - const v128_t v0_1l = wasm_v128_and(v0_1, m4b); - const v128_t v1_1l = wasm_v128_and(v1_1, m4b); - - const v128_t v0_1h = wasm_u8x16_shr(v0_1, 4); - const v128_t v1_1h = wasm_u8x16_shr(v1_1, 4); - - // sub 8 - const v128_t v0_0ls = wasm_i8x16_sub(v0_0l, s8b); - const v128_t v1_0ls = wasm_i8x16_sub(v1_0l, s8b); - - const v128_t v0_0hs = wasm_i8x16_sub(v0_0h, s8b); - const v128_t v1_0hs = wasm_i8x16_sub(v1_0h, s8b); - - const v128_t v0_1ls = wasm_i8x16_sub(v0_1l, s8b); - const v128_t v1_1ls = wasm_i8x16_sub(v1_1l, s8b); - - const v128_t v0_1hs = wasm_i8x16_sub(v0_1h, s8b); - const v128_t v1_1hs = wasm_i8x16_sub(v1_1h, s8b); - - // dot product into int16x8_t - const v128_t pl0l = wasm_i16x8_mul(wasm_i16x8_extend_low_i8x16(v0_0ls), wasm_i16x8_extend_low_i8x16(v1_0ls)); - const v128_t pl0h = wasm_i16x8_mul(wasm_i16x8_extend_high_i8x16(v0_0ls), wasm_i16x8_extend_high_i8x16(v1_0ls)); - - const v128_t ph0l = wasm_i16x8_mul(wasm_i16x8_extend_low_i8x16(v0_0hs), wasm_i16x8_extend_low_i8x16(v1_0hs)); - const v128_t ph0h = wasm_i16x8_mul(wasm_i16x8_extend_high_i8x16(v0_0hs), wasm_i16x8_extend_high_i8x16(v1_0hs)); - - const v128_t pl1l = wasm_i16x8_mul(wasm_i16x8_extend_low_i8x16(v0_1ls), wasm_i16x8_extend_low_i8x16(v1_1ls)); - const v128_t pl1h = wasm_i16x8_mul(wasm_i16x8_extend_high_i8x16(v0_1ls), wasm_i16x8_extend_high_i8x16(v1_1ls)); - - const v128_t ph1l = wasm_i16x8_mul(wasm_i16x8_extend_low_i8x16(v0_1hs), wasm_i16x8_extend_low_i8x16(v1_1hs)); - const v128_t ph1h = wasm_i16x8_mul(wasm_i16x8_extend_high_i8x16(v0_1hs), wasm_i16x8_extend_high_i8x16(v1_1hs)); - - const v128_t pl_0 = wasm_i16x8_add(pl0l, pl0h); - const v128_t ph_0 = wasm_i16x8_add(ph0l, ph0h); - - const v128_t pl_1 = wasm_i16x8_add(pl1l, pl1h); - const v128_t ph_1 = wasm_i16x8_add(ph1l, ph1h); - - const v128_t p_0 = wasm_i16x8_add(pl_0, ph_0); - const v128_t p_1 = wasm_i16x8_add(pl_1, ph_1); - - sum0 += x0->d * y0->d * ( - wasm_i16x8_extract_lane(p_0, 0) + wasm_i16x8_extract_lane(p_0, 1) + - wasm_i16x8_extract_lane(p_0, 2) + wasm_i16x8_extract_lane(p_0, 3) + - wasm_i16x8_extract_lane(p_0, 4) + wasm_i16x8_extract_lane(p_0, 5) + - wasm_i16x8_extract_lane(p_0, 6) + wasm_i16x8_extract_lane(p_0, 7)); - sum1 += x1->d * y1->d * ( - wasm_i16x8_extract_lane(p_1, 0) + wasm_i16x8_extract_lane(p_1, 1) + - wasm_i16x8_extract_lane(p_1, 2) + wasm_i16x8_extract_lane(p_1, 3) + - wasm_i16x8_extract_lane(p_1, 4) + wasm_i16x8_extract_lane(p_1, 5) + - wasm_i16x8_extract_lane(p_1, 6) + wasm_i16x8_extract_lane(p_1, 7)); - } - - sumf = sum0 + sum1; + *s = hsum_float_8(acc); #else // scalar + float sumf = 0.0; for (int i = 0; i < nb; i++) { const float d0 = x[i].d; const float d1 = y[i].d; const uint8_t * restrict p0 = x[i].qs; - const uint8_t * restrict p1 = y[i].qs; + const int8_t * restrict p1 = y[i].qs; int sumi = 0; - for (int j = 0; j < QK/2; j++) { + for (int j = 0; j < QK8_0/2; j++) { const uint8_t v0 = p0[j]; - const uint8_t v1 = p1[j]; - const int8_t i0 = (int8_t) (v0 & 0xf) - 8; - const int8_t i1 = (int8_t) (v0 >> 4) - 8; + const int i0 = (int8_t) (v0 & 0x0F) - 8; + const int i1 = (int8_t) (v0 >> 4) - 8; - const int8_t i2 = (int8_t) (v1 & 0xf) - 8; - const int8_t i3 = (int8_t) (v1 >> 4) - 8; + const int i2 = p1[2*j + 0]; + const int i3 = p1[2*j + 1]; sumi += i0*i2 + i1*i3; } - sumf += d0 * d1 * sumi; + sumf += d0*d1*sumi; } -#endif - *s = sumf; +#endif } -static void ggml_vec_dot_q4_1(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) { - const int nb = n / QK; +static void ggml_vec_dot_q4_1_q8_1(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) { + const int nb = n / QK8_1; + + assert(n % QK8_1 == 0); + assert(nb % 2 == 0); const block_q4_1 * restrict x = vx; - const block_q4_1 * restrict y = vy; + const block_q8_1 * restrict y = vy; - float sumf = 0.0; + // TODO: add AVX / WASM SIMD / etc +#if defined(__ARM_NEON) + float32x4_t sumv0 = vdupq_n_f32(0.0f); + float32x4_t sumv1 = vdupq_n_f32(0.0f); -#if defined(__AVX2__) + float summs = 0; + + for (int i = 0; i < nb; i += 2) { + const block_q4_1 * restrict x0 = &x[i + 0]; + const block_q4_1 * restrict x1 = &x[i + 1]; + const block_q8_1 * restrict y0 = &y[i + 0]; + const block_q8_1 * restrict y1 = &y[i + 1]; + + summs += x0->m * (y0->s0 + y0->s1) + x1->m * (y1->s0 + y1->s1); + + const uint8x16_t m4b = vdupq_n_u8(0x0F); + + const uint8x16_t v0_0 = vld1q_u8(x0->qs); + const uint8x16_t v0_1 = vld1q_u8(x1->qs); + + // 4-bit -> 8-bit + const int8x16_t v0_0l = vreinterpretq_s8_u8(vandq_u8 (v0_0, m4b)); + const int8x16_t v0_0h = vreinterpretq_s8_u8(vshrq_n_u8(v0_0, 4)); + const int8x16_t v0_1l = vreinterpretq_s8_u8(vandq_u8 (v0_1, m4b)); + const int8x16_t v0_1h = vreinterpretq_s8_u8(vshrq_n_u8(v0_1, 4)); + + // interleave + const int8x16_t v0_0lz = vzip1q_s8(v0_0l, v0_0h); + const int8x16_t v0_0hz = vzip2q_s8(v0_0l, v0_0h); + const int8x16_t v0_1lz = vzip1q_s8(v0_1l, v0_1h); + const int8x16_t v0_1hz = vzip2q_s8(v0_1l, v0_1h); + + // load y + const int8x16_t v1_0l = vld1q_s8(y0->qs); + const int8x16_t v1_0h = vld1q_s8(y0->qs + 16); + const int8x16_t v1_1l = vld1q_s8(y1->qs); + const int8x16_t v1_1h = vld1q_s8(y1->qs + 16); + +#if defined(__ARM_FEATURE_DOTPROD) + // dot product into int32x4_t + const int32x4_t p_0 = vdotq_s32(vdotq_s32(vdupq_n_s32(0), v0_0lz, v1_0l), v0_0hz, v1_0h); + const int32x4_t p_1 = vdotq_s32(vdotq_s32(vdupq_n_s32(0), v0_1lz, v1_1l), v0_1hz, v1_1h); + + sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(p_0), x0->d*y0->d); + sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(p_1), x1->d*y1->d); +#else + const int16x8_t pl0l = vmull_s8(vget_low_s8 (v0_0lz), vget_low_s8 (v1_0l)); + const int16x8_t pl0h = vmull_s8(vget_high_s8(v0_0lz), vget_high_s8(v1_0l)); + const int16x8_t ph0l = vmull_s8(vget_low_s8 (v0_0hz), vget_low_s8 (v1_0h)); + const int16x8_t ph0h = vmull_s8(vget_high_s8(v0_0hz), vget_high_s8(v1_0h)); + + const int16x8_t pl1l = vmull_s8(vget_low_s8 (v0_1lz), vget_low_s8 (v1_1l)); + const int16x8_t pl1h = vmull_s8(vget_high_s8(v0_1lz), vget_high_s8(v1_1l)); + const int16x8_t ph1l = vmull_s8(vget_low_s8 (v0_1hz), vget_low_s8 (v1_1h)); + const int16x8_t ph1h = vmull_s8(vget_high_s8(v0_1hz), vget_high_s8(v1_1h)); + + const int32x4_t pl0 = vaddq_s32(vpaddlq_s16(pl0l), vpaddlq_s16(pl0h)); + const int32x4_t ph0 = vaddq_s32(vpaddlq_s16(ph0l), vpaddlq_s16(ph0h)); + const int32x4_t pl1 = vaddq_s32(vpaddlq_s16(pl1l), vpaddlq_s16(pl1h)); + const int32x4_t ph1 = vaddq_s32(vpaddlq_s16(ph1l), vpaddlq_s16(ph1h)); + + sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(pl0, ph0)), x0->d*y0->d); + sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(pl1, ph1)), x1->d*y1->d); +#endif + } + + *s = vaddvq_f32(sumv0) + vaddvq_f32(sumv1) + summs; +#elif defined(__AVX2__) // Initialize accumulator with zeros __m256 acc = _mm256_setzero_ps(); - // Accumulator for constant offsets - float acc_offset = 0.0f; + + float summs = 0; // Main loop for (int i = 0; i < nb; ++i) { const float * d0 = &x[i].d; const float * d1 = &y[i].d; - const float * m0 = &x[i].m; - const float * m1 = &y[i].m; + summs += x[i].m * (y[i].s0 + y[i].s1); const __m256 d0v = _mm256_broadcast_ss( d0 ); const __m256 d1v = _mm256_broadcast_ss( d1 ); - const __m256 m0v = _mm256_broadcast_ss( m0 ); - const __m256 m1v = _mm256_broadcast_ss( m1 ); - // Compute combined scale for the block - const __m256 scale_01 = _mm256_mul_ps( d0v, d1v ); - - // Compute cross scales for the block - const __m256 scale_0 = _mm256_mul_ps( d0v, m1v ); - const __m256 scale_1 = _mm256_mul_ps( m0v, d1v ); - const __m256 cross_scales = _mm256_blend_ps( scale_0, scale_1, 0xAA /* 0b10101010 */ ); + // Compute combined scales + const __m256 d0d1 = _mm256_mul_ps( d0v, d1v ); // Load 16 bytes, and unpack 4 bit fields into bytes, making 32 bytes - __m256i bx = bytesFromNibbles( x[i].qs ); - __m256i by = bytesFromNibbles( y[i].qs ); + const __m256i bx = bytes_from_nibbles_32(x[i].qs); + const __m256i by = _mm256_loadu_si256( (const __m256i *)y[i].qs ); - // Now we have a vector with bytes in [ 0 .. 15 ] interval. + const __m256 xy = mul_sum_i8_pairs_float(bx, by); - // Sign-extend first 16 signed bytes into int16_t - __m256i x16 = _mm256_cvtepi8_epi16( _mm256_castsi256_si128( bx ) ); - __m256i y16 = _mm256_cvtepi8_epi16( _mm256_castsi256_si128( by ) ); - // Compute products of int16_t integers, add pairwise - __m256i i32 = _mm256_madd_epi16( x16, y16 ); - - // Sign-extend last 16 signed bytes into int16_t vectors - __m256i x16_h = _mm256_cvtepi8_epi16( _mm256_extracti128_si256( bx, 1 ) ); - __m256i y16_h = _mm256_cvtepi8_epi16( _mm256_extracti128_si256( by, 1 ) ); - // Accumulate products of int16_t integers - i32 = _mm256_add_epi32( i32, _mm256_madd_epi16( x16_h, y16_h ) ); - - // compute sums of unsigned bytes in bx, by in blocks of 8. - // This results in a layout like X100 0000 X200 0000 X300 0000 X400 0000, - // which we then interleave as X100 Y100 X200 Y200 X300 Y300 X400 Y400. - // so if we then cast to 8 singles, we get 8 floats like [ x0_7, y0_7, x8_15, y8_15, x16_23, y16_23, x24_31, y24_31 ] - __m256i xsumi = _mm256_sad_epu8( bx, _mm256_setzero_si256() ); - __m256i ysumi = _mm256_sad_epu8( by, _mm256_setzero_si256() ); - __m256i sumsi = _mm256_or_si256( xsumi, _mm256_slli_si256( ysumi, 4 ) ); - __m256 sums = _mm256_cvtepi32_ps( sumsi ); - - // Convert int32_t to float - __m256 p = _mm256_cvtepi32_ps( i32 ); - // Apply the scale, and accumulate - // acc += d0*d1*x*y + d0*m1*x + d1*m0*y - acc = _mm256_fmadd_ps( scale_01, p, acc ); - acc = _mm256_fmadd_ps( cross_scales, sums, acc ); - // acc_offset += m0*m1 (for each entry in the block) - acc_offset += (*m0)*(*m1); + // Accumulate d0*d1*x*y + acc = _mm256_fmadd_ps( d0d1, xy, acc ); } - // Return horizontal sum of the acc vector - __m128 res = _mm256_extractf128_ps( acc, 1 ); - res = _mm_add_ps( res, _mm256_castps256_ps128( acc ) ); - res = _mm_add_ps( res, _mm_movehl_ps( res, res ) ); - res = _mm_add_ss( res, _mm_movehdup_ps( res ) ); - - sumf = _mm_cvtss_f32( res ) + acc_offset * QK; -#elif defined(__ARM_NEON) - float sum00 = 0.0f; - float sum01 = 0.0f; - float sum10 = 0.0f; - float sum11 = 0.0f; - - for (int i = 0; i < nb; i += 2) { - const block_q4_1 * restrict x0 = &x[i + 0]; - const block_q4_1 * restrict y0 = &y[i + 0]; - const block_q4_1 * restrict x1 = &x[i + 1]; - const block_q4_1 * restrict y1 = &y[i + 1]; - - const uint8x16_t m4b = vdupq_n_u8(0xf); - - const uint8x16_t v0_0 = vld1q_u8(x0->qs); - const uint8x16_t v1_0 = vld1q_u8(y0->qs); - const uint8x16_t v0_1 = vld1q_u8(x1->qs); - const uint8x16_t v1_1 = vld1q_u8(y1->qs); - - // 4-bit -> 8-bit - const uint8x16_t v0_0l = vandq_u8(v0_0, m4b); - const uint8x16_t v1_0l = vandq_u8(v1_0, m4b); - const uint8x16_t v0_0h = vshrq_n_u8(v0_0, 4); - const uint8x16_t v1_0h = vshrq_n_u8(v1_0, 4); - - const uint8x16_t v0_1l = vandq_u8(v0_1, m4b); - const uint8x16_t v1_1l = vandq_u8(v1_1, m4b); - const uint8x16_t v0_1h = vshrq_n_u8(v0_1, 4); - const uint8x16_t v1_1h = vshrq_n_u8(v1_1, 4); - - sum00 += x0->m*y0->m; - sum01 += y0->m*x0->d*(vaddvq_u8(v0_0l) + vaddvq_u8(v0_0h)); - sum10 += x0->m*y0->d*(vaddvq_u8(v1_0l) + vaddvq_u8(v1_0h)); - - sum00 += x1->m*y1->m; - sum01 += y1->m*x1->d*(vaddvq_u8(v0_1l) + vaddvq_u8(v0_1h)); - sum10 += x1->m*y1->d*(vaddvq_u8(v1_1l) + vaddvq_u8(v1_1h)); - -#if defined(__ARM_FEATURE_DOTPROD) - // dot product into int32x4_t - uint32x4_t p_0 = vdotq_u32(vdupq_n_u32(0), v0_0l, v1_0l); - uint32x4_t p_1 = vdotq_u32(vdupq_n_u32(0), v0_1l, v1_1l); - - p_0 = vdotq_u32(p_0, v0_0h, v1_0h); - p_1 = vdotq_u32(p_1, v0_1h, v1_1h); - - sum11 += x0->d*y0->d*vaddvq_u32(p_0); - sum11 += x1->d*y1->d*vaddvq_u32(p_1); -#else - const uint16x8_t pl0l = vmull_u8(vget_low_u8 (v0_0l), vget_low_u8 (v1_0l)); - const uint16x8_t pl0h = vmull_u8(vget_high_u8(v0_0l), vget_high_u8(v1_0l)); - const uint16x8_t ph0l = vmull_u8(vget_low_u8 (v0_0h), vget_low_u8 (v1_0h)); - const uint16x8_t ph0h = vmull_u8(vget_high_u8(v0_0h), vget_high_u8(v1_0h)); - - const uint16x8_t pl1l = vmull_u8(vget_low_u8 (v0_1l), vget_low_u8 (v1_1l)); - const uint16x8_t pl1h = vmull_u8(vget_high_u8(v0_1l), vget_high_u8(v1_1l)); - const uint16x8_t ph1l = vmull_u8(vget_low_u8 (v0_1h), vget_low_u8 (v1_1h)); - const uint16x8_t ph1h = vmull_u8(vget_high_u8(v0_1h), vget_high_u8(v1_1h)); - - const uint16x8_t pl_0 = vaddq_u16(pl0l, pl0h); - const uint16x8_t ph_0 = vaddq_u16(ph0l, ph0h); - - const uint16x8_t pl_1 = vaddq_u16(pl1l, pl1h); - const uint16x8_t ph_1 = vaddq_u16(ph1l, ph1h); - - const uint16x8_t p_0 = vaddq_u16(pl_0, ph_0); - const uint16x8_t p_1 = vaddq_u16(pl_1, ph_1); - - sum11 += x0->d*y0->d*vaddvq_u16(p_0); - sum11 += x1->d*y1->d*vaddvq_u16(p_1); -#endif - } - - sumf = QK*sum00 + sum01 + sum10 + sum11; + *s = hsum_float_8(acc) + summs; #else // scalar + float sumf = 0.0; for (int i = 0; i < nb; i++) { const float d0 = x[i].d; + const float m0 = x[i].m; const float d1 = y[i].d; - const float m0 = x[i].m; - const float m1 = y[i].m; - const uint8_t * restrict p0 = x[i].qs; - const uint8_t * restrict p1 = y[i].qs; + const int8_t * restrict p1 = y[i].qs; - for (int j = 0; j < QK/2; j++) { + // TODO: this is very slow .. + for (int j = 0; j < QK8_1/2; j++) { const uint8_t v0 = p0[j]; - const uint8_t v1 = p1[j]; - const float f0 = d0*(v0 & 0xf) + m0; - const float f1 = d0*(v0 >> 4) + m0; + const float f0 = d0*(v0 & 0x0F) + m0; + const float f1 = d0*(v0 >> 4) + m0; - const float f2 = d1*(v1 & 0xf) + m1; - const float f3 = d1*(v1 >> 4) + m1; + const float f2 = d1*p1[2*j + 0]; + const float f3 = d1*p1[2*j + 1]; sumf += f0*f2 + f1*f3; } } + *s = sumf; #endif +} + +static void ggml_vec_dot_q4_2_q8_0(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) { + const int nb = n / QK8_0; + + assert(n % QK8_0 == 0); + assert(nb % 2 == 0); + assert(QK8_0 == 2*QK4_2); + + const block_q4_2 * restrict x = vx; + const block_q8_0 * restrict y = vy; + +#if defined(__ARM_NEON) + float32x4_t sumv0 = vdupq_n_f32(0.0f); + float32x4_t sumv1 = vdupq_n_f32(0.0f); + + for (int i = 0; i < nb; i += 2) { + const block_q4_2 * restrict x0_0 = &x[2*(i + 0) + 0]; + const block_q4_2 * restrict x0_1 = &x[2*(i + 0) + 1]; + const block_q4_2 * restrict x1_0 = &x[2*(i + 1) + 0]; + const block_q4_2 * restrict x1_1 = &x[2*(i + 1) + 1]; + + const block_q8_0 * restrict y0 = &y[i + 0]; + const block_q8_0 * restrict y1 = &y[i + 1]; + + const uint8x16_t m4b = vdupq_n_u8(0x0F); + const int8x16_t s8b = vdupq_n_s8(0x8); + + const uint8x16_t v0_0 = vcombine_u8(vld1_u8(x0_0->qs), vld1_u8(x0_1->qs)); + const uint8x16_t v0_1 = vcombine_u8(vld1_u8(x1_0->qs), vld1_u8(x1_1->qs)); + + // 4-bit -> 8-bit + const int8x16_t v0_0l = vreinterpretq_s8_u8(vandq_u8 (v0_0, m4b)); + const int8x16_t v0_0h = vreinterpretq_s8_u8(vshrq_n_u8(v0_0, 4)); + const int8x16_t v0_1l = vreinterpretq_s8_u8(vandq_u8 (v0_1, m4b)); + const int8x16_t v0_1h = vreinterpretq_s8_u8(vshrq_n_u8(v0_1, 4)); + + // sub 8 + const int8x16_t v0_0ls = vsubq_s8(v0_0l, s8b); + const int8x16_t v0_0hs = vsubq_s8(v0_0h, s8b); + const int8x16_t v0_1ls = vsubq_s8(v0_1l, s8b); + const int8x16_t v0_1hs = vsubq_s8(v0_1h, s8b); + + // interleave + const int8x16_t v0_0lz = vzip1q_s8(v0_0ls, v0_0hs); + const int8x16_t v0_0hz = vzip2q_s8(v0_0ls, v0_0hs); + const int8x16_t v0_1lz = vzip1q_s8(v0_1ls, v0_1hs); + const int8x16_t v0_1hz = vzip2q_s8(v0_1ls, v0_1hs); + + // load y + const int8x16_t v1_0l = vld1q_s8(y0->qs); + const int8x16_t v1_0h = vld1q_s8(y0->qs + 16); + const int8x16_t v1_1l = vld1q_s8(y1->qs); + const int8x16_t v1_1h = vld1q_s8(y1->qs + 16); + +#if defined(__ARM_FEATURE_DOTPROD) + sumv0 = vmlaq_n_f32(sumv0, vaddq_f32( + vmulq_n_f32(vcvtq_f32_s32(vdotq_s32(vdupq_n_s32(0), v0_0lz, v1_0l)), GGML_FP16_TO_FP32(x0_0->d)), + vmulq_n_f32(vcvtq_f32_s32(vdotq_s32(vdupq_n_s32(0), v0_0hz, v1_0h)), GGML_FP16_TO_FP32(x0_1->d))), y0->d); + + sumv1 = vmlaq_n_f32(sumv1, vaddq_f32( + vmulq_n_f32(vcvtq_f32_s32(vdotq_s32(vdupq_n_s32(0), v0_1lz, v1_1l)), GGML_FP16_TO_FP32(x1_0->d)), + vmulq_n_f32(vcvtq_f32_s32(vdotq_s32(vdupq_n_s32(0), v0_1hz, v1_1h)), GGML_FP16_TO_FP32(x1_1->d))), y1->d); +#else + const int16x8_t pl0l = vmull_s8(vget_low_s8 (v0_0lz), vget_low_s8 (v1_0l)); + const int16x8_t pl0h = vmull_s8(vget_high_s8(v0_0lz), vget_high_s8(v1_0l)); + const int16x8_t ph0l = vmull_s8(vget_low_s8 (v0_0hz), vget_low_s8 (v1_0h)); + const int16x8_t ph0h = vmull_s8(vget_high_s8(v0_0hz), vget_high_s8(v1_0h)); + + const int16x8_t pl1l = vmull_s8(vget_low_s8 (v0_1lz), vget_low_s8 (v1_1l)); + const int16x8_t pl1h = vmull_s8(vget_high_s8(v0_1lz), vget_high_s8(v1_1l)); + const int16x8_t ph1l = vmull_s8(vget_low_s8 (v0_1hz), vget_low_s8 (v1_1h)); + const int16x8_t ph1h = vmull_s8(vget_high_s8(v0_1hz), vget_high_s8(v1_1h)); + + const int32x4_t pl0 = vaddq_s32(vpaddlq_s16(pl0l), vpaddlq_s16(pl0h)); + const int32x4_t ph0 = vaddq_s32(vpaddlq_s16(ph0l), vpaddlq_s16(ph0h)); + const int32x4_t pl1 = vaddq_s32(vpaddlq_s16(pl1l), vpaddlq_s16(pl1h)); + const int32x4_t ph1 = vaddq_s32(vpaddlq_s16(ph1l), vpaddlq_s16(ph1h)); + + sumv0 = vmlaq_n_f32(sumv0, vaddq_f32( + vmulq_n_f32(vcvtq_f32_s32(pl0), GGML_FP16_TO_FP32(x0_0->d)), + vmulq_n_f32(vcvtq_f32_s32(ph0), GGML_FP16_TO_FP32(x0_1->d))), y0->d); + + sumv1 = vmlaq_n_f32(sumv1, vaddq_f32( + vmulq_n_f32(vcvtq_f32_s32(pl1), GGML_FP16_TO_FP32(x1_0->d)), + vmulq_n_f32(vcvtq_f32_s32(ph1), GGML_FP16_TO_FP32(x1_1->d))), y1->d); +#endif + } + + *s = vaddvq_f32(sumv0) + vaddvq_f32(sumv1); +#elif defined(__AVX2__) + // Initialize accumulator with zeros + __m256 acc = _mm256_setzero_ps(); + + // Main loop + for (int i = 0; i < nb; i++) { + /* Compute combined scale for the block */ + const __m128 d0 = _mm_set1_ps(GGML_FP16_TO_FP32(x[2*i + 0].d)); + const __m128 d1 = _mm_set1_ps(GGML_FP16_TO_FP32(x[2*i + 1].d)); + const __m256 d = _mm256_mul_ps(_mm256_set_m128(d1, d0), _mm256_broadcast_ss(&y[i].d)); + + __m128i bx0 = bytes_from_nibbles_16(x[2*i + 0].qs); + __m128i bx1 = bytes_from_nibbles_16(x[2*i + 1].qs); + __m256i bx = _mm256_set_m128i(bx1, bx0); + + // Now we have a vector with bytes in [ 0 .. 15 ] interval. Offset them into [ -8 .. +7 ] interval. + const __m256i off = _mm256_set1_epi8(8); + bx = _mm256_sub_epi8(bx, off); + + __m256i by = _mm256_loadu_si256((const __m256i *)y[i].qs); + + const __m256 q = mul_sum_i8_pairs_float(bx, by); + + /* Multiply q with scale and accumulate */ + acc = _mm256_fmadd_ps(d, q, acc); + } + + *s = hsum_float_8(acc); +#else + // scalar + float sumf = 0.0; + for (int i = 0; i < nb; i++) { + const uint8_t * restrict x0 = x[2*i + 0].qs; + const uint8_t * restrict x1 = x[2*i + 1].qs; + const int8_t * restrict y0 = y[i].qs; + + const float d0 = GGML_FP16_TO_FP32(x[2*i + 0].d); + const float d1 = GGML_FP16_TO_FP32(x[2*i + 1].d); + + int sumi_0 = 0; + int sumi_1 = 0; + + for (int j = 0; j < QK8_0/4; j++) { + const uint8_t v0 = x0[j]; + const uint8_t v1 = x1[j]; + + const int i0_0 = (int8_t) (v0 & 0x0F) - 8; + const int i1_0 = (int8_t) (v0 >> 4) - 8; + + const int i0_1 = (int8_t) (v1 & 0x0F) - 8; + const int i1_1 = (int8_t) (v1 >> 4) - 8; + + const int i2_0 = y0[2*j + 0]; + const int i3_0 = y0[2*j + 1]; + + const int i2_1 = y0[2*(j + QK8_0/4) + 0]; + const int i3_1 = y0[2*(j + QK8_0/4) + 1]; + + sumi_0 += i0_0*i2_0 + i1_0*i3_0; + sumi_1 += i0_1*i2_1 + i1_1*i3_1; + } + + sumf += (d0 * y[i].d) * sumi_0; + sumf += (d1 * y[i].d) * sumi_1; + } + *s = sumf; +#endif +} + +static void ggml_vec_dot_q5_0_q8_0(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) { + const int nb = n / QK8_0; + + assert(n % QK8_0 == 0); + assert(nb % 2 == 0); + assert(QK8_0 == QK5_0); + + const block_q5_0 * restrict x = vx; + const block_q8_0 * restrict y = vy; + +#if defined(__ARM_NEON) + float32x4_t sumv = vdupq_n_f32(0.0f); + + uint64_t tmp[4]; + + for (int i = 0; i < nb; ++i) { + const block_q5_0 * restrict x0 = &x[i]; + const block_q8_0 * restrict y0 = &y[i]; + + const uint8x16_t m4b = vdupq_n_u8(0x0F); + const int8x16_t s16b = vdupq_n_s8(0x10); + + // extract the 5th bit + uint32_t qh; + memcpy(&qh, x0->qh, sizeof(qh)); + + tmp[0] = table_b2b_u[(qh >> 0) & 0xFF]; + tmp[1] = table_b2b_u[(qh >> 8) & 0xFF]; + tmp[2] = table_b2b_u[(qh >> 16) & 0xFF]; + tmp[3] = table_b2b_u[(qh >> 24) ]; + + const int8x16_t qhl = vld1q_s8((const int8_t *)(tmp + 0)); + const int8x16_t qhh = vld1q_s8((const int8_t *)(tmp + 2)); + + const uint8x16_t v0 = vld1q_u8(x0->qs); + + // 4-bit -> 8-bit + const int8x16_t v0l = vreinterpretq_s8_u8(vandq_u8 (v0, m4b)); + const int8x16_t v0h = vreinterpretq_s8_u8(vshrq_n_u8(v0, 4)); + + // interleave + const int8x16_t v0lz = vzip1q_s8(v0l, v0h); + const int8x16_t v0hz = vzip2q_s8(v0l, v0h); + + // add high bit and sub 16 + const int8x16_t v0lf = vsubq_s8(vorrq_s8(v0lz, qhl), s16b); + const int8x16_t v0hf = vsubq_s8(vorrq_s8(v0hz, qhh), s16b); + + // load y + const int8x16_t v1l = vld1q_s8(y0->qs); + const int8x16_t v1h = vld1q_s8(y0->qs + 16); + + const float x0d = GGML_FP16_TO_FP32(x0->d); + +#if defined(__ARM_FEATURE_DOTPROD) + sumv = vmlaq_n_f32(sumv, vcvtq_f32_s32(vaddq_s32( + vdotq_s32(vdupq_n_s32(0), v0lf, v1l), + vdotq_s32(vdupq_n_s32(0), v0hf, v1h))), x0d*y0->d); +#else + const int16x8_t pl0l = vmull_s8(vget_low_s8 (v0lf), vget_low_s8 (v1l)); + const int16x8_t pl0h = vmull_s8(vget_high_s8(v0lf), vget_high_s8(v1l)); + const int16x8_t ph0l = vmull_s8(vget_low_s8 (v0hf), vget_low_s8 (v1h)); + const int16x8_t ph0h = vmull_s8(vget_high_s8(v0hf), vget_high_s8(v1h)); + + const int32x4_t pl0 = vaddq_s32(vpaddlq_s16(pl0l), vpaddlq_s16(pl0h)); + const int32x4_t ph0 = vaddq_s32(vpaddlq_s16(ph0l), vpaddlq_s16(ph0h)); + + sumv = vmlaq_n_f32(sumv, vcvtq_f32_s32(vaddq_s32(pl0, ph0)), x0d*y0->d); +#endif + } + + *s = vaddvq_f32(sumv); +#elif defined(__AVX2__) + // Initialize accumulator with zeros + __m256 acc = _mm256_setzero_ps(); + + // Main loop + for (int i = 0; i < nb; i++) { + /* Compute combined scale for the block */ + const __m256 d = _mm256_mul_ps(_mm256_set1_ps(GGML_FP16_TO_FP32(x[i].d)), _mm256_broadcast_ss(&y[i].d)); + + __m256i bx = bytes_from_nibbles_32(x[i].qs); + __m256i bxhi = bytes_from_bits_32(x[i].qh); + bxhi = _mm256_andnot_si256(bxhi, _mm256_set1_epi8((char)0xF0)); + bx = _mm256_or_si256(bx, bxhi); + + __m256i by = _mm256_loadu_si256((const __m256i *)y[i].qs); + + const __m256 q = mul_sum_i8_pairs_float(bx, by); + + /* Multiply q with scale and accumulate */ + acc = _mm256_fmadd_ps(d, q, acc); + } + + *s = hsum_float_8(acc); +#else + // scalar + float sumf = 0.0; + for (int i = 0; i < nb; i++) { + const uint8_t * restrict x0 = x[i].qs; + const int8_t * restrict y0 = y[i].qs; + + uint32_t qh; + memcpy(&qh, x[i].qh, sizeof(qh)); + + const float d = GGML_FP16_TO_FP32(x[i].d); + + int sxy = 0; + + for (int j = 0; j < QK8_0/2; j++) { + const uint8_t v0 = x0[j]; + + const int x0_0h = ((qh & (1 << (2*j + 0))) >> (2*j + 0)) << 4; + const int x1_0h = ((qh & (1 << (2*j + 1))) >> (2*j + 1)) << 4; + + const int x0_0 = ((v0 & 0x0F) | x0_0h) - 16; + const int x1_0 = ((v0 >> 4) | x1_0h) - 16; + + const int y0_0 = y0[2*j + 0]; + const int y1_0 = y0[2*j + 1]; + + sxy += x0_0*y0_0 + x1_0*y1_0; + } + + sumf += (d*sxy)*y[i].d; + } + *s = sumf; +#endif +} + +static void ggml_vec_dot_q5_1_q8_1(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) { + const int nb = n / QK8_1; + + assert(n % QK8_1 == 0); + assert(nb % 2 == 0); + assert(QK8_1 == QK5_1); + + const block_q5_1 * restrict x = vx; + const block_q8_1 * restrict y = vy; + +#if defined(__ARM_NEON) + float32x4_t sumv = vdupq_n_f32(0.0f); + + float summs = 0.0f; + + uint64_t tmp[4]; + + for (int i = 0; i < nb; ++i) { + const block_q5_1 * restrict x0 = &x[i]; + const block_q8_1 * restrict y0 = &y[i]; + + summs += GGML_FP16_TO_FP32(x0->m) * (y0->s0 + y0->s1); + + // extract the 5th bit + uint32_t qh; + memcpy(&qh, x0->qh, sizeof(qh)); + + tmp[0] = table_b2b_u[(qh >> 0) & 0xFF]; + tmp[1] = table_b2b_u[(qh >> 8) & 0xFF]; + tmp[2] = table_b2b_u[(qh >> 16) & 0xFF]; + tmp[3] = table_b2b_u[(qh >> 24) ]; + + const int8x16_t qhl = vld1q_s8((const int8_t *)(tmp + 0)); + const int8x16_t qhh = vld1q_s8((const int8_t *)(tmp + 2)); + + const uint8x16_t v0 = vld1q_u8(x0->qs); + + // 4-bit -> 8-bit + const int8x16_t v0l = vreinterpretq_s8_u8(vandq_u8 (v0, vdupq_n_u8(0x0F))); + const int8x16_t v0h = vreinterpretq_s8_u8(vshrq_n_u8(v0, 4)); + + // interleave + const int8x16_t v0lz = vzip1q_s8(v0l, v0h); + const int8x16_t v0hz = vzip2q_s8(v0l, v0h); + + // add + const int8x16_t v0lf = vorrq_s8(v0lz, qhl); + const int8x16_t v0hf = vorrq_s8(v0hz, qhh); + + // load y + const int8x16_t v1l = vld1q_s8(y0->qs); + const int8x16_t v1h = vld1q_s8(y0->qs + 16); + + const float x0d = GGML_FP16_TO_FP32(x0->d); + +#if defined(__ARM_FEATURE_DOTPROD) + sumv = vmlaq_n_f32(sumv, vcvtq_f32_s32(vaddq_s32( + vdotq_s32(vdupq_n_s32(0), v0lf, v1l), + vdotq_s32(vdupq_n_s32(0), v0hf, v1h))), x0d*y0->d); +#else + const int16x8_t pl0l = vmull_s8(vget_low_s8 (v0lf), vget_low_s8 (v1l)); + const int16x8_t pl0h = vmull_s8(vget_high_s8(v0lf), vget_high_s8(v1l)); + const int16x8_t ph0l = vmull_s8(vget_low_s8 (v0hf), vget_low_s8 (v1h)); + const int16x8_t ph0h = vmull_s8(vget_high_s8(v0hf), vget_high_s8(v1h)); + + const int32x4_t pl0 = vaddq_s32(vpaddlq_s16(pl0l), vpaddlq_s16(pl0h)); + const int32x4_t ph0 = vaddq_s32(vpaddlq_s16(ph0l), vpaddlq_s16(ph0h)); + + sumv = vmlaq_n_f32(sumv, vcvtq_f32_s32(vaddq_s32(pl0, ph0)), x0d*y0->d); +#endif + } + + *s = vaddvq_f32(sumv) + summs; +#elif defined(__AVX2__) + // Initialize accumulator with zeros + __m256 acc = _mm256_setzero_ps(); + float summs = 0.0f; + + // Main loop + for (int i = 0; i < nb; i++) { + const __m256 dx = _mm256_set1_ps(GGML_FP16_TO_FP32(x[i].d)); + + summs += GGML_FP16_TO_FP32(x[i].m) * (y[i].s0 + y[i].s1); + + __m256i bx = bytes_from_nibbles_32(x[i].qs); + __m256i bxhi = bytes_from_bits_32(x[i].qh); + bxhi = _mm256_and_si256(bxhi, _mm256_set1_epi8(0x10)); + bx = _mm256_or_si256(bx, bxhi); + + const __m256 dy = _mm256_broadcast_ss(&y[i].d); + const __m256i by = _mm256_loadu_si256((const __m256i *)y[i].qs); + + const __m256 q = mul_sum_i8_pairs_float(bx, by); + + acc = _mm256_fmadd_ps(q, _mm256_mul_ps(dx, dy), acc); + } + + *s = hsum_float_8(acc) + summs; +#else + float sumf = 0.0; + + for (int i = 0; i < nb; i++) { + const uint8_t * restrict x0 = x[i].qs; + const int8_t * restrict y0 = y[i].qs; + + uint32_t qh; + memcpy(&qh, x[i].qh, sizeof(qh)); + + const float d = GGML_FP16_TO_FP32(x[i].d); + const float m = GGML_FP16_TO_FP32(x[i].m); + + int sxy = 0; + + for (int j = 0; j < QK8_1/2; j++) { + const uint8_t v0 = x0[j]; + + const int x0_0h = ((qh & (1 << (2*j + 0))) >> (2*j + 0)) << 4; + const int x1_0h = ((qh & (1 << (2*j + 1))) >> (2*j + 1)) << 4; + + const int x0_0 = (v0 & 0x0F) | x0_0h; + const int x1_0 = (v0 >> 4) | x1_0h; + + const int y0_0 = y0[2*j + 0]; + const int y1_0 = y0[2*j + 1]; + + sxy += x0_0*y0_0 + x1_0*y1_0; + } + + sumf += (d*sxy)*y[i].d + m*(y[i].s0 + y[i].s1); + } *s = sumf; +#endif +} + +static void ggml_vec_dot_q8_0_q8_0(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) { + const int nb = n / QK8_0; + + assert(n % QK8_0 == 0); + assert(nb % 2 == 0); + assert(QK8_0 == QK8_0); + + const block_q8_0 * restrict x = vx; + const block_q8_0 * restrict y = vy; + +#if defined(__ARM_NEON) + float32x4_t sumv0 = vdupq_n_f32(0.0f); + float32x4_t sumv1 = vdupq_n_f32(0.0f); + + for (int i = 0; i < nb; i += 2) { + const block_q8_0 * restrict x0 = &x[i + 0]; + const block_q8_0 * restrict x1 = &x[i + 1]; + const block_q8_0 * restrict y0 = &y[i + 0]; + const block_q8_0 * restrict y1 = &y[i + 1]; + + const int8x16_t x0_0 = vld1q_s8(x0->qs); + const int8x16_t x0_1 = vld1q_s8(x0->qs + 16); + const int8x16_t x1_0 = vld1q_s8(x1->qs); + const int8x16_t x1_1 = vld1q_s8(x1->qs + 16); + + // load y + const int8x16_t y0_0 = vld1q_s8(y0->qs); + const int8x16_t y0_1 = vld1q_s8(y0->qs + 16); + const int8x16_t y1_0 = vld1q_s8(y1->qs); + const int8x16_t y1_1 = vld1q_s8(y1->qs + 16); + +#if defined(__ARM_FEATURE_DOTPROD) + sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32( + vdotq_s32(vdupq_n_s32(0), x0_0, y0_0), + vdotq_s32(vdupq_n_s32(0), x0_1, y0_1))), x0->d*y0->d); + + sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32( + vdotq_s32(vdupq_n_s32(0), x1_0, y1_0), + vdotq_s32(vdupq_n_s32(0), x1_1, y1_1))), x1->d*y1->d); + +#else + const int16x8_t p0_0 = vmull_s8(vget_low_s8 (x0_0), vget_low_s8 (y0_0)); + const int16x8_t p0_1 = vmull_s8(vget_high_s8(x0_0), vget_high_s8(y0_0)); + const int16x8_t p0_2 = vmull_s8(vget_low_s8 (x0_1), vget_low_s8 (y0_1)); + const int16x8_t p0_3 = vmull_s8(vget_high_s8(x0_1), vget_high_s8(y0_1)); + + const int16x8_t p1_0 = vmull_s8(vget_low_s8 (x1_0), vget_low_s8 (y1_0)); + const int16x8_t p1_1 = vmull_s8(vget_high_s8(x1_0), vget_high_s8(y1_0)); + const int16x8_t p1_2 = vmull_s8(vget_low_s8 (x1_1), vget_low_s8 (y1_1)); + const int16x8_t p1_3 = vmull_s8(vget_high_s8(x1_1), vget_high_s8(y1_1)); + + const int32x4_t p0 = vaddq_s32(vpaddlq_s16(p0_0), vpaddlq_s16(p0_1)); + const int32x4_t p1 = vaddq_s32(vpaddlq_s16(p0_2), vpaddlq_s16(p0_3)); + const int32x4_t p2 = vaddq_s32(vpaddlq_s16(p1_0), vpaddlq_s16(p1_1)); + const int32x4_t p3 = vaddq_s32(vpaddlq_s16(p1_2), vpaddlq_s16(p1_3)); + + sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(p0, p1)), x0->d*y0->d); + sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(p2, p3)), x1->d*y1->d); +#endif + } + + *s = vaddvq_f32(sumv0) + vaddvq_f32(sumv1); +#elif defined(__AVX2__) + // Initialize accumulator with zeros + __m256 acc = _mm256_setzero_ps(); + + // Main loop + for (int i = 0; i < nb; ++i) { + // Compute combined scale for the block + const __m256 d = _mm256_mul_ps( _mm256_broadcast_ss( &x[i].d ), _mm256_broadcast_ss( &y[i].d ) ); + __m256i bx = _mm256_loadu_si256((const __m256i *)x[i].qs); + __m256i by = _mm256_loadu_si256((const __m256i *)y[i].qs); + + const __m256 q = mul_sum_i8_pairs_float(bx, by); + + // Multiply q with scale and accumulate + acc = _mm256_fmadd_ps( d, q, acc ); + } + + *s = hsum_float_8(acc); +#else + // scalar + float sumf = 0.0; + + for (int i = 0; i < nb; i++) { + const int8_t * restrict x0 = x[i].qs; + const int8_t * restrict y0 = y[i].qs; + + int sumi = 0; + + for (int j = 0; j < QK8_0; j++) { + const int v0 = x0[j]; + const int v1 = y0[j]; + + sumi += v0*v1; + } + + sumf += (x[i].d*y[i].d)*sumi; + } + + *s = sumf; +#endif } // compute GGML_VEC_DOT_UNROLL dot products at once @@ -2613,6 +3646,14 @@ inline static void ggml_vec_sum_f32(const int n, float * s, const float * x) { #endif } +inline static void ggml_vec_sum_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_max_f32(const int n, float * s, const float * x) { #ifndef GGML_USE_ACCELERATE float max = -INFINITY; @@ -2661,24 +3702,67 @@ inline static void ggml_vec_norm_inv_f32(const int n, float * s, const float * x static const int GGML_BLCK_SIZE[GGML_TYPE_COUNT] = { [GGML_TYPE_F32] = 1, [GGML_TYPE_F16] = 1, - [GGML_TYPE_Q4_0] = QK, - [GGML_TYPE_Q4_1] = QK, + [GGML_TYPE_Q4_0] = QK4_0, + [GGML_TYPE_Q4_1] = QK4_1, + [GGML_TYPE_Q4_2] = QK4_2, + [GGML_TYPE_Q5_0] = QK5_0, + [GGML_TYPE_Q5_1] = QK5_1, + [GGML_TYPE_Q8_0] = QK8_0, + [GGML_TYPE_Q8_1] = QK8_1, [GGML_TYPE_I8] = 1, [GGML_TYPE_I16] = 1, [GGML_TYPE_I32] = 1, }; -static_assert(GGML_TYPE_COUNT == 7, "GGML_BLCK_SIZE is outdated"); +static_assert(GGML_TYPE_COUNT == 13, "GGML_BLCK_SIZE is outdated"); static const size_t GGML_TYPE_SIZE[GGML_TYPE_COUNT] = { [GGML_TYPE_F32] = sizeof(float), [GGML_TYPE_F16] = sizeof(ggml_fp16_t), [GGML_TYPE_Q4_0] = sizeof(block_q4_0), [GGML_TYPE_Q4_1] = sizeof(block_q4_1), + [GGML_TYPE_Q4_2] = sizeof(block_q4_2), + [GGML_TYPE_Q5_0] = sizeof(block_q5_0), + [GGML_TYPE_Q5_1] = sizeof(block_q5_1), + [GGML_TYPE_Q8_0] = sizeof(block_q8_0), + [GGML_TYPE_Q8_1] = sizeof(block_q8_1), [GGML_TYPE_I8] = sizeof(int8_t), [GGML_TYPE_I16] = sizeof(int16_t), [GGML_TYPE_I32] = sizeof(int32_t), }; -static_assert(GGML_TYPE_COUNT == 7, "GGML_TYPE_SIZE is outdated"); +static_assert(GGML_TYPE_COUNT == 13, "GGML_TYPE_SIZE is outdated"); + + +static const char * GGML_TYPE_NAME[GGML_TYPE_COUNT] = { + [GGML_TYPE_F32] = "f32", + [GGML_TYPE_F16] = "f16", + [GGML_TYPE_Q4_0] = "q4_0", + [GGML_TYPE_Q4_1] = "q4_1", + [GGML_TYPE_Q4_2] = "q4_2", + [GGML_TYPE_Q5_0] = "q5_0", + [GGML_TYPE_Q5_1] = "q5_1", + [GGML_TYPE_Q8_0] = "q8_0", + [GGML_TYPE_Q8_1] = "q8_1", + [GGML_TYPE_I8] = "i8", + [GGML_TYPE_I16] = "i16", + [GGML_TYPE_I32] = "i32", +}; +static_assert(GGML_TYPE_COUNT == 13, "GGML_TYPE_NAME is outdated"); + +static bool GGML_IS_QUANTIZED[GGML_TYPE_COUNT] = { + [GGML_TYPE_F32] = false, + [GGML_TYPE_F16] = false, + [GGML_TYPE_Q4_0] = true, + [GGML_TYPE_Q4_1] = true, + [GGML_TYPE_Q4_2] = true, + [GGML_TYPE_Q5_0] = true, + [GGML_TYPE_Q5_1] = true, + [GGML_TYPE_Q8_0] = true, + [GGML_TYPE_Q8_1] = true, + [GGML_TYPE_I8] = false, + [GGML_TYPE_I16] = false, + [GGML_TYPE_I32] = false, +}; +static_assert(GGML_TYPE_COUNT == 13, "GGML_IS_QUANTIZED is outdated"); static const char * GGML_OP_LABEL[GGML_OP_COUNT] = { "NONE", @@ -2726,7 +3810,7 @@ static const char * GGML_OP_LABEL[GGML_OP_COUNT] = { "MAP_BINARY", }; -static_assert(GGML_OP_COUNT == 38, "GGML_OP_COUNT != 38"); +static_assert(GGML_OP_COUNT == 39, "GGML_OP_COUNT != 39"); static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = { "none", @@ -2774,7 +3858,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = { "f(x,y)", }; -static_assert(GGML_OP_COUNT == 38, "GGML_OP_COUNT != 38"); +static_assert(GGML_OP_COUNT == 39, "GGML_OP_COUNT != 39"); 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"); @@ -2904,6 +3988,11 @@ float ggml_type_sizef(enum ggml_type type) { return ((float)(GGML_TYPE_SIZE[type]))/GGML_BLCK_SIZE[type]; } +const char * ggml_type_name(enum ggml_type type) { + return GGML_TYPE_NAME[type]; +} + + size_t ggml_element_size(const struct ggml_tensor * tensor) { return GGML_TYPE_SIZE[tensor->type]; } @@ -2935,6 +4024,10 @@ static inline bool ggml_can_mul_mat(const struct ggml_tensor * t0, const struct (t0->ne[3] == t1->ne[3]); } +bool ggml_is_quantized(enum ggml_type type) { + return GGML_IS_QUANTIZED[type]; +} + static inline bool ggml_is_transposed(const struct ggml_tensor * tensor) { return tensor->nb[0] > tensor->nb[1]; } @@ -3045,6 +4138,13 @@ struct ggml_context * ggml_init(struct ggml_init_params params) { GGML_PRINT_DEBUG("%s: g_state initialized in %f ms\n", __func__, (t_end - t_start)/1000.0f); } + // initialize cuBLAS + #if defined(GGML_USE_CUBLAS) + ggml_init_cublas(); + #elif defined(GGML_USE_CLBLAST) + ggml_cl_init(); + #endif + is_first_call = false; } @@ -3346,14 +4446,6 @@ struct ggml_tensor * ggml_set_i32 (struct ggml_tensor * tensor, int32_t value) { char * const data = tensor->data; switch (tensor->type) { - case GGML_TYPE_Q4_0: - { - GGML_ASSERT(false); - } break; - case GGML_TYPE_Q4_1: - { - GGML_ASSERT(false); - } break; case GGML_TYPE_I8: { assert(tensor->nb[0] == sizeof(int8_t)); @@ -3389,7 +4481,7 @@ struct ggml_tensor * ggml_set_i32 (struct ggml_tensor * tensor, int32_t value) { ggml_vec_set_f32(nc, (float *)(data + i*n1), value); } } break; - case GGML_TYPE_COUNT: + default: { GGML_ASSERT(false); } break; @@ -3406,14 +4498,6 @@ struct ggml_tensor * ggml_set_f32(struct ggml_tensor * tensor, float value) { char * const data = tensor->data; switch (tensor->type) { - case GGML_TYPE_Q4_0: - { - GGML_ASSERT(false); - } break; - case GGML_TYPE_Q4_1: - { - GGML_ASSERT(false); - } break; case GGML_TYPE_I8: { assert(tensor->nb[0] == sizeof(int8_t)); @@ -3449,7 +4533,7 @@ struct ggml_tensor * ggml_set_f32(struct ggml_tensor * tensor, float value) { ggml_vec_set_f32(nc, (float *)(data + i*n1), value); } } break; - case GGML_TYPE_COUNT: + default: { GGML_ASSERT(false); } break; @@ -3460,14 +4544,6 @@ struct ggml_tensor * ggml_set_f32(struct ggml_tensor * tensor, float value) { int32_t ggml_get_i32_1d(const struct ggml_tensor * tensor, int i) { switch (tensor->type) { - case GGML_TYPE_Q4_0: - { - GGML_ASSERT(false); - } break; - case GGML_TYPE_Q4_1: - { - GGML_ASSERT(false); - } break; case GGML_TYPE_I8: { GGML_ASSERT(tensor->nb[0] == sizeof(int8_t)); @@ -3493,7 +4569,7 @@ int32_t ggml_get_i32_1d(const struct ggml_tensor * tensor, int i) { GGML_ASSERT(tensor->nb[0] == sizeof(float)); return ((float *)(tensor->data))[i]; } break; - case GGML_TYPE_COUNT: + default: { GGML_ASSERT(false); } break; @@ -3504,14 +4580,6 @@ int32_t ggml_get_i32_1d(const struct ggml_tensor * tensor, int i) { void ggml_set_i32_1d(const struct ggml_tensor * tensor, int i, int32_t value) { switch (tensor->type) { - case GGML_TYPE_Q4_0: - { - GGML_ASSERT(false); - } break; - case GGML_TYPE_Q4_1: - { - GGML_ASSERT(false); - } break; case GGML_TYPE_I8: { GGML_ASSERT(tensor->nb[0] == sizeof(int8_t)); @@ -3537,7 +4605,7 @@ void ggml_set_i32_1d(const struct ggml_tensor * tensor, int i, int32_t value) { GGML_ASSERT(tensor->nb[0] == sizeof(float)); ((float *)(tensor->data))[i] = value; } break; - case GGML_TYPE_COUNT: + default: { GGML_ASSERT(false); } break; @@ -3546,14 +4614,6 @@ void ggml_set_i32_1d(const struct ggml_tensor * tensor, int i, int32_t value) { float ggml_get_f32_1d(const struct ggml_tensor * tensor, int i) { switch (tensor->type) { - case GGML_TYPE_Q4_0: - { - GGML_ASSERT(false); - } break; - case GGML_TYPE_Q4_1: - { - GGML_ASSERT(false); - } break; case GGML_TYPE_I8: { GGML_ASSERT(tensor->nb[0] == sizeof(int8_t)); @@ -3579,7 +4639,7 @@ float ggml_get_f32_1d(const struct ggml_tensor * tensor, int i) { GGML_ASSERT(tensor->nb[0] == sizeof(float)); return ((float *)(tensor->data))[i]; } break; - case GGML_TYPE_COUNT: + default: { GGML_ASSERT(false); } break; @@ -3590,14 +4650,6 @@ float ggml_get_f32_1d(const struct ggml_tensor * tensor, int i) { void ggml_set_f32_1d(const struct ggml_tensor * tensor, int i, float value) { switch (tensor->type) { - case GGML_TYPE_Q4_0: - { - GGML_ASSERT(false); - } break; - case GGML_TYPE_Q4_1: - { - GGML_ASSERT(false); - } break; case GGML_TYPE_I8: { GGML_ASSERT(tensor->nb[0] == sizeof(int8_t)); @@ -3623,7 +4675,7 @@ void ggml_set_f32_1d(const struct ggml_tensor * tensor, int i, float value) { GGML_ASSERT(tensor->nb[0] == sizeof(float)); ((float *)(tensor->data))[i] = value; } break; - case GGML_TYPE_COUNT: + default: { GGML_ASSERT(false); } break; @@ -4804,6 +5856,37 @@ struct ggml_tensor * ggml_rope( return result; } +// ggml_alibi + +struct ggml_tensor * ggml_alibi( + struct ggml_context * ctx, + struct ggml_tensor * a, + int n_past, + int n_head) { + GGML_ASSERT(n_past >= 0); + bool is_node = false; + + if (a->grad) { + GGML_ASSERT(false); // TODO: implement backward + is_node = true; + } + + // TODO: when implement backward, fix this: + //struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); + struct ggml_tensor * result = ggml_view_tensor(ctx, a); + + struct ggml_tensor * b = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 2); + ((int32_t *) b->data)[0] = n_past; + ((int32_t *) b->data)[1] = n_head; + + result->op = GGML_OP_ALIBI; + result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->src0 = a; + result->src1 = b; + + return result; +} + // ggml_conv_1d_1s struct ggml_tensor * ggml_conv_1d_1s( @@ -5023,7 +6106,6 @@ static void ggml_compute_forward_dup_f16( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { - GGML_ASSERT(params->ith == 0); GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0)); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { @@ -5035,6 +6117,11 @@ static void ggml_compute_forward_dup_f16( const int64_t ne02 = src0->ne[2]; const int64_t ne03 = src0->ne[3]; + const int64_t ne0 = dst->ne[0]; + const int64_t ne1 = dst->ne[1]; + const int64_t ne2 = dst->ne[2]; + const int64_t ne3 = dst->ne[3]; + const size_t nb00 = src0->nb[0]; const size_t nb01 = src0->nb[1]; const size_t nb02 = src0->nb[2]; @@ -5045,19 +6132,40 @@ static void ggml_compute_forward_dup_f16( const size_t nb2 = dst->nb[2]; const size_t nb3 = dst->nb[3]; + const int ith = params->ith; // thread index + const int nth = params->nth; // number of threads + if (ggml_is_contiguous(src0) && ggml_is_contiguous(dst) && src0->type == dst->type) { - memcpy(dst->data, src0->data, ggml_nelements(dst) * GGML_TYPE_SIZE[src0->type]); + // parallelize by elements + const int ne = ggml_nelements(dst); + const int dr = (ne + nth - 1) / nth; + const int ie0 = dr * ith; + const int ie1 = MIN(ie0 + dr, ne); + + memcpy( + ((char *) dst->data + ie0*nb0), + ((char *) src0->data + ie0*nb00), + (ie1 - ie0) * GGML_TYPE_SIZE[src0->type]); + return; } + // parallelize by rows + const int nr = ne01; + // number of rows per thread + const int dr = (nr + nth - 1) / nth; + // row range for this thread + const int ir0 = dr * ith; + const int ir1 = MIN(ir0 + dr, nr); + if (src0->type == dst->type && - src0->ne[0] == dst->ne[0] && - src0->nb[0] == GGML_TYPE_SIZE[src0->type] && dst->nb[0] == GGML_TYPE_SIZE[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 = 0; i01 < ne01; i01++) { + 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), @@ -5071,21 +6179,21 @@ static void ggml_compute_forward_dup_f16( // TODO: add more special-case implementations for tensor shapes/strides that can benefit from memcpy if (ggml_is_contiguous(dst)) { - if (src0->nb[0] == sizeof(ggml_fp16_t)) { + if (nb00 == sizeof(ggml_fp16_t)) { if (dst->type == GGML_TYPE_F16) { size_t id = 0; - const size_t rs = ne00*nb00; + 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++) { - for (int i01 = 0; i01 < ne01; i01++) { + id += rs * ir0; + for (int i01 = ir0; i01 < ir1; i01++) { const char * src0_ptr = (char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03; - char * dst_ptr = (char *) dst->data + id*rs; - - memcpy(dst_ptr, src0_ptr, rs); - - id++; + memcpy(dst_ptr + id, src0_ptr, rs); + id += rs; } + id += rs * (ne01 - ir1); } } } else if (dst->type == GGML_TYPE_F32) { @@ -5094,14 +6202,39 @@ static void ggml_compute_forward_dup_f16( for (int i03 = 0; i03 < ne03; i03++) { for (int i02 = 0; i02 < ne02; i02++) { - for (int i01 = 0; i01 < ne01; i01++) { + 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++) { - 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); + dst_ptr[id] = GGML_FP16_TO_FP32(src0_ptr[i00]); id++; } } + id += ne00 * (ne01 - ir1); + } + } + } else if (ggml_is_quantized(dst->type)) { + quantize_row_q_t const quantize_row_q = quantize_fns[dst->type].quantize_row_q; + float * src0_f32 = (float *) params->wdata + (ne00 + CACHE_LINE_SIZE_F32) * ith; + + size_t id = 0; + size_t rs = nb0 * (ne00 / GGML_BLCK_SIZE[dst->type]); + char * dst_ptr = (char *) dst->data; + + for (int i03 = 0; i03 < ne03; i03++) { + for (int i02 = 0; i02 < ne02; i02++) { + id += rs * ir0; + for (int i01 = ir0; i01 < ir1; i01++) { + const ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); + + for (int i00 = 0; i00 < ne00; i00++) { + src0_f32[i00] = GGML_FP16_TO_FP32(src0_ptr[i00]); + } + + quantize_row_q(src0_f32, dst_ptr + id, ne00); + id += rs; + } + id += rs * (ne01 - ir1); } } } else { @@ -5116,7 +6249,8 @@ static void ggml_compute_forward_dup_f16( for (int i03 = 0; i03 < ne03; i03++) { for (int i02 = 0; i02 < ne02; i02++) { - for (int i01 = 0; i01 < ne01; i01++) { + 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); @@ -5124,6 +6258,7 @@ static void ggml_compute_forward_dup_f16( id++; } } + id += ne00 * (ne01 - ir1); } } } else if (dst->type == GGML_TYPE_F16) { @@ -5132,7 +6267,8 @@ static void ggml_compute_forward_dup_f16( for (int i03 = 0; i03 < ne03; i03++) { for (int i02 = 0; i02 < ne02; i02++) { - for (int i01 = 0; i01 < ne01; i01++) { + 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); @@ -5140,6 +6276,7 @@ static void ggml_compute_forward_dup_f16( id++; } } + id += ne00 * (ne01 - ir1); } } } else { @@ -5158,7 +6295,20 @@ static void ggml_compute_forward_dup_f16( if (dst->type == GGML_TYPE_F16) { for (int64_t i03 = 0; i03 < ne03; i03++) { for (int64_t i02 = 0; i02 < ne02; i02++) { - for (int64_t i01 = 0; i01 < ne01; i01++) { + 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); @@ -5179,25 +6329,51 @@ static void ggml_compute_forward_dup_f16( } } } + 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++) { - for (int64_t i01 = 0; i01 < ne01; i01++) { + 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 == ne00) { + if (++i10 == ne0) { i10 = 0; - if (++i11 == ne01) { + if (++i11 == ne1) { i11 = 0; - if (++i12 == ne02) { + if (++i12 == ne2) { i12 = 0; - if (++i13 == ne03) { + if (++i13 == ne3) { i13 = 0; } } @@ -5205,6 +6381,19 @@ static void ggml_compute_forward_dup_f16( } } } + 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 { @@ -5216,7 +6405,6 @@ static void ggml_compute_forward_dup_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, struct ggml_tensor * dst) { - GGML_ASSERT(params->ith == 0); GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0)); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { @@ -5228,6 +6416,11 @@ static void ggml_compute_forward_dup_f32( const int64_t ne02 = src0->ne[2]; const int64_t ne03 = src0->ne[3]; + const int64_t ne0 = dst->ne[0]; + const int64_t ne1 = dst->ne[1]; + const int64_t ne2 = dst->ne[2]; + const int64_t ne3 = dst->ne[3]; + const size_t nb00 = src0->nb[0]; const size_t nb01 = src0->nb[1]; const size_t nb02 = src0->nb[2]; @@ -5238,19 +6431,40 @@ static void ggml_compute_forward_dup_f32( const size_t nb2 = dst->nb[2]; const size_t nb3 = dst->nb[3]; + const int ith = params->ith; // thread index + const int nth = params->nth; // number of threads + if (ggml_is_contiguous(src0) && ggml_is_contiguous(dst) && src0->type == dst->type) { - memcpy(dst->data, src0->data, ggml_nelements(dst) * GGML_TYPE_SIZE[src0->type]); + // parallelize by elements + const int ne = ggml_nelements(dst); + const int dr = (ne + nth - 1) / nth; + const int ie0 = dr * ith; + const int ie1 = MIN(ie0 + dr, ne); + + memcpy( + ((char *) dst->data + ie0*nb0), + ((char *) src0->data + ie0*nb00), + (ie1 - ie0) * GGML_TYPE_SIZE[src0->type]); + return; } + // parallelize by rows + const int nr = ne01; + // number of rows per thread + const int dr = (nr + nth - 1) / nth; + // row range for this thread + const int ir0 = dr * ith; + const int ir1 = MIN(ir0 + dr, nr); + if (src0->type == dst->type && - src0->ne[0] == dst->ne[0] && - src0->nb[0] == GGML_TYPE_SIZE[src0->type] && dst->nb[0] == GGML_TYPE_SIZE[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 = 0; i01 < ne01; i01++) { + 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), @@ -5263,21 +6477,21 @@ static void ggml_compute_forward_dup_f32( if (ggml_is_contiguous(dst)) { // TODO: simplify - if (src0->nb[0] == sizeof(float)) { + if (nb00 == sizeof(float)) { if (dst->type == GGML_TYPE_F32) { size_t id = 0; - const size_t rs = ne00*nb00; + 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++) { - for (int i01 = 0; i01 < ne01; i01++) { + id += rs * ir0; + for (int i01 = ir0; i01 < ir1; i01++) { const char * src0_ptr = (char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03; - char * dst_ptr = (char *) dst->data + id*rs; - - memcpy(dst_ptr, src0_ptr, rs); - - id++; + memcpy(dst_ptr + id, src0_ptr, rs); + id += rs; } + id += rs * (ne01 - ir1); } } } else if (dst->type == GGML_TYPE_F16) { @@ -5286,7 +6500,8 @@ static void ggml_compute_forward_dup_f32( for (int i03 = 0; i03 < ne03; i03++) { for (int i02 = 0; i02 < ne02; i02++) { - for (int i01 = 0; i01 < ne01; i01++) { + 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); @@ -5294,6 +6509,25 @@ static void ggml_compute_forward_dup_f32( id++; } } + id += ne00 * (ne01 - ir1); + } + } + } else if (ggml_is_quantized(dst->type)) { + quantize_row_q_t const quantize_row_q = quantize_fns[dst->type].quantize_row_q; + + size_t id = 0; + size_t rs = nb0 * (ne00 / GGML_BLCK_SIZE[dst->type]); + char * dst_ptr = (char *) dst->data; + + for (int i03 = 0; i03 < ne03; i03++) { + for (int i02 = 0; i02 < ne02; i02++) { + id += rs * ir0; + for (int i01 = ir0; i01 < ir1; i01++) { + const float * src0_ptr = (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); + quantize_row_q(src0_ptr, dst_ptr + id, ne00); + id += rs; + } + id += rs * (ne01 - ir1); } } } else { @@ -5308,7 +6542,8 @@ static void ggml_compute_forward_dup_f32( for (int i03 = 0; i03 < ne03; i03++) { for (int i02 = 0; i02 < ne02; i02++) { - for (int i01 = 0; i01 < ne01; i01++) { + 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); @@ -5316,6 +6551,7 @@ static void ggml_compute_forward_dup_f32( id++; } } + id += ne00 * (ne01 - ir1); } } } else if (dst->type == GGML_TYPE_F16) { @@ -5324,7 +6560,8 @@ static void ggml_compute_forward_dup_f32( for (int i03 = 0; i03 < ne03; i03++) { for (int i02 = 0; i02 < ne02; i02++) { - for (int i01 = 0; i01 < ne01; i01++) { + 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); @@ -5332,6 +6569,7 @@ static void ggml_compute_forward_dup_f32( id++; } } + id += ne00 * (ne01 - ir1); } } } else { @@ -5343,6 +6581,7 @@ static void ggml_compute_forward_dup_f32( } // dst counters + int64_t i10 = 0; int64_t i11 = 0; int64_t i12 = 0; @@ -5351,20 +6590,33 @@ static void ggml_compute_forward_dup_f32( if (dst->type == GGML_TYPE_F32) { for (int64_t i03 = 0; i03 < ne03; i03++) { for (int64_t i02 = 0; i02 < ne02; i02++) { - for (int64_t i01 = 0; i01 < ne01; i01++) { + 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 == dst->ne[0]) { + if (++i10 == ne0) { i10 = 0; - if (++i11 == dst->ne[1]) { + if (++i11 == ne1) { i11 = 0; - if (++i12 == dst->ne[2]) { + if (++i12 == ne2) { i12 = 0; - if (++i13 == dst->ne[3]) { + if (++i13 == ne3) { i13 = 0; } } @@ -5372,25 +6624,51 @@ static void ggml_compute_forward_dup_f32( } } } + 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++) { - for (int64_t i01 = 0; i01 < ne01; i01++) { + 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 == dst->ne[0]) { + if (++i10 == ne0) { i10 = 0; - if (++i11 == dst->ne[1]) { + if (++i11 == ne1) { i11 = 0; - if (++i12 == dst->ne[2]) { + if (++i12 == ne2) { i12 = 0; - if (++i13 == dst->ne[3]) { + if (++i13 == ne3) { i13 = 0; } } @@ -5398,6 +6676,19 @@ static void ggml_compute_forward_dup_f32( } } } + 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 { @@ -5418,12 +6709,7 @@ static void ggml_compute_forward_dup( { ggml_compute_forward_dup_f32(params, src0, dst); } break; - case GGML_TYPE_Q4_0: - case GGML_TYPE_Q4_1: - case GGML_TYPE_I8: - case GGML_TYPE_I16: - case GGML_TYPE_I32: - case GGML_TYPE_COUNT: + default: { GGML_ASSERT(false); } break; @@ -5489,6 +6775,212 @@ static void ggml_compute_forward_add_f32( } } +static void ggml_compute_forward_add_f16_f32( + const struct ggml_compute_params * params, + const struct ggml_tensor * src0, + const struct ggml_tensor * src1, + struct ggml_tensor * dst) { + GGML_ASSERT(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst)); + + if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + return; + } + + const int ith = params->ith; + const int nth = params->nth; + + const int n = ggml_nrows(src0); + const int nc = src0->ne[0]; + + const size_t nb00 = src0->nb[0]; + const size_t nb01 = src0->nb[1]; + + const size_t nb10 = src1->nb[0]; + const size_t nb11 = src1->nb[1]; + + const size_t nb0 = dst->nb[0]; + const size_t nb1 = dst->nb[1]; + + GGML_ASSERT(src0->type == GGML_TYPE_F16); + GGML_ASSERT(src1->type == GGML_TYPE_F32); + GGML_ASSERT(dst->type == GGML_TYPE_F16); + + GGML_ASSERT( nb0 == sizeof(ggml_fp16_t)); + GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); + + if (nb10 == sizeof(float)) { + for (int j = ith; j < n; j += nth) { + ggml_fp16_t * dst_ptr = (ggml_fp16_t *) ((char *) dst->data + j*nb1); + ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + j*nb01); + for (int i = 0; i < nc; i++) { + float * src1_ptr = (float *) ((char *) src1->data + j*nb11 + i*nb10); + dst_ptr[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(src0_ptr[i]) + *src1_ptr); + } + } + } + else { + // src1 is not contiguous + GGML_ASSERT(false); + } +} + +static void ggml_compute_forward_add_f16_f16( + const struct ggml_compute_params * params, + const struct ggml_tensor * src0, + const struct ggml_tensor * src1, + struct ggml_tensor * dst) { + GGML_ASSERT(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst)); + + if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + return; + } + + const int ith = params->ith; + const int nth = params->nth; + + const int n = ggml_nrows(src0); + const int nc = src0->ne[0]; + + const size_t nb00 = src0->nb[0]; + const size_t nb01 = src0->nb[1]; + + const size_t nb10 = src1->nb[0]; + const size_t nb11 = src1->nb[1]; + + const size_t nb0 = dst->nb[0]; + const size_t nb1 = dst->nb[1]; + + GGML_ASSERT(src0->type == GGML_TYPE_F16); + GGML_ASSERT(src1->type == GGML_TYPE_F16); + GGML_ASSERT(dst->type == GGML_TYPE_F16); + + GGML_ASSERT( nb0 == sizeof(ggml_fp16_t)); + GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); + + if (nb10 == sizeof(ggml_fp16_t)) { + for (int j = ith; j < n; j += nth) { + ggml_fp16_t * dst_ptr = (ggml_fp16_t *) ((char *) dst->data + j*nb1); + ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + j*nb01); + for (int i = 0; i < nc; i++) { + ggml_fp16_t * src1_ptr = (ggml_fp16_t *) ((char *) src1->data + j*nb11 + i*nb10); + dst_ptr[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(src0_ptr[i]) + GGML_FP16_TO_FP32(*src1_ptr)); + } + } + } + else { + // src1 is not contiguous + GGML_ASSERT(false); + } +} + +static void ggml_compute_forward_add_q_f32( + const struct ggml_compute_params * params, + const struct ggml_tensor * src0, + const struct ggml_tensor * src1, + struct ggml_tensor * dst) { + GGML_ASSERT(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst)); + + if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + return; + } + + const int64_t ne00 = src0->ne[0]; + const int64_t ne01 = src0->ne[1]; + const int64_t ne02 = src0->ne[2]; + const int64_t ne03 = src0->ne[3]; + + //const int64_t ne10 = src1->ne[0]; + //const int64_t ne11 = src1->ne[1]; + const int64_t ne12 = src1->ne[2]; + const int64_t ne13 = src1->ne[3]; + + //const int64_t ne0 = dst->ne[0]; + //const int64_t ne1 = dst->ne[1]; + const int64_t ne2 = dst->ne[2]; + const int64_t ne3 = dst->ne[3]; + + const int nb00 = src0->nb[0]; + const int nb01 = src0->nb[1]; + const int nb02 = src0->nb[2]; + const int nb03 = src0->nb[3]; + + const int nb10 = src1->nb[0]; + const int nb11 = src1->nb[1]; + const int nb12 = src1->nb[2]; + const int nb13 = src1->nb[3]; + + const int nb0 = dst->nb[0]; + const int nb1 = dst->nb[1]; + const int nb2 = dst->nb[2]; + const int nb3 = dst->nb[3]; + + const int ith = params->ith; + const int nth = params->nth; + + GGML_ASSERT(ne02 == ne12); + GGML_ASSERT(ne03 == ne13); + GGML_ASSERT(ne2 == ne12); + GGML_ASSERT(ne3 == ne13); + + const enum ggml_type type = src0->type; + dequantize_row_q_t const dequantize_row_q = quantize_fns[type].dequantize_row_q; + quantize_row_q_t const quantize_row_q = quantize_fns[type].quantize_row_q; + + // we don't support permuted src0 or src1 + GGML_ASSERT(nb00 == (int) GGML_TYPE_SIZE[type]); + GGML_ASSERT(nb10 == sizeof(float)); + + // dst cannot be transposed or permuted + GGML_ASSERT(nb0 <= nb1); + GGML_ASSERT(nb1 <= nb2); + GGML_ASSERT(nb2 <= nb3); + + GGML_ASSERT(ggml_is_quantized(src0->type)); + GGML_ASSERT(dst->type == src0->type); + GGML_ASSERT(src1->type == GGML_TYPE_F32); + + // total rows in src0 + const int nr = ne01*ne02*ne03; + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + float * wdata = (float *) params->wdata + (ne00 + CACHE_LINE_SIZE_F32) * ith; + + for (int ir = ir0; ir < ir1; ++ir) { + // src0 indices + const int i03 = ir/(ne02*ne01); + const int i02 = (ir - i03*ne02*ne01)/ne01; + const int i01 = (ir - i03*ne02*ne01 - i02*ne01); + + // src1 and dst are same shape as src0 => same indices + const int i13 = i03; + const int i12 = i02; + const int i11 = i01; + + const int i3 = i03; + const int i2 = i02; + const int i1 = i01; + + void * src0_row = (void *) ((char *) src0->data + (i01*nb01 + i02*nb02 + i03*nb03)); + float * src1_row = (float *)((char *) src1->data + (i11*nb11 + i12*nb12 + i13*nb13)); + void * dst_row = (void *) ((char *) dst->data + ( i1*nb1 + i2*nb2 + i3*nb0)); + + assert(ne00 % 32 == 0); + + // unquantize row from src0 to temp buffer + dequantize_row_q(src0_row, wdata, ne00); + // add src1 + ggml_vec_acc_f32(ne00, wdata, src1_row); + // quantize row to dst + quantize_row_q(wdata, dst_row, ne00); + } +} + static void ggml_compute_forward_add( const struct ggml_compute_params * params, const struct ggml_tensor * src0, @@ -5499,13 +6991,28 @@ static void ggml_compute_forward_add( { ggml_compute_forward_add_f32(params, src0, src1, dst); } break; + case GGML_TYPE_F16: + { + if (src1->type == GGML_TYPE_F16) { + ggml_compute_forward_add_f16_f16(params, src0, src1, dst); + } + else if (src1->type == GGML_TYPE_F32) { + ggml_compute_forward_add_f16_f32(params, src0, src1, dst); + } + else { + GGML_ASSERT(false); + } + } break; case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_1: - case GGML_TYPE_I8: - case GGML_TYPE_I16: - case GGML_TYPE_I32: - case GGML_TYPE_F16: - case GGML_TYPE_COUNT: + case GGML_TYPE_Q4_2: + case GGML_TYPE_Q5_0: + case GGML_TYPE_Q5_1: + case GGML_TYPE_Q8_0: + { + ggml_compute_forward_add_q_f32(params, src0, src1, dst); + } break; + default: { GGML_ASSERT(false); } break; @@ -5551,13 +7058,7 @@ static void ggml_compute_forward_sub( { ggml_compute_forward_sub_f32(params, src0, src1, dst); } break; - case GGML_TYPE_Q4_0: - case GGML_TYPE_Q4_1: - case GGML_TYPE_I8: - case GGML_TYPE_I16: - case GGML_TYPE_I32: - case GGML_TYPE_F16: - case GGML_TYPE_COUNT: + default: { GGML_ASSERT(false); } break; @@ -5603,13 +7104,7 @@ static void ggml_compute_forward_mul( { ggml_compute_forward_mul_f32(params, src0, src1, dst); } break; - case GGML_TYPE_Q4_0: - case GGML_TYPE_Q4_1: - case GGML_TYPE_I8: - case GGML_TYPE_I16: - case GGML_TYPE_I32: - case GGML_TYPE_F16: - case GGML_TYPE_COUNT: + default: { GGML_ASSERT(false); } break; @@ -5655,13 +7150,7 @@ static void ggml_compute_forward_div( { ggml_compute_forward_div_f32(params, src0, src1, dst); } break; - case GGML_TYPE_Q4_0: - case GGML_TYPE_Q4_1: - case GGML_TYPE_I8: - case GGML_TYPE_I16: - case GGML_TYPE_I32: - case GGML_TYPE_F16: - case GGML_TYPE_COUNT: + default: { GGML_ASSERT(false); } break; @@ -5703,13 +7192,7 @@ static void ggml_compute_forward_sqr( { ggml_compute_forward_sqr_f32(params, src0, dst); } break; - case GGML_TYPE_Q4_0: - case GGML_TYPE_Q4_1: - case GGML_TYPE_I8: - case GGML_TYPE_I16: - case GGML_TYPE_I32: - case GGML_TYPE_F16: - case GGML_TYPE_COUNT: + default: { GGML_ASSERT(false); } break; @@ -5751,13 +7234,7 @@ static void ggml_compute_forward_sqrt( { ggml_compute_forward_sqrt_f32(params, src0, dst); } break; - case GGML_TYPE_Q4_0: - case GGML_TYPE_Q4_1: - case GGML_TYPE_I8: - case GGML_TYPE_I16: - case GGML_TYPE_I32: - case GGML_TYPE_F16: - case GGML_TYPE_COUNT: + default: { GGML_ASSERT(false); } break; @@ -5789,15 +7266,20 @@ static void ggml_compute_forward_sum_f32( const size_t nb02 = src0->nb[2]; const size_t nb03 = src0->nb[3]; + 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(ne00, - (float *) (dst->data), + ggml_vec_sum_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( @@ -5809,13 +7291,7 @@ static void ggml_compute_forward_sum( { ggml_compute_forward_sum_f32(params, src0, dst); } break; - case GGML_TYPE_Q4_0: - case GGML_TYPE_Q4_1: - case GGML_TYPE_I8: - case GGML_TYPE_I16: - case GGML_TYPE_I32: - case GGML_TYPE_F16: - case GGML_TYPE_COUNT: + default: { GGML_ASSERT(false); } break; @@ -5886,13 +7362,7 @@ static void ggml_compute_forward_mean( { ggml_compute_forward_mean_f32(params, src0, dst); } break; - case GGML_TYPE_Q4_0: - case GGML_TYPE_Q4_1: - case GGML_TYPE_I8: - case GGML_TYPE_I16: - case GGML_TYPE_I32: - case GGML_TYPE_F16: - case GGML_TYPE_COUNT: + default: { GGML_ASSERT(false); } break; @@ -5950,13 +7420,7 @@ static void ggml_compute_forward_repeat( { ggml_compute_forward_repeat_f32(params, src0, dst); } break; - case GGML_TYPE_Q4_0: - case GGML_TYPE_Q4_1: - case GGML_TYPE_I8: - case GGML_TYPE_I16: - case GGML_TYPE_I32: - case GGML_TYPE_F16: - case GGML_TYPE_COUNT: + default: { GGML_ASSERT(false); } break; @@ -5998,13 +7462,7 @@ static void ggml_compute_forward_abs( { ggml_compute_forward_abs_f32(params, src0, dst); } break; - case GGML_TYPE_Q4_0: - case GGML_TYPE_Q4_1: - case GGML_TYPE_I8: - case GGML_TYPE_I16: - case GGML_TYPE_I32: - case GGML_TYPE_F16: - case GGML_TYPE_COUNT: + default: { GGML_ASSERT(false); } break; @@ -6046,13 +7504,7 @@ static void ggml_compute_forward_sgn( { ggml_compute_forward_sgn_f32(params, src0, dst); } break; - case GGML_TYPE_Q4_0: - case GGML_TYPE_Q4_1: - case GGML_TYPE_I8: - case GGML_TYPE_I16: - case GGML_TYPE_I32: - case GGML_TYPE_F16: - case GGML_TYPE_COUNT: + default: { GGML_ASSERT(false); } break; @@ -6094,13 +7546,7 @@ static void ggml_compute_forward_neg( { ggml_compute_forward_neg_f32(params, src0, dst); } break; - case GGML_TYPE_Q4_0: - case GGML_TYPE_Q4_1: - case GGML_TYPE_I8: - case GGML_TYPE_I16: - case GGML_TYPE_I32: - case GGML_TYPE_F16: - case GGML_TYPE_COUNT: + default: { GGML_ASSERT(false); } break; @@ -6142,13 +7588,7 @@ static void ggml_compute_forward_step( { ggml_compute_forward_step_f32(params, src0, dst); } break; - case GGML_TYPE_Q4_0: - case GGML_TYPE_Q4_1: - case GGML_TYPE_I8: - case GGML_TYPE_I16: - case GGML_TYPE_I32: - case GGML_TYPE_F16: - case GGML_TYPE_COUNT: + default: { GGML_ASSERT(false); } break; @@ -6190,13 +7630,7 @@ static void ggml_compute_forward_relu( { ggml_compute_forward_relu_f32(params, src0, dst); } break; - case GGML_TYPE_Q4_0: - case GGML_TYPE_Q4_1: - case GGML_TYPE_I8: - case GGML_TYPE_I16: - case GGML_TYPE_I32: - case GGML_TYPE_F16: - case GGML_TYPE_COUNT: + default: { GGML_ASSERT(false); } break; @@ -6255,13 +7689,7 @@ static void ggml_compute_forward_gelu( { ggml_compute_forward_gelu_f32(params, src0, dst); } break; - case GGML_TYPE_Q4_0: - case GGML_TYPE_Q4_1: - case GGML_TYPE_I8: - case GGML_TYPE_I16: - case GGML_TYPE_I32: - case GGML_TYPE_F16: - case GGML_TYPE_COUNT: + default: { GGML_ASSERT(false); } break; @@ -6322,13 +7750,7 @@ static void ggml_compute_forward_silu( { ggml_compute_forward_silu_f32(params, src0, dst); } break; - case GGML_TYPE_Q4_0: - case GGML_TYPE_Q4_1: - case GGML_TYPE_I8: - case GGML_TYPE_I16: - case GGML_TYPE_I32: - case GGML_TYPE_F16: - case GGML_TYPE_COUNT: + default: { GGML_ASSERT(false); } break; @@ -6408,13 +7830,7 @@ static void ggml_compute_forward_norm( { ggml_compute_forward_norm_f32(params, src0, dst); } break; - case GGML_TYPE_Q4_0: - case GGML_TYPE_Q4_1: - case GGML_TYPE_I8: - case GGML_TYPE_I16: - case GGML_TYPE_I32: - case GGML_TYPE_F16: - case GGML_TYPE_COUNT: + default: { GGML_ASSERT(false); } break; @@ -6488,13 +7904,7 @@ static void ggml_compute_forward_rms_norm( { ggml_compute_forward_rms_norm_f32(params, src0, dst); } break; - case GGML_TYPE_Q4_0: - case GGML_TYPE_Q4_1: - case GGML_TYPE_I8: - case GGML_TYPE_I16: - case GGML_TYPE_I32: - case GGML_TYPE_F16: - case GGML_TYPE_COUNT: + default: { GGML_ASSERT(false); } break; @@ -6504,7 +7914,7 @@ static void ggml_compute_forward_rms_norm( // ggml_compute_forward_mul_mat -#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) +#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST) // 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( @@ -6520,8 +7930,12 @@ static bool ggml_compute_forward_mul_mat_use_blas( const int64_t ne1 = dst->ne[1]; // TODO: find the optimal values for these - if (ggml_is_contiguous(src0) && - ggml_is_contiguous(src1) && ((ne0 >= 32 && ne1 >= 32 && ne10 >= 32))) { + if ( +#if !defined(GGML_USE_CUBLAS) + ggml_is_contiguous(src0) && + ggml_is_contiguous(src1) && +#endif + ((ne0 >= 32 && ne1 >= 32 && ne10 >= 32))) { /*printf("BLAS: %d %d %d %d %d\n", ne0, ne1, ne10, ne00, ne01);*/ return true; @@ -6529,6 +7943,7 @@ static bool ggml_compute_forward_mul_mat_use_blas( return false; } + #endif static void ggml_compute_forward_mul_mat_f32( @@ -6544,7 +7959,7 @@ static void ggml_compute_forward_mul_mat_f32( const int64_t ne02 = src0->ne[2]; const int64_t ne03 = src0->ne[3]; -#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) +#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST) const int64_t ne10 = src1->ne[0]; #endif const int64_t ne11 = src1->ne[1]; @@ -6601,7 +8016,7 @@ static void ggml_compute_forward_mul_mat_f32( // nb01 >= nb00 - src0 is not transposed // compute by src0 rows -#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) +#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST) if (ggml_compute_forward_mul_mat_use_blas(src0, src1, dst)) { if (params->ith != 0) { return; @@ -6615,22 +8030,65 @@ static void ggml_compute_forward_mul_mat_f32( return; } +#if defined(GGML_USE_CUBLAS) + const float alpha = 1.0f; + const float beta = 0.0f; + const int x_ne = ne01 * ne00; + const int y_ne = ne11 * ne10; + const int d_ne = ne11 * ne01; + + size_t x_size, y_size, d_size; + float *d_X = ggml_cuda_pool_malloc(sizeof(float) * x_ne, &x_size); + float *d_Y = ggml_cuda_pool_malloc(sizeof(float) * y_ne, &y_size); + float *d_D = ggml_cuda_pool_malloc(sizeof(float) * d_ne, &d_size); +#endif + for (int64_t i03 = 0; i03 < ne03; i03++) { for (int64_t i02 = 0; i02 < ne02; i02++) { +#if !defined(GGML_USE_CUBLAS) const float * x = (float *) ((char *) src0->data + i02*nb02 + i03*nb03); const float * y = (float *) ((char *) src1->data + i02*nb12 + i03*nb13); - +#endif float * d = (float *) ((char *) dst->data + i02*nb2 + i03*nb3); +#if defined(GGML_USE_CUBLAS) + // copy data to device + CUDA_CHECK(ggml_cuda_h2d_tensor_2d(d_X, src0, i03, i02, g_cudaStream)); + CUDA_CHECK(ggml_cuda_h2d_tensor_2d(d_Y, src1, i03, i02, g_cudaStream)); + + // compute + CUBLAS_CHECK( + cublasSgemm(g_cublasH, CUBLAS_OP_T, CUBLAS_OP_N, + ne01, ne11, ne10, + &alpha, d_X, ne00, + d_Y, ne10, + &beta, d_D, ne01)); + + // copy data to host + CUDA_CHECK(cudaMemcpyAsync(d, d_D, sizeof(float) * d_ne, cudaMemcpyDeviceToHost, g_cudaStream)); +#elif defined(GGML_USE_CLBLAST) // zT = y * xT + ggml_cl_sgemm_wrapper(GGML_BLAS_ORDER_ROW_MAJOR, GGML_BLAS_OP_N, GGML_BLAS_OP_T, + ne11, ne01, ne10, + 1.0f, y, ne10, + x, ne10, + 0.0f, d, ne01, + GGML_TYPE_F32); +#else cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasTrans, ne11, ne01, ne10, 1.0f, y, ne10, x, ne00, 0.0f, d, ne01); +#endif } } - +#if defined(GGML_USE_CUBLAS) + CUDA_CHECK(cudaStreamSynchronize(g_cudaStream)); + ggml_cuda_pool_free(d_X, x_size); + ggml_cuda_pool_free(d_Y, y_size); + ggml_cuda_pool_free(d_D, d_size); +#endif //printf("CBLAS F32 = %f ms, %d x %d x %d x %d\n", (ggml_perf_time_us() - t0)/1000.0, ne0, ne1, ne2, ne3); return; @@ -6760,7 +8218,7 @@ static void ggml_compute_forward_mul_mat_f16_f32( // nb01 >= nb00 - src0 is not transposed // compute by src0 rows -#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) +#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST) if (ggml_compute_forward_mul_mat_use_blas(src0, src1, dst)) { GGML_ASSERT(nb10 == sizeof(float)); @@ -6776,10 +8234,37 @@ static void ggml_compute_forward_mul_mat_f16_f32( return; } - float * const wdata = params->wdata; +#if defined(GGML_USE_CUBLAS) + const float alpha = 1.0f; + const float beta = 0.0f; + const int x_ne = ne01 * ne00; + const int y_ne = ne11 * ne10; + const int d_ne = ne11 * ne01; + size_t x_size, y_size, d_size; + ggml_fp16_t * d_X = ggml_cuda_pool_malloc(sizeof(float) * x_ne, &x_size); + ggml_fp16_t * d_Y = ggml_cuda_pool_malloc(sizeof(float) * y_ne, &y_size); + float * d_D = ggml_cuda_pool_malloc(sizeof(float) * d_ne, &d_size); +#else + float * const wdata = params->wdata; +#endif for (int64_t i03 = 0; i03 < ne03; i03++) { for (int64_t i02 = 0; i02 < ne02; i02++) { +#if defined(GGML_USE_CUBLAS) + // copy src0 while converting src1 + CUDA_CHECK(ggml_cuda_h2d_tensor_2d(d_X, src0, i03, i02, g_cudaStream)); + + // with cuBlAS, instead of converting src0 to fp32, we convert src1 to fp16 + ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + (ne11 * ne10) * (i03 * ne02 + i02); + { + size_t id = 0; + for (int64_t i01 = 0; i01 < ne11; ++i01) { + for (int64_t i00 = 0; i00 < ne10; ++i00) { + wdata[id++] = GGML_FP32_TO_FP16(*(float *) ((char *) src1->data + i03*nb13 + i02*nb12 + i01*nb11 + i00*nb10)); + } + } + } +#else { size_t id = 0; for (int64_t i01 = 0; i01 < ne01; ++i01) { @@ -6788,7 +8273,41 @@ static void ggml_compute_forward_mul_mat_f16_f32( } } } +#endif +#if defined(GGML_USE_CUBLAS) + const ggml_fp16_t * y = (ggml_fp16_t *) wdata; + float * d = (float *) ((char *) dst->data + i02*nb2 + i03*nb3); + + // copy data to device + CUDA_CHECK(cudaMemcpyAsync(d_Y, y, sizeof(ggml_fp16_t) * y_ne, cudaMemcpyHostToDevice, g_cudaStream)); + + // compute + CUBLAS_CHECK( + cublasGemmEx(g_cublasH, CUBLAS_OP_T, CUBLAS_OP_N, + ne01, ne11, ne10, + &alpha, d_X, CUDA_R_16F, ne00, + d_Y, CUDA_R_16F, ne10, + &beta, d_D, CUDA_R_32F, ne01, + CUBLAS_COMPUTE_32F, + CUBLAS_GEMM_DEFAULT)); + + // copy data to host + CUDA_CHECK(cudaMemcpyAsync(d, d_D, sizeof(float) * d_ne, cudaMemcpyDeviceToHost, g_cudaStream)); +#elif defined(GGML_USE_CLBLAST) + const float * x = wdata; + const float * y = (float *) ((char *) src1->data + i02*nb12 + i03*nb13); + + float * d = (float *) ((char *) dst->data + i02*nb2 + i03*nb3); + + // zT = y * xT + ggml_cl_sgemm_wrapper(GGML_BLAS_ORDER_ROW_MAJOR, GGML_BLAS_OP_N, GGML_BLAS_OP_T, + ne11, ne01, ne10, + 1.0f, y, ne10, + x, ne10, + 0.0f, d, ne01, + GGML_TYPE_F32); +#else const float * x = wdata; const float * y = (float *) ((char *) src1->data + i02*nb12 + i03*nb13); @@ -6800,9 +8319,16 @@ static void ggml_compute_forward_mul_mat_f16_f32( 1.0f, y, ne10, x, ne00, 0.0f, d, ne01); +#endif } } +#if defined(GGML_USE_CUBLAS) + CUDA_CHECK(cudaStreamSynchronize(g_cudaStream)); + ggml_cuda_pool_free(d_X, x_size); + ggml_cuda_pool_free(d_Y, y_size); + ggml_cuda_pool_free(d_D, d_size); +#endif /*printf("CBLAS F16 = %f ms, %d x %d x %d x %d\n", (ggml_perf_time_us() - t0)/1000.0, ne0, ne1, ne2, ne3);*/ return; @@ -6886,27 +8412,6 @@ static void ggml_compute_forward_mul_mat_f16_f32( //} } -static const quantize_fns_t quantize_fns[GGML_TYPE_COUNT] = { - [GGML_TYPE_Q4_0] = { - .dequantize_row_q = dequantize_row_q4_0, - .quantize_row_q = quantize_row_q4_0, - .quantize_row_q_reference = (quantize_row_q_t) quantize_row_q4_0_reference, - .vec_dot_q = ggml_vec_dot_q4_0, - }, - [GGML_TYPE_Q4_1] = { - .dequantize_row_q = dequantize_row_q4_1, - .quantize_row_q = quantize_row_q4_1, - .quantize_row_q_reference = (quantize_row_q_t) quantize_row_q4_1_reference, - .vec_dot_q = ggml_vec_dot_q4_1, - }, -}; - -// For internal test use -quantize_fns_t ggml_internal_get_quantize_fn(size_t i) { - GGML_ASSERT(i < GGML_TYPE_COUNT); - return quantize_fns[i]; -} - static void ggml_compute_forward_mul_mat_q_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, @@ -6954,8 +8459,9 @@ static void ggml_compute_forward_mul_mat_q_f32( GGML_ASSERT(ne3 == ne13); const enum ggml_type type = src0->type; - quantize_row_q_t const quantize_row_q = quantize_fns[type].quantize_row_q; - vec_dot_q_t const vec_dot_q = quantize_fns[type].vec_dot_q; + quantize_row_q_t const quantize_row_q_dot = quantize_fns[type].quantize_row_q_dot; + vec_dot_q_t const vec_dot_q = quantize_fns[type].vec_dot_q; + enum ggml_type const vec_dot_type = quantize_fns[type].vec_dot_type; // we don't support permuted src0 or src1 GGML_ASSERT(nb00 == (int) GGML_TYPE_SIZE[type]); @@ -6975,7 +8481,7 @@ static void ggml_compute_forward_mul_mat_q_f32( // nb01 >= nb00 - src0 is not transposed // compute by src0 rows -#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) +#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST) if (ggml_compute_forward_mul_mat_use_blas(src0, src1, dst)) { if (params->ith != 0) { return; @@ -6989,11 +8495,42 @@ static void ggml_compute_forward_mul_mat_q_f32( return; } +#if defined(GGML_USE_CUBLAS) + const float alpha = 1.0f; + const float beta = 0.0f; + const int x_ne = ne01 * ne00; + const int y_ne = ne11 * ne10; + const int d_ne = ne11 * ne01; + + size_t x_size, y_size, d_size, q_size; + float * d_X = ggml_cuda_pool_malloc(sizeof(float) * x_ne, &x_size); + float * d_Y = ggml_cuda_pool_malloc(sizeof(float) * y_ne, &y_size); + float * d_D = ggml_cuda_pool_malloc(sizeof(float) * d_ne, &d_size); + void * d_Q = ggml_cuda_pool_malloc(GGML_TYPE_SIZE[type] * x_ne / GGML_BLCK_SIZE[type], &q_size); + + const dequantize_row_q_cuda_t dequantize_row_q_cuda = ggml_get_dequantize_row_q_cuda(type); + GGML_ASSERT(dequantize_row_q_cuda != NULL); +#else float * const wdata = params->wdata; dequantize_row_q_t const dequantize_row_q = quantize_fns[type].dequantize_row_q; +#endif for (int64_t i03 = 0; i03 < ne03; i03++) { for (int64_t i02 = 0; i02 < ne02; i02++) { + const float * y = (float *) ((char *) src1->data + i02*nb12 + i03*nb13); + + float * d = (float *) ((char *) dst->data + i02*nb2 + i03*nb3); + +#if defined(GGML_USE_CUBLAS) + // copy and dequantize on device + CUDA_CHECK(ggml_cuda_h2d_tensor_2d(d_Q, src0, i03, i02, g_cudaStream2)); + + dequantize_row_q_cuda(d_Q, d_X, x_ne, g_cudaStream2); + CUDA_CHECK(cudaGetLastError()); + CUDA_CHECK(cudaEventRecord(g_cudaEvent, g_cudaStream2)); +#elif defined(GGML_USE_CLBLAST) + const void* x = (char *) src0->data + i03*nb03 + i02*nb02; +#else { size_t id = 0; for (int64_t i01 = 0; i01 < ne01; ++i01) { @@ -7001,21 +8538,51 @@ static void ggml_compute_forward_mul_mat_q_f32( id += ne00; } } - const float * x = wdata; - const float * y = (float *) ((char *) src1->data + i02*nb12 + i03*nb13); +#endif - float * d = (float *) ((char *) dst->data + i02*nb2 + i03*nb3); +#if defined(GGML_USE_CUBLAS) + // copy data to device + CUDA_CHECK(ggml_cuda_h2d_tensor_2d(d_Y, src1, i03, i02, g_cudaStream)); + // wait for dequantization + CUDA_CHECK(cudaStreamWaitEvent(g_cudaStream, g_cudaEvent, 0)); + + // compute + CUBLAS_CHECK( + cublasSgemm(g_cublasH, CUBLAS_OP_T, CUBLAS_OP_N, + ne01, ne11, ne10, + &alpha, d_X, ne00, + d_Y, ne10, + &beta, d_D, ne01)); + + // copy data to host + CUDA_CHECK(cudaMemcpyAsync(d, d_D, sizeof(float) * d_ne, cudaMemcpyDeviceToHost, g_cudaStream)); +#elif defined(GGML_USE_CLBLAST) // zT = y * xT + ggml_cl_sgemm_wrapper(GGML_BLAS_ORDER_ROW_MAJOR, GGML_BLAS_OP_N, GGML_BLAS_OP_T, + ne11, ne01, ne10, + 1.0f, y, ne10, + x, ne10, + 0.0f, d, ne01, + type); +#else cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasTrans, ne11, ne01, ne10, 1.0f, y, ne10, x, ne00, 0.0f, d, ne01); +#endif } } +#if defined(GGML_USE_CUBLAS) + CUDA_CHECK(cudaStreamSynchronize(g_cudaStream)); + ggml_cuda_pool_free(d_X, x_size); + ggml_cuda_pool_free(d_Y, y_size); + ggml_cuda_pool_free(d_D, d_size); + ggml_cuda_pool_free(d_Q, q_size); +#endif //printf("CBLAS = %f ms, %d x %d x %d x %d\n", (ggml_perf_time_us() - t0)/1000.0, ne0, ne1, ne2, ne3); return; @@ -7024,12 +8591,12 @@ static void ggml_compute_forward_mul_mat_q_f32( if (params->type == GGML_TASK_INIT) { char * wdata = params->wdata; - const size_t row_size = ne10*GGML_TYPE_SIZE[type]/GGML_BLCK_SIZE[type]; + const size_t row_size = ne10*GGML_TYPE_SIZE[vec_dot_type]/GGML_BLCK_SIZE[vec_dot_type]; for (int64_t i13 = 0; i13 < ne13; ++i13) { for (int64_t i12 = 0; i12 < ne12; ++i12) { for (int64_t i11 = 0; i11 < ne11; ++i11) { - quantize_row_q((float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11), (void *) wdata, ne10); + quantize_row_q_dot((float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11), (void *) wdata, ne10); wdata += row_size; } } @@ -7055,7 +8622,7 @@ static void ggml_compute_forward_mul_mat_q_f32( const int ir1 = MIN(ir0 + dr, nr); void * wdata = params->wdata; - const size_t row_size = ne00*GGML_TYPE_SIZE[type]/GGML_BLCK_SIZE[type]; + const size_t row_size = ne00*GGML_TYPE_SIZE[vec_dot_type]/GGML_BLCK_SIZE[vec_dot_type]; for (int ir = ir0; ir < ir1; ++ir) { // src0 indices @@ -7103,6 +8670,11 @@ static void ggml_compute_forward_mul_mat( switch (src0->type) { case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_1: + case GGML_TYPE_Q4_2: + case GGML_TYPE_Q5_0: + case GGML_TYPE_Q5_1: + case GGML_TYPE_Q8_0: + case GGML_TYPE_Q8_1: { ggml_compute_forward_mul_mat_q_f32(params, src0, src1, dst); } break; @@ -7114,42 +8686,11 @@ static void ggml_compute_forward_mul_mat( { ggml_compute_forward_mul_mat_f32(params, src0, src1, dst); } break; - case GGML_TYPE_I8: - case GGML_TYPE_I16: - case GGML_TYPE_I32: - case GGML_TYPE_COUNT: + default: { GGML_ASSERT(false); } break; } - -#if 0 - if (src0->type == GGML_TYPE_F16 || src0->type == GGML_TYPE_Q4_1) { - static int first = 8; - printf("src0: ne0 = %5d, ne1 = %5d, ne2 = %5d\n", src0->ne[0], src0->ne[1], src0->ne[2]); - printf("src1: ne0 = %5d, ne1 = %5d, ne2 = %5d\n", src1->ne[0], src1->ne[1], src1->ne[2]); - printf("dst: ne0 = %5d, ne1 = %5d, ne2 = %5d\n", dst->ne[0], dst->ne[1], dst->ne[2]); - if (first) { - --first; - } 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); - } - } else { - printf("aaaa src0: ne0 = %5d, ne1 = %5d, ne2 = %5d\n", src0->ne[0], src0->ne[1], src0->ne[2]); - printf("aaaa src1: ne0 = %5d, ne1 = %5d, ne2 = %5d\n", src1->ne[0], src1->ne[1], src1->ne[2]); - printf("aaaa dst: ne0 = %5d, ne1 = %5d, ne2 = %5d\n", dst->ne[0], dst->ne[1], dst->ne[2]); - } -#endif } // ggml_compute_forward_scale @@ -7199,13 +8740,7 @@ static void ggml_compute_forward_scale( { ggml_compute_forward_scale_f32(params, src0, src1, dst); } break; - case GGML_TYPE_Q4_0: - case GGML_TYPE_Q4_1: - case GGML_TYPE_I8: - case GGML_TYPE_I16: - case GGML_TYPE_I32: - case GGML_TYPE_F16: - case GGML_TYPE_COUNT: + default: { GGML_ASSERT(false); } break; @@ -7366,6 +8901,11 @@ static void ggml_compute_forward_get_rows( switch (src0->type) { case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_1: + case GGML_TYPE_Q4_2: + case GGML_TYPE_Q5_0: + case GGML_TYPE_Q5_1: + case GGML_TYPE_Q8_0: + case GGML_TYPE_Q8_1: { ggml_compute_forward_get_rows_q(params, src0, src1, dst); } break; @@ -7377,10 +8917,7 @@ static void ggml_compute_forward_get_rows( { ggml_compute_forward_get_rows_f32(params, src0, src1, dst); } break; - case GGML_TYPE_I8: - case GGML_TYPE_I16: - case GGML_TYPE_I32: - case GGML_TYPE_COUNT: + default: { GGML_ASSERT(false); } break; @@ -7453,13 +8990,7 @@ static void ggml_compute_forward_diag_mask_inf( { ggml_compute_forward_diag_mask_inf_f32(params, src0, src1, dst); } break; - case GGML_TYPE_Q4_0: - case GGML_TYPE_Q4_1: - case GGML_TYPE_I8: - case GGML_TYPE_I16: - case GGML_TYPE_I32: - case GGML_TYPE_F16: - case GGML_TYPE_COUNT: + default: { GGML_ASSERT(false); } break; @@ -7512,6 +9043,7 @@ static void ggml_compute_forward_soft_max_f32( uint16_t scvt; for (int i = 0; i < nc; i++) { + //printf("p[%3d] = %8.4f\n", i, p[i]); if (p[i] == -INFINITY) { p[i] = 0.0f; } else { @@ -7547,12 +9079,161 @@ static void ggml_compute_forward_soft_max( { ggml_compute_forward_soft_max_f32(params, src0, dst); } break; + default: + { + GGML_ASSERT(false); + } break; + } +} + +// ggml_compute_forward_alibi + +static void ggml_compute_forward_alibi_f32( + const struct ggml_compute_params * params, + const struct ggml_tensor * src0, + const struct ggml_tensor * src1, + struct ggml_tensor * dst) { + assert(params->ith == 0); + assert(src1->type == GGML_TYPE_I32); + assert(ggml_nelements(src1) == 2); + + if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + return; + } + + const int n_past = ((int32_t *) src1->data)[0]; + const int n_head = ((int32_t *) src1->data)[1]; + + const int ne0 = src0->ne[0]; // all_seq_len = n_past + ne1 + const int ne1 = src0->ne[1]; // seq_len_without_past + //const int ne2 = src0->ne[2]; // n_head -> this is k + //const int ne3 = src0->ne[3]; // 1 -> bsz + + const int n = ggml_nrows(src0); + const int ne2_ne3 = n/ne1; // ne2*ne3 + + const int nb0 = src0->nb[0]; + const int nb1 = src0->nb[1]; + const int nb2 = src0->nb[2]; + //const int nb3 = src0->nb[3]; + + assert(nb0 == sizeof(float)); + assert(ne1+n_past == ne0); + + // add alibi to src0 (KQ_scaled) + const int n_heads_log2_floor = 1 << (int) floor(log2(n_head)); + + const float m0 = powf(2.0f, -8.0f / n_heads_log2_floor); + const float m1 = powf(2.0f, -4.0f / n_heads_log2_floor); + + for (int i = 0; i < ne0; i++) { + for (int j = 0; j < ne1; j++) { + for (int k = 0; k < ne2_ne3; k++) { + float * const src = (float *)((char *) src0->data + i*nb0 + j*nb1 + k*nb2); + float * pdst = (float *)((char *) dst->data + i*nb0 + j*nb1 + k*nb2); + + // TODO: k*nb2 or k*nb3 + + float m_k; + + if (k < n_heads_log2_floor) { + m_k = powf(m0, k + 1); + } else { + m_k = powf(m1, 2 * (k - n_heads_log2_floor) + 1); + } + + pdst[0] = (j+1) * m_k + src[0]; + } + } + } +} + + +static void ggml_compute_forward_alibi_f16( + const struct ggml_compute_params * params, + const struct ggml_tensor * src0, + const struct ggml_tensor * src1, + struct ggml_tensor * dst) { + assert(params->ith == 0); + assert(src1->type == GGML_TYPE_I32); + assert(ggml_nelements(src1) == 2); + + if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + return; + } + + const int n_past = ((int32_t *) src1->data)[0]; + const int n_head = ((int32_t *) src1->data)[1]; + + const int ne0 = src0->ne[0]; // all_seq_len = n_past + ne1 + const int ne1 = src0->ne[1]; // seq_len_without_past + //const int ne2 = src0->ne[2]; // n_head -> this is k + //const int ne3 = src0->ne[3]; // 1 -> bsz + + const int n = ggml_nrows(src0); + const int ne2_ne3 = n/ne1; // ne2*ne3 + + const int nb0 = src0->nb[0]; + const int nb1 = src0->nb[1]; + const int nb2 = src0->nb[2]; + //const int nb3 = src0->nb[3]; + + assert(nb0 == sizeof(ggml_fp16_t)); + assert(ne1+n_past == ne0); + + // add alibi to src0 (KQ_scaled) + const int n_heads_log2_floor = 1 << (int) floor(log2(n_head)); + + const float m0 = powf(2.0f, -8.0f / n_heads_log2_floor); + const float m1 = powf(2.0f, -4.0f / n_heads_log2_floor); + + for (int i = 0; i < ne0; i++) { + for (int j = 0; j < ne1; j++) { + for (int k = 0; k < ne2_ne3; k++) { + ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i*nb0 + j*nb1 + k*nb2); + float * pdst = (float *)((char *) dst->data + i*nb0 + j*nb1 + k*nb2); + + // TODO: k*nb2 or k*nb3 + + float m_k; + + if (k < n_heads_log2_floor) { + m_k = powf(m0, k + 1); + } else { + m_k = powf(m1, 2 * (k - n_heads_log2_floor) + 1); + } + + // we return F32 + pdst[0] = (j+1) * m_k + GGML_FP16_TO_FP32(src[0]); + } + } + } +} + +static void ggml_compute_forward_alibi( + const struct ggml_compute_params * params, + const struct ggml_tensor * src0, + const struct ggml_tensor * src1, + struct ggml_tensor * dst) { + switch (src0->type) { + case GGML_TYPE_F16: + { + ggml_compute_forward_alibi_f16(params, src0, src1, dst); + } break; + case GGML_TYPE_F32: + { + ggml_compute_forward_alibi_f32(params, src0, src1, dst); + } break; case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_1: + case GGML_TYPE_Q4_2: + case GGML_TYPE_Q5_0: + case GGML_TYPE_Q5_1: + case GGML_TYPE_Q8_0: + case GGML_TYPE_Q8_1: case GGML_TYPE_I8: case GGML_TYPE_I16: case GGML_TYPE_I32: - case GGML_TYPE_F16: case GGML_TYPE_COUNT: { GGML_ASSERT(false); @@ -7610,9 +9291,11 @@ static void ggml_compute_forward_rope_f32( const float theta_scale = powf(10000.0, -2.0f/n_dims); + const bool is_neox = mode & 2; + for (int64_t i3 = 0; i3 < ne3; i3++) { - for (int64_t i2 = (mode == 0 ? 0 : n_past); i2 < ne2; i2++) { - const int p = (mode == 0 ? n_past + i2 : i2); + for (int64_t i2 = ((mode & 1) == 0 ? 0 : n_past); i2 < ne2; i2++) { + const int p = ((mode & 1) == 0 ? n_past + i2 : i2); for (int64_t i1 = 0; i1 < ne1; i1++) { if (ir++ < ir0) continue; if (ir > ir1) break; @@ -7625,14 +9308,25 @@ static void ggml_compute_forward_rope_f32( theta *= theta_scale; - const float * const src = (float *)((char *) src0->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); - float * dst_data = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); + if (!is_neox) { + const float * const src = (float *)((char *) src0->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); + float * dst_data = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); - const float x0 = src[0]; - const float x1 = src[1]; + const float x0 = src[0]; + const float x1 = src[1]; - dst_data[0] = x0*cos_theta - x1*sin_theta; - dst_data[1] = x0*sin_theta + x1*cos_theta; + dst_data[0] = x0*cos_theta - x1*sin_theta; + dst_data[1] = x0*sin_theta + x1*cos_theta; + } else { + const float * const src = (float *)((char *) src0->data + i3*nb3 + i2*nb2 + i1*nb1 + (i0/2)*nb0); + float * dst_data = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + (i0/2)*nb0); + + const float x0 = src[0]; + const float x1 = src[n_dims/2]; + + dst_data[0] = x0*cos_theta - x1*sin_theta; + dst_data[n_dims/2] = x0*sin_theta + x1*cos_theta; + } } } } @@ -7687,9 +9381,11 @@ static void ggml_compute_forward_rope_f16( const float theta_scale = powf(10000.0, -2.0f/n_dims); + const bool is_neox = mode & 2; + for (int64_t i3 = 0; i3 < ne3; i3++) { - for (int64_t i2 = (mode == 0 ? 0 : n_past); i2 < ne2; i2++) { - const int p = (mode == 0 ? n_past + i2 : i2); + for (int64_t i2 = ((mode & 1) == 0 ? 0 : n_past); i2 < ne2; i2++) { + const int p = ((mode & 1) == 0 ? n_past + i2 : i2); for (int64_t i1 = 0; i1 < ne1; i1++) { if (ir++ < ir0) continue; if (ir > ir1) break; @@ -7702,14 +9398,25 @@ static void ggml_compute_forward_rope_f16( theta *= theta_scale; - const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); - ggml_fp16_t * dst_data = (ggml_fp16_t *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); + if (!is_neox) { + const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); + ggml_fp16_t * dst_data = (ggml_fp16_t *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); - const float x0 = ggml_fp16_to_fp32(src[0]); - const float x1 = ggml_fp16_to_fp32(src[1]); + 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); + dst_data[0] = GGML_FP32_TO_FP16(x0*cos_theta - x1*sin_theta); + dst_data[1] = GGML_FP32_TO_FP16(x0*sin_theta + x1*cos_theta); + } else { + const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb3 + i2*nb2 + i1*nb1 + (i0/2)*nb0); + ggml_fp16_t * dst_data = (ggml_fp16_t *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + (i0/2)*nb0); + + const float x0 = GGML_FP16_TO_FP32(src[0]); + const float x1 = GGML_FP16_TO_FP32(src[n_dims/2]); + + dst_data[0] = GGML_FP32_TO_FP16(x0*cos_theta - x1*sin_theta); + dst_data[n_dims/2] = GGML_FP32_TO_FP16(x0*sin_theta + x1*cos_theta); + } } } } @@ -7730,12 +9437,7 @@ static void ggml_compute_forward_rope( { ggml_compute_forward_rope_f32(params, src0, src1, dst); } break; - case GGML_TYPE_Q4_0: - case GGML_TYPE_Q4_1: - case GGML_TYPE_I8: - case GGML_TYPE_I16: - case GGML_TYPE_I32: - case GGML_TYPE_COUNT: + default: { GGML_ASSERT(false); } break; @@ -7998,12 +9700,7 @@ static void ggml_compute_forward_conv_1d_1s( { ggml_compute_forward_conv_1d_1s_f32(params, src0, src1, dst); } break; - case GGML_TYPE_Q4_0: - case GGML_TYPE_Q4_1: - case GGML_TYPE_I8: - case GGML_TYPE_I16: - case GGML_TYPE_I32: - case GGML_TYPE_COUNT: + default: { GGML_ASSERT(false); } break; @@ -8266,12 +9963,7 @@ static void ggml_compute_forward_conv_1d_2s( { ggml_compute_forward_conv_1d_2s_f32(params, src0, src1, dst); } break; - case GGML_TYPE_Q4_0: - case GGML_TYPE_Q4_1: - case GGML_TYPE_I8: - case GGML_TYPE_I16: - case GGML_TYPE_I32: - case GGML_TYPE_COUNT: + default: { GGML_ASSERT(false); } break; @@ -8751,12 +10443,7 @@ static void ggml_compute_forward_flash_attn( { ggml_compute_forward_flash_attn_f32(params, q, k, v, masked, dst); } break; - case GGML_TYPE_Q4_0: - case GGML_TYPE_Q4_1: - case GGML_TYPE_I8: - case GGML_TYPE_I16: - case GGML_TYPE_I32: - case GGML_TYPE_COUNT: + default: { GGML_ASSERT(false); } break; @@ -8962,12 +10649,7 @@ static void ggml_compute_forward_flash_ff( { GGML_ASSERT(false); // TODO } break; - case GGML_TYPE_Q4_0: - case GGML_TYPE_Q4_1: - case GGML_TYPE_I8: - case GGML_TYPE_I16: - case GGML_TYPE_I32: - case GGML_TYPE_COUNT: + default: { GGML_ASSERT(false); } break; @@ -9011,13 +10693,7 @@ static void ggml_compute_forward_map_unary( { ggml_compute_forward_map_unary_f32(params, src0, dst, fun); } break; - case GGML_TYPE_Q4_0: - case GGML_TYPE_Q4_1: - case GGML_TYPE_I8: - case GGML_TYPE_I16: - case GGML_TYPE_I32: - case GGML_TYPE_F16: - case GGML_TYPE_COUNT: + default: { GGML_ASSERT(false); } break; @@ -9066,13 +10742,7 @@ static void ggml_compute_forward_map_binary( { ggml_compute_forward_map_binary_f32(params, src0, src1, dst, fun); } break; - case GGML_TYPE_Q4_0: - case GGML_TYPE_Q4_1: - case GGML_TYPE_I8: - case GGML_TYPE_I16: - case GGML_TYPE_I32: - case GGML_TYPE_F16: - case GGML_TYPE_COUNT: + default: { GGML_ASSERT(false); } break; @@ -9209,6 +10879,10 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm { ggml_compute_forward_rope(params, tensor->src0, tensor->src1, tensor); } break; + case GGML_OP_ALIBI: + { + ggml_compute_forward_alibi(params, tensor->src0, tensor->src1, tensor); + } break; case GGML_OP_CONV_1D_1S: { ggml_compute_forward_conv_1d_1s(params, tensor->src0, tensor->src1, tensor); @@ -9411,6 +11085,10 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor { GGML_ASSERT(false); // TODO: not implemented } break; + case GGML_OP_ALIBI: + { + GGML_ASSERT(false); // TODO: not implemented + } break; case GGML_OP_SILU: { GGML_ASSERT(false); // TODO: not implemented @@ -9822,13 +11500,29 @@ void ggml_graph_compute(struct ggml_context * ctx, struct ggml_cgraph * cgraph) struct ggml_tensor * node = cgraph->nodes[i]; switch (node->op) { + case GGML_OP_CPY: case GGML_OP_DUP: { - node->n_tasks = 1; + node->n_tasks = n_threads; + + size_t cur = 0; + if (ggml_is_quantized(node->type)) { + cur = GGML_TYPE_SIZE[GGML_TYPE_F32] * node->ne[0] * n_threads; + } + + work_size = MAX(work_size, cur); } break; case GGML_OP_ADD: { node->n_tasks = n_threads; + + size_t cur = 0; + + if (ggml_is_quantized(node->src0->type)) { + cur = GGML_TYPE_SIZE[GGML_TYPE_F32] * node->src0->ne[0] * n_threads; + } + + work_size = MAX(work_size, cur); } break; case GGML_OP_SUB: case GGML_OP_MUL: @@ -9873,14 +11567,17 @@ void ggml_graph_compute(struct ggml_context * ctx, struct ggml_cgraph * cgraph) size_t cur = 0; if (node->src0->type == GGML_TYPE_F16 && node->src1->type == GGML_TYPE_F32) { -#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) +#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST) if (ggml_compute_forward_mul_mat_use_blas(node->src0, node->src1, node)) { node->n_tasks = 1; // TODO: this actually is doing nothing // the threads are still spinning +#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) + // here we need memory just for single 2D matrix from src0 cur = GGML_TYPE_SIZE[GGML_TYPE_F32]*(node->src0->ne[0]*node->src0->ne[1]); - //printf("src0: ne0 = %d, ne1 = %d, ne = %d\n", node->src0->ne[0], node->src0->ne[1], node->src0->ne[0]*node->src0->ne[1]); - //printf("src1: ne0 = %d, ne1 = %d, ne = %d\n", node->src1->ne[0], node->src1->ne[1], node->src1->ne[0]*node->src1->ne[1]); - //printf("cur = %zu\n", cur); +#else + // with GPU, we need memory for the full 3D / 4D data + cur = GGML_TYPE_SIZE[GGML_TYPE_F32]*MAX(ggml_nelements(node->src1), ggml_nelements(node->src0)); +#endif } else { cur = GGML_TYPE_SIZE[GGML_TYPE_F16]*ggml_nelements(node->src1); } @@ -9889,15 +11586,21 @@ void ggml_graph_compute(struct ggml_context * ctx, struct ggml_cgraph * cgraph) #endif } else if (node->src0->type == GGML_TYPE_F32 && node->src1->type == GGML_TYPE_F32) { cur = 0; - } else if (quantize_fns[node->src0->type].vec_dot_q && node->src1->type == GGML_TYPE_F32) { -#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) +#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CUBLAS) + if (ggml_compute_forward_mul_mat_use_blas(node->src0, node->src1, node)) { + node->n_tasks = 1; + } +#endif + } else if (ggml_is_quantized(node->src0->type) && node->src1->type == GGML_TYPE_F32) { +#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST) if (ggml_compute_forward_mul_mat_use_blas(node->src0, node->src1, node)) { node->n_tasks = 1; cur = GGML_TYPE_SIZE[GGML_TYPE_F32]*(node->src0->ne[0]*node->src0->ne[1]); } else #endif { - cur = GGML_TYPE_SIZE[node->src0->type]*ggml_nelements(node->src1)/GGML_BLCK_SIZE[node->src0->type]; + const enum ggml_type type_q = quantize_fns[node->src0->type].vec_dot_type; + cur = GGML_TYPE_SIZE[type_q]*ggml_nelements(node->src1)/GGML_BLCK_SIZE[type_q]; } } else { GGML_ASSERT(false); @@ -9909,7 +11612,6 @@ void ggml_graph_compute(struct ggml_context * ctx, struct ggml_cgraph * cgraph) { node->n_tasks = n_threads; } break; - case GGML_OP_CPY: case GGML_OP_CONT: case GGML_OP_RESHAPE: case GGML_OP_VIEW: @@ -9928,6 +11630,10 @@ void ggml_graph_compute(struct ggml_context * ctx, struct ggml_cgraph * cgraph) { node->n_tasks = n_threads; } break; + case GGML_OP_ALIBI: + { + node->n_tasks = 1; //TODO + } break; case GGML_OP_CONV_1D_1S: case GGML_OP_CONV_1D_2S: { @@ -10226,9 +11932,9 @@ void ggml_graph_print(const struct ggml_cgraph * cgraph) { for (int i = 0; i < cgraph->n_nodes; i++) { struct ggml_tensor * node = cgraph->nodes[i]; - perf_total_per_op_us[node->op] += node->perf_time_us; + perf_total_per_op_us[node->op] += MAX(1, node->perf_time_us); - GGML_PRINT(" - %3d: [ %" PRId64 ", %" PRId64 ", %" PRId64 "] %16s %s (%3d) cpu = %7.3f / %7.3f ms, wall = %7.3f / %7.3f ms\n", + 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_LABEL[node->op], node->is_param ? "x" : node->grad ? "g" : " ", node->perf_runs, @@ -10242,13 +11948,17 @@ void ggml_graph_print(const struct ggml_cgraph * cgraph) { for (int i = 0; i < cgraph->n_leafs; i++) { struct ggml_tensor * node = cgraph->leafs[i]; - GGML_PRINT(" - %3d: [ %" PRId64 ", %" PRId64 "] %8s\n", + GGML_PRINT(" - %3d: [ %5" PRId64 ", %5" PRId64 "] %8s\n", i, node->ne[0], node->ne[1], GGML_OP_LABEL[node->op]); } for (int i = 0; i < GGML_OP_COUNT; i++) { + if (perf_total_per_op_us[i] == 0) { + continue; + } + GGML_PRINT("perf_total_per_op_us[%16s] = %7.3f ms\n", GGML_OP_LABEL[i], (double) perf_total_per_op_us[i] / 1000.0); } @@ -11072,17 +12782,17 @@ enum ggml_opt_result ggml_opt( //////////////////////////////////////////////////////////////////////////////// size_t ggml_quantize_q4_0(const float * src, void * dst, int n, int k, int64_t * hist) { - assert(k % QK == 0); - const int nb = k / QK; + assert(k % QK4_0 == 0); + const int nb = k / QK4_0; for (int j = 0; j < n; j += k) { - block_q4_0 * restrict y = (block_q4_0 *)dst + j/QK; + block_q4_0 * restrict y = (block_q4_0 *)dst + j/QK4_0; quantize_row_q4_0_reference(src + j, y, k); for (int i = 0; i < nb; i++) { - for (int l = 0; l < QK; l += 2) { - const uint8_t vi0 = y[i].qs[l/2] & 0xF; + for (int l = 0; l < QK4_0; l += 2) { + const uint8_t vi0 = y[i].qs[l/2] & 0x0F; const uint8_t vi1 = y[i].qs[l/2] >> 4; hist[vi0]++; @@ -11091,21 +12801,21 @@ size_t ggml_quantize_q4_0(const float * src, void * dst, int n, int k, int64_t * } } - return (n/QK*sizeof(block_q4_0)); + return (n/QK4_0*sizeof(block_q4_0)); } size_t ggml_quantize_q4_1(const float * src, void * dst, int n, int k, int64_t * hist) { - assert(k % QK == 0); - const int nb = k / QK; + assert(k % QK4_1 == 0); + const int nb = k / QK4_1; for (int j = 0; j < n; j += k) { - block_q4_1 * restrict y = (block_q4_1 *)dst + j/QK; + block_q4_1 * restrict y = (block_q4_1 *)dst + j/QK4_1; quantize_row_q4_1_reference(src + j, y, k); for (int i = 0; i < nb; i++) { - for (int l = 0; l < QK; l += 2) { - const uint8_t vi0 = y[i].qs[l/2] & 0xF; + for (int l = 0; l < QK4_1; l += 2) { + const uint8_t vi0 = y[i].qs[l/2] & 0x0F; const uint8_t vi1 = y[i].qs[l/2] >> 4; hist[vi0]++; @@ -11114,7 +12824,156 @@ size_t ggml_quantize_q4_1(const float * src, void * dst, int n, int k, int64_t * } } - return (n/QK*sizeof(block_q4_1)); + return (n/QK4_1*sizeof(block_q4_1)); +} + +size_t ggml_quantize_q4_2(const float * src, void * dst, int n, int k, int64_t * hist) { + assert(k % QK4_2 == 0); + const int nb = k / QK4_2; + + for (int j = 0; j < n; j += k) { + block_q4_2 * restrict y = (block_q4_2 *)dst + j/QK4_2; + + quantize_row_q4_2_reference(src + j, y, k); + + for (int i = 0; i < nb; i++) { + for (int l = 0; l < QK4_2; l += 2) { + const uint8_t vi0 = y[i].qs[l/2] & 0x0F; + const uint8_t vi1 = y[i].qs[l/2] >> 4; + + hist[vi0]++; + hist[vi1]++; + } + } + } + + return (n/QK4_2*sizeof(block_q4_2)); +} + +size_t ggml_quantize_q5_0(const float * src, void * dst, int n, int k, int64_t * hist) { + assert(k % QK5_0 == 0); + const int nb = k / QK5_0; + + for (int j = 0; j < n; j += k) { + block_q5_0 * restrict y = (block_q5_0 *)dst + j/QK5_0; + + quantize_row_q5_0_reference(src + j, y, k); + + for (int i = 0; i < nb; i++) { + uint32_t qh; + memcpy(&qh, &y[i].qh, sizeof(qh)); + + for (int l = 0; l < QK5_0; l += 2) { + const uint8_t vh0 = ((qh & (1 << (l + 0))) >> (l + 0)) << 4; + const uint8_t vh1 = ((qh & (1 << (l + 1))) >> (l + 1)) << 4; + + // cast to 16 bins + const uint8_t vi0 = ((y[i].qs[l/2] & 0x0F) | vh0) / 2; + const uint8_t vi1 = ((y[i].qs[l/2] >> 4) | vh1) / 2; + + hist[vi0]++; + hist[vi1]++; + } + } + } + + return (n/QK5_0*sizeof(block_q5_0)); +} + +size_t ggml_quantize_q5_1(const float * src, void * dst, int n, int k, int64_t * hist) { + assert(k % QK5_1 == 0); + const int nb = k / QK5_1; + + for (int j = 0; j < n; j += k) { + block_q5_1 * restrict y = (block_q5_1 *)dst + j/QK5_1; + + quantize_row_q5_1_reference(src + j, y, k); + + for (int i = 0; i < nb; i++) { + uint32_t qh; + memcpy(&qh, &y[i].qh, sizeof(qh)); + + for (int l = 0; l < QK5_1; l += 2) { + const uint8_t vh0 = ((qh & (1 << (l + 0))) >> (l + 0)) << 4; + const uint8_t vh1 = ((qh & (1 << (l + 1))) >> (l + 1)) << 4; + + // cast to 16 bins + const uint8_t vi0 = ((y[i].qs[l/2] & 0x0F) | vh0) / 2; + const uint8_t vi1 = ((y[i].qs[l/2] >> 4) | vh1) / 2; + + hist[vi0]++; + hist[vi1]++; + } + } + } + + return (n/QK5_1*sizeof(block_q5_1)); +} + +size_t ggml_quantize_q8_0(const float * src, void * dst, int n, int k, int64_t * hist) { + assert(k % QK8_0 == 0); + const int nb = k / QK8_0; + + for (int j = 0; j < n; j += k) { + block_q8_0 * restrict y = (block_q8_0 *)dst + j/QK8_0; + + quantize_row_q8_0_reference(src + j, y, k); + + for (int i = 0; i < nb; i++) { + for (int l = 0; l < QK8_0; ++l) { + const int8_t vi = y[i].qs[l]; + + hist[vi/16 + 8]++; + } + } + } + + return (n/QK8_0*sizeof(block_q8_0)); +} + +size_t ggml_quantize_chunk(enum ggml_type type, const float * src, void * dst, int start, int n, int64_t * hist) { + size_t result = 0; + switch (type) { + case GGML_TYPE_Q4_0: + { + GGML_ASSERT(start % QK4_0 == 0); + block_q4_0 * block = (block_q4_0*)dst + start / QK4_0; + result = ggml_quantize_q4_0(src + start, block, n, n, hist); + } break; + case GGML_TYPE_Q4_1: + { + GGML_ASSERT(start % QK4_1 == 0); + block_q4_1 * block = (block_q4_1*)dst + start / QK4_1; + result = ggml_quantize_q4_1(src + start, block, n, n, hist); + } break; + case GGML_TYPE_Q4_2: + { + GGML_ASSERT(start % QK4_2 == 0); + block_q4_2 * block = (block_q4_2*)dst + start / QK4_2; + result = ggml_quantize_q4_2(src + start, block, n, n, hist); + } break; + case GGML_TYPE_Q5_0: + { + GGML_ASSERT(start % QK5_0 == 0); + block_q5_0 * block = (block_q5_0*)dst + start / QK5_0; + result = ggml_quantize_q5_0(src + start, block, n, n, hist); + } break; + case GGML_TYPE_Q5_1: + { + GGML_ASSERT(start % QK5_1 == 0); + block_q5_1 * block = (block_q5_1*)dst + start / QK5_1; + result = ggml_quantize_q5_1(src + start, block, n, n, hist); + } break; + case GGML_TYPE_Q8_0: + { + GGML_ASSERT(start % QK8_0 == 0); + block_q8_0 * block = (block_q8_0*)dst + start / QK8_0; + result = ggml_quantize_q8_0(src + start, block, n, n, hist); + } break; + default: + assert(false); + } + return result; } //////////////////////////////////////////////////////////////////////////////// @@ -11143,6 +13002,22 @@ int ggml_cpu_has_avx512(void) { #endif } +int ggml_cpu_has_avx512_vbmi(void) { +#if defined(__AVX512VBMI__) + return 1; +#else + return 0; +#endif +} + +int ggml_cpu_has_avx512_vnni(void) { +#if defined(__AVX512VNNI__) + return 1; +#else + return 0; +#endif +} + int ggml_cpu_has_fma(void) { #if defined(__FMA__) return 1; @@ -11192,13 +13067,33 @@ int ggml_cpu_has_wasm_simd(void) { } int ggml_cpu_has_blas(void) { -#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) +#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST) return 1; #else return 0; #endif } +int ggml_cpu_has_cublas(void) { +#if defined(GGML_USE_CUBLAS) + return 1; +#else + return 0; +#endif +} + +int ggml_cpu_has_clblast(void) { +#if defined(GGML_USE_CLBLAST) + return 1; +#else + return 0; +#endif +} + +int ggml_cpu_has_gpublas(void) { + return ggml_cpu_has_cublas() || ggml_cpu_has_clblast(); +} + int ggml_cpu_has_sse3(void) { #if defined(__SSE3__) return 1; diff --git a/ggml.h b/ggml.h index bdff0b4..38ae9a6 100644 --- a/ggml.h +++ b/ggml.h @@ -169,14 +169,27 @@ // // -#ifdef __cplusplus -extern "C" { +#ifdef GGML_SHARED +# if defined(_WIN32) && !defined(__MINGW32__) +# ifdef GGML_BUILD +# define GGML_API __declspec(dllexport) +# else +# define GGML_API __declspec(dllimport) +# endif +# else +# define GGML_API __attribute__ ((visibility ("default"))) +# endif +#else +# define GGML_API #endif #include #include #include +#define GGML_FILE_MAGIC 0x67676d6c // "ggml" +#define GGML_FILE_VERSION 1 + #define GGML_MAX_DIMS 4 #define GGML_MAX_NODES 4096 #define GGML_MAX_PARAMS 16 @@ -184,660 +197,704 @@ extern "C" { #define GGML_MAX_OPT 4 #define GGML_DEFAULT_N_THREADS 4 -#ifdef __ARM_NEON -// we use the built-in 16-bit float type -typedef __fp16 ggml_fp16_t; -#else -typedef uint16_t ggml_fp16_t; +#ifdef __cplusplus +extern "C" { #endif -// convert FP16 <-> FP32 -float ggml_fp16_to_fp32(ggml_fp16_t x); -ggml_fp16_t ggml_fp32_to_fp16(float x); - -struct ggml_object; -struct ggml_context; - -enum ggml_type { - // explicitly numbered values are used in llama.cpp files - GGML_TYPE_F32 = 0, - GGML_TYPE_F16 = 1, - GGML_TYPE_Q4_0 = 2, - GGML_TYPE_Q4_1 = 3, - GGML_TYPE_I8, - GGML_TYPE_I16, - GGML_TYPE_I32, - GGML_TYPE_COUNT, -}; - -// available tensor operations: -enum ggml_op { - GGML_OP_NONE = 0, - - GGML_OP_DUP, - GGML_OP_ADD, - GGML_OP_SUB, - GGML_OP_MUL, - GGML_OP_DIV, - GGML_OP_SQR, - GGML_OP_SQRT, - GGML_OP_SUM, - GGML_OP_MEAN, - GGML_OP_REPEAT, - GGML_OP_ABS, - GGML_OP_SGN, - GGML_OP_NEG, - GGML_OP_STEP, - GGML_OP_RELU, - GGML_OP_GELU, - GGML_OP_SILU, - GGML_OP_NORM, // normalize - GGML_OP_RMS_NORM, - - GGML_OP_MUL_MAT, - - GGML_OP_SCALE, - GGML_OP_CPY, - GGML_OP_CONT, - GGML_OP_RESHAPE, - GGML_OP_VIEW, - GGML_OP_PERMUTE, - GGML_OP_TRANSPOSE, - GGML_OP_GET_ROWS, - GGML_OP_DIAG_MASK_INF, - GGML_OP_SOFT_MAX, - GGML_OP_ROPE, - GGML_OP_CONV_1D_1S, - GGML_OP_CONV_1D_2S, - - GGML_OP_FLASH_ATTN, - GGML_OP_FLASH_FF, - - GGML_OP_MAP_UNARY, - GGML_OP_MAP_BINARY, - - GGML_OP_COUNT, -}; - - -// ggml object -struct ggml_object { - size_t offs; - size_t size; - - struct ggml_object * next; - - char padding[8]; -}; - -static const size_t GGML_OBJECT_SIZE = sizeof(struct ggml_object); - -// n-dimensional tensor -struct ggml_tensor { - enum ggml_type type; - - int n_dims; - int64_t ne[GGML_MAX_DIMS]; // number of elements - size_t nb[GGML_MAX_DIMS]; // stride in bytes: - // nb[0] = sizeof(type) - // nb[1] = nb[0] * ne[0] + padding - // nb[i] = nb[i-1] * ne[i-1] - - // compute data - enum ggml_op op; - - bool is_param; - - struct ggml_tensor * grad; - struct ggml_tensor * src0; - struct ggml_tensor * src1; - struct ggml_tensor * opt[GGML_MAX_OPT]; - - // thread scheduling - int n_tasks; - - // performance - int perf_runs; - int64_t perf_cycles; - int64_t perf_time_us; - - void * data; - char padding[8]; -}; - -// computation graph -struct ggml_cgraph { - int n_nodes; - int n_leafs; - int n_threads; - - size_t work_size; - struct ggml_tensor * work; - - struct ggml_tensor * nodes[GGML_MAX_NODES]; - struct ggml_tensor * grads[GGML_MAX_NODES]; - struct ggml_tensor * leafs[GGML_MAX_NODES]; - - // performance - int perf_runs; - int64_t perf_cycles; - int64_t perf_time_us; -}; - -// scratch buffer -struct ggml_scratch { - size_t offs; - size_t size; - void * data; -}; - -struct ggml_init_params { - // memory pool - size_t mem_size; // bytes - void * mem_buffer; // if NULL, memory will be allocated internally - bool no_alloc; // don't allocate memory for the tensor data -}; - -void ggml_time_init(void); // call this once at the beginning of the program -int64_t ggml_time_ms(void); -int64_t ggml_time_us(void); -int64_t ggml_cycles(void); -int64_t ggml_cycles_per_ms(void); - -void ggml_print_object (const struct ggml_object * obj); -void ggml_print_objects(const struct ggml_context * ctx); - -int64_t ggml_nelements(const struct ggml_tensor * tensor); -size_t ggml_nbytes (const struct ggml_tensor * tensor); +#ifdef __ARM_NEON + // we use the built-in 16-bit float type + typedef __fp16 ggml_fp16_t; +#else + typedef uint16_t ggml_fp16_t; +#endif + + // convert FP16 <-> FP32 + GGML_API float ggml_fp16_to_fp32(ggml_fp16_t x); + GGML_API ggml_fp16_t ggml_fp32_to_fp16(float x); + + struct ggml_object; + struct ggml_context; + + enum ggml_type { + GGML_TYPE_F32 = 0, + GGML_TYPE_F16 = 1, + GGML_TYPE_Q4_0 = 2, + GGML_TYPE_Q4_1 = 3, + GGML_TYPE_Q4_2 = 4, + // GGML_TYPE_Q4_3 (5) support has been removed + GGML_TYPE_Q5_0 = 6, + GGML_TYPE_Q5_1 = 7, + GGML_TYPE_Q8_0 = 8, + GGML_TYPE_Q8_1 = 9, + GGML_TYPE_I8, + GGML_TYPE_I16, + GGML_TYPE_I32, + GGML_TYPE_COUNT, + }; + + // available tensor operations: + enum ggml_op { + GGML_OP_NONE = 0, + + GGML_OP_DUP, + GGML_OP_ADD, + GGML_OP_SUB, + GGML_OP_MUL, + GGML_OP_DIV, + GGML_OP_SQR, + GGML_OP_SQRT, + GGML_OP_SUM, + GGML_OP_MEAN, + GGML_OP_REPEAT, + GGML_OP_ABS, + GGML_OP_SGN, + GGML_OP_NEG, + GGML_OP_STEP, + GGML_OP_RELU, + GGML_OP_GELU, + GGML_OP_SILU, + GGML_OP_NORM, // normalize + GGML_OP_RMS_NORM, + + GGML_OP_MUL_MAT, + + GGML_OP_SCALE, + GGML_OP_CPY, + GGML_OP_CONT, + GGML_OP_RESHAPE, + GGML_OP_VIEW, + GGML_OP_PERMUTE, + GGML_OP_TRANSPOSE, + GGML_OP_GET_ROWS, + GGML_OP_DIAG_MASK_INF, + GGML_OP_SOFT_MAX, + GGML_OP_ROPE, + GGML_OP_ALIBI, + GGML_OP_CONV_1D_1S, + GGML_OP_CONV_1D_2S, + + GGML_OP_FLASH_ATTN, + GGML_OP_FLASH_FF, + + GGML_OP_MAP_UNARY, + GGML_OP_MAP_BINARY, + + GGML_OP_COUNT, + }; + + + // ggml object + struct ggml_object { + size_t offs; + size_t size; + + struct ggml_object * next; + + char padding[8]; + }; + + static const size_t GGML_OBJECT_SIZE = sizeof(struct ggml_object); + + // n-dimensional tensor + struct ggml_tensor { + enum ggml_type type; + + int n_dims; + int64_t ne[GGML_MAX_DIMS]; // number of elements + size_t nb[GGML_MAX_DIMS]; // stride in bytes: + // nb[0] = sizeof(type) + // nb[1] = nb[0] * ne[0] + padding + // nb[i] = nb[i-1] * ne[i-1] + + // compute data + enum ggml_op op; + + bool is_param; + + struct ggml_tensor * grad; + struct ggml_tensor * src0; + struct ggml_tensor * src1; + struct ggml_tensor * opt[GGML_MAX_OPT]; + + // thread scheduling + int n_tasks; + + // performance + int perf_runs; + int64_t perf_cycles; + int64_t perf_time_us; + + void * data; + char padding[8]; + }; + + // computation graph + struct ggml_cgraph { + int n_nodes; + int n_leafs; + int n_threads; + + size_t work_size; + struct ggml_tensor * work; + + struct ggml_tensor * nodes[GGML_MAX_NODES]; + struct ggml_tensor * grads[GGML_MAX_NODES]; + struct ggml_tensor * leafs[GGML_MAX_NODES]; + + // performance + int perf_runs; + int64_t perf_cycles; + int64_t perf_time_us; + }; + + // scratch buffer + struct ggml_scratch { + size_t offs; + size_t size; + void * data; + }; + + struct ggml_init_params { + // memory pool + size_t mem_size; // bytes + void * mem_buffer; // if NULL, memory will be allocated internally + bool no_alloc; // don't allocate memory for the tensor data + }; + + // misc + + GGML_API void ggml_time_init(void); // call this once at the beginning of the program + GGML_API int64_t ggml_time_ms(void); + GGML_API int64_t ggml_time_us(void); + GGML_API int64_t ggml_cycles(void); + GGML_API int64_t ggml_cycles_per_ms(void); + + GGML_API void ggml_print_object (const struct ggml_object * obj); + GGML_API void ggml_print_objects(const struct ggml_context * ctx); + + GGML_API int64_t ggml_nelements(const struct ggml_tensor * tensor); + GGML_API size_t ggml_nbytes (const struct ggml_tensor * tensor); + + GGML_API int ggml_blck_size (enum ggml_type type); + GGML_API size_t ggml_type_size (enum ggml_type type); // size in bytes for all elements in a block + GGML_API float ggml_type_sizef(enum ggml_type type); // ggml_type_size()/ggml_blck_size() as float + + GGML_API const char * ggml_type_name(enum ggml_type type); + + GGML_API size_t ggml_element_size(const struct ggml_tensor * tensor); + + GGML_API bool ggml_is_quantized(enum ggml_type type); + + // main + + GGML_API struct ggml_context * ggml_init(struct ggml_init_params params); + GGML_API void ggml_free(struct ggml_context * ctx); + + GGML_API size_t ggml_used_mem(const struct ggml_context * ctx); + + GGML_API size_t ggml_set_scratch(struct ggml_context * ctx, struct ggml_scratch scratch); + + GGML_API struct ggml_tensor * ggml_new_tensor( + struct ggml_context * ctx, + enum ggml_type type, + int n_dims, + const int64_t *ne); + + GGML_API struct ggml_tensor * ggml_new_tensor_1d( + struct ggml_context * ctx, + enum ggml_type type, + int64_t ne0); + + GGML_API struct ggml_tensor * ggml_new_tensor_2d( + struct ggml_context * ctx, + enum ggml_type type, + int64_t ne0, + int64_t ne1); + + GGML_API struct ggml_tensor * ggml_new_tensor_3d( + struct ggml_context * ctx, + enum ggml_type type, + int64_t ne0, + int64_t ne1, + int64_t ne2); + + GGML_API 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); + + GGML_API struct ggml_tensor * ggml_new_i32(struct ggml_context * ctx, int32_t value); + GGML_API struct ggml_tensor * ggml_new_f32(struct ggml_context * ctx, float value); + + GGML_API struct ggml_tensor * ggml_dup_tensor (struct ggml_context * ctx, const struct ggml_tensor * src); + GGML_API struct ggml_tensor * ggml_view_tensor(struct ggml_context * ctx, const struct ggml_tensor * src); + + GGML_API struct ggml_tensor * ggml_set_zero(struct ggml_tensor * tensor); + GGML_API struct ggml_tensor * ggml_set_i32 (struct ggml_tensor * tensor, int32_t value); + GGML_API struct ggml_tensor * ggml_set_f32 (struct ggml_tensor * tensor, float value); + + GGML_API int32_t ggml_get_i32_1d(const struct ggml_tensor * tensor, int i); + GGML_API void ggml_set_i32_1d(const struct ggml_tensor * tensor, int i, int32_t value); + + GGML_API float ggml_get_f32_1d(const struct ggml_tensor * tensor, int i); + GGML_API void ggml_set_f32_1d(const struct ggml_tensor * tensor, int i, float value); + + GGML_API void * ggml_get_data (const struct ggml_tensor * tensor); + GGML_API float * ggml_get_data_f32(const struct ggml_tensor * tensor); -int ggml_blck_size (enum ggml_type type); -size_t ggml_type_size (enum ggml_type type); // size in bytes for all elements in a block -float ggml_type_sizef(enum ggml_type type); // ggml_type_size()/ggml_blck_size() as float - -size_t ggml_element_size(const struct ggml_tensor * tensor); - -struct ggml_context * ggml_init(struct ggml_init_params params); -void ggml_free(struct ggml_context * ctx); - -size_t ggml_used_mem(const struct ggml_context * ctx); - -size_t ggml_set_scratch(struct ggml_context * ctx, struct ggml_scratch scratch); - -struct ggml_tensor * ggml_new_tensor( - struct ggml_context * ctx, - enum ggml_type type, - int n_dims, - const int64_t *ne); - -struct ggml_tensor * ggml_new_tensor_1d( - struct ggml_context * ctx, - enum ggml_type type, - int64_t ne0); - -struct ggml_tensor * ggml_new_tensor_2d( - struct ggml_context * ctx, - enum ggml_type type, - int64_t ne0, - int64_t ne1); - -struct ggml_tensor * ggml_new_tensor_3d( - struct ggml_context * ctx, - enum ggml_type type, - int64_t ne0, - int64_t ne1, - int64_t ne2); - -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); - -struct ggml_tensor * ggml_new_i32(struct ggml_context * ctx, int32_t value); -struct ggml_tensor * ggml_new_f32(struct ggml_context * ctx, float value); - -struct ggml_tensor * ggml_dup_tensor (struct ggml_context * ctx, const struct ggml_tensor * src); -struct ggml_tensor * ggml_view_tensor(struct ggml_context * ctx, const struct ggml_tensor * src); - -struct ggml_tensor * ggml_set_zero(struct ggml_tensor * tensor); -struct ggml_tensor * ggml_set_i32 (struct ggml_tensor * tensor, int32_t value); -struct ggml_tensor * ggml_set_f32 (struct ggml_tensor * tensor, float value); - -int32_t ggml_get_i32_1d(const struct ggml_tensor * tensor, int i); -void ggml_set_i32_1d(const struct ggml_tensor * tensor, int i, int32_t value); - -float ggml_get_f32_1d(const struct ggml_tensor * tensor, int i); -void ggml_set_f32_1d(const struct ggml_tensor * tensor, int i, float value); - - void * ggml_get_data (const struct ggml_tensor * tensor); -float * ggml_get_data_f32(const struct ggml_tensor * tensor); - -// -// operations on tensors with backpropagation -// - -struct ggml_tensor * ggml_dup( - struct ggml_context * ctx, - struct ggml_tensor * a); - -struct ggml_tensor * ggml_add( - struct ggml_context * ctx, - struct ggml_tensor * a, - struct ggml_tensor * b); - -struct ggml_tensor * ggml_sub( - struct ggml_context * ctx, - struct ggml_tensor * a, - struct ggml_tensor * b); - -struct ggml_tensor * ggml_mul( - struct ggml_context * ctx, - struct ggml_tensor * a, - struct ggml_tensor * b); - -struct ggml_tensor * ggml_div( - struct ggml_context * ctx, - struct ggml_tensor * a, - struct ggml_tensor * b); - -struct ggml_tensor * ggml_sqr( - struct ggml_context * ctx, - struct ggml_tensor * a); - -struct ggml_tensor * ggml_sqrt( - struct ggml_context * ctx, - struct ggml_tensor * a); - -// return scalar -// TODO: compute sum along rows -struct ggml_tensor * ggml_sum( - struct ggml_context * ctx, - struct ggml_tensor * a); - -// mean along rows -struct ggml_tensor * ggml_mean( - struct ggml_context * ctx, - struct ggml_tensor * a); - -// if a is the same shape as b, and a is not parameter, return a -// otherwise, return a new tensor: repeat(a) to fit in b -struct ggml_tensor * ggml_repeat( - struct ggml_context * ctx, - struct ggml_tensor * a, - struct ggml_tensor * b); - -struct ggml_tensor * ggml_abs( - struct ggml_context * ctx, - struct ggml_tensor * a); - -struct ggml_tensor * ggml_sgn( - struct ggml_context * ctx, - struct ggml_tensor * a); - -struct ggml_tensor * ggml_neg( - struct ggml_context * ctx, - struct ggml_tensor * a); - -struct ggml_tensor * ggml_step( - struct ggml_context * ctx, - struct ggml_tensor * a); - -struct ggml_tensor * ggml_relu( - struct ggml_context * ctx, - struct ggml_tensor * a); - -// TODO: double-check this computation is correct -struct ggml_tensor * ggml_gelu( - struct ggml_context * ctx, - struct ggml_tensor * a); - -struct ggml_tensor * ggml_silu( - struct ggml_context * ctx, - struct ggml_tensor * a); - -// normalize along rows -// TODO: eps is hardcoded to 1e-5 for now -struct ggml_tensor * ggml_norm( - struct ggml_context * ctx, - struct ggml_tensor * a); - -struct ggml_tensor * ggml_rms_norm( - struct ggml_context * ctx, - struct ggml_tensor * a); - -// A: m rows, n columns -// B: p rows, n columns (i.e. we transpose it internally) -// result is m columns, p rows -struct ggml_tensor * ggml_mul_mat( - struct ggml_context * ctx, - struct ggml_tensor * a, - struct ggml_tensor * b); - -// -// operations on tensors without backpropagation -// - -// in-place, returns view(a) -struct ggml_tensor * ggml_scale( - struct ggml_context * ctx, - struct ggml_tensor * a, - struct ggml_tensor * b); - -// a -> b, return view(b) -struct ggml_tensor * ggml_cpy( - struct ggml_context * ctx, - struct ggml_tensor * a, - struct ggml_tensor * b); - -// make contiguous -struct ggml_tensor * ggml_cont( - struct ggml_context * ctx, - struct ggml_tensor * a); - -// return view(a), b specifies the new shape -// TODO: when we start computing gradient, make a copy instead of view -struct ggml_tensor * ggml_reshape( - struct ggml_context * ctx, - struct ggml_tensor * a, - struct ggml_tensor * b); - -// return view(a) -// TODO: when we start computing gradient, make a copy instead of view -struct ggml_tensor * ggml_reshape_2d( - struct ggml_context * ctx, - struct ggml_tensor * a, - int64_t ne0, - int64_t ne1); - -// return view(a) -// TODO: when we start computing gradient, make a copy instead of view -struct ggml_tensor * ggml_reshape_3d( - struct ggml_context * ctx, - struct ggml_tensor * a, - int64_t ne0, - int64_t ne1, - int64_t ne2); - -// offset in bytes -struct ggml_tensor * ggml_view_1d( - struct ggml_context * ctx, - struct ggml_tensor * a, - int64_t ne0, - size_t offset); - -struct ggml_tensor * ggml_view_2d( - struct ggml_context * ctx, - struct ggml_tensor * a, - int64_t ne0, - int64_t ne1, - size_t nb1, // row stride in bytes - size_t offset); - -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, // row stride in bytes - size_t nb2, // slice stride in bytes - size_t offset); - -struct ggml_tensor * ggml_permute( - struct ggml_context * ctx, - struct ggml_tensor * a, - int axis0, - int axis1, - int axis2, - int axis3); - -// alias for ggml_permute(ctx, a, 1, 0, 2, 3) -struct ggml_tensor * ggml_transpose( - struct ggml_context * ctx, - struct ggml_tensor * a); - -struct ggml_tensor * ggml_get_rows( - struct ggml_context * ctx, - struct ggml_tensor * a, - struct ggml_tensor * b); - -// set elements above the diagonal to -INF -// in-place, returns view(a) -struct ggml_tensor * ggml_diag_mask_inf( - struct ggml_context * ctx, - struct ggml_tensor * a, - int n_past); - -// in-place, returns view(a) -struct ggml_tensor * ggml_soft_max( - struct ggml_context * ctx, - struct ggml_tensor * a); - -// rotary position embedding -// in-place, returns view(a) -// if mode == 1, skip n_past elements -// TODO: avoid creating a new tensor every time -struct ggml_tensor * ggml_rope( - struct ggml_context * ctx, - struct ggml_tensor * a, - int n_past, - int n_dims, - int mode); - -// padding = 1 -// TODO: we don't support extra parameters for now -// that's why we are hard-coding the stride, padding, and dilation -// not great .. -struct ggml_tensor * ggml_conv_1d_1s( - struct ggml_context * ctx, - struct ggml_tensor * a, - struct ggml_tensor * b); - -struct ggml_tensor * ggml_conv_1d_2s( - struct ggml_context * ctx, - struct ggml_tensor * a, - struct ggml_tensor * b); - -struct ggml_tensor * ggml_flash_attn( - struct ggml_context * ctx, - struct ggml_tensor * q, - struct ggml_tensor * k, - struct ggml_tensor * v, - bool masked); - -struct ggml_tensor * ggml_flash_ff( - struct ggml_context * ctx, - struct ggml_tensor * a, - struct ggml_tensor * b0, - struct ggml_tensor * b1, - struct ggml_tensor * c0, - struct ggml_tensor * c1); - -// Mapping operations -typedef void (*ggml_unary_op_f32_t)(const int, float *, const float *); -typedef void (*ggml_binary_op_f32_t)(const int, float *, const float *, const float *); - -struct ggml_tensor * ggml_map_unary_f32( - struct ggml_context * ctx, - struct ggml_tensor * a, - const ggml_unary_op_f32_t fun); - -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); - -// -// automatic differentiation -// - -void ggml_set_param( - struct ggml_context * ctx, - struct ggml_tensor * tensor); - -void ggml_build_forward_expand(struct ggml_cgraph * cgraph, struct ggml_tensor * tensor); - -struct ggml_cgraph ggml_build_forward (struct ggml_tensor * tensor); -struct ggml_cgraph ggml_build_backward(struct ggml_context * ctx, struct ggml_cgraph * gf, bool keep); - -void ggml_graph_compute(struct ggml_context * ctx, struct ggml_cgraph * cgraph); -void ggml_graph_reset (struct ggml_cgraph * cgraph); - -// print info and performance information for the graph -void ggml_graph_print(const struct ggml_cgraph * cgraph); - -// dump the graph into a file using the dot format -void ggml_graph_dump_dot(const struct ggml_cgraph * gb, const struct ggml_cgraph * gf, const char * filename); - -// -// optimization -// - -// optimization methods -enum ggml_opt_type { - GGML_OPT_ADAM, - GGML_OPT_LBFGS, -}; - -// linesearch methods -enum ggml_linesearch { - GGML_LINESEARCH_DEFAULT = 1, - - GGML_LINESEARCH_BACKTRACKING_ARMIJO = 0, - GGML_LINESEARCH_BACKTRACKING_WOLFE = 1, - GGML_LINESEARCH_BACKTRACKING_STRONG_WOLFE = 2, -}; - -// optimization return values -enum ggml_opt_result { - GGML_OPT_OK = 0, - GGML_OPT_DID_NOT_CONVERGE, - GGML_OPT_NO_CONTEXT, - GGML_OPT_INVALID_WOLFE, - GGML_OPT_FAIL, - - GGML_LINESEARCH_FAIL = -128, - GGML_LINESEARCH_MINIMUM_STEP, - GGML_LINESEARCH_MAXIMUM_STEP, - GGML_LINESEARCH_MAXIMUM_ITERATIONS, - GGML_LINESEARCH_INVALID_PARAMETERS, -}; - -// optimization parameters -// -// see ggml.c (ggml_opt_default_params) for default values -// -struct ggml_opt_params { - enum ggml_opt_type type; - - int n_threads; - - // delta-based convergence test // - // if past == 0 - disabled - // if past > 0: - // stop if |f(x) - f(x_past)| < delta * max(1, |f(x)|) + // operations on tensors with backpropagation // - int past; - float delta; - // maximum number of iterations without improvement + GGML_API struct ggml_tensor * ggml_dup( + struct ggml_context * ctx, + struct ggml_tensor * a); + + GGML_API struct ggml_tensor * ggml_add( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b); + + GGML_API struct ggml_tensor * ggml_add_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b); + + GGML_API struct ggml_tensor * ggml_sub( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b); + + GGML_API struct ggml_tensor * ggml_mul( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b); + + GGML_API struct ggml_tensor * ggml_div( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b); + + GGML_API struct ggml_tensor * ggml_sqr( + struct ggml_context * ctx, + struct ggml_tensor * a); + + GGML_API struct ggml_tensor * ggml_sqrt( + struct ggml_context * ctx, + struct ggml_tensor * a); + + // return scalar + // TODO: compute sum along rows + GGML_API struct ggml_tensor * ggml_sum( + struct ggml_context * ctx, + struct ggml_tensor * a); + + // mean along rows + GGML_API struct ggml_tensor * ggml_mean( + struct ggml_context * ctx, + struct ggml_tensor * a); + + // if a is the same shape as b, and a is not parameter, return a + // otherwise, return a new tensor: repeat(a) to fit in b + GGML_API struct ggml_tensor * ggml_repeat( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b); + + GGML_API struct ggml_tensor * ggml_abs( + struct ggml_context * ctx, + struct ggml_tensor * a); + + GGML_API struct ggml_tensor * ggml_sgn( + struct ggml_context * ctx, + struct ggml_tensor * a); + + GGML_API struct ggml_tensor * ggml_neg( + struct ggml_context * ctx, + struct ggml_tensor * a); + + GGML_API struct ggml_tensor * ggml_step( + struct ggml_context * ctx, + struct ggml_tensor * a); + + GGML_API struct ggml_tensor * ggml_relu( + struct ggml_context * ctx, + struct ggml_tensor * a); + + // TODO: double-check this computation is correct + GGML_API struct ggml_tensor * ggml_gelu( + struct ggml_context * ctx, + struct ggml_tensor * a); + + GGML_API struct ggml_tensor * ggml_silu( + struct ggml_context * ctx, + struct ggml_tensor * a); + + // normalize along rows + // TODO: eps is hardcoded to 1e-5 for now + GGML_API struct ggml_tensor * ggml_norm( + struct ggml_context * ctx, + struct ggml_tensor * a); + + GGML_API struct ggml_tensor * ggml_rms_norm( + struct ggml_context * ctx, + struct ggml_tensor * a); + + // A: m rows, n columns + // B: p rows, n columns (i.e. we transpose it internally) + // result is m columns, p rows + GGML_API struct ggml_tensor * ggml_mul_mat( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b); + // - // if 0 - disabled - // if > 0: - // assume convergence if no cost improvement in this number of iterations + // operations on tensors without backpropagation // - int max_no_improvement; - bool print_forward_graph; - bool print_backward_graph; + // in-place, returns view(a) + GGML_API struct ggml_tensor * ggml_scale( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b); - // ADAM parameters - struct { - int n_iter; + // a -> b, return view(b) + GGML_API struct ggml_tensor * ggml_cpy( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b); - float alpha; // learning rate - float beta1; - float beta2; - float eps; // epsilon for numerical stability - float eps_f; // epsilon for convergence test - float eps_g; // epsilon for convergence test - } adam; + // make contiguous + GGML_API struct ggml_tensor * ggml_cont( + struct ggml_context * ctx, + struct ggml_tensor * a); - // LBFGS parameters - struct { - int m; // number of corrections to approximate the inv. Hessian - int n_iter; - int max_linesearch; + // return view(a), b specifies the new shape + // TODO: when we start computing gradient, make a copy instead of view + GGML_API struct ggml_tensor * ggml_reshape( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b); - float eps; // convergence tolerance - float ftol; // line search tolerance - float wolfe; - float min_step; - float max_step; + // return view(a) + // TODO: when we start computing gradient, make a copy instead of view + GGML_API struct ggml_tensor * ggml_reshape_2d( + struct ggml_context * ctx, + struct ggml_tensor * a, + int64_t ne0, + int64_t ne1); - enum ggml_linesearch linesearch; - } lbfgs; -}; + // return view(a) + // TODO: when we start computing gradient, make a copy instead of view + GGML_API struct ggml_tensor * ggml_reshape_3d( + struct ggml_context * ctx, + struct ggml_tensor * a, + int64_t ne0, + int64_t ne1, + int64_t ne2); -struct ggml_opt_params ggml_opt_default_params(enum ggml_opt_type type); + // offset in bytes + GGML_API struct ggml_tensor * ggml_view_1d( + struct ggml_context * ctx, + struct ggml_tensor * a, + int64_t ne0, + size_t offset); -// optimize the function defined by the tensor f -enum ggml_opt_result ggml_opt( - struct ggml_context * ctx, - struct ggml_opt_params params, - struct ggml_tensor * f); + GGML_API struct ggml_tensor * ggml_view_2d( + struct ggml_context * ctx, + struct ggml_tensor * a, + int64_t ne0, + int64_t ne1, + size_t nb1, // row stride in bytes + size_t offset); -// -// quantization -// + GGML_API 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, // row stride in bytes + size_t nb2, // slice stride in bytes + size_t offset); -size_t ggml_quantize_q4_0(const float * src, void * dst, int n, int k, int64_t * hist); -size_t ggml_quantize_q4_1(const float * src, void * dst, int n, int k, int64_t * hist); + GGML_API struct ggml_tensor * ggml_permute( + struct ggml_context * ctx, + struct ggml_tensor * a, + int axis0, + int axis1, + int axis2, + int axis3); -// -// system info -// + // alias for ggml_permute(ctx, a, 1, 0, 2, 3) + GGML_API struct ggml_tensor * ggml_transpose( + struct ggml_context * ctx, + struct ggml_tensor * a); -int ggml_cpu_has_avx(void); -int ggml_cpu_has_avx2(void); -int ggml_cpu_has_avx512(void); -int ggml_cpu_has_fma(void); -int ggml_cpu_has_neon(void); -int ggml_cpu_has_arm_fma(void); -int ggml_cpu_has_f16c(void); -int ggml_cpu_has_fp16_va(void); -int ggml_cpu_has_wasm_simd(void); -int ggml_cpu_has_blas(void); -int ggml_cpu_has_sse3(void); -int ggml_cpu_has_vsx(void); + GGML_API struct ggml_tensor * ggml_get_rows( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b); + // set elements above the diagonal to -INF + // in-place, returns view(a) + GGML_API struct ggml_tensor * ggml_diag_mask_inf( + struct ggml_context * ctx, + struct ggml_tensor * a, + int n_past); -// -// Internal types and functions exposed for tests and benchmarks -// + // in-place, returns view(a) + GGML_API struct ggml_tensor * ggml_soft_max( + struct ggml_context * ctx, + struct ggml_tensor * a); + + // rotary position embedding + // in-place, returns view(a) + // if mode & 1 == 1, skip n_past elements + // if mode & 2 == 1, GPT-NeoX style + // TODO: avoid creating a new tensor every time + GGML_API struct ggml_tensor * ggml_rope( + struct ggml_context * ctx, + struct ggml_tensor * a, + int n_past, + int n_dims, + int mode); + + // alibi position embedding + // in-place, returns view(a) + struct ggml_tensor * ggml_alibi( + struct ggml_context * ctx, + struct ggml_tensor * a, + int n_past, + int n_head); + + // padding = 1 + // TODO: we don't support extra parameters for now + // that's why we are hard-coding the stride, padding, and dilation + // not great .. + GGML_API struct ggml_tensor * ggml_conv_1d_1s( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b); + + GGML_API struct ggml_tensor * ggml_conv_1d_2s( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b); + + GGML_API struct ggml_tensor * ggml_flash_attn( + struct ggml_context * ctx, + struct ggml_tensor * q, + struct ggml_tensor * k, + struct ggml_tensor * v, + bool masked); + + GGML_API struct ggml_tensor * ggml_flash_ff( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b0, + struct ggml_tensor * b1, + struct ggml_tensor * c0, + struct ggml_tensor * c1); + + // Mapping operations + GGML_API typedef void (*ggml_unary_op_f32_t)(const int, float *, const float *); + GGML_API typedef void (*ggml_binary_op_f32_t)(const int, float *, const float *, const float *); + + GGML_API struct ggml_tensor * ggml_map_unary_f32( + struct ggml_context * ctx, + struct ggml_tensor * a, + const ggml_unary_op_f32_t fun); + + GGML_API 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); + + // + // automatic differentiation + // + + GGML_API void ggml_set_param( + struct ggml_context * ctx, + struct ggml_tensor * tensor); + + GGML_API void ggml_build_forward_expand(struct ggml_cgraph * cgraph, struct ggml_tensor * tensor); + + GGML_API struct ggml_cgraph ggml_build_forward (struct ggml_tensor * tensor); + GGML_API struct ggml_cgraph ggml_build_backward(struct ggml_context * ctx, struct ggml_cgraph * gf, bool keep); + + GGML_API void ggml_graph_compute(struct ggml_context * ctx, struct ggml_cgraph * cgraph); + GGML_API void ggml_graph_reset (struct ggml_cgraph * cgraph); + + // print info and performance information for the graph + GGML_API void ggml_graph_print(const struct ggml_cgraph * cgraph); + + // dump the graph into a file using the dot format + GGML_API void ggml_graph_dump_dot(const struct ggml_cgraph * gb, const struct ggml_cgraph * gf, const char * filename); + + // + // optimization + // + + // optimization methods + enum ggml_opt_type { + GGML_OPT_ADAM, + GGML_OPT_LBFGS, + }; + + // linesearch methods + enum ggml_linesearch { + GGML_LINESEARCH_DEFAULT = 1, + + GGML_LINESEARCH_BACKTRACKING_ARMIJO = 0, + GGML_LINESEARCH_BACKTRACKING_WOLFE = 1, + GGML_LINESEARCH_BACKTRACKING_STRONG_WOLFE = 2, + }; + + // optimization return values + enum ggml_opt_result { + GGML_OPT_OK = 0, + GGML_OPT_DID_NOT_CONVERGE, + GGML_OPT_NO_CONTEXT, + GGML_OPT_INVALID_WOLFE, + GGML_OPT_FAIL, + + GGML_LINESEARCH_FAIL = -128, + GGML_LINESEARCH_MINIMUM_STEP, + GGML_LINESEARCH_MAXIMUM_STEP, + GGML_LINESEARCH_MAXIMUM_ITERATIONS, + GGML_LINESEARCH_INVALID_PARAMETERS, + }; + + // optimization parameters + // + // see ggml.c (ggml_opt_default_params) for default values + // + struct ggml_opt_params { + enum ggml_opt_type type; + + int n_threads; + + // delta-based convergence test + // + // if past == 0 - disabled + // if past > 0: + // stop if |f(x) - f(x_past)| < delta * max(1, |f(x)|) + // + int past; + float delta; + + // maximum number of iterations without improvement + // + // if 0 - disabled + // if > 0: + // assume convergence if no cost improvement in this number of iterations + // + int max_no_improvement; + + bool print_forward_graph; + bool print_backward_graph; + + // ADAM parameters + struct { + int n_iter; + + float alpha; // learning rate + float beta1; + float beta2; + float eps; // epsilon for numerical stability + float eps_f; // epsilon for convergence test + float eps_g; // epsilon for convergence test + } adam; + + // LBFGS parameters + struct { + int m; // number of corrections to approximate the inv. Hessian + int n_iter; + int max_linesearch; + + float eps; // convergence tolerance + float ftol; // line search tolerance + float wolfe; + float min_step; + float max_step; + + enum ggml_linesearch linesearch; + } lbfgs; + }; + + GGML_API struct ggml_opt_params ggml_opt_default_params(enum ggml_opt_type type); + + // optimize the function defined by the tensor f + GGML_API enum ggml_opt_result ggml_opt( + struct ggml_context * ctx, + struct ggml_opt_params params, + struct ggml_tensor * f); + + // + // quantization + // + + GGML_API size_t ggml_quantize_q4_0(const float * src, void * dst, int n, int k, int64_t * hist); + GGML_API size_t ggml_quantize_q4_1(const float * src, void * dst, int n, int k, int64_t * hist); + GGML_API size_t ggml_quantize_q4_2(const float * src, void * dst, int n, int k, int64_t * hist); + GGML_API size_t ggml_quantize_q5_0(const float * src, void * dst, int n, int k, int64_t * hist); + GGML_API size_t ggml_quantize_q5_1(const float * src, void * dst, int n, int k, int64_t * hist); + GGML_API size_t ggml_quantize_q8_0(const float * src, void * dst, int n, int k, int64_t * hist); + + GGML_API size_t ggml_quantize_chunk(enum ggml_type type, const float * src, void * dst, int start, int n, int64_t * hist); + + // + // system info + // + + GGML_API int ggml_cpu_has_avx (void); + GGML_API int ggml_cpu_has_avx2 (void); + GGML_API int ggml_cpu_has_avx512 (void); + GGML_API int ggml_cpu_has_avx512_vbmi(void); + GGML_API int ggml_cpu_has_avx512_vnni(void); + GGML_API int ggml_cpu_has_fma (void); + GGML_API int ggml_cpu_has_neon (void); + GGML_API int ggml_cpu_has_arm_fma (void); + GGML_API int ggml_cpu_has_f16c (void); + GGML_API int ggml_cpu_has_fp16_va (void); + GGML_API int ggml_cpu_has_wasm_simd (void); + GGML_API int ggml_cpu_has_blas (void); + GGML_API int ggml_cpu_has_cublas (void); + GGML_API int ggml_cpu_has_clblast (void); + GGML_API int ggml_cpu_has_gpublas (void); + GGML_API int ggml_cpu_has_sse3 (void); + GGML_API int ggml_cpu_has_vsx (void); + + // + // Internal types and functions exposed for tests and benchmarks + // #ifdef __cplusplus -// restrict not standard in C++ + // restrict not standard in C++ #define GGML_RESTRICT #else #define GGML_RESTRICT restrict #endif -typedef void (*dequantize_row_q_t)(const void * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); -typedef void (*quantize_row_q_t)(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); -typedef void (*vec_dot_q_t)(const int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT x, const void * GGML_RESTRICT y); + typedef void (*dequantize_row_q_t)(const void * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); + typedef void (*quantize_row_q_t) (const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); + typedef void (*vec_dot_q_t) (const int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT x, const void * GGML_RESTRICT y); -typedef struct { - dequantize_row_q_t dequantize_row_q; - quantize_row_q_t quantize_row_q; - quantize_row_q_t quantize_row_q_reference; - vec_dot_q_t vec_dot_q; -} quantize_fns_t; + typedef struct { + dequantize_row_q_t dequantize_row_q; + quantize_row_q_t quantize_row_q; + quantize_row_q_t quantize_row_q_reference; + quantize_row_q_t quantize_row_q_dot; + vec_dot_q_t vec_dot_q; + enum ggml_type vec_dot_type; + } quantize_fns_t; -quantize_fns_t ggml_internal_get_quantize_fn(size_t i); + quantize_fns_t ggml_internal_get_quantize_fn(size_t i); #ifdef __cplusplus }